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TST: Add skip test to excelwriter contextmanager
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 2cc94524b5d19..38b3ee192ab7a 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -21,12 +21,12 @@ import pandas as pd -def _skip_if_no_xlrd(version=(0, 9)): +def _skip_if_no_xlrd(): try: import xlrd ver = tuple(map(int, xlrd.__VERSION__.split(".")[:2])) - if ver < version: - raise nose.SkipTest('xlrd < %s, skipping' % str(version)) + if ver < (0, 9): + raise nose.SkipTest('xlrd < 0.9, skipping') except ImportError: raise nose.SkipTest('xlrd not installed, skipping') @@ -343,6 +343,7 @@ def test_excel_sheet_by_name_raise(self): self.assertRaises(xlrd.XLRDError, xl.parse, '0') def test_excelwriter_contextmanager(self): + _skip_if_no_xlrd() ext = self.ext pth = os.path.join(self.dirpath, 'testit.{0}'.format(ext)) @@ -350,10 +351,7 @@ def test_excelwriter_contextmanager(self): with ExcelWriter(pth) as writer: self.frame.to_excel(writer, 'Data1') self.frame2.to_excel(writer, 'Data2') - # If above test passes with outdated xlrd, next test - # does require fresh xlrd - # http://nipy.bic.berkeley.edu/builders/pandas-py2.x-wheezy-sparc/builds/148/steps/shell_4/logs/stdio - _skip_if_no_xlrd((0, 9)) + with ExcelFile(pth) as reader: found_df = reader.parse('Data1') found_df2 = reader.parse('Data2')
Fixes #5094.
https://api.github.com/repos/pandas-dev/pandas/pulls/5095
2013-10-03T03:22:10Z
2013-10-03T22:07:48Z
2013-10-03T22:07:48Z
2014-07-16T08:32:54Z
BUG: MultiIndex.get_level_values() replaces NA by another value (#5074)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 216b7f2ca6e5a..40ad07aea1ecf 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -569,6 +569,7 @@ Bug Fixes - Fixed a bug where default options were being overwritten in the option parser cleaning (:issue:`5121`). - Treat a list/ndarray identically for ``iloc`` indexing with list-like (:issue:`5006`) + - Fix ``MultiIndex.get_level_values()`` with missing values (:issue:`5074`) pandas 0.12.0 ------------- diff --git a/pandas/core/index.py b/pandas/core/index.py index 8e98cc6fb25bb..465a0439c6eb3 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -393,6 +393,9 @@ def values(self): def get_values(self): return self.values + _na_value = np.nan + """The expected NA value to use with this index.""" + @property def is_monotonic(self): return self._engine.is_monotonic @@ -2256,7 +2259,8 @@ def get_level_values(self, level): num = self._get_level_number(level) unique_vals = self.levels[num] # .values labels = self.labels[num] - values = unique_vals.take(labels) + values = Index(com.take_1d(unique_vals.values, labels, + fill_value=unique_vals._na_value)) values.name = self.names[num] return values diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 5404b30af8d1c..7e801c0a202db 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -1445,6 +1445,39 @@ def test_get_level_values(self): expected = self.index.get_level_values(0) self.assert_(np.array_equal(result, expected)) + def test_get_level_values_na(self): + arrays = [['a', 'b', 'b'], [1, np.nan, 2]] + index = pd.MultiIndex.from_arrays(arrays) + values = index.get_level_values(1) + expected = [1, np.nan, 2] + assert_array_equal(values.values.astype(float), expected) + + arrays = [['a', 'b', 'b'], [np.nan, np.nan, 2]] + index = pd.MultiIndex.from_arrays(arrays) + values = index.get_level_values(1) + expected = [np.nan, np.nan, 2] + assert_array_equal(values.values.astype(float), expected) + + arrays = [[np.nan, np.nan, np.nan], ['a', np.nan, 1]] + index = pd.MultiIndex.from_arrays(arrays) + values = index.get_level_values(0) + expected = [np.nan, np.nan, np.nan] + assert_array_equal(values.values.astype(float), expected) + values = index.get_level_values(1) + expected = ['a', np.nan, 1] + assert_array_equal(values.values, expected) + + arrays = [['a', 'b', 'b'], pd.DatetimeIndex([0, 1, pd.NaT])] + index = pd.MultiIndex.from_arrays(arrays) + values = index.get_level_values(1) + expected = pd.DatetimeIndex([0, 1, pd.NaT]) + assert_array_equal(values.values, expected.values) + + arrays = [[], []] + index = pd.MultiIndex.from_arrays(arrays) + values = index.get_level_values(0) + self.assertEqual(values.shape, (0,)) + def test_reorder_levels(self): # this blows up assertRaisesRegexp(IndexError, '^Too many levels', diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 281ac0cc8a35a..ce1bea96b9d4c 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -495,6 +495,9 @@ def _mpl_repr(self): # how to represent ourselves to matplotlib return tslib.ints_to_pydatetime(self.asi8, self.tz) + _na_value = tslib.NaT + """The expected NA value to use with this index.""" + def __unicode__(self): from pandas.core.format import _format_datetime64 values = self.values
closes #5074 Do you really prefer using a mask? It seems to have a lower memory footprint but it requires some tricks for corner cases. ``` num = self._get_level_number(level) unique_vals = self.levels[num] # .values labels = self.labels[num] mask = labels == -1 if len(unique_vals) > 0: values = unique_vals.take(labels) else: values = np.empty(len(labels)) values[:] = np.nan values = pd.Index(values) if mask.any(): values = values.get_values() values[mask] = np.nan values = pd.Index(values) values.name = self.names[num] return values ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5090
2013-10-02T21:44:18Z
2013-10-07T21:25:49Z
2013-10-07T21:25:49Z
2014-06-21T03:19:29Z
TST: Tests for multi-index construction of an all-nan frame (GH4078)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 4e5178d8a554a..057802dc51af5 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -524,6 +524,7 @@ Bug Fixes - Bug in setting with ``ix/loc`` and a mixed int/string index (:issue:`4544`) - Make sure series-series boolean comparions are label based (:issue:`4947`) - Bug in multi-level indexing with a Timestamp partial indexer (:issue:`4294`) + - Tests/fix for multi-index construction of an all-nan frame (:isue:`4078`) pandas 0.12.0 ------------- diff --git a/pandas/core/internals.py b/pandas/core/internals.py index fd9aed58798fe..6fddc44d7552e 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3530,9 +3530,16 @@ def _shape_compat(x): if ref_items.is_unique: items = ref_items[ref_items.isin(names)] else: - items = _ensure_index([n for n in names if n in ref_items]) - if len(items) != len(stacked): - raise Exception("invalid names passed _stack_arrays") + # a mi + if isinstance(ref_items, MultiIndex): + names = MultiIndex.from_tuples(names) + items = ref_items[ref_items.isin(names)] + + # plain old dups + else: + items = _ensure_index([n for n in names if n in ref_items]) + if len(items) != len(stacked): + raise ValueError("invalid names passed _stack_arrays") return items, stacked, placement diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index e8d9f3a7fc7cc..1e4e988431f43 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -2263,10 +2263,6 @@ def test_constructor_overflow_int64(self): df_crawls = DataFrame(data) self.assert_(df_crawls['uid'].dtype == object) - def test_is_mixed_type(self): - self.assert_(not self.frame._is_mixed_type) - self.assert_(self.mixed_frame._is_mixed_type) - def test_constructor_ordereddict(self): import random nitems = 100 @@ -2319,6 +2315,19 @@ def test_constructor_dict(self): frame = DataFrame({'A': [], 'B': []}, columns=['A', 'B']) self.assert_(frame.index.equals(Index([]))) + def test_constructor_multi_index(self): + # GH 4078 + # construction error with mi and all-nan frame + tuples = [(2, 3), (3, 3), (3, 3)] + mi = MultiIndex.from_tuples(tuples) + df = DataFrame(index=mi,columns=mi) + self.assert_(pd.isnull(df).values.ravel().all()) + + tuples = [(3, 3), (2, 3), (3, 3)] + mi = MultiIndex.from_tuples(tuples) + df = DataFrame(index=mi,columns=mi) + self.assert_(pd.isnull(df).values.ravel().all()) + def test_constructor_error_msgs(self): msg = "Mixing dicts with non-Series may lead to ambiguous ordering." # mix dict and array, wrong size @@ -9489,6 +9498,10 @@ def test_get_X_columns(self): self.assert_(np.array_equal(df._get_numeric_data().columns, ['a', 'b', 'e'])) + def test_is_mixed_type(self): + self.assert_(not self.frame._is_mixed_type) + self.assert_(self.mixed_frame._is_mixed_type) + def test_get_numeric_data(self): intname = np.dtype(np.int_).name floatname = np.dtype(np.float_).name
closes #4078
https://api.github.com/repos/pandas-dev/pandas/pulls/5089
2013-10-02T16:51:14Z
2013-10-02T20:54:59Z
2013-10-02T20:54:59Z
2014-06-23T22:01:54Z
BUG: fixed a bug in multi-level indexing with a Timestamp partial indexer (GH4294)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 058ea165120a6..4e5178d8a554a 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -523,6 +523,7 @@ Bug Fixes and other reshaping issues. - Bug in setting with ``ix/loc`` and a mixed int/string index (:issue:`4544`) - Make sure series-series boolean comparions are label based (:issue:`4947`) + - Bug in multi-level indexing with a Timestamp partial indexer (:issue:`4294`) pandas 0.12.0 ------------- diff --git a/pandas/core/index.py b/pandas/core/index.py index f6a88f4164191..d6e74e16c8dae 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -1,4 +1,5 @@ # pylint: disable=E1101,E1103,W0232 +import datetime from functools import partial from pandas.compat import range, zip, lrange, lzip, u from pandas import compat @@ -2224,16 +2225,20 @@ def get_value(self, series, key): # Label-based s = _values_from_object(series) k = _values_from_object(key) + + def _try_mi(k): + # TODO: what if a level contains tuples?? + loc = self.get_loc(k) + new_values = series.values[loc] + new_index = self[loc] + new_index = _maybe_droplevels(new_index, k) + return Series(new_values, index=new_index, name=series.name) + try: return self._engine.get_value(s, k) except KeyError as e1: try: - # TODO: what if a level contains tuples?? - loc = self.get_loc(key) - new_values = series.values[loc] - new_index = self[loc] - new_index = _maybe_droplevels(new_index, key) - return Series(new_values, index=new_index, name=series.name) + return _try_mi(key) except KeyError: pass @@ -2250,6 +2255,16 @@ def get_value(self, series, key): except Exception: # pragma: no cover raise e1 except TypeError: + + # a Timestamp will raise a TypeError in a multi-index + # rather than a KeyError, try it here + if isinstance(key, (datetime.datetime,np.datetime64)) or ( + compat.PY3 and isinstance(key, compat.string_types)): + try: + return _try_mi(Timestamp(key)) + except: + pass + raise InvalidIndexError(key) def get_level_values(self, level): @@ -2779,6 +2794,7 @@ def reindex(self, target, method=None, level=None, limit=None, if level is not None: if method is not None: raise TypeError('Fill method not supported if level passed') + target = _ensure_index(target) target, indexer, _ = self._join_level(target, level, how='right', return_indexers=True) else: diff --git a/pandas/index.pyx b/pandas/index.pyx index 53c96b1c55605..8aa4f69a1ec8e 100644 --- a/pandas/index.pyx +++ b/pandas/index.pyx @@ -408,7 +408,7 @@ cdef class Float64Engine(IndexEngine): limit=limit) -cdef Py_ssize_t _bin_search(ndarray values, object val): +cdef Py_ssize_t _bin_search(ndarray values, object val) except -1: cdef: Py_ssize_t mid, lo = 0, hi = len(values) - 1 object pval diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index e4504420bacc2..f3598dd2d210b 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -2751,6 +2751,24 @@ def f(): df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')] self.assertRaises(KeyError, f) + # GH 4294 + # partial slice on a series mi + s = pd.DataFrame(randn(1000, 1000), index=pd.date_range('2000-1-1', periods=1000)).stack() + + s2 = s[:-1].copy() + expected = s2['2000-1-4'] + result = s2[pd.Timestamp('2000-1-4')] + assert_series_equal(result, expected) + + result = s[pd.Timestamp('2000-1-4')] + expected = s['2000-1-4'] + assert_series_equal(result, expected) + + df2 = pd.DataFrame(s) + expected = df2.ix['2000-1-4'] + result = df2.ix[pd.Timestamp('2000-1-4')] + assert_frame_equal(result, expected) + def test_date_range_normalize(self): snap = datetime.today() n = 50
closes #4294
https://api.github.com/repos/pandas-dev/pandas/pulls/5088
2013-10-02T16:16:55Z
2013-10-02T19:10:22Z
2013-10-02T19:10:22Z
2014-06-24T23:01:59Z
DOC: CONTRIBUTING.md: Gold plating: syntax, punctuation, Markdown format...
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ac972b47e7b60..2966aed5f57ee 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -3,147 +3,161 @@ All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. -The Github "issues" tab contains some issues labels "Good as first PR", these are +The [GitHub "issues" tab](https://github.com/pydata/pandas/issues) +contains some issues labeled "Good as first PR"; these are tasks which do not require deep knowledge of the package. Look those up if you're looking for a quick way to help out. Please try and follow these guidelines, as this makes it easier for us to accept your contribution or address the issue you're having. -- When submitting a bug report: - - Please include a short, self-contained python snippet reproducing the problem. - You can have the code formatted nicely by using [GitHub Flavored Markdown](http://github.github.com/github-flavored-markdown/) : +#### Bug Reports - ``` - ```python + - Please include a short, self-contained Python snippet reproducing the problem. + You can have the code formatted nicely by using [GitHub Flavored Markdown](http://github.github.com/github-flavored-markdown/) : - print("I ♥ pandas!") + ```python + + print("I ♥ pandas!") - ``' - ``` + ``` - - Specify the pandas (and numpy) version used. (you can check `pandas.__version__`). + - A [test case](https://github.com/pydata/pandas/tree/master/pandas/tests) may be more helpful. + - Specify the pandas (and NumPy) version used. (check `pandas.__version__` + and `numpy.__version__`) - Explain what the expected behavior was, and what you saw instead. - - If the issue seems to involve some of pandas' dependencies such as matplotlib - or PyTables, you should include (the relavent parts of) the output of - [ci/print_versions.py](https://github.com/pydata/pandas/blob/master/ci/print_versions.py) - -- When submitting a Pull Request - - **Make sure the test suite passes**., and that means on python3 as well. - You can use "test_fast.sh", or tox locally and/or enable Travis-CI on your fork. - See the "Getting Travis-CI going" below. - - If you are changing any code, you need to enable Travis-CI on your fork, + - If the issue seems to involve some of [pandas' dependencies](https://github.com/pydata/pandas#dependencies) + such as + [NumPy](http://numpy.org), + [matplotlib](http://matplotlib.org/), and + [PyTables](http://www.pytables.org/) + you should include (the relevant parts of) the output of + [`ci/print_versions.py`](https://github.com/pydata/pandas/blob/master/ci/print_versions.py). + +#### Pull Requests + + - **Make sure the test suite passes** for both python2 and python3. + You can use `test_fast.sh`, **tox** locally, and/or enable **Travis-CI** on your fork. + See "Getting Travis-CI going" below. + - An informal commit message format is in effect for the project. Please try + and adhere to it. Check `git log` for examples. Here are some common prefixes + along with general guidelines for when to use them: + - **ENH**: Enhancement, new functionality + - **BUG**: Bug fix + - **DOC**: Additions/updates to documentation + - **TST**: Additions/updates to tests + - **BLD**: Updates to the build process/scripts + - **PERF**: Performance improvement + - **CLN**: Code cleanup + - Commit messages should have: + - a subject line with `< 80` chars + - one blank line + - a commit message body, if there's a need for one + - If you are changing any code, you should enable Travis-CI on your fork to make it easier for the team to see that the PR does indeed pass all the tests. - - Back-compatibility **really** matters. Pandas already has a large user-base and - a lot of existing user code. Don't break old code if you can avoid it - Explain the need if there is one in the PR. - Changes to method signatures should be made in a way which doesn't break existing - code, for example you should beware of changes to ordering and naming of keyword - arguments. Add deprecation warnings when needed. - - Performance matters. You can use the included "test_perf.sh" - script to make sure your PR does not introduce any performance regressions + - **Backward-compatibility really matters**. Pandas already has a large user base and + a lot of existing user code. + - Don't break old code if you can avoid it. + - If there is a need, explain it in the PR. + - Changes to method signatures should be made in a way which doesn't break existing + code. For example, you should beware of changes to ordering and naming of keyword + arguments. + - Add deprecation warnings where needed. + - Performance matters. You can use the included `test_perf.sh` + script to make sure your PR does not introduce any new performance regressions in the library. - - docstrings follow the [numpydoc](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt) format. - - **Don't** merge upstream into a branch you're going to submit as a PR, - This can create all sorts of problems. Use "git rebase" instead. This ensures - no merge conflicts occur when you're code is merged by the core team. - - An informal commit message format is in effect for the project, please try - and adhere to it. View "git log" for examples. Here are some common prefixes - along with general guidelines for when to use them: - - ENH: Enhancement, new functionality - - BUG: Bug fix - - DOC: Additions/updates to documentation - - TST: Additions/updates to tests - - BLD: Updates to the build process/scripts - - PERF: Performance improvement - - CLN: Code cleanup - - Commit messages should have subject line <80 chars, followed by one blank line, - and finally a commit message body if there's a need for one. - - Please reference the GH issue number in your commit message using GH1234 - or #1234, either style is fine. - - Use "raise AssertionError" rather then plain `assert` in library code (using assert is fine - for test code). python -o strips assertions. better safe then sorry. - - When writing tests, don't use "new" assertion methods added to the unittest module + - Docstrings follow the [numpydoc](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt) format. + - **Don't** merge upstream into a branch you're going to submit as a PR. + This can create all sorts of problems. Use `git rebase` instead. This ensures + no merge conflicts occur when your code is merged by the core team. + - Please reference the GH issue number in your commit message using `GH1234` + or `#1234`. Either style is fine. + - Use `raise AssertionError` rather then plain `assert` in library code (`assert` is fine + for test code). `python -o` strips assertions. Better safe than sorry. + - When writing tests, don't use "new" assertion methods added to the `unittest` module in 2.7 since pandas currently supports 2.6. The most common pitfall is: - with self.assertRaises(ValueError): - foo + with self.assertRaises(ValueError): + foo + - which fails on python 2.6. You need to use `assertRaises` from + which fails with Python 2.6. You need to use `assertRaises` from `pandas.util.testing` instead (or use `self.assertRaises(TheException,func,args)`). - - doc/source/release.rst and doc/source/vx.y.z.txt contain an on-going - changelog for each release as it is worked on. Add entries to these files - as needed in a separate commit in your PR, documenting the fix, enhancement + - `doc/source/release.rst` and `doc/source/vx.y.z.txt` contain an ongoing + changelog for each release. Add entries to these files + as needed in a separate commit in your PR: document the fix, enhancement, or (unavoidable) breaking change. - - For extra brownie points, use "git rebase -i" to squash and reorder + - For extra brownie points, use `git rebase -i` to squash and reorder commits in your PR so that the history makes the most sense. Use your own judgment to decide what history needs to be preserved. - - Pandas source code should not (with some exceptions, such as 3rd party licensed code), - generally speaking, include an "Authors:" list or attribution to individuals in source code. - The RELEASE.rst details changes and enhancements to the code over time, - a "thanks goes to @JohnSmith." as part of the appropriate entry is a suitable way to acknowledge - contributions, the rest is git blame/log. + - Pandas source code should not -- with some exceptions, such as 3rd party licensed code -- + generally speaking, include an "Authors" list or attribution to individuals in source code. + `RELEASE.rst` details changes and enhancements to the code over time. + A "thanks goes to @JohnSmith." as part of the appropriate entry is a suitable way to acknowledge + contributions. The rest is `git blame`/`git log`. Feel free to ask the commiter who merges your code to include such an entry - or include it directly yourself as part of the PR if you'd like to. We're always glad to have - new contributors join us from the ever-growing pandas community. + or include it directly yourself as part of the PR if you'd like to. + **We're always glad to have new contributors join us from the ever-growing pandas community.** You may also be interested in the copyright policy as detailed in the pandas [LICENSE](https://github.com/pydata/pandas/blob/master/LICENSE). - On the subject of [PEP8](http://www.python.org/dev/peps/pep-0008/): yes. - On the subject of massive PEP8 fix PRs touching everything, please consider the following: - - They create merge conflicts for people working in their own fork. - - They makes git blame less effective. + - They create noisy merge conflicts for people working in their own fork. + - They make `git blame` less effective. - Different tools / people achieve PEP8 in different styles. This can create "style wars" and churn that produces little real benefit. - If your code changes are intermixed with style fixes, they are harder to review before merging. Keep style fixes in separate commits. - - it's fine to clean-up a little around an area you just worked on. - - Generally its a BAD idea to PEP8 on documentation. + - It's fine to clean-up a little around an area you just worked on. + - Generally it's a BAD idea to PEP8 on documentation. Having said that, if you still feel a PEP8 storm is in order, go for it. -### Notes on plotting functions convention +### Notes on plotting function conventions https://groups.google.com/forum/#!topic/pystatsmodels/biNlCvJPNNY/discussion -###Getting Travis-CI going +### Getting Travis-CI going -Instructions for getting Travis-CI installed are available [here](http://about.travis-ci.org/docs/user/getting-started/). For those users who are new to travis-ci and continuous-integration in particular, +Instructions for getting Travis-CI installed are available [here](http://about.travis-ci.org/docs/user/getting-started/). +For those users who are new to Travis-CI and [continuous integration](https://en.wikipedia.org/wiki/Continuous_integration) in particular, Here's a few high-level notes: -- Travis-CI is a free service (with premium account available), that integrates -well with Github. -- Enabling Travis-CI on your github fork of a project will cause any *new* commit -pushed to the repo to trigger a full build+test on Travis-CI's servers. -- All the configuration for travis's builds is already specified by .travis.yml in the repo, -That means all you have to do is enable Travis-CI once, and then just push commits -and you'll get full testing across py2/3 with pandas's considerable test-suite. -- Enabling travis-CI will attach the test results (red/green) to the Pull-Request -page for any PR you submit. For example: +- Travis-CI is a free service (with premium account upgrades available) that integrates + well with GitHub. +- Enabling Travis-CI on your GitHub fork of a project will cause any *new* commit + pushed to the repo to trigger a full build+test on Travis-CI's servers. +- All the configuration for Travis-CI builds is already specified by `.travis.yml` in the repo. + That means all you have to do is enable Travis-CI once, and then just push commits + and you'll get full testing across py2/3 with pandas' considerable + [test-suite](https://github.com/pydata/pandas/tree/master/pandas/tests). +- Enabling Travis-CI will attach the test results (red/green) to the Pull-Request + page for any PR you submit. For example: https://github.com/pydata/pandas/pull/2532, See the Green "Good to merge!" banner? that's it. This is especially important for new contributors, as members of the pandas dev team -like to know the test suite passes before considering it for merging. +like to know that the test suite passes before considering it for merging. Even regular contributors who test religiously on their local box (using tox for example) often rely on a PR+travis=green to make double sure everything works ok on another system, as occasionally, it doesn't. -####Steps to enable Travis-CI +#### Steps to enable Travis-CI -- go to https://travis-ci.org/ -- "Sign in with Github", on top panel. -- \[your username\]/Account, on top-panel. -- 'sync now' to refresh the list of repos on your GH account. -- flip the switch on the repos you want Travis-CI enabled for, -"pandas" obviously. +- Open https://travis-ci.org/ +- Select "Sign in with GitHub" (Top Navbar) +- Select \[your username\] -> "Accounts" (Top Navbar) +- Select 'Sync now' to refresh the list of repos from your GH account. +- Flip the switch for the repos you want Travis-CI enabled for. + "pandas", obviously. - Then, pushing a *new* commit to a certain branch on that repo -will trigger a build/test for that branch, for example the branch -might be "master" or "PR1234_fix_all_the_things", if that's the -name of your PR branch. + will trigger a build/test for that branch. For example, the branch + might be `master` or `PR1234_fix_everything__atomically`, if that's the + name of your PR branch. You can see the build history and current builds for your fork -on: https://travis-ci.org/(your_GH_username)/pandas. +at: https://travis-ci.org/(your_GH_username)/pandas. For example, the builds for the main pandas repo can be seen at: https://travis-ci.org/pydata/pandas.
...ting :sparkles:
https://api.github.com/repos/pandas-dev/pandas/pulls/5086
2013-10-02T14:13:43Z
2013-10-08T03:52:50Z
2013-10-08T03:52:50Z
2014-06-16T06:29:44Z
CLN: Remove internal classes from MultiIndex pickle.
diff --git a/pandas/core/index.py b/pandas/core/index.py index e966912e509e2..3f491b4271ddc 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -2458,8 +2458,9 @@ def __contains__(self, key): def __reduce__(self): """Necessary for making this object picklable""" object_state = list(np.ndarray.__reduce__(self)) - subclass_state = (list(self.levels), list( - self.labels), self.sortorder, list(self.names)) + subclass_state = ([lev.view(np.ndarray) for lev in self.levels], + [label.view(np.ndarray) for label in self.labels], + self.sortorder, list(self.names)) object_state[2] = (object_state[2], subclass_state) return tuple(object_state)
FrozenNDArray had made it into the MI pickle. No reason to do that and just complicates pickle compat going forward. Now they just output ndarray instead (which also avoids the unnecessary nested pickle previously occurring). Fixes #5076.
https://api.github.com/repos/pandas-dev/pandas/pulls/5084
2013-10-02T12:01:23Z
2013-10-02T23:36:29Z
2013-10-02T23:36:29Z
2014-06-25T18:39:41Z
CLN: Fix order of index methods.
diff --git a/pandas/core/index.py b/pandas/core/index.py index 6d0a7d2f9f86a..f6a88f4164191 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -93,35 +93,6 @@ class Index(FrozenNDArray): _engine_type = _index.ObjectEngine - def is_(self, other): - """ - More flexible, faster check like ``is`` but that works through views - - Note: this is *not* the same as ``Index.identical()``, which checks - that metadata is also the same. - - Parameters - ---------- - other : object - other object to compare against. - - Returns - ------- - True if both have same underlying data, False otherwise : bool - """ - # use something other than None to be clearer - return self._id is getattr(other, '_id', Ellipsis) - - def _reset_identity(self): - "Initializes or resets ``_id`` attribute with new object" - self._id = _Identity() - - def view(self, *args, **kwargs): - result = super(Index, self).view(*args, **kwargs) - if isinstance(result, Index): - result._id = self._id - return result - def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False, **kwargs): @@ -187,6 +158,35 @@ def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False, subarr._set_names([name]) return subarr + def is_(self, other): + """ + More flexible, faster check like ``is`` but that works through views + + Note: this is *not* the same as ``Index.identical()``, which checks + that metadata is also the same. + + Parameters + ---------- + other : object + other object to compare against. + + Returns + ------- + True if both have same underlying data, False otherwise : bool + """ + # use something other than None to be clearer + return self._id is getattr(other, '_id', Ellipsis) + + def _reset_identity(self): + "Initializes or resets ``_id`` attribute with new object" + self._id = _Identity() + + def view(self, *args, **kwargs): + result = super(Index, self).view(*args, **kwargs) + if isinstance(result, Index): + result._id = self._id + return result + # construction helpers @classmethod def _scalar_data_error(cls, data):
I moved `__new__` below other methods on Index. Makes it look weird.
https://api.github.com/repos/pandas-dev/pandas/pulls/5081
2013-10-02T02:19:06Z
2013-10-02T03:41:17Z
2013-10-02T03:41:17Z
2014-07-16T08:32:42Z
BUG: Make Index, Int64Index and MI repr evalable
diff --git a/pandas/core/base.py b/pandas/core/base.py index f390592a6f6c4..2acc045156720 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -175,4 +175,4 @@ def __unicode__(self): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True) - return '%s(%s, dtype=%s)' % (type(self).__name__, prepr, self.dtype) + return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) diff --git a/pandas/core/index.py b/pandas/core/index.py index 465a0439c6eb3..98f190360bc33 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -2044,7 +2044,7 @@ def __repr__(self): attrs.append(('sortorder', default_pprint(self.sortorder))) space = ' ' * (len(self.__class__.__name__) + 1) - prepr = (u("\n%s") % space).join([u("%s=%s") % (k, v) + prepr = (u(",\n%s") % space).join([u("%s=%s") % (k, v) for k, v in attrs]) res = u("%s(%s)") % (self.__class__.__name__, prepr) diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 55f70e9e4fe28..d9bf8adb71298 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -1456,7 +1456,7 @@ def test_to_html_with_classes(self): <table border="1" class="dataframe sortable draggable"> <tbody> <tr> - <td>Index([], dtype=object)</td> + <td>Index([], dtype='object')</td> <td>Empty DataFrame</td> </tr> </tbody> diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 11538ae8b3ab8..cd26016acba5c 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -36,21 +36,23 @@ class TestIndex(unittest.TestCase): _multiprocess_can_split_ = True def setUp(self): - self.unicodeIndex = tm.makeUnicodeIndex(100) - self.strIndex = tm.makeStringIndex(100) - self.dateIndex = tm.makeDateIndex(100) - self.intIndex = tm.makeIntIndex(100) - self.floatIndex = tm.makeFloatIndex(100) - self.empty = Index([]) - self.tuples = Index(lzip(['foo', 'bar', 'baz'], [1, 2, 3])) + self.indices = dict( + unicodeIndex = tm.makeUnicodeIndex(100), + strIndex = tm.makeStringIndex(100), + dateIndex = tm.makeDateIndex(100), + intIndex = tm.makeIntIndex(100), + floatIndex = tm.makeFloatIndex(100), + empty = Index([]), + tuples = Index(lzip(['foo', 'bar', 'baz'], [1, 2, 3])), + ) + for name, ind in self.indices.items(): + setattr(self, name, ind) def test_wrong_number_names(self): def testit(ind): ind.names = ["apple", "banana", "carrot"] - indices = (self.dateIndex, self.unicodeIndex, self.strIndex, - self.intIndex, self.floatIndex, self.empty, self.tuples) - for ind in indices: + for ind in self.indices.values(): assertRaisesRegexp(ValueError, "^Length", testit, ind) def test_set_name_methods(self): @@ -700,6 +702,10 @@ def test_hash_error(self): type(self.float).__name__): hash(self.float) + def test_repr_roundtrip(self): + for ind in (self.mixed, self.float): + tm.assert_index_equal(eval(repr(ind)), ind) + def check_is_index(self, i): self.assert_(isinstance(i, Index) and not isinstance(i, Float64Index)) @@ -1167,6 +1173,9 @@ def test_repr_summary(self): self.assertTrue(len(r) < 100) self.assertTrue("..." in r) + def test_repr_roundtrip(self): + tm.assert_index_equal(eval(repr(self.index)), self.index) + def test_unicode_string_with_unicode(self): idx = Index(lrange(1000)) @@ -2291,6 +2300,9 @@ def test_repr_with_unicode_data(self): index = pd.DataFrame(d).set_index(["a", "b"]).index self.assertFalse("\\u" in repr(index)) # we don't want unicode-escaped + def test_repr_roundtrip(self): + tm.assert_index_equal(eval(repr(self.index)), self.index) + def test_unicode_string_with_unicode(self): d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} idx = pd.DataFrame(d).set_index(["a", "b"]).index
MI repr was missing a comma between its arguments and Index reprs needed to quote their dtypes. Only talking about Index, Int64Index and MultiIndex. Tseries indices (like PeriodIndex and DatetimeIndex) are more complicated and could be covered separately.
https://api.github.com/repos/pandas-dev/pandas/pulls/5077
2013-10-02T00:43:52Z
2013-10-12T17:21:01Z
2013-10-12T17:21:01Z
2014-09-02T13:21:29Z
TST/CI: make sure that locales are tested
diff --git a/.travis.yml b/.travis.yml index 387dec1ed2658..818278eebf5b5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,13 +6,13 @@ python: matrix: include: - python: 2.6 - env: NOSE_ARGS="not slow" CLIPBOARD=xclip + env: NOSE_ARGS="not slow" CLIPBOARD=xclip LOCALE_OVERRIDE="it_IT.UTF-8" - python: 2.7 env: NOSE_ARGS="slow and not network" LOCALE_OVERRIDE="zh_CN.GB18030" FULL_DEPS=true JOB_TAG=_LOCALE - python: 2.7 - env: NOSE_ARGS="not slow" FULL_DEPS=true GUI=gtk2 + env: NOSE_ARGS="not slow" FULL_DEPS=true CLIPBOARD_GUI=gtk2 - python: 3.2 - env: NOSE_ARGS="not slow" FULL_DEPS=true GUI=qt4 + env: NOSE_ARGS="not slow" FULL_DEPS=true CLIPBOARD_GUI=qt4 - python: 3.3 env: NOSE_ARGS="not slow" FULL_DEPS=true CLIPBOARD=xsel exclude: @@ -25,28 +25,25 @@ virtualenv: system_site_packages: true before_install: - - echo "Waldo1" + - echo "before_install" - echo $VIRTUAL_ENV - df -h - date - # - export PIP_ARGS=-q # comment this this to debug travis install issues - # - export APT_ARGS=-qq # comment this to debug travis install issues - # - set -x # enable this to see bash commands - - export ZIP_FLAGS=-q # comment this to debug travis install issues - ci/before_install.sh - python -V + # Xvfb stuff for clipboard functionality; see the travis-ci documentation - export DISPLAY=:99.0 - sh -e /etc/init.d/xvfb start install: - - echo "Waldo2" + - echo "install" - ci/install.sh before_script: - mysql -e 'create database pandas_nosetest;' script: - - echo "Waldo3" + - echo "script" - ci/script.sh after_script: diff --git a/ci/install.sh b/ci/install.sh index a30aba9338db2..528d669ae693c 100755 --- a/ci/install.sh +++ b/ci/install.sh @@ -13,20 +13,37 @@ # (no compiling needed), then directly goto script and collect 200$. # -echo "inside $0" +function edit_init() +{ + if [ -n "$LOCALE_OVERRIDE" ]; then + echo "Adding locale to the first line of pandas/__init__.py" + rm -f pandas/__init__.pyc + sedc="3iimport locale\nlocale.setlocale(locale.LC_ALL, '$LOCALE_OVERRIDE')\n" + sed -i "$sedc" pandas/__init__.py + echo "head -4 pandas/__init__.py" + head -4 pandas/__init__.py + echo + fi +} + +edit_init # Install Dependencies -# as of pip 1.4rc2, wheel files are still being broken regularly, this is a known good -# commit. should revert to pypi when a final release is out -pip install -I git+https://github.com/pypa/pip@42102e9deaea99db08b681d06906c2945f6f95e2#egg=pip -pv="${TRAVIS_PYTHON_VERSION:0:1}" -[ "$pv" == "2" ] && pv="" +# as of pip 1.4rc2, wheel files are still being broken regularly, this is a +# known good commit. should revert to pypi when a final release is out +pip_commit=42102e9deaea99db08b681d06906c2945f6f95e2 +pip install -I git+https://github.com/pypa/pip@$pip_commit#egg=pip + +python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" +[ "$python_major_version" == "2" ] && python_major_version="" pip install -I -U setuptools pip install wheel # comment this line to disable the fetching of wheel files -PIP_ARGS+=" -I --use-wheel --find-links=http://cache27diy-cpycloud.rhcloud.com/${TRAVIS_PYTHON_VERSION}${JOB_TAG}/" +base_url=http://cache27diy-cpycloud.rhcloud.com +wheel_box=${TRAVIS_PYTHON_VERSION}${JOB_TAG} +PIP_ARGS+=" -I --use-wheel --find-links=$base_url/$wheel_box/" # Force virtualenv to accpet system_site_packages rm -f $VIRTUAL_ENV/lib/python$TRAVIS_PYTHON_VERSION/no-global-site-packages.txt @@ -35,25 +52,37 @@ rm -f $VIRTUAL_ENV/lib/python$TRAVIS_PYTHON_VERSION/no-global-site-packages.txt if [ -n "$LOCALE_OVERRIDE" ]; then # make sure the locale is available # probably useless, since you would need to relogin - sudo locale-gen "$LOCALE_OVERRIDE" + time sudo locale-gen "$LOCALE_OVERRIDE" fi - # show-skipped is working at this particular commit -time pip install git+git://github.com/cpcloud/nose-show-skipped.git@fa4ff84e53c09247753a155b428c1bf2c69cb6c3 -time pip install $PIP_ARGS -r ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.txt -time sudo apt-get install libatlas-base-dev gfortran +show_skipped_commit=fa4ff84e53c09247753a155b428c1bf2c69cb6c3 +time pip install git+git://github.com/cpcloud/nose-show-skipped.git@$show_skipped_commit +time pip install $PIP_ARGS -r ci/requirements-${wheel_box}.txt + +# we need these for numpy +time sudo apt-get $APT_ARGS install libatlas-base-dev gfortran + + +# Need to enable for locale testing. The location of the locale file(s) is +# distro specific. For example, on Arch Linux all of the locales are in a +# commented file--/etc/locale.gen--that must be commented in to be used +# whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales +# based on what's in the files in that folder +time echo 'it_CH.UTF-8 UTF-8' | sudo tee -a /var/lib/locales/supported.d/it +time sudo locale-gen # install gui for clipboard testing -if [ -n "$GUI" ]; then - echo "Using GUI clipboard: $GUI" - [ -n "$pv" ] && py="py" - time sudo apt-get $APT_ARGS install python${pv}-${py}${GUI} +if [ -n "$CLIPBOARD_GUI" ]; then + echo "Using CLIPBOARD_GUI: $CLIPBOARD_GUI" + [ -n "$python_major_version" ] && py="py" + python_cb_gui_pkg=python${python_major_version}-${py}${CLIPBOARD_GUI} + time sudo apt-get $APT_ARGS install $python_cb_gui_pkg fi -# install a clipboard +# install a clipboard if $CLIPBOARD is not empty if [ -n "$CLIPBOARD" ]; then echo "Using clipboard: $CLIPBOARD" time sudo apt-get $APT_ARGS install $CLIPBOARD @@ -61,13 +90,15 @@ fi # Optional Deps -if [ x"$FULL_DEPS" == x"true" ]; then +if [ -n "$FULL_DEPS" ]; then echo "Installing FULL_DEPS" - # for pytables gets the lib as well + + # need libhdf5 for PyTables time sudo apt-get $APT_ARGS install libhdf5-serial-dev fi -# build pandas + +# build and install pandas time python setup.py build_ext install true diff --git a/ci/script.sh b/ci/script.sh index 2bafe13687505..67dadde2b20fb 100755 --- a/ci/script.sh +++ b/ci/script.sh @@ -5,8 +5,8 @@ echo "inside $0" if [ -n "$LOCALE_OVERRIDE" ]; then export LC_ALL="$LOCALE_OVERRIDE"; echo "Setting LC_ALL to $LOCALE_OVERRIDE" - (cd /; python -c 'import pandas; print("pandas detected console encoding: %s" % pandas.get_option("display.encoding"))') - + pycmd='import pandas; print("pandas detected console encoding: %s" % pandas.get_option("display.encoding"))' + python -c "$pycmd" fi echo nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --show-skipped diff --git a/doc/source/release.rst b/doc/source/release.rst index 7776ee1efba4f..4a25a98f2cfbe 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -578,6 +578,9 @@ Bug Fixes - Fix a bug with ``NDFrame.replace()`` which made replacement appear as though it was (incorrectly) using regular expressions (:issue:`5143`). - Fix better error message for to_datetime (:issue:`4928`) + - Made sure different locales are tested on travis-ci (:issue:`4918`). Also + adds a couple of utilities for getting locales and setting locales with a + context manager. pandas 0.12.0 ------------- diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index f647b217fb260..4e2331f05001d 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -13,6 +13,7 @@ from pandas.io.data import DataReader, SymbolWarning from pandas.util.testing import (assert_series_equal, assert_produces_warning, network, assert_frame_equal) +import pandas.util.testing as tm from numpy.testing import assert_array_equal @@ -35,6 +36,15 @@ def assert_n_failed_equals_n_null_columns(wngs, obj, cls=SymbolWarning): class TestGoogle(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.locales = tm.get_locales(prefix='en_US') + if not cls.locales: + raise nose.SkipTest("US English locale not available for testing") + + @classmethod + def tearDownClass(cls): + del cls.locales @network def test_google(self): @@ -44,9 +54,10 @@ def test_google(self): start = datetime(2010, 1, 1) end = datetime(2013, 1, 27) - self.assertEquals( - web.DataReader("F", 'google', start, end)['Close'][-1], - 13.68) + for locale in self.locales: + with tm.set_locale(locale): + panel = web.DataReader("F", 'google', start, end) + self.assertEquals(panel.Close[-1], 13.68) self.assertRaises(Exception, web.DataReader, "NON EXISTENT TICKER", 'google', start, end) @@ -58,38 +69,40 @@ def test_get_quote_fails(self): @network def test_get_goog_volume(self): - df = web.get_data_google('GOOG') - self.assertEqual(df.Volume.ix['OCT-08-2010'], 2863473) + for locale in self.locales: + with tm.set_locale(locale): + df = web.get_data_google('GOOG').sort_index() + self.assertEqual(df.Volume.ix['OCT-08-2010'], 2863473) @network def test_get_multi1(self): - sl = ['AAPL', 'AMZN', 'GOOG'] - pan = web.get_data_google(sl, '2012') - - def testit(): + for locale in self.locales: + sl = ['AAPL', 'AMZN', 'GOOG'] + with tm.set_locale(locale): + pan = web.get_data_google(sl, '2012') ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG] - self.assertEquals(ts[0].dayofyear, 96) - - if (hasattr(pan, 'Close') and hasattr(pan.Close, 'GOOG') and - hasattr(pan.Close, 'AAPL')): - testit() - else: - self.assertRaises(AttributeError, testit) + if (hasattr(pan, 'Close') and hasattr(pan.Close, 'GOOG') and + hasattr(pan.Close, 'AAPL')): + self.assertEquals(ts[0].dayofyear, 96) + else: + self.assertRaises(AttributeError, lambda: pan.Close) @network def test_get_multi2(self): with warnings.catch_warnings(record=True) as w: - pan = web.get_data_google(['GE', 'MSFT', 'INTC'], 'JAN-01-12', - 'JAN-31-12') - result = pan.Close.ix['01-18-12'] - assert_n_failed_equals_n_null_columns(w, result) - - # sanity checking - - assert np.issubdtype(result.dtype, np.floating) - result = pan.Open.ix['Jan-15-12':'Jan-20-12'] - self.assertEqual((4, 3), result.shape) - assert_n_failed_equals_n_null_columns(w, result) + for locale in self.locales: + with tm.set_locale(locale): + pan = web.get_data_google(['GE', 'MSFT', 'INTC'], + 'JAN-01-12', 'JAN-31-12') + result = pan.Close.ix['01-18-12'] + assert_n_failed_equals_n_null_columns(w, result) + + # sanity checking + + assert np.issubdtype(result.dtype, np.floating) + result = pan.Open.ix['Jan-15-12':'Jan-20-12'] + self.assertEqual((4, 3), result.shape) + assert_n_failed_equals_n_null_columns(w, result) class TestYahoo(unittest.TestCase): diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py index 8c7d89641bdd4..6d392eb265752 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/test_json/test_pandas.py @@ -1,11 +1,9 @@ # pylint: disable-msg=W0612,E1101 from pandas.compat import range, lrange, StringIO from pandas import compat -from pandas.io.common import URLError import os import unittest -import nose import numpy as np from pandas import Series, DataFrame, DatetimeIndex, Timestamp @@ -16,7 +14,6 @@ assert_series_equal, network, ensure_clean, assert_index_equal) import pandas.util.testing as tm -from numpy.testing.decorators import slow _seriesd = tm.getSeriesData() _tsd = tm.getTimeSeriesData() @@ -53,17 +50,35 @@ def setUp(self): self.tsframe = _tsframe.copy() self.mixed_frame = _mixed_frame.copy() + def tearDown(self): + del self.dirpath + + del self.ts + + del self.series + + del self.objSeries + + del self.empty_series + del self.empty_frame + + del self.frame + del self.frame2 + del self.intframe + del self.tsframe + del self.mixed_frame + def test_frame_double_encoded_labels(self): df = DataFrame([['a', 'b'], ['c', 'd']], index=['index " 1', 'index / 2'], columns=['a \\ b', 'y / z']) - assert_frame_equal( - df, read_json(df.to_json(orient='split'), orient='split')) - assert_frame_equal( - df, read_json(df.to_json(orient='columns'), orient='columns')) - assert_frame_equal( - df, read_json(df.to_json(orient='index'), orient='index')) + assert_frame_equal(df, read_json(df.to_json(orient='split'), + orient='split')) + assert_frame_equal(df, read_json(df.to_json(orient='columns'), + orient='columns')) + assert_frame_equal(df, read_json(df.to_json(orient='index'), + orient='index')) df_unser = read_json(df.to_json(orient='records'), orient='records') assert_index_equal(df.columns, df_unser.columns) np.testing.assert_equal(df.values, df_unser.values) @@ -75,10 +90,10 @@ def test_frame_non_unique_index(self): self.assertRaises(ValueError, df.to_json, orient='index') self.assertRaises(ValueError, df.to_json, orient='columns') - assert_frame_equal( - df, read_json(df.to_json(orient='split'), orient='split')) + assert_frame_equal(df, read_json(df.to_json(orient='split'), + orient='split')) unser = read_json(df.to_json(orient='records'), orient='records') - self.assert_(df.columns.equals(unser.columns)) + self.assertTrue(df.columns.equals(unser.columns)) np.testing.assert_equal(df.values, unser.values) unser = read_json(df.to_json(orient='values'), orient='values') np.testing.assert_equal(df.values, unser.values) @@ -102,7 +117,8 @@ def test_frame_non_unique_columns(self): assert_frame_equal(result, df) def _check(df): - result = read_json(df.to_json(orient='split'), orient='split', convert_dates=['x']) + result = read_json(df.to_json(orient='split'), orient='split', + convert_dates=['x']) assert_frame_equal(result, df) for o in [[['a','b'],['c','d']], @@ -112,15 +128,15 @@ def _check(df): _check(DataFrame(o, index=[1,2], columns=['x','x'])) def test_frame_from_json_to_json(self): - - def _check_orient(df, orient, dtype=None, numpy=False, convert_axes=True, check_dtype=True, raise_ok=None): + def _check_orient(df, orient, dtype=None, numpy=False, + convert_axes=True, check_dtype=True, raise_ok=None): df = df.sort() dfjson = df.to_json(orient=orient) try: unser = read_json(dfjson, orient=orient, dtype=dtype, numpy=numpy, convert_axes=convert_axes) - except (Exception) as detail: + except Exception as detail: if raise_ok is not None: if isinstance(detail, raise_ok): return @@ -151,7 +167,8 @@ def _check_orient(df, orient, dtype=None, numpy=False, convert_axes=True, check_ if convert_axes: assert_frame_equal(df, unser, check_dtype=check_dtype) else: - assert_frame_equal(df, unser, check_less_precise=False, check_dtype=check_dtype) + assert_frame_equal(df, unser, check_less_precise=False, + check_dtype=check_dtype) def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None): @@ -171,17 +188,27 @@ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None): # numpy=True and raise_ok might be not None, so ignore the error if convert_axes: - _check_orient(df, "columns", dtype=dtype, numpy=True, raise_ok=raise_ok) - _check_orient(df, "records", dtype=dtype, numpy=True, raise_ok=raise_ok) - _check_orient(df, "split", dtype=dtype, numpy=True, raise_ok=raise_ok) - _check_orient(df, "index", dtype=dtype, numpy=True, raise_ok=raise_ok) - _check_orient(df, "values", dtype=dtype, numpy=True, raise_ok=raise_ok) - - _check_orient(df, "columns", dtype=dtype, numpy=True, convert_axes=False, raise_ok=raise_ok) - _check_orient(df, "records", dtype=dtype, numpy=True, convert_axes=False, raise_ok=raise_ok) - _check_orient(df, "split", dtype=dtype, numpy=True, convert_axes=False, raise_ok=raise_ok) - _check_orient(df, "index", dtype=dtype, numpy=True, convert_axes=False, raise_ok=raise_ok) - _check_orient(df, "values", dtype=dtype, numpy=True, convert_axes=False, raise_ok=raise_ok) + _check_orient(df, "columns", dtype=dtype, numpy=True, + raise_ok=raise_ok) + _check_orient(df, "records", dtype=dtype, numpy=True, + raise_ok=raise_ok) + _check_orient(df, "split", dtype=dtype, numpy=True, + raise_ok=raise_ok) + _check_orient(df, "index", dtype=dtype, numpy=True, + raise_ok=raise_ok) + _check_orient(df, "values", dtype=dtype, numpy=True, + raise_ok=raise_ok) + + _check_orient(df, "columns", dtype=dtype, numpy=True, + convert_axes=False, raise_ok=raise_ok) + _check_orient(df, "records", dtype=dtype, numpy=True, + convert_axes=False, raise_ok=raise_ok) + _check_orient(df, "split", dtype=dtype, numpy=True, + convert_axes=False, raise_ok=raise_ok) + _check_orient(df, "index", dtype=dtype, numpy=True, + convert_axes=False, raise_ok=raise_ok) + _check_orient(df, "values", dtype=dtype, numpy=True, + convert_axes=False, raise_ok=raise_ok) # basic _check_all_orients(self.frame) @@ -202,9 +229,10 @@ def _check_all_orients(df, dtype=None, convert_axes=True, raise_ok=None): # dtypes _check_all_orients(DataFrame(biggie, dtype=np.float64), dtype=np.float64, convert_axes=False) - _check_all_orients(DataFrame(biggie, dtype=np.int), dtype=np.int, convert_axes=False) - _check_all_orients(DataFrame(biggie, dtype='U3'), dtype='U3', convert_axes=False, - raise_ok=ValueError) + _check_all_orients(DataFrame(biggie, dtype=np.int), dtype=np.int, + convert_axes=False) + _check_all_orients(DataFrame(biggie, dtype='U3'), dtype='U3', + convert_axes=False, raise_ok=ValueError) # empty _check_all_orients(self.empty_frame) @@ -258,37 +286,37 @@ def test_frame_from_json_bad_data(self): def test_frame_from_json_nones(self): df = DataFrame([[1, 2], [4, 5, 6]]) unser = read_json(df.to_json()) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) df = DataFrame([['1', '2'], ['4', '5', '6']]) unser = read_json(df.to_json()) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) unser = read_json(df.to_json(),dtype=False) - self.assert_(unser[2][0] is None) + self.assertTrue(unser[2][0] is None) unser = read_json(df.to_json(),convert_axes=False,dtype=False) - self.assert_(unser['2']['0'] is None) + self.assertTrue(unser['2']['0'] is None) unser = read_json(df.to_json(), numpy=False) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) unser = read_json(df.to_json(), numpy=False, dtype=False) - self.assert_(unser[2][0] is None) + self.assertTrue(unser[2][0] is None) unser = read_json(df.to_json(), numpy=False, convert_axes=False, dtype=False) - self.assert_(unser['2']['0'] is None) + self.assertTrue(unser['2']['0'] is None) # infinities get mapped to nulls which get mapped to NaNs during # deserialisation df = DataFrame([[1, 2], [4, 5, 6]]) df[2][0] = np.inf unser = read_json(df.to_json()) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) unser = read_json(df.to_json(), dtype=False) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) df[2][0] = np.NINF unser = read_json(df.to_json()) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) unser = read_json(df.to_json(),dtype=False) - self.assert_(np.isnan(unser[2][0])) + self.assertTrue(np.isnan(unser[2][0])) def test_frame_to_json_except(self): df = DataFrame([1, 2, 3]) @@ -345,7 +373,7 @@ def _check_orient(series, orient, dtype=None, numpy=False): except: raise if orient == "split": - self.assert_(series.name == unser.name) + self.assertEqual(series.name, unser.name) def _check_all_orients(series, dtype=None): _check_orient(series, "columns", dtype=dtype) @@ -403,12 +431,12 @@ def test_reconstruction_index(self): result = read_json(df.to_json()) # the index is serialized as strings....correct? - #assert_frame_equal(result,df) + assert_frame_equal(result, df) def test_path(self): with ensure_clean('test.json') as path: - - for df in [ self.frame, self.frame2, self.intframe, self.tsframe, self.mixed_frame ]: + for df in [self.frame, self.frame2, self.intframe, self.tsframe, + self.mixed_frame]: df.to_json(path) read_json(path) @@ -512,7 +540,6 @@ def test_date_unit(self): assert_frame_equal(result, df) def test_weird_nested_json(self): - # this used to core dump the parser s = r'''{ "status": "success", @@ -528,9 +555,9 @@ def test_weird_nested_json(self): "title": "Another blog post", "body": "More content" } - ] - } -}''' + ] + } + }''' read_json(s) @@ -550,18 +577,19 @@ def test_misc_example(self): # parsing unordered input fails result = read_json('[{"a": 1, "b": 2}, {"b":2, "a" :1}]',numpy=True) expected = DataFrame([[1,2],[1,2]],columns=['a','b']) - #assert_frame_equal(result,expected) + with tm.assertRaisesRegexp(AssertionError, + '\[index\] left \[.+\], right \[.+\]'): + assert_frame_equal(result, expected) result = read_json('[{"a": 1, "b": 2}, {"b":2, "a" :1}]') expected = DataFrame([[1,2],[1,2]],columns=['a','b']) assert_frame_equal(result,expected) @network - @slow def test_round_trip_exception_(self): # GH 3867 - - df = pd.read_csv('https://raw.github.com/hayd/lahman2012/master/csvs/Teams.csv') + csv = 'https://raw.github.com/hayd/lahman2012/master/csvs/Teams.csv' + df = pd.read_csv(csv) s = df.to_json() result = pd.read_json(s) assert_frame_equal(result.reindex(index=df.index,columns=df.columns),df) @@ -569,12 +597,9 @@ def test_round_trip_exception_(self): @network def test_url(self): url = 'https://api.github.com/repos/pydata/pandas/issues?per_page=5' - result = read_json(url,convert_dates=True) - for c in ['created_at','closed_at','updated_at']: - self.assert_(result[c].dtype == 'datetime64[ns]') - - url = 'http://search.twitter.com/search.json?q=pandas%20python' - result = read_json(url) + result = read_json(url, convert_dates=True) + for c in ['created_at', 'closed_at', 'updated_at']: + self.assertEqual(result[c].dtype, 'datetime64[ns]') def test_default_handler(self): from datetime import timedelta @@ -585,6 +610,6 @@ def test_default_handler(self): expected, pd.read_json(frame.to_json(default_handler=str))) def my_handler_raises(obj): - raise TypeError - self.assertRaises( - TypeError, frame.to_json, default_handler=my_handler_raises) + raise TypeError("raisin") + self.assertRaises(TypeError, frame.to_json, + default_handler=my_handler_raises) diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/test_json/test_ujson.py index 0b3bff7a151cc..06ff5abf7cd13 100644 --- a/pandas/io/tests/test_json/test_ujson.py +++ b/pandas/io/tests/test_json/test_ujson.py @@ -32,6 +32,7 @@ def _skip_if_python_ver(skip_major, skip_minor=None): if major == skip_major and (skip_minor is None or minor == skip_minor): raise nose.SkipTest("skipping Python version %d.%d" % (major, minor)) + json_unicode = (json.dumps if sys.version_info[0] >= 3 else partial(json.dumps, encoding="utf-8")) @@ -194,7 +195,6 @@ def test_invalidDoublePrecision(self): # will throw typeError self.assertRaises(TypeError, ujson.encode, input, double_precision = None) - def test_encodeStringConversion(self): input = "A string \\ / \b \f \n \r \t" output = ujson.encode(input) @@ -220,7 +220,6 @@ def test_encodeControlEscaping(self): self.assertEquals(input, dec) self.assertEquals(enc, json_unicode(input)) - def test_encodeUnicodeConversion2(self): input = "\xe6\x97\xa5\xd1\x88" enc = ujson.encode(input) @@ -259,7 +258,6 @@ def test_encodeUnicode4BytesUTF8Highest(self): self.assertEquals(enc, json_unicode(input)) self.assertEquals(dec, json.loads(enc)) - def test_encodeArrayInArray(self): input = [[[[]]]] output = ujson.encode(input) @@ -286,7 +284,6 @@ def test_encodeIntNegConversion(self): self.assertEquals(input, ujson.decode(output)) pass - def test_encodeLongNegConversion(self): input = -9223372036854775808 output = ujson.encode(input) @@ -448,7 +445,6 @@ def test_encodeDoubleNegInf(self): input = -np.inf assert ujson.encode(input) == 'null', "Expected null" - def test_decodeJibberish(self): input = "fdsa sda v9sa fdsa" try: @@ -566,7 +562,6 @@ def test_decodeNullBroken(self): return assert False, "Wrong exception" - def test_decodeBrokenDictKeyTypeLeakTest(self): input = '{{1337:""}}' for x in range(1000): @@ -667,7 +662,6 @@ def test_decodeNullCharacter(self): input = "\"31337 \\u0000 31337\"" self.assertEquals(ujson.decode(input), json.loads(input)) - def test_encodeListLongConversion(self): input = [9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807, 9223372036854775807 ] @@ -1147,6 +1141,7 @@ def testArrayNumpyLabelled(self): self.assertTrue((np.array(['1','2','3']) == output[1]).all()) self.assertTrue((np.array(['a', 'b']) == output[2]).all()) + class PandasJSONTests(TestCase): def testDataFrame(self): @@ -1178,7 +1173,6 @@ def testDataFrame(self): assert_array_equal(df.transpose().columns, outp.columns) assert_array_equal(df.transpose().index, outp.index) - def testDataFrameNumpy(self): df = DataFrame([[1,2,3], [4,5,6]], index=['a', 'b'], columns=['x', 'y', 'z']) @@ -1486,7 +1480,6 @@ def test_decodeArrayFaultyUnicode(self): else: assert False, "expected ValueError" - def test_decodeFloatingPointAdditionalTests(self): places = 15 @@ -1529,39 +1522,10 @@ def test_encodeSet(self): self.assertTrue(v in s) -""" -def test_decodeNumericIntFrcOverflow(self): -input = "X.Y" -raise NotImplementedError("Implement this test!") - - -def test_decodeStringUnicodeEscape(self): -input = "\u3131" -raise NotImplementedError("Implement this test!") - -def test_decodeStringUnicodeBrokenEscape(self): -input = "\u3131" -raise NotImplementedError("Implement this test!") - -def test_decodeStringUnicodeInvalidEscape(self): -input = "\u3131" -raise NotImplementedError("Implement this test!") - -def test_decodeStringUTF8(self): -input = "someutfcharacters" -raise NotImplementedError("Implement this test!") - - - -""" - def _clean_dict(d): return dict((str(k), v) for k, v in compat.iteritems(d)) + if __name__ == '__main__': - # unittest.main() - import nose - # nose.runmodule(argv=[__file__,'-vvs','-x', '--ipdb-failure'], - # exit=False) nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py index 1888f2ede35e0..614f5ecc39e9d 100644 --- a/pandas/tools/tests/test_util.py +++ b/pandas/tools/tests/test_util.py @@ -1,12 +1,21 @@ import os -import nose +import locale +import codecs import unittest +import nose + import numpy as np from numpy.testing import assert_equal +import pandas.util.testing as tm from pandas.tools.util import cartesian_product + +CURRENT_LOCALE = locale.getlocale() +LOCALE_OVERRIDE = os.environ.get('LOCALE_OVERRIDE', None) + + class TestCartesianProduct(unittest.TestCase): def test_simple(self): @@ -16,6 +25,61 @@ def test_simple(self): np.array([ 1, 22, 1, 22, 1, 22])] assert_equal(result, expected) + +class TestLocaleUtils(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.locales = tm.get_locales() + + if not cls.locales: + raise nose.SkipTest("No locales found") + + if os.name == 'nt': # we're on windows + raise nose.SkipTest("Running on Windows") + + @classmethod + def tearDownClass(cls): + del cls.locales + + def test_get_locales(self): + # all systems should have at least a single locale + assert len(tm.get_locales()) > 0 + + def test_get_locales_prefix(self): + if len(self.locales) == 1: + raise nose.SkipTest("Only a single locale found, no point in " + "trying to test filtering locale prefixes") + first_locale = self.locales[0] + assert len(tm.get_locales(prefix=first_locale[:2])) > 0 + + def test_set_locale(self): + if len(self.locales) == 1: + raise nose.SkipTest("Only a single locale found, no point in " + "trying to test setting another locale") + + if LOCALE_OVERRIDE is not None: + lang, enc = LOCALE_OVERRIDE.split('.') + else: + lang, enc = 'it_CH', 'UTF-8' + + enc = codecs.lookup(enc).name + new_locale = lang, enc + + if not tm._can_set_locale('.'.join(new_locale)): + with tm.assertRaises(locale.Error): + with tm.set_locale(new_locale): + pass + else: + with tm.set_locale(new_locale) as normalized_locale: + new_lang, new_enc = normalized_locale.split('.') + new_enc = codecs.lookup(enc).name + normalized_locale = new_lang, new_enc + self.assertEqual(normalized_locale, new_locale) + + current_locale = locale.getlocale() + self.assertEqual(current_locale, CURRENT_LOCALE) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tseries/converter.py b/pandas/tseries/converter.py index bfbd28f7bb4a4..d059d229ef22e 100644 --- a/pandas/tseries/converter.py +++ b/pandas/tseries/converter.py @@ -10,7 +10,7 @@ from matplotlib.ticker import Formatter, AutoLocator, Locator from matplotlib.transforms import nonsingular -from pandas.compat import range, lrange +from pandas.compat import lrange import pandas.compat as compat import pandas.lib as lib import pandas.core.common as com diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index cfbde75f6ae21..a5e249b77fa52 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -317,7 +317,8 @@ def _test(ax): result = ax.get_xlim() self.assertEqual(int(result[0]), expected[0].ordinal) self.assertEqual(int(result[1]), expected[1].ordinal) - plt.close(ax.get_figure()) + fig = ax.get_figure() + plt.close(fig) ser = tm.makeTimeSeries() ax = ser.plot() diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 3dcfa3621895e..c6c2b418f553d 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -35,6 +35,11 @@ from datetime import timedelta, datetime from datetime import time as datetime_time from pandas.compat import parse_date +from sys import version_info + +# GH3363 +cdef bint PY2 = version_info[0] == 2 + # initialize numpy import_array() #import_ufunc() @@ -1757,20 +1762,20 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, bint infer_dst=False): # timestamp falls to the right side of the DST transition if v + deltas[pos] == vals[i]: result_b[i] = v - - + + if infer_dst: dst_hours = np.empty(n, dtype=np.int64) dst_hours.fill(NPY_NAT) - + # Get the ambiguous hours (given the above, these are the hours - # where result_a != result_b and neither of them are NAT) + # where result_a != result_b and neither of them are NAT) both_nat = np.logical_and(result_a != NPY_NAT, result_b != NPY_NAT) both_eq = result_a == result_b trans_idx = np.squeeze(np.nonzero(np.logical_and(both_nat, ~both_eq))) if trans_idx.size == 1: stamp = Timestamp(vals[trans_idx]) - raise pytz.AmbiguousTimeError("Cannot infer dst time from %s as" + raise pytz.AmbiguousTimeError("Cannot infer dst time from %s as" "there are no repeated times" % stamp) # Split the array into contiguous chunks (where the difference between # indices is 1). These are effectively dst transitions in different years @@ -1779,21 +1784,21 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, bint infer_dst=False): if trans_idx.size > 0: one_diff = np.where(np.diff(trans_idx)!=1)[0]+1 trans_grp = np.array_split(trans_idx, one_diff) - + # Iterate through each day, if there are no hours where the delta is negative # (indicates a repeat of hour) the switch cannot be inferred for grp in trans_grp: - + delta = np.diff(result_a[grp]) if grp.size == 1 or np.all(delta>0): stamp = Timestamp(vals[grp[0]]) raise pytz.AmbiguousTimeError(stamp) - + # Find the index for the switch and pull from a for dst and b for standard switch_idx = (delta<=0).nonzero()[0] if switch_idx.size > 1: raise pytz.AmbiguousTimeError("There are %i dst switches " - "when there should only be 1." + "when there should only be 1." % switch_idx.size) switch_idx = switch_idx[0]+1 # Pull the only index and adjust a_idx = grp[:switch_idx] @@ -1812,7 +1817,7 @@ def tz_localize_to_utc(ndarray[int64_t] vals, object tz, bint infer_dst=False): else: stamp = Timestamp(vals[i]) raise pytz.AmbiguousTimeError("Cannot infer dst time from %r, "\ - "try using the 'infer_dst' argument" + "try using the 'infer_dst' argument" % stamp) elif left != NPY_NAT: result[i] = left @@ -2549,8 +2554,9 @@ cdef list extra_fmts = [(b"%q", b"^`AB`^"), cdef list str_extra_fmts = ["^`AB`^", "^`CD`^", "^`EF`^", "^`GH`^", "^`IJ`^", "^`KL`^"] -cdef _period_strftime(int64_t value, int freq, object fmt): +cdef object _period_strftime(int64_t value, int freq, object fmt): import sys + cdef: Py_ssize_t i date_info dinfo @@ -2595,13 +2601,8 @@ cdef _period_strftime(int64_t value, int freq, object fmt): result = result.replace(str_extra_fmts[i], repl) - # Py3? - if not PyString_Check(result): - result = str(result) - - # GH3363 - if sys.version_info[0] == 2: - result = result.decode('utf-8','strict') + if PY2: + result = result.decode('utf-8', 'ignore') return result diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 946a4d94b6045..4787c82282a1f 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -9,6 +9,8 @@ import warnings import inspect import os +import subprocess +import locale from datetime import datetime from functools import wraps, partial @@ -20,6 +22,7 @@ import nose +import pandas as pd from pandas.core.common import isnull, _is_sequence import pandas.core.index as index import pandas.core.series as series @@ -28,7 +31,7 @@ import pandas.core.panel4d as panel4d import pandas.compat as compat from pandas.compat import( - map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter, + filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter, raise_with_traceback, httplib ) @@ -97,6 +100,172 @@ def setUpClass(cls): return cls +#------------------------------------------------------------------------------ +# locale utilities + +def check_output(*popenargs, **kwargs): # shamelessly taken from Python 2.7 source + r"""Run command with arguments and return its output as a byte string. + + If the exit code was non-zero it raises a CalledProcessError. The + CalledProcessError object will have the return code in the returncode + attribute and output in the output attribute. + + The arguments are the same as for the Popen constructor. Example: + + >>> check_output(["ls", "-l", "/dev/null"]) + 'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n' + + The stdout argument is not allowed as it is used internally. + To capture standard error in the result, use stderr=STDOUT. + + >>> check_output(["/bin/sh", "-c", + ... "ls -l non_existent_file ; exit 0"], + ... stderr=STDOUT) + 'ls: non_existent_file: No such file or directory\n' + """ + if 'stdout' in kwargs: + raise ValueError('stdout argument not allowed, it will be overridden.') + process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs) + output, unused_err = process.communicate() + retcode = process.poll() + if retcode: + cmd = kwargs.get("args") + if cmd is None: + cmd = popenargs[0] + raise subprocess.CalledProcessError(retcode, cmd, output=output) + return output + + +def _default_locale_getter(): + try: + raw_locales = check_output(['locale -a'], shell=True) + except subprocess.CalledProcessError as e: + raise type(e)("%s, the 'locale -a' command cannot be foundon your " + "system" % e) + return raw_locales + + +def get_locales(prefix=None, normalize=True, + locale_getter=_default_locale_getter): + """Get all the locales that are available on the system. + + Parameters + ---------- + prefix : str + If not ``None`` then return only those locales with the prefix + provided. For example to get all English language locales (those that + start with ``"en"``), pass ``prefix="en"``. + normalize : bool + Call ``locale.normalize`` on the resulting list of available locales. + If ``True``, only locales that can be set without throwing an + ``Exception`` are returned. + locale_getter : callable + The function to use to retrieve the current locales. This should return + a string with each locale separated by a newline character. + + Returns + ------- + locales : list of strings + A list of locale strings that can be set with ``locale.setlocale()``. + For example:: + + locale.setlocale(locale.LC_ALL, locale_string) + """ + raw_locales = locale_getter() + + try: + raw_locales = str(raw_locales, encoding=pd.options.display.encoding) + except TypeError: + pass + + if prefix is None: + return _valid_locales(raw_locales.splitlines(), normalize) + + found = re.compile('%s.*' % prefix).findall(raw_locales) + return _valid_locales(found, normalize) + + +@contextmanager +def set_locale(new_locale, lc_var=locale.LC_ALL): + """Context manager for temporarily setting a locale. + + Parameters + ---------- + new_locale : str or tuple + A string of the form <language_country>.<encoding>. For example to set + the current locale to US English with a UTF8 encoding, you would pass + "en_US.UTF-8". + + Notes + ----- + This is useful when you want to run a particular block of code under a + particular locale, without globally setting the locale. This probably isn't + thread-safe. + """ + current_locale = locale.getlocale() + + try: + locale.setlocale(lc_var, new_locale) + + try: + normalized_locale = locale.getlocale() + except ValueError: + yield new_locale + else: + if all(lc is not None for lc in normalized_locale): + yield '.'.join(normalized_locale) + else: + yield new_locale + finally: + locale.setlocale(lc_var, current_locale) + + +def _can_set_locale(lc): + """Check to see if we can set a locale without throwing an exception. + + Parameters + ---------- + lc : str + The locale to attempt to set. + + Returns + ------- + isvalid : bool + Whether the passed locale can be set + """ + try: + with set_locale(lc): + pass + except locale.Error: # horrible name for a Exception subclass + return False + else: + return True + + +def _valid_locales(locales, normalize): + """Return a list of normalized locales that do not throw an ``Exception`` + when set. + + Parameters + ---------- + locales : str + A string where each locale is separated by a newline. + normalize : bool + Whether to call ``locale.normalize`` on each locale. + + Returns + ------- + valid_locales : list + A list of valid locales. + """ + if normalize: + normalizer = lambda x: locale.normalize(x.strip()) + else: + normalizer = lambda x: x.strip() + + return list(filter(_can_set_locale, map(normalizer, locales))) + + #------------------------------------------------------------------------------ # Console debugging tools @@ -169,6 +338,7 @@ def assert_isinstance(obj, class_type_or_tuple): "Expected object to be of type %r, found %r instead" % ( type(obj), class_type_or_tuple)) + def assert_equal(a, b, msg=""): """asserts that a equals b, like nose's assert_equal, but allows custom message to start. Passes a and b to format string as well. So you can use '{0}' and '{1}' to display a and b. @@ -198,11 +368,11 @@ def assert_attr_equal(attr, left, right): right_attr = getattr(right, attr) assert_equal(left_attr,right_attr,"attr is not equal [{0}]" .format(attr)) + def isiterable(obj): return hasattr(obj, '__iter__') - def assert_almost_equal(a, b, check_less_precise=False): if isinstance(a, dict) or isinstance(b, dict): return assert_dict_equal(a, b) @@ -378,6 +548,7 @@ def assert_contains_all(iterable, dic): for k in iterable: assert k in dic, "Did not contain item: '%r'" % k + def assert_copy(iter1, iter2, **eql_kwargs): """ iter1, iter2: iterables that produce elements comparable with assert_almost_equal @@ -412,6 +583,7 @@ def makeFloatIndex(k=10): values = sorted(np.random.random_sample(k)) - np.random.random_sample(1) return Index(values * (10 ** np.random.randint(0, 9))) + def makeDateIndex(k=10): dt = datetime(2000, 1, 1) dr = bdate_range(dt, periods=k) @@ -446,6 +618,7 @@ def getSeriesData(): index = makeStringIndex(N) return dict((c, Series(randn(N), index=index)) for c in getCols(K)) + def makeTimeSeries(nper=None): if nper is None: nper = N @@ -503,11 +676,13 @@ def makePanel(nper=None): data = dict((c, makeTimeDataFrame(nper)) for c in cols) return Panel.fromDict(data) + def makePeriodPanel(nper=None): cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]] data = dict((c, makePeriodFrame(nper)) for c in cols) return Panel.fromDict(data) + def makePanel4D(nper=None): return Panel4D(dict(l1=makePanel(nper), l2=makePanel(nper), l3=makePanel(nper)))
related #4918
https://api.github.com/repos/pandas-dev/pandas/pulls/5073
2013-10-01T20:55:32Z
2013-10-09T04:07:26Z
2013-10-09T04:07:25Z
2014-06-25T23:07:36Z
CI/ENH: use nose-show-skipped plugin to show skipped tests
diff --git a/ci/install.sh b/ci/install.sh index 357d962d9610d..a30aba9338db2 100755 --- a/ci/install.sh +++ b/ci/install.sh @@ -38,6 +38,9 @@ if [ -n "$LOCALE_OVERRIDE" ]; then sudo locale-gen "$LOCALE_OVERRIDE" fi + +# show-skipped is working at this particular commit +time pip install git+git://github.com/cpcloud/nose-show-skipped.git@fa4ff84e53c09247753a155b428c1bf2c69cb6c3 time pip install $PIP_ARGS -r ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.txt time sudo apt-get install libatlas-base-dev gfortran diff --git a/ci/print_skipped.py b/ci/print_skipped.py deleted file mode 100755 index 9fb05df64bcea..0000000000000 --- a/ci/print_skipped.py +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env python - -import sys -import math -import xml.etree.ElementTree as et - - -def parse_results(filename): - tree = et.parse(filename) - root = tree.getroot() - skipped = [] - - current_class = old_class = '' - i = 1 - assert i - 1 == len(skipped) - for el in root.findall('testcase'): - cn = el.attrib['classname'] - for sk in el.findall('skipped'): - old_class = current_class - current_class = cn - name = '{classname}.{name}'.format(classname=current_class, - name=el.attrib['name']) - msg = sk.attrib['message'] - out = '' - if old_class != current_class: - ndigits = int(math.log(i, 10) + 1) - out += ('-' * (len(name + msg) + 4 + ndigits) + '\n') # 4 for : + space + # + space - out += '#{i} {name}: {msg}'.format(i=i, name=name, msg=msg) - skipped.append(out) - i += 1 - assert i - 1 == len(skipped) - assert i - 1 == len(skipped) - assert len(skipped) == int(root.attrib['skip']) - return '\n'.join(skipped) - - -def main(args): - print('SKIPPED TESTS:') - print(parse_results(args.filename)) - return 0 - - -def parse_args(): - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('filename', help='XUnit file to parse') - return parser.parse_args() - - -if __name__ == '__main__': - sys.exit(main(parse_args())) diff --git a/ci/script.sh b/ci/script.sh index 2e466e58bf377..2bafe13687505 100755 --- a/ci/script.sh +++ b/ci/script.sh @@ -9,5 +9,5 @@ if [ -n "$LOCALE_OVERRIDE" ]; then fi -echo nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --with-xunit --xunit-file=/tmp/nosetests.xml -nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --with-xunit --xunit-file=/tmp/nosetests.xml +echo nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --show-skipped +nosetests --exe -w /tmp -A "$NOSE_ARGS" pandas --show-skipped
https://api.github.com/repos/pandas-dev/pandas/pulls/5072
2013-10-01T20:51:21Z
2013-10-01T23:53:13Z
2013-10-01T23:53:13Z
2014-07-16T08:32:30Z
API: default export for to_clipboard is now csv/tsv suitable for excel (GH3368)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 6ea4e5a3046b2..34cc4e499a0d5 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -207,6 +207,8 @@ API Changes - ``Series.get`` with negative indexers now returns the same as ``[]`` (:issue:`4390`) - allow ``ix/loc`` for Series/DataFrame/Panel to set on any axis even when the single-key is not currently contained in the index for that axis (:issue:`2578`, :issue:`5226`) + - Default export for ``to_clipboard`` is now csv with a sep of `\t` for + compat (:issue:`3368`) - ``at`` now will enlarge the object inplace (and return the same) (:issue:`2578`) - ``HDFStore`` diff --git a/pandas/core/generic.py b/pandas/core/generic.py index bc5d84b9ff0f5..8a2ed9926d630 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -868,9 +868,15 @@ def load(self, path): # TODO remove in 0.14 warnings.warn("load is deprecated, use pd.read_pickle", FutureWarning) return read_pickle(path) - def to_clipboard(self): + def to_clipboard(self, sep=None, **kwargs): """ Attempt to write text representation of object to the system clipboard + This can be pasted into Excel, for example. + + Parameters + ---------- + sep : optional, defaults to comma + other keywords are passed to to_csv Notes ----- @@ -880,7 +886,7 @@ def to_clipboard(self): - OS X: none """ from pandas.io import clipboard - clipboard.to_clipboard(self) + clipboard.to_clipboard(self, sep, **kwargs) #---------------------------------------------------------------------- # Fancy Indexing diff --git a/pandas/io/clipboard.py b/pandas/io/clipboard.py index c4bea55ce2714..401a0689fe000 100644 --- a/pandas/io/clipboard.py +++ b/pandas/io/clipboard.py @@ -26,9 +26,10 @@ def read_clipboard(**kwargs): # pragma: no cover return read_table(StringIO(text), **kwargs) -def to_clipboard(obj): # pragma: no cover +def to_clipboard(obj, sep=None, **kwargs): # pragma: no cover """ Attempt to write text representation of object to the system clipboard + The clipboard can be then pasted into Excel for example. Notes ----- @@ -38,4 +39,12 @@ def to_clipboard(obj): # pragma: no cover - OS X: """ from pandas.util.clipboard import clipboard_set - clipboard_set(str(obj)) + try: + if sep is None: + sep = '\t' + buf = StringIO() + obj.to_csv(buf,sep=sep, **kwargs) + clipboard_set(buf.getvalue()) + except: + clipboard_set(str(obj)) + diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py index f5b5ba745d83c..90ec2d6fed0ce 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/io/tests/test_clipboard.py @@ -39,12 +39,19 @@ def setUpClass(cls): def tearDownClass(cls): del cls.data_types, cls.data - def check_round_trip_frame(self, data_type): + def check_round_trip_frame(self, data_type, sep=None): data = self.data[data_type] - data.to_clipboard() - result = read_clipboard() + data.to_clipboard(sep=sep) + if sep is not None: + result = read_clipboard(sep=sep,index_col=0) + else: + result = read_clipboard() tm.assert_frame_equal(data, result, check_dtype=False) + def test_round_trip_frame_sep(self): + for dt in self.data_types: + self.check_round_trip_frame(dt,sep=',') + def test_round_trip_frame(self): for dt in self.data_types: self.check_round_trip_frame(dt)
closes #3368
https://api.github.com/repos/pandas-dev/pandas/pulls/5070
2013-10-01T16:42:18Z
2013-10-16T14:49:07Z
2013-10-16T14:49:07Z
2014-06-12T07:22:42Z
CLN: remove unreachable code in tslib.pyx
diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index a77b0afb20b52..0f7a356e84664 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -13,8 +13,7 @@ DateOffset, Week, YearBegin, YearEnd, Hour, Minute, Second, Day, Micro, Milli, Nano, WeekOfMonth, format, ole2datetime, QuarterEnd, to_datetime, normalize_date, - get_offset, get_offset_name, inferTimeRule, hasOffsetName, - get_standard_freq) + get_offset, get_offset_name, hasOffsetName, get_standard_freq) from pandas.tseries.frequencies import _offset_map from pandas.tseries.index import _to_m8, DatetimeIndex, _daterange_cache @@ -532,7 +531,7 @@ def test_repr(self): self.assertEqual(repr(Week(weekday=0)), "<Week: weekday=0>") self.assertEqual(repr(Week(n=-1, weekday=0)), "<-1 * Week: weekday=0>") self.assertEqual(repr(Week(n=-2, weekday=0)), "<-2 * Weeks: weekday=0>") - + def test_corner(self): self.assertRaises(ValueError, Week, weekday=7) assertRaisesRegexp(ValueError, "Day must be", Week, weekday=-1) @@ -905,7 +904,7 @@ def test_onOffset(self): class TestBQuarterBegin(unittest.TestCase): - + def test_repr(self): self.assertEqual(repr(BQuarterBegin()),"<BusinessQuarterBegin: startingMonth=3>") self.assertEqual(repr(BQuarterBegin(startingMonth=3)), "<BusinessQuarterBegin: startingMonth=3>") @@ -1000,7 +999,7 @@ def test_repr(self): self.assertEqual(repr(BQuarterEnd()),"<BusinessQuarterEnd: startingMonth=3>") self.assertEqual(repr(BQuarterEnd(startingMonth=3)), "<BusinessQuarterEnd: startingMonth=3>") self.assertEqual(repr(BQuarterEnd(startingMonth=1)), "<BusinessQuarterEnd: startingMonth=1>") - + def test_isAnchored(self): self.assert_(BQuarterEnd(startingMonth=1).isAnchored()) self.assert_(BQuarterEnd().isAnchored()) @@ -1107,7 +1106,7 @@ def test_repr(self): self.assertEqual(repr(QuarterBegin()), "<QuarterBegin: startingMonth=3>") self.assertEqual(repr(QuarterBegin(startingMonth=3)), "<QuarterBegin: startingMonth=3>") self.assertEqual(repr(QuarterBegin(startingMonth=1)),"<QuarterBegin: startingMonth=1>") - + def test_isAnchored(self): self.assert_(QuarterBegin(startingMonth=1).isAnchored()) self.assert_(QuarterBegin().isAnchored()) @@ -1181,7 +1180,7 @@ def test_repr(self): self.assertEqual(repr(QuarterEnd()), "<QuarterEnd: startingMonth=3>") self.assertEqual(repr(QuarterEnd(startingMonth=3)), "<QuarterEnd: startingMonth=3>") self.assertEqual(repr(QuarterEnd(startingMonth=1)), "<QuarterEnd: startingMonth=1>") - + def test_isAnchored(self): self.assert_(QuarterEnd(startingMonth=1).isAnchored()) self.assert_(QuarterEnd().isAnchored()) @@ -1631,6 +1630,7 @@ def assertEq(offset, base, expected): "\nAt Date: %s" % (expected, actual, offset, base)) + def test_Hour(): assertEq(Hour(), datetime(2010, 1, 1), datetime(2010, 1, 1, 1)) assertEq(Hour(-1), datetime(2010, 1, 1, 1), datetime(2010, 1, 1)) @@ -1698,6 +1698,8 @@ def test_Microsecond(): def test_NanosecondGeneric(): + if _np_version_under1p7: + raise nose.SkipTest('numpy >= 1.7 required') timestamp = Timestamp(datetime(2010, 1, 1)) assert timestamp.nanosecond == 0 @@ -1710,7 +1712,6 @@ def test_NanosecondGeneric(): def test_Nanosecond(): if _np_version_under1p7: - import nose raise nose.SkipTest('numpy >= 1.7 required') timestamp = Timestamp(datetime(2010, 1, 1)) @@ -1815,8 +1816,6 @@ def setUp(self): pass def test_alias_equality(self): - from pandas.tseries.frequencies import _offset_map - for k, v in compat.iteritems(_offset_map): if v is None: continue @@ -1872,7 +1871,8 @@ def test_freq_offsets(): off = BDay(1, offset=timedelta(0, -1800)) assert(off.freqstr == 'B-30Min') - + + def get_all_subclasses(cls): ret = set() this_subclasses = cls.__subclasses__() @@ -1881,40 +1881,41 @@ def get_all_subclasses(cls): ret | get_all_subclasses(this_subclass) return ret -class TestCaching(unittest.TestCase): + +class TestCaching(unittest.TestCase): def test_should_cache_month_end(self): self.assertTrue(MonthEnd()._should_cache()) - + def test_should_cache_bmonth_end(self): self.assertTrue(BusinessMonthEnd()._should_cache()) - + def test_should_cache_week_month(self): self.assertTrue(WeekOfMonth(weekday=1, week=2)._should_cache()) - + def test_all_cacheableoffsets(self): for subclass in get_all_subclasses(CacheableOffset): if subclass in [WeekOfMonth]: continue self.run_X_index_creation(subclass) - + def setUp(self): _daterange_cache.clear() - + def run_X_index_creation(self, cls): inst1 = cls() if not inst1.isAnchored(): self.assertFalse(inst1._should_cache(), cls) return - + self.assertTrue(inst1._should_cache(), cls) - + DatetimeIndex(start=datetime(2013,1,31), end=datetime(2013,3,31), freq=inst1, normalize=True) self.assertTrue(cls() in _daterange_cache, cls) - + def test_month_end_index_creation(self): DatetimeIndex(start=datetime(2013,1,31), end=datetime(2013,3,31), freq=MonthEnd(), normalize=True) self.assertTrue(MonthEnd() in _daterange_cache) - + def test_bmonth_end_index_creation(self): DatetimeIndex(start=datetime(2013,1,31), end=datetime(2013,3,29), freq=BusinessMonthEnd(), normalize=True) self.assertTrue(BusinessMonthEnd() in _daterange_cache) @@ -1924,9 +1925,8 @@ def test_week_of_month_index_creation(self): DatetimeIndex(start=datetime(2013,1,31), end=datetime(2013,3,29), freq=inst1, normalize=True) inst2 = WeekOfMonth(weekday=1, week=2) self.assertTrue(inst2 in _daterange_cache) - - + + if __name__ == '__main__': - import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 0df0fc377d000..a8c27806c2c1e 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -336,7 +336,7 @@ class NaTType(_NaT): def __hash__(self): return iNaT - + def weekday(self): return -1 @@ -573,22 +573,22 @@ cdef class _Timestamp(datetime): dts.us, ts.tzinfo) def __add__(self, other): + cdef Py_ssize_t other_int + if is_timedelta64_object(other): - return Timestamp(self.value + other.astype('timedelta64[ns]').item(), tz=self.tzinfo) - + other_int = other.astype('timedelta64[ns]').astype(int) + return Timestamp(self.value + other_int, tz=self.tzinfo) + if is_integer_object(other): if self.offset is None: - return Timestamp(self.value + other, tz=self.tzinfo) - msg = ("Cannot add integral value to Timestamp " - "without offset.") - raise ValueError(msg) - else: - return Timestamp((self.offset.__mul__(other)).apply(self)) - + raise ValueError("Cannot add integral value to Timestamp " + "without offset.") + return Timestamp((self.offset * other).apply(self)) + if isinstance(other, timedelta) or hasattr(other, 'delta'): nanos = _delta_to_nanoseconds(other) return Timestamp(self.value + nanos, tz=self.tzinfo) - + result = datetime.__add__(self, other) if isinstance(result, datetime): result = Timestamp(result) @@ -597,9 +597,9 @@ cdef class _Timestamp(datetime): def __sub__(self, other): if is_integer_object(other): - return self.__add__(-other) - else: - return datetime.__sub__(self, other) + neg_other = -other + return self + neg_other + return super(_Timestamp, self).__sub__(other) cpdef _get_field(self, field): out = get_date_field(np.array([self.value], dtype=np.int64), field) @@ -2329,7 +2329,7 @@ cpdef int64_t period_asfreq(int64_t period_ordinal, int freq1, int freq2, """ cdef: int64_t retval - + if end: retval = asfreq(period_ordinal, freq1, freq2, END) else:
https://api.github.com/repos/pandas-dev/pandas/pulls/5067
2013-10-01T04:23:23Z
2013-10-01T12:34:06Z
2013-10-01T12:34:06Z
2014-07-13T18:10:17Z
BUG: Bug in setting with ix/loc and a mixed int/string index (GH4544)
diff --git a/doc/source/release.rst b/doc/source/release.rst index f5e7e66c98a64..65e6ca0e1d95c 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -520,6 +520,7 @@ Bug Fixes chunks of the same file. Now coerces to numerical type or raises warning. (:issue:`3866`) - Fix a bug where reshaping a ``Series`` to its own shape raised ``TypeError`` (:issue:`4554`) and other reshaping issues. + - Bug in setting with ``ix/loc`` and a mixed int/string index (:issue:`4544`) pandas 0.12.0 ------------- diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index eb377c4b7955f..0d19736ed8083 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -783,9 +783,25 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): - No, prefer label-based indexing """ labels = self.obj._get_axis(axis) + + # if we are a scalar indexer and not type correct raise + obj = self._convert_scalar_indexer(obj, axis) + + # see if we are positional in nature is_int_index = labels.is_integer() + is_int_positional = com.is_integer(obj) and not is_int_index - if com.is_integer(obj) and not is_int_index: + # if we are a label return me + try: + return labels.get_loc(obj) + except (KeyError, TypeError): + pass + except (ValueError): + if not is_int_positional: + raise + + # a positional + if is_int_positional: # if we are setting and its not a valid location # its an insert which fails by definition @@ -795,11 +811,6 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): return obj - try: - return labels.get_loc(obj) - except (KeyError, TypeError): - pass - if isinstance(obj, slice): return self._convert_slice_indexer(obj, axis) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 837acb90407ea..67c87277647c8 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1070,6 +1070,26 @@ def test_ix_assign_column_mixed(self): df['b'].ix[[1,3]] = [100,-100] assert_frame_equal(df,expected) + def test_ix_get_set_consistency(self): + + # GH 4544 + # ix/loc get/set not consistent when + # a mixed int/string index + df = DataFrame(np.arange(16).reshape((4, 4)), + columns=['a', 'b', 8, 'c'], + index=['e', 7, 'f', 'g']) + + self.assert_(df.ix['e', 8] == 2) + self.assert_(df.loc['e', 8] == 2) + + df.ix['e', 8] = 42 + self.assert_(df.ix['e', 8] == 42) + self.assert_(df.loc['e', 8] == 42) + + df.loc['e', 8] = 45 + self.assert_(df.ix['e', 8] == 45) + self.assert_(df.loc['e', 8] == 45) + def test_iloc_mask(self): # GH 3631, iloc with a mask (of a series) should raise diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 1f0f7a5564142..a70f2931e36fe 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -878,7 +878,7 @@ def test_setitem_float_labels(self): tmp = s.copy() s.ix[1] = 'zoo' - tmp.values[1] = 'zoo' + tmp.iloc[2] = 'zoo' assert_series_equal(s, tmp)
closes #4544
https://api.github.com/repos/pandas-dev/pandas/pulls/5064
2013-09-30T23:00:52Z
2013-10-01T01:21:47Z
2013-10-01T01:21:47Z
2014-06-24T06:52:45Z
VIS: let scatter plots obey mpl color scheme (#3338)
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 6631a3cf8c6f1..d6c0482d86be4 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -229,6 +229,7 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, >>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D']) >>> scatter_matrix(df, alpha=0.2) """ + import matplotlib.pyplot as plt from matplotlib.artist import setp df = frame._get_numeric_data() @@ -246,6 +247,9 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, hist_kwds = hist_kwds or {} density_kwds = density_kwds or {} + # workaround because `c='b'` is hardcoded in matplotlibs scatter method + kwds.setdefault('c', plt.rcParams['patch.facecolor']) + for i, a in zip(lrange(n), df.columns): for j, b in zip(lrange(n), df.columns): ax = axes[i, j] @@ -653,6 +657,10 @@ def lag_plot(series, lag=1, ax=None, **kwds): ax: Matplotlib axis object """ import matplotlib.pyplot as plt + + # workaround because `c='b'` is hardcoded in matplotlibs scatter method + kwds.setdefault('c', plt.rcParams['patch.facecolor']) + data = series.values y1 = data[:-lag] y2 = data[lag:] @@ -1889,6 +1897,9 @@ def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False, **kwarg """ import matplotlib.pyplot as plt + # workaround because `c='b'` is hardcoded in matplotlibs scatter method + kwargs.setdefault('c', plt.rcParams['patch.facecolor']) + def plot_group(group, ax): xvals = group[x].values yvals = group[y].values
Closes #3338. Fixes the remaining plot functions that don't follow the matplotlib style (functions based on scatter plots). Rationale: - set the color keyword `c` to default one defined in matplotlibs rcParams if `c` was not specified by the user (because `c='b'` is harcoded in matplotlib `scatter` method). - the `color` keyword is not checked because, if specified by the user, it already is given preference by matplotlib over `c` if both are given. There is a new PR on scatter based plotting methods (#3473), but I suppose that it can be handled in that PR to ensure this new plotting method also follows this approach?
https://api.github.com/repos/pandas-dev/pandas/pulls/5060
2013-09-30T18:36:20Z
2013-10-04T23:17:53Z
2013-10-04T23:17:52Z
2014-07-16T08:32:22Z
BF: import isnull
diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index d5bd1072f6a3e..3a99793937096 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -19,7 +19,7 @@ from pandas.util.decorators import cache_readonly, Appender, Substitution from pandas.core.common import (PandasError, ABCSeries, is_timedelta64_dtype, is_datetime64_dtype, - is_integer_dtype) + is_integer_dtype, isnull) import pandas.core.common as com
Used in the code see http://nipy.bic.berkeley.edu/builders/pandas-py2.x-wheezy-sparc/builds/148/steps/shell_4/logs/stdio for the failure
https://api.github.com/repos/pandas-dev/pandas/pulls/5059
2013-09-30T16:21:52Z
2013-09-30T16:42:04Z
2013-09-30T16:42:04Z
2014-07-16T08:32:21Z
TST: allow to check for specific xlrd version and skip reader test if xlrd < 0.9
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index 0c6332205ffe5..2cc94524b5d19 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -21,12 +21,12 @@ import pandas as pd -def _skip_if_no_xlrd(): +def _skip_if_no_xlrd(version=(0, 9)): try: import xlrd ver = tuple(map(int, xlrd.__VERSION__.split(".")[:2])) - if ver < (0, 9): - raise nose.SkipTest('xlrd < 0.9, skipping') + if ver < version: + raise nose.SkipTest('xlrd < %s, skipping' % str(version)) except ImportError: raise nose.SkipTest('xlrd not installed, skipping') @@ -350,7 +350,10 @@ def test_excelwriter_contextmanager(self): with ExcelWriter(pth) as writer: self.frame.to_excel(writer, 'Data1') self.frame2.to_excel(writer, 'Data2') - + # If above test passes with outdated xlrd, next test + # does require fresh xlrd + # http://nipy.bic.berkeley.edu/builders/pandas-py2.x-wheezy-sparc/builds/148/steps/shell_4/logs/stdio + _skip_if_no_xlrd((0, 9)) with ExcelFile(pth) as reader: found_df = reader.parse('Data1') found_df2 = reader.parse('Data2')
See http://nipy.bic.berkeley.edu/builders/pandas-py2.x-wheezy-sparc/builds/148/steps/shell_4/logs/stdio for failure when outdated xlrd found (e.g. on debian stable wheezy)
https://api.github.com/repos/pandas-dev/pandas/pulls/5058
2013-09-30T16:17:57Z
2013-10-02T23:08:16Z
2013-10-02T23:08:16Z
2014-06-28T20:27:27Z
DOC: proposed fix for #4699: Period() docstring inconsistent with code when freq has a mult != 1
diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index b28da7c9d7e0b..819777c2350a5 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -51,7 +51,7 @@ class Period(PandasObject): value : Period or compat.string_types, default None The time period represented (e.g., '4Q2005') freq : str, default None - e.g., 'B' for businessday, ('T', 5) or '5T' for 5 minutes + e.g., 'B' for businessday. Must be a singlular rule-code (e.g. 5T is not allowed). year : int, default None month : int, default 1 quarter : int, default None
closes #4699
https://api.github.com/repos/pandas-dev/pandas/pulls/5057
2013-09-30T14:42:25Z
2013-10-04T12:50:04Z
2013-10-04T12:50:04Z
2014-06-25T16:32:09Z
BUG: fix Index's __iadd__ methods
diff --git a/doc/source/release.rst b/doc/source/release.rst index daee460fc50a1..05626096fe6fe 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -214,6 +214,7 @@ API Changes data - allowing metadata changes. - ``MultiIndex.astype()`` now only allows ``np.object_``-like dtypes and now returns a ``MultiIndex`` rather than an ``Index``. (:issue:`4039`) + - Aliased ``__iadd__`` to ``__add__``. (:issue:`4996`) - Added ``is_`` method to ``Index`` that allows fast equality comparison of views (similar to ``np.may_share_memory`` but no false positives, and changes on ``levels`` and ``labels`` setting on ``MultiIndex``). diff --git a/pandas/core/index.py b/pandas/core/index.py index d488a29182a18..081968f47c090 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -832,6 +832,7 @@ def __add__(self, other): else: return Index(self.view(np.ndarray) + other) + __iadd__ = __add__ __eq__ = _indexOp('__eq__') __ne__ = _indexOp('__ne__') __lt__ = _indexOp('__lt__') diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 857836fa698ce..53398b92c6d2e 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -382,6 +382,14 @@ def test_add_string(self): self.assert_('a' not in index2) self.assert_('afoo' in index2) + def test_iadd_string(self): + index = pd.Index(['a', 'b', 'c']) + # doesn't fail test unless there is a check before `+=` + self.assert_('a' in index) + + index += '_x' + self.assert_('a_x' in index) + def test_diff(self): first = self.strIndex[5:20] second = self.strIndex[:10]
closes #4996 Aliases **iadd** = **add**. Not doing anything else for now until time to consider.
https://api.github.com/repos/pandas-dev/pandas/pulls/5053
2013-09-30T03:13:38Z
2013-10-02T21:17:01Z
2013-10-02T21:17:01Z
2014-07-07T01:51:22Z
ENH/DOC: Cleanup docstrings on NDFrame
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index f7d2b161759ed..b256d76fbcdd2 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -26,7 +26,7 @@ _default_index, _maybe_upcast, _is_sequence, _infer_dtype_from_scalar, _values_from_object, _coerce_to_dtypes, _DATELIKE_DTYPES, is_list_like) -from pandas.core.generic import NDFrame +from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.core.indexing import (_maybe_droplevels, _convert_to_index_sliceable, @@ -62,6 +62,9 @@ #---------------------------------------------------------------------- # Docstring templates +_shared_doc_kwargs = dict(axes='index, columns', + klass='DataFrame', + axes_single_arg="{0,1,'index','columns'}") _numeric_only_doc = """numeric_only : boolean, default None Include only float, int, boolean data. If None, will attempt to use @@ -1380,6 +1383,7 @@ def ftypes(self): return self.apply(lambda x: x.ftype, reduce=False) def transpose(self): + """Transpose index and columns""" return super(DataFrame, self).transpose(1, 0) T = property(transpose) @@ -2157,6 +2161,24 @@ def _reindex_multi(self, axes, copy, fill_value): return self._reindex_with_indexers({0: [new_index, row_indexer], 1: [new_columns, col_indexer]}, copy=copy, fill_value=fill_value) + @Appender(_shared_docs['reindex'] % _shared_doc_kwargs) + def reindex(self, index=None, columns=None, **kwargs): + return super(DataFrame, self).reindex(index=index, columns=columns, + **kwargs) + + @Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs) + def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, + limit=None, fill_value=np.nan): + return super(DataFrame, self).reindex_axis(labels=labels, axis=axis, + method=method, level=level, + copy=copy, limit=limit, + fill_value=fill_value) + + @Appender(_shared_docs['rename'] % _shared_doc_kwargs) + def rename(self, index=None, columns=None, **kwargs): + return super(DataFrame, self).rename(index=index, columns=columns, + **kwargs) + def reindex_like(self, other, method=None, copy=True, limit=None, fill_value=NA): """ diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 18a03eb313dd2..f92496173854f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -24,6 +24,15 @@ import pandas.core.nanops as nanops from pandas.util.decorators import Appender, Substitution +# goal is to be able to define the docs close to function, while still being +# able to share +_shared_docs = dict() +_shared_doc_kwargs = dict(axes='keywords for axes', + klass='NDFrame', + axes_single_arg='int or labels for object', + args_transpose='axes to permute (int or label for' + ' object)') + def is_dictlike(x): return isinstance(x, (dict, com.ABCSeries)) @@ -348,13 +357,12 @@ def _set_axis(self, axis, labels): self._data.set_axis(axis, labels) self._clear_item_cache() - def transpose(self, *args, **kwargs): - """ - Permute the dimensions of the Object + _shared_docs['transpose'] = """ + Permute the dimensions of the %(klass)s Parameters ---------- - axes : int or name (or alias) + args : %(args_transpose)s copy : boolean, default False Make a copy of the underlying data. Mixed-dtype data will always result in a copy @@ -368,6 +376,8 @@ def transpose(self, *args, **kwargs): ------- y : same as input """ + @Appender(_shared_docs['transpose'] % _shared_doc_kwargs) + def transpose(self, *args, **kwargs): # construct the args axes, kwargs = self._construct_axes_from_arguments( @@ -451,31 +461,31 @@ def swaplevel(self, i, j, axis=0): #---------------------------------------------------------------------- # Rename - def rename(self, *args, **kwargs): - """ - Alter axes input function or - functions. Function / dict values must be unique (1-to-1). Labels not - contained in a dict / Series will be left as-is. + # TODO: define separate funcs for DataFrame, Series and Panel so you can + # get completion on keyword arguments. + _shared_docs['rename'] = """ + Alter axes input function or functions. Function / dict values must be + unique (1-to-1). Labels not contained in a dict / Series will be left + as-is. Parameters ---------- - axis keywords for this object - (e.g. index for Series, - index,columns for DataFrame, - items,major_axis,minor_axis for Panel) - : dict-like or function, optional + %(axes)s : dict-like or function, optional Transformation to apply to that axis values copy : boolean, default True Also copy underlying data inplace : boolean, default False - Whether to return a new PandasObject. If True then value of copy is + Whether to return a new %(klass)s. If True then value of copy is ignored. Returns ------- - renamed : PandasObject (new object) + renamed : %(klass)s (new object) """ + @Appender(_shared_docs['rename'] % dict(axes='axes keywords for this' + ' object', klass='NDFrame')) + def rename(self, *args, **kwargs): axes, kwargs = self._construct_axes_from_arguments(args, kwargs) copy = kwargs.get('copy', True) @@ -518,6 +528,8 @@ def f(x): else: return result._propogate_attributes(self) + rename.__doc__ = _shared_docs['rename'] + def rename_axis(self, mapper, axis=0, copy=True, inplace=False): """ Alter index and / or columns using input function or functions. @@ -527,7 +539,7 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): Parameters ---------- mapper : dict-like or function, optional - axis : int, default 0 + axis : int or string, default 0 copy : boolean, default True Also copy underlying data inplace : boolean, default False @@ -568,16 +580,19 @@ def __iter__(self): """ return iter(self._info_axis) + # can we get a better explanation of this? def keys(self): """ return the info axis names """ return self._info_axis + # what does info axis actually mean? def iteritems(self): for h in self._info_axis: yield h, self[h] # originally used to get around 2to3's changes to iteritems. - # Now unnecessary. + # Now unnecessary. Sidenote: don't want to deprecate this for a while, + # otherwise libraries that use 2to3 will have issues. def iterkv(self, *args, **kwargs): warnings.warn("iterkv is deprecated and will be removed in a future " "release, use ``iteritems`` instead.", DeprecationWarning) @@ -782,13 +797,13 @@ def to_pickle(self, path): from pandas.io.pickle import to_pickle return to_pickle(self, path) - def save(self, path): # TODO remove in 0.13 + def save(self, path): # TODO remove in 0.14 import warnings from pandas.io.pickle import to_pickle warnings.warn("save is deprecated, use to_pickle", FutureWarning) return to_pickle(self, path) - def load(self, path): # TODO remove in 0.13 + def load(self, path): # TODO remove in 0.14 import warnings from pandas.io.pickle import read_pickle warnings.warn("load is deprecated, use pd.read_pickle", FutureWarning) @@ -802,8 +817,8 @@ def to_clipboard(self): ----- Requirements for your platform - Linux: xclip, or xsel (with gtk or PyQt4 modules) - - Windows: - - OS X: + - Windows: none + - OS X: none """ from pandas.io import clipboard clipboard.to_clipboard(self) @@ -945,6 +960,7 @@ def take(self, indices, axis=0, convert=True): new_data = self._data.take(indices, axis=baxis) return self._constructor(new_data) + # TODO: Check if this was clearer in 0.12 def select(self, crit, axis=0): """ Return data corresponding to axis labels matching criteria @@ -1095,16 +1111,15 @@ def sort_index(self, axis=0, ascending=True): new_axis = labels.take(sort_index) return self.reindex(**{axis_name: new_axis}) - - def reindex(self, *args, **kwargs): - """Conform DataFrame to new index with optional filling logic, placing + _shared_docs['reindex'] = """ + Conform %(klass)s to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False Parameters ---------- - axes : array-like, optional (can be specified in order, or as keywords) + %(axes)s : array-like, optional (can be specified in order, or as keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None @@ -1130,8 +1145,12 @@ def reindex(self, *args, **kwargs): Returns ------- - reindexed : same type as calling instance + reindexed : %(klass)s """ + # TODO: Decide if we care about having different examples for different + # kinds + @Appender(_shared_docs['reindex'] % dict(axes="axes", klass="NDFrame")) + def reindex(self, *args, **kwargs): # construct the args axes, kwargs = self._construct_axes_from_arguments(args, kwargs) @@ -1189,8 +1208,7 @@ def _needs_reindex_multi(self, axes, method, level): def _reindex_multi(self, axes, copy, fill_value): return NotImplemented - def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, - limit=None, fill_value=np.nan): + _shared_docs['reindex_axis'] = ( """Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and @@ -1201,9 +1219,9 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, index : array-like, optional New labels / index to conform to. Preferably an Index object to avoid duplicating data - axis : allowed axis for the input + axis : %(axes_single_arg)s method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None - Method to use for filling holes in reindexed DataFrame + Method to use for filling holes in reindexed object. pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation to fill gap copy : boolean, default True @@ -1220,12 +1238,15 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, See also -------- - DataFrame.reindex, DataFrame.reindex_like + reindex, reindex_like Returns ------- - reindexed : same type as calling instance - """ + reindexed : %(klass)s + """) + @Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs) + def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, + limit=None, fill_value=np.nan): self._consolidate_inplace() axis_name = self._get_axis_name(axis) @@ -1432,7 +1453,7 @@ def as_matrix(self, columns=None): Returns ------- values : ndarray - If the DataFrame is heterogeneous and contains booleans or objects, + If the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object """ self._consolidate_inplace() @@ -1568,10 +1589,9 @@ def fillna(self, value=None, method=None, axis=0, inplace=False, 0: fill column-by-column 1: fill row-by-row inplace : boolean, default False - If True, fill the DataFrame in place. Note: this will modify any - other views on this DataFrame, like if you took a no-copy slice of - an existing DataFrame, for example a column in a DataFrame. Returns - a reference to the filled object, which is self if inplace=True + If True, fill in place. Note: this will modify any + other views on this object, (e.g. a no-copy slice for a column in a + DataFrame). Still returns the object. limit : int, default None Maximum size gap to forward or backward fill downcast : dict, default is None, a dict of item->dtype of what to @@ -1584,7 +1604,7 @@ def fillna(self, value=None, method=None, axis=0, inplace=False, Returns ------- - filled : DataFrame + filled : same type as caller """ if isinstance(value, (list, tuple)): raise TypeError('"value" parameter must be a scalar or dict, but ' @@ -1714,10 +1734,9 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed. inplace : boolean, default False - If True, fill the DataFrame in place. Note: this will modify any - other views on this DataFrame, like if you took a no-copy slice of - an existing DataFrame, for example a column in a DataFrame. Returns - a reference to the filled object, which is self if inplace=True + If True, in place. Note: this will modify any + other views on this object (e.g. a column form a DataFrame). + Returns the caller if this is True. limit : int, default None Maximum size gap to forward or backward fill regex : bool or same types as `to_replace`, default False @@ -1916,9 +1935,9 @@ def interpolate(self, to_replace, method='pad', axis=0, inplace=False, reindex, replace, fillna """ from warnings import warn - warn('DataFrame.interpolate will be removed in v0.13, please use ' - 'either DataFrame.fillna or DataFrame.replace instead', - FutureWarning) + warn('{klass}.interpolate will be removed in v0.14, please use ' + 'either {klass}.fillna or {klass}.replace ' + 'instead'.format(klass=self.__class__), FutureWarning) if self._is_mixed_type and axis == 1: return self.T.replace(to_replace, method=method, limit=limit).T @@ -2381,8 +2400,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, Parameters ---------- - cond : boolean DataFrame or array - other : scalar or DataFrame + cond : boolean NDFrame or array + other : scalar or NDFrame inplace : boolean, default False Whether to perform the operation in place on the data axis : alignment axis if needed, default None @@ -2395,7 +2414,7 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, Returns ------- - wh : DataFrame + wh : same type as caller """ if isinstance(cond, NDFrame): cond = cond.reindex(**self._construct_axes_dict()) @@ -2430,7 +2449,7 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, # slice me out of the other else: - raise NotImplemented("cannot align with a bigger dimensional PandasObject") + raise NotImplemented("cannot align with a higher dimensional NDFrame") elif is_list_like(other): @@ -2512,12 +2531,12 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, def mask(self, cond): """ - Returns copy of self whose values are replaced with nan if the + Returns copy whose values are replaced with nan if the inverted condition is True Parameters ---------- - cond: boolean object or array + cond : boolean NDFrame or array Returns ------- @@ -2528,8 +2547,7 @@ def mask(self, cond): def shift(self, periods=1, freq=None, axis=0, **kwds): """ - Shift the index of the DataFrame by desired number of periods with an - optional time freq + Shift index by desired number of periods with an optional time freq Parameters ---------- @@ -2545,7 +2563,7 @@ def shift(self, periods=1, freq=None, axis=0, **kwds): Returns ------- - shifted : DataFrame + shifted : same type as caller """ if periods == 0: return self @@ -2621,15 +2639,15 @@ def tshift(self, periods=1, freq=None, axis=0, **kwds): return self._constructor(new_data) def truncate(self, before=None, after=None, copy=True): - """Function truncate a sorted DataFrame / Series before and/or after - some particular dates. + """Truncates a sorted NDFrame before and/or after some particular + dates. Parameters ---------- before : date - Truncate before date + Truncate before date after : date - Truncate after date + Truncate after date Returns ------- @@ -2778,8 +2796,9 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, Returns ------- - chg : Series or DataFrame + chg : same type as caller """ + # TODO: Not sure if above is correct - need someone to confirm. if fill_method is None: data = self else: diff --git a/pandas/core/panel.py b/pandas/core/panel.py index f0bad6b796e7c..a23d8160bb91a 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -18,7 +18,7 @@ create_block_manager_from_arrays, create_block_manager_from_blocks) from pandas.core.frame import DataFrame -from pandas.core.generic import NDFrame +from pandas.core.generic import NDFrame, _shared_docs from pandas import compat from pandas.util.decorators import deprecate, Appender, Substitution import pandas.core.common as com @@ -27,6 +27,15 @@ import pandas.computation.expressions as expressions +_shared_doc_kwargs = dict( + axes='items, major_axis, minor_axis', + klass="Panel", + axes_single_arg="{0,1,2,'items','major_axis','minor_axis'}") +_shared_doc_kwargs['args_transpose'] = ("three positional arguments: each one" + "of\n %s" % + _shared_doc_kwargs['axes_single_arg']) + + def _ensure_like_indices(time, panels): """ Makes sure that time and panels are conformable @@ -871,6 +880,31 @@ def _wrap_result(self, result, axis): return self._construct_return_type(result, axes) + @Appender(_shared_docs['reindex'] % _shared_doc_kwargs) + def reindex(self, items=None, major_axis=None, minor_axis=None, **kwargs): + major_axis = major_axis if major_axis is not None else kwargs.pop('major', None) + minor_axis = minor_axis if minor_axis is not None else kwargs.pop('minor', None) + return super(Panel, self).reindex(items=items, major_axis=major_axis, + minor_axis=minor_axis, **kwargs) + + @Appender(_shared_docs['rename'] % _shared_doc_kwargs) + def rename(self, items=None, major_axis=None, minor_axis=None, **kwargs): + major_axis = major_axis if major_axis is not None else kwargs.pop('major', None) + minor_axis = minor_axis if minor_axis is not None else kwargs.pop('minor', None) + return super(Panel, self).rename(items=items, major_axis=major_axis, + minor_axis=minor_axis, **kwargs) + + @Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs) + def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, + limit=None, fill_value=np.nan): + return super(Panel, self).reindex_axis(labels=labels, axis=axis, + method=method, level=level, + copy=copy, limit=limit, + fill_value=fill_value) + @Appender(_shared_docs['transpose'] % _shared_doc_kwargs) + def transpose(self, *args, **kwargs): + return super(Panel, self).transpose(*args, **kwargs) + def count(self, axis='major'): """ Return number of observations over requested axis. diff --git a/pandas/core/series.py b/pandas/core/series.py index 884e737f357a7..0bbdbc89879ff 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -53,6 +53,11 @@ __all__ = ['Series'] +_shared_doc_kwargs = dict( + axes='index', + klass='Series', + axes_single_arg="{0,'index'}" +) def _coerce_method(converter): """ install the scalar coercion methods """ @@ -1977,6 +1982,14 @@ def _needs_reindex_multi(self, axes, method, level): """ check if we do need a multi reindex; this is for compat with higher dims """ return False + @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs) + def rename(self, index=None, **kwargs): + return super(Series, self).rename(index=index, **kwargs) + + @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs) + def reindex(self, index=None, **kwargs): + return super(Series, self).reindex(index=index, **kwargs) + def reindex_axis(self, labels, axis=0, **kwargs): """ for compatibility with higher dims """ if axis != 0:
Resolves much of #4717. Basically cleans up docstrings in a few places plus, for the few functions that need them, uses a shared dict of function definitions to share them. This has the side-benefit of letting the function docstrings reside right next to the functions themselves, maintaining readability.
https://api.github.com/repos/pandas-dev/pandas/pulls/5052
2013-09-30T02:58:12Z
2013-10-01T04:00:14Z
2013-10-01T04:00:14Z
2014-07-16T08:32:14Z
API: provide __dir__ method (and local context) for tab completion / remove ipython completers code
diff --git a/doc/source/release.rst b/doc/source/release.rst index 801158a00b9ab..23b8252cd57e5 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -274,6 +274,8 @@ API Changes support ``pow`` or ``mod`` with non-scalars. (:issue:`3765`) - Provide numpy compatibility with 1.7 for a calling convention like ``np.prod(pandas_object)`` as numpy call with additional keyword args (:issue:`4435`) + - Provide __dir__ method (and local context) for tab completion / remove ipython completers code + (:issue:`4501`) Internal Refactoring diff --git a/pandas/core/base.py b/pandas/core/base.py index 14070d8825393..f390592a6f6c4 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -48,6 +48,16 @@ def __repr__(self): """ return str(self) + def _local_dir(self): + """ provide addtional __dir__ for this object """ + return [] + + def __dir__(self): + """ + Provide method name lookup and completion + Only provide 'public' methods + """ + return list(sorted(list(set(dir(type(self)) + self._local_dir())))) class PandasObject(StringMixin): """baseclass for various pandas objects""" diff --git a/pandas/core/frame.py b/pandas/core/frame.py index dbd67aa3c5c25..05514372de6fc 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4674,25 +4674,6 @@ def _put_str(s, space): return ('%s' % s)[:space].ljust(space) -def install_ipython_completers(): # pragma: no cover - """Register the DataFrame type with IPython's tab completion machinery, so - that it knows about accessing column names as attributes.""" - from IPython.utils.generics import complete_object - - @complete_object.when_type(DataFrame) - def complete_dataframe(obj, prev_completions): - return prev_completions + [c for c in obj.columns - if isinstance(c, compat.string_types) and compat.isidentifier(c)] - - -# Importing IPython brings in about 200 modules, so we want to avoid it unless -# we're in IPython (when those modules are loaded anyway). -if "IPython" in sys.modules: # pragma: no cover - try: - install_ipython_completers() - except Exception: - pass - #---------------------------------------------------------------------- # Add plotting methods to DataFrame diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 18a03eb313dd2..7d304209168b1 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -16,7 +16,7 @@ import pandas.core.common as com import pandas.core.datetools as datetools from pandas import compat, _np_version_under1p7 -from pandas.compat import map, zip, lrange +from pandas.compat import map, zip, lrange, string_types, isidentifier from pandas.core.common import (isnull, notnull, is_list_like, _values_from_object, _infer_dtype_from_scalar, _maybe_promote, @@ -109,6 +109,11 @@ def __unicode__(self): prepr = '[%s]' % ','.join(map(com.pprint_thing, self)) return '%s(%s)' % (self.__class__.__name__, prepr) + def _local_dir(self): + """ add the string-like attributes from the info_axis """ + return [c for c in self._info_axis + if isinstance(c, string_types) and isidentifier(c) ] + @property def _constructor_sliced(self): raise NotImplementedError @@ -252,7 +257,7 @@ def _get_axis_number(self, axis): def _get_axis_name(self, axis): axis = self._AXIS_ALIASES.get(axis, axis) - if isinstance(axis, compat.string_types): + if isinstance(axis, string_types): if axis in self._AXIS_NUMBERS: return axis else: @@ -1311,7 +1316,7 @@ def filter(self, items=None, like=None, regex=None, axis=None): if items is not None: return self.reindex(**{axis_name: [r for r in items if r in axis_values]}) elif like: - matchf = lambda x: (like in x if isinstance(x, compat.string_types) + matchf = lambda x: (like in x if isinstance(x, string_types) else like in str(x)) return self.select(matchf, axis=axis_name) elif regex: @@ -2601,7 +2606,7 @@ def tshift(self, periods=1, freq=None, axis=0, **kwds): offset = _resolve_offset(freq, kwds) - if isinstance(offset, compat.string_types): + if isinstance(offset, string_types): offset = datetools.to_offset(offset) block_axis = self._get_block_manager_axis(axis) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 2e07662bffbfe..e70c01ffcb12f 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2704,30 +2704,3 @@ def numpy_groupby(data, labels, axis=0): group_sums = np.add.reduceat(ordered_data, groups_at, axis=axis) return group_sums - -#----------------------------------------------------------------------- -# Helper functions - - -from pandas import compat -import sys - - -def install_ipython_completers(): # pragma: no cover - """Register the DataFrame type with IPython's tab completion machinery, so - that it knows about accessing column names as attributes.""" - from IPython.utils.generics import complete_object - - @complete_object.when_type(DataFrameGroupBy) - def complete_dataframe(obj, prev_completions): - return prev_completions + [c for c in obj.obj.columns - if isinstance(c, compat.string_types) and compat.isidentifier(c)] - - -# Importing IPython brings in about 200 modules, so we want to avoid it unless -# we're in IPython (when those modules are loaded anyway). -if "IPython" in sys.modules: # pragma: no cover - try: - install_ipython_completers() - except Exception: - pass diff --git a/pandas/core/panel.py b/pandas/core/panel.py index f0bad6b796e7c..b6054c1a96781 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -1230,21 +1230,3 @@ def f(self, other, axis=0): LongPanel = DataFrame -def install_ipython_completers(): # pragma: no cover - """Register the Panel type with IPython's tab completion machinery, so - that it knows about accessing column names as attributes.""" - from IPython.utils.generics import complete_object - - @complete_object.when_type(Panel) - def complete_dataframe(obj, prev_completions): - return prev_completions + [c for c in obj.keys() - if isinstance(c, compat.string_types) - and compat.isidentifier(c)] - -# Importing IPython brings in about 200 modules, so we want to avoid it unless -# we're in IPython (when those modules are loaded anyway). -if "IPython" in sys.modules: # pragma: no cover - try: - install_ipython_completers() - except Exception: - pass
closes #4501 this removes the ipython completers code and instead defines `__dir__`, with a `_local_dir` that a class can override NDFrame objects will have a pre-defined local of the info_axis (e.g. columns in a DataFrame) for example: ``` In [1]: df = DataFrame(columns=list('ABC')) In [2]: df. Display all 199 possibilities? (y or n) df.A df.at_time df.corrwith df.eq df.groupby df.itertuples df.min df.reindex df.shape df.to_dict df.tshift df.B df.axes df.count df.eval df.gt df.ix df.mod df.reindex_axis df.shift df.to_excel df.tz_convert df.C df.between_time df.cov df.ffill df.head df.join df.mul df.reindex_like df.skew df.to_hdf df.tz_localize df.T df.bfill df.cummax df.fillna df.hist df.keys df.multiply df.rename df.sort df.to_html df.unstack df.abs df.blocks df.cummin df.filter df.iat df.kurt df.ndim df.rename_axis df.sort_index df.to_json df.update df.add df.boxplot df.cumprod df.first df.icol df.kurtosis df.ne df.reorder_levels df.sortlevel df.to_latex df.values df.add_prefix df.clip df.cumsum df.first_valid_index df.idxmax df.last df.pct_change df.replace df.squeeze df.to_panel df.var df.add_suffix df.clip_lower df.delevel df.floordiv df.idxmin df.last_valid_index df.pivot df.resample df.stack df.to_period df.where df.align df.clip_upper df.describe df.from_csv df.iget_value df.le df.pivot_table df.reset_index df.std df.to_pickle df.xs df.all df.columns df.diff df.from_dict df.iloc df.load df.plot df.rfloordiv df.sub df.to_records df.any df.combine df.div df.from_items df.index df.loc df.pop df.rmod df.subtract df.to_sparse df.append df.combineAdd df.divide df.from_records df.info df.lookup df.pow df.rmul df.sum df.to_sql df.apply df.combineMult df.dot df.ftypes df.insert df.lt df.prod df.rpow df.swapaxes df.to_stata df.applymap df.combine_first df.drop df.ge df.interpolate df.mad df.product df.rsub df.swaplevel df.to_string df.as_blocks df.compound df.drop_duplicates df.get df.irow df.mask df.quantile df.rtruediv df.tail df.to_timestamp df.as_matrix df.consolidate df.dropna df.get_dtype_counts df.isin df.max df.query df.save df.take df.to_wide df.asfreq df.convert_objects df.dtypes df.get_ftype_counts df.iteritems df.mean df.radd df.select df.to_clipboard df.transpose df.astype df.copy df.duplicated df.get_value df.iterkv df.median df.rank df.set_index df.to_csv df.truediv df.at df.corr df.empty df.get_values df.iterrows df.merge df.rdiv df.set_value df.to_dense df.truncate ```
https://api.github.com/repos/pandas-dev/pandas/pulls/5050
2013-09-30T02:01:15Z
2013-10-01T00:04:14Z
2013-10-01T00:04:14Z
2014-06-25T01:17:18Z
CLN: pytables cleanup added functiones (previously deleted/moved to comp...
diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 42a434c005a4c..ff0e1b08d7247 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -3966,227 +3966,6 @@ def _need_convert(kind): return True return False - -class Coordinates(object): - - """ holds a returned coordinates list, useful to select the same rows from different tables - - coordinates : holds the array of coordinates - group : the source group - where : the source where - """ - - _ops = ['<=', '<', '>=', '>', '!=', '==', '='] - _search = re.compile( - "^\s*(?P<field>\w+)\s*(?P<op>%s)\s*(?P<value>.+)\s*$" % '|'.join(_ops)) - _max_selectors = 31 - - def __init__(self, field, op=None, value=None, queryables=None, encoding=None): - self.field = None - self.op = None - self.value = None - self.q = queryables or dict() - self.filter = None - self.condition = None - self.encoding = encoding - - # unpack lists/tuples in field - while(isinstance(field, (tuple, list))): - f = field - field = f[0] - if len(f) > 1: - op = f[1] - if len(f) > 2: - value = f[2] - - # backwards compatible - if isinstance(field, dict): - self.field = field.get('field') - self.op = field.get('op') or '==' - self.value = field.get('value') - - # passed a term - elif isinstance(field, Term): - self.field = field.field - self.op = field.op - self.value = field.value - - # a string expression (or just the field) - elif isinstance(field, compat.string_types): - - # is a term is passed - s = self._search.match(field) - if s is not None: - self.field = s.group('field') - self.op = s.group('op') - self.value = s.group('value') - - else: - self.field = field - - # is an op passed? - if isinstance(op, compat.string_types) and op in self._ops: - self.op = op - self.value = value - else: - self.op = '==' - self.value = op - - else: - raise ValueError( - "Term does not understand the supplied field [%s]" % field) - - # we have valid fields - if self.field is None or self.op is None or self.value is None: - raise ValueError("Could not create this term [%s]" % str(self)) - - # = vs == - if self.op == '=': - self.op = '==' - - # we have valid conditions - if self.op in ['>', '>=', '<', '<=']: - if hasattr(self.value, '__iter__') and len(self.value) > 1 and not isinstance(self.value, compat.string_types): - raise ValueError( - "an inequality condition cannot have multiple values [%s]" % str(self)) - - if not is_list_like(self.value): - self.value = [self.value] - - if len(self.q): - self.eval() - - def __unicode__(self): - attrs = lmap(pprint_thing, (self.field, self.op, self.value)) - return "field->%s,op->%s,value->%s" % tuple(attrs) - - @property - def is_valid(self): - """ return True if this is a valid field """ - return self.field in self.q - - @property - def is_in_table(self): - """ return True if this is a valid column name for generation (e.g. an actual column in the table) """ - return self.q.get(self.field) is not None - - @property - def kind(self): - """ the kind of my field """ - return self.q.get(self.field) - - def generate(self, v): - """ create and return the op string for this TermValue """ - val = v.tostring(self.encoding) - return "(%s %s %s)" % (self.field, self.op, val) - - def eval(self): - """ set the numexpr expression for this term """ - - if not self.is_valid: - raise ValueError("query term is not valid [{0}]\n" - " all queries terms must include a reference to\n" - " either an axis (e.g. index or column), or a data_columns\n".format(str(self))) - - # convert values if we are in the table - if self.is_in_table: - values = [self.convert_value(v) for v in self.value] - else: - values = [TermValue(v, v, self.kind) for v in self.value] - - # equality conditions - if self.op in ['==', '!=']: - - # our filter op expression - if self.op == '!=': - filter_op = lambda axis, vals: not axis.isin(vals) - else: - filter_op = lambda axis, vals: axis.isin(vals) - - if self.is_in_table: - - # too many values to create the expression? - if len(values) <= self._max_selectors: - vs = [self.generate(v) for v in values] - self.condition = "(%s)" % ' | '.join(vs) - - # use a filter after reading - else: - self.filter = ( - self.field, filter_op, Index([v.value for v in values])) - - else: - - self.filter = ( - self.field, filter_op, Index([v.value for v in values])) - - else: - - if self.is_in_table: - - self.condition = self.generate(values[0]) - - else: - - raise TypeError( - "passing a filterable condition to a Fixed format indexer [%s]" % str(self)) - - def convert_value(self, v): - """ convert the expression that is in the term to something that is accepted by pytables """ - - def stringify(value): - value = str(value) - if self.encoding is not None: - value = value.encode(self.encoding) - return value - - kind = _ensure_decoded(self.kind) - if kind == u('datetime64') or kind == u('datetime'): - v = lib.Timestamp(v) - if v.tz is not None: - v = v.tz_convert('UTC') - return TermValue(v, v.value, kind) - elif kind == u('timedelta64') or kind == u('timedelta'): - v = _coerce_scalar_to_timedelta_type(v,unit='s').item() - return TermValue(int(v), v, kind) - elif (isinstance(v, datetime) or hasattr(v, 'timetuple')): - v = time.mktime(v.timetuple()) - return TermValue(v, Timestamp(v), kind) - elif kind == u('date'): - v = v.toordinal() - return TermValue(v, Timestamp.fromordinal(v), kind) - elif kind == u('integer'): - v = int(float(v)) - return TermValue(v, v, kind) - elif kind == u('float'): - v = float(v) - return TermValue(v, v, kind) - elif kind == u('bool'): - if isinstance(v, compat.string_types): - poss_vals = [u('false'), u('f'), u('no'), - u('n'), u('none'), u('0'), - u('[]'), u('{}'), u('')] - v = not v.strip().lower() in poss_vals - else: - v = bool(v) - return TermValue(v, v, kind) - elif not isinstance(v, compat.string_types): - v = stringify(v) - return TermValue(v, stringify(v), u('string')) - - # string quoting - return TermValue(v, stringify(v), u('string')) - - - - def __len__(self): - return len(self.values) - - def __getitem__(self, key): - """ return a new coordinates object, sliced by the key """ - return Coordinates(self.values[key], self.group, self.where) - - class Selection(object): """
...utation/pytables.py)
https://api.github.com/repos/pandas-dev/pandas/pulls/5047
2013-09-29T21:09:35Z
2013-09-29T21:23:13Z
2013-09-29T21:23:13Z
2014-07-16T08:32:08Z
BUG: Fix unbound local in exception handling in core/index
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 935dff44ad49e..f7d2b161759ed 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3342,6 +3342,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): else: # pragma : no cover raise AssertionError('Axis must be 0 or 1, got %s' % str(axis)) + i = None keys = [] results = {} if ignore_failures: @@ -3362,14 +3363,12 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): results[i] = func(v) keys.append(v.name) except Exception as e: - try: - if hasattr(e, 'args'): + if hasattr(e, 'args'): + # make sure i is defined + if i is not None: k = res_index[i] e.args = e.args + ('occurred at index %s' % - com.pprint_thing(k),) - except (NameError, UnboundLocalError): # pragma: no cover - # no k defined yet - pass + com.pprint_thing(k),) raise if len(results) > 0 and _is_sequence(results[0]):
Only would occur if both enumerations failed. better way to handle than try/except.
https://api.github.com/repos/pandas-dev/pandas/pulls/5046
2013-09-29T20:37:00Z
2013-09-29T22:48:09Z
2013-09-29T22:48:09Z
2014-07-16T08:32:07Z
TST: sparc test fixups
diff --git a/pandas/core/series.py b/pandas/core/series.py index 90d535e51580c..4a77a5669948a 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -135,6 +135,12 @@ def __init__(self, data=None, index=None, dtype=None, name=None, if isinstance(data, MultiIndex): raise NotImplementedError + elif isinstance(data, Index): + # need to copy to avoid aliasing issues + if name is None: + name = data.name + data = data.values + copy = True elif isinstance(data, pa.Array): pass elif isinstance(data, Series): diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index bb46d563b904e..852d02764affc 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -2221,9 +2221,10 @@ def test_tolist(self): self.assertEqual(result, exp) def test_repr_with_unicode_data(self): - d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} - index = pd.DataFrame(d).set_index(["a", "b"]).index - self.assertFalse("\\u" in repr(index)) # we don't want unicode-escaped + with pd.core.config.option_context("display.encoding",'UTF-8'): + d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} + index = pd.DataFrame(d).set_index(["a", "b"]).index + self.assertFalse("\\u" in repr(index)) # we don't want unicode-escaped def test_unicode_string_with_unicode(self): d = {"a": [u("\u05d0"), 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
closes #4396
https://api.github.com/repos/pandas-dev/pandas/pulls/5043
2013-09-29T19:53:09Z
2013-09-29T20:16:10Z
2013-09-29T20:16:10Z
2014-06-22T11:58:23Z
EHN: Allow load_data to load problematic R datasets
diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index 79a87cb49f027..4f5c5a03a1be5 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -20,7 +20,7 @@ its release 2.3, while the current interface is designed for the 2.2.x series. We recommend to use 2.2.x over other series unless you are prepared to fix parts of the code, yet the rpy2-2.3.0 introduces improvements such as a better R-Python bridge memory management -layer so I might be a good idea to bite the bullet and submit patches for +layer so it might be a good idea to bite the bullet and submit patches for the few minor differences that need to be fixed. diff --git a/doc/source/release.rst b/doc/source/release.rst index 058ea165120a6..0894c84809a13 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -160,6 +160,10 @@ Improvements to existing features :issue:`4998`) - ``to_dict`` now takes ``records`` as a possible outtype. Returns an array of column-keyed dictionaries. (:issue:`4936`) + - Improve support for converting R datasets to pandas objects (more + informative index for timeseries and numeric, support for factors, dist, and + high-dimensional arrays). + API Changes ~~~~~~~~~~~ diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 90d2989de65c2..3f6919e3b3df0 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -480,6 +480,12 @@ Enhancements dfi[mask.any(1)] :ref:`See the docs<indexing.basics.indexing_isin>` for more. + - All R datasets listed here http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html can now be loaded into Pandas objects + + .. code-block:: python + + import pandas.rpy.common as com + com.load_data('Titanic') .. _whatsnew_0130.experimental: diff --git a/pandas/rpy/common.py b/pandas/rpy/common.py index a640b43ab97e6..5747285deb988 100644 --- a/pandas/rpy/common.py +++ b/pandas/rpy/common.py @@ -15,6 +15,9 @@ from rpy2.robjects import r import rpy2.robjects as robj +import itertools as IT + + __all__ = ['convert_robj', 'load_data', 'convert_to_r_dataframe', 'convert_to_r_matrix'] @@ -46,38 +49,44 @@ def _is_null(obj): def _convert_list(obj): """ - Convert named Vector to dict + Convert named Vector to dict, factors to list """ - values = [convert_robj(x) for x in obj] - return dict(zip(obj.names, values)) + try: + values = [convert_robj(x) for x in obj] + keys = r['names'](obj) + return dict(zip(keys, values)) + except TypeError: + # For state.division and state.region + factors = list(r['factor'](obj)) + level = list(r['levels'](obj)) + result = [level[index-1] for index in factors] + return result def _convert_array(obj): """ - Convert Array to ndarray + Convert Array to DataFrame """ - # this royally sucks. "Matrices" (arrays) with dimension > 3 in R aren't - # really matrices-- things come out Fortran order in the first two - # dimensions. Maybe I'm wrong? - + def _list(item): + try: + return list(item) + except TypeError: + return [] + + # For iris3, HairEyeColor, UCBAdmissions, Titanic dim = list(obj.dim) values = np.array(list(obj)) - - if len(dim) == 3: - arr = values.reshape(dim[-1:] + dim[:-1]).swapaxes(1, 2) - - if obj.names is not None: - name_list = [list(x) for x in obj.names] - if len(dim) == 2: - return pd.DataFrame(arr, index=name_list[0], columns=name_list[1]) - elif len(dim) == 3: - return pd.Panel(arr, items=name_list[2], - major_axis=name_list[0], - minor_axis=name_list[1]) - else: - print('Cannot handle dim=%d' % len(dim)) - else: - return arr + names = r['dimnames'](obj) + try: + columns = list(r['names'](names))[::-1] + except TypeError: + columns = ['X{:d}'.format(i) for i in range(len(names))][::-1] + columns.append('value') + name_list = [(_list(x) or range(d)) for x, d in zip(names, dim)][::-1] + arr = np.array(list(IT.product(*name_list))) + arr = np.column_stack([arr,values]) + df = pd.DataFrame(arr, columns=columns) + return df def _convert_vector(obj): @@ -85,8 +94,24 @@ def _convert_vector(obj): return _convert_int_vector(obj) elif isinstance(obj, robj.StrVector): return _convert_str_vector(obj) - - return list(obj) + # Check if the vector has extra information attached to it that can be used + # as an index + try: + attributes = set(r['attributes'](obj).names) + except AttributeError: + return list(obj) + if 'names' in attributes: + return pd.Series(list(obj), index=r['names'](obj)) + elif 'tsp' in attributes: + return pd.Series(list(obj), index=r['time'](obj)) + elif 'labels' in attributes: + return pd.Series(list(obj), index=r['labels'](obj)) + if _rclass(obj) == 'dist': + # For 'eurodist'. WARNING: This results in a DataFrame, not a Series or list. + matrix = r['as.matrix'](obj) + return convert_robj(matrix) + else: + return list(obj) NA_INTEGER = -2147483648 @@ -141,8 +166,7 @@ def _convert_Matrix(mat): rows = mat.rownames columns = None if _is_null(columns) else list(columns) - index = None if _is_null(rows) else list(rows) - + index = r['time'](mat) if _is_null(rows) else list(rows) return pd.DataFrame(np.array(mat), index=_check_int(index), columns=columns) @@ -197,7 +221,7 @@ def convert_robj(obj, use_pandas=True): if isinstance(obj, rpy_type): return converter(obj) - raise Exception('Do not know what to do with %s object' % type(obj)) + raise TypeError('Do not know what to do with %s object' % type(obj)) def convert_to_r_posixct(obj): @@ -329,117 +353,5 @@ def convert_to_r_matrix(df, strings_as_factors=False): return r_matrix - -def test_convert_list(): - obj = r('list(a=1, b=2, c=3)') - - converted = convert_robj(obj) - expected = {'a': [1], 'b': [2], 'c': [3]} - - _test.assert_dict_equal(converted, expected) - - -def test_convert_nested_list(): - obj = r('list(a=list(foo=1, bar=2))') - - converted = convert_robj(obj) - expected = {'a': {'foo': [1], 'bar': [2]}} - - _test.assert_dict_equal(converted, expected) - - -def test_convert_frame(): - # built-in dataset - df = r['faithful'] - - converted = convert_robj(df) - - assert np.array_equal(converted.columns, ['eruptions', 'waiting']) - assert np.array_equal(converted.index, np.arange(1, 273)) - - -def _test_matrix(): - r('mat <- matrix(rnorm(9), ncol=3)') - r('colnames(mat) <- c("one", "two", "three")') - r('rownames(mat) <- c("a", "b", "c")') - - return r['mat'] - - -def test_convert_matrix(): - mat = _test_matrix() - - converted = convert_robj(mat) - - assert np.array_equal(converted.index, ['a', 'b', 'c']) - assert np.array_equal(converted.columns, ['one', 'two', 'three']) - - -def test_convert_r_dataframe(): - - is_na = robj.baseenv.get("is.na") - - seriesd = _test.getSeriesData() - frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A']) - - # Null data - frame["E"] = [np.nan for item in frame["A"]] - # Some mixed type data - frame["F"] = ["text" if item % 2 == 0 else np.nan for item in range(30)] - - r_dataframe = convert_to_r_dataframe(frame) - - assert np.array_equal(convert_robj(r_dataframe.rownames), frame.index) - assert np.array_equal(convert_robj(r_dataframe.colnames), frame.columns) - assert all(is_na(item) for item in r_dataframe.rx2("E")) - - for column in frame[["A", "B", "C", "D"]]: - coldata = r_dataframe.rx2(column) - original_data = frame[column] - assert np.array_equal(convert_robj(coldata), original_data) - - for column in frame[["D", "E"]]: - for original, converted in zip(frame[column], - r_dataframe.rx2(column)): - - if pd.isnull(original): - assert is_na(converted) - else: - assert original == converted - - -def test_convert_r_matrix(): - - is_na = robj.baseenv.get("is.na") - - seriesd = _test.getSeriesData() - frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A']) - # Null data - frame["E"] = [np.nan for item in frame["A"]] - - r_dataframe = convert_to_r_matrix(frame) - - assert np.array_equal(convert_robj(r_dataframe.rownames), frame.index) - assert np.array_equal(convert_robj(r_dataframe.colnames), frame.columns) - assert all(is_na(item) for item in r_dataframe.rx(True, "E")) - - for column in frame[["A", "B", "C", "D"]]: - coldata = r_dataframe.rx(True, column) - original_data = frame[column] - assert np.array_equal(convert_robj(coldata), - original_data) - - # Pandas bug 1282 - frame["F"] = ["text" if item % 2 == 0 else np.nan for item in range(30)] - - # FIXME: Ugly, this whole module needs to be ported to nose/unittest - try: - wrong_matrix = convert_to_r_matrix(frame) - except TypeError: - pass - except Exception: - raise - - if __name__ == '__main__': pass diff --git a/pandas/rpy/tests/__init__.py b/pandas/rpy/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/pandas/rpy/tests/test_common.py b/pandas/rpy/tests/test_common.py new file mode 100644 index 0000000000000..a2e6d08d07b58 --- /dev/null +++ b/pandas/rpy/tests/test_common.py @@ -0,0 +1,213 @@ +""" +Testing that functions from rpy work as expected +""" + +import pandas as pd +import numpy as np +import unittest +import nose +import pandas.util.testing as tm + +try: + import pandas.rpy.common as com + from rpy2.robjects import r + import rpy2.robjects as robj +except ImportError: + raise nose.SkipTest('R not installed') + + +class TestCommon(unittest.TestCase): + def test_convert_list(self): + obj = r('list(a=1, b=2, c=3)') + + converted = com.convert_robj(obj) + expected = {'a': [1], 'b': [2], 'c': [3]} + + tm.assert_dict_equal(converted, expected) + + def test_convert_nested_list(self): + obj = r('list(a=list(foo=1, bar=2))') + + converted = com.convert_robj(obj) + expected = {'a': {'foo': [1], 'bar': [2]}} + + tm.assert_dict_equal(converted, expected) + + def test_convert_frame(self): + # built-in dataset + df = r['faithful'] + + converted = com.convert_robj(df) + + assert np.array_equal(converted.columns, ['eruptions', 'waiting']) + assert np.array_equal(converted.index, np.arange(1, 273)) + + def _test_matrix(self): + r('mat <- matrix(rnorm(9), ncol=3)') + r('colnames(mat) <- c("one", "two", "three")') + r('rownames(mat) <- c("a", "b", "c")') + + return r['mat'] + + def test_convert_matrix(self): + mat = self._test_matrix() + + converted = com.convert_robj(mat) + + assert np.array_equal(converted.index, ['a', 'b', 'c']) + assert np.array_equal(converted.columns, ['one', 'two', 'three']) + + def test_convert_r_dataframe(self): + + is_na = robj.baseenv.get("is.na") + + seriesd = tm.getSeriesData() + frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A']) + + # Null data + frame["E"] = [np.nan for item in frame["A"]] + # Some mixed type data + frame["F"] = ["text" if item % + 2 == 0 else np.nan for item in range(30)] + + r_dataframe = com.convert_to_r_dataframe(frame) + + assert np.array_equal( + com.convert_robj(r_dataframe.rownames), frame.index) + assert np.array_equal( + com.convert_robj(r_dataframe.colnames), frame.columns) + assert all(is_na(item) for item in r_dataframe.rx2("E")) + + for column in frame[["A", "B", "C", "D"]]: + coldata = r_dataframe.rx2(column) + original_data = frame[column] + assert np.array_equal(com.convert_robj(coldata), original_data) + + for column in frame[["D", "E"]]: + for original, converted in zip(frame[column], + r_dataframe.rx2(column)): + + if pd.isnull(original): + assert is_na(converted) + else: + assert original == converted + + def test_convert_r_matrix(self): + + is_na = robj.baseenv.get("is.na") + + seriesd = tm.getSeriesData() + frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A']) + # Null data + frame["E"] = [np.nan for item in frame["A"]] + + r_dataframe = com.convert_to_r_matrix(frame) + + assert np.array_equal( + com.convert_robj(r_dataframe.rownames), frame.index) + assert np.array_equal( + com.convert_robj(r_dataframe.colnames), frame.columns) + assert all(is_na(item) for item in r_dataframe.rx(True, "E")) + + for column in frame[["A", "B", "C", "D"]]: + coldata = r_dataframe.rx(True, column) + original_data = frame[column] + assert np.array_equal(com.convert_robj(coldata), + original_data) + + # Pandas bug 1282 + frame["F"] = ["text" if item % + 2 == 0 else np.nan for item in range(30)] + + try: + wrong_matrix = com.convert_to_r_matrix(frame) + except TypeError: + pass + except Exception: + raise + + def test_dist(self): + for name in ('eurodist',): + df = com.load_data(name) + dist = r[name] + labels = r['labels'](dist) + assert np.array_equal(df.index, labels) + assert np.array_equal(df.columns, labels) + + def test_timeseries(self): + """ + Test that the series has an informative index. + Unfortunately the code currently does not build a DateTimeIndex + """ + for name in ( + 'austres', 'co2', 'fdeaths', 'freeny.y', 'JohnsonJohnson', + 'ldeaths', 'mdeaths', 'nottem', 'presidents', 'sunspot.month', 'sunspots', + 'UKDriverDeaths', 'UKgas', 'USAccDeaths', + 'airmiles', 'discoveries', 'EuStockMarkets', + 'LakeHuron', 'lh', 'lynx', 'nhtemp', 'Nile', + 'Seatbelts', 'sunspot.year', 'treering', 'uspop'): + series = com.load_data(name) + ts = r[name] + assert np.array_equal(series.index, r['time'](ts)) + + def test_numeric(self): + for name in ('euro', 'islands', 'precip'): + series = com.load_data(name) + numeric = r[name] + names = numeric.names + assert np.array_equal(series.index, names) + + def test_table(self): + iris3 = pd.DataFrame({'X0': {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'}, + 'X1': {0: 'Sepal L.', + 1: 'Sepal L.', + 2: 'Sepal L.', + 3: 'Sepal L.', + 4: 'Sepal L.'}, + 'X2': {0: 'Setosa', + 1: 'Setosa', + 2: 'Setosa', + 3: 'Setosa', + 4: 'Setosa'}, + 'value': {0: '5.1', 1: '4.9', 2: '4.7', 3: '4.6', 4: '5.0'}}) + hec = pd.DataFrame( + { + 'Eye': {0: 'Brown', 1: 'Brown', 2: 'Brown', 3: 'Brown', 4: 'Blue'}, + 'Hair': {0: 'Black', 1: 'Brown', 2: 'Red', 3: 'Blond', 4: 'Black'}, + 'Sex': {0: 'Male', 1: 'Male', 2: 'Male', 3: 'Male', 4: 'Male'}, + 'value': {0: '32.0', 1: '53.0', 2: '10.0', 3: '3.0', 4: '11.0'}}) + titanic = pd.DataFrame( + { + 'Age': {0: 'Child', 1: 'Child', 2: 'Child', 3: 'Child', 4: 'Child'}, + 'Class': {0: '1st', 1: '2nd', 2: '3rd', 3: 'Crew', 4: '1st'}, + 'Sex': {0: 'Male', 1: 'Male', 2: 'Male', 3: 'Male', 4: 'Female'}, + 'Survived': {0: 'No', 1: 'No', 2: 'No', 3: 'No', 4: 'No'}, + 'value': {0: '0.0', 1: '0.0', 2: '35.0', 3: '0.0', 4: '0.0'}}) + for name, expected in zip(('HairEyeColor', 'Titanic', 'iris3'), + (hec, titanic, iris3)): + df = com.load_data(name) + table = r[name] + names = r['dimnames'](table) + try: + columns = list(r['names'](names))[::-1] + except TypeError: + columns = ['X{:d}'.format(i) for i in range(len(names))][::-1] + columns.append('value') + assert np.array_equal(df.columns, columns) + result = df.head() + cond = ((result.sort(axis=1) == expected.sort(axis=1))).values + assert np.all(cond) + + def test_factor(self): + for name in ('state.division', 'state.region'): + vector = r[name] + factors = list(r['factor'](vector)) + level = list(r['levels'](vector)) + factors = [level[index - 1] for index in factors] + result = com.load_data(name) + assert np.equal(result, factors) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + # '--with-coverage', '--cover-package=pandas.core'], + exit=False)
TST: Move tests from rpy/common.py to tests/test_rpy.py TST: Add tests to demonstrate the enhancements made to rpy/common.py. DOC: Add explanation to doc/source/release.rst
https://api.github.com/repos/pandas-dev/pandas/pulls/5042
2013-09-29T17:08:06Z
2013-10-02T21:26:47Z
2013-10-02T21:26:47Z
2014-06-23T00:59:28Z
TST: added null conversions for TimeDeltaBlock in core/internals related (GH4396)
diff --git a/pandas/core/internals.py b/pandas/core/internals.py index f10e1612f7fe9..fd9aed58798fe 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -23,7 +23,7 @@ from pandas.tslib import Timestamp from pandas import compat from pandas.compat import range, lrange, lmap, callable, map, zip - +from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type class Block(PandasObject): @@ -1083,6 +1083,20 @@ def _try_fill(self, value): return value + def _try_coerce_args(self, values, other): + """ provide coercion to our input arguments + we are going to compare vs i8, so coerce to integer + values is always ndarra like, other may not be """ + values = values.view('i8') + if isnull(other) or (np.isscalar(other) and other == tslib.iNaT): + other = tslib.iNaT + elif isinstance(other, np.timedelta64): + other = _coerce_scalar_to_timedelta_type(other,unit='s').item() + else: + other = other.view('i8') + + return values, other + def _try_operate(self, values): """ return a version to operate on """ return values.view('i8')
closes #4396 NaT wraparound bug again, fixed
https://api.github.com/repos/pandas-dev/pandas/pulls/5040
2013-09-29T15:50:44Z
2013-09-29T17:07:57Z
2013-09-29T17:07:57Z
2014-07-09T23:52:42Z
CLN: PEP8 cleanup
diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index a2531ebd43c82..982b5de49e6fa 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -120,7 +120,8 @@ def iteritems(obj, **kwargs): """replacement for six's iteritems for Python2/3 compat uses 'iteritems' if available and otherwise uses 'items'. - Passes kwargs to method.""" + Passes kwargs to method. + """ func = getattr(obj, "iteritems", None) if not func: func = obj.items @@ -180,6 +181,7 @@ class to receive bound method def u(s): return s + def u_safe(s): return s else: @@ -243,8 +245,7 @@ def wrapper(cls): class _OrderedDict(dict): - - 'Dictionary that remembers insertion order' + """Dictionary that remembers insertion order""" # An inherited dict maps keys to values. # The inherited dict provides __getitem__, __len__, __contains__, and get. # The remaining methods are order-aware. @@ -258,11 +259,10 @@ class _OrderedDict(dict): # KEY]. def __init__(self, *args, **kwds): - '''Initialize an ordered dictionary. Signature is the same as for + """Initialize an ordered dictionary. Signature is the same as for regular dictionaries, but keyword arguments are not recommended because their insertion order is arbitrary. - - ''' + """ if len(args) > 1: raise TypeError('expected at most 1 arguments, got %d' % len(args)) try: @@ -274,7 +274,7 @@ def __init__(self, *args, **kwds): self.__update(*args, **kwds) def __setitem__(self, key, value, dict_setitem=dict.__setitem__): - 'od.__setitem__(i, y) <==> od[i]=y' + """od.__setitem__(i, y) <==> od[i]=y""" # Setting a new item creates a new link which goes at the end of the # linked list, and the inherited dictionary is updated with the new # key/value pair. @@ -285,7 +285,7 @@ def __setitem__(self, key, value, dict_setitem=dict.__setitem__): dict_setitem(self, key, value) def __delitem__(self, key, dict_delitem=dict.__delitem__): - 'od.__delitem__(y) <==> del od[y]' + """od.__delitem__(y) <==> del od[y]""" # Deleting an existing item uses self.__map to find the link which is # then removed by updating the links in the predecessor and successor # nodes. @@ -295,7 +295,7 @@ def __delitem__(self, key, dict_delitem=dict.__delitem__): link_next[0] = link_prev def __iter__(self): - 'od.__iter__() <==> iter(od)' + """od.__iter__() <==> iter(od)""" root = self.__root curr = root[1] while curr is not root: @@ -303,7 +303,7 @@ def __iter__(self): curr = curr[1] def __reversed__(self): - 'od.__reversed__() <==> reversed(od)' + """od.__reversed__() <==> reversed(od)""" root = self.__root curr = root[0] while curr is not root: @@ -311,7 +311,7 @@ def __reversed__(self): curr = curr[0] def clear(self): - 'od.clear() -> None. Remove all items from od.' + """od.clear() -> None. Remove all items from od.""" try: for node in itervalues(self.__map): del node[:] @@ -323,10 +323,11 @@ def clear(self): dict.clear(self) def popitem(self, last=True): - '''od.popitem() -> (k, v), return and remove a (key, value) pair. + """od.popitem() -> (k, v), return and remove a (key, value) pair. + Pairs are returned in LIFO order if last is true or FIFO order if false. - ''' + """ if not self: raise KeyError('dictionary is empty') root = self.__root @@ -348,39 +349,39 @@ def popitem(self, last=True): # -- the following methods do not depend on the internal structure -- def keys(self): - 'od.keys() -> list of keys in od' + """od.keys() -> list of keys in od""" return list(self) def values(self): - 'od.values() -> list of values in od' + """od.values() -> list of values in od""" return [self[key] for key in self] def items(self): - 'od.items() -> list of (key, value) pairs in od' + """od.items() -> list of (key, value) pairs in od""" return [(key, self[key]) for key in self] def iterkeys(self): - 'od.iterkeys() -> an iterator over the keys in od' + """od.iterkeys() -> an iterator over the keys in od""" return iter(self) def itervalues(self): - 'od.itervalues -> an iterator over the values in od' + """od.itervalues -> an iterator over the values in od""" for k in self: yield self[k] def iteritems(self): - 'od.iteritems -> an iterator over the (key, value) items in od' + """od.iteritems -> an iterator over the (key, value) items in od""" for k in self: yield (k, self[k]) def update(*args, **kwds): - '''od.update(E, **F) -> None. Update od from dict/iterable E and F. + """od.update(E, **F) -> None. Update od from dict/iterable E and F. If E is a dict instance, does: for k in E: od[k] = E[k] If E has a .keys() method, does: for k in E.keys(): od[k] = E[k] Or if E is an iterable of items, does:for k, v in E: od[k] = v In either case, this is followed by: for k, v in F.items(): od[k] = v - ''' + """ if len(args) > 2: raise TypeError('update() takes at most 2 positional ' 'arguments (%d given)' % (len(args),)) @@ -408,10 +409,10 @@ def update(*args, **kwds): __marker = object() def pop(self, key, default=__marker): - '''od.pop(k[,d]) -> v, remove specified key and return the\ + """od.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised. - ''' + """ if key in self: result = self[key] del self[key] @@ -421,14 +422,15 @@ def pop(self, key, default=__marker): return default def setdefault(self, key, default=None): - 'od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od' + """od.setdefault(k[,d]) -> od.get(k,d), also set od[k]=d if k not in od + """ if key in self: return self[key] self[key] = default return default def __repr__(self, _repr_running={}): - 'od.__repr__() <==> repr(od)' + """od.__repr__() <==> repr(od)""" call_key = id(self), _get_ident() if call_key in _repr_running: return '...' @@ -441,7 +443,7 @@ def __repr__(self, _repr_running={}): del _repr_running[call_key] def __reduce__(self): - 'Return state information for pickling' + """Return state information for pickling""" items = [[k, self[k]] for k in self] inst_dict = vars(self).copy() for k in vars(OrderedDict()): @@ -451,24 +453,24 @@ def __reduce__(self): return self.__class__, (items,) def copy(self): - 'od.copy() -> a shallow copy of od' + """od.copy() -> a shallow copy of od""" return self.__class__(self) @classmethod def fromkeys(cls, iterable, value=None): - '''OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S and + """OD.fromkeys(S[, v]) -> New ordered dictionary with keys from S and values equal to v (which defaults to None). - ''' + """ d = cls() for key in iterable: d[key] = value return d def __eq__(self, other): - '''od.__eq__(y) <==> od==y. Comparison to another OD is + """od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive while comparison to a regular mapping is order-insensitive. - ''' + """ if isinstance(other, OrderedDict): return (len(self) == len(other) and list(self.items()) == list(other.items())) @@ -480,15 +482,16 @@ def __ne__(self, other): # -- the following methods are only used in Python 2.7 -- def viewkeys(self): - "od.viewkeys() -> a set-like object providing a view on od's keys" + """od.viewkeys() -> a set-like object providing a view on od's keys""" return KeysView(self) def viewvalues(self): - "od.viewvalues() -> an object providing a view on od's values" + """od.viewvalues() -> an object providing a view on od's values""" return ValuesView(self) def viewitems(self): - "od.viewitems() -> a set-like object providing a view on od's items" + """od.viewitems() -> a set-like object providing a view on od's items + """ return ItemsView(self) @@ -502,18 +505,17 @@ def viewitems(self): class _Counter(dict): - - '''Dict subclass for counting hashable objects. Sometimes called a bag + """Dict subclass for counting hashable objects. Sometimes called a bag or multiset. Elements are stored as dictionary keys and their counts are stored as dictionary values. >>> Counter('zyzygy') Counter({'y': 3, 'z': 2, 'g': 1}) - ''' + """ def __init__(self, iterable=None, **kwds): - '''Create a new, empty Counter object. And if given, count elements + """Create a new, empty Counter object. And if given, count elements from an input iterable. Or, initialize the count from another mapping of elements to their counts. @@ -522,26 +524,26 @@ def __init__(self, iterable=None, **kwds): >>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping >>> c = Counter(a=4, b=2) # a new counter from keyword args - ''' + """ self.update(iterable, **kwds) def __missing__(self, key): return 0 def most_common(self, n=None): - '''List the n most common elements and their counts from the most + """List the n most common elements and their counts from the most common to the least. If n is None, then list all element counts. >>> Counter('abracadabra').most_common(3) [('a', 5), ('r', 2), ('b', 2)] - ''' + """ if n is None: return sorted(iteritems(self), key=itemgetter(1), reverse=True) return nlargest(n, iteritems(self), key=itemgetter(1)) def elements(self): - '''Iterator over elements repeating each as many times as its count. + """Iterator over elements repeating each as many times as its count. >>> c = Counter('ABCABC') >>> sorted(c.elements()) @@ -550,7 +552,7 @@ def elements(self): If an element's count has been set to zero or is a negative number, elements() will ignore it. - ''' + """ for elem, count in iteritems(self): for _ in range(count): yield elem @@ -563,7 +565,7 @@ def fromkeys(cls, iterable, v=None): 'Counter.fromkeys() is undefined. Use Counter(iterable) instead.') def update(self, iterable=None, **kwds): - '''Like dict.update() but add counts instead of replacing them. + """Like dict.update() but add counts instead of replacing them. Source can be an iterable, a dictionary, or another Counter instance. @@ -574,7 +576,7 @@ def update(self, iterable=None, **kwds): >>> c['h'] # four 'h' in which, witch, and watch 4 - ''' + """ if iterable is not None: if hasattr(iterable, 'iteritems'): if self: @@ -592,12 +594,14 @@ def update(self, iterable=None, **kwds): self.update(kwds) def copy(self): - 'Like dict.copy() but returns a Counter instance instead of a dict.' + """Like dict.copy() but returns a Counter instance instead of a dict. + """ return Counter(self) def __delitem__(self, elem): - '''Like dict.__delitem__() but does not raise KeyError for missing - values.''' + """Like dict.__delitem__() but does not raise KeyError for missing + values. + """ if elem in self: dict.__delitem__(self, elem) @@ -617,13 +621,12 @@ def __repr__(self): # c += Counter() def __add__(self, other): - '''Add counts from two counters. + """Add counts from two counters. >>> Counter('abbb') + Counter('bcc') Counter({'b': 4, 'c': 2, 'a': 1}) - - ''' + """ if not isinstance(other, Counter): return NotImplemented result = Counter() @@ -634,12 +637,12 @@ def __add__(self, other): return result def __sub__(self, other): - ''' Subtract count, but keep only results with positive counts. + """Subtract count, but keep only results with positive counts. >>> Counter('abbbc') - Counter('bccd') Counter({'b': 2, 'a': 1}) - ''' + """ if not isinstance(other, Counter): return NotImplemented result = Counter() @@ -650,12 +653,12 @@ def __sub__(self, other): return result def __or__(self, other): - '''Union is the maximum of value in either of the input counters. + """Union is the maximum of value in either of the input counters. >>> Counter('abbb') | Counter('bcc') Counter({'b': 3, 'c': 2, 'a': 1}) - ''' + """ if not isinstance(other, Counter): return NotImplemented _max = max @@ -667,12 +670,12 @@ def __or__(self, other): return result def __and__(self, other): - ''' Intersection is the minimum of corresponding counts. + """Intersection is the minimum of corresponding counts. >>> Counter('abbb') & Counter('bcc') Counter({'b': 1}) - ''' + """ if not isinstance(other, Counter): return NotImplemented _min = min @@ -705,10 +708,9 @@ def raise_with_traceback(exc, traceback=Ellipsis): raise exc, None, traceback """) -raise_with_traceback.__doc__ = ( -"""Raise exception with existing traceback. +raise_with_traceback.__doc__ = """Raise exception with existing traceback. If traceback is not passed, uses sys.exc_info() to get traceback.""" -) + # http://stackoverflow.com/questions/4126348 # Thanks to @martineau at SO @@ -723,6 +725,7 @@ def parse_date(timestr, *args, **kwargs): else: parse_date = _date_parser.parse + class OrderedDefaultdict(OrderedDict): def __init__(self, *args, **kwargs): diff --git a/pandas/compat/pickle_compat.py b/pandas/compat/pickle_compat.py index bf52fc30a9ea3..3365f1bb630b9 100644 --- a/pandas/compat/pickle_compat.py +++ b/pandas/compat/pickle_compat.py @@ -9,6 +9,7 @@ from pandas.core.series import Series, TimeSeries from pandas.sparse.series import SparseSeries, SparseTimeSeries + def load_reduce(self): stack = self.stack args = stack.pop() @@ -18,7 +19,8 @@ def load_reduce(self): if n == u('DeprecatedSeries') or n == u('DeprecatedTimeSeries'): stack[-1] = object.__new__(Series) return - elif n == u('DeprecatedSparseSeries') or n == u('DeprecatedSparseTimeSeries'): + elif (n == u('DeprecatedSparseSeries') or + n == u('DeprecatedSparseTimeSeries')): stack[-1] = object.__new__(SparseSeries) return @@ -28,7 +30,9 @@ def load_reduce(self): # try to reencode the arguments if self.encoding is not None: - args = tuple([ arg.encode(self.encoding) if isinstance(arg, string_types) else arg for arg in args ]) + args = tuple([arg.encode(self.encoding) + if isinstance(arg, string_types) + else arg for arg in args]) try: stack[-1] = func(*args) return @@ -51,9 +55,9 @@ class Unpickler(pkl.Unpickler): Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce + def load(fh, encoding=None, compat=False, is_verbose=False): - """ - load a pickle, with a provided encoding + """load a pickle, with a provided encoding if compat is True: fake the old class hierarchy @@ -90,14 +94,18 @@ def load(fh, encoding=None, compat=False, is_verbose=False): pandas.sparse.series.SparseSeries = SparseSeries pandas.sparse.series.SparseTimeSeries = SparseTimeSeries + class DeprecatedSeries(np.ndarray, Series): pass + class DeprecatedTimeSeries(DeprecatedSeries): pass + class DeprecatedSparseSeries(DeprecatedSeries): pass + class DeprecatedSparseTimeSeries(DeprecatedSparseSeries): pass diff --git a/pandas/compat/scipy.py b/pandas/compat/scipy.py index 3dab5b1f0451e..81601ffe25609 100644 --- a/pandas/compat/scipy.py +++ b/pandas/compat/scipy.py @@ -7,8 +7,7 @@ def scoreatpercentile(a, per, limit=(), interpolation_method='fraction'): - """ - Calculate the score at the given `per` percentile of the sequence `a`. + """Calculate the score at the given `per` percentile of the sequence `a`. For example, the score at `per=50` is the median. If the desired quantile lies between two data points, we interpolate between them, according to @@ -65,7 +64,7 @@ def scoreatpercentile(a, per, limit=(), interpolation_method='fraction'): values = values[(limit[0] <= values) & (values <= limit[1])] idx = per / 100. * (values.shape[0] - 1) - if (idx % 1 == 0): + if idx % 1 == 0: score = values[idx] else: if interpolation_method == 'fraction': @@ -153,8 +152,7 @@ def fastsort(a): def percentileofscore(a, score, kind='rank'): - ''' - The percentile rank of a score relative to a list of scores. + """The percentile rank of a score relative to a list of scores. A `percentileofscore` of, for example, 80% means that 80% of the scores in `a` are below the given score. In the case of gaps or @@ -217,7 +215,7 @@ def percentileofscore(a, score, kind='rank'): >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='mean') 60.0 - ''' + """ a = np.array(a) n = len(a) diff --git a/pandas/computation/align.py b/pandas/computation/align.py index f420d0dacf34c..233f2b61dc463 100644 --- a/pandas/computation/align.py +++ b/pandas/computation/align.py @@ -101,8 +101,8 @@ def wrapper(terms): @_filter_special_cases def _align_core(terms): - term_index = [i for i, term in enumerate(terms) if hasattr(term.value, - 'axes')] + term_index = [i for i, term in enumerate(terms) + if hasattr(term.value, 'axes')] term_dims = [terms[i].value.ndim for i in term_index] ndims = pd.Series(dict(zip(term_index, term_dims))) @@ -139,10 +139,10 @@ def _align_core(terms): ordm = np.log10(abs(reindexer_size - term_axis_size)) if ordm >= 1 and reindexer_size >= 10000: - warnings.warn("Alignment difference on axis {0} is larger" - " than an order of magnitude on term {1!r}, " - "by more than {2:.4g}; performance may suffer" - "".format(axis, terms[i].name, ordm), + warnings.warn('Alignment difference on axis {0} is larger ' + 'than an order of magnitude on term {1!r}, ' + 'by more than {2:.4g}; performance may ' + 'suffer'.format(axis, terms[i].name, ordm), category=pd.io.common.PerformanceWarning) if transpose: @@ -237,7 +237,7 @@ def _reconstruct_object(typ, obj, axes, dtype): res_t = dtype if (not isinstance(typ, partial) and - issubclass(typ, pd.core.generic.PandasObject)): + issubclass(typ, pd.core.generic.PandasObject)): return typ(obj, dtype=res_t, **axes) # special case for pathological things like ~True/~False diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index 64bceee118fd1..1af41acd34ede 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -91,7 +91,8 @@ class Scope(StringMixin): __slots__ = ('globals', 'locals', 'resolvers', '_global_resolvers', 'resolver_keys', '_resolver', 'level', 'ntemps', 'target') - def __init__(self, gbls=None, lcls=None, level=1, resolvers=None, target=None): + def __init__(self, gbls=None, lcls=None, level=1, resolvers=None, + target=None): self.level = level self.resolvers = tuple(resolvers or []) self.globals = dict() @@ -133,11 +134,12 @@ def __init__(self, gbls=None, lcls=None, level=1, resolvers=None, target=None): self.resolver_dict.update(dict(o)) def __unicode__(self): - return com.pprint_thing("locals: {0}\nglobals: {0}\nresolvers: " - "{0}\ntarget: {0}".format(list(self.locals.keys()), - list(self.globals.keys()), - list(self.resolver_keys), - self.target)) + return com.pprint_thing( + 'locals: {0}\nglobals: {0}\nresolvers: ' + '{0}\ntarget: {0}'.format(list(self.locals.keys()), + list(self.globals.keys()), + list(self.resolver_keys), + self.target)) def __getitem__(self, key): return self.resolve(key, globally=False) @@ -499,9 +501,8 @@ def _possibly_evaluate_binop(self, op, op_class, lhs, rhs, maybe_eval_in_python=('==', '!=')): res = op(lhs, rhs) - if (res.op in _cmp_ops_syms and - lhs.is_datetime or rhs.is_datetime and - self.engine != 'pytables'): + if (res.op in _cmp_ops_syms and lhs.is_datetime or rhs.is_datetime and + self.engine != 'pytables'): # all date ops must be done in python bc numexpr doesn't work well # with NaT return self._possibly_eval(res, self.binary_ops) @@ -594,18 +595,20 @@ def visit_Assign(self, node, **kwargs): if len(node.targets) != 1: raise SyntaxError('can only assign a single expression') if not isinstance(node.targets[0], ast.Name): - raise SyntaxError('left hand side of an assignment must be a single name') + raise SyntaxError('left hand side of an assignment must be a ' + 'single name') if self.env.target is None: raise ValueError('cannot assign without a target object') try: assigner = self.visit(node.targets[0], **kwargs) - except (UndefinedVariableError): + except UndefinedVariableError: assigner = node.targets[0].id - self.assigner = getattr(assigner,'name',assigner) + self.assigner = getattr(assigner, 'name', assigner) if self.assigner is None: - raise SyntaxError('left hand side of an assignment must be a single resolvable name') + raise SyntaxError('left hand side of an assignment must be a ' + 'single resolvable name') return self.visit(node.value, **kwargs) @@ -622,7 +625,7 @@ def visit_Attribute(self, node, **kwargs): name = self.env.add_tmp(v) return self.term_type(name, self.env) except AttributeError: - # something like datetime.datetime where scope is overriden + # something like datetime.datetime where scope is overridden if isinstance(value, ast.Name) and value.id == attr: return resolved @@ -699,8 +702,7 @@ def visitor(x, y): return reduce(visitor, operands) -_python_not_supported = frozenset(['Dict', 'Call', 'BoolOp', - 'In', 'NotIn']) +_python_not_supported = frozenset(['Dict', 'Call', 'BoolOp', 'In', 'NotIn']) _numexpr_supported_calls = frozenset(_reductions + _mathops) @@ -744,7 +746,7 @@ def __init__(self, expr, engine='numexpr', parser='pandas', env=None, @property def assigner(self): - return getattr(self._visitor,'assigner',None) + return getattr(self._visitor, 'assigner', None) def __call__(self): self.env.locals['truediv'] = self.truediv diff --git a/pandas/computation/expressions.py b/pandas/computation/expressions.py index f1007cbc81eb7..035878e20c645 100644 --- a/pandas/computation/expressions.py +++ b/pandas/computation/expressions.py @@ -2,7 +2,7 @@ Expressions ----------- -Offer fast expression evaluation thru numexpr +Offer fast expression evaluation through numexpr """ @@ -22,9 +22,10 @@ _where = None # the set of dtypes that we will allow pass to numexpr -_ALLOWED_DTYPES = dict( - evaluate=set(['int64', 'int32', 'float64', 'float32', 'bool']), - where=set(['int64', 'float64', 'bool'])) +_ALLOWED_DTYPES = { + 'evaluate': set(['int64', 'int32', 'float64', 'float32', 'bool']), + 'where': set(['int64', 'float64', 'bool']) +} # the minimum prod shape that we will use numexpr _MIN_ELEMENTS = 10000 @@ -100,10 +101,10 @@ def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, truediv=True, 'b_value': b_value}, casting='safe', truediv=truediv, **eval_kwargs) - except (ValueError) as detail: + except ValueError as detail: if 'unknown type object' in str(detail): pass - except (Exception) as detail: + except Exception as detail: if raise_on_error: raise @@ -135,10 +136,10 @@ def _where_numexpr(cond, a, b, raise_on_error=False): 'a_value': a_value, 'b_value': b_value}, casting='safe') - except (ValueError) as detail: + except ValueError as detail: if 'unknown type object' in str(detail): pass - except (Exception) as detail: + except Exception as detail: if raise_on_error: raise TypeError(str(detail)) diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index fd5ee159fe2b4..0510ee86760a3 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -207,7 +207,6 @@ def name(self): return self.value - _bool_op_map = {'not': '~', 'and': '&', 'or': '|'} diff --git a/pandas/computation/pytables.py b/pandas/computation/pytables.py index eb675d6230c8c..8afe8e909a434 100644 --- a/pandas/computation/pytables.py +++ b/pandas/computation/pytables.py @@ -16,6 +16,7 @@ from pandas.computation.common import _ensure_decoded from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type + class Scope(expr.Scope): __slots__ = 'globals', 'locals', 'queryables' @@ -85,7 +86,7 @@ def _disallow_scalar_only_bool_ops(self): def prune(self, klass): def pr(left, right): - """ create and return a new specilized BinOp from myself """ + """ create and return a new specialized BinOp from myself """ if left is None: return right @@ -95,7 +96,7 @@ def pr(left, right): k = klass if isinstance(left, ConditionBinOp): if (isinstance(left, ConditionBinOp) and - isinstance(right, ConditionBinOp)): + isinstance(right, ConditionBinOp)): k = JointConditionBinOp elif isinstance(left, k): return left @@ -104,7 +105,7 @@ def pr(left, right): elif isinstance(left, FilterBinOp): if (isinstance(left, FilterBinOp) and - isinstance(right, FilterBinOp)): + isinstance(right, FilterBinOp)): k = JointFilterBinOp elif isinstance(left, k): return left @@ -177,11 +178,12 @@ def stringify(value): if v.tz is not None: v = v.tz_convert('UTC') return TermValue(v, v.value, kind) - elif isinstance(v, datetime) or hasattr(v, 'timetuple') or kind == u('date'): + elif (isinstance(v, datetime) or hasattr(v, 'timetuple') or + kind == u('date')): v = time.mktime(v.timetuple()) return TermValue(v, pd.Timestamp(v), kind) elif kind == u('timedelta64') or kind == u('timedelta'): - v = _coerce_scalar_to_timedelta_type(v,unit='s').item() + v = _coerce_scalar_to_timedelta_type(v, unit='s').item() return TermValue(int(v), v, kind) elif kind == u('integer'): v = int(float(v)) @@ -293,7 +295,8 @@ def invert(self): #if self.condition is not None: # self.condition = "~(%s)" % self.condition #return self - raise NotImplementedError("cannot use an invert condition when passing to numexpr") + raise NotImplementedError("cannot use an invert condition when " + "passing to numexpr") def format(self): """ return the actual ne format """ @@ -352,10 +355,10 @@ def prune(self, klass): operand = operand.prune(klass) if operand is not None: - if issubclass(klass,ConditionBinOp): + if issubclass(klass, ConditionBinOp): if operand.condition is not None: return operand.invert() - elif issubclass(klass,FilterBinOp): + elif issubclass(klass, FilterBinOp): if operand.filter is not None: return operand.invert() @@ -364,6 +367,7 @@ def prune(self, klass): _op_classes = {'unary': UnaryOp} + class ExprVisitor(BaseExprVisitor): const_type = Constant term_type = Term @@ -401,7 +405,7 @@ def visit_Subscript(self, node, **kwargs): return self.const_type(value[slobj], self.env) except TypeError: raise ValueError("cannot subscript {0!r} with " - "{1!r}".format(value, slobj)) + "{1!r}".format(value, slobj)) def visit_Attribute(self, node, **kwargs): attr = node.attr @@ -435,7 +439,8 @@ class Expr(expr.Expr): Parameters ---------- where : string term expression, Expr, or list-like of Exprs - queryables : a "kinds" map (dict of column name -> kind), or None if column is non-indexable + queryables : a "kinds" map (dict of column name -> kind), or None if column + is non-indexable encoding : an encoding that will encode the query terms Returns @@ -538,13 +543,13 @@ def evaluate(self): try: self.condition = self.terms.prune(ConditionBinOp) except AttributeError: - raise ValueError( - "cannot process expression [{0}], [{1}] is not a valid condition".format(self.expr,self)) + raise ValueError("cannot process expression [{0}], [{1}] is not a " + "valid condition".format(self.expr, self)) try: self.filter = self.terms.prune(FilterBinOp) except AttributeError: - raise ValueError( - "cannot process expression [{0}], [{1}] is not a valid filter".format(self.expr,self)) + raise ValueError("cannot process expression [{0}], [{1}] is not a " + "valid filter".format(self.expr, self)) return self.condition, self.filter diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 2699dd0a25a2b..24c14a5d7f215 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -153,7 +153,8 @@ def factorize(values, sort=False, order=None, na_sentinel=-1): return labels, uniques -def value_counts(values, sort=True, ascending=False, normalize=False, bins=None): +def value_counts(values, sort=True, ascending=False, normalize=False, + bins=None): """ Compute a histogram of the counts of non-null values @@ -191,7 +192,7 @@ def value_counts(values, sort=True, ascending=False, normalize=False, bins=None) values = com._ensure_int64(values) keys, counts = htable.value_count_int64(values) - elif issubclass(values.dtype.type, (np.datetime64,np.timedelta64)): + elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): dtype = values.dtype values = values.view(np.int64) keys, counts = htable.value_count_int64(values) @@ -223,7 +224,7 @@ def value_counts(values, sort=True, ascending=False, normalize=False, bins=None) def mode(values): - "Returns the mode or mode(s) of the passed Series or ndarray (sorted)" + """Returns the mode or mode(s) of the passed Series or ndarray (sorted)""" # must sort because hash order isn't necessarily defined. from pandas.core.series import Series @@ -239,7 +240,7 @@ def mode(values): values = com._ensure_int64(values) result = constructor(sorted(htable.mode_int64(values)), dtype=dtype) - elif issubclass(values.dtype.type, (np.datetime64,np.timedelta64)): + elif issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): dtype = values.dtype values = values.view(np.int64) result = constructor(sorted(htable.mode_int64(values)), dtype=dtype) @@ -324,7 +325,7 @@ def _get_score(at): return np.nan idx = at * (len(values) - 1) - if (idx % 1 == 0): + if idx % 1 == 0: score = values[idx] else: if interpolation_method == 'fraction': diff --git a/pandas/core/api.py b/pandas/core/api.py index 36081cc34cc3a..28118c60776ce 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -27,8 +27,8 @@ # legacy from pandas.core.daterange import DateRange # deprecated -from pandas.core.common import save, load # deprecated, remove in 0.13 +from pandas.core.common import save, load # deprecated, remove in 0.13 import pandas.core.datetools as datetools -from pandas.core.config import get_option, set_option, reset_option,\ - describe_option, options +from pandas.core.config import (get_option, set_option, reset_option, + describe_option, options) diff --git a/pandas/core/array.py b/pandas/core/array.py index 6847ba073b92a..209b00cf8bb3c 100644 --- a/pandas/core/array.py +++ b/pandas/core/array.py @@ -37,6 +37,7 @@ #### a series-like ndarray #### + class SNDArray(Array): def __new__(cls, data, index=None, name=None): @@ -49,4 +50,3 @@ def __new__(cls, data, index=None, name=None): @property def values(self): return self.view(Array) - diff --git a/pandas/core/base.py b/pandas/core/base.py index 6b9fa78d45406..a702e7c87c0a9 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -5,10 +5,15 @@ import numpy as np from pandas.core import common as com + class StringMixin(object): - """implements string methods so long as object defines a `__unicode__` method. - Handles Python2/3 compatibility transparently.""" - # side note - this could be made into a metaclass if more than one object nees + """implements string methods so long as object defines a `__unicode__` + method. + + Handles Python2/3 compatibility transparently. + """ + # side note - this could be made into a metaclass if more than one + # object needs #---------------------------------------------------------------------- # Formatting @@ -96,7 +101,8 @@ class FrozenList(PandasObject, list): because it's technically non-hashable, will be used for lookups, appropriately, etc. """ - # Sidenote: This has to be of type list, otherwise it messes up PyTables typechecks + # Sidenote: This has to be of type list, otherwise it messes up PyTables + # typechecks def __add__(self, other): if isinstance(other, tuple): @@ -146,7 +152,7 @@ def _disabled(self, *args, **kwargs): def __unicode__(self): from pandas.core.common import pprint_thing return pprint_thing(self, quote_strings=True, - escape_chars=('\t', '\r', '\n')) + escape_chars=('\t', '\r', '\n')) def __repr__(self): return "%s(%s)" % (self.__class__.__name__, @@ -185,7 +191,9 @@ def __unicode__(self): """ Return a string representation for this object. - Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. + Invoked by unicode(df) in py2 only. Yields a Unicode String in both + py2/py3. """ - prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'),quote_strings=True) + prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'), + quote_strings=True) return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index f412947f92255..fec9cd4ff4274 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -31,6 +31,7 @@ def f(self, other): return f + class Categorical(PandasObject): """ Represents a categorical variable in classic R / S-plus fashion @@ -167,8 +168,8 @@ def _repr_footer(self): def _get_repr(self, name=False, length=True, na_rep='NaN', footer=True): formatter = fmt.CategoricalFormatter(self, name=name, - length=length, na_rep=na_rep, - footer=footer) + length=length, na_rep=na_rep, + footer=footer) result = formatter.to_string() return compat.text_type(result) @@ -226,7 +227,8 @@ def describe(self): grouped = DataFrame(self.labels).groupby(0) counts = grouped.count().values.squeeze() freqs = counts/float(counts.sum()) - return DataFrame.from_dict(dict( - counts=counts, - freqs=freqs, - levels=self.levels)).set_index('levels') + return DataFrame.from_dict({ + 'counts': counts, + 'freqs': freqs, + 'levels': self.levels + }).set_index('levels') diff --git a/pandas/core/common.py b/pandas/core/common.py index 42964c9d48537..6fc015d2cb575 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -26,20 +26,23 @@ class PandasError(Exception): pass + class SettingWithCopyError(ValueError): pass + class SettingWithCopyWarning(Warning): pass + class AmbiguousIndexError(PandasError, KeyError): pass _POSSIBLY_CAST_DTYPES = set([np.dtype(t) - for t in ['M8[ns]', 'm8[ns]', 'O', 'int8', - 'uint8', 'int16', 'uint16', 'int32', - 'uint32', 'int64', 'uint64']]) + for t in ['M8[ns]', 'm8[ns]', 'O', 'int8', + 'uint8', 'int16', 'uint16', 'int32', + 'uint32', 'int64', 'uint64']]) _NS_DTYPE = np.dtype('M8[ns]') _TD_DTYPE = np.dtype('m8[ns]') @@ -136,8 +139,7 @@ def _isnull_new(obj): def _isnull_old(obj): - ''' - Detect missing values. Treat None, NaN, INF, -INF as null. + """Detect missing values. Treat None, NaN, INF, -INF as null. Parameters ---------- @@ -146,7 +148,7 @@ def _isnull_old(obj): Returns ------- boolean ndarray or boolean - ''' + """ if lib.isscalar(obj): return lib.checknull_old(obj) # hack (for now) because MI registers as ndarray @@ -155,7 +157,8 @@ def _isnull_old(obj): elif isinstance(obj, (ABCSeries, np.ndarray)): return _isnull_ndarraylike_old(obj) elif isinstance(obj, ABCGeneric): - return obj._constructor(obj._data.apply(lambda x: _isnull_old(x.values))) + return obj._constructor(obj._data.apply( + lambda x: _isnull_old(x.values))) elif isinstance(obj, list) or hasattr(obj, '__array__'): return _isnull_ndarraylike_old(np.asarray(obj)) else: @@ -165,7 +168,7 @@ def _isnull_old(obj): def _use_inf_as_null(key): - '''Option change callback for null/inf behaviour + """Option change callback for null/inf behaviour Choose which replacement for numpy.isnan / -numpy.isfinite is used. Parameters @@ -182,7 +185,7 @@ def _use_inf_as_null(key): * http://stackoverflow.com/questions/4859217/ programmatically-creating-variables-in-python/4859312#4859312 - ''' + """ flag = get_option(key) if flag: globals()['_isnull'] = _isnull_old @@ -192,7 +195,7 @@ def _use_inf_as_null(key): def _isnull_ndarraylike(obj): - values = getattr(obj,'values',obj) + values = getattr(obj, 'values', obj) dtype = values.dtype if dtype.kind in ('O', 'S', 'U'): @@ -221,7 +224,7 @@ def _isnull_ndarraylike(obj): def _isnull_ndarraylike_old(obj): - values = getattr(obj,'values',obj) + values = getattr(obj, 'values', obj) dtype = values.dtype if dtype.kind in ('O', 'S', 'U'): @@ -775,13 +778,15 @@ def diff(arr, n, axis=0): def _coerce_to_dtypes(result, dtypes): - """ given a dtypes and a result set, coerce the result elements to the dtypes """ + """ given a dtypes and a result set, coerce the result elements to the + dtypes + """ if len(result) != len(dtypes): raise AssertionError("_coerce_to_dtypes requires equal len arrays") from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type - def conv(r,dtype): + def conv(r, dtype): try: if isnull(r): pass @@ -800,7 +805,7 @@ def conv(r,dtype): return r - return np.array([ conv(r,dtype) for r, dtype in zip(result,dtypes) ]) + return np.array([conv(r, dtype) for r, dtype in zip(result, dtypes)]) def _infer_dtype_from_scalar(val): @@ -850,7 +855,9 @@ def _infer_dtype_from_scalar(val): def _maybe_cast_scalar(dtype, value): - """ if we a scalar value and are casting to a dtype that needs nan -> NaT conversion """ + """ if we a scalar value and are casting to a dtype that needs nan -> NaT + conversion + """ if np.isscalar(value) and dtype in _DATELIKE_DTYPES and isnull(value): return tslib.iNaT return value @@ -882,8 +889,8 @@ def _maybe_promote(dtype, fill_value=np.nan): try: fill_value = lib.Timestamp(fill_value).value except: - # the proper thing to do here would probably be to upcast to - # object (but numpy 1.6.1 doesn't do this properly) + # the proper thing to do here would probably be to upcast + # to object (but numpy 1.6.1 doesn't do this properly) fill_value = tslib.iNaT else: fill_value = tslib.iNaT @@ -920,10 +927,10 @@ def _maybe_promote(dtype, fill_value=np.nan): def _maybe_upcast_putmask(result, mask, other, dtype=None, change=None): """ a safe version of put mask that (potentially upcasts the result - return the result - if change is not None, then MUTATE the change (and change the dtype) - return a changed flag - """ + return the result + if change is not None, then MUTATE the change (and change the dtype) + return a changed flag + """ if mask.any(): @@ -964,15 +971,17 @@ def changeit(): return r, True # we want to decide whether putmask will work - # if we have nans in the False portion of our mask then we need to upcast (possibily) - # otherwise we DON't want to upcast (e.g. if we are have values, say integers in - # the success portion then its ok to not upcast) + # if we have nans in the False portion of our mask then we need to + # upcast (possibily) otherwise we DON't want to upcast (e.g. if we are + # have values, say integers in the success portion then its ok to not + # upcast) new_dtype, fill_value = _maybe_promote(result.dtype, other) if new_dtype != result.dtype: # we have a scalar or len 0 ndarray # and its nan and we are changing some values - if np.isscalar(other) or (isinstance(other, np.ndarray) and other.ndim < 1): + if (np.isscalar(other) or + (isinstance(other, np.ndarray) and other.ndim < 1)): if isnull(other): return changeit() @@ -991,14 +1000,15 @@ def changeit(): def _maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): - """ provide explicty type promotion and coercion + """ provide explict type promotion and coercion - Parameters - ---------- - values : the ndarray that we want to maybe upcast - fill_value : what we want to fill with - dtype : if None, then use the dtype of the values, else coerce to this type - copy : if True always make a copy even if no upcast is required """ + Parameters + ---------- + values : the ndarray that we want to maybe upcast + fill_value : what we want to fill with + dtype : if None, then use the dtype of the values, else coerce to this type + copy : if True always make a copy even if no upcast is required + """ if dtype is None: dtype = values.dtype @@ -1022,7 +1032,8 @@ def _possibly_cast_item(obj, item, dtype): def _possibly_downcast_to_dtype(result, dtype): """ try to cast to the specified dtype (e.g. convert back to bool/int - or could be an astype of float64->float32 """ + or could be an astype of float64->float32 + """ if np.isscalar(result) or not len(result): return result @@ -1065,22 +1076,25 @@ def _possibly_downcast_to_dtype(result, dtype): # do a test on the first element, if it fails then we are done r = result.ravel() - arr = np.array([ r[0] ]) - if not np.allclose(arr,trans(arr).astype(dtype)): + arr = np.array([r[0]]) + if not np.allclose(arr, trans(arr).astype(dtype)): return result # a comparable, e.g. a Decimal may slip in here - elif not isinstance(r[0], (np.integer,np.floating,np.bool,int,float,bool)): + elif not isinstance(r[0], (np.integer, np.floating, np.bool, int, + float, bool)): return result - if issubclass(result.dtype.type, (np.object_,np.number)) and notnull(result).all(): + if (issubclass(result.dtype.type, (np.object_, np.number)) and + notnull(result).all()): new_result = trans(result).astype(dtype) try: - if np.allclose(new_result,result): + if np.allclose(new_result, result): return new_result except: - # comparison of an object dtype with a number type could hit here + # comparison of an object dtype with a number type could + # hit here if (new_result == result).all(): return new_result except: @@ -1119,8 +1133,9 @@ def _lcd_dtypes(a_dtype, b_dtype): def _fill_zeros(result, y, fill): """ if we have an integer value (or array in y) - and we have 0's, fill them with the fill, - return the result """ + and we have 0's, fill them with the fill, + return the result + """ if fill is not None: if not isinstance(y, np.ndarray): @@ -1155,7 +1170,6 @@ def wrapper(arr, mask, limit=None): np.int64) - def pad_1d(values, limit=None, mask=None): dtype = values.dtype.name @@ -1357,8 +1371,8 @@ def _interp_limit(invalid, limit): new_x = new_x[firstIndex:] xvalues = xvalues[firstIndex:] - result[firstIndex:][invalid] = _interpolate_scipy_wrapper(valid_x, - valid_y, new_x, method=method, fill_value=fill_value, + result[firstIndex:][invalid] = _interpolate_scipy_wrapper( + valid_x, valid_y, new_x, method=method, fill_value=fill_value, bounds_error=bounds_error, **kwargs) if limit: result[violate_limit] = np.nan @@ -1384,7 +1398,7 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, 'barycentric': interpolate.barycentric_interpolate, 'krogh': interpolate.krogh_interpolate, 'piecewise_polynomial': interpolate.piecewise_polynomial_interpolate, - } + } try: alt_methods['pchip'] = interpolate.pchip_interpolate @@ -1411,16 +1425,18 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None): - """ perform an actual interpolation of values, values will be make 2-d if needed - fills inplace, returns the result """ + """ perform an actual interpolation of values, values will be make 2-d if + needed fills inplace, returns the result + """ transf = (lambda x: x) if axis == 0 else (lambda x: x.T) # reshape a 1 dim if needed ndim = values.ndim if values.ndim == 1: - if axis != 0: # pragma: no cover - raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0") + if axis != 0: # pragma: no cover + raise AssertionError("cannot interpolate on a ndim == 1 with " + "axis != 0") values = values.reshape(tuple((1,) + values.shape)) if fill_value is None: @@ -1451,6 +1467,7 @@ def _consensus_name_attr(objs): _fill_methods = {'pad': pad_1d, 'backfill': backfill_1d} + def _get_fill_func(method): method = _clean_fill_method(method) return _fill_methods[method] @@ -1478,8 +1495,9 @@ def _values_from_object(o): return o -def _possibly_convert_objects(values, convert_dates=True, convert_numeric=True): - """ if we have an object dtype, try to coerce dates and/or numers """ +def _possibly_convert_objects(values, convert_dates=True, + convert_numeric=True): + """ if we have an object dtype, try to coerce dates and/or numbers """ # if we have passed in a list or scalar if isinstance(values, (list, tuple)): @@ -1537,7 +1555,9 @@ def _possibly_convert_platform(values): def _possibly_cast_to_datetime(value, dtype, coerce=False): - """ try to cast the array/value to a datetimelike dtype, converting float nan to iNaT """ + """ try to cast the array/value to a datetimelike dtype, converting float + nan to iNaT + """ if dtype is not None: if isinstance(dtype, compat.string_types): @@ -1573,21 +1593,26 @@ def _possibly_cast_to_datetime(value, dtype, coerce=False): from pandas.tseries.tools import to_datetime value = to_datetime(value, coerce=coerce).values elif is_timedelta64: - from pandas.tseries.timedeltas import _possibly_cast_to_timedelta + from pandas.tseries.timedeltas import \ + _possibly_cast_to_timedelta value = _possibly_cast_to_timedelta(value) except: pass else: - # only do this if we have an array and the dtype of the array is not setup already - # we are not an integer/object, so don't bother with this conversion - if isinstance(value, np.ndarray) and not (issubclass(value.dtype.type, np.integer) or value.dtype == np.object_): + # only do this if we have an array and the dtype of the array is not + # setup already we are not an integer/object, so don't bother with this + # conversion + if (isinstance(value, np.ndarray) and not + (issubclass(value.dtype.type, np.integer) or + value.dtype == np.object_)): pass else: - # we might have a array (or single object) that is datetime like, and no dtype is passed - # don't change the value unless we find a datetime set + # we might have a array (or single object) that is datetime like, + # and no dtype is passed don't change the value unless we find a + # datetime set v = value if not is_list_like(v): v = [v] @@ -1599,7 +1624,8 @@ def _possibly_cast_to_datetime(value, dtype, coerce=False): except: pass elif inferred_type in ['timedelta', 'timedelta64']: - from pandas.tseries.timedeltas import _possibly_cast_to_timedelta + from pandas.tseries.timedeltas import \ + _possibly_cast_to_timedelta value = _possibly_cast_to_timedelta(value) return value @@ -1874,9 +1900,9 @@ def _asarray_tuplesafe(values, dtype=None): try: result = np.empty(len(values), dtype=object) result[:] = values - except (ValueError): + except ValueError: # we have a list-of-list - result[:] = [ tuple(x) for x in values ] + result[:] = [tuple(x) for x in values] return result @@ -1977,7 +2003,8 @@ def is_timedelta64_dtype(arr_or_dtype): def needs_i8_conversion(arr_or_dtype): - return is_datetime64_dtype(arr_or_dtype) or is_timedelta64_dtype(arr_or_dtype) + return (is_datetime64_dtype(arr_or_dtype) or + is_timedelta64_dtype(arr_or_dtype)) def is_float_dtype(arr_or_dtype): @@ -2010,7 +2037,8 @@ def is_re_compilable(obj): def is_list_like(arg): - return hasattr(arg, '__iter__') and not isinstance(arg, compat.string_and_binary_types) + return (hasattr(arg, '__iter__') and + not isinstance(arg, compat.string_and_binary_types)) def _is_sequence(x): @@ -2044,8 +2072,8 @@ def _astype_nansafe(arr, dtype, copy=True): elif dtype == np.int64: return arr.view(dtype) elif dtype != _NS_DTYPE: - raise TypeError( - "cannot astype a datetimelike from [%s] to [%s]" % (arr.dtype, dtype)) + raise TypeError("cannot astype a datetimelike from [%s] to [%s]" % + (arr.dtype, dtype)) return arr.astype(_NS_DTYPE) elif is_timedelta64_dtype(arr): if dtype == np.int64: @@ -2054,7 +2082,8 @@ def _astype_nansafe(arr, dtype, copy=True): return arr.astype(object) # in py3, timedelta64[ns] are int64 - elif (compat.PY3 and dtype not in [_INT64_DTYPE,_TD_DTYPE]) or (not compat.PY3 and dtype != _TD_DTYPE): + elif ((compat.PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or + (not compat.PY3 and dtype != _TD_DTYPE)): # allow frequency conversions if dtype.kind == 'm': @@ -2063,7 +2092,8 @@ def _astype_nansafe(arr, dtype, copy=True): result[mask] = np.nan return result - raise TypeError("cannot astype a timedelta from [%s] to [%s]" % (arr.dtype,dtype)) + raise TypeError("cannot astype a timedelta from [%s] to [%s]" % + (arr.dtype, dtype)) return arr.astype(_TD_DTYPE) elif (np.issubdtype(arr.dtype, np.floating) and @@ -2083,7 +2113,8 @@ def _astype_nansafe(arr, dtype, copy=True): def _clean_fill_method(method): - if method is None: return None + if method is None: + return None method = method.lower() if method == 'ffill': method = 'pad' @@ -2130,8 +2161,9 @@ def next(self): def _get_handle(path, mode, encoding=None, compression=None): """Gets file handle for given path and mode. - NOTE: Under Python 3.2, getting a compressed file handle means reading in the entire file, - decompressing it and decoding it to ``str`` all at once and then wrapping it in a StringIO. + NOTE: Under Python 3.2, getting a compressed file handle means reading in + the entire file, decompressing it and decoding it to ``str`` all at once + and then wrapping it in a StringIO. """ if compression is not None: if encoding is not None and not compat.PY3: @@ -2327,8 +2359,10 @@ def in_qtconsole(): """ try: ip = get_ipython() - front_end = (ip.config.get('KernelApp', {}).get('parent_appname', "") or - ip.config.get('IPKernelApp', {}).get('parent_appname', "")) + front_end = ( + ip.config.get('KernelApp', {}).get('parent_appname', "") or + ip.config.get('IPKernelApp', {}).get('parent_appname', "") + ) if 'qtconsole' in front_end.lower(): return True except: @@ -2342,8 +2376,10 @@ def in_ipnb(): """ try: ip = get_ipython() - front_end = (ip.config.get('KernelApp', {}).get('parent_appname', "") or - ip.config.get('IPKernelApp', {}).get('parent_appname', "")) + front_end = ( + ip.config.get('KernelApp', {}).get('parent_appname', "") or + ip.config.get('IPKernelApp', {}).get('parent_appname', "") + ) if 'notebook' in front_end.lower(): return True except: @@ -2399,7 +2435,7 @@ def _pprint_seq(seq, _nest_lvl=0, **kwds): bounds length of printed sequence, depending on options """ - if isinstance(seq,set): + if isinstance(seq, set): fmt = u("set([%s])") else: fmt = u("[%s]") if hasattr(seq, '__setitem__') else u("(%s)") @@ -2433,8 +2469,8 @@ def _pprint_dict(seq, _nest_lvl=0, **kwds): nitems = get_option("max_seq_items") or len(seq) for k, v in list(seq.items())[:nitems]: - pairs.append(pfmt % (pprint_thing(k,_nest_lvl+1,**kwds), - pprint_thing(v,_nest_lvl+1,**kwds))) + pairs.append(pfmt % (pprint_thing(k, _nest_lvl+1, **kwds), + pprint_thing(v, _nest_lvl+1, **kwds))) if nitems < len(seq): return fmt % (", ".join(pairs) + ", ...") @@ -2505,7 +2541,7 @@ def as_escaped_unicode(thing, escape_chars=escape_chars): get_option("display.pprint_nest_depth"): result = _pprint_seq(thing, _nest_lvl, escape_chars=escape_chars, quote_strings=quote_strings) - elif isinstance(thing,compat.string_types) and quote_strings: + elif isinstance(thing, compat.string_types) and quote_strings: if compat.PY3: fmt = "'%s'" else: @@ -2539,8 +2575,8 @@ def load(path): # TODO remove in 0.13 Load pickled pandas object (or any other pickled object) from the specified file path - Warning: Loading pickled data received from untrusted sources can be unsafe. - See: http://docs.python.org/2.7/library/pickle.html + Warning: Loading pickled data received from untrusted sources can be + unsafe. See: http://docs.python.org/2.7/library/pickle.html Parameters ---------- @@ -2558,7 +2594,7 @@ def load(path): # TODO remove in 0.13 def save(obj, path): # TODO remove in 0.13 - ''' + """ Pickle (serialize) object to input file path Parameters @@ -2566,7 +2602,7 @@ def save(obj, path): # TODO remove in 0.13 obj : any object path : string File path - ''' + """ import warnings warnings.warn("save is deprecated, use obj.to_pickle", FutureWarning) from pandas.io.pickle import to_pickle @@ -2574,8 +2610,8 @@ def save(obj, path): # TODO remove in 0.13 def _maybe_match_name(a, b): - a_name = getattr(a,'name',None) - b_name = getattr(b,'name',None) + a_name = getattr(a, 'name', None) + b_name = getattr(b, 'name', None) if a_name == b_name: return a_name return None diff --git a/pandas/core/config.py b/pandas/core/config.py index 20ec30398fd64..6eb947119578f 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -173,16 +173,19 @@ def _reset_option(pat): if len(keys) > 1 and len(pat) < 4 and pat != 'all': raise ValueError('You must specify at least 4 characters when ' - 'resetting multiple keys, use the special keyword "all" ' - 'to reset all the options to their default value') + 'resetting multiple keys, use the special keyword ' + '"all" to reset all the options to their default ' + 'value') for k in keys: _set_option(k, _registered_options[k].defval) + def get_default_val(pat): - key = _get_single_key(pat, silent=True) + key = _get_single_key(pat, silent=True) return _get_registered_option(key).defval + class DictWrapper(object): """ provide attribute-style access to a nested dict """ @@ -242,7 +245,8 @@ def __doc__(self): return self.__doc_tmpl__.format(opts_desc=opts_desc, opts_list=opts_list) -_get_option_tmpl = """"get_option(pat) - Retrieves the value of the specified option +_get_option_tmpl = """ +get_option(pat) - Retrieves the value of the specified option Available options: {opts_list} @@ -266,7 +270,8 @@ def __doc__(self): {opts_desc} """ -_set_option_tmpl = """set_option(pat,value) - Sets the value of the specified option +_set_option_tmpl = """ +set_option(pat,value) - Sets the value of the specified option Available options: {opts_list} @@ -292,7 +297,8 @@ def __doc__(self): {opts_desc} """ -_describe_option_tmpl = """describe_option(pat,_print_desc=False) Prints the description +_describe_option_tmpl = """ +describe_option(pat,_print_desc=False) Prints the description for one or more registered options. Call with not arguments to get a listing for all registered options. @@ -317,7 +323,8 @@ def __doc__(self): {opts_desc} """ -_reset_option_tmpl = """reset_option(pat) - Reset one or more options to their default value. +_reset_option_tmpl = """ +reset_option(pat) - Reset one or more options to their default value. Pass "all" as argument to reset all options. @@ -353,9 +360,11 @@ def __doc__(self): class option_context(object): def __init__(self, *args): - if not ( len(args) % 2 == 0 and len(args) >= 2): - errmsg = "Need to invoke as option_context(pat,val,[(pat,val),..))." - raise AssertionError(errmsg) + if not (len(args) % 2 == 0 and len(args) >= 2): + raise AssertionError( + 'Need to invoke as' + 'option_context(pat, val, [(pat, val), ...)).' + ) ops = list(zip(args[::2], args[1::2])) undo = [] @@ -425,20 +434,21 @@ def register_option(key, defval, doc='', validator=None, cb=None): for i, p in enumerate(path[:-1]): if not isinstance(cursor, dict): raise OptionError("Path prefix to option '%s' is already an option" - % '.'.join(path[:i])) + % '.'.join(path[:i])) if p not in cursor: cursor[p] = {} cursor = cursor[p] if not isinstance(cursor, dict): raise OptionError("Path prefix to option '%s' is already an option" - % '.'.join(path[:-1])) + % '.'.join(path[:-1])) cursor[path[-1]] = defval # initialize # save the option metadata _registered_options[key] = RegisteredOption(key=key, defval=defval, - doc=doc, validator=validator, cb=cb) + doc=doc, validator=validator, + cb=cb) def deprecate_option(key, msg=None, rkey=None, removal_ver=None): @@ -484,7 +494,7 @@ def deprecate_option(key, msg=None, rkey=None, removal_ver=None): if key in _deprecated_options: raise OptionError("Option '%s' has already been defined as deprecated." - % key) + % key) _deprecated_options[key] = DeprecatedOption(key, msg, rkey, removal_ver) @@ -512,6 +522,7 @@ def _get_root(key): cursor = cursor[p] return cursor, path[-1] + def _get_option_fast(key): """ internal quick access routine, no error checking """ path = key.split('.') @@ -520,6 +531,7 @@ def _get_option_fast(key): cursor = cursor[p] return cursor + def _is_deprecated(key): """ Returns True if the given option has been deprecated """ @@ -603,7 +615,8 @@ def _build_option_description(k): s = u('%s: ') % k if o: - s += u('[default: %s] [currently: %s]') % (o.defval, _get_option(k, True)) + s += u('[default: %s] [currently: %s]') % (o.defval, + _get_option(k, True)) if o.doc: s += '\n' + '\n '.join(o.doc.strip().split('\n')) @@ -755,12 +768,14 @@ def inner(x): return inner + def is_one_of_factory(legal_values): def inner(x): from pandas.core.common import pprint_thing as pp if not x in legal_values: pp_values = lmap(pp, legal_values) - raise ValueError("Value must be one of %s" % pp("|".join(pp_values))) + raise ValueError("Value must be one of %s" + % pp("|".join(pp_values))) return inner diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index 9e95759ac088b..b9b934769793f 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -1,8 +1,3 @@ -import pandas.core.config as cf -from pandas.core.config import (is_int, is_bool, is_text, is_float, - is_instance_factory,is_one_of_factory,get_default_val) -from pandas.core.format import detect_console_encoding - """ This module is imported from the pandas package __init__.py file in order to ensure that the core.config options registered here will @@ -15,6 +10,12 @@ """ +import pandas.core.config as cf +from pandas.core.config import (is_int, is_bool, is_text, is_float, + is_instance_factory, is_one_of_factory, + get_default_val) +from pandas.core.format import detect_console_encoding + ########################################### # options from the "display" namespace @@ -113,8 +114,8 @@ pc_expand_repr_doc = """ : boolean - Whether to print out the full DataFrame repr for wide DataFrames - across multiple lines, `max_columns` is still respected, but the output will + Whether to print out the full DataFrame repr for wide DataFrames across + multiple lines, `max_columns` is still respected, but the output will wrap-around across multiple "pages" if it's width exceeds `display.width`. """ @@ -124,7 +125,8 @@ """ pc_line_width_deprecation_warning = """\ -line_width has been deprecated, use display.width instead (currently both are identical) +line_width has been deprecated, use display.width instead (currently both are +identical) """ pc_height_deprecation_warning = """\ @@ -134,8 +136,8 @@ pc_width_doc = """ : int Width of the display in characters. In case python/IPython is running in - a terminal this can be set to None and pandas will correctly auto-detect the - width. + a terminal this can be set to None and pandas will correctly auto-detect + the width. Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to correctly detect the width. """ @@ -155,8 +157,8 @@ : int or None when pretty-printing a long sequence, no more then `max_seq_items` - will be printed. If items are ommitted, they will be denoted by the addition - of "..." to the resulting string. + will be printed. If items are omitted, they will be denoted by the + addition of "..." to the resulting string. If set to None, the number of items to be printed is unlimited. """ @@ -182,6 +184,8 @@ """ style_backup = dict() + + def mpl_style_cb(key): import sys from pandas.tools.plotting import mpl_stylesheet @@ -190,15 +194,14 @@ def mpl_style_cb(key): val = cf.get_option(key) if 'matplotlib' not in sys.modules.keys(): - if not(val): # starting up, we get reset to None + if not(val): # starting up, we get reset to None return val raise Exception("matplotlib has not been imported. aborting") import matplotlib.pyplot as plt - if val == 'default': - style_backup = dict([(k,plt.rcParams[k]) for k in mpl_stylesheet]) + style_backup = dict([(k, plt.rcParams[k]) for k in mpl_stylesheet]) plt.rcParams.update(mpl_stylesheet) elif not val: if style_backup: @@ -241,10 +244,11 @@ def mpl_style_cb(key): cb=mpl_style_cb) cf.register_option('height', 60, pc_height_doc, validator=is_instance_factory([type(None), int])) - cf.register_option('width',80, pc_width_doc, + cf.register_option('width', 80, pc_width_doc, validator=is_instance_factory([type(None), int])) # redirected to width, make defval identical - cf.register_option('line_width', get_default_val('display.width'), pc_line_width_doc) + cf.register_option('line_width', get_default_val('display.width'), + pc_line_width_doc) cf.deprecate_option('display.line_width', msg=pc_line_width_deprecation_warning, @@ -271,6 +275,7 @@ def mpl_style_cb(key): # We don't want to start importing everything at the global context level # or we'll hit circular deps. + def use_inf_as_null_cb(key): from pandas.core.common import _use_inf_as_null _use_inf_as_null(key) @@ -283,7 +288,8 @@ def use_inf_as_null_cb(key): # user warnings chained_assignment = """ : string - Raise an exception, warn, or no action if trying to use chained assignment, The default is warn + Raise an exception, warn, or no action if trying to use chained assignment, + The default is warn """ with cf.config_prefix('mode'): @@ -294,7 +300,8 @@ def use_inf_as_null_cb(key): # Set up the io.excel specific configuration. writer_engine_doc = """ : string - The default Excel writer engine for '{ext}' files. Available options: '{default}' (the default){others}. + The default Excel writer engine for '{ext}' files. Available options: + '{default}' (the default){others}. """ with cf.config_prefix('io.excel'): @@ -309,12 +316,13 @@ def use_inf_as_null_cb(key): doc = writer_engine_doc.format(ext=ext, default=default, others=options) cf.register_option(ext + '.writer', default, doc, validator=str) + def _register_xlsx(engine, other): cf.register_option('xlsx.writer', engine, writer_engine_doc.format(ext='xlsx', default=engine, others=", '%s'" % other), - validator=str) + validator=str) try: # better memory footprint diff --git a/pandas/core/datetools.py b/pandas/core/datetools.py index 91a29259d8f2f..1fb6ae4225f25 100644 --- a/pandas/core/datetools.py +++ b/pandas/core/datetools.py @@ -36,6 +36,7 @@ isMonthEnd = MonthEnd().onOffset isBMonthEnd = BMonthEnd().onOffset + def _resolve_offset(freq, kwds): if 'timeRule' in kwds or 'offset' in kwds: offset = kwds.get('offset', None) @@ -54,4 +55,3 @@ def _resolve_offset(freq, kwds): FutureWarning) return offset - diff --git a/pandas/core/format.py b/pandas/core/format.py index ae0d95b1c3074..9abfe3c43b8e5 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -62,6 +62,7 @@ ------- formatted : string (or unicode, depending on data and options)""" + class CategoricalFormatter(object): def __init__(self, categorical, buf=None, length=True, na_rep='NaN', name=False, footer=True): @@ -78,8 +79,8 @@ def _get_footer(self): if self.name: name = com.pprint_thing(self.categorical.name, escape_chars=('\t', '\r', '\n')) - footer += ('Name: %s' % - name) if self.categorical.name is not None else "" + footer += ('Name: %s' % name if self.categorical.name is not None + else '') if self.length: if footer: @@ -88,7 +89,7 @@ def _get_footer(self): levheader = 'Levels (%d): ' % len(self.categorical.levels) - #TODO: should max_line_width respect a setting? + # TODO: should max_line_width respect a setting? levstring = np.array_repr(self.categorical.levels, max_line_width=60) indent = ' ' * (levstring.find('[') + len(levheader) + 1) lines = levstring.split('\n') @@ -140,7 +141,7 @@ def __init__(self, series, buf=None, header=True, length=True, if float_format is None: float_format = get_option("display.float_format") self.float_format = float_format - self.dtype = dtype + self.dtype = dtype def _get_footer(self): footer = u('') @@ -163,10 +164,11 @@ def _get_footer(self): footer += 'Length: %d' % len(self.series) if self.dtype: - if getattr(self.series.dtype,'name',None): + name = getattr(self.series.dtype, 'name', None) + if name: if footer: footer += ', ' - footer += 'dtype: %s' % com.pprint_thing(self.series.dtype.name) + footer += 'dtype: %s' % com.pprint_thing(name) return compat.text_type(footer) @@ -213,6 +215,7 @@ def to_string(self): return compat.text_type(u('\n').join(result)) + def _strlen_func(): if compat.PY3: # pragma: no cover _strlen = len @@ -420,9 +423,10 @@ def get_col_type(dtype): column_format = 'l%s' % ''.join(map(get_col_type, dtypes)) else: column_format = '%s' % ''.join(map(get_col_type, dtypes)) - elif not isinstance(column_format, compat.string_types): # pragma: no cover - raise AssertionError(('column_format must be str or unicode, not %s' - % type(column_format))) + elif not isinstance(column_format, + compat.string_types): # pragma: no cover + raise AssertionError('column_format must be str or unicode, not %s' + % type(column_format)) def write(buf, frame, column_format, strcols): buf.write('\\begin{tabular}{%s}\n' % column_format) @@ -482,10 +486,9 @@ def is_numeric_dtype(dtype): fmt_columns = lzip(*fmt_columns) dtypes = self.frame.dtypes.values need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes))) - str_columns = list(zip(*[[' ' + y - if y not in self.formatters and need_leadsp[x] - else y for y in x] - for x in fmt_columns])) + str_columns = list(zip(*[ + [' ' + y if y not in self.formatters and need_leadsp[x] + else y for y in x] for x in fmt_columns])) if self.sparsify: str_columns = _sparsify(str_columns) @@ -690,11 +693,12 @@ def _column_header(): sentinal = com.sentinal_factory() levels = self.columns.format(sparsify=sentinal, adjoin=False, names=False) - level_lengths = _get_level_lengths(levels,sentinal) + level_lengths = _get_level_lengths(levels, sentinal) row_levels = self.frame.index.nlevels - for lnum, (records, values) in enumerate(zip(level_lengths, levels)): + for lnum, (records, values) in enumerate(zip(level_lengths, + levels)): name = self.columns.names[lnum] row = [''] * (row_levels - 1) + ['' if name is None else str(name)] @@ -784,8 +788,9 @@ def _write_hierarchical_rows(self, fmt_values, indent): # GH3547 sentinal = com.sentinal_factory() - levels = frame.index.format(sparsify=sentinal, adjoin=False, names=False) - level_lengths = _get_level_lengths(levels,sentinal) + levels = frame.index.format(sparsify=sentinal, adjoin=False, + names=False) + level_lengths = _get_level_lengths(levels, sentinal) for i in range(len(frame)): row = [] @@ -810,15 +815,16 @@ def _write_hierarchical_rows(self, fmt_values, indent): else: for i in range(len(frame)): idx_values = list(zip(*frame.index.format(sparsify=False, - adjoin=False, - names=False))) + adjoin=False, + names=False))) row = [] row.extend(idx_values[i]) row.extend(fmt_values[j][i] for j in range(ncols)) self.write_tr(row, indent, self.indent_delta, tags=None, nindex_levels=frame.index.nlevels) -def _get_level_lengths(levels,sentinal=''): + +def _get_level_lengths(levels, sentinal=''): from itertools import groupby def _make_grouper(): @@ -882,8 +888,8 @@ def __init__(self, obj, path_or_buf, sep=",", na_rep='', float_format=None, #GH3457 if not self.obj.columns.is_unique and engine == 'python': - msg= "columns.is_unique == False not supported with engine='python'" - raise NotImplementedError(msg) + raise NotImplementedError("columns.is_unique == False not " + "supported with engine='python'") self.tupleize_cols = tupleize_cols self.has_mi_columns = isinstance(obj.columns, MultiIndex @@ -892,24 +898,27 @@ def __init__(self, obj, path_or_buf, sep=",", na_rep='', float_format=None, # validate mi options if self.has_mi_columns: if cols is not None: - raise TypeError("cannot specify cols with a MultiIndex on the columns") + raise TypeError("cannot specify cols with a MultiIndex on the " + "columns") if cols is not None: - if isinstance(cols,Index): - cols = cols.to_native_types(na_rep=na_rep,float_format=float_format, + if isinstance(cols, Index): + cols = cols.to_native_types(na_rep=na_rep, + float_format=float_format, date_format=date_format) else: - cols=list(cols) - self.obj = self.obj.loc[:,cols] + cols = list(cols) + self.obj = self.obj.loc[:, cols] # update columns to include possible multiplicity of dupes # and make sure sure cols is just a list of labels cols = self.obj.columns - if isinstance(cols,Index): - cols = cols.to_native_types(na_rep=na_rep,float_format=float_format, + if isinstance(cols, Index): + cols = cols.to_native_types(na_rep=na_rep, + float_format=float_format, date_format=date_format) else: - cols=list(cols) + cols = list(cols) # save it self.cols = cols @@ -917,19 +926,22 @@ def __init__(self, obj, path_or_buf, sep=",", na_rep='', float_format=None, # preallocate data 2d list self.blocks = self.obj._data.blocks ncols = sum(len(b.items) for b in self.blocks) - self.data =[None] * ncols + self.data = [None] * ncols self.column_map = self.obj._data.get_items_map(use_cached=False) if chunksize is None: - chunksize = (100000/ (len(self.cols) or 1)) or 1 + chunksize = (100000 / (len(self.cols) or 1)) or 1 self.chunksize = chunksize self.data_index = obj.index if isinstance(obj.index, PeriodIndex): self.data_index = obj.index.to_timestamp() - if isinstance(self.data_index, DatetimeIndex) and date_format is not None: - self.data_index = Index([x.strftime(date_format) if notnull(x) else '' for x in self.data_index]) + if (isinstance(self.data_index, DatetimeIndex) and + date_format is not None): + self.data_index = Index([x.strftime(date_format) + if notnull(x) else '' + for x in self.data_index]) self.nlevels = getattr(self.data_index, 'nlevels', 1) if not index: @@ -961,7 +973,8 @@ def _helper_csv(self, writer, na_rep=None, cols=None, index_label = [''] else: index_label = [index_label] - elif not isinstance(index_label, (list, tuple, np.ndarray)): + elif not isinstance(index_label, + (list, tuple, np.ndarray)): # given a string for a DF with Index index_label = [index_label] @@ -1004,8 +1017,9 @@ def strftime_with_nulls(x): values = self.obj.copy() values.index = data_index - values.columns = values.columns.to_native_types(na_rep=na_rep,float_format=float_format, - date_format=date_format) + values.columns = values.columns.to_native_types( + na_rep=na_rep, float_format=float_format, + date_format=date_format) values = values[cols] series = {} @@ -1018,7 +1032,7 @@ def strftime_with_nulls(x): if index: if nlevels == 1: row_fields = [idx] - else: # handle MultiIndex + else: # handle MultiIndex row_fields = list(idx) for i, col in enumerate(cols): val = series[col][j] @@ -1040,7 +1054,8 @@ def save(self): f = self.path_or_buf close = False else: - f = com._get_handle(self.path_or_buf, self.mode, encoding=self.encoding) + f = com._get_handle(self.path_or_buf, self.mode, + encoding=self.encoding) close = True try: @@ -1056,14 +1071,15 @@ def save(self): if self.engine == 'python': # to be removed in 0.13 self._helper_csv(self.writer, na_rep=self.na_rep, - float_format=self.float_format, cols=self.cols, - header=self.header, index=self.index, - index_label=self.index_label, date_format=self.date_format) + float_format=self.float_format, + cols=self.cols, header=self.header, + index=self.index, + index_label=self.index_label, + date_format=self.date_format) else: self._save() - finally: if close: f.close() @@ -1127,7 +1143,8 @@ def _save_header(self): if has_mi_columns: columns = obj.columns - # write out the names for each level, then ALL of the values for each level + # write out the names for each level, then ALL of the values for + # each level for i in range(columns.nlevels): # we need at least 1 index column to write our col names @@ -1135,10 +1152,10 @@ def _save_header(self): if self.index: # name is the first column - col_line.append( columns.names[i] ) + col_line.append(columns.names[i]) - if isinstance(index_label,list) and len(index_label)>1: - col_line.extend([ '' ] * (len(index_label)-1)) + if isinstance(index_label, list) and len(index_label) > 1: + col_line.extend([''] * (len(index_label)-1)) col_line.extend(columns.get_level_values(i)) @@ -1146,7 +1163,7 @@ def _save_header(self): # add blanks for the columns, so that we # have consistent seps - encoded_labels.extend([ '' ] * len(columns)) + encoded_labels.extend([''] * len(columns)) # write out the index label line writer.writerow(encoded_labels) @@ -1171,14 +1188,15 @@ def _save(self): def _save_chunk(self, start_i, end_i): - data_index = self.data_index + data_index = self.data_index # create the data for a chunk - slicer = slice(start_i,end_i) + slicer = slice(start_i, end_i) for i in range(len(self.blocks)): b = self.blocks[i] d = b.to_native_types(slicer=slicer, na_rep=self.na_rep, - float_format=self.float_format, date_format=self.date_format) + float_format=self.float_format, + date_format=self.date_format) for i, item in enumerate(b.items): @@ -1186,7 +1204,8 @@ def _save_chunk(self, start_i, end_i): self.data[self.column_map[b][i]] = d[i] ix = data_index.to_native_types(slicer=slicer, na_rep=self.na_rep, - float_format=self.float_format, date_format=self.date_format) + float_format=self.float_format, + date_format=self.date_format) lib.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer) @@ -1194,6 +1213,7 @@ def _save_chunk(self, start_i, end_i): # ExcelCell = namedtuple("ExcelCell", # 'row, col, val, style, mergestart, mergeend') + class ExcelCell(object): __fields__ = ('row', 'col', 'val', 'style', 'mergestart', 'mergeend') __slots__ = __fields__ @@ -1539,8 +1559,8 @@ def _format_strings(self): else: float_format = self.float_format - formatter = (lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n'))) \ - if self.formatter is None else self.formatter + formatter = self.formatter if self.formatter is not None else \ + (lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n'))) def _format(x): if self.na_rep is not None and lib.checknull(x): @@ -1584,19 +1604,20 @@ def __init__(self, *args, **kwargs): def _format_with(self, fmt_str): def _val(x, threshold): if notnull(x): - if threshold is None or abs(x) > get_option("display.chop_threshold"): - return fmt_str % x + if (threshold is None or + abs(x) > get_option("display.chop_threshold")): + return fmt_str % x else: - if fmt_str.endswith("e"): # engineering format - return "0" + if fmt_str.endswith("e"): # engineering format + return "0" else: - return fmt_str % 0 + return fmt_str % 0 else: return self.na_rep threshold = get_option("display.chop_threshold") - fmt_values = [ _val(x, threshold) for x in self.values] + fmt_values = [_val(x, threshold) for x in self.values] return _trim_zeros(fmt_values, self.na_rep) def get_result(self): @@ -1654,6 +1675,7 @@ def get_result(self): fmt_values = [formatter(x) for x in self.values] return _make_fixed_width(fmt_values, self.justify) + def _format_datetime64(x, tz=None): if isnull(x): return 'NaT' @@ -1674,12 +1696,14 @@ def get_result(self): fmt_values = [formatter(x) for x in self.values] return _make_fixed_width(fmt_values, self.justify) + def _format_timedelta64(x): if isnull(x): return 'NaT' return lib.repr_timedelta64(x) + def _make_fixed_width(strings, justify='right', minimum=None): if len(strings) == 0: return strings @@ -1762,6 +1786,8 @@ def _has_names(index): # Global formatting options _initial_defencoding = None + + def detect_console_encoding(): """ Try to find the most capable encoding supported by the console. @@ -1776,13 +1802,15 @@ def detect_console_encoding(): except AttributeError: pass - if not encoding or 'ascii' in encoding.lower(): # try again for something better + # try again for something better + if not encoding or 'ascii' in encoding.lower(): try: encoding = locale.getpreferredencoding() except Exception: pass - if not encoding or 'ascii' in encoding.lower(): # when all else fails. this will usually be "ascii" + # when all else fails. this will usually be "ascii" + if not encoding or 'ascii' in encoding.lower(): encoding = sys.getdefaultencoding() # GH3360, save the reported defencoding at import time @@ -1804,8 +1832,8 @@ def get_console_size(): # Consider # interactive shell terminal, can detect term size - # interactive non-shell terminal (ipnb/ipqtconsole), cannot detect term size - # non-interactive script, should disregard term size + # interactive non-shell terminal (ipnb/ipqtconsole), cannot detect term + # size non-interactive script, should disregard term size # in addition # width,height have default values, but setting to 'None' signals @@ -1823,7 +1851,7 @@ def get_console_size(): # pure terminal terminal_width, terminal_height = get_terminal_size() else: - terminal_width, terminal_height = None,None + terminal_width, terminal_height = None, None # Note if the User sets width/Height to None (auto-detection) # and we're in a script (non-inter), this will return (None,None) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 1222b5b93799d..b194c938b13cc 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -60,9 +60,8 @@ #---------------------------------------------------------------------- # Docstring templates -_shared_doc_kwargs = dict(axes='index, columns', - klass='DataFrame', - axes_single_arg="{0,1,'index','columns'}") +_shared_doc_kwargs = dict(axes='index, columns', klass='DataFrame', + axes_single_arg="{0,1,'index','columns'}") _numeric_only_doc = """numeric_only : boolean, default None Include only float, int, boolean data. If None, will attempt to use @@ -196,15 +195,16 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, data = data._data if isinstance(data, BlockManager): - mgr = self._init_mgr( - data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy) + mgr = self._init_mgr(data, axes=dict(index=index, columns=columns), + dtype=dtype, copy=copy) elif isinstance(data, dict): mgr = self._init_dict(data, index, columns, dtype=dtype) elif isinstance(data, ma.MaskedArray): # masked recarray if isinstance(data, ma.mrecords.MaskedRecords): - mgr = _masked_rec_array_to_mgr(data, index, columns, dtype, copy) + mgr = _masked_rec_array_to_mgr(data, index, columns, dtype, + copy) # a masked array else: @@ -224,8 +224,9 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, if columns is None: columns = data_columns mgr = self._init_dict(data, index, columns, dtype=dtype) - elif getattr(data,'name',None): - mgr = self._init_dict({ data.name : data }, index, columns, dtype=dtype) + elif getattr(data, 'name', None): + mgr = self._init_dict({data.name: data}, index, columns, + dtype=dtype) else: mgr = self._init_ndarray(data, index, columns, dtype=dtype, copy=copy) @@ -236,7 +237,7 @@ def __init__(self, data=None, index=None, columns=None, dtype=None, if index is None and isinstance(data[0], Series): index = _get_names_from_index(data) - if is_list_like(data[0]) and getattr(data[0],'ndim',1) == 1: + if is_list_like(data[0]) and getattr(data[0], 'ndim', 1) == 1: arrays, columns = _to_arrays(data, columns, dtype=dtype) columns = _ensure_index(columns) @@ -283,7 +284,8 @@ def _init_dict(self, data, index, columns, dtype=None): # prefilter if columns passed - data = dict((k, v) for k, v in compat.iteritems(data) if k in columns) + data = dict((k, v) for k, v in compat.iteritems(data) + if k in columns) if index is None: index = extract_index(list(data.values())) @@ -395,7 +397,8 @@ def _repr_fits_horizontal_(self, ignore_width=False): return False if (ignore_width # used by repr_html under IPython notebook - or not com.in_interactive_session()): # scripts ignore terminal dims + # scripts ignore terminal dims + or not com.in_interactive_session()): return True if (get_option('display.width') is not None or @@ -671,22 +674,25 @@ def to_dict(self, outtype='dict'): else: # pragma: no cover raise ValueError("outtype %s not understood" % outtype) - def to_gbq(self, destination_table, schema=None, col_order=None, if_exists='fail', **kwargs): + def to_gbq(self, destination_table, schema=None, col_order=None, + if_exists='fail', **kwargs): """Write a DataFrame to a Google BigQuery table. - If the table exists, the DataFrame will be appended. If not, a new table - will be created, in which case the schema will have to be specified. By default, - rows will be written in the order they appear in the DataFrame, though - the user may specify an alternative order. + If the table exists, the DataFrame will be appended. If not, a new + table will be created, in which case the schema will have to be + specified. By default, rows will be written in the order they appear + in the DataFrame, though the user may specify an alternative order. Parameters --------------- destination_table : string name of table to be written, in the form 'dataset.tablename' schema : sequence (optional) - list of column types in order for data to be inserted, e.g. ['INTEGER', 'TIMESTAMP', 'BOOLEAN'] + list of column types in order for data to be inserted, e.g. + ['INTEGER', 'TIMESTAMP', 'BOOLEAN'] col_order : sequence (optional) - order which columns are to be inserted, e.g. ['primary_key', 'birthday', 'username'] + order which columns are to be inserted, e.g. ['primary_key', + 'birthday', 'username'] if_exists : {'fail', 'replace', 'append'} (optional) - fail: If table exists, do nothing. - replace: If table exists, drop it, recreate it, and insert data. @@ -696,15 +702,19 @@ def to_gbq(self, destination_table, schema=None, col_order=None, if_exists='fail Raises ------ SchemaMissing : - Raised if the 'if_exists' parameter is set to 'replace', but no schema is specified + Raised if the 'if_exists' parameter is set to 'replace', but no + schema is specified TableExists : - Raised if the specified 'destination_table' exists but the 'if_exists' parameter is set to 'fail' (the default) + Raised if the specified 'destination_table' exists but the + 'if_exists' parameter is set to 'fail' (the default) InvalidSchema : - Raised if the 'schema' parameter does not match the provided DataFrame + Raised if the 'schema' parameter does not match the provided + DataFrame """ from pandas.io import gbq - return gbq.to_gbq(self, destination_table, schema=None, col_order=None, if_exists='fail', **kwargs) + return gbq.to_gbq(self, destination_table, schema=None, col_order=None, + if_exists='fail', **kwargs) @classmethod def from_records(cls, data, index=None, exclude=None, columns=None, @@ -757,7 +767,7 @@ def from_records(cls, data, index=None, exclude=None, columns=None, values = [first_row] #if unknown length iterable (generator) - if nrows == None: + if nrows is None: #consume whole generator values += list(data) else: @@ -785,7 +795,8 @@ def from_records(cls, data, index=None, exclude=None, columns=None, arr_columns.append(k) arrays.append(v) - arrays, arr_columns = _reorder_arrays(arrays, arr_columns, columns) + arrays, arr_columns = _reorder_arrays(arrays, arr_columns, + columns) elif isinstance(data, (np.ndarray, DataFrame)): arrays, columns = _to_arrays(data, columns) @@ -864,7 +875,7 @@ def to_records(self, index=True, convert_datetime64=True): else: if isinstance(self.index, MultiIndex): # array of tuples to numpy cols. copy copy copy - ix_vals = lmap(np.array,zip(*self.index.values)) + ix_vals = lmap(np.array, zip(*self.index.values)) else: ix_vals = [self.index.values] @@ -1017,13 +1028,13 @@ def to_panel(self): from pandas.core.reshape import block2d_to_blocknd # only support this kind for now - if (not isinstance(self.index, MultiIndex) or # pragma: no cover + if (not isinstance(self.index, MultiIndex) or # pragma: no cover len(self.index.levels) != 2): raise NotImplementedError('Only 2-level MultiIndex are supported.') if not self.index.is_unique: raise ValueError("Can't convert non-uniquely indexed " - "DataFrame to Panel") + "DataFrame to Panel") self._consolidate_inplace() @@ -1228,8 +1239,8 @@ def to_stata( >>> writer.write_file() """ from pandas.io.stata import StataWriter - writer = StataWriter( - fname, self, convert_dates=convert_dates, encoding=encoding, byteorder=byteorder) + writer = StataWriter(fname, self, convert_dates=convert_dates, + encoding=encoding, byteorder=byteorder) writer.write_file() def to_sql(self, name, con, flavor='sqlite', if_exists='fail', **kwargs): @@ -1407,7 +1418,7 @@ def info(self, verbose=True, buf=None, max_cols=None): len(self.columns)) space = max([len(com.pprint_thing(k)) for k in self.columns]) + 4 counts = self.count() - if len(cols) != len(counts): # pragma: no cover + if len(cols) != len(counts): # pragma: no cover raise AssertionError('Columns must equal counts (%d != %d)' % (len(cols), len(counts))) for col, count in compat.iteritems(counts): @@ -1516,8 +1527,8 @@ def set_value(self, index, col, value): except KeyError: # set using a non-recursive method & reset the cache - self.loc[index,col] = value - self._item_cache.pop(col,None) + self.loc[index, col] = value + self._item_cache.pop(col, None) return self @@ -1581,7 +1592,7 @@ def _ixs(self, i, axis=0, copy=False): # a numpy error (as numpy should really raise) values = self._data.iget(i) if not len(values): - values = np.array([np.nan]*len(self.index),dtype=object) + values = np.array([np.nan]*len(self.index), dtype=object) return self._constructor_sliced.from_array( values, index=self.index, name=label, fastpath=True) @@ -1824,7 +1835,8 @@ def _box_item_values(self, key, values): def _box_col_values(self, values, items): """ provide boxed values for a column """ - return self._constructor_sliced.from_array(values, index=self.index, name=items, fastpath=True) + return self._constructor_sliced.from_array(values, index=self.index, + name=items, fastpath=True) def __setitem__(self, key, value): # see if we can slice the rows @@ -1877,11 +1889,13 @@ def _setitem_frame(self, key, value): def _ensure_valid_index(self, value): """ - ensure that if we don't have an index, that we can create one from the passed value + ensure that if we don't have an index, that we can create one from the + passed value """ if not len(self.index): if not isinstance(value, Series): - raise ValueError("cannot set a frame with no defined index and a non-series") + raise ValueError('Cannot set a frame with no defined index ' + 'and a non-series') self._data.set_axis(1, value.index.copy(), check_axis=False) def _set_item(self, key, value): @@ -1909,7 +1923,8 @@ def _set_item(self, key, value): def insert(self, loc, column, value, allow_duplicates=False): """ Insert column into DataFrame at specified location. - if allow_duplicates is False, Raises Exception if column is already contained in the DataFrame + if allow_duplicates is False, Raises Exception if column is already + contained in the DataFrame Parameters ---------- @@ -1945,8 +1960,8 @@ def _sanitize_column(self, key, value): value = value.T elif isinstance(value, Index) or _is_sequence(value): if len(value) != len(self.index): - raise ValueError('Length of values does not match ' - 'length of index') + raise ValueError('Length of values does not match length of ' + 'index') if not isinstance(value, (np.ndarray, Index)): if isinstance(value, list) and len(value) > 0: @@ -1967,7 +1982,8 @@ def _sanitize_column(self, key, value): # broadcast across multiple columns if necessary if key in self.columns and value.ndim == 1: - if not self.columns.is_unique or isinstance(self.columns, MultiIndex): + if not self.columns.is_unique or isinstance(self.columns, + MultiIndex): existing_piece = self[key] if isinstance(existing_piece, DataFrame): value = np.tile(value, (len(existing_piece.columns), 1)) @@ -2053,7 +2069,7 @@ def xs(self, key, axis=0, level=None, copy=True, drop_level=True): labels = self._get_axis(axis) if level is not None: loc, new_ax = labels.get_loc_level(key, level=level, - drop_level=drop_level) + drop_level=drop_level) if not copy and not isinstance(loc, slice): raise ValueError('Cannot retrieve view (copy=False)') @@ -2088,7 +2104,7 @@ def xs(self, key, axis=0, level=None, copy=True, drop_level=True): index = self.index if isinstance(index, MultiIndex): loc, new_index = self.index.get_loc_level(key, - drop_level=drop_level) + drop_level=drop_level) else: loc = self.index.get_loc(key) @@ -2146,8 +2162,7 @@ def lookup(self, row_labels, col_labels): """ n = len(row_labels) if n != len(col_labels): - raise ValueError('Row labels must have same size as ' - 'column labels') + raise ValueError('Row labels must have same size as column labels') thresh = 1000 if not self._is_mixed_type or n > thresh: @@ -2173,13 +2188,14 @@ def lookup(self, row_labels, col_labels): #---------------------------------------------------------------------- # Reindexing and alignment - def _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable=False): + def _reindex_axes(self, axes, level, limit, method, fill_value, copy, + takeable=False): frame = self columns = axes['columns'] if columns is not None: - frame = frame._reindex_columns(columns, copy, level, - fill_value, limit, takeable=takeable) + frame = frame._reindex_columns(columns, copy, level, fill_value, + limit, takeable=takeable) index = axes['index'] if index is not None: @@ -2191,18 +2207,22 @@ def _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable=F def _reindex_index(self, new_index, method, copy, level, fill_value=NA, limit=None, takeable=False): new_index, indexer = self.index.reindex(new_index, method, level, - limit=limit, copy_if_needed=True, + limit=limit, + copy_if_needed=True, takeable=takeable) return self._reindex_with_indexers({0: [new_index, indexer]}, - copy=copy, fill_value=fill_value, allow_dups=takeable) + copy=copy, fill_value=fill_value, + allow_dups=takeable) def _reindex_columns(self, new_columns, copy, level, fill_value=NA, limit=None, takeable=False): new_columns, indexer = self.columns.reindex(new_columns, level=level, - limit=limit, copy_if_needed=True, + limit=limit, + copy_if_needed=True, takeable=takeable) return self._reindex_with_indexers({1: [new_columns, indexer]}, - copy=copy, fill_value=fill_value, allow_dups=takeable) + copy=copy, fill_value=fill_value, + allow_dups=takeable) def _reindex_multi(self, axes, copy, fill_value): """ we are guaranteed non-Nones in the axes! """ @@ -2218,7 +2238,9 @@ def _reindex_multi(self, axes, copy, fill_value): columns=new_columns) else: return self._reindex_with_indexers({0: [new_index, row_indexer], - 1: [new_columns, col_indexer]}, copy=copy, fill_value=fill_value) + 1: [new_columns, col_indexer]}, + copy=copy, + fill_value=fill_value) @Appender(_shared_docs['reindex'] % _shared_doc_kwargs) def reindex(self, index=None, columns=None, **kwargs): @@ -2434,7 +2456,8 @@ def _maybe_cast(values, labels=None): #---------------------------------------------------------------------- # Reindex-based selection methods - def dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False): + def dropna(self, axis=0, how='any', thresh=None, subset=None, + inplace=False): """ Return object with labels on given axis omitted where alternately any or all of the data are missing @@ -2493,7 +2516,6 @@ def dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False): else: return result - def drop_duplicates(self, cols=None, take_last=False, inplace=False): """ Return DataFrame with duplicate rows removed, optionally only @@ -2630,14 +2652,15 @@ def sort_index(self, axis=0, by=None, ascending=True, inplace=False, from pandas.core.groupby import _lexsort_indexer axis = self._get_axis_number(axis) - if axis not in [0, 1]: # pragma: no cover + if axis not in [0, 1]: # pragma: no cover raise AssertionError('Axis must be 0 or 1, got %s' % str(axis)) labels = self._get_axis(axis) if by is not None: if axis != 0: - raise ValueError('When sorting by column, axis must be 0 (rows)') + raise ValueError('When sorting by column, axis must be 0 ' + '(rows)') if not isinstance(by, (tuple, list)): by = [by] if com._is_sequence(ascending) and len(by) != len(ascending): @@ -2721,9 +2744,9 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False): ax = 'index' if axis == 0 else 'columns' if new_axis.is_unique: - d = { ax : new_axis } + d = {ax: new_axis} else: - d = { ax : indexer, 'takeable' : True } + d = {ax: indexer, 'takeable': True} return self.reindex(**d) if inplace: @@ -2816,18 +2839,23 @@ def _arith_op(left, right): def f(col): r = _arith_op(this[col].values, other[col].values) - return self._constructor_sliced(r,index=new_index,dtype=r.dtype) + return self._constructor_sliced(r, index=new_index, + dtype=r.dtype) - result = dict([ (col, f(col)) for col in this ]) + result = dict([(col, f(col)) for col in this]) # non-unique else: def f(i): - r = _arith_op(this.iloc[:,i].values, other.iloc[:,i].values) - return self._constructor_sliced(r,index=new_index,dtype=r.dtype) - - result = dict([ (i,f(i)) for i, col in enumerate(this.columns) ]) + r = _arith_op(this.iloc[:, i].values, + other.iloc[:, i].values) + return self._constructor_sliced(r, index=new_index, + dtype=r.dtype) + + result = dict([ + (i, f(i)) for i, col in enumerate(this.columns) + ]) result = self._constructor(result, index=new_index, copy=False) result.columns = new_columns return result @@ -2894,7 +2922,6 @@ def _combine_const(self, other, func, raise_on_error=True): new_data = self._data.eval(func, other, raise_on_error=raise_on_error) return self._constructor(new_data) - def _compare_frame_evaluate(self, other, func, str_rep): # unique @@ -2907,7 +2934,8 @@ def _compare(a, b): # non-unique else: def _compare(a, b): - return dict([(i,func(a.iloc[:,i], b.iloc[:,i])) for i, col in enumerate(a.columns)]) + return dict([(i, func(a.iloc[:, i], b.iloc[:, i])) + for i, col in enumerate(a.columns)]) new_data = expressions.evaluate(_compare, str_rep, self, other) result = self._constructor(data=new_data, index=self.index, copy=False) @@ -2917,7 +2945,7 @@ def _compare(a, b): def _compare_frame(self, other, func, str_rep): if not self._indexed_same(other): raise ValueError('Can only compare identically-labeled ' - 'DataFrame objects') + 'DataFrame objects') return self._compare_frame_evaluate(other, func, str_rep) def _flex_compare_frame(self, other, func, str_rep, level): @@ -3046,7 +3074,8 @@ def combiner(x, y, needs_i8_conversion=False): else: mask = isnull(x_values) - return expressions.where(mask, y_values, x_values, raise_on_error=True) + return expressions.where(mask, y_values, x_values, + raise_on_error=True) return self.combine(other, combiner, overwrite=False) @@ -3070,7 +3099,7 @@ def update(self, other, join='left', overwrite=True, filter_func=None, contain data in the same place. """ # TODO: Support other joins - if join != 'left': # pragma: no cover + if join != 'left': # pragma: no cover raise NotImplementedError("Only left join is supported") if not isinstance(other, DataFrame): @@ -3413,7 +3442,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): series_gen = (Series.from_array(arr, index=res_columns, name=name) for i, (arr, name) in enumerate(zip(values, res_index))) - else: # pragma : no cover + else: # pragma : no cover raise AssertionError('Axis must be 0 or 1, got %s' % str(axis)) i = None @@ -3442,7 +3471,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): if i is not None: k = res_index[i] e.args = e.args + ('occurred at index %s' % - com.pprint_thing(k),) + com.pprint_thing(k),) raise if len(results) > 0 and _is_sequence(results[0]): @@ -3837,13 +3866,13 @@ def pretty_name(x): destat = [] for i in range(len(numdata.columns)): - series = numdata.iloc[:,i] + series = numdata.iloc[:, i] destat.append([series.count(), series.mean(), series.std(), series.min(), series.quantile(lb), series.median(), series.quantile(ub), series.max()]) - return self._constructor(lmap(list, zip(*destat)), index=destat_columns, - columns=numdata.columns) + return self._constructor(lmap(list, zip(*destat)), + index=destat_columns, columns=numdata.columns) #---------------------------------------------------------------------- # ndarray-like stats methods @@ -3920,7 +3949,8 @@ def _count_level(self, level, axis=0, numeric_only=False): else: return result - def any(self, axis=None, bool_only=None, skipna=True, level=None, **kwargs): + def any(self, axis=None, bool_only=None, skipna=True, level=None, + **kwargs): """ Return whether any element is True over requested axis. %(na_action)s @@ -3950,7 +3980,8 @@ def any(self, axis=None, bool_only=None, skipna=True, level=None, **kwargs): return self._reduce(nanops.nanany, axis=axis, skipna=skipna, numeric_only=bool_only, filter_type='bool') - def all(self, axis=None, bool_only=None, skipna=True, level=None, **kwargs): + def all(self, axis=None, bool_only=None, skipna=True, level=None, + **kwargs): """ Return whether all elements are True over requested axis. %(na_action)s @@ -3987,7 +4018,8 @@ def _reduce(self, op, axis=0, skipna=True, numeric_only=None, labels = self._get_agg_axis(axis) # exclude timedelta/datetime unless we are uniform types - if axis == 1 and self._is_mixed_type and len(set(self.dtypes) & _DATELIKE_DTYPES): + if axis == 1 and self._is_mixed_type and len(set(self.dtypes) & + _DATELIKE_DTYPES): numeric_only = True if numeric_only is None: @@ -4020,7 +4052,7 @@ def _reduce(self, op, axis=0, skipna=True, numeric_only=None, data = self._get_numeric_data() elif filter_type == 'bool': data = self._get_bool_data() - else: # pragma: no cover + else: # pragma: no cover msg = ("Generating numeric_only data with filter_type %s" "not supported." % filter_type) raise NotImplementedError(msg) @@ -4167,6 +4199,7 @@ def f(arr): data = self._get_numeric_data() if numeric_only else self return data.apply(f, axis=axis) + def rank(self, axis=0, numeric_only=None, method='average', na_option='keep', ascending=True): """ @@ -4242,7 +4275,7 @@ def to_timestamp(self, freq=None, how='start', axis=0, copy=True): new_data.set_axis(1, self.index.to_timestamp(freq=freq, how=how)) elif axis == 1: new_data.set_axis(0, self.columns.to_timestamp(freq=freq, how=how)) - else: # pragma: no cover + else: # pragma: no cover raise AssertionError('Axis must be 0 or 1. Got %s' % str(axis)) return self._constructor(new_data) @@ -4277,7 +4310,7 @@ def to_period(self, freq=None, axis=0, copy=True): if freq is None: freq = self.columns.freqstr or self.columns.inferred_freq new_data.set_axis(0, self.columns.to_period(freq=freq)) - else: # pragma: no cover + else: # pragma: no cover raise AssertionError('Axis must be 0 or 1. Got %s' % str(axis)) return self._constructor(new_data) @@ -4510,7 +4543,7 @@ def extract_index(data): elif isinstance(v, dict): have_dicts = True indexes.append(list(v.keys())) - elif is_list_like(v) and getattr(v,'ndim',1) == 1: + elif is_list_like(v) and getattr(v, 'ndim', 1) == 1: have_raw_arrays = True raw_lengths.append(len(v)) @@ -4658,7 +4691,8 @@ def _masked_rec_array_to_mgr(data, index, columns, dtype, copy): def _reorder_arrays(arrays, arr_columns, columns): # reorder according to the columns - if columns is not None and len(columns) and arr_columns is not None and len(arr_columns): + if (columns is not None and len(columns) and arr_columns is not None and + len(arr_columns)): indexer = _ensure_index( arr_columns).get_indexer(columns) arr_columns = _ensure_index( @@ -4681,13 +4715,15 @@ def _list_of_series_to_arrays(data, columns, coerce_float=False, dtype=None): from pandas.core.index import _get_combined_index if columns is None: - columns = _get_combined_index([s.index for s in data if getattr(s,'index',None) is not None ]) + columns = _get_combined_index([ + s.index for s in data if getattr(s, 'index', None) is not None + ]) indexer_cache = {} aligned_values = [] for s in data: - index = getattr(s,'index',None) + index = getattr(s, 'index', None) if index is None: index = _default_index(len(s)) @@ -4741,13 +4777,13 @@ def _convert_object_array(content, columns, coerce_float=False, dtype=None): def _get_names_from_index(data): index = lrange(len(data)) - has_some_name = any([getattr(s,'name',None) is not None for s in data]) + has_some_name = any([getattr(s, 'name', None) is not None for s in data]) if not has_some_name: return index count = 0 for i, s in enumerate(data): - n = getattr(s,'name',None) + n = getattr(s, 'name', None) if n is not None: index[i] = n else: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index efa083e239f63..f960f64e7be16 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -7,7 +7,8 @@ import pandas as pd from pandas.core.base import PandasObject -from pandas.core.index import Index, MultiIndex, _ensure_index, InvalidIndexError +from pandas.core.index import (Index, MultiIndex, _ensure_index, + InvalidIndexError) import pandas.core.indexing as indexing from pandas.core.indexing import _maybe_convert_indices from pandas.tseries.index import DatetimeIndex @@ -34,6 +35,7 @@ args_transpose='axes to permute (int or label for' ' object)') + def is_dictlike(x): return isinstance(x, (dict, com.ABCSeries)) @@ -49,7 +51,8 @@ def _single_replace(self, to_replace, method, inplace, limit): if values.dtype == orig_dtype and inplace: return - result = pd.Series(values, index=self.index, dtype=self.dtype).__finalize__(self) + result = pd.Series(values, index=self.index, + dtype=self.dtype).__finalize__(self) if inplace: self._data = result._data @@ -70,13 +73,14 @@ class NDFrame(PandasObject): axes : list copy : boolean, default False """ - _internal_names = [ - '_data', 'name', '_cacher', '_is_copy', '_subtyp', '_index', '_default_kind', '_default_fill_value'] + _internal_names = ['_data', 'name', '_cacher', '_is_copy', '_subtyp', + '_index', '_default_kind', '_default_fill_value'] _internal_names_set = set(_internal_names) _metadata = [] _is_copy = None - def __init__(self, data, axes=None, copy=False, dtype=None, fastpath=False): + def __init__(self, data, axes=None, copy=False, dtype=None, + fastpath=False): if not fastpath: if dtype is not None: @@ -101,7 +105,8 @@ def _validate_dtype(self, dtype): # a compound dtype if dtype.kind == 'V': raise NotImplementedError("compound dtypes are not implemented" - "in the {0} constructor".format(self.__class__.__name__)) + "in the {0} constructor" + .format(self.__class__.__name__)) return dtype def _init_mgr(self, mgr, axes=None, dtype=None, copy=False): @@ -136,7 +141,7 @@ def __unicode__(self): def _local_dir(self): """ add the string-like attributes from the info_axis """ return [c for c in self._info_axis - if isinstance(c, string_types) and isidentifier(c) ] + if isinstance(c, string_types) and isidentifier(c)] @property def _constructor_sliced(self): @@ -156,7 +161,8 @@ def _setup_axes( stat_axis_num : the number of axis for the default stats (int) aliases : other names for a single axis (dict) slicers : how axes slice to others (dict) - axes_are_reversed : boolean whether to treat passed axes as reversed (DataFrame) + axes_are_reversed : boolean whether to treat passed axes as + reversed (DataFrame) build_axes : setup the axis properties (default True) """ @@ -238,7 +244,9 @@ def _construct_axes_from_arguments(self, args, kwargs, require_all=False): if a in kwargs: if alias in kwargs: raise TypeError( - "arguments are multually exclusive for [%s,%s]" % (a, alias)) + "arguments are mutually exclusive for [%s,%s]" % + (a, alias) + ) continue if alias in kwargs: kwargs[a] = kwargs.pop(alias) @@ -277,7 +285,8 @@ def _get_axis_number(self, axis): return self._AXIS_NUMBERS[axis] except: pass - raise ValueError('No axis named {0} for object type {1}'.format(axis,type(self))) + raise ValueError('No axis named {0} for object type {1}' + .format(axis, type(self))) def _get_axis_name(self, axis): axis = self._AXIS_ALIASES.get(axis, axis) @@ -289,7 +298,8 @@ def _get_axis_name(self, axis): return self._AXIS_NAMES[axis] except: pass - raise ValueError('No axis named {0} for object type {1}'.format(axis,type(self))) + raise ValueError('No axis named {0} for object type {1}' + .format(axis, type(self))) def _get_axis(self, axis): name = self._get_axis_name(axis) @@ -399,6 +409,7 @@ def _set_axis(self, axis, labels): ------- y : same as input """ + @Appender(_shared_docs['transpose'] % _shared_doc_kwargs) def transpose(self, *args, **kwargs): @@ -458,7 +469,8 @@ def pop(self, item): def squeeze(self): """ squeeze length 1 dimensions """ try: - return self.ix[tuple([slice(None) if len(a) > 1 else a[0] for a in self.axes])] + return self.ix[tuple([slice(None) if len(a) > 1 else a[0] + for a in self.axes])] except: return self @@ -506,6 +518,7 @@ def swaplevel(self, i, j, axis=0): ------- renamed : %(klass)s (new object) """ + @Appender(_shared_docs['rename'] % dict(axes='axes keywords for this' ' object', klass='NDFrame')) def rename(self, *args, **kwargs): @@ -530,14 +543,14 @@ def f(x): return f - self._consolidate_inplace() result = self if inplace else self.copy(deep=copy) # start in the axis order to eliminate too many copies for axis in lrange(self._AXIS_LEN): v = axes.get(self._AXIS_NAMES[axis]) - if v is None: continue + if v is None: + continue f = _get_rename_function(v) baxis = self._get_block_manager_axis(axis) @@ -572,7 +585,7 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): renamed : type of caller """ axis = self._get_axis_name(axis) - d = { 'copy' : copy, 'inplace' : inplace } + d = {'copy': copy, 'inplace': inplace} d[axis] = mapper return self.rename(**d) @@ -580,7 +593,8 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): # Comparisons def _indexed_same(self, other): - return all([self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS]) + return all([self._get_axis(a).equals(other._get_axis(a)) + for a in self._AXIS_ORDERS]) def __neg__(self): arr = operator.neg(_values_from_object(self)) @@ -626,7 +640,8 @@ def iteritems(self): def iterkv(self, *args, **kwargs): "iteritems alias used to get around 2to3. Deprecated" warnings.warn("iterkv is deprecated and will be removed in a future " - "release, use ``iteritems`` instead.", DeprecationWarning) + "release, use ``iteritems`` instead.", + DeprecationWarning) return self.iteritems(*args, **kwargs) def __len__(self): @@ -644,7 +659,8 @@ def empty(self): def __nonzero__(self): raise ValueError("The truth value of a {0} is ambiguous. " - "Use a.empty, a.bool(), a.item(), a.any() or a.all().".format(self.__class__.__name__)) + "Use a.empty, a.bool(), a.item(), a.any() or a.all()." + .format(self.__class__.__name__)) __bool__ = __nonzero__ @@ -655,10 +671,11 @@ def bool(self): Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean """ v = self.squeeze() - if isinstance(v, (bool,np.bool_)): + if isinstance(v, (bool, np.bool_)): return bool(v) elif np.isscalar(v): - raise ValueError("bool cannot act on a non-boolean single element {0}".format(self.__class__.__name__)) + raise ValueError("bool cannot act on a non-boolean single element " + "{0}".format(self.__class__.__name__)) self.__nonzero__() @@ -823,9 +840,9 @@ def to_hdf(self, path_or_buf, key, **kwargs): fixed(f) : Fixed format Fast writing/reading. Not-appendable, nor searchable table(t) : Table format - Write as a PyTables Table structure which may perform worse but - allow more flexible operations like searching / selecting subsets - of the data + Write as a PyTables Table structure which may perform + worse but allow more flexible operations like searching + / selecting subsets of the data append : boolean, default False For Table formats, append the input data to the existing complevel : int, 1-9, default 0 @@ -852,10 +869,11 @@ def to_msgpack(self, path_or_buf=None, **kwargs): Parameters ---------- path : string File path, buffer-like, or None - if None, return generated string + if None, return generated string append : boolean whether to append to an existing msgpack - (default is False) - compress : type of compressor (zlib or blosc), default to None (no compression) + (default is False) + compress : type of compressor (zlib or blosc), default to None (no + compression) """ from pandas.io import packers @@ -956,7 +974,7 @@ def _get_item_cache(self, item): values = self._data.get(item) res = self._box_item_values(item, values) cache[item] = res - res._cacher = (item,weakref.ref(self)) + res._cacher = (item, weakref.ref(self)) return res def _box_item_values(self, key, values): @@ -970,10 +988,10 @@ def _maybe_cache_changed(self, item, value): def _maybe_update_cacher(self, clear=False): """ see if we need to update our parent cacher if clear, then clear our cache """ - cacher = getattr(self,'_cacher',None) + cacher = getattr(self, '_cacher', None) if cacher is not None: try: - cacher[1]()._maybe_cache_changed(cacher[0],self) + cacher[1]()._maybe_cache_changed(cacher[0], self) except: # our referant is dead @@ -984,7 +1002,7 @@ def _maybe_update_cacher(self, clear=False): def _clear_item_cache(self, i=None): if i is not None: - self._item_cache.pop(i,None) + self._item_cache.pop(i, None) else: self._item_cache.clear() @@ -1002,11 +1020,13 @@ def _check_setitem_copy(self): if self._is_copy: value = config._get_option_fast('mode.chained_assignment') - t = "A value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_index,col_indexer] = value instead" + t = ("A value is trying to be set on a copy of a slice from a " + "DataFrame.\nTry using .loc[row_index,col_indexer] = value " + "instead") if value == 'raise': raise SettingWithCopyError(t) elif value == 'warn': - warnings.warn(t,SettingWithCopyWarning) + warnings.warn(t, SettingWithCopyWarning) def __delitem__(self, key): """ @@ -1066,10 +1086,13 @@ def take(self, indices, axis=0, convert=True): if baxis == 0: labels = self._get_axis(axis) new_items = labels.take(indices) - new_data = self._data.reindex_axis(new_items, indexer=indices, axis=0) + new_data = self._data.reindex_axis(new_items, indexer=indices, + axis=0) else: new_data = self._data.take(indices, axis=baxis) - return self._constructor(new_data)._setitem_copy(True).__finalize__(self) + return self._constructor(new_data)\ + ._setitem_copy(True)\ + .__finalize__(self) # TODO: Check if this was clearer in 0.12 def select(self, crit, axis=0): @@ -1149,7 +1172,7 @@ def drop(self, labels, axis=0, level=None, inplace=False, **kwargs): new_axis = axis.drop(labels, level=level) else: new_axis = axis.drop(labels) - dropped = self.reindex(**{ axis_name: new_axis }) + dropped = self.reindex(**{axis_name: new_axis}) try: dropped.axes[axis_].set_names(axis.names, inplace=True) except AttributeError: @@ -1247,7 +1270,8 @@ def sort_index(self, axis=0, ascending=True): Parameters ---------- - %(axes)s : array-like, optional (can be specified in order, or as keywords) + %(axes)s : array-like, optional (can be specified in order, or as + keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None @@ -1277,6 +1301,7 @@ def sort_index(self, axis=0, ascending=True): """ # TODO: Decide if we care about having different examples for different # kinds + @Appender(_shared_docs['reindex'] % dict(axes="axes", klass="NDFrame")) def reindex(self, *args, **kwargs): @@ -1298,18 +1323,21 @@ def reindex(self, *args, **kwargs): except: pass - # if all axes that are requested to reindex are equal, then only copy if indicated - # must have index names equal here as well as values - if all([ self._get_axis(axis).identical(ax) for axis, ax in axes.items() if ax is not None ]): + # if all axes that are requested to reindex are equal, then only copy + # if indicated must have index names equal here as well as values + if all([self._get_axis(axis).identical(ax) + for axis, ax in axes.items() if ax is not None]): if copy: return self.copy() return self # perform the reindex on the axes return self._reindex_axes(axes, level, limit, - method, fill_value, copy, takeable=takeable).__finalize__(self) + method, fill_value, copy, + takeable=takeable).__finalize__(self) - def _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable=False): + def _reindex_axes(self, axes, level, limit, method, fill_value, copy, + takeable=False): """ perform the reinxed for all the axes """ obj = self for a in self._AXIS_ORDERS: @@ -1324,35 +1352,42 @@ def _reindex_axes(self, axes, level, limit, method, fill_value, copy, takeable=F axis = self._get_axis_number(a) ax = self._get_axis(a) try: - new_index, indexer = ax.reindex(labels, level=level, - limit=limit, method=method, takeable=takeable) + new_index, indexer = ax.reindex( + labels, level=level, limit=limit, method=method, + takeable=takeable) except (ValueError): - # catch trying to reindex a non-monotonic index with a specialized indexer - # e.g. pad, so fallback to the regular indexer - # this will show up on reindexing a not-naturally ordering series, e.g. - # Series([1,2,3,4],index=['a','b','c','d']).reindex(['c','b','g'],method='pad') - new_index, indexer = ax.reindex(labels, level=level, - limit=limit, method=None, takeable=takeable) + # catch trying to reindex a non-monotonic index with a + # specialized indexer e.g. pad, so fallback to the regular + # indexer this will show up on reindexing a not-naturally + # ordering series, + # e.g. + # Series( + # [1,2,3,4], index=['a','b','c','d'] + # ).reindex(['c','b','g'], method='pad') + new_index, indexer = ax.reindex( + labels, level=level, limit=limit, method=None, + takeable=takeable) obj = obj._reindex_with_indexers( - {axis: [new_index, indexer]}, method=method, fill_value=fill_value, - limit=limit, copy=copy) + {axis: [new_index, indexer]}, method=method, + fill_value=fill_value, limit=limit, copy=copy) return obj def _needs_reindex_multi(self, axes, method, level): """ check if we do need a multi reindex """ - return (com._count_not_none(*axes.values()) == self._AXIS_LEN) and method is None and level is None and not self._is_mixed_type + return ((com._count_not_none(*axes.values()) == self._AXIS_LEN) and + method is None and level is None and not self._is_mixed_type) def _reindex_multi(self, axes, copy, fill_value): return NotImplemented _shared_docs['reindex_axis'] = ( - """Conform input object to new index with optional filling logic, placing - NA/NaN in locations having no value in the previous index. A new object - is produced unless the new index is equivalent to the current one and - copy=False + """Conform input object to new index with optional filling logic, + placing NA/NaN in locations having no value in the previous index. A + new object is produced unless the new index is equivalent to the + current one and copy=False Parameters ---------- @@ -1384,6 +1419,7 @@ def _reindex_multi(self, axes, copy, fill_value): ------- reindexed : %(klass)s """) + @Appender(_shared_docs['reindex_axis'] % _shared_doc_kwargs) def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=np.nan): @@ -1392,12 +1428,15 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, axis_name = self._get_axis_name(axis) axis_values = self._get_axis(axis_name) method = com._clean_fill_method(method) - new_index, indexer = axis_values.reindex(labels, method, level, - limit=limit, copy_if_needed=True) - return self._reindex_with_indexers({axis: [new_index, indexer]}, method=method, fill_value=fill_value, - limit=limit, copy=copy).__finalize__(self) - - def _reindex_with_indexers(self, reindexers, method=None, fill_value=np.nan, limit=None, copy=False, allow_dups=False): + new_index, indexer = axis_values.reindex( + labels, method, level, limit=limit, copy_if_needed=True) + return self._reindex_with_indexers( + {axis: [new_index, indexer]}, method=method, fill_value=fill_value, + limit=limit, copy=copy).__finalize__(self) + + def _reindex_with_indexers(self, reindexers, method=None, + fill_value=np.nan, limit=None, copy=False, + allow_dups=False): """ allow_dups indicates an internal call here """ # reindex doing multiple operations on different axes if indiciated @@ -1420,13 +1459,16 @@ def _reindex_with_indexers(self, reindexers, method=None, fill_value=np.nan, lim # TODO: speed up on homogeneous DataFrame objects indexer = com._ensure_int64(indexer) new_data = new_data.reindex_indexer(index, indexer, axis=baxis, - fill_value=fill_value, allow_dups=allow_dups) + fill_value=fill_value, + allow_dups=allow_dups) - elif baxis == 0 and index is not None and index is not new_data.axes[baxis]: + elif (baxis == 0 and index is not None and + index is not new_data.axes[baxis]): new_data = new_data.reindex_items(index, copy=copy, fill_value=fill_value) - elif baxis > 0 and index is not None and index is not new_data.axes[baxis]: + elif (baxis > 0 and index is not None and + index is not new_data.axes[baxis]): new_data = new_data.copy(deep=copy) new_data.set_axis(baxis, index) @@ -1470,14 +1512,16 @@ def filter(self, items=None, like=None, regex=None, axis=None): axis_values = self._get_axis(axis_name) if items is not None: - return self.reindex(**{axis_name: [r for r in items if r in axis_values]}) + return self.reindex(**{axis_name: [r for r in items + if r in axis_values]}) elif like: matchf = lambda x: (like in x if isinstance(x, string_types) else like in str(x)) return self.select(matchf, axis=axis_name) elif regex: matcher = re.compile(regex) - return self.select(lambda x: matcher.search(x) is not None, axis=axis_name) + return self.select(lambda x: matcher.search(x) is not None, + axis=axis_name) else: raise TypeError('Must pass either `items`, `like`, or `regex`') @@ -1508,9 +1552,10 @@ def __finalize__(self, other, method=None, **kwargs): Parameters ---------- - other : the object from which to get the attributes that we are going to propagate - method : optional, a passed method name ; possibily to take different types - of propagation actions based on this + other : the object from which to get the attributes that we are going + to propagate + method : optional, a passed method name ; possibly to take different + types of propagation actions based on this """ for name in self._metadata: @@ -1518,8 +1563,11 @@ def __finalize__(self, other, method=None, **kwargs): return self def __getattr__(self, name): - """After regular attribute access, try looking up the name of a the info - This allows simpler access to columns for interactive use.""" + """After regular attribute access, try looking up the name of a the + info. + + This allows simpler access to columns for interactive use. + """ if name in self._info_axis: return self[name] raise AttributeError("'%s' object has no attribute '%s'" % @@ -1594,7 +1642,8 @@ def _protect_consolidate(self, f): return result def _get_numeric_data(self): - return self._constructor(self._data.get_numeric_data()).__finalize__(self) + return self._constructor( + self._data.get_numeric_data()).__finalize__(self) def _get_bool_data(self): return self._constructor(self._data.get_bool_data()).__finalize__(self) @@ -1608,9 +1657,10 @@ def as_matrix(self, columns=None): are presented in sorted order unless a specific list of columns is provided. - NOTE: the dtype will be a lower-common-denominator dtype (implicit upcasting) - that is to say if the dtypes (even of numeric types) are mixed, the one that accomodates all will be chosen - use this with care if you are not dealing with the blocks + NOTE: the dtype will be a lower-common-denominator dtype (implicit + upcasting) that is to say if the dtypes (even of numeric types) + are mixed, the one that accommodates all will be chosen use this + with care if you are not dealing with the blocks e.g. if the dtypes are float16,float32 -> float32 float16,float32,float64 -> float64 @@ -1654,11 +1704,14 @@ def get_ftype_counts(self): def as_blocks(self, columns=None): """ - Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. + Convert the frame to a dict of dtype -> Constructor Types that each has + a homogeneous dtype. + are presented in sorted order unless a specific list of columns is provided. - NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) + NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in + as_matrix) Parameters ---------- @@ -1720,23 +1773,27 @@ def copy(self, deep=True): data = data.copy() return self._constructor(data).__finalize__(self) - def convert_objects(self, convert_dates=True, convert_numeric=False, copy=True): + def convert_objects(self, convert_dates=True, convert_numeric=False, + copy=True): """ Attempt to infer better dtype for object columns Parameters ---------- - convert_dates : if True, attempt to soft convert_dates, if 'coerce', force conversion (and non-convertibles get NaT) - convert_numeric : if True attempt to coerce to numerbers (including strings), non-convertibles get NaN + convert_dates : if True, attempt to soft convert_dates, if 'coerce', + force conversion (and non-convertibles get NaT) + convert_numeric : if True attempt to coerce to numbers (including + strings), non-convertibles get NaN copy : Boolean, if True, return copy, default is True Returns ------- converted : asm as input object """ - return self._constructor(self._data.convert(convert_dates=convert_dates, - convert_numeric=convert_numeric, - copy=copy)).__finalize__(self) + return self._constructor( + self._data.convert(convert_dates=convert_dates, + convert_numeric=convert_numeric, + copy=copy)).__finalize__(self) #---------------------------------------------------------------------- # Filling NA's @@ -1767,7 +1824,8 @@ def fillna(self, value=None, method=None, axis=0, inplace=False, Maximum size gap to forward or backward fill downcast : dict, default is None, a dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to - downcast to an appropriate equal type (e.g. float64 to int64 if possible) + downcast to an appropriate equal type (e.g. float64 to int64 if + possible) See also -------- @@ -1800,13 +1858,16 @@ def fillna(self, value=None, method=None, axis=0, inplace=False, # > 3d if self.ndim > 3: - raise NotImplementedError('cannot fillna with a method for > 3dims') + raise NotImplementedError( + 'Cannot fillna with a method for > 3dims' + ) # 3d elif self.ndim == 3: # fill in 2d chunks - result = dict([ (col,s.fillna(method=method, value=value)) for col, s in compat.iteritems(self) ]) + result = dict([(col, s.fillna(method=method, value=value)) + for col, s in compat.iteritems(self)]) return self._constructor.from_dict(result).__finalize__(self) # 2d or less @@ -2036,7 +2097,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None, raise TypeError('Fill value must be scalar, dict, or ' 'Series') - elif com.is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] + elif com.is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] if com.is_list_like(value): if len(to_replace) != len(value): raise ValueError('Replacement lists must match ' @@ -2212,8 +2273,8 @@ def isnull(self): return isnull(self).__finalize__(self) def notnull(self): - """ - Return a boolean same-sized object indicating if the values are not null + """Return a boolean same-sized object indicating if the values are + not null """ return notnull(self).__finalize__(self) @@ -2305,8 +2366,8 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys : boolean, default True When calling apply, add group keys to index to identify pieces squeeze : boolean, default False - reduce the dimensionaility of the return type if possible, otherwise - return a consistent type + reduce the dimensionaility of the return type if possible, + otherwise return a consistent type Examples -------- @@ -2590,7 +2651,8 @@ def _align_series(self, other, join='outer', axis=None, level=None, # series/series compat if isinstance(self, ABCSeries) and isinstance(other, ABCSeries): if axis: - raise ValueError('cannot align series to a series other than axis 0') + raise ValueError('cannot align series to a series other than ' + 'axis 0') join_index, lidx, ridx = self.index.join(other.index, how=join, level=level, @@ -2607,8 +2669,8 @@ def _align_series(self, other, join='outer', axis=None, level=None, join_index = self.index lidx, ridx = None, None if not self.index.equals(other.index): - join_index, lidx, ridx = self.index.join(other.index, how=join, - return_indexers=True) + join_index, lidx, ridx = self.index.join( + other.index, how=join, return_indexers=True) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=1) @@ -2617,8 +2679,8 @@ def _align_series(self, other, join='outer', axis=None, level=None, lidx, ridx = None, None if not self.columns.equals(other.index): join_index, lidx, ridx = \ - self.columns.join(other.index, how=join, - return_indexers=True) + self.columns.join(other.index, how=join, + return_indexers=True) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=0) @@ -2639,7 +2701,8 @@ def _align_series(self, other, join='outer', axis=None, level=None, right_result.fillna(fill_value, method=method, limit=limit)) else: - return left_result.__finalize__(self), right_result.__finalize__(other) + return (left_result.__finalize__(self), + right_result.__finalize__(other)) def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True): @@ -2669,8 +2732,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, cond = cond.reindex(**self._construct_axes_dict()) else: if not hasattr(cond, 'shape'): - raise ValueError('where requires an ndarray like object for its ' - 'condition') + raise ValueError('where requires an ndarray like object for ' + 'its condition') if cond.shape != self.shape: raise ValueError( 'Array conditional must be same shape as self') @@ -2693,12 +2756,16 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, fill_value=np.nan) # if we are NOT aligned, raise as we cannot where index - if axis is None and not all([ other._get_axis(i).equals(ax) for i, ax in enumerate(self.axes) ]): + if (axis is None and + not all([other._get_axis(i).equals(ax) + for i, ax in enumerate(self.axes)])): raise InvalidIndexError # slice me out of the other else: - raise NotImplemented("cannot align with a higher dimensional NDFrame") + raise NotImplemented( + "cannot align with a higher dimensional NDFrame" + ) elif is_list_like(other): @@ -2770,11 +2837,13 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, if inplace: # we may have different type blocks come out of putmask, so # reconstruct the block manager - self._data = self._data.putmask(cond, other, align=axis is None, inplace=True) + self._data = self._data.putmask(cond, other, align=axis is None, + inplace=True) else: - new_data = self._data.where( - other, cond, align=axis is None, raise_on_error=raise_on_error, try_cast=try_cast) + new_data = self._data.where(other, cond, align=axis is None, + raise_on_error=raise_on_error, + try_cast=try_cast) return self._constructor(new_data).__finalize__(self) @@ -2793,7 +2862,6 @@ def mask(self, cond): """ return self.where(~cond, np.nan) - def shift(self, periods=1, freq=None, axis=0, **kwds): """ Shift index by desired number of periods with an optional time freq @@ -2862,7 +2930,6 @@ def tshift(self, periods=1, freq=None, axis=0, **kwds): msg = 'Freq was not given and was not set in the index' raise ValueError(msg) - if periods == 0: return self @@ -2923,12 +2990,13 @@ def truncate(self, before=None, after=None, axis=None, copy=True): raise ValueError('Truncate: %s must be after %s' % (after, before)) - slicer = [ slice(None, None) ] * self._AXIS_LEN - slicer[axis] = slice(before,after) + slicer = [slice(None, None)] * self._AXIS_LEN + slicer[axis] = slice(before, after) result = self.ix[tuple(slicer)] if isinstance(ax, MultiIndex): - setattr(result,self._get_axis_name(axis),ax.truncate(before, after)) + setattr(result, self._get_axis_name(axis), + ax.truncate(before, after)) if copy: result = result.copy() @@ -3083,8 +3151,11 @@ def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwds): def _add_numeric_operations(cls): """ add the operations to the cls; evaluate the doc strings again """ - axis_descr = "{" + ', '.join([ "{0} ({1})".format(a,i) for i, a in enumerate(cls._AXIS_ORDERS)]) + "}" - name = cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else 'scalar' + axis_descr = "{%s}" % ', '.join([ + "{0} ({1})".format(a, i) for i, a in enumerate(cls._AXIS_ORDERS) + ]) + name = (cls._constructor_sliced.__name__ + if cls._AXIS_LEN > 1 else 'scalar') _num_doc = """ %(desc)s @@ -3123,8 +3194,8 @@ def _make_stat_function(name, desc, f): @Substitution(outname=name, desc=desc) @Appender(_num_doc) - def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, - **kwargs): + def stat_func(self, axis=None, skipna=None, level=None, + numeric_only=None, **kwargs): if skipna is None: skipna = True if axis is None: @@ -3137,24 +3208,40 @@ def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, stat_func.__name__ = name return stat_func - cls.sum = _make_stat_function('sum',"Return the sum of the values for the requested axis", nanops.nansum) - cls.mean = _make_stat_function('mean',"Return the mean of the values for the requested axis", nanops.nanmean) - cls.skew = _make_stat_function('skew',"Return unbiased skew over requested axis\nNormalized by N-1", nanops.nanskew) - cls.kurt = _make_stat_function('kurt',"Return unbiased kurtosis over requested axis\nNormalized by N-1", nanops.nankurt) + cls.sum = _make_stat_function( + 'sum', 'Return the sum of the values for the requested axis', + nanops.nansum) + cls.mean = _make_stat_function( + 'mean', 'Return the mean of the values for the requested axis', + nanops.nanmean) + cls.skew = _make_stat_function( + 'skew', + 'Return unbiased skew over requested axis\nNormalized by N-1', + nanops.nanskew) + cls.kurt = _make_stat_function( + 'kurt', + 'Return unbiased kurtosis over requested axis\nNormalized by N-1', + nanops.nankurt) cls.kurtosis = cls.kurt - cls.prod = _make_stat_function('prod',"Return the product of the values for the requested axis", nanops.nanprod) + cls.prod = _make_stat_function( + 'prod', 'Return the product of the values for the requested axis', + nanops.nanprod) cls.product = cls.prod - cls.median = _make_stat_function('median',"Return the median of the values for the requested axis", nanops.nanmedian) - cls.max = _make_stat_function('max',""" + cls.median = _make_stat_function( + 'median', 'Return the median of the values for the requested axis', + nanops.nanmedian) + cls.max = _make_stat_function('max', """ This method returns the maximum of the values in the object. If you want the *index* of the maximum, use ``idxmax``. This is the equivalent of the ``numpy.ndarray`` method ``argmax``.""", nanops.nanmax) - cls.min = _make_stat_function('min',""" + cls.min = _make_stat_function('min', """ This method returns the minimum of the values in the object. If you want the *index* of the minimum, use ``idxmin``. This is the equivalent of the ``numpy.ndarray`` method ``argmin``.""", nanops.nanmin) - @Substitution(outname='mad', desc="Return the mean absolute deviation of the values for the requested axis") + @Substitution(outname='mad', + desc="Return the mean absolute deviation of the values " + "for the requested axis") @Appender(_num_doc) def mad(self, axis=None, skipna=None, level=None, **kwargs): if skipna is None: @@ -3173,7 +3260,9 @@ def mad(self, axis=None, skipna=None, level=None, **kwargs): return np.abs(demeaned).mean(axis=axis, skipna=skipna) cls.mad = mad - @Substitution(outname='variance',desc="Return unbiased variance over requested axis\nNormalized by N-1") + @Substitution(outname='variance', + desc="Return unbiased variance over requested " + "axis\nNormalized by N-1") @Appender(_num_doc) def var(self, axis=None, skipna=None, level=None, ddof=1, **kwargs): if skipna is None: @@ -3184,10 +3273,13 @@ def var(self, axis=None, skipna=None, level=None, ddof=1, **kwargs): return self._agg_by_level('var', axis=axis, level=level, skipna=skipna, ddof=ddof) - return self._reduce(nanops.nanvar, axis=axis, skipna=skipna, ddof=ddof) + return self._reduce(nanops.nanvar, axis=axis, skipna=skipna, + ddof=ddof) cls.var = var - @Substitution(outname='stdev',desc="Return unbiased standard deviation over requested axis\nNormalized by N-1") + @Substitution(outname='stdev', + desc="Return unbiased standard deviation over requested " + "axis\nNormalized by N-1") @Appender(_num_doc) def std(self, axis=None, skipna=None, level=None, ddof=1, **kwargs): if skipna is None: @@ -3198,12 +3290,14 @@ def std(self, axis=None, skipna=None, level=None, ddof=1, **kwargs): return self._agg_by_level('std', axis=axis, level=level, skipna=skipna, ddof=ddof) result = self.var(axis=axis, skipna=skipna, ddof=ddof) - if getattr(result,'ndim',0) > 0: + if getattr(result, 'ndim', 0) > 0: return result.apply(np.sqrt) return np.sqrt(result) cls.std = std - @Substitution(outname='compounded',desc="Return the compound percentage of the values for the requested axis") + @Substitution(outname='compounded', + desc="Return the compound percentage of the values for " + "the requested axis") @Appender(_num_doc) def compound(self, axis=None, skipna=None, level=None, **kwargs): if skipna is None: @@ -3214,15 +3308,17 @@ def compound(self, axis=None, skipna=None, level=None, **kwargs): def _make_cum_function(name, accum_func, mask_a, mask_b): @Substitution(outname=name) - @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) - def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): + @Appender("Return cumulative {0} over requested axis.".format(name) + + _cnum_doc) + def func(self, axis=None, dtype=None, out=None, skipna=True, + **kwargs): if axis is None: axis = self._stat_axis_number else: axis = self._get_axis_number(axis) y = _values_from_object(self).copy() - if not issubclass(y.dtype.type, (np.integer,np.bool_)): + if not issubclass(y.dtype.type, (np.integer, np.bool_)): mask = isnull(self) if skipna: np.putmask(y, mask, mask_a) @@ -3239,11 +3335,16 @@ def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): func.__name__ = name return func - - cls.cummin = _make_cum_function('min', lambda y, axis: np.minimum.accumulate(y, axis), np.inf, np.nan) - cls.cumsum = _make_cum_function('sum', lambda y, axis: y.cumsum(axis), 0., np.nan) - cls.cumprod = _make_cum_function('prod', lambda y, axis: y.cumprod(axis), 1., np.nan) - cls.cummax = _make_cum_function('max', lambda y, axis: np.maximum.accumulate(y, axis), -np.inf, np.nan) + cls.cummin = _make_cum_function( + 'min', lambda y, axis: np.minimum.accumulate(y, axis), + np.inf, np.nan) + cls.cumsum = _make_cum_function( + 'sum', lambda y, axis: y.cumsum(axis), 0., np.nan) + cls.cumprod = _make_cum_function( + 'prod', lambda y, axis: y.cumprod(axis), 1., np.nan) + cls.cummax = _make_cum_function( + 'max', lambda y, axis: np.maximum.accumulate(y, axis), + -np.inf, np.nan) # install the indexerse for _name, _indexer in indexing.get_indexers_list(): diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index f37b94cd7f689..18f41917067f2 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -60,7 +60,6 @@ 'fillna', 'dtype']) | _plotting_methods - class GroupByError(Exception): pass @@ -482,17 +481,17 @@ def picker(arr): return self.agg(picker) def cumcount(self): - ''' - Number each item in each group from 0 to the length of that group. + """Number each item in each group from 0 to the length of that group. Essentially this is equivalent to - - >>> self.apply(lambda x: Series(np.arange(len(x)), x.index)). + + >>> self.apply(lambda x: Series(np.arange(len(x)), x.index)) Example ------- - >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], columns=['A']) + >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], + ... columns=['A']) >>> df A 0 a @@ -510,14 +509,13 @@ def cumcount(self): 5 3 dtype: int64 - ''' + """ index = self.obj.index cumcounts = np.zeros(len(index), dtype='int64') for v in self.indices.values(): cumcounts[v] = np.arange(len(v), dtype='int64') return Series(cumcounts, index) - def _try_cast(self, result, obj): """ try to cast the result to our obj original type, @@ -578,7 +576,7 @@ def _python_agg_general(self, func, *args, **kwargs): if _is_numeric_dtype(values.dtype): values = com.ensure_float(values) - output[name] = self._try_cast(values[mask],result) + output[name] = self._try_cast(values[mask], result) return self._wrap_aggregated_output(output) @@ -620,7 +618,7 @@ def _apply_filter(self, indices, dropna): mask[indices.astype(int)] = True # mask fails to broadcast when passed to where; broadcast manually. mask = np.tile(mask, list(self.obj.shape[1:]) + [1]).T - filtered = self.obj.where(mask) # Fill with NaNs. + filtered = self.obj.where(mask) # Fill with NaNs. return filtered @@ -710,7 +708,7 @@ def apply(self, f, data, axis=0): # oh boy if (f.__name__ not in _plotting_methods and - hasattr(splitter, 'fast_apply') and axis == 0): + hasattr(splitter, 'fast_apply') and axis == 0): try: values, mutated = splitter.fast_apply(f, group_keys) return group_keys, values, mutated @@ -840,16 +838,21 @@ def get_group_levels(self): # Aggregation functions _cython_functions = { - 'add' : 'group_add', - 'prod' : 'group_prod', - 'min' : 'group_min', - 'max' : 'group_max', - 'mean' : 'group_mean', - 'median': dict(name = 'group_median'), - 'var' : 'group_var', - 'std' : 'group_var', - 'first': dict(name = 'group_nth', f = lambda func, a, b, c, d: func(a, b, c, d, 1)), - 'last' : 'group_last', + 'add': 'group_add', + 'prod': 'group_prod', + 'min': 'group_min', + 'max': 'group_max', + 'mean': 'group_mean', + 'median': { + 'name': 'group_median' + }, + 'var': 'group_var', + 'std': 'group_var', + 'first': { + 'name': 'group_nth', + 'f': lambda func, a, b, c, d: func(a, b, c, d, 1) + }, + 'last': 'group_last', } _cython_transforms = { @@ -867,18 +870,19 @@ def get_group_levels(self): def _get_aggregate_function(self, how, values): dtype_str = values.dtype.name - def get_func(fname): - # find the function, or use the object function, or return a generic - for dt in [dtype_str,'object']: - f = getattr(_algos,"%s_%s" % (fname,dtype_str),None) + def get_func(fname): + # find the function, or use the object function, or return a + # generic + for dt in [dtype_str, 'object']: + f = getattr(_algos, "%s_%s" % (fname, dtype_str), None) if f is not None: return f - return getattr(_algos,fname,None) + return getattr(_algos, fname, None) ftype = self._cython_functions[how] - if isinstance(ftype,dict): + if isinstance(ftype, dict): func = afunc = get_func(ftype['name']) # a sub-function @@ -895,7 +899,9 @@ def wrapper(*args, **kwargs): func = get_func(ftype) if func is None: - raise NotImplementedError("function is not implemented for this dtype: [how->%s,dtype->%s]" % (how,dtype_str)) + raise NotImplementedError("function is not implemented for this" + "dtype: [how->%s,dtype->%s]" % + (how, dtype_str)) return func, dtype_str def aggregate(self, values, how, axis=0): @@ -934,11 +940,11 @@ def aggregate(self, values, how, axis=0): if self._filter_empty_groups: if result.ndim == 2: if is_numeric: - result = lib.row_bool_subset(result, - (counts > 0).view(np.uint8)) + result = lib.row_bool_subset( + result, (counts > 0).view(np.uint8)) else: - result = lib.row_bool_subset_object(result, - (counts > 0).view(np.uint8)) + result = lib.row_bool_subset_object( + result, (counts > 0).view(np.uint8)) else: result = result[counts > 0] @@ -957,8 +963,8 @@ def aggregate(self, values, how, axis=0): return result, names def _aggregate(self, result, counts, values, how, is_numeric): - agg_func,dtype = self._get_aggregate_function(how, values) - trans_func = self._cython_transforms.get(how, lambda x: x) + agg_func, dtype = self._get_aggregate_function(how, values) + trans_func = self._cython_transforms.get(how, lambda x: x) comp_ids, _, ngroups = self.group_info if values.ndim > 3: @@ -989,7 +995,7 @@ def _aggregate_series_fast(self, obj, func): group_index, _, ngroups = self.group_info # avoids object / Series creation overhead - dummy = obj._get_values(slice(None,0)).to_dense() + dummy = obj._get_values(slice(None, 0)).to_dense() indexer = _algos.groupsort_indexer(group_index, ngroups)[0] obj = obj.take(indexer, convert=False) group_index = com.take_nd(group_index, indexer, allow_fill=False) @@ -1010,7 +1016,8 @@ def _aggregate_series_pure_python(self, obj, func): for label, group in splitter: res = func(group) if result is None: - if isinstance(res, (Series, np.ndarray)) or isinstance(res, list): + if (isinstance(res, (Series, np.ndarray)) or + isinstance(res, list)): raise ValueError('Function does not reduce') result = np.empty(ngroups, dtype='O') @@ -1158,16 +1165,19 @@ def names(self): # cython aggregation _cython_functions = { - 'add' : 'group_add_bin', - 'prod' : 'group_prod_bin', - 'mean' : 'group_mean_bin', - 'min' : 'group_min_bin', - 'max' : 'group_max_bin', - 'var' : 'group_var_bin', - 'std' : 'group_var_bin', - 'ohlc' : 'group_ohlc', - 'first': dict(name = 'group_nth_bin', f = lambda func, a, b, c, d: func(a, b, c, d, 1)), - 'last' : 'group_last_bin', + 'add': 'group_add_bin', + 'prod': 'group_prod_bin', + 'mean': 'group_mean_bin', + 'min': 'group_min_bin', + 'max': 'group_max_bin', + 'var': 'group_var_bin', + 'std': 'group_var_bin', + 'ohlc': 'group_ohlc', + 'first': { + 'name': 'group_nth_bin', + 'f': lambda func, a, b, c, d: func(a, b, c, d, 1) + }, + 'last': 'group_last_bin', } _name_functions = { @@ -1178,8 +1188,8 @@ def names(self): def _aggregate(self, result, counts, values, how, is_numeric=True): - agg_func,dtype = self._get_aggregate_function(how, values) - trans_func = self._cython_transforms.get(how, lambda x: x) + agg_func, dtype = self._get_aggregate_function(how, values) + trans_func = self._cython_transforms.get(how, lambda x: x) if values.ndim > 3: # punting for now @@ -1295,14 +1305,14 @@ def __init__(self, index, grouper=None, name=None, level=None, # no level passed if not isinstance(self.grouper, (Series, np.ndarray)): self.grouper = self.index.map(self.grouper) - if not (hasattr(self.grouper,"__len__") and \ - len(self.grouper) == len(self.index)): - errmsg = "Grouper result violates len(labels) == len(data)\n" - errmsg += "result: %s" % com.pprint_thing(self.grouper) - self.grouper = None # Try for sanity + if not (hasattr(self.grouper, "__len__") and + len(self.grouper) == len(self.index)): + errmsg = ('Grouper result violates len(labels) == ' + 'len(data)\nresult: %s' % + com.pprint_thing(self.grouper)) + self.grouper = None # Try for sanity raise AssertionError(errmsg) - def __repr__(self): return 'Grouping(%s)' % self.name @@ -1357,7 +1367,8 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True): if not isinstance(group_axis, MultiIndex): if isinstance(level, compat.string_types): if obj.index.name != level: - raise ValueError('level name %s is not the name of the index' % level) + raise ValueError('level name %s is not the name of the ' + 'index' % level) elif level > 0: raise ValueError('level > 0 only valid with MultiIndex') @@ -1416,7 +1427,7 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True): name = gpr gpr = obj[gpr] - if (isinstance(gpr,Categorical) and len(gpr) != len(obj)): + if isinstance(gpr, Categorical) and len(gpr) != len(obj): errmsg = "Categorical grouper must have len(grouper) == len(data)" raise AssertionError(errmsg) @@ -1628,7 +1639,7 @@ def transform(self, func, *args, **kwargs): transformed : Series """ result = self.obj.copy() - if hasattr(result,'values'): + if hasattr(result, 'values'): result = result.values dtype = result.dtype @@ -1642,7 +1653,7 @@ def transform(self, func, *args, **kwargs): group = com.ensure_float(group) object.__setattr__(group, 'name', name) res = wrapper(group) - if hasattr(res,'values'): + if hasattr(res, 'values'): res = res.values # need to do a safe put here, as the dtype may be different @@ -1653,7 +1664,8 @@ def transform(self, func, *args, **kwargs): # downcast if we can (and need) result = _possibly_downcast_to_dtype(result, dtype) - return self.obj.__class__(result,index=self.obj.index,name=self.obj.name) + return self.obj.__class__(result, index=self.obj.index, + name=self.obj.name) def filter(self, func, dropna=True, *args, **kwargs): """ @@ -1686,8 +1698,8 @@ def true_and_notnull(x, *args, **kwargs): return b and notnull(b) try: - indices = [self.indices[name] if true_and_notnull(group) else [] - for name, group in self] + indices = [self.indices[name] if true_and_notnull(group) else [] + for name, group in self] except ValueError: raise TypeError("the filter must return a boolean result") except TypeError: @@ -1880,7 +1892,7 @@ def _aggregate_multiple_funcs(self, arg): grouper=self.grouper) results.append(colg.aggregate(arg)) keys.append(col) - except (TypeError, DataError) : + except (TypeError, DataError): pass except SpecificationError: raise @@ -1901,14 +1913,16 @@ def _aggregate_generic(self, func, *args, **kwargs): for name, data in self: # for name in self.indices: # data = self.get_group(name, obj=obj) - result[name] = self._try_cast(func(data, *args, **kwargs),data) + result[name] = self._try_cast(func(data, *args, **kwargs), + data) except Exception: return self._aggregate_item_by_item(func, *args, **kwargs) else: for name in self.indices: try: data = self.get_group(name, obj=obj) - result[name] = self._try_cast(func(data, *args, **kwargs), data) + result[name] = self._try_cast(func(data, *args, **kwargs), + data) except Exception: wrapper = lambda x: func(x, *args, **kwargs) result[name] = data.apply(wrapper, axis=axis) @@ -1929,7 +1943,8 @@ def _aggregate_item_by_item(self, func, *args, **kwargs): data = obj[item] colg = SeriesGroupBy(data, selection=item, grouper=self.grouper) - result[item] = self._try_cast(colg.aggregate(func, *args, **kwargs), data) + result[item] = self._try_cast( + colg.aggregate(func, *args, **kwargs), data) except ValueError: cannot_agg.append(item) continue @@ -1987,12 +2002,15 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): if isinstance(values[0], (np.ndarray, Series)): if isinstance(values[0], Series): - applied_index = self.obj._get_axis(self.axis) - all_indexed_same = _all_indexes_same([x.index for x in values]) - singular_series = len(values) == 1 and applied_index.nlevels == 1 + applied_index = self.obj._get_axis(self.axis) + all_indexed_same = _all_indexes_same([x.index + for x in values]) + singular_series = (len(values) == 1 and + applied_index.nlevels == 1) # GH3596 - # provide a reduction (Frame -> Series) if groups are unique + # provide a reduction (Frame -> Series) if groups are + # unique if self.squeeze: # assign the name to this series @@ -2000,15 +2018,19 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): values[0].name = keys[0] # GH2893 - # we have series in the values array, we want to produce a series: + # we have series in the values array, we want to + # produce a series: # if any of the sub-series are not indexed the same - # OR we don't have a multi-index and we have only a single values - return self._concat_objects(keys, values, - not_indexed_same=not_indexed_same) + # OR we don't have a multi-index and we have only a + # single values + return self._concat_objects( + keys, values, not_indexed_same=not_indexed_same + ) if not all_indexed_same: - return self._concat_objects(keys, values, - not_indexed_same=not_indexed_same) + return self._concat_objects( + keys, values, not_indexed_same=not_indexed_same + ) try: if self.axis == 0: @@ -2079,7 +2101,7 @@ def transform(self, func, *args, **kwargs): except TypeError: return self._transform_item_by_item(obj, fast_path) except Exception: # pragma: no cover - res = fast_path(group) + res = fast_path(group) path = fast_path else: res = path(group) @@ -2104,15 +2126,17 @@ def transform(self, func, *args, **kwargs): def _define_paths(self, func, *args, **kwargs): if isinstance(func, compat.string_types): fast_path = lambda group: getattr(group, func)(*args, **kwargs) - slow_path = lambda group: group.apply(lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis) + slow_path = lambda group: group.apply( + lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis) else: fast_path = lambda group: func(group, *args, **kwargs) - slow_path = lambda group: group.apply(lambda x: func(x, *args, **kwargs), axis=self.axis) + slow_path = lambda group: group.apply( + lambda x: func(x, *args, **kwargs), axis=self.axis) return fast_path, slow_path def _choose_path(self, fast_path, slow_path, group): path = slow_path - res = slow_path(group) + res = slow_path(group) # if we make it here, test if we can use the fast path try: @@ -2190,7 +2214,7 @@ def filter(self, func, dropna=True, *args, **kwargs): try: path, res = self._choose_path(fast_path, slow_path, group) except Exception: # pragma: no cover - res = fast_path(group) + res = fast_path(group) path = fast_path else: res = path(group) @@ -2199,11 +2223,11 @@ def add_indices(): indices.append(self.indices[name]) # interpret the result of the filter - if isinstance(res,(bool,np.bool_)): + if isinstance(res, (bool, np.bool_)): if res: add_indices() else: - if getattr(res,'ndim',None) == 1: + if getattr(res, 'ndim', None) == 1: val = res.ravel()[0] if val and notnull(val): add_indices() @@ -2224,7 +2248,8 @@ def __getitem__(self, key): if self._selection is not None: raise Exception('Column(s) %s already selected' % self._selection) - if isinstance(key, (list, tuple, Series, np.ndarray)) or not self.as_index: + if (isinstance(key, (list, tuple, Series, np.ndarray)) or + not self.as_index): return DataFrameGroupBy(self.obj, self.grouper, selection=key, grouper=self.grouper, exclusions=self.exclusions, @@ -2324,16 +2349,17 @@ def _wrap_agged_blocks(self, blocks): def _iterate_column_groupbys(self): for i, colname in enumerate(self.obj.columns): - yield colname, SeriesGroupBy(self.obj.iloc[:, i], selection=colname, + yield colname, SeriesGroupBy(self.obj.iloc[:, i], + selection=colname, grouper=self.grouper, exclusions=self.exclusions) def _apply_to_column_groupbys(self, func): from pandas.tools.merge import concat - return concat((func(col_groupby) - for _, col_groupby in self._iterate_column_groupbys()), - keys=self.obj.columns, - axis=1) + return concat( + (func(col_groupby) for _, col_groupby + in self._iterate_column_groupbys()), + keys=self.obj.columns, axis=1) def ohlc(self): """ @@ -2341,7 +2367,8 @@ def ohlc(self): For multiple groupings, the result index will be a MultiIndex """ - return self._apply_to_column_groupbys(lambda x: x._cython_agg_general('ohlc')) + return self._apply_to_column_groupbys( + lambda x: x._cython_agg_general('ohlc')) from pandas.tools.plotting import boxplot_frame_groupby diff --git a/pandas/core/index.py b/pandas/core/index.py index 096aff548dc9c..65eb8486c36d2 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -56,6 +56,7 @@ def _shouldbe_timestamp(obj): _Identity = object + class Index(FrozenNDArray): """ @@ -144,7 +145,7 @@ def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False, inferred = lib.infer_dtype(subarr) if inferred == 'integer': return Int64Index(subarr.astype('i8'), copy=copy, name=name) - elif inferred in ['floating','mixed-integer-float']: + elif inferred in ['floating', 'mixed-integer-float']: return Float64Index(subarr, copy=copy, name=name) elif inferred != 'string': if (inferred.startswith('datetime') or @@ -179,7 +180,7 @@ def is_(self, other): return self._id is getattr(other, '_id', Ellipsis) def _reset_identity(self): - "Initializes or resets ``_id`` attribute with new object" + """Initializes or resets ``_id`` attribute with new object""" self._id = _Identity() def view(self, *args, **kwargs): @@ -191,8 +192,10 @@ def view(self, *args, **kwargs): # construction helpers @classmethod def _scalar_data_error(cls, data): - raise TypeError('{0}(...) must be called with a collection ' - 'of some kind, {1} was passed'.format(cls.__name__,repr(data))) + raise TypeError( + '{0}(...) must be called with a collection of some kind, {1} was ' + 'passed'.format(cls.__name__, repr(data)) + ) @classmethod def _string_data_error(cls, data): @@ -411,7 +414,7 @@ def is_integer(self): return self.inferred_type in ['integer'] def is_floating(self): - return self.inferred_type in ['floating','mixed-integer-float'] + return self.inferred_type in ['floating', 'mixed-integer-float'] def is_numeric(self): return self.inferred_type in ['integer', 'floating'] @@ -423,8 +426,9 @@ def holds_integer(self): return self.inferred_type in ['integer', 'mixed-integer'] def _convert_scalar_indexer(self, key, typ=None): - """ convert a scalar indexer, right now we are converting floats -> ints - if the index supports it """ + """ convert a scalar indexer, right now we are converting + floats -> ints if the index supports it + """ def to_int(): ikey = int(key) @@ -463,7 +467,7 @@ def _convert_slice_indexer_getitem(self, key, is_index_slice=False): whether positional or not """ if self.is_integer() or is_index_slice: return key - return self._convert_slice_indexer(key) + return self._convert_slice_indexer(key) def _convert_slice_indexer(self, key, typ=None): """ convert a slice indexer. disallow floats in the start/stop/step """ @@ -494,7 +498,8 @@ def is_int(v): if typ == 'iloc': return self._convert_slice_indexer_iloc(key) elif typ == 'getitem': - return self._convert_slice_indexer_getitem(key, is_index_slice=is_index_slice) + return self._convert_slice_indexer_getitem( + key, is_index_slice=is_index_slice) # convert the slice to an indexer here @@ -535,9 +540,9 @@ def _convert_list_indexer(self, key, typ=None): def _convert_indexer_error(self, key, msg=None): if msg is None: msg = 'label' - raise TypeError("the {0} [{1}] is not a proper indexer for this index type ({2})".format(msg, - key, - self.__class__.__name__)) + raise TypeError("the {0} [{1}] is not a proper indexer for this index " + "type ({2})".format(msg, key, self.__class__.__name__)) + def get_duplicates(self): from collections import defaultdict counter = defaultdict(lambda: 0) @@ -750,11 +755,12 @@ def equals(self, other): return np.array_equal(self, other) def identical(self, other): + """Similar to equals, but check that other comparable attributes are + also equal """ - Similar to equals, but check that other comparable attributes are also equal - """ - return self.equals(other) and all( - (getattr(self, c, None) == getattr(other, c, None) for c in self._comparables)) + return (self.equals(other) and + all((getattr(self, c, None) == getattr(other, c, None) + for c in self._comparables))) def asof(self, label): """ @@ -1213,7 +1219,8 @@ def reindex(self, target, method=None, level=None, limit=None, indexer = None # to avoid aliasing an existing index - if copy_if_needed and target.name != self.name and self.name is not None: + if (copy_if_needed and target.name != self.name and + self.name is not None): if target.name is None: target = self.copy() @@ -1621,9 +1628,10 @@ class Int64Index(Index): """ Immutable ndarray implementing an ordered, sliceable set. The basic object - storing axis labels for all pandas objects. Int64Index is a special case of `Index` - with purely integer labels. This is the default index type used by the DataFrame - and Series ctors when no explicit index is provided by the user. + storing axis labels for all pandas objects. Int64Index is a special case + of `Index` with purely integer labels. This is the default index type used + by the DataFrame and Series ctors when no explicit index is provided by the + user. Parameters ---------- @@ -1664,7 +1672,8 @@ def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False): elif issubclass(data.dtype.type, np.integer): # don't force the upcast as we may be dealing # with a platform int - if dtype is None or not issubclass(np.dtype(dtype).type, np.integer): + if dtype is None or not issubclass(np.dtype(dtype).type, + np.integer): dtype = np.int64 subarr = np.array(data, dtype=dtype, copy=copy) @@ -1719,8 +1728,8 @@ def _wrap_joined_index(self, joined, other): class Float64Index(Index): """ Immutable ndarray implementing an ordered, sliceable set. The basic object - storing axis labels for all pandas objects. Float64Index is a special case of `Index` - with purely floating point labels. + storing axis labels for all pandas objects. Float64Index is a special case + of `Index` with purely floating point labels. Parameters ---------- @@ -1774,14 +1783,15 @@ def inferred_type(self): def astype(self, dtype): if np.dtype(dtype) != np.object_: - raise TypeError( - "Setting %s dtype to anything other than object is not supported" % self.__class__) - return Index(self.values,name=self.name,dtype=object) + raise TypeError('Setting %s dtype to anything other than object ' + 'is not supported' % self.__class__) + return Index(self.values, name=self.name, dtype=object) def _convert_scalar_indexer(self, key, typ=None): if typ == 'iloc': - return super(Float64Index, self)._convert_scalar_indexer(key, typ=typ) + return super(Float64Index, self)._convert_scalar_indexer(key, + typ=typ) return key def _convert_slice_indexer(self, key, typ=None): @@ -1793,10 +1803,11 @@ def _convert_slice_indexer(self, key, typ=None): pass # allow floats here - self._validate_slicer(key, lambda v: v is None or is_integer(v) or is_float(v)) + self._validate_slicer( + key, lambda v: v is None or is_integer(v) or is_float(v)) # translate to locations - return self.slice_indexer(key.start,key.stop,key.step) + return self.slice_indexer(key.start, key.stop, key.step) def get_value(self, series, key): """ we always want to get an index value, never a value """ @@ -1980,8 +1991,8 @@ def _set_labels(self, labels, copy=False, validate=True, verify_integrity=False): if validate and len(labels) != self.nlevels: raise ValueError("Length of labels must match length of levels") - self._labels = FrozenList(_ensure_frozen(labs, copy=copy)._shallow_copy() - for labs in labels) + self._labels = FrozenList( + _ensure_frozen(labs, copy=copy)._shallow_copy() for labs in labels) self._tuples = None self._reset_cache() @@ -2108,12 +2119,12 @@ def __repr__(self): res = res.encode(encoding) return res - def __unicode__(self): """ Return a string representation for a particular Index - Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. + Invoked by unicode(df) in py2 only. Yields a Unicode String in both + py2/py3. """ rows = self.format(names=True) max_rows = get_option('display.max_rows') @@ -2133,7 +2144,7 @@ def _convert_slice_indexer(self, key, typ=None): if typ == 'iloc': return self._convert_slice_indexer_iloc(key) - return super(MultiIndex,self)._convert_slice_indexer(key, typ=typ) + return super(MultiIndex, self)._convert_slice_indexer(key, typ=typ) def _get_names(self): return FrozenList(level.name for level in self.levels) @@ -2142,8 +2153,8 @@ def _set_names(self, values, validate=True): """ sets names on levels. WARNING: mutates! - Note that you generally want to set this *after* changing levels, so that it only - acts on copies""" + Note that you generally want to set this *after* changing levels, so + that it only acts on copies""" values = list(values) if validate and len(values) != self.nlevels: raise ValueError('Length of names must match length of levels') @@ -2189,8 +2200,8 @@ def _get_level_number(self, level): level += self.nlevels # Note: levels are zero-based elif level >= self.nlevels: - raise IndexError('Too many levels: Index has only %d levels, not %d' - % (self.nlevels, level + 1)) + raise IndexError('Too many levels: Index has only %d levels, ' + 'not %d' % (self.nlevels, level + 1)) return level _tuples = None @@ -2288,8 +2299,8 @@ def _try_mi(k): # a Timestamp will raise a TypeError in a multi-index # rather than a KeyError, try it here - if isinstance(key, (datetime.datetime,np.datetime64)) or ( - compat.PY3 and isinstance(key, compat.string_types)): + if isinstance(key, (datetime.datetime, np.datetime64)) or ( + compat.PY3 and isinstance(key, compat.string_types)): try: return _try_mi(Timestamp(key)) except: @@ -2338,7 +2349,8 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, else: # weird all NA case - formatted = [com.pprint_thing(na_rep if isnull(x) else x, escape_chars=('\t', '\r', '\n')) + formatted = [com.pprint_thing(na_rep if isnull(x) else x, + escape_chars=('\t', '\r', '\n')) for x in com.take_1d(lev.values, lab)] stringified_levels.append(formatted) @@ -2347,7 +2359,8 @@ def format(self, space=2, sparsify=None, adjoin=True, names=False, level = [] if names: - level.append(com.pprint_thing(name, escape_chars=('\t', '\r', '\n')) + level.append(com.pprint_thing(name, + escape_chars=('\t', '\r', '\n')) if name is not None else '') level.extend(np.array(lev, dtype=object)) @@ -2847,7 +2860,7 @@ def reindex(self, target, method=None, level=None, limit=None, else: if takeable: if method is not None or limit is not None: - raise ValueError("cannot do a takeable reindex with " + raise ValueError("cannot do a takeable reindex " "with a method or limit") return self[target], target @@ -3039,17 +3052,24 @@ def partial_selection(key): raise KeyError(key) ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)] - return indexer, _maybe_drop_levels(indexer, ilevels, drop_level) + return indexer, _maybe_drop_levels(indexer, ilevels, + drop_level) if len(key) == self.nlevels: if self.is_unique: - # here we have a completely specified key, but are using some partial string matching here + # here we have a completely specified key, but are + # using some partial string matching here # GH4758 - can_index_exactly = any( - [l.is_all_dates and not isinstance(k, compat.string_types) for k, l in zip(key, self.levels)]) - if any([l.is_all_dates for k, l in zip(key, self.levels)]) and not can_index_exactly: + can_index_exactly = any([ + (l.is_all_dates and + not isinstance(k, compat.string_types)) + for k, l in zip(key, self.levels) + ]) + if any([ + l.is_all_dates for k, l in zip(key, self.levels) + ]) and not can_index_exactly: indexer = slice(*self.slice_locs(key, key)) # we have a multiple selection here @@ -3058,7 +3078,8 @@ def partial_selection(key): key = tuple(self[indexer].tolist()[0]) - return self._engine.get_loc(_values_from_object(key)), None + return (self._engine.get_loc(_values_from_object(key)), + None) else: return partial_selection(key) else: @@ -3089,7 +3110,8 @@ def partial_selection(key): indexer = slice(None, None) ilevels = [i for i in range(len(key)) if key[i] != slice(None, None)] - return indexer, _maybe_drop_levels(indexer, ilevels, drop_level) + return indexer, _maybe_drop_levels(indexer, ilevels, + drop_level) else: indexer = self._get_level_indexer(key, level=level) new_index = _maybe_drop_levels(indexer, [level], drop_level) @@ -3277,8 +3299,8 @@ def _assert_can_do_setop(self, other): def astype(self, dtype): if np.dtype(dtype) != np.object_: - raise TypeError( - "Setting %s dtype to anything other than object is not supported" % self.__class__) + raise TypeError('Setting %s dtype to anything other than object ' + 'is not supported' % self.__class__) return self._shallow_copy() def insert(self, loc, item): @@ -3530,8 +3552,9 @@ def _get_consensus_names(indexes): # find the non-none names, need to tupleify to make # the set hashable, then reverse on return - consensus_names = set([tuple(i.names) - for i in indexes if all(n is not None for n in i.names)]) + consensus_names = set([ + tuple(i.names) for i in indexes if all(n is not None for n in i.names) + ]) if len(consensus_names) == 1: return list(list(consensus_names)[0]) return [None] * indexes[0].nlevels diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index b462624dde1f5..ab9000fd21a0a 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -12,15 +12,16 @@ import numpy as np + # the supported indexers def get_indexers_list(): return [ - ('ix' ,_IXIndexer ), - ('iloc',_iLocIndexer ), - ('loc' ,_LocIndexer ), - ('at' ,_AtIndexer ), - ('iat' ,_iAtIndexer ), + ('ix', _IXIndexer), + ('iloc', _iLocIndexer), + ('loc', _LocIndexer), + ('at', _AtIndexer), + ('iat', _iAtIndexer), ] # "null slice" @@ -33,7 +34,7 @@ class IndexingError(Exception): class _NDFrameIndexer(object): _valid_types = None - _exception = KeyError + _exception = KeyError def __init__(self, obj, name): self.obj = obj @@ -70,7 +71,8 @@ def _get_loc(self, key, axis=0): return self.obj._ixs(key, axis=axis) def _slice(self, obj, axis=0, raise_on_error=False, typ=None): - return self.obj._slice(obj, axis=axis, raise_on_error=raise_on_error, typ=typ) + return self.obj._slice(obj, axis=axis, raise_on_error=raise_on_error, + typ=typ) def __setitem__(self, key, value): # kludgetastic @@ -101,8 +103,9 @@ def _has_valid_tuple(self, key): for i, k in enumerate(key): if i >= self.obj.ndim: raise IndexingError('Too many indexers') - if not self._has_valid_type(k,i): - raise ValueError("Location based indexing can only have [%s] types" % self._valid_types) + if not self._has_valid_type(k, i): + raise ValueError("Location based indexing can only have [%s] " + "types" % self._valid_types) def _convert_tuple(self, key, is_setter=False): keyidx = [] @@ -113,13 +116,13 @@ def _convert_tuple(self, key, is_setter=False): def _convert_scalar_indexer(self, key, axis): # if we are accessing via lowered dim, use the last dim - ax = self.obj._get_axis(min(axis,self.ndim-1)) + ax = self.obj._get_axis(min(axis, self.ndim-1)) # a scalar return ax._convert_scalar_indexer(key, typ=self.name) def _convert_slice_indexer(self, key, axis): # if we are accessing via lowered dim, use the last dim - ax = self.obj._get_axis(min(axis,self.ndim-1)) + ax = self.obj._get_axis(min(axis, self.ndim-1)) return ax._convert_slice_indexer(key, typ=self.name) def _has_valid_setitem_indexer(self, indexer): @@ -129,11 +132,12 @@ def _has_valid_positional_setitem_indexer(self, indexer): """ validate that an positional indexer cannot enlarge its target will raise if needed, does not modify the indexer externally """ if isinstance(indexer, dict): - raise IndexError("{0} cannot enlarge its target object".format(self.name)) + raise IndexError("{0} cannot enlarge its target object" + .format(self.name)) else: if not isinstance(indexer, tuple): indexer = self._tuplify(indexer) - for ax, i in zip(self.obj.axes,indexer): + for ax, i in zip(self.obj.axes, indexer): if isinstance(i, slice): # should check the stop slice? pass @@ -142,9 +146,11 @@ def _has_valid_positional_setitem_indexer(self, indexer): pass elif com.is_integer(i): if i >= len(ax): - raise IndexError("{0} cannot enlarge its target object".format(self.name)) + raise IndexError("{0} cannot enlarge its target object" + .format(self.name)) elif isinstance(i, dict): - raise IndexError("{0} cannot enlarge its target object".format(self.name)) + raise IndexError("{0} cannot enlarge its target object" + .format(self.name)) return True @@ -157,34 +163,41 @@ def _setitem_with_indexer(self, indexer, value): # maybe partial set take_split_path = self.obj._is_mixed_type - if isinstance(indexer,tuple): + if isinstance(indexer, tuple): nindexer = [] for i, idx in enumerate(indexer): if isinstance(idx, dict): # reindex the axis to the new value # and set inplace - key,_ = _convert_missing_indexer(idx) + key, _ = _convert_missing_indexer(idx) - # if this is the items axes, then take the main missing path - # first; this correctly sets the dtype and avoids cache issues - # essentially this separates out the block that is needed to possibly - # be modified + # if this is the items axes, then take the main missing + # path first + # this correctly sets the dtype and avoids cache issues + # essentially this separates out the block that is needed + # to possibly be modified if self.ndim > 1 and i == self.obj._info_axis_number: # add the new item, and set the value # must have all defined axes if we have a scalar - # or a list-like on the non-info axes if we have a list-like - len_non_info_axes = [ len(_ax) for _i, _ax in enumerate(self.obj.axes) if _i != i ] - if any([ not l for l in len_non_info_axes ]): + # or a list-like on the non-info axes if we have a + # list-like + len_non_info_axes = [ + len(_ax) for _i, _ax in enumerate(self.obj.axes) + if _i != i + ] + if any([not l for l in len_non_info_axes]): if not is_list_like(value): - raise ValueError("cannot set a frame with no defined index and a scalar") + raise ValueError("cannot set a frame with no " + "defined index and a scalar") self.obj[key] = value return self.obj self.obj[key] = np.nan - new_indexer = _convert_from_missing_indexer_tuple(indexer, self.obj.axes) + new_indexer = _convert_from_missing_indexer_tuple( + indexer, self.obj.axes) self._setitem_with_indexer(new_indexer, value) return self.obj @@ -194,10 +207,10 @@ def _setitem_with_indexer(self, indexer, value): # so the object is the same index = self.obj._get_axis(i) labels = _safe_append_to_index(index, key) - self.obj._data = self.obj.reindex_axis(labels,i)._data + self.obj._data = self.obj.reindex_axis(labels, i)._data self.obj._maybe_update_cacher(clear=True) - if isinstance(labels,MultiIndex): + if isinstance(labels, MultiIndex): self.obj.sortlevel(inplace=True) labels = self.obj._get_axis(i) @@ -225,10 +238,11 @@ def _setitem_with_indexer(self, indexer, value): # this preserves dtype of the value new_values = Series([value]).values if len(self.obj.values): - new_values = np.concatenate([self.obj.values, new_values]) + new_values = np.concatenate([self.obj.values, + new_values]) - self.obj._data = self.obj._constructor(new_values, - index=new_index, name=self.obj.name)._data + self.obj._data = self.obj._constructor( + new_values, index=new_index, name=self.obj.name)._data self.obj._maybe_update_cacher(clear=True) return self.obj @@ -236,24 +250,28 @@ def _setitem_with_indexer(self, indexer, value): # no columns and scalar if not len(self.obj.columns): - raise ValueError("cannot set a frame with no defined columns") + raise ValueError( + "cannot set a frame with no defined columns" + ) index = self.obj._get_axis(0) labels = _safe_append_to_index(index, indexer) - self.obj._data = self.obj.reindex_axis(labels,0)._data + self.obj._data = self.obj.reindex_axis(labels, 0)._data self.obj._maybe_update_cacher(clear=True) - return getattr(self.obj,self.name).__setitem__(indexer,value) + return getattr(self.obj, self.name).__setitem__(indexer, + value) # set using setitem (Panel and > dims) elif self.ndim >= 3: - return self.obj.__setitem__(indexer,value) + return self.obj.__setitem__(indexer, value) # set info_axis = self.obj._info_axis_number item_labels = self.obj._get_axis(info_axis) # if we have a complicated setup, take the split path - if isinstance(indexer, tuple) and any([ isinstance(ax,MultiIndex) for ax in self.obj.axes ]): + if (isinstance(indexer, tuple) and + any([isinstance(ax, MultiIndex) for ax in self.obj.axes])): take_split_path = True # align and set the values @@ -270,8 +288,10 @@ def _setitem_with_indexer(self, indexer, value): info_idx = [info_idx] labels = item_labels[info_idx] - # if we have a partial multiindex, then need to adjust the plane indexer here - if len(labels) == 1 and isinstance(self.obj[labels[0]].index,MultiIndex): + # if we have a partial multiindex, then need to adjust the plane + # indexer here + if (len(labels) == 1 and + isinstance(self.obj[labels[0]].index, MultiIndex)): item = labels[0] obj = self.obj[item] index = obj.index @@ -282,19 +302,23 @@ def _setitem_with_indexer(self, indexer, value): except: pass plane_indexer = tuple([idx]) + indexer[info_axis + 1:] - lplane_indexer = _length_of_indexer(plane_indexer[0],index) + lplane_indexer = _length_of_indexer(plane_indexer[0], index) - # require that we are setting the right number of values that we are indexing + # require that we are setting the right number of values that + # we are indexing if is_list_like(value) and lplane_indexer != len(value): if len(obj[idx]) != len(value): - raise ValueError("cannot set using a multi-index selection indexer with a different length than the value") + raise ValueError( + "cannot set using a multi-index selection indexer " + "with a different length than the value" + ) # we can directly set the series here # as we select a slice indexer on the mi idx = index._convert_slice_indexer(idx) obj = obj.copy() - obj._data = obj._data.setitem(tuple([idx]),value) + obj._data = obj._data.setitem(tuple([idx]), value) self.obj[item] = obj return @@ -303,7 +327,8 @@ def _setitem_with_indexer(self, indexer, value): plane_indexer = indexer[:info_axis] + indexer[info_axis + 1:] if info_axis > 0: plane_axis = self.obj.axes[:info_axis][0] - lplane_indexer = _length_of_indexer(plane_indexer[0],plane_axis) + lplane_indexer = _length_of_indexer(plane_indexer[0], + plane_axis) else: lplane_indexer = 0 @@ -313,7 +338,7 @@ def setter(item, v): # set the item, possibly having a dtype change s = s.copy() - s._data = s._data.setitem(pi,v) + s._data = s._data.setitem(pi, v) s._maybe_update_cacher(clear=True) self.obj[item] = s @@ -352,11 +377,11 @@ def can_do_equal_len(): # we have an equal len ndarray to our labels elif isinstance(value, np.ndarray) and value.ndim == 2: if len(labels) != value.shape[1]: - raise ValueError('Must have equal len keys and value when' - ' setting with an ndarray') + raise ValueError('Must have equal len keys and value ' + 'when setting with an ndarray') for i, item in enumerate(labels): - setter(item, value[:,i]) + setter(item, value[:, i]) # we have an equal len list/ndarray elif can_do_equal_len(): @@ -366,8 +391,8 @@ def can_do_equal_len(): else: if len(labels) != len(value): - raise ValueError('Must have equal len keys and value when' - ' setting with an iterable') + raise ValueError('Must have equal len keys and value' + 'when setting with an iterable') for item, v in zip(labels, value): setter(item, v) @@ -390,14 +415,14 @@ def can_do_equal_len(): if isinstance(value, ABCPanel): value = self._align_panel(indexer, value) - self.obj._data = self.obj._data.setitem(indexer,value) + self.obj._data = self.obj._data.setitem(indexer, value) self.obj._maybe_update_cacher(clear=True) def _align_series(self, indexer, ser): # indexer to assign Series can be tuple or scalar if isinstance(indexer, tuple): - aligners = [ not _is_null_slice(idx) for idx in indexer ] + aligners = [not _is_null_slice(idx) for idx in indexer] sum_aligners = sum(aligners) single_aligner = sum_aligners == 1 is_frame = self.obj.ndim == 2 @@ -415,15 +440,17 @@ def _align_series(self, indexer, ser): # panel elif is_panel: - single_aligner = single_aligner and (aligners[1] or aligners[2]) - - # we have a frame, with multiple indexers on both axes; and a series, - # so need to broadcast (see GH5206) - if sum_aligners == self.ndim and all([ com._is_sequence(_) for _ in indexer ]): - - ser = ser.reindex(obj.axes[0][indexer[0].ravel()],copy=True).values + single_aligner = (single_aligner and + (aligners[1] or aligners[2])) + + # we have a frame, with multiple indexers on both axes; and a + # series, so need to broadcast (see GH5206) + if (sum_aligners == self.ndim and + all([com._is_sequence(_) for _ in indexer])): + ser = ser.reindex(obj.axes[0][indexer[0].ravel()], + copy=True).values l = len(indexer[1].ravel()) - ser = np.tile(ser,l).reshape(l,-1).T + ser = np.tile(ser, l).reshape(l, -1).T return ser for i, idx in enumerate(indexer): @@ -462,14 +489,14 @@ def _align_series(self, indexer, ser): if len(labels & ser.index): ser = ser.reindex(labels) else: - broadcast.append((n,len(labels))) + broadcast.append((n, len(labels))) # broadcast along other dims ser = ser.values.copy() - for (axis,l) in broadcast: - shape = [ -1 ] * (len(broadcast)+1) + for (axis, l) in broadcast: + shape = [-1] * (len(broadcast)+1) shape[axis] = l - ser = np.tile(ser,l).reshape(shape) + ser = np.tile(ser, l).reshape(shape) if self.obj.ndim == 3: ser = ser.T @@ -509,7 +536,7 @@ def _align_frame(self, indexer, df): if len(sindexers) == 1 and idx is None and cols is None: if sindexers[0] == 0: df = df.T - return self.obj.conform(df,axis=sindexers[0]) + return self.obj.conform(df, axis=sindexers[0]) df = df.T if idx is not None and cols is not None: @@ -551,7 +578,8 @@ def _align_frame(self, indexer, df): def _align_panel(self, indexer, df): is_frame = self.obj.ndim == 2 is_panel = self.obj.ndim >= 3 - raise NotImplementedError("cannot set using an indexer with a Panel yet!") + raise NotImplementedError("cannot set using an indexer with a Panel " + "yet!") def _getitem_tuple(self, tup): try: @@ -575,7 +603,7 @@ def _getitem_tuple(self, tup): if _is_null_slice(key): continue - retval = getattr(retval,self.name)._getitem_axis(key, axis=i) + retval = getattr(retval, self.name)._getitem_axis(key, axis=i) return retval @@ -590,7 +618,7 @@ def _multi_take_opportunity(self, tup): return False # just too complicated - for indexer, ax in zip(tup,self.obj._data.axes): + for indexer, ax in zip(tup, self.obj._data.axes): if isinstance(ax, MultiIndex): return False elif com._is_bool_indexer(indexer): @@ -599,11 +627,15 @@ def _multi_take_opportunity(self, tup): return True def _multi_take(self, tup): - """ create the reindex map for our objects, raise the _exception if we can't create the indexer """ - + """ create the reindex map for our objects, raise the _exception if we + can't create the indexer + """ try: o = self.obj - d = dict([ (a,self._convert_for_reindex(t, axis=o._get_axis_number(a))) for t, a in zip(tup, o._AXIS_ORDERS) ]) + d = dict([ + (a, self._convert_for_reindex(t, axis=o._get_axis_number(a))) + for t, a in zip(tup, o._AXIS_ORDERS) + ]) return o.reindex(**d) except: raise self._exception @@ -682,7 +714,7 @@ def _getitem_lowerdim(self, tup): if len(new_key) == 1: new_key, = new_key - return getattr(section,self.name)[new_key] + return getattr(section, self.name)[new_key] raise IndexingError('not applicable') @@ -769,7 +801,8 @@ def _reindex(keys, level=None): else: indexer, missing = labels.get_indexer_non_unique(keyarr) check = indexer != -1 - result = self.obj.take(indexer[check], axis=axis, convert=False) + result = self.obj.take(indexer[check], axis=axis, + convert=False) # need to merge the result labels and the missing labels if len(missing): @@ -781,33 +814,39 @@ def _reindex(keys, level=None): cur_labels = result._get_axis(axis).values cur_indexer = com._ensure_int64(l[check]) - new_labels = np.empty(tuple([len(indexer)]),dtype=object) - new_labels[cur_indexer] = cur_labels + new_labels = np.empty(tuple([len(indexer)]), dtype=object) + new_labels[cur_indexer] = cur_labels new_labels[missing_indexer] = missing_labels # reindex with the specified axis ndim = self.obj.ndim if axis+1 > ndim: - raise AssertionError("invalid indexing error with non-unique index") + raise AssertionError("invalid indexing error with " + "non-unique index") # a unique indexer if keyarr_is_unique: - new_indexer = (Index(cur_indexer) + Index(missing_indexer)).values + new_indexer = (Index(cur_indexer) + + Index(missing_indexer)).values new_indexer[missing_indexer] = -1 - # we have a non_unique selector, need to use the original indexer here + # we have a non_unique selector, need to use the original + # indexer here else: # need to retake to have the same size as the indexer rindexer = indexer.values rindexer[~check] = 0 - result = self.obj.take(rindexer, axis=axis, convert=False) + result = self.obj.take(rindexer, axis=axis, + convert=False) # reset the new indexer to account for the new size new_indexer = np.arange(len(result)) new_indexer[~check] = -1 - result = result._reindex_with_indexers({ axis : [ new_labels, new_indexer ] }, copy=True, allow_dups=True) + result = result._reindex_with_indexers({ + axis: [new_labels, new_indexer] + }, copy=True, allow_dups=True) return result @@ -853,11 +892,12 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): # always valid if self.name == 'loc': - return { 'key' : obj } + return {'key': obj} # a positional if obj >= len(self.obj) and not isinstance(labels, MultiIndex): - raise ValueError("cannot set by positional indexing with enlargement") + raise ValueError("cannot set by positional indexing with " + "enlargement") return obj @@ -898,7 +938,8 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): # non-unique (dups) else: - indexer, missing = labels.get_indexer_non_unique(objarr) + (indexer, + missing) = labels.get_indexer_non_unique(objarr) check = indexer mask = check == -1 @@ -906,7 +947,7 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): # mi here if isinstance(obj, tuple) and is_setter: - return { 'key' : obj } + return {'key': obj} raise KeyError('%s not in index' % objarr[mask]) return indexer @@ -914,11 +955,10 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): else: try: return labels.get_loc(obj) - except (KeyError): - + except KeyError: # allow a not found key only if we are a setter if not is_list_like(obj) and is_setter: - return { 'key' : obj } + return {'key': obj} raise def _tuplify(self, loc): @@ -938,6 +978,7 @@ def _get_slice_axis(self, slice_obj, axis=0): else: return self.obj.take(indexer, axis=axis) + class _IXIndexer(_NDFrameIndexer): """ A primarily location based indexer, with integer fallback """ @@ -959,8 +1000,9 @@ def _has_valid_type(self, key, axis): return True + class _LocationIndexer(_NDFrameIndexer): - _exception = Exception + _exception = Exception def __getitem__(self, key): if type(key) is tuple: @@ -977,8 +1019,9 @@ def _getbool_axis(self, key, axis=0): inds, = key.nonzero() try: return self.obj.take(inds, axis=axis, convert=False) - except (Exception) as detail: + except Exception as detail: raise self._exception(detail) + def _get_slice_axis(self, slice_obj, axis=0): """ this is pretty simple as we just have to deal with labels """ obj = self.obj @@ -986,17 +1029,21 @@ def _get_slice_axis(self, slice_obj, axis=0): return obj labels = obj._get_axis(axis) - indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, slice_obj.step) + indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop, + slice_obj.step) if isinstance(indexer, slice): return self._slice(indexer, axis=axis, typ='iloc') else: return self.obj.take(indexer, axis=axis) + class _LocIndexer(_LocationIndexer): """ purely label based location based indexing """ - _valid_types = "labels (MUST BE IN THE INDEX), slices of labels (BOTH endpoints included! Can be slices of integers if the index is integers), listlike of labels, boolean" - _exception = KeyError + _valid_types = ("labels (MUST BE IN THE INDEX), slices of labels (BOTH " + "endpoints included! Can be slices of integers if the " + "index is integers), listlike of labels, boolean") + _exception = KeyError def _has_valid_type(self, key, axis): ax = self.obj._get_axis(axis) @@ -1016,10 +1063,16 @@ def _has_valid_type(self, key, axis): else: if key.start is not None: if key.start not in ax: - raise KeyError("start bound [%s] is not the [%s]" % (key.start,self.obj._get_axis_name(axis))) + raise KeyError( + "start bound [%s] is not the [%s]" % + (key.start, self.obj._get_axis_name(axis)) + ) if key.stop is not None: if key.stop not in ax: - raise KeyError("stop bound [%s] is not in the [%s]" % (key.stop,self.obj._get_axis_name(axis))) + raise KeyError( + "stop bound [%s] is not in the [%s]" % + (key.stop, self.obj._get_axis_name(axis)) + ) elif com._is_bool_indexer(key): return True @@ -1033,7 +1086,8 @@ def _has_valid_type(self, key, axis): # require all elements in the index idx = _ensure_index(key) if not idx.isin(ax).all(): - raise KeyError("[%s] are not in ALL in the [%s]" % (key,self.obj._get_axis_name(axis))) + raise KeyError("[%s] are not in ALL in the [%s]" % + (key, self.obj._get_axis_name(axis))) return True @@ -1041,8 +1095,10 @@ def _has_valid_type(self, key, axis): def error(): if isnull(key): - raise ValueError("cannot use label indexing with a null key") - raise KeyError("the label [%s] is not in the [%s]" % (key,self.obj._get_axis_name(axis))) + raise ValueError( + "cannot use label indexing with a null key") + raise KeyError("the label [%s] is not in the [%s]" % + (key, self.obj._get_axis_name(axis))) try: key = self._convert_scalar_indexer(key, axis) @@ -1063,7 +1119,7 @@ def _getitem_axis(self, key, axis=0): labels = self.obj._get_axis(axis) if isinstance(key, slice): - self._has_valid_type(key,axis) + self._has_valid_type(key, axis) return self._get_slice_axis(key, axis=axis) elif com._is_bool_indexer(key): return self._getbool_axis(key, axis=axis) @@ -1075,23 +1131,31 @@ def _getitem_axis(self, key, axis=0): return self._getitem_iterable(key, axis=axis) else: - self._has_valid_type(key,axis) + self._has_valid_type(key, axis) return self._get_label(key, axis=axis) + class _iLocIndexer(_LocationIndexer): """ purely integer based location based indexing """ - _valid_types = "integer, integer slice (START point is INCLUDED, END point is EXCLUDED), listlike of integers, boolean array" - _exception = IndexError + _valid_types = ("integer, integer slice (START point is INCLUDED, END " + "point is EXCLUDED), listlike of integers, boolean array") + _exception = IndexError def _has_valid_type(self, key, axis): if com._is_bool_indexer(key): - if hasattr(key,'index') and isinstance(key.index,Index): + if hasattr(key, 'index') and isinstance(key.index, Index): if key.index.inferred_type == 'integer': - raise NotImplementedError("iLocation based boolean indexing on an integer type is not available") - raise ValueError("iLocation based boolean indexing cannot use an indexable as a mask") + raise NotImplementedError( + "iLocation based boolean indexing on an integer type " + "is not available" + ) + raise ValueError("iLocation based boolean indexing cannot use " + "an indexable as a mask") return True - return isinstance(key, slice) or com.is_integer(key) or _is_list_like(key) + return (isinstance(key, slice) or + com.is_integer(key) or + _is_list_like(key)) def _has_valid_setitem_indexer(self, indexer): self._has_valid_positional_setitem_indexer(indexer) @@ -1112,7 +1176,7 @@ def _getitem_tuple(self, tup): if _is_null_slice(key): continue - retval = getattr(retval,self.name)._getitem_axis(key, axis=i) + retval = getattr(retval, self.name)._getitem_axis(key, axis=i) return retval @@ -1123,18 +1187,19 @@ def _get_slice_axis(self, slice_obj, axis=0): return obj if isinstance(slice_obj, slice): - return self._slice(slice_obj, axis=axis, raise_on_error=True, typ='iloc') + return self._slice(slice_obj, axis=axis, raise_on_error=True, + typ='iloc') else: return self.obj.take(slice_obj, axis=axis) def _getitem_axis(self, key, axis=0): if isinstance(key, slice): - self._has_valid_type(key,axis) + self._has_valid_type(key, axis) return self._get_slice_axis(key, axis=axis) elif com._is_bool_indexer(key): - self._has_valid_type(key,axis) + self._has_valid_type(key, axis) return self._getbool_axis(key, axis=axis) # a single integer or a list of integers @@ -1148,16 +1213,18 @@ def _getitem_axis(self, key, axis=0): key = self._convert_scalar_indexer(key, axis) if not com.is_integer(key): - raise TypeError("Cannot index by location index with a non-integer key") + raise TypeError("Cannot index by location index with a " + "non-integer key") - return self._get_loc(key,axis=axis) + return self._get_loc(key, axis=axis) def _convert_to_indexer(self, obj, axis=0, is_setter=False): """ much simpler as we only have to deal with our valid types """ - if self._has_valid_type(obj,axis): + if self._has_valid_type(obj, axis): return obj - raise ValueError("Can only index by location with a [%s]" % self._valid_types) + raise ValueError("Can only index by location with a [%s]" % + self._valid_types) class _ScalarAccessIndexer(_NDFrameIndexer): @@ -1171,7 +1238,7 @@ def __getitem__(self, key): # we could have a convertible item here (e.g. Timestamp) if not _is_list_like(key): - key = tuple([ key ]) + key = tuple([key]) else: raise ValueError('Invalid call for scalar access (getting)!') @@ -1182,15 +1249,18 @@ def __setitem__(self, key, value): if not isinstance(key, tuple): key = self._tuplify(key) if len(key) != self.obj.ndim: - raise ValueError('Not enough indexers for scalar access (setting)!') + raise ValueError('Not enough indexers for scalar access ' + '(setting)!') key = self._convert_key(key) key.append(value) self.obj.set_value(*key) + class _AtIndexer(_ScalarAccessIndexer): """ label based scalar accessor """ pass + class _iAtIndexer(_ScalarAccessIndexer): """ integer based scalar accessor """ @@ -1200,17 +1270,20 @@ def _has_valid_setitem_indexer(self, indexer): def _convert_key(self, key): """ require integer args (and convert to label arguments) """ ckey = [] - for a, i in zip(self.obj.axes,key): + for a, i in zip(self.obj.axes, key): if not com.is_integer(i): - raise ValueError("iAt based indexing can only have integer indexers") + raise ValueError("iAt based indexing can only have integer " + "indexers") ckey.append(a[i]) return ckey # 32-bit floating point machine epsilon _eps = np.finfo('f4').eps -def _length_of_indexer(indexer,target=None): - """ return the length of a single non-tuple indexer which could be a slice """ + +def _length_of_indexer(indexer, target=None): + """return the length of a single non-tuple indexer which could be a slice + """ if target is not None and isinstance(indexer, slice): l = len(target) start = indexer.start @@ -1235,8 +1308,10 @@ def _length_of_indexer(indexer,target=None): return 1 raise AssertionError("cannot find the length of the indexer") + def _convert_to_index_sliceable(obj, key): - """ if we are index sliceable, then return my slicer, otherwise return None """ + """if we are index sliceable, then return my slicer, otherwise return None + """ idx = obj.index if isinstance(key, slice): return idx._convert_slice_indexer(key, typ='getitem') @@ -1256,6 +1331,7 @@ def _convert_to_index_sliceable(obj, key): return None + def _is_index_slice(obj): def _is_valid_index(x): return (com.is_integer(x) or com.is_float(x) @@ -1301,11 +1377,13 @@ def _setitem_with_indexer(self, indexer, value): # need to delegate to the super setter if isinstance(indexer, dict): - return super(_SeriesIndexer, self)._setitem_with_indexer(indexer, value) + return super(_SeriesIndexer, self)._setitem_with_indexer(indexer, + value) # fast access self.obj._set_values(indexer, value) + def _check_bool_indexer(ax, key): # boolean indexing, need to check that the data are aligned, otherwise # disallowed @@ -1344,14 +1422,18 @@ def _convert_missing_indexer(indexer): return indexer, False + def _convert_from_missing_indexer_tuple(indexer, axes): """ create a filtered indexer that doesn't have any missing indexers """ def get_indexer(_i, _idx): - return axes[_i].get_loc(_idx['key']) if isinstance(_idx,dict) else _idx - return tuple([ get_indexer(_i, _idx) for _i, _idx in enumerate(indexer) ]) + return (axes[_i].get_loc(_idx['key']) + if isinstance(_idx, dict) else _idx) + return tuple([get_indexer(_i, _idx) for _i, _idx in enumerate(indexer)]) + def _safe_append_to_index(index, key): - """ a safe append to an index, if incorrect type, then catch and recreate """ + """ a safe append to an index, if incorrect type, then catch and recreate + """ try: return index.insert(len(index), key) except: @@ -1359,23 +1441,26 @@ def _safe_append_to_index(index, key): # raise here as this is basically an unsafe operation and we want # it to be obvious that you are doing something wrong - raise ValueError("unsafe appending to index of " - "type {0} with a key {1}".format(index.__class__.__name__,key)) + raise ValueError("unsafe appending to index of type {0} with a key " + "{1}".format(index.__class__.__name__, key)) + def _maybe_convert_indices(indices, n): """ if we have negative indicies, translate to postive here - if have indicies that are out-of-bounds, raise an IndexError """ + if have indicies that are out-of-bounds, raise an IndexError + """ if isinstance(indices, list): indices = np.array(indices) - mask = indices<0 + mask = indices < 0 if mask.any(): indices[mask] += n - mask = (indices>=n) | (indices<0) + mask = (indices >= n) | (indices < 0) if mask.any(): raise IndexError("indices are out-of-bounds") return indices + def _maybe_convert_ix(*args): """ We likely want to take the cross-product @@ -1426,6 +1511,7 @@ def _check_slice_bounds(slobj, values): if stop < -l-1 or stop > l: raise IndexError("out-of-bounds on slice (end)") + def _maybe_droplevels(index, key): # drop levels original_index = index diff --git a/pandas/core/internals.py b/pandas/core/internals.py index c5e245d2e320c..bb719722fd090 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -8,8 +8,9 @@ from pandas.core.base import PandasObject from pandas.core.common import (_possibly_downcast_to_dtype, isnull, notnull, - _NS_DTYPE, _TD_DTYPE, ABCSeries, ABCSparseSeries, - is_list_like, _infer_dtype_from_scalar, _values_from_object) + _NS_DTYPE, _TD_DTYPE, ABCSeries, is_list_like, + ABCSparseSeries, _infer_dtype_from_scalar, + _values_from_object) from pandas.core.index import (Index, MultiIndex, _ensure_index, _handle_legacy_indexes) from pandas.core.indexing import (_check_slice_bounds, _maybe_convert_indices, @@ -25,6 +26,7 @@ from pandas.compat import range, lrange, lmap, callable, map, zip, u from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type + class Block(PandasObject): """ @@ -49,7 +51,8 @@ class Block(PandasObject): _verify_integrity = True _ftype = 'dense' - def __init__(self, values, items, ref_items, ndim=None, fastpath=False, placement=None): + def __init__(self, values, items, ref_items, ndim=None, fastpath=False, + placement=None): if ndim is None: ndim = values.ndim @@ -58,8 +61,8 @@ def __init__(self, values, items, ref_items, ndim=None, fastpath=False, placemen raise ValueError('Wrong number of dimensions') if len(items) != len(values): - raise ValueError('Wrong number of items passed %d, indices imply %d' - % (len(items), len(values))) + raise ValueError('Wrong number of items passed %d, indices imply ' + '%d' % (len(items), len(values))) self.set_ref_locs(placement) self.values = values @@ -100,10 +103,11 @@ def ref_locs(self): # this means that we have nan's in our block try: - indexer[indexer == -1] = np.arange(len(self.items))[isnull(self.items)] + indexer[indexer == -1] = np.arange( + len(self.items))[isnull(self.items)] except: - raise AssertionError('Some block items were not in block ' - 'ref_items') + raise AssertionError('Some block items were not in ' + 'block ref_items') self._ref_locs = indexer return self._ref_locs @@ -113,7 +117,9 @@ def reset_ref_locs(self): self._ref_locs = np.empty(len(self.items), dtype='int64') def set_ref_locs(self, placement): - """ explicity set the ref_locs indexer, only necessary for duplicate indicies """ + """ explicity set the ref_locs indexer, only necessary for duplicate + indicies + """ if placement is None: self._ref_locs = None else: @@ -195,7 +201,8 @@ def merge(self, other): # union_ref = self.ref_items + other.ref_items return _merge_blocks([self, other], self.ref_items) - def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, limit=None, mask_info=None): + def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, + limit=None, mask_info=None): """ Reindex using pre-computed indexer information """ @@ -206,11 +213,12 @@ def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, limit=None new_values = com.take_nd(self.values, indexer, axis, fill_value=fill_value, mask_info=mask_info) - return make_block( - new_values, self.items, self.ref_items, ndim=self.ndim, fastpath=True, - placement=self._ref_locs) + return make_block(new_values, self.items, self.ref_items, + ndim=self.ndim, fastpath=True, + placement=self._ref_locs) - def reindex_items_from(self, new_ref_items, indexer=None, method=None, fill_value=None, limit=None, copy=True): + def reindex_items_from(self, new_ref_items, indexer=None, method=None, + fill_value=None, limit=None, copy=True): """ Reindex to only those items contained in the input set of items @@ -222,7 +230,8 @@ def reindex_items_from(self, new_ref_items, indexer=None, method=None, fill_valu reindexed : Block """ if indexer is None: - new_ref_items, indexer = self.items.reindex(new_ref_items, limit=limit) + new_ref_items, indexer = self.items.reindex(new_ref_items, + limit=limit) needs_fill = method is not None and limit is None if fill_value is None: @@ -247,9 +256,11 @@ def reindex_items_from(self, new_ref_items, indexer=None, method=None, fill_valu # fill if needed if needs_fill: - new_values = com.interpolate_2d(new_values, method=method, limit=limit, fill_value=fill_value) + new_values = com.interpolate_2d(new_values, method=method, + limit=limit, fill_value=fill_value) - block = make_block(new_values, new_items, new_ref_items, ndim=self.ndim, fastpath=True) + block = make_block(new_values, new_items, new_ref_items, + ndim=self.ndim, fastpath=True) # down cast if needed if not self.is_float and (needs_fill or notnull(fill_value)): @@ -284,7 +295,8 @@ def delete(self, item): loc = self.items.get_loc(item) new_items = self.items.delete(loc) new_values = np.delete(self.values, loc, 0) - return make_block(new_values, new_items, self.ref_items, ndim=self.ndim, klass=self.__class__, fastpath=True) + return make_block(new_values, new_items, self.ref_items, + ndim=self.ndim, klass=self.__class__, fastpath=True) def split_block_at(self, item): """ @@ -344,7 +356,7 @@ def downcast(self, dtypes=None): # turn it off completely if dtypes is False: - return [ self ] + return [self] values = self.values @@ -356,14 +368,16 @@ def downcast(self, dtypes=None): dtypes = 'infer' nv = _possibly_downcast_to_dtype(values, dtypes) - return [ make_block(nv, self.items, self.ref_items, ndim=self.ndim, fastpath=True) ] + return [make_block(nv, self.items, self.ref_items, ndim=self.ndim, + fastpath=True)] # ndim > 1 if dtypes is None: - return [ self ] + return [self] if not (dtypes == 'infer' or isinstance(dtypes, dict)): - raise ValueError("downcast must have a dictionary or 'infer' as its argument") + raise ValueError("downcast must have a dictionary or 'infer' as " + "its argument") # item-by-item # this is expensive as it splits the blocks items-by-item @@ -376,12 +390,13 @@ def downcast(self, dtypes=None): dtype = dtypes.get(item, self._downcast_dtype) if dtype is None: - nv = _block_shape(values[i],ndim=self.ndim) + nv = _block_shape(values[i], ndim=self.ndim) else: nv = _possibly_downcast_to_dtype(values[i], dtype) - nv = _block_shape(nv,ndim=self.ndim) + nv = _block_shape(nv, ndim=self.ndim) - blocks.append(make_block(nv, Index([item]), self.ref_items, ndim=self.ndim, fastpath=True)) + blocks.append(make_block(nv, Index([item]), self.ref_items, + ndim=self.ndim, fastpath=True)) return blocks @@ -405,9 +420,9 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, # force the copy here if values is None: values = com._astype_nansafe(self.values, dtype, copy=True) - newb = make_block( - values, self.items, self.ref_items, ndim=self.ndim, placement=self._ref_locs, - fastpath=True, dtype=dtype, klass=klass) + newb = make_block(values, self.items, self.ref_items, + ndim=self.ndim, placement=self._ref_locs, + fastpath=True, dtype=dtype, klass=klass) except: if raise_on_error is True: raise @@ -418,15 +433,16 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, raise TypeError("cannot set astype for copy = [%s] for dtype " "(%s [%s]) with smaller itemsize that current " "(%s [%s])" % (copy, self.dtype.name, - self.itemsize, newb.dtype.name, newb.itemsize)) - return [ newb ] + self.itemsize, newb.dtype.name, + newb.itemsize)) + return [newb] def convert(self, copy=True, **kwargs): """ attempt to coerce any object types to better types return a copy of the block (if copy = True) by definition we are not an ObjectBlock here! """ - return [ self.copy() ] if copy else [ self ] + return [self.copy()] if copy else [self] def prepare_for_merge(self, **kwargs): """ a regular block is ok to merge as is """ @@ -445,8 +461,8 @@ def post_merge(self, items, **kwargs): # this is a safe bet with multiple dtypes dtype = list(dtypes)[0] if len(dtypes) == 1 else np.float64 - b = make_block( - SparseArray(self.get(item), dtype=dtype), [item], self.ref_items) + b = make_block(SparseArray(self.get(item), dtype=dtype), + [item], self.ref_items) new_blocks.append(b) return new_blocks @@ -470,18 +486,18 @@ def _try_cast_result(self, result, dtype=None): elif self.is_float and result.dtype == self.dtype: # protect against a bool/object showing up here - if isinstance(dtype,compat.string_types) and dtype == 'infer': + if isinstance(dtype, compat.string_types) and dtype == 'infer': return result - if not isinstance(dtype,type): + if not isinstance(dtype, type): dtype = dtype.type - if issubclass(dtype,(np.bool_,np.object_)): - if issubclass(dtype,np.bool_): + if issubclass(dtype, (np.bool_, np.object_)): + if issubclass(dtype, np.bool_): if isnull(result).all(): return result.astype(np.bool_) else: result = result.astype(np.object_) - result[result==1] = True - result[result==0] = False + result[result == 1] = True + result[result == 0] = False return result else: return result.astype(np.object_) @@ -524,9 +540,9 @@ def copy(self, deep=True, ref_items=None): values = values.copy() if ref_items is None: ref_items = self.ref_items - return make_block( - values, self.items, ref_items, ndim=self.ndim, klass=self.__class__, - fastpath=True, placement=self._ref_locs) + return make_block(values, self.items, ref_items, ndim=self.ndim, + klass=self.__class__, fastpath=True, + placement=self._ref_locs) def replace(self, to_replace, value, inplace=False, filter=None, regex=False): @@ -547,8 +563,12 @@ def replace(self, to_replace, value, inplace=False, filter=None, return self.putmask(mask, value, inplace=inplace) def setitem(self, indexer, value): - """ set the value inplace; return a new block (of a possibly different dtype) - indexer is a direct slice/positional indexer; value must be a compaitable shape """ + """ set the value inplace; return a new block (of a possibly different + dtype) + + indexer is a direct slice/positional indexer; value must be a + compatible shape + """ # coerce args values, value = self._try_coerce_args(self.values, value) @@ -567,15 +587,19 @@ def setitem(self, indexer, value): # boolean with truth values == len of the value is ok too if isinstance(indexer, (np.ndarray, list)): if is_list_like(value) and len(indexer) != len(value): - if not (isinstance(indexer, np.ndarray) and indexer.dtype == np.bool_ and len(indexer[indexer]) == len(value)): - raise ValueError("cannot set using a list-like indexer with a different length than the value") + if not (isinstance(indexer, np.ndarray) and + indexer.dtype == np.bool_ and + len(indexer[indexer]) == len(value)): + raise ValueError("cannot set using a list-like indexer " + "with a different length than the value") # slice elif isinstance(indexer, slice): if is_list_like(value) and l: if len(value) != _length_of_indexer(indexer, values): - raise ValueError("cannot set using a slice indexer with a different length than the value") + raise ValueError("cannot set using a slice indexer with a " + "different length than the value") try: # set and return a block @@ -583,22 +607,25 @@ def setitem(self, indexer, value): # coerce and try to infer the dtypes of the result if np.isscalar(value): - dtype,_ = _infer_dtype_from_scalar(value) + dtype, _ = _infer_dtype_from_scalar(value) else: dtype = 'infer' values = self._try_coerce_result(values) values = self._try_cast_result(values, dtype) - return [make_block(transf(values), self.items, self.ref_items, ndim=self.ndim, fastpath=True)] + return [make_block(transf(values), self.items, self.ref_items, + ndim=self.ndim, fastpath=True)] except (ValueError, TypeError) as detail: raise - except (Exception) as detail: + except Exception as detail: pass - return [ self ] + return [self] def putmask(self, mask, new, align=True, inplace=False): - """ putmask the data to the block; it is possible that we may create a new dtype of block - return the resulting block(s) + """ putmask the data to the block; it is possible that we may create a + new dtype of block + + return the resulting block(s) Parameters ---------- @@ -618,7 +645,8 @@ def putmask(self, mask, new, align=True, inplace=False): if hasattr(new, 'reindex_axis'): if align: axis = getattr(new, '_info_axis_number', 0) - new = new.reindex_axis(self.items, axis=axis, copy=False).values.T + new = new.reindex_axis(self.items, axis=axis, + copy=False).values.T else: new = new.values.T @@ -639,8 +667,8 @@ def putmask(self, mask, new, align=True, inplace=False): new = self._try_cast(new) # pseudo-broadcast - if isinstance(new,np.ndarray) and new.ndim == self.ndim-1: - new = np.repeat(new,self.shape[-1]).reshape(self.shape) + if isinstance(new, np.ndarray) and new.ndim == self.ndim-1: + new = np.repeat(new, self.shape[-1]).reshape(self.shape) np.putmask(new_values, mask, new) @@ -712,16 +740,16 @@ def create_block(v, m, n, item, reshape=True): new_blocks.append(block) else: - - new_blocks.append( - create_block(new_values, mask, new, self.items, reshape=False)) + new_blocks.append(create_block(new_values, mask, new, + self.items, reshape=False)) return new_blocks if inplace: return [self] - return [make_block(new_values, self.items, self.ref_items, placement=self._ref_locs, fastpath=True)] + return [make_block(new_values, self.items, self.ref_items, + placement=self._ref_locs, fastpath=True)] def interpolate(self, method='pad', axis=0, index=None, values=None, inplace=False, limit=None, @@ -761,7 +789,8 @@ def interpolate(self, method='pad', axis=0, index=None, raise ValueError("invalid method '{0}' to interpolate.".format(method)) def _interpolate_with_fill(self, method='pad', axis=0, inplace=False, - limit=None, fill_value=None, coerce=False, downcast=None): + limit=None, fill_value=None, coerce=False, + downcast=None): """ fillna but using the interpolate machinery """ # if we are coercing, then don't force the conversion @@ -779,7 +808,9 @@ def _interpolate_with_fill(self, method='pad', axis=0, inplace=False, values = com.interpolate_2d(values, method, axis, limit, fill_value) values = self._try_coerce_result(values) - blocks = [ make_block(values, self.items, self.ref_items, ndim=self.ndim, klass=self.__class__, fastpath=True) ] + blocks = [make_block(values, self.items, self.ref_items, + ndim=self.ndim, klass=self.__class__, + fastpath=True)] return self._maybe_downcast(blocks, downcast) def _interpolate(self, method=None, index=None, values=None, @@ -810,8 +841,8 @@ def func(x): # should the axis argument be handled below in apply_along_axis? # i.e. not an arg to com.interpolate_1d return com.interpolate_1d(index, x, method=method, limit=limit, - fill_value=fill_value, bounds_error=False, - **kwargs) + fill_value=fill_value, + bounds_error=False, **kwargs) # interp each column independently interp_values = np.apply_along_axis(func, axis, data) @@ -825,7 +856,8 @@ def take(self, indexer, ref_items, axis=1): raise AssertionError('axis must be at least 1, got %d' % axis) new_values = com.take_nd(self.values, indexer, axis=axis, allow_fill=False) - return [make_block(new_values, self.items, ref_items, ndim=self.ndim, klass=self.__class__, fastpath=True)] + return [make_block(new_values, self.items, ref_items, ndim=self.ndim, + klass=self.__class__, fastpath=True)] def get_values(self, dtype=None): return self.values @@ -836,7 +868,8 @@ def get_merge_length(self): def diff(self, n): """ return block for the diff of the values """ new_values = com.diff(self.values, n, axis=1) - return [make_block(new_values, self.items, self.ref_items, ndim=self.ndim, fastpath=True)] + return [make_block(new_values, self.items, self.ref_items, + ndim=self.ndim, fastpath=True)] def shift(self, indexer, periods, axis=0): """ shift the block by periods, possibly upcast """ @@ -859,7 +892,8 @@ def shift(self, indexer, periods, axis=0): new_values[:, :periods] = fill_value else: new_values[:, periods:] = fill_value - return [make_block(new_values, self.items, self.ref_items, ndim=self.ndim, fastpath=True)] + return [make_block(new_values, self.items, self.ref_items, + ndim=self.ndim, fastpath=True)] def eval(self, func, other, raise_on_error=True, try_cast=False): """ @@ -869,8 +903,8 @@ def eval(self, func, other, raise_on_error=True, try_cast=False): ---------- func : how to combine self, other other : a ndarray/object - raise_on_error : if True, raise when I can't perform the function, False by default (and just return - the data that we had coming in) + raise_on_error : if True, raise when I can't perform the function, + False by default (and just return the data that we had coming in) Returns ------- @@ -896,8 +930,9 @@ def eval(self, func, other, raise_on_error=True, try_cast=False): is_transposed = True else: # this is a broadcast error heree - raise ValueError("cannot broadcast shape [%s] with block values [%s]" - % (values.T.shape,other.shape)) + raise ValueError("cannot broadcast shape [%s] with block " + "values [%s]" % (values.T.shape, + other.shape)) transf = (lambda x: x.T) if is_transposed else (lambda x: x) @@ -925,21 +960,22 @@ def handle_error(): result = get_result(other) # if we have an invalid shape/broadcast error - # GH4576, so raise instead of allowing to pass thru - except (ValueError) as detail: + # GH4576, so raise instead of allowing to pass through + except ValueError as detail: raise - except (Exception) as detail: + except Exception as detail: result = handle_error() - # technically a broadcast error in numpy can 'work' by returning a boolean False + # technically a broadcast error in numpy can 'work' by returning a + # boolean False if not isinstance(result, np.ndarray): if not isinstance(result, np.ndarray): - # differentiate between an invalid ndarray-ndarray comparsion and - # an invalid type comparison + # differentiate between an invalid ndarray-ndarray comparison + # and an invalid type comparison if isinstance(values, np.ndarray) and is_list_like(other): - raise ValueError('Invalid broadcasting comparison [%s] with block values' - % repr(other)) + raise ValueError('Invalid broadcasting comparison [%s] ' + 'with block values' % repr(other)) raise TypeError('Could not compare [%s] with block values' % repr(other)) @@ -951,9 +987,11 @@ def handle_error(): if try_cast: result = self._try_cast_result(result) - return [make_block(result, self.items, self.ref_items, ndim=self.ndim, fastpath=True)] + return [make_block(result, self.items, self.ref_items, ndim=self.ndim, + fastpath=True)] - def where(self, other, cond, align=True, raise_on_error=True, try_cast=False): + def where(self, other, cond, align=True, raise_on_error=True, + try_cast=False): """ evaluate the block; return result block(s) from the result @@ -962,8 +1000,8 @@ def where(self, other, cond, align=True, raise_on_error=True, try_cast=False): other : a ndarray/object cond : the condition to respect align : boolean, perform alignment on other/cond - raise_on_error : if True, raise when I can't perform the function, False by default (and just return - the data that we had coming in) + raise_on_error : if True, raise when I can't perform the function, + False by default (and just return the data that we had coming in) Returns ------- @@ -976,7 +1014,8 @@ def where(self, other, cond, align=True, raise_on_error=True, try_cast=False): if hasattr(other, 'reindex_axis'): if align: axis = getattr(other, '_info_axis_number', 0) - other = other.reindex_axis(self.items, axis=axis, copy=True).values + other = other.reindex_axis(self.items, axis=axis, + copy=True).values else: other = other.values @@ -985,8 +1024,10 @@ def where(self, other, cond, align=True, raise_on_error=True, try_cast=False): if hasattr(other, 'ndim') and hasattr(values, 'ndim'): if values.ndim != other.ndim or values.shape == other.shape[::-1]: - # pseodo broadcast (its a 2d vs 1d say and where needs it in a specific direction) - if other.ndim >= 1 and values.ndim-1 == other.ndim and values.shape[0] != other.shape[0]: + # pseodo broadcast (its a 2d vs 1d say and where needs it in a + # specific direction) + if (other.ndim >= 1 and values.ndim-1 == other.ndim and + values.shape[0] != other.shape[0]): other = _block_shape(other).T else: values = values.T @@ -1016,11 +1057,13 @@ def func(c, v, o): v, o = self._try_coerce_args(v, o) try: - return self._try_coerce_result(expressions.where(c, v, o, raise_on_error=True)) - except (Exception) as detail: + return self._try_coerce_result( + expressions.where(c, v, o, raise_on_error=True) + ) + except Exception as detail: if raise_on_error: - raise TypeError('Could not operate [%s] with block values [%s]' - % (repr(o), str(detail))) + raise TypeError('Could not operate [%s] with block values ' + '[%s]' % (repr(o), str(detail))) else: # return the values result = np.empty(v.shape, dtype='float64') @@ -1043,7 +1086,8 @@ def func(c, v, o): if try_cast: result = self._try_cast_result(result) - return make_block(result, self.items, self.ref_items, ndim=self.ndim) + return make_block(result, self.items, self.ref_items, + ndim=self.ndim) # might need to separate out blocks axis = cond.ndim - 1 @@ -1076,7 +1120,8 @@ def _can_hold_element(self, element): if is_list_like(element): element = np.array(element) return issubclass(element.dtype.type, (np.floating, np.integer)) - return isinstance(element, (float, int, np.float_, np.int_)) and not isinstance(bool,np.bool_) + return (isinstance(element, (float, int, np.float_, np.int_)) and + not isinstance(bool, np.bool_)) def _try_cast(self, element): try: @@ -1084,7 +1129,8 @@ def _try_cast(self, element): except: # pragma: no cover return element - def to_native_types(self, slicer=None, na_rep='', float_format=None, **kwargs): + def to_native_types(self, slicer=None, na_rep='', float_format=None, + **kwargs): """ convert to our native types format, slicing if desired """ values = self.values @@ -1102,7 +1148,8 @@ def to_native_types(self, slicer=None, na_rep='', float_format=None, **kwargs): def should_store(self, value): # when inserting a column should not coerce integers to floats # unnecessarily - return issubclass(value.dtype.type, np.floating) and value.dtype == self.dtype + return (issubclass(value.dtype.type, np.floating) and + value.dtype == self.dtype) class ComplexBlock(NumericBlock): @@ -1176,7 +1223,7 @@ def masker(v): if isnull(other) or (np.isscalar(other) and other == tslib.iNaT): other = np.nan elif isinstance(other, np.timedelta64): - other = _coerce_scalar_to_timedelta_type(other,unit='s').item() + other = _coerce_scalar_to_timedelta_type(other, unit='s').item() if other == tslib.iNaT: other = np.nan else: @@ -1191,7 +1238,7 @@ def _try_operate(self, values): def _try_coerce_result(self, result): """ reverse of try_coerce_args / try_operate """ if isinstance(result, np.ndarray): - if result.dtype.kind in ['i','f','O']: + if result.dtype.kind in ['i', 'f', 'O']: result = result.astype('m8[ns]') elif isinstance(result, np.integer): result = np.timedelta64(result) @@ -1214,7 +1261,8 @@ def to_native_types(self, slicer=None, na_rep=None, **kwargs): rvalues[mask] = na_rep imask = (-mask).ravel() rvalues.flat[imask] = np.array([lib.repr_timedelta64(val) - for val in values.ravel()[imask]], dtype=object) + for val in values.ravel()[imask]], + dtype=object) return rvalues.tolist() @@ -1242,19 +1290,24 @@ class ObjectBlock(Block): is_object = True _can_hold_na = True - def __init__(self, values, items, ref_items, ndim=2, fastpath=False, placement=None): + def __init__(self, values, items, ref_items, ndim=2, fastpath=False, + placement=None): if issubclass(values.dtype.type, compat.string_types): values = np.array(values, dtype=object) - super(ObjectBlock, self).__init__(values, items, ref_items, - ndim=ndim, fastpath=fastpath, placement=placement) + super(ObjectBlock, self).__init__(values, items, ref_items, ndim=ndim, + fastpath=fastpath, + placement=placement) @property def is_bool(self): - """ we can be a bool if we have only bool values but are of type object """ + """ we can be a bool if we have only bool values but are of type + object + """ return lib.is_bool_array(self.values.ravel()) - def convert(self, convert_dates=True, convert_numeric=True, copy=True, by_item=True): + def convert(self, convert_dates=True, convert_numeric=True, copy=True, + by_item=True): """ attempt to coerce any object types to better types return a copy of the block (if copy = True) by definition we ARE an ObjectBlock!!!!! @@ -1271,20 +1324,24 @@ def convert(self, convert_dates=True, convert_numeric=True, copy=True, by_item=T values = self.iget(i) values = com._possibly_convert_objects( - values.ravel(), convert_dates=convert_dates, convert_numeric=convert_numeric).reshape(values.shape) + values.ravel(), convert_dates=convert_dates, + convert_numeric=convert_numeric + ).reshape(values.shape) values = _block_shape(values, ndim=self.ndim) items = self.items.take([i]) placement = None if is_unique else [i] - newb = make_block( - values, items, self.ref_items, ndim=self.ndim, placement=placement) + newb = make_block(values, items, self.ref_items, + ndim=self.ndim, placement=placement) blocks.append(newb) else: values = com._possibly_convert_objects( - self.values.ravel(), convert_dates=convert_dates, convert_numeric=convert_numeric).reshape(self.values.shape) - blocks.append( - make_block(values, self.items, self.ref_items, ndim=self.ndim)) + self.values.ravel(), convert_dates=convert_dates, + convert_numeric=convert_numeric + ).reshape(self.values.shape) + blocks.append(make_block(values, self.items, self.ref_items, + ndim=self.ndim)) return blocks @@ -1296,7 +1353,8 @@ def _maybe_downcast(self, blocks, downcast=None): # split and convert the blocks result_blocks = [] for blk in blocks: - result_blocks.extend(blk.convert(convert_dates=True,convert_numeric=False)) + result_blocks.extend(blk.convert(convert_dates=True, + convert_numeric=False)) return result_blocks def _can_hold_element(self, element): @@ -1376,7 +1434,8 @@ def _replace_single(self, to_replace, value, inplace=False, filter=None, # the superclass method -> to_replace is some kind of object result = super(ObjectBlock, self).replace(to_replace, value, inplace=inplace, - filter=filter, regex=regex) + filter=filter, + regex=regex) if not isinstance(result, list): result = [result] return result @@ -1417,18 +1476,22 @@ class DatetimeBlock(Block): is_datetime = True _can_hold_na = True - def __init__(self, values, items, ref_items, fastpath=False, placement=None, **kwargs): + def __init__(self, values, items, ref_items, fastpath=False, + placement=None, **kwargs): if values.dtype != _NS_DTYPE: values = tslib.cast_to_nanoseconds(values) super(DatetimeBlock, self).__init__(values, items, ref_items, - fastpath=True, placement=placement, **kwargs) + fastpath=True, placement=placement, + **kwargs) def _can_hold_element(self, element): if is_list_like(element): element = np.array(element) return element.dtype == _NS_DTYPE or element.dtype == np.int64 - return com.is_integer(element) or isinstance(element, datetime) or isnull(element) + return (com.is_integer(element) or + isinstance(element, datetime) or + isnull(element)) def _try_cast(self, element): try: @@ -1460,7 +1523,7 @@ def _try_coerce_result(self, result): if result.dtype == 'i8': result = tslib.array_to_datetime( result.astype(object).ravel()).reshape(result.shape) - elif result.dtype.kind in ['i','f','O']: + elif result.dtype.kind in ['i', 'f', 'O']: result = result.astype('M8[ns]') elif isinstance(result, (np.integer, np.datetime64)): result = lib.Timestamp(result) @@ -1477,11 +1540,12 @@ def fillna(self, value, inplace=False, downcast=None): values = self.values if inplace else self.values.copy() mask = com.isnull(self.values) value = self._try_fill(value) - np.putmask(values,mask,value) - return [self if inplace else make_block(values, self.items, - self.ref_items, fastpath=True)] + np.putmask(values, mask, value) + return [self if inplace else + make_block(values, self.items, self.ref_items, fastpath=True)] - def to_native_types(self, slicer=None, na_rep=None, date_format=None, **kwargs): + def to_native_types(self, slicer=None, na_rep=None, date_format=None, + **kwargs): """ convert to our native types format, slicing if desired """ values = self.values @@ -1515,7 +1579,8 @@ def astype(self, dtype, copy=False, raise_on_error=True): klass = None if np.dtype(dtype).type == np.object_: klass = ObjectBlock - return self._astype(dtype, copy=copy, raise_on_error=raise_on_error, klass=klass) + return self._astype(dtype, copy=copy, raise_on_error=raise_on_error, + klass=klass) def set(self, item, value): """ @@ -1535,7 +1600,8 @@ def set(self, item, value): def get_values(self, dtype=None): # return object dtype as Timestamps if dtype == object: - return lib.map_infer(self.values.ravel(), lib.Timestamp).reshape(self.values.shape) + return lib.map_infer(self.values.ravel(), lib.Timestamp)\ + .reshape(self.values.shape) return self.values @@ -1550,7 +1616,8 @@ class SparseBlock(Block): _verify_integrity = False _ftype = 'sparse' - def __init__(self, values, items, ref_items, ndim=None, fastpath=False, placement=None): + def __init__(self, values, items, ref_items, ndim=None, fastpath=False, + placement=None): # kludgetastic if ndim is not None: @@ -1600,8 +1667,9 @@ def sp_values(self): @sp_values.setter def sp_values(self, v): # reset the sparse values - self.values = SparseArray( - v, sparse_index=self.sp_index, kind=self.kind, dtype=v.dtype, fill_value=self.fill_value, copy=False) + self.values = SparseArray(v, sparse_index=self.sp_index, + kind=self.kind, dtype=v.dtype, + fill_value=self.fill_value, copy=False) @property def sp_index(self): @@ -1651,9 +1719,9 @@ def get_values(self, dtype=None): def get_merge_length(self): return 1 - def make_block( - self, values, items=None, ref_items=None, sparse_index=None, kind=None, dtype=None, fill_value=None, - copy=False, fastpath=True): + def make_block(self, values, items=None, ref_items=None, sparse_index=None, + kind=None, dtype=None, fill_value=None, copy=False, + fastpath=True): """ return a new block """ if dtype is None: dtype = self.dtype @@ -1664,8 +1732,10 @@ def make_block( if ref_items is None: ref_items = self.ref_items new_values = SparseArray(values, sparse_index=sparse_index, - kind=kind or self.kind, dtype=dtype, fill_value=fill_value, copy=copy) - return make_block(new_values, items, ref_items, ndim=self.ndim, fastpath=fastpath) + kind=kind or self.kind, dtype=dtype, + fill_value=fill_value, copy=copy) + return make_block(new_values, items, ref_items, ndim=self.ndim, + fastpath=fastpath) def interpolate(self, method='pad', axis=0, inplace=False, limit=None, fill_value=None, **kwargs): @@ -1679,7 +1749,7 @@ def fillna(self, value, inplace=False, downcast=None): if issubclass(self.dtype.type, np.floating): value = float(value) values = self.values if inplace else self.values.copy() - return [ self.make_block(values.get_values(value), fill_value=value) ] + return [self.make_block(values.get_values(value), fill_value=value)] def shift(self, indexer, periods, axis=0): """ shift the block by periods """ @@ -1692,7 +1762,7 @@ def shift(self, indexer, periods, axis=0): new_values[:periods] = fill_value else: new_values[periods:] = fill_value - return [ self.make_block(new_values) ] + return [self.make_block(new_values)] def take(self, indexer, ref_items, axis=1): """ going to take our items @@ -1700,9 +1770,10 @@ def take(self, indexer, ref_items, axis=1): if axis < 1: raise AssertionError('axis must be at least 1, got %d' % axis) - return [ self.make_block(self.values.take(indexer)) ] + return [self.make_block(self.values.take(indexer))] - def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, limit=None, mask_info=None): + def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, + limit=None, mask_info=None): """ Reindex using pre-computed indexer information """ @@ -1712,9 +1783,11 @@ def reindex_axis(self, indexer, method=None, axis=1, fill_value=None, limit=None # taking on the 0th axis always here if fill_value is None: fill_value = self.fill_value - return self.make_block(self.values.take(indexer), items=self.items, fill_value=fill_value) + return self.make_block(self.values.take(indexer), items=self.items, + fill_value=fill_value) - def reindex_items_from(self, new_ref_items, indexer=None, method=None, fill_value=None, limit=None, copy=True): + def reindex_items_from(self, new_ref_items, indexer=None, method=None, + fill_value=None, limit=None, copy=True): """ Reindex to only those items contained in the input set of items @@ -1728,7 +1801,8 @@ def reindex_items_from(self, new_ref_items, indexer=None, method=None, fill_valu # 1-d always if indexer is None: - new_ref_items, indexer = self.items.reindex(new_ref_items, limit=limit) + new_ref_items, indexer = self.items.reindex(new_ref_items, + limit=limit) if indexer is None: indexer = np.arange(len(self.items)) @@ -1751,9 +1825,11 @@ def reindex_items_from(self, new_ref_items, indexer=None, method=None, fill_valu if method is not None or limit is not None: if fill_value is None: fill_value = self.fill_value - new_values = com.interpolate_2d(new_values, method=method, limit=limit, fill_value=fill_value) + new_values = com.interpolate_2d(new_values, method=method, + limit=limit, fill_value=fill_value) - return self.make_block(new_values, items=new_items, ref_items=new_ref_items, copy=copy) + return self.make_block(new_values, items=new_items, + ref_items=new_ref_items, copy=copy) def sparse_reindex(self, new_index): """ sparse reindex and return a new block @@ -1772,8 +1848,8 @@ def _try_cast_result(self, result, dtype=None): return result -def make_block(values, items, ref_items, klass=None, ndim=None, dtype=None, fastpath=False, placement=None): - +def make_block(values, items, ref_items, klass=None, ndim=None, dtype=None, + fastpath=False, placement=None): if klass is None: dtype = dtype or values.dtype vtype = dtype.type @@ -1782,9 +1858,11 @@ def make_block(values, items, ref_items, klass=None, ndim=None, dtype=None, fast klass = SparseBlock elif issubclass(vtype, np.floating): klass = FloatBlock - elif issubclass(vtype, np.integer) and issubclass(vtype, np.timedelta64): + elif (issubclass(vtype, np.integer) and + issubclass(vtype, np.timedelta64)): klass = TimeDeltaBlock - elif issubclass(vtype, np.integer) and not issubclass(vtype, np.datetime64): + elif (issubclass(vtype, np.integer) and + not issubclass(vtype, np.datetime64)): klass = IntBlock elif dtype == np.bool_: klass = BoolBlock @@ -1799,10 +1877,10 @@ def make_block(values, items, ref_items, klass=None, ndim=None, dtype=None, fast if np.prod(values.shape): flat = values.ravel() inferred_type = lib.infer_dtype(flat) - if inferred_type in ['datetime','datetime64']: + if inferred_type in ['datetime', 'datetime64']: - # we have an object array that has been inferred as datetime, so - # convert it + # we have an object array that has been inferred as + # datetime, so convert it try: values = tslib.array_to_datetime( flat).reshape(values.shape) @@ -1814,7 +1892,9 @@ def make_block(values, items, ref_items, klass=None, ndim=None, dtype=None, fast if klass is None: klass = ObjectBlock - return klass(values, items, ref_items, ndim=ndim, fastpath=fastpath, placement=placement) + return klass(values, items, ref_items, ndim=ndim, fastpath=fastpath, + placement=placement) + # TODO: flexible with index=None and/or items=None @@ -1863,11 +1943,13 @@ def __init__(self, blocks, axes, do_integrity_check=True, fastpath=True): def make_empty(self, axes=None): """ return an empty BlockManager with the items axis of len 0 """ if axes is None: - axes = [_ensure_index([]) ] + [ _ensure_index(a) for a in self.axes[1:] ] + axes = [_ensure_index([])] + [ + _ensure_index(a) for a in self.axes[1:] + ] # preserve dtype if possible dtype = self.dtype if self.ndim == 1 else object - return self.__class__(np.array([],dtype=dtype), axes) + return self.__class__(np.array([], dtype=dtype), axes) def __nonzero__(self): return True @@ -1892,8 +1974,9 @@ def set_axis(self, axis, value, maybe_rename=True, check_axis=True): value = _ensure_index(value) if check_axis and len(value) != len(cur_axis): - raise ValueError('Length mismatch: Expected axis has %d elements, new values have %d elements' - % (len(cur_axis), len(value))) + raise ValueError('Length mismatch: Expected axis has %d elements, ' + 'new values have %d elements' % (len(cur_axis), + len(value))) self.axes[axis] = value self._shape = None @@ -1929,9 +2012,10 @@ def _reset_ref_locs(self): self._items_map = None def _rebuild_ref_locs(self): - """ take _ref_locs and set the individual block ref_locs, skipping Nones - no effect on a unique index """ - if getattr(self,'_ref_locs',None) is not None: + """Take _ref_locs and set the individual block ref_locs, skipping Nones + no effect on a unique index + """ + if getattr(self, '_ref_locs', None) is not None: item_count = 0 for v in self._ref_locs: if v is not None: @@ -1984,9 +2068,10 @@ def _set_ref_locs(self, labels=None, do_refs=False): try: rl = block.ref_locs except: - raise AssertionError("cannot create BlockManager._ref_locs because " - "block [%s] with duplicate items [%s] " - "does not have _ref_locs set" % (block, labels)) + raise AssertionError( + 'Cannot create BlockManager._ref_locs because ' + 'block [%s] with duplicate items [%s] does not ' + 'have _ref_locs set' % (block, labels)) m = maybe_create_block_in_items_map(im, block) for i, item in enumerate(block.items): @@ -2138,7 +2223,8 @@ def apply(self, f, *args, **kwargs): ---------- f : the callable or function name to operate on at the block level axes : optional (if not supplied, use self.axes) - filter : list, if supplied, only call the block if the filter is in the block + filter : list, if supplied, only call the block if the filter is in + the block """ axes = kwargs.pop('axes', None) @@ -2169,8 +2255,8 @@ def apply(self, f, *args, **kwargs): result_blocks.append(applied) if len(result_blocks) == 0: return self.make_empty(axes or self.axes) - bm = self.__class__( - result_blocks, axes or self.axes, do_integrity_check=do_integrity_check) + bm = self.__class__(result_blocks, axes or self.axes, + do_integrity_check=do_integrity_check) bm._consolidate_inplace() return bm @@ -2254,7 +2340,9 @@ def comp(s): return bm def prepare_for_merge(self, *args, **kwargs): - """ prepare for merging, return a new block manager with Sparse -> Dense """ + """ prepare for merging, return a new block manager with + Sparse -> Dense + """ self._consolidate_inplace() if self._has_sparse: return self.apply('prepare_for_merge', *args, **kwargs) @@ -2305,7 +2393,8 @@ def is_numeric_mixed_type(self): self._consolidate_inplace() return all([block.is_numeric for block in self.blocks]) - def get_block_map(self, copy=False, typ=None, columns=None, is_numeric=False, is_bool=False): + def get_block_map(self, copy=False, typ=None, columns=None, + is_numeric=False, is_bool=False): """ return a dictionary mapping the ftype -> block list Parameters @@ -2316,7 +2405,8 @@ def get_block_map(self, copy=False, typ=None, columns=None, is_numeric=False, is filter if the type is indicated """ # short circuit - mainly for merging - if typ == 'dict' and columns is None and not is_numeric and not is_bool and not copy: + if (typ == 'dict' and columns is None and not is_numeric and + not is_bool and not copy): bm = defaultdict(list) for b in self.blocks: bm[str(b.ftype)].append(b) @@ -2414,15 +2504,13 @@ def get_slice(self, slobj, axis=0, raise_on_error=False): new_items = new_axes[0] if len(self.blocks) == 1: blk = self.blocks[0] - newb = make_block(blk._slice(slobj), - new_items, - new_items, - klass=blk.__class__, - fastpath=True, + newb = make_block(blk._slice(slobj), new_items, new_items, + klass=blk.__class__, fastpath=True, placement=blk._ref_locs) new_blocks = [newb] else: - return self.reindex_items(new_items, indexer=np.arange(len(self.items))[slobj]) + return self.reindex_items( + new_items, indexer=np.arange(len(self.items))[slobj]) else: new_blocks = self._slice_blocks(slobj, axis) @@ -2477,7 +2565,7 @@ def copy(self, deep=True): else: new_axes = list(self.axes) return self.apply('copy', axes=new_axes, deep=deep, - ref_items=new_axes[0], do_integrity_check=False) + ref_items=new_axes[0], do_integrity_check=False) def as_matrix(self, items=None): if len(self.blocks) == 0: @@ -2947,7 +3035,7 @@ def _add_new_block(self, item, value, loc=None): # need to shift elements to the right if self._ref_locs[loc] is not None: - for i in reversed(lrange(loc+1,len(self._ref_locs))): + for i in reversed(lrange(loc+1, len(self._ref_locs))): self._ref_locs[i] = self._ref_locs[i-1] self._ref_locs[loc] = (new_block, 0) @@ -2966,7 +3054,8 @@ def _check_have(self, item): if item not in self.items: raise KeyError('no item named %s' % com.pprint_thing(item)) - def reindex_axis(self, new_axis, indexer=None, method=None, axis=0, fill_value=None, limit=None, copy=True): + def reindex_axis(self, new_axis, indexer=None, method=None, axis=0, + fill_value=None, limit=None, copy=True): new_axis = _ensure_index(new_axis) cur_axis = self.axes[axis] @@ -2987,19 +3076,25 @@ def reindex_axis(self, new_axis, indexer=None, method=None, axis=0, fill_value=N if axis == 0: if method is not None or limit is not None: - return self.reindex_axis0_with_method(new_axis, indexer=indexer, - method=method, fill_value=fill_value, limit=limit, copy=copy) - return self.reindex_items(new_axis, indexer=indexer, copy=copy, fill_value=fill_value) + return self.reindex_axis0_with_method( + new_axis, indexer=indexer, method=method, + fill_value=fill_value, limit=limit, copy=copy + ) + return self.reindex_items(new_axis, indexer=indexer, copy=copy, + fill_value=fill_value) new_axis, indexer = cur_axis.reindex( new_axis, method, copy_if_needed=True) - return self.reindex_indexer(new_axis, indexer, axis=axis, fill_value=fill_value) + return self.reindex_indexer(new_axis, indexer, axis=axis, + fill_value=fill_value) - def reindex_axis0_with_method(self, new_axis, indexer=None, method=None, fill_value=None, limit=None, copy=True): + def reindex_axis0_with_method(self, new_axis, indexer=None, method=None, + fill_value=None, limit=None, copy=True): raise AssertionError('method argument not supported for ' 'axis == 0') - def reindex_indexer(self, new_axis, indexer, axis=1, fill_value=None, allow_dups=False): + def reindex_indexer(self, new_axis, indexer, axis=1, fill_value=None, + allow_dups=False): """ pandas-indexer with -1's only. """ @@ -3063,7 +3158,8 @@ def _reindex_indexer_items(self, new_items, indexer, fill_value): return self.__class__(new_blocks, new_axes) - def reindex_items(self, new_items, indexer=None, copy=True, fill_value=None): + def reindex_items(self, new_items, indexer=None, copy=True, + fill_value=None): """ """ @@ -3071,10 +3167,12 @@ def reindex_items(self, new_items, indexer=None, copy=True, fill_value=None): data = self if not data.is_consolidated(): data = data.consolidate() - return data.reindex_items(new_items, copy=copy, fill_value=fill_value) + return data.reindex_items(new_items, copy=copy, + fill_value=fill_value) if indexer is None: - new_items, indexer = self.items.reindex(new_items, copy_if_needed=True) + new_items, indexer = self.items.reindex(new_items, + copy_if_needed=True) new_axes = [new_items] + self.axes[1:] # could have so me pathological (MultiIndex) issues here @@ -3103,12 +3201,9 @@ def reindex_items(self, new_items, indexer=None, copy=True, fill_value=None): for i, idx in enumerate(indexer): blk, lidx = rl[idx] item = new_items.take([i]) - blk = make_block(_block_shape(blk.iget(lidx)), - item, - new_items, - ndim=self.ndim, - fastpath=True, - placement = [i]) + blk = make_block(_block_shape(blk.iget(lidx)), item, + new_items, ndim=self.ndim, fastpath=True, + placement=[i]) new_blocks.append(blk) # add a na block if we are missing items @@ -3122,7 +3217,8 @@ def reindex_items(self, new_items, indexer=None, copy=True, fill_value=None): return self.__class__(new_blocks, new_axes) - def _make_na_block(self, items, ref_items, placement=None, fill_value=None): + def _make_na_block(self, items, ref_items, placement=None, + fill_value=None): # TODO: infer dtypes other than float64 from fill_value if fill_value is None: @@ -3157,7 +3253,8 @@ def take(self, indexer, new_index=None, axis=1, verify=True): new_index = self.axes[axis].take(indexer) new_axes[axis] = new_index - return self.apply('take', axes=new_axes, indexer=indexer, ref_items=new_axes[0], axis=axis) + return self.apply('take', axes=new_axes, indexer=indexer, + ref_items=new_axes[0], axis=axis) def merge(self, other, lsuffix=None, rsuffix=None): if not self._is_indexed_like(other): @@ -3220,7 +3317,8 @@ def rename_axis(self, mapper, axis=1): index = self.axes[axis] if isinstance(index, MultiIndex): new_axis = MultiIndex.from_tuples( - [tuple(mapper(y) for y in x) for x in index], names=index.names) + [tuple(mapper(y) for y in x) for x in index], + names=index.names) else: new_axis = Index([mapper(x) for x in index], name=index.name) @@ -3307,8 +3405,8 @@ def __init__(self, block, axis, do_integrity_check=False, fastpath=True): self.axes = [axis] if isinstance(block, list): if len(block) != 1: - raise ValueError( - "cannot create SingleBlockManager with more than 1 block") + raise ValueError('Cannot create SingleBlockManager with ' + 'more than 1 block') block = block[0] if not isinstance(block, Block): block = make_block(block, axis, axis, ndim=1, fastpath=True) @@ -3327,8 +3425,8 @@ def __init__(self, block, axis, do_integrity_check=False, fastpath=True): block = _consolidate(block, axis) if len(block) != 1: - raise ValueError( - "cannot create SingleBlockManager with more than 1 block") + raise ValueError('Cannot create SingleBlockManager with ' + 'more than 1 block') block = block[0] if not isinstance(block, Block): @@ -3349,39 +3447,46 @@ def shape(self): self._shape = tuple([len(self.axes[0])]) return self._shape - def reindex(self, new_axis, indexer=None, method=None, fill_value=None, limit=None, copy=True): - + def reindex(self, new_axis, indexer=None, method=None, fill_value=None, + limit=None, copy=True): # if we are the same and don't copy, just return if not copy and self.index.equals(new_axis): return self - block = self._block.reindex_items_from(new_axis, indexer=indexer, method=method, - fill_value=fill_value, limit=limit, copy=copy) + block = self._block.reindex_items_from(new_axis, indexer=indexer, + method=method, + fill_value=fill_value, + limit=limit, copy=copy) mgr = SingleBlockManager(block, new_axis) mgr._consolidate_inplace() return mgr def _reindex_indexer_items(self, new_items, indexer, fill_value): # equiv to a reindex - return self.reindex(new_items, indexer=indexer, fill_value=fill_value, copy=False) + return self.reindex(new_items, indexer=indexer, fill_value=fill_value, + copy=False) - def reindex_axis0_with_method(self, new_axis, indexer=None, method=None, fill_value=None, limit=None, copy=True): + def reindex_axis0_with_method(self, new_axis, indexer=None, method=None, + fill_value=None, limit=None, copy=True): if method is None: indexer = None - return self.reindex(new_axis, indexer=indexer, method=method, fill_value=fill_value, limit=limit, copy=copy) + return self.reindex(new_axis, indexer=indexer, method=method, + fill_value=fill_value, limit=limit, copy=copy) def get_slice(self, slobj, raise_on_error=False): if raise_on_error: _check_slice_bounds(slobj, self.index) - return self.__class__(self._block._slice(slobj), self.index._getitem_slice(slobj), fastpath=True) + return self.__class__(self._block._slice(slobj), + self.index._getitem_slice(slobj), fastpath=True) def set_axis(self, axis, value): cur_axis = self.axes[axis] value = _ensure_index(value) if len(value) != len(cur_axis): - raise ValueError('Length mismatch: Expected axis has %d elements, new values have %d elements' - % (len(cur_axis), len(value))) + raise ValueError('Length mismatch: Expected axis has %d elements, ' + 'new values have %d elements' % (len(cur_axis), + len(value))) self.axes[axis] = value self._shape = None @@ -3575,7 +3680,9 @@ def form_blocks(arrays, names, axes): def _simple_blockify(tuples, ref_items, dtype, is_unique=True): - """ return a single array of a block that has a single dtype; if dtype is not None, coerce to this dtype """ + """ return a single array of a block that has a single dtype; if dtype is + not None, coerce to this dtype + """ block_items, values, placement = _stack_arrays(tuples, ref_items, dtype) # CHECK DTYPE? @@ -3608,7 +3715,9 @@ def _multi_blockify(tuples, ref_items, dtype=None, is_unique=True): def _sparse_blockify(tuples, ref_items, dtype=None): - """ return an array of blocks that potentially have different dtypes (and are sparse) """ + """ return an array of blocks that potentially have different dtypes (and + are sparse) + """ new_blocks = [] for i, names, array in tuples: @@ -3748,8 +3857,8 @@ def _consolidate(blocks, items): new_blocks = [] for (_can_consolidate, dtype), group_blocks in grouper: - merged_blocks = _merge_blocks( - list(group_blocks), items, dtype=dtype, _can_consolidate=_can_consolidate) + merged_blocks = _merge_blocks(list(group_blocks), items, dtype=dtype, + _can_consolidate=_can_consolidate) if isinstance(merged_blocks, list): new_blocks.extend(merged_blocks) else: @@ -3810,6 +3919,6 @@ def _vstack(to_stack, dtype): def _possibly_convert_to_indexer(loc): if com._is_bool_indexer(loc): loc = [i for i, v in enumerate(loc) if v] - elif isinstance(loc,slice): - loc = lrange(loc.start,loc.stop) + elif isinstance(loc, slice): + loc = lrange(loc.start, loc.stop) return loc diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 45e6a54721bd2..b6ebeb7f96489 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -105,6 +105,7 @@ def _has_infs(result): return False return np.isinf(result) or np.isneginf(result) + def _get_fill_value(dtype, fill_value=None, fill_value_typ=None): """ return the correct fill value for the dtype of the values """ if fill_value is not None: @@ -127,7 +128,9 @@ def _get_fill_value(dtype, fill_value=None, fill_value_typ=None): else: return tslib.iNaT -def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=False, copy=True): + +def _get_values(values, skipna, fill_value=None, fill_value_typ=None, + isfinite=False, copy=True): """ utility to get the values view, mask, dtype if necessary copy and mask using the specified fill_value copy = True will force the copy """ @@ -137,11 +140,13 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=F else: mask = isnull(values) - dtype = values.dtype + dtype = values.dtype dtype_ok = _na_ok_dtype(dtype) - # get our fill value (in case we need to provide an alternative dtype for it) - fill_value = _get_fill_value(dtype, fill_value=fill_value, fill_value_typ=fill_value_typ) + # get our fill value (in case we need to provide an alternative + # dtype for it) + fill_value = _get_fill_value(dtype, fill_value=fill_value, + fill_value_typ=fill_value_typ) if skipna: if copy: @@ -151,7 +156,8 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=F # promote if needed else: - values, changed = com._maybe_upcast_putmask(values, mask, fill_value) + values, changed = com._maybe_upcast_putmask(values, mask, + fill_value) elif copy: values = values.copy() @@ -159,20 +165,25 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=F values = _view_if_needed(values) return values, mask, dtype + def _isfinite(values): - if issubclass(values.dtype.type, (np.timedelta64,np.datetime64)): + if issubclass(values.dtype.type, (np.timedelta64, np.datetime64)): return isnull(values) return -np.isfinite(values) + def _na_ok_dtype(dtype): - return not issubclass(dtype.type, (np.integer, np.datetime64, np.timedelta64)) + return not issubclass(dtype.type, (np.integer, np.datetime64, + np.timedelta64)) + def _view_if_needed(values): - if issubclass(values.dtype.type, (np.datetime64,np.timedelta64)): + if issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): return values.view(np.int64) return values -def _wrap_results(result,dtype): + +def _wrap_results(result, dtype): """ wrap our results if needed """ if issubclass(dtype.type, np.datetime64): @@ -185,27 +196,30 @@ def _wrap_results(result,dtype): # this is a scalar timedelta result! # we have series convert then take the element (scalar) - # as series will do the right thing in py3 (and deal with numpy 1.6.2 - # bug in that it results dtype of timedelta64[us] + # as series will do the right thing in py3 (and deal with numpy + # 1.6.2 bug in that it results dtype of timedelta64[us] from pandas import Series # coerce float to results if is_float(result): result = int(result) - result = Series([result],dtype='timedelta64[ns]') + result = Series([result], dtype='timedelta64[ns]') else: result = result.view(dtype) return result + def nanany(values, axis=None, skipna=True): values, mask, dtype = _get_values(values, skipna, False, copy=skipna) return values.any(axis) + def nanall(values, axis=None, skipna=True): values, mask, dtype = _get_values(values, skipna, True, copy=skipna) return values.all(axis) + @disallow('M8') @bottleneck_switch(zero_value=0) def nansum(values, axis=None, skipna=True): @@ -214,6 +228,7 @@ def nansum(values, axis=None, skipna=True): the_sum = _maybe_null_out(the_sum, axis, mask) return the_sum + @disallow('M8') @bottleneck_switch() def nanmean(values, axis=None, skipna=True): @@ -229,7 +244,8 @@ def nanmean(values, axis=None, skipna=True): else: the_mean = the_sum / count if count > 0 else np.nan - return _wrap_results(the_mean,dtype) + return _wrap_results(the_mean, dtype) + @disallow('M8') @bottleneck_switch() @@ -265,7 +281,7 @@ def get_median(x): return ret # otherwise return a scalar value - return _wrap_results(get_median(values),dtype) if notempty else np.nan + return _wrap_results(get_median(values), dtype) if notempty else np.nan @disallow('M8') @@ -292,7 +308,7 @@ def nanvar(values, axis=None, skipna=True, ddof=1): @bottleneck_switch() def nanmin(values, axis=None, skipna=True): - values, mask, dtype = _get_values(values, skipna, fill_value_typ = '+inf') + values, mask, dtype = _get_values(values, skipna, fill_value_typ='+inf') # numpy 1.6.1 workaround in Python 3.x if (values.dtype == np.object_ and compat.PY3): @@ -315,13 +331,13 @@ def nanmin(values, axis=None, skipna=True): else: result = values.min(axis) - result = _wrap_results(result,dtype) + result = _wrap_results(result, dtype) return _maybe_null_out(result, axis, mask) @bottleneck_switch() def nanmax(values, axis=None, skipna=True): - values, mask, dtype = _get_values(values, skipna, fill_value_typ ='-inf') + values, mask, dtype = _get_values(values, skipna, fill_value_typ='-inf') # numpy 1.6.1 workaround in Python 3.x if (values.dtype == np.object_ and compat.PY3): @@ -345,7 +361,7 @@ def nanmax(values, axis=None, skipna=True): else: result = values.max(axis) - result = _wrap_results(result,dtype) + result = _wrap_results(result, dtype) return _maybe_null_out(result, axis, mask) @@ -353,7 +369,8 @@ def nanargmax(values, axis=None, skipna=True): """ Returns -1 in the NA case """ - values, mask, dtype = _get_values(values, skipna, fill_value_typ = '-inf', isfinite=True) + values, mask, dtype = _get_values(values, skipna, fill_value_typ='-inf', + isfinite=True) result = values.argmax(axis) result = _maybe_arg_null_out(result, axis, mask, skipna) return result @@ -363,7 +380,8 @@ def nanargmin(values, axis=None, skipna=True): """ Returns -1 in the NA case """ - values, mask, dtype = _get_values(values, skipna, fill_value_typ = '+inf', isfinite=True) + values, mask, dtype = _get_values(values, skipna, fill_value_typ='+inf', + isfinite=True) result = values.argmin(axis) result = _maybe_arg_null_out(result, axis, mask, skipna) return result diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 249468c332e0c..0836ac7bc22a6 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -317,13 +317,14 @@ def _convert_to_array(self, values, name=None, other=None): if inferred_type in ('datetime64', 'datetime', 'date', 'time'): # if we have a other of timedelta, but use pd.NaT here we # we are in the wrong path - if other is not None and other.dtype == 'timedelta64[ns]' and all(isnull(v) for v in values): - values = np.empty(values.shape,dtype=other.dtype) + if (other is not None and other.dtype == 'timedelta64[ns]' and + all(isnull(v) for v in values)): + values = np.empty(values.shape, dtype=other.dtype) values[:] = tslib.iNaT # a datetlike elif not (isinstance(values, (pa.Array, pd.Series)) and - com.is_datetime64_dtype(values)): + com.is_datetime64_dtype(values)): values = tslib.array_to_datetime(values) elif isinstance(values, pd.DatetimeIndex): values = values.to_series() @@ -353,11 +354,12 @@ def _convert_to_array(self, values, name=None, other=None): # all nan, so ok, use the other dtype (e.g. timedelta or datetime) if isnull(values).all(): - values = np.empty(values.shape,dtype=other.dtype) + values = np.empty(values.shape, dtype=other.dtype) values[:] = tslib.iNaT else: - raise TypeError("incompatible type [{0}] for a datetime/timedelta" - " operation".format(pa.array(values).dtype)) + raise TypeError( + 'incompatible type [{0}] for a datetime/timedelta ' + 'operation'.format(pa.array(values).dtype)) else: raise TypeError("incompatible type [{0}] for a datetime/timedelta" " operation".format(pa.array(values).dtype)) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 885ec2714c47a..c695dc44dbdb5 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -3,13 +3,13 @@ """ # pylint: disable=E1103,W0231,W0212,W0621 from __future__ import division -from pandas.compat import map, zip, range, lrange, lmap, u, OrderedDict, OrderedDefaultdict +from pandas.compat import (map, zip, range, lrange, lmap, u, OrderedDict, + OrderedDefaultdict) from pandas import compat import sys import numpy as np -from pandas.core.common import (PandasError, - _try_sort, _default_index, _infer_dtype_from_scalar, - notnull) +from pandas.core.common import (PandasError, _try_sort, _default_index, + _infer_dtype_from_scalar, notnull) from pandas.core.categorical import Categorical from pandas.core.index import (Index, MultiIndex, _ensure_index, _get_combined_index) @@ -100,8 +100,6 @@ def panel_index(time, panels, names=['time', 'panel']): verify_integrity=False) - - class Panel(NDFrame): """ @@ -130,9 +128,8 @@ def _constructor(self): def __init__(self, data=None, items=None, major_axis=None, minor_axis=None, copy=False, dtype=None): - self._init_data( - data=data, items=items, major_axis=major_axis, minor_axis=minor_axis, - copy=copy, dtype=dtype) + self._init_data(data=data, items=items, major_axis=major_axis, + minor_axis=minor_axis, copy=copy, dtype=dtype) def _init_data(self, data, copy, dtype, **kwargs): """ @@ -327,8 +324,8 @@ def axis_pretty(a): v = getattr(self, a) if len(v) > 0: return u('%s axis: %s to %s') % (a.capitalize(), - com.pprint_thing(v[0]), - com.pprint_thing(v[-1])) + com.pprint_thing(v[0]), + com.pprint_thing(v[-1])) else: return u('%s axis: None') % a.capitalize() @@ -535,9 +532,9 @@ def __setitem__(self, key, value): mat = value.values elif isinstance(value, np.ndarray): if value.shape != shape[1:]: - raise ValueError('shape of value must be {0}, shape of given ' - 'object was {1}'.format(shape[1:], - tuple(map(int, value.shape)))) + raise ValueError( + 'shape of value must be {0}, shape of given object was ' + '{1}'.format(shape[1:], tuple(map(int, value.shape)))) mat = np.asarray(value) elif np.isscalar(value): dtype, value = _infer_dtype_from_scalar(value) @@ -589,7 +586,10 @@ def tail(self, n=5): def _needs_reindex_multi(self, axes, method, level): # only allowing multi-index on Panel (and not > dims) - return method is None and not self._is_mixed_type and self._AXIS_LEN <= 3 and com._count_not_none(*axes.values()) == 3 + return (method is None and + not self._is_mixed_type and + self._AXIS_LEN <= 3 and + com._count_not_none(*axes.values()) == 3) def _reindex_multi(self, axes, copy, fill_value): """ we are guaranteed non-Nones in the axes! """ @@ -780,13 +780,13 @@ def _ixs(self, i, axis=0): # xs cannot handle a non-scalar key, so just reindex here if _is_list_like(key): - indexer = { self._get_axis_name(axis): key } + indexer = {self._get_axis_name(axis): key} return self.reindex(**indexer) # a reduction if axis == 0: values = self._data.iget(i) - return self._box_item_values(key,values) + return self._box_item_values(key, values) # xs by position self._consolidate_inplace() @@ -904,11 +904,11 @@ def _construct_return_type(self, result, axes=None, **kwargs): elif self.ndim == ndim + 1: if axes is None: return self._constructor_sliced(result) - return self._constructor_sliced(result, - **self._extract_axes_for_slice(self, axes)) + return self._constructor_sliced( + result, **self._extract_axes_for_slice(self, axes)) - raise PandasError("invalid _construct_return_type [self->%s] [result->%s]" % - (self.ndim, result.ndim)) + raise PandasError('invalid _construct_return_type [self->%s] ' + '[result->%s]' % (self.ndim, result.ndim)) def _wrap_result(self, result, axis): axis = self._get_axis_name(axis) @@ -920,15 +920,19 @@ def _wrap_result(self, result, axis): @Appender(_shared_docs['reindex'] % _shared_doc_kwargs) def reindex(self, items=None, major_axis=None, minor_axis=None, **kwargs): - major_axis = major_axis if major_axis is not None else kwargs.pop('major', None) - minor_axis = minor_axis if minor_axis is not None else kwargs.pop('minor', None) + major_axis = (major_axis if major_axis is not None + else kwargs.pop('major', None)) + minor_axis = (minor_axis if minor_axis is not None + else kwargs.pop('minor', None)) return super(Panel, self).reindex(items=items, major_axis=major_axis, minor_axis=minor_axis, **kwargs) @Appender(_shared_docs['rename'] % _shared_doc_kwargs) def rename(self, items=None, major_axis=None, minor_axis=None, **kwargs): - major_axis = major_axis if major_axis is not None else kwargs.pop('major', None) - minor_axis = minor_axis if minor_axis is not None else kwargs.pop('minor', None) + major_axis = (major_axis if major_axis is not None + else kwargs.pop('major', None)) + minor_axis = (minor_axis if minor_axis is not None + else kwargs.pop('minor', None)) return super(Panel, self).rename(items=items, major_axis=major_axis, minor_axis=minor_axis, **kwargs) @@ -939,6 +943,7 @@ def reindex_axis(self, labels, axis=0, method=None, level=None, copy=True, method=method, level=level, copy=copy, limit=limit, fill_value=fill_value) + @Appender(_shared_docs['transpose'] % _shared_doc_kwargs) def transpose(self, *args, **kwargs): return super(Panel, self).transpose(*args, **kwargs) @@ -1225,11 +1230,11 @@ def _add_aggregate_operations(cls, use_numexpr=True): # doc strings substitors _agg_doc = """ -Wrapper method for %s +Wrapper method for %%s Parameters ---------- -other : """ + "%s or %s" % (cls._constructor_sliced.__name__, cls.__name__) + """ +other : %s or %s""" % (cls._constructor_sliced.__name__, cls.__name__) + """ axis : {""" + ', '.join(cls._AXIS_ORDERS) + "}" + """ Axis to broadcast over @@ -1237,19 +1242,22 @@ def _add_aggregate_operations(cls, use_numexpr=True): ------- """ + cls.__name__ + "\n" - def _panel_arith_method(op, name, str_rep = None, default_axis=None, + def _panel_arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None, **eval_kwargs): def na_op(x, y): try: - result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) + result = expressions.evaluate(op, str_rep, x, y, + raise_on_error=True, + **eval_kwargs) except TypeError: result = op(x, y) - # handles discrepancy between numpy and numexpr on division/mod by 0 - # though, given that these are generally (always?) non-scalars, I'm - # not sure whether it's worth it at the moment - result = com._fill_zeros(result,y,fill_zeros) + # handles discrepancy between numpy and numexpr on division/mod + # by 0 though, given that these are generally (always?) + # non-scalars, I'm not sure whether it's worth it at the moment + result = com._fill_zeros(result, y, fill_zeros) return result + @Substitution(name) @Appender(_agg_doc) def f(self, other, axis=0): @@ -1258,9 +1266,9 @@ def f(self, other, axis=0): return f # add `div`, `mul`, `pow`, etc.. - ops.add_flex_arithmetic_methods(cls, _panel_arith_method, - use_numexpr=use_numexpr, - flex_comp_method=ops._comp_method_PANEL) + ops.add_flex_arithmetic_methods( + cls, _panel_arith_method, use_numexpr=use_numexpr, + flex_comp_method=ops._comp_method_PANEL) Panel._setup_axes(axes=['items', 'major_axis', 'minor_axis'], info_axis=0, @@ -1276,5 +1284,3 @@ def f(self, other, axis=0): WidePanel = Panel LongPanel = DataFrame - - diff --git a/pandas/core/panel4d.py b/pandas/core/panel4d.py index 5679506cc6bb8..3d480464388c8 100644 --- a/pandas/core/panel4d.py +++ b/pandas/core/panel4d.py @@ -5,15 +5,14 @@ Panel4D = create_nd_panel_factory( klass_name='Panel4D', - orders =['labels', 'items', 'major_axis', 'minor_axis'], - slices ={'labels': 'labels', 'items': 'items', - 'major_axis': 'major_axis', - 'minor_axis': 'minor_axis'}, + orders=['labels', 'items', 'major_axis', 'minor_axis'], + slices={'labels': 'labels', 'items': 'items', 'major_axis': 'major_axis', + 'minor_axis': 'minor_axis'}, slicer=Panel, - aliases ={'major': 'major_axis', 'minor': 'minor_axis'}, + aliases={'major': 'major_axis', 'minor': 'minor_axis'}, stat_axis=2, - ns=dict(__doc__= """ - Represents a 4 dimensonal structured + ns=dict(__doc__=""" + Represents a 4 dimensional structured Parameters ---------- @@ -28,10 +27,9 @@ Data type to force, otherwise infer copy : boolean, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input - """ + """) +) - ) - ) def panel4d_init(self, data=None, labels=None, items=None, major_axis=None, minor_axis=None, copy=False, dtype=None): diff --git a/pandas/core/panelnd.py b/pandas/core/panelnd.py index 9ccce1edc9067..8ac84c0d91adc 100644 --- a/pandas/core/panelnd.py +++ b/pandas/core/panelnd.py @@ -5,27 +5,24 @@ import pandas.compat as compat - -def create_nd_panel_factory(klass_name, orders, slices, slicer, aliases=None, stat_axis=2, info_axis=0, ns=None): +def create_nd_panel_factory(klass_name, orders, slices, slicer, aliases=None, + stat_axis=2, info_axis=0, ns=None): """ manufacture a n-d class: - parameters + Parameters ---------- klass_name : the klass name - orders : the names of the axes in order (highest to lowest) - slices : a dictionary that defines how the axes map to the sliced axis - slicer : the class representing a slice of this panel - aliases : a dictionary defining aliases for various axes - default = { major : major_axis, minor : minor_axis } - stat_axis : the default statistic axis - default = 2 - info_axis : the info axis - - - returns + orders : the names of the axes in order (highest to lowest) + slices : a dictionary that defines how the axes map to the slice axis + slicer : the class representing a slice of this panel + aliases : a dictionary defining aliases for various axes + default = { major : major_axis, minor : minor_axis } + stat_axis : the default statistic axis default = 2 + info_axis : the info axis + + Returns ------- - a class object reprsenting this panel - + a class object representing this panel """ @@ -42,11 +39,8 @@ def create_nd_panel_factory(klass_name, orders, slices, slicer, aliases=None, st klass = type(klass_name, (slicer,), ns) # setup the axes - klass._setup_axes(axes = orders, - info_axis = info_axis, - stat_axis = stat_axis, - aliases = aliases, - slicers = slices) + klass._setup_axes(axes=orders, info_axis=info_axis, stat_axis=stat_axis, + aliases=aliases, slicers=slices) klass._constructor_sliced = slicer @@ -101,7 +95,8 @@ def _combine_with_constructor(self, other, func): klass._combine_with_constructor = _combine_with_constructor # set as NonImplemented operations which we don't support - for f in ['to_frame', 'to_excel', 'to_sparse', 'groupby', 'join', 'filter', 'dropna', 'shift']: + for f in ['to_frame', 'to_excel', 'to_sparse', 'groupby', 'join', 'filter', + 'dropna', 'shift']: def func(self, *args, **kwargs): raise NotImplementedError setattr(klass, f, func) diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py index c2c1a2931d4aa..24a4797759dab 100644 --- a/pandas/core/reshape.py +++ b/pandas/core/reshape.py @@ -71,12 +71,14 @@ def __init__(self, values, index, level=-1, value_columns=None): levels = index.levels labels = index.labels - def _make_index(lev,lab): - i = lev.__class__(_make_index_array_level(lev.values,lab)) + + def _make_index(lev, lab): + i = lev.__class__(_make_index_array_level(lev.values, lab)) i.name = lev.name return i - self.new_index_levels = list([ _make_index(lev,lab) for lev,lab in zip(levels,labels) ]) + self.new_index_levels = [_make_index(lev, lab) + for lev, lab in zip(levels, labels)] self.new_index_names = list(index.names) self.removed_name = self.new_index_names.pop(self.level) @@ -154,7 +156,8 @@ def get_result(self): mask = isnull(index) if mask.any(): l = np.arange(len(index)) - values, orig_values = np.empty((len(index),values.shape[1])), values + values, orig_values = (np.empty((len(index), values.shape[1])), + values) values.fill(np.nan) values_indexer = com._ensure_int64(l[~mask]) for i, j in enumerate(values_indexer): @@ -224,7 +227,7 @@ def get_new_index(self): result_labels = [] for cur in self.sorted_labels[:-1]: labels = cur.take(self.compressor) - labels = _make_index_array_level(labels,cur) + labels = _make_index_array_level(labels, cur) result_labels.append(labels) # construct the new index @@ -240,26 +243,27 @@ def get_new_index(self): return new_index -def _make_index_array_level(lev,lab): +def _make_index_array_level(lev, lab): """ create the combined index array, preserving nans, return an array """ mask = lab == -1 if not mask.any(): return lev l = np.arange(len(lab)) - mask_labels = np.empty(len(mask[mask]),dtype=object) + mask_labels = np.empty(len(mask[mask]), dtype=object) mask_labels.fill(np.nan) mask_indexer = com._ensure_int64(l[mask]) labels = lev labels_indexer = com._ensure_int64(l[~mask]) - new_labels = np.empty(tuple([len(lab)]),dtype=object) + new_labels = np.empty(tuple([len(lab)]), dtype=object) new_labels[labels_indexer] = labels - new_labels[mask_indexer] = mask_labels + new_labels[mask_indexer] = mask_labels return new_labels + def _unstack_multiple(data, clocs): if len(clocs) == 0: return data @@ -341,7 +345,8 @@ def pivot(self, index=None, columns=None, values=None): return indexed.unstack(columns) else: indexed = Series(self[values].values, - index=MultiIndex.from_arrays([self[index], self[columns]])) + index=MultiIndex.from_arrays([self[index], + self[columns]])) return indexed.unstack(columns) @@ -540,9 +545,10 @@ def _stack_multi_columns(frame, level=-1, dropna=True): # tuple list excluding level for grouping columns if len(frame.columns.levels) > 2: - tuples = list(zip(*[lev.values.take(lab) - for lev, lab in zip(this.columns.levels[:-1], - this.columns.labels[:-1])])) + tuples = list(zip(*[ + lev.values.take(lab) for lev, lab in + zip(this.columns.levels[:-1], this.columns.labels[:-1]) + ])) unique_groups = [key for key, _ in itertools.groupby(tuples)] new_names = this.columns.names[:-1] new_columns = MultiIndex.from_tuples(unique_groups, names=new_names) @@ -678,7 +684,8 @@ def melt(frame, id_vars=None, value_vars=None, frame = frame.copy() if col_level is not None: # allow list or other? - frame.columns = frame.columns.get_level_values(col_level) # frame is a copy + # frame is a copy + frame.columns = frame.columns.get_level_values(col_level) if var_name is None: if isinstance(frame.columns, MultiIndex): @@ -848,7 +855,8 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False): 2 0 0 1 """ - cat = Categorical.from_array(Series(data)) # Series avoids inconsistent NaN handling + # Series avoids inconsistent NaN handling + cat = Categorical.from_array(Series(data)) levels = cat.levels # if all NaN @@ -957,6 +965,9 @@ def block2d_to_blocknd(values, items, shape, labels, ref_items=None): def factor_indexer(shape, labels): - """ given a tuple of shape and a list of Categorical labels, return the expanded label indexer """ + """ given a tuple of shape and a list of Categorical labels, return the + expanded label indexer + """ mult = np.array(shape)[::-1].cumprod()[::-1] - return com._ensure_platform_int(np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T) + return com._ensure_platform_int( + np.sum(np.array(labels).T * np.append(mult, [1]), axis=1).T) diff --git a/pandas/core/series.py b/pandas/core/series.py index 3e8202c7ec0b6..cf704e9aef174 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -22,8 +22,8 @@ _values_from_object, _possibly_cast_to_datetime, _possibly_castable, _possibly_convert_platform, - ABCSparseArray, _maybe_match_name, _ensure_object, - SettingWithCopyError) + ABCSparseArray, _maybe_match_name, + _ensure_object, SettingWithCopyError) from pandas.core.index import (Index, MultiIndex, InvalidIndexError, _ensure_index, _handle_legacy_indexes) @@ -63,6 +63,7 @@ axes_single_arg="{0,'index'}" ) + def _coerce_method(converter): """ install the scalar coercion methods """ @@ -224,8 +225,8 @@ def __init__(self, data=None, index=None, dtype=None, name=None, self._set_axis(0, index, fastpath=True) @classmethod - def from_array(cls, arr, index=None, name=None, copy=False, fastpath=False): - + def from_array(cls, arr, index=None, name=None, copy=False, + fastpath=False): # return a sparse series here if isinstance(arr, ABCSparseArray): from pandas.sparse.series import SparseSeries @@ -336,7 +337,8 @@ def __len__(self): return len(self._data) def view(self, dtype=None): - return self._constructor(self.values.view(dtype), index=self.index).__finalize__(self) + return self._constructor(self.values.view(dtype), + index=self.index).__finalize__(self) def __array__(self, result=None): """ the array interface, return my values """ @@ -346,7 +348,8 @@ def __array_wrap__(self, result): """ Gets called prior to a ufunc (and after) """ - return self._constructor(result, index=self.index, copy=False).__finalize__(self) + return self._constructor(result, index=self.index, + copy=False).__finalize__(self) def __contains__(self, key): return key in self.index @@ -455,7 +458,7 @@ def _ixs(self, i, axis=0): raise except: if isinstance(i, slice): - indexer = self.index._convert_slice_indexer(i,typ='iloc') + indexer = self.index._convert_slice_indexer(i, typ='iloc') return self._get_values(indexer) else: label = self.index[i] @@ -472,8 +475,9 @@ def _is_mixed_type(self): def _slice(self, slobj, axis=0, raise_on_error=False, typ=None): if raise_on_error: _check_slice_bounds(slobj, self.values) - slobj = self.index._convert_slice_indexer(slobj,typ=typ or 'getitem') - return self._constructor(self.values[slobj], index=self.index[slobj]).__finalize__(self) + slobj = self.index._convert_slice_indexer(slobj, typ=typ or 'getitem') + return self._constructor(self.values[slobj], + index=self.index[slobj]).__finalize__(self) def __getitem__(self, key): try: @@ -510,7 +514,7 @@ def __getitem__(self, key): def _get_with(self, key): # other: fancy integer or otherwise if isinstance(key, slice): - indexer = self.index._convert_slice_indexer(key,typ='getitem') + indexer = self.index._convert_slice_indexer(key, typ='getitem') return self._get_values(indexer) else: if isinstance(key, tuple): @@ -564,11 +568,13 @@ def _get_values_tuple(self, key): # If key is contained, would have returned by now indexer, new_index = self.index.get_loc_level(key) - return self._constructor(self.values[indexer], index=new_index).__finalize__(self) + return self._constructor(self.values[indexer], + index=new_index).__finalize__(self) def _get_values(self, indexer): try: - return self._constructor(self._data.get_slice(indexer), fastpath=True).__finalize__(self) + return self._constructor(self._data.get_slice(indexer), + fastpath=True).__finalize__(self) except Exception: return self.values[indexer] @@ -605,7 +611,8 @@ def __setitem__(self, key, value): return except TypeError as e: - if isinstance(key, tuple) and not isinstance(self.index, MultiIndex): + if isinstance(key, tuple) and not isinstance(self.index, + MultiIndex): raise ValueError("Can only tuple-index with a MultiIndex") # python 3 type errors should be raised @@ -635,7 +642,7 @@ def _set_with_engine(self, key, value): def _set_with(self, key, value): # other: fancy integer or otherwise if isinstance(key, slice): - indexer = self.index._convert_slice_indexer(key,typ='getitem') + indexer = self.index._convert_slice_indexer(key, typ='getitem') return self._set_values(indexer, value) else: if isinstance(key, tuple): @@ -677,7 +684,7 @@ def _set_labels(self, key, value): def _set_values(self, key, value): if isinstance(key, Series): key = key.values - self._data = self._data.setitem(key,value) + self._data = self._data.setitem(key, value) # help out SparseSeries _get_val_at = ndarray.__getitem__ @@ -705,7 +712,8 @@ def repeat(self, reps): """ new_index = self.index.repeat(reps) new_values = self.values.repeat(reps) - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) def reshape(self, *args, **kwargs): """ @@ -722,7 +730,6 @@ def reshape(self, *args, **kwargs): return self.values.reshape(shape, **kwargs) - def get(self, label, default=None): """ Returns value occupying requested label, default to specified @@ -824,7 +831,8 @@ def reset_index(self, level=None, drop=False, name=None, inplace=False): # set name if it was passed, otherwise, keep the previous name self.name = name or self.name else: - return self._constructor(self.values.copy(), index=new_index).__finalize__(self) + return self._constructor(self.values.copy(), + index=new_index).__finalize__(self) elif inplace: raise TypeError('Cannot reset_index inplace on a Series ' 'to create a DataFrame') @@ -1035,7 +1043,8 @@ def to_frame(self, name=None): Parameters ---------- name : object, default None - The passed name should substitute for the series name (if it has one). + The passed name should substitute for the series name (if it has + one). Returns ------- @@ -1094,18 +1103,21 @@ def count(self, level=None): level_index = self.index.levels[level] if len(self) == 0: - return self._constructor(0, index=level_index).__finalize__(self) + return self._constructor(0, index=level_index)\ + .__finalize__(self) # call cython function max_bin = len(level_index) labels = com._ensure_int64(self.index.labels[level]) counts = lib.count_level_1d(mask.view(pa.uint8), labels, max_bin) - return self._constructor(counts, index=level_index).__finalize__(self) + return self._constructor(counts, + index=level_index).__finalize__(self) return notnull(_values_from_object(self)).sum() - def value_counts(self, normalize=False, sort=True, ascending=False, bins=None): + def value_counts(self, normalize=False, sort=True, ascending=False, + bins=None): """ Returns Series containing counts of unique values. The resulting Series will be in descending order so that the first element is the most @@ -1195,7 +1207,6 @@ def drop_duplicates(self, take_last=False, inplace=False): else: return result - def duplicated(self, take_last=False): """ Return boolean Series denoting duplicate values @@ -1211,7 +1222,8 @@ def duplicated(self, take_last=False): """ keys = _ensure_object(self.values) duplicated = lib.duplicated(keys, take_last=take_last) - return self._constructor(duplicated, index=self.index).__finalize__(self) + return self._constructor(duplicated, + index=self.index).__finalize__(self) def idxmin(self, axis=None, out=None, skipna=True): """ @@ -1276,7 +1288,8 @@ def round(self, decimals=0, out=None): """ result = _values_from_object(self).round(decimals, out=out) if out is None: - result = self._constructor(result, index=self.index).__finalize__(self) + result = self._constructor(result, + index=self.index).__finalize__(self) return result @@ -1448,7 +1461,8 @@ def autocorr(self): def dot(self, other): """ - Matrix multiplication with DataFrame or inner-product with Series objects + Matrix multiplication with DataFrame or inner-product with Series + objects Parameters ---------- @@ -1692,7 +1706,8 @@ def sort_index(self, ascending=True): ascending=ascending) new_values = self.values.take(indexer) - return self._constructor(new_values, index=new_labels).__finalize__(self) + return self._constructor(new_values, + index=new_labels).__finalize__(self) def argsort(self, axis=0, kind='quicksort', order=None): """ @@ -1720,7 +1735,8 @@ def argsort(self, axis=0, kind='quicksort', order=None): -1, index=self.index, name=self.name, dtype='int64') notmask = -mask result[notmask] = np.argsort(values[notmask], kind=kind) - return self._constructor(result, index=self.index).__finalize__(self) + return self._constructor(result, + index=self.index).__finalize__(self) else: return self._constructor( np.argsort(values, kind=kind), index=self.index, @@ -1802,8 +1818,8 @@ def _try_kind_sort(arr): sortedIdx[n:] = idx[good][argsorted] sortedIdx[:n] = idx[bad] - return self._constructor(arr[sortedIdx], - index=self.index[sortedIdx]).__finalize__(self) + return self._constructor(arr[sortedIdx], index=self.index[sortedIdx])\ + .__finalize__(self) def sortlevel(self, level=0, ascending=True): """ @@ -1825,7 +1841,8 @@ def sortlevel(self, level=0, ascending=True): new_index, indexer = self.index.sortlevel(level, ascending=ascending) new_values = self.values.take(indexer) - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) def swaplevel(self, i, j, copy=True): """ @@ -1954,10 +1971,12 @@ def map_f(values, f): indexer = arg.index.get_indexer(values) new_values = com.take_1d(arg.values, indexer) - return self._constructor(new_values, index=self.index).__finalize__(self) + return self._constructor(new_values, + index=self.index).__finalize__(self) else: mapped = map_f(values, arg) - return self._constructor(mapped, index=self.index).__finalize__(self) + return self._constructor(mapped, + index=self.index).__finalize__(self) def apply(self, func, convert_dtype=True, args=(), **kwds): """ @@ -2000,7 +2019,8 @@ def apply(self, func, convert_dtype=True, args=(), **kwds): from pandas.core.frame import DataFrame return DataFrame(mapped.tolist(), index=self.index) else: - return self._constructor(mapped, index=self.index).__finalize__(self) + return self._constructor(mapped, + index=self.index).__finalize__(self) def _reduce(self, op, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): @@ -2018,7 +2038,9 @@ def _reindex_indexer(self, new_index, indexer, copy): return self._constructor(new_values, index=new_index) def _needs_reindex_multi(self, axes, method, level): - """ check if we do need a multi reindex; this is for compat with higher dims """ + """ check if we do need a multi reindex; this is for compat with + higher dims + """ return False @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs) @@ -2057,7 +2079,8 @@ def take(self, indices, axis=0, convert=True): indices = com._ensure_platform_int(indices) new_index = self.index.take(indices) new_values = self.values.take(indices) - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) def isin(self, values): """ @@ -2314,7 +2337,8 @@ def asof(self, where): @property def weekday(self): - return self._constructor([d.weekday() for d in self.index], index=self.index).__finalize__(self) + return self._constructor([d.weekday() for d in self.index], + index=self.index).__finalize__(self) def tz_convert(self, tz, copy=True): """ @@ -2336,7 +2360,8 @@ def tz_convert(self, tz, copy=True): if copy: new_values = new_values.copy() - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) def tz_localize(self, tz, copy=True, infer_dst=False): """ @@ -2373,7 +2398,8 @@ def tz_localize(self, tz, copy=True, infer_dst=False): if copy: new_values = new_values.copy() - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) @cache_readonly def str(self): @@ -2401,7 +2427,8 @@ def to_timestamp(self, freq=None, how='start', copy=True): new_values = new_values.copy() new_index = self.index.to_timestamp(freq=freq, how=how) - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) def to_period(self, freq=None, copy=True): """ @@ -2423,7 +2450,8 @@ def to_period(self, freq=None, copy=True): if freq is None: freq = self.index.freqstr or self.index.inferred_freq new_index = self.index.to_period(freq=freq) - return self._constructor(new_values, index=new_index).__finalize__(self) + return self._constructor(new_values, + index=new_index).__finalize__(self) Series._setup_axes(['index'], info_axis=0, stat_axis=0, aliases={'rows': 0}) diff --git a/pandas/core/sparse.py b/pandas/core/sparse.py index 7b9caaa3a0139..84149e5598f82 100644 --- a/pandas/core/sparse.py +++ b/pandas/core/sparse.py @@ -1,6 +1,6 @@ """ -Data structures for sparse float data. Life is made simpler by dealing only with -float64 data +Data structures for sparse float data. Life is made simpler by dealing only +with float64 data """ # pylint: disable=W0611 diff --git a/pandas/core/strings.py b/pandas/core/strings.py index c1bd369686969..0df9db2ebd06c 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -9,6 +9,7 @@ import pandas.lib as lib import warnings + def _get_array_list(arr, others): if isinstance(others[0], (list, np.ndarray)): arrays = [arr] + list(others) @@ -115,6 +116,7 @@ def g(x): else: return lib.map_infer(arr, f) + def str_title(arr): """ Convert strings to titlecased version @@ -399,29 +401,31 @@ def f(x): return None m = regex.search(x) if m: - return m.groups()[0] # may be None + return m.groups()[0] # may be None else: return None else: empty_row = Series(regex.groups*[None]) + def f(x): if not isinstance(x, compat.string_types): return empty_row m = regex.search(x) if m: - return Series(list(m.groups())) # may contain None + return Series(list(m.groups())) # may contain None else: return empty_row result = arr.apply(f) result.replace({None: np.nan}, inplace=True) if regex.groups > 1: - result = DataFrame(result) # Don't rely on the wrapper; name columns. + result = DataFrame(result) # Don't rely on the wrapper; name columns. names = dict(zip(regex.groupindex.values(), regex.groupindex.keys())) result.columns = [names.get(1 + i, i) for i in range(regex.groups)] else: result.name = regex.groupindex.get(0) return result + def str_join(arr, sep): """ Join lists contained as elements in array, a la str.join
I stated a PEP8 cleanup and did it for a few modules. Changed only the things where this doesn't produce ugly code. If this is acceptable, I can continue doing this and go through the whole pandas codebase. There are just a few things that I would like to check if this is acceptable: 1. PEP8: "For flowing long blocks of text with fewer structural restrictions (docstrings or comments), the line length should be limited to 72 characters." - should I do this? 2. Should I clean the unused imports? There are a lot of unused imports, not just from pandas but also from the standard library. Also duplicate imports (importing `timedelta` 2 times for example in the cleanup that I did)... 3. Should I discard the unused variables? (There are a lot of them especially in the tests). 4. `a[i+2:3]` this is against PEP8, it should look like `a[i + 2:3]`, but I don't think these things should be changed, the second one is uglier even if it's against PEP8. Also one of the problems with this cleanup is that it changes a lot of things and should be merged as soon as possible :( Because it will soon become unmergable...
https://api.github.com/repos/pandas-dev/pandas/pulls/5038
2013-09-29T13:33:56Z
2013-11-17T01:09:10Z
2013-11-17T01:09:10Z
2014-07-16T08:32:03Z
DOC: make experimental functionality more visible in release notes
diff --git a/doc/source/release.rst b/doc/source/release.rst index 66c3dcd203a6a..1f0e447429d6a 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -60,6 +60,21 @@ New features - Clipboard functionality now works with PySide (:issue:`4282`) - New ``extract`` string method returns regex matches more conveniently (:issue:`4685`) +Experimental Features +~~~~~~~~~~~~~~~~~~~~~ + +- The new :func:`~pandas.eval` function implements expression evaluation using + ``numexpr`` behind the scenes. This results in large speedups for complicated + expressions involving large DataFrames/Series. +- :class:`~pandas.DataFrame` has a new :meth:`~pandas.DataFrame.eval` that + evaluates an expression in the context of the ``DataFrame``. +- A :meth:`~pandas.DataFrame.query` method has been added that allows + you to select elements of a ``DataFrame`` using a natural query syntax nearly + identical to Python syntax. +- ``pd.eval`` and friends now evaluate operations involving ``datetime64`` + objects in Python space because ``numexpr`` cannot handle ``NaT`` values + (:issue:`4897`). + Improvements to existing features ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -327,21 +342,6 @@ See :ref:`Internal Refactoring<whatsnew_0130.refactoring>` - Complex compat for ``Series`` with ``ndarray``. (:issue:`4819`) - Removed unnecessary ``rwproperty`` from codebase in favor of builtin property. (:issue:`4843`) -Experimental Features -~~~~~~~~~~~~~~~~~~~~~ - -- The new :func:`~pandas.eval` function implements expression evaluation using - ``numexpr`` behind the scenes. This results in large speedups for complicated - expressions involving large DataFrames/Series. -- :class:`~pandas.DataFrame` has a new :meth:`~pandas.DataFrame.eval` that - evaluates an expression in the context of the ``DataFrame``. -- A :meth:`~pandas.DataFrame.query` method has been added that allows - you to select elements of a ``DataFrame`` using a natural query syntax nearly - identical to Python syntax. -- ``pd.eval`` and friends now evaluate operations involving ``datetime64`` - objects in Python space because ``numexpr`` cannot handle ``NaT`` values - (:issue:`4897`). - .. _release.bug_fixes-0.13.0:
closes #5031
https://api.github.com/repos/pandas-dev/pandas/pulls/5037
2013-09-29T04:47:48Z
2013-09-29T04:48:11Z
2013-09-29T04:48:11Z
2014-07-16T08:32:01Z
BUG/CLN: numpy compat with pandas numeric functions and cln of same (GH4435)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 73e7e3affd944..2c975e58d9575 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -267,6 +267,9 @@ API Changes ``SparsePanel``, etc.), now support the entire set of arithmetic operators and arithmetic flex methods (add, sub, mul, etc.). ``SparsePanel`` does not support ``pow`` or ``mod`` with non-scalars. (:issue:`3765`) + - Provide numpy compatibility with 1.7 for a calling convention like ``np.prod(pandas_object)`` as numpy + call with additional keyword args (:issue:`4435`) + Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ @@ -345,6 +348,10 @@ See :ref:`Internal Refactoring<whatsnew_0130.refactoring>` etc.) into a separate, cleaned up wrapper class. (:issue:`4613`) - Complex compat for ``Series`` with ``ndarray``. (:issue:`4819`) - Removed unnecessary ``rwproperty`` from codebase in favor of builtin property. (:issue:`4843`) +- Refactor object level numeric methods (mean/sum/min/max...) from object level modules to + ``core/generic.py``(:issue:`4435`). +- Refactor cum objects to core/generic.py (:issue:`4435`), note that these have a more numpy-like + function signature. .. _release.bug_fixes-0.13.0: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index c6727f91644fc..935dff44ad49e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -63,28 +63,6 @@ # Docstring templates -_stat_doc = """ -Return %(name)s over requested axis. -%(na_action)s - -Parameters ----------- -axis : {0, 1} - 0 for row-wise, 1 for column-wise -skipna : boolean, default True - Exclude NA/null values. If an entire row/column is NA, the result - will be NA -level : int, default None - If the axis is a MultiIndex (hierarchical), count along a - particular level, collapsing into a DataFrame -%(extras)s -Returns -------- -%(shortname)s : Series (or DataFrame if level specified) -""" - -_doc_exclude_na = "NA/null values are excluded" - _numeric_only_doc = """numeric_only : boolean, default None Include only float, int, boolean data. If None, will attempt to use everything, then use only numeric data @@ -3869,7 +3847,7 @@ def _count_level(self, level, axis=0, numeric_only=False): else: return result - def any(self, axis=0, bool_only=None, skipna=True, level=None): + def any(self, axis=None, bool_only=None, skipna=True, level=None, **kwargs): """ Return whether any element is True over requested axis. %(na_action)s @@ -3891,13 +3869,15 @@ def any(self, axis=0, bool_only=None, skipna=True, level=None): ------- any : Series (or DataFrame if level specified) """ + if axis is None: + axis = self._stat_axis_number if level is not None: return self._agg_by_level('any', axis=axis, level=level, skipna=skipna) return self._reduce(nanops.nanany, axis=axis, skipna=skipna, numeric_only=bool_only, filter_type='bool') - def all(self, axis=0, bool_only=None, skipna=True, level=None): + def all(self, axis=None, bool_only=None, skipna=True, level=None, **kwargs): """ Return whether all elements are True over requested axis. %(na_action)s @@ -3919,169 +3899,14 @@ def all(self, axis=0, bool_only=None, skipna=True, level=None): ------- any : Series (or DataFrame if level specified) """ + if axis is None: + axis = self._stat_axis_number if level is not None: return self._agg_by_level('all', axis=axis, level=level, skipna=skipna) return self._reduce(nanops.nanall, axis=axis, skipna=skipna, numeric_only=bool_only, filter_type='bool') - @Substitution(name='sum', shortname='sum', na_action=_doc_exclude_na, - extras=_numeric_only_doc) - @Appender(_stat_doc) - def sum(self, axis=0, numeric_only=None, skipna=True, level=None): - if level is not None: - return self._agg_by_level('sum', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nansum, axis=axis, skipna=skipna, - numeric_only=numeric_only) - - @Substitution(name='mean', shortname='mean', na_action=_doc_exclude_na, - extras='') - @Appender(_stat_doc) - def mean(self, axis=0, skipna=True, level=None): - if level is not None: - return self._agg_by_level('mean', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nanmean, axis=axis, skipna=skipna, - numeric_only=None) - - @Substitution(name='minimum', shortname='min', na_action=_doc_exclude_na, - extras='') - @Appender(_stat_doc) - def min(self, axis=0, skipna=True, level=None): - """ - Notes - ----- - This method returns the minimum of the values in the DataFrame. If you - want the *index* of the minimum, use ``DataFrame.idxmin``. This is the - equivalent of the ``numpy.ndarray`` method ``argmin``. - - See Also - -------- - DataFrame.idxmin - Series.idxmin - """ - if level is not None: - return self._agg_by_level('min', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nanmin, axis=axis, skipna=skipna, - numeric_only=None) - - @Substitution(name='maximum', shortname='max', na_action=_doc_exclude_na, - extras='') - @Appender(_stat_doc) - def max(self, axis=0, skipna=True, level=None): - """ - Notes - ----- - This method returns the maximum of the values in the DataFrame. If you - want the *index* of the maximum, use ``DataFrame.idxmax``. This is the - equivalent of the ``numpy.ndarray`` method ``argmax``. - - See Also - -------- - DataFrame.idxmax - Series.idxmax - """ - if level is not None: - return self._agg_by_level('max', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nanmax, axis=axis, skipna=skipna, - numeric_only=None) - - @Substitution(name='product', shortname='product', - na_action='NA/null values are treated as 1', extras='') - @Appender(_stat_doc) - def prod(self, axis=0, skipna=True, level=None): - if level is not None: - return self._agg_by_level('prod', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nanprod, axis=axis, skipna=skipna, - numeric_only=None) - - product = prod - - @Substitution(name='median', shortname='median', na_action=_doc_exclude_na, - extras='') - @Appender(_stat_doc) - def median(self, axis=0, skipna=True, level=None): - if level is not None: - return self._agg_by_level('median', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nanmedian, axis=axis, skipna=skipna, - numeric_only=None) - - @Substitution(name='mean absolute deviation', shortname='mad', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def mad(self, axis=0, skipna=True, level=None): - if level is not None: - return self._agg_by_level('mad', axis=axis, level=level, - skipna=skipna) - - frame = self._get_numeric_data() - - axis = self._get_axis_number(axis) - if axis == 0: - demeaned = frame - frame.mean(axis=0) - else: - demeaned = frame.sub(frame.mean(axis=1), axis=0) - return np.abs(demeaned).mean(axis=axis, skipna=skipna) - - @Substitution(name='variance', shortname='var', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc + - """ - Normalized by N-1 (unbiased estimator). - """) - def var(self, axis=0, skipna=True, level=None, ddof=1): - if level is not None: - return self._agg_by_level('var', axis=axis, level=level, - skipna=skipna, ddof=ddof) - return self._reduce(nanops.nanvar, axis=axis, skipna=skipna, - numeric_only=None, ddof=ddof) - - @Substitution(name='standard deviation', shortname='std', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc + - """ - Normalized by N-1 (unbiased estimator). - """) - def std(self, axis=0, skipna=True, level=None, ddof=1): - if level is not None: - return self._agg_by_level('std', axis=axis, level=level, - skipna=skipna, ddof=ddof) - return np.sqrt(self.var(axis=axis, skipna=skipna, ddof=ddof)) - - @Substitution(name='unbiased skewness', shortname='skew', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def skew(self, axis=0, skipna=True, level=None): - if level is not None: - return self._agg_by_level('skew', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nanskew, axis=axis, skipna=skipna, - numeric_only=None) - - @Substitution(name='unbiased kurtosis', shortname='kurt', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def kurt(self, axis=0, skipna=True, level=None): - if level is not None: - return self._agg_by_level('kurt', axis=axis, level=level, - skipna=skipna) - return self._reduce(nanops.nankurt, axis=axis, skipna=skipna, - numeric_only=None) - - def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwds): - grouped = self.groupby(level=level, axis=axis) - if hasattr(grouped, name) and skipna: - return getattr(grouped, name)(**kwds) - axis = self._get_axis_number(axis) - method = getattr(type(self), name) - applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwds) - return grouped.aggregate(applyf) - def _reduce(self, op, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): axis = self._get_axis_number(axis) @@ -4440,7 +4265,7 @@ def combineMult(self, other): DataFrame._setup_axes( ['index', 'columns'], info_axis=1, stat_axis=0, axes_are_reversed=True) - +DataFrame._add_numeric_operations() _EMPTY_SERIES = Series([]) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 705679136c3d2..18a03eb313dd2 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -21,6 +21,8 @@ _values_from_object, _infer_dtype_from_scalar, _maybe_promote, ABCSeries) +import pandas.core.nanops as nanops +from pandas.util.decorators import Appender, Substitution def is_dictlike(x): return isinstance(x, (dict, com.ABCSeries)) @@ -1949,33 +1951,6 @@ def interpolate(self, to_replace, method='pad', axis=0, inplace=False, #---------------------------------------------------------------------- # Action Methods - def abs(self): - """ - Return an object with absolute value taken. Only applicable to objects - that are all numeric - - Returns - ------- - abs: type of caller - """ - obj = np.abs(self) - - # suprimo numpy 1.6 hacking - if _np_version_under1p7: - if self.ndim == 1: - if obj.dtype == 'm8[us]': - obj = obj.astype('m8[ns]') - elif self.ndim == 2: - def f(x): - if x.dtype == 'm8[us]': - x = x.astype('m8[ns]') - return x - - if 'm8[us]' in obj.dtypes.values: - obj = obj.apply(f) - - return obj - def clip(self, lower=None, upper=None, out=None): """ Trim values at input threshold(s) @@ -2550,178 +2525,6 @@ def mask(self, cond): """ return self.where(~cond, np.nan) - def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, - **kwds): - """ - Percent change over given number of periods - - Parameters - ---------- - periods : int, default 1 - Periods to shift for forming percent change - fill_method : str, default 'pad' - How to handle NAs before computing percent changes - limit : int, default None - The number of consecutive NAs to fill before stopping - freq : DateOffset, timedelta, or offset alias string, optional - Increment to use from time series API (e.g. 'M' or BDay()) - - Returns - ------- - chg : Series or DataFrame - """ - if fill_method is None: - data = self - else: - data = self.fillna(method=fill_method, limit=limit) - rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1 - if freq is None: - mask = com.isnull(_values_from_object(self)) - np.putmask(rs.values, mask, np.nan) - return rs - - def cumsum(self, axis=None, skipna=True): - """ - Return DataFrame of cumulative sums over requested axis. - - Parameters - ---------- - axis : {0, 1} - 0 for row-wise, 1 for column-wise - skipna : boolean, default True - Exclude NA/null values. If an entire row/column is NA, the result - will be NA - - Returns - ------- - y : DataFrame - """ - if axis is None: - axis = self._stat_axis_number - else: - axis = self._get_axis_number(axis) - - y = _values_from_object(self).copy() - if not issubclass(y.dtype.type, np.integer): - mask = np.isnan(_values_from_object(self)) - - if skipna: - np.putmask(y, mask, 0.) - - result = y.cumsum(axis) - - if skipna: - np.putmask(result, mask, np.nan) - else: - result = y.cumsum(axis) - return self._wrap_array(result, self.axes, copy=False) - - def cumprod(self, axis=None, skipna=True): - """ - Return cumulative product over requested axis as DataFrame - - Parameters - ---------- - axis : {0, 1} - 0 for row-wise, 1 for column-wise - skipna : boolean, default True - Exclude NA/null values. If an entire row/column is NA, the result - will be NA - - Returns - ------- - y : DataFrame - """ - if axis is None: - axis = self._stat_axis_number - else: - axis = self._get_axis_number(axis) - - y = _values_from_object(self).copy() - if not issubclass(y.dtype.type, np.integer): - mask = np.isnan(_values_from_object(self)) - - if skipna: - np.putmask(y, mask, 1.) - result = y.cumprod(axis) - - if skipna: - np.putmask(result, mask, np.nan) - else: - result = y.cumprod(axis) - return self._wrap_array(result, self.axes, copy=False) - - def cummax(self, axis=None, skipna=True): - """ - Return DataFrame of cumulative max over requested axis. - - Parameters - ---------- - axis : {0, 1} - 0 for row-wise, 1 for column-wise - skipna : boolean, default True - Exclude NA/null values. If an entire row/column is NA, the result - will be NA - - Returns - ------- - y : DataFrame - """ - if axis is None: - axis = self._stat_axis_number - else: - axis = self._get_axis_number(axis) - - y = _values_from_object(self).copy() - if not issubclass(y.dtype.type, np.integer): - mask = np.isnan(_values_from_object(self)) - - if skipna: - np.putmask(y, mask, -np.inf) - - result = np.maximum.accumulate(y, axis) - - if skipna: - np.putmask(result, mask, np.nan) - else: - result = np.maximum.accumulate(y, axis) - return self._wrap_array(result, self.axes, copy=False) - - def cummin(self, axis=None, skipna=True): - """ - Return DataFrame of cumulative min over requested axis. - - Parameters - ---------- - axis : {0, 1} - 0 for row-wise, 1 for column-wise - skipna : boolean, default True - Exclude NA/null values. If an entire row/column is NA, the result - will be NA - - Returns - ------- - y : DataFrame - """ - if axis is None: - axis = self._stat_axis_number - else: - axis = self._get_axis_number(axis) - - y = _values_from_object(self).copy() - if not issubclass(y.dtype.type, np.integer): - mask = np.isnan(_values_from_object(self)) - - if skipna: - np.putmask(y, mask, np.inf) - - result = np.minimum.accumulate(y, axis) - - if skipna: - np.putmask(result, mask, np.nan) - else: - result = np.minimum.accumulate(y, axis) - return self._wrap_array(result, self.axes, copy=False) def shift(self, periods=1, freq=None, axis=0, **kwds): """ @@ -2928,6 +2731,240 @@ def tz_localize(self, tz, axis=0, copy=True): return new_obj + #---------------------------------------------------------------------- + # Numeric Methods + def abs(self): + """ + Return an object with absolute value taken. Only applicable to objects + that are all numeric + + Returns + ------- + abs: type of caller + """ + obj = np.abs(self) + + # suprimo numpy 1.6 hacking + if _np_version_under1p7: + if self.ndim == 1: + if obj.dtype == 'm8[us]': + obj = obj.astype('m8[ns]') + elif self.ndim == 2: + def f(x): + if x.dtype == 'm8[us]': + x = x.astype('m8[ns]') + return x + + if 'm8[us]' in obj.dtypes.values: + obj = obj.apply(f) + + return obj + + def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, + **kwds): + """ + Percent change over given number of periods + + Parameters + ---------- + periods : int, default 1 + Periods to shift for forming percent change + fill_method : str, default 'pad' + How to handle NAs before computing percent changes + limit : int, default None + The number of consecutive NAs to fill before stopping + freq : DateOffset, timedelta, or offset alias string, optional + Increment to use from time series API (e.g. 'M' or BDay()) + + Returns + ------- + chg : Series or DataFrame + """ + if fill_method is None: + data = self + else: + data = self.fillna(method=fill_method, limit=limit) + rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1 + if freq is None: + mask = com.isnull(_values_from_object(self)) + np.putmask(rs.values, mask, np.nan) + return rs + + def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwds): + grouped = self.groupby(level=level, axis=axis) + if hasattr(grouped, name) and skipna: + return getattr(grouped, name)(**kwds) + axis = self._get_axis_number(axis) + method = getattr(type(self), name) + applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwds) + return grouped.aggregate(applyf) + + @classmethod + def _add_numeric_operations(cls): + """ add the operations to the cls; evaluate the doc strings again """ + + axis_descr = "{" + ', '.join([ "{0} ({1})".format(a,i) for i, a in enumerate(cls._AXIS_ORDERS)]) + "}" + name = cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else 'scalar' + _num_doc = """ + +%(desc)s + +Parameters +---------- +axis : """ + axis_descr + """ +skipna : boolean, default True + Exclude NA/null values. If an entire row/column is NA, the result + will be NA +level : int, default None + If the axis is a MultiIndex (hierarchical), count along a + particular level, collapsing into a """ + name + """ +numeric_only : boolean, default None + Include only float, int, boolean data. If None, will attempt to use + everything, then use only numeric data + +Returns +------- +%(outname)s : """ + name + " or " + cls.__name__ + " (if level specified)\n" + + _cnum_doc = """ + +Parameters +---------- +axis : """ + axis_descr + """ +skipna : boolean, default True + Exclude NA/null values. If an entire row/column is NA, the result + will be NA + +Returns +------- +%(outname)s : """ + name + "\n" + + def _make_stat_function(name, desc, f): + + @Substitution(outname=name, desc=desc) + @Appender(_num_doc) + def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, + **kwargs): + if skipna is None: + skipna = True + if axis is None: + axis = self._stat_axis_number + if level is not None: + return self._agg_by_level(name, axis=axis, level=level, + skipna=skipna) + return self._reduce(f, axis=axis, + skipna=skipna, numeric_only=numeric_only) + stat_func.__name__ = name + return stat_func + + cls.sum = _make_stat_function('sum',"Return the sum of the values for the requested axis", nanops.nansum) + cls.mean = _make_stat_function('mean',"Return the mean of the values for the requested axis", nanops.nanmean) + cls.skew = _make_stat_function('skew',"Return unbiased skew over requested axis\nNormalized by N-1", nanops.nanskew) + cls.kurt = _make_stat_function('kurt',"Return unbiased kurtosis over requested axis\nNormalized by N-1", nanops.nankurt) + cls.kurtosis = cls.kurt + cls.prod = _make_stat_function('prod',"Return the product of the values for the requested axis", nanops.nanprod) + cls.product = cls.prod + cls.median = _make_stat_function('median',"Return the median of the values for the requested axis", nanops.nanmedian) + cls.max = _make_stat_function('max',""" +This method returns the maximum of the values in the object. If you +want the *index* of the maximum, use ``idxmax``. This is the +equivalent of the ``numpy.ndarray`` method ``argmax``.""", nanops.nanmax) + cls.min = _make_stat_function('min',""" +This method returns the minimum of the values in the object. If you +want the *index* of the minimum, use ``idxmin``. This is the +equivalent of the ``numpy.ndarray`` method ``argmin``.""", nanops.nanmin) + + @Substitution(outname='mad', desc="Return the mean absolute deviation of the values for the requested axis") + @Appender(_num_doc) + def mad(self, axis=None, skipna=None, level=None, **kwargs): + if skipna is None: + skipna = True + if axis is None: + axis = self._stat_axis_number + if level is not None: + return self._agg_by_level('mad', axis=axis, level=level, + skipna=skipna) + + data = self._get_numeric_data() + if axis == 0: + demeaned = data - data.mean(axis=0) + else: + demeaned = data.sub(data.mean(axis=1), axis=0) + return np.abs(demeaned).mean(axis=axis, skipna=skipna) + cls.mad = mad + + @Substitution(outname='variance',desc="Return unbiased variance over requested axis\nNormalized by N-1") + @Appender(_num_doc) + def var(self, axis=None, skipna=None, level=None, ddof=1, **kwargs): + if skipna is None: + skipna = True + if axis is None: + axis = self._stat_axis_number + if level is not None: + return self._agg_by_level('var', axis=axis, level=level, + skipna=skipna, ddof=ddof) + + return self._reduce(nanops.nanvar, axis=axis, skipna=skipna, ddof=ddof) + cls.var = var + + @Substitution(outname='stdev',desc="Return unbiased standard deviation over requested axis\nNormalized by N-1") + @Appender(_num_doc) + def std(self, axis=None, skipna=None, level=None, ddof=1, **kwargs): + if skipna is None: + skipna = True + if axis is None: + axis = self._stat_axis_number + if level is not None: + return self._agg_by_level('std', axis=axis, level=level, + skipna=skipna, ddof=ddof) + result = self.var(axis=axis, skipna=skipna, ddof=ddof) + if getattr(result,'ndim',0) > 0: + return result.apply(np.sqrt) + return np.sqrt(result) + cls.std = std + + @Substitution(outname='compounded',desc="Return the compound percentage of the values for the requested axis") + @Appender(_num_doc) + def compound(self, axis=None, skipna=None, level=None, **kwargs): + if skipna is None: + skipna = True + return (1 + self).prod(axis=axis, skipna=skipna, level=level) - 1 + cls.compound = compound + + def _make_cum_function(name, accum_func, mask_a, mask_b): + + @Substitution(outname=name) + @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) + def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): + if axis is None: + axis = self._stat_axis_number + else: + axis = self._get_axis_number(axis) + + y = _values_from_object(self).copy() + if not issubclass(y.dtype.type, (np.integer,np.bool_)): + mask = isnull(self) + if skipna: + np.putmask(y, mask, mask_a) + result = accum_func(y, axis) + if skipna: + np.putmask(result, mask, mask_b) + else: + result = accum_func(y, axis) + + d = self._construct_axes_dict() + d['copy'] = False + return self._constructor(result, **d)._propogate_attributes(self) + + func.__name__ = name + return func + + + cls.cummin = _make_cum_function('min', lambda y, axis: np.minimum.accumulate(y, axis), np.inf, np.nan) + cls.cumsum = _make_cum_function('sum', lambda y, axis: y.cumsum(axis), 0., np.nan) + cls.cumprod = _make_cum_function('prod', lambda y, axis: y.cumprod(axis), 1., np.nan) + cls.cummax = _make_cum_function('max', lambda y, axis: np.maximum.accumulate(y, axis), -np.inf, np.nan) + # install the indexerse for _name, _indexer in indexing.get_indexers_list(): NDFrame._create_indexer(_name, _indexer) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 7208ceff7d1a7..f0bad6b796e7c 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -831,10 +831,11 @@ def apply(self, func, axis='major'): result = np.apply_along_axis(func, i, self.values) return self._wrap_result(result, axis=axis) - def _reduce(self, op, axis=0, skipna=True): + def _reduce(self, op, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): axis_name = self._get_axis_name(axis) axis_number = self._get_axis_number(axis_name) - f = lambda x: op(x, axis=axis_number, skipna=skipna) + f = lambda x: op(x, axis=axis_number, skipna=skipna, **kwds) result = f(self.values) @@ -1207,89 +1208,11 @@ def f(self, other, axis=0): return self._combine(other, na_op, axis=axis) f.__name__ = name return f + # add `div`, `mul`, `pow`, etc.. ops.add_flex_arithmetic_methods(cls, _panel_arith_method, use_numexpr=use_numexpr, flex_comp_method=ops._comp_method_PANEL) - _agg_doc = """ -Return %(desc)s over requested axis - -Parameters ----------- -axis : {""" + ', '.join(cls._AXIS_ORDERS) + "} or {" \ - + ', '.join([str(i) for i in range(cls._AXIS_LEN)]) + """} -skipna : boolean, default True - Exclude NA/null values. If an entire row/column is NA, the result - will be NA - -Returns -------- -%(outname)s : """ + cls._constructor_sliced.__name__ + "\n" - - _na_info = """ - -NA/null values are %s. -If all values are NA, result will be NA""" - - @Substitution(desc='sum', outname='sum') - @Appender(_agg_doc) - def sum(self, axis='major', skipna=True): - return self._reduce(nanops.nansum, axis=axis, skipna=skipna) - cls.sum = sum - - @Substitution(desc='mean', outname='mean') - @Appender(_agg_doc) - def mean(self, axis='major', skipna=True): - return self._reduce(nanops.nanmean, axis=axis, skipna=skipna) - cls.mean = mean - - @Substitution(desc='unbiased variance', outname='variance') - @Appender(_agg_doc) - def var(self, axis='major', skipna=True): - return self._reduce(nanops.nanvar, axis=axis, skipna=skipna) - cls.var = var - - @Substitution(desc='unbiased standard deviation', outname='stdev') - @Appender(_agg_doc) - def std(self, axis='major', skipna=True): - return self.var(axis=axis, skipna=skipna).apply(np.sqrt) - cls.std = std - - @Substitution(desc='unbiased skewness', outname='skew') - @Appender(_agg_doc) - def skew(self, axis='major', skipna=True): - return self._reduce(nanops.nanskew, axis=axis, skipna=skipna) - cls.skew = skew - - @Substitution(desc='product', outname='prod') - @Appender(_agg_doc) - def prod(self, axis='major', skipna=True): - return self._reduce(nanops.nanprod, axis=axis, skipna=skipna) - cls.prod = prod - - @Substitution(desc='compounded percentage', outname='compounded') - @Appender(_agg_doc) - def compound(self, axis='major', skipna=True): - return (1 + self).prod(axis=axis, skipna=skipna) - 1 - cls.compound = compound - - @Substitution(desc='median', outname='median') - @Appender(_agg_doc) - def median(self, axis='major', skipna=True): - return self._reduce(nanops.nanmedian, axis=axis, skipna=skipna) - cls.median = median - - @Substitution(desc='maximum', outname='maximum') - @Appender(_agg_doc) - def max(self, axis='major', skipna=True): - return self._reduce(nanops.nanmax, axis=axis, skipna=skipna) - cls.max = max - - @Substitution(desc='minimum', outname='minimum') - @Appender(_agg_doc) - def min(self, axis='major', skipna=True): - return self._reduce(nanops.nanmin, axis=axis, skipna=skipna) - cls.min = min Panel._setup_axes(axes=['items', 'major_axis', 'minor_axis'], info_axis=0, @@ -1301,6 +1224,7 @@ def min(self, axis='major', skipna=True): ops.add_special_arithmetic_methods(Panel, **ops.panel_special_funcs) Panel._add_aggregate_operations() +Panel._add_numeric_operations() WidePanel = Panel LongPanel = DataFrame diff --git a/pandas/core/panelnd.py b/pandas/core/panelnd.py index 8f427568a4102..9ccce1edc9067 100644 --- a/pandas/core/panelnd.py +++ b/pandas/core/panelnd.py @@ -108,5 +108,6 @@ def func(self, *args, **kwargs): # add the aggregate operations klass._add_aggregate_operations() + klass._add_numeric_operations() return klass diff --git a/pandas/core/series.py b/pandas/core/series.py index 38e22e7a9ed3a..90d535e51580c 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -77,40 +77,6 @@ def f(self, *args, **kwargs): f.__name__ = func.__name__ return f -_stat_doc = """ -Return %(name)s of values -%(na_action)s - -Parameters ----------- -skipna : boolean, default True - Exclude NA/null values -level : int, default None - If the axis is a MultiIndex (hierarchical), count along a - particular level, collapsing into a smaller Series -%(extras)s -Returns -------- -%(shortname)s : float (or Series if level specified) -""" -_doc_exclude_na = "NA/null values are excluded" -_doc_ndarray_interface = ("Extra parameters are to preserve ndarray" - "interface.\n") - - -def _make_stat_func(nanop, name, shortname, na_action=_doc_exclude_na, - extras=_doc_ndarray_interface): - - @Substitution(name=name, shortname=shortname, - na_action=na_action, extras=extras) - @Appender(_stat_doc) - def f(self, axis=0, dtype=None, out=None, skipna=True, level=None): - if level is not None: - return self._agg_by_level(shortname, level=level, skipna=skipna) - return nanop(_values_from_object(self), skipna=skipna) - f.__name__ = shortname - return f - #---------------------------------------------------------------------- # Series class @@ -1194,113 +1160,6 @@ def duplicated(self, take_last=False): duplicated = lib.duplicated(keys, take_last=take_last) return self._constructor(duplicated, index=self.index, name=self.name) - sum = _make_stat_func(nanops.nansum, 'sum', 'sum') - mean = _make_stat_func(nanops.nanmean, 'mean', 'mean') - median = _make_stat_func(nanops.nanmedian, 'median', 'median', extras='') - prod = _make_stat_func(nanops.nanprod, 'product', 'prod', extras='') - - @Substitution(name='mean absolute deviation', shortname='mad', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def mad(self, skipna=True, level=None): - if level is not None: - return self._agg_by_level('mad', level=level, skipna=skipna) - - demeaned = self - self.mean(skipna=skipna) - return np.abs(demeaned).mean(skipna=skipna) - - @Substitution(name='minimum', shortname='min', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def min(self, axis=None, out=None, skipna=True, level=None): - """ - Notes - ----- - This method returns the minimum of the values in the Series. If you - want the *index* of the minimum, use ``Series.idxmin``. This is the - equivalent of the ``numpy.ndarray`` method ``argmin``. - - See Also - -------- - Series.idxmin - DataFrame.idxmin - """ - if level is not None: - return self._agg_by_level('min', level=level, skipna=skipna) - return nanops.nanmin(_values_from_object(self), skipna=skipna) - - @Substitution(name='maximum', shortname='max', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def max(self, axis=None, out=None, skipna=True, level=None): - """ - Notes - ----- - This method returns the maximum of the values in the Series. If you - want the *index* of the maximum, use ``Series.idxmax``. This is the - equivalent of the ``numpy.ndarray`` method ``argmax``. - - See Also - -------- - Series.idxmax - DataFrame.idxmax - """ - if level is not None: - return self._agg_by_level('max', level=level, skipna=skipna) - return nanops.nanmax(_values_from_object(self), skipna=skipna) - - @Substitution(name='standard deviation', shortname='stdev', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc + - """ - Normalized by N-1 (unbiased estimator). - """) - def std(self, axis=None, dtype=None, out=None, ddof=1, skipna=True, - level=None): - if level is not None: - return self._agg_by_level('std', level=level, skipna=skipna, - ddof=ddof) - return np.sqrt(nanops.nanvar(_values_from_object(self), skipna=skipna, ddof=ddof)) - - @Substitution(name='variance', shortname='var', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc + - """ - Normalized by N-1 (unbiased estimator). - """) - def var(self, axis=None, dtype=None, out=None, ddof=1, skipna=True, - level=None): - if level is not None: - return self._agg_by_level('var', level=level, skipna=skipna, - ddof=ddof) - return nanops.nanvar(_values_from_object(self), skipna=skipna, ddof=ddof) - - @Substitution(name='unbiased skewness', shortname='skew', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def skew(self, skipna=True, level=None): - if level is not None: - return self._agg_by_level('skew', level=level, skipna=skipna) - - return nanops.nanskew(_values_from_object(self), skipna=skipna) - - @Substitution(name='unbiased kurtosis', shortname='kurt', - na_action=_doc_exclude_na, extras='') - @Appender(_stat_doc) - def kurt(self, skipna=True, level=None): - if level is not None: - return self._agg_by_level('kurt', level=level, skipna=skipna) - - return nanops.nankurt(_values_from_object(self), skipna=skipna) - - def _agg_by_level(self, name, level=0, skipna=True, **kwds): - grouped = self.groupby(level=level) - if hasattr(grouped, name) and skipna: - return getattr(grouped, name)(**kwds) - method = getattr(type(self), name) - applyf = lambda x: method(x, skipna=skipna, **kwds) - return grouped.aggregate(applyf) - def idxmin(self, axis=None, out=None, skipna=True): """ Index of first occurrence of minimum of values. @@ -1357,124 +1216,6 @@ def idxmax(self, axis=None, out=None, skipna=True): argmin = idxmin argmax = idxmax - def cumsum(self, axis=0, dtype=None, out=None, skipna=True): - """ - Cumulative sum of values. Preserves locations of NaN values - - Extra parameters are to preserve ndarray interface. - - Parameters - ---------- - skipna : boolean, default True - Exclude NA/null values - - Returns - ------- - cumsum : Series - """ - arr = _values_from_object(self).copy() - - do_mask = skipna and not issubclass(self.dtype.type, - (np.integer, np.bool_)) - if do_mask: - mask = isnull(arr) - np.putmask(arr, mask, 0.) - - result = arr.cumsum() - - if do_mask: - np.putmask(result, mask, pa.NA) - - return self._constructor(result, index=self.index, name=self.name) - - def cumprod(self, axis=0, dtype=None, out=None, skipna=True): - """ - Cumulative product of values. Preserves locations of NaN values - - Extra parameters are to preserve ndarray interface. - - Parameters - ---------- - skipna : boolean, default True - Exclude NA/null values - - Returns - ------- - cumprod : Series - """ - arr = _values_from_object(self).copy() - - do_mask = skipna and not issubclass(self.dtype.type, - (np.integer, np.bool_)) - if do_mask: - mask = isnull(arr) - np.putmask(arr, mask, 1.) - - result = arr.cumprod() - - if do_mask: - np.putmask(result, mask, pa.NA) - - return self._constructor(result, index=self.index, name=self.name) - - def cummax(self, axis=0, dtype=None, out=None, skipna=True): - """ - Cumulative max of values. Preserves locations of NaN values - - Extra parameters are to preserve ndarray interface. - - Parameters - ---------- - skipna : boolean, default True - Exclude NA/null values - - Returns - ------- - cummax : Series - """ - arr = _values_from_object(self).copy() - - do_mask = skipna and not issubclass(self.dtype.type, np.integer) - if do_mask: - mask = isnull(arr) - np.putmask(arr, mask, -np.inf) - - result = np.maximum.accumulate(arr) - - if do_mask: - np.putmask(result, mask, pa.NA) - - return self._constructor(result, index=self.index, name=self.name) - - def cummin(self, axis=0, dtype=None, out=None, skipna=True): - """ - Cumulative min of values. Preserves locations of NaN values - - Extra parameters are to preserve ndarray interface. - - Parameters - ---------- - skipna : boolean, default True - Exclude NA/null values - - Returns - ------- - cummin : Series - """ - arr = _values_from_object(self).copy() - - do_mask = skipna and not issubclass(self.dtype.type, np.integer) - if do_mask: - mask = isnull(arr) - np.putmask(arr, mask, np.inf) - - result = np.minimum.accumulate(arr) - - if do_mask: - np.putmask(result, mask, pa.NA) - - return self._constructor(result, index=self.index, name=self.name) - @Appender(pa.Array.round.__doc__) def round(self, decimals=0, out=None): """ @@ -2208,6 +1949,11 @@ def apply(self, func, convert_dtype=True, args=(), **kwds): else: return self._constructor(mapped, index=self.index, name=self.name) + def _reduce(self, op, axis=0, skipna=True, numeric_only=None, + filter_type=None, **kwds): + """ perform a reduction operation """ + return op(_values_from_object(self), skipna=skipna, **kwds) + def _reindex_indexer(self, new_index, indexer, copy): if indexer is None: if copy: @@ -2647,7 +2393,8 @@ def to_period(self, freq=None, copy=True): new_index = self.index.to_period(freq=freq) return self._constructor(new_values, index=new_index, name=self.name) -Series._setup_axes(['index'], info_axis=0) +Series._setup_axes(['index'], info_axis=0, stat_axis=0) +Series._add_numeric_operations() _INDEX_TYPES = ndarray, Index, list, tuple # reinstall the SeriesIndexer diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index a41072d97ddc3..fd37717e73ba0 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -17,7 +17,7 @@ map, zip, range, long, lrange, lmap, lzip, OrderedDict, cPickle as pickle, u, StringIO ) -from pandas import compat +from pandas import compat, _np_version_under1p7 from numpy import random, nan from numpy.random import randn, rand diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 6ea58ec997e23..7f50cb2453a21 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -9,7 +9,7 @@ import pandas as pd from pandas import (Index, Series, DataFrame, Panel, - isnull, notnull,date_range) + isnull, notnull,date_range, _np_version_under1p7) from pandas.core.index import Index, MultiIndex from pandas.tseries.index import Timestamp, DatetimeIndex @@ -118,6 +118,7 @@ def test_get_numeric_data(self): self._compare(result, o) # _get_numeric_data is includes _get_bool_data, so can't test for non-inclusion + def test_nonzero(self): # GH 4633 @@ -154,6 +155,20 @@ def f(): self.assertRaises(ValueError, lambda : obj1 or obj2) self.assertRaises(ValueError, lambda : not obj1) + def test_numpy_1_7_compat_numeric_methods(self): + if _np_version_under1p7: + raise nose.SkipTest("numpy < 1.7") + + # GH 4435 + # numpy in 1.7 tries to pass addtional arguments to pandas functions + + o = self._construct(shape=4) + for op in ['min','max','max','var','std','prod','sum','cumsum','cumprod', + 'median','skew','kurt','compound','cummax','cummin','all','any']: + f = getattr(np,op,None) + if f is not None: + f(o) + class TestSeries(unittest.TestCase, Generic): _typ = Series _comparator = lambda self, x, y: assert_series_equal(x,y) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 46ab0fe022e78..fec6460ea31f3 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -18,7 +18,7 @@ from pandas.compat import( range, long, lrange, StringIO, lmap, lzip, map, zip, builtins, OrderedDict ) -from pandas import compat +from pandas import compat, _np_version_under1p7 from pandas.core.panel import Panel from pandas.tools.merge import concat from collections import defaultdict
related #4787 (keywords now pass thru to _reduce), default for `numeric` is still `None` closes #4435 BUG: provide numpy compatibility with 1.7 when implicity using `__array__`, IOW: `np.prod(s)`, or `np.mean(df,axis=1)` will now work (because numpy _decided_ to try to see if the object has this function and then pass all kinds of keywords to it, so pandas gets the right function called with `dtype`, and `out` passed in (currently ignored) CLN: refactored all numeric and stat like function (sum/mean/mad/min/max) and cums (cummin/sum..) to `core/generic.py` from Series/DataFrame/Panel
https://api.github.com/repos/pandas-dev/pandas/pulls/5034
2013-09-29T02:25:49Z
2013-09-29T19:20:49Z
2013-09-29T19:20:49Z
2014-06-24T03:57:23Z
CLN/ENH: Provide full suite of arithmetic (and flex) methods to all NDFrame objects.
diff --git a/doc/source/api.rst b/doc/source/api.rst index 8dcf9c0f52de4..f74f5f0d28a58 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -275,12 +275,30 @@ Binary operator functions :toctree: generated/ Series.add - Series.div - Series.mul Series.sub + Series.mul + Series.div + Series.truediv + Series.floordiv + Series.mod + Series.pow + Series.radd + Series.rsub + Series.rmul + Series.rdiv + Series.rtruediv + Series.rfloordiv + Series.rmod + Series.rpow Series.combine Series.combine_first Series.round + Series.lt + Series.gt + Series.le + Series.ge + Series.ne + Series.eq Function application, GroupBy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -480,13 +498,27 @@ Binary operator functions :toctree: generated/ DataFrame.add - DataFrame.div - DataFrame.mul DataFrame.sub + DataFrame.mul + DataFrame.div + DataFrame.truediv + DataFrame.floordiv + DataFrame.mod + DataFrame.pow DataFrame.radd - DataFrame.rdiv - DataFrame.rmul DataFrame.rsub + DataFrame.rmul + DataFrame.rdiv + DataFrame.rtruediv + DataFrame.rfloordiv + DataFrame.rmod + DataFrame.rpow + DataFrame.lt + DataFrame.gt + DataFrame.le + DataFrame.ge + DataFrame.ne + DataFrame.eq DataFrame.combine DataFrame.combineAdd DataFrame.combine_first @@ -710,9 +742,27 @@ Binary operator functions :toctree: generated/ Panel.add - Panel.div - Panel.mul Panel.sub + Panel.mul + Panel.div + Panel.truediv + Panel.floordiv + Panel.mod + Panel.pow + Panel.radd + Panel.rsub + Panel.rmul + Panel.rdiv + Panel.rtruediv + Panel.rfloordiv + Panel.rmod + Panel.rpow + Panel.lt + Panel.gt + Panel.le + Panel.ge + Panel.ne + Panel.eq Function application, GroupBy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/release.rst b/doc/source/release.rst index 1f0e447429d6a..73e7e3affd944 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -263,6 +263,10 @@ API Changes - Begin removing methods that don't make sense on ``GroupBy`` objects (:issue:`4887`). - Remove deprecated ``read_clipboard/to_clipboard/ExcelFile/ExcelWriter`` from ``pandas.io.parsers`` (:issue:`3717`) + - All non-Index NDFrames (``Series``, ``DataFrame``, ``Panel``, ``Panel4D``, + ``SparsePanel``, etc.), now support the entire set of arithmetic operators + and arithmetic flex methods (add, sub, mul, etc.). ``SparsePanel`` does not + support ``pow`` or ``mod`` with non-scalars. (:issue:`3765`) Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index c7f80a49b9b6c..0796f34ead839 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -68,6 +68,11 @@ API changes df1 and df2 s1 and s2 + - All non-Index NDFrames (``Series``, ``DataFrame``, ``Panel``, ``Panel4D``, + ``SparsePanel``, etc.), now support the entire set of arithmetic operators + and arithmetic flex methods (add, sub, mul, etc.). ``SparsePanel`` does not + support ``pow`` or ``mod`` with non-scalars. (:issue:`3765`) + Prior Version Deprecations/Changes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/computation/expressions.py b/pandas/computation/expressions.py index 45c9a2d5259cb..3c1fb091ab823 100644 --- a/pandas/computation/expressions.py +++ b/pandas/computation/expressions.py @@ -15,6 +15,8 @@ except ImportError: # pragma: no cover _NUMEXPR_INSTALLED = False +_TEST_MODE = None +_TEST_RESULT = None _USE_NUMEXPR = _NUMEXPR_INSTALLED _evaluate = None _where = None @@ -55,9 +57,10 @@ def set_numexpr_threads(n=None): def _evaluate_standard(op, op_str, a, b, raise_on_error=True, **eval_kwargs): """ standard evaluation """ + if _TEST_MODE: + _store_test_result(False) return op(a, b) - def _can_use_numexpr(op, op_str, a, b, dtype_check): """ return a boolean if we WILL be using numexpr """ if op_str is not None: @@ -88,11 +91,8 @@ def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, **eval_kwargs): if _can_use_numexpr(op, op_str, a, b, 'evaluate'): try: - a_value, b_value = a, b - if hasattr(a_value, 'values'): - a_value = a_value.values - if hasattr(b_value, 'values'): - b_value = b_value.values + a_value = getattr(a, "values", a) + b_value = getattr(b, "values", b) result = ne.evaluate('a_value %s b_value' % op_str, local_dict={'a_value': a_value, 'b_value': b_value}, @@ -104,6 +104,9 @@ def _evaluate_numexpr(op, op_str, a, b, raise_on_error=False, **eval_kwargs): if raise_on_error: raise + if _TEST_MODE: + _store_test_result(result is not None) + if result is None: result = _evaluate_standard(op, op_str, a, b, raise_on_error) @@ -119,13 +122,9 @@ def _where_numexpr(cond, a, b, raise_on_error=False): if _can_use_numexpr(None, 'where', a, b, 'where'): try: - cond_value, a_value, b_value = cond, a, b - if hasattr(cond_value, 'values'): - cond_value = cond_value.values - if hasattr(a_value, 'values'): - a_value = a_value.values - if hasattr(b_value, 'values'): - b_value = b_value.values + cond_value = getattr(cond, 'values', cond) + a_value = getattr(a, 'values', a) + b_value = getattr(b, 'values', b) result = ne.evaluate('where(cond_value, a_value, b_value)', local_dict={'cond_value': cond_value, 'a_value': a_value, @@ -189,3 +188,28 @@ def where(cond, a, b, raise_on_error=False, use_numexpr=True): if use_numexpr: return _where(cond, a, b, raise_on_error=raise_on_error) return _where_standard(cond, a, b, raise_on_error=raise_on_error) + + +def set_test_mode(v = True): + """ + Keeps track of whether numexpr was used. Stores an additional ``True`` for + every successful use of evaluate with numexpr since the last + ``get_test_result`` + """ + global _TEST_MODE, _TEST_RESULT + _TEST_MODE = v + _TEST_RESULT = [] + + +def _store_test_result(used_numexpr): + global _TEST_RESULT + if used_numexpr: + _TEST_RESULT.append(used_numexpr) + + +def get_test_result(): + """get test result and reset test_results""" + global _TEST_RESULT + res = _TEST_RESULT + _TEST_RESULT = [] + return res diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index e9201c233753f..aa5c0cc5d50f6 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -2,9 +2,7 @@ import unittest import functools -import numbers from itertools import product -import ast import nose from nose.tools import assert_raises, assert_true, assert_false, assert_equal @@ -250,12 +248,6 @@ def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2): not np.isscalar(rhs_new) and binop in skip_these): with tm.assertRaises(TypeError): _eval_single_bin(lhs_new, binop, rhs_new, self.engine) - elif _bool_and_frame(lhs_new, rhs_new): - with tm.assertRaises(TypeError): - _eval_single_bin(lhs_new, binop, rhs_new, self.engine) - with tm.assertRaises(TypeError): - pd.eval('lhs_new & rhs_new'.format(binop), - engine=self.engine, parser=self.parser) else: expected = _eval_single_bin(lhs_new, binop, rhs_new, self.engine) result = pd.eval(ex, engine=self.engine, parser=self.parser) @@ -301,28 +293,15 @@ def check_operands(left, right, cmp_op): rhs_new = check_operands(mid, rhs, cmp2) if lhs_new is not None and rhs_new is not None: - # these are not compatible operands - if isinstance(lhs_new, Series) and isinstance(rhs_new, DataFrame): - self.assertRaises(TypeError, _eval_single_bin, lhs_new, '&', - rhs_new, self.engine) - elif (_bool_and_frame(lhs_new, rhs_new)): - self.assertRaises(TypeError, _eval_single_bin, lhs_new, '&', - rhs_new, self.engine) - elif _series_and_2d_ndarray(lhs_new, rhs_new): - # TODO: once #4319 is fixed add this test back in - #self.assertRaises(Exception, _eval_single_bin, lhs_new, '&', - #rhs_new, self.engine) - pass - else: - ex1 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2) - ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp1, cmp2) - ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp1, cmp2) - expected = _eval_single_bin(lhs_new, '&', rhs_new, self.engine) - - for ex in (ex1, ex2, ex3): - result = pd.eval(ex, engine=self.engine, - parser=self.parser) - assert_array_equal(result, expected) + ex1 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2) + ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp1, cmp2) + ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp1, cmp2) + expected = _eval_single_bin(lhs_new, '&', rhs_new, self.engine) + + for ex in (ex1, ex2, ex3): + result = pd.eval(ex, engine=self.engine, + parser=self.parser) + assert_array_equal(result, expected) @skip_incompatible_operand def check_simple_cmp_op(self, lhs, cmp1, rhs): diff --git a/pandas/core/common.py b/pandas/core/common.py index d3fa10abc7681..2c5ca42c7be86 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -8,6 +8,7 @@ import codecs import csv import sys +import types from datetime import timedelta @@ -27,6 +28,7 @@ from pandas.core.config import get_option from pandas.core import array as pa + class PandasError(Exception): pass @@ -74,6 +76,31 @@ def __instancecheck__(cls, inst): ABCGeneric = _ABCGeneric("ABCGeneric", tuple(), {}) + +def bind_method(cls, name, func): + """Bind a method to class, python 2 and python 3 compatible. + + Parameters + ---------- + + cls : type + class to receive bound method + name : basestring + name of method on class instance + func : function + function to be bound as method + + + Returns + ------- + None + """ + # only python 2 has bound/unbound method issue + if not compat.PY3: + setattr(cls, name, types.MethodType(func, None, cls)) + else: + setattr(cls, name, func) + def isnull(obj): """Detect missing values (NaN in numeric arrays, None/NaN in object arrays) @@ -360,10 +387,10 @@ def _take_2d_multi_generic(arr, indexer, out, fill_value, mask_info): if col_needs: out[:, col_mask] = fill_value for i in range(len(row_idx)): - u = row_idx[i] + u_ = row_idx[i] for j in range(len(col_idx)): v = col_idx[j] - out[i, j] = arr[u, v] + out[i, j] = arr[u_, v] def _take_nd_generic(arr, indexer, out, axis, fill_value, mask_info): @@ -2348,3 +2375,10 @@ def save(obj, path): # TODO remove in 0.13 warnings.warn("save is deprecated, use obj.to_pickle", FutureWarning) from pandas.io.pickle import to_pickle return to_pickle(obj, path) + + +def _maybe_match_name(a, b): + name = None + if a.name == b.name: + name = a.name + return name diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 01e0d74ef8ce6..c6727f91644fc 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -34,7 +34,7 @@ from pandas.core.internals import (BlockManager, create_block_manager_from_arrays, create_block_manager_from_blocks) -from pandas.core.series import Series, _radd_compat +from pandas.core.series import Series import pandas.computation.expressions as expressions from pandas.computation.eval import eval as _eval from pandas.computation.expr import _ensure_scope @@ -42,7 +42,6 @@ from pandas.compat import(range, zip, lrange, lmap, lzip, StringIO, u, OrderedDict, raise_with_traceback) from pandas import compat -from pandas.util.terminal import get_terminal_size from pandas.util.decorators import deprecate, Appender, Substitution from pandas.tseries.period import PeriodIndex @@ -53,6 +52,7 @@ import pandas.core.common as com import pandas.core.format as fmt import pandas.core.nanops as nanops +import pandas.core.ops as ops import pandas.lib as lib import pandas.algos as _algos @@ -62,31 +62,6 @@ #---------------------------------------------------------------------- # Docstring templates -_arith_doc = """ -Binary operator %s with support to substitute a fill_value for missing data in -one of the inputs - -Parameters ----------- -other : Series, DataFrame, or constant -axis : {0, 1, 'index', 'columns'} - For Series input, axis to match Series index on -fill_value : None or float value, default None - Fill missing (NaN) values with this value. If both DataFrame locations are - missing, the result will be missing -level : int or name - Broadcast across a level, matching Index values on the - passed MultiIndex level - -Notes ------ -Mismatched indices will be unioned together - -Returns -------- -result : DataFrame -""" - _stat_doc = """ Return %(name)s over requested axis. @@ -181,153 +156,6 @@ merged : DataFrame """ -#---------------------------------------------------------------------- -# Factory helper methods - - -def _arith_method(op, name, str_rep=None, default_axis='columns', fill_zeros=None, **eval_kwargs): - def na_op(x, y): - try: - result = expressions.evaluate( - op, str_rep, x, y, raise_on_error=True, **eval_kwargs) - result = com._fill_zeros(result, y, fill_zeros) - - except TypeError: - xrav = x.ravel() - result = np.empty(x.size, dtype=x.dtype) - if isinstance(y, (np.ndarray, Series)): - yrav = y.ravel() - mask = notnull(xrav) & notnull(yrav) - result[mask] = op(xrav[mask], yrav[mask]) - else: - mask = notnull(xrav) - result[mask] = op(xrav[mask], y) - - result, changed = com._maybe_upcast_putmask(result, -mask, np.nan) - result = result.reshape(x.shape) - - return result - - @Appender(_arith_doc % name) - def f(self, other, axis=default_axis, level=None, fill_value=None): - if isinstance(other, DataFrame): # Another DataFrame - return self._combine_frame(other, na_op, fill_value, level) - elif isinstance(other, Series): - return self._combine_series(other, na_op, fill_value, axis, level) - elif isinstance(other, (list, tuple)): - if axis is not None and self._get_axis_name(axis) == 'index': - casted = Series(other, index=self.index) - else: - casted = Series(other, index=self.columns) - return self._combine_series(casted, na_op, fill_value, axis, level) - elif isinstance(other, np.ndarray): - if other.ndim == 1: - if axis is not None and self._get_axis_name(axis) == 'index': - casted = Series(other, index=self.index) - else: - casted = Series(other, index=self.columns) - return self._combine_series(casted, na_op, fill_value, - axis, level) - elif other.ndim == 2: - casted = DataFrame(other, index=self.index, - columns=self.columns) - return self._combine_frame(casted, na_op, fill_value, level) - else: - raise ValueError("Incompatible argument shape %s" % (other.shape,)) - else: - return self._combine_const(other, na_op) - - f.__name__ = name - - return f - - -def _flex_comp_method(op, name, str_rep=None, default_axis='columns'): - - def na_op(x, y): - try: - result = op(x, y) - except TypeError: - xrav = x.ravel() - result = np.empty(x.size, dtype=x.dtype) - if isinstance(y, (np.ndarray, Series)): - yrav = y.ravel() - mask = notnull(xrav) & notnull(yrav) - result[mask] = op(np.array(list(xrav[mask])), - np.array(list(yrav[mask]))) - else: - mask = notnull(xrav) - result[mask] = op(np.array(list(xrav[mask])), y) - - if op == operator.ne: # pragma: no cover - np.putmask(result, -mask, True) - else: - np.putmask(result, -mask, False) - result = result.reshape(x.shape) - - return result - - @Appender('Wrapper for flexible comparison methods %s' % name) - def f(self, other, axis=default_axis, level=None): - if isinstance(other, DataFrame): # Another DataFrame - return self._flex_compare_frame(other, na_op, str_rep, level) - - elif isinstance(other, Series): - return self._combine_series(other, na_op, None, axis, level) - - elif isinstance(other, (list, tuple)): - if axis is not None and self._get_axis_name(axis) == 'index': - casted = Series(other, index=self.index) - else: - casted = Series(other, index=self.columns) - - return self._combine_series(casted, na_op, None, axis, level) - - elif isinstance(other, np.ndarray): - if other.ndim == 1: - if axis is not None and self._get_axis_name(axis) == 'index': - casted = Series(other, index=self.index) - else: - casted = Series(other, index=self.columns) - - return self._combine_series(casted, na_op, None, axis, level) - - elif other.ndim == 2: - casted = DataFrame(other, index=self.index, - columns=self.columns) - - return self._flex_compare_frame(casted, na_op, str_rep, level) - - else: - raise ValueError("Incompatible argument shape: %s" % - (other.shape,)) - - else: - return self._combine_const(other, na_op) - - f.__name__ = name - - return f - - -def _comp_method(func, name, str_rep): - @Appender('Wrapper for comparison method %s' % name) - def f(self, other): - if isinstance(other, DataFrame): # Another DataFrame - return self._compare_frame(other, func, str_rep) - elif isinstance(other, Series): - return self._combine_series_infer(other, func) - else: - - # straight boolean comparisions we want to allow all columns - # (regardless of dtype to pass thru) - return self._combine_const(other, func, raise_on_error=False).fillna(True).astype(bool) - - f.__name__ = name - - return f - - #---------------------------------------------------------------------- # DataFrame class @@ -752,79 +580,6 @@ def __len__(self): """Returns length of info axis, but here we use the index """ return len(self.index) - #---------------------------------------------------------------------- - # Arithmetic methods - - add = _arith_method(operator.add, 'add', '+') - mul = _arith_method(operator.mul, 'multiply', '*') - sub = _arith_method(operator.sub, 'subtract', '-') - div = divide = _arith_method(lambda x, y: x / y, 'divide', '/') - pow = _arith_method(operator.pow, 'pow', '**') - mod = _arith_method(lambda x, y: x % y, 'mod') - - radd = _arith_method(_radd_compat, 'radd') - rmul = _arith_method(operator.mul, 'rmultiply') - rsub = _arith_method(lambda x, y: y - x, 'rsubtract') - rdiv = _arith_method(lambda x, y: y / x, 'rdivide') - rpow = _arith_method(lambda x, y: y ** x, 'rpow') - rmod = _arith_method(lambda x, y: y % x, 'rmod') - - __add__ = _arith_method(operator.add, '__add__', '+', default_axis=None) - __sub__ = _arith_method(operator.sub, '__sub__', '-', default_axis=None) - __mul__ = _arith_method(operator.mul, '__mul__', '*', default_axis=None) - __truediv__ = _arith_method(operator.truediv, '__truediv__', '/', - default_axis=None, fill_zeros=np.inf, truediv=True) - # numexpr produces a different value (python/numpy: 0.000, numexpr: inf) - # when dividing by zero, so can't use floordiv speed up (yet) - # __floordiv__ = _arith_method(operator.floordiv, '__floordiv__', '//', - __floordiv__ = _arith_method(operator.floordiv, '__floordiv__', - default_axis=None, fill_zeros=np.inf) - __pow__ = _arith_method(operator.pow, '__pow__', '**', default_axis=None) - - # currently causes a floating point exception to occur - so sticking with unaccelerated for now - # __mod__ = _arith_method(operator.mod, '__mod__', '%', default_axis=None, fill_zeros=np.nan) - __mod__ = _arith_method( - operator.mod, '__mod__', default_axis=None, fill_zeros=np.nan) - - __radd__ = _arith_method(_radd_compat, '__radd__', default_axis=None) - __rmul__ = _arith_method(operator.mul, '__rmul__', default_axis=None) - __rsub__ = _arith_method(lambda x, y: y - x, '__rsub__', default_axis=None) - __rtruediv__ = _arith_method(lambda x, y: y / x, '__rtruediv__', - default_axis=None, fill_zeros=np.inf) - __rfloordiv__ = _arith_method(lambda x, y: y // x, '__rfloordiv__', - default_axis=None, fill_zeros=np.inf) - __rpow__ = _arith_method(lambda x, y: y ** x, '__rpow__', - default_axis=None) - __rmod__ = _arith_method(lambda x, y: y % x, '__rmod__', default_axis=None, - fill_zeros=np.nan) - - # boolean operators - __and__ = _arith_method(operator.and_, '__and__', '&') - __or__ = _arith_method(operator.or_, '__or__', '|') - __xor__ = _arith_method(operator.xor, '__xor__') - - # Python 2 division methods - if not compat.PY3: - __div__ = _arith_method(operator.div, '__div__', '/', - default_axis=None, fill_zeros=np.inf, truediv=False) - __rdiv__ = _arith_method(lambda x, y: y / x, '__rdiv__', - default_axis=None, fill_zeros=np.inf) - - # Comparison methods - __eq__ = _comp_method(operator.eq, '__eq__', '==') - __ne__ = _comp_method(operator.ne, '__ne__', '!=') - __lt__ = _comp_method(operator.lt, '__lt__', '<') - __gt__ = _comp_method(operator.gt, '__gt__', '>') - __le__ = _comp_method(operator.le, '__le__', '<=') - __ge__ = _comp_method(operator.ge, '__ge__', '>=') - - eq = _flex_comp_method(operator.eq, 'eq', '==') - ne = _flex_comp_method(operator.ne, 'ne', '!=') - lt = _flex_comp_method(operator.lt, 'lt', '<') - gt = _flex_comp_method(operator.gt, 'gt', '>') - le = _flex_comp_method(operator.le, 'le', '<=') - ge = _flex_comp_method(operator.ge, 'ge', '>=') - def dot(self, other): """ Matrix multiplication with DataFrame or Series objects @@ -5152,6 +4907,8 @@ def boxplot(self, column=None, by=None, ax=None, fontsize=None, return ax DataFrame.boxplot = boxplot +ops.add_flex_arithmetic_methods(DataFrame, **ops.frame_flex_funcs) +ops.add_special_arithmetic_methods(DataFrame, **ops.frame_special_funcs) if __name__ == '__main__': import nose diff --git a/pandas/core/ops.py b/pandas/core/ops.py new file mode 100644 index 0000000000000..4ce2143fdd92c --- /dev/null +++ b/pandas/core/ops.py @@ -0,0 +1,911 @@ +""" +Arithmetic operations for PandasObjects + +This is not a public API. +""" +import operator +import numpy as np +import pandas as pd +from pandas import compat, lib, tslib +import pandas.index as _index +from pandas.util.decorators import Appender +import pandas.core.common as com +import pandas.core.array as pa +import pandas.computation.expressions as expressions +from pandas.core.common import(bind_method, is_list_like, notnull, isnull, + _values_from_object, _maybe_match_name) + +# ----------------------------------------------------------------------------- +# Functions that add arithmetic methods to objects, given arithmetic factory +# methods + +def _create_methods(arith_method, radd_func, comp_method, bool_method, + use_numexpr, special=False, default_axis='columns'): + # NOTE: Only frame cares about default_axis, specifically: special methods + # have default axis None, whereas flex methods have default axis 'columns' + # if we're not using numexpr, then don't pass a str_rep + if use_numexpr: + op = lambda x: x + else: + op = lambda x: None + if special: + def names(x): + if x[-1] == "_": + return "__%s_" % x + else: + return "__%s__" % x + else: + names = lambda x: x + radd_func = radd_func or operator.add + # Inframe, all special methods have default_axis=None, flex methods have default_axis set to the default (columns) + new_methods = dict( + add=arith_method(operator.add, names('add'), op('+'), default_axis=default_axis), + radd=arith_method(radd_func, names('radd'), op('+'), default_axis=default_axis), + sub=arith_method(operator.sub, names('sub'), op('-'), default_axis=default_axis), + mul=arith_method(operator.mul, names('mul'), op('*'), default_axis=default_axis), + truediv=arith_method(operator.truediv, names('truediv'), op('/'), + truediv=True, fill_zeros=np.inf, default_axis=default_axis), + floordiv=arith_method(operator.floordiv, names('floordiv'), op('//'), + default_axis=default_axis, fill_zeros=np.inf), + # Causes a floating point exception in the tests when numexpr + # enabled, so for now no speedup + mod=arith_method(operator.mod, names('mod'), default_axis=default_axis, + fill_zeros=np.nan), + pow=arith_method(operator.pow, names('pow'), op('**'), default_axis=default_axis), + # not entirely sure why this is necessary, but previously was included + # so it's here to maintain compatibility + rmul=arith_method(operator.mul, names('rmul'), default_axis=default_axis), + rsub=arith_method(lambda x, y: y - x, names('rsub'), default_axis=default_axis), + rtruediv=arith_method(lambda x, y: operator.truediv(y, x), names('rtruediv'), + truediv=True, fill_zeros=np.inf, default_axis=default_axis), + rfloordiv=arith_method(lambda x, y: operator.floordiv(y, x), names('rfloordiv'), + default_axis=default_axis, fill_zeros=np.inf), + rpow=arith_method(lambda x, y: y ** x, names('rpow'), default_axis=default_axis), + rmod=arith_method(lambda x, y: y % x, names('rmod'), default_axis=default_axis), + ) + if not compat.PY3: + new_methods["div"] = arith_method(operator.div, names('div'), op('/'), + truediv=False, fill_zeros=np.inf, default_axis=default_axis) + new_methods["rdiv"] = arith_method(lambda x, y: operator.div(y, x), names('rdiv'), + truediv=False, fill_zeros=np.inf, default_axis=default_axis) + else: + new_methods["div"] = arith_method(operator.truediv, names('div'), op('/'), + truediv=True, fill_zeros=np.inf, default_axis=default_axis) + new_methods["rdiv"] = arith_method(lambda x, y: operator.truediv(y, x), names('rdiv'), + truediv=False, fill_zeros=np.inf, default_axis=default_axis) + # Comp methods never had a default axis set + if comp_method: + new_methods.update(dict( + eq=comp_method(operator.eq, names('eq'), op('==')), + ne=comp_method(operator.ne, names('ne'), op('!='), masker=True), + lt=comp_method(operator.lt, names('lt'), op('<')), + gt=comp_method(operator.gt, names('gt'), op('>')), + le=comp_method(operator.le, names('le'), op('<=')), + ge=comp_method(operator.ge, names('ge'), op('>=')), + )) + if bool_method: + new_methods.update(dict( + and_=bool_method(operator.and_, names('and_ [&]'), op('&')), + or_=bool_method(operator.or_, names('or_ [|]'), op('|')), + # For some reason ``^`` wasn't used in original. + xor=bool_method(operator.xor, names('xor [^]')), + rand_=bool_method(lambda x, y: operator.and_(y, x), names('rand_[&]')), + ror_=bool_method(lambda x, y: operator.or_(y, x), names('ror_ [|]')), + rxor=bool_method(lambda x, y: operator.xor(y, x), names('rxor [^]')) + )) + + new_methods = dict((names(k), v) for k, v in new_methods.items()) + return new_methods + + +def add_methods(cls, new_methods, force, select, exclude): + if select and exclude: + raise TypeError("May only pass either select or exclude") + methods = new_methods + if select: + select = set(select) + methods = {} + for key, method in new_methods.items(): + if key in select: + methods[key] = method + if exclude: + for k in exclude: + new_methods.pop(k, None) + + for name, method in new_methods.items(): + if force or name not in cls.__dict__: + bind_method(cls, name, method) + +#---------------------------------------------------------------------- +# Arithmetic +def add_special_arithmetic_methods(cls, arith_method=None, radd_func=None, + comp_method=None, bool_method=None, + use_numexpr=True, force=False, select=None, + exclude=None): + """ + Adds the full suite of special arithmetic methods (``__add__``, ``__sub__``, etc.) to the class. + + Parameters + ---------- + arith_method : function (optional) + factory for special arithmetic methods, with op string: + f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs) + radd_func : function (optional) + Possible replacement for ``operator.add`` for compatibility + comp_method : function, optional, + factory for rich comparison - signature: f(op, name, str_rep) + use_numexpr : bool, default True + whether to accelerate with numexpr, defaults to True + force : bool, default False + if False, checks whether function is defined **on ``cls.__dict__``** before defining + if True, always defines functions on class base + select : iterable of strings (optional) + if passed, only sets functions with names in select + exclude : iterable of strings (optional) + if passed, will not set functions with names in exclude + """ + radd_func = radd_func or operator.add + # in frame, special methods have default_axis = None, comp methods use 'columns' + new_methods = _create_methods(arith_method, radd_func, comp_method, bool_method, use_numexpr, default_axis=None, + special=True) + + # inplace operators (I feel like these should get passed an `inplace=True` + # or just be removed + new_methods.update(dict( + __iadd__=new_methods["__add__"], + __isub__=new_methods["__sub__"], + __imul__=new_methods["__mul__"], + __itruediv__=new_methods["__truediv__"], + __ipow__=new_methods["__pow__"] + )) + if not compat.PY3: + new_methods["__idiv__"] = new_methods["__div__"] + + add_methods(cls, new_methods=new_methods, force=force, select=select, exclude=exclude) + + +def add_flex_arithmetic_methods(cls, flex_arith_method, radd_func=None, + flex_comp_method=None, flex_bool_method=None, + use_numexpr=True, force=False, select=None, + exclude=None): + """ + Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``) to the class. + + Parameters + ---------- + flex_arith_method : function (optional) + factory for special arithmetic methods, with op string: + f(op, name, str_rep, default_axis=None, fill_zeros=None, **eval_kwargs) + radd_func : function (optional) + Possible replacement for ``lambda x, y: operator.add(y, x)`` for compatibility + flex_comp_method : function, optional, + factory for rich comparison - signature: f(op, name, str_rep) + use_numexpr : bool, default True + whether to accelerate with numexpr, defaults to True + force : bool, default False + if False, checks whether function is defined **on ``cls.__dict__``** before defining + if True, always defines functions on class base + select : iterable of strings (optional) + if passed, only sets functions with names in select + exclude : iterable of strings (optional) + if passed, will not set functions with names in exclude + """ + radd_func = radd_func or (lambda x, y: operator.add(y, x)) + # in frame, default axis is 'columns', doesn't matter for series and panel + new_methods = _create_methods( + flex_arith_method, radd_func, flex_comp_method, flex_bool_method, + use_numexpr, default_axis='columns', special=False) + new_methods.update(dict( + multiply=new_methods['mul'], + subtract=new_methods['sub'], + divide=new_methods['div'] + )) + # opt out of bool flex methods for now + for k in ('ror_', 'rxor', 'rand_'): + if k in new_methods: + new_methods.pop(k) + + add_methods(cls, new_methods=new_methods, force=force, select=select, exclude=exclude) + +def cleanup_name(name): + """cleanup special names + >>> cleanup_name("__rsub__") + sub + >>> cleanup_name("rand_") + and_ + """ + if name[:2] == "__": + name = name[2:-2] + if name[0] == "r": + name = name[1:] + # readd last _ for operator names. + if name == "or": + name = "or_" + elif name == "and": + name = "and_" + return name + + +# direct copy of original Series _TimeOp +class _TimeOp(object): + """ + Wrapper around Series datetime/time/timedelta arithmetic operations. + Generally, you should use classmethod ``maybe_convert_for_time_op`` as an + entry point. + """ + fill_value = tslib.iNaT + wrap_results = staticmethod(lambda x: x) + dtype = None + + def __init__(self, left, right, name): + self.name = name + + lvalues = self._convert_to_array(left, name=name) + rvalues = self._convert_to_array(right, name=name) + + self.is_timedelta_lhs = com.is_timedelta64_dtype(left) + self.is_datetime_lhs = com.is_datetime64_dtype(left) + self.is_integer_lhs = left.dtype.kind in ['i','u'] + self.is_datetime_rhs = com.is_datetime64_dtype(rvalues) + self.is_timedelta_rhs = (com.is_timedelta64_dtype(rvalues) + or (not self.is_datetime_rhs + and pd._np_version_under1p7)) + self.is_integer_rhs = rvalues.dtype.kind in ('i','u') + + self._validate() + + self._convert_for_datetime(lvalues, rvalues) + + def _validate(self): + # timedelta and integer mul/div + + if (self.is_timedelta_lhs and self.is_integer_rhs) or\ + (self.is_integer_lhs and self.is_timedelta_rhs): + + if self.name not in ('__truediv__','__div__','__mul__'): + raise TypeError("can only operate on a timedelta and an integer for " + "division, but the operator [%s] was passed" % self.name) + + # 2 datetimes + elif self.is_datetime_lhs and self.is_datetime_rhs: + if self.name != '__sub__': + raise TypeError("can only operate on a datetimes for subtraction, " + "but the operator [%s] was passed" % self.name) + + + # 2 timedeltas + elif self.is_timedelta_lhs and self.is_timedelta_rhs: + + if self.name not in ('__div__', '__truediv__', '__add__', '__sub__'): + raise TypeError("can only operate on a timedeltas for " + "addition, subtraction, and division, but the operator [%s] was passed" % self.name) + + # datetime and timedelta + elif self.is_datetime_lhs and self.is_timedelta_rhs: + + if self.name not in ('__add__','__sub__'): + raise TypeError("can only operate on a datetime with a rhs of a timedelta for " + "addition and subtraction, but the operator [%s] was passed" % self.name) + + elif self.is_timedelta_lhs and self.is_datetime_rhs: + + if self.name != '__add__': + raise TypeError("can only operate on a timedelta and a datetime for " + "addition, but the operator [%s] was passed" % self.name) + else: + raise TypeError('cannot operate on a series with out a rhs ' + 'of a series/ndarray of type datetime64[ns] ' + 'or a timedelta') + + def _convert_to_array(self, values, name=None): + """converts values to ndarray""" + from pandas.tseries.timedeltas import _possibly_cast_to_timedelta + + coerce = 'compat' if pd._np_version_under1p7 else True + if not is_list_like(values): + values = np.array([values]) + inferred_type = lib.infer_dtype(values) + if inferred_type in ('datetime64','datetime','date','time'): + # a datetlike + if not (isinstance(values, (pa.Array, pd.Series)) and com.is_datetime64_dtype(values)): + values = tslib.array_to_datetime(values) + elif isinstance(values, pd.DatetimeIndex): + values = values.to_series() + elif inferred_type in ('timedelta', 'timedelta64'): + # have a timedelta, convert to to ns here + values = _possibly_cast_to_timedelta(values, coerce=coerce) + elif inferred_type == 'integer': + # py3 compat where dtype is 'm' but is an integer + if values.dtype.kind == 'm': + values = values.astype('timedelta64[ns]') + elif isinstance(values, pd.PeriodIndex): + values = values.to_timestamp().to_series() + elif name not in ('__truediv__','__div__','__mul__'): + raise TypeError("incompatible type for a datetime/timedelta " + "operation [{0}]".format(name)) + elif isinstance(values[0], pd.DateOffset): + # handle DateOffsets + os = pa.array([ getattr(v,'delta',None) for v in values ]) + mask = isnull(os) + if mask.any(): + raise TypeError("cannot use a non-absolute DateOffset in " + "datetime/timedelta operations [{0}]".format( + ','.join([ com.pprint_thing(v) for v in values[mask] ]))) + values = _possibly_cast_to_timedelta(os, coerce=coerce) + else: + raise TypeError("incompatible type [{0}] for a datetime/timedelta operation".format(pa.array(values).dtype)) + + return values + + def _convert_for_datetime(self, lvalues, rvalues): + mask = None + # datetimes require views + if self.is_datetime_lhs or self.is_datetime_rhs: + # datetime subtraction means timedelta + if self.is_datetime_lhs and self.is_datetime_rhs: + self.dtype = 'timedelta64[ns]' + else: + self.dtype = 'datetime64[ns]' + mask = isnull(lvalues) | isnull(rvalues) + lvalues = lvalues.view(np.int64) + rvalues = rvalues.view(np.int64) + + # otherwise it's a timedelta + else: + self.dtype = 'timedelta64[ns]' + mask = isnull(lvalues) | isnull(rvalues) + lvalues = lvalues.astype(np.int64) + rvalues = rvalues.astype(np.int64) + + # time delta division -> unit less + # integer gets converted to timedelta in np < 1.6 + if (self.is_timedelta_lhs and self.is_timedelta_rhs) and\ + not self.is_integer_rhs and\ + not self.is_integer_lhs and\ + self.name in ('__div__', '__truediv__'): + self.dtype = 'float64' + self.fill_value = np.nan + lvalues = lvalues.astype(np.float64) + rvalues = rvalues.astype(np.float64) + + # if we need to mask the results + if mask is not None: + if mask.any(): + def f(x): + x = pa.array(x,dtype=self.dtype) + np.putmask(x,mask,self.fill_value) + return x + self.wrap_results = f + self.lvalues = lvalues + self.rvalues = rvalues + + @classmethod + def maybe_convert_for_time_op(cls, left, right, name): + """ + if ``left`` and ``right`` are appropriate for datetime arithmetic with + operation ``name``, processes them and returns a ``_TimeOp`` object + that stores all the required values. Otherwise, it will generate + either a ``NotImplementedError`` or ``None``, indicating that the + operation is unsupported for datetimes (e.g., an unsupported r_op) or + that the data is not the right type for time ops. + """ + # decide if we can do it + is_timedelta_lhs = com.is_timedelta64_dtype(left) + is_datetime_lhs = com.is_datetime64_dtype(left) + if not (is_datetime_lhs or is_timedelta_lhs): + return None + # rops are allowed. No need for special checks, just strip off + # r part. + if name.startswith('__r'): + name = "__" + name[3:] + return cls(left, right, name) + + +def _arith_method_SERIES(op, name, str_rep=None, fill_zeros=None, default_axis=None, **eval_kwargs): + """ + Wrapper function for Series arithmetic operations, to avoid + code duplication. + """ + def na_op(x, y): + try: + result = expressions.evaluate(op, str_rep, x, y, + raise_on_error=True, **eval_kwargs) + except TypeError: + result = pa.empty(len(x), dtype=x.dtype) + if isinstance(y, (pa.Array, pd.Series)): + mask = notnull(x) & notnull(y) + result[mask] = op(x[mask], y[mask]) + else: + mask = notnull(x) + result[mask] = op(x[mask], y) + + result, changed = com._maybe_upcast_putmask(result, -mask, pa.NA) + + result = com._fill_zeros(result, y, fill_zeros) + return result + + def wrapper(left, right, name=name): + + time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name) + + if time_converted is None: + lvalues, rvalues = left, right + dtype = None + wrap_results = lambda x: x + elif time_converted == NotImplemented: + return NotImplemented + else: + lvalues = time_converted.lvalues + rvalues = time_converted.rvalues + dtype = time_converted.dtype + wrap_results = time_converted.wrap_results + + if isinstance(rvalues, pd.Series): + join_idx, lidx, ridx = left.index.join(rvalues.index, how='outer', + return_indexers=True) + rindex = rvalues.index + name = _maybe_match_name(left, rvalues) + lvalues = getattr(lvalues, 'values', lvalues) + rvalues = getattr(rvalues, 'values', rvalues) + if left.index.equals(rindex): + index = left.index + else: + index = join_idx + + if lidx is not None: + lvalues = com.take_1d(lvalues, lidx) + + if ridx is not None: + rvalues = com.take_1d(rvalues, ridx) + + arr = na_op(lvalues, rvalues) + + return left._constructor(wrap_results(arr), index=index, + name=name, dtype=dtype) + elif isinstance(right, pd.DataFrame): + return NotImplemented + else: + # scalars + if hasattr(lvalues, 'values'): + lvalues = lvalues.values + return left._constructor(wrap_results(na_op(lvalues, rvalues)), + index=left.index, name=left.name, dtype=dtype) + return wrapper + +def _comp_method_SERIES(op, name, str_rep=None, masker=False): + """ + Wrapper function for Series arithmetic operations, to avoid + code duplication. + """ + def na_op(x, y): + if x.dtype == np.object_: + if isinstance(y, list): + y = lib.list_to_object_array(y) + + if isinstance(y, (pa.Array, pd.Series)): + if y.dtype != np.object_: + result = lib.vec_compare(x, y.astype(np.object_), op) + else: + result = lib.vec_compare(x, y, op) + else: + result = lib.scalar_compare(x, y, op) + else: + + try: + result = getattr(x,name)(y) + if result is NotImplemented: + raise TypeError("invalid type comparison") + except (AttributeError): + result = op(x, y) + + return result + + def wrapper(self, other): + if isinstance(other, pd.Series): + name = _maybe_match_name(self, other) + if len(self) != len(other): + raise ValueError('Series lengths must match to compare') + return self._constructor(na_op(self.values, other.values), + index=self.index, name=name) + elif isinstance(other, pd.DataFrame): # pragma: no cover + return NotImplemented + elif isinstance(other, (pa.Array, pd.Series)): + if len(self) != len(other): + raise ValueError('Lengths must match to compare') + return self._constructor(na_op(self.values, np.asarray(other)), + index=self.index, name=self.name) + else: + + mask = isnull(self) + + values = self.values + other = _index.convert_scalar(values, other) + + if issubclass(values.dtype.type, np.datetime64): + values = values.view('i8') + + # scalars + res = na_op(values, other) + if np.isscalar(res): + raise TypeError('Could not compare %s type with Series' + % type(other)) + + # always return a full value series here + res = _values_from_object(res) + + res = pd.Series(res, index=self.index, name=self.name, dtype='bool') + + # mask out the invalids + if mask.any(): + res[mask.values] = masker + + return res + return wrapper + + +def _bool_method_SERIES(op, name, str_rep=None): + """ + Wrapper function for Series arithmetic operations, to avoid + code duplication. + """ + def na_op(x, y): + try: + result = op(x, y) + except TypeError: + if isinstance(y, list): + y = lib.list_to_object_array(y) + + if isinstance(y, (pa.Array, pd.Series)): + if (x.dtype == np.bool_ and + y.dtype == np.bool_): # pragma: no cover + result = op(x, y) # when would this be hit? + else: + x = com._ensure_object(x) + y = com._ensure_object(y) + result = lib.vec_binop(x, y, op) + else: + result = lib.scalar_binop(x, y, op) + + return result + + def wrapper(self, other): + if isinstance(other, pd.Series): + name = _maybe_match_name(self, other) + return self._constructor(na_op(self.values, other.values), + index=self.index, name=name) + elif isinstance(other, pd.DataFrame): + return NotImplemented + else: + # scalars + return self._constructor(na_op(self.values, other), + index=self.index, name=self.name) + return wrapper + + +# original Series _radd_compat method +def _radd_compat(left, right): + radd = lambda x, y: y + x + # GH #353, NumPy 1.5.1 workaround + try: + output = radd(left, right) + except TypeError: + cond = (pd._np_version_under1p6 and + left.dtype == np.object_) + if cond: # pragma: no cover + output = np.empty_like(left) + output.flat[:] = [radd(x, right) for x in left.flat] + else: + raise + + return output + + +def _flex_method_SERIES(op, name, str_rep=None, default_axis=None, + fill_zeros=None, **eval_kwargs): + doc = """ + Binary operator %s with support to substitute a fill_value for missing data + in one of the inputs + + Parameters + ---------- + other: Series or scalar value + fill_value : None or float value, default None (NaN) + Fill missing (NaN) values with this value. If both Series are + missing, the result will be missing + level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level + + Returns + ------- + result : Series + """ % name + + @Appender(doc) + def f(self, other, level=None, fill_value=None): + if isinstance(other, pd.Series): + return self._binop(other, op, level=level, fill_value=fill_value) + elif isinstance(other, (pa.Array, pd.Series, list, tuple)): + if len(other) != len(self): + raise ValueError('Lengths must be equal') + return self._binop(self._constructor(other, self.index), op, + level=level, fill_value=fill_value) + else: + return self._constructor(op(self.values, other), self.index, + name=self.name) + + f.__name__ = name + return f + +series_flex_funcs = dict(flex_arith_method=_flex_method_SERIES, + radd_func=_radd_compat, + flex_comp_method=_comp_method_SERIES) + +series_special_funcs = dict(arith_method=_arith_method_SERIES, + radd_func=_radd_compat, + comp_method=_comp_method_SERIES, + bool_method=_bool_method_SERIES) + + +_arith_doc_FRAME = """ +Binary operator %s with support to substitute a fill_value for missing data in +one of the inputs + +Parameters +---------- +other : Series, DataFrame, or constant +axis : {0, 1, 'index', 'columns'} + For Series input, axis to match Series index on +fill_value : None or float value, default None + Fill missing (NaN) values with this value. If both DataFrame locations are + missing, the result will be missing +level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level + +Notes +----- +Mismatched indices will be unioned together + +Returns +------- +result : DataFrame +""" + + +def _arith_method_FRAME(op, name, str_rep=None, default_axis='columns', fill_zeros=None, **eval_kwargs): + def na_op(x, y): + try: + result = expressions.evaluate( + op, str_rep, x, y, raise_on_error=True, **eval_kwargs) + except TypeError: + xrav = x.ravel() + result = np.empty(x.size, dtype=x.dtype) + if isinstance(y, (np.ndarray, pd.Series)): + yrav = y.ravel() + mask = notnull(xrav) & notnull(yrav) + result[mask] = op(xrav[mask], yrav[mask]) + else: + mask = notnull(xrav) + result[mask] = op(xrav[mask], y) + + result, changed = com._maybe_upcast_putmask(result, -mask, np.nan) + result = result.reshape(x.shape) + + result = com._fill_zeros(result, y, fill_zeros) + + return result + + @Appender(_arith_doc_FRAME % name) + def f(self, other, axis=default_axis, level=None, fill_value=None): + if isinstance(other, pd.DataFrame): # Another DataFrame + return self._combine_frame(other, na_op, fill_value, level) + elif isinstance(other, pd.Series): + return self._combine_series(other, na_op, fill_value, axis, level) + elif isinstance(other, (list, tuple)): + if axis is not None and self._get_axis_name(axis) == 'index': + # casted = self._constructor_sliced(other, index=self.index) + casted = pd.Series(other, index=self.index) + else: + # casted = self._constructor_sliced(other, index=self.columns) + casted = pd.Series(other, index=self.columns) + return self._combine_series(casted, na_op, fill_value, axis, level) + elif isinstance(other, np.ndarray): + if other.ndim == 1: + if axis is not None and self._get_axis_name(axis) == 'index': + # casted = self._constructor_sliced(other, index=self.index) + casted = pd.Series(other, index=self.index) + else: + # casted = self._constructor_sliced(other, index=self.columns) + casted = pd.Series(other, index=self.columns) + return self._combine_series(casted, na_op, fill_value, + axis, level) + elif other.ndim == 2: + # casted = self._constructor(other, index=self.index, + # columns=self.columns) + casted = pd.DataFrame(other, index=self.index, + columns=self.columns) + return self._combine_frame(casted, na_op, fill_value, level) + else: + raise ValueError("Incompatible argument shape: %s" % + (other.shape,)) + else: + return self._combine_const(other, na_op) + + f.__name__ = name + + return f + + +# Masker unused for now +def _flex_comp_method_FRAME(op, name, str_rep=None, default_axis='columns', + masker=False): + + def na_op(x, y): + try: + result = op(x, y) + except TypeError: + xrav = x.ravel() + result = np.empty(x.size, dtype=x.dtype) + if isinstance(y, (np.ndarray, pd.Series)): + yrav = y.ravel() + mask = notnull(xrav) & notnull(yrav) + result[mask] = op(np.array(list(xrav[mask])), + np.array(list(yrav[mask]))) + else: + mask = notnull(xrav) + result[mask] = op(np.array(list(xrav[mask])), y) + + if op == operator.ne: # pragma: no cover + np.putmask(result, -mask, True) + else: + np.putmask(result, -mask, False) + result = result.reshape(x.shape) + + return result + + @Appender('Wrapper for flexible comparison methods %s' % name) + def f(self, other, axis=default_axis, level=None): + if isinstance(other, pd.DataFrame): # Another DataFrame + return self._flex_compare_frame(other, na_op, str_rep, level) + + elif isinstance(other, pd.Series): + return self._combine_series(other, na_op, None, axis, level) + + elif isinstance(other, (list, tuple)): + if axis is not None and self._get_axis_name(axis) == 'index': + casted = pd.Series(other, index=self.index) + else: + casted = pd.Series(other, index=self.columns) + + return self._combine_series(casted, na_op, None, axis, level) + + elif isinstance(other, np.ndarray): + if other.ndim == 1: + if axis is not None and self._get_axis_name(axis) == 'index': + casted = pd.Series(other, index=self.index) + else: + casted = pd.Series(other, index=self.columns) + + return self._combine_series(casted, na_op, None, axis, level) + + elif other.ndim == 2: + casted = pd.DataFrame(other, index=self.index, + columns=self.columns) + + return self._flex_compare_frame(casted, na_op, str_rep, level) + + else: + raise ValueError("Incompatible argument shape: %s" % + (other.shape,)) + + else: + return self._combine_const(other, na_op) + + f.__name__ = name + + return f + + +def _comp_method_FRAME(func, name, str_rep, masker=False): + @Appender('Wrapper for comparison method %s' % name) + def f(self, other): + if isinstance(other, pd.DataFrame): # Another DataFrame + return self._compare_frame(other, func, str_rep) + elif isinstance(other, pd.Series): + return self._combine_series_infer(other, func) + else: + + # straight boolean comparisions we want to allow all columns + # (regardless of dtype to pass thru) See #4537 for discussion. + return self._combine_const(other, func, raise_on_error=False).fillna(True).astype(bool) + + f.__name__ = name + + return f + + +frame_flex_funcs = dict(flex_arith_method=_arith_method_FRAME, + radd_func=_radd_compat, + flex_comp_method=_flex_comp_method_FRAME) + + +frame_special_funcs = dict(arith_method=_arith_method_FRAME, + radd_func=_radd_compat, + comp_method=_comp_method_FRAME, + bool_method=_arith_method_FRAME) + + +def _arith_method_PANEL(op, name, str_rep=None, fill_zeros=None, + default_axis=None, **eval_kwargs): + # copied from Series na_op above, but without unnecessary branch for + # non-scalar + def na_op(x, y): + try: + result = expressions.evaluate(op, str_rep, x, y, + raise_on_error=True, **eval_kwargs) + except TypeError: + result = pa.empty(len(x), dtype=x.dtype) + mask = notnull(x) + result[mask] = op(x[mask], y) + result, changed = com._maybe_upcast_putmask(result, -mask, pa.NA) + + result = com._fill_zeros(result, y, fill_zeros) + return result + # work only for scalars + + def f(self, other): + if not np.isscalar(other): + raise ValueError('Simple arithmetic with %s can only be ' + 'done with scalar values' % self._constructor.__name__) + + return self._combine(other, op) + f.__name__ = name + return f + + +def _comp_method_PANEL(op, name, str_rep=None, masker=False): + + def na_op(x, y): + try: + result = expressions.evaluate(op, str_rep, x, y, + raise_on_error=True) + except TypeError: + xrav = x.ravel() + result = np.empty(x.size, dtype=bool) + if isinstance(y, np.ndarray): + yrav = y.ravel() + mask = notnull(xrav) & notnull(yrav) + result[mask] = op(np.array(list(xrav[mask])), + np.array(list(yrav[mask]))) + else: + mask = notnull(xrav) + result[mask] = op(np.array(list(xrav[mask])), y) + + if op == operator.ne: # pragma: no cover + np.putmask(result, -mask, True) + else: + np.putmask(result, -mask, False) + result = result.reshape(x.shape) + + return result + + @Appender('Wrapper for comparison method %s' % name) + def f(self, other): + if isinstance(other, self._constructor): + return self._compare_constructor(other, na_op) + elif isinstance(other, (self._constructor_sliced, pd.DataFrame, + pd.Series)): + raise Exception("input needs alignment for this object [%s]" % + self._constructor) + else: + return self._combine_const(other, na_op) + + f.__name__ = name + + return f + + +panel_special_funcs = dict(arith_method=_arith_method_PANEL, + comp_method=_comp_method_PANEL, + bool_method=_arith_method_PANEL) diff --git a/pandas/core/panel.py b/pandas/core/panel.py index 697344639c41b..7208ceff7d1a7 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -5,7 +5,6 @@ from pandas.compat import map, zip, range, lrange, lmap, u, OrderedDict, OrderedDefaultdict from pandas import compat -import operator import sys import numpy as np from pandas.core.common import (PandasError, @@ -18,14 +17,14 @@ from pandas.core.internals import (BlockManager, create_block_manager_from_arrays, create_block_manager_from_blocks) -from pandas.core.series import Series from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame from pandas import compat from pandas.util.decorators import deprecate, Appender, Substitution import pandas.core.common as com +import pandas.core.ops as ops import pandas.core.nanops as nanops -import pandas.lib as lib +import pandas.computation.expressions as expressions def _ensure_like_indices(time, panels): @@ -91,57 +90,6 @@ def panel_index(time, panels, names=['time', 'panel']): return MultiIndex(levels, labels, sortorder=None, names=names) -def _arith_method(func, name): - # work only for scalars - - def f(self, other): - if not np.isscalar(other): - raise ValueError('Simple arithmetic with %s can only be ' - 'done with scalar values' % self._constructor.__name__) - - return self._combine(other, func) - f.__name__ = name - return f - - -def _comp_method(func, name): - - def na_op(x, y): - try: - result = func(x, y) - except TypeError: - xrav = x.ravel() - result = np.empty(x.size, dtype=x.dtype) - if isinstance(y, np.ndarray): - yrav = y.ravel() - mask = notnull(xrav) & notnull(yrav) - result[mask] = func(np.array(list(xrav[mask])), - np.array(list(yrav[mask]))) - else: - mask = notnull(xrav) - result[mask] = func(np.array(list(xrav[mask])), y) - - if func == operator.ne: # pragma: no cover - np.putmask(result, -mask, True) - else: - np.putmask(result, -mask, False) - result = result.reshape(x.shape) - - return result - - @Appender('Wrapper for comparison method %s' % name) - def f(self, other): - if isinstance(other, self._constructor): - return self._compare_constructor(other, func) - elif isinstance(other, (self._constructor_sliced, DataFrame, Series)): - raise Exception("input needs alignment for this object [%s]" % - self._constructor) - else: - return self._combine_const(other, na_op) - - f.__name__ = name - - return f class Panel(NDFrame): @@ -289,25 +237,6 @@ def from_dict(cls, data, intersect=False, orient='items', dtype=None): d[cls._info_axis_name] = Index(ks) return cls(**d) - # Comparison methods - __add__ = _arith_method(operator.add, '__add__') - __sub__ = _arith_method(operator.sub, '__sub__') - __truediv__ = _arith_method(operator.truediv, '__truediv__') - __floordiv__ = _arith_method(operator.floordiv, '__floordiv__') - __mul__ = _arith_method(operator.mul, '__mul__') - __pow__ = _arith_method(operator.pow, '__pow__') - - __radd__ = _arith_method(operator.add, '__radd__') - __rmul__ = _arith_method(operator.mul, '__rmul__') - __rsub__ = _arith_method(lambda x, y: y - x, '__rsub__') - __rtruediv__ = _arith_method(lambda x, y: y / x, '__rtruediv__') - __rfloordiv__ = _arith_method(lambda x, y: y // x, '__rfloordiv__') - __rpow__ = _arith_method(lambda x, y: y ** x, '__rpow__') - - if not compat.PY3: - __div__ = _arith_method(operator.div, '__div__') - __rdiv__ = _arith_method(lambda x, y: y / x, '__rdiv__') - def __getitem__(self, key): if isinstance(self._info_axis, MultiIndex): return self._getitem_multilevel(key) @@ -365,26 +294,6 @@ def _compare_constructor(self, other, func): d = self._construct_axes_dict(copy=False) return self._constructor(data=new_data, **d) - # boolean operators - __and__ = _arith_method(operator.and_, '__and__') - __or__ = _arith_method(operator.or_, '__or__') - __xor__ = _arith_method(operator.xor, '__xor__') - - # Comparison methods - __eq__ = _comp_method(operator.eq, '__eq__') - __ne__ = _comp_method(operator.ne, '__ne__') - __lt__ = _comp_method(operator.lt, '__lt__') - __gt__ = _comp_method(operator.gt, '__gt__') - __le__ = _comp_method(operator.le, '__le__') - __ge__ = _comp_method(operator.ge, '__ge__') - - eq = _comp_method(operator.eq, 'eq') - ne = _comp_method(operator.ne, 'ne') - gt = _comp_method(operator.gt, 'gt') - lt = _comp_method(operator.lt, 'lt') - ge = _comp_method(operator.ge, 'ge') - le = _comp_method(operator.le, 'le') - #---------------------------------------------------------------------- # Magic methods @@ -1262,7 +1171,7 @@ def _extract_axis(self, data, axis=0, intersect=False): return _ensure_index(index) @classmethod - def _add_aggregate_operations(cls): + def _add_aggregate_operations(cls, use_numexpr=True): """ add the operations to the cls; evaluate the doc strings again """ # doc strings substitors @@ -1279,25 +1188,29 @@ def _add_aggregate_operations(cls): ------- """ + cls.__name__ + "\n" - def _panel_arith_method(op, name): + def _panel_arith_method(op, name, str_rep = None, default_axis=None, + fill_zeros=None, **eval_kwargs): + def na_op(x, y): + try: + result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) + except TypeError: + result = op(x, y) + + # handles discrepancy between numpy and numexpr on division/mod by 0 + # though, given that these are generally (always?) non-scalars, I'm + # not sure whether it's worth it at the moment + result = com._fill_zeros(result,y,fill_zeros) + return result @Substitution(op) @Appender(_agg_doc) def f(self, other, axis=0): - return self._combine(other, op, axis=axis) + return self._combine(other, na_op, axis=axis) f.__name__ = name return f - - cls.add = _panel_arith_method(operator.add, 'add') - cls.subtract = cls.sub = _panel_arith_method(operator.sub, 'subtract') - cls.multiply = cls.mul = _panel_arith_method(operator.mul, 'multiply') - - try: - cls.divide = cls.div = _panel_arith_method(operator.div, 'divide') - except AttributeError: # pragma: no cover - # Python 3 - cls.divide = cls.div = _panel_arith_method( - operator.truediv, 'divide') - + # add `div`, `mul`, `pow`, etc.. + ops.add_flex_arithmetic_methods(cls, _panel_arith_method, + use_numexpr=use_numexpr, + flex_comp_method=ops._comp_method_PANEL) _agg_doc = """ Return %(desc)s over requested axis @@ -1385,6 +1298,8 @@ def min(self, axis='major', skipna=True): 'minor': 'minor_axis'}, slicers={'major_axis': 'index', 'minor_axis': 'columns'}) + +ops.add_special_arithmetic_methods(Panel, **ops.panel_special_funcs) Panel._add_aggregate_operations() WidePanel = Panel diff --git a/pandas/core/series.py b/pandas/core/series.py index aeb63ecbe268f..38e22e7a9ed3a 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -6,7 +6,6 @@ # pylint: disable=W0703,W0622,W0613,W0201 import operator -from distutils.version import LooseVersion import types from numpy import nan, ndarray @@ -21,7 +20,7 @@ _values_from_object, _possibly_cast_to_datetime, _possibly_castable, _possibly_convert_platform, - ABCSparseArray) + ABCSparseArray, _maybe_match_name) from pandas.core.index import (Index, MultiIndex, InvalidIndexError, _ensure_index, _handle_legacy_indexes) from pandas.core.indexing import ( @@ -32,13 +31,12 @@ from pandas.core.categorical import Categorical from pandas.tseries.index import DatetimeIndex from pandas.tseries.period import PeriodIndex, Period -from pandas.tseries.offsets import DateOffset -from pandas.tseries.timedeltas import _possibly_cast_to_timedelta from pandas import compat from pandas.util.terminal import get_terminal_size from pandas.compat import zip, lzip, u, OrderedDict import pandas.core.array as pa +import pandas.core.ops as ops import pandas.core.common as com import pandas.core.datetools as datetools @@ -55,387 +53,6 @@ __all__ = ['Series'] -_np_version = np.version.short_version -_np_version_under1p6 = LooseVersion(_np_version) < '1.6' -_np_version_under1p7 = LooseVersion(_np_version) < '1.7' - -class _TimeOp(object): - """ - Wrapper around Series datetime/time/timedelta arithmetic operations. - Generally, you should use classmethod ``maybe_convert_for_time_op`` as an - entry point. - """ - fill_value = tslib.iNaT - wrap_results = staticmethod(lambda x: x) - dtype = None - - def __init__(self, left, right, name): - self.name = name - - lvalues = self._convert_to_array(left, name=name) - rvalues = self._convert_to_array(right, name=name) - - self.is_timedelta_lhs = com.is_timedelta64_dtype(left) - self.is_datetime_lhs = com.is_datetime64_dtype(left) - self.is_integer_lhs = left.dtype.kind in ['i','u'] - self.is_datetime_rhs = com.is_datetime64_dtype(rvalues) - self.is_timedelta_rhs = com.is_timedelta64_dtype(rvalues) or (not self.is_datetime_rhs and _np_version_under1p7) - self.is_integer_rhs = rvalues.dtype.kind in ('i','u') - - self._validate() - - self._convert_for_datetime(lvalues, rvalues) - - def _validate(self): - # timedelta and integer mul/div - - if (self.is_timedelta_lhs and self.is_integer_rhs) or\ - (self.is_integer_lhs and self.is_timedelta_rhs): - - if self.name not in ('__truediv__','__div__','__mul__'): - raise TypeError("can only operate on a timedelta and an integer for " - "division, but the operator [%s] was passed" % self.name) - - # 2 datetimes - elif self.is_datetime_lhs and self.is_datetime_rhs: - if self.name != '__sub__': - raise TypeError("can only operate on a datetimes for subtraction, " - "but the operator [%s] was passed" % self.name) - - - # 2 timedeltas - elif self.is_timedelta_lhs and self.is_timedelta_rhs: - - if self.name not in ('__div__', '__truediv__', '__add__', '__sub__'): - raise TypeError("can only operate on a timedeltas for " - "addition, subtraction, and division, but the operator [%s] was passed" % self.name) - - # datetime and timedelta - elif self.is_datetime_lhs and self.is_timedelta_rhs: - - if self.name not in ('__add__','__sub__'): - raise TypeError("can only operate on a datetime with a rhs of a timedelta for " - "addition and subtraction, but the operator [%s] was passed" % self.name) - - elif self.is_timedelta_lhs and self.is_datetime_rhs: - - if self.name != '__add__': - raise TypeError("can only operate on a timedelta and a datetime for " - "addition, but the operator [%s] was passed" % self.name) - else: - raise TypeError('cannot operate on a series with out a rhs ' - 'of a series/ndarray of type datetime64[ns] ' - 'or a timedelta') - - def _convert_to_array(self, values, name=None): - """converts values to ndarray""" - coerce = 'compat' if _np_version_under1p7 else True - if not is_list_like(values): - values = np.array([values]) - inferred_type = lib.infer_dtype(values) - if inferred_type in ('datetime64','datetime','date','time'): - # a datetlike - if not (isinstance(values, (pa.Array, Series)) and com.is_datetime64_dtype(values)): - values = tslib.array_to_datetime(values) - elif isinstance(values, DatetimeIndex): - values = values.to_series() - elif inferred_type in ('timedelta', 'timedelta64'): - # have a timedelta, convert to to ns here - values = _possibly_cast_to_timedelta(values, coerce=coerce) - elif inferred_type == 'integer': - # py3 compat where dtype is 'm' but is an integer - if values.dtype.kind == 'm': - values = values.astype('timedelta64[ns]') - elif isinstance(values, PeriodIndex): - values = values.to_timestamp().to_series() - elif name not in ('__truediv__','__div__','__mul__'): - raise TypeError("incompatible type for a datetime/timedelta " - "operation [{0}]".format(name)) - elif isinstance(values[0],DateOffset): - # handle DateOffsets - os = pa.array([ getattr(v,'delta',None) for v in values ]) - mask = isnull(os) - if mask.any(): - raise TypeError("cannot use a non-absolute DateOffset in " - "datetime/timedelta operations [{0}]".format( - ','.join([ com.pprint_thing(v) for v in values[mask] ]))) - values = _possibly_cast_to_timedelta(os, coerce=coerce) - else: - raise TypeError("incompatible type [{0}] for a datetime/timedelta operation".format(pa.array(values).dtype)) - - return values - - def _convert_for_datetime(self, lvalues, rvalues): - mask = None - # datetimes require views - if self.is_datetime_lhs or self.is_datetime_rhs: - # datetime subtraction means timedelta - if self.is_datetime_lhs and self.is_datetime_rhs: - self.dtype = 'timedelta64[ns]' - else: - self.dtype = 'datetime64[ns]' - mask = isnull(lvalues) | isnull(rvalues) - lvalues = lvalues.view(np.int64) - rvalues = rvalues.view(np.int64) - - # otherwise it's a timedelta - else: - self.dtype = 'timedelta64[ns]' - mask = isnull(lvalues) | isnull(rvalues) - lvalues = lvalues.astype(np.int64) - rvalues = rvalues.astype(np.int64) - - # time delta division -> unit less - # integer gets converted to timedelta in np < 1.6 - if (self.is_timedelta_lhs and self.is_timedelta_rhs) and\ - not self.is_integer_rhs and\ - not self.is_integer_lhs and\ - self.name in ('__div__', '__truediv__'): - self.dtype = 'float64' - self.fill_value = np.nan - lvalues = lvalues.astype(np.float64) - rvalues = rvalues.astype(np.float64) - - # if we need to mask the results - if mask is not None: - if mask.any(): - def f(x): - x = pa.array(x,dtype=self.dtype) - np.putmask(x,mask,self.fill_value) - return x - self.wrap_results = f - self.lvalues = lvalues - self.rvalues = rvalues - - @classmethod - def maybe_convert_for_time_op(cls, left, right, name): - """ - if ``left`` and ``right`` are appropriate for datetime arithmetic with - operation ``name``, processes them and returns a ``_TimeOp`` object - that stores all the required values. Otherwise, it will generate - either a ``NotImplementedError`` or ``None``, indicating that the - operation is unsupported for datetimes (e.g., an unsupported r_op) or - that the data is not the right type for time ops. - """ - # decide if we can do it - is_timedelta_lhs = com.is_timedelta64_dtype(left) - is_datetime_lhs = com.is_datetime64_dtype(left) - if not (is_datetime_lhs or is_timedelta_lhs): - return None - # rops currently disabled - if name.startswith('__r'): - return NotImplemented - - return cls(left, right, name) - -#---------------------------------------------------------------------- -# Wrapper function for Series arithmetic methods - -def _arith_method(op, name, fill_zeros=None): - """ - Wrapper function for Series arithmetic operations, to avoid - code duplication. - """ - def na_op(x, y): - try: - - result = op(x, y) - result = com._fill_zeros(result, y, fill_zeros) - - except TypeError: - result = pa.empty(len(x), dtype=x.dtype) - if isinstance(y, (pa.Array, Series)): - mask = notnull(x) & notnull(y) - result[mask] = op(x[mask], y[mask]) - else: - mask = notnull(x) - result[mask] = op(x[mask], y) - - result, changed = com._maybe_upcast_putmask(result, -mask, pa.NA) - - return result - - def wrapper(left, right, name=name): - from pandas.core.frame import DataFrame - - time_converted = _TimeOp.maybe_convert_for_time_op(left, right, name) - - if time_converted is None: - lvalues, rvalues = left, right - dtype = None - wrap_results = lambda x: x - elif time_converted == NotImplemented: - return NotImplemented - else: - lvalues = time_converted.lvalues - rvalues = time_converted.rvalues - dtype = time_converted.dtype - wrap_results = time_converted.wrap_results - - if isinstance(rvalues, Series): - - join_idx, lidx, ridx = left.index.join(rvalues.index, how='outer', - return_indexers=True) - rindex = rvalues.index - name = _maybe_match_name(left, rvalues) - lvalues = getattr(lvalues, 'values', lvalues) - rvalues = getattr(rvalues, 'values', rvalues) - if left.index.equals(rindex): - index = left.index - else: - index = join_idx - - if lidx is not None: - lvalues = com.take_1d(lvalues, lidx) - - if ridx is not None: - rvalues = com.take_1d(rvalues, ridx) - - arr = na_op(lvalues, rvalues) - - return left._constructor(wrap_results(arr), index=index, - name=name, dtype=dtype) - elif isinstance(right, DataFrame): - return NotImplemented - else: - # scalars - if hasattr(lvalues, 'values'): - lvalues = lvalues.values - return left._constructor(wrap_results(na_op(lvalues, rvalues)), - index=left.index, name=left.name, dtype=dtype) - return wrapper - - -def _comp_method(op, name, masker=False): - """ - Wrapper function for Series arithmetic operations, to avoid - code duplication. - """ - def na_op(x, y): - if x.dtype == np.object_: - if isinstance(y, list): - y = lib.list_to_object_array(y) - - if isinstance(y, (pa.Array, Series)): - if y.dtype != np.object_: - result = lib.vec_compare(x, y.astype(np.object_), op) - else: - result = lib.vec_compare(x, y, op) - else: - result = lib.scalar_compare(x, y, op) - else: - - try: - result = getattr(x,name)(y) - if result is NotImplemented: - raise TypeError("invalid type comparison") - except (AttributeError): - result = op(x, y) - - return result - - def wrapper(self, other): - from pandas.core.frame import DataFrame - - if isinstance(other, Series): - name = _maybe_match_name(self, other) - if len(self) != len(other): - raise ValueError('Series lengths must match to compare') - return self._constructor(na_op(self.values, other.values), - index=self.index, name=name) - elif isinstance(other, DataFrame): # pragma: no cover - return NotImplemented - elif isinstance(other, (pa.Array, Series)): - if len(self) != len(other): - raise ValueError('Lengths must match to compare') - return self._constructor(na_op(self.values, np.asarray(other)), - index=self.index, name=self.name) - else: - - mask = isnull(self) - - values = self.values - other = _index.convert_scalar(values, other) - - if issubclass(values.dtype.type, np.datetime64): - values = values.view('i8') - - # scalars - res = na_op(values, other) - if np.isscalar(res): - raise TypeError('Could not compare %s type with Series' - % type(other)) - - # always return a full value series here - res = _values_from_object(res) - - res = Series(res, index=self.index, name=self.name, dtype='bool') - - # mask out the invalids - if mask.any(): - res[mask.values] = masker - - return res - return wrapper - - -def _bool_method(op, name): - """ - Wrapper function for Series arithmetic operations, to avoid - code duplication. - """ - def na_op(x, y): - try: - result = op(x, y) - except TypeError: - if isinstance(y, list): - y = lib.list_to_object_array(y) - - if isinstance(y, (pa.Array, Series)): - if (x.dtype == np.bool_ and - y.dtype == np.bool_): # pragma: no cover - result = op(x, y) # when would this be hit? - else: - x = com._ensure_object(x) - y = com._ensure_object(y) - result = lib.vec_binop(x, y, op) - else: - result = lib.scalar_binop(x, y, op) - - return result - - def wrapper(self, other): - from pandas.core.frame import DataFrame - - if isinstance(other, Series): - name = _maybe_match_name(self, other) - return self._constructor(na_op(self.values, other.values), - index=self.index, name=name) - elif isinstance(other, DataFrame): - return NotImplemented - else: - # scalars - return self._constructor(na_op(self.values, other), - index=self.index, name=self.name) - return wrapper - - -def _radd_compat(left, right): - radd = lambda x, y: y + x - # GH #353, NumPy 1.5.1 workaround - try: - output = radd(left, right) - except TypeError: - cond = (_np_version_under1p6 and - left.dtype == np.object_) - if cond: # pragma: no cover - output = np.empty_like(left) - output.flat[:] = [radd(x, right) for x in left.flat] - else: - raise - - return output - def _coerce_method(converter): """ install the scalar coercion methods """ @@ -448,50 +65,6 @@ def wrapper(self): return wrapper -def _maybe_match_name(a, b): - name = None - if a.name == b.name: - name = a.name - return name - - -def _flex_method(op, name): - doc = """ - Binary operator %s with support to substitute a fill_value for missing data - in one of the inputs - - Parameters - ---------- - other: Series or scalar value - fill_value : None or float value, default None (NaN) - Fill missing (NaN) values with this value. If both Series are - missing, the result will be missing - level : int or name - Broadcast across a level, matching Index values on the - passed MultiIndex level - - Returns - ------- - result : Series - """ % name - - @Appender(doc) - def f(self, other, level=None, fill_value=None): - if isinstance(other, Series): - return self._binop(other, op, level=level, fill_value=fill_value) - elif isinstance(other, (pa.Array, Series, list, tuple)): - if len(other) != len(self): - raise ValueError('Lengths must be equal') - return self._binop(self._constructor(other, self.index), op, - level=level, fill_value=fill_value) - else: - return self._constructor(op(self.values, other), self.index, - name=self.name) - - f.__name__ = name - return f - - def _unbox(func): @Appender(func.__doc__) def f(self, *args, **kwargs): @@ -1423,37 +996,6 @@ def iteritems(self): if compat.PY3: # pragma: no cover items = iteritems - #---------------------------------------------------------------------- - # Arithmetic operators - - __add__ = _arith_method(operator.add, '__add__') - __sub__ = _arith_method(operator.sub, '__sub__') - __mul__ = _arith_method(operator.mul, '__mul__') - __truediv__ = _arith_method( - operator.truediv, '__truediv__', fill_zeros=np.inf) - __floordiv__ = _arith_method( - operator.floordiv, '__floordiv__', fill_zeros=np.inf) - __pow__ = _arith_method(operator.pow, '__pow__') - __mod__ = _arith_method(operator.mod, '__mod__', fill_zeros=np.nan) - - __radd__ = _arith_method(_radd_compat, '__add__') - __rmul__ = _arith_method(operator.mul, '__mul__') - __rsub__ = _arith_method(lambda x, y: y - x, '__sub__') - __rtruediv__ = _arith_method( - lambda x, y: y / x, '__truediv__', fill_zeros=np.inf) - __rfloordiv__ = _arith_method( - lambda x, y: y // x, '__floordiv__', fill_zeros=np.inf) - __rpow__ = _arith_method(lambda x, y: y ** x, '__pow__') - __rmod__ = _arith_method(lambda x, y: y % x, '__mod__', fill_zeros=np.nan) - - # comparisons - __gt__ = _comp_method(operator.gt, '__gt__') - __ge__ = _comp_method(operator.ge, '__ge__') - __lt__ = _comp_method(operator.lt, '__lt__') - __le__ = _comp_method(operator.le, '__le__') - __eq__ = _comp_method(operator.eq, '__eq__') - __ne__ = _comp_method(operator.ne, '__ne__', True) - # inversion def __neg__(self): arr = operator.neg(self.values) @@ -1463,26 +1005,6 @@ def __invert__(self): arr = operator.inv(self.values) return self._constructor(arr, self.index, name=self.name) - # binary logic - __or__ = _bool_method(operator.or_, '__or__') - __and__ = _bool_method(operator.and_, '__and__') - __xor__ = _bool_method(operator.xor, '__xor__') - - # Inplace operators - __iadd__ = __add__ - __isub__ = __sub__ - __imul__ = __mul__ - __itruediv__ = __truediv__ - __ifloordiv__ = __floordiv__ - __ipow__ = __pow__ - - # Python 2 division operators - if not compat.PY3: - __div__ = _arith_method(operator.div, '__div__', fill_zeros=np.inf) - __rdiv__ = _arith_method( - lambda x, y: y / x, '__div__', fill_zeros=np.inf) - __idiv__ = __div__ - #---------------------------------------------------------------------- # unbox reductions @@ -2245,16 +1767,6 @@ def _binop(self, other, func, level=None, fill_value=None): name = _maybe_match_name(self, other) return self._constructor(result, index=new_index, name=name) - add = _flex_method(operator.add, 'add') - sub = _flex_method(operator.sub, 'subtract') - mul = _flex_method(operator.mul, 'multiply') - try: - div = _flex_method(operator.div, 'divide') - except AttributeError: # pragma: no cover - # Python 3 - div = _flex_method(operator.truediv, 'divide') - mod = _flex_method(operator.mod, 'mod') - def combine(self, other, func, fill_value=nan): """ Perform elementwise binary operation on two Series using given function @@ -3281,3 +2793,7 @@ def _try_cast(arr, take_fast_path): Series.plot = _gfx.plot_series Series.hist = _gfx.hist_series + +# Add arithmetic! +ops.add_flex_arithmetic_methods(Series, **ops.series_flex_funcs) +ops.add_special_arithmetic_methods(Series, **ops.series_special_funcs) diff --git a/pandas/sparse/array.py b/pandas/sparse/array.py index 8a50a000a9526..bed4ede6ce5f3 100644 --- a/pandas/sparse/array.py +++ b/pandas/sparse/array.py @@ -7,7 +7,6 @@ from numpy import nan, ndarray import numpy as np -import operator from pandas.core.base import PandasObject import pandas.core.common as com @@ -17,21 +16,26 @@ from pandas._sparse import BlockIndex, IntIndex import pandas._sparse as splib import pandas.index as _index +import pandas.core.ops as ops -def _sparse_op_wrap(op, name): +def _arith_method(op, name, str_rep=None, default_axis=None, + fill_zeros=None, **eval_kwargs): """ Wrapper function for Series arithmetic operations, to avoid code duplication. """ - def wrapper(self, other): if isinstance(other, np.ndarray): if len(self) != len(other): - raise AssertionError("Operands must be of the same size") - if not isinstance(other, SparseArray): + raise AssertionError("length mismatch: %d vs. %d" % + (len(self), len(other))) + if not isinstance(other, com.ABCSparseArray): other = SparseArray(other, fill_value=self.fill_value) - return _sparse_array_op(self, other, op, name) + if name[0] == 'r': + return _sparse_array_op(other, self, op, name[1:]) + else: + return _sparse_array_op(self, other, op, name) elif np.isscalar(other): new_fill_value = op(np.float64(self.fill_value), np.float64(other)) @@ -41,7 +45,8 @@ def wrapper(self, other): fill_value=new_fill_value) else: # pragma: no cover raise TypeError('operation with %s not supported' % type(other)) - + if name.startswith("__"): + name = name[2:-2] wrapper.__name__ = name return wrapper @@ -218,23 +223,6 @@ def __unicode__(self): com.pprint_thing(self.fill_value), com.pprint_thing(self.sp_index)) - # Arithmetic operators - - __add__ = _sparse_op_wrap(operator.add, 'add') - __sub__ = _sparse_op_wrap(operator.sub, 'sub') - __mul__ = _sparse_op_wrap(operator.mul, 'mul') - __truediv__ = _sparse_op_wrap(operator.truediv, 'truediv') - __floordiv__ = _sparse_op_wrap(operator.floordiv, 'floordiv') - __pow__ = _sparse_op_wrap(operator.pow, 'pow') - - # reverse operators - __radd__ = _sparse_op_wrap(operator.add, 'add') - __rsub__ = _sparse_op_wrap(lambda x, y: y - x, 'rsub') - __rmul__ = _sparse_op_wrap(operator.mul, 'mul') - __rtruediv__ = _sparse_op_wrap(lambda x, y: y / x, 'rtruediv') - __rfloordiv__ = _sparse_op_wrap(lambda x, y: y // x, 'rfloordiv') - __rpow__ = _sparse_op_wrap(lambda x, y: y ** x, 'rpow') - def disable(self, other): raise NotImplementedError('inplace binary ops not supported') # Inplace operators @@ -247,8 +235,6 @@ def disable(self, other): # Python 2 division operators if not compat.PY3: - __div__ = _sparse_op_wrap(operator.div, 'div') - __rdiv__ = _sparse_op_wrap(lambda x, y: y / x, '__rdiv__') __idiv__ = disable @property @@ -539,3 +525,7 @@ def make_sparse(arr, kind='block', fill_value=nan): sparsified_values = arr[mask] return sparsified_values, index + +ops.add_special_arithmetic_methods(SparseArray, + arith_method=_arith_method, + use_numexpr=False) diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index 93b29cbf91b91..6f83ee90dd9da 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -25,6 +25,7 @@ from pandas.core.generic import NDFrame from pandas.sparse.series import SparseSeries, SparseArray from pandas.util.decorators import Appender +import pandas.core.ops as ops class SparseDataFrame(DataFrame): @@ -815,3 +816,9 @@ def homogenize(series_dict): output = series_dict return output + +# use unaccelerated ops for sparse objects +ops.add_flex_arithmetic_methods(SparseDataFrame, use_numexpr=False, + **ops.frame_flex_funcs) +ops.add_special_arithmetic_methods(SparseDataFrame, use_numexpr=False, + **ops.frame_special_funcs) diff --git a/pandas/sparse/panel.py b/pandas/sparse/panel.py index 286b683b1ea88..dd0204f11edfb 100644 --- a/pandas/sparse/panel.py +++ b/pandas/sparse/panel.py @@ -16,6 +16,7 @@ from pandas.util.decorators import deprecate import pandas.core.common as com +import pandas.core.ops as ops class SparsePanelAxis(object): @@ -462,6 +463,19 @@ def minor_xs(self, key): default_fill_value=self.default_fill_value, default_kind=self.default_kind) + # TODO: allow SparsePanel to work with flex arithmetic. + # pow and mod only work for scalars for now + def pow(self, val, *args, **kwargs): + """wrapper around `__pow__` (only works for scalar values)""" + return self.__pow__(val) + + def mod(self, val, *args, **kwargs): + """wrapper around `__mod__` (only works for scalar values""" + return self.__mod__(val) + +# Sparse objects opt out of numexpr +SparsePanel._add_aggregate_operations(use_numexpr=False) +ops.add_special_arithmetic_methods(SparsePanel, use_numexpr=False, **ops.panel_special_funcs) SparseWidePanel = SparsePanel diff --git a/pandas/sparse/series.py b/pandas/sparse/series.py index 50e80e0c202d5..eb97eec75be36 100644 --- a/pandas/sparse/series.py +++ b/pandas/sparse/series.py @@ -10,13 +10,14 @@ import operator -from pandas.core.common import isnull, _values_from_object +from pandas.core.common import isnull, _values_from_object, _maybe_match_name from pandas.core.index import Index, _ensure_index -from pandas.core.series import Series, _maybe_match_name +from pandas.core.series import Series from pandas.core.frame import DataFrame from pandas.core.internals import SingleBlockManager from pandas.core import generic import pandas.core.common as com +import pandas.core.ops as ops import pandas.core.datetools as datetools import pandas.index as _index @@ -32,10 +33,14 @@ # Wrapper function for Series arithmetic methods -def _sparse_op_wrap(op, name): +def _arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None, + **eval_kwargs): """ Wrapper function for Series arithmetic operations, to avoid code duplication. + + str_rep, default_axis, fill_zeros and eval_kwargs are not used, but are present + for compatibility. """ def wrapper(self, other): @@ -61,6 +66,10 @@ def wrapper(self, other): raise TypeError('operation with %s not supported' % type(other)) wrapper.__name__ = name + if name.startswith("__"): + # strip special method names, e.g. `__add__` needs to be `add` when passed + # to _sparse_series_op + name = name[2:-2] return wrapper @@ -272,36 +281,6 @@ def __unicode__(self): rep = '%s\n%s' % (series_rep, repr(self.sp_index)) return rep - # Arithmetic operators - - __add__ = _sparse_op_wrap(operator.add, 'add') - __sub__ = _sparse_op_wrap(operator.sub, 'sub') - __mul__ = _sparse_op_wrap(operator.mul, 'mul') - __truediv__ = _sparse_op_wrap(operator.truediv, 'truediv') - __floordiv__ = _sparse_op_wrap(operator.floordiv, 'floordiv') - __pow__ = _sparse_op_wrap(operator.pow, 'pow') - - # Inplace operators - __iadd__ = __add__ - __isub__ = __sub__ - __imul__ = __mul__ - __itruediv__ = __truediv__ - __ifloordiv__ = __floordiv__ - __ipow__ = __pow__ - - # reverse operators - __radd__ = _sparse_op_wrap(operator.add, '__radd__') - __rsub__ = _sparse_op_wrap(lambda x, y: y - x, '__rsub__') - __rmul__ = _sparse_op_wrap(operator.mul, '__rmul__') - __rtruediv__ = _sparse_op_wrap(lambda x, y: y / x, '__rtruediv__') - __rfloordiv__ = _sparse_op_wrap(lambda x, y: y // x, 'floordiv') - __rpow__ = _sparse_op_wrap(lambda x, y: y ** x, '__rpow__') - - # Python 2 division operators - if not compat.PY3: - __div__ = _sparse_op_wrap(operator.div, 'div') - __rdiv__ = _sparse_op_wrap(lambda x, y: y / x, '__rdiv__') - def __array_wrap__(self, result): """ Gets called prior to a ufunc (and after) @@ -659,5 +638,16 @@ def combine_first(self, other): dense_combined = self.to_dense().combine_first(other) return dense_combined.to_sparse(fill_value=self.fill_value) +# overwrite series methods with unaccelerated versions +ops.add_special_arithmetic_methods(SparseSeries, use_numexpr=False, + **ops.series_special_funcs) +ops.add_flex_arithmetic_methods(SparseSeries, use_numexpr=False, + **ops.series_flex_funcs) +# overwrite basic arithmetic to use SparseSeries version +# force methods to overwrite previous definitions. +ops.add_special_arithmetic_methods(SparseSeries, _arith_method, + radd_func=operator.add, comp_method=None, + bool_method=None, use_numexpr=False, force=True) + # backwards compatiblity SparseTimeSeries = SparseSeries diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py index 56f52447aadfe..85f5ba1f08b1d 100644 --- a/pandas/tests/test_expressions.py +++ b/pandas/tests/test_expressions.py @@ -10,12 +10,16 @@ import numpy as np from numpy.testing import assert_array_equal -from pandas.core.api import DataFrame +from pandas.core.api import DataFrame, Panel from pandas.computation import expressions as expr - -from pandas.util.testing import assert_series_equal, assert_frame_equal from pandas import compat +from pandas.util.testing import (assert_almost_equal, assert_series_equal, + assert_frame_equal, assert_panel_equal, + assert_panel4d_equal) +import pandas.util.testing as tm +from numpy.testing.decorators import slow + if not expr._USE_NUMEXPR: try: @@ -31,6 +35,18 @@ _mixed = DataFrame({ 'A' : _frame['A'].copy(), 'B' : _frame['B'].astype('float32'), 'C' : _frame['C'].astype('int64'), 'D' : _frame['D'].astype('int32') }) _mixed2 = DataFrame({ 'A' : _frame2['A'].copy(), 'B' : _frame2['B'].astype('float32'), 'C' : _frame2['C'].astype('int64'), 'D' : _frame2['D'].astype('int32') }) _integer = DataFrame(np.random.randint(1, 100, size=(10001, 4)), columns = list('ABCD'), dtype='int64') +_integer2 = DataFrame(np.random.randint(1, 100, size=(101, 4)), + columns=list('ABCD'), dtype='int64') +_frame_panel = Panel(dict(ItemA=_frame.copy(), ItemB=(_frame.copy() + 3), ItemC=_frame.copy(), ItemD=_frame.copy())) +_frame2_panel = Panel(dict(ItemA=_frame2.copy(), ItemB=(_frame2.copy() + 3), + ItemC=_frame2.copy(), ItemD=_frame2.copy())) +_integer_panel = Panel(dict(ItemA=_integer, + ItemB=(_integer + 34).astype('int64'))) +_integer2_panel = Panel(dict(ItemA=_integer2, + ItemB=(_integer2 + 34).astype('int64'))) +_mixed_panel = Panel(dict(ItemA=_mixed, ItemB=(_mixed + 3))) +_mixed2_panel = Panel(dict(ItemA=_mixed2, ItemB=(_mixed2 + 3))) + class TestExpressions(unittest.TestCase): @@ -48,20 +64,27 @@ def setUp(self): def tearDown(self): expr._MIN_ELEMENTS = self._MIN_ELEMENTS - #TODO: add test for Panel - #TODO: add tests for binary operations @nose.tools.nottest - def run_arithmetic_test(self, df, assert_func, check_dtype=False): + def run_arithmetic_test(self, df, other, assert_func, check_dtype=False, + test_flex=True): expr._MIN_ELEMENTS = 0 - operations = ['add', 'sub', 'mul','mod','truediv','floordiv','pow'] + operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv', 'pow'] if not compat.PY3: operations.append('div') for arith in operations: - op = getattr(operator, arith) + if test_flex: + op = getattr(df, arith) + else: + op = getattr(operator, arith) + if test_flex: + op = lambda x, y: getattr(df, arith)(y) + op.__name__ = arith + else: + op = getattr(operator, arith) expr.set_use_numexpr(False) - expected = op(df, df) + expected = op(df, other) expr.set_use_numexpr(True) - result = op(df, df) + result = op(df, other) try: if check_dtype: if arith == 'div': @@ -74,24 +97,150 @@ def run_arithmetic_test(self, df, assert_func, check_dtype=False): raise def test_integer_arithmetic(self): - self.run_arithmetic_test(self.integer, assert_frame_equal) - self.run_arithmetic_test(self.integer.icol(0), assert_series_equal, - check_dtype=True) + self.run_arithmetic_test(self.integer, self.integer, + assert_frame_equal) + self.run_arithmetic_test(self.integer.icol(0), self.integer.icol(0), + assert_series_equal, check_dtype=True) + + @nose.tools.nottest + def run_binary_test(self, df, other, assert_func, check_dtype=False, + test_flex=False, numexpr_ops=set(['gt', 'lt', 'ge', + 'le', 'eq', 'ne'])): + """ + tests solely that the result is the same whether or not numexpr is + enabled. Need to test whether the function does the correct thing + elsewhere. + """ + expr._MIN_ELEMENTS = 0 + expr.set_test_mode(True) + operations = ['gt', 'lt', 'ge', 'le', 'eq', 'ne'] + for arith in operations: + if test_flex: + op = lambda x, y: getattr(df, arith)(y) + op.__name__ = arith + else: + op = getattr(operator, arith) + expr.set_use_numexpr(False) + expected = op(df, other) + expr.set_use_numexpr(True) + expr.get_test_result() + result = op(df, other) + used_numexpr = expr.get_test_result() + try: + if check_dtype: + if arith == 'div': + assert expected.dtype.kind == result.dtype.kind + if arith == 'truediv': + assert result.dtype.kind == 'f' + if arith in numexpr_ops: + assert used_numexpr, "Did not use numexpr as expected." + else: + assert not used_numexpr, "Used numexpr unexpectedly." + assert_func(expected, result) + except Exception: + print("Failed test with operation %r" % arith) + print("test_flex was %r" % test_flex) + raise + + def run_frame(self, df, other, binary_comp=None, run_binary=True, + **kwargs): + self.run_arithmetic_test(df, other, assert_frame_equal, + test_flex=False, **kwargs) + self.run_arithmetic_test(df, other, assert_frame_equal, test_flex=True, + **kwargs) + if run_binary: + if binary_comp is None: + expr.set_use_numexpr(False) + binary_comp = other + 1 + expr.set_use_numexpr(True) + self.run_binary_test(df, binary_comp, assert_frame_equal, + test_flex=False, **kwargs) + self.run_binary_test(df, binary_comp, assert_frame_equal, + test_flex=True, **kwargs) + + def run_series(self, ser, other, binary_comp=None, **kwargs): + self.run_arithmetic_test(ser, other, assert_series_equal, + test_flex=False, **kwargs) + self.run_arithmetic_test(ser, other, assert_almost_equal, + test_flex=True, **kwargs) + # series doesn't uses vec_compare instead of numexpr... + # if binary_comp is None: + # binary_comp = other + 1 + # self.run_binary_test(ser, binary_comp, assert_frame_equal, test_flex=False, + # **kwargs) + # self.run_binary_test(ser, binary_comp, assert_frame_equal, test_flex=True, + # **kwargs) + + def run_panel(self, panel, other, binary_comp=None, run_binary=True, + assert_func=assert_panel_equal, **kwargs): + self.run_arithmetic_test(panel, other, assert_func, test_flex=False, + **kwargs) + self.run_arithmetic_test(panel, other, assert_func, test_flex=True, + **kwargs) + if run_binary: + if binary_comp is None: + binary_comp = other + 1 + self.run_binary_test(panel, binary_comp, assert_func, + test_flex=False, **kwargs) + self.run_binary_test(panel, binary_comp, assert_func, + test_flex=True, **kwargs) + + def test_integer_arithmetic_frame(self): + self.run_frame(self.integer, self.integer) + + def test_integer_arithmetic_series(self): + self.run_series(self.integer.icol(0), self.integer.icol(0)) + + @slow + def test_integer_panel(self): + self.run_panel(_integer2_panel, np.random.randint(1, 100)) + + def test_float_arithemtic_frame(self): + self.run_frame(self.frame2, self.frame2) + + def test_float_arithmetic_series(self): + self.run_series(self.frame2.icol(0), self.frame2.icol(0)) + + @slow + def test_float_panel(self): + self.run_panel(_frame2_panel, np.random.randn() + 0.1, binary_comp=0.8) + + @slow + def test_panel4d(self): + self.run_panel(tm.makePanel4D(), np.random.randn() + 0.5, + assert_func=assert_panel4d_equal, binary_comp=3) + + def test_mixed_arithmetic_frame(self): + # TODO: FIGURE OUT HOW TO GET IT TO WORK... + # can't do arithmetic because comparison methods try to do *entire* + # frame instead of by-column + self.run_frame(self.mixed2, self.mixed2, run_binary=False) + + def test_mixed_arithmetic_series(self): + for col in self.mixed2.columns: + self.run_series(self.mixed2[col], self.mixed2[col], binary_comp=4) + + @slow + def test_mixed_panel(self): + self.run_panel(_mixed2_panel, np.random.randint(1, 100), + binary_comp=-2) def test_float_arithemtic(self): - self.run_arithmetic_test(self.frame, assert_frame_equal) - self.run_arithmetic_test(self.frame.icol(0), assert_series_equal, - check_dtype=True) + self.run_arithmetic_test(self.frame, self.frame, assert_frame_equal) + self.run_arithmetic_test(self.frame.icol(0), self.frame.icol(0), + assert_series_equal, check_dtype=True) def test_mixed_arithmetic(self): - self.run_arithmetic_test(self.mixed, assert_frame_equal) + self.run_arithmetic_test(self.mixed, self.mixed, assert_frame_equal) for col in self.mixed.columns: - self.run_arithmetic_test(self.mixed[col], assert_series_equal) + self.run_arithmetic_test(self.mixed[col], self.mixed[col], + assert_series_equal) def test_integer_with_zeros(self): self.integer *= np.random.randint(0, 2, size=np.shape(self.integer)) - self.run_arithmetic_test(self.integer, assert_frame_equal) - self.run_arithmetic_test(self.integer.icol(0), assert_series_equal) + self.run_arithmetic_test(self.integer, self.integer, assert_frame_equal) + self.run_arithmetic_test(self.integer.icol(0), self.integer.icol(0), + assert_series_equal) def test_invalid(self): diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 8266502ccdece..a41072d97ddc3 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4554,35 +4554,72 @@ def test_first_last_valid(self): self.assert_(index == frame.index[-6]) def test_arith_flex_frame(self): - ops = ['add', 'sub', 'mul', 'div', 'pow'] - aliases = {'div': 'truediv'} + ops = ['add', 'sub', 'mul', 'div', 'truediv', 'pow', 'floordiv', 'mod'] + if not compat.PY3: + aliases = {} + else: + aliases = {'div': 'truediv'} for op in ops: - alias = aliases.get(op, op) - f = getattr(operator, alias) - result = getattr(self.frame, op)(2 * self.frame) - exp = f(self.frame, 2 * self.frame) - assert_frame_equal(result, exp) - - # vs mix float - result = getattr(self.mixed_float, op)(2 * self.mixed_float) - exp = f(self.mixed_float, 2 * self.mixed_float) - assert_frame_equal(result, exp) - _check_mixed_float(result, dtype = dict(C = None)) - - # vs mix int - if op in ['add','sub','mul']: - result = getattr(self.mixed_int, op)(2 + self.mixed_int) - exp = f(self.mixed_int, 2 + self.mixed_int) - - # overflow in the uint - dtype = None - if op in ['sub']: - dtype = dict(B = 'object', C = None) - elif op in ['add','mul']: - dtype = dict(C = None) + try: + alias = aliases.get(op, op) + f = getattr(operator, alias) + result = getattr(self.frame, op)(2 * self.frame) + exp = f(self.frame, 2 * self.frame) + assert_frame_equal(result, exp) + + # vs mix float + result = getattr(self.mixed_float, op)(2 * self.mixed_float) + exp = f(self.mixed_float, 2 * self.mixed_float) assert_frame_equal(result, exp) - _check_mixed_int(result, dtype = dtype) + _check_mixed_float(result, dtype = dict(C = None)) + + # vs mix int + if op in ['add','sub','mul']: + result = getattr(self.mixed_int, op)(2 + self.mixed_int) + exp = f(self.mixed_int, 2 + self.mixed_int) + + # overflow in the uint + dtype = None + if op in ['sub']: + dtype = dict(B = 'object', C = None) + elif op in ['add','mul']: + dtype = dict(C = None) + assert_frame_equal(result, exp) + _check_mixed_int(result, dtype = dtype) + + # rops + r_f = lambda x, y: f(y, x) + result = getattr(self.frame, 'r' + op)(2 * self.frame) + exp = r_f(self.frame, 2 * self.frame) + assert_frame_equal(result, exp) + + # vs mix float + result = getattr(self.mixed_float, op)(2 * self.mixed_float) + exp = f(self.mixed_float, 2 * self.mixed_float) + assert_frame_equal(result, exp) + _check_mixed_float(result, dtype = dict(C = None)) + + result = getattr(self.intframe, op)(2 * self.intframe) + exp = f(self.intframe, 2 * self.intframe) + assert_frame_equal(result, exp) + + # vs mix int + if op in ['add','sub','mul']: + result = getattr(self.mixed_int, op)(2 + self.mixed_int) + exp = f(self.mixed_int, 2 + self.mixed_int) + + # overflow in the uint + dtype = None + if op in ['sub']: + dtype = dict(B = 'object', C = None) + elif op in ['add','mul']: + dtype = dict(C = None) + assert_frame_equal(result, exp) + _check_mixed_int(result, dtype = dtype) + except: + print("Failing operation %r" % op) + raise # ndim >= 3 ndim_5 = np.ones(self.frame.shape + (3, 4, 5)) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 289bcb9db0c7e..5d3f7b350250d 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1,8 +1,6 @@ # pylint: disable=W0612,E1101 from datetime import datetime -from pandas.compat import range, lrange, StringIO, cPickle, OrderedDict -from pandas import compat import operator import unittest import nose @@ -16,6 +14,7 @@ from pandas.core.series import remove_na import pandas.core.common as com from pandas import compat +from pandas.compat import range, lrange, StringIO, cPickle, OrderedDict from pandas.util.testing import (assert_panel_equal, assert_frame_equal, @@ -50,7 +49,7 @@ def test_cumsum(self): def not_hashable(self): c_empty = Panel() - c = Panel(pd.Panel([[[1]]])) + c = Panel(Panel([[[1]]])) self.assertRaises(TypeError, hash, c_empty) self.assertRaises(TypeError, hash, c) @@ -313,14 +312,32 @@ def check_op(op, name): assert_frame_equal(result.minor_xs(idx), op(self.panel.minor_xs(idx), xs)) + from pandas import SparsePanel + ops = ['add', 'sub', 'mul', 'truediv', 'floordiv'] + if not compat.PY3: + ops.append('div') + # pow, mod not supported for SparsePanel as flex ops (for now) + if not isinstance(self.panel, SparsePanel): + ops.extend(['pow', 'mod']) + else: + idx = self.panel.minor_axis[1] + with assertRaisesRegexp(ValueError, "Simple arithmetic.*scalar"): + self.panel.pow(self.panel.minor_xs(idx), axis='minor') + with assertRaisesRegexp(ValueError, "Simple arithmetic.*scalar"): + self.panel.mod(self.panel.minor_xs(idx), axis='minor') - check_op(operator.add, 'add') - check_op(operator.sub, 'subtract') - check_op(operator.mul, 'multiply') + for op in ops: + try: + check_op(getattr(operator, op), op) + except: + print("Failing operation: %r" % op) + raise if compat.PY3: - check_op(operator.truediv, 'divide') - else: - check_op(operator.div, 'divide') + try: + check_op(operator.truediv, 'div') + except: + print("Failing operation: %r" % name) + raise def test_combinePanel(self): result = self.panel.add(self.panel) @@ -1737,6 +1754,31 @@ def test_operators(self): result = (self.panel + 1).to_panel() assert_frame_equal(wp['ItemA'] + 1, result['ItemA']) + def test_arith_flex_panel(self): + ops = ['add', 'sub', 'mul', 'div', 'truediv', 'pow', 'floordiv', 'mod'] + if not compat.PY3: + aliases = {} + else: + aliases = {'div': 'truediv'} + self.panel = self.panel.to_panel() + n = np.random.randint(-50, 50) + for op in ops: + try: + alias = aliases.get(op, op) + f = getattr(operator, alias) + result = getattr(self.panel, op)(n) + exp = f(self.panel, n) + assert_panel_equal(result, exp, check_panel_type=True) + + # rops + r_f = lambda x, y: f(y, x) + result = getattr(self.panel, 'r' + op)(n) + exp = r_f(self.panel, n) + assert_panel_equal(result, exp) + except: + print("Failing operation %r" % op) + raise + def test_sort(self): def is_sorted(arr): return (arr[1:] > arr[:-1]).any() diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index f8320149f4ac6..479d627e72346 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -269,7 +269,6 @@ class SafeForSparse(object): _ts = tm.makeTimeSeries() - class TestSeries(unittest.TestCase, CheckNameIntegration): _multiprocess_can_split_ = True @@ -1946,21 +1945,27 @@ def test_all_any(self): self.assert_(bool_series.any()) def test_op_method(self): - def _check_op(series, other, op, alt): - result = op(series, other) - expected = alt(series, other) - tm.assert_almost_equal(result, expected) - - def check(series, other): - simple_ops = ['add', 'sub', 'mul'] + def check(series, other, check_reverse=False): + simple_ops = ['add', 'sub', 'mul', 'floordiv', 'truediv', 'pow'] + if not compat.PY3: + simple_ops.append('div') for opname in simple_ops: - _check_op(series, other, getattr(Series, opname), - getattr(operator, opname)) + op = getattr(Series, opname) + alt = getattr(operator, opname) + result = op(series, other) + expected = alt(series, other) + tm.assert_almost_equal(result, expected) + if check_reverse: + rop = getattr(Series, "r" + opname) + result = rop(series, other) + expected = alt(other, series) + tm.assert_almost_equal(result, expected) check(self.ts, self.ts * 2) check(self.ts, self.ts[::2]) - check(self.ts, 5) + check(self.ts, 5, check_reverse=True) + check(tm.makeFloatSeries(), tm.makeFloatSeries(), check_reverse=True) def test_neg(self): assert_series_equal(-self.series, -1 * self.series) @@ -2186,13 +2191,18 @@ def test_timedeltas_with_DateOffset(self): s = Series([Timestamp('20130101 9:01'), Timestamp('20130101 9:02')]) result = s + pd.offsets.Second(5) + result2 = pd.offsets.Second(5) + s expected = Series( [Timestamp('20130101 9:01:05'), Timestamp('20130101 9:02:05')]) + assert_series_equal(result, expected) + assert_series_equal(result2, expected) result = s + pd.offsets.Milli(5) + result2 = pd.offsets.Milli(5) + s expected = Series( [Timestamp('20130101 9:01:00.005'), Timestamp('20130101 9:02:00.005')]) assert_series_equal(result, expected) + assert_series_equal(result2, expected) result = s + pd.offsets.Minute(5) + pd.offsets.Milli(5) expected = Series( @@ -2203,20 +2213,25 @@ def test_timedeltas_with_DateOffset(self): # operate with np.timedelta64 correctly result = s + np.timedelta64(1, 's') + result2 = np.timedelta64(1, 's') + s expected = Series( [Timestamp('20130101 9:01:01'), Timestamp('20130101 9:02:01')]) assert_series_equal(result, expected) + assert_series_equal(result2, expected) result = s + np.timedelta64(5, 'ms') + result2 = np.timedelta64(5, 'ms') + s expected = Series( [Timestamp('20130101 9:01:00.005'), Timestamp('20130101 9:02:00.005')]) assert_series_equal(result, expected) + assert_series_equal(result2, expected) # valid DateOffsets for do in [ 'Hour', 'Minute', 'Second', 'Day', 'Micro', 'Milli', 'Nano' ]: op = getattr(pd.offsets,do) s + op(5) + op(5) + s # invalid DateOffsets for do in [ 'Week', 'BDay', 'BQuarterEnd', 'BMonthEnd', 'BYearEnd', @@ -2225,6 +2240,7 @@ def test_timedeltas_with_DateOffset(self): 'MonthBegin', 'QuarterBegin' ]: op = getattr(pd.offsets,do) self.assertRaises(TypeError, s.__add__, op(5)) + self.assertRaises(TypeError, s.__radd__, op(5)) def test_timedelta64_operations_with_timedeltas(self): @@ -2237,6 +2253,11 @@ def test_timedelta64_operations_with_timedeltas(self): self.assert_(result.dtype == 'm8[ns]') assert_series_equal(result, expected) + result2 = td2 - td1 + expected = (Series([timedelta(seconds=1)] * 3) - + Series([timedelta(seconds=0)] * 3)) + assert_series_equal(result2, expected) + # roundtrip assert_series_equal(result + td2,td1) @@ -2318,6 +2339,10 @@ def test_timedelta64_conversions(self): result = s1 / np.timedelta64(m,unit) assert_series_equal(result, expected) + # reverse op + expected = s1.apply(lambda x: np.timedelta64(m,unit) / x) + result = np.timedelta64(m,unit) / s1 + def test_timedelta64_equal_timedelta_supported_ops(self): ser = Series([Timestamp('20130301'), Timestamp('20130228 23:00:00'), Timestamp('20130228 22:00:00'), @@ -2351,44 +2376,58 @@ def timedelta64(*args): def test_operators_datetimelike(self): - # timedelta64 ### - td1 = Series([timedelta(minutes=5, seconds=3)] * 3) - td2 = timedelta(minutes=5, seconds=4) - for op in ['__mul__', '__floordiv__', '__pow__']: - op = getattr(td1, op, None) - if op is not None: - self.assertRaises(TypeError, op, td2) + def run_ops(ops, get_ser, test_ser): + for op in ops: + try: + op = getattr(get_ser, op, None) + if op is not None: + self.assertRaises(TypeError, op, test_ser) + except: + print("Failed on op %r" % op) + raise + ### timedelta64 ### + td1 = Series([timedelta(minutes=5,seconds=3)]*3) + td2 = timedelta(minutes=5,seconds=4) + ops = ['__mul__','__floordiv__','__pow__', + '__rmul__','__rfloordiv__','__rpow__'] + run_ops(ops, td1, td2) td1 + td2 + td2 + td1 td1 - td2 + td2 - td1 td1 / td2 - - # datetime64 ### - dt1 = Series( - [Timestamp('20111230'), Timestamp('20120101'), Timestamp('20120103')]) - dt2 = Series( - [Timestamp('20111231'), Timestamp('20120102'), Timestamp('20120104')]) - for op in ['__add__', '__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__']: - sop = getattr(dt1, op, None) - if sop is not None: - self.assertRaises(TypeError, sop, dt2) + td2 / td1 + + ### datetime64 ### + dt1 = Series([Timestamp('20111230'), Timestamp('20120101'), + Timestamp('20120103')]) + dt2 = Series([Timestamp('20111231'), Timestamp('20120102'), + Timestamp('20120104')]) + ops = ['__add__', '__mul__', '__floordiv__', '__truediv__', '__div__', + '__pow__', '__radd__', '__rmul__', '__rfloordiv__', + '__rtruediv__', '__rdiv__', '__rpow__'] + run_ops(ops, dt1, dt2) dt1 - dt2 + dt2 - dt1 - # datetime64 with timetimedelta ### - for op in ['__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__']: - sop = getattr(dt1, op, None) - if sop is not None: - self.assertRaises(TypeError, sop, td1) + ### datetime64 with timetimedelta ### + ops = ['__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__', + '__rmul__', '__rfloordiv__', '__rtruediv__', '__rdiv__', + '__rpow__'] + run_ops(ops, dt1, td1) dt1 + td1 + td1 + dt1 dt1 - td1 - - # timetimedelta with datetime64 ### - for op in ['__sub__', '__mul__', '__floordiv__', '__truediv__', '__div__', '__pow__']: - sop = getattr(td1, op, None) - if sop is not None: - self.assertRaises(TypeError, sop, dt1) - - # timedelta + datetime ok + # TODO: Decide if this ought to work. + # td1 - dt1 + + ### timetimedelta with datetime64 ### + ops = ['__sub__', '__mul__', '__floordiv__', '__truediv__', '__div__', + '__pow__', '__rsub__', '__rmul__', '__rfloordiv__', + '__rtruediv__', '__rdiv__', '__rpow__'] + run_ops(ops, td1, dt1) td1 + dt1 + dt1 + td1 def test_timedelta64_functions(self): @@ -2517,6 +2556,9 @@ def test_sub_of_datetime_from_TimeSeries(self): result = _possibly_cast_to_timedelta(np.abs(a - b)) self.assert_(result.dtype == 'timedelta64[ns]') + result = _possibly_cast_to_timedelta(np.abs(b - a)) + self.assert_(result.dtype == 'timedelta64[ns]') + def test_datetime64_with_index(self): # arithmetic integer ops with an index @@ -2537,8 +2579,8 @@ def test_datetime64_with_index(self): df = DataFrame(np.random.randn(5,2),index=date_range('20130101',periods=5)) df['date'] = Timestamp('20130102') - df['expected'] = df['date']-df.index.to_series() - df['result'] = df['date']-df.index + df['expected'] = df['date'] - df.index.to_series() + df['result'] = df['date'] - df.index assert_series_equal(df['result'],df['expected']) def test_timedelta64_nan(self): @@ -2586,7 +2628,9 @@ def test_operators_na_handling(self): index=[date(2012, 1, 1), date(2012, 1, 2)]) result = s + s.shift(1) + result2 = s.shift(1) + s self.assert_(isnull(result[0])) + self.assert_(isnull(result2[0])) s = Series(['foo', 'bar', 'baz', np.nan]) result = 'prefix_' + s @@ -2616,7 +2660,7 @@ def test_comparison_operators_with_nas(self): s = Series(bdate_range('1/1/2000', periods=10), dtype=object) s[::2] = np.nan - # test that comparions work + # test that comparisons work ops = ['lt', 'le', 'gt', 'ge', 'eq', 'ne'] for op in ops: val = s[5] @@ -2753,7 +2797,10 @@ def tester(a, b): assert_series_equal(tester(s, list(s)), s) d = DataFrame({'A': s}) - self.assertRaises(TypeError, tester, s, d) + # TODO: Fix this exception - needs to be fixed! (see GH5035) + # (previously this was a TypeError because series returned + # NotImplemented + self.assertRaises(ValueError, tester, s, d) def test_idxmin(self): # test idxmin @@ -2942,19 +2989,13 @@ def test_series_frame_radd_bug(self): self.assertRaises(TypeError, operator.add, datetime.now(), self.ts) def test_operators_frame(self): - import sys - buf = StringIO() - tmp = sys.stderr - sys.stderr = buf # rpow does not work with DataFrame - try: - df = DataFrame({'A': self.ts}) + df = DataFrame({'A': self.ts}) - tm.assert_almost_equal(self.ts + self.ts, (self.ts + df)['A']) - tm.assert_almost_equal(self.ts ** self.ts, (self.ts ** df)['A']) - tm.assert_almost_equal(self.ts < self.ts, (self.ts < df)['A']) - finally: - sys.stderr = tmp + tm.assert_almost_equal(self.ts + self.ts, (self.ts + df)['A']) + tm.assert_almost_equal(self.ts ** self.ts, (self.ts ** df)['A']) + tm.assert_almost_equal(self.ts < self.ts, (self.ts < df)['A']) + tm.assert_almost_equal(self.ts / self.ts, (self.ts / df)['A']) def test_operators_combine(self): def _check_fill(meth, op, a, b, fill_value=0): @@ -2987,8 +3028,10 @@ def _check_fill(meth, op, a, b, fill_value=0): a = Series([nan, 1., 2., 3., nan], index=np.arange(5)) b = Series([nan, 1, nan, 3, nan, 4.], index=np.arange(6)) - ops = [Series.add, Series.sub, Series.mul, Series.div] - equivs = [operator.add, operator.sub, operator.mul] + ops = [Series.add, Series.sub, Series.mul, Series.pow, + Series.truediv, Series.div] + equivs = [operator.add, operator.sub, operator.mul, operator.pow, + operator.truediv] if compat.PY3: equivs.append(operator.truediv) else: @@ -3253,9 +3296,12 @@ def test_value_counts_nunique(self): # timedelta64[ns] from datetime import timedelta td = df.dt - df.dt + timedelta(1) + td2 = timedelta(1) + (df.dt - df.dt) result = td.value_counts() + result2 = td2.value_counts() #self.assert_(result.index.dtype == 'timedelta64[ns]') self.assert_(result.index.dtype == 'int64') + self.assert_(result2.index.dtype == 'int64') # basics.rst doc example series = Series(np.random.randn(500)) diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 92ed1e415d11a..232ebd2c3726c 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -19,6 +19,10 @@ #---------------------------------------------------------------------- # DateOffset +class ApplyTypeError(TypeError): + # sentinel class for catching the apply error to return NotImplemented + pass + class CacheableOffset(object): @@ -128,7 +132,7 @@ def __repr__(self): kwds_new[key] = self.kwds[key] if len(kwds_new) > 0: attrs.append('='.join((attr, repr(kwds_new)))) - else: + else: if attr not in exclude: attrs.append('='.join((attr, repr(getattr(self, attr))))) @@ -136,7 +140,7 @@ def __repr__(self): plural = 's' else: plural = '' - + n_str = "" if self.n != 1: n_str = "%s * " % self.n @@ -170,19 +174,21 @@ def __call__(self, other): return self.apply(other) def __add__(self, other): - return self.apply(other) + try: + return self.apply(other) + except ApplyTypeError: + return NotImplemented def __radd__(self, other): return self.__add__(other) def __sub__(self, other): if isinstance(other, datetime): - raise TypeError('Cannot subtract datetime from offset!') + raise TypeError('Cannot subtract datetime from offset.') elif type(other) == type(self): return self.__class__(self.n - other.n, **self.kwds) else: # pragma: no cover - raise TypeError('Cannot subtract %s from %s' - % (type(other), type(self))) + return NotImplemented def __rsub__(self, other): return self.__class__(-self.n, **self.kwds) + other @@ -273,7 +279,7 @@ def __repr__(self): #TODO: Figure out if this should be merged into DateOffset plural = 's' else: plural = '' - + n_str = "" if self.n != 1: n_str = "%s * " % self.n @@ -370,8 +376,8 @@ def apply(self, other): return BDay(self.n, offset=self.offset + other, normalize=self.normalize) else: - raise TypeError('Only know how to combine business day with ' - 'datetime or timedelta!') + raise ApplyTypeError('Only know how to combine business day with ' + 'datetime or timedelta.') @classmethod def onOffset(cls, dt): @@ -463,8 +469,8 @@ def apply(self, other): return BDay(self.n, offset=self.offset + other, normalize=self.normalize) else: - raise TypeError('Only know how to combine trading day with ' - 'datetime, datetime64 or timedelta!') + raise ApplyTypeError('Only know how to combine trading day with ' + 'datetime, datetime64 or timedelta.') dt64 = self._to_dt64(other) day64 = dt64.astype('datetime64[D]') @@ -1177,7 +1183,10 @@ def __add__(self, other): return type(self)(self.n + other.n) else: return _delta_to_tick(self.delta + other.delta) - return self.apply(other) + try: + return self.apply(other) + except ApplyTypeError: + return NotImplemented def __eq__(self, other): if isinstance(other, compat.string_types): @@ -1220,8 +1229,8 @@ def apply(self, other): return other + self.delta elif isinstance(other, type(self)): return type(self)(self.n + other.n) - else: # pragma: no cover - raise TypeError('Unhandled type: %s' % type(other)) + else: + raise ApplyTypeError('Unhandled type: %s' % type(other).__name__) _rule_base = 'undefined'
There's a lot of overlap right now, this is the first step to trying to make this cleaner. - Abstract all arithmetic methods into core/ops - Add full range of flex arithmetic methods to all NDFrame/ndarray PandasObjects (except for SparsePanel pow and mod, which only work for scalars) - Normalize arithmetic methods signature (see `ops.add_special_arithmetic_methods` and `ops.add_flex_arithmetic_methods` for signature). - Opt-in more arithmetic operations with numexpr (except for SparsePanel, which has to opt-out because it doesn't respond to `shape`). - BUG: Fix `_fill_zeros` call to work even if TypeError (previously was inconsistent). - Add bind method to core/common Closes #3765. Closes #4334. Closes #4051. Closes #5033. Closes #4331.
https://api.github.com/repos/pandas-dev/pandas/pulls/5022
2013-09-28T20:37:55Z
2013-09-29T17:02:25Z
2013-09-29T17:02:25Z
2014-07-09T23:49:20Z
ER: give a better error message for hashing indices
diff --git a/pandas/core/index.py b/pandas/core/index.py index d488a29182a18..ffa1d7cfa0c3b 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -600,7 +600,7 @@ def __contains__(self, key): return False def __hash__(self): - return hash(self.view(np.ndarray)) + raise TypeError("unhashable type: %r" % type(self).__name__) def __getitem__(self, key): """Override numpy.ndarray's __getitem__ method to work as desired""" @@ -1852,6 +1852,7 @@ def equals(self, other): # e.g. fails in numpy 1.6 with DatetimeIndex #1681 return False + class MultiIndex(Index): """ diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 857836fa698ce..87772b5a86326 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -12,7 +12,8 @@ import numpy as np from numpy.testing import assert_array_equal -from pandas.core.index import Index, Float64Index, Int64Index, MultiIndex, InvalidIndexError +from pandas.core.index import (Index, Float64Index, Int64Index, MultiIndex, + InvalidIndexError) from pandas.core.frame import DataFrame from pandas.core.series import Series from pandas.util.testing import (assert_almost_equal, assertRaisesRegexp, @@ -75,7 +76,10 @@ def test_set_name_methods(self): self.assertEqual(ind.names, [name]) def test_hash_error(self): - self.assertRaises(TypeError, hash, self.strIndex) + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(self.strIndex).__name__): + hash(self.strIndex) def test_new_axis(self): new_index = self.dateIndex[None, :] @@ -661,6 +665,12 @@ def setUp(self): self.mixed = Float64Index([1.5, 2, 3, 4, 5]) self.float = Float64Index(np.arange(5) * 2.5) + def test_hash_error(self): + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(self.float).__name__): + hash(self.float) + def check_is_index(self, i): self.assert_(isinstance(i, Index) and not isinstance(i, Float64Index)) @@ -736,6 +746,7 @@ def test_astype(self): self.assert_(i.equals(result)) self.check_is_index(result) + class TestInt64Index(unittest.TestCase): _multiprocess_can_split_ = True @@ -779,6 +790,12 @@ def test_constructor_corner(self): arr = np.array([1, '2', 3, '4'], dtype=object) self.assertRaises(TypeError, Int64Index, arr) + def test_hash_error(self): + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(self.index).__name__): + hash(self.index) + def test_copy(self): i = Int64Index([], name='Foo') i_copy = i.copy() @@ -1155,6 +1172,12 @@ def setUp(self): labels=[major_labels, minor_labels], names=self.index_names) + def test_hash_error(self): + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(self.index).__name__): + hash(self.index) + def test_set_names_and_rename(self): # so long as these are synonyms, we don't need to test set_names self.assert_(self.index.rename == self.index.set_names) @@ -2231,6 +2254,7 @@ def test_get_combined_index(): result = _get_combined_index([]) assert(result.equals(Index([]))) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 96e96607ad9de..173ebeb199b3b 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -1057,6 +1057,13 @@ class TestPeriodIndex(TestCase): def setUp(self): pass + def test_hash_error(self): + index = period_range('20010101', periods=10) + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(index).__name__): + hash(index) + def test_make_time_series(self): index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009') series = Series(1, index=index) diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 0e5e3d1922ec4..d44ae94bdb718 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1716,6 +1716,13 @@ def _simple_ts(start, end, freq='D'): class TestDatetimeIndex(unittest.TestCase): _multiprocess_can_split_ = True + def test_hash_error(self): + index = date_range('20010101', periods=10) + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(index).__name__): + hash(index) + def test_stringified_slice_with_tz(self): #GH2658 import datetime
https://api.github.com/repos/pandas-dev/pandas/pulls/5019
2013-09-28T19:55:19Z
2013-09-28T20:57:43Z
2013-09-28T20:57:43Z
2014-07-16T08:31:55Z
BUG: Fix a bug when indexing np.nan via loc/iloc (GH5016)
diff --git a/doc/source/release.rst b/doc/source/release.rst index daee460fc50a1..66c3dcd203a6a 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -497,6 +497,7 @@ Bug Fixes - Fixed wrong index name during read_csv if using usecols. Applies to c parser only. (:issue:`4201`) - ``Timestamp`` objects can now appear in the left hand side of a comparison operation with a ``Series`` or ``DataFrame`` object (:issue:`4982`). + - Fix a bug when indexing with ``np.nan`` via ``iloc/loc`` (:issue:`5016`) pandas 0.12.0 ------------- diff --git a/pandas/core/index.py b/pandas/core/index.py index d488a29182a18..63bda40932647 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -424,7 +424,7 @@ def _convert_scalar_indexer(self, key, typ=None): def to_int(): ikey = int(key) if ikey != key: - self._convert_indexer_error(key, 'label') + return self._convert_indexer_error(key, 'label') return ikey if typ == 'iloc': diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index afbeb53d857e2..eb377c4b7955f 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1,12 +1,12 @@ # pylint: disable=W0223 from datetime import datetime -from pandas.core.common import _asarray_tuplesafe, is_list_like from pandas.core.index import Index, MultiIndex, _ensure_index from pandas.compat import range, zip import pandas.compat as compat import pandas.core.common as com from pandas.core.common import (_is_bool_indexer, is_integer_dtype, + _asarray_tuplesafe, is_list_like, isnull, ABCSeries, ABCDataFrame, ABCPanel) import pandas.lib as lib @@ -979,12 +979,20 @@ def _has_valid_type(self, key, axis): else: def error(): + if isnull(key): + raise ValueError("cannot use label indexing with a null key") raise KeyError("the label [%s] is not in the [%s]" % (key,self.obj._get_axis_name(axis))) - key = self._convert_scalar_indexer(key, axis) try: + key = self._convert_scalar_indexer(key, axis) if not key in ax: error() + except (TypeError) as e: + + # python 3 type errors should be raised + if 'unorderable' in str(e): # pragma: no cover + error() + raise except: error() diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 8fcb64e6d0eda..f10e1612f7fe9 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -97,8 +97,13 @@ def ref_locs(self): indexer = self.ref_items.get_indexer(self.items) indexer = com._ensure_platform_int(indexer) if (indexer == -1).any(): - raise AssertionError('Some block items were not in block ' - 'ref_items') + + # this means that we have nan's in our block + try: + indexer[indexer == -1] = np.arange(len(self.items))[isnull(self.items)] + except: + raise AssertionError('Some block items were not in block ' + 'ref_items') self._ref_locs = indexer return self._ref_locs @@ -2500,9 +2505,18 @@ def _consolidate_inplace(self): def get(self, item): if self.items.is_unique: + + if isnull(item): + indexer = np.arange(len(self.items))[isnull(self.items)] + return self.get_for_nan_indexer(indexer) + _, block = self._find_block(item) return block.get(item) else: + + if isnull(item): + raise ValueError("cannot label index with a null key") + indexer = self.items.get_loc(item) ref_locs = np.array(self._set_ref_locs()) @@ -2528,14 +2542,31 @@ def get(self, item): def iget(self, i): item = self.items[i] + + # unique if self.items.is_unique: - return self.get(item) + if notnull(item): + return self.get(item) + return self.get_for_nan_indexer(i) - # compute the duplicative indexer if needed ref_locs = self._set_ref_locs() b, loc = ref_locs[i] return b.iget(loc) + def get_for_nan_indexer(self, indexer): + + # allow a single nan location indexer + if not np.isscalar(indexer): + if len(indexer) == 1: + indexer = indexer.item() + else: + raise ValueError("cannot label index with a null key") + + # take a nan indexer and return the values + ref_locs = self._set_ref_locs(do_refs='force') + b, loc = ref_locs[indexer] + return b.iget(loc) + def get_scalar(self, tup): """ Retrieve single item diff --git a/pandas/core/series.py b/pandas/core/series.py index d9e9a0034b56b..77c777042ab5f 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1053,10 +1053,10 @@ def __setitem__(self, key, value): except TypeError as e: if isinstance(key, tuple) and not isinstance(self.index, MultiIndex): raise ValueError("Can only tuple-index with a MultiIndex") + # python 3 type errors should be raised if 'unorderable' in str(e): # pragma: no cover raise IndexError(key) - # Could not hash item if _is_bool_indexer(key): key = _check_bool_indexer(self.index, key) diff --git a/pandas/hashtable.pyx b/pandas/hashtable.pyx index 164fc8c94924e..1b132ea91f515 100644 --- a/pandas/hashtable.pyx +++ b/pandas/hashtable.pyx @@ -643,6 +643,8 @@ cdef class Float64HashTable(HashTable): return uniques.to_array() +na_sentinel = object + cdef class PyObjectHashTable(HashTable): # cdef kh_pymap_t *table @@ -660,6 +662,8 @@ cdef class PyObjectHashTable(HashTable): def __contains__(self, object key): cdef khiter_t k hash(key) + if key != key or key is None: + key = na_sentinel k = kh_get_pymap(self.table, <PyObject*>key) return k != self.table.n_buckets @@ -669,6 +673,8 @@ cdef class PyObjectHashTable(HashTable): cpdef get_item(self, object val): cdef khiter_t k + if val != val or val is None: + val = na_sentinel k = kh_get_pymap(self.table, <PyObject*>val) if k != self.table.n_buckets: return self.table.vals[k] @@ -677,6 +683,8 @@ cdef class PyObjectHashTable(HashTable): def get_iter_test(self, object key, Py_ssize_t iterations): cdef Py_ssize_t i, val + if key != key or key is None: + key = na_sentinel for i in range(iterations): k = kh_get_pymap(self.table, <PyObject*>key) if k != self.table.n_buckets: @@ -689,6 +697,8 @@ cdef class PyObjectHashTable(HashTable): char* buf hash(key) + if key != key or key is None: + key = na_sentinel k = kh_put_pymap(self.table, <PyObject*>key, &ret) # self.table.keys[k] = key if kh_exist_pymap(self.table, k): @@ -706,6 +716,9 @@ cdef class PyObjectHashTable(HashTable): for i in range(n): val = values[i] hash(val) + if val != val or val is None: + val = na_sentinel + k = kh_put_pymap(self.table, <PyObject*>val, &ret) self.table.vals[k] = i @@ -720,6 +733,9 @@ cdef class PyObjectHashTable(HashTable): for i in range(n): val = values[i] hash(val) + if val != val or val is None: + val = na_sentinel + k = kh_get_pymap(self.table, <PyObject*>val) if k != self.table.n_buckets: locs[i] = self.table.vals[k] diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index e5d2bb17ec7a8..eeb2c34ea9394 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -642,6 +642,8 @@ def test_setitem_clear_caches(self): def test_setitem_None(self): # GH #766 self.frame[None] = self.frame['A'] + assert_series_equal(self.frame.iloc[:,-1], self.frame['A']) + assert_series_equal(self.frame.loc[:,None], self.frame['A']) assert_series_equal(self.frame[None], self.frame['A']) repr(self.frame) @@ -4475,6 +4477,41 @@ def test_constructor_lists_to_object_dtype(self): self.assert_(d['a'].dtype == np.object_) self.assert_(d['a'][1] is False) + def test_constructor_with_nas(self): + # GH 5016 + # na's in indicies + + def check(df): + for i in range(len(df.columns)): + df.iloc[:,i] + + # allow single nans to succeed + indexer = np.arange(len(df.columns))[isnull(df.columns)] + + if len(indexer) == 1: + assert_series_equal(df.iloc[:,indexer[0]],df.loc[:,np.nan]) + + + # multiple nans should fail + else: + + def f(): + df.loc[:,np.nan] + self.assertRaises(ValueError, f) + + + df = DataFrame([[1,2,3],[4,5,6]], index=[1,np.nan]) + check(df) + + df = DataFrame([[1,2,3],[4,5,6]], columns=[1.1,2.2,np.nan]) + check(df) + + df = DataFrame([[0,1,2,3],[4,5,6,7]], columns=[np.nan,1.1,2.2,np.nan]) + check(df) + + df = DataFrame([[0.0,1,2,3.0],[4,5,6,7]], columns=[np.nan,1.1,2.2,np.nan]) + check(df) + def test_logical_with_nas(self): d = DataFrame({'a': [np.nan, False], 'b': [True, True]})
closes #5016
https://api.github.com/repos/pandas-dev/pandas/pulls/5018
2013-09-28T16:19:45Z
2013-09-28T19:22:45Z
2013-09-28T19:22:45Z
2014-07-03T18:50:32Z
ENH: PySide support for qtpandas.
diff --git a/doc/source/release.rst b/doc/source/release.rst index 058ea165120a6..488fd8deacc2e 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -77,6 +77,7 @@ Experimental Features (:issue:`4897`). - Add msgpack support via ``pd.read_msgpack()`` and ``pd.to_msgpack()`` / ``df.to_msgpack()`` for serialization of arbitrary pandas (and python objects) in a lightweight portable binary format (:issue:`686`) + - Added PySide support for the qtpandas DataFrameModel and DataFrameWidget. Improvements to existing features ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 90d2989de65c2..3f5989856902e 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -600,6 +600,8 @@ Experimental os.remove('foo.msg') +- Added PySide support for the qtpandas DataFrameModel and DataFrameWidget. + .. _whatsnew_0130.refactoring: Internal Refactoring diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 6e357d6d38e49..a9926947013fb 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -594,3 +594,79 @@ Andrews curves charts: @savefig andrews_curve_winter.png andrews_curves(data, 'Name', colormap='winter') + + +**************************************** +Visualizing your data in Qt applications +**************************************** + +There is an experimental support for visualizing DataFrames in PyQt4 and PySide +applications. At the moment you can display and edit the values of the cells +in the DataFrame. Qt will take care of displaying just the portion of the +DataFrame that is currently visible and the edits will be immediately saved to +the underlying DataFrame + +To demonstrate this we will create a simple PySide application that will switch +between two editable DataFrames. For this will use the ``DataFrameModel`` class +that handles the access to the DataFrame, and the ``DataFrameWidget``, which is +just a thin layer around the ``QTableView``. + +.. code-block:: python + + import numpy as np + import pandas as pd + from pandas.sandbox.qtpandas import DataFrameModel, DataFrameWidget + from PySide import QtGui, QtCore + + # Or if you use PyQt4: + # from PyQt4 import QtGui, QtCore + + class MainWidget(QtGui.QWidget): + def __init__(self, parent=None): + super(MainWidget, self).__init__(parent) + + # Create two DataFrames + self.df1 = pd.DataFrame(np.arange(9).reshape(3, 3), + columns=['foo', 'bar', 'baz']) + self.df2 = pd.DataFrame({ + 'int': [1, 2, 3], + 'float': [1.5, 2.5, 3.5], + 'string': ['a', 'b', 'c'], + 'nan': [np.nan, np.nan, np.nan] + }, index=['AAA', 'BBB', 'CCC'], + columns=['int', 'float', 'string', 'nan']) + + # Create the widget and set the first DataFrame + self.widget = DataFrameWidget(self.df1) + + # Create the buttons for changing DataFrames + self.button_first = QtGui.QPushButton('First') + self.button_first.clicked.connect(self.on_first_click) + self.button_second = QtGui.QPushButton('Second') + self.button_second.clicked.connect(self.on_second_click) + + # Set the layout + vbox = QtGui.QVBoxLayout() + vbox.addWidget(self.widget) + hbox = QtGui.QHBoxLayout() + hbox.addWidget(self.button_first) + hbox.addWidget(self.button_second) + vbox.addLayout(hbox) + self.setLayout(vbox) + + def on_first_click(self): + '''Sets the first DataFrame''' + self.widget.setDataFrame(self.df1) + + def on_second_click(self): + '''Sets the second DataFrame''' + self.widget.setDataFrame(self.df2) + + if __name__ == '__main__': + import sys + + # Initialize the application + app = QtGui.QApplication(sys.argv) + mw = MainWidget() + mw.show() + app.exec_() diff --git a/pandas/sandbox/qtpandas.py b/pandas/sandbox/qtpandas.py index 35aa28fea1678..3f284990efd40 100644 --- a/pandas/sandbox/qtpandas.py +++ b/pandas/sandbox/qtpandas.py @@ -3,10 +3,15 @@ @author: Jev Kuznetsov ''' -from PyQt4.QtCore import ( - QAbstractTableModel, Qt, QVariant, QModelIndex, SIGNAL) -from PyQt4.QtGui import ( - QApplication, QDialog, QVBoxLayout, QTableView, QWidget) +try: + from PyQt4.QtCore import QAbstractTableModel, Qt, QVariant, QModelIndex + from PyQt4.QtGui import ( + QApplication, QDialog, QVBoxLayout, QTableView, QWidget) +except ImportError: + from PySide.QtCore import QAbstractTableModel, Qt, QModelIndex + from PySide.QtGui import ( + QApplication, QDialog, QVBoxLayout, QTableView, QWidget) + QVariant = lambda value=None: value from pandas import DataFrame, Index @@ -57,9 +62,17 @@ def flags(self, index): return flags def setData(self, index, value, role): - self.df.set_value(self.df.index[index.row()], - self.df.columns[index.column()], - value.toPyObject()) + row = self.df.index[index.row()] + col = self.df.columns[index.column()] + if hasattr(value, 'toPyObject'): + # PyQt4 gets a QVariant + value = value.toPyObject() + else: + # PySide gets an unicode + dtype = self.df[col].dtype + if dtype != object: + value = None if value == '' else dtype.type(value) + self.df.set_value(row, col, value) return True def rowCount(self, index=QModelIndex()): @@ -75,17 +88,18 @@ def __init__(self, dataFrame, parent=None): super(DataFrameWidget, self).__init__(parent) self.dataModel = DataFrameModel() - self.dataModel.setDataFrame(dataFrame) - self.dataTable = QTableView() self.dataTable.setModel(self.dataModel) - self.dataModel.signalUpdate() layout = QVBoxLayout() layout.addWidget(self.dataTable) self.setLayout(layout) + # Set DataFrame + self.setDataFrame(dataFrame) - def resizeColumnsToContents(self): + def setDataFrame(self, dataFrame): + self.dataModel.setDataFrame(dataFrame) + self.dataModel.signalUpdate() self.dataTable.resizeColumnsToContents() #-----------------stand alone test code
Added PySide support for the qtpandas DataFrameWidget.
https://api.github.com/repos/pandas-dev/pandas/pulls/5013
2013-09-27T23:39:07Z
2013-10-02T21:12:03Z
2013-10-02T21:12:03Z
2014-06-25T08:10:52Z
ER: give concat a better error message for incompatible types
diff --git a/doc/source/release.rst b/doc/source/release.rst index 13e2d5a136c21..daee460fc50a1 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -136,6 +136,8 @@ Improvements to existing features - Both ExcelFile and read_excel to accept an xlrd.Book for the io (formerly path_or_buf) argument; this requires engine to be set. (:issue:`4961`). + - ``concat`` now gives a more informative error message when passed objects + that cannot be concatenated (:issue:`4608`). API Changes ~~~~~~~~~~~ diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 5792161e0171e..ba60566a7fc55 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -1245,7 +1245,11 @@ def _get_comb_axis(self, i): if self._is_series: all_indexes = [x.index for x in self.objs] else: - all_indexes = [x._data.axes[i] for x in self.objs] + try: + all_indexes = [x._data.axes[i] for x in self.objs] + except IndexError: + types = [type(x).__name__ for x in self.objs] + raise TypeError("Cannot concatenate list of %s" % types) return _get_combined_index(all_indexes, intersect=self.intersect) @@ -1256,6 +1260,10 @@ def _get_concat_axis(self): elif self.keys is None: names = [] for x in self.objs: + if not isinstance(x, Series): + raise TypeError("Cannot concatenate type 'Series' " + "with object of type " + "%r" % type(x).__name__) if x.name is not None: names.append(x.name) else: diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 203769e731022..d44564db4b830 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1804,6 +1804,15 @@ def test_concat_invalid_first_argument(self): # generator ok though concat(DataFrame(np.random.rand(5,5)) for _ in range(3)) + def test_concat_mixed_types_fails(self): + df = DataFrame(randn(10, 1)) + + with tm.assertRaisesRegexp(TypeError, "Cannot concatenate.+"): + concat([df[0], df], axis=1) + + with tm.assertRaisesRegexp(TypeError, "Cannot concatenate.+"): + concat([df, df[0]], axis=1) + class TestOrderedMerge(unittest.TestCase): def setUp(self):
closes #4608
https://api.github.com/repos/pandas-dev/pandas/pulls/5011
2013-09-27T20:32:30Z
2013-09-27T21:29:12Z
2013-09-27T21:29:12Z
2014-06-24T15:20:06Z
API: Remove deprecated read_clipboard/to_clipboard/ExcelFile/ExcelWriter from pandas.io.parsers (GH3717)
diff --git a/doc/source/api.rst b/doc/source/api.rst index 28c1515e93bc5..8dcf9c0f52de4 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -31,6 +31,15 @@ Flat File read_table read_csv read_fwf + +Clipboard +~~~~~~~~~ + +.. currentmodule:: pandas.io.clipboard + +.. autosummary:: + :toctree: generated/ + read_clipboard Excel diff --git a/doc/source/install.rst b/doc/source/install.rst index 91e86a6aa1e29..532c90b83ebb0 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -111,7 +111,7 @@ Optional Dependencies <http://www.pygtk.org/>`__, `xsel <http://www.vergenet.net/~conrad/software/xsel/>`__, or `xclip <http://sourceforge.net/projects/xclip/>`__: necessary to use - :func:`~pandas.io.parsers.read_clipboard`. Most package managers on Linux + :func:`~pandas.io.clipboard.read_clipboard`. Most package managers on Linux distributions will have xclip and/or xsel immediately available for installation. * One of the following combinations of libraries is needed to use the diff --git a/doc/source/release.rst b/doc/source/release.rst index e49812b207921..f871a412d2ff6 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -245,6 +245,7 @@ API Changes - Remove deprecated ``_verbose_info`` (:issue:`3215`) - Begin removing methods that don't make sense on ``GroupBy`` objects (:issue:`4887`). + - Remove deprecated ``read_clipboard/to_clipboard/ExcelFile/ExcelWriter`` from ``pandas.io.parsers`` (:issue:`3717`) Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 426d71b05e30a..26f15d5ae2aea 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -2048,27 +2048,3 @@ def __init__(self, f, **kwds): def _make_reader(self, f): self.data = FixedWidthReader(f, self.colspecs, self.delimiter, encoding=self.encoding) - - -##### deprecations in 0.12 ##### -##### remove in 0.12 ##### - -from pandas.io import clipboard -def read_clipboard(**kwargs): - warn("read_clipboard is now a top-level accessible via pandas.read_clipboard", FutureWarning) - clipboard.read_clipboard(**kwargs) - -def to_clipboard(obj): - warn("to_clipboard is now an object level method accessible via obj.to_clipboard()", FutureWarning) - clipboard.to_clipboard(obj) - -from pandas.io import excel -class ExcelWriter(excel.ExcelWriter): - def __init__(self, path): - warn("ExcelWriter can now be imported from: pandas.io.excel", FutureWarning) - super(ExcelWriter, self).__init__(path) - -class ExcelFile(excel.ExcelFile): - def __init__(self, path_or_buf, **kwds): - warn("ExcelFile can now be imported from: pandas.io.excel", FutureWarning) - super(ExcelFile, self).__init__(path_or_buf, **kwds) diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index cd101d325f21d..0c6332205ffe5 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -254,20 +254,20 @@ def test_excel_read_buffer(self): f = open(pth, 'rb') xl = ExcelFile(f) xl.parse('Sheet1', index_col=0, parse_dates=True) - + def test_read_xlrd_Book(self): _skip_if_no_xlrd() _skip_if_no_xlwt() - + import xlrd - + pth = '__tmp_excel_read_worksheet__.xls' df = self.frame - + with ensure_clean(pth) as pth: df.to_excel(pth, "SheetA") book = xlrd.open_workbook(pth) - + with ExcelFile(book, engine="xlrd") as xl: result = xl.parse("SheetA") tm.assert_frame_equal(df, result) @@ -1004,26 +1004,6 @@ def check_called(func): check_called(lambda: df.to_excel('something.xls', engine='dummy')) set_option('io.excel.xlsx.writer', val) - -class ExcelLegacyTests(SharedItems, unittest.TestCase): - def test_deprecated_from_parsers(self): - - # since 0.12 changed the import path - import warnings - - with warnings.catch_warnings(): - warnings.filterwarnings(action='ignore', category=FutureWarning) - - _skip_if_no_xlrd() - from pandas.io.parsers import ExcelFile as xf - xf(self.xls1) - - _skip_if_no_xlwt() - with ensure_clean('test.xls') as path: - from pandas.io.parsers import ExcelWriter as xw - xw(path) - - if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False)
closes #3717
https://api.github.com/repos/pandas-dev/pandas/pulls/5009
2013-09-27T19:21:02Z
2013-09-27T19:55:18Z
2013-09-27T19:55:18Z
2014-07-16T08:31:45Z
ENH: Support for "52–53-week fiscal year" / "4–4–5 calendar" and LastWeekOfMonth DateOffset
diff --git a/doc/source/release.rst b/doc/source/release.rst index ed1834f14fc2e..a2015a3b361ac 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -62,7 +62,8 @@ New features - Auto-detect field widths in read_fwf when unspecified (:issue:`4488`) - ``to_csv()`` now outputs datetime objects according to a specified format string via the ``date_format`` keyword (:issue:`4313`) - + - Added ``LastWeekOfMonth`` DateOffset (:issue:`4637`) + - Added ``FY5253``, and ``FY5253Quarter`` DateOffsets (:issue:`4511`) Experimental Features ~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index cd12cc65dcd43..875ba2de93956 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -420,6 +420,7 @@ frequency increment. Specific offset logic like "month", "business day", or CDay, "custom business day (experimental)" Week, "one week, optionally anchored on a day of the week" WeekOfMonth, "the x-th day of the y-th week of each month" + LastWeekOfMonth, "the x-th day of the last week of each month" MonthEnd, "calendar month end" MonthBegin, "calendar month begin" BMonthEnd, "business month end" @@ -428,10 +429,12 @@ frequency increment. Specific offset logic like "month", "business day", or QuarterBegin, "calendar quarter begin" BQuarterEnd, "business quarter end" BQuarterBegin, "business quarter begin" + FY5253Quarter, "retail (aka 52-53 week) quarter" YearEnd, "calendar year end" YearBegin, "calendar year begin" BYearEnd, "business year end" BYearBegin, "business year begin" + FY5253, "retail (aka 52-53 week) year" Hour, "one hour" Minute, "one minute" Second, "one second" diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 4c1ece032310f..02f231170ab97 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -511,6 +511,8 @@ Enhancements - Python csv parser now supports usecols (:issue:`4335`) - DataFrame has a new ``interpolate`` method, similar to Series (:issue:`4434`, :issue:`1892`) +- Added ``LastWeekOfMonth`` DateOffset (:issue:`4637`) +- Added ``FY5253``, and ``FY5253Quarter`` DateOffsets (:issue:`4511`) .. ipython:: python diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 4878ebfccf915..cfe874484231b 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -95,6 +95,8 @@ def get_freq_code(freqstr): code = _period_str_to_code(freqstr[0]) stride = freqstr[1] except: + if com.is_integer(freqstr[1]): + raise code = _period_str_to_code(freqstr[1]) stride = freqstr[0] return code, stride @@ -227,10 +229,10 @@ def get_period_alias(offset_str): 'us': 'U' } +#TODO: Can this be killed? for _i, _weekday in enumerate(['MON', 'TUE', 'WED', 'THU', 'FRI']): for _iweek in range(4): _name = 'WOM-%d%s' % (_iweek + 1, _weekday) - _offset_map[_name] = offsets.WeekOfMonth(week=_iweek, weekday=_i) _rule_aliases[_name.replace('-', '@')] = _name # Note that _rule_aliases is not 1:1 (d[BA]==d[A@DEC]), and so traversal @@ -301,7 +303,7 @@ def to_offset(freqstr): # hack to handle WOM-1MON -opattern = re.compile(r'([\-]?\d*)\s*([A-Za-z]+([\-@]\d*[A-Za-z]+)?)') +opattern = re.compile(r'([\-]?\d*)\s*([A-Za-z]+([\-@][\dA-Za-z\-]+)?)') def _base_and_stride(freqstr): @@ -356,16 +358,16 @@ def get_offset(name): else: if name in _rule_aliases: name = _rule_aliases[name] - try: - if name not in _offset_map: + + if name not in _offset_map: + try: # generate and cache offset offset = _make_offset(name) - _offset_map[name] = offset - return _offset_map[name] - except (ValueError, TypeError, KeyError): - # bad prefix or suffix - pass - raise ValueError('Bad rule name requested: %s.' % name) + except (ValueError, TypeError, KeyError): + # bad prefix or suffix + raise ValueError('Bad rule name requested: %s.' % name) + _offset_map[name] = offset + return _offset_map[name] getOffset = get_offset @@ -401,9 +403,6 @@ def get_legacy_offset_name(offset): name = offset.name return _legacy_reverse_map.get(name, name) -get_offset_name = get_offset_name - - def get_standard_freq(freq): """ Return the standardized frequency string @@ -621,8 +620,12 @@ def _period_str_to_code(freqstr): try: freqstr = freqstr.upper() return _period_code_map[freqstr] - except: - alias = _period_alias_dict[freqstr] + except KeyError: + try: + alias = _period_alias_dict[freqstr] + except KeyError: + raise ValueError("Unknown freqstr: %s" % freqstr) + return _period_code_map[alias] @@ -839,16 +842,21 @@ def _get_monthly_rule(self): 'ce': 'M', 'be': 'BM'}.get(pos_check) def _get_wom_rule(self): - wdiffs = unique(np.diff(self.index.week)) - if not lib.ismember(wdiffs, set([4, 5])).all(): - return None +# wdiffs = unique(np.diff(self.index.week)) + #We also need -47, -49, -48 to catch index spanning year boundary +# if not lib.ismember(wdiffs, set([4, 5, -47, -49, -48])).all(): +# return None weekdays = unique(self.index.weekday) if len(weekdays) > 1: return None + + week_of_months = unique((self.index.day - 1) // 7) + if len(week_of_months) > 1: + return None # get which week - week = (self.index[0].day - 1) // 7 + 1 + week = week_of_months[0] + 1 wd = _weekday_rule_aliases[weekdays[0]] return 'WOM-%d%s' % (week, wd) diff --git a/pandas/tseries/offsets.py b/pandas/tseries/offsets.py index 9c60e77363b9b..07efbcfdcd7ba 100644 --- a/pandas/tseries/offsets.py +++ b/pandas/tseries/offsets.py @@ -6,7 +6,7 @@ from pandas.tseries.tools import to_datetime # import after tools, dateutil check -from dateutil.relativedelta import relativedelta +from dateutil.relativedelta import relativedelta, weekday import pandas.tslib as tslib from pandas.tslib import Timestamp from pandas import _np_version_under1p7 @@ -15,6 +15,7 @@ 'MonthBegin', 'BMonthBegin', 'MonthEnd', 'BMonthEnd', 'YearBegin', 'BYearBegin', 'YearEnd', 'BYearEnd', 'QuarterBegin', 'BQuarterBegin', 'QuarterEnd', 'BQuarterEnd', + 'LastWeekOfMonth', 'FY5253Quarter', 'FY5253', 'Week', 'WeekOfMonth', 'Hour', 'Minute', 'Second', 'Milli', 'Micro', 'Nano'] @@ -108,8 +109,8 @@ def _should_cache(self): def _params(self): attrs = [(k, v) for k, v in compat.iteritems(vars(self)) - if k not in ['kwds', '_offset', 'name', 'normalize', - 'busdaycalendar', '_named']] + if (k not in ['kwds', 'name', 'normalize', + 'busdaycalendar']) and (k[0] != '_')] attrs.extend(list(self.kwds.items())) attrs = sorted(set(attrs)) @@ -701,15 +702,23 @@ def _from_name(cls, suffix=None): weekday = _weekday_to_int[suffix] return cls(weekday=weekday) +class WeekDay(object): + MON = 0 + TUE = 1 + WED = 2 + THU = 3 + FRI = 4 + SAT = 5 + SUN = 6 _int_to_weekday = { - 0: 'MON', - 1: 'TUE', - 2: 'WED', - 3: 'THU', - 4: 'FRI', - 5: 'SAT', - 6: 'SUN' + WeekDay.MON: 'MON', + WeekDay.TUE: 'TUE', + WeekDay.WED: 'WED', + WeekDay.THU: 'THU', + WeekDay.FRI: 'FRI', + WeekDay.SAT: 'SAT', + WeekDay.SUN: 'SUN' } _weekday_to_int = dict((v, k) for k, v in _int_to_weekday.items()) @@ -800,6 +809,80 @@ def _from_name(cls, suffix=None): week = int(suffix[0]) - 1 weekday = _weekday_to_int[suffix[1:]] return cls(week=week, weekday=weekday) + +class LastWeekOfMonth(CacheableOffset, DateOffset): + """ + Describes monthly dates in last week of month like "the last Tuesday of each month" + + Parameters + ---------- + n : int + weekday : {0, 1, ..., 6} + 0: Mondays + 1: Tuesdays + 2: Wednesdays + 3: Thursdays + 4: Fridays + 5: Saturdays + 6: Sundays + """ + def __init__(self, n=1, **kwds): + self.n = n + self.weekday = kwds['weekday'] + + if self.n == 0: + raise ValueError('N cannot be 0') + + if self.weekday < 0 or self.weekday > 6: + raise ValueError('Day must be 0<=day<=6, got %d' % + self.weekday) + + self.kwds = kwds + + def apply(self, other): + offsetOfMonth = self.getOffsetOfMonth(other) + + if offsetOfMonth > other: + if self.n > 0: + months = self.n - 1 + else: + months = self.n + elif offsetOfMonth == other: + months = self.n + else: + if self.n > 0: + months = self.n + else: + months = self.n + 1 + + return self.getOffsetOfMonth(other + relativedelta(months=months, day=1)) + + def getOffsetOfMonth(self, dt): + m = MonthEnd() + d = datetime(dt.year, dt.month, 1) + + eom = m.rollforward(d) + + w = Week(weekday=self.weekday) + + return w.rollback(eom) + + def onOffset(self, dt): + return dt == self.getOffsetOfMonth(dt) + + @property + def rule_code(self): + return '%s-%s' % (self._prefix, _int_to_weekday.get(self.weekday, '')) + + _prefix = 'LWOM' + + @classmethod + def _from_name(cls, suffix=None): + if not suffix: + raise ValueError("Prefix %r requires a suffix." % (cls._prefix)) + # TODO: handle n here... + weekday = _weekday_to_int[suffix] + return cls(weekday=weekday) class QuarterOffset(DateOffset): @@ -876,7 +959,319 @@ def onOffset(self, dt): modMonth = (dt.month - self.startingMonth) % 3 return BMonthEnd().onOffset(dt) and modMonth == 0 +class FY5253(CacheableOffset, DateOffset): + """ + Describes 52-53 week fiscal year. This is also known as a 4-4-5 calendar. + + It is used by companies that desire that their + fiscal year always end on the same day of the week. + + It is a method of managing accounting periods. + It is a common calendar structure for some industries, + such as retail, manufacturing and parking industry. + + For more information see: + http://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar + + + The year may either: + - end on the last X day of the Y month. + - end on the last X day closest to the last day of the Y month. + + X is a specific day of the week. + Y is a certain month of the year + + Parameters + ---------- + n : int + weekday : {0, 1, ..., 6} + 0: Mondays + 1: Tuesdays + 2: Wednesdays + 3: Thursdays + 4: Fridays + 5: Saturdays + 6: Sundays + startingMonth : The month in which fiscal years end. {1, 2, ... 12} + variation : str + {"nearest", "last"} for "LastOfMonth" or "NearestEndMonth" + """ + + _prefix = 'RE' + _suffix_prefix_last = 'L' + _suffix_prefix_nearest = 'N' + + def __init__(self, n=1, **kwds): + self.n = n + self.startingMonth = kwds['startingMonth'] + self.weekday = kwds["weekday"] + + self.variation = kwds["variation"] + + self.kwds = kwds + + if self.n == 0: + raise ValueError('N cannot be 0') + + if self.variation not in ["nearest", "last"]: + raise ValueError('%s is not a valid variation' % self.variation) + + if self.variation == "nearest": + self._rd_forward = relativedelta(weekday=weekday(self.weekday)) + self._rd_backward = relativedelta(weekday=weekday(self.weekday)(-1)) + else: + self._offset_lwom = LastWeekOfMonth(n=1, weekday=self.weekday) + + def isAnchored(self): + return self.n == 1 \ + and self.startingMonth is not None \ + and self.weekday is not None + + def onOffset(self, dt): + year_end = self.get_year_end(dt) + return year_end == dt + + def apply(self, other): + n = self.n + if n > 0: + year_end = self.get_year_end(other) + if other < year_end: + other = year_end + n -= 1 + elif other > year_end: + other = self.get_year_end(other + relativedelta(years=1)) + n -= 1 + + return self.get_year_end(other + relativedelta(years=n)) + else: + n = -n + year_end = self.get_year_end(other) + if other > year_end: + other = year_end + n -= 1 + elif other < year_end: + other = self.get_year_end(other + relativedelta(years=-1)) + n -= 1 + + return self.get_year_end(other + relativedelta(years=-n)) + + def get_year_end(self, dt): + if self.variation == "nearest": + return self._get_year_end_nearest(dt) + else: + return self._get_year_end_last(dt) + + def get_target_month_end(self, dt): + target_month = datetime(year=dt.year, month=self.startingMonth, day=1) + next_month_first_of = target_month + relativedelta(months=+1) + return next_month_first_of + relativedelta(days=-1) + + def _get_year_end_nearest(self, dt): + target_date = self.get_target_month_end(dt) + if target_date.weekday() == self.weekday: + return target_date + else: + forward = target_date + self._rd_forward + backward = target_date + self._rd_backward + + if forward - target_date < target_date - backward: + return forward + else: + return backward + + def _get_year_end_last(self, dt): + current_year = datetime(year=dt.year, month=self.startingMonth, day=1) + return current_year + self._offset_lwom + + @property + def rule_code(self): + suffix = self.get_rule_code_suffix() + return "%s-%s" % (self._get_prefix(), suffix) + + def _get_prefix(self): + return self._prefix + + def _get_suffix_prefix(self): + if self.variation == "nearest": + return self._suffix_prefix_nearest + else: + return self._suffix_prefix_last + + def get_rule_code_suffix(self): + return '%s-%s-%s' % (self._get_suffix_prefix(), \ + _int_to_month[self.startingMonth], \ + _int_to_weekday[self.weekday]) + + @classmethod + def _parse_suffix(cls, varion_code, startingMonth_code, weekday_code): + if varion_code == "N": + variation = "nearest" + elif varion_code == "L": + variation = "last" + else: + raise ValueError("Unable to parse varion_code: %s" % (varion_code,)) + + startingMonth = _month_to_int[startingMonth_code] + weekday = _weekday_to_int[weekday_code] + + return { + "weekday":weekday, + "startingMonth":startingMonth, + "variation":variation, + } + + @classmethod + def _from_name(cls, *args): + return cls(**cls._parse_suffix(*args)) + +class FY5253Quarter(CacheableOffset, DateOffset): + """ + DateOffset increments between business quarter dates + for 52-53 week fiscal year (also known as a 4-4-5 calendar). + + It is used by companies that desire that their + fiscal year always end on the same day of the week. + + It is a method of managing accounting periods. + It is a common calendar structure for some industries, + such as retail, manufacturing and parking industry. + + For more information see: + http://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar + + The year may either: + - end on the last X day of the Y month. + - end on the last X day closest to the last day of the Y month. + + X is a specific day of the week. + Y is a certain month of the year + + startingMonth = 1 corresponds to dates like 1/31/2007, 4/30/2007, ... + startingMonth = 2 corresponds to dates like 2/28/2007, 5/31/2007, ... + startingMonth = 3 corresponds to dates like 3/30/2007, 6/29/2007, ... + + Parameters + ---------- + n : int + weekday : {0, 1, ..., 6} + 0: Mondays + 1: Tuesdays + 2: Wednesdays + 3: Thursdays + 4: Fridays + 5: Saturdays + 6: Sundays + startingMonth : The month in which fiscal years end. {1, 2, ... 12} + qtr_with_extra_week : The quarter number that has the leap + or 14 week when needed. {1,2,3,4} + variation : str + {"nearest", "last"} for "LastOfMonth" or "NearestEndMonth" + """ + + _prefix = 'REQ' + + def __init__(self, n=1, **kwds): + self.n = n + + self.qtr_with_extra_week = kwds["qtr_with_extra_week"] + + self.kwds = kwds + + if self.n == 0: + raise ValueError('N cannot be 0') + + self._offset = FY5253( \ + startingMonth=kwds['startingMonth'], \ + weekday=kwds["weekday"], + variation=kwds["variation"]) + + def isAnchored(self): + return self.n == 1 and self._offset.isAnchored() + + def apply(self, other): + n = self.n + + if n > 0: + while n > 0: + if not self._offset.onOffset(other): + qtr_lens = self.get_weeks(other) + start = other - self._offset + else: + start = other + qtr_lens = self.get_weeks(other + self._offset) + + for weeks in qtr_lens: + start += relativedelta(weeks=weeks) + if start > other: + other = start + n -= 1 + break + + else: + n = -n + while n > 0: + if not self._offset.onOffset(other): + qtr_lens = self.get_weeks(other) + end = other + self._offset + else: + end = other + qtr_lens = self.get_weeks(other) + + for weeks in reversed(qtr_lens): + end -= relativedelta(weeks=weeks) + if end < other: + other = end + n -= 1 + break + return other + + def get_weeks(self, dt): + ret = [13] * 4 + + year_has_extra_week = self.year_has_extra_week(dt) + + if year_has_extra_week: + ret[self.qtr_with_extra_week-1] = 14 + + return ret + + def year_has_extra_week(self, dt): + if self._offset.onOffset(dt): + prev_year_end = dt - self._offset + next_year_end = dt + else: + next_year_end = dt + self._offset + prev_year_end = dt - self._offset + + week_in_year = (next_year_end - prev_year_end).days/7 + + return week_in_year == 53 + + def onOffset(self, dt): + if self._offset.onOffset(dt): + return True + + next_year_end = dt - self._offset + + qtr_lens = self.get_weeks(dt) + + current = next_year_end + for qtr_len in qtr_lens[0:4]: + current += relativedelta(weeks=qtr_len) + if dt == current: + return True + return False + + @property + def rule_code(self): + suffix = self._offset.get_rule_code_suffix() + return "%s-%s" %(self._prefix, "%s-%d" % (suffix, self.qtr_with_extra_week)) + + @classmethod + def _from_name(cls, *args): + return cls(**dict(FY5253._parse_suffix(*args[:-1]), qtr_with_extra_week=int(args[-1]))) + _int_to_month = { 1: 'JAN', 2: 'FEB', @@ -1452,6 +1847,8 @@ def generate_range(start=None, end=None, periods=None, Hour, # 'H' Day, # 'D' WeekOfMonth, # 'WOM' + FY5253, + FY5253Quarter, ]) if not _np_version_under1p7: diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index 00a3d392a45c0..f1078f44efd13 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -148,17 +148,22 @@ def _check_tick(self, base_delta, code): self.assert_(infer_freq(index) is None) def test_weekly(self): - days = ['MON', 'TUE', 'WED', 'THU', 'FRI'] + days = ['MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT', 'SUN'] for day in days: self._check_generated_range('1/1/2000', 'W-%s' % day) def test_week_of_month(self): - days = ['MON', 'TUE', 'WED', 'THU', 'FRI'] + days = ['MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT', 'SUN'] for day in days: for i in range(1, 5): self._check_generated_range('1/1/2000', 'WOM-%d%s' % (i, day)) + + def test_week_of_month_fake(self): + #All of these dates are on same day of week and are 4 or 5 weeks apart + index = DatetimeIndex(["2013-08-27","2013-10-01","2013-10-29","2013-11-26"]) + assert infer_freq(index) != 'WOM-4TUE' def test_monthly(self): self._check_generated_range('1/1/2000', 'M') @@ -195,7 +200,7 @@ def _check_generated_range(self, start, freq): gen = date_range(start, periods=7, freq=freq) index = _dti(gen.values) if not freq.startswith('Q-'): - self.assert_(infer_freq(index) == gen.freqstr) + self.assertEqual(infer_freq(index), gen.freqstr) else: inf_freq = infer_freq(index) self.assert_((inf_freq == 'Q-DEC' and diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 8592a2c2d8d9c..7ebe6c0cfb728 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -1,4 +1,5 @@ from datetime import date, datetime, timedelta +from dateutil.relativedelta import relativedelta from pandas.compat import range from pandas import compat import unittest @@ -24,7 +25,8 @@ from pandas.lib import Timestamp from pandas.util.testing import assertRaisesRegexp import pandas.util.testing as tm -from pandas.tseries.offsets import BusinessMonthEnd, CacheableOffset +from pandas.tseries.offsets import BusinessMonthEnd, CacheableOffset, \ + LastWeekOfMonth, FY5253, FY5253Quarter, WeekDay from pandas import _np_version_under1p7 @@ -524,7 +526,9 @@ def test_weekmask_and_holidays(self): def assertOnOffset(offset, date, expected): actual = offset.onOffset(date) - assert actual == expected + assert actual == expected, ("\nExpected: %s\nActual: %s\nFor Offset: %s)" + "\nAt Date: %s" % + (expected, actual, offset, date)) class TestWeek(unittest.TestCase): @@ -674,6 +678,75 @@ def test_onOffset(self): offset = WeekOfMonth(week=week, weekday=weekday) self.assert_(offset.onOffset(date) == expected) +class TestLastWeekOfMonth(unittest.TestCase): + def test_constructor(self): + assertRaisesRegexp(ValueError, "^N cannot be 0", \ + LastWeekOfMonth, n=0, weekday=1) + + assertRaisesRegexp(ValueError, "^Day", LastWeekOfMonth, n=1, weekday=-1) + assertRaisesRegexp(ValueError, "^Day", LastWeekOfMonth, n=1, weekday=7) + + def test_offset(self): + #### Saturday + last_sat = datetime(2013,8,31) + next_sat = datetime(2013,9,28) + offset_sat = LastWeekOfMonth(n=1, weekday=5) + + one_day_before = (last_sat + timedelta(days=-1)) + self.assert_(one_day_before + offset_sat == last_sat) + + one_day_after = (last_sat + timedelta(days=+1)) + self.assert_(one_day_after + offset_sat == next_sat) + + #Test On that day + self.assert_(last_sat + offset_sat == next_sat) + + #### Thursday + + offset_thur = LastWeekOfMonth(n=1, weekday=3) + last_thurs = datetime(2013,1,31) + next_thurs = datetime(2013,2,28) + + one_day_before = last_thurs + timedelta(days=-1) + self.assert_(one_day_before + offset_thur == last_thurs) + + one_day_after = last_thurs + timedelta(days=+1) + self.assert_(one_day_after + offset_thur == next_thurs) + + # Test on that day + self.assert_(last_thurs + offset_thur == next_thurs) + + three_before = last_thurs + timedelta(days=-3) + self.assert_(three_before + offset_thur == last_thurs) + + two_after = last_thurs + timedelta(days=+2) + self.assert_(two_after + offset_thur == next_thurs) + + offset_sunday = LastWeekOfMonth(n=1, weekday=WeekDay.SUN) + self.assert_(datetime(2013,7,31) + offset_sunday == datetime(2013,8,25)) + + def test_onOffset(self): + test_cases = [ + (WeekDay.SUN, datetime(2013, 1, 27), True), + (WeekDay.SAT, datetime(2013, 3, 30), True), + (WeekDay.MON, datetime(2013, 2, 18), False), #Not the last Mon + (WeekDay.SUN, datetime(2013, 2, 25), False), #Not a SUN + (WeekDay.MON, datetime(2013, 2, 25), True), + (WeekDay.SAT, datetime(2013, 11, 30), True), + + (WeekDay.SAT, datetime(2006, 8, 26), True), + (WeekDay.SAT, datetime(2007, 8, 25), True), + (WeekDay.SAT, datetime(2008, 8, 30), True), + (WeekDay.SAT, datetime(2009, 8, 29), True), + (WeekDay.SAT, datetime(2010, 8, 28), True), + (WeekDay.SAT, datetime(2011, 8, 27), True), + (WeekDay.SAT, datetime(2019, 8, 31), True), + ] + + for weekday, date, expected in test_cases: + offset = LastWeekOfMonth(weekday=weekday) + self.assert_(offset.onOffset(date) == expected, date) + class TestBMonthBegin(unittest.TestCase): def test_offset(self): @@ -1101,7 +1174,379 @@ def test_onOffset(self): for offset, date, expected in tests: assertOnOffset(offset, date, expected) +def makeFY5253LastOfMonthQuarter(*args, **kwds): + return FY5253Quarter(*args, variation="last", **kwds) +def makeFY5253NearestEndMonthQuarter(*args, **kwds): + return FY5253Quarter(*args, variation="nearest", **kwds) + +def makeFY5253NearestEndMonth(*args, **kwds): + return FY5253(*args, variation="nearest", **kwds) + +def makeFY5253LastOfMonth(*args, **kwds): + return FY5253(*args, variation="last", **kwds) + +class TestFY5253LastOfMonth(unittest.TestCase): + def test_onOffset(self): + + offset_lom_sat_aug = makeFY5253LastOfMonth(1, startingMonth=8, weekday=WeekDay.SAT) + offset_lom_sat_sep = makeFY5253LastOfMonth(1, startingMonth=9, weekday=WeekDay.SAT) + + tests = [ + #From Wikipedia (see: http://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar#Last_Saturday_of_the_month_at_fiscal_year_end) + (offset_lom_sat_aug, datetime(2006, 8, 26), True), + (offset_lom_sat_aug, datetime(2007, 8, 25), True), + (offset_lom_sat_aug, datetime(2008, 8, 30), True), + (offset_lom_sat_aug, datetime(2009, 8, 29), True), + (offset_lom_sat_aug, datetime(2010, 8, 28), True), + (offset_lom_sat_aug, datetime(2011, 8, 27), True), + (offset_lom_sat_aug, datetime(2012, 8, 25), True), + (offset_lom_sat_aug, datetime(2013, 8, 31), True), + (offset_lom_sat_aug, datetime(2014, 8, 30), True), + (offset_lom_sat_aug, datetime(2015, 8, 29), True), + (offset_lom_sat_aug, datetime(2016, 8, 27), True), + (offset_lom_sat_aug, datetime(2017, 8, 26), True), + (offset_lom_sat_aug, datetime(2018, 8, 25), True), + (offset_lom_sat_aug, datetime(2019, 8, 31), True), + + (offset_lom_sat_aug, datetime(2006, 8, 27), False), + (offset_lom_sat_aug, datetime(2007, 8, 28), False), + (offset_lom_sat_aug, datetime(2008, 8, 31), False), + (offset_lom_sat_aug, datetime(2009, 8, 30), False), + (offset_lom_sat_aug, datetime(2010, 8, 29), False), + (offset_lom_sat_aug, datetime(2011, 8, 28), False), + + (offset_lom_sat_aug, datetime(2006, 8, 25), False), + (offset_lom_sat_aug, datetime(2007, 8, 24), False), + (offset_lom_sat_aug, datetime(2008, 8, 29), False), + (offset_lom_sat_aug, datetime(2009, 8, 28), False), + (offset_lom_sat_aug, datetime(2010, 8, 27), False), + (offset_lom_sat_aug, datetime(2011, 8, 26), False), + (offset_lom_sat_aug, datetime(2019, 8, 30), False), + + #From GMCR (see for example: http://yahoo.brand.edgar-online.com/Default.aspx?companyid=3184&formtypeID=7) + (offset_lom_sat_sep, datetime(2010, 9, 25), True), + (offset_lom_sat_sep, datetime(2011, 9, 24), True), + (offset_lom_sat_sep, datetime(2012, 9, 29), True), + + ] + + for offset, date, expected in tests: + assertOnOffset(offset, date, expected) + + def test_apply(self): + offset_lom_aug_sat = makeFY5253LastOfMonth(startingMonth=8, weekday=WeekDay.SAT) + offset_lom_aug_sat_1 = makeFY5253LastOfMonth(n=1, startingMonth=8, weekday=WeekDay.SAT) + + date_seq_lom_aug_sat = [datetime(2006, 8, 26), datetime(2007, 8, 25), + datetime(2008, 8, 30), datetime(2009, 8, 29), + datetime(2010, 8, 28), datetime(2011, 8, 27), + datetime(2012, 8, 25), datetime(2013, 8, 31), + datetime(2014, 8, 30), datetime(2015, 8, 29), + datetime(2016, 8, 27)] + + tests = [ + (offset_lom_aug_sat, date_seq_lom_aug_sat), + (offset_lom_aug_sat_1, date_seq_lom_aug_sat), + (offset_lom_aug_sat, [datetime(2006, 8, 25)] + date_seq_lom_aug_sat), + (offset_lom_aug_sat_1, [datetime(2006, 8, 27)] + date_seq_lom_aug_sat[1:]), + (makeFY5253LastOfMonth(n=-1, startingMonth=8, weekday=WeekDay.SAT), list(reversed(date_seq_lom_aug_sat))), + ] + for test in tests: + offset, data = test + current = data[0] + for datum in data[1:]: + current = current + offset + self.assertEqual(current, datum) + +class TestFY5253NearestEndMonth(unittest.TestCase): + def test_get_target_month_end(self): + self.assertEqual(makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.SAT).get_target_month_end(datetime(2013,1,1)), datetime(2013,8,31)) + self.assertEqual(makeFY5253NearestEndMonth(startingMonth=12, weekday=WeekDay.SAT).get_target_month_end(datetime(2013,1,1)), datetime(2013,12,31)) + self.assertEqual(makeFY5253NearestEndMonth(startingMonth=2, weekday=WeekDay.SAT).get_target_month_end(datetime(2013,1,1)), datetime(2013,2,28)) + + def test_get_year_end(self): + self.assertEqual(makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.SAT).get_year_end(datetime(2013,1,1)), datetime(2013,8,31)) + self.assertEqual(makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.SUN).get_year_end(datetime(2013,1,1)), datetime(2013,9,1)) + self.assertEqual(makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.FRI).get_year_end(datetime(2013,1,1)), datetime(2013,8,30)) + + def test_onOffset(self): + offset_lom_aug_sat = makeFY5253NearestEndMonth(1, startingMonth=8, weekday=WeekDay.SAT) + offset_lom_aug_thu = makeFY5253NearestEndMonth(1, startingMonth=8, weekday=WeekDay.THU) + + tests = [ +# From Wikipedia (see: http://en.wikipedia.org/wiki/4%E2%80%934%E2%80%935_calendar#Saturday_nearest_the_end_of_month) +# 2006-09-02 2006 September 2 +# 2007-09-01 2007 September 1 +# 2008-08-30 2008 August 30 (leap year) +# 2009-08-29 2009 August 29 +# 2010-08-28 2010 August 28 +# 2011-09-03 2011 September 3 +# 2012-09-01 2012 September 1 (leap year) +# 2013-08-31 2013 August 31 +# 2014-08-30 2014 August 30 +# 2015-08-29 2015 August 29 +# 2016-09-03 2016 September 3 (leap year) +# 2017-09-02 2017 September 2 +# 2018-09-01 2018 September 1 +# 2019-08-31 2019 August 31 + (offset_lom_aug_sat, datetime(2006, 9, 2), True), + (offset_lom_aug_sat, datetime(2007, 9, 1), True), + (offset_lom_aug_sat, datetime(2008, 8, 30), True), + (offset_lom_aug_sat, datetime(2009, 8, 29), True), + (offset_lom_aug_sat, datetime(2010, 8, 28), True), + (offset_lom_aug_sat, datetime(2011, 9, 3), True), + + (offset_lom_aug_sat, datetime(2016, 9, 3), True), + (offset_lom_aug_sat, datetime(2017, 9, 2), True), + (offset_lom_aug_sat, datetime(2018, 9, 1), True), + (offset_lom_aug_sat, datetime(2019, 8, 31), True), + + (offset_lom_aug_sat, datetime(2006, 8, 27), False), + (offset_lom_aug_sat, datetime(2007, 8, 28), False), + (offset_lom_aug_sat, datetime(2008, 8, 31), False), + (offset_lom_aug_sat, datetime(2009, 8, 30), False), + (offset_lom_aug_sat, datetime(2010, 8, 29), False), + (offset_lom_aug_sat, datetime(2011, 8, 28), False), + + (offset_lom_aug_sat, datetime(2006, 8, 25), False), + (offset_lom_aug_sat, datetime(2007, 8, 24), False), + (offset_lom_aug_sat, datetime(2008, 8, 29), False), + (offset_lom_aug_sat, datetime(2009, 8, 28), False), + (offset_lom_aug_sat, datetime(2010, 8, 27), False), + (offset_lom_aug_sat, datetime(2011, 8, 26), False), + (offset_lom_aug_sat, datetime(2019, 8, 30), False), + + #From Micron, see: http://google.brand.edgar-online.com/?sym=MU&formtypeID=7 + (offset_lom_aug_thu, datetime(2012, 8, 30), True), + (offset_lom_aug_thu, datetime(2011, 9, 1), True), + + ] + + for offset, date, expected in tests: + assertOnOffset(offset, date, expected) + + def test_apply(self): + date_seq_nem_8_sat = [datetime(2006, 9, 2), datetime(2007, 9, 1), datetime(2008, 8, 30), datetime(2009, 8, 29), datetime(2010, 8, 28), datetime(2011, 9, 3)] + + tests = [ + (makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.SAT), date_seq_nem_8_sat), + (makeFY5253NearestEndMonth(n=1, startingMonth=8, weekday=WeekDay.SAT), date_seq_nem_8_sat), + (makeFY5253NearestEndMonth(startingMonth=8, weekday=WeekDay.SAT), [datetime(2006, 9, 1)] + date_seq_nem_8_sat), + (makeFY5253NearestEndMonth(n=1, startingMonth=8, weekday=WeekDay.SAT), [datetime(2006, 9, 3)] + date_seq_nem_8_sat[1:]), + (makeFY5253NearestEndMonth(n=-1, startingMonth=8, weekday=WeekDay.SAT), list(reversed(date_seq_nem_8_sat))), + ] + for test in tests: + offset, data = test + current = data[0] + for datum in data[1:]: + current = current + offset + self.assertEqual(current, datum) + +class TestFY5253LastOfMonthQuarter(unittest.TestCase): + + def test_isAnchored(self): + self.assert_(makeFY5253LastOfMonthQuarter(startingMonth=1, weekday=WeekDay.SAT, qtr_with_extra_week=4).isAnchored()) + self.assert_(makeFY5253LastOfMonthQuarter(weekday=WeekDay.SAT, startingMonth=3, qtr_with_extra_week=4).isAnchored()) + self.assert_(not makeFY5253LastOfMonthQuarter(2, startingMonth=1, weekday=WeekDay.SAT, qtr_with_extra_week=4).isAnchored()) + + def test_equality(self): + self.assertEqual(makeFY5253LastOfMonthQuarter(startingMonth=1, weekday=WeekDay.SAT, qtr_with_extra_week=4), makeFY5253LastOfMonthQuarter(startingMonth=1, weekday=WeekDay.SAT, qtr_with_extra_week=4)) + self.assertNotEqual(makeFY5253LastOfMonthQuarter(startingMonth=1, weekday=WeekDay.SAT, qtr_with_extra_week=4), makeFY5253LastOfMonthQuarter(startingMonth=1, weekday=WeekDay.SUN, qtr_with_extra_week=4)) + self.assertNotEqual(makeFY5253LastOfMonthQuarter(startingMonth=1, weekday=WeekDay.SAT, qtr_with_extra_week=4), makeFY5253LastOfMonthQuarter(startingMonth=2, weekday=WeekDay.SAT, qtr_with_extra_week=4)) + + def test_offset(self): + offset = makeFY5253LastOfMonthQuarter(1, startingMonth=9, weekday=WeekDay.SAT, qtr_with_extra_week=4) + offset2 = makeFY5253LastOfMonthQuarter(2, startingMonth=9, weekday=WeekDay.SAT, qtr_with_extra_week=4) + offset4 = makeFY5253LastOfMonthQuarter(4, startingMonth=9, weekday=WeekDay.SAT, qtr_with_extra_week=4) + + offset_neg1 = makeFY5253LastOfMonthQuarter(-1, startingMonth=9, weekday=WeekDay.SAT, qtr_with_extra_week=4) + offset_neg2 = makeFY5253LastOfMonthQuarter(-2, startingMonth=9, weekday=WeekDay.SAT, qtr_with_extra_week=4) + + GMCR = [datetime(2010, 3, 27), + datetime(2010, 6, 26), + datetime(2010, 9, 25), + datetime(2010, 12, 25), + datetime(2011, 3, 26), + datetime(2011, 6, 25), + datetime(2011, 9, 24), + datetime(2011, 12, 24), + datetime(2012, 3, 24), + datetime(2012, 6, 23), + datetime(2012, 9, 29), + datetime(2012, 12, 29), + datetime(2013, 3, 30), + datetime(2013, 6, 29)] + + + assertEq(offset, base=GMCR[0], expected=GMCR[1]) + assertEq(offset, base=GMCR[0] + relativedelta(days=-1), expected=GMCR[0]) + assertEq(offset, base=GMCR[1], expected=GMCR[2]) + + assertEq(offset2, base=GMCR[0], expected=GMCR[2]) + assertEq(offset4, base=GMCR[0], expected=GMCR[4]) + + assertEq(offset_neg1, base=GMCR[-1], expected=GMCR[-2]) + assertEq(offset_neg1, base=GMCR[-1] + relativedelta(days=+1), expected=GMCR[-1]) + assertEq(offset_neg2, base=GMCR[-1], expected=GMCR[-3]) + + date = GMCR[0] + relativedelta(days=-1) + for expected in GMCR: + assertEq(offset, date, expected) + date = date + offset + + date = GMCR[-1] + relativedelta(days=+1) + for expected in reversed(GMCR): + assertEq(offset_neg1, date, expected) + date = date + offset_neg1 + + + def test_onOffset(self): + lomq_aug_sat_4 = makeFY5253LastOfMonthQuarter(1, startingMonth=8, weekday=WeekDay.SAT, qtr_with_extra_week=4) + lomq_sep_sat_4 = makeFY5253LastOfMonthQuarter(1, startingMonth=9, weekday=WeekDay.SAT, qtr_with_extra_week=4) + + tests = [ + #From Wikipedia + (lomq_aug_sat_4, datetime(2006, 8, 26), True), + (lomq_aug_sat_4, datetime(2007, 8, 25), True), + (lomq_aug_sat_4, datetime(2008, 8, 30), True), + (lomq_aug_sat_4, datetime(2009, 8, 29), True), + (lomq_aug_sat_4, datetime(2010, 8, 28), True), + (lomq_aug_sat_4, datetime(2011, 8, 27), True), + (lomq_aug_sat_4, datetime(2019, 8, 31), True), + + (lomq_aug_sat_4, datetime(2006, 8, 27), False), + (lomq_aug_sat_4, datetime(2007, 8, 28), False), + (lomq_aug_sat_4, datetime(2008, 8, 31), False), + (lomq_aug_sat_4, datetime(2009, 8, 30), False), + (lomq_aug_sat_4, datetime(2010, 8, 29), False), + (lomq_aug_sat_4, datetime(2011, 8, 28), False), + + (lomq_aug_sat_4, datetime(2006, 8, 25), False), + (lomq_aug_sat_4, datetime(2007, 8, 24), False), + (lomq_aug_sat_4, datetime(2008, 8, 29), False), + (lomq_aug_sat_4, datetime(2009, 8, 28), False), + (lomq_aug_sat_4, datetime(2010, 8, 27), False), + (lomq_aug_sat_4, datetime(2011, 8, 26), False), + (lomq_aug_sat_4, datetime(2019, 8, 30), False), + + #From GMCR + (lomq_sep_sat_4, datetime(2010, 9, 25), True), + (lomq_sep_sat_4, datetime(2011, 9, 24), True), + (lomq_sep_sat_4, datetime(2012, 9, 29), True), + + (lomq_sep_sat_4, datetime(2013, 6, 29), True), + (lomq_sep_sat_4, datetime(2012, 6, 23), True), + (lomq_sep_sat_4, datetime(2012, 6, 30), False), + + (lomq_sep_sat_4, datetime(2013, 3, 30), True), + (lomq_sep_sat_4, datetime(2012, 3, 24), True), + + (lomq_sep_sat_4, datetime(2012, 12, 29), True), + (lomq_sep_sat_4, datetime(2011, 12, 24), True), + + #INTC (extra week in Q1) + #See: http://www.intc.com/releasedetail.cfm?ReleaseID=542844 + (makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1), datetime(2011, 4, 2), True), + + #see: http://google.brand.edgar-online.com/?sym=INTC&formtypeID=7 + (makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1), datetime(2012, 12, 29), True), + (makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1), datetime(2011, 12, 31), True), + (makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1), datetime(2010, 12, 25), True), + + ] + + for offset, date, expected in tests: + assertOnOffset(offset, date, expected) + + def test_year_has_extra_week(self): + #End of long Q1 + self.assertTrue(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(2011, 4, 2))) + + #Start of long Q1 + self.assertTrue(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(2010, 12, 26))) + + #End of year before year with long Q1 + self.assertFalse(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(2010, 12, 25))) + + for year in [x for x in range(1994, 2011+1) if x not in [2011, 2005, 2000, 1994]]: + self.assertFalse(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(year, 4, 2))) + + #Other long years + self.assertTrue(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(2005, 4, 2))) + self.assertTrue(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(2000, 4, 2))) + self.assertTrue(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).year_has_extra_week(datetime(1994, 4, 2))) + + def test_get_weeks(self): + self.assertEqual(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).get_weeks(datetime(2011, 4, 2)), [14, 13, 13, 13]) + self.assertEqual(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=4).get_weeks(datetime(2011, 4, 2)), [13, 13, 13, 14]) + self.assertEqual(makeFY5253LastOfMonthQuarter(1, startingMonth=12, weekday=WeekDay.SAT, qtr_with_extra_week=1).get_weeks(datetime(2010, 12, 25)), [13, 13, 13, 13]) + +class TestFY5253NearestEndMonthQuarter(unittest.TestCase): + + def test_onOffset(self): + + offset_nem_sat_aug_4 = makeFY5253NearestEndMonthQuarter(1, startingMonth=8, weekday=WeekDay.SAT, qtr_with_extra_week=4) + offset_nem_thu_aug_4 = makeFY5253NearestEndMonthQuarter(1, startingMonth=8, weekday=WeekDay.THU, qtr_with_extra_week=4) + tests = [ + #From Wikipedia + (offset_nem_sat_aug_4, datetime(2006, 9, 2), True), + (offset_nem_sat_aug_4, datetime(2007, 9, 1), True), + (offset_nem_sat_aug_4, datetime(2008, 8, 30), True), + (offset_nem_sat_aug_4, datetime(2009, 8, 29), True), + (offset_nem_sat_aug_4, datetime(2010, 8, 28), True), + (offset_nem_sat_aug_4, datetime(2011, 9, 3), True), + + (offset_nem_sat_aug_4, datetime(2016, 9, 3), True), + (offset_nem_sat_aug_4, datetime(2017, 9, 2), True), + (offset_nem_sat_aug_4, datetime(2018, 9, 1), True), + (offset_nem_sat_aug_4, datetime(2019, 8, 31), True), + + (offset_nem_sat_aug_4, datetime(2006, 8, 27), False), + (offset_nem_sat_aug_4, datetime(2007, 8, 28), False), + (offset_nem_sat_aug_4, datetime(2008, 8, 31), False), + (offset_nem_sat_aug_4, datetime(2009, 8, 30), False), + (offset_nem_sat_aug_4, datetime(2010, 8, 29), False), + (offset_nem_sat_aug_4, datetime(2011, 8, 28), False), + + (offset_nem_sat_aug_4, datetime(2006, 8, 25), False), + (offset_nem_sat_aug_4, datetime(2007, 8, 24), False), + (offset_nem_sat_aug_4, datetime(2008, 8, 29), False), + (offset_nem_sat_aug_4, datetime(2009, 8, 28), False), + (offset_nem_sat_aug_4, datetime(2010, 8, 27), False), + (offset_nem_sat_aug_4, datetime(2011, 8, 26), False), + (offset_nem_sat_aug_4, datetime(2019, 8, 30), False), + + #From Micron, see: http://google.brand.edgar-online.com/?sym=MU&formtypeID=7 + (offset_nem_thu_aug_4, datetime(2012, 8, 30), True), + (offset_nem_thu_aug_4, datetime(2011, 9, 1), True), + + #See: http://google.brand.edgar-online.com/?sym=MU&formtypeID=13 + (offset_nem_thu_aug_4, datetime(2013, 5, 30), True), + (offset_nem_thu_aug_4, datetime(2013, 2, 28), True), + (offset_nem_thu_aug_4, datetime(2012, 11, 29), True), + (offset_nem_thu_aug_4, datetime(2012, 5, 31), True), + (offset_nem_thu_aug_4, datetime(2007, 3, 1), True), + (offset_nem_thu_aug_4, datetime(1994, 3, 3), True), + + ] + + for offset, date, expected in tests: + assertOnOffset(offset, date, expected) + + def test_offset(self): + offset = makeFY5253NearestEndMonthQuarter(1, startingMonth=8, weekday=WeekDay.THU, qtr_with_extra_week=4) + + MU = [datetime(2012, 5, 31), datetime(2012, 8, 30), datetime(2012, 11, 29), datetime(2013, 2, 28), datetime(2013, 5, 30)] + + date = MU[0] + relativedelta(days=-1) + for expected in MU: + assertEq(offset, date, expected) + date = date + offset + + assertEq(offset, datetime(2012, 5, 31), datetime(2012, 8, 30)) + assertEq(offset, datetime(2012, 5, 30), datetime(2012, 5, 31)) + class TestQuarterBegin(unittest.TestCase): def test_repr(self): self.assertEqual(repr(QuarterBegin()), "<QuarterBegin: startingMonth=3>") @@ -1748,32 +2193,43 @@ def test_compare_ticks(): assert(kls(3) != kls(4)) -def test_get_offset_name(): - assertRaisesRegexp(ValueError, 'Bad rule.*BusinessDays', get_offset_name, BDay(2)) - - assert get_offset_name(BDay()) == 'B' - assert get_offset_name(BMonthEnd()) == 'BM' - assert get_offset_name(Week(weekday=0)) == 'W-MON' - assert get_offset_name(Week(weekday=1)) == 'W-TUE' - assert get_offset_name(Week(weekday=2)) == 'W-WED' - assert get_offset_name(Week(weekday=3)) == 'W-THU' - assert get_offset_name(Week(weekday=4)) == 'W-FRI' +class TestOffsetNames(unittest.TestCase): + def test_get_offset_name(self): + assertRaisesRegexp(ValueError, 'Bad rule.*BusinessDays', get_offset_name, BDay(2)) + + assert get_offset_name(BDay()) == 'B' + assert get_offset_name(BMonthEnd()) == 'BM' + assert get_offset_name(Week(weekday=0)) == 'W-MON' + assert get_offset_name(Week(weekday=1)) == 'W-TUE' + assert get_offset_name(Week(weekday=2)) == 'W-WED' + assert get_offset_name(Week(weekday=3)) == 'W-THU' + assert get_offset_name(Week(weekday=4)) == 'W-FRI' + self.assertEqual(get_offset_name(LastWeekOfMonth(weekday=WeekDay.SUN)), "LWOM-SUN") + self.assertEqual(get_offset_name(makeFY5253LastOfMonthQuarter(weekday=1, startingMonth=3, qtr_with_extra_week=4)),"REQ-L-MAR-TUE-4") + self.assertEqual(get_offset_name(makeFY5253NearestEndMonthQuarter(weekday=1, startingMonth=3, qtr_with_extra_week=3)), "REQ-N-MAR-TUE-3") def test_get_offset(): assertRaisesRegexp(ValueError, "rule.*GIBBERISH", get_offset, 'gibberish') assertRaisesRegexp(ValueError, "rule.*QS-JAN-B", get_offset, 'QS-JAN-B') - pairs = [('B', BDay()), ('b', BDay()), ('bm', BMonthEnd()), + pairs = [ + ('B', BDay()), ('b', BDay()), ('bm', BMonthEnd()), ('Bm', BMonthEnd()), ('W-MON', Week(weekday=0)), ('W-TUE', Week(weekday=1)), ('W-WED', Week(weekday=2)), ('W-THU', Week(weekday=3)), ('W-FRI', Week(weekday=4)), - ('w@Sat', Week(weekday=5))] + ('w@Sat', Week(weekday=5)), + ("RE-N-DEC-MON", makeFY5253NearestEndMonth(weekday=0, startingMonth=12)), + ("RE-L-DEC-TUE", makeFY5253LastOfMonth(weekday=1, startingMonth=12)), + ("REQ-L-MAR-TUE-4", makeFY5253LastOfMonthQuarter(weekday=1, startingMonth=3, qtr_with_extra_week=4)), + ("REQ-L-DEC-MON-3", makeFY5253LastOfMonthQuarter(weekday=0, startingMonth=12, qtr_with_extra_week=3)), + ("REQ-N-DEC-MON-3", makeFY5253NearestEndMonthQuarter(weekday=0, startingMonth=12, qtr_with_extra_week=3)), + ] for name, expected in pairs: offset = get_offset(name) assert offset == expected, ("Expected %r to yield %r (actual: %r)" % (name, expected, offset)) - + def test_parse_time_string(): (date, parsed, reso) = parse_time_string('4Q1984') @@ -1879,8 +2335,11 @@ def get_all_subclasses(cls): ret | get_all_subclasses(this_subclass) return ret - class TestCaching(unittest.TestCase): + no_simple_ctr = [WeekOfMonth, FY5253, + FY5253Quarter, + LastWeekOfMonth] + def test_should_cache_month_end(self): self.assertTrue(MonthEnd()._should_cache()) @@ -1892,7 +2351,8 @@ def test_should_cache_week_month(self): def test_all_cacheableoffsets(self): for subclass in get_all_subclasses(CacheableOffset): - if subclass in [WeekOfMonth]: + if subclass.__name__[0] == "_" \ + or subclass in TestCaching.no_simple_ctr: continue self.run_X_index_creation(subclass) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 0fc7101a99856..312a88bcbc5a9 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -199,7 +199,7 @@ def test_period_constructor(self): self.assertRaises(ValueError, Period, ordinal=200701) - self.assertRaises(KeyError, Period, '2007-1-1', freq='X') + self.assertRaises(ValueError, Period, '2007-1-1', freq='X') def test_freq_str(self): i1 = Period('1982', freq='Min') @@ -1136,8 +1136,8 @@ def test_constructor_field_arrays(self): self.assert_(idx.equals(exp)) def test_constructor_U(self): - # X was used as undefined period - self.assertRaises(KeyError, period_range, '2007-1-1', periods=500, + # U was used as undefined period + self.assertRaises(ValueError, period_range, '2007-1-1', periods=500, freq='X') def test_constructor_arrays_negative_year(self): diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 7f11fa5873fe7..dee0587aaaa02 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -2664,7 +2664,7 @@ def test_frequency_misc(self): expected = offsets.Minute(5) self.assertEquals(result, expected) - self.assertRaises(KeyError, fmod.get_freq_code, (5, 'baz')) + self.assertRaises(ValueError, fmod.get_freq_code, (5, 'baz')) self.assertRaises(ValueError, fmod.to_offset, '100foo') @@ -3031,6 +3031,15 @@ def test_frame_apply_dont_convert_datetime64(self): df = df.applymap(lambda x: x + BDay()) self.assertTrue(df.x1.dtype == 'M8[ns]') + + def test_date_range_fy5252(self): + dr = date_range(start="2013-01-01", + periods=2, + freq=offsets.FY5253(startingMonth=1, + weekday=3, + variation="nearest")) + self.assertEqual(dr[0], Timestamp('2013-01-31')) + self.assertEqual(dr[1], Timestamp('2014-01-30')) if __name__ == '__main__': diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index cfc93a22c454b..40dbb2d3712af 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -283,6 +283,10 @@ def test_period_ordinal_business_day(self): # Tuesday self.assertEqual(11418, period_ordinal(2013, 10, 8, 0, 0, 0, 0, 0, get_freq('B'))) +class TestTomeStampOps(unittest.TestCase): + def test_timestamp_and_datetime(self): + self.assertEqual((Timestamp(datetime.datetime(2013, 10,13)) - datetime.datetime(2013, 10,12)).days, 1) + self.assertEqual((datetime.datetime(2013, 10, 12) - Timestamp(datetime.datetime(2013, 10,13))).days, -1) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index d95956261bc44..c487202c4c0f9 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -609,7 +609,8 @@ cdef class _Timestamp(datetime): if is_integer_object(other): neg_other = -other return self + neg_other - return super(_Timestamp, self).__sub__(other) + # This calling convention is required + return datetime.__sub__(self, other) cpdef _get_field(self, field): out = get_date_field(np.array([self.value], dtype=np.int64), field)
Closes #4511 and #4637 - Added `LastWeekOfMonth` DateOffset #4637 - Added `FY5253`, and `FY5253Quarter` DateOffsets #4511 - Improved error handling in `get_freq_code`, `_period_str_to_code` and `_base_and_stride` - Fix issue with datetime - Timestamp
https://api.github.com/repos/pandas-dev/pandas/pulls/5004
2013-09-27T03:55:08Z
2013-10-22T20:37:52Z
2013-10-22T20:37:52Z
2014-06-12T15:33:59Z
BUG: wrong index name during read_csv if using usecols
diff --git a/doc/source/release.rst b/doc/source/release.rst index ce08a1ca0a175..056292322c297 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -480,6 +480,7 @@ Bug Fixes - Fixed wrong check for overlapping in ``DatetimeIndex.union`` (:issue:`4564`) - Fixed conflict between thousands separator and date parser in csv_parser (:issue:`4678`) - Fix appending when dtypes are not the same (error showing mixing float/np.datetime64) (:issue:`4993`) + - Fixed wrong index name during read_csv if using usecols. Applies to c parser only. (:issue:`4201`) pandas 0.12.0 ------------- diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 21b791d2a1acc..426d71b05e30a 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -2,7 +2,7 @@ Module contains tools for processing files into DataFrames or other objects """ from __future__ import print_function -from pandas.compat import range, lrange, StringIO, lzip, zip +from pandas.compat import range, lrange, StringIO, lzip, zip, string_types from pandas import compat import re import csv @@ -15,7 +15,6 @@ import datetime import pandas.core.common as com from pandas.core.config import get_option -from pandas import compat from pandas.io.date_converters import generic_parser from pandas.io.common import get_filepath_or_buffer @@ -24,7 +23,7 @@ import pandas.lib as lib import pandas.tslib as tslib import pandas.parser as _parser -from pandas.tseries.period import Period + _parser_params = """Also supports optionally iterating or breaking of the file into chunks. @@ -982,7 +981,19 @@ def __init__(self, src, **kwds): else: self.names = lrange(self._reader.table_width) - # XXX + # If the names were inferred (not passed by user) and usedcols is defined, + # then ensure names refers to the used columns, not the document's columns. + if self.usecols and passed_names: + col_indices = [] + for u in self.usecols: + if isinstance(u, string_types): + col_indices.append(self.names.index(u)) + else: + col_indices.append(u) + self.names = [n for i, n in enumerate(self.names) if i in col_indices] + if len(self.names) < len(self.usecols): + raise ValueError("Usecols do not match names.") + self._set_noconvert_columns() self.orig_names = self.names diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 48c47238aec6f..fadf70877409f 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -1865,6 +1865,32 @@ def test_parse_integers_above_fp_precision(self): self.assertTrue(np.array_equal(result['Numbers'], expected['Numbers'])) + def test_usecols_index_col_conflict(self): + # Issue 4201 Test that index_col as integer reflects usecols + data = """SecId,Time,Price,P2,P3 +10000,2013-5-11,100,10,1 +500,2013-5-12,101,11,1 +""" + expected = DataFrame({'Price': [100, 101]}, index=[datetime(2013, 5, 11), datetime(2013, 5, 12)]) + expected.index.name = 'Time' + + df = pd.read_csv(StringIO(data), usecols=['Time', 'Price'], parse_dates=True, index_col=0) + tm.assert_frame_equal(expected, df) + + df = pd.read_csv(StringIO(data), usecols=['Time', 'Price'], parse_dates=True, index_col='Time') + tm.assert_frame_equal(expected, df) + + df = pd.read_csv(StringIO(data), usecols=[1, 2], parse_dates=True, index_col='Time') + tm.assert_frame_equal(expected, df) + + df = pd.read_csv(StringIO(data), usecols=[1, 2], parse_dates=True, index_col=0) + tm.assert_frame_equal(expected, df) + + expected = DataFrame({'P3': [1, 1], 'Price': (100, 101), 'P2': (10, 11)}) + expected = expected.set_index(['Price', 'P2']) + df = pd.read_csv(StringIO(data), usecols=['Price', 'P2', 'P3'], parse_dates=True, index_col=['Price', 'P2']) + tm.assert_frame_equal(expected, df) + class TestPythonParser(ParserTests, unittest.TestCase):
Closes #4201 If user passes usecols and not names, then ensure that the inferred names refer to the used columns, not the document's columns.
https://api.github.com/repos/pandas-dev/pandas/pulls/5003
2013-09-27T03:26:01Z
2013-09-27T12:39:31Z
2013-09-27T12:39:31Z
2014-07-29T17:20:31Z
API: Remove set_printoptions/reset_printoptions (:issue:3046)
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 7d2555e8cba81..b167b00b58ef1 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -983,7 +983,7 @@ Methods like ``replace`` and ``findall`` take regular expressions, too: s3.str.replace('^.a|dog', 'XX-XX ', case=False) The method ``match`` returns the groups in a regular expression in one tuple. - Starting in pandas version 0.13, the method ``extract`` is available to + Starting in pandas version 0.13, the method ``extract`` is available to accomplish this more conveniently. Extracting a regular expression with one group returns a Series of strings. @@ -992,16 +992,16 @@ Extracting a regular expression with one group returns a Series of strings. Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)') -Elements that do not match return ``NaN``. Extracting a regular expression +Elements that do not match return ``NaN``. Extracting a regular expression with more than one group returns a DataFrame with one column per group. .. ipython:: python Series(['a1', 'b2', 'c3']).str.extract('([ab])(\d)') -Elements that do not match return a row of ``NaN``s. -Thus, a Series of messy strings can be "converted" into a -like-indexed Series or DataFrame of cleaned-up or more useful strings, +Elements that do not match return a row of ``NaN``s. +Thus, a Series of messy strings can be "converted" into a +like-indexed Series or DataFrame of cleaned-up or more useful strings, without necessitating ``get()`` to access tuples or ``re.match`` objects. Named groups like @@ -1411,11 +1411,6 @@ Console Output Formatting .. _basics.console_output: -**Note:** ``set_printoptions``/ ``reset_printoptions`` are now deprecated (but functioning), -and both, as well as ``set_eng_float_format``, use the options API behind the scenes. -The corresponding options now live under "print.XYZ", and you can set them directly with -``get/set_option``. - Use the ``set_eng_float_format`` function in the ``pandas.core.common`` module to alter the floating-point formatting of pandas objects to produce a particular format. diff --git a/doc/source/release.rst b/doc/source/release.rst index 8ba0574df97cb..8c72235297bd0 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -235,6 +235,8 @@ API Changes on indexes on non ``Float64Index`` will raise a ``TypeError``, e.g. ``Series(range(5))[3.5:4.5]`` (:issue:`263`) - Make Categorical repr nicer (:issue:`4368`) - Remove deprecated ``Factor`` (:issue:`3650`) + - Remove deprecated ``set_printoptions/reset_printoptions`` (:issue:``3046``) + - Remove deprecated ``_verbose_info`` (:issue:`3215`) Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 13aff4d21e802..b1c40fe3b2ced 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -68,6 +68,10 @@ API changes df1 and df2 s1 and s2 + - Remove deprecated ``Factor`` (:issue:`3650`) + - Remove deprecated ``set_printoptions/reset_printoptions`` (:issue:``3046``) + - Remove deprecated ``_verbose_info`` (:issue:`3215`) + Indexing API Changes ~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/__init__.py b/pandas/__init__.py index ddd4cd49e6ec6..803cda264b250 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -21,7 +21,6 @@ # XXX: HACK for NumPy 1.5.1 to suppress warnings try: np.seterr(all='ignore') - # np.set_printoptions(suppress=True) except Exception: # pragma: no cover pass diff --git a/pandas/core/api.py b/pandas/core/api.py index 2b4063eae1f74..36081cc34cc3a 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -6,8 +6,7 @@ from pandas.core.algorithms import factorize, match, unique, value_counts from pandas.core.common import isnull, notnull from pandas.core.categorical import Categorical -from pandas.core.format import (set_printoptions, reset_printoptions, - set_eng_float_format) +from pandas.core.format import set_eng_float_format from pandas.core.index import Index, Int64Index, Float64Index, MultiIndex from pandas.core.series import Series, TimeSeries diff --git a/pandas/core/format.py b/pandas/core/format.py index be6ad4d2bc5ef..190ef3fb5f1ab 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -49,7 +49,7 @@ multiindex key at each row, default True justify : {'left', 'right'}, default None Left or right-justify the column labels. If None uses the option from - the print configuration (controlled by set_printoptions), 'right' out + the print configuration (controlled by set_option), 'right' out of the box. index_names : bool, optional Prints the names of the indexes, default True @@ -1669,78 +1669,6 @@ def _has_names(index): #------------------------------------------------------------------------------ # Global formatting options - -def set_printoptions(precision=None, column_space=None, max_rows=None, - max_columns=None, colheader_justify=None, - max_colwidth=None, notebook_repr_html=None, - date_dayfirst=None, date_yearfirst=None, - pprint_nest_depth=None, multi_sparse=None, encoding=None): - """ - Alter default behavior of DataFrame.toString - - precision : int - Floating point output precision (number of significant digits). This is - only a suggestion - column_space : int - Default space for DataFrame columns, defaults to 12 - max_rows : int - max_columns : int - max_rows and max_columns are used in __repr__() methods to decide if - to_string() or info() is used to render an object to a string. - Either one, or both can be set to 0 (experimental). Pandas will figure - out how big the terminal is and will not display more rows or/and - columns that can fit on it. - colheader_justify - notebook_repr_html : boolean - When True (default), IPython notebook will use html representation for - pandas objects (if it is available). - date_dayfirst : boolean - When True, prints and parses dates with the day first, eg 20/01/2005 - date_yearfirst : boolean - When True, prints and parses dates with the year first, eg 2005/01/20 - pprint_nest_depth : int - Defaults to 3. - Controls the number of nested levels to process when pretty-printing - nested sequences. - multi_sparse : boolean - Default True, "sparsify" MultiIndex display (don't display repeated - elements in outer levels within groups) - """ - import warnings - warnings.warn("set_printoptions is deprecated, use set_option instead", - FutureWarning) - if precision is not None: - set_option("display.precision", precision) - if column_space is not None: - set_option("display.column_space", column_space) - if max_rows is not None: - set_option("display.max_rows", max_rows) - if max_colwidth is not None: - set_option("display.max_colwidth", max_colwidth) - if max_columns is not None: - set_option("display.max_columns", max_columns) - if colheader_justify is not None: - set_option("display.colheader_justify", colheader_justify) - if notebook_repr_html is not None: - set_option("display.notebook_repr_html", notebook_repr_html) - if date_dayfirst is not None: - set_option("display.date_dayfirst", date_dayfirst) - if date_yearfirst is not None: - set_option("display.date_yearfirst", date_yearfirst) - if pprint_nest_depth is not None: - set_option("display.pprint_nest_depth", pprint_nest_depth) - if multi_sparse is not None: - set_option("display.multi_sparse", multi_sparse) - if encoding is not None: - set_option("display.encoding", encoding) - - -def reset_printoptions(): - import warnings - warnings.warn("reset_printoptions is deprecated, use reset_option instead", - FutureWarning) - reset_option("^display\.") - _initial_defencoding = None def detect_console_encoding(): """ diff --git a/pandas/core/frame.py b/pandas/core/frame.py index c98790fdc38ff..7b9a75753136e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -371,7 +371,6 @@ class DataFrame(NDFrame): read_csv / read_table / read_clipboard """ _auto_consolidate = True - _verbose_info = True @property def _constructor(self): @@ -554,12 +553,6 @@ def _init_ndarray(self, values, index, columns, dtype=None, return create_block_manager_from_blocks([values.T], [columns, index]) - @property - def _verbose_info(self): - warnings.warn('The _verbose_info property will be removed in version ' - '0.13. please use "max_info_rows"', FutureWarning) - return get_option('display.max_info_rows') is None - @property def axes(self): return [self.index, self.columns] diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py index a1b630dedaaab..53fabb0160a88 100644 --- a/pandas/sparse/frame.py +++ b/pandas/sparse/frame.py @@ -46,7 +46,6 @@ class SparseDataFrame(DataFrame): Default fill_value for converting Series to SparseSeries. Will not override SparseSeries passed in """ - _verbose_info = False _constructor_sliced = SparseSeries _subtyp = 'sparse_frame' diff --git a/pandas/tests/test_config.py b/pandas/tests/test_config.py index ed6f641cbcb2c..80a3fe9be7003 100644 --- a/pandas/tests/test_config.py +++ b/pandas/tests/test_config.py @@ -437,5 +437,3 @@ def f3(key): options.c = 1 self.assertEqual(len(holder), 1) -# fmt.reset_printoptions and fmt.set_printoptions were altered -# to use core.config, test_format exercises those paths.
closes #3046 closes #3215
https://api.github.com/repos/pandas-dev/pandas/pulls/5001
2013-09-26T21:24:22Z
2013-09-27T00:36:48Z
2013-09-27T00:36:48Z
2014-06-14T13:53:48Z
API: Remove deprecated Factor (GH3650)
diff --git a/doc/source/release.rst b/doc/source/release.rst index bd3fbf53de039..8ba0574df97cb 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -234,6 +234,7 @@ API Changes Indexing on other index types are preserved (and positional fallback for ``[],ix``), with the exception, that floating point slicing on indexes on non ``Float64Index`` will raise a ``TypeError``, e.g. ``Series(range(5))[3.5:4.5]`` (:issue:`263`) - Make Categorical repr nicer (:issue:`4368`) + - Remove deprecated ``Factor`` (:issue:`3650`) Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/core/api.py b/pandas/core/api.py index b4afe90d46842..2b4063eae1f74 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -5,7 +5,7 @@ from pandas.core.algorithms import factorize, match, unique, value_counts from pandas.core.common import isnull, notnull -from pandas.core.categorical import Categorical, Factor +from pandas.core.categorical import Categorical from pandas.core.format import (set_printoptions, reset_printoptions, set_eng_float_format) from pandas.core.index import Index, Int64Index, Float64Index, MultiIndex diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 97ed0fdb0da30..f412947f92255 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -230,16 +230,3 @@ def describe(self): counts=counts, freqs=freqs, levels=self.levels)).set_index('levels') - - -class Factor(Categorical): - def __init__(self, labels, levels=None, name=None): - from warnings import warn - warn("Factor is deprecated. Use Categorical instead", FutureWarning) - super(Factor, self).__init__(labels, levels, name) - - @classmethod - def from_array(cls, data): - from warnings import warn - warn("Factor is deprecated. Use Categorical instead", FutureWarning) - return super(Factor, cls).from_array(data)
closes #3650
https://api.github.com/repos/pandas-dev/pandas/pulls/5000
2013-09-26T21:21:26Z
2013-09-26T21:22:21Z
2013-09-26T21:22:21Z
2014-06-14T18:21:49Z
added halflife to exponentially weighted moving functions
diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 207e2796c468d..85c6b88d740da 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -453,15 +453,16 @@ average as y_t = (1 - \alpha) y_{t-1} + \alpha x_t One must have :math:`0 < \alpha \leq 1`, but rather than pass :math:`\alpha` -directly, it's easier to think about either the **span** or **center of mass -(com)** of an EW moment: +directly, it's easier to think about either the **span**, **center of mass +(com)** or **halflife** of an EW moment: .. math:: \alpha = \begin{cases} \frac{2}{s + 1}, s = \text{span}\\ - \frac{1}{1 + c}, c = \text{center of mass} + \frac{1}{1 + c}, c = \text{center of mass}\\ + 1 - \exp^{\frac{\log 0.5}{h}}, h = \text{half life} \end{cases} .. note:: @@ -474,11 +475,12 @@ directly, it's easier to think about either the **span** or **center of mass where :math:`\alpha' = 1 - \alpha`. -You can pass one or the other to these functions but not both. **Span** +You can pass one of the three to these functions but not more. **Span** corresponds to what is commonly called a "20-day EW moving average" for example. **Center of mass** has a more physical interpretation. For example, -**span** = 20 corresponds to **com** = 9.5. Here is the list of functions -available: +**span** = 20 corresponds to **com** = 9.5. **Halflife** is the period of +time for the exponential weight to reduce to one half. Here is the list of +functions available: .. csv-table:: :header: "Function", "Description" diff --git a/doc/source/release.rst b/doc/source/release.rst index 66c3dcd203a6a..cd1cd669152ec 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -138,6 +138,8 @@ Improvements to existing features (:issue:`4961`). - ``concat`` now gives a more informative error message when passed objects that cannot be concatenated (:issue:`4608`). + - Add ``halflife`` option to exponentially weighted moving functions (PR + :issue:`4998`) API Changes ~~~~~~~~~~~ diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index fd81bd119fe09..f3ec3880ec8b5 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -59,6 +59,8 @@ Center of mass: :math:`\alpha = 1 / (1 + com)`, span : float, optional Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)` +halflife : float, optional + Specify decay in terms of halflife, :math: `\alpha = 1 - exp(log(0.5) / halflife)` min_periods : int, default 0 Number of observations in sample to require (only affects beginning) @@ -338,25 +340,29 @@ def _process_data_structure(arg, kill_inf=True): # Exponential moving moments -def _get_center_of_mass(com, span): - if span is not None: - if com is not None: - raise Exception("com and span are mutually exclusive") +def _get_center_of_mass(com, span, halflife): + valid_count = len([x for x in [com, span, halflife] if x is not None]) + if valid_count > 1: + raise Exception("com, span, and halflife are mutually exclusive") + if span is not None: # convert span to center of mass com = (span - 1) / 2. - + elif halflife is not None: + # convert halflife to center of mass + decay = 1 - np.exp(np.log(0.5) / halflife) + com = 1 / decay - 1 elif com is None: - raise Exception("Must pass either com or span") + raise Exception("Must pass one of com, span, or halflife") return float(com) @Substitution("Exponentially-weighted moving average", _unary_arg, "") @Appender(_ewm_doc) -def ewma(arg, com=None, span=None, min_periods=0, freq=None, time_rule=None, +def ewma(arg, com=None, span=None, halflife=None, min_periods=0, freq=None, time_rule=None, adjust=True): - com = _get_center_of_mass(com, span) + com = _get_center_of_mass(com, span, halflife) arg = _conv_timerule(arg, freq, time_rule) def _ewma(v): @@ -377,9 +383,9 @@ def _first_valid_index(arr): @Substitution("Exponentially-weighted moving variance", _unary_arg, _bias_doc) @Appender(_ewm_doc) -def ewmvar(arg, com=None, span=None, min_periods=0, bias=False, +def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, freq=None, time_rule=None): - com = _get_center_of_mass(com, span) + com = _get_center_of_mass(com, span, halflife) arg = _conv_timerule(arg, freq, time_rule) moment2nd = ewma(arg * arg, com=com, min_periods=min_periods) moment1st = ewma(arg, com=com, min_periods=min_periods) @@ -393,9 +399,9 @@ def ewmvar(arg, com=None, span=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving std", _unary_arg, _bias_doc) @Appender(_ewm_doc) -def ewmstd(arg, com=None, span=None, min_periods=0, bias=False, +def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, time_rule=None): - result = ewmvar(arg, com=com, span=span, time_rule=time_rule, + result = ewmvar(arg, com=com, span=span, halflife=halflife, time_rule=time_rule, min_periods=min_periods, bias=bias) return _zsqrt(result) @@ -404,17 +410,17 @@ def ewmstd(arg, com=None, span=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving covariance", _binary_arg, "") @Appender(_ewm_doc) -def ewmcov(arg1, arg2, com=None, span=None, min_periods=0, bias=False, +def ewmcov(arg1, arg2, com=None, span=None, halflife=None, min_periods=0, bias=False, freq=None, time_rule=None): X, Y = _prep_binary(arg1, arg2) X = _conv_timerule(X, freq, time_rule) Y = _conv_timerule(Y, freq, time_rule) - mean = lambda x: ewma(x, com=com, span=span, min_periods=min_periods) + mean = lambda x: ewma(x, com=com, span=span, halflife=halflife, min_periods=min_periods) result = (mean(X * Y) - mean(X) * mean(Y)) - com = _get_center_of_mass(com, span) + com = _get_center_of_mass(com, span, halflife) if not bias: result *= (1.0 + 2.0 * com) / (2.0 * com) @@ -423,15 +429,15 @@ def ewmcov(arg1, arg2, com=None, span=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving " "correlation", _binary_arg, "") @Appender(_ewm_doc) -def ewmcorr(arg1, arg2, com=None, span=None, min_periods=0, +def ewmcorr(arg1, arg2, com=None, span=None, halflife=None, min_periods=0, freq=None, time_rule=None): X, Y = _prep_binary(arg1, arg2) X = _conv_timerule(X, freq, time_rule) Y = _conv_timerule(Y, freq, time_rule) - mean = lambda x: ewma(x, com=com, span=span, min_periods=min_periods) - var = lambda x: ewmvar(x, com=com, span=span, min_periods=min_periods, + mean = lambda x: ewma(x, com=com, span=span, halflife=halflife, min_periods=min_periods) + var = lambda x: ewmvar(x, com=com, span=span, halflife=halflife, min_periods=min_periods, bias=True) return (mean(X * Y) - mean(X) * mean(Y)) / _zsqrt(var(X) * var(Y)) diff --git a/pandas/stats/tests/test_moments.py b/pandas/stats/tests/test_moments.py index 70653d9d96bef..1f7df9894a97d 100644 --- a/pandas/stats/tests/test_moments.py +++ b/pandas/stats/tests/test_moments.py @@ -535,6 +535,16 @@ def test_ewma_span_com_args(self): self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20) self.assertRaises(Exception, mom.ewma, self.arr) + def test_ewma_halflife_arg(self): + A = mom.ewma(self.arr, com=13.932726172912965) + B = mom.ewma(self.arr, halflife=10.0) + assert_almost_equal(A, B) + + self.assertRaises(Exception, mom.ewma, self.arr, span=20, halflife=50) + self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, halflife=50) + self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, halflife=50) + self.assertRaises(Exception, mom.ewma, self.arr) + def test_ew_empty_arrays(self): arr = np.array([], dtype=np.float64)
Currently for the exponentially weighted moving functions (ewma, ewmstd, ewmvol, ewmvar, ewmcov) there are two ways (span, center of mass) to specify how fast the exponential decay is. It would be nice to support a "half life" option as well. The half life is basically just the number of periods in which the exponential weight drops to one half, i.e., (1 - \alpha)^h = 0.5, h: half life
https://api.github.com/repos/pandas-dev/pandas/pulls/4998
2013-09-26T19:47:13Z
2013-09-29T17:51:40Z
2013-09-29T17:51:40Z
2015-04-25T23:32:49Z
BUG: Fix appending when dtypes are not the same (error showing mixing float/np.datetime64 (GH4993)
diff --git a/doc/source/release.rst b/doc/source/release.rst index 0da7337977851..bd3fbf53de039 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -474,6 +474,7 @@ Bug Fixes explicitly passing labels (:issue:`3415`) - Fixed wrong check for overlapping in ``DatetimeIndex.union`` (:issue:`4564`) - Fixed conflict between thousands separator and date parser in csv_parser (:issue:`4678`) + - Fix appending when dtypes are not the same (error showing mixing float/np.datetime64) (:issue:`4993`) pandas 0.12.0 ------------- diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 2cc9d586a05a3..5792161e0171e 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -17,13 +17,16 @@ from pandas.core.internals import (IntBlock, BoolBlock, BlockManager, make_block, _consolidate) from pandas.util.decorators import cache_readonly, Appender, Substitution -from pandas.core.common import PandasError, ABCSeries +from pandas.core.common import (PandasError, ABCSeries, + is_timedelta64_dtype, is_datetime64_dtype, + is_integer_dtype) + import pandas.core.common as com import pandas.lib as lib import pandas.algos as algos import pandas.hashtable as _hash - +import pandas.tslib as tslib @Substitution('\nleft : DataFrame') @Appender(_merge_doc, indents=0) @@ -1128,6 +1131,8 @@ def _concat_blocks(self, blocks): return block def _concat_single_item(self, objs, item): + # this is called if we don't have consistent dtypes in a row-wise append + all_values = [] dtypes = set() @@ -1141,22 +1146,57 @@ def _concat_single_item(self, objs, item): else: all_values.append(None) - # this stinks - have_object = False + # figure out the resulting dtype of the combination + alls = set() + seen = [] for dtype in dtypes: + d = dict([ (t,False) for t in ['object','datetime','timedelta','other'] ]) if issubclass(dtype.type, (np.object_, np.bool_)): - have_object = True - if have_object: - empty_dtype = np.object_ - else: - empty_dtype = np.float64 + d['object'] = True + alls.add('object') + elif is_datetime64_dtype(dtype): + d['datetime'] = True + alls.add('datetime') + elif is_timedelta64_dtype(dtype): + d['timedelta'] = True + alls.add('timedelta') + else: + d['other'] = True + alls.add('other') + seen.append(d) + + if 'datetime' in alls or 'timedelta' in alls: + + if 'object' in alls or 'other' in alls: + for v, s in zip(all_values,seen): + if s.get('datetime') or s.get('timedelta'): + pass + + # if we have all null, then leave a date/time like type + # if we have only that type left + elif isnull(v).all(): + + alls.remove('other') + alls.remove('object') + + # create the result + if 'object' in alls: + empty_dtype, fill_value = np.object_, np.nan + elif 'other' in alls: + empty_dtype, fill_value = np.float64, np.nan + elif 'datetime' in alls: + empty_dtype, fill_value = 'M8[ns]', tslib.iNaT + elif 'timedelta' in alls: + empty_dtype, fill_value = 'm8[ns]', tslib.iNaT + else: # pragma + raise AssertionError("invalid dtype determination in concat_single_item") to_concat = [] for obj, item_values in zip(objs, all_values): if item_values is None: shape = obj.shape[1:] missing_arr = np.empty(shape, dtype=empty_dtype) - missing_arr.fill(np.nan) + missing_arr.fill(fill_value) to_concat.append(missing_arr) else: to_concat.append(item_values) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 0eeb68c4691eb..203769e731022 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -742,6 +742,30 @@ def test_merge_nan_right(self): assert_frame_equal(result, expected) + def test_append_dtype_coerce(self): + + # GH 4993 + # appending with datetime will incorrectly convert datetime64 + import datetime as dt + from pandas import NaT + + df1 = DataFrame(index=[1,2], data=[dt.datetime(2013,1,1,0,0), + dt.datetime(2013,1,2,0,0)], + columns=['start_time']) + df2 = DataFrame(index=[4,5], data=[[dt.datetime(2013,1,3,0,0), + dt.datetime(2013,1,3,6,10)], + [dt.datetime(2013,1,4,0,0), + dt.datetime(2013,1,4,7,10)]], + columns=['start_time','end_time']) + + expected = concat([ + Series([NaT,NaT,dt.datetime(2013,1,3,6,10),dt.datetime(2013,1,4,7,10)],name='end_time'), + Series([dt.datetime(2013,1,1,0,0),dt.datetime(2013,1,2,0,0),dt.datetime(2013,1,3,0,0),dt.datetime(2013,1,4,0,0)],name='start_time'), + ],axis=1) + result = df1.append(df2,ignore_index=True) + assert_frame_equal(result, expected) + + def test_overlapping_columns_error_message(self): # #2649 df = DataFrame({'key': [1, 2, 3],
closes #4993
https://api.github.com/repos/pandas-dev/pandas/pulls/4995
2013-09-26T13:59:00Z
2013-09-26T18:31:38Z
2013-09-26T18:31:38Z
2014-06-24T01:41:21Z
TST: fix indexing test for windows failure
diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 0eab5ab834533..837acb90407ea 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -1532,7 +1532,7 @@ def test_floating_index(self): # related 236 # scalar/slicing of a float index - s = Series(np.arange(5), index=np.arange(5) * 2.5) + s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) # label based slicing result1 = s[1.0:3.0]
Fixes new / remaining test failure in #4866
https://api.github.com/repos/pandas-dev/pandas/pulls/4992
2013-09-26T06:28:00Z
2013-09-26T10:49:39Z
2013-09-26T10:49:39Z
2014-07-16T08:31:19Z
BUG: Warn when dtypes differ in between chunks in csv parser
diff --git a/doc/source/release.rst b/doc/source/release.rst index ce08a1ca0a175..810889cbc4b26 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -480,6 +480,8 @@ Bug Fixes - Fixed wrong check for overlapping in ``DatetimeIndex.union`` (:issue:`4564`) - Fixed conflict between thousands separator and date parser in csv_parser (:issue:`4678`) - Fix appending when dtypes are not the same (error showing mixing float/np.datetime64) (:issue:`4993`) + - Fixed a bug where low memory c parser could create different types in different + chunks of the same file. Now coerces to numerical type or raises warning. (:issue:`3866`) pandas 0.12.0 ------------- diff --git a/pandas/io/common.py b/pandas/io/common.py index 02242c5a91493..aa5fdb29f3b5b 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -36,10 +36,15 @@ def urlopen(*args, **kwargs): _VALID_URLS = set(uses_relative + uses_netloc + uses_params) _VALID_URLS.discard('') + class PerformanceWarning(Warning): pass +class DtypeWarning(Warning): + pass + + def _is_url(url): """Check to see if a URL has a valid protocol. diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 48c47238aec6f..24ec88cff727b 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -11,6 +11,7 @@ from numpy import nan import numpy as np +from pandas.io.common import DtypeWarning from pandas import DataFrame, Series, Index, MultiIndex, DatetimeIndex from pandas.compat import( @@ -1865,6 +1866,24 @@ def test_parse_integers_above_fp_precision(self): self.assertTrue(np.array_equal(result['Numbers'], expected['Numbers'])) + def test_chunks_have_consistent_numerical_type(self): + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers) + + with tm.assert_produces_warning(False): + df = self.read_csv(StringIO(data)) + self.assertTrue(type(df.a[0]) is np.float64) # Assert that types were coerced. + self.assertEqual(df.a.dtype, np.float) + + def test_warn_if_chunks_have_mismatched_type(self): + # See test in TestCParserLowMemory. + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) + + with tm.assert_produces_warning(False): + df = self.read_csv(StringIO(data)) + self.assertEqual(df.a.dtype, np.object) + class TestPythonParser(ParserTests, unittest.TestCase): @@ -2301,7 +2320,6 @@ def test_usecols_dtypes(self): self.assertTrue((result.dtypes == [object, np.int, np.float]).all()) self.assertTrue((result2.dtypes == [object, np.float]).all()) - def test_usecols_implicit_index_col(self): # #2654 data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10' @@ -2528,16 +2546,22 @@ def test_tokenize_CR_with_quoting(self): def test_raise_on_no_columns(self): # single newline - data = """ -""" + data = "\n" self.assertRaises(ValueError, self.read_csv, StringIO(data)) # test with more than a single newline - data = """ + data = "\n\n\n" + self.assertRaises(ValueError, self.read_csv, StringIO(data)) + def test_warn_if_chunks_have_mismatched_type(self): + # Issue #3866 If chunks are different types and can't + # be coerced using numerical types, then issue warning. + integers = [str(i) for i in range(499999)] + data = "a\n" + "\n".join(integers + ['a', 'b'] + integers) -""" - self.assertRaises(ValueError, self.read_csv, StringIO(data)) + with tm.assert_produces_warning(DtypeWarning): + df = self.read_csv(StringIO(data)) + self.assertEqual(df.a.dtype, np.object) class TestParseSQL(unittest.TestCase): diff --git a/pandas/parser.pyx b/pandas/parser.pyx index b97929023adb6..d08c020c9e9bc 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -5,10 +5,12 @@ from libc.stdio cimport fopen, fclose from libc.stdlib cimport malloc, free from libc.string cimport strncpy, strlen, strcmp, strcasecmp cimport libc.stdio as stdio +import warnings from cpython cimport (PyObject, PyBytes_FromString, PyBytes_AsString, PyBytes_Check, PyUnicode_Check, PyUnicode_AsUTF8String) +from io.common import DtypeWarning cdef extern from "Python.h": @@ -1735,11 +1737,28 @@ def _concatenate_chunks(list chunks): cdef: list names = list(chunks[0].keys()) object name + list warning_columns + object warning_names + object common_type result = {} + warning_columns = list() for name in names: arrs = [chunk.pop(name) for chunk in chunks] + # Check each arr for consistent types. + dtypes = set([a.dtype for a in arrs]) + if len(dtypes) > 1: + common_type = np.find_common_type(dtypes, []) + if common_type == np.object: + warning_columns.append(str(name)) result[name] = np.concatenate(arrs) + + if warning_columns: + warning_names = ','.join(warning_columns) + warning_message = " ".join(["Columns (%s) have mixed types." % warning_names, + "Specify dtype option on import or set low_memory=False." + ]) + warnings.warn(warning_message, DtypeWarning) return result #----------------------------------------------------------------------
Closes #3866
https://api.github.com/repos/pandas-dev/pandas/pulls/4991
2013-09-26T03:52:21Z
2013-09-29T19:28:11Z
2013-09-29T19:28:11Z
2014-07-03T21:50:15Z
CLN: fix py2to3 issues in categorical.py
diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 0868ead2c1558..97ed0fdb0da30 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -2,6 +2,9 @@ import numpy as np +from pandas import compat +from pandas.compat import u + from pandas.core.algorithms import factorize from pandas.core.base import PandasObject from pandas.core.index import Index @@ -147,7 +150,7 @@ def _tidy_repr(self, max_vals=20): #TODO: tidy_repr for footer since there may be a ton of levels? result = '%s\n%s' % (result, self._repr_footer()) - return result + return compat.text_type(result) def _repr_footer(self): levheader = 'Levels (%d): ' % len(self.levels) @@ -158,17 +161,16 @@ def _repr_footer(self): levstring = '\n'.join([lines[0]] + [indent + x.lstrip() for x in lines[1:]]) - namestr = u"Name: %s, " % com.pprint_thing( - self.name) if self.name is not None else "" - return u'%s\n%sLength: %d' % (levheader + levstring, namestr, - len(self)) + namestr = "Name: %s, " % self.name if self.name is not None else "" + return u('%s\n%sLength: %d' % (levheader + levstring, namestr, + len(self))) def _get_repr(self, name=False, length=True, na_rep='NaN', footer=True): formatter = fmt.CategoricalFormatter(self, name=name, length=length, na_rep=na_rep, footer=footer) result = formatter.to_string() - return result + return compat.text_type(result) def __unicode__(self): width, height = get_terminal_size() @@ -180,10 +182,10 @@ def __unicode__(self): result = self._get_repr(length=len(self) > 50, name=True) else: - result = u'Categorical([], %s' % self._get_repr(name=True, - length=False, - footer=True, - ) + result = 'Categorical([], %s' % self._get_repr(name=True, + length=False, + footer=True, + ) return result diff --git a/pandas/core/format.py b/pandas/core/format.py index 749120f8732c2..be6ad4d2bc5ef 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -65,14 +65,14 @@ class CategoricalFormatter(object): def __init__(self, categorical, buf=None, length=True, na_rep='NaN', name=False, footer=True): self.categorical = categorical - self.buf = buf if buf is not None else StringIO(u"") + self.buf = buf if buf is not None else StringIO(u("")) self.name = name self.na_rep = na_rep self.length = length self.footer = footer def _get_footer(self): - footer = u'' + footer = '' if self.name: name = com.pprint_thing(self.categorical.name, @@ -82,7 +82,7 @@ def _get_footer(self): if self.length: if footer: - footer += u', ' + footer += ', ' footer += "Length: %d" % len(self.categorical) levheader = 'Levels (%d): ' % len(self.categorical.levels) @@ -94,10 +94,10 @@ def _get_footer(self): levstring = '\n'.join([lines[0]] + [indent + x.lstrip() for x in lines[1:]]) if footer: - footer += u', ' + footer += ', ' footer += levheader + levstring - return footer + return compat.text_type(footer) def _get_formatted_values(self): return format_array(np.asarray(self.categorical), None, @@ -111,18 +111,18 @@ def to_string(self): if self.footer: return self._get_footer() else: - return u'' + return u('') fmt_values = self._get_formatted_values() pad_space = 10 - result = [u'%s' % i for i in fmt_values] + result = ['%s' % i for i in fmt_values] if self.footer: footer = self._get_footer() if footer: result.append(footer) - return u'\n'.join(result) + return compat.text_type(u('\n').join(result)) class SeriesFormatter(object): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index f4d1c6a0116a9..e47ba0c8e1569 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1,7 +1,7 @@ # pylint: disable=E1101,E1103,W0232 from datetime import datetime -from pandas.compat import range, lrange +from pandas.compat import range, lrange, u import unittest import nose import re
https://api.github.com/repos/pandas-dev/pandas/pulls/4990
2013-09-26T02:18:37Z
2013-09-26T03:12:20Z
2013-09-26T03:12:19Z
2014-07-16T08:31:17Z
BUG: Bug in concatenation with duplicate columns across dtypes, GH4975
diff --git a/doc/source/release.rst b/doc/source/release.rst index 26529072f15bf..1f11ce414ae56 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -432,7 +432,7 @@ Bug Fixes - Bug in multi-indexing with a partial string selection as one part of a MultIndex (:issue:`4758`) - Bug with reindexing on the index with a non-unique index will now raise ``ValueError`` (:issue:`4746`) - Bug in setting with ``loc/ix`` a single indexer with a multi-index axis and a numpy array, related to (:issue:`3777`) - - Bug in concatenation with duplicate columns across dtypes not merging with axis=0 (:issue:`4771`) + - Bug in concatenation with duplicate columns across dtypes not merging with axis=0 (:issue:`4771`, :issue:`4975`) - Bug in ``iloc`` with a slice index failing (:issue:`4771`) - Incorrect error message with no colspecs or width in ``read_fwf``. (:issue:`4774`) - Fix bugs in indexing in a Series with a duplicate index (:issue:`4548`, :issue:`4550`) diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index d7fedecdb0ef2..2cc9d586a05a3 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -655,6 +655,7 @@ def __init__(self, data_list, join_index, indexers, axis=1, copy=True): self.join_index = join_index self.axis = axis self.copy = copy + self.offsets = None # do NOT sort self.result_items = _concat_indexes([d.items for d in data_list]) @@ -683,14 +684,29 @@ def get_result(self): blockmaps = self._prepare_blocks() kinds = _get_merge_block_kinds(blockmaps) - result_blocks = [] - # maybe want to enable flexible copying <-- what did I mean? + kind_blocks = [] for klass in kinds: klass_blocks = [] for unit, mapping in blockmaps: if klass in mapping: klass_blocks.extend((unit, b) for b in mapping[klass]) + + # blocks that we are going to merge + kind_blocks.append(klass_blocks) + + # create the merge offsets, essentially where the resultant blocks go in the result + if not self.result_items.is_unique: + + # length of the merges for each of the klass blocks + self.offsets = np.zeros(len(blockmaps)) + for kb in kind_blocks: + kl = list(b.get_merge_length() for unit, b in kb) + self.offsets += np.array(kl) + + # merge the blocks to create the result blocks + result_blocks = [] + for klass_blocks in kind_blocks: res_blk = self._get_merged_block(klass_blocks) result_blocks.append(res_blk) @@ -726,7 +742,8 @@ def _merge_blocks(self, merge_chunks): n = len(fidx) if fidx is not None else out_shape[self.axis] - out_shape[0] = sum(blk.get_merge_length() for unit, blk in merge_chunks) + merge_lengths = list(blk.get_merge_length() for unit, blk in merge_chunks) + out_shape[0] = sum(merge_lengths) out_shape[self.axis] = n # Should use Fortran order?? @@ -746,9 +763,8 @@ def _merge_blocks(self, merge_chunks): # calculate by the existing placement plus the offset in the result set placement = None if not self.result_items.is_unique: - nchunks = len(merge_chunks) - offsets = np.array([0] + [ len(self.result_items) / nchunks ] * (nchunks-1)).cumsum() placement = [] + offsets = np.append(np.array([0]),self.offsets.cumsum()[:-1]) for (unit, blk), offset in zip(merge_chunks,offsets): placement.extend(blk.ref_locs+offset) diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index f7eb3c125db61..0eeb68c4691eb 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -15,7 +15,8 @@ from pandas.tools.merge import merge, concat, ordered_merge, MergeError from pandas.util.testing import (assert_frame_equal, assert_series_equal, assert_almost_equal, rands, - makeCustomDataframe as mkdf) + makeCustomDataframe as mkdf, + assertRaisesRegexp) from pandas import isnull, DataFrame, Index, MultiIndex, Panel, Series, date_range import pandas.algos as algos import pandas.util.testing as tm @@ -1435,6 +1436,8 @@ def test_dups_index(self): assert_frame_equal(result, expected) def test_join_dups(self): + + # joining dups df = concat([DataFrame(np.random.randn(10,4),columns=['A','A','B','B']), DataFrame(np.random.randint(0,10,size=20).reshape(10,2),columns=['A','C'])], axis=1) @@ -1444,6 +1447,18 @@ def test_join_dups(self): result.columns = expected.columns assert_frame_equal(result, expected) + # GH 4975, invalid join on dups + w = DataFrame(np.random.randn(4,2), columns=["x", "y"]) + x = DataFrame(np.random.randn(4,2), columns=["x", "y"]) + y = DataFrame(np.random.randn(4,2), columns=["x", "y"]) + z = DataFrame(np.random.randn(4,2), columns=["x", "y"]) + + dta = x.merge(y, left_index=True, right_index=True).merge(z, left_index=True, right_index=True, how="outer") + dta = dta.merge(w, left_index=True, right_index=True) + expected = concat([x,y,z,w],axis=1) + expected.columns=['x_x','y_x','x_y','y_y','x_x','y_x','x_y','y_y'] + assert_frame_equal(dta,expected) + def test_handle_empty_objects(self): df = DataFrame(np.random.randn(10, 4), columns=list('abcd'))
partially fixed in #4771 closes #4975 ``` In [1]: w = DataFrame(np.random.randn(4,2), columns=["x", "y"]) In [2]: x = DataFrame(np.random.randn(4,2), columns=["x", "y"]) In [3]: y = DataFrame(np.random.randn(4,2), columns=["x", "y"]) In [4]: z = DataFrame(np.random.randn(4,2), columns=["x", "y"]) In [5]: dta = x.merge(y, left_index=True, right_index=True).merge(z, left_index=True, right_index=True, how="outer") In [6]: dta = dta.merge(w, left_index=True, right_index=True) In [7]: dta Out[7]: x_x y_x x_y y_y x_x y_x x_y y_y 0 0.393625 -0.340291 0.035043 -0.195235 -0.892856 -0.357269 0.820424 0.142803 1 -1.600176 0.737261 -0.571140 1.352393 0.201634 1.403633 0.590919 -1.003057 2 -1.046113 2.148139 2.406527 -1.460300 0.881712 0.949246 -0.061758 -0.386265 3 -0.472761 -0.055612 -0.449152 -0.209876 1.076689 0.294275 -0.684433 0.925683 ``` Via direct concat ``` In [11]: concat([x,y,z,w],axis=1) Out[11]: x y x y x y x y 0 0.393625 -0.340291 0.035043 -0.195235 -0.892856 -0.357269 0.820424 0.142803 1 -1.600176 0.737261 -0.571140 1.352393 0.201634 1.403633 0.590919 -1.003057 2 -1.046113 2.148139 2.406527 -1.460300 0.881712 0.949246 -0.061758 -0.386265 3 -0.472761 -0.055612 -0.449152 -0.209876 1.076689 0.294275 -0.684433 0.925683 In [8]: expected = concat([x,y,z,w],axis=1) In [9]: expected.columns=['x_x','y_x','x_y','y_y','x_x','y_x','x_y','y_y'] In [10]: expected Out[10]: x_x y_x x_y y_y x_x y_x x_y y_y 0 0.393625 -0.340291 0.035043 -0.195235 -0.892856 -0.357269 0.820424 0.142803 1 -1.600176 0.737261 -0.571140 1.352393 0.201634 1.403633 0.590919 -1.003057 2 -1.046113 2.148139 2.406527 -1.460300 0.881712 0.949246 -0.061758 -0.386265 3 -0.472761 -0.055612 -0.449152 -0.209876 1.076689 0.294275 -0.684433 0.925683 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4989
2013-09-25T23:53:33Z
2013-09-26T00:21:08Z
2013-09-26T00:21:08Z
2014-06-13T14:32:17Z
API: properly box numeric timedelta ops on Series (GH4984)
diff --git a/doc/source/release.rst b/doc/source/release.rst index a50a0f9c90b73..8d0f2c6a599e8 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -93,6 +93,7 @@ Improvements to existing features is frequency conversion. - Timedelta64 support ``fillna/ffill/bfill`` with an integer interpreted as seconds, or a ``timedelta`` (:issue:`3371`) + - Box numeric ops on ``timedelta`` Series (:issue:`4984`) - Datetime64 support ``ffill/bfill`` - Performance improvements with ``__getitem__`` on ``DataFrames`` with when the key is a column diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index bcb738d8a89cb..85ac48c379aad 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -1204,6 +1204,25 @@ pass a timedelta to get a particular value. y.fillna(10) y.fillna(timedelta(days=-1,seconds=5)) +.. _timeseries.timedeltas_reductions: + +Time Deltas & Reductions +------------------------ + +.. warning:: + + A numeric reduction operation for ``timedelta64[ns]`` will return a single-element ``Series`` of + dtype ``timedelta64[ns]``. + +You can do numeric reduction operations on timedeltas. + +.. ipython:: python + + y2 = y.fillna(timedelta(days=-1,seconds=5)) + y2 + y2.mean() + y2.quantile(.1) + .. _timeseries.timedeltas_convert: Time Deltas & Conversions diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index bda6fa4cdf021..982ae939fc085 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -292,6 +292,14 @@ Enhancements td.fillna(0) td.fillna(timedelta(days=1,seconds=5)) + - You can do numeric reduction operations on timedeltas. Note that these will return + a single-element Series. + + .. ipython:: python + + td.mean() + td.quantile(.1) + - ``plot(kind='kde')`` now accepts the optional parameters ``bw_method`` and ``ind``, passed to scipy.stats.gaussian_kde() (for scipy >= 0.11.0) to set the bandwidth, and to gkde.evaluate() to specify the indicies at which it diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index 247f429d4b331..f9aeb1f726ff7 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -5,7 +5,7 @@ import numpy as np -from pandas.core.common import isnull, notnull, _values_from_object +from pandas.core.common import isnull, notnull, _values_from_object, is_float import pandas.core.common as com import pandas.lib as lib import pandas.algos as algos @@ -188,6 +188,10 @@ def _wrap_results(result,dtype): # as series will do the right thing in py3 (and deal with numpy 1.6.2 # bug in that it results dtype of timedelta64[us] from pandas import Series + + # coerce float to results + if is_float(result): + result = int(result) result = Series([result],dtype='timedelta64[ns]') else: result = result.view(dtype) @@ -224,11 +228,15 @@ def nanmean(values, axis=None, skipna=True): the_mean[ct_mask] = np.nan else: the_mean = the_sum / count if count > 0 else np.nan - return the_mean + + return _wrap_results(the_mean,dtype) @disallow('M8') @bottleneck_switch() def nanmedian(values, axis=None, skipna=True): + + values, mask, dtype = _get_values(values, skipna) + def get_median(x): mask = notnull(x) if not skipna and not mask.all(): @@ -257,7 +265,7 @@ def get_median(x): return ret # otherwise return a scalar value - return get_median(values) if notempty else np.nan + return _wrap_results(get_median(values),dtype) if notempty else np.nan @disallow('M8') diff --git a/pandas/core/series.py b/pandas/core/series.py index 942bb700a3718..8713ffb58392e 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1981,7 +1981,12 @@ def quantile(self, q=0.5): valid_values = self.dropna().values if len(valid_values) == 0: return pa.NA - return _quantile(valid_values, q * 100) + result = _quantile(valid_values, q * 100) + if result.dtype == _TD_DTYPE: + from pandas.tseries.timedeltas import to_timedelta + return to_timedelta(result) + + return result def ptp(self, axis=None, out=None): return _values_from_object(self).ptp(axis, out) diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 551507039112b..64e5728f0f549 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd -from pandas import (Index, Series, DataFrame, isnull, notnull, +from pandas import (Index, Series, DataFrame, Timestamp, isnull, notnull, bdate_range, date_range, _np_version_under1p7) import pandas.core.common as com from pandas.compat import StringIO, lrange, range, zip, u, OrderedDict, long @@ -123,8 +123,8 @@ def conv(v): def test_nat_converters(self): _skip_if_numpy_not_friendly() - self.assert_(to_timedelta('nat') == tslib.iNaT) - self.assert_(to_timedelta('nan') == tslib.iNaT) + self.assert_(to_timedelta('nat',box=False) == tslib.iNaT) + self.assert_(to_timedelta('nan',box=False) == tslib.iNaT) def test_to_timedelta(self): _skip_if_numpy_not_friendly() @@ -133,11 +133,11 @@ def conv(v): return v.astype('m8[ns]') d1 = np.timedelta64(1,'D') - self.assert_(to_timedelta('1 days 06:05:01.00003') == conv(d1+np.timedelta64(6*3600+5*60+1,'s')+np.timedelta64(30,'us'))) - self.assert_(to_timedelta('15.5us') == conv(np.timedelta64(15500,'ns'))) + self.assert_(to_timedelta('1 days 06:05:01.00003',box=False) == conv(d1+np.timedelta64(6*3600+5*60+1,'s')+np.timedelta64(30,'us'))) + self.assert_(to_timedelta('15.5us',box=False) == conv(np.timedelta64(15500,'ns'))) # empty string - result = to_timedelta('') + result = to_timedelta('',box=False) self.assert_(result == tslib.iNaT) result = to_timedelta(['', '']) @@ -150,7 +150,7 @@ def conv(v): # ints result = np.timedelta64(0,'ns') - expected = to_timedelta(0) + expected = to_timedelta(0,box=False) self.assert_(result == expected) # Series @@ -163,6 +163,35 @@ def conv(v): expected = to_timedelta([0,10],unit='s') tm.assert_series_equal(result, expected) + # single element conversion + v = timedelta(seconds=1) + result = to_timedelta(v,box=False) + expected = to_timedelta([v]) + + v = np.timedelta64(timedelta(seconds=1)) + result = to_timedelta(v,box=False) + expected = to_timedelta([v]) + + def test_timedelta_ops(self): + _skip_if_numpy_not_friendly() + + # GH4984 + # make sure ops return timedeltas + s = Series([Timestamp('20130101') + timedelta(seconds=i*i) for i in range(10) ]) + td = s.diff() + + result = td.mean() + expected = to_timedelta(timedelta(seconds=9)) + tm.assert_series_equal(result, expected) + + result = td.quantile(.1) + expected = to_timedelta('00:00:02.6') + tm.assert_series_equal(result, expected) + + result = td.median() + expected = to_timedelta('00:00:08') + tm.assert_series_equal(result, expected) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 4d8633546e017..24e4b1377cc45 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -58,7 +58,7 @@ def _convert_listlike(arg, box): elif is_list_like(arg): return _convert_listlike(arg, box=box) - return _convert_listlike([ arg ], box=False)[0] + return _convert_listlike([ arg ], box=box) _short_search = re.compile( "^\s*(?P<neg>-?)\s*(?P<value>\d*\.?\d*)\s*(?P<unit>d|s|ms|us|ns)?\s*$",re.IGNORECASE)
closes #4984
https://api.github.com/repos/pandas-dev/pandas/pulls/4985
2013-09-25T19:10:51Z
2013-09-25T20:07:40Z
2013-09-25T20:07:40Z
2014-06-27T14:49:50Z
BUG: allow Timestamp comparisons on the left
diff --git a/doc/source/release.rst b/doc/source/release.rst index 3b5bb04344d25..74e54526cfe9a 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -487,6 +487,8 @@ Bug Fixes - Fix repr for DateOffset. No longer show duplicate entries in kwds. Removed unused offset fields. (:issue:`4638`) - Fixed wrong index name during read_csv if using usecols. Applies to c parser only. (:issue:`4201`) + - ``Timestamp`` objects can now appear in the left hand side of a comparison + operation with a ``Series`` or ``DataFrame`` object (:issue:`4982`). pandas 0.12.0 ------------- diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 82be82ea57dae..a6f806d5ce097 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4335,6 +4335,31 @@ def check(df,df2): df2 = DataFrame({'a': date_range('20010101', periods=len(df)), 'b': date_range('20100101', periods=len(df))}) check(df,df2) + def test_timestamp_compare(self): + # make sure we can compare Timestamps on the right AND left hand side + # GH4982 + df = DataFrame({'dates1': date_range('20010101', periods=10), + 'dates2': date_range('20010102', periods=10), + 'intcol': np.random.randint(1000000000, size=10), + 'floatcol': np.random.randn(10), + 'stringcol': list(tm.rands(10))}) + df.loc[np.random.rand(len(df)) > 0.5, 'dates2'] = pd.NaT + ops = {'gt': 'lt', 'lt': 'gt', 'ge': 'le', 'le': 'ge', 'eq': 'eq', + 'ne': 'ne'} + for left, right in ops.items(): + left_f = getattr(operator, left) + right_f = getattr(operator, right) + + # no nats + expected = left_f(df, Timestamp('20010109')) + result = right_f(Timestamp('20010109'), df) + tm.assert_frame_equal(result, expected) + + # nats + expected = left_f(df, Timestamp('nat')) + result = right_f(Timestamp('nat'), df) + tm.assert_frame_equal(result, expected) + def test_modulo(self): # GH3590, modulo as ints diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 51a010f9d4ead..0e5e3d1922ec4 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -3,6 +3,7 @@ import sys import os import unittest +import operator import nose @@ -2010,6 +2011,7 @@ def test_join_self(self): joined = index.join(index, how=kind) self.assert_(index is joined) + class TestDatetime64(unittest.TestCase): """ Also test supoprt for datetime64[ns] in Series / DataFrame @@ -2507,6 +2509,74 @@ def test_hash_equivalent(self): stamp = Timestamp(datetime(2011, 1, 1)) self.assertEquals(d[stamp], 5) + def test_timestamp_compare_scalars(self): + # case where ndim == 0 + lhs = np.datetime64(datetime(2013, 12, 6)) + rhs = Timestamp('now') + nat = Timestamp('nat') + + ops = {'gt': 'lt', 'lt': 'gt', 'ge': 'le', 'le': 'ge', 'eq': 'eq', + 'ne': 'ne'} + + for left, right in ops.items(): + left_f = getattr(operator, left) + right_f = getattr(operator, right) + + if pd._np_version_under1p7: + # you have to convert to timestamp for this to work with numpy + # scalars + expected = left_f(Timestamp(lhs), rhs) + + # otherwise a TypeError is thrown + if left not in ('eq', 'ne'): + with tm.assertRaises(TypeError): + left_f(lhs, rhs) + else: + expected = left_f(lhs, rhs) + + result = right_f(rhs, lhs) + self.assertEqual(result, expected) + + expected = left_f(rhs, nat) + result = right_f(nat, rhs) + self.assertEqual(result, expected) + + def test_timestamp_compare_series(self): + # make sure we can compare Timestamps on the right AND left hand side + # GH4982 + s = Series(date_range('20010101', periods=10), name='dates') + s_nat = s.copy(deep=True) + + s[0] = pd.Timestamp('nat') + s[3] = pd.Timestamp('nat') + + ops = {'lt': 'gt', 'le': 'ge', 'eq': 'eq', 'ne': 'ne'} + + for left, right in ops.items(): + left_f = getattr(operator, left) + right_f = getattr(operator, right) + + # no nats + expected = left_f(s, Timestamp('20010109')) + result = right_f(Timestamp('20010109'), s) + tm.assert_series_equal(result, expected) + + # nats + expected = left_f(s, Timestamp('nat')) + result = right_f(Timestamp('nat'), s) + tm.assert_series_equal(result, expected) + + # compare to timestamp with series containing nats + expected = left_f(s_nat, Timestamp('20010109')) + result = right_f(Timestamp('20010109'), s_nat) + tm.assert_series_equal(result, expected) + + # compare to nat with series containing nats + expected = left_f(s_nat, Timestamp('nat')) + result = right_f(Timestamp('nat'), s_nat) + tm.assert_series_equal(result, expected) + + class TestSlicing(unittest.TestCase): def test_slice_year(self): @@ -2775,6 +2845,7 @@ def test_frame_apply_dont_convert_datetime64(self): self.assertTrue(df.x1.dtype == 'M8[ns]') + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 075102dd63100..99b09446be232 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -9,12 +9,15 @@ from cpython cimport ( PyTypeObject, PyFloat_Check, PyObject_RichCompareBool, - PyString_Check + PyObject_RichCompare, + PyString_Check, + Py_GT, Py_GE, Py_EQ, Py_NE, Py_LT, Py_LE ) # Cython < 0.17 doesn't have this in cpython cdef extern from "Python.h": cdef PyTypeObject *Py_TYPE(object) + int PySlice_Check(object) from libc.stdlib cimport free @@ -30,9 +33,6 @@ from datetime import timedelta, datetime from datetime import time as datetime_time from pandas.compat import parse_date -cdef extern from "Python.h": - int PySlice_Check(object) - # initialize numpy import_array() #import_ufunc() @@ -350,6 +350,11 @@ NaT = NaTType() iNaT = util.get_nat() + +cdef inline bint _cmp_nat_dt(_NaT lhs, _Timestamp rhs, int op) except -1: + return _nat_scalar_rules[op] + + cdef _tz_format(object obj, object zone): try: return obj.strftime(' %%Z, tz=%s' % zone) @@ -437,9 +442,35 @@ def apply_offset(ndarray[object] values, object offset): result = np.empty(n, dtype='M8[ns]') new_values = result.view('i8') - pass +cdef inline bint _cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1: + if op == Py_EQ: + return lhs == rhs + elif op == Py_NE: + return lhs != rhs + elif op == Py_LT: + return lhs < rhs + elif op == Py_LE: + return lhs <= rhs + elif op == Py_GT: + return lhs > rhs + elif op == Py_GE: + return lhs >= rhs + + +cdef int _reverse_ops[6] + +_reverse_ops[Py_LT] = Py_GT +_reverse_ops[Py_LE] = Py_GE +_reverse_ops[Py_EQ] = Py_EQ +_reverse_ops[Py_NE] = Py_NE +_reverse_ops[Py_GT] = Py_LT +_reverse_ops[Py_GE] = Py_LE + + +cdef str _NDIM_STRING = "ndim" + # This is PITA. Because we inherit from datetime, which has very specific # construction requirements, we need to do object instantiation in python # (see Timestamp class above). This will serve as a C extension type that @@ -449,18 +480,21 @@ cdef class _Timestamp(datetime): int64_t value, nanosecond object offset # frequency reference - def __hash__(self): + def __hash__(_Timestamp self): if self.nanosecond: return hash(self.value) - else: - return datetime.__hash__(self) + return datetime.__hash__(self) def __richcmp__(_Timestamp self, object other, int op): - cdef _Timestamp ots + cdef: + _Timestamp ots + int ndim if isinstance(other, _Timestamp): + if isinstance(other, _NaT): + return _cmp_nat_dt(other, self, _reverse_ops[op]) ots = other - elif type(other) is datetime: + elif isinstance(other, datetime): if self.nanosecond == 0: val = self.to_datetime() return PyObject_RichCompareBool(val, other, op) @@ -470,70 +504,60 @@ cdef class _Timestamp(datetime): except ValueError: return self._compare_outside_nanorange(other, op) else: - if op == 2: - return False - elif op == 3: - return True + ndim = getattr(other, _NDIM_STRING, -1) + + if ndim != -1: + if ndim == 0: + if isinstance(other, np.datetime64): + other = Timestamp(other) + else: + raise TypeError('Cannot compare type %r with type %r' % + (type(self).__name__, + type(other).__name__)) + return PyObject_RichCompare(other, self, _reverse_ops[op]) else: - raise TypeError('Cannot compare Timestamp with ' - '{0!r}'.format(other.__class__.__name__)) + if op == Py_EQ: + return False + elif op == Py_NE: + return True + raise TypeError('Cannot compare type %r with type %r' % + (type(self).__name__, type(other).__name__)) self._assert_tzawareness_compat(other) + return _cmp_scalar(self.value, ots.value, op) - if op == 2: # == - return self.value == ots.value - elif op == 3: # != - return self.value != ots.value - elif op == 0: # < - return self.value < ots.value - elif op == 1: # <= - return self.value <= ots.value - elif op == 4: # > - return self.value > ots.value - elif op == 5: # >= - return self.value >= ots.value - - cdef _compare_outside_nanorange(self, object other, int op): - dtval = self.to_datetime() + cdef bint _compare_outside_nanorange(_Timestamp self, datetime other, + int op) except -1: + cdef datetime dtval = self.to_datetime() self._assert_tzawareness_compat(other) if self.nanosecond == 0: - if op == 2: # == - return dtval == other - elif op == 3: # != - return dtval != other - elif op == 0: # < - return dtval < other - elif op == 1: # <= - return dtval <= other - elif op == 4: # > - return dtval > other - elif op == 5: # >= - return dtval >= other + return PyObject_RichCompareBool(dtval, other, op) else: - if op == 2: # == + if op == Py_EQ: return False - elif op == 3: # != + elif op == Py_NE: return True - elif op == 0: # < + elif op == Py_LT: return dtval < other - elif op == 1: # <= + elif op == Py_LE: return dtval < other - elif op == 4: # > + elif op == Py_GT: return dtval >= other - elif op == 5: # >= + elif op == Py_GE: return dtval >= other - cdef _assert_tzawareness_compat(self, object other): + cdef int _assert_tzawareness_compat(_Timestamp self, + object other) except -1: if self.tzinfo is None: if other.tzinfo is not None: - raise Exception('Cannot compare tz-naive and ' - 'tz-aware timestamps') + raise ValueError('Cannot compare tz-naive and tz-aware ' + 'timestamps') elif other.tzinfo is None: - raise Exception('Cannot compare tz-naive and tz-aware timestamps') + raise ValueError('Cannot compare tz-naive and tz-aware timestamps') - cpdef to_datetime(self): + cpdef datetime to_datetime(_Timestamp self): cdef: pandas_datetimestruct dts _TSObject ts @@ -580,6 +604,16 @@ cdef inline bint is_timestamp(object o): return Py_TYPE(o) == ts_type # isinstance(o, Timestamp) +cdef bint _nat_scalar_rules[6] + +_nat_scalar_rules[Py_EQ] = False +_nat_scalar_rules[Py_NE] = True +_nat_scalar_rules[Py_LT] = False +_nat_scalar_rules[Py_LE] = False +_nat_scalar_rules[Py_GT] = False +_nat_scalar_rules[Py_GE] = False + + cdef class _NaT(_Timestamp): def __hash__(_NaT self): @@ -587,23 +621,18 @@ cdef class _NaT(_Timestamp): return hash(self.value) def __richcmp__(_NaT self, object other, int op): - # if not isinstance(other, (_NaT, _Timestamp)): - # raise TypeError('Cannot compare %s with NaT' % type(other)) - - if op == 2: # == - return False - elif op == 3: # != - return True - elif op == 0: # < - return False - elif op == 1: # <= - return False - elif op == 4: # > - return False - elif op == 5: # >= - return False + cdef int ndim = getattr(other, 'ndim', -1) + if ndim == -1: + return _nat_scalar_rules[op] + if ndim == 0: + if isinstance(other, np.datetime64): + other = Timestamp(other) + else: + raise TypeError('Cannot compare type %r with type %r' % + (type(self).__name__, type(other).__name__)) + return PyObject_RichCompare(other, self, _reverse_ops[op]) def _delta_to_nanoseconds(delta): diff --git a/vb_suite/binary_ops.py b/vb_suite/binary_ops.py index 3f076f9f922a3..8293f650425e3 100644 --- a/vb_suite/binary_ops.py +++ b/vb_suite/binary_ops.py @@ -102,3 +102,15 @@ frame_multi_and_no_ne = \ Benchmark("df[(df>0) & (df2>0)]", setup, name='frame_multi_and_no_ne',cleanup="expr.set_use_numexpr(True)", start_date=datetime(2013, 2, 26)) + +setup = common_setup + """ +N = 1000000 +halfway = N // 2 - 1 +s = Series(date_range('20010101', periods=N, freq='D')) +ts = s[halfway] +""" + +timestamp_series_compare = Benchmark("ts >= s", setup, + start_date=datetime(2013, 9, 27)) +series_timestamp_compare = Benchmark("s <= ts", setup, + start_date=datetime(2012, 2, 21)) diff --git a/vb_suite/index_object.py b/vb_suite/index_object.py index cf87a9af500fb..8b348ddc6e6cc 100644 --- a/vb_suite/index_object.py +++ b/vb_suite/index_object.py @@ -22,6 +22,16 @@ index_datetime_intersection = Benchmark("rng.intersection(rng2)", setup) index_datetime_union = Benchmark("rng.union(rng2)", setup) +setup = common_setup + """ +rng = date_range('1/1/2000', periods=10000, freq='T') +rng2 = rng[:-1] +""" + +datetime_index_intersection = Benchmark("rng.intersection(rng2)", setup, + start_date=datetime(2013, 9, 27)) +datetime_index_union = Benchmark("rng.union(rng2)", setup, + start_date=datetime(2013, 9, 27)) + # integers setup = common_setup + """ N = 1000000
closes #4982
https://api.github.com/repos/pandas-dev/pandas/pulls/4983
2013-09-25T16:53:47Z
2013-09-27T15:53:27Z
2013-09-27T15:53:26Z
2014-08-18T17:03:33Z
CI/ENH: test against an older version of statsmodels
diff --git a/ci/install.sh b/ci/install.sh index 86226c530541c..357d962d9610d 100755 --- a/ci/install.sh +++ b/ci/install.sh @@ -62,19 +62,6 @@ if [ x"$FULL_DEPS" == x"true" ]; then echo "Installing FULL_DEPS" # for pytables gets the lib as well time sudo apt-get $APT_ARGS install libhdf5-serial-dev - - # fool statsmodels into thinking pandas was already installed - # so it won't refuse to install itself. - - SITE_PKG_DIR=$VIRTUAL_ENV/lib/python$TRAVIS_PYTHON_VERSION/site-packages - echo "Using SITE_PKG_DIR: $SITE_PKG_DIR" - - mkdir $SITE_PKG_DIR/pandas - touch $SITE_PKG_DIR/pandas/__init__.py - echo "version='0.10.0-phony'" > $SITE_PKG_DIR/pandas/version.py - time pip install $PIP_ARGS git+git://github.com/statsmodels/statsmodels@c9062e43b8a5f7385537ca95#egg=statsmodels - - rm -Rf $SITE_PKG_DIR/pandas # scrub phoney pandas fi # build pandas diff --git a/ci/requirements-2.7.txt b/ci/requirements-2.7.txt index 2e903102de7b1..686dc87f7d009 100644 --- a/ci/requirements-2.7.txt +++ b/ci/requirements-2.7.txt @@ -17,3 +17,4 @@ scikits.timeseries==0.91.3 MySQL-python==1.2.4 scipy==0.10.0 beautifulsoup4==4.2.1 +statsmodels==0.5.0 diff --git a/ci/requirements-2.7_LOCALE.txt b/ci/requirements-2.7_LOCALE.txt index 056b63bbb8591..e4cdf0733a7d3 100644 --- a/ci/requirements-2.7_LOCALE.txt +++ b/ci/requirements-2.7_LOCALE.txt @@ -15,3 +15,4 @@ html5lib==1.0b2 lxml==3.2.1 scipy==0.10.0 beautifulsoup4==4.2.1 +statsmodels==0.5.0 diff --git a/ci/requirements-3.2.txt b/ci/requirements-3.2.txt index b689047019ed7..b44a708c4fffc 100644 --- a/ci/requirements-3.2.txt +++ b/ci/requirements-3.2.txt @@ -12,3 +12,4 @@ patsy==0.1.0 lxml==3.2.1 scipy==0.12.0 beautifulsoup4==4.2.1 +statsmodels==0.4.3 diff --git a/ci/requirements-3.3.txt b/ci/requirements-3.3.txt index 326098be5f7f4..318030e733158 100644 --- a/ci/requirements-3.3.txt +++ b/ci/requirements-3.3.txt @@ -13,3 +13,4 @@ patsy==0.1.0 lxml==3.2.1 scipy==0.12.0 beautifulsoup4==4.2.1 +statsmodels==0.4.3 diff --git a/ci/speedpack/build.sh b/ci/speedpack/build.sh index d19c6da8a86ed..689f9aa5db8ea 100755 --- a/ci/speedpack/build.sh +++ b/ci/speedpack/build.sh @@ -26,6 +26,42 @@ apt-get build-dep python-lxml -y export PYTHONIOENCODING='utf-8' export VIRTUALENV_DISTRIBUTE=0 + +function create_fake_pandas() { + local site_pkg_dir="$1" + rm -rf $site_pkg_dir/pandas + mkdir $site_pkg_dir/pandas + touch $site_pkg_dir/pandas/__init__.py + echo "version = '0.10.0-phony'" > $site_pkg_dir/pandas/version.py +} + + +function get_site_pkgs_dir() { + python$1 -c 'import distutils; print(distutils.sysconfig.get_python_lib())' +} + + +function create_wheel() { + local pip_args="$1" + local wheelhouse="$2" + local n="$3" + local pyver="$4" + + local site_pkgs_dir="$(get_site_pkgs_dir $pyver)" + + + if [[ "$n" == *statsmodels* ]]; then + create_fake_pandas $site_pkgs_dir && \ + pip wheel $pip_args --wheel-dir=$wheelhouse $n && \ + pip install $pip_args --no-index $n && \ + rm -Rf $site_pkgs_dir + else + pip wheel $pip_args --wheel-dir=$wheelhouse $n + pip install $pip_args --no-index $n + fi +} + + function generate_wheels() { # get the requirements file local reqfile="$1" @@ -62,8 +98,7 @@ function generate_wheels() { # install and build the wheels cat $reqfile | while read N; do - pip wheel $PIP_ARGS --wheel-dir=$WHEELHOUSE $N - pip install $PIP_ARGS --no-index $N + create_wheel "$PIP_ARGS" "$WHEELHOUSE" "$N" "$PY_VER" done } diff --git a/doc/source/release.rst b/doc/source/release.rst index eec2e91f0a755..a50a0f9c90b73 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -128,6 +128,8 @@ Improvements to existing features - ``read_stata`` now accepts Stata 13 format (:issue:`4291`) - ``ExcelWriter`` and ``ExcelFile`` can be used as contextmanagers. (:issue:`3441`, :issue:`4933`) + - ``pandas`` is now tested with two different versions of ``statsmodels`` + (0.4.3 and 0.5.0) (:issue:`4981`). API Changes ~~~~~~~~~~~ diff --git a/pandas/stats/tests/test_ols.py b/pandas/stats/tests/test_ols.py index a2271731b6de9..ad9184e698316 100644 --- a/pandas/stats/tests/test_ols.py +++ b/pandas/stats/tests/test_ols.py @@ -6,6 +6,7 @@ from __future__ import division +from distutils.version import LooseVersion from datetime import datetime from pandas import compat import unittest @@ -98,11 +99,10 @@ def testOLSWithDatasets_scotland(self): def testWLS(self): # WLS centered SS changed (fixed) in 0.5.0 - v = sm.version.version.split('.') - if int(v[0]) >= 0 and int(v[1]) <= 5: - if int(v[2]) < 1: - raise nose.SkipTest - print( "Make sure you're using statsmodels 0.5.0.dev-cec4f26 or later.") + sm_version = sm.version.version + if sm_version < LooseVersion('0.5.0'): + raise nose.SkipTest("WLS centered SS not fixed in statsmodels" + " version {0}".format(sm_version)) X = DataFrame(np.random.randn(30, 4), columns=['A', 'B', 'C', 'D']) Y = Series(np.random.randn(30))
https://api.github.com/repos/pandas-dev/pandas/pulls/4981
2013-09-25T14:56:18Z
2013-09-25T17:12:39Z
2013-09-25T17:12:39Z
2014-06-16T11:01:48Z
FIX: JSON support for non C locales
diff --git a/doc/source/release.rst b/doc/source/release.rst index 97150cbeb53a2..8584fe564f8b0 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -358,6 +358,8 @@ Bug Fixes dtypes, surfaced in (:issue:`4377`) - Fixed bug with duplicate columns and type conversion in ``read_json`` when ``orient='split'`` (:issue:`4377`) + - Fixed JSON bug where locales with decimal separators other than '.' threw + exceptions when encoding / decoding certain values. (:issue:`4918`) - Fix ``.iat`` indexing with a ``PeriodIndex`` (:issue:`4390`) - Fixed an issue where ``PeriodIndex`` joining with self was returning a new instance rather than the same instance (:issue:`4379`); also adds a test diff --git a/pandas/io/tests/test_json/test_ujson.py b/pandas/io/tests/test_json/test_ujson.py index 4d6218d3dbc35..38a30b8baf459 100644 --- a/pandas/io/tests/test_json/test_ujson.py +++ b/pandas/io/tests/test_json/test_ujson.py @@ -83,6 +83,19 @@ def test_doubleLongDecimalIssue(self): decoded = ujson.decode(encoded) self.assertEqual(sut, decoded) + def test_encodeNonCLocale(self): + import locale + savedlocale = locale.getlocale(locale.LC_NUMERIC) + try: + locale.setlocale(locale.LC_NUMERIC, 'it_IT.UTF-8') + except: + try: + locale.setlocale(locale.LC_NUMERIC, 'Italian_Italy') + except: + raise nose.SkipTest('Could not set locale for testing') + self.assertEqual(ujson.loads(ujson.dumps(4.78e60)), 4.78e60) + self.assertEqual(ujson.loads('4.78', precise_float=True), 4.78) + locale.setlocale(locale.LC_NUMERIC, savedlocale) def test_encodeDecodeLongDecimal(self): sut = {u('a'): -528656961.4399388} diff --git a/pandas/src/ujson/lib/ultrajsondec.c b/pandas/src/ujson/lib/ultrajsondec.c index c5cf341ad3092..85a8387547641 100644 --- a/pandas/src/ujson/lib/ultrajsondec.c +++ b/pandas/src/ujson/lib/ultrajsondec.c @@ -43,6 +43,7 @@ Numeric decoder derived from from TCL library #include <wchar.h> #include <stdlib.h> #include <errno.h> +#include <locale.h> #ifndef TRUE #define TRUE 1 @@ -824,7 +825,7 @@ FASTCALL_ATTR JSOBJ FASTCALL_MSVC decode_object( struct DecoderState *ds) default: ds->dec->releaseObject(ds->prv, newObj, ds->dec); - return SetError(ds, -1, "Unexpected character in found when decoding object value"); + return SetError(ds, -1, "Unexpected character found when decoding object value"); } } } @@ -874,6 +875,7 @@ JSOBJ JSON_DecodeObject(JSONObjectDecoder *dec, const char *buffer, size_t cbBuf { /* FIXME: Base the size of escBuffer of that of cbBuffer so that the unicode escaping doesn't run into the wall each time */ + char *locale; struct DecoderState ds; wchar_t escBuffer[(JSON_MAX_STACK_BUFFER_SIZE / sizeof(wchar_t))]; JSOBJ ret; @@ -892,7 +894,15 @@ JSOBJ JSON_DecodeObject(JSONObjectDecoder *dec, const char *buffer, size_t cbBuf ds.dec = dec; + locale = strdup(setlocale(LC_NUMERIC, NULL)); + if (!locale) + { + return SetError(&ds, -1, "Could not reserve memory block"); + } + setlocale(LC_NUMERIC, "C"); ret = decode_any (&ds); + setlocale(LC_NUMERIC, locale); + free(locale); if (ds.escHeap) { diff --git a/pandas/src/ujson/lib/ultrajsonenc.c b/pandas/src/ujson/lib/ultrajsonenc.c index 15d92d42f6753..17048bd86adc2 100644 --- a/pandas/src/ujson/lib/ultrajsonenc.c +++ b/pandas/src/ujson/lib/ultrajsonenc.c @@ -41,6 +41,7 @@ Numeric decoder derived from from TCL library #include <string.h> #include <stdlib.h> #include <math.h> +#include <locale.h> #include <float.h> @@ -877,6 +878,7 @@ void encode(JSOBJ obj, JSONObjectEncoder *enc, const char *name, size_t cbName) char *JSON_EncodeObject(JSOBJ obj, JSONObjectEncoder *enc, char *_buffer, size_t _cbBuffer) { + char *locale; enc->malloc = enc->malloc ? enc->malloc : malloc; enc->free = enc->free ? enc->free : free; enc->realloc = enc->realloc ? enc->realloc : realloc; @@ -915,7 +917,16 @@ char *JSON_EncodeObject(JSOBJ obj, JSONObjectEncoder *enc, char *_buffer, size_t enc->end = enc->start + _cbBuffer; enc->offset = enc->start; + locale = strdup(setlocale(LC_NUMERIC, NULL)); + if (!locale) + { + SetError(NULL, enc, "Could not reserve memory block"); + return NULL; + } + setlocale(LC_NUMERIC, "C"); encode (obj, enc, NULL, 0); + setlocale(LC_NUMERIC, locale); + free(locale); Buffer_Reserve(enc, 1); if (enc->errorMsg)
As reported in #4918. Tested on Linux and Windows.
https://api.github.com/repos/pandas-dev/pandas/pulls/4976
2013-09-25T02:08:52Z
2013-09-25T12:55:17Z
2013-09-25T12:55:17Z
2014-07-16T08:31:04Z
TST/CLN: pare down the eval test suite
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index d5bcf85d4de03..7dc2ebc3d54e1 100755 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -129,8 +129,8 @@ def setup_data(self): Series([1, 2, np.nan, np.nan, 5]), nan_df1) self.pandas_rhses = (DataFrame(randn(10, 5)), Series(randn(5)), Series([1, 2, np.nan, np.nan, 5]), nan_df2) - self.scalar_lhses = randn(), np.float64(randn()), np.nan - self.scalar_rhses = randn(), np.float64(randn()), np.nan + self.scalar_lhses = randn(), + self.scalar_rhses = randn(), self.lhses = self.pandas_lhses + self.scalar_lhses self.rhses = self.pandas_rhses + self.scalar_rhses @@ -180,7 +180,6 @@ def test_floor_division(self): for lhs, rhs in product(self.lhses, self.rhses): self.check_floor_division(lhs, '//', rhs) - @slow def test_pow(self): for lhs, rhs in product(self.lhses, self.rhses): self.check_pow(lhs, '**', rhs) @@ -198,13 +197,13 @@ def test_compound_invert_op(self): @slow def test_chained_cmp_op(self): mids = self.lhses - cmp_ops = tuple(set(self.cmp_ops) - set(['==', '!=', '<=', '>='])) + cmp_ops = '<', '>'# tuple(set(self.cmp_ops) - set(['==', '!=', '<=', '>='])) for lhs, cmp1, mid, cmp2, rhs in product(self.lhses, cmp_ops, mids, cmp_ops, self.rhses): self.check_chained_cmp_op(lhs, cmp1, mid, cmp2, rhs) def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2): - skip_these = 'in', 'not in' + skip_these = _scalar_skip ex = '(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)'.format(cmp1=cmp1, binop=binop, cmp2=cmp2) @@ -264,7 +263,7 @@ def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2): @skip_incompatible_operand def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs): - skip_these = 'in', 'not in' + skip_these = _scalar_skip def check_operands(left, right, cmp_op): if (np.isscalar(left) and np.isnan(left) and not np.isscalar(right) @@ -318,11 +317,7 @@ def check_operands(left, right, cmp_op): ex1 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2) ex2 = 'lhs {0} mid and mid {1} rhs'.format(cmp1, cmp2) ex3 = '(lhs {0} mid) & (mid {1} rhs)'.format(cmp1, cmp2) - try: - expected = _eval_single_bin(lhs_new, '&', rhs_new, self.engine) - except TypeError: - import ipdb; ipdb.set_trace() - raise + expected = _eval_single_bin(lhs_new, '&', rhs_new, self.engine) for ex in (ex1, ex2, ex3): result = pd.eval(ex, engine=self.engine, @@ -729,9 +724,8 @@ def setup_ops(self): def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs): ex1 = 'lhs {0} mid {1} rhs'.format(cmp1, cmp2) - self.assertRaises(NotImplementedError, pd.eval, ex1, - local_dict={'lhs': lhs, 'mid': mid, 'rhs': rhs}, - engine=self.engine, parser=self.parser) + with tm.assertRaises(NotImplementedError): + pd.eval(ex1, engine=self.engine, parser=self.parser) class TestEvalPythonPython(TestEvalNumexprPython): @@ -783,7 +777,8 @@ def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs): class TestAlignment(object): - index_types = 'i', 'f', 's', 'u', 'dt', # 'p' + index_types = 'i', 'u', 'dt' + lhs_index_types = index_types + ('f', 's') # 'p' def check_align_nested_unary_op(self, engine, parser): skip_if_no_ne(engine) @@ -798,23 +793,23 @@ def test_align_nested_unary_op(self): def check_basic_frame_alignment(self, engine, parser): skip_if_no_ne(engine) - args = product(self.index_types, repeat=2) - for r_idx_type, c_idx_type in args: - df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, + args = product(self.lhs_index_types, self.index_types, + self.index_types) + for lr_idx_type, rr_idx_type, c_idx_type in args: + df = mkdf(10, 10, data_gen_f=f, r_idx_type=lr_idx_type, c_idx_type=c_idx_type) - df2 = mkdf(20, 10, data_gen_f=f, r_idx_type=r_idx_type, + df2 = mkdf(20, 10, data_gen_f=f, r_idx_type=rr_idx_type, c_idx_type=c_idx_type) res = pd.eval('df + df2', engine=engine, parser=parser) assert_frame_equal(res, df + df2) - @slow def test_basic_frame_alignment(self): for engine, parser in ENGINES_PARSERS: yield self.check_basic_frame_alignment, engine, parser def check_frame_comparison(self, engine, parser): skip_if_no_ne(engine) - args = product(self.index_types, repeat=2) + args = product(self.lhs_index_types, repeat=2) for r_idx_type, c_idx_type in args: df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type) @@ -826,18 +821,19 @@ def check_frame_comparison(self, engine, parser): res = pd.eval('df < df3', engine=engine, parser=parser) assert_frame_equal(res, df < df3) - @slow def test_frame_comparison(self): for engine, parser in ENGINES_PARSERS: yield self.check_frame_comparison, engine, parser def check_medium_complex_frame_alignment(self, engine, parser): skip_if_no_ne(engine) - args = product(self.index_types, repeat=4) + args = product(self.lhs_index_types, self.index_types, + self.index_types, self.index_types) + for r1, c1, r2, c2 in args: - df = mkdf(5, 2, data_gen_f=f, r_idx_type=r1, c_idx_type=c1) - df2 = mkdf(10, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2) - df3 = mkdf(15, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2) + df = mkdf(3, 2, data_gen_f=f, r_idx_type=r1, c_idx_type=c1) + df2 = mkdf(4, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2) + df3 = mkdf(5, 2, data_gen_f=f, r_idx_type=r2, c_idx_type=c2) res = pd.eval('df + df2 + df3', engine=engine, parser=parser) assert_frame_equal(res, df + df2 + df3) @@ -864,12 +860,11 @@ def testit(r_idx_type, c_idx_type, index_name): expected = df + s assert_frame_equal(res, expected) - args = product(self.index_types, self.index_types, ('index', - 'columns')) + args = product(self.lhs_index_types, self.index_types, + ('index', 'columns')) for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) - @slow def test_basic_frame_series_alignment(self): for engine, parser in ENGINES_PARSERS: yield self.check_basic_frame_series_alignment, engine, parser @@ -877,7 +872,7 @@ def test_basic_frame_series_alignment(self): def check_basic_series_frame_alignment(self, engine, parser): skip_if_no_ne(engine) def testit(r_idx_type, c_idx_type, index_name): - df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, + df = mkdf(10, 7, data_gen_f=f, r_idx_type=r_idx_type, c_idx_type=c_idx_type) index = getattr(df, index_name) s = Series(np.random.randn(5), index[:5]) @@ -892,19 +887,18 @@ def testit(r_idx_type, c_idx_type, index_name): expected = s + df assert_frame_equal(res, expected) - args = product(self.index_types, self.index_types, ('index', - 'columns')) + args = product(self.lhs_index_types, self.index_types, + ('index', 'columns')) for r_idx_type, c_idx_type, index_name in args: testit(r_idx_type, c_idx_type, index_name) - @slow def test_basic_series_frame_alignment(self): for engine, parser in ENGINES_PARSERS: yield self.check_basic_series_frame_alignment, engine, parser def check_series_frame_commutativity(self, engine, parser): skip_if_no_ne(engine) - args = product(self.index_types, self.index_types, ('+', '*'), + args = product(self.lhs_index_types, self.index_types, ('+', '*'), ('index', 'columns')) for r_idx_type, c_idx_type, op, index_name in args: df = mkdf(10, 10, data_gen_f=f, r_idx_type=r_idx_type, @@ -921,20 +915,28 @@ def check_series_frame_commutativity(self, engine, parser): if engine == 'numexpr': assert_frame_equal(a, b) - @slow def test_series_frame_commutativity(self): for engine, parser in ENGINES_PARSERS: yield self.check_series_frame_commutativity, engine, parser def check_complex_series_frame_alignment(self, engine, parser): skip_if_no_ne(engine) - index_types = [self.index_types] * 4 - args = product(('index', 'columns'), ('df', 'df2'), *index_types) - for index_name, obj, r1, r2, c1, c2 in args: - df = mkdf(10, 5, data_gen_f=f, r_idx_type=r1, c_idx_type=c1) - df2 = mkdf(20, 5, data_gen_f=f, r_idx_type=r2, c_idx_type=c2) - index = getattr(locals()[obj], index_name) - s = Series(np.random.randn(5), index[:5]) + + import random + args = product(self.lhs_index_types, self.index_types, + self.index_types, self.index_types) + n = 3 + m1 = 5 + m2 = 2 * m1 + + for r1, r2, c1, c2 in args: + index_name = random.choice(['index', 'columns']) + obj_name = random.choice(['df', 'df2']) + + df = mkdf(m1, n, data_gen_f=f, r_idx_type=r1, c_idx_type=c1) + df2 = mkdf(m2, n, data_gen_f=f, r_idx_type=r2, c_idx_type=c2) + index = getattr(locals().get(obj_name), index_name) + s = Series(np.random.randn(n), index[:n]) if r2 == 'dt' or c2 == 'dt': if engine == 'numexpr': @@ -1004,7 +1006,6 @@ def check_performance_warning_for_poor_alignment(self, engine, parser): "".format(1, 's', np.log10(s.size - df.shape[1]))) assert_equal(msg, expected) - def test_performance_warning_for_poor_alignment(self): for engine, parser in ENGINES_PARSERS: yield self.check_performance_warning_for_poor_alignment, engine, parser
Current running time of the `eval` test suite (not including `query`) ``` $ nosetests pandas/computation/tests/test_eval.py 1 ↵ ......../home/phillip/Documents/code/py/pandas/pandas/core/frame.py:3088: FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index FutureWarning) .........................................................................................S....................................................................................................................... ---------------------------------------------------------------------- Ran 217 tests in 190.685s OK (SKIP=1) ``` New running time :speedboat: : ``` $ nosetests pandas/computation/tests/test_eval.py ......../home/phillip/Documents/code/py/pandas/pandas/core/frame.py:3088: FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index FutureWarning) .........................................................................................S....................................................................................................................... ---------------------------------------------------------------------- Ran 217 tests in 39.299s OK (SKIP=1) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/4974
2013-09-24T22:27:33Z
2013-09-25T01:28:32Z
2013-09-25T01:28:32Z
2014-07-16T08:31:00Z
CLN: General print statement cleanup.
diff --git a/bench/io_roundtrip.py b/bench/io_roundtrip.py index e389481d1aabc..fa4e0755f40df 100644 --- a/bench/io_roundtrip.py +++ b/bench/io_roundtrip.py @@ -62,8 +62,8 @@ def rountrip_archive(N, K=50, iterations=10): pickle_time = timeit(pickle_f, iterations) / iterations print('pandas (pickle) %7.4f seconds' % pickle_time) - # print 'Numpy (npz) %7.4f seconds' % numpy_time - # print 'larry (HDF5) %7.4f seconds' % larry_time + # print('Numpy (npz) %7.4f seconds' % numpy_time) + # print('larry (HDF5) %7.4f seconds' % larry_time) # Delete old files try: diff --git a/doc/sphinxext/docscrape.py b/doc/sphinxext/docscrape.py index a6a42ac40042e..9a8ac59b32714 100755 --- a/doc/sphinxext/docscrape.py +++ b/doc/sphinxext/docscrape.py @@ -463,7 +463,7 @@ def __str__(self): if self._role: if not roles.has_key(self._role): - print "Warning: invalid role %s" % self._role + print("Warning: invalid role %s" % self._role) out += '.. %s:: %s\n \n\n' % (roles.get(self._role, ''), func_name) diff --git a/doc/sphinxext/ipython_console_highlighting.py b/doc/sphinxext/ipython_console_highlighting.py index 569335311aeab..dfb489e49394d 100644 --- a/doc/sphinxext/ipython_console_highlighting.py +++ b/doc/sphinxext/ipython_console_highlighting.py @@ -39,7 +39,7 @@ class IPythonConsoleLexer(Lexer): In [2]: a Out[2]: 'foo' - In [3]: print a + In [3]: print(a) foo In [4]: 1 / 0 diff --git a/doc/sphinxext/ipython_directive.py b/doc/sphinxext/ipython_directive.py index f05330c371885..114a3d56f36c8 100644 --- a/doc/sphinxext/ipython_directive.py +++ b/doc/sphinxext/ipython_directive.py @@ -158,8 +158,8 @@ def block_parser(part, rgxin, rgxout, fmtin, fmtout): nextline = lines[i] matchout = rgxout.match(nextline) - # print "nextline=%s, continuation=%s, starts=%s"%(nextline, - # continuation, nextline.startswith(continuation)) + # print("nextline=%s, continuation=%s, starts=%s"%(nextline, + # continuation, nextline.startswith(continuation))) if matchout or nextline.startswith('#'): break elif nextline.startswith(continuation): @@ -245,7 +245,7 @@ def clear_cout(self): def process_input_line(self, line, store_history=True): """process the input, capturing stdout""" - # print "input='%s'"%self.input + # print("input='%s'"%self.input) stdout = sys.stdout splitter = self.IP.input_splitter try: @@ -293,7 +293,7 @@ def process_input(self, data, input_prompt, lineno): decorator, input, rest = data image_file = None image_directive = None - # print 'INPUT:', data # dbg + # print('INPUT:', data) # dbg is_verbatim = decorator == '@verbatim' or self.is_verbatim is_doctest = decorator == '@doctest' or self.is_doctest is_suppress = decorator == '@suppress' or self.is_suppress @@ -361,7 +361,7 @@ def _remove_first_space_if_any(line): self.cout.truncate(0) return (ret, input_lines, output, is_doctest, image_file, image_directive) - # print 'OUTPUT', output # dbg + # print('OUTPUT', output) # dbg def process_output(self, data, output_prompt, input_lines, output, is_doctest, image_file): @@ -390,9 +390,9 @@ def process_output(self, data, output_prompt, 'found_output="%s" and submitted output="%s"' % (input_lines, found, submitted)) raise RuntimeError(e) - # print 'doctest PASSED for input_lines="%s" with - # found_output="%s" and submitted output="%s"'%(input_lines, - # found, submitted) + # print('''doctest PASSED for input_lines="%s" with + # found_output="%s" and submitted output="%s"''' % (input_lines, + # found, submitted)) def process_comment(self, data): """Process data fPblock for COMMENT token.""" @@ -406,7 +406,7 @@ def save_image(self, image_file): self.ensure_pyplot() command = ('plt.gcf().savefig("%s", bbox_inches="tight", ' 'dpi=100)' % image_file) - # print 'SAVEFIG', command # dbg + # print('SAVEFIG', command) # dbg self.process_input_line('bookmark ipy_thisdir', store_history=False) self.process_input_line('cd -b ipy_savedir', store_history=False) self.process_input_line(command, store_history=False) @@ -737,12 +737,12 @@ def run(self): lines.extend(figure.split('\n')) lines.append('') - # print lines + # print(lines) if len(lines) > 2: if debug: - print '\n'.join(lines) + print('\n'.join(lines)) else: # NOTE: this raises some errors, what's it for? - # print 'INSERTING %d lines'%len(lines) + # print('INSERTING %d lines' % len(lines)) self.state_machine.insert_input( lines, self.state_machine.input_lines.source(0)) @@ -813,7 +813,7 @@ def test(): In [130]: url = 'http://ichart.finance.yahoo.com/table.csv?s=CROX\ .....: &d=9&e=22&f=2009&g=d&a=1&br=8&c=2006&ignore=.csv' -In [131]: print url.split('&') +In [131]: print(url.split('&')) ['http://ichart.finance.yahoo.com/table.csv?s=CROX', 'd=9', 'e=22', 'f=2009', 'g=d', 'a=1', 'b=8', 'c=2006', 'ignore=.csv'] In [60]: import urllib @@ -843,12 +843,12 @@ def test(): """, r""" -In [106]: print x +In [106]: print(x) jdh In [109]: for i in range(10): n -.....: print i +.....: print(i) .....: .....: 0 @@ -920,4 +920,4 @@ def test(): if not os.path.isdir('_static'): os.mkdir('_static') test() - print 'All OK? Check figures in _static/' + print('All OK? Check figures in _static/') diff --git a/doc/sphinxext/phantom_import.py b/doc/sphinxext/phantom_import.py index 926641827e937..b69f09ea612a0 100755 --- a/doc/sphinxext/phantom_import.py +++ b/doc/sphinxext/phantom_import.py @@ -31,7 +31,7 @@ def setup(app): def initialize(app): fn = app.config.phantom_import_file if (fn and os.path.isfile(fn)): - print "[numpydoc] Phantom importing modules from", fn, "..." + print("[numpydoc] Phantom importing modules from", fn, "...") import_phantom_module(fn) #------------------------------------------------------------------------------ diff --git a/doc/sphinxext/tests/test_docscrape.py b/doc/sphinxext/tests/test_docscrape.py index 96c9d5639b5c2..a66e4222b380d 100755 --- a/doc/sphinxext/tests/test_docscrape.py +++ b/doc/sphinxext/tests/test_docscrape.py @@ -85,13 +85,13 @@ >>> mean = (1,2) >>> cov = [[1,0],[1,0]] >>> x = multivariate_normal(mean,cov,(3,3)) - >>> print x.shape + >>> print(x.shape) (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: - >>> print list( (x[0,0,:] - mean) < 0.6 ) + >>> print(list( (x[0,0,:] - mean) < 0.6 )) [True, True] .. index:: random @@ -153,7 +153,7 @@ def test_examples(): def test_index(): assert_equal(doc['index']['default'], 'random') - print doc['index'] + print(doc['index']) assert_equal(len(doc['index']), 2) assert_equal(len(doc['index']['refguide']), 2) @@ -247,13 +247,13 @@ def test_str(): >>> mean = (1,2) >>> cov = [[1,0],[1,0]] >>> x = multivariate_normal(mean,cov,(3,3)) ->>> print x.shape +>>> print(x.shape) (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: ->>> print list( (x[0,0,:] - mean) < 0.6 ) +>>> print(list( (x[0,0,:] - mean) < 0.6 )) [True, True] .. index:: random @@ -351,13 +351,13 @@ def test_sphinx_str(): >>> mean = (1,2) >>> cov = [[1,0],[1,0]] >>> x = multivariate_normal(mean,cov,(3,3)) ->>> print x.shape +>>> print(x.shape) (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: ->>> print list( (x[0,0,:] - mean) < 0.6 ) +>>> print(list( (x[0,0,:] - mean) < 0.6 )) [True, True] """) diff --git a/examples/regressions.py b/examples/regressions.py index 2203165825ccb..6351c6730d838 100644 --- a/examples/regressions.py +++ b/examples/regressions.py @@ -31,7 +31,7 @@ def makeSeries(): model = ols(y=Y, x=X) -print (model) +print(model) #------------------------------------------------------------------------------- # Panel regression @@ -48,4 +48,4 @@ def makeSeries(): model = ols(y=Y, x=data) -print (panelModel) +print(panelModel) diff --git a/pandas/__init__.py b/pandas/__init__.py index c4c012d6c5095..ddd4cd49e6ec6 100644 --- a/pandas/__init__.py +++ b/pandas/__init__.py @@ -7,7 +7,7 @@ except Exception: # pragma: no cover import sys e = sys.exc_info()[1] # Py25 and Py3 current exception syntax conflict - print (e) + print(e) if 'No module named lib' in str(e): raise ImportError('C extensions not built: if you installed already ' 'verify that you are not importing from the source ' diff --git a/pandas/core/config.py b/pandas/core/config.py index f81958a0e58fc..9f864e720dbfb 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -154,7 +154,7 @@ def _describe_option(pat='', _print_desc=True): s += _build_option_description(k) if _print_desc: - print (s) + print(s) else: return s @@ -631,7 +631,7 @@ def pp(name, ks): ls += pp(k, ks) s = '\n'.join(ls) if _print: - print (s) + print(s) else: return s diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 799d96f46a15b..0fd02c2bdc3a4 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -724,9 +724,9 @@ def iterrows(self): >>> df = DataFrame([[1, 1.0]], columns=['x', 'y']) >>> row = next(df.iterrows())[1] - >>> print row['x'].dtype + >>> print(row['x'].dtype) float64 - >>> print df['x'].dtype + >>> print(df['x'].dtype) int64 Returns diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index ce07981793f7b..186277777abe8 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -1432,7 +1432,7 @@ def aggregate(self, func_or_funcs, *args, **kwargs): ret = Series(result, index=index) if not self.as_index: # pragma: no cover - print ('Warning, ignoring as_index=True') + print('Warning, ignoring as_index=True') return ret diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 7b9347a821fad..aac8bd0890169 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -516,7 +516,7 @@ def _clean_options(self, options, engine): sep = options['delimiter'] if (sep is None and not options['delim_whitespace']): if engine == 'c': - print ('Using Python parser to sniff delimiter') + print('Using Python parser to sniff delimiter') engine = 'python' elif sep is not None and len(sep) > 1: # wait until regex engine integrated diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 491e090cab4fe..42a434c005a4c 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -503,7 +503,7 @@ def open(self, mode='a'): self._handle = h5_open(self._path, self._mode) except IOError as e: # pragma: no cover if 'can not be written' in str(e): - print ('Opening %s in read-only mode' % self._path) + print('Opening %s in read-only mode' % self._path) self._handle = h5_open(self._path, 'r') else: raise diff --git a/pandas/io/sql.py b/pandas/io/sql.py index b65c35e6b352a..e269d14f72712 100644 --- a/pandas/io/sql.py +++ b/pandas/io/sql.py @@ -97,7 +97,7 @@ def tquery(sql, con=None, cur=None, retry=True): except Exception as e: excName = e.__class__.__name__ if excName == 'OperationalError': # pragma: no cover - print ('Failed to commit, may need to restart interpreter') + print('Failed to commit, may need to restart interpreter') else: raise @@ -131,7 +131,7 @@ def uquery(sql, con=None, cur=None, retry=True, params=None): traceback.print_exc() if retry: - print ('Looks like your connection failed, reconnecting...') + print('Looks like your connection failed, reconnecting...') return uquery(sql, con, retry=False) return result diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index a79ac9d3f9e40..35b9dfbdb6f77 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -1328,7 +1328,7 @@ def test_big_table_frame(self): recons = store.select('df') assert isinstance(recons, DataFrame) - print ("\nbig_table frame [%s] -> %5.2f" % (rows, time.time() - x)) + print("\nbig_table frame [%s] -> %5.2f" % (rows, time.time() - x)) def test_big_table2_frame(self): # this is a really big table: 1m rows x 60 float columns, 20 string, 20 datetime @@ -1336,7 +1336,7 @@ def test_big_table2_frame(self): raise nose.SkipTest('no big table2 frame') # create and write a big table - print ("\nbig_table2 start") + print("\nbig_table2 start") import time start_time = time.time() df = DataFrame(np.random.randn(1000 * 1000, 60), index=range(int( @@ -1346,8 +1346,8 @@ def test_big_table2_frame(self): for x in range(20): df['datetime%03d' % x] = datetime.datetime(2001, 1, 2, 0, 0) - print ("\nbig_table2 frame (creation of df) [rows->%s] -> %5.2f" - % (len(df.index), time.time() - start_time)) + print("\nbig_table2 frame (creation of df) [rows->%s] -> %5.2f" + % (len(df.index), time.time() - start_time)) def f(chunksize): with ensure_clean(self.path,mode='w') as store: @@ -1357,15 +1357,15 @@ def f(chunksize): for c in [10000, 50000, 250000]: start_time = time.time() - print ("big_table2 frame [chunk->%s]" % c) + print("big_table2 frame [chunk->%s]" % c) rows = f(c) - print ("big_table2 frame [rows->%s,chunk->%s] -> %5.2f" - % (rows, c, time.time() - start_time)) + print("big_table2 frame [rows->%s,chunk->%s] -> %5.2f" + % (rows, c, time.time() - start_time)) def test_big_put_frame(self): raise nose.SkipTest('no big put frame') - print ("\nbig_put start") + print("\nbig_put start") import time start_time = time.time() df = DataFrame(np.random.randn(1000 * 1000, 60), index=range(int( @@ -1375,17 +1375,17 @@ def test_big_put_frame(self): for x in range(20): df['datetime%03d' % x] = datetime.datetime(2001, 1, 2, 0, 0) - print ("\nbig_put frame (creation of df) [rows->%s] -> %5.2f" - % (len(df.index), time.time() - start_time)) + print("\nbig_put frame (creation of df) [rows->%s] -> %5.2f" + % (len(df.index), time.time() - start_time)) with ensure_clean(self.path, mode='w') as store: start_time = time.time() store = HDFStore(self.path, mode='w') store.put('df', df) - print (df.get_dtype_counts()) - print ("big_put frame [shape->%s] -> %5.2f" - % (df.shape, time.time() - start_time)) + print(df.get_dtype_counts()) + print("big_put frame [shape->%s] -> %5.2f" + % (df.shape, time.time() - start_time)) def test_big_table_panel(self): raise nose.SkipTest('no big table panel') @@ -1410,7 +1410,7 @@ def test_big_table_panel(self): recons = store.select('wp') assert isinstance(recons, Panel) - print ("\nbig_table panel [%s] -> %5.2f" % (rows, time.time() - x)) + print("\nbig_table panel [%s] -> %5.2f" % (rows, time.time() - x)) def test_append_diff_item_order(self): diff --git a/pandas/io/wb.py b/pandas/io/wb.py index 7c50c0b41e897..a585cb9adccbb 100644 --- a/pandas/io/wb.py +++ b/pandas/io/wb.py @@ -68,7 +68,7 @@ def download(country=['MX', 'CA', 'US'], indicator=['GDPPCKD', 'GDPPCKN'], # Warn if len(bad_indicators) > 0: print('Failed to obtain indicator(s): %s' % '; '.join(bad_indicators)) - print ('The data may still be available for download at http://data.worldbank.org') + print('The data may still be available for download at http://data.worldbank.org') if len(bad_countries) > 0: print('Invalid ISO-2 codes: %s' % ' '.join(bad_countries)) # Merge WDI series diff --git a/pandas/stats/interface.py b/pandas/stats/interface.py index d93eb83820822..6d7bf329b4bee 100644 --- a/pandas/stats/interface.py +++ b/pandas/stats/interface.py @@ -64,7 +64,7 @@ def ols(**kwargs): # Run rolling simple OLS with window of size 10. result = ols(y=y, x=x, window_type='rolling', window=10) - print result.beta + print(result.beta) result = ols(y=y, x=x, nw_lags=1) diff --git a/pandas/stats/math.py b/pandas/stats/math.py index 64548b90dade8..505415bebf89e 100644 --- a/pandas/stats/math.py +++ b/pandas/stats/math.py @@ -84,9 +84,9 @@ def newey_west(m, max_lags, nobs, df, nw_overlap=False): if nw_overlap and not is_psd(Xeps): new_max_lags = int(np.ceil(max_lags * 1.5)) -# print ('nw_overlap is True and newey_west generated a non positive ' -# 'semidefinite matrix, so using newey_west with max_lags of %d.' -# % new_max_lags) +# print('nw_overlap is True and newey_west generated a non positive ' +# 'semidefinite matrix, so using newey_west with max_lags of %d.' +# % new_max_lags) return newey_west(m, new_max_lags, nobs, df) return Xeps diff --git a/pandas/stats/plm.py b/pandas/stats/plm.py index 2c4e4c47c684a..450ddac78e06a 100644 --- a/pandas/stats/plm.py +++ b/pandas/stats/plm.py @@ -58,7 +58,7 @@ def __init__(self, y, x, weights=None, intercept=True, nw_lags=None, def log(self, msg): if self._verbose: # pragma: no cover - print (msg) + print(msg) def _prepare_data(self): """Cleans and stacks input data into DataFrame objects diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 7b753f5d6a367..cc8f8e91b928e 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -9600,7 +9600,7 @@ def _check_stat_op(self, name, alternative, frame=None, has_skipna=True, if not ('max' in name or 'min' in name or 'count' in name): df = DataFrame({'b': date_range('1/1/2001', periods=2)}) _f = getattr(df, name) - print (df) + print(df) self.assertFalse(len(_f())) df['a'] = lrange(len(df)) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 4bd44fcf26bb3..02eb4015c133f 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -504,9 +504,9 @@ def test_agg_item_by_item_raise_typeerror(self): df = DataFrame(randint(10, size=(20, 10))) def raiseException(df): - print ('----------------------------------------') - print(df.to_string()) - raise TypeError + print('----------------------------------------') + print(df.to_string()) + raise TypeError self.assertRaises(TypeError, df.groupby(0).agg, raiseException) diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index be0c5dfad9071..20d42f7211f55 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -86,7 +86,7 @@ def resample(self, obj): offset = to_offset(self.freq) if offset.n > 1: if self.kind == 'period': # pragma: no cover - print ('Warning: multiple of frequency -> timestamps') + print('Warning: multiple of frequency -> timestamps') # Cannot have multiple of periods, convert to timestamp self.kind = 'timestamp' diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index dd78bea385c61..5dda1a9b352d9 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -21,7 +21,7 @@ raise Exception('dateutil 2.0 incompatible with Python 2.x, you must ' 'install version 1.5 or 2.1+!') except ImportError: # pragma: no cover - print ('Please install python-dateutil via easy_install or some method!') + print('Please install python-dateutil via easy_install or some method!') raise # otherwise a 2nd import won't show the message diff --git a/scripts/bench_join.py b/scripts/bench_join.py index 5e50e8da61fdb..c9f2475566519 100644 --- a/scripts/bench_join.py +++ b/scripts/bench_join.py @@ -133,7 +133,7 @@ def do_left_join_frame(a, b): # a = np.array([1, 2, 3, 4, 5], dtype=np.int64) # b = np.array([0, 3, 5, 7, 9], dtype=np.int64) -# print lib.inner_join_indexer(a, b) +# print(lib.inner_join_indexer(a, b)) out = np.empty((10, 120000)) diff --git a/scripts/git-mrb b/scripts/git-mrb index 5b48cd9c50b6b..c15e6dbf9f51a 100644 --- a/scripts/git-mrb +++ b/scripts/git-mrb @@ -26,7 +26,7 @@ import sys def sh(cmd): cmd = cmd.format(**shvars) - print '$', cmd + print('$', cmd) check_call(cmd, shell=True) #----------------------------------------------------------------------------- @@ -46,7 +46,7 @@ try: except: import traceback as tb tb.print_exc() - print __doc__ + print(__doc__) sys.exit(1) onto = argv[1] if narg >= 2 else 'master' @@ -65,7 +65,7 @@ sh('git fetch {remote}') sh('git checkout -b {branch_spec} {onto}') sh('git merge {remote}/{branch}') -print """ +print(""" ************************************************************* Run test suite. If tests pass, run the following to merge: @@ -74,7 +74,7 @@ git merge {branch_spec} git push {upstream} {onto} ************************************************************* -""".format(**shvars) +""".format(**shvars)) ans = raw_input("Revert to master and delete temporary branch? [Y/n]: ") if ans.strip().lower() in ('', 'y', 'yes'): diff --git a/scripts/groupby_test.py b/scripts/groupby_test.py index 3425f0cd98723..5acf7da7534a3 100644 --- a/scripts/groupby_test.py +++ b/scripts/groupby_test.py @@ -21,15 +21,15 @@ dtype=object) shape, labels, idicts = gp.labelize(key1, key2) -print tseries.group_labels(key1) +print(tseries.group_labels(key1)) -# print shape -# print labels -# print idicts +# print(shape) +# print(labels) +# print(idicts) result = tseries.group_aggregate(values, labels, shape) -print tseries.groupby_indices(key2) +print(tseries.groupby_indices(key2)) df = DataFrame({'key1' : key1, 'key2' : key2, @@ -43,7 +43,7 @@ # r2 = gp.multi_groupby(df, np.sum, k1, k2) -# print result +# print(result) gen = gp.generate_groups(df['v1'], labels, shape, axis=1, factory=DataFrame) @@ -51,8 +51,8 @@ res = defaultdict(dict) for a, gen1 in gen: for b, group in gen1: - print a, b - print group + print(a, b) + print(group) # res[b][a] = group['values'].sum() res[b][a] = group.sum() @@ -82,10 +82,10 @@ # exp = DataFrame(expd).T.stack() # result = grouped.sum()['C'] -# print 'wanted' -# print exp -# print 'got' -# print result +# print('wanted') +# print(exp) +# print('got') +# print(result) # tm.N = 10000 diff --git a/scripts/json_manip.py b/scripts/json_manip.py index 72d0bbb34d6b6..7ff4547825568 100644 --- a/scripts/json_manip.py +++ b/scripts/json_manip.py @@ -205,9 +205,9 @@ def _denorm(queries,thing): fields = [] results = [] for q in queries: - #print q + #print(q) r = Ql(q,thing) - #print "-- result: ", r + #print("-- result: ", r) if not r: r = [default] if isinstance(r[0], type({})): @@ -217,15 +217,15 @@ def _denorm(queries,thing): results.append(r) - #print results - #print fields + #print(results) + #print(fields) flist = list(flatten(*map(iter,fields))) prod = itertools.product(*results) for p in prod: U = dict() for (ii,thing) in enumerate(p): - #print ii,thing + #print(ii,thing) if isinstance(thing, type({})): U.update(thing) else: @@ -285,7 +285,7 @@ def _Q(filter_, thing): T = type(thing) if isinstance({}, T): for k,v in compat.iteritems(thing): - #print k,v + #print(k,v) if filter_ == k: if isinstance(v, type([])): yield iter(v) @@ -297,7 +297,7 @@ def _Q(filter_, thing): elif isinstance([], T): for k in thing: - #print k + #print(k) yield Q(filter_,k) else: @@ -321,9 +321,9 @@ def Q(filter_,thing): return flatten(*[_Q(x,thing) for x in filter_]) elif isinstance(filter_, type({})): d = dict.fromkeys(list(filter_.keys())) - #print d + #print(d) for k in d: - #print flatten(Q(k,thing)) + #print(flatten(Q(k,thing))) d[k] = Q(k,thing) return d @@ -380,32 +380,32 @@ def printout(queries,things,default=None, f=sys.stdout, **kwargs): fields = set(itertools.chain(*(x.keys() for x in results))) W = csv.DictWriter(f=f,fieldnames=fields,**kwargs) - #print "---prod---" - #print list(prod) + #print("---prod---") + #print(list(prod)) W.writeheader() for r in results: W.writerow(r) def test_run(): - print("\n>>> print list(Q('url',ex1))") + print("\n>>> print(list(Q('url',ex1)))") print(list(Q('url',ex1))) assert list(Q('url',ex1)) == ['url1','url2','url3'] assert Ql('url',ex1) == ['url1','url2','url3'] - print("\n>>> print list(Q(['name','id'],ex1))") + print("\n>>> print(list(Q(['name','id'],ex1)))") print(list(Q(['name','id'],ex1))) assert Ql(['name','id'],ex1) == ['Gregg','hello','gbye'] - print("\n>>> print Ql('more url',ex1)") + print("\n>>> print(Ql('more url',ex1))") print(Ql('more url',ex1)) print("\n>>> list(Q('extensions',ex1))") print(list(Q('extensions',ex1))) - print("\n>>> print Ql('extensions',ex1)") + print("\n>>> print(Ql('extensions',ex1))") print(Ql('extensions',ex1)) print("\n>>> printout(['name','extensions'],[ex1,], extrasaction='ignore')") diff --git a/scripts/use_build_cache.py b/scripts/use_build_cache.py index 361ac59e5e852..f8c2df2a8a45d 100755 --- a/scripts/use_build_cache.py +++ b/scripts/use_build_cache.py @@ -39,7 +39,7 @@ class Foo(object): args = Foo() # for 2.6, no argparse -#print args.accumulate(args.integers) +#print(args.accumulate(args.integers)) shim=""" import os diff --git a/vb_suite/suite.py b/vb_suite/suite.py index f3c8dfe3032e0..57920fcbf7c19 100644 --- a/vb_suite/suite.py +++ b/vb_suite/suite.py @@ -92,15 +92,15 @@ def generate_rst_files(benchmarks): fig_base_path = os.path.join(vb_path, 'figures') if not os.path.exists(vb_path): - print 'creating %s' % vb_path + print('creating %s' % vb_path) os.makedirs(vb_path) if not os.path.exists(fig_base_path): - print 'creating %s' % fig_base_path + print('creating %s' % fig_base_path) os.makedirs(fig_base_path) for bmk in benchmarks: - print 'Generating rst file for %s' % bmk.name + print('Generating rst file for %s' % bmk.name) rst_path = os.path.join(RST_BASE, 'vbench/%s.txt' % bmk.name) fig_full_path = os.path.join(fig_base_path, '%s.png' % bmk.name) diff --git a/vb_suite/test_perf.py b/vb_suite/test_perf.py index c1a91786ab6d0..bb3a0d123f9b1 100755 --- a/vb_suite/test_perf.py +++ b/vb_suite/test_perf.py @@ -216,7 +216,7 @@ def profile_comparative(benchmarks): # ARGH. reparse the repo, without discarding any commits, # then overwrite the previous parse results - # prprint ("Slaughtering kittens..." ) + # prprint("Slaughtering kittens...") (repo.shas, repo.messages, repo.timestamps, repo.authors) = _parse_commit_log(None,REPO_PATH, args.base_commit)
Changed all print statements in comments docstrings and supporting scripts to print "function calls".
https://api.github.com/repos/pandas-dev/pandas/pulls/4973
2013-09-24T21:23:23Z
2013-09-24T22:43:17Z
2013-09-24T22:43:17Z
2014-06-14T18:07:23Z
CLN: change print to print() in docs
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index fad62c1a17deb..ce42ee3b7bc88 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -860,7 +860,7 @@ Thus, for example: .. ipython:: In [0]: for col in df: - ...: print col + ...: print(col) ...: iteritems @@ -878,8 +878,8 @@ For example: .. ipython:: In [0]: for item, frame in wp.iteritems(): - ...: print item - ...: print frame + ...: print(item) + ...: print(frame) ...: @@ -895,7 +895,7 @@ containing the data in each row: .. ipython:: In [0]: for row_index, row in df2.iterrows(): - ...: print '%s\n%s' % (row_index, row) + ...: print('%s\n%s' % (row_index, row)) ...: For instance, a contrived way to transpose the dataframe would be: @@ -903,11 +903,11 @@ For instance, a contrived way to transpose the dataframe would be: .. ipython:: python df2 = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]}) - print df2 - print df2.T + print(df2) + print(df2.T) df2_t = DataFrame(dict((idx,values) for idx, values in df2.iterrows())) - print df2_t + print(df2_t) .. note:: @@ -918,8 +918,8 @@ For instance, a contrived way to transpose the dataframe would be: df_iter = DataFrame([[1, 1.0]], columns=['x', 'y']) row = next(df_iter.iterrows())[1] - print row['x'].dtype - print df_iter['x'].dtype + print(row['x'].dtype) + print(df_iter['x'].dtype) itertuples ~~~~~~~~~~ @@ -932,7 +932,8 @@ For instance, .. ipython:: python - for r in df2.itertuples(): print r + for r in df2.itertuples(): + print(r) .. _basics.string_methods: diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index c2e5aae80f978..08ef25b178af9 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -586,7 +586,7 @@ R package): .. ipython:: python baseball = read_csv('data/baseball.csv') - print baseball + print(baseball) .. ipython:: python :suppress: @@ -599,7 +599,7 @@ DataFrame in tabular form, though it won't always fit the console width: .. ipython:: python - print baseball.iloc[-20:, :12].to_string() + print(baseball.iloc[-20:, :12].to_string()) New since 0.10.0, wide DataFrames will now be printed across multiple rows by default: diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst index 909aa5e2e4c97..58eb6dccfc967 100644 --- a/doc/source/gotchas.rst +++ b/doc/source/gotchas.rst @@ -372,7 +372,7 @@ of the new set of columns rather than the original ones: .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) date_spec = {'nominal': [1, 2], 'actual': [1, 3]} df = read_csv('tmp.csv', header=None, diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index 98d3d702e24d8..a8900bd83309f 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -282,8 +282,8 @@ natural and functions similarly to ``itertools.groupby``: In [4]: grouped = df.groupby('A') In [5]: for name, group in grouped: - ...: print name - ...: print group + ...: print(name) + ...: print(group) ...: In the case of grouping by multiple keys, the group name will be a tuple: @@ -291,8 +291,8 @@ In the case of grouping by multiple keys, the group name will be a tuple: .. ipython:: In [5]: for name, group in df.groupby(['A', 'B']): - ...: print name - ...: print group + ...: print(name) + ...: print(group) ...: It's standard Python-fu but remember you can unpack the tuple in the for loop diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index a8b9a4be01ae8..9f238c22850b7 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -1149,7 +1149,7 @@ and stop are **inclusive** in the label-based case: .. ipython:: python date1, date2 = dates[[2, 4]] - print date1, date2 + print(date1, date2) df.ix[date1:date2] df['A'].ix[date1:date2] @@ -1211,10 +1211,10 @@ scalar values, though setting arbitrary vectors is not yet supported: df2 = df[:4] df2['foo'] = 'bar' - print df2 + print(df2) df2.ix[2] = np.nan - print df2 - print df2.dtypes + print(df2) + print(df2.dtypes) .. _indexing.view_versus_copy: @@ -1639,13 +1639,13 @@ instance: midx = MultiIndex(levels=[['zero', 'one'], ['x','y']], labels=[[1,1,0,0],[1,0,1,0]]) df = DataFrame(randn(4,2), index=midx) - print df + print(df) df2 = df.mean(level=0) - print df2 - print df2.reindex(df.index, level=0) + print(df2) + print(df2.reindex(df.index, level=0)) df_aligned, df2_aligned = df.align(df2, level=0) - print df_aligned - print df2_aligned + print(df_aligned) + print(df2_aligned) The need for sortedness with :class:`~pandas.MultiIndex` diff --git a/doc/source/io.rst b/doc/source/io.rst index a0e41a96181a2..f31b4043da370 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -167,7 +167,7 @@ Consider a typical CSV file containing, in this case, some time series data: .. ipython:: python - print open('foo.csv').read() + print(open('foo.csv').read()) The default for `read_csv` is to create a DataFrame with simple numbered rows: @@ -209,7 +209,7 @@ Suppose you had data with unenclosed quotes: .. ipython:: python - print data + print(data) By default, ``read_csv`` uses the Excel dialect and treats the double quote as the quote character, which causes it to fail when it finds a newline before it @@ -236,7 +236,7 @@ after a delimiter: .. ipython:: python data = 'a, b, c\n1, 2, 3\n4, 5, 6' - print data + print(data) pd.read_csv(StringIO(data), skipinitialspace=True) The parsers make every attempt to "do the right thing" and not be very @@ -255,7 +255,7 @@ individual columns: .. ipython:: python data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9' - print data + print(data) df = pd.read_csv(StringIO(data), dtype=object) df @@ -275,7 +275,7 @@ used as the column names: from StringIO import StringIO data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9' - print data + print(data) pd.read_csv(StringIO(data)) By specifying the ``names`` argument in conjunction with ``header`` you can @@ -284,7 +284,7 @@ any): .. ipython:: python - print data + print(data) pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0) pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None) @@ -356,7 +356,7 @@ index column inference and discard the last column, pass ``index_col=False``: .. ipython:: python data = 'a,b,c\n4,apple,bat,\n8,orange,cow,' - print data + print(data) pd.read_csv(StringIO(data)) pd.read_csv(StringIO(data), index_col=False) @@ -411,7 +411,7 @@ column names: .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]]) df @@ -499,7 +499,7 @@ DD/MM/YYYY instead. For convenience, a ``dayfirst`` keyword is provided: .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) pd.read_csv('tmp.csv', parse_dates=[0]) pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0]) @@ -527,7 +527,7 @@ By default, numbers with a thousands separator will be parsed as strings .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) df = pd.read_csv('tmp.csv', sep='|') df @@ -537,7 +537,7 @@ The ``thousands`` keyword allows integers to be parsed correctly .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) df = pd.read_csv('tmp.csv', sep='|', thousands=',') df @@ -614,7 +614,7 @@ Sometimes comments or meta data may be included in a file: .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) By default, the parse includes the comments in the output: @@ -654,7 +654,7 @@ as a ``Series``: .. ipython:: python - print open('tmp.csv').read() + print(open('tmp.csv').read()) output = pd.read_csv('tmp.csv', squeeze=True) output @@ -679,7 +679,7 @@ options: .. ipython:: python data= 'a,b,c\n1,Yes,2\n3,No,4' - print data + print(data) pd.read_csv(StringIO(data)) pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No']) @@ -730,7 +730,7 @@ should pass the ``escapechar`` option: .. ipython:: python data = 'a,b\n"hello, \\"Bob\\", nice to see you",5' - print data + print(data) pd.read_csv(StringIO(data), escapechar='\\') .. _io.fwf: @@ -763,7 +763,7 @@ Consider a typical fixed-width data file: .. ipython:: python - print open('bar.csv').read() + print(open('bar.csv').read()) In order to parse this file into a DataFrame, we simply need to supply the column specifications to the `read_fwf` function along with the file name: @@ -809,7 +809,7 @@ column: .. ipython:: python - print open('foo.csv').read() + print(open('foo.csv').read()) In this special case, ``read_csv`` assumes that the first column is to be used as the index of the DataFrame: @@ -841,7 +841,7 @@ Suppose you have data indexed by two columns: .. ipython:: python - print open('data/mindex_ex.csv').read() + print(open('data/mindex_ex.csv').read()) The ``index_col`` argument to ``read_csv`` and ``read_table`` can take a list of column numbers to turn multiple columns into a ``MultiIndex`` for the index of the @@ -868,7 +868,7 @@ of tupleizing columns, specify ``tupleize_cols=True``. from pandas.util.testing import makeCustomDataframe as mkdf df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4) df.to_csv('mi.csv') - print open('mi.csv').read() + print(open('mi.csv').read()) pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1]) Note: If an ``index_col`` is not specified (e.g. you don't have an index, or wrote it @@ -898,7 +898,7 @@ class of the csv module. .. ipython:: python - print open('tmp2.sv').read() + print(open('tmp2.sv').read()) pd.read_csv('tmp2.sv') .. _io.chunking: @@ -912,7 +912,7 @@ rather than reading the entire file into memory, such as the following: .. ipython:: python - print open('tmp.sv').read() + print(open('tmp.sv').read()) table = pd.read_table('tmp.sv', sep='|') table @@ -926,7 +926,7 @@ value will be an iterable object of type ``TextFileReader``: reader for chunk in reader: - print chunk + print(chunk) Specifying ``iterator=True`` will also return the ``TextFileReader`` object: @@ -1333,7 +1333,7 @@ Specify an HTML attribute dfs1 = read_html(url, attrs={'id': 'table'}) dfs2 = read_html(url, attrs={'class': 'sortable'}) - print np.array_equal(dfs1[0], dfs2[0]) # Should be True + print(np.array_equal(dfs1[0], dfs2[0])) # Should be True Use some combination of the above @@ -1400,7 +1400,7 @@ in the method ``to_string`` described above. df = DataFrame(randn(2, 2)) df - print df.to_html() # raw html + print(df.to_html()) # raw html .. ipython:: python :suppress: @@ -1416,7 +1416,7 @@ The ``columns`` argument will limit the columns shown .. ipython:: python - print df.to_html(columns=[0]) + print(df.to_html(columns=[0])) .. ipython:: python :suppress: @@ -1433,7 +1433,7 @@ point values .. ipython:: python - print df.to_html(float_format='{0:.10f}'.format) + print(df.to_html(float_format='{0:.10f}'.format)) .. ipython:: python :suppress: @@ -1450,7 +1450,7 @@ off .. ipython:: python - print df.to_html(bold_rows=False) + print(df.to_html(bold_rows=False)) .. ipython:: python :suppress: @@ -1466,7 +1466,7 @@ table CSS classes. Note that these classes are *appended* to the existing .. ipython:: python - print df.to_html(classes=['awesome_table_class', 'even_more_awesome_class']) + print(df.to_html(classes=['awesome_table_class', 'even_more_awesome_class'])) Finally, the ``escape`` argument allows you to control whether the "<", ">" and "&" characters escaped in the resulting HTML (by default it is @@ -1487,7 +1487,7 @@ Escaped: .. ipython:: python - print df.to_html() + print(df.to_html()) .. raw:: html :file: _static/escape.html @@ -1496,7 +1496,7 @@ Not escaped: .. ipython:: python - print df.to_html(escape=False) + print(df.to_html(escape=False)) .. raw:: html :file: _static/noescape.html @@ -1746,7 +1746,7 @@ for some advanced strategies .. ipython:: python store = HDFStore('store.h5') - print store + print(store) Objects can be written to the file just like adding key-value pairs to a dict: @@ -2209,7 +2209,7 @@ The default is 50,000 rows returned in a chunk. .. ipython:: python for df in store.select('df', chunksize=3): - print df + print(df) .. note:: @@ -2221,7 +2221,7 @@ The default is 50,000 rows returned in a chunk. .. code-block:: python for df in read_hdf('store.h5','df', chunsize=3): - print df + print(df) Note, that the chunksize keyword applies to the **returned** rows. So if you are doing a query, then that set will be subdivided and returned in the diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index d375b3da38d82..79a87cb49f027 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -74,8 +74,8 @@ DataFrames into the equivalent R object (that is, **data.frame**): index=["one", "two", "three"]) r_dataframe = com.convert_to_r_dataframe(df) - print type(r_dataframe) - print r_dataframe + print(type(r_dataframe)) + print(r_dataframe) The DataFrame's index is stored as the ``rownames`` attribute of the @@ -90,8 +90,8 @@ R matrices bear no information on the data type): r_matrix = com.convert_to_r_matrix(df) - print type(r_matrix) - print r_matrix + print(type(r_matrix)) + print(r_matrix) Calling R functions with pandas objects diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index 178ac0fce55dc..b950876738852 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -126,7 +126,7 @@ Bank's servers: In [3]: dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2005, end=2008) - In [4]: print dat + In [4]: print(dat) NY.GDP.PCAP.KD country year Canada 2008 36005.5004978584 @@ -175,7 +175,7 @@ Notice that this second search was much faster than the first one because In [13]: ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS'] In [14]: dat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna() In [15]: dat.columns = ['gdp', 'cellphone'] - In [16]: print dat.tail() + In [16]: print(dat.tail()) gdp cellphone country year Swaziland 2011 2413.952853 94.9 @@ -193,7 +193,7 @@ populations in rich countries tend to use cellphones at a higher rate: In [17]: import numpy as np In [18]: import statsmodels.formula.api as smf In [19]: mod = smf.ols("cellphone ~ np.log(gdp)", dat).fit() - In [20]: print mod.summary() + In [20]: print(mod.summary()) OLS Regression Results ============================================================================== Dep. Variable: cellphone R-squared: 0.297 diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst index 99af4afc71a66..5dedfa1ad144d 100644 --- a/doc/source/reshaping.rst +++ b/doc/source/reshaping.rst @@ -287,7 +287,7 @@ calling ``to_string`` if you wish: .. ipython:: python table = pivot_table(df, rows=['A', 'B'], cols=['C']) - print table.to_string(na_rep='') + print(table.to_string(na_rep='')) Note that ``pivot_table`` is also available as an instance method on DataFrame. diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index 5dbf1ce77bad8..bcb738d8a89cb 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -514,10 +514,10 @@ calendars which account for local holidays and local weekend conventions. holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')] bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt) dt = datetime(2013, 4, 30) - print dt + 2 * bday_egypt + print(dt + 2 * bday_egypt) dts = date_range(dt, periods=5, freq=bday_egypt).to_series() - print dts - print Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split())) + print(dts) + print(Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split()))) .. note:: diff --git a/doc/source/v0.10.0.txt b/doc/source/v0.10.0.txt index 0c86add1225ad..2e59c420fbd01 100644 --- a/doc/source/v0.10.0.txt +++ b/doc/source/v0.10.0.txt @@ -111,7 +111,7 @@ Note: .. ipython:: python data= 'a,b,c\n1,Yes,2\n3,No,4' - print data + print(data) pd.read_csv(StringIO(data), header=None) pd.read_csv(StringIO(data), header=None, prefix='X') @@ -121,7 +121,7 @@ Note: .. ipython:: python - print data + print(data) pd.read_csv(StringIO(data)) pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No']) diff --git a/doc/source/v0.12.0.txt b/doc/source/v0.12.0.txt index beb62df505a37..43b5479159b38 100644 --- a/doc/source/v0.12.0.txt +++ b/doc/source/v0.12.0.txt @@ -188,10 +188,10 @@ I/O Enhancements .. ipython :: python df = DataFrame({'a': range(3), 'b': list('abc')}) - print df + print(df) html = df.to_html() alist = pd.read_html(html, infer_types=True, index_col=0) - print df == alist[0] + print(df == alist[0]) Note that ``alist`` here is a Python ``list`` so ``pd.read_html()`` and ``DataFrame.to_html()`` are not inverses. @@ -237,7 +237,7 @@ I/O Enhancements from pandas.util.testing import makeCustomDataframe as mkdf df = mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4) df.to_csv('mi.csv',tupleize_cols=False) - print open('mi.csv').read() + print(open('mi.csv').read()) pd.read_csv('mi.csv',header=[0,1,2,3],index_col=[0,1],tupleize_cols=False) .. ipython:: python @@ -256,7 +256,7 @@ I/O Enhancements path = 'store_iterator.h5' DataFrame(randn(10,2)).to_hdf(path,'df',table=True) for df in read_hdf(path,'df', chunksize=3): - print df + print(df) .. ipython:: python :suppress: @@ -376,9 +376,9 @@ Experimental Features holidays = ['2012-05-01', datetime(2013, 5, 1), np.datetime64('2014-05-01')] bday_egypt = CustomBusinessDay(holidays=holidays, weekmask=weekmask_egypt) dt = datetime(2013, 4, 30) - print dt + 2 * bday_egypt + print(dt + 2 * bday_egypt) dts = date_range(dt, periods=5, freq=bday_egypt).to_series() - print Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split())) + print(Series(dts.weekday, dts).map(Series('Mon Tue Wed Thu Fri Sat Sun'.split()))) Bug Fixes ~~~~~~~~~ @@ -404,7 +404,7 @@ Bug Fixes ds = Series(strs) for s in ds.str: - print s + print(s) s s.dropna().values.item() == 'w' diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index bc16f549f0cf1..bda6fa4cdf021 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -161,7 +161,7 @@ HDFStore API Changes df.to_hdf(path,'df_table2',append=True) df.to_hdf(path,'df_fixed') with get_store(path) as store: - print store + print(store) .. ipython:: python :suppress:
closes #4967 `print` statements refactoring
https://api.github.com/repos/pandas-dev/pandas/pulls/4972
2013-09-24T20:06:41Z
2013-09-24T20:35:53Z
2013-09-24T20:35:53Z
2014-07-16T08:30:39Z
API: raise a TypeError on invalid comparison ops on Series (e.g. integer/datetime) GH4968
diff --git a/doc/source/release.rst b/doc/source/release.rst index 285cea7938f91..b95509f70f56f 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -225,6 +225,7 @@ API Changes - moved timedeltas support to pandas.tseries.timedeltas.py; add timedeltas string parsing, add top-level ``to_timedelta`` function - ``NDFrame`` now is compatible with Python's toplevel ``abs()`` function (:issue:`4821`). + - raise a ``TypeError`` on invalid comparison ops on Series/DataFrame (e.g. integer/datetime) (:issue:`4968`) Internal Refactoring ~~~~~~~~~~~~~~~~~~~~ diff --git a/pandas/core/series.py b/pandas/core/series.py index 9f7ab0cb0346b..942bb700a3718 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -324,7 +324,13 @@ def na_op(x, y): else: result = lib.scalar_compare(x, y, op) else: - result = op(x, y) + + try: + result = getattr(x,name)(y) + if result is NotImplemented: + raise TypeError("invalid type comparison") + except (AttributeError): + result = op(x, y) return result diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 7b753f5d6a367..04ee6abcbac18 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4296,6 +4296,31 @@ def test_operators_none_as_na(self): result = op(df.fillna(7), df) assert_frame_equal(result, expected) + def test_comparison_invalid(self): + + def check(df,df2): + + for (x, y) in [(df,df2),(df2,df)]: + self.assertRaises(TypeError, lambda : x == y) + self.assertRaises(TypeError, lambda : x != y) + self.assertRaises(TypeError, lambda : x >= y) + self.assertRaises(TypeError, lambda : x > y) + self.assertRaises(TypeError, lambda : x < y) + self.assertRaises(TypeError, lambda : x <= y) + + # GH4968 + # invalid date/int comparisons + df = DataFrame(np.random.randint(10, size=(10, 1)), columns=['a']) + df['dates'] = date_range('20010101', periods=len(df)) + + df2 = df.copy() + df2['dates'] = df['a'] + check(df,df2) + + df = DataFrame(np.random.randint(10, size=(10, 2)), columns=['a', 'b']) + df2 = DataFrame({'a': date_range('20010101', periods=len(df)), 'b': date_range('20100101', periods=len(df))}) + check(df,df2) + def test_modulo(self): # GH3590, modulo as ints diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index b2c5782d56b1f..6d3b052154147 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -2663,6 +2663,21 @@ def test_comparison_object_numeric_nas(self): expected = f(s.astype(float), shifted.astype(float)) assert_series_equal(result, expected) + def test_comparison_invalid(self): + + # GH4968 + # invalid date/int comparisons + s = Series(range(5)) + s2 = Series(date_range('20010101', periods=5)) + + for (x, y) in [(s,s2),(s2,s)]: + self.assertRaises(TypeError, lambda : x == y) + self.assertRaises(TypeError, lambda : x != y) + self.assertRaises(TypeError, lambda : x >= y) + self.assertRaises(TypeError, lambda : x > y) + self.assertRaises(TypeError, lambda : x < y) + self.assertRaises(TypeError, lambda : x <= y) + def test_more_na_comparisons(self): left = Series(['a', np.nan, 'c']) right = Series(['a', np.nan, 'd'])
closes #4968
https://api.github.com/repos/pandas-dev/pandas/pulls/4970
2013-09-24T19:26:03Z
2013-09-24T20:28:02Z
2013-09-24T20:28:02Z
2014-07-02T07:32:26Z
BUG: fix skiprows option for python parser in read_csv
diff --git a/doc/source/release.rst b/doc/source/release.rst index 285cea7938f91..9c2032212e3c8 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -457,6 +457,7 @@ Bug Fixes weren't strings (:issue:`4956`) - Fixed ``copy()`` to shallow copy axes/indices as well and thereby keep separate metadata. (:issue:`4202`, :issue:`4830`) + - Fixed skiprows option in Python parser for read_csv (:issue:`4382`) pandas 0.12.0 ------------- diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 7b9347a821fad..380fd04fb4433 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -1283,7 +1283,6 @@ def __init__(self, f, **kwds): # needs to be cleaned/refactored # multiple date column thing turning into a real spaghetti factory - if not self._has_complex_date_col: (index_names, self.orig_names, _) = self._get_index_name(self.columns) @@ -1561,8 +1560,6 @@ def _get_index_name(self, columns): except StopIteration: next_line = None - index_name = None - # implicitly index_col=0 b/c 1 fewer column names implicit_first_cols = 0 if line is not None: @@ -1647,11 +1644,20 @@ def _get_lines(self, rows=None): if self.pos > len(source): raise StopIteration if rows is None: - lines.extend(source[self.pos:]) - self.pos = len(source) + new_rows = source[self.pos:] + new_pos = len(source) else: - lines.extend(source[self.pos:self.pos + rows]) - self.pos += rows + new_rows = source[self.pos:self.pos + rows] + new_pos = self.pos + rows + + # Check for stop rows. n.b.: self.skiprows is a set. + if self.skiprows: + new_rows = [row for i, row in enumerate(new_rows) + if i + self.pos not in self.skiprows] + + lines.extend(new_rows) + self.pos = new_pos + else: new_rows = [] try: @@ -1673,6 +1679,9 @@ def _get_lines(self, rows=None): raise Exception(msg) raise except StopIteration: + if self.skiprows: + new_rows = [row for i, row in enumerate(new_rows) + if self.pos + i not in self.skiprows] lines.extend(new_rows) if len(lines) == 0: raise diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index fb2b3fdd33bf1..16cc53976e862 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -735,6 +735,14 @@ def test_skiprows_bug(self): tm.assert_frame_equal(data, expected) tm.assert_frame_equal(data, data2) + def test_deep_skiprows(self): + # GH #4382 + text = "a,b,c\n" + "\n".join([",".join([str(i), str(i+1), str(i+2)]) for i in range(10)]) + condensed_text = "a,b,c\n" + "\n".join([",".join([str(i), str(i+1), str(i+2)]) for i in [0, 1, 2, 3, 4, 6, 8, 9]]) + data = self.read_csv(StringIO(text), skiprows=[6, 8]) + condensed_data = self.read_csv(StringIO(condensed_text)) + tm.assert_frame_equal(data, condensed_data) + def test_detect_string_na(self): data = """A,B foo,bar
Closes #4382
https://api.github.com/repos/pandas-dev/pandas/pulls/4969
2013-09-24T19:11:39Z
2013-09-24T20:37:18Z
2013-09-24T20:37:17Z
2014-06-24T14:29:29Z
CLN: do not use mutable default arguments
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 18109e8c612b9..6631a3cf8c6f1 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -201,8 +201,8 @@ def use(self, key, value): def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, - diagonal='hist', marker='.', density_kwds={}, hist_kwds={}, - **kwds): + diagonal='hist', marker='.', density_kwds=None, + hist_kwds=None, **kwds): """ Draw a matrix of scatter plots. @@ -243,6 +243,9 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, marker = _get_marker_compat(marker) + hist_kwds = hist_kwds or {} + density_kwds = density_kwds or {} + for i, a in zip(lrange(n), df.columns): for j, b in zip(lrange(n), df.columns): ax = axes[i, j]
https://api.github.com/repos/pandas-dev/pandas/pulls/4966
2013-09-24T18:26:28Z
2013-09-24T20:30:03Z
2013-09-24T20:30:03Z
2014-07-16T08:30:25Z
DOC: remote_data.rst: Fama/French punctuation
diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst index bda532317ffe8..178ac0fce55dc 100644 --- a/doc/source/remote_data.rst +++ b/doc/source/remote_data.rst @@ -86,8 +86,8 @@ FRED Fama/French ----------- -Tthe dataset names are listed at `Fama/French Data Library -<http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>`__) +Dataset names are listed at `Fama/French Data Library +<http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>`__. .. ipython:: python
https://api.github.com/repos/pandas-dev/pandas/pulls/4965
2013-09-24T16:24:26Z
2013-09-24T18:12:12Z
2013-09-24T18:12:12Z
2014-07-16T08:30:23Z
GH9570 allow timedelta string conversion without leading zero
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index e1e930ee21efe..dc6093b31633b 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -50,6 +50,8 @@ Enhancements - Allow conversion of values with dtype ``datetime64`` or ``timedelta64`` to strings using ``astype(str)`` (:issue:`9757`) - ``get_dummies`` function now accepts ``sparse`` keyword. If set to ``True``, the return ``DataFrame`` is sparse, e.g. ``SparseDataFrame``. (:issue:`8823`) +- Allow timedelta string conversion when leading zero is missing from time definition, ie `0:00:00` vs `00:00:00`. (:issue:`9570`) + .. _whatsnew_0161.api: API changes diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index b74a3a59d3bca..bc51e01ca9bdf 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -64,6 +64,13 @@ def test_construction(self): self.assertEqual(Timedelta(123072001000000).value, 123072001000000) self.assertTrue('1 days 10:11:12.001' in str(Timedelta(123072001000000))) + # string conversion with/without leading zero + # GH 9570 + self.assertEqual(Timedelta('0:00:00'), timedelta(hours=0)) + self.assertEqual(Timedelta('00:00:00'), timedelta(hours=0)) + self.assertEqual(Timedelta('-1:00:00'), -timedelta(hours=1)) + self.assertEqual(Timedelta('-01:00:00'), -timedelta(hours=1)) + # more strings # GH 8190 self.assertEqual(Timedelta('1 h'), timedelta(hours=1)) diff --git a/pandas/tseries/timedeltas.py b/pandas/tseries/timedeltas.py index 91e75da1b551c..5b353058f0093 100644 --- a/pandas/tseries/timedeltas.py +++ b/pandas/tseries/timedeltas.py @@ -119,7 +119,7 @@ def _validate_timedelta_unit(arg): _short_search = re.compile( "^\s*(?P<neg>-?)\s*(?P<value>\d*\.?\d*)\s*(?P<unit>d|s|ms|us|ns)?\s*$",re.IGNORECASE) _full_search = re.compile( - "^\s*(?P<neg>-?)\s*(?P<days>\d*\.?\d*)?\s*(days|d|day)?,?\s*\+?(?P<time>\d{2}:\d{2}:\d{2})?(?P<frac>\.\d+)?\s*$",re.IGNORECASE) + "^\s*(?P<neg>-?)\s*(?P<days>\d*?\.?\d*?)?\s*(days|d|day)?,?\s*\+?(?P<time>\d{1,2}:\d{2}:\d{2})?(?P<frac>\.\d+)?\s*$",re.IGNORECASE) _nat_search = re.compile( "^\s*(nat|nan)\s*$",re.IGNORECASE) _whitespace = re.compile('^\s*$') @@ -209,13 +209,12 @@ def convert(r=None, unit=None, m=m): is_neg = gd['neg'] if gd['days']: days = int((float(gd['days'] or 0) * 86400)*1e9) - if gd['neg']: + if is_neg: days *= -1 value += days else: - if gd['neg']: + if is_neg: value *= -1 - return tslib.cast_from_unit(value, 'ns') return convert
See https://github.com/pydata/pandas/issues/9570 This commit allows for the `Timedelta` class to take a construction like `Timedelta('0:00:00')` or `Timedelta('00:00:00')`.
https://api.github.com/repos/pandas-dev/pandas/pulls/9868
2015-04-13T14:26:00Z
2015-04-13T16:34:46Z
2015-04-13T16:34:46Z
2015-04-13T16:34:46Z
DOC/CLN: fixed bloolean indexing example, cleaned up typos
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index fc074802353ee..2eabc35fd831d 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -30,9 +30,9 @@ The axis labeling information in pandas objects serves many purposes: In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in -this area. Expect more work to be invested higher-dimensional data structures -(including ``Panel``) in the future, especially in label-based advanced -indexing. +this area. Expect more work to be invested in higher-dimensional data +structures (including ``Panel``) in the future, especially in label-based +advanced indexing. .. note:: @@ -54,7 +54,7 @@ indexing. .. warning:: - In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray`` + In 0.15.0 ``Index`` has internally been refactored to no longer subclass ``ndarray`` but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This should be a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`) @@ -225,9 +225,9 @@ new column. sa.a = 5 sa - dfa.A = list(range(len(dfa.index))) # ok if A already exists + dfa.A = list(range(len(dfa.index))) # ok if A already exists dfa - dfa['A'] = list(range(len(dfa.index))) # use this form to create a new column + dfa['A'] = list(range(len(dfa.index))) # use this form to create a new column dfa .. warning:: @@ -314,7 +314,7 @@ Selection By Label dfl.loc['20130102':'20130104'] pandas provides a suite of methods in order to have **purely label based indexing**. This is a strict inclusion based protocol. -**at least 1** of the labels for which you ask, must be in the index or a ``KeyError`` will be raised! When slicing, the start bound is *included*, **AND** the stop bound is *included*. Integers are valid labels, but they refer to the label **and not the position**. +**At least 1** of the labels for which you ask, must be in the index or a ``KeyError`` will be raised! When slicing, the start bound is *included*, **AND** the stop bound is *included*. Integers are valid labels, but they refer to the label **and not the position**. The ``.loc`` attribute is the primary access method. The following are valid inputs: @@ -578,9 +578,10 @@ Using a boolean vector to index a Series works exactly as in a numpy ndarray: .. ipython:: python + s = Series(range(-3, 4)) + s s[s > 0] - s[(s < 0) & (s > -0.5)] - s[(s < -1) | (s > 1 )] + s[(s < -1) | (s > 0.5)] s[~(s < 0)] You may select rows from a DataFrame using a boolean vector the same length as diff --git a/doc/source/options.rst b/doc/source/options.rst index 7e36f369bc7e7..4b69015353612 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -18,7 +18,7 @@ Overview pandas has an options system that lets you customize some aspects of its behaviour, display-related options being those the user is most likely to adjust. -Options have a full "dotted-style", case-insensitive name (e.g. ``display.max_rows``), +Options have a full "dotted-style", case-insensitive name (e.g. ``display.max_rows``). You can get/set options directly as attributes of the top-level ``options`` attribute: .. ipython:: python @@ -29,7 +29,7 @@ You can get/set options directly as attributes of the top-level ``options`` attr pd.options.display.max_rows There is also an API composed of 5 relevant functions, available directly from the ``pandas`` -namespace, and they are: +namespace: - :func:`~pandas.get_option` / :func:`~pandas.set_option` - get/set the value of a single option. - :func:`~pandas.reset_option` - reset one or more options to their default value. @@ -412,7 +412,7 @@ mode.use_inf_as_null False True means treat None, NaN, -INF, Number Formatting ------------------ -pandas also allow you to set how numbers are displayed in the console. +pandas also allows you to set how numbers are displayed in the console. This option is not set through the ``set_options`` API. Use the ``set_eng_float_format`` function
The boolean indexing example http://pandas.pydata.org/pandas-docs/version/0.16.0/indexing.html#boolean-indexing seems to be broken for a while. The variable `s` is being set in a much earlier part of the page and is not suitable for this example. Also cleaned up a few minor typos.
https://api.github.com/repos/pandas-dev/pandas/pulls/9866
2015-04-13T02:46:35Z
2015-04-13T07:28:03Z
2015-04-13T07:28:03Z
2015-04-13T19:04:07Z
DOC/CLN: fixed several typos in categorical.rst
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index d03e0fb117c5c..11e7fb0fd4117 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -23,11 +23,11 @@ Categorical Data .. versionadded:: 0.15 .. note:: - While there was in `pandas.Categorical` in earlier versions, the ability to use + While there was `pandas.Categorical` in earlier versions, the ability to use categorical data in `Series` and `DataFrame` is new. -This is a introduction to pandas categorical data type, including a short comparison +This is an introduction to pandas categorical data type, including a short comparison with R's ``factor``. `Categoricals` are a pandas data type, which correspond to categorical variables in @@ -276,7 +276,7 @@ Sorting and Order .. warning:: - The default for construction has change in v0.16.0 to ``ordered=False``, from the prior implicit ``ordered=True`` + The default for construction has changed in v0.16.0 to ``ordered=False``, from the prior implicit ``ordered=True`` If categorical data is ordered (``s.cat.ordered == True``), then the order of the categories has a meaning and certain operations are possible. If the categorical is unordered, ``.min()/.max()`` will raise a `TypeError`. @@ -347,15 +347,15 @@ Multi Column Sorting ~~~~~~~~~~~~~~~~~~~~ A categorical dtyped column will partcipate in a multi-column sort in a similar manner to other columns. -The ordering of the categorical is determined by the ``categories`` of that columns. +The ordering of the categorical is determined by the ``categories`` of that column. .. ipython:: python - dfs = DataFrame({'A' : Categorical(list('bbeebbaa'),categories=['e','a','b'],ordered=True), + dfs = DataFrame({'A' : Categorical(list('bbeebbaa'), categories=['e','a','b'], ordered=True), 'B' : [1,2,1,2,2,1,2,1] }) - dfs.sort(['A','B']) + dfs.sort(['A', 'B']) -Reordering the ``categories``, changes a future sort. +Reordering the ``categories`` changes a future sort. .. ipython:: python @@ -380,7 +380,7 @@ categories or a categorical with any list-like object, will raise a TypeError. Any "non-equality" comparisons of categorical data with a `Series`, `np.array`, `list` or categorical data with different categories or ordering will raise an `TypeError` because custom - categories ordering could be interpreted in two ways: one with taking in account the + categories ordering could be interpreted in two ways: one with taking into account the ordering and one without. .. ipython:: python @@ -471,7 +471,7 @@ Data munging ------------ The optimized pandas data access methods ``.loc``, ``.iloc``, ``.ix`` ``.at``, and ``.iat``, -work as normal, the only difference is the return type (for getting) and +work as normal. The only difference is the return type (for getting) and that only values already in `categories` can be assigned. Getting @@ -707,8 +707,8 @@ an ``object`` dtype is a constant times the length of the data. .. note:: - If the number of categories approaches the length of the data, the ``Categorical`` will use nearly (or more) memory than an - equivalent ``object`` dtype representation. + If the number of categories approaches the length of the data, the ``Categorical`` will use nearly the same or + more memory than an equivalent ``object`` dtype representation. .. ipython:: python
https://api.github.com/repos/pandas-dev/pandas/pulls/9863
2015-04-12T18:54:33Z
2015-04-12T21:08:15Z
2015-04-12T21:08:15Z
2015-04-13T19:06:46Z
DOC: add more examples to StringMethods on Index
diff --git a/doc/source/text.rst b/doc/source/text.rst index ee91ea3c166b6..f417f56f51fbc 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -37,6 +37,32 @@ the equivalent (scalar) built-in string methods: idx.str.lstrip() idx.str.rstrip() +The string methods on Index are especially useful for cleaning up or +transforming DataFrame columns. For instance, you may have columns with +leading or trailing whitespace: + +.. ipython:: python + + df = DataFrame(randn(3, 2), columns=[' Column A ', ' Column B '], + index=range(3)) + df + +Since ``df.columns`` is an Index object, we can use the ``.str`` accessor + +.. ipython:: python + + df.columns.str.strip() + df.columns.str.lower() + +These string methods can then be used to clean up the columns as needed. +Here we are removing leading and trailing whitespaces, lowercasing all names, +and replacing any remaining whitespaces with underscores: + +.. ipython:: python + + df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_') + df + Splitting and Replacing Strings -------------------------------
as discussed in https://github.com/pydata/pandas/pull/9667 @jorisvandenbossche please take a look, thanks
https://api.github.com/repos/pandas-dev/pandas/pulls/9858
2015-04-11T21:11:03Z
2015-04-12T08:01:34Z
2015-04-12T08:01:34Z
2015-04-13T19:07:10Z
BUG: plot(kind=hist) results in TypeError if it contains non-numeric data
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index ab57f1fb6ea10..a3fd8ae1b86fb 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -107,6 +107,7 @@ Bug Fixes - Bug in plotting continuously using ``secondary_y`` may not show legend properly. (:issue:`9610`, :issue:`9779`) +- Bug in ``DataFrame.plot(kind="hist")`` results in ``TypeError`` when ``DataFrame`` contains non-numeric columns (:issue:`9853`) - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) - Bug in ``where`` causing incorrect results when upcasting was required (:issue:`9731`) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 36c19cd39f76c..7d489ce66c288 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -678,6 +678,18 @@ def test_hist_df_kwargs(self): ax = df.plot(kind='hist', bins=5) self.assertEqual(len(ax.patches), 10) + @slow + def test_hist_df_with_nonnumerics(self): + # GH 9853 + with tm.RNGContext(1): + df = DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) + df['E'] = ['x', 'y'] * 5 + ax = df.plot(kind='hist', bins=5) + self.assertEqual(len(ax.patches), 20) + + ax = df.plot(kind='hist') # bins=10 + self.assertEqual(len(ax.patches), 40) + @slow def test_hist_legacy(self): _check_plot_works(self.ts.hist) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 358c5b0dd5940..1accc48b0d3c4 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -1948,7 +1948,8 @@ def __init__(self, data, bins=10, bottom=0, **kwargs): def _args_adjust(self): if com.is_integer(self.bins): # create common bin edge - values = np.ravel(self.data.values) + values = self.data.convert_objects()._get_numeric_data() + values = np.ravel(values) values = values[~com.isnull(values)] hist, self.bins = np.histogram(values, bins=self.bins,
``` df = pd.DataFrame(np.random.rand(20, 5), columns=['A', 'B', 'C', 'D', 'E']) df['C'] = ['A', 'B', 'C', 'D'] * 5 df['D'] = df['D'] * 100 df.plot(kind='hist') # TypeError: cannot concatenate 'str' and 'float' objects ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9853
2015-04-11T12:24:14Z
2015-04-11T15:18:00Z
2015-04-11T15:18:00Z
2015-04-11T15:58:20Z
BUG/CLN: Repeated time-series plot may raise TypeError
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index b80e341d4156a..1750ec64025a7 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -108,6 +108,7 @@ Bug Fixes - Bug in plotting continuously using ``secondary_y`` may not show legend properly. (:issue:`9610`, :issue:`9779`) - Bug in ``DataFrame.plot(kind="hist")`` results in ``TypeError`` when ``DataFrame`` contains non-numeric columns (:issue:`9853`) +- Bug where repeated plotting of ``DataFrame`` with a ``DatetimeIndex`` may raise ``TypeError`` (:issue:`9852`) - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) - Bug in ``where`` causing incorrect results when upcasting was required (:issue:`9731`) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 1accc48b0d3c4..6a284e547433a 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -885,28 +885,16 @@ def _iter_data(self, data=None, keep_index=False, fillna=None): if fillna is not None: data = data.fillna(fillna) - from pandas.core.frame import DataFrame - if isinstance(data, (Series, np.ndarray, Index)): - label = self.label if self.label is not None else data.name + if self.sort_columns: + columns = com._try_sort(data.columns) + else: + columns = data.columns + + for col in columns: if keep_index is True: - yield label, data + yield col, data[col] else: - yield label, np.asarray(data) - elif isinstance(data, DataFrame): - if self.sort_columns: - columns = com._try_sort(data.columns) - else: - columns = data.columns - - for col in columns: - # # is this right? - # empty = df[col].count() == 0 - # values = df[col].values if not empty else np.zeros(len(df)) - - if keep_index is True: - yield col, data[col] - else: - yield col, data[col].values + yield col, data[col].values @property def nseries(self): @@ -1006,7 +994,15 @@ def result(self): return self.axes[0] def _compute_plot_data(self): - numeric_data = self.data.convert_objects()._get_numeric_data() + data = self.data + + if isinstance(data, Series): + label = self.kwds.pop('label', None) + if label is None and data.name is None: + label = 'None' + data = data.to_frame(name=label) + + numeric_data = data.convert_objects()._get_numeric_data() try: is_empty = numeric_data.empty @@ -1027,12 +1023,7 @@ def _add_table(self): if self.table is False: return elif self.table is True: - from pandas.core.frame import DataFrame - if isinstance(self.data, Series): - data = DataFrame(self.data, columns=[self.data.name]) - elif isinstance(self.data, DataFrame): - data = self.data - data = data.transpose() + data = self.data.transpose() else: data = self.table ax = self._get_ax(0) @@ -1099,18 +1090,15 @@ def _apply_axis_properties(self, axis, rot=None, fontsize=None): @property def legend_title(self): - if hasattr(self.data, 'columns'): - if not isinstance(self.data.columns, MultiIndex): - name = self.data.columns.name - if name is not None: - name = com.pprint_thing(name) - return name - else: - stringified = map(com.pprint_thing, - self.data.columns.names) - return ','.join(stringified) + if not isinstance(self.data.columns, MultiIndex): + name = self.data.columns.name + if name is not None: + name = com.pprint_thing(name) + return name else: - return None + stringified = map(com.pprint_thing, + self.data.columns.names) + return ','.join(stringified) def _add_legend_handle(self, handle, label, index=None): if not label is None: @@ -1256,12 +1244,10 @@ def _get_ax(self, i): return ax def on_right(self, i): - from pandas.core.frame import DataFrame if isinstance(self.secondary_y, bool): return self.secondary_y - if (isinstance(self.data, DataFrame) and - isinstance(self.secondary_y, (tuple, list, np.ndarray, Index))): + if isinstance(self.secondary_y, (tuple, list, np.ndarray, Index)): return self.data.columns[i] in self.secondary_y def _get_style(self, i, col_name): @@ -1553,16 +1539,14 @@ def __init__(self, data, **kwargs): self.x_compat = bool(self.kwds.pop('x_compat')) def _index_freq(self): - from pandas.core.frame import DataFrame - if isinstance(self.data, (Series, DataFrame)): - freq = getattr(self.data.index, 'freq', None) - if freq is None: - freq = getattr(self.data.index, 'inferred_freq', None) - if freq == 'B': - weekdays = np.unique(self.data.index.dayofweek) - if (5 in weekdays) or (6 in weekdays): - freq = None - return freq + freq = getattr(self.data.index, 'freq', None) + if freq is None: + freq = getattr(self.data.index, 'inferred_freq', None) + if freq == 'B': + weekdays = np.unique(self.data.index.dayofweek) + if (5 in weekdays) or (6 in weekdays): + freq = None + return freq def _is_dynamic_freq(self, freq): if isinstance(freq, DateOffset): @@ -1574,9 +1558,7 @@ def _is_dynamic_freq(self, freq): def _no_base(self, freq): # hack this for 0.10.1, creating more technical debt...sigh - from pandas.core.frame import DataFrame - if (isinstance(self.data, (Series, DataFrame)) - and isinstance(self.data.index, DatetimeIndex)): + if isinstance(self.data.index, DatetimeIndex): base = frequencies.get_freq(freq) x = self.data.index if (base <= frequencies.FreqGroup.FR_DAY): @@ -1686,17 +1668,13 @@ def _update_prior(self, y): def _maybe_convert_index(self, data): # tsplot converts automatically, but don't want to convert index # over and over for DataFrames - from pandas.core.frame import DataFrame - if (isinstance(data.index, DatetimeIndex) and - isinstance(data, DataFrame)): + if isinstance(data.index, DatetimeIndex): freq = getattr(data.index, 'freq', None) if freq is None: freq = getattr(data.index, 'inferred_freq', None) if isinstance(freq, DateOffset): freq = freq.rule_code - freq = frequencies.get_base_alias(freq) - freq = frequencies.get_period_alias(freq) if freq is None: ax = self._get_ax(0) @@ -1705,9 +1683,10 @@ def _maybe_convert_index(self, data): if freq is None: raise ValueError('Could not get frequency alias for plotting') - data = DataFrame(data.values, - index=data.index.to_period(freq=freq), - columns=data.columns) + freq = frequencies.get_base_alias(freq) + freq = frequencies.get_period_alias(freq) + + data.index = data.index.to_period(freq=freq) return data def _post_plot_logic(self): @@ -2522,9 +2501,7 @@ def plot_series(data, kind='line', ax=None, # Series unique if ax is None and len(plt.get_fignums()) > 0: ax = _gca() ax = getattr(ax, 'left_ax', ax) - # is there harm in this? - if label is None: - label = data.name + return _plot(data, kind=kind, ax=ax, figsize=figsize, use_index=use_index, title=title, grid=grid, legend=legend, diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index c4e642ffe43b0..bdc0aa02f2715 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -636,6 +636,38 @@ def test_mixed_freq_irregular_first(self): x2 = lines[1].get_xdata() assert_array_equal(x2, s1.index.asobject.values) + def test_mixed_freq_regular_first_df(self): + # GH 9852 + import matplotlib.pyplot as plt + s1 = tm.makeTimeSeries().to_frame() + s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :] + ax = s1.plot() + ax2 = s2.plot(style='g', ax=ax) + lines = ax2.get_lines() + idx1 = PeriodIndex(lines[0].get_xdata()) + idx2 = PeriodIndex(lines[1].get_xdata()) + self.assertTrue(idx1.equals(s1.index.to_period('B'))) + self.assertTrue(idx2.equals(s2.index.to_period('B'))) + left, right = ax2.get_xlim() + pidx = s1.index.to_period() + self.assertEqual(left, pidx[0].ordinal) + self.assertEqual(right, pidx[-1].ordinal) + + @slow + def test_mixed_freq_irregular_first_df(self): + # GH 9852 + import matplotlib.pyplot as plt + s1 = tm.makeTimeSeries().to_frame() + s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :] + ax = s2.plot(style='g') + ax = s1.plot(ax=ax) + self.assertFalse(hasattr(ax, 'freq')) + lines = ax.get_lines() + x1 = lines[0].get_xdata() + assert_array_equal(x1, s2.index.asobject.values) + x2 = lines[1].get_xdata() + assert_array_equal(x2, s1.index.asobject.values) + def test_mixed_freq_hf_first(self): idxh = date_range('1/1/1999', periods=365, freq='D') idxl = date_range('1/1/1999', periods=12, freq='M')
This repeated time-series plotting works: ``` import pandas.util.testing as tm s1 = tm.makeTimeSeries() s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]] ax = s1.plot() ax2 = s2.plot(style='g') ``` ![line_incorrectsecondary](https://cloud.githubusercontent.com/assets/1696302/7101339/f74b566a-e08f-11e4-8b7f-5ed58327cc5c.png) But if converted to `DateFrame`, it doesn't: ``` s1 = s1.to_frame() s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15]] print(s2.index.freq) ax = s1.plot() ax2 = s2.plot(style='g', ax=ax) # TypeError: expected string or buffer ``` Fixed the problem, and cleaned up the code to merge `Series` and `DataFrame` flows. #### After the Fix: ``` import pandas.util.testing as tm fig, axes = plt.subplots(2, 1) s1 = tm.makeTimeSeries() s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]] ax = s1.plot(ax=axes[0]) ax2 = s2.plot(style='g', ax=axes[0]) s1 = s1.to_frame(name='x') s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15]] ax = s1.plot(ax=axes[1]) ax2 = s2.plot(style='g', ax=axes[1]) ``` ![line_incorrectsecondary](https://cloud.githubusercontent.com/assets/1696302/7103495/55f95316-e0e4-11e4-8086-6aedfae82006.png)
https://api.github.com/repos/pandas-dev/pandas/pulls/9852
2015-04-11T12:18:15Z
2015-04-12T13:39:44Z
2015-04-12T13:39:44Z
2015-04-18T14:26:06Z
BUG: memory access bug in read_csv causing segfault
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index f5b158d717357..3ff2406220e35 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -91,6 +91,7 @@ Bug Fixes - Fixed bug (:issue:`9542`) where labels did not appear properly in legend of ``DataFrame.plot()``. Passing ``label=`` args also now works, and series indices are no longer mutated. - Bug in json serialization when frame has length zero.(:issue:`9805`) +- Bug in `read_csv` where missing trailing delimiters would cause segfault. (:issue:`5664`) - Bug in ``scatter_matrix`` draws unexpected axis ticklabels (:issue:`5662`) diff --git a/pandas/io/tests/test_cparser.py b/pandas/io/tests/test_cparser.py index ad6f071d738ff..93d55c654de90 100644 --- a/pandas/io/tests/test_cparser.py +++ b/pandas/io/tests/test_cparser.py @@ -336,6 +336,28 @@ def test_empty_field_eof(self): 2: np.array(['3', ''], dtype=object)} assert_array_dicts_equal(result, expected) + # GH5664 + a = DataFrame([['b'], [nan]], columns=['a'], index=['a', 'c']) + b = DataFrame([[1, 1, 1, 0], [1, 1, 1, 0]], + columns=list('abcd'), + index=[1, 1]) + c = DataFrame([[1, 2, 3, 4], [6, nan, nan, nan], + [8, 9, 10, 11], [13, 14, nan, nan]], + columns=list('abcd'), + index=[0, 5, 7, 12]) + + for _ in range(100): + df = read_csv(StringIO('a,b\nc\n'), skiprows=0, + names=['a'], engine='c') + assert_frame_equal(df, a) + + df = read_csv(StringIO('1,1,1,1,0\n'*2 + '\n'*2), + names=list("abcd"), engine='c') + assert_frame_equal(df, b) + + df = read_csv(StringIO('0,1,2,3,4\n5,6\n7,8,9,10,11\n12,13,14'), + names=list('abcd'), engine='c') + assert_frame_equal(df, c) def assert_array_dicts_equal(left, right): for k, v in compat.iteritems(left): diff --git a/pandas/parser.pyx b/pandas/parser.pyx index d13781d6fa132..73a03fc5cef7c 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -175,7 +175,7 @@ cdef extern from "parser/tokenizer.h": int col void coliter_setup(coliter_t *it, parser_t *parser, int i, int start) - char* COLITER_NEXT(coliter_t it) + void COLITER_NEXT(coliter_t, const char *) parser_t* parser_new() @@ -212,7 +212,7 @@ cdef extern from "parser/tokenizer.h": inline int to_longlong(char *item, long long *p_value) # inline int to_longlong_thousands(char *item, long long *p_value, # char tsep) - int to_boolean(char *item, uint8_t *val) + int to_boolean(const char *item, uint8_t *val) cdef extern from "parser/io.h": @@ -1279,7 +1279,7 @@ cdef _string_box_factorize(parser_t *parser, int col, Py_ssize_t i size_t lines coliter_t it - char *word + const char *word = NULL ndarray[object] result int ret = 0 @@ -1296,7 +1296,7 @@ cdef _string_box_factorize(parser_t *parser, int col, coliter_setup(&it, parser, col, line_start) for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) if na_filter: k = kh_get_str(na_hashset, word) @@ -1333,7 +1333,7 @@ cdef _string_box_utf8(parser_t *parser, int col, Py_ssize_t i size_t lines coliter_t it - char *word + const char *word = NULL ndarray[object] result int ret = 0 @@ -1350,7 +1350,7 @@ cdef _string_box_utf8(parser_t *parser, int col, coliter_setup(&it, parser, col, line_start) for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) if na_filter: k = kh_get_str(na_hashset, word) @@ -1388,7 +1388,7 @@ cdef _string_box_decode(parser_t *parser, int col, Py_ssize_t i, size size_t lines coliter_t it - char *word + const char *word = NULL ndarray[object] result int ret = 0 @@ -1407,7 +1407,7 @@ cdef _string_box_decode(parser_t *parser, int col, coliter_setup(&it, parser, col, line_start) for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) if na_filter: k = kh_get_str(na_hashset, word) @@ -1444,7 +1444,7 @@ cdef _to_fw_string(parser_t *parser, int col, int line_start, int error Py_ssize_t i, j coliter_t it - char *word + const char *word = NULL char *data ndarray result @@ -1454,7 +1454,7 @@ cdef _to_fw_string(parser_t *parser, int col, int line_start, coliter_setup(&it, parser, col, line_start) for i in range(line_end - line_start): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) strncpy(data, word, width) data += width @@ -1469,7 +1469,7 @@ cdef _try_double(parser_t *parser, int col, int line_start, int line_end, int error, na_count = 0 size_t i, lines coliter_t it - char *word + const char *word = NULL char *p_end double *data double NA = na_values[np.float64] @@ -1485,7 +1485,7 @@ cdef _try_double(parser_t *parser, int col, int line_start, int line_end, if na_filter: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) k = kh_get_str(na_hashset, word) # in the hash table @@ -1509,7 +1509,7 @@ cdef _try_double(parser_t *parser, int col, int line_start, int line_end, data += 1 else: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) data[0] = parser.converter(word, &p_end, parser.decimal, parser.sci, parser.thousands, 1) if errno != 0 or p_end[0] or p_end == word: @@ -1530,7 +1530,7 @@ cdef _try_int64(parser_t *parser, int col, int line_start, int line_end, int error, na_count = 0 size_t i, lines coliter_t it - char *word + const char *word = NULL int64_t *data ndarray result @@ -1544,7 +1544,7 @@ cdef _try_int64(parser_t *parser, int col, int line_start, int line_end, if na_filter: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) k = kh_get_str(na_hashset, word) # in the hash table if k != na_hashset.n_buckets: @@ -1561,7 +1561,7 @@ cdef _try_int64(parser_t *parser, int col, int line_start, int line_end, return None, None else: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) data[i] = str_to_int64(word, INT64_MIN, INT64_MAX, &error, parser.thousands) if error != 0: @@ -1578,7 +1578,7 @@ cdef _try_bool(parser_t *parser, int col, int line_start, int line_end, int error, na_count = 0 size_t i, lines coliter_t it - char *word + const char *word = NULL uint8_t *data ndarray result @@ -1592,7 +1592,7 @@ cdef _try_bool(parser_t *parser, int col, int line_start, int line_end, if na_filter: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) k = kh_get_str(na_hashset, word) # in the hash table @@ -1608,7 +1608,7 @@ cdef _try_bool(parser_t *parser, int col, int line_start, int line_end, data += 1 else: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) error = to_boolean(word, data) if error != 0: @@ -1625,7 +1625,7 @@ cdef _try_bool_flex(parser_t *parser, int col, int line_start, int line_end, int error, na_count = 0 size_t i, lines coliter_t it - char *word + const char *word = NULL uint8_t *data ndarray result @@ -1639,7 +1639,7 @@ cdef _try_bool_flex(parser_t *parser, int col, int line_start, int line_end, if na_filter: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) k = kh_get_str(na_hashset, word) # in the hash table @@ -1667,7 +1667,7 @@ cdef _try_bool_flex(parser_t *parser, int col, int line_start, int line_end, data += 1 else: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) k = kh_get_str(true_hashset, word) if k != true_hashset.n_buckets: @@ -1688,33 +1688,6 @@ cdef _try_bool_flex(parser_t *parser, int col, int line_start, int line_end, return result.view(np.bool_), na_count -cdef _get_na_mask(parser_t *parser, int col, int line_start, int line_end, - kh_str_t *na_hashset): - cdef: - int error - Py_ssize_t i - size_t lines - coliter_t it - char *word - ndarray[uint8_t, cast=True] result - khiter_t k - - lines = line_end - line_start - result = np.empty(lines, dtype=np.bool_) - - coliter_setup(&it, parser, col, line_start) - for i in range(lines): - word = COLITER_NEXT(it) - - k = kh_get_str(na_hashset, word) - # in the hash table - if k != na_hashset.n_buckets: - result[i] = 1 - else: - result[i] = 0 - - return result - cdef kh_str_t* kset_from_list(list values) except NULL: # caller takes responsibility for freeing the hash table cdef: @@ -1897,7 +1870,7 @@ cdef _apply_converter(object f, parser_t *parser, int col, Py_ssize_t i size_t lines coliter_t it - char *word + const char *word = NULL char *errors = "strict" ndarray[object] result object val @@ -1909,17 +1882,17 @@ cdef _apply_converter(object f, parser_t *parser, int col, if not PY3 and c_encoding == NULL: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) val = PyBytes_FromString(word) result[i] = f(val) elif ((PY3 and c_encoding == NULL) or c_encoding == b'utf-8'): for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) val = PyUnicode_FromString(word) result[i] = f(val) else: for i in range(lines): - word = COLITER_NEXT(it) + COLITER_NEXT(it, word) val = PyUnicode_Decode(word, strlen(word), c_encoding, errors) result[i] = f(val) diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 1bc4096658b29..1850aab50b55a 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -38,7 +38,7 @@ See LICENSE for the license * RESTORE_FINAL (2): * Put the file position at the next byte after the * data read from the file_buffer. -* +* #define RESTORE_NOT 0 #define RESTORE_INITIAL 1 #define RESTORE_FINAL 2 @@ -304,7 +304,7 @@ static int make_stream_space(parser_t *self, size_t nbytes) { self->stream_len, &self->stream_cap, nbytes * 2, sizeof(char), &status); - TRACE(("make_stream_space: self->stream=%p, self->stream_len = %zu, self->stream_cap=%zu, status=%zu\n", + TRACE(("make_stream_space: self->stream=%p, self->stream_len = %zu, self->stream_cap=%zu, status=%zu\n", self->stream, self->stream_len, self->stream_cap, status)) if (status != 0) { @@ -334,7 +334,7 @@ static int make_stream_space(parser_t *self, size_t nbytes) { self->words_len, &self->words_cap, nbytes, sizeof(char*), &status); - TRACE(("make_stream_space: grow_buffer(self->self->words, %zu, %zu, %zu, %d)\n", + TRACE(("make_stream_space: grow_buffer(self->self->words, %zu, %zu, %zu, %d)\n", self->words_len, self->words_cap, nbytes, status)) if (status != 0) { return PARSER_OUT_OF_MEMORY; @@ -371,7 +371,7 @@ static int make_stream_space(parser_t *self, size_t nbytes) { self->lines + 1, &self->lines_cap, nbytes, sizeof(int), &status); - TRACE(("make_stream_space: grow_buffer(self->line_start, %zu, %zu, %zu, %d)\n", + TRACE(("make_stream_space: grow_buffer(self->line_start, %zu, %zu, %zu, %d)\n", self->lines + 1, self->lines_cap, nbytes, status)) if (status != 0) { return PARSER_OUT_OF_MEMORY; @@ -398,7 +398,7 @@ static int push_char(parser_t *self, char c) { /* TRACE(("pushing %c \n", c)) */ TRACE(("push_char: self->stream[%zu] = %x, stream_cap=%zu\n", self->stream_len+1, c, self->stream_cap)) if (self->stream_len >= self->stream_cap) { - TRACE(("push_char: ERROR!!! self->stream_len(%d) >= self->stream_cap(%d)\n", + TRACE(("push_char: ERROR!!! self->stream_len(%d) >= self->stream_cap(%d)\n", self->stream_len, self->stream_cap)) self->error_msg = (char*) malloc(64); sprintf(self->error_msg, "Buffer overflow caught - possible malformed input file.\n"); @@ -463,7 +463,6 @@ static void append_warning(parser_t *self, const char *msg) { static int end_line(parser_t *self) { int fields; - khiter_t k; /* for hash set detection */ int ex_fields = self->expected_fields; char *msg; @@ -483,7 +482,7 @@ static int end_line(parser_t *self) { TRACE(("end_line: Skipping row %d\n", self->file_lines)); // increment file line count self->file_lines++; - + // skip the tokens from this bad line self->line_start[self->lines] += fields; @@ -605,12 +604,11 @@ int parser_set_skipfirstnrows(parser_t *self, int64_t nrows) { static int parser_buffer_bytes(parser_t *self, size_t nbytes) { int status; size_t bytes_read; - void *src = self->source; status = 0; self->datapos = 0; self->data = self->cb_io(self->source, nbytes, &bytes_read, &status); - TRACE(("parser_buffer_bytes self->cb_io: nbytes=%zu, datalen: %d, status=%d\n", + TRACE(("parser_buffer_bytes self->cb_io: nbytes=%zu, datalen: %d, status=%d\n", nbytes, bytes_read, status)); self->datalen = bytes_read; @@ -704,7 +702,7 @@ typedef int (*parser_op)(parser_t *self, size_t line_limit); int skip_this_line(parser_t *self, int64_t rownum) { if (self->skipset != NULL) { - return ( kh_get_int64((kh_int64_t*) self->skipset, self->file_lines) != + return ( kh_get_int64((kh_int64_t*) self->skipset, self->file_lines) != ((kh_int64_t*)self->skipset)->n_buckets ); } else { @@ -784,7 +782,7 @@ int tokenize_delimited(parser_t *self, size_t line_limit) else self->state = EAT_CRNL; break; - } + } else if (c == self->commentchar) { self->state = EAT_LINE_COMMENT; break; @@ -1750,7 +1748,7 @@ int parser_trim_buffers(parser_t *self) { /* trim stream */ new_cap = _next_pow2(self->stream_len) + 1; - TRACE(("parser_trim_buffers: new_cap = %zu, stream_cap = %zu, lines_cap = %zu\n", + TRACE(("parser_trim_buffers: new_cap = %zu, stream_cap = %zu, lines_cap = %zu\n", new_cap, self->stream_cap, self->lines_cap)); if (new_cap < self->stream_cap) { TRACE(("parser_trim_buffers: new_cap < self->stream_cap, calling safe_realloc\n")); @@ -1871,7 +1869,7 @@ int _tokenize_helper(parser_t *self, size_t nrows, int all) { } } - TRACE(("_tokenize_helper: Trying to process %d bytes, datalen=%d, datapos= %d\n", + TRACE(("_tokenize_helper: Trying to process %d bytes, datalen=%d, datapos= %d\n", self->datalen - self->datapos, self->datalen, self->datapos)); /* TRACE(("sourcetype: %c, status: %d\n", self->sourcetype, status)); */ @@ -2033,7 +2031,7 @@ int P_INLINE to_longlong_thousands(char *item, long long *p_value, char tsep) return status; }*/ -int to_boolean(char *item, uint8_t *val) { +int to_boolean(const char *item, uint8_t *val) { char *tmp; int i, status = 0; @@ -2357,7 +2355,7 @@ double precise_xstrtod(const char *str, char **endptr, char decimal, num_digits++; num_decimals++; } - + if (num_digits >= max_digits) // consume extra decimal digits while (isdigit(*p)) ++p; @@ -2653,4 +2651,4 @@ uint64_t str_to_uint64(const char *p_item, uint64_t uint_max, int *error) *error = 0; return number; } -*/ \ No newline at end of file +*/ diff --git a/pandas/src/parser/tokenizer.h b/pandas/src/parser/tokenizer.h index 694a73ec78153..d3777e858b6ca 100644 --- a/pandas/src/parser/tokenizer.h +++ b/pandas/src/parser/tokenizer.h @@ -228,9 +228,12 @@ coliter_t *coliter_new(parser_t *self, int i); /* #define COLITER_NEXT(iter) iter->words[iter->line_start[iter->line++] + iter->col] */ // #define COLITER_NEXT(iter) iter.words[iter.line_start[iter.line++] + iter.col] -#define COLITER_NEXT(iter) iter.words[*iter.line_start++ + iter.col] +#define COLITER_NEXT(iter, word) do { \ + const int i = *iter.line_start++ + iter.col; \ + word = i < *iter.line_start ? iter.words[i]: ""; \ + } while(0) -parser_t* parser_new(); +parser_t* parser_new(void); int parser_init(parser_t *self); @@ -270,6 +273,6 @@ double round_trip(const char *p, char **q, char decimal, char sci, char tsep, in //int P_INLINE to_complex(char *item, double *p_real, double *p_imag, char sci, char decimal); int P_INLINE to_longlong(char *item, long long *p_value); //int P_INLINE to_longlong_thousands(char *item, long long *p_value, char tsep); -int to_boolean(char *item, uint8_t *val); +int to_boolean(const char *item, uint8_t *val); #endif // _PARSER_COMMON_H_
closes https://github.com/pydata/pandas/issues/5664 the added test covers the original issue, though I cannot reproduce that on master; [the one mentioned in the comments](https://github.com/pydata/pandas/issues/5664#issuecomment-49332533) still segfaults on master (if repeated few times) and is fixed by this pr.
https://api.github.com/repos/pandas-dev/pandas/pulls/9846
2015-04-10T02:41:58Z
2015-04-12T13:37:27Z
2015-04-12T13:37:27Z
2015-04-12T13:57:14Z
BUG: raw_locales unreachable in util.testing.get_locales
diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py index 2e22b33dc769a..bb8bd3df96b71 100644 --- a/pandas/tests/test_util.py +++ b/pandas/tests/test_util.py @@ -79,6 +79,10 @@ def test_warning(self): with tm.assert_produces_warning(FutureWarning): self.assertNotAlmostEquals(1, 2) + def test_locale(self): + #GH9744 + locales = pandas.util.testing.get_locales() + self.assertTrue(len(locales) >= 1) def test_rands(): r = tm.rands(10) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 3d9a0e7b43634..b4baedada46e1 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -331,19 +331,21 @@ def get_locales(prefix=None, normalize=True, # raw_locales is "\n" seperated list of locales # it may contain non-decodable parts, so split # extract what we can and then rejoin. - raw_locales = [] + raw_locales = raw_locales.split(b'\n') + out_locales = [] for x in raw_locales: - try: - raw_locales.append(str(x, encoding=pd.options.display.encoding)) - except: - pass + if compat.PY3: + out_locales.append(str(x, encoding=pd.options.display.encoding)) + else: + out_locales.append(str(x)) + except TypeError: pass if prefix is None: - return _valid_locales(raw_locales, normalize) + return _valid_locales(out_locales, normalize) - found = re.compile('%s.*' % prefix).findall('\n'.join(raw_locales)) + found = re.compile('%s.*' % prefix).findall('\n'.join(out_locales)) return _valid_locales(found, normalize)
Fixes #9744
https://api.github.com/repos/pandas-dev/pandas/pulls/9845
2015-04-10T02:41:56Z
2015-04-12T13:38:22Z
2015-04-12T13:38:22Z
2015-04-12T13:38:26Z
DOC/CLN: Revise StringMethods docs
diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 6d20907373014..3506338afd9d4 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -27,19 +27,42 @@ def _get_array_list(arr, others): def str_cat(arr, others=None, sep=None, na_rep=None): """ - Concatenate arrays of strings with given separator + Concatenate strings in the Series/Index with given separator. Parameters ---------- - arr : list or array-like - others : list or array, or list of arrays + others : list-like, or list of list-likes + If None, returns str concatenating strings of the Series sep : string or None, default None na_rep : string or None, default None If None, an NA in any array will propagate Returns ------- - concat : array + concat : Series/Index of objects or str + + Examples + -------- + If ``others`` is specified, corresponding values are + concatenated with the separator. Result will be a Series of strings. + + >>> Series(['a', 'b', 'c']).str.cat(['A', 'B', 'C'], sep=',') + 0 a,A + 1 b,B + 2 c,C + dtype: object + + Otherwise, strings in the Series are concatenated. Result will be a string. + + >>> Series(['a', 'b', 'c']).str.cat(sep=',') + 'a,b,c' + + Also, you can pass a list of list-likes. + + >>> Series(['a', 'b']).str.cat([['x', 'y'], ['1', '2']], sep=',') + 0 a,x,1 + 1 b,y,2 + dtype: object """ if sep is None: sep = '' @@ -130,18 +153,17 @@ def g(x): def str_count(arr, pat, flags=0): """ - Count occurrences of pattern in each string + Count occurrences of pattern in each string of the Series/Index. Parameters ---------- - arr : list or array-like pat : string, valid regular expression flags : int, default 0 (no flags) re module flags, e.g. re.IGNORECASE Returns ------- - counts : arrays + counts : Series/Index of integer values """ regex = re.compile(pat, flags=flags) f = lambda x: len(regex.findall(x)) @@ -150,7 +172,8 @@ def str_count(arr, pat, flags=0): def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True): """ - Check whether given pattern is contained in each string in the array + Return boolean Series/``array`` whether given pattern/regex is + contained in each string in the Series/Index. Parameters ---------- @@ -166,7 +189,7 @@ def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True): Returns ------- - Series of boolean values + contained : Series/array of boolean values See Also -------- @@ -197,8 +220,9 @@ def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True): def str_startswith(arr, pat, na=np.nan): """ - Return boolean array indicating whether each string starts with passed - pattern + Return boolean Series/``array`` indicating whether each string in the + Series/Index starts with passed pattern. Equivalent to + :meth:`str.startswith`. Parameters ---------- @@ -208,7 +232,7 @@ def str_startswith(arr, pat, na=np.nan): Returns ------- - startswith : array (boolean) + startswith : Series/array of boolean values """ f = lambda x: x.startswith(pat) return _na_map(f, arr, na, dtype=bool) @@ -216,8 +240,9 @@ def str_startswith(arr, pat, na=np.nan): def str_endswith(arr, pat, na=np.nan): """ - Return boolean array indicating whether each string ends with passed - pattern + Return boolean Series indicating whether each string in the + Series/Index ends with passed pattern. Equivalent to + :meth:`str.endswith`. Parameters ---------- @@ -227,7 +252,7 @@ def str_endswith(arr, pat, na=np.nan): Returns ------- - endswith : array (boolean) + endswith : Series/array of boolean values """ f = lambda x: x.endswith(pat) return _na_map(f, arr, na, dtype=bool) @@ -235,7 +260,9 @@ def str_endswith(arr, pat, na=np.nan): def str_replace(arr, pat, repl, n=-1, case=True, flags=0): """ - Replace + Replace occurrences of pattern/regex in the Series/Index with + some other string. Equivalent to :meth:`str.replace` or + :func:`re.sub`. Parameters ---------- @@ -252,7 +279,7 @@ def str_replace(arr, pat, repl, n=-1, case=True, flags=0): Returns ------- - replaced : array + replaced : Series/Index of objects """ use_re = not case or len(pat) > 1 or flags @@ -272,7 +299,8 @@ def f(x): def str_repeat(arr, repeats): """ - Duplicate each string in the array by indicated number of times + Duplicate each string in the Series/Index by indicated number + of times. Parameters ---------- @@ -281,7 +309,7 @@ def str_repeat(arr, repeats): Returns ------- - repeated : array + repeated : Series/Index of objects """ if np.isscalar(repeats): def rep(x): @@ -305,7 +333,8 @@ def rep(x, r): def str_match(arr, pat, case=True, flags=0, na=np.nan, as_indexer=False): """ - Deprecated: Find groups in each string using passed regular expression. + Deprecated: Find groups in each string in the Series/Index + using passed regular expression. If as_indexer=True, determine if each string matches a regular expression. Parameters @@ -322,9 +351,9 @@ def str_match(arr, pat, case=True, flags=0, na=np.nan, as_indexer=False): Returns ------- - Series of boolean values + Series/array of boolean values if as_indexer=True - Series of tuples + Series/Index of tuples if as_indexer=False, default but deprecated See Also @@ -359,6 +388,7 @@ def str_match(arr, pat, case=True, flags=0, na=np.nan, as_indexer=False): if (not as_indexer) and regex.groups > 0: dtype = object + def f(x): m = regex.match(x) if m: @@ -382,7 +412,8 @@ def _get_single_group_name(rx): def str_extract(arr, pat, flags=0): """ - Find groups in each string using passed regular expression + Find groups in each string in the Series using passed regular + expression. Parameters ---------- @@ -441,6 +472,7 @@ def str_extract(arr, pat, flags=0): if regex.groups == 0: raise ValueError("This pattern contains no groups to capture.") empty_row = [np.nan]*regex.groups + def f(x): if not isinstance(x, compat.string_types): return empty_row @@ -468,7 +500,17 @@ def f(x): def str_get_dummies(arr, sep='|'): """ - Split each string by sep and return a frame of dummy/indicator variables. + Split each string in the Series by sep and return a frame of + dummy/indicator variables. + + Parameters + ---------- + sep : string, default "|" + String to split on. + + Returns + ------- + dummies : DataFrame Examples -------- @@ -478,14 +520,15 @@ def str_get_dummies(arr, sep='|'): 1 1 0 0 2 1 0 1 - >>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies() + >>> Series(['a|b', np.nan, 'a|c']).str.get_dummies() a b c 0 1 1 0 1 0 0 0 2 1 0 1 - See also ``pd.get_dummies``. - + See Also + -------- + pandas.get_dummies """ from pandas.core.frame import DataFrame @@ -511,7 +554,8 @@ def str_get_dummies(arr, sep='|'): def str_join(arr, sep): """ - Join lists contained as elements in array, a la str.join + Join lists contained as elements in the Series/Index with + passed delimiter. Equivalent to :meth:`str.join`. Parameters ---------- @@ -520,14 +564,15 @@ def str_join(arr, sep): Returns ------- - joined : array + joined : Series/Index of objects """ return _na_map(sep.join, arr) def str_findall(arr, pat, flags=0): """ - Find all occurrences of pattern or regular expression + Find all occurrences of pattern or regular expression in the + Series/Index. Equivalent to :func:`re.findall`. Parameters ---------- @@ -538,7 +583,7 @@ def str_findall(arr, pat, flags=0): Returns ------- - matches : array + matches : Series/Index of lists """ regex = re.compile(pat, flags=flags) return _na_map(regex.findall, arr) @@ -546,8 +591,8 @@ def str_findall(arr, pat, flags=0): def str_find(arr, sub, start=0, end=None, side='left'): """ - Return indexes in each strings where the substring is - fully contained between [start:end]. Return -1 on failure. + Return indexes in each strings in the Series/Index where the + substring is fully contained between [start:end]. Return -1 on failure. Parameters ---------- @@ -562,7 +607,7 @@ def str_find(arr, sub, start=0, end=None, side='left'): Returns ------- - found : array + found : Series/Index of integer values """ if not isinstance(sub, compat.string_types): @@ -586,11 +631,11 @@ def str_find(arr, sub, start=0, end=None, side='left'): def str_pad(arr, width, side='left', fillchar=' '): """ - Pad strings with an additional character + Pad strings in the Series/Index with an additional character to + specified side. Parameters ---------- - arr : list or array-like width : int Minimum width of resulting string; additional characters will be filled with spaces @@ -600,7 +645,7 @@ def str_pad(arr, width, side='left', fillchar=' '): Returns ------- - padded : array + padded : Series/Index of objects """ if not isinstance(fillchar, compat.string_types): @@ -624,8 +669,8 @@ def str_pad(arr, width, side='left', fillchar=' '): def str_split(arr, pat=None, n=None, return_type='series'): """ - Split each string (a la re.split) in array by given pattern, propagating NA - values + Split each string (a la re.split) in the Series/Index by given + pattern, propagating NA values. Equivalent to :meth:`str.split`. Parameters ---------- @@ -643,7 +688,7 @@ def str_split(arr, pat=None, n=None, return_type='series'): Returns ------- - split : array + split : Series/Index of objects or DataFrame """ from pandas.core.series import Series from pandas.core.frame import DataFrame @@ -677,7 +722,7 @@ def str_split(arr, pat=None, n=None, return_type='series'): def str_slice(arr, start=None, stop=None, step=None): """ - Slice substrings from each element in array + Slice substrings from each element in the Series/Index Parameters ---------- @@ -687,7 +732,7 @@ def str_slice(arr, start=None, stop=None, step=None): Returns ------- - sliced : array + sliced : Series/Index of objects """ obj = slice(start, stop, step) f = lambda x: x[obj] @@ -696,17 +741,19 @@ def str_slice(arr, start=None, stop=None, step=None): def str_slice_replace(arr, start=None, stop=None, repl=None): """ - Replace a slice of each string with another string. + Replace a slice of each string in the Series/Index with another + string. Parameters ---------- start : int or None stop : int or None repl : str or None + String for replacement Returns ------- - replaced : array + replaced : Series/Index of objects """ if repl is None: repl = '' @@ -726,56 +773,35 @@ def f(x): return _na_map(f, arr) -def str_strip(arr, to_strip=None): +def str_strip(arr, to_strip=None, side='both'): """ - Strip whitespace (including newlines) from each string in the array + Strip whitespace (including newlines) from each string in the + Series/Index. Parameters ---------- to_strip : str or unicode + side : {'left', 'right', 'both'}, default 'both' Returns ------- - stripped : array + stripped : Series/Index of objects """ - return _na_map(lambda x: x.strip(to_strip), arr) - - -def str_lstrip(arr, to_strip=None): - """ - Strip whitespace (including newlines) from left side of each string in the - array - - Parameters - ---------- - to_strip : str or unicode - - Returns - ------- - stripped : array - """ - return _na_map(lambda x: x.lstrip(to_strip), arr) - - -def str_rstrip(arr, to_strip=None): - """ - Strip whitespace (including newlines) from right side of each string in the - array - - Parameters - ---------- - to_strip : str or unicode - - Returns - ------- - stripped : array - """ - return _na_map(lambda x: x.rstrip(to_strip), arr) + if side == 'both': + f = lambda x: x.strip(to_strip) + elif side == 'left': + f = lambda x: x.lstrip(to_strip) + elif side == 'right': + f = lambda x: x.rstrip(to_strip) + else: # pragma: no cover + raise ValueError('Invalid side') + return _na_map(f, arr) def str_wrap(arr, width, **kwargs): - r""" - Wrap long strings to be formatted in paragraphs. + """ + Wrap long strings in the Series/Index to be formatted in + paragraphs with length less than a given width. This method has the same keyword parameters and defaults as :class:`textwrap.TextWrapper`. @@ -787,31 +813,32 @@ def str_wrap(arr, width, **kwargs): expand_tabs : bool, optional If true, tab characters will be expanded to spaces (default: True) replace_whitespace : bool, optional - If true, each whitespace character (as defined by string.whitespace) remaining - after tab expansion will be replaced by a single space (default: True) + If true, each whitespace character (as defined by string.whitespace) + remaining after tab expansion will be replaced by a single space + (default: True) drop_whitespace : bool, optional - If true, whitespace that, after wrapping, happens to end up at the beginning - or end of a line is dropped (default: True) + If true, whitespace that, after wrapping, happens to end up at the + beginning or end of a line is dropped (default: True) break_long_words : bool, optional - If true, then words longer than width will be broken in order to ensure that - no lines are longer than width. If it is false, long words will not be broken, - and some lines may be longer than width. (default: True) + If true, then words longer than width will be broken in order to ensure + that no lines are longer than width. If it is false, long words will + not be broken, and some lines may be longer than width. (default: True) break_on_hyphens : bool, optional - If true, wrapping will occur preferably on whitespace and right after hyphens - in compound words, as it is customary in English. If false, only whitespaces - will be considered as potentially good places for line breaks, but you need - to set break_long_words to false if you want truly insecable words. - (default: True) + If true, wrapping will occur preferably on whitespace and right after + hyphens in compound words, as it is customary in English. If false, + only whitespaces will be considered as potentially good places for line + breaks, but you need to set break_long_words to false if you want truly + insecable words. (default: True) Returns ------- - wrapped : array + wrapped : Series/Index of objects Notes ----- - Internally, this method uses a :class:`textwrap.TextWrapper` instance with default - settings. To achieve behavior matching R's stringr library str_wrap function, use - the arguments: + Internally, this method uses a :class:`textwrap.TextWrapper` instance with + default settings. To achieve behavior matching R's stringr library str_wrap + function, use the arguments: - expand_tabs = False - replace_whitespace = True @@ -836,7 +863,8 @@ def str_wrap(arr, width, **kwargs): def str_get(arr, i): """ - Extract element from lists, tuples, or strings in each element in the array + Extract element from lists, tuples, or strings in each element in the + Series/Index. Parameters ---------- @@ -845,7 +873,7 @@ def str_get(arr, i): Returns ------- - items : array + items : Series/Index of objects """ f = lambda x: x[i] if len(x) > i else np.nan return _na_map(f, arr) @@ -853,7 +881,8 @@ def str_get(arr, i): def str_decode(arr, encoding, errors="strict"): """ - Decode character string to unicode using indicated encoding + Decode character string in the Series/Index to unicode + using indicated encoding. Equivalent to :meth:`str.decode`. Parameters ---------- @@ -862,7 +891,7 @@ def str_decode(arr, encoding, errors="strict"): Returns ------- - decoded : array + decoded : Series/Index of objects """ f = lambda x: x.decode(encoding, errors) return _na_map(f, arr) @@ -870,7 +899,8 @@ def str_decode(arr, encoding, errors="strict"): def str_encode(arr, encoding, errors="strict"): """ - Encode character string to some other encoding using indicated encoding + Encode character string in the Series/Index to some other encoding + using indicated encoding. Equivalent to :meth:`str.encode`. Parameters ---------- @@ -879,7 +909,7 @@ def str_encode(arr, encoding, errors="strict"): Returns ------- - encoded : array + encoded : Series/Index of objects """ f = lambda x: x.encode(encoding, errors) return _na_map(f, arr) @@ -1011,7 +1041,7 @@ def contains(self, pat, case=True, flags=0, na=np.nan, regex=True): @copy(str_match) def match(self, pat, case=True, flags=0, na=np.nan, as_indexer=False): result = str_match(self.series, pat, case=case, flags=flags, - na=na, as_indexer=as_indexer) + na=na, as_indexer=as_indexer) return self._wrap_result(result) @copy(str_replace) @@ -1031,7 +1061,8 @@ def pad(self, width, side='left', fillchar=' '): return self._wrap_result(result) _shared_docs['str_pad'] = (""" - Filling %s side of strings with an additional character + Filling %(side)s side of strings in the Series/Index with an + additional character. Equivalent to :meth:`str.%(method)s`. Parameters ---------- @@ -1043,34 +1074,36 @@ def pad(self, width, side='left', fillchar=' '): Returns ------- - filled : array + filled : Series/Index of objects """) - @Appender(_shared_docs['str_pad'] % 'left and right') + @Appender(_shared_docs['str_pad'] % dict(side='left and right', + method='center')) def center(self, width, fillchar=' '): return self.pad(width, side='both', fillchar=fillchar) - @Appender(_shared_docs['str_pad'] % 'right') + @Appender(_shared_docs['str_pad'] % dict(side='right', method='right')) def ljust(self, width, fillchar=' '): return self.pad(width, side='right', fillchar=fillchar) - @Appender(_shared_docs['str_pad'] % 'left') + @Appender(_shared_docs['str_pad'] % dict(side='left', method='left')) def rjust(self, width, fillchar=' '): return self.pad(width, side='left', fillchar=fillchar) def zfill(self, width): """" - Filling left side with 0 + Filling left side of strings in the Series/Index with 0. + Equivalent to :meth:`str.zfill`. Parameters ---------- width : int - Minimum width of resulting string; additional characters will be filled - with 0 + Minimum width of resulting string; additional characters will be + filled with 0 Returns ------- - filled : array + filled : Series/Index of objects """ result = str_pad(self.series, width, side='left', fillchar='0') return self._wrap_result(result) @@ -1095,19 +1128,31 @@ def encode(self, encoding, errors="strict"): result = str_encode(self.series, encoding, errors) return self._wrap_result(result) - @copy(str_strip) + _shared_docs['str_strip'] = (""" + Strip whitespace (including newlines) from each string in the + Series/Index from %(side)s. Equivalent to :meth:`str.%(method)s`. + + Returns + ------- + stripped : Series/Index of objects + """) + + @Appender(_shared_docs['str_strip'] % dict(side='left and right sides', + method='strip')) def strip(self, to_strip=None): - result = str_strip(self.series, to_strip) + result = str_strip(self.series, to_strip, side='both') return self._wrap_result(result) - @copy(str_lstrip) + @Appender(_shared_docs['str_strip'] % dict(side='left side', + method='lstrip')) def lstrip(self, to_strip=None): - result = str_lstrip(self.series, to_strip) + result = str_strip(self.series, to_strip, side='left') return self._wrap_result(result) - @copy(str_rstrip) + @Appender(_shared_docs['str_strip'] % dict(side='right side', + method='rstrip')) def rstrip(self, to_strip=None): - result = str_rstrip(self.series, to_strip) + result = str_strip(self.series, to_strip, side='right') return self._wrap_result(result) @copy(str_wrap) @@ -1127,9 +1172,9 @@ def get_dummies(self, sep='|'): extract = _pat_wrapper(str_extract, flags=True) _shared_docs['find'] = (""" - Return %(side)s indexes in each strings where the substring is - fully contained between [start:end]. Return -1 on failure. - Equivalent to standard ``str.%(method)s``. + Return %(side)s indexes in each strings in the Series/Index + where the substring is fully contained between [start:end]. + Return -1 on failure. Equivalent to standard :meth:`str.%(method)s`. Parameters ---------- @@ -1142,7 +1187,7 @@ def get_dummies(self, sep='|'): Returns ------- - found : array + found : Series/Index of integer values See Also -------- @@ -1162,45 +1207,51 @@ def rfind(self, sub, start=0, end=None): return self._wrap_result(result) _shared_docs['len'] = (""" - Compute length of each string in array. + Compute length of each string in the Series/Index. Returns ------- - lengths : array + lengths : Series/Index of integer values """) len = _noarg_wrapper(len, docstring=_shared_docs['len'], dtype=int) _shared_docs['casemethods'] = (""" - Convert strings in array to %(type)s. - Equivalent to ``str.%(method)s``. + Convert strings in the Series/Index to %(type)s. + Equivalent to :meth:`str.%(method)s`. Returns ------- - converted : array + converted : Series/Index of objects """) _shared_docs['lower'] = dict(type='lowercase', method='lower') _shared_docs['upper'] = dict(type='uppercase', method='upper') _shared_docs['title'] = dict(type='titlecase', method='title') - _shared_docs['capitalize'] = dict(type='be capitalized', method='capitalize') + _shared_docs['capitalize'] = dict(type='be capitalized', + method='capitalize') _shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase') lower = _noarg_wrapper(lambda x: x.lower(), - docstring=_shared_docs['casemethods'] % _shared_docs['lower']) + docstring=_shared_docs['casemethods'] % + _shared_docs['lower']) upper = _noarg_wrapper(lambda x: x.upper(), - docstring=_shared_docs['casemethods'] % _shared_docs['upper']) + docstring=_shared_docs['casemethods'] % + _shared_docs['upper']) title = _noarg_wrapper(lambda x: x.title(), - docstring=_shared_docs['casemethods'] % _shared_docs['title']) + docstring=_shared_docs['casemethods'] % + _shared_docs['title']) capitalize = _noarg_wrapper(lambda x: x.capitalize(), - docstring=_shared_docs['casemethods'] % _shared_docs['capitalize']) + docstring=_shared_docs['casemethods'] % + _shared_docs['capitalize']) swapcase = _noarg_wrapper(lambda x: x.swapcase(), - docstring=_shared_docs['casemethods'] % _shared_docs['swapcase']) + docstring=_shared_docs['casemethods'] % + _shared_docs['swapcase']) _shared_docs['ismethods'] = (""" - Check whether all characters in each string in the array are %(type)s. - Equivalent to ``str.%(method)s``. + Check whether all characters in each string in the Series/Index + are %(type)s. Equivalent to :meth:`str.%(method)s`. Returns ------- - Series of boolean values + is : Series/array of boolean values """) _shared_docs['isalnum'] = dict(type='alphanumeric', method='isalnum') _shared_docs['isalpha'] = dict(type='alphabetic', method='isalpha') @@ -1212,20 +1263,29 @@ def rfind(self, sub, start=0, end=None): _shared_docs['isnumeric'] = dict(type='numeric', method='isnumeric') _shared_docs['isdecimal'] = dict(type='decimal', method='isdecimal') isalnum = _noarg_wrapper(lambda x: x.isalnum(), - docstring=_shared_docs['ismethods'] % _shared_docs['isalnum']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isalnum']) isalpha = _noarg_wrapper(lambda x: x.isalpha(), - docstring=_shared_docs['ismethods'] % _shared_docs['isalpha']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isalpha']) isdigit = _noarg_wrapper(lambda x: x.isdigit(), - docstring=_shared_docs['ismethods'] % _shared_docs['isdigit']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isdigit']) isspace = _noarg_wrapper(lambda x: x.isspace(), - docstring=_shared_docs['ismethods'] % _shared_docs['isspace']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isspace']) islower = _noarg_wrapper(lambda x: x.islower(), - docstring=_shared_docs['ismethods'] % _shared_docs['islower']) + docstring=_shared_docs['ismethods'] % + _shared_docs['islower']) isupper = _noarg_wrapper(lambda x: x.isupper(), - docstring=_shared_docs['ismethods'] % _shared_docs['isupper']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isupper']) istitle = _noarg_wrapper(lambda x: x.istitle(), - docstring=_shared_docs['ismethods'] % _shared_docs['istitle']) + docstring=_shared_docs['ismethods'] % + _shared_docs['istitle']) isnumeric = _noarg_wrapper(lambda x: compat.u_safe(x).isnumeric(), - docstring=_shared_docs['ismethods'] % _shared_docs['isnumeric']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isnumeric']) isdecimal = _noarg_wrapper(lambda x: compat.u_safe(x).isdecimal(), - docstring=_shared_docs['ismethods'] % _shared_docs['isdecimal']) + docstring=_shared_docs['ismethods'] % + _shared_docs['isdecimal'])
Derived from #9773, #9667. Fix docstrings to meet what current `.str` accessors does.
https://api.github.com/repos/pandas-dev/pandas/pulls/9843
2015-04-09T14:54:56Z
2015-05-01T16:56:23Z
2015-05-01T16:56:23Z
2015-05-04T02:56:30Z
DOC: Fix release note for v0.16
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index aa35434802799..f9bef3d9c7f4a 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -474,10 +474,11 @@ Other API Changes - ``Series.values_counts`` and ``Series.describe`` for categorical data will now put ``NaN`` entries at the end. (:issue:`9443`) - ``Series.describe`` for categorical data will now give counts and frequencies of 0, not ``NaN``, for unused categories (:issue:`9443`) -- Due to a bug fix, looking up a partial string label with ``DatetimeIndex.asof`` now includes values that match the string, even if they are after the start of the partial string label (:issue:`9258`). Old behavior: +- Due to a bug fix, looking up a partial string label with ``DatetimeIndex.asof`` now includes values that match the string, even if they are after the start of the partial string label (:issue:`9258`). - .. ipython:: python - :verbatim: + Old behavior: + + .. code-block:: python In [4]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') Out[4]: Timestamp('2000-01-31 00:00:00')
Release note for v0.16 is not rendered properly.
https://api.github.com/repos/pandas-dev/pandas/pulls/9840
2015-04-09T13:41:17Z
2015-04-09T15:12:51Z
2015-04-09T15:12:51Z
2015-04-11T13:09:45Z
TST: Fix tests in TestGoogle
diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index 70a25a45c0ad4..9b27d612cdeee 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -33,7 +33,7 @@ def assert_n_failed_equals_n_null_columns(wngs, obj, cls=SymbolWarning): all_nan_cols = pd.Series(dict((k, pd.isnull(v).all()) for k, v in compat.iteritems(obj))) n_all_nan_cols = all_nan_cols.sum() - valid_warnings = pd.Series([wng for wng in wngs if isinstance(wng, cls)]) + valid_warnings = pd.Series([wng for wng in wngs if wng.category == cls]) assert_equal(len(valid_warnings), n_all_nan_cols) failed_symbols = all_nan_cols[all_nan_cols].index msgs = valid_warnings.map(lambda x: x.message) @@ -79,7 +79,7 @@ def test_get_goog_volume(self): for locale in self.locales: with tm.set_locale(locale): df = web.get_data_google('GOOG').sort_index() - self.assertEqual(df.Volume.ix['OCT-08-2010'], 2863473) + self.assertEqual(df.Volume.ix['JAN-02-2015'], 1446662) @network def test_get_multi1(self): @@ -87,10 +87,10 @@ def test_get_multi1(self): sl = ['AAPL', 'AMZN', 'GOOG'] with tm.set_locale(locale): pan = web.get_data_google(sl, '2012') - ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG] + ts = pan.Close.GOOG.index[pan.Close.AAPL < pan.Close.GOOG] if (hasattr(pan, 'Close') and hasattr(pan.Close, 'GOOG') and hasattr(pan.Close, 'AAPL')): - self.assertEqual(ts[0].dayofyear, 96) + self.assertEqual(ts[0].dayofyear, 3) else: self.assertRaises(AttributeError, lambda: pan.Close) @@ -135,7 +135,7 @@ def test_dtypes(self): def test_unicode_date(self): #GH8967 data = web.get_data_google('F', start='JAN-01-10', end='JAN-27-13') - self.assertEquals(data.index.name, 'Date') + self.assertEqual(data.index.name, 'Date') class TestYahoo(tm.TestCase):
Fixes the tests in data reader that will be run when the bug in #9744 is fixed. This needs to be merged before I can fix #9744.
https://api.github.com/repos/pandas-dev/pandas/pulls/9839
2015-04-09T03:28:12Z
2015-04-09T15:13:42Z
2015-04-09T15:13:42Z
2015-04-10T01:55:02Z
ERR: raise when index_col=True is passed
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index dcddcaaad36d0..9f989b2cf0ea9 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -96,3 +96,5 @@ Bug Fixes - Fixed bug where ``DataFrame.plot()`` raised an error when both ``color`` and ``style`` keywords were passed and there was no color symbol in the style strings (:issue:`9671`) - Bug in ``read_csv`` and ``read_table`` when using ``skip_rows`` parameter if blank lines are present. (:issue:`9832`) + +- Bug in ``read_csv()`` interprets ``index_col=True`` as ``1`` (:issue:`9798`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 637612d5fb09d..786d308c6770f 100644 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -652,6 +652,8 @@ def _clean_options(self, options, engine): # really delete this one keep_default_na = result.pop('keep_default_na') + if index_col is True: + raise ValueError("The value of index_col couldn't be 'True'") if _is_index_col(index_col): if not isinstance(index_col, (list, tuple, np.ndarray)): index_col = [index_col] diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index e549ec674b18d..b7016ad6cffae 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -520,6 +520,11 @@ def test_usecols_index_col_False(self): df = self.read_csv(StringIO(s_malformed), usecols=cols, index_col=False) tm.assert_frame_equal(expected, df) + def test_index_col_is_True(self): + # Issue 9798 + self.assertRaises(ValueError, self.read_csv, StringIO(self.ts_data), + index_col=True) + def test_converter_index_col_bug(self): # 1835 data = "A;B\n1;2\n3;4"
closes #9798 Add a check to see whether the `index_col` is `True`.
https://api.github.com/repos/pandas-dev/pandas/pulls/9835
2015-04-08T13:58:47Z
2015-04-09T15:03:48Z
2015-04-09T15:03:48Z
2015-04-09T15:03:53Z
BUG: skiprows doesn't handle blank lines properly when engine='c'
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index bd79d9d93fd04..54892a35462d5 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -88,3 +88,4 @@ Bug Fixes - Bug in ``FloatArrayFormatter`` where decision boundary for displaying "small" floats in decimal format is off by one order of magnitude for a given display.precision (:issue:`9764`) - Fixed bug where ``DataFrame.plot()`` raised an error when both ``color`` and ``style`` keywords were passed and there was no color symbol in the style strings (:issue:`9671`) +- Bug in ``read_csv`` and ``read_table`` when using ``skip_rows`` parameter if blank lines are present. (:issue:`9832`) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 35530a7f5e07f..e549ec674b18d 100644 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -839,6 +839,28 @@ def test_deep_skiprows(self): condensed_data = self.read_csv(StringIO(condensed_text)) tm.assert_frame_equal(data, condensed_data) + def test_skiprows_blank(self): + # GH 9832 + text = """#foo,a,b,c +#foo,a,b,c + +#foo,a,b,c +#foo,a,b,c + +1/1/2000,1.,2.,3. +1/2/2000,4,5,6 +1/3/2000,7,8,9 +""" + data = self.read_csv(StringIO(text), skiprows=6, header=None, + index_col=0, parse_dates=True) + + expected = DataFrame(np.arange(1., 10.).reshape((3, 3)), + columns=[1, 2, 3], + index=[datetime(2000, 1, 1), datetime(2000, 1, 2), + datetime(2000, 1, 3)]) + expected.index.name = 0 + tm.assert_frame_equal(data, expected) + def test_detect_string_na(self): data = """A,B foo,bar diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 975142ebacc2a..1bc4096658b29 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -757,11 +757,9 @@ int tokenize_delimited(parser_t *self, size_t line_limit) case START_RECORD: // start of record if (skip_this_line(self, self->file_lines)) { + self->state = SKIP_LINE; if (c == '\n') { - END_LINE() - } - else { - self->state = SKIP_LINE; + END_LINE(); } break; } @@ -1093,11 +1091,9 @@ int tokenize_delim_customterm(parser_t *self, size_t line_limit) case START_RECORD: // start of record if (skip_this_line(self, self->file_lines)) { + self->state = SKIP_LINE; if (c == self->lineterminator) { - END_LINE() - } - else { - self->state = SKIP_LINE; + END_LINE(); } break; } @@ -1391,11 +1387,9 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) case START_RECORD: // start of record if (skip_this_line(self, self->file_lines)) { + self->state = SKIP_LINE; if (c == '\n') { - END_LINE() - } - else { - self->state = SKIP_LINE; + END_LINE(); } break; } else if (c == '\n') {
Fixes GH #9832
https://api.github.com/repos/pandas-dev/pandas/pulls/9834
2015-04-08T12:54:05Z
2015-04-08T18:05:47Z
2015-04-08T18:05:47Z
2015-09-19T00:38:21Z
DOC: Clean up documentation for convert_objects
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e05709d7a180f..cda3d1d82aa3f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2260,19 +2260,23 @@ def convert_objects(self, convert_dates=True, convert_numeric=False, Parameters ---------- - convert_dates : if True, attempt to soft convert dates, if 'coerce', - force conversion (and non-convertibles get NaT) - convert_numeric : if True attempt to coerce to numbers (including - strings), non-convertibles get NaN - convert_timedeltas : if True, attempt to soft convert timedeltas, if 'coerce', - force conversion (and non-convertibles get NaT) - copy : Boolean, if True, return copy even if no copy is necessary - (e.g. no conversion was done), default is True. - It is meant for internal use, not to be confused with `inplace` kw. + convert_dates : boolean, default True + If True, convert to date where possible. If 'coerce', force + conversion, with unconvertible values becoming NaT. + convert_numeric : boolean, default False + If True, attempt to coerce to numbers (including strings), with + unconvertible values becoming NaN. + convert_timedeltas : boolean, default True + If True, convert to timedelta where possible. If 'coerce', force + conversion, with unconvertible values becoming NaT. + copy : boolean, default True + If True, return a copy even if no copy is necessary (e.g. no + conversion was done). Note: This is meant for internal use, and + should not be confused with inplace. Returns ------- - converted : asm as input object + converted : same as input object """ return self._constructor( self._data.convert(convert_dates=convert_dates,
https://api.github.com/repos/pandas-dev/pandas/pulls/9830
2015-04-07T12:34:08Z
2015-04-07T13:01:56Z
2015-04-07T13:01:56Z
2015-09-19T00:38:05Z
Allow tz-aware inputs in Holiday.dates
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 3b3542b760d6f..c31e25115c6a4 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -203,7 +203,10 @@ def dates(self, start_date, end_date, return_name=False): end_date = Timestamp(end_date) year_offset = DateOffset(years=1) - base_date = Timestamp(datetime(start_date.year, self.month, self.day)) + base_date = Timestamp( + datetime(start_date.year, self.month, self.day), + tz=start_date.tz, + ) dates = DatetimeIndex(start=base_date, end=end_date, freq=year_offset) holiday_dates = self._apply_rule(dates) if self.days_of_week is not None: diff --git a/pandas/tseries/tests/test_holiday.py b/pandas/tseries/tests/test_holiday.py index c2300481eca43..0880e84f1fcde 100644 --- a/pandas/tseries/tests/test_holiday.py +++ b/pandas/tseries/tests/test_holiday.py @@ -9,6 +9,7 @@ HolidayCalendarFactory, next_workday, previous_workday, before_nearest_workday, EasterMonday, GoodFriday, after_nearest_workday, weekend_to_monday) +from pytz import utc import nose class TestCalendar(tm.TestCase): @@ -55,87 +56,119 @@ def setUp(self): self.start_date = datetime(2011, 1, 1) self.end_date = datetime(2020, 12, 31) + def check_results(self, holiday, start, end, expected): + self.assertEqual(list(holiday.dates(start, end)), expected) + # Verify that timezone info is preserved. + self.assertEqual( + list( + holiday.dates( + utc.localize(Timestamp(start)), + utc.localize(Timestamp(end)), + ) + ), + [utc.localize(dt) for dt in expected], + ) + def test_usmemorialday(self): - holidays = USMemorialDay.dates(self.start_date, - self.end_date) - holidayList = [ - datetime(2011, 5, 30), - datetime(2012, 5, 28), - datetime(2013, 5, 27), - datetime(2014, 5, 26), - datetime(2015, 5, 25), - datetime(2016, 5, 30), - datetime(2017, 5, 29), - datetime(2018, 5, 28), - datetime(2019, 5, 27), - datetime(2020, 5, 25), - ] - self.assertEqual(list(holidays), holidayList) + self.check_results( + holiday=USMemorialDay, + start=self.start_date, + end=self.end_date, + expected=[ + datetime(2011, 5, 30), + datetime(2012, 5, 28), + datetime(2013, 5, 27), + datetime(2014, 5, 26), + datetime(2015, 5, 25), + datetime(2016, 5, 30), + datetime(2017, 5, 29), + datetime(2018, 5, 28), + datetime(2019, 5, 27), + datetime(2020, 5, 25), + ], + ) def test_non_observed_holiday(self): - july_3rd = Holiday('July 4th Eve', month=7, day=3) - result = july_3rd.dates("2001-01-01", "2003-03-03") - expected = [Timestamp('2001-07-03 00:00:00'), - Timestamp('2002-07-03 00:00:00')] - self.assertEqual(list(result), expected) - july_3rd = Holiday('July 4th Eve', month=7, day=3, - days_of_week=(0, 1, 2, 3)) - result = july_3rd.dates("2001-01-01", "2008-03-03") - expected = [Timestamp('2001-07-03 00:00:00'), - Timestamp('2002-07-03 00:00:00'), - Timestamp('2003-07-03 00:00:00'), - Timestamp('2006-07-03 00:00:00'), - Timestamp('2007-07-03 00:00:00')] - self.assertEqual(list(result), expected) + + self.check_results( + Holiday('July 4th Eve', month=7, day=3), + start="2001-01-01", + end="2003-03-03", + expected=[ + Timestamp('2001-07-03 00:00:00'), + Timestamp('2002-07-03 00:00:00') + ] + ) + + self.check_results( + Holiday('July 4th Eve', month=7, day=3, days_of_week=(0, 1, 2, 3)), + start="2001-01-01", + end="2008-03-03", + expected=[ + Timestamp('2001-07-03 00:00:00'), + Timestamp('2002-07-03 00:00:00'), + Timestamp('2003-07-03 00:00:00'), + Timestamp('2006-07-03 00:00:00'), + Timestamp('2007-07-03 00:00:00'), + ] + ) def test_easter(self): - holidays = EasterMonday.dates(self.start_date, - self.end_date) - holidayList = [Timestamp('2011-04-25 00:00:00'), - Timestamp('2012-04-09 00:00:00'), - Timestamp('2013-04-01 00:00:00'), - Timestamp('2014-04-21 00:00:00'), - Timestamp('2015-04-06 00:00:00'), - Timestamp('2016-03-28 00:00:00'), - Timestamp('2017-04-17 00:00:00'), - Timestamp('2018-04-02 00:00:00'), - Timestamp('2019-04-22 00:00:00'), - Timestamp('2020-04-13 00:00:00')] - - - self.assertEqual(list(holidays), holidayList) - holidays = GoodFriday.dates(self.start_date, - self.end_date) - holidayList = [Timestamp('2011-04-22 00:00:00'), - Timestamp('2012-04-06 00:00:00'), - Timestamp('2013-03-29 00:00:00'), - Timestamp('2014-04-18 00:00:00'), - Timestamp('2015-04-03 00:00:00'), - Timestamp('2016-03-25 00:00:00'), - Timestamp('2017-04-14 00:00:00'), - Timestamp('2018-03-30 00:00:00'), - Timestamp('2019-04-19 00:00:00'), - Timestamp('2020-04-10 00:00:00')] - self.assertEqual(list(holidays), holidayList) - + + self.check_results( + EasterMonday, + start=self.start_date, + end=self.end_date, + expected=[ + Timestamp('2011-04-25 00:00:00'), + Timestamp('2012-04-09 00:00:00'), + Timestamp('2013-04-01 00:00:00'), + Timestamp('2014-04-21 00:00:00'), + Timestamp('2015-04-06 00:00:00'), + Timestamp('2016-03-28 00:00:00'), + Timestamp('2017-04-17 00:00:00'), + Timestamp('2018-04-02 00:00:00'), + Timestamp('2019-04-22 00:00:00'), + Timestamp('2020-04-13 00:00:00'), + ], + ) + self.check_results( + GoodFriday, + start=self.start_date, + end=self.end_date, + expected=[ + Timestamp('2011-04-22 00:00:00'), + Timestamp('2012-04-06 00:00:00'), + Timestamp('2013-03-29 00:00:00'), + Timestamp('2014-04-18 00:00:00'), + Timestamp('2015-04-03 00:00:00'), + Timestamp('2016-03-25 00:00:00'), + Timestamp('2017-04-14 00:00:00'), + Timestamp('2018-03-30 00:00:00'), + Timestamp('2019-04-19 00:00:00'), + Timestamp('2020-04-10 00:00:00'), + ], + ) def test_usthanksgivingday(self): - holidays = USThanksgivingDay.dates(self.start_date, - self.end_date) - holidayList = [ - datetime(2011, 11, 24), - datetime(2012, 11, 22), - datetime(2013, 11, 28), - datetime(2014, 11, 27), - datetime(2015, 11, 26), - datetime(2016, 11, 24), - datetime(2017, 11, 23), - datetime(2018, 11, 22), - datetime(2019, 11, 28), - datetime(2020, 11, 26), - ] - - self.assertEqual(list(holidays), holidayList) + + self.check_results( + USThanksgivingDay, + start=self.start_date, + end=self.end_date, + expected=[ + datetime(2011, 11, 24), + datetime(2012, 11, 22), + datetime(2013, 11, 28), + datetime(2014, 11, 27), + datetime(2015, 11, 26), + datetime(2016, 11, 24), + datetime(2017, 11, 23), + datetime(2018, 11, 22), + datetime(2019, 11, 28), + datetime(2020, 11, 26), + ], + ) def test_argument_types(self): holidays = USThanksgivingDay.dates(self.start_date,
closes #9825 Previously, passing tz-aware inputs to Holiday.dates would always result in an error because the the input start_date was converted in a way that destroyed timezone information. This PR makes that conversion correctly preserve tz info, and converts several of the existing tests to run with both tz-aware and tz-naive inputs. Noticed while working on a new trading calendar implementation here: https://github.com/quantopian/zipline/pull/556.
https://api.github.com/repos/pandas-dev/pandas/pulls/9824
2015-04-07T00:44:21Z
2015-04-08T14:00:34Z
2015-04-08T14:00:34Z
2015-04-08T14:27:32Z
DOC: fix some various doc warnings
diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index 826d9e980538e..2207c823f43b1 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -56,6 +56,7 @@ appropriate pandas object (most likely a DataFrame): .. ipython:: python + :okwarning: import pandas.rpy.common as com infert = com.load_data('infert') diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 93ad2066d0e12..2b24531b2ef54 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -769,12 +769,14 @@ def str_rstrip(arr, to_strip=None): def str_wrap(arr, width, **kwargs): - """ - Wrap long strings to be formatted in paragraphs + r""" + Wrap long strings to be formatted in paragraphs. + + This method has the same keyword parameters and defaults as + :class:`textwrap.TextWrapper`. Parameters ---------- - Same keyword parameters and defaults as :class:`textwrap.TextWrapper` width : int Maximum line-width expand_tabs : bool, optional @@ -806,11 +808,11 @@ def str_wrap(arr, width, **kwargs): settings. To achieve behavior matching R's stringr library str_wrap function, use the arguments: - expand_tabs = False - replace_whitespace = True - drop_whitespace = True - break_long_words = False - break_on_hyphens = False + - expand_tabs = False + - replace_whitespace = True + - drop_whitespace = True + - break_long_words = False + - break_on_hyphens = False Examples -------- diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 8430e0209fd78..ef37e003ab67f 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -210,10 +210,13 @@ def to_datetime(arg, errors='ignore', dayfirst=False, utc=None, box=True, Returns ------- - ret : datetime if parsing succeeded. Return type depends on input: + ret : datetime if parsing succeeded. + Return type depends on input: + - list-like: DatetimeIndex - Series: Series of datetime64 dtype - scalar: Timestamp + In case when it is not possible to return designated types (e.g. when any element of input is before Timestamp.min or after Timestamp.max) return will have datetime.datetime type (or correspoding array/Series).
https://api.github.com/repos/pandas-dev/pandas/pulls/9819
2015-04-06T13:24:05Z
2015-04-06T21:16:59Z
2015-04-06T21:16:59Z
2015-04-06T21:17:00Z
API: Sort keys for DataFrame.assign
diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index e1c14029f1cf9..adcf2fca9b4c5 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -461,7 +461,7 @@ Inspired by `dplyr's <http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html#mutate>`__ ``mutate`` verb, DataFrame has an :meth:`~pandas.DataFrame.assign` method that allows you to easily create new columns that are potentially -derived from existing columns. +derived from existing columns. .. ipython:: python @@ -511,7 +511,9 @@ DataFrame is returned, with the new values inserted. .. warning:: Since the function signature of ``assign`` is ``**kwargs``, a dictionary, - the order of the new columns in the resulting DataFrame cannot be guaranteed. + the order of the new columns in the resulting DataFrame cannot be guaranteed + to match the order you pass in. To make things predictable, items are inserted + alphabetically (by key) at the end of the DataFrame. All expressions are computed first, and then assigned. So you can't refer to another column being assigned in the same call to ``assign``. For example: diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index f691b0842f071..653c296023c4e 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -45,6 +45,10 @@ API changes - Add support for separating years and quarters using dashes, for example 2014-Q1. (:issue:`9688`) +- :meth:`~pandas.DataFrame.assign` now inserts new columns in alphabetical order. Previously + the order was arbitrary. (:issue:`9777`) + + .. _whatsnew_0161.performance: Performance Improvements diff --git a/pandas/core/frame.py b/pandas/core/frame.py index f700d4316842c..8b683ad89558a 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -2244,10 +2244,11 @@ def assign(self, **kwargs): Notes ----- Since ``kwargs`` is a dictionary, the order of your - arguments may not be preserved, and so the order of the - new columns is not well defined. Assigning multiple - columns within the same ``assign`` is possible, but you cannot - reference other columns created within the same ``assign`` call. + arguments may not be preserved. The make things predicatable, + the columns are inserted in alphabetical order, at the end of + your DataFrame. Assigning multiple columns within the same + ``assign`` is possible, but you cannot reference other columns + created within the same ``assign`` call. Examples -------- @@ -2296,7 +2297,7 @@ def assign(self, **kwargs): results[k] = v # ... and then assign - for k, v in results.items(): + for k, v in sorted(results.items()): data[k] = v return data diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 3e4c16f63035f..e4abe15dee493 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -14073,12 +14073,21 @@ def test_assign(self): assert_frame_equal(result, expected) def test_assign_multiple(self): - df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) + df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=['A', 'B']) result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B) - expected = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9], - 'D': [1, 2, 3], 'E': [4, 5, 6]}) - # column order isn't preserved - assert_frame_equal(result.reindex_like(expected), expected) + expected = DataFrame([[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], + [3, 6, 9, 3, 6]], columns=list('ABCDE')) + assert_frame_equal(result, expected) + + def test_assign_alphabetical(self): + # GH 9818 + df = DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) + result = df.assign(D=df.A + df.B, C=df.A - df.B) + expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], + columns=list('ABCD')) + assert_frame_equal(result, expected) + result = df.assign(C=df.A - df.B, D=df.A + df.B) + assert_frame_equal(result, expected) def test_assign_bad(self): df = DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
Closes #9777 Previously the order of new columns from `.assign` was arbitrary. For predictability, we'll sort before inserting. We need to be comfortable with this change since we can't change behavior later with a keyword arg. Technically we _could_ allow referencing the a column defined within the same call to `assign` as long as they are sorted. e.g. `df.assign(C=df.A + df.B, D=df.C**2)` would work, but not `df.assign(df.D=df.A +df.B, C=df.D**2)`. But I don't think we should. cc @mrocklin
https://api.github.com/repos/pandas-dev/pandas/pulls/9818
2015-04-06T00:25:16Z
2015-04-07T22:36:59Z
2015-04-07T22:36:59Z
2015-04-07T22:37:18Z
BUG: Repeated time-series plot causes memory leak
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index d59b6120163ff..287963ed2c825 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -381,7 +381,7 @@ Bug Fixes - Bug in ``Series.plot(kind='hist')`` Y Label not informative (:issue:`10485`) - Bug in ``read_csv`` when using a converter which generates a ``uint8`` type (:issue:`9266`) - +- Bug causes memory leak in time-series line and area plot (:issue:`9003`) - Bug in line and kde plot cannot accept multiple colors when ``subplots=True`` (:issue:`9894`) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 800c6f83f4902..3271493f59219 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -3281,6 +3281,36 @@ def test_sharey_and_ax(self): self.assertTrue(ax.yaxis.get_label().get_visible(), "y label is invisible but shouldn't") + def test_memory_leak(self): + """ Check that every plot type gets properly collected. """ + import weakref + import gc + + results = {} + for kind in plotting._plot_klass.keys(): + args = {} + if kind in ['hexbin', 'scatter', 'pie']: + df = self.hexbin_df + args = {'x': 'A', 'y': 'B'} + elif kind == 'area': + df = self.tdf.abs() + else: + df = self.tdf + + # Use a weakref so we can see if the object gets collected without + # also preventing it from being collected + results[kind] = weakref.proxy(df.plot(kind=kind, **args)) + + # have matplotlib delete all the figures + tm.close() + # force a garbage collection + gc.collect() + for key in results: + # check that every plot was collected + with tm.assertRaises(ReferenceError): + # need to actually access something to get an error + results[key].lines + @slow def test_df_grid_settings(self): # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 6a822a0231a2b..c16e2686c5a3a 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -774,6 +774,7 @@ class MPLPlot(object): data : """ + _kind = 'base' _layout_type = 'vertical' _default_rot = 0 orientation = None @@ -830,10 +831,7 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None, self._rot_set = True else: self._rot_set = False - if isinstance(self._default_rot, dict): - self.rot = self._default_rot[self.kind] - else: - self.rot = self._default_rot + self.rot = self._default_rot if grid is None: grid = False if secondary_y else self.plt.rcParams['axes.grid'] @@ -1217,34 +1215,25 @@ def _get_xticks(self, convert_period=False): return x - def _is_datetype(self): - index = self.data.index - return (isinstance(index, (PeriodIndex, DatetimeIndex)) or - index.inferred_type in ('datetime', 'date', 'datetime64', - 'time')) + @classmethod + def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds): + mask = com.isnull(y) + if mask.any(): + y = np.ma.array(y) + y = np.ma.masked_where(mask, y) - def _get_plot_function(self): - ''' - Returns the matplotlib plotting function (plot or errorbar) based on - the presence of errorbar keywords. - ''' - errorbar = any(e is not None for e in self.errors.values()) - def plotf(ax, x, y, style=None, **kwds): - mask = com.isnull(y) - if mask.any(): - y = np.ma.array(y) - y = np.ma.masked_where(mask, y) - - if errorbar: - return self.plt.Axes.errorbar(ax, x, y, **kwds) + if isinstance(x, Index): + x = x._mpl_repr() + + if is_errorbar: + return ax.errorbar(x, y, **kwds) + else: + # prevent style kwarg from going to errorbar, where it is unsupported + if style is not None: + args = (x, y, style) else: - # prevent style kwarg from going to errorbar, where it is unsupported - if style is not None: - args = (ax, x, y, style) - else: - args = (ax, x, y) - return self.plt.Axes.plot(*args, **kwds) - return plotf + args = (x, y) + return ax.plot(*args, **kwds) def _get_index_name(self): if isinstance(self.data.index, MultiIndex): @@ -1431,6 +1420,7 @@ def _get_axes_layout(self): return (len(y_set), len(x_set)) class ScatterPlot(MPLPlot): + _kind = 'scatter' _layout_type = 'single' def __init__(self, data, x, y, c=None, **kwargs): @@ -1509,6 +1499,7 @@ def _post_plot_logic(self): class HexBinPlot(MPLPlot): + _kind = 'hexbin' _layout_type = 'single' def __init__(self, data, x, y, C=None, **kwargs): @@ -1564,7 +1555,7 @@ def _post_plot_logic(self): class LinePlot(MPLPlot): - + _kind = 'line' _default_rot = 0 orientation = 'vertical' @@ -1576,65 +1567,30 @@ def __init__(self, data, **kwargs): if 'x_compat' in self.kwds: self.x_compat = bool(self.kwds.pop('x_compat')) - def _index_freq(self): - freq = getattr(self.data.index, 'freq', None) - if freq is None: - freq = getattr(self.data.index, 'inferred_freq', None) - if freq == 'B': - weekdays = np.unique(self.data.index.dayofweek) - if (5 in weekdays) or (6 in weekdays): - freq = None - return freq - - def _is_dynamic_freq(self, freq): - if isinstance(freq, DateOffset): - freq = freq.rule_code - else: - freq = frequencies.get_base_alias(freq) - freq = frequencies.get_period_alias(freq) - return freq is not None and self._no_base(freq) - - def _no_base(self, freq): - # hack this for 0.10.1, creating more technical debt...sigh - if isinstance(self.data.index, DatetimeIndex): - base = frequencies.get_freq(freq) - x = self.data.index - if (base <= frequencies.FreqGroup.FR_DAY): - return x[:1].is_normalized - - return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0] - return True - - def _use_dynamic_x(self): - freq = self._index_freq() - - ax = self._get_ax(0) - ax_freq = getattr(ax, 'freq', None) - if freq is None: # convert irregular if axes has freq info - freq = ax_freq - else: # do not use tsplot if irregular was plotted first - if (ax_freq is None) and (len(ax.get_lines()) > 0): - return False - - return (freq is not None) and self._is_dynamic_freq(freq) - def _is_ts_plot(self): # this is slightly deceptive return not self.x_compat and self.use_index and self._use_dynamic_x() - def _make_plot(self): - self._initialize_prior(len(self.data)) + def _use_dynamic_x(self): + from pandas.tseries.plotting import _use_dynamic_x + return _use_dynamic_x(self._get_ax(0), self.data) + def _make_plot(self): if self._is_ts_plot(): - data = self._maybe_convert_index(self.data) + from pandas.tseries.plotting import _maybe_convert_index + data = _maybe_convert_index(self._get_ax(0), self.data) + x = data.index # dummy, not used - plotf = self._get_ts_plot_function() + plotf = self._ts_plot it = self._iter_data(data=data, keep_index=True) else: x = self._get_xticks(convert_period=True) - plotf = self._get_plot_function() + plotf = self._plot it = self._iter_data() + stacking_id = self._get_stacking_id() + is_errorbar = any(e is not None for e in self.errors.values()) + colors = self._get_colors() for i, (label, y) in enumerate(it): ax = self._get_ax(i) @@ -1647,84 +1603,87 @@ def _make_plot(self): label = com.pprint_thing(label) # .encode('utf-8') kwds['label'] = label - newlines = plotf(ax, x, y, style=style, column_num=i, **kwds) + newlines = plotf(ax, x, y, style=style, column_num=i, + stacking_id=stacking_id, + is_errorbar=is_errorbar, + **kwds) self._add_legend_handle(newlines[0], label, index=i) lines = _get_all_lines(ax) left, right = _get_xlim(lines) ax.set_xlim(left, right) - def _get_stacked_values(self, y, label): + @classmethod + def _plot(cls, ax, x, y, style=None, column_num=None, + stacking_id=None, **kwds): + # column_num is used to get the target column from protf in line and area plots + if column_num == 0: + cls._initialize_stacker(ax, stacking_id, len(y)) + y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) + lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds) + cls._update_stacker(ax, stacking_id, y) + return lines + + @classmethod + def _ts_plot(cls, ax, x, data, style=None, **kwds): + from pandas.tseries.plotting import (_maybe_resample, + _decorate_axes, + format_dateaxis) + # accept x to be consistent with normal plot func, + # x is not passed to tsplot as it uses data.index as x coordinate + # column_num must be in kwds for stacking purpose + freq, data = _maybe_resample(data, ax, kwds) + + # Set ax with freq info + _decorate_axes(ax, freq, kwds) + ax._plot_data.append((data, cls._kind, kwds)) + + lines = cls._plot(ax, data.index, data.values, style=style, **kwds) + # set date formatter, locators and rescale limits + format_dateaxis(ax, ax.freq) + return lines + + def _get_stacking_id(self): if self.stacked: - if (y >= 0).all(): - return self._pos_prior + y - elif (y <= 0).all(): - return self._neg_prior + y - else: - raise ValueError('When stacked is True, each column must be either all positive or negative.' - '{0} contains both positive and negative values'.format(label)) + return id(self.data) else: - return y - - def _get_plot_function(self): - f = MPLPlot._get_plot_function(self) - def plotf(ax, x, y, style=None, column_num=None, **kwds): - # column_num is used to get the target column from protf in line and area plots - if column_num == 0: - self._initialize_prior(len(self.data)) - y_values = self._get_stacked_values(y, kwds['label']) - lines = f(ax, x, y_values, style=style, **kwds) - self._update_prior(y) - return lines - return plotf - - def _get_ts_plot_function(self): - from pandas.tseries.plotting import tsplot - plotf = self._get_plot_function() - def _plot(ax, x, data, style=None, **kwds): - # accept x to be consistent with normal plot func, - # x is not passed to tsplot as it uses data.index as x coordinate - lines = tsplot(data, plotf, ax=ax, style=style, **kwds) - return lines - return _plot - - def _initialize_prior(self, n): - self._pos_prior = np.zeros(n) - self._neg_prior = np.zeros(n) - - def _update_prior(self, y): - if self.stacked and not self.subplots: - # tsplot resample may changedata length - if len(self._pos_prior) != len(y): - self._initialize_prior(len(y)) - if (y >= 0).all(): - self._pos_prior += y - elif (y <= 0).all(): - self._neg_prior += y - - def _maybe_convert_index(self, data): - # tsplot converts automatically, but don't want to convert index - # over and over for DataFrames - if isinstance(data.index, DatetimeIndex): - freq = getattr(data.index, 'freq', None) - - if freq is None: - freq = getattr(data.index, 'inferred_freq', None) - if isinstance(freq, DateOffset): - freq = freq.rule_code - - if freq is None: - ax = self._get_ax(0) - freq = getattr(ax, 'freq', None) - - if freq is None: - raise ValueError('Could not get frequency alias for plotting') - - freq = frequencies.get_base_alias(freq) - freq = frequencies.get_period_alias(freq) - - data.index = data.index.to_period(freq=freq) - return data + return None + + @classmethod + def _initialize_stacker(cls, ax, stacking_id, n): + if stacking_id is None: + return + if not hasattr(ax, '_stacker_pos_prior'): + ax._stacker_pos_prior = {} + if not hasattr(ax, '_stacker_neg_prior'): + ax._stacker_neg_prior = {} + ax._stacker_pos_prior[stacking_id] = np.zeros(n) + ax._stacker_neg_prior[stacking_id] = np.zeros(n) + + @classmethod + def _get_stacked_values(cls, ax, stacking_id, values, label): + if stacking_id is None: + return values + if not hasattr(ax, '_stacker_pos_prior'): + # stacker may not be initialized for subplots + cls._initialize_stacker(ax, stacking_id, len(values)) + + if (values >= 0).all(): + return ax._stacker_pos_prior[stacking_id] + values + elif (values <= 0).all(): + return ax._stacker_neg_prior[stacking_id] + values + + raise ValueError('When stacked is True, each column must be either all positive or negative.' + '{0} contains both positive and negative values'.format(label)) + + @classmethod + def _update_stacker(cls, ax, stacking_id, values): + if stacking_id is None: + return + if (values >= 0).all(): + ax._stacker_pos_prior[stacking_id] += values + elif (values <= 0).all(): + ax._stacker_neg_prior[stacking_id] += values def _post_plot_logic(self): df = self.data @@ -1749,6 +1708,7 @@ def _post_plot_logic(self): class AreaPlot(LinePlot): + _kind = 'area' def __init__(self, data, **kwargs): kwargs.setdefault('stacked', True) @@ -1759,35 +1719,36 @@ def __init__(self, data, **kwargs): # use smaller alpha to distinguish overlap self.kwds.setdefault('alpha', 0.5) - def _get_plot_function(self): if self.logy or self.loglog: raise ValueError("Log-y scales are not supported in area plot") - else: - f = MPLPlot._get_plot_function(self) - def plotf(ax, x, y, style=None, column_num=None, **kwds): - if column_num == 0: - self._initialize_prior(len(self.data)) - y_values = self._get_stacked_values(y, kwds['label']) - lines = f(ax, x, y_values, style=style, **kwds) - - # get data from the line to get coordinates for fill_between - xdata, y_values = lines[0].get_data(orig=False) - - if (y >= 0).all(): - start = self._pos_prior - elif (y <= 0).all(): - start = self._neg_prior - else: - start = np.zeros(len(y)) - if not 'color' in kwds: - kwds['color'] = lines[0].get_color() + @classmethod + def _plot(cls, ax, x, y, style=None, column_num=None, + stacking_id=None, is_errorbar=False, **kwds): + if column_num == 0: + cls._initialize_stacker(ax, stacking_id, len(y)) + y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label']) + lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds) + + # get data from the line to get coordinates for fill_between + xdata, y_values = lines[0].get_data(orig=False) + + # unable to use ``_get_stacked_values`` here to get starting point + if stacking_id is None: + start = np.zeros(len(y)) + elif (y >= 0).all(): + start = ax._stacker_pos_prior[stacking_id] + elif (y <= 0).all(): + start = ax._stacker_neg_prior[stacking_id] + else: + start = np.zeros(len(y)) - self.plt.Axes.fill_between(ax, xdata, start, y_values, **kwds) - self._update_prior(y) - return lines + if not 'color' in kwds: + kwds['color'] = lines[0].get_color() - return plotf + ax.fill_between(xdata, start, y_values, **kwds) + cls._update_stacker(ax, stacking_id, y) + return lines def _add_legend_handle(self, handle, label, index=None): from matplotlib.patches import Rectangle @@ -1810,8 +1771,9 @@ def _post_plot_logic(self): class BarPlot(MPLPlot): - - _default_rot = {'bar': 90, 'barh': 0} + _kind = 'bar' + _default_rot = 90 + orientation = 'vertical' def __init__(self, data, **kwargs): self.bar_width = kwargs.pop('width', 0.5) @@ -1848,20 +1810,13 @@ def _args_adjust(self): if com.is_list_like(self.left): self.left = np.array(self.left) - def _get_plot_function(self): - if self.kind == 'bar': - def f(ax, x, y, w, start=None, **kwds): - start = start + self.bottom - return ax.bar(x, y, w, bottom=start, log=self.log, **kwds) - elif self.kind == 'barh': - - def f(ax, x, y, w, start=None, log=self.log, **kwds): - start = start + self.left - return ax.barh(x, y, w, left=start, log=self.log, **kwds) - else: - raise ValueError("BarPlot kind must be either 'bar' or 'barh'") + @classmethod + def _plot(cls, ax, x, y, w, start=0, log=False, **kwds): + return ax.bar(x, y, w, bottom=start, log=log, **kwds) - return f + @property + def _start_base(self): + return self.bottom def _make_plot(self): import matplotlib as mpl @@ -1869,7 +1824,6 @@ def _make_plot(self): colors = self._get_colors() ncolors = len(colors) - bar_f = self._get_plot_function() pos_prior = neg_prior = np.zeros(len(self.data)) K = self.nseries @@ -1890,24 +1844,25 @@ def _make_plot(self): start = 0 if self.log and (y >= 1).all(): start = 1 + start = start + self._start_base if self.subplots: w = self.bar_width / 2 - rect = bar_f(ax, self.ax_pos + w, y, self.bar_width, - start=start, label=label, **kwds) + rect = self._plot(ax, self.ax_pos + w, y, self.bar_width, + start=start, label=label, log=self.log, **kwds) ax.set_title(label) elif self.stacked: mask = y > 0 - start = np.where(mask, pos_prior, neg_prior) + start = np.where(mask, pos_prior, neg_prior) + self._start_base w = self.bar_width / 2 - rect = bar_f(ax, self.ax_pos + w, y, self.bar_width, - start=start, label=label, **kwds) + rect = self._plot(ax, self.ax_pos + w, y, self.bar_width, + start=start, label=label, log=self.log, **kwds) pos_prior = pos_prior + np.where(mask, y, 0) neg_prior = neg_prior + np.where(mask, 0, y) else: w = self.bar_width / K - rect = bar_f(ax, self.ax_pos + (i + 0.5) * w, y, w, - start=start, label=label, **kwds) + rect = self._plot(ax, self.ax_pos + (i + 0.5) * w, y, w, + start=start, label=label, log=self.log, **kwds) self._add_legend_handle(rect, label, index=i) def _post_plot_logic(self): @@ -1922,33 +1877,40 @@ def _post_plot_logic(self): s_edge = self.ax_pos[0] - 0.25 + self.lim_offset e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset - if self.kind == 'bar': - ax.set_xlim((s_edge, e_edge)) - ax.set_xticks(self.tick_pos) - ax.set_xticklabels(str_index) - if name is not None and self.use_index: - ax.set_xlabel(name) - elif self.kind == 'barh': - # horizontal bars - ax.set_ylim((s_edge, e_edge)) - ax.set_yticks(self.tick_pos) - ax.set_yticklabels(str_index) - if name is not None and self.use_index: - ax.set_ylabel(name) - else: - raise NotImplementedError(self.kind) + self._decorate_ticks(ax, name, str_index, s_edge, e_edge) + + def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge): + ax.set_xlim((start_edge, end_edge)) + ax.set_xticks(self.tick_pos) + ax.set_xticklabels(ticklabels) + if name is not None and self.use_index: + ax.set_xlabel(name) + + +class BarhPlot(BarPlot): + _kind = 'barh' + _default_rot = 0 + orientation = 'horizontal' @property - def orientation(self): - if self.kind == 'bar': - return 'vertical' - elif self.kind == 'barh': - return 'horizontal' - else: - raise NotImplementedError(self.kind) + def _start_base(self): + return self.left + + @classmethod + def _plot(cls, ax, x, y, w, start=0, log=False, **kwds): + return ax.barh(x, y, w, left=start, log=log, **kwds) + + def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge): + # horizontal bars + ax.set_ylim((start_edge, end_edge)) + ax.set_yticks(self.tick_pos) + ax.set_yticklabels(ticklabels) + if name is not None and self.use_index: + ax.set_ylabel(name) class HistPlot(LinePlot): + _kind = 'hist' def __init__(self, data, bins=10, bottom=0, **kwargs): self.bins = bins # use mpl default @@ -1971,22 +1933,24 @@ def _args_adjust(self): if com.is_list_like(self.bottom): self.bottom = np.array(self.bottom) - def _get_plot_function(self): - def plotf(ax, y, style=None, column_num=None, **kwds): - if column_num == 0: - self._initialize_prior(len(self.bins) - 1) - y = y[~com.isnull(y)] - bottom = self._pos_prior + self.bottom - # ignore style - n, bins, patches = self.plt.Axes.hist(ax, y, bins=self.bins, - bottom=bottom, **kwds) - self._update_prior(n) - return patches - return plotf + @classmethod + def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, + stacking_id=None, **kwds): + if column_num == 0: + cls._initialize_stacker(ax, stacking_id, len(bins) - 1) + y = y[~com.isnull(y)] + + base = np.zeros(len(bins) - 1) + bottom = bottom + cls._get_stacked_values(ax, stacking_id, base, kwds['label']) + # ignore style + n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) + cls._update_stacker(ax, stacking_id, n) + return patches def _make_plot(self): - plotf = self._get_plot_function() colors = self._get_colors() + stacking_id = self._get_stacking_id() + for i, (label, y) in enumerate(self._iter_data()): ax = self._get_ax(i) @@ -1999,9 +1963,18 @@ def _make_plot(self): if style is not None: kwds['style'] = style - artists = plotf(ax, y, column_num=i, **kwds) + kwds = self._make_plot_keywords(kwds, y) + artists = self._plot(ax, y, column_num=i, + stacking_id=stacking_id, **kwds) self._add_legend_handle(artists[0], label, index=i) + def _make_plot_keywords(self, kwds, y): + """merge BoxPlot/KdePlot properties to passed kwds""" + # y is required for KdePlot + kwds['bottom'] = self.bottom + kwds['bins'] = self.bins + return kwds + def _post_plot_logic(self): if self.orientation == 'horizontal': for ax in self.axes: @@ -2019,6 +1992,7 @@ def orientation(self): class KdePlot(HistPlot): + _kind = 'kde' orientation = 'vertical' def __init__(self, data, bw_method=None, ind=None, **kwargs): @@ -2038,26 +2012,31 @@ def _get_ind(self, y): ind = self.ind return ind - def _get_plot_function(self): + @classmethod + def _plot(cls, ax, y, style=None, bw_method=None, ind=None, + column_num=None, stacking_id=None, **kwds): from scipy.stats import gaussian_kde from scipy import __version__ as spv - f = MPLPlot._get_plot_function(self) - def plotf(ax, y, style=None, column_num=None, **kwds): - y = remove_na(y) - if LooseVersion(spv) >= '0.11.0': - gkde = gaussian_kde(y, bw_method=self.bw_method) - else: - gkde = gaussian_kde(y) - if self.bw_method is not None: - msg = ('bw_method was added in Scipy 0.11.0.' + - ' Scipy version in use is %s.' % spv) - warnings.warn(msg) - - ind = self._get_ind(y) - y = gkde.evaluate(ind) - lines = f(ax, ind, y, style=style, **kwds) - return lines - return plotf + + y = remove_na(y) + + if LooseVersion(spv) >= '0.11.0': + gkde = gaussian_kde(y, bw_method=bw_method) + else: + gkde = gaussian_kde(y) + if bw_method is not None: + msg = ('bw_method was added in Scipy 0.11.0.' + + ' Scipy version in use is %s.' % spv) + warnings.warn(msg) + + y = gkde.evaluate(ind) + lines = MPLPlot._plot(ax, ind, y, style=style, **kwds) + return lines + + def _make_plot_keywords(self, kwds, y): + kwds['bw_method'] = self.bw_method + kwds['ind'] = self._get_ind(y) + return kwds def _post_plot_logic(self): for ax in self.axes: @@ -2065,6 +2044,7 @@ def _post_plot_logic(self): class PiePlot(MPLPlot): + _kind = 'pie' _layout_type = 'horizontal' def __init__(self, data, kind=None, **kwargs): @@ -2083,8 +2063,8 @@ def _validate_color_args(self): pass def _make_plot(self): - self.kwds.setdefault('colors', self._get_colors(num_colors=len(self.data), - color_kwds='colors')) + colors = self._get_colors(num_colors=len(self.data), color_kwds='colors') + self.kwds.setdefault('colors', colors) for i, (label, y) in enumerate(self._iter_data()): ax = self._get_ax(i) @@ -2129,6 +2109,7 @@ def blank_labeler(label, value): class BoxPlot(LinePlot): + _kind = 'box' _layout_type = 'horizontal' _valid_return_types = (None, 'axes', 'dict', 'both') @@ -2151,25 +2132,24 @@ def _args_adjust(self): else: self.sharey = False - def _get_plot_function(self): - def plotf(ax, y, column_num=None, **kwds): - if y.ndim == 2: - y = [remove_na(v) for v in y] - # Boxplot fails with empty arrays, so need to add a NaN - # if any cols are empty - # GH 8181 - y = [v if v.size > 0 else np.array([np.nan]) for v in y] - else: - y = remove_na(y) - bp = ax.boxplot(y, **kwds) + @classmethod + def _plot(cls, ax, y, column_num=None, return_type=None, **kwds): + if y.ndim == 2: + y = [remove_na(v) for v in y] + # Boxplot fails with empty arrays, so need to add a NaN + # if any cols are empty + # GH 8181 + y = [v if v.size > 0 else np.array([np.nan]) for v in y] + else: + y = remove_na(y) + bp = ax.boxplot(y, **kwds) - if self.return_type == 'dict': - return bp, bp - elif self.return_type == 'both': - return self.BP(ax=ax, lines=bp), bp - else: - return ax, bp - return plotf + if return_type == 'dict': + return bp, bp + elif return_type == 'both': + return cls.BP(ax=ax, lines=bp), bp + else: + return ax, bp def _validate_color_args(self): if 'color' in self.kwds: @@ -2223,7 +2203,6 @@ def maybe_color_bp(self, bp): setp(bp['caps'], color=caps, alpha=1) def _make_plot(self): - plotf = self._get_plot_function() if self.subplots: self._return_obj = compat.OrderedDict() @@ -2231,7 +2210,8 @@ def _make_plot(self): ax = self._get_ax(i) kwds = self.kwds.copy() - ret, bp = plotf(ax, y, column_num=i, **kwds) + ret, bp = self._plot(ax, y, column_num=i, + return_type=self.return_type, **kwds) self.maybe_color_bp(bp) self._return_obj[label] = ret @@ -2242,7 +2222,8 @@ def _make_plot(self): ax = self._get_ax(0) kwds = self.kwds.copy() - ret, bp = plotf(ax, y, column_num=0, **kwds) + ret, bp = self._plot(ax, y, column_num=0, + return_type=self.return_type, **kwds) self.maybe_color_bp(bp) self._return_obj = ret @@ -2287,10 +2268,12 @@ def result(self): _series_kinds = ['pie'] _all_kinds = _common_kinds + _dataframe_kinds + _series_kinds -_plot_klass = {'line': LinePlot, 'bar': BarPlot, 'barh': BarPlot, - 'kde': KdePlot, 'hist': HistPlot, 'box': BoxPlot, - 'scatter': ScatterPlot, 'hexbin': HexBinPlot, - 'area': AreaPlot, 'pie': PiePlot} +_klasses = [LinePlot, BarPlot, BarhPlot, KdePlot, HistPlot, BoxPlot, + ScatterPlot, HexBinPlot, AreaPlot, PiePlot] + +_plot_klass = {} +for klass in _klasses: + _plot_klass[klass._kind] = klass def _plot(data, x=None, y=None, subplots=False, diff --git a/pandas/tseries/plotting.py b/pandas/tseries/plotting.py index 9d28fa11f646f..ad27b412cddb9 100644 --- a/pandas/tseries/plotting.py +++ b/pandas/tseries/plotting.py @@ -4,12 +4,16 @@ """ #!!! TODO: Use the fact that axis can have units to simplify the process + +import numpy as np + from matplotlib import pylab from pandas.tseries.period import Period from pandas.tseries.offsets import DateOffset import pandas.tseries.frequencies as frequencies from pandas.tseries.index import DatetimeIndex import pandas.core.common as com +import pandas.compat as compat from pandas.tseries.converter import (TimeSeries_DateLocator, TimeSeries_DateFormatter) @@ -18,7 +22,7 @@ # Plotting functions and monkey patches -def tsplot(series, plotf, **kwargs): +def tsplot(series, plotf, ax=None, **kwargs): """ Plots a Series on the given Matplotlib axes or the current axes @@ -33,46 +37,33 @@ def tsplot(series, plotf, **kwargs): """ # Used inferred freq is possible, need a test case for inferred - if 'ax' in kwargs: - ax = kwargs.pop('ax') - else: + if ax is None: import matplotlib.pyplot as plt ax = plt.gca() - freq = _get_freq(ax, series) - # resample against axes freq if necessary - if freq is None: # pragma: no cover - raise ValueError('Cannot use dynamic axis without frequency info') - else: - # Convert DatetimeIndex to PeriodIndex - if isinstance(series.index, DatetimeIndex): - series = series.to_period(freq=freq) - freq, ax_freq, series = _maybe_resample(series, ax, freq, plotf, - kwargs) + freq, series = _maybe_resample(series, ax, kwargs) # Set ax with freq info _decorate_axes(ax, freq, kwargs) - - # how to make sure ax.clear() flows through? - if not hasattr(ax, '_plot_data'): - ax._plot_data = [] ax._plot_data.append((series, plotf, kwargs)) lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs) # set date formatter, locators and rescale limits format_dateaxis(ax, ax.freq) + return lines - # x and y coord info - ax.format_coord = lambda t, y: ("t = {0} " - "y = {1:8f}".format(Period(ordinal=int(t), - freq=ax.freq), - y)) - return lines +def _maybe_resample(series, ax, kwargs): + # resample against axes freq if necessary + freq, ax_freq = _get_freq(ax, series) + + if freq is None: # pragma: no cover + raise ValueError('Cannot use dynamic axis without frequency info') + # Convert DatetimeIndex to PeriodIndex + if isinstance(series.index, DatetimeIndex): + series = series.to_period(freq=freq) -def _maybe_resample(series, ax, freq, plotf, kwargs): - ax_freq = _get_ax_freq(ax) if ax_freq is not None and freq != ax_freq: if frequencies.is_superperiod(freq, ax_freq): # upsample input series = series.copy() @@ -84,21 +75,11 @@ def _maybe_resample(series, ax, freq, plotf, kwargs): series = series.resample(ax_freq, how=how).dropna() freq = ax_freq elif frequencies.is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq): - _upsample_others(ax, freq, plotf, kwargs) + _upsample_others(ax, freq, kwargs) ax_freq = freq else: # pragma: no cover raise ValueError('Incompatible frequency conversion') - return freq, ax_freq, series - - -def _get_ax_freq(ax): - ax_freq = getattr(ax, 'freq', None) - if ax_freq is None: - if hasattr(ax, 'left_ax'): - ax_freq = getattr(ax.left_ax, 'freq', None) - elif hasattr(ax, 'right_ax'): - ax_freq = getattr(ax.right_ax, 'freq', None) - return ax_freq + return freq, series def _is_sub(f1, f2): @@ -111,9 +92,10 @@ def _is_sup(f1, f2): (f2.startswith('W') and frequencies.is_superperiod(f1, 'D'))) -def _upsample_others(ax, freq, plotf, kwargs): +def _upsample_others(ax, freq, kwargs): legend = ax.get_legend() lines, labels = _replot_ax(ax, freq, kwargs) + _replot_ax(ax, freq, kwargs) other_ax = None if hasattr(ax, 'left_ax'): @@ -136,8 +118,11 @@ def _upsample_others(ax, freq, plotf, kwargs): def _replot_ax(ax, freq, kwargs): data = getattr(ax, '_plot_data', None) + + # clear current axes and data ax._plot_data = [] ax.clear() + _decorate_axes(ax, freq, kwargs) lines = [] @@ -147,7 +132,13 @@ def _replot_ax(ax, freq, kwargs): series = series.copy() idx = series.index.asfreq(freq, how='S') series.index = idx - ax._plot_data.append(series) + ax._plot_data.append((series, plotf, kwds)) + + # for tsplot + if isinstance(plotf, compat.string_types): + from pandas.tools.plotting import _plot_klass + plotf = _plot_klass[plotf]._plot + lines.append(plotf(ax, series.index._mpl_repr(), series.values, **kwds)[0]) labels.append(com.pprint_thing(series.name)) @@ -155,6 +146,10 @@ def _replot_ax(ax, freq, kwargs): def _decorate_axes(ax, freq, kwargs): + """Initialize axes for time-series plotting""" + if not hasattr(ax, '_plot_data'): + ax._plot_data = [] + ax.freq = freq xaxis = ax.get_xaxis() xaxis.freq = freq @@ -173,6 +168,11 @@ def _get_freq(ax, series): freq = getattr(series.index, 'inferred_freq', None) ax_freq = getattr(ax, 'freq', None) + if ax_freq is None: + if hasattr(ax, 'left_ax'): + ax_freq = getattr(ax.left_ax, 'freq', None) + elif hasattr(ax, 'right_ax'): + ax_freq = getattr(ax.right_ax, 'freq', None) # use axes freq if no data freq if freq is None: @@ -185,10 +185,76 @@ def _get_freq(ax, series): freq = frequencies.get_base_alias(freq) freq = frequencies.get_period_alias(freq) + return freq, ax_freq + + +def _use_dynamic_x(ax, data): + freq = _get_index_freq(data) + ax_freq = getattr(ax, 'freq', None) + + if freq is None: # convert irregular if axes has freq info + freq = ax_freq + else: # do not use tsplot if irregular was plotted first + if (ax_freq is None) and (len(ax.get_lines()) > 0): + return False + + if freq is None: + return False + + if isinstance(freq, DateOffset): + freq = freq.rule_code + else: + freq = frequencies.get_base_alias(freq) + freq = frequencies.get_period_alias(freq) + if freq is None: + return False + + # hack this for 0.10.1, creating more technical debt...sigh + if isinstance(data.index, DatetimeIndex): + base = frequencies.get_freq(freq) + x = data.index + if (base <= frequencies.FreqGroup.FR_DAY): + return x[:1].is_normalized + return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0] + return True + + +def _get_index_freq(data): + freq = getattr(data.index, 'freq', None) + if freq is None: + freq = getattr(data.index, 'inferred_freq', None) + if freq == 'B': + weekdays = np.unique(data.index.dayofweek) + if (5 in weekdays) or (6 in weekdays): + freq = None return freq +def _maybe_convert_index(ax, data): + # tsplot converts automatically, but don't want to convert index + # over and over for DataFrames + if isinstance(data.index, DatetimeIndex): + freq = getattr(data.index, 'freq', None) + + if freq is None: + freq = getattr(data.index, 'inferred_freq', None) + if isinstance(freq, DateOffset): + freq = freq.rule_code + + if freq is None: + freq = getattr(ax, 'freq', None) + + if freq is None: + raise ValueError('Could not get frequency alias for plotting') + + freq = frequencies.get_base_alias(freq) + freq = frequencies.get_period_alias(freq) + + data = data.to_period(freq=freq) + return data + + # Patch methods for subplot. Only format_dateaxis is currently used. # Do we need the rest for convenience? @@ -219,4 +285,9 @@ def format_dateaxis(subplot, freq): plot_obj=subplot) subplot.xaxis.set_major_formatter(majformatter) subplot.xaxis.set_minor_formatter(minformatter) + + # x and y coord info + subplot.format_coord = lambda t, y: ("t = {0} " + "y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y)) + pylab.draw_if_interactive() diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index 2ba65c07aa114..74f2a4550780b 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -105,6 +105,12 @@ def test_tsplot(self): for s in self.datetime_ser: _check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s) + for s in self.period_ser: + _check_plot_works(s.plot, ax=ax) + + for s in self.datetime_ser: + _check_plot_works(s.plot, ax=ax) + ax = ts.plot(style='k') self.assertEqual((0., 0., 0.), ax.get_lines()[0].get_color()) @@ -151,6 +157,15 @@ def check_format_of_first_point(ax, expected_string): # note this is added to the annual plot already in existence, and changes its freq field daily = Series(1, index=date_range('2014-01-01', periods=3, freq='D')) check_format_of_first_point(daily.plot(), 't = 2014-01-01 y = 1.000000') + tm.close() + + # tsplot + import matplotlib.pyplot as plt + from pandas.tseries.plotting import tsplot + tsplot(annual, plt.Axes.plot) + check_format_of_first_point(plt.gca(), 't = 2014 y = 1.000000') + tsplot(daily, plt.Axes.plot) + check_format_of_first_point(plt.gca(), 't = 2014-01-01 y = 1.000000') @slow def test_line_plot_period_series(self): @@ -746,6 +761,15 @@ def test_to_weekly_resampling(self): for l in ax.get_lines(): self.assertTrue(PeriodIndex(data=l.get_xdata()).freq.startswith('W')) + # tsplot + from pandas.tseries.plotting import tsplot + import matplotlib.pyplot as plt + + tsplot(high, plt.Axes.plot) + lines = tsplot(low, plt.Axes.plot) + for l in lines: + self.assertTrue(PeriodIndex(data=l.get_xdata()).freq.startswith('W')) + @slow def test_from_weekly_resampling(self): idxh = date_range('1/1/1999', periods=52, freq='W') @@ -760,7 +784,22 @@ def test_from_weekly_resampling(self): 1553, 1558, 1562]) for l in ax.get_lines(): self.assertTrue(PeriodIndex(data=l.get_xdata()).freq.startswith('W')) + xdata = l.get_xdata(orig=False) + if len(xdata) == 12: # idxl lines + self.assert_numpy_array_equal(xdata, expected_l) + else: + self.assert_numpy_array_equal(xdata, expected_h) + tm.close() + + # tsplot + from pandas.tseries.plotting import tsplot + import matplotlib.pyplot as plt + + tsplot(low, plt.Axes.plot) + lines = tsplot(high, plt.Axes.plot) + for l in lines: + self.assertTrue(PeriodIndex(data=l.get_xdata()).freq.startswith('W')) xdata = l.get_xdata(orig=False) if len(xdata) == 12: # idxl lines self.assert_numpy_array_equal(xdata, expected_l)
Closes #9003, Closes #9307. - Not to clutter plot functions outside of classes. - Changed to use classmethods rather than closures for plot funcs. - Not cache plot function itself on `ax._plot_data`. Changed to store `str` indicates plot kind. - Moved `tsplot` related codes to `tseries.plotting.py`. Because the change is little big, I think it is better to wait until 0.17. Further simplification seems to be possible if we can remove `tsplot` as not public function. CC: @qwhelan, @TomAugspurger
https://api.github.com/repos/pandas-dev/pandas/pulls/9814
2015-04-05T09:55:45Z
2015-07-29T14:05:50Z
2015-07-29T14:05:50Z
2015-08-03T13:23:33Z
TST: Split graphics_test to main and others
diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index d72bc420b2388..dd526720d2605 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -30,13 +30,11 @@ import pandas.tools.plotting as plotting -def _skip_if_mpl_14_or_dev_boxplot(): - # GH 8382 - # Boxplot failures on 1.4 and 1.4.1 - # Don't need try / except since that's done at class level - import matplotlib - if str(matplotlib.__version__) >= LooseVersion('1.4'): - raise nose.SkipTest("Matplotlib Regression in 1.4 and current dev.") +""" +These tests are for ``Dataframe.plot`` and ``Series.plot``. +Other plot methods such as ``.hist``, ``.boxplot`` and other miscellaneous +are tested in test_graphics_others.py +""" def _skip_if_no_scipy_gaussian_kde(): @@ -46,6 +44,7 @@ def _skip_if_no_scipy_gaussian_kde(): except ImportError: raise nose.SkipTest("scipy version doesn't support gaussian_kde") + def _ok_for_gaussian_kde(kind): if kind in ['kde','density']: try: @@ -55,6 +54,7 @@ def _ok_for_gaussian_kde(kind): return False return True + @tm.mplskip class TestPlotBase(tm.TestCase): @@ -943,20 +943,6 @@ def test_plot_fails_with_dupe_color_and_style(self): with tm.assertRaises(ValueError): x.plot(style='k--', color='k') - @slow - def test_hist_by_no_extra_plots(self): - df = self.hist_df - axes = df.height.hist(by=df.gender) - self.assertEqual(len(self.plt.get_fignums()), 1) - - def test_plot_fails_when_ax_differs_from_figure(self): - from pylab import figure - fig1 = figure() - fig2 = figure() - ax1 = fig1.add_subplot(111) - with tm.assertRaises(AssertionError): - self.ts.hist(ax=ax1, figure=fig2) - @slow def test_hist_kde(self): ax = self.ts.plot(kind='hist', logy=True) @@ -1037,25 +1023,6 @@ def test_boxplot_series(self): self._check_text_labels(ylabels, [''] * len(ylabels)) @slow - def test_autocorrelation_plot(self): - from pandas.tools.plotting import autocorrelation_plot - _check_plot_works(autocorrelation_plot, self.ts) - _check_plot_works(autocorrelation_plot, self.ts.values) - - ax = autocorrelation_plot(self.ts, label='Test') - self._check_legend_labels(ax, labels=['Test']) - - @slow - def test_lag_plot(self): - from pandas.tools.plotting import lag_plot - _check_plot_works(lag_plot, self.ts) - _check_plot_works(lag_plot, self.ts, lag=5) - - @slow - def test_bootstrap_plot(self): - from pandas.tools.plotting import bootstrap_plot - _check_plot_works(bootstrap_plot, self.ts, size=10) - def test_invalid_plot_data(self): s = Series(list('abcd')) for kind in plotting._common_kinds: @@ -1193,6 +1160,30 @@ def test_standard_colors_all(self): result = plotting._get_standard_colors(num_colors=3, color=[c]) self.assertEqual(result, [c] * 3) + def test_series_plot_color_kwargs(self): + # GH1890 + ax = Series(np.arange(12) + 1).plot(color='green') + self._check_colors(ax.get_lines(), linecolors=['green']) + + def test_time_series_plot_color_kwargs(self): + # #1890 + ax = Series(np.arange(12) + 1, index=date_range( + '1/1/2000', periods=12)).plot(color='green') + self._check_colors(ax.get_lines(), linecolors=['green']) + + def test_time_series_plot_color_with_empty_kwargs(self): + import matplotlib as mpl + + def_colors = mpl.rcParams['axes.color_cycle'] + index = date_range('1/1/2000', periods=12) + s = Series(np.arange(1, 13), index=index) + + ncolors = 3 + + for i in range(ncolors): + ax = s.plot() + self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors]) + @tm.mplskip class TestDataFramePlots(TestPlotBase): @@ -2276,112 +2267,6 @@ def test_boxplot_subplots_return_type(self): expected_keys=['height', 'weight', 'category'], check_ax_title=False) - @slow - def test_boxplot_legacy(self): - df = DataFrame(randn(6, 4), - index=list(string.ascii_letters[:6]), - columns=['one', 'two', 'three', 'four']) - df['indic'] = ['foo', 'bar'] * 3 - df['indic2'] = ['foo', 'bar', 'foo'] * 2 - - _check_plot_works(df.boxplot, return_type='dict') - _check_plot_works(df.boxplot, column=['one', 'two'], return_type='dict') - _check_plot_works(df.boxplot, column=['one', 'two'], by='indic') - _check_plot_works(df.boxplot, column='one', by=['indic', 'indic2']) - _check_plot_works(df.boxplot, by='indic') - _check_plot_works(df.boxplot, by=['indic', 'indic2']) - _check_plot_works(plotting.boxplot, df['one'], return_type='dict') - _check_plot_works(df.boxplot, notch=1, return_type='dict') - _check_plot_works(df.boxplot, by='indic', notch=1) - - df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2']) - df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']) - df['Y'] = Series(['A'] * 10) - _check_plot_works(df.boxplot, by='X') - - # When ax is supplied and required number of axes is 1, - # passed ax should be used: - fig, ax = self.plt.subplots() - axes = df.boxplot('Col1', by='X', ax=ax) - self.assertIs(ax.get_axes(), axes) - - fig, ax = self.plt.subplots() - axes = df.groupby('Y').boxplot(ax=ax, return_type='axes') - self.assertIs(ax.get_axes(), axes['A']) - - # Multiple columns with an ax argument should use same figure - fig, ax = self.plt.subplots() - axes = df.boxplot(column=['Col1', 'Col2'], by='X', ax=ax, return_type='axes') - self.assertIs(axes['Col1'].get_figure(), fig) - - # When by is None, check that all relevant lines are present in the dict - fig, ax = self.plt.subplots() - d = df.boxplot(ax=ax, return_type='dict') - lines = list(itertools.chain.from_iterable(d.values())) - self.assertEqual(len(ax.get_lines()), len(lines)) - - @slow - def test_boxplot_return_type_legacy(self): - # API change in https://github.com/pydata/pandas/pull/7096 - import matplotlib as mpl - - df = DataFrame(randn(6, 4), - index=list(string.ascii_letters[:6]), - columns=['one', 'two', 'three', 'four']) - with tm.assertRaises(ValueError): - df.boxplot(return_type='NOTATYPE') - - with tm.assert_produces_warning(FutureWarning): - result = df.boxplot() - # change to Axes in future - self._check_box_return_type(result, 'dict') - - with tm.assert_produces_warning(False): - result = df.boxplot(return_type='dict') - self._check_box_return_type(result, 'dict') - - with tm.assert_produces_warning(False): - result = df.boxplot(return_type='axes') - self._check_box_return_type(result, 'axes') - - with tm.assert_produces_warning(False): - result = df.boxplot(return_type='both') - self._check_box_return_type(result, 'both') - - @slow - def test_boxplot_axis_limits(self): - - def _check_ax_limits(col, ax): - y_min, y_max = ax.get_ylim() - self.assertTrue(y_min <= col.min()) - self.assertTrue(y_max >= col.max()) - - df = self.hist_df.copy() - df['age'] = np.random.randint(1, 20, df.shape[0]) - # One full row - height_ax, weight_ax = df.boxplot(['height', 'weight'], by='category') - _check_ax_limits(df['height'], height_ax) - _check_ax_limits(df['weight'], weight_ax) - self.assertEqual(weight_ax._sharey, height_ax) - - # Two rows, one partial - p = df.boxplot(['height', 'weight', 'age'], by='category') - height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0] - dummy_ax = p[1, 1] - _check_ax_limits(df['height'], height_ax) - _check_ax_limits(df['weight'], weight_ax) - _check_ax_limits(df['age'], age_ax) - self.assertEqual(weight_ax._sharey, height_ax) - self.assertEqual(age_ax._sharey, height_ax) - self.assertIsNone(dummy_ax._sharey) - - @slow - def test_boxplot_empty_column(self): - _skip_if_mpl_14_or_dev_boxplot() - df = DataFrame(np.random.randn(20, 4)) - df.loc[:, 0] = np.nan - _check_plot_works(df.boxplot, return_type='axes') - @slow def test_kde_df(self): tm._skip_if_no_scipy() @@ -2529,251 +2414,6 @@ def test_hist_df_coord(self): self._check_box_coord(axes[2].patches, expected_x=np.array([0, 0, 0, 0, 0]), expected_w=np.array([6, 7, 8, 9, 10])) - @slow - def test_hist_df_legacy(self): - _check_plot_works(self.hist_df.hist) - - # make sure layout is handled - df = DataFrame(randn(100, 3)) - axes = _check_plot_works(df.hist, grid=False) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - self.assertFalse(axes[1, 1].get_visible()) - - df = DataFrame(randn(100, 1)) - _check_plot_works(df.hist) - - # make sure layout is handled - df = DataFrame(randn(100, 6)) - axes = _check_plot_works(df.hist, layout=(4, 2)) - self._check_axes_shape(axes, axes_num=6, layout=(4, 2)) - - # make sure sharex, sharey is handled - _check_plot_works(df.hist, sharex=True, sharey=True) - - # handle figsize arg - _check_plot_works(df.hist, figsize=(8, 10)) - - # check bins argument - _check_plot_works(df.hist, bins=5) - - # make sure xlabelsize and xrot are handled - ser = df[0] - xf, yf = 20, 18 - xrot, yrot = 30, 40 - axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) - self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - - xf, yf = 20, 18 - xrot, yrot = 30, 40 - axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) - self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - - tm.close() - # make sure kwargs to hist are handled - ax = ser.hist(normed=True, cumulative=True, bins=4) - # height of last bin (index 5) must be 1.0 - self.assertAlmostEqual(ax.get_children()[5].get_height(), 1.0) - - tm.close() - ax = ser.hist(log=True) - # scale of y must be 'log' - self._check_ax_scales(ax, yaxis='log') - - tm.close() - - # propagate attr exception from matplotlib.Axes.hist - with tm.assertRaises(AttributeError): - ser.hist(foo='bar') - - @slow - def test_hist_layout(self): - df = DataFrame(randn(100, 3)) - - layout_to_expected_size = ( - {'layout': None, 'expected_size': (2, 2)}, # default is 2x2 - {'layout': (2, 2), 'expected_size': (2, 2)}, - {'layout': (4, 1), 'expected_size': (4, 1)}, - {'layout': (1, 4), 'expected_size': (1, 4)}, - {'layout': (3, 3), 'expected_size': (3, 3)}, - {'layout': (-1, 4), 'expected_size': (1, 4)}, - {'layout': (4, -1), 'expected_size': (4, 1)}, - {'layout': (-1, 2), 'expected_size': (2, 2)}, - {'layout': (2, -1), 'expected_size': (2, 2)} - ) - - for layout_test in layout_to_expected_size: - axes = df.hist(layout=layout_test['layout']) - expected = layout_test['expected_size'] - self._check_axes_shape(axes, axes_num=3, layout=expected) - - # layout too small for all 4 plots - with tm.assertRaises(ValueError): - df.hist(layout=(1, 1)) - - # invalid format for layout - with tm.assertRaises(ValueError): - df.hist(layout=(1,)) - with tm.assertRaises(ValueError): - df.hist(layout=(-1, -1)) - - - @slow - def test_scatter(self): - tm._skip_if_no_scipy() - - df = DataFrame(randn(100, 2)) - - def scat(**kwds): - return plotting.scatter_matrix(df, **kwds) - - _check_plot_works(scat) - _check_plot_works(scat, marker='+') - _check_plot_works(scat, vmin=0) - if _ok_for_gaussian_kde('kde'): - _check_plot_works(scat, diagonal='kde') - if _ok_for_gaussian_kde('density'): - _check_plot_works(scat, diagonal='density') - _check_plot_works(scat, diagonal='hist') - _check_plot_works(scat, range_padding=.1) - - def scat2(x, y, by=None, ax=None, figsize=None): - return plotting.scatter_plot(df, x, y, by, ax, figsize=None) - - _check_plot_works(scat2, 0, 1) - grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index) - _check_plot_works(scat2, 0, 1, by=grouper) - - def test_scatter_matrix_axis(self): - tm._skip_if_no_scipy() - scatter_matrix = plotting.scatter_matrix - - with tm.RNGContext(42): - df = DataFrame(randn(100, 3)) - - axes = _check_plot_works(scatter_matrix, df, range_padding=.1) - axes0_labels = axes[0][0].yaxis.get_majorticklabels() - # GH 5662 - expected = ['-2', '-1', '0', '1', '2'] - self._check_text_labels(axes0_labels, expected) - self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) - - df[0] = ((df[0] - 2) / 3) - axes = _check_plot_works(scatter_matrix, df, range_padding=.1) - axes0_labels = axes[0][0].yaxis.get_majorticklabels() - expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] - self._check_text_labels(axes0_labels, expected) - self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) - - @slow - def test_andrews_curves(self): - from pandas.tools.plotting import andrews_curves - from matplotlib import cm - - df = self.iris - - _check_plot_works(andrews_curves, df, 'Name') - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works(andrews_curves, df, 'Name', color=rgba) - self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - ax = _check_plot_works(andrews_curves, df, 'Name', color=cnames) - self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) - - ax = _check_plot_works(andrews_curves, df, 'Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) - - colors = ['b', 'g', 'r'] - df = DataFrame({"A": [1, 2, 3], - "B": [1, 2, 3], - "C": [1, 2, 3], - "Name": colors}) - ax = andrews_curves(df, 'Name', color=colors) - handles, labels = ax.get_legend_handles_labels() - self._check_colors(handles, linecolors=colors) - - with tm.assert_produces_warning(FutureWarning): - andrews_curves(data=df, class_column='Name') - - @slow - def test_parallel_coordinates(self): - from pandas.tools.plotting import parallel_coordinates - from matplotlib import cm - - df = self.iris - - ax = _check_plot_works(parallel_coordinates, df, 'Name') - nlines = len(ax.get_lines()) - nxticks = len(ax.xaxis.get_ticklabels()) - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works(parallel_coordinates, df, 'Name', color=rgba) - self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - ax = _check_plot_works(parallel_coordinates, df, 'Name', color=cnames) - self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) - - ax = _check_plot_works(parallel_coordinates, df, 'Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) - - ax = _check_plot_works(parallel_coordinates, df, 'Name', axvlines=False) - assert len(ax.get_lines()) == (nlines - nxticks) - - colors = ['b', 'g', 'r'] - df = DataFrame({"A": [1, 2, 3], - "B": [1, 2, 3], - "C": [1, 2, 3], - "Name": colors}) - ax = parallel_coordinates(df, 'Name', color=colors) - handles, labels = ax.get_legend_handles_labels() - self._check_colors(handles, linecolors=colors) - - with tm.assert_produces_warning(FutureWarning): - parallel_coordinates(data=df, class_column='Name') - with tm.assert_produces_warning(FutureWarning): - parallel_coordinates(df, 'Name', colors=colors) - - @slow - def test_radviz(self): - from pandas.tools.plotting import radviz - from matplotlib import cm - - df = self.iris - _check_plot_works(radviz, df, 'Name') - - rgba = ('#556270', '#4ECDC4', '#C7F464') - ax = _check_plot_works(radviz, df, 'Name', color=rgba) - # skip Circle drawn as ticks - patches = [p for p in ax.patches[:20] if p.get_label() != ''] - self._check_colors(patches[:10], facecolors=rgba, mapping=df['Name'][:10]) - - cnames = ['dodgerblue', 'aquamarine', 'seagreen'] - _check_plot_works(radviz, df, 'Name', color=cnames) - patches = [p for p in ax.patches[:20] if p.get_label() != ''] - self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10]) - - _check_plot_works(radviz, df, 'Name', colormap=cm.jet) - cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) - patches = [p for p in ax.patches[:20] if p.get_label() != ''] - self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10]) - - colors = [[0., 0., 1., 1.], - [0., 0.5, 1., 1.], - [1., 0., 0., 1.]] - df = DataFrame({"A": [1, 2, 3], - "B": [2, 1, 3], - "C": [3, 2, 1], - "Name": ['b', 'g', 'r']}) - ax = radviz(df, 'Name', color=colors) - handles, labels = ax.get_legend_handles_labels() - self._check_colors(handles, facecolors=colors) - @slow def test_plot_int_columns(self): df = DataFrame(randn(100, 4)).cumsum() @@ -3522,399 +3162,12 @@ def test_sharey_and_ax(self): self.assertTrue(ax.yaxis.get_label().get_visible(), "y label is invisible but shouldn't") - @slow def test_df_grid_settings(self): # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792 self._check_grid_settings(DataFrame({'a':[1,2,3],'b':[2,3,4]}), plotting._dataframe_kinds, kws={'x':'a','y':'b'}) - -@tm.mplskip -class TestDataFrameGroupByPlots(TestPlotBase): - - @slow - def test_boxplot(self): - grouped = self.hist_df.groupby(by='gender') - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - axes = _check_plot_works(grouped.boxplot, return_type='axes') - self._check_axes_shape(list(axes.values()), axes_num=2, layout=(1, 2)) - - axes = _check_plot_works(grouped.boxplot, subplots=False, - return_type='axes') - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - tuples = lzip(string.ascii_letters[:10], range(10)) - df = DataFrame(np.random.rand(10, 3), - index=MultiIndex.from_tuples(tuples)) - - grouped = df.groupby(level=1) - axes = _check_plot_works(grouped.boxplot, return_type='axes') - self._check_axes_shape(list(axes.values()), axes_num=10, layout=(4, 3)) - - axes = _check_plot_works(grouped.boxplot, subplots=False, - return_type='axes') - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - - grouped = df.unstack(level=1).groupby(level=0, axis=1) - axes = _check_plot_works(grouped.boxplot, return_type='axes') - self._check_axes_shape(list(axes.values()), axes_num=3, layout=(2, 2)) - - axes = _check_plot_works(grouped.boxplot, subplots=False, - return_type='axes') - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - - @slow - def test_grouped_plot_fignums(self): - n = 10 - weight = Series(np.random.normal(166, 20, size=n)) - height = Series(np.random.normal(60, 10, size=n)) - with tm.RNGContext(42): - gender = tm.choice(['male', 'female'], size=n) - df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) - gb = df.groupby('gender') - - res = gb.plot() - self.assertEqual(len(self.plt.get_fignums()), 2) - self.assertEqual(len(res), 2) - tm.close() - - res = gb.boxplot(return_type='axes') - self.assertEqual(len(self.plt.get_fignums()), 1) - self.assertEqual(len(res), 2) - tm.close() - - # now works with GH 5610 as gender is excluded - res = df.groupby('gender').hist() - tm.close() - - def test_series_plot_color_kwargs(self): - # GH1890 - ax = Series(np.arange(12) + 1).plot(color='green') - self._check_colors(ax.get_lines(), linecolors=['green']) - - def test_time_series_plot_color_kwargs(self): - # #1890 - ax = Series(np.arange(12) + 1, index=date_range( - '1/1/2000', periods=12)).plot(color='green') - self._check_colors(ax.get_lines(), linecolors=['green']) - - def test_time_series_plot_color_with_empty_kwargs(self): - import matplotlib as mpl - - def_colors = mpl.rcParams['axes.color_cycle'] - index = date_range('1/1/2000', periods=12) - s = Series(np.arange(1, 13), index=index) - - ncolors = 3 - - for i in range(ncolors): - ax = s.plot() - self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors]) - - @slow - def test_grouped_hist(self): - df = DataFrame(randn(500, 2), columns=['A', 'B']) - df['C'] = np.random.randint(0, 4, 500) - df['D'] = ['X'] * 500 - - axes = plotting.grouped_hist(df.A, by=df.C) - self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) - - tm.close() - axes = df.hist(by=df.C) - self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) - - tm.close() - # group by a key with single value - axes = df.hist(by='D', rot=30) - self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) - self._check_ticks_props(axes, xrot=30) - - tm.close() - # make sure kwargs to hist are handled - xf, yf = 20, 18 - xrot, yrot = 30, 40 - axes = plotting.grouped_hist(df.A, by=df.C, normed=True, - cumulative=True, bins=4, - xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) - # height of last bin (index 5) must be 1.0 - for ax in axes.ravel(): - height = ax.get_children()[5].get_height() - self.assertAlmostEqual(height, 1.0) - self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, - ylabelsize=yf, yrot=yrot) - - tm.close() - axes = plotting.grouped_hist(df.A, by=df.C, log=True) - # scale of y must be 'log' - self._check_ax_scales(axes, yaxis='log') - - tm.close() - # propagate attr exception from matplotlib.Axes.hist - with tm.assertRaises(AttributeError): - plotting.grouped_hist(df.A, by=df.C, foo='bar') - - with tm.assert_produces_warning(FutureWarning): - df.hist(by='C', figsize='default') - - @slow - def test_grouped_hist2(self): - n = 10 - weight = Series(np.random.normal(166, 20, size=n)) - height = Series(np.random.normal(60, 10, size=n)) - with tm.RNGContext(42): - gender_int = tm.choice([0, 1], size=n) - df_int = DataFrame({'height': height, 'weight': weight, - 'gender': gender_int}) - gb = df_int.groupby('gender') - axes = gb.hist() - self.assertEqual(len(axes), 2) - self.assertEqual(len(self.plt.get_fignums()), 2) - tm.close() - - @slow - def test_grouped_box_return_type(self): - df = self.hist_df - - # old style: return_type=None - result = df.boxplot(by='gender') - self.assertIsInstance(result, np.ndarray) - self._check_box_return_type(result, None, - expected_keys=['height', 'weight', 'category']) - - # now for groupby - with tm.assert_produces_warning(FutureWarning): - result = df.groupby('gender').boxplot() - self._check_box_return_type(result, 'dict', expected_keys=['Male', 'Female']) - - columns2 = 'X B C D A G Y N Q O'.split() - df2 = DataFrame(random.randn(50, 10), columns=columns2) - categories2 = 'A B C D E F G H I J'.split() - df2['category'] = categories2 * 5 - - for t in ['dict', 'axes', 'both']: - returned = df.groupby('classroom').boxplot(return_type=t) - self._check_box_return_type(returned, t, expected_keys=['A', 'B', 'C']) - - returned = df.boxplot(by='classroom', return_type=t) - self._check_box_return_type(returned, t, - expected_keys=['height', 'weight', 'category']) - - returned = df2.groupby('category').boxplot(return_type=t) - self._check_box_return_type(returned, t, expected_keys=categories2) - - returned = df2.boxplot(by='category', return_type=t) - self._check_box_return_type(returned, t, expected_keys=columns2) - - @slow - def test_grouped_box_layout(self): - df = self.hist_df - - self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], - by=df.gender, layout=(1, 1)) - self.assertRaises(ValueError, df.boxplot, column=['height', 'weight', 'category'], - layout=(2, 1), return_type='dict') - self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], - by=df.gender, layout=(-1, -1)) - - box = _check_plot_works(df.groupby('gender').boxplot, column='height', - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2)) - - box = _check_plot_works(df.groupby('category').boxplot, column='height', - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2)) - - # GH 6769 - box = _check_plot_works(df.groupby('classroom').boxplot, - column='height', return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) - - # GH 5897 - axes = df.boxplot(column=['height', 'weight', 'category'], by='gender', - return_type='axes') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) - for ax in [axes['height']]: - self._check_visible(ax.get_xticklabels(), visible=False) - self._check_visible([ax.xaxis.get_label()], visible=False) - for ax in [axes['weight'], axes['category']]: - self._check_visible(ax.get_xticklabels()) - self._check_visible([ax.xaxis.get_label()]) - - box = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) - - box = _check_plot_works(df.groupby('category').boxplot, column='height', - layout=(3, 2), return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) - box = _check_plot_works(df.groupby('category').boxplot, column='height', - layout=(3, -1), return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) - - box = df.boxplot(column=['height', 'weight', 'category'], by='gender', - layout=(4, 1)) - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1)) - - box = df.boxplot(column=['height', 'weight', 'category'], by='gender', - layout=(-1, 1)) - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1)) - - box = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], layout=(1, 4), - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4)) - - box = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], layout=(1, -1), - return_type='dict') - self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3)) - - - @slow - def test_grouped_box_multiple_axes(self): - # GH 6970, GH 7069 - df = self.hist_df - - # check warning to ignore sharex / sharey - # this check should be done in the first function which - # passes multiple axes to plot, hist or boxplot - # location should be changed if other test is added - # which has earlier alphabetical order - with tm.assert_produces_warning(UserWarning): - fig, axes = self.plt.subplots(2, 2) - df.groupby('category').boxplot(column='height', return_type='axes', ax=axes) - self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2)) - - fig, axes = self.plt.subplots(2, 3) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - returned = df.boxplot(column=['height', 'weight', 'category'], - by='gender', return_type='axes', ax=axes[0]) - returned = np.array(list(returned.values())) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[0]) - self.assertIs(returned[0].figure, fig) - - # draw on second row - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - returned = df.groupby('classroom').boxplot( - column=['height', 'weight', 'category'], - return_type='axes', ax=axes[1]) - returned = np.array(list(returned.values())) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[1]) - self.assertIs(returned[0].figure, fig) - - with tm.assertRaises(ValueError): - fig, axes = self.plt.subplots(2, 3) - # pass different number of axes from required - axes = df.groupby('classroom').boxplot(ax=axes) - - @slow - def test_grouped_hist_layout(self): - - df = self.hist_df - self.assertRaises(ValueError, df.hist, column='weight', by=df.gender, - layout=(1, 1)) - self.assertRaises(ValueError, df.hist, column='height', by=df.category, - layout=(1, 3)) - self.assertRaises(ValueError, df.hist, column='height', by=df.category, - layout=(-1, -1)) - - axes = _check_plot_works(df.hist, column='height', by=df.gender, - layout=(2, 1)) - self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) - - axes = _check_plot_works(df.hist, column='height', by=df.gender, - layout=(2, -1)) - self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) - - axes = df.hist(column='height', by=df.category, layout=(4, 1)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - - axes = df.hist(column='height', by=df.category, layout=(-1, 1)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) - - axes = df.hist(column='height', by=df.category, layout=(4, 2), figsize=(12, 8)) - self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 8)) - tm.close() - - # GH 6769 - axes = _check_plot_works(df.hist, column='height', by='classroom', layout=(2, 2)) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - # without column - axes = _check_plot_works(df.hist, by='classroom') - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - axes = df.hist(by='gender', layout=(3, 5)) - self._check_axes_shape(axes, axes_num=2, layout=(3, 5)) - - axes = df.hist(column=['height', 'weight', 'category']) - self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) - - @slow - def test_grouped_hist_multiple_axes(self): - # GH 6970, GH 7069 - df = self.hist_df - - fig, axes = self.plt.subplots(2, 3) - returned = df.hist(column=['height', 'weight', 'category'], ax=axes[0]) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[0]) - self.assertIs(returned[0].figure, fig) - returned = df.hist(by='classroom', ax=axes[1]) - self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) - self.assert_numpy_array_equal(returned, axes[1]) - self.assertIs(returned[0].figure, fig) - - with tm.assertRaises(ValueError): - fig, axes = self.plt.subplots(2, 3) - # pass different number of axes from required - axes = df.hist(column='height', ax=axes) - @slow - def test_axis_share_x(self): - df = self.hist_df - # GH4089 - ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True) - - # share x - self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) - - # don't share y - self.assertFalse(ax1._shared_y_axes.joined(ax1, ax2)) - self.assertFalse(ax2._shared_y_axes.joined(ax1, ax2)) - - @slow - def test_axis_share_y(self): - df = self.hist_df - ax1, ax2 = df.hist(column='height', by=df.gender, sharey=True) - - # share y - self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) - - # don't share x - self.assertFalse(ax1._shared_x_axes.joined(ax1, ax2)) - self.assertFalse(ax2._shared_x_axes.joined(ax1, ax2)) - - @slow - def test_axis_share_xy(self): - df = self.hist_df - ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True, - sharey=True) - - # share both x and y - self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) - - self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) - self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) - def test_option_mpl_style(self): set_option('display.mpl_style', 'default') set_option('display.mpl_style', None) @@ -3929,6 +3182,10 @@ def test_invalid_colormap(self): with tm.assertRaises(ValueError): df.plot(colormap='invalid_colormap') + +@tm.mplskip +class TestDataFrameGroupByPlots(TestPlotBase): + def test_series_groupby_plotting_nominally_works(self): n = 10 weight = Series(np.random.normal(166, 20, size=n)) diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py new file mode 100644 index 0000000000000..f461a8ab624dc --- /dev/null +++ b/pandas/tests/test_graphics_others.py @@ -0,0 +1,913 @@ +#!/usr/bin/env python +# coding: utf-8 + +import nose +import itertools +import os +import string +import warnings +from distutils.version import LooseVersion + +from datetime import datetime, date + +from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range, + bdate_range) +from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip, + iteritems, OrderedDict, PY3) +from pandas.util.decorators import cache_readonly +import pandas.core.common as com +import pandas.util.testing as tm +from pandas.util.testing import ensure_clean +from pandas.core.config import set_option + + +import numpy as np +from numpy import random +from numpy.random import rand, randn + +from numpy.testing import assert_array_equal, assert_allclose +from numpy.testing.decorators import slow +import pandas.tools.plotting as plotting + +from pandas.tests.test_graphics import (TestPlotBase, _check_plot_works, + curpath, _ok_for_gaussian_kde) + + +""" +These tests are for ``DataFrame.hist``, ``DataFrame.boxplot`` and +other miscellaneous plots. +`Dataframe.plot`` and ``Series.plot`` are tested in test_graphics.py +""" + + +def _skip_if_mpl_14_or_dev_boxplot(): + # GH 8382 + # Boxplot failures on 1.4 and 1.4.1 + # Don't need try / except since that's done at class level + import matplotlib + if str(matplotlib.__version__) >= LooseVersion('1.4'): + raise nose.SkipTest("Matplotlib Regression in 1.4 and current dev.") + + +@tm.mplskip +class TestSeriesPlots(TestPlotBase): + + def setUp(self): + TestPlotBase.setUp(self) + import matplotlib as mpl + mpl.rcdefaults() + + self.ts = tm.makeTimeSeries() + self.ts.name = 'ts' + + self.series = tm.makeStringSeries() + self.series.name = 'series' + + self.iseries = tm.makePeriodSeries() + self.iseries.name = 'iseries' + + @slow + def test_hist_legacy(self): + _check_plot_works(self.ts.hist) + _check_plot_works(self.ts.hist, grid=False) + _check_plot_works(self.ts.hist, figsize=(8, 10)) + _check_plot_works(self.ts.hist, by=self.ts.index.month) + _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5) + + fig, ax = self.plt.subplots(1, 1) + _check_plot_works(self.ts.hist, ax=ax) + _check_plot_works(self.ts.hist, ax=ax, figure=fig) + _check_plot_works(self.ts.hist, figure=fig) + tm.close() + + fig, (ax1, ax2) = self.plt.subplots(1, 2) + _check_plot_works(self.ts.hist, figure=fig, ax=ax1) + _check_plot_works(self.ts.hist, figure=fig, ax=ax2) + + with tm.assertRaises(ValueError): + self.ts.hist(by=self.ts.index, figure=fig) + + @slow + def test_hist_bins_legacy(self): + df = DataFrame(np.random.randn(10, 2)) + ax = df.hist(bins=2)[0][0] + self.assertEqual(len(ax.patches), 2) + + @slow + def test_hist_layout(self): + df = self.hist_df + with tm.assertRaises(ValueError): + df.height.hist(layout=(1, 1)) + + with tm.assertRaises(ValueError): + df.height.hist(layout=[1, 1]) + + @slow + def test_hist_layout_with_by(self): + df = self.hist_df + + axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + axes = _check_plot_works(df.height.hist, by=df.gender, layout=(3, -1)) + self._check_axes_shape(axes, axes_num=2, layout=(3, 1)) + + axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + axes = _check_plot_works(df.height.hist, by=df.category, layout=(2, -1)) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + axes = _check_plot_works(df.height.hist, by=df.category, layout=(3, -1)) + self._check_axes_shape(axes, axes_num=4, layout=(3, 2)) + + axes = _check_plot_works(df.height.hist, by=df.category, layout=(-1, 4)) + self._check_axes_shape(axes, axes_num=4, layout=(1, 4)) + + axes = _check_plot_works(df.height.hist, by=df.classroom, layout=(2, 2)) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 7)) + + @slow + def test_hist_no_overlap(self): + from matplotlib.pyplot import subplot, gcf + x = Series(randn(2)) + y = Series(randn(2)) + subplot(121) + x.hist() + subplot(122) + y.hist() + fig = gcf() + axes = fig.get_axes() + self.assertEqual(len(axes), 2) + + @slow + def test_hist_by_no_extra_plots(self): + df = self.hist_df + axes = df.height.hist(by=df.gender) + self.assertEqual(len(self.plt.get_fignums()), 1) + + @slow + def test_plot_fails_when_ax_differs_from_figure(self): + from pylab import figure + fig1 = figure() + fig2 = figure() + ax1 = fig1.add_subplot(111) + with tm.assertRaises(AssertionError): + self.ts.hist(ax=ax1, figure=fig2) + + @slow + def test_autocorrelation_plot(self): + from pandas.tools.plotting import autocorrelation_plot + _check_plot_works(autocorrelation_plot, self.ts) + _check_plot_works(autocorrelation_plot, self.ts.values) + + ax = autocorrelation_plot(self.ts, label='Test') + self._check_legend_labels(ax, labels=['Test']) + + @slow + def test_lag_plot(self): + from pandas.tools.plotting import lag_plot + _check_plot_works(lag_plot, self.ts) + _check_plot_works(lag_plot, self.ts, lag=5) + + @slow + def test_bootstrap_plot(self): + from pandas.tools.plotting import bootstrap_plot + _check_plot_works(bootstrap_plot, self.ts, size=10) + + +@tm.mplskip +class TestDataFramePlots(TestPlotBase): + + def setUp(self): + TestPlotBase.setUp(self) + import matplotlib as mpl + mpl.rcdefaults() + + self.tdf = tm.makeTimeDataFrame() + self.hexbin_df = DataFrame({"A": np.random.uniform(size=20), + "B": np.random.uniform(size=20), + "C": np.arange(20) + np.random.uniform(size=20)}) + + from pandas import read_csv + path = os.path.join(curpath(), 'data', 'iris.csv') + self.iris = read_csv(path) + + @slow + def test_boxplot_legacy(self): + df = DataFrame(randn(6, 4), + index=list(string.ascii_letters[:6]), + columns=['one', 'two', 'three', 'four']) + df['indic'] = ['foo', 'bar'] * 3 + df['indic2'] = ['foo', 'bar', 'foo'] * 2 + + _check_plot_works(df.boxplot, return_type='dict') + _check_plot_works(df.boxplot, column=['one', 'two'], return_type='dict') + _check_plot_works(df.boxplot, column=['one', 'two'], by='indic') + _check_plot_works(df.boxplot, column='one', by=['indic', 'indic2']) + _check_plot_works(df.boxplot, by='indic') + _check_plot_works(df.boxplot, by=['indic', 'indic2']) + _check_plot_works(plotting.boxplot, df['one'], return_type='dict') + _check_plot_works(df.boxplot, notch=1, return_type='dict') + _check_plot_works(df.boxplot, by='indic', notch=1) + + df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2']) + df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B']) + df['Y'] = Series(['A'] * 10) + _check_plot_works(df.boxplot, by='X') + + # When ax is supplied and required number of axes is 1, + # passed ax should be used: + fig, ax = self.plt.subplots() + axes = df.boxplot('Col1', by='X', ax=ax) + self.assertIs(ax.get_axes(), axes) + + fig, ax = self.plt.subplots() + axes = df.groupby('Y').boxplot(ax=ax, return_type='axes') + self.assertIs(ax.get_axes(), axes['A']) + + # Multiple columns with an ax argument should use same figure + fig, ax = self.plt.subplots() + axes = df.boxplot(column=['Col1', 'Col2'], by='X', ax=ax, return_type='axes') + self.assertIs(axes['Col1'].get_figure(), fig) + + # When by is None, check that all relevant lines are present in the dict + fig, ax = self.plt.subplots() + d = df.boxplot(ax=ax, return_type='dict') + lines = list(itertools.chain.from_iterable(d.values())) + self.assertEqual(len(ax.get_lines()), len(lines)) + + @slow + def test_boxplot_return_type_legacy(self): + # API change in https://github.com/pydata/pandas/pull/7096 + import matplotlib as mpl + + df = DataFrame(randn(6, 4), + index=list(string.ascii_letters[:6]), + columns=['one', 'two', 'three', 'four']) + with tm.assertRaises(ValueError): + df.boxplot(return_type='NOTATYPE') + + with tm.assert_produces_warning(FutureWarning): + result = df.boxplot() + # change to Axes in future + self._check_box_return_type(result, 'dict') + + with tm.assert_produces_warning(False): + result = df.boxplot(return_type='dict') + self._check_box_return_type(result, 'dict') + + with tm.assert_produces_warning(False): + result = df.boxplot(return_type='axes') + self._check_box_return_type(result, 'axes') + + with tm.assert_produces_warning(False): + result = df.boxplot(return_type='both') + self._check_box_return_type(result, 'both') + + @slow + def test_boxplot_axis_limits(self): + + def _check_ax_limits(col, ax): + y_min, y_max = ax.get_ylim() + self.assertTrue(y_min <= col.min()) + self.assertTrue(y_max >= col.max()) + + df = self.hist_df.copy() + df['age'] = np.random.randint(1, 20, df.shape[0]) + # One full row + height_ax, weight_ax = df.boxplot(['height', 'weight'], by='category') + _check_ax_limits(df['height'], height_ax) + _check_ax_limits(df['weight'], weight_ax) + self.assertEqual(weight_ax._sharey, height_ax) + + # Two rows, one partial + p = df.boxplot(['height', 'weight', 'age'], by='category') + height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0] + dummy_ax = p[1, 1] + _check_ax_limits(df['height'], height_ax) + _check_ax_limits(df['weight'], weight_ax) + _check_ax_limits(df['age'], age_ax) + self.assertEqual(weight_ax._sharey, height_ax) + self.assertEqual(age_ax._sharey, height_ax) + self.assertIsNone(dummy_ax._sharey) + + @slow + def test_boxplot_empty_column(self): + _skip_if_mpl_14_or_dev_boxplot() + df = DataFrame(np.random.randn(20, 4)) + df.loc[:, 0] = np.nan + _check_plot_works(df.boxplot, return_type='axes') + + @slow + def test_hist_df_legacy(self): + _check_plot_works(self.hist_df.hist) + + # make sure layout is handled + df = DataFrame(randn(100, 3)) + axes = _check_plot_works(df.hist, grid=False) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + self.assertFalse(axes[1, 1].get_visible()) + + df = DataFrame(randn(100, 1)) + _check_plot_works(df.hist) + + # make sure layout is handled + df = DataFrame(randn(100, 6)) + axes = _check_plot_works(df.hist, layout=(4, 2)) + self._check_axes_shape(axes, axes_num=6, layout=(4, 2)) + + # make sure sharex, sharey is handled + _check_plot_works(df.hist, sharex=True, sharey=True) + + # handle figsize arg + _check_plot_works(df.hist, figsize=(8, 10)) + + # check bins argument + _check_plot_works(df.hist, bins=5) + + # make sure xlabelsize and xrot are handled + ser = df[0] + xf, yf = 20, 18 + xrot, yrot = 30, 40 + axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) + self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + + xf, yf = 20, 18 + xrot, yrot = 30, 40 + axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) + self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + + tm.close() + # make sure kwargs to hist are handled + ax = ser.hist(normed=True, cumulative=True, bins=4) + # height of last bin (index 5) must be 1.0 + self.assertAlmostEqual(ax.get_children()[5].get_height(), 1.0) + + tm.close() + ax = ser.hist(log=True) + # scale of y must be 'log' + self._check_ax_scales(ax, yaxis='log') + + tm.close() + + # propagate attr exception from matplotlib.Axes.hist + with tm.assertRaises(AttributeError): + ser.hist(foo='bar') + + @slow + def test_hist_layout(self): + df = DataFrame(randn(100, 3)) + + layout_to_expected_size = ( + {'layout': None, 'expected_size': (2, 2)}, # default is 2x2 + {'layout': (2, 2), 'expected_size': (2, 2)}, + {'layout': (4, 1), 'expected_size': (4, 1)}, + {'layout': (1, 4), 'expected_size': (1, 4)}, + {'layout': (3, 3), 'expected_size': (3, 3)}, + {'layout': (-1, 4), 'expected_size': (1, 4)}, + {'layout': (4, -1), 'expected_size': (4, 1)}, + {'layout': (-1, 2), 'expected_size': (2, 2)}, + {'layout': (2, -1), 'expected_size': (2, 2)} + ) + + for layout_test in layout_to_expected_size: + axes = df.hist(layout=layout_test['layout']) + expected = layout_test['expected_size'] + self._check_axes_shape(axes, axes_num=3, layout=expected) + + # layout too small for all 4 plots + with tm.assertRaises(ValueError): + df.hist(layout=(1, 1)) + + # invalid format for layout + with tm.assertRaises(ValueError): + df.hist(layout=(1,)) + with tm.assertRaises(ValueError): + df.hist(layout=(-1, -1)) + + @slow + def test_scatter_plot_legacy(self): + tm._skip_if_no_scipy() + + df = DataFrame(randn(100, 2)) + + def scat(**kwds): + return plotting.scatter_matrix(df, **kwds) + + _check_plot_works(scat) + _check_plot_works(scat, marker='+') + _check_plot_works(scat, vmin=0) + if _ok_for_gaussian_kde('kde'): + _check_plot_works(scat, diagonal='kde') + if _ok_for_gaussian_kde('density'): + _check_plot_works(scat, diagonal='density') + _check_plot_works(scat, diagonal='hist') + _check_plot_works(scat, range_padding=.1) + + def scat2(x, y, by=None, ax=None, figsize=None): + return plotting.scatter_plot(df, x, y, by, ax, figsize=None) + + _check_plot_works(scat2, 0, 1) + grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index) + _check_plot_works(scat2, 0, 1, by=grouper) + + def test_scatter_matrix_axis(self): + tm._skip_if_no_scipy() + scatter_matrix = plotting.scatter_matrix + + with tm.RNGContext(42): + df = DataFrame(randn(100, 3)) + + axes = _check_plot_works(scatter_matrix, df, range_padding=.1) + axes0_labels = axes[0][0].yaxis.get_majorticklabels() + # GH 5662 + expected = ['-2', '-1', '0', '1', '2'] + self._check_text_labels(axes0_labels, expected) + self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) + + df[0] = ((df[0] - 2) / 3) + axes = _check_plot_works(scatter_matrix, df, range_padding=.1) + axes0_labels = axes[0][0].yaxis.get_majorticklabels() + expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0'] + self._check_text_labels(axes0_labels, expected) + self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0) + + @slow + def test_andrews_curves(self): + from pandas.tools.plotting import andrews_curves + from matplotlib import cm + + df = self.iris + + _check_plot_works(andrews_curves, df, 'Name') + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works(andrews_curves, df, 'Name', color=rgba) + self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + ax = _check_plot_works(andrews_curves, df, 'Name', color=cnames) + self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) + + ax = _check_plot_works(andrews_curves, df, 'Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) + + colors = ['b', 'g', 'r'] + df = DataFrame({"A": [1, 2, 3], + "B": [1, 2, 3], + "C": [1, 2, 3], + "Name": colors}) + ax = andrews_curves(df, 'Name', color=colors) + handles, labels = ax.get_legend_handles_labels() + self._check_colors(handles, linecolors=colors) + + with tm.assert_produces_warning(FutureWarning): + andrews_curves(data=df, class_column='Name') + + @slow + def test_parallel_coordinates(self): + from pandas.tools.plotting import parallel_coordinates + from matplotlib import cm + + df = self.iris + + ax = _check_plot_works(parallel_coordinates, df, 'Name') + nlines = len(ax.get_lines()) + nxticks = len(ax.xaxis.get_ticklabels()) + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works(parallel_coordinates, df, 'Name', color=rgba) + self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + ax = _check_plot_works(parallel_coordinates, df, 'Name', color=cnames) + self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10]) + + ax = _check_plot_works(parallel_coordinates, df, 'Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10]) + + ax = _check_plot_works(parallel_coordinates, df, 'Name', axvlines=False) + assert len(ax.get_lines()) == (nlines - nxticks) + + colors = ['b', 'g', 'r'] + df = DataFrame({"A": [1, 2, 3], + "B": [1, 2, 3], + "C": [1, 2, 3], + "Name": colors}) + ax = parallel_coordinates(df, 'Name', color=colors) + handles, labels = ax.get_legend_handles_labels() + self._check_colors(handles, linecolors=colors) + + with tm.assert_produces_warning(FutureWarning): + parallel_coordinates(data=df, class_column='Name') + with tm.assert_produces_warning(FutureWarning): + parallel_coordinates(df, 'Name', colors=colors) + + @slow + def test_radviz(self): + from pandas.tools.plotting import radviz + from matplotlib import cm + + df = self.iris + _check_plot_works(radviz, df, 'Name') + + rgba = ('#556270', '#4ECDC4', '#C7F464') + ax = _check_plot_works(radviz, df, 'Name', color=rgba) + # skip Circle drawn as ticks + patches = [p for p in ax.patches[:20] if p.get_label() != ''] + self._check_colors(patches[:10], facecolors=rgba, mapping=df['Name'][:10]) + + cnames = ['dodgerblue', 'aquamarine', 'seagreen'] + _check_plot_works(radviz, df, 'Name', color=cnames) + patches = [p for p in ax.patches[:20] if p.get_label() != ''] + self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10]) + + _check_plot_works(radviz, df, 'Name', colormap=cm.jet) + cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique())) + patches = [p for p in ax.patches[:20] if p.get_label() != ''] + self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10]) + + colors = [[0., 0., 1., 1.], + [0., 0.5, 1., 1.], + [1., 0., 0., 1.]] + df = DataFrame({"A": [1, 2, 3], + "B": [2, 1, 3], + "C": [3, 2, 1], + "Name": ['b', 'g', 'r']}) + ax = radviz(df, 'Name', color=colors) + handles, labels = ax.get_legend_handles_labels() + self._check_colors(handles, facecolors=colors) + + +@tm.mplskip +class TestDataFrameGroupByPlots(TestPlotBase): + + @slow + def test_boxplot_legacy(self): + grouped = self.hist_df.groupby(by='gender') + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + axes = _check_plot_works(grouped.boxplot, return_type='axes') + self._check_axes_shape(list(axes.values()), axes_num=2, layout=(1, 2)) + + axes = _check_plot_works(grouped.boxplot, subplots=False, + return_type='axes') + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + tuples = lzip(string.ascii_letters[:10], range(10)) + df = DataFrame(np.random.rand(10, 3), + index=MultiIndex.from_tuples(tuples)) + + grouped = df.groupby(level=1) + axes = _check_plot_works(grouped.boxplot, return_type='axes') + self._check_axes_shape(list(axes.values()), axes_num=10, layout=(4, 3)) + + axes = _check_plot_works(grouped.boxplot, subplots=False, + return_type='axes') + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + + grouped = df.unstack(level=1).groupby(level=0, axis=1) + axes = _check_plot_works(grouped.boxplot, return_type='axes') + self._check_axes_shape(list(axes.values()), axes_num=3, layout=(2, 2)) + + axes = _check_plot_works(grouped.boxplot, subplots=False, + return_type='axes') + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + + @slow + def test_grouped_plot_fignums(self): + n = 10 + weight = Series(np.random.normal(166, 20, size=n)) + height = Series(np.random.normal(60, 10, size=n)) + with tm.RNGContext(42): + gender = tm.choice(['male', 'female'], size=n) + df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) + gb = df.groupby('gender') + + res = gb.plot() + self.assertEqual(len(self.plt.get_fignums()), 2) + self.assertEqual(len(res), 2) + tm.close() + + res = gb.boxplot(return_type='axes') + self.assertEqual(len(self.plt.get_fignums()), 1) + self.assertEqual(len(res), 2) + tm.close() + + # now works with GH 5610 as gender is excluded + res = df.groupby('gender').hist() + tm.close() + + @slow + def test_grouped_hist_legacy(self): + df = DataFrame(randn(500, 2), columns=['A', 'B']) + df['C'] = np.random.randint(0, 4, 500) + df['D'] = ['X'] * 500 + + axes = plotting.grouped_hist(df.A, by=df.C) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + tm.close() + axes = df.hist(by=df.C) + self._check_axes_shape(axes, axes_num=4, layout=(2, 2)) + + tm.close() + # group by a key with single value + axes = df.hist(by='D', rot=30) + self._check_axes_shape(axes, axes_num=1, layout=(1, 1)) + self._check_ticks_props(axes, xrot=30) + + tm.close() + # make sure kwargs to hist are handled + xf, yf = 20, 18 + xrot, yrot = 30, 40 + axes = plotting.grouped_hist(df.A, by=df.C, normed=True, + cumulative=True, bins=4, + xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot) + # height of last bin (index 5) must be 1.0 + for ax in axes.ravel(): + height = ax.get_children()[5].get_height() + self.assertAlmostEqual(height, 1.0) + self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot, + ylabelsize=yf, yrot=yrot) + + tm.close() + axes = plotting.grouped_hist(df.A, by=df.C, log=True) + # scale of y must be 'log' + self._check_ax_scales(axes, yaxis='log') + + tm.close() + # propagate attr exception from matplotlib.Axes.hist + with tm.assertRaises(AttributeError): + plotting.grouped_hist(df.A, by=df.C, foo='bar') + + with tm.assert_produces_warning(FutureWarning): + df.hist(by='C', figsize='default') + + @slow + def test_grouped_hist_legacy2(self): + n = 10 + weight = Series(np.random.normal(166, 20, size=n)) + height = Series(np.random.normal(60, 10, size=n)) + with tm.RNGContext(42): + gender_int = tm.choice([0, 1], size=n) + df_int = DataFrame({'height': height, 'weight': weight, + 'gender': gender_int}) + gb = df_int.groupby('gender') + axes = gb.hist() + self.assertEqual(len(axes), 2) + self.assertEqual(len(self.plt.get_fignums()), 2) + tm.close() + + @slow + def test_grouped_box_return_type(self): + df = self.hist_df + + # old style: return_type=None + result = df.boxplot(by='gender') + self.assertIsInstance(result, np.ndarray) + self._check_box_return_type(result, None, + expected_keys=['height', 'weight', 'category']) + + # now for groupby + with tm.assert_produces_warning(FutureWarning): + result = df.groupby('gender').boxplot() + self._check_box_return_type(result, 'dict', expected_keys=['Male', 'Female']) + + columns2 = 'X B C D A G Y N Q O'.split() + df2 = DataFrame(random.randn(50, 10), columns=columns2) + categories2 = 'A B C D E F G H I J'.split() + df2['category'] = categories2 * 5 + + for t in ['dict', 'axes', 'both']: + returned = df.groupby('classroom').boxplot(return_type=t) + self._check_box_return_type(returned, t, expected_keys=['A', 'B', 'C']) + + returned = df.boxplot(by='classroom', return_type=t) + self._check_box_return_type(returned, t, + expected_keys=['height', 'weight', 'category']) + + returned = df2.groupby('category').boxplot(return_type=t) + self._check_box_return_type(returned, t, expected_keys=categories2) + + returned = df2.boxplot(by='category', return_type=t) + self._check_box_return_type(returned, t, expected_keys=columns2) + + @slow + def test_grouped_box_layout(self): + df = self.hist_df + + self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], + by=df.gender, layout=(1, 1)) + self.assertRaises(ValueError, df.boxplot, column=['height', 'weight', 'category'], + layout=(2, 1), return_type='dict') + self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'], + by=df.gender, layout=(-1, -1)) + + box = _check_plot_works(df.groupby('gender').boxplot, column='height', + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2)) + + box = _check_plot_works(df.groupby('category').boxplot, column='height', + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2)) + + # GH 6769 + box = _check_plot_works(df.groupby('classroom').boxplot, + column='height', return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) + + # GH 5897 + axes = df.boxplot(column=['height', 'weight', 'category'], by='gender', + return_type='axes') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) + for ax in [axes['height']]: + self._check_visible(ax.get_xticklabels(), visible=False) + self._check_visible([ax.xaxis.get_label()], visible=False) + for ax in [axes['weight'], axes['category']]: + self._check_visible(ax.get_xticklabels()) + self._check_visible([ax.xaxis.get_label()]) + + box = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2)) + + box = _check_plot_works(df.groupby('category').boxplot, column='height', + layout=(3, 2), return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) + box = _check_plot_works(df.groupby('category').boxplot, column='height', + layout=(3, -1), return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2)) + + box = df.boxplot(column=['height', 'weight', 'category'], by='gender', + layout=(4, 1)) + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1)) + + box = df.boxplot(column=['height', 'weight', 'category'], by='gender', + layout=(-1, 1)) + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1)) + + box = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], layout=(1, 4), + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4)) + + box = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], layout=(1, -1), + return_type='dict') + self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3)) + + @slow + def test_grouped_box_multiple_axes(self): + # GH 6970, GH 7069 + df = self.hist_df + + # check warning to ignore sharex / sharey + # this check should be done in the first function which + # passes multiple axes to plot, hist or boxplot + # location should be changed if other test is added + # which has earlier alphabetical order + with tm.assert_produces_warning(UserWarning): + fig, axes = self.plt.subplots(2, 2) + df.groupby('category').boxplot(column='height', return_type='axes', ax=axes) + self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2)) + + fig, axes = self.plt.subplots(2, 3) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + returned = df.boxplot(column=['height', 'weight', 'category'], + by='gender', return_type='axes', ax=axes[0]) + returned = np.array(list(returned.values())) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[0]) + self.assertIs(returned[0].figure, fig) + + # draw on second row + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + returned = df.groupby('classroom').boxplot( + column=['height', 'weight', 'category'], + return_type='axes', ax=axes[1]) + returned = np.array(list(returned.values())) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[1]) + self.assertIs(returned[0].figure, fig) + + with tm.assertRaises(ValueError): + fig, axes = self.plt.subplots(2, 3) + # pass different number of axes from required + axes = df.groupby('classroom').boxplot(ax=axes) + + @slow + def test_grouped_hist_layout(self): + df = self.hist_df + self.assertRaises(ValueError, df.hist, column='weight', by=df.gender, + layout=(1, 1)) + self.assertRaises(ValueError, df.hist, column='height', by=df.category, + layout=(1, 3)) + self.assertRaises(ValueError, df.hist, column='height', by=df.category, + layout=(-1, -1)) + + axes = _check_plot_works(df.hist, column='height', by=df.gender, + layout=(2, 1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + axes = _check_plot_works(df.hist, column='height', by=df.gender, + layout=(2, -1)) + self._check_axes_shape(axes, axes_num=2, layout=(2, 1)) + + axes = df.hist(column='height', by=df.category, layout=(4, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + axes = df.hist(column='height', by=df.category, layout=(-1, 1)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 1)) + + axes = df.hist(column='height', by=df.category, layout=(4, 2), figsize=(12, 8)) + self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 8)) + tm.close() + + # GH 6769 + axes = _check_plot_works(df.hist, column='height', by='classroom', layout=(2, 2)) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + # without column + axes = _check_plot_works(df.hist, by='classroom') + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + axes = df.hist(by='gender', layout=(3, 5)) + self._check_axes_shape(axes, axes_num=2, layout=(3, 5)) + + axes = df.hist(column=['height', 'weight', 'category']) + self._check_axes_shape(axes, axes_num=3, layout=(2, 2)) + + @slow + def test_grouped_hist_multiple_axes(self): + # GH 6970, GH 7069 + df = self.hist_df + + fig, axes = self.plt.subplots(2, 3) + returned = df.hist(column=['height', 'weight', 'category'], ax=axes[0]) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[0]) + self.assertIs(returned[0].figure, fig) + returned = df.hist(by='classroom', ax=axes[1]) + self._check_axes_shape(returned, axes_num=3, layout=(1, 3)) + self.assert_numpy_array_equal(returned, axes[1]) + self.assertIs(returned[0].figure, fig) + + with tm.assertRaises(ValueError): + fig, axes = self.plt.subplots(2, 3) + # pass different number of axes from required + axes = df.hist(column='height', ax=axes) + + @slow + def test_axis_share_x(self): + df = self.hist_df + # GH4089 + ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True) + + # share x + self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) + + # don't share y + self.assertFalse(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertFalse(ax2._shared_y_axes.joined(ax1, ax2)) + + @slow + def test_axis_share_y(self): + df = self.hist_df + ax1, ax2 = df.hist(column='height', by=df.gender, sharey=True) + + # share y + self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) + + # don't share x + self.assertFalse(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertFalse(ax2._shared_x_axes.joined(ax1, ax2)) + + @slow + def test_axis_share_xy(self): + df = self.hist_df + ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True, + sharey=True) + + # share both x and y + self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2)) + + self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2)) + self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2)) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False)
Because `test_graphics.py` gets little longer, I've splitted it to 2 files. Test methods itself are not changed. - `test_graphics.py` is for `.plot()` methods. - `test_graphics_others.py` is for other plots, such as `.hist()`, `.boxplot` and miscs.
https://api.github.com/repos/pandas-dev/pandas/pulls/9813
2015-04-05T09:41:23Z
2015-07-15T14:31:09Z
2015-07-15T14:31:09Z
2015-07-15T14:33:52Z
BUG: secondary_y may not show legend properly
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 5e75d9ed011a2..05c762b91b925 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -75,7 +75,7 @@ Bug Fixes - Bug in ``DataFrame`` slicing may not retain metadata (:issue:`9776`) - Bug where ``TimdeltaIndex`` were not properly serialized in fixed ``HDFStore`` (:issue:`9635`) - +- Bug in plotting continuously using ``secondary_y`` may not show legend properly. (:issue:`9610`, :issue:`9779`) - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 04e43fabcc1cc..3ce4e150326a2 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -755,6 +755,101 @@ def test_hist_no_overlap(self): axes = fig.get_axes() self.assertEqual(len(axes), 2) + @slow + def test_hist_secondary_legend(self): + # GH 9610 + df = DataFrame(np.random.randn(30, 4), columns=list('abcd')) + + # primary -> secondary + ax = df['a'].plot(kind='hist', legend=True) + df['b'].plot(kind='hist', ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left and right axis must be visible + self._check_legend_labels(ax, labels=['a', 'b (right)']) + self.assertTrue(ax.get_yaxis().get_visible()) + self.assertTrue(ax.right_ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> secondary + ax = df['a'].plot(kind='hist', legend=True, secondary_y=True) + df['b'].plot(kind='hist', ax=ax, legend=True, secondary_y=True) + # both legends are draw on left ax + # left axis must be invisible, right axis must be visible + self._check_legend_labels(ax.left_ax, labels=['a (right)', 'b (right)']) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> primary + ax = df['a'].plot(kind='hist', legend=True, secondary_y=True) + # right axes is returned + df['b'].plot(kind='hist', ax=ax, legend=True) + # both legends are draw on left ax + # left and right axis must be visible + self._check_legend_labels(ax.left_ax, labels=['a (right)', 'b']) + self.assertTrue(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + @slow + def test_df_series_secondary_legend(self): + # GH 9779 + df = DataFrame(np.random.randn(30, 3), columns=list('abc')) + s = Series(np.random.randn(30), name='x') + + # primary -> secondary (without passing ax) + ax = df.plot() + s.plot(legend=True, secondary_y=True) + # both legends are dran on left ax + # left and right axis must be visible + self._check_legend_labels(ax, labels=['a', 'b', 'c', 'x (right)']) + self.assertTrue(ax.get_yaxis().get_visible()) + self.assertTrue(ax.right_ax.get_yaxis().get_visible()) + tm.close() + + # primary -> secondary (with passing ax) + ax = df.plot() + s.plot(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left and right axis must be visible + self._check_legend_labels(ax, labels=['a', 'b', 'c', 'x (right)']) + self.assertTrue(ax.get_yaxis().get_visible()) + self.assertTrue(ax.right_ax.get_yaxis().get_visible()) + tm.close() + + # seconcary -> secondary (without passing ax) + ax = df.plot(secondary_y=True) + s.plot(legend=True, secondary_y=True) + # both legends are dran on left ax + # left axis must be invisible and right axis must be visible + expected = ['a (right)', 'b (right)', 'c (right)', 'x (right)'] + self._check_legend_labels(ax.left_ax, labels=expected) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> secondary (with passing ax) + ax = df.plot(secondary_y=True) + s.plot(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left axis must be invisible and right axis must be visible + expected = ['a (right)', 'b (right)', 'c (right)', 'x (right)'] + self._check_legend_labels(ax.left_ax, expected) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + + # secondary -> secondary (with passing ax) + ax = df.plot(secondary_y=True, mark_right=False) + s.plot(ax=ax, legend=True, secondary_y=True) + # both legends are dran on left ax + # left axis must be invisible and right axis must be visible + expected = ['a', 'b', 'c', 'x (right)'] + self._check_legend_labels(ax.left_ax, expected) + self.assertFalse(ax.left_ax.get_yaxis().get_visible()) + self.assertTrue(ax.get_yaxis().get_visible()) + tm.close() + @slow def test_plot_fails_with_dupe_color_and_style(self): x = Series(randn(2)) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index c7130a144adea..0be030d7c2c8e 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -926,7 +926,19 @@ def generate(self): def _args_adjust(self): pass - def _maybe_right_yaxis(self, ax): + def _has_plotted_object(self, ax): + """check whether ax has data""" + return (len(ax.lines) != 0 or + len(ax.artists) != 0 or + len(ax.containers) != 0) + + def _maybe_right_yaxis(self, ax, axes_num): + if not self.on_right(axes_num): + if hasattr(ax, 'left_ax'): + # secondary axes may be passed as axes + return ax.left_ax + return ax + if hasattr(ax, 'right_ax'): return ax.right_ax else: @@ -936,7 +948,7 @@ def _maybe_right_yaxis(self, ax): orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax new_ax.right_ax = new_ax - if len(orig_ax.get_lines()) == 0: # no data on left y + if not self._has_plotted_object(orig_ax): # no data on left y orig_ax.get_yaxis().set_visible(False) return new_ax @@ -978,7 +990,15 @@ def result(self): else: return self.axes else: - return self.axes[0] + sec_true = isinstance(self.secondary_y, bool) and self.secondary_y + all_sec = (com.is_list_like(self.secondary_y) and + len(self.secondary_y) == self.nseries) + if (sec_true or all_sec): + # if all data is plotted on secondary, + # return secondary axes + return self.axes[0].right_ax + else: + return self.axes[0] def _compute_plot_data(self): numeric_data = self.data.convert_objects()._get_numeric_data() @@ -1128,8 +1148,8 @@ def _make_legend(self): def _get_ax_legend(self, ax): leg = ax.get_legend() - other_ax = (getattr(ax, 'right_ax', None) or - getattr(ax, 'left_ax', None)) + other_ax = (getattr(ax, 'left_ax', None) or + getattr(ax, 'right_ax', None)) other_leg = None if other_ax is not None: other_leg = other_ax.get_legend() @@ -1221,20 +1241,11 @@ def _get_ax(self, i): if self.subplots: ax = self.axes[i] - if self.on_right(i): - ax = self._maybe_right_yaxis(ax) - self.axes[i] = ax + ax = self._maybe_right_yaxis(ax, i) + self.axes[i] = ax else: ax = self.axes[0] - - if self.on_right(i): - ax = self._maybe_right_yaxis(ax) - - sec_true = isinstance(self.secondary_y, bool) and self.secondary_y - all_sec = (com.is_list_like(self.secondary_y) and - len(self.secondary_y) == self.nseries) - if sec_true or all_sec: - self.axes[0] = ax + ax = self._maybe_right_yaxis(ax, i) ax.get_yaxis().set_visible(True) return ax @@ -1971,7 +1982,7 @@ def _make_plot(self): kwds['style'] = style artists = plotf(ax, y, column_num=i, **kwds) - self._add_legend_handle(artists[0], label) + self._add_legend_handle(artists[0], label, index=i) def _post_plot_logic(self): if self.orientation == 'horizontal':
Closes #9610, Closes #9779.
https://api.github.com/repos/pandas-dev/pandas/pulls/9812
2015-04-05T00:55:47Z
2015-04-05T13:52:22Z
2015-04-05T13:52:22Z
2015-04-11T23:35:42Z
DOC: add dev environment creation details to contributing.rst
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f7041dbabdad5..d3eeb820a12eb 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -137,6 +137,69 @@ clear what the branch brings to *pandas*. You can have many shiny-new-features and switch in between them using the git checkout command. +### Creating a Development Environment + +An easy way to create a *pandas* development environment is as follows. + +- Install either Install Anaconda \<install-anaconda\> or + Install miniconda \<install-miniconda\> +- Make sure that you have + cloned the repository \<contributing-forking\> +- `cd` to the pandas source directory + +Tell `conda` to create a new environment, named `pandas_dev`, or any +name you would like for this environment by running: + + conda create -n pandas_dev --file ci/requirements_dev.txt + +For a python 3 environment + + conda create -n pandas_dev python=3 --file ci/requirements_dev.txt + +If you are on `windows`, then you will need to install the compiler +linkages: + + conda install -n pandas_dev libpython + +This will create the new environment, and not touch any of your existing +environments, nor any existing python installation. It will install all +of the basic dependencies of *pandas*, as well as the development and +testing tools. If you would like to install other dependencies, you can +install them as follows: + + conda install -n pandas_dev -c pandas pytables scipy + +To install *all* pandas dependencies you can do the following: + + conda install -n pandas_dev -c pandas --file ci/requirements_all.txt + +To work in this environment, `activate` it as follows: + + activate pandas_dev + +At which point, the prompt will change to indicate you are in the new +development environment. + +> **note** +> +> The above syntax is for `windows` environments. To work on +> `macosx/linux`, use: +> +> source activate pandas_dev + +To view your environments: + + conda info -e + +To return to you home root environment: + + deactivate + +See the full `conda` docs [here](http://conda.pydata.org/docs). + +At this point you can easily do an *in-place* install, as detailed in +the next section. + ### Making changes Before making your code changes, it is often necessary to build the code @@ -231,13 +294,19 @@ docstrings that follow the Numpy Docstring Standard (see above), but you don't need to install this because a local copy of `numpydoc` is included in the *pandas* source code. +It is easiest to +create a development environment \<contributing-dev\_env\>, then +install: + + conda install -n pandas_dev sphinx ipython + Furthermore, it is recommended to have all [optional dependencies](http://pandas.pydata.org/pandas-docs/dev/install.html#optional-dependencies) -installed. This is not needed, but be aware that you will see some error -messages. Because all the code in the documentation is executed during -the doc build, the examples using this optional dependencies will -generate errors. Run `pd.show_versions()` to get an overview of the -installed version of all dependencies. +installed. This is not strictly necessary, but be aware that you will +see some error messages. Because all the code in the documentation is +executed during the doc build, the examples using this optional +dependencies will generate errors. Run `pd.show_versions()` to get an +overview of the installed version of all dependencies. > **warning** > diff --git a/ci/requirements_all.txt b/ci/requirements_all.txt new file mode 100644 index 0000000000000..c70efed96a8dd --- /dev/null +++ b/ci/requirements_all.txt @@ -0,0 +1,21 @@ +nose +sphinx +ipython +dateutil +pytz +openpyxl +xlsxwriter +xlrd +html5lib +patsy +beautiful-soup +numpy +cython +scipy +numexpr +pytables +matplotlib +lxml +sqlalchemy +bottleneck +pymysql diff --git a/ci/requirements_dev.txt b/ci/requirements_dev.txt new file mode 100644 index 0000000000000..b273ca043c4a2 --- /dev/null +++ b/ci/requirements_dev.txt @@ -0,0 +1,5 @@ +dateutil +pytz +numpy +cython +nose diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index b3b2d272e66c6..cc4473e8d355a 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -96,6 +96,8 @@ Getting Started with Git setting up your SSH key, and configuring git. All these steps need to be completed before working seamlessly with your local repository and GitHub. +.. _contributing.forking: + Forking ------- @@ -132,6 +134,84 @@ changes in this branch specific to one bug or feature so it is clear what the branch brings to *pandas*. You can have many shiny-new-features and switch in between them using the git checkout command. +.. _contributing.dev_env: + +Creating a Development Environment +---------------------------------- + +An easy way to create a *pandas* development environment is as follows. + +- Install either :ref:`Install Anaconda <install-anaconda>` or :ref:`Install miniconda <install-miniconda>` +- Make sure that you have :ref:`cloned the repository <contributing-forking>` +- ``cd`` to the pandas source directory + +Tell ``conda`` to create a new environment, named ``pandas_dev``, or any name you would like for this environment by running: + +:: + + conda create -n pandas_dev --file ci/requirements_dev.txt + + +For a python 3 environment + +:: + + conda create -n pandas_dev python=3 --file ci/requirements_dev.txt + + +If you are on ``windows``, then you will need to install the compiler linkages: + +:: + + conda install -n pandas_dev libpython + +This will create the new environment, and not touch any of your existing environments, nor any existing python installation. It will install all of the basic dependencies of *pandas*, as well as the development and testing tools. If you would like to install other dependencies, you can install them as follows: + +:: + + conda install -n pandas_dev -c pandas pytables scipy + +To install *all* pandas dependencies you can do the following: + +:: + + conda install -n pandas_dev -c pandas --file ci/requirements_all.txt + +To work in this environment, ``activate`` it as follows: + +:: + + activate pandas_dev + +At which point, the prompt will change to indicate you are in the new development environment. + +.. note:: + + The above syntax is for ``windows`` environments. To work on ``macosx/linux``, use: + + :: + + source activate pandas_dev + +To view your environments: + +:: + + conda info -e + +To return to you home root environment: + +:: + + deactivate + +See the full ``conda`` docs `here +<http://conda.pydata.org/docs>`_. + +At this point you can easily do an *in-place* install, as detailed in the next section. + +.. _contributing.getting_source: + Making changes -------------- @@ -237,9 +317,15 @@ follow the Numpy Docstring Standard (see above), but you don't need to install this because a local copy of ``numpydoc`` is included in the *pandas* source code. +It is easiest to :ref:`create a development environment <contributing-dev_env>`, then install: + +:: + + conda install -n pandas_dev sphinx ipython + Furthermore, it is recommended to have all `optional dependencies <http://pandas.pydata.org/pandas-docs/dev/install.html#optional-dependencies>`_ -installed. This is not needed, but be aware that you will see some error +installed. This is not strictly necessary, but be aware that you will see some error messages. Because all the code in the documentation is executed during the doc build, the examples using this optional dependencies will generate errors. Run ``pd.show_versions()`` to get an overview of the installed version of all @@ -572,6 +658,3 @@ branch has not actually been merged. The branch will still exist on GitHub, so to delete it there do :: git push origin --delete shiny-new-feature - - - diff --git a/doc/source/install.rst b/doc/source/install.rst index dd9021d0439dc..07c88841e5dcb 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -35,6 +35,8 @@ pandas at all. Simply create an account, and have access to pandas from within your brower via an `IPython Notebook <http://ipython.org/notebook.html>`__ in a few minutes. +.. _install.anaconda + Installing pandas with Anaconda ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -66,6 +68,8 @@ admin rights to install it, it will install in the user's home directory, and this also makes it trivial to delete Anaconda at a later date (just delete that folder). +.. _install.miniconda + Installing pandas with Miniconda ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -173,47 +177,8 @@ Installing using your Linux distribution's package manager. Installing from source ~~~~~~~~~~~~~~~~~~~~~~ -.. note:: - - Installing from the git repository requires a recent installation of `Cython - <http://cython.org>`__ as the cythonized C sources are no longer checked - into source control. Released source distributions will contain the built C - files. I recommend installing the latest Cython via ``easy_install -U - Cython`` - -The source code is hosted at http://github.com/pydata/pandas, it can be checked -out using git and compiled / installed like so: - -:: - - git clone git://github.com/pydata/pandas.git - cd pandas - python setup.py install - -Make sure you have Cython installed when installing from the repository, -rather then a tarball or pypi. -On Windows, I suggest installing the MinGW compiler suite following the -directions linked to above. Once configured property, run the following on the -command line: - -:: - - python setup.py build --compiler=mingw32 - python setup.py install - -Note that you will not be able to import pandas if you open an interpreter in -the source directory unless you build the C extensions in place: - -:: - - python setup.py build_ext --inplace - -The most recent version of MinGW (any installer dated after 2011-08-03) -has removed the '-mno-cygwin' option but Distutils has not yet been updated to -reflect that. Thus, you may run into an error like "unrecognized command line -option '-mno-cygwin'". Until the bug is fixed in Distutils, you may need to -install a slightly older version of MinGW (2011-08-02 installer). +See the :ref:`contributing documentation <contributing>` for complete instructions on building from the git source tree. Further, see :ref:`creating a devevlopment environment <contributing-dev_env>` if you wish to create a *pandas* development environment. Running the test suite ~~~~~~~~~~~~~~~~~~~~~~ @@ -354,4 +319,3 @@ Optional Dependencies work. Hence, it is highly recommended that you install these. A packaged distribution like `Enthought Canopy <http://enthought.com/products/canopy>`__ may be worth considering. -
- provides instructions for creating a development on any platform with conda - cleans up the install.rst a bit
https://api.github.com/repos/pandas-dev/pandas/pulls/9810
2015-04-04T18:13:24Z
2015-04-06T12:18:51Z
2015-04-06T12:18:51Z
2015-04-06T12:54:31Z
ENH: NDFrame.mask supports same kwds as where
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index f6032a65c32f1..efee559e0e86b 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -19,7 +19,7 @@ Enhancements - Added ``StringMethods.capitalize()`` and ``swapcase`` which behave as the same as standard ``str`` (:issue:`9766`) - +- ``DataFrame.mask()`` and ``Series.mask()`` now support same keywords as ``where`` (:issue:`8801`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e05709d7a180f..908d57e28a0cd 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3250,16 +3250,14 @@ def _align_series(self, other, join='outer', axis=None, level=None, return (left_result.__finalize__(self), right_result.__finalize__(other)) - def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, - try_cast=False, raise_on_error=True): - """ + _shared_docs['where'] = (""" Return an object of same shape as self and whose corresponding - entries are from self where cond is True and otherwise are from other. + entries are from self where cond is %(cond)s and otherwise are from other. Parameters ---------- - cond : boolean NDFrame or array - other : scalar or NDFrame + cond : boolean %(klass)s or array + other : scalar or %(klass)s inplace : boolean, default False Whether to perform the operation in place on the data axis : alignment axis if needed, default None @@ -3273,7 +3271,11 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, Returns ------- wh : same type as caller - """ + """) + @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="True")) + def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, + try_cast=False, raise_on_error=True): + if isinstance(cond, NDFrame): cond = cond.reindex(**self._construct_axes_dict()) else: @@ -3400,20 +3402,11 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, return self._constructor(new_data).__finalize__(self) - def mask(self, cond): - """ - Returns copy whose values are replaced with nan if the - inverted condition is True - - Parameters - ---------- - cond : boolean NDFrame or array - - Returns - ------- - wh: same as input - """ - return self.where(~cond, np.nan) + @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="False")) + def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None, + try_cast=False, raise_on_error=True): + return self.where(~cond, other=other, inplace=inplace, axis=axis, + level=level, try_cast=try_cast, raise_on_error=raise_on_error) def shift(self, periods=1, freq=None, axis=0, **kwargs): """ diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1acad4cf978a8..d923138489288 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -9775,6 +9775,27 @@ def test_mask(self): assert_frame_equal(rs, df.mask(df <= 0)) assert_frame_equal(rs, df.mask(~cond)) + other = DataFrame(np.random.randn(5, 3)) + rs = df.where(cond, other) + assert_frame_equal(rs, df.mask(df <= 0, other)) + assert_frame_equal(rs, df.mask(~cond, other)) + + def test_mask_inplace(self): + # GH8801 + df = DataFrame(np.random.randn(5, 3)) + cond = df > 0 + + rdf = df.copy() + + rdf.where(cond, inplace=True) + assert_frame_equal(rdf, df.where(cond)) + assert_frame_equal(rdf, df.mask(~cond)) + + rdf = df.copy() + rdf.where(cond, -df, inplace=True) + assert_frame_equal(rdf, df.where(cond, -df)) + assert_frame_equal(rdf, df.mask(~cond, -df)) + def test_mask_edge_case_1xN_frame(self): # GH4071 df = DataFrame([[1, 2]]) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index e140ffd97051c..c021bb1bf2fd6 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -1821,6 +1821,10 @@ def test_where_broadcast(self): for i, use_item in enumerate(selection)]) assert_series_equal(s, expected) + s = Series(data) + result = s.where(~selection, arr) + assert_series_equal(result, expected) + def test_where_inplace(self): s = Series(np.random.randn(5)) cond = s > 0 @@ -1856,11 +1860,69 @@ def test_where_dups(self): assert_series_equal(comb, expected) def test_mask(self): + # compare with tested results in test_where + s = Series(np.random.randn(5)) + cond = s > 0 + + rs = s.where(~cond, np.nan) + assert_series_equal(rs, s.mask(cond)) + + rs = s.where(~cond) + rs2 = s.mask(cond) + assert_series_equal(rs, rs2) + + rs = s.where(~cond, -s) + rs2 = s.mask(cond, -s) + assert_series_equal(rs, rs2) + + cond = Series([True, False, False, True, False], index=s.index) + s2 = -(s.abs()) + rs = s2.where(~cond[:3]) + rs2 = s2.mask(cond[:3]) + assert_series_equal(rs, rs2) + + rs = s2.where(~cond[:3], -s2) + rs2 = s2.mask(cond[:3], -s2) + assert_series_equal(rs, rs2) + + self.assertRaises(ValueError, s.mask, 1) + self.assertRaises(ValueError, s.mask, cond[:3].values, -s) + + # dtype changes + s = Series([1,2,3,4]) + result = s.mask(s>2, np.nan) + expected = Series([1, 2, np.nan, np.nan]) + assert_series_equal(result, expected) + + def test_mask_broadcast(self): + # GH 8801 + # copied from test_where_broadcast + for size in range(2, 6): + for selection in [np.resize([True, False, False, False, False], size), # First element should be set + # Set alternating elements] + np.resize([True, False], size), + np.resize([False], size)]: # No element should be set + for item in [2.0, np.nan, np.finfo(np.float).max, np.finfo(np.float).min]: + for arr in [np.array([item]), [item], (item,)]: + data = np.arange(size, dtype=float) + s = Series(data) + result = s.mask(selection, arr) + expected = Series([item if use_item else data[i] + for i, use_item in enumerate(selection)]) + assert_series_equal(result, expected) + + def test_mask_inplace(self): s = Series(np.random.randn(5)) cond = s > 0 - rs = s.where(cond, np.nan) - assert_series_equal(rs, s.mask(~cond)) + rs = s.copy() + rs.mask(cond, inplace=True) + assert_series_equal(rs.dropna(), s[~cond]) + assert_series_equal(rs, s.mask(cond)) + + rs = s.copy() + rs.mask(cond, -s, inplace=True) + assert_series_equal(rs, s.mask(cond, -s)) def test_drop(self): @@ -6845,7 +6907,7 @@ def test_repeat(self): def test_unique_data_ownership(self): # it works! #1807 Series(Series(["a", "c", "b"]).unique()).sort() - + def test_datetime_timedelta_quantiles(self): # covers #9694 self.assertTrue(pd.isnull(Series([],dtype='M8[ns]').quantile(.5)))
Closes #8801.
https://api.github.com/repos/pandas-dev/pandas/pulls/9808
2015-04-04T12:57:09Z
2015-04-04T18:28:30Z
2015-04-04T18:28:30Z
2015-04-04T21:27:58Z
BUG: format small floats correctly (GH9764)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..6fc347cfeb273 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -79,3 +79,4 @@ Bug Fixes - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) - Bug in ``where`` causing incorrect results when upcasting was required (:issue:`9731`) +- Bug in ``FloatArrayFormatter`` where decision boundary for displaying "small" floats in decimal format is off by one order of magnitude for a given display.precision (:issue:`9764`) diff --git a/pandas/core/format.py b/pandas/core/format.py index b21ca9050ffd0..7b8a3161b5e05 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -1996,7 +1996,7 @@ def _format_strings(self): # this is pretty arbitrary for now has_large_values = (abs_vals > 1e8).any() - has_small_values = ((abs_vals < 10 ** (-self.digits)) & + has_small_values = ((abs_vals < 10 ** (-self.digits+1)) & (abs_vals > 0)).any() if too_long and has_large_values: diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index ce32c8af99a73..1dcdbf12a6b59 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -2986,6 +2986,25 @@ def test_format(self): self.assertEqual(result[0], " 12") self.assertEqual(result[1], " 0") + def test_output_significant_digits(self): + # Issue #9764 + + # In case default display precision changes: + with pd.option_context('display.precision', 7): + # DataFrame example from issue #9764 + d=pd.DataFrame({'col1':[9.999e-8, 1e-7, 1.0001e-7, 2e-7, 4.999e-7, 5e-7, 5.0001e-7, 6e-7, 9.999e-7, 1e-6, 1.0001e-6, 2e-6, 4.999e-6, 5e-6, 5.0001e-6, 6e-6]}) + + expected_output={ + (0,6):' col1\n0 9.999000e-08\n1 1.000000e-07\n2 1.000100e-07\n3 2.000000e-07\n4 4.999000e-07\n5 5.000000e-07', + (1,6):' col1\n1 1.000000e-07\n2 1.000100e-07\n3 2.000000e-07\n4 4.999000e-07\n5 5.000000e-07', + (1,8):' col1\n1 1.000000e-07\n2 1.000100e-07\n3 2.000000e-07\n4 4.999000e-07\n5 5.000000e-07\n6 5.000100e-07\n7 6.000000e-07', + (8,16):' col1\n8 9.999000e-07\n9 1.000000e-06\n10 1.000100e-06\n11 2.000000e-06\n12 4.999000e-06\n13 5.000000e-06\n14 5.000100e-06\n15 6.000000e-06', + (9,16):' col1\n9 0.000001\n10 0.000001\n11 0.000002\n12 0.000005\n13 0.000005\n14 0.000005\n15 0.000006' + } + + for (start, stop), v in expected_output.items(): + self.assertEqual(str(d[start:stop]), v) + class TestRepr_timedelta64(tm.TestCase):
closes #9764 In order to display a number from range (0,1) in decimal format with N significant digits (and not in scientific format), the number needs to be greater than or equal to 1e(-N+1), not 1e-N. Did not test for range of display.precision, as this does not seem to current practice. Did not include tests as they would imply an output format which might change in the future.
https://api.github.com/repos/pandas-dev/pandas/pulls/9806
2015-04-03T20:35:07Z
2015-04-06T00:18:30Z
2015-04-06T00:18:30Z
2015-04-06T00:18:43Z
BUG: bug in json lib when frame has length zero
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..f6032a65c32f1 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -61,6 +61,7 @@ Bug Fixes ~~~~~~~~~ - Fixed bug (:issue:`9542`) where labels did not appear properly in legend of ``DataFrame.plot()``. Passing ``label=`` args also now works, and series indices are no longer mutated. +- Bug in json serialization when frame has length zero.(:issue:`9805`) - Bug in ``scatter_matrix`` draws unexpected axis ticklabels (:issue:`5662`) diff --git a/pandas/io/tests/test_json/test_pandas.py b/pandas/io/tests/test_json/test_pandas.py index 7fe9cd9ce5cdf..1e8ce7afa9492 100644 --- a/pandas/io/tests/test_json/test_pandas.py +++ b/pandas/io/tests/test_json/test_pandas.py @@ -321,6 +321,16 @@ def test_frame_to_json_except(self): df = DataFrame([1, 2, 3]) self.assertRaises(ValueError, df.to_json, orient="garbage") + def test_frame_empty(self): + df = DataFrame(columns=['jim', 'joe']) + self.assertFalse(df._is_mixed_type) + assert_frame_equal(read_json(df.to_json()), df) + + # mixed type + df['joe'] = df['joe'].astype('i8') + self.assertTrue(df._is_mixed_type) + assert_frame_equal(read_json(df.to_json()), df) + def test_v12_compat(self): df = DataFrame( [[1.56808523, 0.65727391, 1.81021139, -0.17251653], diff --git a/pandas/src/ujson/python/objToJSON.c b/pandas/src/ujson/python/objToJSON.c index 75967bce87f76..38ce67e0fc28e 100644 --- a/pandas/src/ujson/python/objToJSON.c +++ b/pandas/src/ujson/python/objToJSON.c @@ -457,7 +457,7 @@ static void *PyTimeToJSON(JSOBJ _obj, JSONTypeContext *tc, void *outValue, size_ PyErr_SetString(PyExc_ValueError, "Failed to convert time"); return NULL; } - if (PyUnicode_Check(str)) + if (PyUnicode_Check(str)) { tmp = str; str = PyUnicode_AsUTF8String(str); @@ -479,7 +479,7 @@ static int NpyTypeToJSONType(PyObject* obj, JSONTypeContext* tc, int npyType, vo { PRINTMARK(); castfunc = PyArray_GetCastFunc(PyArray_DescrFromType(npyType), NPY_DOUBLE); - if (!castfunc) + if (!castfunc) { PyErr_Format ( PyExc_ValueError, @@ -501,7 +501,7 @@ static int NpyTypeToJSONType(PyObject* obj, JSONTypeContext* tc, int npyType, vo { PRINTMARK(); castfunc = PyArray_GetCastFunc(PyArray_DescrFromType(npyType), NPY_INT64); - if (!castfunc) + if (!castfunc) { PyErr_Format ( PyExc_ValueError, @@ -584,7 +584,12 @@ void NpyArr_iterBegin(JSOBJ _obj, JSONTypeContext *tc) obj = (PyArrayObject *) _obj; } - if (PyArray_SIZE(obj) > 0) + if (PyArray_SIZE(obj) < 0) + { + PRINTMARK(); + GET_TC(tc)->iterNext = NpyArr_iterNextNone; + } + else { PRINTMARK(); npyarr = PyObject_Malloc(sizeof(NpyArrContext)); @@ -624,11 +629,6 @@ void NpyArr_iterBegin(JSOBJ _obj, JSONTypeContext *tc) npyarr->columnLabels = GET_TC(tc)->columnLabels; npyarr->rowLabels = GET_TC(tc)->rowLabels; } - else - { - PRINTMARK(); - GET_TC(tc)->iterNext = NpyArr_iterNextNone; - } } void NpyArr_iterEnd(JSOBJ obj, JSONTypeContext *tc) @@ -1054,8 +1054,11 @@ void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext *tc) npyarr = GET_TC(tc)->npyarr; // set the dataptr to our desired column and initialise - npyarr->dataptr += npyarr->stride * idx; - NpyArr_iterNext(obj, tc); + if (npyarr != NULL) + { + npyarr->dataptr += npyarr->stride * idx; + NpyArr_iterNext(obj, tc); + } GET_TC(tc)->itemValue = NULL; ((PyObjectEncoder*) tc->encoder)->npyCtxtPassthru = NULL; @@ -2624,7 +2627,7 @@ PyObject* objToJSON(PyObject* self, PyObject *args, PyObject *kwargs) if (odefHandler != NULL && odefHandler != Py_None) { - if (!PyCallable_Check(odefHandler)) + if (!PyCallable_Check(odefHandler)) { PyErr_SetString (PyExc_TypeError, "Default handler is not callable"); return NULL;
closes https://github.com/pydata/pandas/issues/9781 on master: ``` >>> df = DataFrame(columns=['jim', 'joe']) >>> df Empty DataFrame Columns: [jim, joe] Index: [] >>> df.to_json() '{}' ``` mixed type will segfault: ``` >>> df['joe'] = df['joe'].astype('i8') >>> df.to_json() Segmentation fault (core dumped) ``` on branch: ``` >>> df.to_json() '{"jim":{},"joe":{}}' >>> df['joe'] = df['joe'].astype('i8') >>> read_json(df.to_json()) Empty DataFrame Columns: [jim, joe] Index: [] ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9805
2015-04-03T18:19:26Z
2015-04-03T22:41:42Z
2015-04-03T22:41:42Z
2015-05-06T02:50:31Z
DOC: Fix broken formatting on docstring examples with first-line comments
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e05709d7a180f..3674cbcca1063 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2723,7 +2723,8 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, Examples -------- - # Filling in NaNs: + Filling in NaNs + >>> s = pd.Series([0, 1, np.nan, 3]) >>> s.interpolate() 0 0 @@ -2902,13 +2903,13 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, Examples -------- - # DataFrame result - >>> data.groupby(func, axis=0).mean() + DataFrame results - # DataFrame result + >>> data.groupby(func, axis=0).mean() >>> data.groupby(['col1', 'col2'])['col3'].mean() - # DataFrame with hierarchical index + DataFrame with hierarchical index + >>> data.groupby(['col1', 'col2']).mean() Returns
Examples for `interpolate` and `groupby` weren't being formatted correctly. See http://pandas.pydata.org/pandas-docs/version/0.16.0/generated/pandas.Series.interpolate.html and http://pandas.pydata.org/pandas-docs/version/0.16.0/generated/pandas.Series.groupby.html
https://api.github.com/repos/pandas-dev/pandas/pulls/9803
2015-04-03T14:40:44Z
2015-04-28T12:00:28Z
2015-04-28T12:00:28Z
2015-09-19T00:38:25Z
API: define _constructor_expanddim for subclassing Series and DataFrame
diff --git a/doc/source/faq.rst b/doc/source/faq.rst index 467ec02b55f20..20762e3fc039f 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -369,3 +369,4 @@ just a thin layer around the ``QTableView``. mw = MainWidget() mw.show() app.exec_() + diff --git a/doc/source/internals.rst b/doc/source/internals.rst index 9418ca5265f1a..bc1189a8961d6 100644 --- a/doc/source/internals.rst +++ b/doc/source/internals.rst @@ -95,3 +95,155 @@ constructors ``from_tuples`` and ``from_arrays`` ensure that this is true, but if you compute the levels and labels yourself, please be careful. +.. _: + +Subclassing pandas Data Structures +---------------------------------- + +.. warning:: There are some easier alternatives before considering subclassing ``pandas`` data structures. + + 1. Monkey-patching: See :ref:`Adding Features to your pandas Installation <ref-monkey-patching>`. + + 2. Use *composition*. See `here <http://en.wikipedia.org/wiki/Composition_over_inheritance>`_. + +This section describes how to subclass ``pandas`` data structures to meet more specific needs. There are 2 points which need attention: + +1. Override constructor properties. +2. Define original properties + +.. note:: You can find a nice example in `geopandas <https://github.com/geopandas/geopandas>`_ project. + +Override Constructor Properties +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Each data structure has constructor properties to specifying data constructors. By overriding these properties, you can retain defined-classes through ``pandas`` data manipulations. + +There are 3 constructors to be defined: + +- ``_constructor``: Used when a manipulation result has the same dimesions as the original. +- ``_constructor_sliced``: Used when a manipulation result has one lower dimension(s) as the original, such as ``DataFrame`` single columns slicing. +- ``_constructor_expanddim``: Used when a manipulation result has one higher dimension as the original, such as ``Series.to_frame()`` and ``DataFrame.to_panel()``. + +Following table shows how ``pandas`` data structures define constructor properties by default. + +=========================== ======================= =================== ======================= +Property Attributes ``Series`` ``DataFrame`` ``Panel`` +=========================== ======================= =================== ======================= +``_constructor`` ``Series`` ``DataFrame`` ``Panel`` +``_constructor_sliced`` ``NotImplementedError`` ``Series`` ``DataFrame`` +``_constructor_expanddim`` ``DataFrame`` ``Panel`` ``NotImplementedError`` +=========================== ======================= =================== ======================= + +Below example shows how to define ``SubclassedSeries`` and ``SubclassedDataFrame`` overriding constructor properties. + +.. code-block:: python + + class SubclassedSeries(Series): + + @property + def _constructor(self): + return SubclassedSeries + + @property + def _constructor_expanddim(self): + return SubclassedDataFrame + + class SubclassedDataFrame(DataFrame): + + @property + def _constructor(self): + return SubclassedDataFrame + + @property + def _constructor_sliced(self): + return SubclassedSeries + +.. code-block:: python + + >>> s = SubclassedSeries([1, 2, 3]) + >>> type(s) + <class '__main__.SubclassedSeries'> + + >>> to_framed = s.to_frame() + >>> type(to_framed) + <class '__main__.SubclassedDataFrame'> + + >>> df = SubclassedDataFrame({'A', [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}) + >>> df + A B C + 0 1 4 7 + 1 2 5 8 + 2 3 6 9 + + >>> type(df) + <class '__main__.SubclassedDataFrame'> + + >>> sliced1 = df[['A', 'B']] + >>> sliced1 + A B + 0 1 4 + 1 2 5 + 2 3 6 + >>> type(sliced1) + <class '__main__.SubclassedDataFrame'> + + >>> sliced2 = df['A'] + >>> sliced2 + 0 1 + 1 2 + 2 3 + Name: A, dtype: int64 + >>> type(sliced2) + <class '__main__.SubclassedSeries'> + +Define Original Properties +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To let original data structures have additional properties, you should let ``pandas`` knows what properties are added. ``pandas`` maps unknown properties to data names overriding ``__getattribute__``. Defining original properties can be done in one of 2 ways: + +1. Define ``_internal_names`` and ``_internal_names_set`` for temporary properties which WILL NOT be passed to manipulation results. +2. Define ``_metadata`` for normal properties which will be passed to manipulation results. + +Below is an example to define 2 original properties, "internal_cache" as a temporary property and "added_property" as a normal property + +.. code-block:: python + + class SubclassedDataFrame2(DataFrame): + + # temporary properties + _internal_names = DataFrame._internal_names + ['internal_cache'] + _internal_names_set = set(_internal_names) + + # normal properties + _metadata = ['added_property'] + + @property + def _constructor(self): + return SubclassedDataFrame2 + +.. code-block:: python + + >>> df = SubclassedDataFrame2({'A', [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}) + >>> df + A B C + 0 1 4 7 + 1 2 5 8 + 2 3 6 9 + + >>> df.internal_cache = 'cached' + >>> df.added_property = 'property' + + >>> df.internal_cache + cached + >>> df.added_property + property + + # properties defined in _internal_names is reset after manipulation + >>> df[['A', 'B']].internal_cache + AttributeError: 'SubclassedDataFrame2' object has no attribute 'internal_cache' + + # properties defined in _metadata are retained + >>> df[['A', 'B']].added_property + property + + diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8bd2939e2c805..7166801b3fbf0 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -56,6 +56,8 @@ Enhancements - Trying to write an excel file now raises ``NotImplementedError`` if the ``DataFrame`` has a ``MultiIndex`` instead of writing a broken Excel file. (:issue:`9794`) +- ``DataFrame`` and ``Series`` now have ``_constructor_expanddim`` property as overridable constructor for one higher dimensionality data. This should be used only when it is really needed, see :ref:`here <ref-subclassing-pandas>` + .. _whatsnew_0161.api: API changes diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 4f7bc11cbf03c..272c401c18761 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -191,6 +191,11 @@ def _constructor(self): _constructor_sliced = Series + @property + def _constructor_expanddim(self): + from pandas.core.panel import Panel + return Panel + def __init__(self, data=None, index=None, columns=None, dtype=None, copy=False): if data is None: @@ -1061,8 +1066,6 @@ def to_panel(self): ------- panel : Panel """ - from pandas.core.panel import Panel - # only support this kind for now if (not isinstance(self.index, MultiIndex) or # pragma: no cover len(self.index.levels) != 2): @@ -1100,7 +1103,7 @@ def to_panel(self): shape=shape, ref_items=selfsorted.columns) - return Panel(new_mgr) + return self._constructor_expanddim(new_mgr) to_wide = deprecate('to_wide', to_panel) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 8bd85a008f077..9624b1308239c 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -155,6 +155,10 @@ def _local_dir(self): def _constructor_sliced(self): raise AbstractMethodError(self) + @property + def _constructor_expanddim(self): + raise NotImplementedError + #---------------------------------------------------------------------- # Axis diff --git a/pandas/core/series.py b/pandas/core/series.py index f9c56db018639..7bcf6c6671152 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -236,6 +236,11 @@ def from_array(cls, arr, index=None, name=None, dtype=None, copy=False, def _constructor(self): return Series + @property + def _constructor_expanddim(self): + from pandas.core.frame import DataFrame + return DataFrame + # types @property def _can_hold_na(self): @@ -1047,11 +1052,10 @@ def to_frame(self, name=None): ------- data_frame : DataFrame """ - from pandas.core.frame import DataFrame if name is None: - df = DataFrame(self) + df = self._constructor_expanddim(self) else: - df = DataFrame({name: self}) + df = self._constructor_expanddim({name: self}) return df diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index bcba891ee7e9d..c001f35ab65cc 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -31,7 +31,7 @@ import pandas.core.common as com import pandas.core.format as fmt import pandas.core.datetools as datetools -from pandas import (DataFrame, Index, Series, notnull, isnull, +from pandas import (DataFrame, Index, Series, Panel, notnull, isnull, MultiIndex, DatetimeIndex, Timestamp, date_range, read_csv, timedelta_range, Timedelta, option_context) @@ -14214,6 +14214,26 @@ def _constructor(self): # GH9776 self.assertEqual(df.iloc[0:1, :].testattr, 'XXX') + def test_to_panel_expanddim(self): + # GH 9762 + + class SubclassedFrame(DataFrame): + @property + def _constructor_expanddim(self): + return SubclassedPanel + + class SubclassedPanel(Panel): + pass + + index = MultiIndex.from_tuples([(0, 0), (0, 1), (0, 2)]) + df = SubclassedFrame({'X':[1, 2, 3], 'Y': [4, 5, 6]}, index=index) + result = df.to_panel() + self.assertTrue(isinstance(result, SubclassedPanel)) + expected = SubclassedPanel([[[1, 2, 3]], [[4, 5, 6]]], + items=['X', 'Y'], major_axis=[0], + minor_axis=[0, 1, 2]) + tm.assert_panel_equal(result, expected) + def skip_if_no_ne(engine='numexpr'): if engine == 'numexpr': diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index c3b43f3ec70c0..b5ada4cf39b5e 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -6851,6 +6851,22 @@ def test_searchsorted_sorter(self): e = np.array([0, 2]) tm.assert_array_equal(r, e) + def test_to_frame_expanddim(self): + # GH 9762 + + class SubclassedSeries(Series): + @property + def _constructor_expanddim(self): + return SubclassedFrame + + class SubclassedFrame(DataFrame): + pass + + s = SubclassedSeries([1, 2, 3], name='X') + result = s.to_frame() + self.assertTrue(isinstance(result, SubclassedFrame)) + expected = SubclassedFrame({'X': [1, 2, 3]}) + assert_frame_equal(result, expected) class TestSeriesNonUnique(tm.TestCase):
Closes #9762. Does this needs release note?
https://api.github.com/repos/pandas-dev/pandas/pulls/9802
2015-04-03T14:39:25Z
2015-04-18T23:18:34Z
2015-04-18T23:18:33Z
2015-04-22T13:55:12Z
DOC: str.split to use return_type in an example
diff --git a/doc/source/text.rst b/doc/source/text.rst index 2d46b37853cee..a98153e277fae 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -42,18 +42,18 @@ Methods like ``split`` return a Series of lists: s2 = Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h']) s2.str.split('_') -Easy to expand this to return a DataFrame +Elements in the split lists can be accessed using ``get`` or ``[]`` notation: .. ipython:: python - s2.str.split('_').apply(Series) + s2.str.split('_').str.get(1) + s2.str.split('_').str[1] -Elements in the split lists can be accessed using ``get`` or ``[]`` notation: +Easy to expand this to return a DataFrame using ``return_type``. .. ipython:: python - s2.str.split('_').str.get(1) - s2.str.split('_').str[1] + s2.str.split('_', return_type='frame') Methods like ``replace`` and ``findall`` take `regular expressions <https://docs.python.org/2/library/re.html>`__, too:
https://api.github.com/repos/pandas-dev/pandas/pulls/9801
2015-04-03T13:54:35Z
2015-04-03T18:52:09Z
2015-04-03T18:52:09Z
2015-04-06T10:10:41Z
DOC: regenerate CONTRIBUTING.md
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5329bad1d90e4..f7041dbabdad5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,105 +1,571 @@ -### Guidelines +Contributing to pandas +====================== + +Where to start? +--------------- All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. -The [GitHub "issues" tab](https://github.com/pydata/pandas/issues) -contains some issues labeled "Good as first PR"; Look those up if you're -looking for a quick way to help out. +If you are simply looking to start working with the *pandas* codebase, +navigate to the [GitHub "issues" +tab](https://github.com/pydata/pandas/issues) and start looking through +interesting issues. There are a number of issues listed under +[Docs](https://github.com/pydata/pandas/issues?labels=Docs&sort=updated&state=open) +and [Good as first +PR](https://github.com/pydata/pandas/issues?labels=Good+as+first+PR&sort=updated&state=open) +where you could start out. -#### Bug Reports +Or maybe through using *pandas* you have an idea of you own or are +looking for something in the documentation and thinking 'this can be +improved'...you can do something about it! - - Please include a short, self-contained Python snippet reproducing the problem. - You can have the code formatted nicely by using [GitHub Flavored Markdown](http://github.github.com/github-flavored-markdown/) : +Feel free to ask questions on [mailing +list](https://groups.google.com/forum/?fromgroups#!forum/pydata) - ```python +Bug Reports/Enhancement Requests +-------------------------------- + +Bug reports are an important part of making *pandas* more stable. Having +a complete bug report will allow others to reproduce the bug and provide +insight into fixing. Since many versions of *pandas* are supported, +knowing version information will also identify improvements made since +previous versions. Often trying the bug-producing code out on the +*master* branch is a worthwhile exercise to confirm the bug still +exists. It is also worth searching existing bug reports and pull +requests to see if the issue has already been reported and/or fixed. + +Bug reports must: + +1. Include a short, self-contained Python snippet reproducing the + problem. You can have the code formatted nicely by using [GitHub + Flavored + Markdown](http://github.github.com/github-flavored-markdown/): : + ```python >>> from pandas import DataFrame >>> df = DataFrame(...) ... ``` - - Include the full version string of pandas and its dependencies. In recent (>0.12) versions - of pandas you can use a built in function: - - ```python - >>> from pandas.util.print_versions import show_versions - >>> show_versions() - ``` - - and in 0.13.1 onwards: - ```python - >>> pd.show_versions() - ``` - - Explain what the expected behavior was, and what you saw instead. - -#### Pull Requests - -##### Testing: - - Every addition to the codebase whether it be a bug or new feature should have associated tests. The can be placed in the `tests` directory where your code change occurs. - - When writing tests, use 2.6 compatible `self.assertFoo` methods. Some polyfills such as `assertRaises` - can be found in `pandas.util.testing`. - - Do not attach doctrings to tests. Make the test itself readable and use comments if needed. - - **Make sure the test suite passes** on your box, use the provided `test_*.sh` scripts or tox. Pandas tests a variety of platforms and Python versions so be cognizant of cross-platorm considerations. - - Performance matters. Make sure your PR hasn't introduced performance regressions by using `test_perf.sh`. See [vbench performance tests](https://github.com/pydata/pandas/wiki/Performance-Testing) wiki for more information on running these tests. - - For more information on testing see [Testing advice and best practices in `pandas`](https://github.com/pydata/pandas/wiki/Testing) - -##### Documentation / Commit Messages: - - Docstrings follow the [numpydoc](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt) format. - - Keep style fixes to a separate commit to make your PR more readable. - - An informal commit message format is in effect for the project. Please try - and adhere to it. Check `git log` for examples. Here are some common prefixes - along with general guidelines for when to use them: - - **ENH**: Enhancement, new functionality - - **BUG**: Bug fix - - **DOC**: Additions/updates to documentation - - **TST**: Additions/updates to tests - - **BLD**: Updates to the build process/scripts - - **PERF**: Performance improvement - - **CLN**: Code cleanup - - Use [proper commit messages](http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html): - - a subject line with `< 80` chars. - - One blank line. - - Optionally, a commit message body. - - Please reference relevant Github issues in your commit message using `GH1234` - or `#1234`. Either style is fine but the '#' style generates noise when your rebase your PR. - - `doc/source/vx.y.z.txt` contains an ongoing - changelog for each release. Add an entry to this file - as needed in your PR: document the fix, enhancement, - or (unavoidable) breaking change. - - Maintain backward-compatibility. Pandas has lots of users with lots of existing code. Don't break it. - - If you think breakage is required clearly state why as part of the PR. - - Be careful when changing method signatures. - - Add deprecation warnings where needed. - - Generally, pandas source files should not contain attributions. You can include a "thanks to..." - in the release changelog. The rest is `git blame`/`git log`. - -##### Workflow/Git - - When you start working on a PR, start by creating a new branch pointing at the latest - commit on github master. - - **Do not** merge upstream into a branch you're going to submit as a PR. - Use `git rebase` against the current github master. - - For extra brownie points, you can squash and reorder the commits in your PR using `git rebase -i`. - Use your own judgment to decide what history needs to be preserved. If git frightens you, that's OK too. - - Use `raise AssertionError` over `assert` unless you want the assertion stripped by `python -o`. - - The pandas copyright policy is detailed in the pandas [LICENSE](https://github.com/pydata/pandas/blob/master/LICENSE). - - On the subject of [PEP8](http://www.python.org/dev/peps/pep-0008/): yes. - - [Git tips and tricks](https://github.com/pydata/pandas/wiki/Using-Git) - -##### Code standards: - - We've written a tool to check that your commits are PEP8 great, - [`pip install pep8radius`](https://github.com/hayd/pep8radius). Look at PEP8 fixes in your branch - vs master with `pep8radius master --diff` and make these changes with - `pep8radius master --diff --in-place`. - - On the subject of a massive PEP8-storm touching everything: not too often (once per release works). - - Additional standards are outlined on the [code style wiki page](https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions) - -### Notes on plotting function conventions - -https://groups.google.com/forum/#!topic/pystatsmodels/biNlCvJPNNY/discussion - -#### More developer docs -* See the [developers](http://pandas.pydata.org/developers.html) page on the - project website for more details. -* [`pandas` wiki](https://github.com/pydata/pandas/wiki) constains useful pages for development and general pandas usage -* [Tips and tricks](https://github.com/pydata/pandas/wiki/Tips-&-Tricks) +2. Include the full version string of *pandas* and its dependencies. In + recent (\>0.12) versions of *pandas* you can use a built in + function: : + + >>> from pandas.util.print_versions import show_versions + >>> show_versions() + + and in 0.13.1 onwards: : + + >>> pd.show_versions() + +3. Explain why the current behavior is wrong/not desired and what you + expect instead. + +The issue will then show up to the *pandas* community and be open to +comments/ideas from others. + +Working with the code +--------------------- + +Now that you have an issue you want to fix, enhancement to add, or +documentation to improve, you need to learn how to work with GitHub and +the *pandas* code base. + +### Version Control, Git, and GitHub + +To the new user, working with Git is one of the more daunting aspects of +contributing to *pandas*. It can very quickly become overwhelming, but +sticking to the guidelines below will make the process straightforward +and will work without much trouble. As always, if you are having +difficulties please feel free to ask for help. + +The code is hosted on [GitHub](https://www.github.com/pydata/pandas). To +contribute you will need to sign up for a [free GitHub +account](https://github.com/signup/free). We use +[Git](http://git-scm.com/) for version control to allow many people to +work together on the project. + +Some great resources for learning git: + +- the [GitHub help pages](http://help.github.com/). +- the [NumPy's + documentation](http://docs.scipy.org/doc/numpy/dev/index.html). +- Matthew Brett's + [Pydagogue](http://matthew-brett.github.com/pydagogue/). + +### Getting Started with Git + +[GitHub has instructions](http://help.github.com/set-up-git-redirect) +for installing git, setting up your SSH key, and configuring git. All +these steps need to be completed before working seamlessly with your +local repository and GitHub. + +### Forking + +You will need your own fork to work on the code. Go to the [pandas +project page](https://github.com/pydata/pandas) and hit the *fork* +button. You will want to clone your fork to your machine: : + + git clone git@github.com:your-user-name/pandas.git pandas-yourname + cd pandas-yourname + git remote add upstream git://github.com/pydata/pandas.git + +This creates the directory pandas-yourname and connects your repository +to the upstream (main project) *pandas* repository. + +You will also need to hook up Travis-CI to your GitHub repository so the +suite is automatically run when a Pull Request is submitted. +Instructions are +[here](http://about.travis-ci.org/docs/user/getting-started/). + +### Creating a Branch + +You want your master branch to reflect only production-ready code, so +create a feature branch for making your changes. For example: + + git branch shiny-new-feature + git checkout shiny-new-feature + +The above can be simplified to: + + git checkout -b shiny-new-feature + +This changes your working directory to the shiny-new-feature branch. +Keep any changes in this branch specific to one bug or feature so it is +clear what the branch brings to *pandas*. You can have many +shiny-new-features and switch in between them using the git checkout +command. + +### Making changes + +Before making your code changes, it is often necessary to build the code +that was just checked out. There are two primary methods of doing this. + +1. The best way to develop *pandas* is to build the C extensions + in-place by running: + + python setup.py build_ext --inplace + + If you startup the Python interpreter in the *pandas* source + directory you will call the built C extensions + +2. Another very common option is to do a `develop` install of *pandas*: + + python setup.py develop + + This makes a symbolic link that tells the Python interpreter to + import *pandas* from your development directory. Thus, you can + always be using the development version on your system without being + inside the clone directory. + +Contributing to the documentation +--------------------------------- + +If you're not the developer type, contributing to the documentation is +still of huge value. You don't even have to be an expert on *pandas* to +do so! Something as simple as rewriting small passages for clarity as +you reference the docs is a simple but effective way to contribute. The +next person to read that passage will be in your debt! + +Actually, there are sections of the docs that are worse off by being +written by experts. If something in the docs doesn't make sense to you, +updating the relevant section after you figure it out is a simple way to +ensure it will help the next person. + +### About the pandas documentation + +The documentation is written in **reStructuredText**, which is almost +like writing in plain English, and built using +[Sphinx](http://sphinx.pocoo.org/). The Sphinx Documentation has an +excellent [introduction to reST](http://sphinx.pocoo.org/rest.html). +Review the Sphinx docs to perform more complex changes to the +documentation as well. + +Some other important things to know about the docs: + +- The *pandas* documentation consists of two parts: the docstrings in + the code itself and the docs in this folder `pandas/doc/`. + + The docstrings provide a clear explanation of the usage of the + individual functions, while the documentation in this folder + consists of tutorial-like overviews per topic together with some + other information (what's new, installation, etc). + +- The docstrings follow the **Numpy Docstring Standard** which is used + widely in the Scientific Python community. This standard specifies + the format of the different sections of the docstring. See [this + document](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt) + for a detailed explanation, or look at some of the existing + functions to extend it in a similar manner. +- The tutorials make heavy use of the [ipython + directive](http://matplotlib.org/sampledoc/ipython_directive.html) + sphinx extension. This directive lets you put code in the + documentation which will be run during the doc build. For example: + + .. ipython:: python + + x = 2 + x**3 + + will be rendered as + + In [1]: x = 2 + + In [2]: x**3 + Out[2]: 8 + + This means that almost all code examples in the docs are always run + (and the output saved) during the doc build. This way, they will + always be up to date, but it makes the doc building a bit more + complex. + +### How to build the pandas documentation + +#### Requirements + +To build the *pandas* docs there are some extra requirements: you will +need to have `sphinx` and `ipython` installed. +[numpydoc](https://github.com/numpy/numpydoc) is used to parse the +docstrings that follow the Numpy Docstring Standard (see above), but you +don't need to install this because a local copy of `numpydoc` is +included in the *pandas* source code. + +Furthermore, it is recommended to have all [optional +dependencies](http://pandas.pydata.org/pandas-docs/dev/install.html#optional-dependencies) +installed. This is not needed, but be aware that you will see some error +messages. Because all the code in the documentation is executed during +the doc build, the examples using this optional dependencies will +generate errors. Run `pd.show_versions()` to get an overview of the +installed version of all dependencies. + +> **warning** +> +> Sphinx version \>= 1.2.2 or the older 1.1.3 is required. + +#### Building the documentation + +So how do you build the docs? Navigate to your local the folder +`pandas/doc/` directory in the console and run: + + python make.py html + +And then you can find the html output in the folder +`pandas/doc/build/html/`. + +The first time it will take quite a while, because it has to run all the +code examples in the documentation and build all generated docstring +pages. In subsequent evocations, sphinx will try to only build the pages +that have been modified. + +If you want to do a full clean build, do: + + python make.py clean + python make.py build + +Starting with 0.13.1 you can tell `make.py` to compile only a single +section of the docs, greatly reducing the turn-around time for checking +your changes. You will be prompted to delete .rst files that aren't +required, since the last committed version can always be restored from +git. + + #omit autosummary and API section + python make.py clean + python make.py --no-api + + # compile the docs with only a single + # section, that which is in indexing.rst + python make.py clean + python make.py --single indexing + +For comparison, a full documentation build may take 10 minutes. a +`-no-api` build may take 3 minutes and a single section may take 15 +seconds. However, subsequent builds only process portions you changed. +Now, open the following file in a web browser to see the full +documentation you just built: + + pandas/docs/build/html/index.html + +And you'll have the satisfaction of seeing your new and improved +documentation! + +Contributing to the code base +----------------------------- + +### Code Standards + +*pandas* uses the [PEP8](http://www.python.org/dev/peps/pep-0008/) +standard. There are several tools to ensure you abide by this standard. + +We've written a tool to check that your commits are PEP8 great, [pip +install pep8radius](https://github.com/hayd/pep8radius). Look at PEP8 +fixes in your branch vs master with: + + pep8radius master --diff` and make these changes with `pep8radius master --diff --in-place` + +Alternatively, use [flake8](http://pypi.python.org/pypi/flake8) tool for +checking the style of your code. Additional standards are outlined on +the [code style wiki +page](https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions). + +Please try to maintain backward-compatibility. *Pandas* has lots of +users with lots of existing code, so don't break it if at all possible. +If you think breakage is required clearly state why as part of the Pull +Request. Also, be careful when changing method signatures and add +deprecation warnings where needed. + +### Test-driven Development/Writing Code + +*Pandas* is serious about [Test-driven Development +(TDD)](http://en.wikipedia.org/wiki/Test-driven_development). This +development process "relies on the repetition of a very short +development cycle: first the developer writes an (initially failing) +automated test case that defines a desired improvement or new function, +then produces the minimum amount of code to pass that test." So, before +actually writing any code, you should write your tests. Often the test +can be taken from the original GitHub issue. However, it is always worth +considering additional use cases and writing corresponding tests. + +Adding tests is one of the most common requests after code is pushed to +*pandas*. It is worth getting in the habit of writing tests ahead of +time so this is never an issue. + +Like many packages, *pandas* uses the [Nose testing +system](http://somethingaboutorange.com/mrl/projects/nose/) and the +convenient extensions in +[numpy.testing](http://docs.scipy.org/doc/numpy/reference/routines.testing.html). + +#### Writing tests + +All tests should go into the *tests* subdirectory of the specific +package. There are probably many examples already there and looking to +these for inspiration is suggested. If you test requires working with +files or network connectivity there is more information on the [testing +page](https://github.com/pydata/pandas/wiki/Testing) of the wiki. + +The `pandas.util.testing` module has many special `assert` functions +that make it easier to make statements about whether Series or DataFrame +objects are equivalent. The easiest way to verify that your code is +correct is to explicitly construct the result you expect, then compare +the actual result to the expected correct result: + + def test_pivot(self): + data = { + 'index' : ['A', 'B', 'C', 'C', 'B', 'A'], + 'columns' : ['One', 'One', 'One', 'Two', 'Two', 'Two'], + 'values' : [1., 2., 3., 3., 2., 1.] + } + + frame = DataFrame(data) + pivoted = frame.pivot(index='index', columns='columns', values='values') + + expected = DataFrame({ + 'One' : {'A' : 1., 'B' : 2., 'C' : 3.}, + 'Two' : {'A' : 1., 'B' : 2., 'C' : 3.} + }) + + assert_frame_equal(pivoted, expected) + +#### Running the test suite + +The tests can then be run directly inside your git clone (without having +to install *pandas*) by typing:: + + nosetests pandas + +The tests suite is exhaustive and takes around 20 minutes to run. Often +it is worth running only a subset of tests first around your changes +before running the entire suite. This is done using one of the following +constructs: + + nosetests pandas/tests/[test-module].py + nosetests pandas/tests/[test-module].py:[TestClass] + nosetests pandas/tests/[test-module].py:[TestClass].[test_method] + +#### Running the performance test suite + +Performance matters and it is worth considering that your code has not +introduced performance regressions. Currently *pandas* uses the [vbench +library](https://github.com/pydata/vbench) to enable easy monitoring of +the performance of critical *pandas* operations. These benchmarks are +all found in the `pandas/vb_suite` directory. vbench currently only +works on python2. + +To install vbench: + + pip install git+https://github.com/pydata/vbench + +Vbench also requires sqlalchemy, gitpython, and psutil which can all be +installed using pip. If you need to run a benchmark, change your +directory to the *pandas* root and run: + + ./test_perf.sh -b master -t HEAD + +This will checkout the master revision and run the suite on both master +and your commit. Running the full test suite can take up to one hour and +use up to 3GB of RAM. Usually it is sufficient to past a subset of the +results in to the Pull Request to show that the committed changes do not +cause unexpected performance regressions. + +You can run specific benchmarks using the *-r* flag which takes a +regular expression. + +See the [performance testing +wiki](https://github.com/pydata/pandas/wiki/Performance-Testing) for +information on how to write a benchmark. + +### Documenting your code + +Changes should be reflected in the release notes located in +doc/source/whatsnew/vx.y.z.txt. This file contains an ongoing change log +for each release. Add an entry to this file to document your fix, +enhancement or (unavoidable) breaking change. Make sure to include the +GitHub issue number when adding your entry. + +If your code is an enhancement, it is most likely necessary to add usage +examples to the existing documentation. This can be done following the +section regarding documentation. + +Contributing your changes to *pandas* +------------------------------------- + +### Committing your code + +Keep style fixes to a separate commit to make your PR more readable. + +Once you've made changes, you can see them by typing: + + git status + +If you've created a new file, it is not being tracked by git. Add it by +typing : + + git add path/to/file-to-be-added.py + +Doing 'git status' again should give something like : + + # On branch shiny-new-feature + # + # modified: /relative/path/to/file-you-added.py + # + +Finally, commit your changes to your local repository with an +explanatory message. An informal commit message format is in effect for +the project. Please try to adhere to it. Here are some common prefixes +along with general guidelines for when to use them: + +> - ENH: Enhancement, new functionality +> - BUG: Bug fix +> - DOC: Additions/updates to documentation +> - TST: Additions/updates to tests +> - BLD: Updates to the build process/scripts +> - PERF: Performance improvement +> - CLN: Code cleanup + +The following defines how a commit message should be structured. Please +reference the relevant GitHub issues in your commit message using GH1234 +or \#1234. Either style is fine, but the former is generally preferred: + +> - a subject line with \< 80 chars. +> - One blank line. +> - Optionally, a commit message body. + +Now you can commit your changes in your local repository: + + git commit -m + +If you have multiple commits, it is common to want to combine them into +one commit, often referred to as "squashing" or "rebasing". This is a +common request by package maintainers when submitting a Pull Request as +it maintains a more compact commit history. To rebase your commits: + + git rebase -i HEAD~# + +Where \# is the number of commits you want to combine. Then you can pick +the relevant commit message and discard others. + +### Pushing your changes + +When you want your changes to appear publicly on your GitHub page, push +your forked feature branch's commits : + + git push origin shiny-new-feature + +Here origin is the default name given to your remote repository on +GitHub. You can see the remote repositories : + + git remote -v + +If you added the upstream repository as described above you will see +something like : + + origin git@github.com:yourname/pandas.git (fetch) + origin git@github.com:yourname/pandas.git (push) + upstream git://github.com/pydata/pandas.git (fetch) + upstream git://github.com/pydata/pandas.git (push) + +Now your code is on GitHub, but it is not yet a part of the *pandas* +project. For that to happen, a Pull Request needs to be submitted on +GitHub. + +### Review your code + +When you're ready to ask for a code review, you will file a Pull +Request. Before you do, again make sure you've followed all the +guidelines outlined in this document regarding code style, tests, +performance tests, and documentation. You should also double check your +branch changes against the branch it was based off of: + +1. Navigate to your repository on + GitHub--<https://github.com/your-user-name/pandas>. +2. Click on Branches. +3. Click on the Compare button for your feature branch. +4. Select the base and compare branches, if necessary. This will be + master and shiny-new-feature, respectively. + +### Finally, make the Pull Request + +If everything looks good you are ready to make a Pull Request. A Pull +Request is how code from a local repository becomes available to the +GitHub community and can be looked at and eventually merged into the +master version. This Pull Request and its associated changes will +eventually be committed to the master branch and available in the next +release. To submit a Pull Request: + +1. Navigate to your repository on GitHub. +2. Click on the Pull Request button. +3. You can then click on Commits and Files Changed to make sure + everything looks okay one last time. +4. Write a description of your changes in the Preview Discussion tab. +5. Click Send Pull Request. + +This request then appears to the repository maintainers, and they will +review the code. If you need to make more changes, you can make them in +your branch, push them to GitHub, and the pull request will be +automatically updated. Pushing them to GitHub again is done by: + + git push -f origin shiny-new-feature + +This will automatically update your Pull Request with the latest code +and restart the Travis-CI tests. + +### Delete your merged branch (optional) + +Once your feature branch is accepted into upstream, you'll probably want +to get rid of the branch. First, merge upstream master into your branch +so git knows it is safe to delete your branch : + + git fetch upstream + git checkout master + git merge upstream/master + +Then you can just do: + + git branch -d shiny-new-feature + +Make sure you use a lower-case -d, or else git won't warn you if your +feature branch has not actually been merged. + +The branch will still exist on GitHub, so to delete it there do : + + git push origin --delete shiny-new-feature diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 68bd6109b85d7..b3b2d272e66c6 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -13,8 +13,8 @@ Where to start? All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. -If you are simply looking to start working with the *pandas* codebase, navigate to the -`GitHub "issues" tab <https://github.com/pydata/pandas/issues>`_ and start looking through +If you are simply looking to start working with the *pandas* codebase, navigate to the +`GitHub "issues" tab <https://github.com/pydata/pandas/issues>`_ and start looking through interesting issues. There are a number of issues listed under `Docs <https://github.com/pydata/pandas/issues?labels=Docs&sort=updated&state=open>`_ and `Good as first PR @@ -31,11 +31,11 @@ Feel free to ask questions on `mailing list Bug Reports/Enhancement Requests ================================ -Bug reports are an important part of making *pandas* more stable. Having a complete bug report -will allow others to reproduce the bug and provide insight into fixing. Since many versions of -*pandas* are supported, knowing version information will also identify improvements made since -previous versions. Often trying the bug-producing code out on the *master* branch is a worthwhile exercise -to confirm the bug still exists. It is also worth searching existing bug reports and pull requests +Bug reports are an important part of making *pandas* more stable. Having a complete bug report +will allow others to reproduce the bug and provide insight into fixing. Since many versions of +*pandas* are supported, knowing version information will also identify improvements made since +previous versions. Often trying the bug-producing code out on the *master* branch is a worthwhile exercise +to confirm the bug still exists. It is also worth searching existing bug reports and pull requests to see if the issue has already been reported and/or fixed. Bug reports must: @@ -59,7 +59,7 @@ Bug reports must: and in 0.13.1 onwards: :: >>> pd.show_versions() - + #. Explain why the current behavior is wrong/not desired and what you expect instead. The issue will then show up to the *pandas* community and be open to comments/ideas from others. @@ -67,15 +67,15 @@ The issue will then show up to the *pandas* community and be open to comments/id Working with the code ===================== -Now that you have an issue you want to fix, enhancement to add, or documentation to improve, +Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the *pandas* code base. Version Control, Git, and GitHub -------------------------------- -To the new user, working with Git is one of the more daunting aspects of contributing to *pandas*. -It can very quickly become overwhelming, but sticking to the guidelines below will make the process -straightforward and will work without much trouble. As always, if you are having difficulties please +To the new user, working with Git is one of the more daunting aspects of contributing to *pandas*. +It can very quickly become overwhelming, but sticking to the guidelines below will make the process +straightforward and will work without much trouble. As always, if you are having difficulties please feel free to ask for help. The code is hosted on `GitHub <https://www.github.com/pydata/pandas>`_. To @@ -85,14 +85,14 @@ version control to allow many people to work together on the project. Some great resources for learning git: - * the `GitHub help pages <http://help.github.com/>`_. - * the `NumPy's documentation <http://docs.scipy.org/doc/numpy/dev/index.html>`_. - * Matthew Brett's `Pydagogue <http://matthew-brett.github.com/pydagogue/>`_. +* the `GitHub help pages <http://help.github.com/>`_. +* the `NumPy's documentation <http://docs.scipy.org/doc/numpy/dev/index.html>`_. +* Matthew Brett's `Pydagogue <http://matthew-brett.github.com/pydagogue/>`_. Getting Started with Git ------------------------ -`GitHub has instructions <http://help.github.com/set-up-git-redirect>`__ for installing git, +`GitHub has instructions <http://help.github.com/set-up-git-redirect>`__ for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before working seamlessly with your local repository and GitHub. @@ -110,7 +110,7 @@ want to clone your fork to your machine: :: This creates the directory `pandas-yourname` and connects your repository to the upstream (main project) *pandas* repository. -You will also need to hook up Travis-CI to your GitHub repository so the suite +You will also need to hook up Travis-CI to your GitHub repository so the suite is automatically run when a Pull Request is submitted. Instructions are `here <http://about.travis-ci.org/docs/user/getting-started/>`_. @@ -127,27 +127,27 @@ The above can be simplified to:: git checkout -b shiny-new-feature -This changes your working directory to the shiny-new-feature branch. Keep any -changes in this branch specific to one bug or feature so it is clear -what the branch brings to *pandas*. You can have many shiny-new-features +This changes your working directory to the shiny-new-feature branch. Keep any +changes in this branch specific to one bug or feature so it is clear +what the branch brings to *pandas*. You can have many shiny-new-features and switch in between them using the git checkout command. Making changes -------------- -Before making your code changes, it is often necessary to build the code that was -just checked out. There are two primary methods of doing this. +Before making your code changes, it is often necessary to build the code that was +just checked out. There are two primary methods of doing this. #. The best way to develop *pandas* is to build the C extensions in-place by running:: - + python setup.py build_ext --inplace - - If you startup the Python interpreter in the *pandas* source directory you + + If you startup the Python interpreter in the *pandas* source directory you will call the built C extensions - + #. Another very common option is to do a ``develop`` install of *pandas*:: - + python setup.py develop This makes a symbolic link that tells the Python interpreter to import *pandas* @@ -155,7 +155,7 @@ just checked out. There are two primary methods of doing this. version on your system without being inside the clone directory. Contributing to the documentation ---------------------------------- +================================= If you're not the developer type, contributing to the documentation is still of huge value. You don't even have to be an expert on @@ -173,7 +173,7 @@ help the next person. About the pandas documentation -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------------------------ The documentation is written in **reStructuredText**, which is almost like writing in plain English, and built using `Sphinx <http://sphinx.pocoo.org/>`__. The @@ -225,10 +225,10 @@ Some other important things to know about the docs: How to build the pandas documentation -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------------------------------- Requirements -"""""""""""" +~~~~~~~~~~~~ To build the *pandas* docs there are some extra requirements: you will need to have ``sphinx`` and ``ipython`` installed. `numpydoc @@ -250,7 +250,7 @@ dependencies. Sphinx version >= 1.2.2 or the older 1.1.3 is required. Building the documentation -"""""""""""""""""""""""""" +~~~~~~~~~~~~~~~~~~~~~~~~~~ So how do you build the docs? Navigate to your local the folder ``pandas/doc/`` directory in the console and run:: @@ -287,8 +287,8 @@ last committed version can always be restored from git. python make.py --single indexing For comparison, a full documentation build may take 10 minutes. a ``-no-api`` build -may take 3 minutes and a single section may take 15 seconds. However, subsequent -builds only process portions you changed. Now, open the following file in a web +may take 3 minutes and a single section may take 15 seconds. However, subsequent +builds only process portions you changed. Now, open the following file in a web browser to see the full documentation you just built:: pandas/docs/build/html/index.html @@ -297,40 +297,40 @@ And you'll have the satisfaction of seeing your new and improved documentation! Contributing to the code base ------------------------------ +============================= .. contents:: Code Base: :local: Code Standards -^^^^^^^^^^^^^^ +-------------- -*pandas* uses the `PEP8 <http://www.python.org/dev/peps/pep-0008/>`_ standard. +*pandas* uses the `PEP8 <http://www.python.org/dev/peps/pep-0008/>`_ standard. There are several tools to ensure you abide by this standard. -We've written a tool to check that your commits are PEP8 great, `pip install pep8radius <https://github.com/hayd/pep8radius>`_. +We've written a tool to check that your commits are PEP8 great, `pip install pep8radius <https://github.com/hayd/pep8radius>`_. Look at PEP8 fixes in your branch vs master with:: pep8radius master --diff` and make these changes with `pep8radius master --diff --in-place` -Alternatively, use `flake8 <http://pypi.python.org/pypi/flake8>`_ tool for checking the style of your code. +Alternatively, use `flake8 <http://pypi.python.org/pypi/flake8>`_ tool for checking the style of your code. Additional standards are outlined on the `code style wiki page <https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions>`_. -Please try to maintain backward-compatibility. *Pandas* has lots of users with lots of existing code, so -don't break it if at all possible. If you think breakage is required clearly state why -as part of the Pull Request. Also, be careful when changing method signatures and add +Please try to maintain backward-compatibility. *Pandas* has lots of users with lots of existing code, so +don't break it if at all possible. If you think breakage is required clearly state why +as part of the Pull Request. Also, be careful when changing method signatures and add deprecation warnings where needed. Test-driven Development/Writing Code -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -*Pandas* is serious about `Test-driven Development (TDD) -<http://en.wikipedia.org/wiki/Test-driven_development>`_. -This development process "relies on the repetition of a very short development cycle: -first the developer writes an (initially failing) automated test case that defines a desired -improvement or new function, then produces the minimum amount of code to pass that test." -So, before actually writing any code, you should write your tests. Often the test can be -taken from the original GitHub issue. However, it is always worth considering additional +------------------------------------ + +*Pandas* is serious about `Test-driven Development (TDD) +<http://en.wikipedia.org/wiki/Test-driven_development>`_. +This development process "relies on the repetition of a very short development cycle: +first the developer writes an (initially failing) automated test case that defines a desired +improvement or new function, then produces the minimum amount of code to pass that test." +So, before actually writing any code, you should write your tests. Often the test can be +taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests. Adding tests is one of the most common requests after code is pushed to *pandas*. It is worth getting @@ -342,10 +342,10 @@ extensions in `numpy.testing <http://docs.scipy.org/doc/numpy/reference/routines.testing.html>`_. Writing tests -""""""""""""" +~~~~~~~~~~~~~ All tests should go into the *tests* subdirectory of the specific package. -There are probably many examples already there and looking to these for +There are probably many examples already there and looking to these for inspiration is suggested. If you test requires working with files or network connectivity there is more information on the `testing page <https://github.com/pydata/pandas/wiki/Testing>`_ of the wiki. @@ -376,64 +376,67 @@ the expected correct result: assert_frame_equal(pivoted, expected) Running the test suite -"""""""""""""""""""""" +~~~~~~~~~~~~~~~~~~~~~~ The tests can then be run directly inside your git clone (without having to install *pandas*) by typing::: nosetests pandas -The tests suite is exhaustive and takes around 20 minutes to run. Often it is -worth running only a subset of tests first around your changes before running the +The tests suite is exhaustive and takes around 20 minutes to run. Often it is +worth running only a subset of tests first around your changes before running the entire suite. This is done using one of the following constructs: :: - + nosetests pandas/tests/[test-module].py nosetests pandas/tests/[test-module].py:[TestClass] nosetests pandas/tests/[test-module].py:[TestClass].[test_method] Running the performance test suite -"""""""""""""""""""""""""""""""""" +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Performance matters and it is worth considering that your code has not introduced -performance regressions. Currently *pandas* uses the `vbench library <https://github.com/pydata/vbench>`__ +Performance matters and it is worth considering that your code has not introduced +performance regressions. Currently *pandas* uses the `vbench library <https://github.com/pydata/vbench>`__ to enable easy monitoring of the performance of critical *pandas* operations. -These benchmarks are all found in the ``pandas/vb_suite`` directory. vbench +These benchmarks are all found in the ``pandas/vb_suite`` directory. vbench currently only works on python2. To install vbench:: pip install git+https://github.com/pydata/vbench -Vbench also requires sqlalchemy, gitpython, and psutil which can all be installed +Vbench also requires sqlalchemy, gitpython, and psutil which can all be installed using pip. If you need to run a benchmark, change your directory to the *pandas* root and run:: ./test_perf.sh -b master -t HEAD -This will checkout the master revision and run the suite on both master and -your commit. Running the full test suite can take up to one hour and use up -to 3GB of RAM. Usually it is sufficient to past a subset of the results in -to the Pull Request to show that the committed changes do not cause unexpected +This will checkout the master revision and run the suite on both master and +your commit. Running the full test suite can take up to one hour and use up +to 3GB of RAM. Usually it is sufficient to past a subset of the results in +to the Pull Request to show that the committed changes do not cause unexpected performance regressions. You can run specific benchmarks using the *-r* flag which takes a regular expression. -See the `performance testing wiki <https://github.com/pydata/pandas/wiki/Performance-Testing>`_ for information +See the `performance testing wiki <https://github.com/pydata/pandas/wiki/Performance-Testing>`_ for information on how to write a benchmark. Documenting your code -^^^^^^^^^^^^^^^^^^^^^ +--------------------- -Changes should be reflected in the release notes located in `doc/source/whatsnew/vx.y.z.txt`. -This file contains an ongoing change log for each release. Add an entry to this file to -document your fix, enhancement or (unavoidable) breaking change. Make sure to include the +Changes should be reflected in the release notes located in `doc/source/whatsnew/vx.y.z.txt`. +This file contains an ongoing change log for each release. Add an entry to this file to +document your fix, enhancement or (unavoidable) breaking change. Make sure to include the GitHub issue number when adding your entry. -If your code is an enhancement, it is most likely necessary to add usage examples to the +If your code is an enhancement, it is most likely necessary to add usage examples to the existing documentation. This can be done following the section regarding documentation. +Contributing your changes to *pandas* +===================================== + Committing your code -------------------- @@ -454,8 +457,8 @@ Doing 'git status' again should give something like :: # modified: /relative/path/to/file-you-added.py # -Finally, commit your changes to your local repository with an explanatory message. An informal -commit message format is in effect for the project. Please try to adhere to it. Here are +Finally, commit your changes to your local repository with an explanatory message. An informal +commit message format is in effect for the project. Please try to adhere to it. Here are some common prefixes along with general guidelines for when to use them: * ENH: Enhancement, new functionality @@ -466,8 +469,8 @@ some common prefixes along with general guidelines for when to use them: * PERF: Performance improvement * CLN: Code cleanup -The following defines how a commit message should be structured. Please reference the -relevant GitHub issues in your commit message using `GH1234` or `#1234`. Either style +The following defines how a commit message should be structured. Please reference the +relevant GitHub issues in your commit message using `GH1234` or `#1234`. Either style is fine, but the former is generally preferred: * a subject line with `< 80` chars. @@ -478,13 +481,13 @@ Now you can commit your changes in your local repository:: git commit -m -If you have multiple commits, it is common to want to combine them into one commit, often -referred to as "squashing" or "rebasing". This is a common request by package maintainers +If you have multiple commits, it is common to want to combine them into one commit, often +referred to as "squashing" or "rebasing". This is a common request by package maintainers when submitting a Pull Request as it maintains a more compact commit history. To rebase your commits:: git rebase -i HEAD~# -Where # is the number of commits you want to combine. Then you can pick the relevant +Where # is the number of commits you want to combine. Then you can pick the relevant commit message and discard others. Pushing your changes @@ -508,33 +511,30 @@ like :: upstream git://github.com/pydata/pandas.git (fetch) upstream git://github.com/pydata/pandas.git (push) -Now your code is on GitHub, but it is not yet a part of the *pandas* project. For that to +Now your code is on GitHub, but it is not yet a part of the *pandas* project. For that to happen, a Pull Request needs to be submitted on GitHub. -Contributing your changes to *pandas* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - Review your code ---------------- -When you're ready to ask for a code review, you will file a Pull Request. Before you do, -again make sure you've followed all the guidelines outlined in this document regarding -code style, tests, performance tests, and documentation. You should also double check +When you're ready to ask for a code review, you will file a Pull Request. Before you do, +again make sure you've followed all the guidelines outlined in this document regarding +code style, tests, performance tests, and documentation. You should also double check your branch changes against the branch it was based off of: #. Navigate to your repository on GitHub--https://github.com/your-user-name/pandas. #. Click on `Branches`. #. Click on the `Compare` button for your feature branch. -#. Select the `base` and `compare` branches, if necessary. This will be `master` and +#. Select the `base` and `compare` branches, if necessary. This will be `master` and `shiny-new-feature`, respectively. Finally, make the Pull Request ------------------------------ -If everything looks good you are ready to make a Pull Request. A Pull Request is how -code from a local repository becomes available to the GitHub community and can be looked -at and eventually merged into the master version. This Pull Request and its associated -changes will eventually be committed to the master branch and available in the next +If everything looks good you are ready to make a Pull Request. A Pull Request is how +code from a local repository becomes available to the GitHub community and can be looked +at and eventually merged into the master version. This Pull Request and its associated +changes will eventually be committed to the master branch and available in the next release. To submit a Pull Request: #. Navigate to your repository on GitHub. @@ -555,7 +555,7 @@ This will automatically update your Pull Request with the latest code and restar Delete your merged branch (optional) ------------------------------------ -Once your feature branch is accepted into upstream, you'll probably want to get rid of +Once your feature branch is accepted into upstream, you'll probably want to get rid of the branch. First, merge upstream master into your branch so git knows it is safe to delete your branch :: git fetch upstream
This is an alternative to #9796. I simply did a couple of changes to the restructuredtext source so it generates cleaner markdown. The main thing was making the categories not quite so nested. (Was that [heavy nesting](http://pandas-docs.github.io/pandas-docs-travis/contributing.html#committing-your-code) intentional?) I think pandoc was running out of header styles or something like that. cc @rockg @jreback @jorisvandenbossche
https://api.github.com/repos/pandas-dev/pandas/pulls/9797
2015-04-03T04:54:57Z
2015-04-03T17:18:52Z
2015-04-03T17:18:52Z
2015-04-03T19:58:47Z
BUG: DataFrame._slice doesnt retain metadata
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index ca316bbac8474..eaf8054c69b18 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -69,7 +69,7 @@ Bug Fixes - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) - +- Bug in ``DataFrame`` slicing may not retain metadata (:issue:`9776`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e05709d7a180f..555954f112f5a 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1179,6 +1179,7 @@ def _slice(self, slobj, axis=0, kind=None): """ axis = self._get_block_manager_axis(axis) result = self._constructor(self._data.get_slice(slobj, axis=axis)) + result = result.__finalize__(self) # this could be a view # but only in a single-dtyped view slicable case diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1acad4cf978a8..31dc7de1e1b67 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -14057,6 +14057,28 @@ def test_assign_bad(self): with tm.assertRaises(KeyError): df.assign(C=df.A, D=lambda x: x['A'] + x['C']) + def test_dataframe_metadata(self): + + class TestDataFrame(DataFrame): + _metadata = ['testattr'] + + @property + def _constructor(self): + return TestDataFrame + + + df = TestDataFrame({'X': [1, 2, 3], 'Y': [1, 2, 3]}, + index=['a', 'b', 'c']) + df.testattr = 'XXX' + + self.assertEqual(df.testattr, 'XXX') + self.assertEqual(df[['X']].testattr, 'XXX') + self.assertEqual(df.loc[['a', 'b'], :].testattr, 'XXX') + self.assertEqual(df.iloc[[0, 1], :].testattr, 'XXX') + # GH9776 + self.assertEqual(df.iloc[0:1, :].testattr, 'XXX') + + def skip_if_no_ne(engine='numexpr'): if engine == 'numexpr': try:
Closes #9776.
https://api.github.com/repos/pandas-dev/pandas/pulls/9793
2015-04-02T21:04:00Z
2015-04-04T18:32:40Z
2015-04-04T18:32:40Z
2015-04-04T21:28:12Z
Fix zlib and blosc imports in to_msgpack
diff --git a/ci/script.sh b/ci/script.sh index b1ba7ba79c816..e1f71e70ded69 100755 --- a/ci/script.sh +++ b/ci/script.sh @@ -16,6 +16,8 @@ fi "$TRAVIS_BUILD_DIR"/ci/build_docs.sh 2>&1 > /tmp/doc.log & # doc build log will be shown after tests +pip install -U blosc # See https://github.com/pydata/pandas/pull/9783 +python -c 'import blosc; blosc.print_versions()' echo nosetests --exe -A "$NOSE_ARGS" pandas --with-xunit --xunit-file=/tmp/nosetests.xml nosetests --exe -A "$NOSE_ARGS" pandas --with-xunit --xunit-file=/tmp/nosetests.xml diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index f0210698e2828..a25c86c1a86e6 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -78,3 +78,5 @@ Bug Fixes - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) + +- Bug in ``to_msgpack`` and ``read_msgpack`` zlib and blosc compression support (:issue:`9783`) diff --git a/pandas/io/packers.py b/pandas/io/packers.py index b3e2e16af54c2..75ca44fd1ef3e 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -65,26 +65,7 @@ # until we can pass this into our conversion functions, # this is pretty hacky compressor = None -_IMPORTS = False -_BLOSC = False -def _importers(): - # import things we need - # but make this done on a first use basis - - global _IMPORTS - if _IMPORTS: - return - - _IMPORTS = True - - global _BLOSC - import zlib - try: - import blosc - _BLOSC = True - except: - pass def to_msgpack(path_or_buf, *args, **kwargs): """ @@ -103,7 +84,6 @@ def to_msgpack(path_or_buf, *args, **kwargs): compress : type of compressor (zlib or blosc), default to None (no compression) """ - _importers() global compressor compressor = kwargs.pop('compress', None) append = kwargs.pop('append', None) @@ -146,7 +126,6 @@ def read_msgpack(path_or_buf, iterator=False, **kwargs): obj : type of object stored in file """ - _importers() path_or_buf, _ = get_filepath_or_buffer(path_or_buf) if iterator: return Iterator(path_or_buf) @@ -232,9 +211,10 @@ def convert(values): # convert to a bytes array v = v.tostring() + import zlib return zlib.compress(v) - elif compressor == 'blosc' and _BLOSC: + elif compressor == 'blosc': # return string arrays like they are if dtype == np.object_: @@ -242,6 +222,7 @@ def convert(values): # convert to a bytes array v = v.tostring() + import blosc return blosc.compress(v, typesize=dtype.itemsize) # ndarray (on original dtype) @@ -253,23 +234,20 @@ def unconvert(values, dtype, compress=None): if dtype == np.object_: return np.array(values, dtype=object) - if compress == 'zlib': + values = values.encode('latin1') + if compress == 'zlib': + import zlib values = zlib.decompress(values) return np.frombuffer(values, dtype=dtype) elif compress == 'blosc': - - if not _BLOSC: - raise Exception("cannot uncompress w/o blosc") - - # decompress + import blosc values = blosc.decompress(values) - return np.frombuffer(values, dtype=dtype) # from a string - return np.fromstring(values.encode('latin1'), dtype=dtype) + return np.fromstring(values, dtype=dtype) def encode(obj): @@ -285,7 +263,8 @@ def encode(obj): 'name': getattr(obj, 'name', None), 'freq': getattr(obj, 'freqstr', None), 'dtype': obj.dtype.num, - 'data': convert(obj.asi8)} + 'data': convert(obj.asi8), + 'compress': compressor} elif isinstance(obj, DatetimeIndex): tz = getattr(obj, 'tz', None) @@ -299,19 +278,22 @@ def encode(obj): 'dtype': obj.dtype.num, 'data': convert(obj.asi8), 'freq': getattr(obj, 'freqstr', None), - 'tz': tz} + 'tz': tz, + 'compress': compressor} elif isinstance(obj, MultiIndex): return {'typ': 'multi_index', 'klass': obj.__class__.__name__, 'names': getattr(obj, 'names', None), 'dtype': obj.dtype.num, - 'data': convert(obj.values)} + 'data': convert(obj.values), + 'compress': compressor} else: return {'typ': 'index', 'klass': obj.__class__.__name__, 'name': getattr(obj, 'name', None), 'dtype': obj.dtype.num, - 'data': convert(obj.values)} + 'data': convert(obj.values), + 'compress': compressor} elif isinstance(obj, Series): if isinstance(obj, SparseSeries): raise NotImplementedError( diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 9633f567ab098..d85e75f5d2818 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -446,6 +446,41 @@ def test_sparse_panel(self): check_panel_type=True) +class TestCompression(TestPackers): + """See https://github.com/pydata/pandas/pull/9783 + """ + + def setUp(self): + super(TestCompression, self).setUp() + data = { + 'A': np.arange(1000, dtype=np.float64), + 'B': np.arange(1000, dtype=np.int32), + 'C': list(100 * 'abcdefghij'), + 'D': date_range(datetime.datetime(2015, 4, 1), periods=1000), + 'E': [datetime.timedelta(days=x) for x in range(1000)], + } + self.frame = { + 'float': DataFrame(dict((k, data[k]) for k in ['A', 'A'])), + 'int': DataFrame(dict((k, data[k]) for k in ['B', 'B'])), + 'mixed': DataFrame(data), + } + + def test_plain(self): + i_rec = self.encode_decode(self.frame) + for k in self.frame.keys(): + assert_frame_equal(self.frame[k], i_rec[k]) + + def test_compression_zlib(self): + i_rec = self.encode_decode(self.frame, compress='zlib') + for k in self.frame.keys(): + assert_frame_equal(self.frame[k], i_rec[k]) + + def test_compression_blosc(self): + i_rec = self.encode_decode(self.frame, compress='blosc') + for k in self.frame.keys(): + assert_frame_equal(self.frame[k], i_rec[k]) + + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
6717aa06dcaa1950ffb46fef454f5df9404209bd removed zlib and blosc from the global namespace. ``` from pandas import read_csv table = read_csv('aadhaar_data.csv') table.to_msgpack('d.msg') # NameError: global name 'blosc' is not defined table.to_msgpack('d-blosc.msg', compress='blosc') # NameError: global name 'zlib' is not defined table.to_msgpack('d-zlib.msg', compress='zlib') ``` This pull request restores zlib and blosc compression in to_msgpack via local imports.
https://api.github.com/repos/pandas-dev/pandas/pulls/9783
2015-04-02T00:52:27Z
2015-04-13T16:01:48Z
2015-04-13T16:01:48Z
2015-04-13T16:01:48Z
ENH: Add StringMethods.partition and rpartition
diff --git a/doc/source/api.rst b/doc/source/api.rst index 2d9fc0df5347d..364b3ba04aefb 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -544,10 +544,12 @@ strings and apply several methods to it. These can be acccessed like Series.str.match Series.str.normalize Series.str.pad + Series.str.partition Series.str.repeat Series.str.replace Series.str.rfind Series.str.rjust + Series.str.rpartition Series.str.rstrip Series.str.slice Series.str.slice_replace diff --git a/doc/source/text.rst b/doc/source/text.rst index 359b6d61dbb64..bb27fe52ba7a5 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -262,6 +262,8 @@ Method Summary :meth:`~Series.str.strip`,Equivalent to ``str.strip`` :meth:`~Series.str.rstrip`,Equivalent to ``str.rstrip`` :meth:`~Series.str.lstrip`,Equivalent to ``str.lstrip`` + :meth:`~Series.str.partition`,Equivalent to ``str.partition`` + :meth:`~Series.str.rpartition`,Equivalent to ``str.rpartition`` :meth:`~Series.str.lower`,Equivalent to ``str.lower`` :meth:`~Series.str.upper`,Equivalent to ``str.upper`` :meth:`~Series.str.find`,Equivalent to ``str.find`` diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 1c2dbaa48832b..493f299b2bf32 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -42,6 +42,7 @@ Enhancements - Added ``StringMethods`` (.str accessor) to ``Index`` (:issue:`9068`) - Added ``StringMethods.normalize()`` which behaves the same as standard :func:`unicodedata.normalizes` (:issue:`10031`) +- Added ``StringMethods.partition()`` and ``rpartition()`` which behave as the same as standard ``str`` (:issue:`9773`) - Allow clip, clip_lower, and clip_upper to accept array-like arguments as thresholds (:issue:`6966`). These methods now have an ``axis`` parameter which determines how the Series or DataFrame will be aligned with the threshold(s). The ``.str`` accessor is now available for both ``Series`` and ``Index``. diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 5cea4c4afe8cc..62e9e0fbc41ae 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -992,6 +992,8 @@ def __iter__(self): g = self.get(i) def _wrap_result(self, result): + # leave as it is to keep extract and get_dummies results + # can be merged to _wrap_result_expand in v0.17 from pandas.core.series import Series from pandas.core.frame import DataFrame from pandas.core.index import Index @@ -1012,6 +1014,33 @@ def _wrap_result(self, result): assert result.ndim < 3 return DataFrame(result, index=self.series.index) + def _wrap_result_expand(self, result, expand=False): + from pandas.core.index import Index + if not hasattr(result, 'ndim'): + return result + + if isinstance(self.series, Index): + name = getattr(result, 'name', None) + # if result is a boolean np.array, return the np.array + # instead of wrapping it into a boolean Index (GH 8875) + if hasattr(result, 'dtype') and is_bool_dtype(result): + return result + + if expand: + result = list(result) + return Index(result, name=name) + else: + index = self.series.index + if expand: + cons_row = self.series._constructor + cons = self.series._constructor_expanddim + data = [cons_row(x) for x in result] + return cons(data, index=index) + else: + name = getattr(result, 'name', None) + cons = self.series._constructor + return cons(result, name=name, index=index) + @copy(str_cat) def cat(self, others=None, sep=None, na_rep=None): result = str_cat(self.series, others=others, sep=sep, na_rep=na_rep) @@ -1022,6 +1051,65 @@ def split(self, pat=None, n=-1, return_type='series'): result = str_split(self.series, pat, n=n, return_type=return_type) return self._wrap_result(result) + _shared_docs['str_partition'] = (""" + Split the string at the %(side)s occurrence of `sep`, and return 3 elements + containing the part before the separator, the separator itself, + and the part after the separator. + If the separator is not found, return %(return)s. + + Parameters + ---------- + pat : string, default whitespace + String to split on. + expand : bool, default True + * If True, return DataFrame/MultiIndex expanding dimensionality. + * If False, return Series/Index + + Returns + ------- + split : DataFrame/MultiIndex or Series/Index of objects + + See Also + -------- + %(also)s + + Examples + -------- + + >>> s = Series(['A_B_C', 'D_E_F', 'X']) + 0 A_B_C + 1 D_E_F + 2 X + dtype: object + + >>> s.str.partition('_') + 0 1 2 + 0 A _ B_C + 1 D _ E_F + 2 X + + >>> s.str.rpartition('_') + 0 1 2 + 0 A_B _ C + 1 D_E _ F + 2 X + """) + @Appender(_shared_docs['str_partition'] % {'side': 'first', + 'return': '3 elements containing the string itself, followed by two empty strings', + 'also': 'rpartition : Split the string at the last occurrence of `sep`'}) + def partition(self, pat=' ', expand=True): + f = lambda x: x.partition(pat) + result = _na_map(f, self.series) + return self._wrap_result_expand(result, expand=expand) + + @Appender(_shared_docs['str_partition'] % {'side': 'last', + 'return': '3 elements containing two empty strings, followed by the string itself', + 'also': 'partition : Split the string at the first occurrence of `sep`'}) + def rpartition(self, pat=' ', expand=True): + f = lambda x: x.rpartition(pat) + result = _na_map(f, self.series) + return self._wrap_result_expand(result, expand=expand) + @copy(str_get) def get(self, i): result = str_get(self.series, i) diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index d3875f0675e9f..1f84e1dc4d155 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -664,6 +664,8 @@ def test_empty_str_methods(self): tm.assert_series_equal(empty_str, empty.str.pad(42)) tm.assert_series_equal(empty_str, empty.str.center(42)) tm.assert_series_equal(empty_list, empty.str.split('a')) + tm.assert_series_equal(empty_list, empty.str.partition('a', expand=False)) + tm.assert_series_equal(empty_list, empty.str.rpartition('a', expand=False)) tm.assert_series_equal(empty_str, empty.str.slice(stop=1)) tm.assert_series_equal(empty_str, empty.str.slice(step=1)) tm.assert_series_equal(empty_str, empty.str.strip()) @@ -687,6 +689,12 @@ def test_empty_str_methods(self): tm.assert_series_equal(empty_str, empty.str.swapcase()) tm.assert_series_equal(empty_str, empty.str.normalize('NFC')) + def test_empty_str_methods_to_frame(self): + empty_str = empty = Series(dtype=str) + empty_df = DataFrame([]) + tm.assert_frame_equal(empty_df, empty.str.partition('a')) + tm.assert_frame_equal(empty_df, empty.str.rpartition('a')) + def test_ismethods(self): values = ['A', 'b', 'Xy', '4', '3A', '', 'TT', '55', '-', ' '] str_s = Series(values) @@ -1175,6 +1183,119 @@ def test_split_to_dataframe(self): with tm.assertRaisesRegexp(ValueError, "return_type must be"): s.str.split('_', return_type="some_invalid_type") + def test_partition_series(self): + values = Series(['a_b_c', 'c_d_e', NA, 'f_g_h']) + + result = values.str.partition('_', expand=False) + exp = Series([['a', '_', 'b_c'], ['c', '_', 'd_e'], NA, ['f', '_', 'g_h']]) + tm.assert_series_equal(result, exp) + + result = values.str.rpartition('_', expand=False) + exp = Series([['a_b', '_', 'c'], ['c_d', '_', 'e'], NA, ['f_g', '_', 'h']]) + tm.assert_series_equal(result, exp) + + # more than one char + values = Series(['a__b__c', 'c__d__e', NA, 'f__g__h']) + result = values.str.partition('__', expand=False) + exp = Series([['a', '__', 'b__c'], ['c', '__', 'd__e'], NA, ['f', '__', 'g__h']]) + tm.assert_series_equal(result, exp) + + result = values.str.rpartition('__', expand=False) + exp = Series([['a__b', '__', 'c'], ['c__d', '__', 'e'], NA, ['f__g', '__', 'h']]) + tm.assert_series_equal(result, exp) + + # None + values = Series(['a b c', 'c d e', NA, 'f g h']) + result = values.str.partition(expand=False) + exp = Series([['a', ' ', 'b c'], ['c', ' ', 'd e'], NA, ['f', ' ', 'g h']]) + tm.assert_series_equal(result, exp) + + result = values.str.rpartition(expand=False) + exp = Series([['a b', ' ', 'c'], ['c d', ' ', 'e'], NA, ['f g', ' ', 'h']]) + tm.assert_series_equal(result, exp) + + # Not splited + values = Series(['abc', 'cde', NA, 'fgh']) + result = values.str.partition('_', expand=False) + exp = Series([['abc', '', ''], ['cde', '', ''], NA, ['fgh', '', '']]) + tm.assert_series_equal(result, exp) + + result = values.str.rpartition('_', expand=False) + exp = Series([['', '', 'abc'], ['', '', 'cde'], NA, ['', '', 'fgh']]) + tm.assert_series_equal(result, exp) + + # unicode + values = Series([u('a_b_c'), u('c_d_e'), NA, u('f_g_h')]) + + result = values.str.partition('_', expand=False) + exp = Series([[u('a'), u('_'), u('b_c')], [u('c'), u('_'), u('d_e')], + NA, [u('f'), u('_'), u('g_h')]]) + tm.assert_series_equal(result, exp) + + result = values.str.rpartition('_', expand=False) + exp = Series([[u('a_b'), u('_'), u('c')], [u('c_d'), u('_'), u('e')], + NA, [u('f_g'), u('_'), u('h')]]) + tm.assert_series_equal(result, exp) + + # compare to standard lib + values = Series(['A_B_C', 'B_C_D', 'E_F_G', 'EFGHEF']) + result = values.str.partition('_', expand=False).tolist() + self.assertEqual(result, [v.partition('_') for v in values]) + result = values.str.rpartition('_', expand=False).tolist() + self.assertEqual(result, [v.rpartition('_') for v in values]) + + def test_partition_index(self): + values = Index(['a_b_c', 'c_d_e', 'f_g_h']) + + result = values.str.partition('_', expand=False) + exp = Index(np.array([('a', '_', 'b_c'), ('c', '_', 'd_e'), ('f', '_', 'g_h')])) + tm.assert_index_equal(result, exp) + self.assertEqual(result.nlevels, 1) + + result = values.str.rpartition('_', expand=False) + exp = Index(np.array([('a_b', '_', 'c'), ('c_d', '_', 'e'), ('f_g', '_', 'h')])) + tm.assert_index_equal(result, exp) + self.assertEqual(result.nlevels, 1) + + result = values.str.partition('_') + exp = Index([('a', '_', 'b_c'), ('c', '_', 'd_e'), ('f', '_', 'g_h')]) + tm.assert_index_equal(result, exp) + self.assertTrue(isinstance(result, MultiIndex)) + self.assertEqual(result.nlevels, 3) + + result = values.str.rpartition('_') + exp = Index([('a_b', '_', 'c'), ('c_d', '_', 'e'), ('f_g', '_', 'h')]) + tm.assert_index_equal(result, exp) + self.assertTrue(isinstance(result, MultiIndex)) + self.assertEqual(result.nlevels, 3) + + def test_partition_to_dataframe(self): + values = Series(['a_b_c', 'c_d_e', NA, 'f_g_h']) + result = values.str.partition('_') + exp = DataFrame({0: ['a', 'c', np.nan, 'f'], + 1: ['_', '_', np.nan, '_'], + 2: ['b_c', 'd_e', np.nan, 'g_h']}) + tm.assert_frame_equal(result, exp) + + result = values.str.rpartition('_') + exp = DataFrame({0: ['a_b', 'c_d', np.nan, 'f_g'], + 1: ['_', '_', np.nan, '_'], + 2: ['c', 'e', np.nan, 'h']}) + tm.assert_frame_equal(result, exp) + + values = Series(['a_b_c', 'c_d_e', NA, 'f_g_h']) + result = values.str.partition('_', expand=True) + exp = DataFrame({0: ['a', 'c', np.nan, 'f'], + 1: ['_', '_', np.nan, '_'], + 2: ['b_c', 'd_e', np.nan, 'g_h']}) + tm.assert_frame_equal(result, exp) + + result = values.str.rpartition('_', expand=True) + exp = DataFrame({0: ['a_b', 'c_d', np.nan, 'f_g'], + 1: ['_', '_', np.nan, '_'], + 2: ['c', 'e', np.nan, 'h']}) + tm.assert_frame_equal(result, exp) + def test_pipe_failures(self): # #2119 s = Series(['A|B|C'])
Derived from #9111.
https://api.github.com/repos/pandas-dev/pandas/pulls/9773
2015-04-01T13:58:57Z
2015-05-07T11:08:58Z
2015-05-07T11:08:58Z
2015-05-07T17:18:42Z
ENH: Add option in read_csv to infer compression type from filename
diff --git a/doc/source/io.rst b/doc/source/io.rst index 1c8a1159ab162..a6c702e1cd874 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -89,6 +89,8 @@ They can take a number of arguments: - ``delim_whitespace``: Parse whitespace-delimited (spaces or tabs) file (much faster than using a regular expression) - ``compression``: decompress ``'gzip'`` and ``'bz2'`` formats on the fly. + Set to ``'infer'`` (the default) to guess a format based on the file + extension. - ``dialect``: string or :class:`python:csv.Dialect` instance to expose more ways to specify the file format - ``dtype``: A data type name or a dict of column name to data type. If not diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt old mode 100644 new mode 100755 index a6e917827b755..659aa6786b366 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -80,6 +80,7 @@ API changes - :meth:`~pandas.DataFrame.assign` now inserts new columns in alphabetical order. Previously the order was arbitrary. (:issue:`9777`) +- By default, ``read_csv`` and ``read_table`` will now try to infer the compression type based on the file extension. Set ``compression=None`` to restore the previous behavior (no decompression). (:issue:`9770`) .. _whatsnew_0161.performance: diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py old mode 100644 new mode 100755 index fef02dcb6e0c5..59ecb29146315 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -56,8 +56,11 @@ class ParserWarning(Warning): dtype : Type name or dict of column -> type Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} (Unsupported with engine='python') -compression : {'gzip', 'bz2', None}, default None - For on-the-fly decompression of on-disk data +compression : {'gzip', 'bz2', 'infer', None}, default 'infer' + For on-the-fly decompression of on-disk data. If 'infer', then use gzip or + bz2 if filepath_or_buffer is a string ending in '.gz' or '.bz2', + respectively, and no decompression otherwise. Set to None for no + decompression. dialect : string or csv.Dialect instance, default None If None defaults to Excel dialect. Ignored if sep longer than 1 char See csv.Dialect documentation for more details @@ -295,7 +298,7 @@ def _read(filepath_or_buffer, kwds): 'verbose': False, 'encoding': None, 'squeeze': False, - 'compression': None, + 'compression': 'infer', 'mangle_dupe_cols': True, 'tupleize_cols': False, 'infer_datetime_format': False, @@ -335,7 +338,7 @@ def _make_parser_function(name, sep=','): def parser_f(filepath_or_buffer, sep=sep, dialect=None, - compression=None, + compression='infer', doublequote=True, escapechar=None, @@ -1317,6 +1320,7 @@ def _wrap_compressed(f, compression, encoding=None): """ compression = compression.lower() encoding = encoding or get_option('display.encoding') + if compression == 'gzip': import gzip @@ -1389,6 +1393,17 @@ def __init__(self, f, **kwds): self.comment = kwds['comment'] self._comment_lines = [] + if self.compression == 'infer': + if isinstance(f, compat.string_types): + if f.endswith('.gz'): + self.compression = 'gzip' + elif f.endswith('.bz2'): + self.compression = 'bz2' + else: + self.compression = None + else: + self.compression = None + if isinstance(f, compat.string_types): f = com._get_handle(f, 'r', encoding=self.encoding, compression=self.compression) diff --git a/pandas/io/tests/data/test1.csv.bz2 b/pandas/io/tests/data/test1.csv.bz2 new file mode 100644 index 0000000000000..f96f26a8e7419 Binary files /dev/null and b/pandas/io/tests/data/test1.csv.bz2 differ diff --git a/pandas/io/tests/data/test1.csv.gz b/pandas/io/tests/data/test1.csv.gz new file mode 100644 index 0000000000000..1336db6e2af7e Binary files /dev/null and b/pandas/io/tests/data/test1.csv.gz differ diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py old mode 100644 new mode 100755 index b7016ad6cffae..799872d036c4f --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -1098,6 +1098,21 @@ def test_read_csv_no_index_name(self): self.assertEqual(df.ix[:, ['A', 'B', 'C', 'D']].values.dtype, np.float64) tm.assert_frame_equal(df, df2) + def test_read_csv_infer_compression(self): + # GH 9770 + expected = self.read_csv(self.csv1, index_col=0, parse_dates=True) + + inputs = [self.csv1, self.csv1 + '.gz', + self.csv1 + '.bz2', open(self.csv1)] + + for f in inputs: + df = self.read_csv(f, index_col=0, parse_dates=True, + compression='infer') + + tm.assert_frame_equal(expected, df) + + inputs[3].close() + def test_read_table_unicode(self): fin = BytesIO(u('\u0141aski, Jan;1').encode('utf-8')) df1 = read_table(fin, sep=";", encoding="utf-8", header=None) diff --git a/pandas/parser.pyx b/pandas/parser.pyx index 73a03fc5cef7c..b28e0587264d4 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -541,6 +541,17 @@ cdef class TextReader: self.parser.cb_io = NULL self.parser.cb_cleanup = NULL + if self.compression == 'infer': + if isinstance(source, basestring): + if source.endswith('.gz'): + self.compression = 'gzip' + elif source.endswith('.bz2'): + self.compression = 'bz2' + else: + self.compression = None + else: + self.compression = None + if self.compression: if self.compression == 'gzip': import gzip
Ideally, I would love for this to be the default, but that wouldn't be backwards-compatible in the case where the filename ends in '.gz' or '.bz2' and you want to treat it as uncompressed. That seems like it would be very rare, though.
https://api.github.com/repos/pandas-dev/pandas/pulls/9770
2015-04-01T12:11:53Z
2015-04-18T02:53:14Z
2015-04-18T02:53:14Z
2015-04-18T03:41:12Z
Added documentation for mode()
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index fad271dbdb224..f700d4316842c 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4411,9 +4411,15 @@ def _get_agg_axis(self, axis_num): def mode(self, axis=0, numeric_only=False): """ - Gets the mode of each element along the axis selected. Empty if nothing + Gets the mode(s) of each element along the axis selected. Empty if nothing has 2+ occurrences. Adds a row for each mode per label, fills in gaps - with nan. + with nan. + + Note that there could be multiple values returned for the selected + axis (when more than one item share the maximum frequency), which is the + reason why a dataframe is returned. If you want to impute missing values + with the mode in a dataframe ``df``, you can just do this: + ``df.fillna(df.mode().iloc[0])`` Parameters ---------- @@ -4426,6 +4432,14 @@ def mode(self, axis=0, numeric_only=False): Returns ------- modes : DataFrame (sorted) + + Examples + -------- + >>> df = pd.DataFrame({'A': [1, 2, 1, 2, 1, 2, 3]}) + >>> df.mode() + A + 0 1 + 1 2 """ data = self if not numeric_only else self._get_numeric_data() f = lambda s: s.mode()
This relates to issue #9750
https://api.github.com/repos/pandas-dev/pandas/pulls/9769
2015-04-01T11:20:03Z
2015-04-02T18:25:44Z
2015-04-02T18:25:44Z
2015-04-02T18:25:54Z
ENH: Add StringMethods.capitalize and swapcase
diff --git a/doc/source/api.rst b/doc/source/api.rst index b617009fe2f13..af9f8c84388bd 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -521,6 +521,7 @@ strings and apply several methods to it. These can be acccessed like :toctree: generated/ :template: autosummary/accessor_method.rst + Series.str.capitalize Series.str.cat Series.str.center Series.str.contains @@ -549,6 +550,7 @@ strings and apply several methods to it. These can be acccessed like Series.str.split Series.str.startswith Series.str.strip + Series.str.swapcase Series.str.title Series.str.upper Series.str.zfill diff --git a/doc/source/text.rst b/doc/source/text.rst index af32549893dde..2d46b37853cee 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -233,6 +233,8 @@ Method Summary :meth:`~Series.str.upper`,Equivalent to ``str.upper`` :meth:`~Series.str.find`,Equivalent to ``str.find`` :meth:`~Series.str.rfind`,Equivalent to ``str.rfind`` + :meth:`~Series.str.capicalize`,Equivalent to ``str.capitalize`` + :meth:`~Series.str.swapcase`,Equivalent to ``str.swapcase`` :meth:`~Series.str.isalnum`,Equivalent to ``str.isalnum`` :meth:`~Series.str.isalpha`,Equivalent to ``str.isalpha`` :meth:`~Series.str.isdigit`,Equivalent to ``str.isdigit`` diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 55922091556c1..f8482f8ded184 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -17,6 +17,8 @@ We recommend that all users upgrade to this version. Enhancements ~~~~~~~~~~~~ +- Added ``StringMethods.capitalize()`` and ``swapcase`` which behave as the same as standard ``str`` (:issue:`9766`) + @@ -58,7 +60,7 @@ Performance Improvements Bug Fixes ~~~~~~~~~ -- Fixed bug (:issue:`9542`) where labels did not appear properly in legend of ``DataFrame.plot()``. Passing ``label=`` args also now works, and series indices are no longer mutated. +- Fixed bug (:issue:`9542`) where labels did not appear properly in legend of ``DataFrame.plot()``. Passing ``label=`` args also now works, and series indices are no longer mutated. diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 93ad2066d0e12..97f6752fb5851 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1157,18 +1157,28 @@ def rfind(self, sub, start=0, end=None): len = _noarg_wrapper(len, docstring=_shared_docs['len'], dtype=int) _shared_docs['casemethods'] = (""" - Convert strings in array to %s + Convert strings in array to %(type)s. + Equivalent to ``str.%(method)s``. Returns ------- - uppercase : array + converted : array """) + _shared_docs['lower'] = dict(type='lowercase', method='lower') + _shared_docs['upper'] = dict(type='uppercase', method='upper') + _shared_docs['title'] = dict(type='titlecase', method='title') + _shared_docs['capitalize'] = dict(type='be capitalized', method='capitalize') + _shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase') lower = _noarg_wrapper(lambda x: x.lower(), - docstring=_shared_docs['casemethods'] % 'lowercase') + docstring=_shared_docs['casemethods'] % _shared_docs['lower']) upper = _noarg_wrapper(lambda x: x.upper(), - docstring=_shared_docs['casemethods'] % 'uppercase') + docstring=_shared_docs['casemethods'] % _shared_docs['upper']) title = _noarg_wrapper(lambda x: x.title(), - docstring=_shared_docs['casemethods'] % 'titlecase') + docstring=_shared_docs['casemethods'] % _shared_docs['title']) + capitalize = _noarg_wrapper(lambda x: x.capitalize(), + docstring=_shared_docs['casemethods'] % _shared_docs['capitalize']) + swapcase = _noarg_wrapper(lambda x: x.swapcase(), + docstring=_shared_docs['casemethods'] % _shared_docs['swapcase']) _shared_docs['ismethods'] = (""" Check whether all characters in each string in the array are %(type)s. diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py index 727ef39aa35e7..9283be566bd8f 100644 --- a/pandas/tests/test_strings.py +++ b/pandas/tests/test_strings.py @@ -328,6 +328,53 @@ def test_lower_upper(self): result = result.str.lower() tm.assert_series_equal(result, values) + def test_capitalize(self): + values = Series(["FOO", "BAR", NA, "Blah", "blurg"]) + result = values.str.capitalize() + exp = Series(["Foo", "Bar", NA, "Blah", "Blurg"]) + tm.assert_series_equal(result, exp) + + # mixed + mixed = Series(["FOO", NA, "bar", True, datetime.today(), + "blah", None, 1, 2.]) + mixed = mixed.str.capitalize() + exp = Series(["Foo", NA, "Bar", NA, NA, "Blah", NA, NA, NA]) + tm.assert_almost_equal(mixed, exp) + + # unicode + values = Series([u("FOO"), NA, u("bar"), u("Blurg")]) + results = values.str.capitalize() + exp = Series([u("Foo"), NA, u("Bar"), u("Blurg")]) + tm.assert_series_equal(results, exp) + + def test_swapcase(self): + values = Series(["FOO", "BAR", NA, "Blah", "blurg"]) + result = values.str.swapcase() + exp = Series(["foo", "bar", NA, "bLAH", "BLURG"]) + tm.assert_series_equal(result, exp) + + # mixed + mixed = Series(["FOO", NA, "bar", True, datetime.today(), + "Blah", None, 1, 2.]) + mixed = mixed.str.swapcase() + exp = Series(["foo", NA, "BAR", NA, NA, "bLAH", NA, NA, NA]) + tm.assert_almost_equal(mixed, exp) + + # unicode + values = Series([u("FOO"), NA, u("bar"), u("Blurg")]) + results = values.str.swapcase() + exp = Series([u("foo"), NA, u("BAR"), u("bLURG")]) + tm.assert_series_equal(results, exp) + + def test_casemethods(self): + values = ['aaa', 'bbb', 'CCC', 'Dddd', 'eEEE'] + s = Series(values) + self.assertEqual(s.str.lower().tolist(), [v.lower() for v in values]) + self.assertEqual(s.str.upper().tolist(), [v.upper() for v in values]) + self.assertEqual(s.str.title().tolist(), [v.title() for v in values]) + self.assertEqual(s.str.capitalize().tolist(), [v.capitalize() for v in values]) + self.assertEqual(s.str.swapcase().tolist(), [v.swapcase() for v in values]) + def test_replace(self): values = Series(['fooBAD__barBAD', NA]) @@ -636,6 +683,8 @@ def test_empty_str_methods(self): tm.assert_series_equal(empty_str, empty.str.istitle()) tm.assert_series_equal(empty_str, empty.str.isnumeric()) tm.assert_series_equal(empty_str, empty.str.isdecimal()) + tm.assert_series_equal(empty_str, empty.str.capitalize()) + tm.assert_series_equal(empty_str, empty.str.swapcase()) def test_ismethods(self): values = ['A', 'b', 'Xy', '4', '3A', '', 'TT', '55', '-', ' ']
Derived from #9111.
https://api.github.com/repos/pandas-dev/pandas/pulls/9766
2015-03-31T20:51:31Z
2015-04-01T19:17:09Z
2015-04-01T19:17:09Z
2015-04-02T13:58:00Z
BUG: allow conversion of Timestamp and Timedelta to string in astype
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 9f989b2cf0ea9..d0f7af2275812 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -30,6 +30,7 @@ Enhancements df = DataFrame(np.random.randn(3, 3), columns=['A', 'B', 'C']) df.drop(['A', 'X'], axis=1, errors='ignore') +- Allow conversion of values with dtype ``datetime64`` or ``timedelta64`` to strings using ``astype(str)`` (:issue:`9757`) .. _whatsnew_0161.api: diff --git a/pandas/core/common.py b/pandas/core/common.py index ec805aba34d48..0fb35c2fb02fc 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2637,7 +2637,12 @@ def _astype_nansafe(arr, dtype, copy=True): if not isinstance(dtype, np.dtype): dtype = _coerce_to_dtype(dtype) - if is_datetime64_dtype(arr): + if issubclass(dtype.type, compat.text_type): + # in Py3 that's str, in Py2 that's unicode + return lib.astype_unicode(arr.ravel()).reshape(arr.shape) + elif issubclass(dtype.type, compat.string_types): + return lib.astype_str(arr.ravel()).reshape(arr.shape) + elif is_datetime64_dtype(arr): if dtype == object: return tslib.ints_to_pydatetime(arr.view(np.int64)) elif dtype == np.int64: @@ -2675,11 +2680,6 @@ def _astype_nansafe(arr, dtype, copy=True): elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer): # work around NumPy brokenness, #1987 return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape) - elif issubclass(dtype.type, compat.text_type): - # in Py3 that's str, in Py2 that's unicode - return lib.astype_unicode(arr.ravel()).reshape(arr.shape) - elif issubclass(dtype.type, compat.string_types): - return lib.astype_str(arr.ravel()).reshape(arr.shape) if copy: return arr.astype(dtype) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index b8bdd2d4e3b40..6ea76710b4de7 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -4192,6 +4192,30 @@ def test_astype_cast_nan_int(self): df = DataFrame(data={"Values": [1.0, 2.0, 3.0, np.nan]}) self.assertRaises(ValueError, df.astype, np.int64) + def test_astype_str(self): + # GH9757 + a = Series(date_range('2010-01-04', periods=5)) + b = Series(date_range('3/6/2012 00:00', periods=5, tz='US/Eastern')) + c = Series([Timedelta(x, unit='d') for x in range(5)]) + d = Series(range(5)) + e = Series([0.0, 0.2, 0.4, 0.6, 0.8]) + + df = DataFrame({'a' : a, 'b' : b, 'c' : c, 'd' : d, 'e' : e}) + + # Test str and unicode on python 2.x and just str on python 3.x + for tt in set([str, compat.text_type]): + result = df.astype(tt) + + expected = DataFrame({ + 'a' : list(map(tt, a.values)), + 'b' : list(map(tt, b.values)), + 'c' : list(map(tt, c.values)), + 'd' : list(map(tt, d.values)), + 'e' : list(map(tt, e.values)), + }) + + assert_frame_equal(result, expected) + def test_array_interface(self): result = np.sqrt(self.frame) tm.assert_isinstance(result, type(self.frame)) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index f044fe540ea24..fec98a37b5017 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -5511,6 +5511,24 @@ def test_astype_str(self): expec = s.map(compat.text_type) assert_series_equal(res, expec) + # GH9757 + # Test str and unicode on python 2.x and just str on python 3.x + for tt in set([str, compat.text_type]): + ts = Series([Timestamp('2010-01-04 00:00:00')]) + s = ts.astype(tt) + expected = Series([tt(ts.values[0])]) + assert_series_equal(s, expected) + + ts = Series([Timestamp('2010-01-04 00:00:00', tz='US/Eastern')]) + s = ts.astype(tt) + expected = Series([tt(ts.values[0])]) + assert_series_equal(s, expected) + + td = Series([Timedelta(1, unit='d')]) + s = td.astype(tt) + expected = Series([tt(td.values[0])]) + assert_series_equal(s, expected) + def test_astype_unicode(self): # GH7758
Fixes GH #9757
https://api.github.com/repos/pandas-dev/pandas/pulls/9758
2015-03-31T11:47:51Z
2015-04-10T06:20:57Z
2015-04-10T06:20:57Z
2015-09-19T00:38:25Z
Start combining various development documentation into one place.
diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 6d76c6e4efd6c..68bd6109b85d7 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -4,13 +4,574 @@ Contributing to pandas ********************** -See the following links: +.. contents:: Table of contents: + :local: + +Where to start? +=============== + +All contributions, bug reports, bug fixes, documentation improvements, +enhancements and ideas are welcome. + +If you are simply looking to start working with the *pandas* codebase, navigate to the +`GitHub "issues" tab <https://github.com/pydata/pandas/issues>`_ and start looking through +interesting issues. There are a number of issues listed under `Docs +<https://github.com/pydata/pandas/issues?labels=Docs&sort=updated&state=open>`_ +and `Good as first PR +<https://github.com/pydata/pandas/issues?labels=Good+as+first+PR&sort=updated&state=open>`_ +where you could start out. + +Or maybe through using *pandas* you have an idea of you own or are looking for something +in the documentation and thinking 'this can be improved'...you can do something +about it! + +Feel free to ask questions on `mailing list +<https://groups.google.com/forum/?fromgroups#!forum/pydata>`_ + +Bug Reports/Enhancement Requests +================================ + +Bug reports are an important part of making *pandas* more stable. Having a complete bug report +will allow others to reproduce the bug and provide insight into fixing. Since many versions of +*pandas* are supported, knowing version information will also identify improvements made since +previous versions. Often trying the bug-producing code out on the *master* branch is a worthwhile exercise +to confirm the bug still exists. It is also worth searching existing bug reports and pull requests +to see if the issue has already been reported and/or fixed. + +Bug reports must: + +#. Include a short, self-contained Python snippet reproducing the problem. + You can have the code formatted nicely by using `GitHub Flavored Markdown + <http://github.github.com/github-flavored-markdown/>`_: :: + + ```python + >>> from pandas import DataFrame + >>> df = DataFrame(...) + ... + ``` + +#. Include the full version string of *pandas* and its dependencies. In recent (>0.12) versions + of *pandas* you can use a built in function: :: + + >>> from pandas.util.print_versions import show_versions + >>> show_versions() + + and in 0.13.1 onwards: :: + + >>> pd.show_versions() + +#. Explain why the current behavior is wrong/not desired and what you expect instead. + +The issue will then show up to the *pandas* community and be open to comments/ideas from others. + +Working with the code +===================== + +Now that you have an issue you want to fix, enhancement to add, or documentation to improve, +you need to learn how to work with GitHub and the *pandas* code base. + +Version Control, Git, and GitHub +-------------------------------- + +To the new user, working with Git is one of the more daunting aspects of contributing to *pandas*. +It can very quickly become overwhelming, but sticking to the guidelines below will make the process +straightforward and will work without much trouble. As always, if you are having difficulties please +feel free to ask for help. + +The code is hosted on `GitHub <https://www.github.com/pydata/pandas>`_. To +contribute you will need to sign up for a `free GitHub account +<https://github.com/signup/free>`_. We use `Git <http://git-scm.com/>`_ for +version control to allow many people to work together on the project. + +Some great resources for learning git: + + * the `GitHub help pages <http://help.github.com/>`_. + * the `NumPy's documentation <http://docs.scipy.org/doc/numpy/dev/index.html>`_. + * Matthew Brett's `Pydagogue <http://matthew-brett.github.com/pydagogue/>`_. + +Getting Started with Git +------------------------ + +`GitHub has instructions <http://help.github.com/set-up-git-redirect>`__ for installing git, +setting up your SSH key, and configuring git. All these steps need to be completed before +working seamlessly with your local repository and GitHub. + +Forking +------- + +You will need your own fork to work on the code. Go to the `pandas project +page <https://github.com/pydata/pandas>`_ and hit the *fork* button. You will +want to clone your fork to your machine: :: + + git clone git@github.com:your-user-name/pandas.git pandas-yourname + cd pandas-yourname + git remote add upstream git://github.com/pydata/pandas.git + +This creates the directory `pandas-yourname` and connects your repository to +the upstream (main project) *pandas* repository. + +You will also need to hook up Travis-CI to your GitHub repository so the suite +is automatically run when a Pull Request is submitted. Instructions are `here +<http://about.travis-ci.org/docs/user/getting-started/>`_. + +Creating a Branch +----------------- + +You want your master branch to reflect only production-ready code, so create a +feature branch for making your changes. For example:: + + git branch shiny-new-feature + git checkout shiny-new-feature + +The above can be simplified to:: + + git checkout -b shiny-new-feature + +This changes your working directory to the shiny-new-feature branch. Keep any +changes in this branch specific to one bug or feature so it is clear +what the branch brings to *pandas*. You can have many shiny-new-features +and switch in between them using the git checkout command. + +Making changes +-------------- + +Before making your code changes, it is often necessary to build the code that was +just checked out. There are two primary methods of doing this. + +#. The best way to develop *pandas* is to build the C extensions in-place by + running:: + + python setup.py build_ext --inplace + + If you startup the Python interpreter in the *pandas* source directory you + will call the built C extensions + +#. Another very common option is to do a ``develop`` install of *pandas*:: + + python setup.py develop + + This makes a symbolic link that tells the Python interpreter to import *pandas* + from your development directory. Thus, you can always be using the development + version on your system without being inside the clone directory. + +Contributing to the documentation +--------------------------------- + +If you're not the developer type, contributing to the documentation is still +of huge value. You don't even have to be an expert on +*pandas* to do so! Something as simple as rewriting small passages for clarity +as you reference the docs is a simple but effective way to contribute. The +next person to read that passage will be in your debt! + +Actually, there are sections of the docs that are worse off by being written +by experts. If something in the docs doesn't make sense to you, updating the +relevant section after you figure it out is a simple way to ensure it will +help the next person. + +.. contents:: Documentation: + :local: + + +About the pandas documentation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The documentation is written in **reStructuredText**, which is almost like writing +in plain English, and built using `Sphinx <http://sphinx.pocoo.org/>`__. The +Sphinx Documentation has an excellent `introduction to reST +<http://sphinx.pocoo.org/rest.html>`__. Review the Sphinx docs to perform more +complex changes to the documentation as well. + +Some other important things to know about the docs: + +- The *pandas* documentation consists of two parts: the docstrings in the code + itself and the docs in this folder ``pandas/doc/``. + + The docstrings provide a clear explanation of the usage of the individual + functions, while the documentation in this folder consists of tutorial-like + overviews per topic together with some other information (what's new, + installation, etc). + +- The docstrings follow the **Numpy Docstring Standard** which is used widely + in the Scientific Python community. This standard specifies the format of + the different sections of the docstring. See `this document + <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_ + for a detailed explanation, or look at some of the existing functions to + extend it in a similar manner. + +- The tutorials make heavy use of the `ipython directive + <http://matplotlib.org/sampledoc/ipython_directive.html>`_ sphinx extension. + This directive lets you put code in the documentation which will be run + during the doc build. For example: + + :: + + .. ipython:: python + + x = 2 + x**3 + + will be rendered as + + :: + + In [1]: x = 2 + + In [2]: x**3 + Out[2]: 8 + + This means that almost all code examples in the docs are always run (and the + output saved) during the doc build. This way, they will always be up to date, + but it makes the doc building a bit more complex. + + +How to build the pandas documentation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Requirements +"""""""""""" + +To build the *pandas* docs there are some extra requirements: you will need to +have ``sphinx`` and ``ipython`` installed. `numpydoc +<https://github.com/numpy/numpydoc>`_ is used to parse the docstrings that +follow the Numpy Docstring Standard (see above), but you don't need to install +this because a local copy of ``numpydoc`` is included in the *pandas* source +code. + +Furthermore, it is recommended to have all `optional dependencies +<http://pandas.pydata.org/pandas-docs/dev/install.html#optional-dependencies>`_ +installed. This is not needed, but be aware that you will see some error +messages. Because all the code in the documentation is executed during the doc +build, the examples using this optional dependencies will generate errors. +Run ``pd.show_versions()`` to get an overview of the installed version of all +dependencies. + +.. warning:: + + Sphinx version >= 1.2.2 or the older 1.1.3 is required. + +Building the documentation +"""""""""""""""""""""""""" + +So how do you build the docs? Navigate to your local the folder +``pandas/doc/`` directory in the console and run:: + + python make.py html + +And then you can find the html output in the folder ``pandas/doc/build/html/``. + +The first time it will take quite a while, because it has to run all the code +examples in the documentation and build all generated docstring pages. +In subsequent evocations, sphinx will try to only build the pages that have +been modified. + +If you want to do a full clean build, do:: + + python make.py clean + python make.py build + + +Starting with 0.13.1 you can tell ``make.py`` to compile only a single section +of the docs, greatly reducing the turn-around time for checking your changes. +You will be prompted to delete `.rst` files that aren't required, since the +last committed version can always be restored from git. + +:: + + #omit autosummary and API section + python make.py clean + python make.py --no-api + + # compile the docs with only a single + # section, that which is in indexing.rst + python make.py clean + python make.py --single indexing + +For comparison, a full documentation build may take 10 minutes. a ``-no-api`` build +may take 3 minutes and a single section may take 15 seconds. However, subsequent +builds only process portions you changed. Now, open the following file in a web +browser to see the full documentation you just built:: + + pandas/docs/build/html/index.html + +And you'll have the satisfaction of seeing your new and improved documentation! + + +Contributing to the code base +----------------------------- + +.. contents:: Code Base: + :local: + +Code Standards +^^^^^^^^^^^^^^ + +*pandas* uses the `PEP8 <http://www.python.org/dev/peps/pep-0008/>`_ standard. +There are several tools to ensure you abide by this standard. + +We've written a tool to check that your commits are PEP8 great, `pip install pep8radius <https://github.com/hayd/pep8radius>`_. +Look at PEP8 fixes in your branch vs master with:: + + pep8radius master --diff` and make these changes with `pep8radius master --diff --in-place` + +Alternatively, use `flake8 <http://pypi.python.org/pypi/flake8>`_ tool for checking the style of your code. +Additional standards are outlined on the `code style wiki page <https://github.com/pydata/pandas/wiki/Code-Style-and-Conventions>`_. + +Please try to maintain backward-compatibility. *Pandas* has lots of users with lots of existing code, so +don't break it if at all possible. If you think breakage is required clearly state why +as part of the Pull Request. Also, be careful when changing method signatures and add +deprecation warnings where needed. + +Test-driven Development/Writing Code +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +*Pandas* is serious about `Test-driven Development (TDD) +<http://en.wikipedia.org/wiki/Test-driven_development>`_. +This development process "relies on the repetition of a very short development cycle: +first the developer writes an (initially failing) automated test case that defines a desired +improvement or new function, then produces the minimum amount of code to pass that test." +So, before actually writing any code, you should write your tests. Often the test can be +taken from the original GitHub issue. However, it is always worth considering additional +use cases and writing corresponding tests. + +Adding tests is one of the most common requests after code is pushed to *pandas*. It is worth getting +in the habit of writing tests ahead of time so this is never an issue. + +Like many packages, *pandas* uses the `Nose testing system +<http://somethingaboutorange.com/mrl/projects/nose/>`_ and the convenient +extensions in `numpy.testing +<http://docs.scipy.org/doc/numpy/reference/routines.testing.html>`_. + +Writing tests +""""""""""""" + +All tests should go into the *tests* subdirectory of the specific package. +There are probably many examples already there and looking to these for +inspiration is suggested. If you test requires working with files or +network connectivity there is more information on the `testing page +<https://github.com/pydata/pandas/wiki/Testing>`_ of the wiki. + +The ``pandas.util.testing`` module has many special ``assert`` functions that +make it easier to make statements about whether Series or DataFrame objects are +equivalent. The easiest way to verify that your code is correct is to +explicitly construct the result you expect, then compare the actual result to +the expected correct result: + +:: + + def test_pivot(self): + data = { + 'index' : ['A', 'B', 'C', 'C', 'B', 'A'], + 'columns' : ['One', 'One', 'One', 'Two', 'Two', 'Two'], + 'values' : [1., 2., 3., 3., 2., 1.] + } + + frame = DataFrame(data) + pivoted = frame.pivot(index='index', columns='columns', values='values') + + expected = DataFrame({ + 'One' : {'A' : 1., 'B' : 2., 'C' : 3.}, + 'Two' : {'A' : 1., 'B' : 2., 'C' : 3.} + }) + + assert_frame_equal(pivoted, expected) + +Running the test suite +"""""""""""""""""""""" + +The tests can then be run directly inside your git clone (without having to +install *pandas*) by typing::: + + nosetests pandas + +The tests suite is exhaustive and takes around 20 minutes to run. Often it is +worth running only a subset of tests first around your changes before running the +entire suite. This is done using one of the following constructs: + +:: + + nosetests pandas/tests/[test-module].py + nosetests pandas/tests/[test-module].py:[TestClass] + nosetests pandas/tests/[test-module].py:[TestClass].[test_method] + + +Running the performance test suite +"""""""""""""""""""""""""""""""""" + +Performance matters and it is worth considering that your code has not introduced +performance regressions. Currently *pandas* uses the `vbench library <https://github.com/pydata/vbench>`__ +to enable easy monitoring of the performance of critical *pandas* operations. +These benchmarks are all found in the ``pandas/vb_suite`` directory. vbench +currently only works on python2. + +To install vbench:: + + pip install git+https://github.com/pydata/vbench + +Vbench also requires sqlalchemy, gitpython, and psutil which can all be installed +using pip. If you need to run a benchmark, change your directory to the *pandas* root and run:: + + ./test_perf.sh -b master -t HEAD + +This will checkout the master revision and run the suite on both master and +your commit. Running the full test suite can take up to one hour and use up +to 3GB of RAM. Usually it is sufficient to past a subset of the results in +to the Pull Request to show that the committed changes do not cause unexpected +performance regressions. + +You can run specific benchmarks using the *-r* flag which takes a regular expression. + +See the `performance testing wiki <https://github.com/pydata/pandas/wiki/Performance-Testing>`_ for information +on how to write a benchmark. + +Documenting your code +^^^^^^^^^^^^^^^^^^^^^ + +Changes should be reflected in the release notes located in `doc/source/whatsnew/vx.y.z.txt`. +This file contains an ongoing change log for each release. Add an entry to this file to +document your fix, enhancement or (unavoidable) breaking change. Make sure to include the +GitHub issue number when adding your entry. + +If your code is an enhancement, it is most likely necessary to add usage examples to the +existing documentation. This can be done following the section regarding documentation. + +Committing your code +-------------------- + +Keep style fixes to a separate commit to make your PR more readable. + +Once you've made changes, you can see them by typing:: + + git status + +If you've created a new file, it is not being tracked by git. Add it by typing :: + + git add path/to/file-to-be-added.py + +Doing 'git status' again should give something like :: + + # On branch shiny-new-feature + # + # modified: /relative/path/to/file-you-added.py + # + +Finally, commit your changes to your local repository with an explanatory message. An informal +commit message format is in effect for the project. Please try to adhere to it. Here are +some common prefixes along with general guidelines for when to use them: + + * ENH: Enhancement, new functionality + * BUG: Bug fix + * DOC: Additions/updates to documentation + * TST: Additions/updates to tests + * BLD: Updates to the build process/scripts + * PERF: Performance improvement + * CLN: Code cleanup + +The following defines how a commit message should be structured. Please reference the +relevant GitHub issues in your commit message using `GH1234` or `#1234`. Either style +is fine, but the former is generally preferred: + + * a subject line with `< 80` chars. + * One blank line. + * Optionally, a commit message body. + +Now you can commit your changes in your local repository:: + + git commit -m + +If you have multiple commits, it is common to want to combine them into one commit, often +referred to as "squashing" or "rebasing". This is a common request by package maintainers +when submitting a Pull Request as it maintains a more compact commit history. To rebase your commits:: + + git rebase -i HEAD~# + +Where # is the number of commits you want to combine. Then you can pick the relevant +commit message and discard others. + +Pushing your changes +-------------------- + +When you want your changes to appear publicly on your GitHub page, push your +forked feature branch's commits :: + + git push origin shiny-new-feature + +Here `origin` is the default name given to your remote repository on GitHub. +You can see the remote repositories :: + + git remote -v + +If you added the upstream repository as described above you will see something +like :: + + origin git@github.com:yourname/pandas.git (fetch) + origin git@github.com:yourname/pandas.git (push) + upstream git://github.com/pydata/pandas.git (fetch) + upstream git://github.com/pydata/pandas.git (push) + +Now your code is on GitHub, but it is not yet a part of the *pandas* project. For that to +happen, a Pull Request needs to be submitted on GitHub. + +Contributing your changes to *pandas* +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Review your code +---------------- + +When you're ready to ask for a code review, you will file a Pull Request. Before you do, +again make sure you've followed all the guidelines outlined in this document regarding +code style, tests, performance tests, and documentation. You should also double check +your branch changes against the branch it was based off of: + +#. Navigate to your repository on GitHub--https://github.com/your-user-name/pandas. +#. Click on `Branches`. +#. Click on the `Compare` button for your feature branch. +#. Select the `base` and `compare` branches, if necessary. This will be `master` and + `shiny-new-feature`, respectively. + +Finally, make the Pull Request +------------------------------ + +If everything looks good you are ready to make a Pull Request. A Pull Request is how +code from a local repository becomes available to the GitHub community and can be looked +at and eventually merged into the master version. This Pull Request and its associated +changes will eventually be committed to the master branch and available in the next +release. To submit a Pull Request: + +#. Navigate to your repository on GitHub. +#. Click on the `Pull Request` button. +#. You can then click on `Commits` and `Files Changed` to make sure everything looks okay one last time. +#. Write a description of your changes in the `Preview Discussion` tab. +#. Click `Send Pull Request`. + +This request then appears to the repository maintainers, and they will review +the code. If you need to make more changes, you can make them in +your branch, push them to GitHub, and the pull request will be automatically +updated. Pushing them to GitHub again is done by:: + + git push -f origin shiny-new-feature + +This will automatically update your Pull Request with the latest code and restart the Travis-CI tests. + +Delete your merged branch (optional) +------------------------------------ + +Once your feature branch is accepted into upstream, you'll probably want to get rid of +the branch. First, merge upstream master into your branch so git knows it is safe to delete your branch :: + + git fetch upstream + git checkout master + git merge upstream/master + +Then you can just do:: + + git branch -d shiny-new-feature + +Make sure you use a lower-case -d, or else git won't warn you if your feature +branch has not actually been merged. + +The branch will still exist on GitHub, so to delete it there do :: + + git push origin --delete shiny-new-feature + -- `The developer pages on the website - <http://pandas.pydata.org/developers.html>`_ -- `Guidelines on bug reports and pull requests - <https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md>`_ -- `Some extra tips on using git - <https://github.com/pydata/pandas/wiki/Using-Git>`_ -.. include:: ../README.rst
Closes #6232 In working with the various "Contributing" documents recently it was clear that we needed to combine them. This is a stab at doing so. I took the various components from pydata.org, CONTRIBUTING.md, various wiki pages, and the already included Documentation documents and created this file. I think it's a good start.
https://api.github.com/repos/pandas-dev/pandas/pulls/9754
2015-03-31T02:23:20Z
2015-04-03T00:41:09Z
2015-04-03T00:41:09Z
2015-05-05T18:03:08Z
BUG: where gives incorrect results when upcasting (GH 9731)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 3c3742c968642..e2b51f1b984cc 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -64,3 +64,4 @@ Bug Fixes - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) +- Bug in ``where`` causing incorrect results when upcasting was required (:issue:`9731`) diff --git a/pandas/core/common.py b/pandas/core/common.py index 78406682473ff..ec805aba34d48 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1081,15 +1081,6 @@ def _infer_dtype_from_scalar(val): return dtype, val -def _maybe_cast_scalar(dtype, value): - """ if we a scalar value and are casting to a dtype that needs nan -> NaT - conversion - """ - if np.isscalar(value) and dtype in _DATELIKE_DTYPES and isnull(value): - return tslib.iNaT - return value - - def _maybe_promote(dtype, fill_value=np.nan): # if we passed an array here, determine the fill value by dtype @@ -1154,16 +1145,39 @@ def _maybe_promote(dtype, fill_value=np.nan): return dtype, fill_value -def _maybe_upcast_putmask(result, mask, other, dtype=None, change=None): - """ a safe version of put mask that (potentially upcasts the result - return the result - if change is not None, then MUTATE the change (and change the dtype) - return a changed flag +def _maybe_upcast_putmask(result, mask, other): """ + A safe version of putmask that potentially upcasts the result - if mask.any(): + Parameters + ---------- + result : ndarray + The destination array. This will be mutated in-place if no upcasting is + necessary. + mask : boolean ndarray + other : ndarray or scalar + The source array or value - other = _maybe_cast_scalar(result.dtype, other) + Returns + ------- + result : ndarray + changed : boolean + Set to true if the result array was upcasted + """ + + if mask.any(): + # Two conversions for date-like dtypes that can't be done automatically + # in np.place: + # NaN -> NaT + # integer or integer array -> date-like array + if result.dtype in _DATELIKE_DTYPES: + if lib.isscalar(other): + if isnull(other): + other = tslib.iNaT + elif is_integer(other): + other = np.array(other, dtype=result.dtype) + elif is_integer_dtype(other): + other = np.array(other, dtype=result.dtype) def changeit(): @@ -1173,39 +1187,26 @@ def changeit(): om = other[mask] om_at = om.astype(result.dtype) if (om == om_at).all(): - new_other = result.values.copy() - new_other[mask] = om_at - result[:] = new_other + new_result = result.values.copy() + new_result[mask] = om_at + result[:] = new_result return result, False except: pass # we are forced to change the dtype of the result as the input # isn't compatible - r, fill_value = _maybe_upcast( - result, fill_value=other, dtype=dtype, copy=True) - np.putmask(r, mask, other) - - # we need to actually change the dtype here - if change is not None: - - # if we are trying to do something unsafe - # like put a bigger dtype in a smaller one, use the smaller one - # pragma: no cover - if change.dtype.itemsize < r.dtype.itemsize: - raise AssertionError( - "cannot change dtype of input to smaller size") - change.dtype = r.dtype - change[:] = r + r, _ = _maybe_upcast(result, fill_value=other, copy=True) + np.place(r, mask, other) return r, True - # we want to decide whether putmask will work + # we want to decide whether place will work # if we have nans in the False portion of our mask then we need to - # upcast (possibily) otherwise we DON't want to upcast (e.g. if we are - # have values, say integers in the success portion then its ok to not + # upcast (possibly), otherwise we DON't want to upcast (e.g. if we + # have values, say integers, in the success portion then it's ok to not # upcast) - new_dtype, fill_value = _maybe_promote(result.dtype, other) + new_dtype, _ = _maybe_promote(result.dtype, other) if new_dtype != result.dtype: # we have a scalar or len 0 ndarray @@ -1222,7 +1223,7 @@ def changeit(): return changeit() try: - np.putmask(result, mask, other) + np.place(result, mask, other) except: return changeit() diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 9b5e36974553b..e140ffd97051c 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -1688,6 +1688,14 @@ def test_where(self): assert_series_equal(s, expected) self.assertEqual(s.dtype, expected.dtype) + # GH 9731 + s = Series(np.arange(10), dtype='int64') + mask = s > 5 + values = [2.5, 3.5, 4.5, 5.5] + s[mask] = values + expected = Series(lrange(6) + values, dtype='float64') + assert_series_equal(s, expected) + # can't do these as we are forced to change the itemsize of the input # to something we cannot for dtype in [np.int8, np.int16, np.int32, np.float16, np.float32]:
closes #9731 The main issue is when the destination and source arrays have different lengths, `np.putmask` doesn't behave like `arr[mask] = values`: "Sets `a.flat[n] = values[n]` for each n where `mask.flat[n]==True`" We have to use `np.place` instead. A secondary issue is that `np.place` doesn't automatically convert an integer to a `datetime64` like `np.putmask` does (I created a numpy issue for this), so we need an additional check for that case. The rest of the commit is just cleaning up `_maybe_upcast_putmask`, which had some parameters that were never used, and a confusing docstring.
https://api.github.com/repos/pandas-dev/pandas/pulls/9743
2015-03-28T14:08:51Z
2015-04-02T21:25:39Z
2015-04-02T21:25:39Z
2015-06-10T13:41:24Z
BUG: Bring pandas up to date with pandas-datareader
diff --git a/pandas/io/data.py b/pandas/io/data.py index ea635e85ed177..3e077bf526ab9 100644 --- a/pandas/io/data.py +++ b/pandas/io/data.py @@ -172,14 +172,14 @@ def _retry_read_url(url, retry_count, pause, name): if len(rs) > 2 and rs.index[-1] == rs.index[-2]: # pragma: no cover rs = rs[:-1] - #Get rid of unicode characters in index name. - try: - rs.index.name = rs.index.name.decode('unicode_escape').encode('ascii', 'ignore') - except AttributeError: - #Python 3 string has no decode method. - rs.index.name = rs.index.name.encode('ascii', 'ignore').decode() + #Get rid of unicode characters in index name. + try: + rs.index.name = rs.index.name.decode('unicode_escape').encode('ascii', 'ignore') + except AttributeError: + #Python 3 string has no decode method. + rs.index.name = rs.index.name.encode('ascii', 'ignore').decode() - return rs + return rs raise IOError("after %d tries, %s did not " "return a 200 for url %r" % (retry_count, name, url)) @@ -326,18 +326,23 @@ def _dl_mult_symbols(symbols, start, end, interval, chunksize, retry_count, paus method): stocks = {} failed = [] + passed = [] for sym_group in _in_chunks(symbols, chunksize): for sym in sym_group: try: stocks[sym] = method(sym, start, end, interval, retry_count, pause) + passed.append(sym) except IOError: warnings.warn('Failed to read symbol: {0!r}, replacing with ' 'NaN.'.format(sym), SymbolWarning) failed.append(sym) + if len(passed) == 0: + raise RemoteDataError("No data fetched using " + "{0!r}".format(method.__name__)) try: - if len(stocks) > 0 and len(failed) > 0: - df_na = stocks.values()[0].copy() + if len(stocks) > 0 and len(failed) > 0 and len(passed) > 0: + df_na = stocks[passed[0]].copy() df_na[:] = np.nan for sym in failed: stocks[sym] = df_na @@ -347,7 +352,6 @@ def _dl_mult_symbols(symbols, start, end, interval, chunksize, retry_count, paus raise RemoteDataError("No data fetched using " "{0!r}".format(method.__name__)) - _source_functions = {'google': _get_hist_google, 'yahoo': _get_hist_yahoo} @@ -701,9 +705,6 @@ def _option_frames_from_url(self, url): calls = frames[self._TABLE_LOC['calls']] puts = frames[self._TABLE_LOC['puts']] - if len(calls) == 0 or len(puts) == 0: - raise RemoteDataError('Received no data from Yahoo at url: %s' % url) - calls = self._process_data(calls, 'call') puts = self._process_data(puts, 'put') diff --git a/pandas/io/tests/data/yahoo_options3.html b/pandas/io/tests/data/yahoo_options3.html new file mode 100644 index 0000000000000..6e79bb9bf9f36 --- /dev/null +++ b/pandas/io/tests/data/yahoo_options3.html @@ -0,0 +1,2807 @@ +<!DOCTYPE html> +<html> +<head> + <!-- customizable : anything you expected. --> + <title>SPWR Option Chain | Yahoo! Inc. Stock - Yahoo! Finance</title> + + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1" /> + <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> + + + + + <link rel="stylesheet" type="text/css" href="https://s.yimg.com/zz/combo?/os/mit/td/stencil-0.1.306/stencil-css/stencil-css-min.css&/os/mit/td/finance-td-app-mobile-web-2.0.356/css.master/css.master-min.css"/><link rel="stylesheet" type="text/css" href="https://s.yimg.com/os/mit/media/m/quotes/quotes-search-gs-smartphone-min-1680382.css"/> + + +<script>(function(html){var c = html.className;c += " JsEnabled";c = c.replace("NoJs","");html.className = c;})(document.documentElement);</script> + + + + <!-- UH --> + <link rel="stylesheet" href="https://s.yimg.com/zz/combo?kx/yucs/uh3/uh/1132/css/uh_non_mail-min.css&amp;kx/yucs/uh_common/meta/3/css/meta-min.css&amp;kx/yucs/uh3/top-bar/366/css/no_icons-min.css&amp;kx/yucs/uh3/search/css/588/blue_border-min.css&amp;kx/yucs/uh3/get-the-app/151/css/get_the_app-min.css&amp;kx/yucs/uh3/uh/1132/css/uh_ssl-min.css&amp;&amp;bm/lib/fi/common/p/d/static/css/2.0.356953/2.0.0/mini/yfi_theme_teal.css&amp;bm/lib/fi/common/p/d/static/css/2.0.356953/2.0.0/mini/yfi_interactive_charts_embedded.css"> + + + + + <style> + .dev-desktop .y-header { + position: fixed; 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font-size: 12px; + z-index: 1000; + line-height: 1.22em; + } + + .ac-form .yui3-highlight, em { + font-weight: bold; + font-style: normal; + } + + .ac-form .yui3-fin-ac-list { + margin: 0; + padding-bottom: .4em; + padding: 0.38em 0; + width: 100%; + } + + .ac-form .yui3-fin-ac-list li { + padding: 0.15em 0.38em; + _width: 100%; + cursor: default; + white-space: nowrap; + list-style: none; + vertical-align: bottom; + margin: 0; + position: relative; + } + + .ac-form .symbol { + width: 8.5em; + display: inline-block; + margin: 0 1em 0 0; + overflow: hidden; + } + + .ac-form .name { + display: inline-block; + left: 0; + width: 25em; + overflow: hidden; + position: relative; + } + + .ac-form .exch_type_wrapper { + color: #aaa; + height: auto; + text-align: right; + font-size: 92%; + _font-size: 72%; + position: absolute; + right: 0; + } + + .ac-form .yui-ac-ft { + font-family: Verdana,sans-serif; + font-size: 92%; + text-align: left; + } + + .ac-form .moreresults { + padding-left: 0.3em; + } + + .yui3-fin-ac-item-hover, .yui3-fin-ac-item-active { + background: #D6F7FF; + cursor: pointer; + } + + .yui-ac-ft a { + color: #039; + text-decoration: none; + font-size: inherit !important; + } + + .yui-ac-ft .tip { + border-top: 1px solid #D6D6D6; + color: #636363; + padding: 0.5em 0 0 0.4em; + margin-top: .25em; + } + +</style> +<div mode="search" class="ticker-search mod" id="searchQuotes"> + <div class="hd"></div> + <div class="bd" > + <form action="/q" name="quote" id="lookupQuote" class="ac-form"> + <h2 class="yfi_signpost">Search for share prices</h2> + <label id="lookupPlaceHolder" class='Hidden'>Enter Symbol</label> + <input placeholder="Enter Symbol" type="text" autocomplete="off" value="" name="s" id="lookupTxtQuotes" class="fin-ac-input yui-ac-input"> + + <input type="hidden" autocomplete="off" value="1" name="ql" id="lookupGet_quote_logic_opt"> + + <div id="yfi_quotes_submit"> + <span> + <span> + <span> + <input type="submit" class="rapid-nf" id="btnQuotes" value="Look Up"> + </span> + </span> + </span> + </div> + </form> + </div> + <div class="ft"><a href="http://finance.search.yahoo.com?fr=fin-v1" data-rapid_p="4">Finance Search</a> + <p><span id="yfs_market_time">Tue, Mar 24 2015, 10:47pm EDT - U.S. Markets closed</span></p></div> +</div> + </div> </div> </div> </div></div><!--END td-applet-mw-quote-search--> + + + + </div> + <div id="yfi_doc"> + <div id="yfi_bd"> + <div id="marketindices"> + + + + <span><a href="/q?s=^DJI">Dow</a></span> + <span id="yfs_pp0_^dji"> + + + <img width="10" height="14" border="0" alt="Down" class="neg_arrow" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" style="margin-right:-2px;"> + + + <b class="yfi-price-change-down">0.58%</b> + </span> + + + + + + <span><a href="/q?s=^IXIC">Nasdaq</a></span> + <span id="yfs_pp0_^ixic"> + + + <img width="10" height="14" border="0" alt="Down" class="neg_arrow" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" style="margin-right:-2px;"> + + + <b class="yfi-price-change-down">0.32%</b> + + + + + + + </div> + + <div data-region="leftNav"> +<div id="yfi_investing_nav"> + <div id="tickerSearch"> + + + </div> + + <div class="hd"> + <h2>More on SPWR</h2> + </div> + <div class="bd"> + + + <h3>Quotes</h3> + <ul> + + + <li ><a href="/q?s=SPWR">Summary</a></li> + + + + <li ><a href="/q/ecn?s=SPWR+Order+Book">Order Book</a></li> + + + + <li class="selected" ><a href="/q/op?s=SPWR+Options">Options</a></li> + + + + <li ><a href="/q/hp?s=SPWR+Historical+Prices">Historical Prices</a></li> + + + </ul> + + <h3>Charts</h3> + <ul> + + + <li ><a href="/echarts?s=SPWR+Interactive">Interactive</a></li> + + + </ul> + + <h3>News &amp; Info</h3> + <ul> + + + <li ><a href="/q/h?s=SPWR+Headlines">Headlines</a></li> + + + + + + <li ><a href="/q/p?s=SPWR+Press+Releases">Press Releases</a></li> + + + + + <li ><a href="/q/ce?s=SPWR+Company+Events">Company Events</a></li> + + + + <li ><a href="/mb?s=SPWR">Message Boards</a></li> + + + + <li ><a href="/marketpulse/?s=SPWR">Market Pulse</a></li> + + + </ul> + + <h3>Company</h3> + <ul> + + + <li ><a href="/q/pr?s=SPWR+Profile">Profile</a></li> + + + + <li ><a href="/q/ks?s=SPWR+Key+Statistics">Key Statistics</a></li> + + + + <li ><a href="/q/sec?s=SPWR+SEC+Filings">SEC Filings</a></li> + + + + <li ><a href="/q/co?s=SPWR+Competitors">Competitors</a></li> + + + + <li ><a href="/q/in?s=SPWR+Industry">Industry</a></li> + + + + + + <li class="deselected">Components</li> + + + + </ul> + + <h3>Analyst Coverage</h3> + <ul> + + + <li ><a href="/q/ao?s=SPWR+Analyst+Opinion">Analyst Opinion</a></li> + + + + <li ><a href="/q/ae?s=SPWR+Analyst+Estimates">Analyst Estimates</a></li> + + + </ul> + + <h3>Ownership</h3> + <ul> + + + <li ><a href="/q/mh?s=SPWR+Major+Holders">Major Holders</a></li> + + + + <li ><a href="/q/it?s=SPWR+Insider+Transactions">Insider Transactions</a></li> + + + + <li ><a href="/q/ir?s=SPWR+Insider+Roster">Insider Roster</a></li> + + + </ul> + + <h3>Financials</h3> + <ul> + + + <li ><a href="/q/is?s=SPWR+Income+Statement">Income Statement</a></li> + + + + <li ><a href="/q/bs?s=SPWR+Balance+Sheet">Balance Sheet</a></li> + + + + <li ><a href="/q/cf?s=SPWR+Cash+Flow">Cash Flow</a></li> + + + </ul> + + </div> + <div class="ft"> + + </div> +</div> + +</div><!--END leftNav--> + <div id="sky"> + <div id="yom-ad-SKY"><div id="yom-ad-SKY-iframe"></div></div><!--ESI Ads for SKY --> + </div> + <div id="yfi_investing_content"> + + <div id="yfi_broker_buttons"> + <div class='yom-ad D-ib W-20'> + <div id="yom-ad-FB2-1"><div id="yom-ad-FB2-1-iframe"><script>var FB2_1_noadPos = document.getElementById("yom-ad-FB2-1"); if (FB2_1_noadPos) {FB2_1_noadPos.style.display="none";}</script></div></div><!--ESI Ads for FB2-1 --> + </div> + <div class='yom-ad D-ib W-25'> + <div id="yom-ad-FB2-2"><div id="yom-ad-FB2-2-iframe"><script>var FB2_2_noadPos = document.getElementById("yom-ad-FB2-2"); if (FB2_2_noadPos) {FB2_2_noadPos.style.display="none";}</script></div></div><!--ESI Ads for FB2-2 --> + </div> + <div class='yom-ad D-ib W-25'> + <div id="yom-ad-FB2-3"><div id="yom-ad-FB2-3-iframe"><script>var FB2_3_noadPos = document.getElementById("yom-ad-FB2-3"); if (FB2_3_noadPos) {FB2_3_noadPos.style.display="none";}</script></div></div><!--ESI Ads for FB2-3 --> + </div> + <div class='yom-ad D-ib W-25'> + <div id="yom-ad-FB2-4"><div id="yom-ad-FB2-4-iframe"><script>var FB2_4_noadPos = document.getElementById("yom-ad-FB2-4"); if (FB2_4_noadPos) {FB2_4_noadPos.style.display="none";}</script></div></div><!--ESI Ads for FB2-4 --> + </div> + </div> + + + <div data-region="td-applet-mw-quote-details"><style>/* +* Stencil defined classes - https://git.corp.yahoo.com/pages/ape/stencil/behavior/index.html +* .PageOverlay +* .ModalDismissBtn.Btn +*/ + +/* +* User defined classes +* #ham-nav-cue-modal - styles for the modal window +* .padd-border - styles for the content box of #ham-nav-cue-modal +* #ham-nav-cue-modal:after, #ham-nav-cue-modal:before - used to create modal window's arrow. +*/ + +.PageOverlay #ham-nav-cue-modal { + left: 49px; + transition: -webkit-transform .3s; + max-width: 240px; +} + +.PageOverlay #ham-nav-cue-modal .padd-border { + border: solid #5300C5 2px; + padding: 5px 5px 10px 15px; +} + +.PageOverlay { + z-index: 201; +} + +#ham-nav-cue-modal:after, +#ham-nav-cue-modal:before { + content: ""; + border-style: solid; + border-width: 10px; + width: 0; + height: 0; + position: absolute; + top: 4%; + left: -20px; +} + +#ham-nav-cue-modal:before { + border-color: transparent #5300C5 transparent transparent; +} + +#ham-nav-cue-modal:after { + margin-left: 3px; + border-color: transparent #fff transparent transparent; +} + +.ModalDismissBtn.Btn { + background: transparent; + border-color: transparent; +} +.follow-quote,.follow-quote-proxy { + color: #999; +} +.Icon.follow-quote-following { + color: #eac02b; +} + +.follow-quote-tooltip { + z-index: 400; + text-align: center; +} + +.follow-quote-area:hover .follow-quote { + display: inline-block; +} + +.follow-quote-area:hover .quote-link,.follow-quote-visible .quote-link { + display: inline-block; + max-width: 50px; + _width: 50px; +}</style><div id="applet_4305521170488091" class="App_v2 js-applet" data-applet-guid="4305521170488091" data-applet-type="td-applet-mw-quote-details"> <div class="App-Bd"> <div class="App-Main" data-region="main"> <div class="js-applet-view-container-main"> + + <style> + img { + vertical-align: baseline; + } + .follow-quote { + margin-left: 5px; + margin-right: 2px; + } + .yfi_rt_quote_summary .rtq_exch { + font: inherit; + } + .up_g.time_rtq_content, span.yfi-price-change-green { + color: #80 !important; + } + .time_rtq, .follow-quote-txt { + color: #979ba2; + } + .yfin_gs span.yfi-price-change-red, .yfin_gs span.yfi-price-change-green { + font-weight: bold; + } + .yfi_rt_quote_summary .hd h2 { + font: inherit; + } + span.yfi-price-change-red { + color: #C00 !important; + } + /* to hide the up/down arrow */ + .yfi_rt_quote_summary_rt_top .time_rtq_content img { + display: none; + } + + .quote_summary { + min-height: 77px; + } + + .app_promo.after_hours, .app_promo.pre_market { + top: 8px; + } + </style> + <div class="rtq_leaf"> + <div class="rtq_div"> + <div class="yui-g quote_summary"> + <div class="yfi_rt_quote_summary" id="yfi_rt_quote_summary"> + <div class="hd"> + <div class="title Fz-xl"> + <h2 class="symbol-name">SunPower Corporation (SPWR)</h2> + <span class="wl_sign Invisible"><button class="follow-quote follow-quote-follow follow-quote-always-visible D-ib Bd-0 O-0 Cur-p Sprite P-0 M-0 Fz-s" data-flw-quote="SPWR"><i class="Icon">&#xe023;</i></button> <span class="follow-quote-txt Fz-m" data-flw-quote="SPWR"> + Watchlist + </span></span> + </div> + </div> + <div class="yfi_rt_quote_summary_rt_top sigfig_promo_1"> + <div> + <span class="time_rtq_ticker Fz-30 Fw-b"> + <span id="yfs_l84_SPWR" data-sq="SPWR:value">33.05</span> + </span> + + + + <span class="up_g time_rtq_content Fz-2xl Fw-b"><span id="yfs_c63_SPWR"><img width="10" height="14" border="0" style="margin-right:-2px;" src="https://s.yimg.com/lq/i/us/fi/03rd/up_g.gif" alt="Up"> <span class="yfi-price-change-green" data-sq="SPWR:chg">+0.07</span></span><span id="yfs_p43_SPWR">(<span class="yfi-price-change-green" data-sq="SPWR:pctChg">0.21%</span>)</span> </span> + + + <span class="time_rtq Fz-m"><span class="rtq_exch">NASDAQ - </span><span id="yfs_t53_SPWR">As of <span data-sq="SPWR:lstTrdTime">4:00PM EDT</span></span></span> + + </div> + <div><span class="rtq_separator">|</span> + + After Hours: + <span class="yfs_rtq_quote"><span id="yfs_l86_SPWR" data-sq="SPWR:ahValue">33.10</span></span> <span class="up_g"><span id="yfs_c85_SPWR"><img width="10" height="14" style="margin-right:-2px;" border="0" src="https://s.yimg.com/os/mit/media/m/base/images/transparent-1093278.png" class="pos_arrow" alt="Up" data-sq="SPWR:ahChg"> +0.05</span> (<span id="yfs_c86_SPWR" data-sq="SPWR:ahPctChg">0.15%</span>)</span><span class="time_rtq"> <span id="yfs_t54_SPWR" data-sq="SPWR:ahLstTrdTime">7:47PM EDT</span></span> + + + </div> + </div> + <style> + #yfi_toolbox_mini_rtq.sigfig_promo { + bottom:45px !important; + } + </style> + <div class="app_promo after_hours " > + <a href="https://mobile.yahoo.com/finance/?src=gta" title="Get the App" target="_blank" ></a> + + </div> + </div> + </div> + </div> + </div> + + + </div> </div> </div> </div></div><!--END td-applet-mw-quote-details--> + + + + <div id="optionsTableApplet"> + + + <div data-region="td-applet-options-table"><style>.App_v2 { + border: none; + margin: 0; + padding: 0; +} + +.options-table { + position: relative; +} + +/*.Icon.up {*/ + /*display: none;*/ +/*}*/ + +.option_column { + width: auto; +} + +.header_text { + float: left; + max-width: 50px; +} +.header_sorts { + color: #00be8c; + float: left; +} + +.size-toggle-menu { + margin-left: 600px; +} + +.in-the-money-banner { + background-color: rgba(224,241,231,1); + padding: 7px; + position: relative; + top: -3px; + width: 95px; +} + +.in-the-money.odd { + background-color: rgba(232,249,239,1); +} + +.in-the-money.even { + background-color: rgba(224,241,231,1); +} + +.toggle li{ + display: inline-block; + cursor: pointer; + border: 1px solid #e2e2e6; + border-right-width: 0; + color: #454545; + background-color: #fff; + float: left; + padding: 0px; + margin: 0px; +} + +.toggle li a { + padding: 7px; + display: block; +} + +.toggle li:hover{ + background-color: #e2e2e6; +} + +.toggle li.active{ + color: #fff; + background-color: #30d3b6; + border-color: #30d3b6; + border-bottom-color: #0c8087; +} + +.toggle li:first-child{ + border-radius: 3px 0 0 3px; +} + +.toggle li:last-child{ + border-radius: 0 3px 3px 0; + border-right-width: 1px; +} + +.high-low .up { + display: none; +} + +.high-low .down { + display: block; +} + +.low-high .down { + display: none; +} + +.low-high .up { + display: block; +} + +.option_column.sortable { + cursor: pointer; +} + +.option-filter-overlay { + background-color: #fff; + border: 1px solid #979ba2; + border-radius: 3px; + float: left; + padding: 15px; + position: absolute; + top: 60px; + z-index: 10; + display: none; +} + +#optionsStraddlesTable .option-filter-overlay { + left: 430px; +} + +.option-filter-overlay.active { + display: block; +} + +.option-filter-overlay .strike-filter{ + height: 25px; + width: 75px; +} + +#straddleTable .column-strike .cell{ + width: 30px; +} + +/**columns**/ + +#quote-table th.column-expires { + width: 102px; +} +.straddle-expire div.option_entry { + min-width: 65px; +} +.column-last .cell { + width: 55px; +} + +.column-change .cell { + width: 70px; +} + +.cell .change { + width: 35px; +} + +.column-percentChange .cell { + width: 85px; +} + +.column-volume .cell { + width: 70px; +} + +.cell .sessionVolume { + width: 37px; +} + +.column-session-volume .cell { + width: 75px; +} + +.column-openInterest .cell, .column-openInterestChange .cell { + width: 75px; +} +.cell .openInterest, .cell .openInterestChange { + width: 37px; +} + +.column-bid .cell { + width: 50px; +} + +.column-ask .cell { + width: 55px; +} + +.column-impliedVolatility .cell { + width: 75px; +} + +.cell .impliedVolatility { + width: 37px; +} + +.column-contractName .cell { + width: 170px; +} + +.options-menu-item { + position: relative; + top: -11px; +} + +.options-table { + margin-bottom: 30px; +} +.options-table.hidden { + display: none; +} +#quote-table table { + width: 100%; +} +#quote-table tr * { + font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; + font-size: 15px; + color: #454545; + font-weight: 200; +} +#quote-table tr a { + color: #1D1DA3; +} +#quote-table tr .Icon { + font-family: YGlyphs; +} +#quote-table tr.odd { + background-color: #f7f7f7; +} +#quote-table tr th { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + text-align: center; + width: 60px; + font-size: 11px !important; + padding-top: 10px; + padding-right: 5px; + padding-bottom: 10px; + vertical-align: middle; +} +#quote-table tr th * { + font-size: 11px; +} +#quote-table tr th .expand-icon { + display: block !important; + margin: 0 auto; + border: 1px solid #e2e2e6; + background-color: #fcfcfc; + -webkit-border-radius: 2px; + border-radius: 2px; + padding: 2px 0; +} +#quote-table tr th.column-strike { + width: 82px; +} +#quote-table tr th .sort-icons { + position: absolute; + margin-left: 2px; +} +#quote-table tr th .Icon { + display: none; +} +#quote-table tr th.low-high .up { + display: block !important; +} +#quote-table tr th.high-low .down { + display: block !important; +} +#quote-table td { + text-align: center; + padding: 7px 5px 7px 5px; +} +#quote-table td:first-child, +#quote-table th:first-child { + border-right: 1px solid #e2e2e6; +} +#quote-table .D-ib .Icon { + color: #66aeb2; +} +#quote-table caption { + background-color: #454545 !important; + color: #fff; + font-size: medium; + padding: 4px; + padding-left: 20px !important; + text-rendering: antialiased; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +#quote-table caption .callStraddles { + width:50%; + text-align:center; + float:left; +} +#quote-table caption .putStraddles { + width:50%; + text-align:center; + float:right; +} +#quote-table .in-the-money.even { + background-color: #f3fdfc; +} +#quote-table .in-the-money.even td:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #d5f8f3; + box-shadow: inset 5px 0 0 0 #d5f8f3; +} +#quote-table .in-the-money.even td:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #d5f8f3; + box-shadow: inset -5px 0 0 0 #d5f8f3; +} +#quote-table .in-the-money.odd { + background-color: #ecf6f4; +} +#quote-table .in-the-money.odd td:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #cff3ec; + box-shadow: inset 5px 0 0 0 #cff3ec; +} +#quote-table .in-the-money.odd td:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #cff3ec; + box-shadow: inset -5px 0 0 0 #cff3ec; +} +#quote-table .column-strike { + text-align: center; + padding: 4px 20px; +} +#quote-table .column-strike .header_text, +#quote-table .column-expires .cell .expiration{ + color: #454545; + font-size: 15px; + font-weight: bold; + max-width: 100%; +} +#quote-table .column-strike .header_text { + width: 100%; +} +#quote-table .column-strike .filter { + border: 1px solid #e2e2e6; + background-color: #fcfcfc; + color: #858585; + display: inline-block; + padding: 1px 10px; + -webkit-border-radius: 3px; + border-radius: 3px; + margin-top: 4px; +} +#quote-table .column-strike .filter span { + position: relative; + top: -2px; + font-weight: bold; + margin-left: -5px; +} + +#quote-table .column-strike .sort-icons { + top: 35px; +} +#quote-table .column-expires .sort-icons { + top: 45px; +} +#optionsStraddlesTable .column-expires .sort-icons { + top: 40px; +} +#quote-table #options_menu { + width: 100%; +} +#quote-table #options_menu .SelectBox-Pick { + background-color: #fcfcfc !important; + border: 1px solid #e2e2e6; + color: #128086; + font-size: 14px; + padding: 5px; + padding-top: 8px; +} +#quote-table #options_menu .SelectBox-Text { + font-weight: bold; +} +#quote-table .size-toggle-menu { + margin-left: 15px !important; +} +#quote-table .options-menu-item { + top: -9px; +} +#quote-table .option_view { + float: right; +} +#quote-table .option-change-pos { + color: #2ac194; +} +#quote-table .option-change-neg { + color: #f90f31; +} +#quote-table .toggle li { + color: #128086; + background-color: #fcfcfc; +} +#quote-table .toggle li.active { + color: #fff; + background-color: #35d2b6; +} +#quote-table .expand-icon { + color: #b5b5b5; + font-size: 12px; + cursor: pointer; +} +#quote-table .straddleCallContractName { + padding-left: 25px; +} +#quote-table .straddlePutContractName { + padding-left: 20px; +} +#quote-table .straddle-row-expand { + display: none; + border-bottom: 1px solid #f9f9f9; +} +#quote-table .straddle-row-expand td { + padding-right: 5px; +} +#quote-table .straddle-row-expand label { + color: #454545; + font-size: 11px; + margin-bottom: 2px; + color: #888; +} +#quote-table .straddle-row-expand label, +#quote-table .straddle-row-expand div { + display: block; + font-weight: 400; + text-align: left; + padding-left: 5px; +} +#quote-table .expand-icon-up { + display: none; +} +#quote-table tr.expanded + .straddle-row-expand { + display: table-row; +} +#quote-table tr.expanded .expand-icon-up { + display: inline-block; +} +#quote-table tr.expanded .expand-icon-down { + display: none; +} +.in-the-money-banner { + color: #7f8584; + font-size: 11px; + background-color: #eefcfa; + border-left: 12px solid #e0faf6; + border-right: 12px solid #e0faf6; + width: 76px !important; + text-align: center; + padding: 5px !important; + margin-top: 5px; + margin-left: 15px; +} +#optionsStraddlesTable td div { + text-align: center; +} +#optionsStraddlesTable .straddle-strike, +#optionsStraddlesTable .column-strike, +#optionsStraddlesTable .straddle-expire{ + border-right: 1px solid #e2e2e6; + border-left: 1px solid #e2e2e6; +} +#optionsStraddlesTable td:first-child, +#optionsStraddlesTable th:first-child { + border-right: none !important; +} +#optionsStraddlesTable .odd td.in-the-money { + background-color: #ecf6f4; +} +#optionsStraddlesTable .odd td.in-the-money:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #cff3ec; + box-shadow: inset 5px 0 0 0 #cff3ec; +} +#optionsStraddlesTable .odd td.in-the-money:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #cff3ec; + box-shadow: inset -5px 0 0 0 #cff3ec; +} +#optionsStraddlesTable .even td.in-the-money { + background-color: #f3fdfc; +} +#optionsStraddlesTable .even td.in-the-money:first-child { + -webkit-box-shadow: inset 5px 0 0 0 #d5f8f3; + box-shadow: inset 5px 0 0 0 #d5f8f3; +} +#optionsStraddlesTable .even td.in-the-money:last-child { + -webkit-box-shadow: inset -5px 0 0 0 #d5f8f3; + box-shadow: inset -5px 0 0 0 #d5f8f3; +} +.column-expand-all { + cursor: pointer; +} +.options-table.expand-all tr + .straddle-row-expand { + display: table-row !important; +} +.options-table.expand-all tr .expand-icon-up { + display: inline-block !important; +} +.options-table.expand-all tr .expand-icon-down { + display: none !important; +} +.options_menu .toggle a { + color: #128086; +} +.options_menu .toggle a:hover { + text-decoration: none; +} +.options_menu .toggle .active a { + color: #fff; +} +#options_menu .symbol_lookup { + float: right; + top: -11px; +} +.symbol_lookup .options-ac-input { + border-radius: 0; + height: 26px; + width: 79%; +} +.goto-icon { + border-left: 1px solid #e2e2e6; + color: #028087; + cursor: pointer; +} +.symbol_lookup .goto-icon { + height: 27px; + line-height: 2.1em; +} +#finAcOutput { + left: 10px; + top: -10px; +} +#finAcOutput .yui3-fin-ac-hidden { + display: none; +} +#finAcOutput .yui3-aclist { + border: 1px solid #DDD; + background: #fefefe; + font-size: 92%; + left: 0 !important; + overflow: visible; + padding: .5em; + position: absolute !important; + text-align: left; + top: 0 !important; + +} +#finAcOutput li.yui3-fin-ac-item-active, +#finAcOutput li.yui3-fin-ac-item-hover { + background: #F1F1F1; + cursor: pointer; +} +#finAcOutput div:first-child { + width: 30em !important; +} +#finAcOutput b.yui3-highlight { + font-weight: bold; +} +#finAcOutput li .name { + display: inline-block; + left: 0; + width: 25em; + overflow: hidden; + position: relative; +} + +#finAcOutput li .symbol { + width: 8.5em; + display: inline-block; + margin: 0 1em 0 0; + overflow: hidden; +} + +#finAcOutput li { + color: #444; + cursor: default; + font-weight: 300; + list-style: none; + margin: 0; + padding: .15em .38em; + position: relative; + vertical-align: bottom; + white-space: nowrap; +} + +.yui3-fin-ac-hidden { + visibility: hidden; +} + +.filterRangeRow { + line-height: 5px; +} +.filterRangeTitle { + padding-bottom: 5px; + font-size: 12px !important; +} +.clear-filter { + padding-left: 20px; +} +.closeFilter { + font-size: 10px !important; + color: red !important; +} +.modify-filter { + font-size: 11px !important; +} +.showModifyFilter { + top: 80px; + left: 630px; +} + +#options_menu { + margin-bottom: -15px; +} + +#optionsTableApplet { + margin-top: 9px; + width: 1070px; +} + +#yfi_charts.desktop #yfi_doc, #yfi_charts.tablet #yfi_doc { + width: 1440px; +} + +#yfi_charts.tablet #yfi_investing_content { + width: 1070px; +} + +#sky { + float: right; + margin-left: 30px; + margin-top: 50px; + width: 170px; +} +</style><div id="applet_4305521169702139" class="App_v2 js-applet" data-applet-guid="4305521169702139" data-applet-type="td-applet-options-table"> <div class="App-Bd"> <div class="App-Main" data-region="main"> <div class="js-applet-view-container-main"> <div id="quote-table"> + <div id="options_menu" class="Grid-U options_menu"> + + <form class="Grid-U SelectBox Disabled"> + <div class="SelectBox-Pick"><b class='SelectBox-Text '>May 1, 2015</b><i class='Icon Va-m'>&#xe002;</i></div> + <select class='Start-0' disabled data-plugin="selectbox"> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1427414400" value="1427414400" >March 27, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1427932800" value="1427932800" >April 2, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1428624000" value="1428624000" >April 10, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1429228800" value="1429228800" >April 17, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1429833600" value="1429833600" >April 24, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1430438400" value="1430438400" selected >May 1, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1434672000" value="1434672000" >June 19, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1442534400" value="1442534400" >September 18, 2015</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1452816000" value="1452816000" >January 15, 2016</option> + + + <option data-selectbox-link="/q/op?s=SPWR&date=1484870400" value="1484870400" >January 20, 2017</option> + + </select> + </form> + + + + <div class="Grid-U options-menu-item symbol_lookup"> + <div class="Cf"> + <div class="fin-ac-container Bd-1 Pos-r M-10"> + <input placeholder="Lookup Option" type="text" autocomplete="off" value="" name="s" class="options-ac-input Bd-0" id="finAcOptions"> + <i class="Icon Fl-end W-20 goto-icon">&#xe015;</i> + </div> + <div id="finAcOutput" class="yui-ac-container Pos-r"></div> + </div> + </div> + <div class="Grid-U option_view options-menu-item"> + <ul class="toggle toggle-view-mode"> + <li class="toggle-list active"> + <a href="/q/op?s=SPWR&date=1430438400">List</a> + </li> + <li class="toggle-straddle "> + <a href="/q/op?s=SPWR&straddle=true&date=1430438400">Straddle</a> + </li> + </ul> + + </div> + <div class="Grid-U in_the_money in-the-money-banner"> + In The Money + </div> + </div> + + + + <div class="options-table " id="optionsCallsTable" data-sec="options-calls-table"> + <div class="strike-filter option-filter-overlay"> + <p>Show Me Strikes From</p> + <div class="My-6"> + $ <input class="filter-low strike-filter" data-filter-type="low" type="text"> + to $ <input class="filter-high strike-filter" data-filter-type="high" type="text"> + </div> + <a data-table-filter="optionsCalls" class="Cur-p apply-filter">Apply Filter</a> + <a class="Cur-p clear-filter">Clear Filter</a> +</div> + + +<div class="follow-quote-area"> + <div class="quote-table-overflow"> + <table class="details-table quote-table Fz-m"> + + + <caption> + Calls + </caption> + + + <thead class="details-header quote-table-headers"> + <tr> + + + + + <th class='column-strike Pstart-38 low-high Fz-xs filterable sortable option_column' style='color: #454545;' data-sort-column='strike' data-col-pos='0'> + <div class="cell"> + <div class="D-ib header_text strike">Strike</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + <div class="filter Cur-p "><span>&#8757;</span> Filter</div> + </th> + + + + + + <th class='column-contractName Pstart-10 '>Contract Name</th> + + + + + + + <th class='column-last Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='lastPrice' data-col-pos='2'> + <div class="cell"> + <div class="D-ib lastPrice">Last</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-bid Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='bid' data-col-pos='3'> + <div class="cell"> + <div class="D-ib bid">Bid</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-ask Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='ask' data-col-pos='4'> + <div class="cell"> + <div class="D-ib ask">Ask</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-change Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='change' data-col-pos='5'> + <div class="cell"> + <div class="D-ib change">Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-percentChange Pstart-16 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='percentChange' data-col-pos='6'> + <div class="cell"> + <div class="D-ib percentChange">%Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-volume Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='volume' data-col-pos='7'> + <div class="cell"> + <div class="D-ib volume">Volume</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-openInterest Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='openInterest' data-col-pos='8'> + <div class="cell"> + <div class="D-ib openInterest">Open Interest</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-impliedVolatility Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='impliedVolatility' data-col-pos='9'> + <div class="cell"> + <div class="D-ib impliedVolatility">Implied Volatility</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + </tr> + + <tr class="filterRangeRow D-n"> + <td colspan="10"> + <div> + <span class="filterRangeTitle"></span> + <span class="closeFilter Cur-p">&#10005;</span> + <span class="modify-filter Cur-p">[modify]</span> + </div> + </td> + </tr> + + </thead> + + <tbody> + + + + <tr data-row="0" data-row-quote="_" class="in-the-money + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=SPWR&strike=30.50">30.50</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=SPWR150501C00030500">SPWR150501C00030500</a></div> + </td> + <td> + <div class="option_entry Fz-m" >3.57</div> + </td> + <td> + <div class="option_entry Fz-m" >3.30</div> + </td> + <td> + <div class="option_entry Fz-m" >4.20</div> + </td> + <td> + <div class="option_entry Fz-m" >-0.49</div> + </td> + <td> + + + + <div class="option_entry Fz-m option-change-neg">-13.73%</div> + + + </td> + <td> + <strong data-sq=":volume" data-raw="10">10</strong> + </td> + <td> + <div class="option_entry Fz-m" >20</div> + </td> + <td> + <div class="option_entry Fz-m" >55.08%</div> + </td> + </tr> + + <tr data-row="1" data-row-quote="_" class=" + + odd + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=SPWR&strike=35.00">35.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=SPWR150501C00035000">SPWR150501C00035000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >1.10</div> + </td> + <td> + <div class="option_entry Fz-m" >1.06</div> + </td> + <td> + <div class="option_entry Fz-m" >1.44</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="107">107</strong> + </td> + <td> + <div class="option_entry Fz-m" >119</div> + </td> + <td> + <div class="option_entry Fz-m" >52.20%</div> + </td> + </tr> + + <tr data-row="2" data-row-quote="_" class=" + + even + + "> + <td> + <strong data-sq=":value" data-raw=""><a href="/q/op?s=SPWR&strike=42.00">42.00</a></strong> + </td> + <td> + <div class="option_entry Fz-m" ><a href="/q?s=SPWR150501C00042000">SPWR150501C00042000</a></div> + </td> + <td> + <div class="option_entry Fz-m" >0.41</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + <div class="option_entry Fz-m" >0.50</div> + </td> + <td> + <div class="option_entry Fz-m" >0.00</div> + </td> + <td> + + + <div class="option_entry Fz-m">0.00%</div> + + + + </td> + <td> + <strong data-sq=":volume" data-raw="20">20</strong> + </td> + <td> + <div class="option_entry Fz-m" >10</div> + </td> + <td> + <div class="option_entry Fz-m" >54.00%</div> + </td> + </tr> + + + + + + + + </tbody> + </table> + </div> +</div> + + + </div> + + <div class="options-table " id="optionsPutsTable" data-sec="options-puts-table"> + <div class="strike-filter option-filter-overlay"> + <p>Show Me Strikes From</p> + <div class="My-6"> + $ <input class="filter-low strike-filter" data-filter-type="low" type="text"> + to $ <input class="filter-high strike-filter" data-filter-type="high" type="text"> + </div> + <a data-table-filter="optionsPuts" class="Cur-p apply-filter">Apply Filter</a> + <a class="Cur-p clear-filter">Clear Filter</a> +</div> + + +<div class="follow-quote-area"> + <div class="quote-table-overflow"> + <table class="details-table quote-table Fz-m"> + + + <caption> + Puts + </caption> + + + <thead class="details-header quote-table-headers"> + <tr> + + + + + <th class='column-strike Pstart-38 low-high Fz-xs filterable sortable option_column' style='color: #454545;' data-sort-column='strike' data-col-pos='0'> + <div class="cell"> + <div class="D-ib header_text strike">Strike</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + <div class="filter Cur-p "><span>&#8757;</span> Filter</div> + </th> + + + + + + <th class='column-contractName Pstart-10 '>Contract Name</th> + + + + + + + <th class='column-last Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='lastPrice' data-col-pos='2'> + <div class="cell"> + <div class="D-ib lastPrice">Last</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-bid Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='bid' data-col-pos='3'> + <div class="cell"> + <div class="D-ib bid">Bid</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-ask Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='ask' data-col-pos='4'> + <div class="cell"> + <div class="D-ib ask">Ask</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-change Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='change' data-col-pos='5'> + <div class="cell"> + <div class="D-ib change">Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-percentChange Pstart-16 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='percentChange' data-col-pos='6'> + <div class="cell"> + <div class="D-ib percentChange">%Change</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-volume Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='volume' data-col-pos='7'> + <div class="cell"> + <div class="D-ib volume">Volume</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-openInterest Pstart-14 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='openInterest' data-col-pos='8'> + <div class="cell"> + <div class="D-ib openInterest">Open Interest</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + + + <th class='column-impliedVolatility Pstart-10 Fz-xs sortable option_column' style='color: #454545;' data-sort-column='impliedVolatility' data-col-pos='9'> + <div class="cell"> + <div class="D-ib impliedVolatility">Implied Volatility</div> + <div class="D-ib sort-icons"> + <i class='Icon up'>&#xe004;</i> + <i class='Icon down'>&#xe002;</i> + </div> + </div> + </th> + + + + + </tr> + + <tr class="filterRangeRow D-n"> + <td colspan="10"> + <div> + <span class="filterRangeTitle"></span> + <span class="closeFilter 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Finance"; + } else { + res.locals.page_title = config.title; + } + callback(); +},function (req, res, data, next) { + /* this would invoke the ESI plugin on YTS */ + res.parentRes.set('X-Esi', '1'); + + var hosts = req.config().hosts, + hostToSet = hosts._default; + + Object.keys(hosts).some(function (host) { + if (req.headers.host.indexOf(host) >= 0) { + hostToSet = hosts[host]; + return true; + } + }); + + /* saving request host server name for esi end point */ + res.locals.requesturl = { + host: hostToSet + }; + + /* saving header x-yahoo-request-url for Darla configuration */ + res.locals.requestxhosturl = req.headers['x-env-host'] ? {host: req.headers['x-env-host']} : {host: hostToSet}; + + //urlPath is used for ./node_modules/assembler/node_modules/dust-helpers/lib/util.js::getSpaceId() + //see: https://git.corp.yahoo.com/sports/sportacular-web + req.context.urlPath = req.path; + + // console.log(JSON.stringify({ + // requesturl: res.locals.requesturl.host, + // requestxhosturl: res.locals.requestxhosturl, + // urlPath: req.context.urlPath + // })); + + next(); +},function (req, res, data, callback) { + + res.locals = res.locals || {}; + if (req.query && req.query.s) { + res.locals.quote = req.query.s; + } + + callback(); +},function (req, res, data, callback) { + var params, + ticker, + config, i; + + req = req || {}; + req.params = req.params || {}; + + config = req.config() || {}; + + + data = data || {}; + + params = req.params || {}; + ticker = (params.ticker || (req.query && req.query.s) || 'YHOO').toUpperCase(); + ticker = ticker.split('+')[0];//Split on + if it's in the ticker + ticker = ticker.split(' ')[0];//Split on space if it's in the ticker + + params.tickers = []; + if (config.default_market_tickers) { + params.tickers = params.tickers.concat(config.default_market_tickers); + } + params.tickers.push(ticker); + params.tickers = params.tickers.join(','); + params.format = 'inflated'; + + //Move this into a new action + res.locals.isTablet = config.isTablet; + + quoteStore.read('finance_quote', params, req, function (err, qData) { + if (!err && qData.quotes && qData.quotes.length > 0) { + res.locals.quoteData = qData; + for (i = 0; i < qData.quotes.length; i = i + 1) { + if (qData.quotes[i].symbol.toUpperCase() === ticker.toUpperCase()) { + params.ticker_securityType = qData.quotes[i].type; + } + } + params.tickers = ticker; + } + callback(); + }); +},function (req, res, data, callback) { + + marketTimeStore.read('markettime', {}, req, function (err, data) { + if (data && data.index) { + res.parentRes.locals.markettime = data.index.markettime; + } + callback(); + }); +}],"after":[]}},"context":{"authed":"0","ynet":"0","ssl":"1","spdy":"0","bucket":"","colo":"gq1","device":"desktop","environment":"prod","lang":"en-US","partner":"none","site":"finance","region":"US","intl":"us","tz":"America\u002FLos_Angeles","edgepipeEnabled":false,"urlPath":"\u002Fq\u002Fop"},"intl":{"locales":"en-US"},"user":{"crumb":"RaKZ96VJ.kK","firstName":null}}; +root.YUI_config = {"version":"3.17.2","base":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?yui:3.17.2\u002F","comboBase":"https:\u002F\u002Fs.yimg.com\u002Fzz\u002Fcombo?","comboSep":"&","root":"yui:3.17.2\u002F","filter":"min","logLevel":"error","combine":true,"patches":[function patchLangBundlesRequires(Y, loader) { + var getRequires = loader.getRequires; + loader.getRequires = function (mod) { + var i, j, m, name, mods, loadDefaultBundle, + locales = Y.config.lang || [], + r = getRequires.apply(this, arguments); + // expanding requirements with optional requires + if (mod.langBundles && !mod.langBundlesExpanded) { + mod.langBundlesExpanded = []; + locales = typeof locales === 'string' ? 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Two bug fixes from pandas-datareader. pydata/pandas-datareader#25 pydata/pandas-datareader#24
https://api.github.com/repos/pandas-dev/pandas/pulls/9742
2015-03-28T03:30:41Z
2015-03-29T17:10:43Z
2015-03-29T17:10:43Z
2015-04-07T09:05:49Z
ENH: support CategoricalIndex (GH7629)
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index 1749409c863df..688935c6b104d 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -594,6 +594,95 @@ faster than fancy indexing. timeit ser.ix[indexer] timeit ser.take(indexer) +.. _indexing.categoricalindex: + +CategoricalIndex +---------------- + +.. versionadded:: 0.16.1 + +We introduce a ``CategoricalIndex``, a new type of index object that is useful for supporting +indexing with duplicates. This is a container around a ``Categorical`` (introduced in v0.15.0) +and allows efficient indexing and storage of an index with a large number of duplicated elements. Prior to 0.16.1, +setting the index of a ``DataFrame/Series`` with a ``category`` dtype would convert this to regular object-based ``Index``. + +.. ipython:: python + + df = DataFrame({'A' : np.arange(6), + 'B' : Series(list('aabbca')).astype('category', + categories=list('cab')) + }) + df + df.dtypes + df.B.cat.categories + +Setting the index, will create create a ``CategoricalIndex`` + +.. ipython:: python + + df2 = df.set_index('B') + df2.index + +Indexing with ``__getitem__/.iloc/.loc/.ix`` works similarly to an ``Index`` with duplicates. +The indexers MUST be in the category or the operation will raise. + +.. ipython:: python + + df2.loc['a'] + +These PRESERVE the ``CategoricalIndex`` + +.. ipython:: python + + df2.loc['a'].index + +Sorting will order by the order of the categories + +.. ipython:: python + + df2.sort_index() + +Groupby operations on the index will preserve the index nature as well + +.. ipython:: python + + df2.groupby(level=0).sum() + df2.groupby(level=0).sum().index + +Reindexing operations, will return a resulting index based on the type of the passed +indexer, meaning that passing a list will return a plain-old-``Index``; indexing with +a ``Categorical`` will return a ``CategoricalIndex``, indexed according to the categories +of the PASSED ``Categorical`` dtype. This allows one to arbitrarly index these even with +values NOT in the categories, similarly to how you can reindex ANY pandas index. + +.. ipython :: python + + df2.reindex(['a','e']) + df2.reindex(['a','e']).index + df2.reindex(pd.Categorical(['a','e'],categories=list('abcde'))) + df2.reindex(pd.Categorical(['a','e'],categories=list('abcde'))).index + +.. warning:: + + Reshaping and Comparision operations on a ``CategoricalIndex`` must have the same categories + or a ``TypeError`` will be raised. + + .. code-block:: python + + In [10]: df3 = DataFrame({'A' : np.arange(6), + 'B' : Series(list('aabbca')).astype('category', + categories=list('abc')) + }).set_index('B') + + In [11]: df3.index + Out[11]: + CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], + categories=[u'a', u'b', u'c'], + ordered=False) + + In [12]: pd.concat([df2,df3] + TypeError: categories must match existing categories when appending + .. _indexing.float64index: Float64Index @@ -706,4 +795,3 @@ Of course if you need integer based selection, then use ``iloc`` .. ipython:: python dfir.iloc[0:5] - diff --git a/doc/source/api.rst b/doc/source/api.rst index af9f8c84388bd..b1540ff528605 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1291,6 +1291,34 @@ Selecting Index.slice_indexer Index.slice_locs +.. _api.categoricalindex: + +CategoricalIndex +---------------- + +.. autosummary:: + :toctree: generated/ + + CategoricalIndex + +Categorical Components +~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated/ + + CategoricalIndex.codes + CategoricalIndex.categories + CategoricalIndex.ordered + CategoricalIndex.rename_categories + CategoricalIndex.reorder_categories + CategoricalIndex.add_categories + CategoricalIndex.remove_categories + CategoricalIndex.remove_unused_categories + CategoricalIndex.set_categories + CategoricalIndex.as_ordered + CategoricalIndex.as_unordered + .. _api.datetimeindex: DatetimeIndex diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index fcb5cd6a5ec30..cbd5ad3f49c18 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -7,6 +7,10 @@ This is a minor bug-fix release from 0.16.0 and includes a a large number of bug fixes along several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version. +Highlights include: + +- Support for a ``CategoricalIndex``, a category based index, see :ref:`here <whatsnew_0161`.enhancements.categoricalindex>` + .. contents:: What's new in v0.16.1 :local: :backlinks: none @@ -31,6 +35,7 @@ Enhancements will return a `np.array` instead of a boolean `Index` (:issue:`8875`). This enables the following expression to work naturally: + .. ipython:: python idx = Index(['a1', 'a2', 'b1', 'b2']) @@ -40,6 +45,7 @@ Enhancements s[s.index.str.startswith('a')] - ``DataFrame.mask()`` and ``Series.mask()`` now support same keywords as ``where`` (:issue:`8801`) + - ``drop`` function can now accept ``errors`` keyword to suppress ValueError raised when any of label does not exist in the target data. (:issue:`6736`) .. ipython:: python @@ -58,6 +64,75 @@ Enhancements - ``DataFrame`` and ``Series`` now have ``_constructor_expanddim`` property as overridable constructor for one higher dimensionality data. This should be used only when it is really needed, see :ref:`here <ref-subclassing-pandas>` +.. _whatsnew_0161.enhancements.categoricalindex: + +CategoricalIndex +^^^^^^^^^^^^^^^^ + +We introduce a ``CategoricalIndex``, a new type of index object that is useful for supporting +indexing with duplicates. This is a container around a ``Categorical`` (introduced in v0.15.0) +and allows efficient indexing and storage of an index with a large number of duplicated elements. Prior to 0.16.1, +setting the index of a ``DataFrame/Series`` with a ``category`` dtype would convert this to regular object-based ``Index``. + +.. ipython :: python + + df = DataFrame({'A' : np.arange(6), + 'B' : Series(list('aabbca')).astype('category', + categories=list('cab')) + }) + df + df.dtypes + df.B.cat.categories + +setting the index, will create create a CategoricalIndex + +.. ipython :: python + + df2 = df.set_index('B') + df2.index + +indexing with ``__getitem__/.iloc/.loc/.ix`` works similarly to an Index with duplicates. +The indexers MUST be in the category or the operation will raise. + +.. ipython :: python + + df2.loc['a'] + +and preserves the ``CategoricalIndex`` + +.. ipython :: python + + df2.loc['a'].index + +sorting will order by the order of the categories + +.. ipython :: python + + df2.sort_index() + +groupby operations on the index will preserve the index nature as well + +.. ipython :: python + + df2.groupby(level=0).sum() + df2.groupby(level=0).sum().index + +reindexing operations, will return a resulting index based on the type of the passed +indexer, meaning that passing a list will return a plain-old-``Index``; indexing with +a ``Categorical`` will return a ``CategoricalIndex``, indexed according to the categories +of the PASSED ``Categorical`` dtype. This allows one to arbitrarly index these even with +values NOT in the categories, similarly to how you can reindex ANY pandas index. + +.. ipython :: python + + df2.reindex(['a','e']) + df2.reindex(['a','e']).index + df2.reindex(pd.Categorical(['a','e'],categories=list('abcde'))) + df2.reindex(pd.Categorical(['a','e'],categories=list('abcde'))).index + +See the :ref:`documentation <advanced.categoricalindex>` for more. (:issue:`7629`) +>>>>>>> support CategoricalIndex + .. _whatsnew_0161.api: API changes diff --git a/pandas/core/api.py b/pandas/core/api.py index a8b10342593ce..fde9bc77c4bd9 100644 --- a/pandas/core/api.py +++ b/pandas/core/api.py @@ -8,7 +8,7 @@ from pandas.core.categorical import Categorical from pandas.core.groupby import Grouper from pandas.core.format import set_eng_float_format -from pandas.core.index import Index, Int64Index, Float64Index, MultiIndex +from pandas.core.index import Index, CategoricalIndex, Int64Index, Float64Index, MultiIndex from pandas.core.series import Series, TimeSeries from pandas.core.frame import DataFrame diff --git a/pandas/core/base.py b/pandas/core/base.py index a25651a73f507..c0233a5a33308 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -121,7 +121,7 @@ def _delegate_method(self, name, *args, **kwargs): raise TypeError("You cannot call method {name}".format(name=name)) @classmethod - def _add_delegate_accessors(cls, delegate, accessors, typ): + def _add_delegate_accessors(cls, delegate, accessors, typ, overwrite=False): """ add accessors to cls from the delegate class @@ -131,6 +131,8 @@ def _add_delegate_accessors(cls, delegate, accessors, typ): delegate : the class to get methods/properties & doc-strings acccessors : string list of accessors to add typ : 'property' or 'method' + overwrite : boolean, default False + overwrite the method/property in the target class if it exists """ @@ -164,7 +166,7 @@ def f(self, *args, **kwargs): f = _create_delegator_method(name) # don't overwrite existing methods/properties - if not hasattr(cls, name): + if overwrite or not hasattr(cls, name): setattr(cls,name,f) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 0d66a89b0a585..9537523380350 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -9,12 +9,11 @@ from pandas.core.algorithms import factorize from pandas.core.base import PandasObject, PandasDelegate -from pandas.core.index import Index, _ensure_index -from pandas.tseries.period import PeriodIndex import pandas.core.common as com from pandas.util.decorators import cache_readonly -from pandas.core.common import (CategoricalDtype, ABCSeries, isnull, notnull, +from pandas.core.common import (CategoricalDtype, ABCSeries, ABCIndexClass, ABCPeriodIndex, ABCCategoricalIndex, + isnull, notnull, is_dtype_equal, is_categorical_dtype, is_integer_dtype, is_object_dtype, _possibly_infer_to_datetimelike, get_dtype_kinds, is_list_like, is_sequence, is_null_slice, is_bool, @@ -22,7 +21,6 @@ _coerce_indexer_dtype, _values_from_object, take_1d) from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option -from pandas.core import format as fmt def _cat_compare_op(op): def f(self, other): @@ -86,7 +84,7 @@ def f(self, other): def maybe_to_categorical(array): """ coerce to a categorical if a series is given """ - if isinstance(array, ABCSeries): + if isinstance(array, (ABCSeries, ABCCategoricalIndex)): return array.values return array @@ -236,15 +234,17 @@ def __init__(self, values, categories=None, ordered=False, name=None, fastpath=F # sanitize input if is_categorical_dtype(values): - # we are either a Series or a Categorical - cat = values - if isinstance(values, ABCSeries): - cat = values.values + # we are either a Series or a CategoricalIndex + if isinstance(values, (ABCSeries, ABCCategoricalIndex)): + values = values.values + + if ordered is None: + ordered = values.ordered if categories is None: - categories = cat.categories + categories = values.categories values = values.__array__() - elif isinstance(values, Index): + elif isinstance(values, ABCIndexClass): pass else: @@ -295,11 +295,11 @@ def __init__(self, values, categories=None, ordered=False, name=None, fastpath=F warn("Values and categories have different dtypes. Did you mean to use\n" "'Categorical.from_codes(codes, categories)'?", RuntimeWarning) - if is_integer_dtype(values) and (codes == -1).all(): + if len(values) and is_integer_dtype(values) and (codes == -1).all(): warn("None of the categories were found in values. Did you mean to use\n" "'Categorical.from_codes(codes, categories)'?", RuntimeWarning) - self.set_ordered(ordered, inplace=True) + self.set_ordered(ordered or False, inplace=True) self.categories = categories self.name = name self._codes = _coerce_indexer_dtype(codes, categories) @@ -309,11 +309,27 @@ def copy(self): return Categorical(values=self._codes.copy(),categories=self.categories, name=self.name, ordered=self.ordered, fastpath=True) + def astype(self, dtype): + """ coerce this type to another dtype """ + if is_categorical_dtype(dtype): + return self + return np.array(self, dtype=dtype) + @cache_readonly def ndim(self): """Number of dimensions of the Categorical """ return self._codes.ndim + @cache_readonly + def size(self): + """ return the len of myself """ + return len(self) + + @cache_readonly + def itemsize(self): + """ return the size of a single category """ + return self.categories.itemsize + def reshape(self, new_shape, **kwargs): """ compat with .reshape """ return self @@ -395,7 +411,8 @@ def _set_codes(self, codes): codes = property(fget=_get_codes, fset=_set_codes, doc=_codes_doc) def _get_labels(self): - """ Get the category labels (deprecated). + """ + Get the category labels (deprecated). Deprecated, use .codes! """ @@ -409,8 +426,10 @@ def _get_labels(self): @classmethod def _validate_categories(cls, categories): - """" Validates that we have good categories """ - if not isinstance(categories, Index): + """ + Validates that we have good categories + """ + if not isinstance(categories, ABCIndexClass): dtype = None if not hasattr(categories, "dtype"): categories = _convert_to_list_like(categories) @@ -421,6 +440,8 @@ def _validate_categories(cls, categories): with_na = np.array(categories) if with_na.dtype != without_na.dtype: dtype = "object" + + from pandas import Index categories = Index(categories, dtype=dtype) if not categories.is_unique: raise ValueError('Categorical categories must be unique') @@ -761,6 +782,8 @@ def remove_unused_categories(self, inplace=False): cat = self if inplace else self.copy() _used = sorted(np.unique(cat._codes)) new_categories = cat.categories.take(_ensure_platform_int(_used)) + + from pandas.core.index import _ensure_index new_categories = _ensure_index(new_categories) cat._codes = _get_codes_for_values(cat.__array__(), new_categories) cat._categories = new_categories @@ -790,7 +813,8 @@ def shape(self): return tuple([len(self._codes)]) def __array__(self, dtype=None): - """ The numpy array interface. + """ + The numpy array interface. Returns ------- @@ -799,7 +823,7 @@ def __array__(self, dtype=None): dtype as categorical.categories.dtype """ ret = take_1d(self.categories.values, self._codes) - if dtype and dtype != self.categories.dtype: + if dtype and not is_dtype_equal(dtype,self.categories.dtype): return np.asarray(ret, dtype) return ret @@ -997,7 +1021,7 @@ def get_values(self): """ # if we are a period index, return a string repr - if isinstance(self.categories, PeriodIndex): + if isinstance(self.categories, ABCPeriodIndex): return take_1d(np.array(self.categories.to_native_types(), dtype=object), self._codes) @@ -1243,7 +1267,8 @@ def __iter__(self): """Returns an Iterator over the values of this Categorical.""" return iter(np.array(self)) - def _tidy_repr(self, max_vals=10): + def _tidy_repr(self, max_vals=10, footer=True): + """ a short repr displaying only max_vals and an optional (but default footer) """ num = max_vals // 2 head = self[:num]._get_repr(length=False, name=False, footer=False) tail = self[-(max_vals - num):]._get_repr(length=False, @@ -1251,23 +1276,31 @@ def _tidy_repr(self, max_vals=10): footer=False) result = '%s, ..., %s' % (head[:-1], tail[1:]) - result = '%s\n%s' % (result, self._repr_footer()) + if footer: + result = '%s\n%s' % (result, self._repr_footer()) return compat.text_type(result) - def _repr_categories_info(self): - """ Returns a string representation of the footer.""" - + def _repr_categories(self): + """ return the base repr for the categories """ max_categories = (10 if get_option("display.max_categories") == 0 else get_option("display.max_categories")) + from pandas.core import format as fmt category_strs = fmt.format_array(self.categories.get_values(), None) if len(category_strs) > max_categories: num = max_categories // 2 head = category_strs[:num] tail = category_strs[-(max_categories - num):] category_strs = head + ["..."] + tail + # Strip all leading spaces, which format_array adds for columns... category_strs = [x.strip() for x in category_strs] + return category_strs + + def _repr_categories_info(self): + """ Returns a string representation of the footer.""" + + category_strs = self._repr_categories() levheader = "Categories (%d, %s): " % (len(self.categories), self.categories.dtype) width, height = get_terminal_size() @@ -1299,8 +1332,11 @@ def _repr_footer(self): len(self), self._repr_categories_info()) def _get_repr(self, name=False, length=True, na_rep='NaN', footer=True): - formatter = fmt.CategoricalFormatter(self, name=name, - length=length, na_rep=na_rep, + from pandas.core import format as fmt + formatter = fmt.CategoricalFormatter(self, + name=name, + length=length, + na_rep=na_rep, footer=footer) result = formatter.to_string() return compat.text_type(result) @@ -1315,9 +1351,9 @@ def __unicode__(self): name=True) else: result = '[], %s' % self._get_repr(name=True, - length=False, - footer=True, - ).replace("\n",", ") + length=False, + footer=True, + ).replace("\n",", ") return result @@ -1358,6 +1394,8 @@ def __setitem__(self, key, value): "categories") rvalue = value if is_list_like(value) else [value] + + from pandas import Index to_add = Index(rvalue).difference(self.categories) # no assignments of values not in categories, but it's always ok to set something to np.nan @@ -1516,11 +1554,27 @@ def equals(self, other): ------- are_equal : boolean """ - if not isinstance(other, Categorical): - return False # TODO: should this also test if name is equal? - return (self.categories.equals(other.categories) and self.ordered == other.ordered and - np.array_equal(self._codes, other._codes)) + return self.is_dtype_equal(other) and np.array_equal(self._codes, other._codes) + + def is_dtype_equal(self, other): + """ + Returns True if categoricals are the same dtype + same categories, and same ordered + + Parameters + ---------- + other : Categorical + + Returns + ------- + are_equal : boolean + """ + + try: + return self.categories.equals(other.categories) and self.ordered == other.ordered + except (AttributeError, TypeError): + return False def describe(self): """ Describes this Categorical @@ -1604,18 +1658,20 @@ def _delegate_method(self, name, *args, **kwargs): ##### utility routines ##### def _get_codes_for_values(values, categories): - """" + """ utility routine to turn values into codes given the specified categories """ from pandas.core.algorithms import _get_data_algo, _hashtables - if values.dtype != categories.dtype: + if not is_dtype_equal(values.dtype,categories.dtype): values = _ensure_object(values) categories = _ensure_object(categories) + (hash_klass, vec_klass), vals = _get_data_algo(values, _hashtables) - t = hash_klass(len(categories)) - t.map_locations(_values_from_object(categories)) - return _coerce_indexer_dtype(t.lookup(values), categories) + (_, _), cats = _get_data_algo(categories, _hashtables) + t = hash_klass(len(cats)) + t.map_locations(cats) + return _coerce_indexer_dtype(t.lookup(vals), cats) def _convert_to_list_like(list_like): if hasattr(list_like, "dtype"): diff --git a/pandas/core/common.py b/pandas/core/common.py index ffe12d0c1546c..3d23aeff942dc 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -83,6 +83,16 @@ def _check(cls, inst): ABCDatetimeIndex = create_pandas_abc_type("ABCDatetimeIndex", "_typ", ("datetimeindex",)) ABCTimedeltaIndex = create_pandas_abc_type("ABCTimedeltaIndex", "_typ", ("timedeltaindex",)) ABCPeriodIndex = create_pandas_abc_type("ABCPeriodIndex", "_typ", ("periodindex",)) +ABCCategoricalIndex = create_pandas_abc_type("ABCCategoricalIndex", "_typ", ("categoricalindex",)) +ABCIndexClass = create_pandas_abc_type("ABCIndexClass", "_typ", ("index", + "int64index", + "float64index", + "multiindex", + "datetimeindex", + "timedeltaindex", + "periodindex", + "categoricalindex")) + ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series",)) ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe",)) ABCPanel = create_pandas_abc_type("ABCPanel", "_typ", ("panel",)) @@ -2455,11 +2465,27 @@ def _get_dtype_type(arr_or_dtype): return np.dtype(arr_or_dtype).type elif isinstance(arr_or_dtype, CategoricalDtype): return CategoricalDtypeType + elif isinstance(arr_or_dtype, compat.string_types): + if is_categorical_dtype(arr_or_dtype): + return CategoricalDtypeType + return _get_dtype_type(np.dtype(arr_or_dtype)) try: return arr_or_dtype.dtype.type except AttributeError: raise ValueError('%r is not a dtype' % arr_or_dtype) +def is_dtype_equal(source, target): + """ return a boolean if the dtypes are equal """ + source = _get_dtype_type(source) + target = _get_dtype_type(target) + + try: + return source == target + except TypeError: + + # invalid comparison + # object == category will hit this + return False def is_any_int_dtype(arr_or_dtype): tipo = _get_dtype_type(arr_or_dtype) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 4ef3bbce85467..e5b1a96f81677 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -14,7 +14,7 @@ from pandas.core.categorical import Categorical from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame -from pandas.core.index import Index, MultiIndex, _ensure_index, _union_indexes +from pandas.core.index import Index, MultiIndex, CategoricalIndex, _ensure_index, _union_indexes from pandas.core.internals import BlockManager, make_block from pandas.core.series import Series from pandas.core.panel import Panel @@ -1928,7 +1928,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, self.grouper = com._asarray_tuplesafe(self.grouper) # a passed Categorical - elif isinstance(self.grouper, Categorical): + elif is_categorical_dtype(self.grouper): # must have an ordered categorical if self.sort: @@ -1942,8 +1942,15 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, # fix bug #GH8868 sort=False being ignored in categorical groupby else: self.grouper = self.grouper.reorder_categories(self.grouper.unique()) + + # we make a CategoricalIndex out of the cat grouper + # preserving the categories / ordered attributes self._labels = self.grouper.codes - self._group_index = self.grouper.categories + + c = self.grouper.categories + self._group_index = CategoricalIndex(Categorical.from_codes(np.arange(len(c)), + categories=c, + ordered=self.grouper.ordered)) if self.name is None: self.name = self.grouper.name @@ -2131,8 +2138,8 @@ def is_in_obj(gpr): else: in_axis, name = False, None - if isinstance(gpr, Categorical) and len(gpr) != len(obj): - raise ValueError("Categorical grouper must have len(grouper) == len(data)") + if is_categorical_dtype(gpr) and len(gpr) != len(obj): + raise ValueError("Categorical dtype grouper must have len(grouper) == len(data)") ping = Grouping(group_axis, gpr, obj=obj, name=name, level=level, sort=sort, in_axis=in_axis) @@ -3252,7 +3259,7 @@ def _reindex_output(self, result): return result elif len(groupings) == 1: return result - elif not any([isinstance(ping.grouper, Categorical) + elif not any([isinstance(ping.grouper, (Categorical, CategoricalIndex)) for ping in groupings]): return result diff --git a/pandas/core/index.py b/pandas/core/index.py index 8b509c6876ec7..8b650fea9b440 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -2,6 +2,7 @@ import datetime import warnings import operator + from functools import partial from pandas.compat import range, zip, lrange, lzip, u, reduce, filter, map from pandas import compat @@ -13,13 +14,13 @@ import pandas.algos as _algos import pandas.index as _index from pandas.lib import Timestamp, Timedelta, is_datetime_array -from pandas.core.base import PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin, _shared_docs +from pandas.core.base import PandasObject, FrozenList, FrozenNDArray, IndexOpsMixin, _shared_docs, PandasDelegate from pandas.util.decorators import (Appender, Substitution, cache_readonly, deprecate) -from pandas.core.common import isnull, array_equivalent import pandas.core.common as com -from pandas.core.common import (_values_from_object, is_float, is_integer, - ABCSeries, _ensure_object, _ensure_int64, is_bool_indexer, +from pandas.core.common import (isnull, array_equivalent, is_dtype_equal, is_object_dtype, + _values_from_object, is_float, is_integer, is_iterator, is_categorical_dtype, + ABCSeries, ABCCategorical, _ensure_object, _ensure_int64, is_bool_indexer, is_list_like, is_bool_dtype, is_null_slice, is_integer_dtype) from pandas.core.config import get_option from pandas.io.common import PerformanceWarning @@ -44,26 +45,6 @@ def _try_get_item(x): except AttributeError: return x -def _indexOp(opname): - """ - Wrapper function for index comparison operations, to avoid - code duplication. - """ - def wrapper(self, other): - func = getattr(self.values, opname) - result = func(np.asarray(other)) - - # technically we could support bool dtyped Index - # for now just return the indexing array directly - if is_bool_dtype(result): - return result - try: - return Index(result) - except: # pragma: no cover - return result - return wrapper - - class InvalidIndexError(Exception): pass @@ -162,6 +143,8 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False, return Float64Index(data, copy=copy, dtype=dtype, name=name) elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data): subarr = data.astype('object') + elif is_categorical_dtype(data) or is_categorical_dtype(dtype): + return CategoricalIndex(data, copy=copy, name=name, **kwargs) else: subarr = com._asarray_tuplesafe(data, dtype=object) @@ -170,6 +153,8 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None, fastpath=False, if copy: subarr = subarr.copy() + elif is_categorical_dtype(data) or is_categorical_dtype(dtype): + return CategoricalIndex(data, copy=copy, name=name, **kwargs) elif hasattr(data, '__array__'): return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs) @@ -258,7 +243,7 @@ def __len__(self): """ return len(self._data) - def __array__(self, result=None): + def __array__(self, dtype=None): """ the array interface, return my values """ return self._data.view(np.ndarray) @@ -282,9 +267,6 @@ def get_values(self): """ return the underlying data as an ndarray """ return self.values - def _array_values(self): - return self._data - # ops compat def tolist(self): """ @@ -410,8 +392,7 @@ def __unicode__(self): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ - prepr = com.pprint_thing(self, escape_chars=('\t', '\r', '\n'), - quote_strings=True) + prepr = default_pprint(self) return "%s(%s, dtype='%s')" % (type(self).__name__, prepr, self.dtype) def to_series(self, **kwargs): @@ -429,9 +410,10 @@ def to_series(self, **kwargs): def _to_embed(self, keep_tz=False): """ + *this is an internal non-public method* + return an array repr of this object, potentially casting to object - This is for internal compat """ return self.values @@ -623,7 +605,10 @@ def is_numeric(self): return self.inferred_type in ['integer', 'floating'] def is_object(self): - return self.dtype == np.object_ + return is_object_dtype(self.dtype) + + def is_categorical(self): + return self.inferred_type in ['categorical'] def is_mixed(self): return 'mixed' in self.inferred_type @@ -772,14 +757,11 @@ def is_int(v): return indexer - def _convert_list_indexer(self, key, kind=None): - """ convert a list indexer. these should be locations """ - return key - - def _convert_list_indexer_for_mixed(self, keyarr, kind=None): - """ passed a key that is tuplesafe that is integer based - and we have a mixed index (e.g. number/labels). figure out - the indexer. return None if we can't help + def _convert_list_indexer(self, keyarr, kind=None): + """ + passed a key that is tuplesafe that is integer based + and we have a mixed index (e.g. number/labels). figure out + the indexer. return None if we can't help """ if (kind is None or kind in ['iloc','ix']) and (is_integer_dtype(keyarr) and not self.is_floating()): if self.inferred_type != 'integer': @@ -954,17 +936,13 @@ def __getitem__(self, key): else: return result - def append(self, other): + def _ensure_compat_append(self, other): """ - Append a collection of Index options together - - Parameters - ---------- - other : Index or list/tuple of indices + prepare the append Returns ------- - appended : Index + list of to_concat, name of result Index """ name = self.name to_concat = [self] @@ -984,7 +962,21 @@ def append(self, other): to_concat = self._ensure_compat_concat(to_concat) to_concat = [x.values if isinstance(x, Index) else x for x in to_concat] + return to_concat, name + + def append(self, other): + """ + Append a collection of Index options together + + Parameters + ---------- + other : Index or list/tuple of indices + Returns + ------- + appended : Index + """ + to_concat, name = self._ensure_compat_append(other) return Index(np.concatenate(to_concat), name=name) @staticmethod @@ -1046,10 +1038,12 @@ def _format_with_header(self, header, na_rep='NaN', **kwargs): from pandas.core.format import format_array - if values.dtype == np.object_: + if is_categorical_dtype(values.dtype): + values = np.array(values) + elif is_object_dtype(values.dtype): values = lib.maybe_convert_objects(values, safe=1) - if values.dtype == np.object_: + if is_object_dtype(values.dtype): result = [com.pprint_thing(x, escape_chars=('\t', '\r', '\n')) for x in values] @@ -1092,9 +1086,6 @@ def equals(self, other): if not isinstance(other, Index): return False - if type(other) != Index: - return other.equals(self) - return array_equivalent(_values_from_object(self), _values_from_object(other)) def identical(self, other): @@ -1201,13 +1192,6 @@ def __sub__(self, other): "use .difference()",FutureWarning) return self.difference(other) - __eq__ = _indexOp('__eq__') - __ne__ = _indexOp('__ne__') - __lt__ = _indexOp('__lt__') - __gt__ = _indexOp('__gt__') - __le__ = _indexOp('__le__') - __ge__ = _indexOp('__ge__') - def __and__(self, other): return self.intersection(other) @@ -1240,7 +1224,7 @@ def union(self, other): self._assert_can_do_setop(other) - if self.dtype != other.dtype: + if not is_dtype_equal(self.dtype,other.dtype): this = self.astype('O') other = other.astype('O') return this.union(other) @@ -1314,7 +1298,7 @@ def intersection(self, other): if self.equals(other): return self - if self.dtype != other.dtype: + if not is_dtype_equal(self.dtype,other.dtype): this = self.astype('O') other = other.astype('O') return this.intersection(other) @@ -1473,7 +1457,7 @@ def get_value(self, series, key): raise except TypeError: # generator/iterator-like - if com.is_iterator(key): + if is_iterator(key): raise InvalidIndexError(key) else: raise e1 @@ -1548,7 +1532,7 @@ def get_indexer(self, target, method=None, limit=None): if pself is not self or ptarget is not target: return pself.get_indexer(ptarget, method=method, limit=limit) - if self.dtype != target.dtype: + if not is_dtype_equal(self.dtype,target.dtype): this = self.astype(object) target = target.astype(object) return this.get_indexer(target, method=method, limit=limit) @@ -1647,7 +1631,8 @@ def get_indexer_for(self, target, **kwargs): """ guaranteed return of an indexer even when non-unique """ if self.is_unique: return self.get_indexer(target, **kwargs) - return self.get_indexer_non_unique(target, **kwargs)[0] + indexer, _ = self.get_indexer_non_unique(target, **kwargs) + return indexer def _possibly_promote(self, other): # A hack, but it works @@ -1655,7 +1640,7 @@ def _possibly_promote(self, other): if self.inferred_type == 'date' and isinstance(other, DatetimeIndex): return DatetimeIndex(self), other elif self.inferred_type == 'boolean': - if self.dtype != 'object': + if not is_object_dtype(self.dtype): return self.astype('object'), other.astype('object') return self, other @@ -1707,12 +1692,35 @@ def isin(self, values, level=None): value_set = set(values) if level is not None: self._validate_index_level(level) - return lib.ismember(self._array_values(), value_set) + return lib.ismember(np.array(self), value_set) + + def _can_reindex(self, indexer): + """ + *this is an internal non-public method* + + Check if we are allowing reindexing with this particular indexer + + Parameters + ---------- + indexer : an integer indexer + + Raises + ------ + ValueError if its a duplicate axis + """ + + # trying to reindex on an axis with duplicates + if not self.is_unique and len(indexer): + raise ValueError("cannot reindex from a duplicate axis") def reindex(self, target, method=None, level=None, limit=None): """ Create index with target's values (move/add/delete values as necessary) + Parameters + ---------- + target : an iterable + Returns ------- new_index : pd.Index @@ -1733,6 +1741,7 @@ def reindex(self, target, method=None, level=None, limit=None): target = self._simple_new(np.empty(0, dtype=self.dtype), **attrs) else: target = _ensure_index(target) + if level is not None: if method is not None: raise TypeError('Fill method not supported if level passed') @@ -1757,9 +1766,72 @@ def reindex(self, target, method=None, level=None, limit=None): return target, indexer + def _reindex_non_unique(self, target): + """ + *this is an internal non-public method* + + Create a new index with target's values (move/add/delete values as necessary) + use with non-unique Index and a possibly non-unique target + + Parameters + ---------- + target : an iterable + + Returns + ------- + new_index : pd.Index + Resulting index + indexer : np.ndarray or None + Indices of output values in original index + + """ + + target = _ensure_index(target) + indexer, missing = self.get_indexer_non_unique(target) + check = indexer != -1 + new_labels = self.take(indexer[check]) + new_indexer = None + + if len(missing): + l = np.arange(len(indexer)) + + missing = com._ensure_platform_int(missing) + missing_labels = target.take(missing) + missing_indexer = com._ensure_int64(l[~check]) + cur_labels = self.take(indexer[check]).values + cur_indexer = com._ensure_int64(l[check]) + + new_labels = np.empty(tuple([len(indexer)]), dtype=object) + new_labels[cur_indexer] = cur_labels + new_labels[missing_indexer] = missing_labels + + # a unique indexer + if target.is_unique: + + # see GH5553, make sure we use the right indexer + new_indexer = np.arange(len(indexer)) + new_indexer[cur_indexer] = np.arange(len(cur_labels)) + new_indexer[missing_indexer] = -1 + + # we have a non_unique selector, need to use the original + # indexer here + else: + + # need to retake to have the same size as the indexer + indexer = indexer.values + indexer[~check] = 0 + + # reset the new indexer to account for the new size + new_indexer = np.arange(len(self.take(indexer))) + new_indexer[~check] = -1 + + return self._shallow_copy(new_labels), indexer, new_indexer + def join(self, other, how='left', level=None, return_indexers=False): """ - Internal API method. Compute join_index and indexers to conform data + *this is an internal non-public method* + + Compute join_index and indexers to conform data structures to the new index. Parameters @@ -1818,7 +1890,7 @@ def join(self, other, how='left', level=None, return_indexers=False): result = x, z, y return result - if self.dtype != other.dtype: + if not is_dtype_equal(self.dtype,other.dtype): this = self.astype('O') other = other.astype('O') return this.join(other, how=how, @@ -2369,6 +2441,34 @@ def _evaluate_with_timedelta_like(self, other, op, opstr): def _evaluate_with_datetime_like(self, other, op, opstr): raise TypeError("can only perform ops with datetime like values") + @classmethod + def _add_comparison_methods(cls): + """ add in comparison methods """ + + def _make_compare(op): + + def _evaluate_compare(self, other): + func = getattr(self.values, op) + result = func(np.asarray(other)) + + # technically we could support bool dtyped Index + # for now just return the indexing array directly + if is_bool_dtype(result): + return result + try: + return Index(result) + except TypeError: + return result + + return _evaluate_compare + + cls.__eq__ = _make_compare('__eq__') + cls.__ne__ = _make_compare('__ne__') + cls.__lt__ = _make_compare('__lt__') + cls.__gt__ = _make_compare('__gt__') + cls.__le__ = _make_compare('__le__') + cls.__ge__ = _make_compare('__ge__') + @classmethod def _add_numeric_methods_disabled(cls): """ add in numeric methods to disable """ @@ -2423,7 +2523,7 @@ def _evaluate_numeric_binop(self, other): elif isinstance(other, (Timestamp, np.datetime64)): return self._evaluate_with_datetime_like(other, op, opstr) else: - if not (com.is_float(other) or com.is_integer(other)): + if not (is_float(other) or is_integer(other)): raise TypeError("can only perform ops with scalar values") # if we are a reversed non-communative op @@ -2487,7 +2587,7 @@ def _make_logical_function(name, desc, f): @Appender(_doc) def logical_func(self, *args, **kwargs): result = f(self.values) - if isinstance(result, (np.ndarray, com.ABCSeries, Index)) \ + if isinstance(result, (np.ndarray, ABCSeries, Index)) \ and result.ndim == 0: # return NumPy type return result.dtype.type(result.item()) @@ -2519,6 +2619,539 @@ def invalid_op(self, other=None): Index._add_numeric_methods_disabled() Index._add_logical_methods() +Index._add_comparison_methods() + +class CategoricalIndex(Index, PandasDelegate): + """ + + Immutable Index implementing an ordered, sliceable set. CategoricalIndex + represents a sparsely populated Index with an underlying Categorical. + + Parameters + ---------- + data : array-like or Categorical, (1-dimensional) + categories : optional, array-like + categories for the CategoricalIndex + ordered : boolean, + designating if the categories are ordered + copy : bool + Make a copy of input ndarray + name : object + Name to be stored in the index + + """ + + _typ = 'categoricalindex' + _engine_type = _index.Int64Engine + _attributes = ['name','categories','ordered'] + + def __new__(cls, data=None, categories=None, ordered=None, dtype=None, copy=False, name=None, fastpath=False, **kwargs): + + if fastpath: + return cls._simple_new(data, name=name) + + if isinstance(data, ABCCategorical): + data = cls._create_categorical(cls, data, categories, ordered) + elif isinstance(data, CategoricalIndex): + data = data._data + data = cls._create_categorical(cls, data, categories, ordered) + else: + + # don't allow scalars + # if data is None, then categories must be provided + if lib.isscalar(data): + if data is not None or categories is None: + cls._scalar_data_error(data) + data = [] + data = cls._create_categorical(cls, data, categories, ordered) + + if copy: + data = data.copy() + + return cls._simple_new(data, name=name) + + def _create_from_codes(self, codes, categories=None, ordered=None, name=None): + """ + *this is an internal non-public method* + + create the correct categorical from codes + + Parameters + ---------- + codes : new codes + categories : optional categories, defaults to existing + ordered : optional ordered attribute, defaults to existing + name : optional name attribute, defaults to existing + + Returns + ------- + CategoricalIndex + """ + + from pandas.core.categorical import Categorical + if categories is None: + categories = self.categories + if ordered is None: + ordered = self.ordered + if name is None: + name = self.name + cat = Categorical.from_codes(codes, categories=categories, ordered=self.ordered) + return CategoricalIndex(cat, name=name) + + @staticmethod + def _create_categorical(self, data, categories=None, ordered=None): + """ + *this is an internal non-public method* + + create the correct categorical from data and the properties + + Parameters + ---------- + data : data for new Categorical + categories : optional categories, defaults to existing + ordered : optional ordered attribute, defaults to existing + + Returns + ------- + Categorical + """ + + if not isinstance(data, ABCCategorical): + from pandas.core.categorical import Categorical + data = Categorical(data, categories=categories, ordered=ordered) + else: + if categories is not None: + data = data.set_categories(categories) + if ordered is not None: + data = data.set_ordered(ordered) + return data + + @classmethod + def _simple_new(cls, values, name=None, categories=None, ordered=None, **kwargs): + result = object.__new__(cls) + + values = cls._create_categorical(cls, values, categories, ordered) + result._data = values + result.name = name + for k, v in compat.iteritems(kwargs): + setattr(result,k,v) + + result._reset_identity() + return result + + def _is_dtype_compat(self, other): + """ + *this is an internal non-public method* + + provide a comparison between the dtype of self and other (coercing if needed) + + Raises + ------ + TypeError if the dtypes are not compatible + """ + + if is_categorical_dtype(other): + if isinstance(other, CategoricalIndex): + other = other.values + if not other.is_dtype_equal(self): + raise TypeError("categories must match existing categories when appending") + else: + values = other + other = CategoricalIndex(self._create_categorical(self, other, categories=self.categories, ordered=self.ordered)) + if not other.isin(values).all(): + raise TypeError("cannot append a non-category item to a CategoricalIndex") + + return other + + def equals(self, other): + """ + Determines if two CategorialIndex objects contain the same elements. + """ + if self.is_(other): + return True + + try: + other = self._is_dtype_compat(other) + return array_equivalent(self._data, other) + except (TypeError, ValueError): + pass + + return False + + def __unicode__(self): + """ + Return a string representation for this object. + + Invoked by unicode(df) in py2 only. Yields a Unicode String in both + py2/py3. + """ + + # currently doesn't use the display.max_categories, or display.max_seq_len + # for head/tail printing + values = default_pprint(self.values.get_values()) + cats = default_pprint(self.categories.get_values()) + space = ' ' * (len(self.__class__.__name__) + 1) + name = self.name + if name is not None: + name = default_pprint(name) + + result = u("{klass}({values},\n{space}categories={categories},\n{space}ordered={ordered},\n{space}name={name})").format( + klass=self.__class__.__name__, + values=values, + categories=cats, + ordered=self.ordered, + name=name, + space=space) + + return result + + @property + def inferred_type(self): + return 'categorical' + + @property + def values(self): + """ return the underlying data, which is a Categorical """ + return self._data + + @property + def codes(self): + return self._data.codes + + @property + def categories(self): + return self._data.categories + + @property + def ordered(self): + return self._data.ordered + + def __contains__(self, key): + hash(key) + return key in self.values + + def __array__(self, dtype=None): + """ the array interface, return my values """ + return np.array(self._data, dtype=dtype) + + def argsort(self, *args, **kwargs): + return self.values.argsort(*args, **kwargs) + + @cache_readonly + def _engine(self): + + # we are going to look things up with the codes themselves + return self._engine_type(lambda: self.codes.astype('i8'), len(self)) + + @cache_readonly + def is_unique(self): + return not self.duplicated().any() + + @Appender(_shared_docs['duplicated'] % _index_doc_kwargs) + def duplicated(self, take_last=False): + from pandas.hashtable import duplicated_int64 + return duplicated_int64(self.codes.astype('i8'), take_last) + + def get_loc(self, key, method=None): + """ + Get integer location for requested label + + Parameters + ---------- + key : label + method : {None} + * default: exact matches only. + + Returns + ------- + loc : int if unique index, possibly slice or mask if not + """ + codes = self.categories.get_loc(key) + if (codes == -1): + raise KeyError(key) + indexer, _ = self._engine.get_indexer_non_unique(np.array([codes])) + if (indexer==-1).any(): + raise KeyError(key) + + return indexer + + def _can_reindex(self, indexer): + """ always allow reindexing """ + pass + + def reindex(self, target, method=None, level=None, limit=None): + """ + Create index with target's values (move/add/delete values as necessary) + + Returns + ------- + new_index : pd.Index + Resulting index + indexer : np.ndarray or None + Indices of output values in original index + + """ + + if method is not None: + raise NotImplementedError("argument method is not implemented for CategoricalIndex.reindex") + if level is not None: + raise NotImplementedError("argument level is not implemented for CategoricalIndex.reindex") + if limit is not None: + raise NotImplementedError("argument limit is not implemented for CategoricalIndex.reindex") + + target = _ensure_index(target) + + if not is_categorical_dtype(target) and not target.is_unique: + raise ValueError("cannot reindex with a non-unique indexer") + + indexer, missing = self.get_indexer_non_unique(np.array(target)) + new_target = self.take(indexer) + + + # filling in missing if needed + if len(missing): + cats = self.categories.get_indexer(target) + if (cats==-1).any(): + + # coerce to a regular index here! + result = Index(np.array(self),name=self.name) + new_target, indexer, _ = result._reindex_non_unique(np.array(target)) + + else: + + codes = new_target.codes.copy() + codes[indexer==-1] = cats[missing] + new_target = self._create_from_codes(codes) + + # we always want to return an Index type here + # to be consistent with .reindex for other index types (e.g. they don't coerce + # based on the actual values, only on the dtype) + # unless we had an inital Categorical to begin with + # in which case we are going to conform to the passed Categorical + new_target = np.asarray(new_target) + if is_categorical_dtype(target): + new_target = target._shallow_copy(new_target, name=self.name) + else: + new_target = Index(new_target, name=self.name) + + return new_target, indexer + + def _reindex_non_unique(self, target): + """ reindex from a non-unique; which CategoricalIndex's are almost always """ + new_target, indexer = self.reindex(target) + new_indexer = None + + check = indexer==-1 + if check.any(): + new_indexer = np.arange(len(self.take(indexer))) + new_indexer[check] = -1 + + return new_target, indexer, new_indexer + + def get_indexer(self, target, method=None, limit=None): + """ + Compute indexer and mask for new index given the current index. The + indexer should be then used as an input to ndarray.take to align the + current data to the new index. The mask determines whether labels are + found or not in the current index + + Parameters + ---------- + target : MultiIndex or Index (of tuples) + method : {'pad', 'ffill', 'backfill', 'bfill'} + pad / ffill: propagate LAST valid observation forward to next valid + backfill / bfill: use NEXT valid observation to fill gap + + Notes + ----- + This is a low-level method and probably should be used at your own risk + + Examples + -------- + >>> indexer, mask = index.get_indexer(new_index) + >>> new_values = cur_values.take(indexer) + >>> new_values[-mask] = np.nan + + Returns + ------- + (indexer, mask) : (ndarray, ndarray) + """ + method = com._clean_reindex_fill_method(method) + target = _ensure_index(target) + + if isinstance(target, CategoricalIndex): + target = target.categories + + if method == 'pad' or method == 'backfill': + raise NotImplementedError("method='pad' and method='backfill' not implemented yet " + 'for CategoricalIndex') + elif method == 'nearest': + raise NotImplementedError("method='nearest' not implemented yet " + 'for CategoricalIndex') + else: + + codes = self.categories.get_indexer(target) + indexer, _ = self._engine.get_indexer_non_unique(codes) + + return com._ensure_platform_int(indexer) + + def get_indexer_non_unique(self, target): + """ this is the same for a CategoricalIndex for get_indexer; the API returns the missing values as well """ + target = _ensure_index(target) + + if isinstance(target, CategoricalIndex): + target = target.categories + + codes = self.categories.get_indexer(target) + return self._engine.get_indexer_non_unique(codes) + + def _convert_list_indexer(self, keyarr, kind=None): + """ + we are passed a list indexer. + Return our indexer or raise if all of the values are not included in the categories + """ + codes = self.categories.get_indexer(keyarr) + if (codes==-1).any(): + raise KeyError("a list-indexer must only include values that are in the categories") + + return None + + def take(self, indexer, axis=0): + """ + return a new CategoricalIndex of the values selected by the indexer + + See also + -------- + numpy.ndarray.take + """ + + indexer = com._ensure_platform_int(indexer) + taken = self.codes.take(indexer) + return self._create_from_codes(taken) + + def delete(self, loc): + """ + Make new Index with passed location(-s) deleted + + Returns + ------- + new_index : Index + """ + return self._create_from_codes(np.delete(self.codes, loc)) + + def insert(self, loc, item): + """ + Make new Index inserting new item at location. Follows + Python list.append semantics for negative values + + Parameters + ---------- + loc : int + item : object + + Returns + ------- + new_index : Index + + Raises + ------ + ValueError if the item is not in the categories + + """ + code = self.categories.get_indexer([item]) + if (code == -1): + raise TypeError("cannot insert an item into a CategoricalIndex that is not already an existing category") + + codes = self.codes + codes = np.concatenate( + (codes[:loc], code, codes[loc:])) + return self._create_from_codes(codes) + + def append(self, other): + """ + Append a collection of CategoricalIndex options together + + Parameters + ---------- + other : Index or list/tuple of indices + + Returns + ------- + appended : Index + + Raises + ------ + ValueError if other is not in the categories + """ + to_concat, name = self._ensure_compat_append(other) + to_concat = [ self._is_dtype_compat(c) for c in to_concat ] + codes = np.concatenate([ c.codes for c in to_concat ]) + return self._create_from_codes(codes, name=name) + + @classmethod + def _add_comparison_methods(cls): + """ add in comparison methods """ + + def _make_compare(op): + + def _evaluate_compare(self, other): + + # if we have a Categorical type, then must have the same categories + if isinstance(other, CategoricalIndex): + other = other.values + elif isinstance(other, Index): + other = self._create_categorical(self, other.values, categories=self.categories, ordered=self.ordered) + + if isinstance(other, ABCCategorical): + if not (self.values.is_dtype_equal(other) and len(self.values) == len(other)): + raise TypeError("categorical index comparisions must have the same categories and ordered attributes") + + return getattr(self.values, op)(other) + + return _evaluate_compare + + cls.__eq__ = _make_compare('__eq__') + cls.__ne__ = _make_compare('__ne__') + cls.__lt__ = _make_compare('__lt__') + cls.__gt__ = _make_compare('__gt__') + cls.__le__ = _make_compare('__le__') + cls.__ge__ = _make_compare('__ge__') + + + def _delegate_method(self, name, *args, **kwargs): + """ method delegation to the .values """ + method = getattr(self.values, name) + if 'inplace' in kwargs: + raise ValueError("cannot use inplace with CategoricalIndex") + res = method(*args, **kwargs) + if lib.isscalar(res): + return res + return CategoricalIndex(res, name=self.name) + + @classmethod + def _add_accessors(cls): + """ add in Categorical accessor methods """ + + from pandas.core.categorical import Categorical + CategoricalIndex._add_delegate_accessors(delegate=Categorical, + accessors=["rename_categories", + "reorder_categories", + "add_categories", + "remove_categories", + "remove_unused_categories", + "set_categories", + "as_ordered", + "as_unordered", + "min", + "max"], + typ='method', + overwrite=True) + + +CategoricalIndex._add_numeric_methods_disabled() +CategoricalIndex._add_logical_methods_disabled() +CategoricalIndex._add_comparison_methods() +CategoricalIndex._add_accessors() class NumericIndex(Index): @@ -2791,7 +3424,7 @@ def equals(self, other): try: if not isinstance(other, Float64Index): other = self._constructor(other) - if self.dtype != other.dtype or self.shape != other.shape: + if not is_dtype_equal(self.dtype,other.dtype) or self.shape != other.shape: return False left, right = self.values, other.values return ((left == right) | (self._isnan & other._isnan)).all() @@ -2857,7 +3490,7 @@ def isin(self, values, level=None): value_set = set(values) if level is not None: self._validate_index_level(level) - return lib.ismember_nans(self._array_values(), value_set, + return lib.ismember_nans(np.array(self), value_set, isnull(list(value_set)).any()) @@ -3197,7 +3830,7 @@ def copy(self, names=None, dtype=None, levels=None, labels=None, verify_integrity=False, _set_identity=_set_identity) - def __array__(self, result=None): + def __array__(self, dtype=None): """ the array interface, return my values """ return self.values @@ -3209,10 +3842,6 @@ def view(self, cls=None): _shallow_copy = view - def _array_values(self): - # hack for various methods - return self.values - @cache_readonly def dtype(self): return np.dtype('O') @@ -3359,7 +3988,7 @@ def values(self): taken = com.take_1d(lev._box_values(lev.values), lab, fill_value=_get_na_value(lev.dtype.type)) else: - taken = com.take_1d(lev.values, lab) + taken = com.take_1d(np.asarray(lev.values), lab) values.append(taken) self._tuples = lib.fast_zip(values) @@ -3424,7 +4053,7 @@ def _try_mi(k): raise except TypeError: # generator/iterator-like - if com.is_iterator(key): + if is_iterator(key): raise InvalidIndexError(key) else: raise e1 @@ -4095,7 +4724,7 @@ def get_indexer(self, target, method=None, limit=None): if isinstance(target, MultiIndex): target_index = target._tuple_index - if target_index.dtype != object: + if not is_object_dtype(target_index.dtype): return np.ones(len(target_index)) * -1 if not self.is_unique: @@ -4654,9 +5283,9 @@ def equals(self, other): return False for i in range(self.nlevels): - svalues = com.take_nd(self.levels[i].values, self.labels[i], + svalues = com.take_nd(np.asarray(self.levels[i].values), self.labels[i], allow_fill=False) - ovalues = com.take_nd(other.levels[i].values, other.labels[i], + ovalues = com.take_nd(np.asarray(other.levels[i].values), other.labels[i], allow_fill=False) if not array_equivalent(svalues, ovalues): return False @@ -4772,7 +5401,7 @@ def _assert_can_do_setop(self, other): pass def astype(self, dtype): - if np.dtype(dtype) != np.object_: + if not is_object_dtype(np.dtype(dtype)): raise TypeError('Setting %s dtype to anything other than object ' 'is not supported' % self.__class__) return self._shallow_copy() @@ -4852,7 +5481,7 @@ def _wrap_joined_index(self, joined, other): @Appender(Index.isin.__doc__) def isin(self, values, level=None): if level is None: - return lib.ismember(self._array_values(), set(values)) + return lib.ismember(np.array(self), set(values)) else: num = self._get_level_number(level) levs = self.levels[num] diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index 8154eb1bb6c8b..41950bf8b0e88 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -253,7 +253,7 @@ def _setitem_with_indexer(self, indexer, value): # just replacing the block manager here # so the object is the same index = self.obj._get_axis(i) - labels = safe_append_to_index(index, key) + labels = index.insert(len(index),key) self.obj._data = self.obj.reindex_axis(labels, i)._data self.obj._maybe_update_cacher(clear=True) self.obj.is_copy=None @@ -274,10 +274,7 @@ def _setitem_with_indexer(self, indexer, value): # and set inplace if self.ndim == 1: index = self.obj.index - if len(index) == 0: - new_index = Index([indexer]) - else: - new_index = safe_append_to_index(index, indexer) + new_index = index.insert(len(index),indexer) # this preserves dtype of the value new_values = Series([value]).values @@ -928,24 +925,6 @@ def _getitem_iterable(self, key, axis=0): labels = self.obj._get_axis(axis) - def _reindex(keys, level=None): - - try: - result = self.obj.reindex_axis(keys, axis=axis, level=level) - except AttributeError: - # Series - if axis != 0: - raise AssertionError('axis must be 0') - return self.obj.reindex(keys, level=level) - - # this is an error as we are trying to find - # keys in a multi-index that don't exist - if isinstance(labels, MultiIndex) and level is not None: - if hasattr(result,'ndim') and not np.prod(result.shape) and len(keys): - raise KeyError("cannot index a multi-index axis with these keys") - - return result - if is_bool_indexer(key): key = check_bool_indexer(labels, key) inds, = key.nonzero() @@ -958,8 +937,9 @@ def _reindex(keys, level=None): # asarray can be unsafe, NumPy strings are weird keyarr = _asarray_tuplesafe(key) - # handle a mixed integer scenario - indexer = labels._convert_list_indexer_for_mixed(keyarr, kind=self.name) + # have the index handle the indexer and possibly return + # an indexer or raising + indexer = labels._convert_list_indexer(keyarr, kind=self.name) if indexer is not None: return self.obj.take(indexer, axis=axis) @@ -970,65 +950,48 @@ def _reindex(keys, level=None): else: level = None - keyarr_is_unique = Index(keyarr).is_unique + # existing labels are unique and indexer are unique + if labels.is_unique and Index(keyarr).is_unique: + + try: + result = self.obj.reindex_axis(keyarr, axis=axis, level=level) + + # this is an error as we are trying to find + # keys in a multi-index that don't exist + if isinstance(labels, MultiIndex) and level is not None: + if hasattr(result,'ndim') and not np.prod(result.shape) and len(keyarr): + raise KeyError("cannot index a multi-index axis with these keys") + + return result - # existing labels are unique and indexer is unique - if labels.is_unique and keyarr_is_unique: - return _reindex(keyarr, level=level) + except AttributeError: + # Series + if axis != 0: + raise AssertionError('axis must be 0') + return self.obj.reindex(keyarr, level=level) + + # existing labels are non-unique else: - indexer, missing = labels.get_indexer_non_unique(keyarr) - check = indexer != -1 - result = self.obj.take(indexer[check], axis=axis, - convert=False) - - # need to merge the result labels and the missing labels - if len(missing): - l = np.arange(len(indexer)) - - missing = com._ensure_platform_int(missing) - missing_labels = keyarr.take(missing) - missing_indexer = com._ensure_int64(l[~check]) - cur_labels = result._get_axis(axis).values - cur_indexer = com._ensure_int64(l[check]) - - new_labels = np.empty(tuple([len(indexer)]), dtype=object) - new_labels[cur_indexer] = cur_labels - new_labels[missing_indexer] = missing_labels - - # reindex with the specified axis - ndim = self.obj.ndim - if axis + 1 > ndim: - raise AssertionError("invalid indexing error with " - "non-unique index") - - # a unique indexer - if keyarr_is_unique: - - # see GH5553, make sure we use the right indexer - new_indexer = np.arange(len(indexer)) - new_indexer[cur_indexer] = np.arange( - len(result._get_axis(axis)) - ) - new_indexer[missing_indexer] = -1 - # we have a non_unique selector, need to use the original - # indexer here - else: + # reindex with the specified axis + if axis + 1 > self.obj.ndim: + raise AssertionError("invalid indexing error with " + "non-unique index") - # need to retake to have the same size as the indexer - rindexer = indexer.values - rindexer[~check] = 0 - result = self.obj.take(rindexer, axis=axis, - convert=False) + new_target, indexer, new_indexer = labels._reindex_non_unique(keyarr) - # reset the new indexer to account for the new size - new_indexer = np.arange(len(result)) - new_indexer[~check] = -1 + if new_indexer is not None: + result = self.obj.take(indexer[indexer!=-1], axis=axis, + convert=False) result = result._reindex_with_indexers({ - axis: [new_labels, new_indexer] - }, copy=True, allow_dups=True) + axis: [new_target, new_indexer] + }, copy=True, allow_dups=True) + + else: + result = self.obj.take(indexer, axis=axis, + convert=False) return result @@ -1105,8 +1068,9 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False): else: objarr = _asarray_tuplesafe(obj) - # If have integer labels, defer to label-based indexing - indexer = labels._convert_list_indexer_for_mixed(objarr, kind=self.name) + # The index may want to handle a list indexer differently + # by returning an indexer or raising + indexer = labels._convert_list_indexer(objarr, kind=self.name) if indexer is not None: return indexer @@ -1719,19 +1683,6 @@ def get_indexer(_i, _idx): return tuple([get_indexer(_i, _idx) for _i, _idx in enumerate(indexer)]) -def safe_append_to_index(index, key): - """ a safe append to an index, if incorrect type, then catch and recreate - """ - try: - return index.insert(len(index), key) - except: - - # raise here as this is basically an unsafe operation and we want - # it to be obvious that you are doing something wrong - raise ValueError("unsafe appending to index of type {0} with a key " - "{1}".format(index.__class__.__name__, key)) - - def maybe_convert_indices(indices, n): """ if we have negative indicies, translate to postive here if have indicies that are out-of-bounds, raise an IndexError diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 864dc0dd46de2..440892f8e8b59 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3134,7 +3134,6 @@ def reindex_indexer(self, new_axis, indexer, axis, fill_value=None, pandas-indexer with -1's only. """ - if indexer is None: if new_axis is self.axes[axis] and not copy: return self @@ -3146,10 +3145,9 @@ def reindex_indexer(self, new_axis, indexer, axis, fill_value=None, self._consolidate_inplace() - # trying to reindex on an axis with duplicates - if (not allow_dups and not self.axes[axis].is_unique - and len(indexer)): - raise ValueError("cannot reindex from a duplicate axis") + # some axes don't allow reindexing with dups + if not allow_dups: + self.axes[axis]._can_reindex(indexer) if axis >= self.ndim: raise IndexError("Requested axis not found in manager") diff --git a/pandas/core/series.py b/pandas/core/series.py index 7bcf6c6671152..685d44acafe53 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2594,8 +2594,9 @@ def _try_cast(arr, take_fast_path): # GH #846 if isinstance(data, (np.ndarray, Index, Series)): - subarr = np.array(data, copy=False) + if dtype is not None: + subarr = np.array(data, copy=False) # possibility of nan -> garbage if com.is_float_dtype(data.dtype) and com.is_integer_dtype(dtype): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index af48774492b11..97fa442595893 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -11,7 +11,7 @@ import numpy as np import pandas as pd -from pandas import Categorical, Index, Series, DataFrame, PeriodIndex, Timestamp +from pandas import Categorical, Index, Series, DataFrame, PeriodIndex, Timestamp, CategoricalIndex from pandas.core.config import option_context import pandas.core.common as com @@ -93,6 +93,24 @@ def test_constructor_unsortable(self): else: Categorical.from_array(arr, ordered=True) + def test_is_equal_dtype(self): + + # test dtype comparisons between cats + + c1 = Categorical(list('aabca'),categories=list('abc'),ordered=False) + c2 = Categorical(list('aabca'),categories=list('cab'),ordered=False) + c3 = Categorical(list('aabca'),categories=list('cab'),ordered=True) + self.assertTrue(c1.is_dtype_equal(c1)) + self.assertTrue(c2.is_dtype_equal(c2)) + self.assertTrue(c3.is_dtype_equal(c3)) + self.assertFalse(c1.is_dtype_equal(c2)) + self.assertFalse(c1.is_dtype_equal(c3)) + self.assertFalse(c1.is_dtype_equal(Index(list('aabca')))) + self.assertFalse(c1.is_dtype_equal(c1.astype(object))) + self.assertTrue(c1.is_dtype_equal(CategoricalIndex(c1))) + self.assertFalse(c1.is_dtype_equal(CategoricalIndex(c1,categories=list('cab')))) + self.assertFalse(c1.is_dtype_equal(CategoricalIndex(c1,ordered=True))) + def test_constructor(self): exp_arr = np.array(["a", "b", "c", "a", "b", "c"]) @@ -224,6 +242,18 @@ def f(): c_old2 = Categorical([0, 1, 2, 0, 1, 2], [1, 2, 3]) cat = Categorical([1,2], categories=[1,2,3]) + # this is a legitimate constructor + with tm.assert_produces_warning(None): + c = Categorical(np.array([],dtype='int64'),categories=[3,2,1],ordered=True) + + def test_constructor_with_index(self): + + ci = CategoricalIndex(list('aabbca'),categories=list('cab')) + self.assertTrue(ci.values.equals(Categorical(ci))) + + ci = CategoricalIndex(list('aabbca'),categories=list('cab')) + self.assertTrue(ci.values.equals(Categorical(ci.astype(object),categories=ci.categories))) + def test_constructor_with_generator(self): # This was raising an Error in isnull(single_val).any() because isnull returned a scalar # for a generator @@ -2562,6 +2592,8 @@ def f(): dfx['grade'].cat.categories self.assert_numpy_array_equal(df['grade'].cat.categories, dfx['grade'].cat.categories) + def test_concat_preserve(self): + # GH 8641 # series concat not preserving category dtype s = Series(list('abc'),dtype='category') @@ -2579,6 +2611,28 @@ def f(): expected = Series(list('abcabc'),index=[0,1,2,0,1,2]).astype('category') tm.assert_series_equal(result, expected) + a = Series(np.arange(6,dtype='int64')) + b = Series(list('aabbca')) + + df2 = DataFrame({'A' : a, 'B' : b.astype('category',categories=list('cab')) }) + result = pd.concat([df2,df2]) + expected = DataFrame({'A' : pd.concat([a,a]), 'B' : pd.concat([b,b]).astype('category',categories=list('cab')) }) + tm.assert_frame_equal(result, expected) + + def test_categorical_index_preserver(self): + + a = Series(np.arange(6,dtype='int64')) + b = Series(list('aabbca')) + + df2 = DataFrame({'A' : a, 'B' : b.astype('category',categories=list('cab')) }).set_index('B') + result = pd.concat([df2,df2]) + expected = DataFrame({'A' : pd.concat([a,a]), 'B' : pd.concat([b,b]).astype('category',categories=list('cab')) }).set_index('B') + tm.assert_frame_equal(result, expected) + + # wrong catgories + df3 = DataFrame({'A' : a, 'B' : b.astype('category',categories=list('abc')) }).set_index('B') + self.assertRaises(TypeError, lambda : pd.concat([df2,df3])) + def test_append(self): cat = pd.Categorical(["a","b"], categories=["a","b"]) vals = [1,2] @@ -2714,6 +2768,14 @@ def cmp(a,b): self.assertRaises(TypeError, lambda : invalid(s)) + def test_astype_categorical(self): + + cat = Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c']) + tm.assert_categorical_equal(cat,cat.astype('category')) + tm.assert_almost_equal(np.array(cat),cat.astype('object')) + + self.assertRaises(ValueError, lambda : cat.astype(float)) + def test_to_records(self): # GH8626 diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index a35e03d53cb31..5912ccb1494fe 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -33,7 +33,7 @@ import pandas.core.datetools as datetools from pandas import (DataFrame, Index, Series, Panel, notnull, isnull, MultiIndex, DatetimeIndex, Timestamp, date_range, - read_csv, timedelta_range, Timedelta, + read_csv, timedelta_range, Timedelta, CategoricalIndex, option_context) import pandas as pd from pandas.parser import CParserError @@ -2386,6 +2386,32 @@ def test_set_index_pass_arrays(self): expected = df.set_index(['A', 'B'], drop=False) assert_frame_equal(result, expected, check_names=False) # TODO should set_index check_names ? + def test_construction_with_categorical_index(self): + + ci = tm.makeCategoricalIndex(10) + + # with Categorical + df = DataFrame({'A' : np.random.randn(10), + 'B' : ci.values }) + idf = df.set_index('B') + str(idf) + tm.assert_index_equal(idf.index,ci) + + # from a CategoricalIndex + df = DataFrame({'A' : np.random.randn(10), + 'B' : ci }) + idf = df.set_index('B') + str(idf) + tm.assert_index_equal(idf.index,ci) + + idf = df.set_index('B').reset_index().set_index('B') + str(idf) + tm.assert_index_equal(idf.index,ci) + + new_df = idf.reset_index() + new_df.index = df.B + tm.assert_index_equal(new_df.index,ci) + def test_set_index_cast_datetimeindex(self): df = DataFrame({'A': [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)], @@ -10744,6 +10770,19 @@ def test_sort_index(self): with assertRaisesRegexp(ValueError, msg): frame.sort_index(by=['A', 'B'], axis=0, ascending=[True] * 5) + def test_sort_index_categorical_index(self): + + df = DataFrame({'A' : np.arange(6,dtype='int64'), + 'B' : Series(list('aabbca')).astype('category',categories=list('cab')) }).set_index('B') + + result = df.sort_index() + expected = df.iloc[[4,0,1,5,2,3]] + assert_frame_equal(result, expected) + + result = df.sort_index(ascending=False) + expected = df.iloc[[3,2,5,1,0,4]] + assert_frame_equal(result, expected) + def test_sort_nan(self): # GH3917 nan = np.nan diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 87536b9bf0ff8..c5a338520df21 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -8,7 +8,7 @@ from numpy import nan from pandas import date_range,bdate_range, Timestamp -from pandas.core.index import Index, MultiIndex, Int64Index +from pandas.core.index import Index, MultiIndex, Int64Index, CategoricalIndex from pandas.core.api import Categorical, DataFrame from pandas.core.groupby import (SpecificationError, DataError, _nargsort, _lexsort_indexer) @@ -3378,12 +3378,11 @@ def test_groupby_datetime_categorical(self): cats = Categorical.from_codes(codes, levels, name='myfactor', ordered=True) data = DataFrame(np.random.randn(100, 4)) - result = data.groupby(cats).mean() expected = data.groupby(np.asarray(cats)).mean() expected = expected.reindex(levels) - expected.index.name = 'myfactor' + expected.index = CategoricalIndex(expected.index,categories=expected.index,name='myfactor',ordered=True) assert_frame_equal(result, expected) self.assertEqual(result.index.name, cats.name) @@ -3398,6 +3397,26 @@ def test_groupby_datetime_categorical(self): expected.index.names = ['myfactor', None] assert_frame_equal(desc_result, expected) + def test_groupby_categorical_index(self): + + levels = ['foo', 'bar', 'baz', 'qux'] + codes = np.random.randint(0, 4, size=20) + cats = Categorical.from_codes(codes, levels, name='myfactor', ordered=True) + df = DataFrame(np.repeat(np.arange(20),4).reshape(-1,4), columns=list('abcd')) + df['cats'] = cats + + # with a cat index + result = df.set_index('cats').groupby(level=0).sum() + expected = df[list('abcd')].groupby(cats.codes).sum() + expected.index = CategoricalIndex(Categorical.from_codes([0,1,2,3], levels, ordered=True),name='cats') + assert_frame_equal(result, expected) + + # with a cat column, should produce a cat index + result = df.groupby('cats').sum() + expected = df[list('abcd')].groupby(cats.codes).sum() + expected.index = CategoricalIndex(Categorical.from_codes([0,1,2,3], levels, ordered=True),name='cats') + assert_frame_equal(result, expected) + def test_groupby_groups_datetimeindex(self): # #1430 from pandas.tseries.api import DatetimeIndex @@ -3526,6 +3545,8 @@ def test_groupby_categorical_no_compress(self): result = data.groupby(cats).mean() exp = data.groupby(codes).mean() + + exp.index = CategoricalIndex(exp.index,categories=cats.categories,ordered=cats.ordered) assert_series_equal(result, exp) codes = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3]) @@ -3533,6 +3554,7 @@ def test_groupby_categorical_no_compress(self): result = data.groupby(cats).mean() exp = data.groupby(codes).mean().reindex(cats.categories) + exp.index = CategoricalIndex(exp.index,categories=cats.categories,ordered=cats.ordered) assert_series_equal(result, exp) cats = Categorical(["a", "a", "a", "b", "b", "b", "c", "c", "c"], diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 336340dd95991..1d59d1f3fbfe3 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -12,14 +12,10 @@ import numpy as np from numpy.testing import assert_array_equal -from pandas import period_range, date_range - -from pandas.core.index import (Index, Float64Index, Int64Index, MultiIndex, - InvalidIndexError, NumericIndex) -from pandas.tseries.index import DatetimeIndex -from pandas.tseries.tdi import TimedeltaIndex -from pandas.tseries.period import PeriodIndex -from pandas.core.series import Series +from pandas import (period_range, date_range, Categorical, Series, + Index, Float64Index, Int64Index, MultiIndex, + CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex) +from pandas.core.index import InvalidIndexError, NumericIndex from pandas.util.testing import (assert_almost_equal, assertRaisesRegexp, assert_copy) from pandas import compat @@ -41,6 +37,11 @@ class Base(object): _holder = None _compat_props = ['shape', 'ndim', 'size', 'itemsize', 'nbytes'] + def setup_indices(self): + # setup the test indices in the self.indicies dict + for name, ind in self.indices.items(): + setattr(self, name, ind) + def verify_pickle(self,index): unpickled = self.round_trip_pickle(index) self.assertTrue(index.equals(unpickled)) @@ -98,6 +99,7 @@ def f(): def test_reindex_base(self): idx = self.create_index() expected = np.arange(idx.size) + actual = idx.get_indexer(idx) assert_array_equal(expected, actual) @@ -118,29 +120,6 @@ def test_ndarray_compat_properties(self): idx.nbytes idx.values.nbytes - -class TestIndex(Base, tm.TestCase): - _holder = Index - _multiprocess_can_split_ = True - - def setUp(self): - self.indices = dict( - unicodeIndex = tm.makeUnicodeIndex(100), - strIndex = tm.makeStringIndex(100), - dateIndex = tm.makeDateIndex(100), - intIndex = tm.makeIntIndex(100), - floatIndex = tm.makeFloatIndex(100), - boolIndex = Index([True,False]), - empty = Index([]), - tuples = MultiIndex.from_tuples(lzip(['foo', 'bar', 'baz'], - [1, 2, 3])) - ) - for name, ind in self.indices.items(): - setattr(self, name, ind) - - def create_index(self): - return Index(list('abcde')) - def test_wrong_number_names(self): def testit(ind): ind.names = ["apple", "banana", "carrot"] @@ -150,14 +129,18 @@ def testit(ind): def test_set_name_methods(self): new_name = "This is the new name for this index" - indices = (self.dateIndex, self.intIndex, self.unicodeIndex, - self.empty) - for ind in indices: + for ind in self.indices.values(): + + # don't tests a MultiIndex here (as its tested separated) + if isinstance(ind, MultiIndex): + continue + original_name = ind.name new_ind = ind.set_names([new_name]) self.assertEqual(new_ind.name, new_name) self.assertEqual(ind.name, original_name) res = ind.rename(new_name, inplace=True) + # should return None self.assertIsNone(res) self.assertEqual(ind.name, new_name) @@ -167,46 +150,128 @@ def test_set_name_methods(self): # ind.set_names("a") with assertRaisesRegexp(ValueError, "Level must be None"): ind.set_names("a", level=0) - # rename in place just leaves tuples and other containers alone - name = ('A', 'B') - ind = self.intIndex - ind.rename(name, inplace=True) - self.assertEqual(ind.name, name) - self.assertEqual(ind.names, [name]) - def test_hash_error(self): - with tm.assertRaisesRegexp(TypeError, - "unhashable type: %r" % - type(self.strIndex).__name__): - hash(self.strIndex) + # rename in place just leaves tuples and other containers alone + name = ('A', 'B') + ind.rename(name, inplace=True) + self.assertEqual(ind.name, name) + self.assertEqual(ind.names, [name]) - def test_new_axis(self): - new_index = self.dateIndex[None, :] - self.assertEqual(new_index.ndim, 2) - tm.assert_isinstance(new_index, np.ndarray) + def test_hash_error(self): + for ind in self.indices.values(): + with tm.assertRaisesRegexp(TypeError, + "unhashable type: %r" % + type(ind).__name__): + hash(ind) def test_copy_and_deepcopy(self): from copy import copy, deepcopy - for func in (copy, deepcopy): - idx_copy = func(self.strIndex) - self.assertIsNot(idx_copy, self.strIndex) - self.assertTrue(idx_copy.equals(self.strIndex)) + for ind in self.indices.values(): - new_copy = self.strIndex.copy(deep=True, name="banana") - self.assertEqual(new_copy.name, "banana") - new_copy2 = self.intIndex.copy(dtype=int) - self.assertEqual(new_copy2.dtype.kind, 'i') + # don't tests a MultiIndex here (as its tested separated) + if isinstance(ind, MultiIndex): + continue + + for func in (copy, deepcopy): + idx_copy = func(ind) + self.assertIsNot(idx_copy, ind) + self.assertTrue(idx_copy.equals(ind)) + + new_copy = ind.copy(deep=True, name="banana") + self.assertEqual(new_copy.name, "banana") def test_duplicates(self): - idx = Index([0, 0, 0]) - self.assertFalse(idx.is_unique) + for ind in self.indices.values(): + + if not len(ind): + continue + idx = self._holder([ind[0]]*5) + self.assertFalse(idx.is_unique) + self.assertTrue(idx.has_duplicates) def test_sort(self): - self.assertRaises(TypeError, self.strIndex.sort) + for ind in self.indices.values(): + self.assertRaises(TypeError, ind.sort) def test_mutability(self): - self.assertRaises(TypeError, self.strIndex.__setitem__, 0, 'foo') + for ind in self.indices.values(): + if not len(ind): + continue + self.assertRaises(TypeError, ind.__setitem__, 0, ind[0]) + + def test_view(self): + for ind in self.indices.values(): + i_view = ind.view() + self.assertEqual(i_view.name, ind.name) + + def test_compat(self): + for ind in self.indices.values(): + self.assertEqual(ind.tolist(),list(ind)) + + def test_argsort(self): + for k, ind in self.indices.items(): + + # sep teststed + if k in ['catIndex']: + continue + + result = ind.argsort() + expected = np.array(ind).argsort() + self.assert_numpy_array_equal(result, expected) + + def test_pickle(self): + for ind in self.indices.values(): + self.verify_pickle(ind) + ind.name = 'foo' + self.verify_pickle(ind) + + def test_take(self): + indexer = [4, 3, 0, 2] + for k, ind in self.indices.items(): + + # separate + if k in ['boolIndex','tuples','empty']: + continue + + result = ind.take(indexer) + expected = ind[indexer] + self.assertTrue(result.equals(expected)) + +class TestIndex(Base, tm.TestCase): + _holder = Index + _multiprocess_can_split_ = True + + def setUp(self): + self.indices = dict( + unicodeIndex = tm.makeUnicodeIndex(100), + strIndex = tm.makeStringIndex(100), + dateIndex = tm.makeDateIndex(100), + periodIndex = tm.makePeriodIndex(100), + tdIndex = tm.makeTimedeltaIndex(100), + intIndex = tm.makeIntIndex(100), + floatIndex = tm.makeFloatIndex(100), + boolIndex = Index([True,False]), + catIndex = tm.makeCategoricalIndex(100), + empty = Index([]), + tuples = MultiIndex.from_tuples(lzip(['foo', 'bar', 'baz'], + [1, 2, 3])) + ) + self.setup_indices() + + def create_index(self): + return Index(list('abcde')) + + def test_new_axis(self): + new_index = self.dateIndex[None, :] + self.assertEqual(new_index.ndim, 2) + tm.assert_isinstance(new_index, np.ndarray) + + def test_copy_and_deepcopy(self): + super(TestIndex, self).test_copy_and_deepcopy() + + new_copy2 = self.intIndex.copy(dtype=int) + self.assertEqual(new_copy2.dtype.kind, 'i') def test_constructor(self): # regular instance creation @@ -297,18 +362,22 @@ def test_constructor_simple_new(self): result = idx._simple_new(idx, 'obj') self.assertTrue(result.equals(idx)) - def test_copy(self): - i = Index([], name='Foo') - i_copy = i.copy() - self.assertEqual(i_copy.name, 'Foo') + def test_view_with_args(self): - def test_view(self): - i = Index([], name='Foo') - i_view = i.view() - self.assertEqual(i_view.name, 'Foo') + restricted = ['unicodeIndex','strIndex','catIndex','boolIndex','empty'] + + for i in restricted: + ind = self.indices[i] - # with arguments - self.assertRaises(TypeError, lambda : i.view('i8')) + # with arguments + self.assertRaises(TypeError, lambda : ind.view('i8')) + + # these are ok + for i in list(set(self.indices.keys())-set(restricted)): + ind = self.indices[i] + + # with arguments + ind.view('i8') def test_legacy_pickle_identity(self): @@ -330,9 +399,6 @@ def test_astype(self): casted = self.intIndex.astype('i8') self.assertEqual(casted.name, 'foobar') - def test_compat(self): - self.strIndex.tolist() - def test_equals(self): # same self.assertTrue(Index(['a', 'b', 'c']).equals(Index(['a', 'b', 'c']))) @@ -459,11 +525,6 @@ def test_nanosecond_index_access(self): self.assertEqual(first_value, x[Timestamp(np.datetime64('2013-01-01 00:00:00.000000050+0000', 'ns'))]) - def test_argsort(self): - result = self.strIndex.argsort() - expected = np.array(self.strIndex).argsort() - self.assert_numpy_array_equal(result, expected) - def test_comparators(self): index = self.dateIndex element = index[len(index) // 2] @@ -760,22 +821,17 @@ def test_symmetric_diff(self): with tm.assertRaises(TypeError): Index(idx1,dtype='object') - 1 - def test_pickle(self): - - self.verify_pickle(self.strIndex) - self.strIndex.name = 'foo' - self.verify_pickle(self.strIndex) - self.verify_pickle(self.dateIndex) - def test_is_numeric(self): self.assertFalse(self.dateIndex.is_numeric()) self.assertFalse(self.strIndex.is_numeric()) self.assertTrue(self.intIndex.is_numeric()) self.assertTrue(self.floatIndex.is_numeric()) + self.assertFalse(self.catIndex.is_numeric()) def test_is_object(self): self.assertTrue(self.strIndex.is_object()) self.assertTrue(self.boolIndex.is_object()) + self.assertFalse(self.catIndex.is_object()) self.assertFalse(self.intIndex.is_object()) self.assertFalse(self.dateIndex.is_object()) self.assertFalse(self.floatIndex.is_object()) @@ -839,12 +895,6 @@ def test_format_none(self): idx.format() self.assertIsNone(idx[3]) - def test_take(self): - indexer = [4, 3, 0, 2] - result = self.dateIndex.take(indexer) - expected = self.dateIndex[indexer] - self.assertTrue(result.equals(expected)) - def test_logical_compat(self): idx = self.create_index() self.assertEqual(idx.all(), idx.values.all()) @@ -857,6 +907,7 @@ def _check_method_works(self, method): method(self.strIndex) method(self.intIndex) method(self.tuples) + method(self.catIndex) def test_get_indexer(self): idx1 = Index([1, 2, 3, 4, 5]) @@ -1338,6 +1389,352 @@ def test_equals_op(self): index_b == index_a, ) +class TestCategoricalIndex(Base, tm.TestCase): + _holder = CategoricalIndex + + def setUp(self): + self.indices = dict(catIndex = tm.makeCategoricalIndex(100)) + self.setup_indices() + + def create_index(self, categories=None, ordered=False): + if categories is None: + categories = list('cab') + return CategoricalIndex(list('aabbca'), categories=categories, ordered=ordered) + + def test_construction(self): + + ci = self.create_index(categories=list('abcd')) + categories = ci.categories + + result = Index(ci) + tm.assert_index_equal(result,ci,exact=True) + self.assertFalse(result.ordered) + + result = Index(ci.values) + tm.assert_index_equal(result,ci,exact=True) + self.assertFalse(result.ordered) + + # empty + result = CategoricalIndex(categories=categories) + self.assertTrue(result.categories.equals(Index(categories))) + self.assert_numpy_array_equal(result.codes,np.array([],dtype='int8')) + self.assertFalse(result.ordered) + + # passing categories + result = CategoricalIndex(list('aabbca'),categories=categories) + self.assertTrue(result.categories.equals(Index(categories))) + self.assert_numpy_array_equal(result.codes,np.array([0,0,1,1,2,0],dtype='int8')) + + c = pd.Categorical(list('aabbca')) + result = CategoricalIndex(c) + self.assertTrue(result.categories.equals(Index(list('abc')))) + self.assert_numpy_array_equal(result.codes,np.array([0,0,1,1,2,0],dtype='int8')) + self.assertFalse(result.ordered) + + result = CategoricalIndex(c,categories=categories) + self.assertTrue(result.categories.equals(Index(categories))) + self.assert_numpy_array_equal(result.codes,np.array([0,0,1,1,2,0],dtype='int8')) + self.assertFalse(result.ordered) + + ci = CategoricalIndex(c,categories=list('abcd')) + result = CategoricalIndex(ci) + self.assertTrue(result.categories.equals(Index(categories))) + self.assert_numpy_array_equal(result.codes,np.array([0,0,1,1,2,0],dtype='int8')) + self.assertFalse(result.ordered) + + result = CategoricalIndex(ci, categories=list('ab')) + self.assertTrue(result.categories.equals(Index(list('ab')))) + self.assert_numpy_array_equal(result.codes,np.array([0,0,1,1,-1,0],dtype='int8')) + self.assertFalse(result.ordered) + + result = CategoricalIndex(ci, categories=list('ab'), ordered=True) + self.assertTrue(result.categories.equals(Index(list('ab')))) + self.assert_numpy_array_equal(result.codes,np.array([0,0,1,1,-1,0],dtype='int8')) + self.assertTrue(result.ordered) + + # turn me to an Index + result = Index(np.array(ci)) + self.assertIsInstance(result, Index) + self.assertNotIsInstance(result, CategoricalIndex) + + def test_construction_with_dtype(self): + + # specify dtype + ci = self.create_index(categories=list('abc')) + + result = Index(np.array(ci), dtype='category') + tm.assert_index_equal(result,ci,exact=True) + + result = Index(np.array(ci).tolist(), dtype='category') + tm.assert_index_equal(result,ci,exact=True) + + # these are generally only equal when the categories are reordered + ci = self.create_index() + + result = Index(np.array(ci), dtype='category').reorder_categories(ci.categories) + tm.assert_index_equal(result,ci,exact=True) + + # make sure indexes are handled + expected = CategoricalIndex([0,1,2], categories=[0,1,2], ordered=True) + idx = Index(range(3)) + result = CategoricalIndex(idx, categories=idx, ordered=True) + tm.assert_index_equal(result, expected, exact=True) + + def test_method_delegation(self): + + ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) + result = ci.set_categories(list('cab')) + tm.assert_index_equal(result, CategoricalIndex(list('aabbca'), categories=list('cab'))) + + ci = CategoricalIndex(list('aabbca'), categories=list('cab')) + result = ci.rename_categories(list('efg')) + tm.assert_index_equal(result, CategoricalIndex(list('ffggef'), categories=list('efg'))) + + ci = CategoricalIndex(list('aabbca'), categories=list('cab')) + result = ci.add_categories(['d']) + tm.assert_index_equal(result, CategoricalIndex(list('aabbca'), categories=list('cabd'))) + + ci = CategoricalIndex(list('aabbca'), categories=list('cab')) + result = ci.remove_categories(['c']) + tm.assert_index_equal(result, CategoricalIndex(list('aabb') + [np.nan] + ['a'], categories=list('ab'))) + + ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) + result = ci.as_unordered() + tm.assert_index_equal(result, ci) + + ci = CategoricalIndex(list('aabbca'), categories=list('cabdef')) + result = ci.as_ordered() + tm.assert_index_equal(result, CategoricalIndex(list('aabbca'), categories=list('cabdef'), ordered=True)) + + # invalid + self.assertRaises(ValueError, lambda : ci.set_categories(list('cab'), inplace=True)) + + def test_contains(self): + + ci = self.create_index(categories=list('cabdef')) + + self.assertTrue('a' in ci) + self.assertTrue('z' not in ci) + self.assertTrue('e' not in ci) + self.assertTrue(np.nan not in ci) + + # assert codes NOT in index + self.assertFalse(0 in ci) + self.assertFalse(1 in ci) + + ci = CategoricalIndex(list('aabbca'), categories=list('cabdef') + [np.nan]) + self.assertFalse(np.nan in ci) + + ci = CategoricalIndex(list('aabbca') + [np.nan], categories=list('cabdef') + [np.nan]) + self.assertTrue(np.nan in ci) + + def test_min_max(self): + + ci = self.create_index(ordered=False) + self.assertRaises(TypeError, lambda : ci.min()) + self.assertRaises(TypeError, lambda : ci.max()) + + ci = self.create_index(ordered=True) + + self.assertEqual(ci.min(),'c') + self.assertEqual(ci.max(),'b') + + def test_append(self): + + ci = self.create_index() + categories = ci.categories + + # append cats with the same categories + result = ci[:3].append(ci[3:]) + tm.assert_index_equal(result,ci,exact=True) + + foos = [ci[:1], ci[1:3], ci[3:]] + result = foos[0].append(foos[1:]) + tm.assert_index_equal(result,ci,exact=True) + + # empty + result = ci.append([]) + tm.assert_index_equal(result,ci,exact=True) + + # appending with different categories or reoreded is not ok + self.assertRaises(TypeError, lambda : ci.append(ci.values.set_categories(list('abcd')))) + self.assertRaises(TypeError, lambda : ci.append(ci.values.reorder_categories(list('abc')))) + + # with objects + result = ci.append(['c','a']) + expected = CategoricalIndex(list('aabbcaca'), categories=categories) + tm.assert_index_equal(result,expected,exact=True) + + # invalid objects + self.assertRaises(TypeError, lambda : ci.append(['a','d'])) + + def test_insert(self): + + ci = self.create_index() + categories = ci.categories + + #test 0th element + result = ci.insert(0, 'a') + expected = CategoricalIndex(list('aaabbca'),categories=categories) + tm.assert_index_equal(result,expected,exact=True) + + #test Nth element that follows Python list behavior + result = ci.insert(-1, 'a') + expected = CategoricalIndex(list('aabbcaa'),categories=categories) + tm.assert_index_equal(result,expected,exact=True) + + #test empty + result = CategoricalIndex(categories=categories).insert(0, 'a') + expected = CategoricalIndex(['a'],categories=categories) + tm.assert_index_equal(result,expected,exact=True) + + # invalid + self.assertRaises(TypeError, lambda : ci.insert(0,'d')) + + def test_delete(self): + + ci = self.create_index() + categories = ci.categories + + result = ci.delete(0) + expected = CategoricalIndex(list('abbca'),categories=categories) + tm.assert_index_equal(result,expected,exact=True) + + result = ci.delete(-1) + expected = CategoricalIndex(list('aabbc'),categories=categories) + tm.assert_index_equal(result,expected,exact=True) + + with tm.assertRaises((IndexError, ValueError)): + # either depeidnig on numpy version + result = ci.delete(10) + + def test_astype(self): + + ci = self.create_index() + result = ci.astype('category') + tm.assert_index_equal(result,ci,exact=True) + + result = ci.astype(object) + self.assertTrue(result.equals(Index(np.array(ci)))) + + # this IS equal, but not the same class + self.assertTrue(result.equals(ci)) + self.assertIsInstance(result, Index) + self.assertNotIsInstance(result, CategoricalIndex) + + def test_reindex_base(self): + + # determined by cat ordering + idx = self.create_index() + expected = np.array([4,0,1,5,2,3]) + + actual = idx.get_indexer(idx) + assert_array_equal(expected, actual) + + with tm.assertRaisesRegexp(ValueError, 'Invalid fill method'): + idx.get_indexer(idx, method='invalid') + + def test_reindexing(self): + + ci = self.create_index() + oidx = Index(np.array(ci)) + + for n in [1,2,5,len(ci)]: + finder = oidx[np.random.randint(0,len(ci),size=n)] + expected = oidx.get_indexer_non_unique(finder)[0] + + actual = ci.get_indexer(finder) + assert_array_equal(expected, actual) + + def test_duplicates(self): + + idx = CategoricalIndex([0, 0, 0]) + self.assertFalse(idx.is_unique) + self.assertTrue(idx.has_duplicates) + + def test_get_indexer(self): + + idx1 = CategoricalIndex(list('aabcde'),categories=list('edabc')) + idx2 = CategoricalIndex(list('abf')) + + for indexer in [idx2, list('abf'), Index(list('abf'))]: + r1 = idx1.get_indexer(idx2) + assert_almost_equal(r1, [0, 1, 2, -1]) + + self.assertRaises(NotImplementedError, lambda : idx2.get_indexer(idx1, method='pad')) + self.assertRaises(NotImplementedError, lambda : idx2.get_indexer(idx1, method='backfill')) + self.assertRaises(NotImplementedError, lambda : idx2.get_indexer(idx1, method='nearest')) + + def test_repr(self): + + ci = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) + str(ci) + tm.assert_index_equal(eval(repr(ci)),ci,exact=True) + + # formatting + if compat.PY3: + str(ci) + else: + compat.text_type(ci) + + # long format + ci = CategoricalIndex(np.random.randint(0,5,size=100)) + result = str(ci) + tm.assert_index_equal(eval(repr(ci)),ci,exact=True) + + def test_isin(self): + + ci = CategoricalIndex(list('aabca') + [np.nan],categories=['c','a','b',np.nan]) + self.assert_numpy_array_equal(ci.isin(['c']),np.array([False,False,False,True,False,False])) + self.assert_numpy_array_equal(ci.isin(['c','a','b']),np.array([True]*5 + [False])) + self.assert_numpy_array_equal(ci.isin(['c','a','b',np.nan]),np.array([True]*6)) + + # mismatched categorical -> coerced to ndarray so doesn't matter + self.assert_numpy_array_equal(ci.isin(ci.set_categories(list('abcdefghi'))),np.array([True]*6)) + self.assert_numpy_array_equal(ci.isin(ci.set_categories(list('defghi'))),np.array([False]*5 + [True])) + + def test_identical(self): + + ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) + ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'], ordered=True) + self.assertTrue(ci1.identical(ci1)) + self.assertTrue(ci1.identical(ci1.copy())) + self.assertFalse(ci1.identical(ci2)) + + def test_equals(self): + + ci1 = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) + ci2 = CategoricalIndex(['a', 'b'], categories=['a', 'b', 'c'], ordered=True) + + self.assertTrue(ci1.equals(ci1)) + self.assertFalse(ci1.equals(ci2)) + self.assertTrue(ci1.equals(ci1.astype(object))) + self.assertTrue(ci1.astype(object).equals(ci1)) + + self.assertTrue((ci1 == ci1).all()) + self.assertFalse((ci1 != ci1).all()) + self.assertFalse((ci1 > ci1).all()) + self.assertFalse((ci1 < ci1).all()) + self.assertTrue((ci1 <= ci1).all()) + self.assertTrue((ci1 >= ci1).all()) + + self.assertFalse((ci1 == 1).all()) + self.assertTrue((ci1 == Index(['a','b'])).all()) + self.assertTrue((ci1 == ci1.values).all()) + + # invalid comparisons + self.assertRaises(TypeError, lambda : ci1 == Index(['a','b','c'])) + self.assertRaises(TypeError, lambda : ci1 == ci2) + self.assertRaises(TypeError, lambda : ci1 == Categorical(ci1.values, ordered=False)) + self.assertRaises(TypeError, lambda : ci1 == Categorical(ci1.values, categories=list('abc'))) + + # tests + # make sure that we are testing for category inclusion properly + self.assertTrue(CategoricalIndex(list('aabca'),categories=['c','a','b']).equals(list('aabca'))) + self.assertTrue(CategoricalIndex(list('aabca'),categories=['c','a','b',np.nan]).equals(list('aabca'))) + + self.assertFalse(CategoricalIndex(list('aabca') + [np.nan],categories=['c','a','b',np.nan]).equals(list('aabca'))) + self.assertTrue(CategoricalIndex(list('aabca') + [np.nan],categories=['c','a','b',np.nan]).equals(list('aabca') + [np.nan])) class Numeric(Base): @@ -1417,18 +1814,13 @@ class TestFloat64Index(Numeric, tm.TestCase): _multiprocess_can_split_ = True def setUp(self): - self.mixed = Float64Index([1.5, 2, 3, 4, 5]) - self.float = Float64Index(np.arange(5) * 2.5) + self.indices = dict(mixed = Float64Index([1.5, 2, 3, 4, 5]), + float = Float64Index(np.arange(5) * 2.5)) + self.setup_indices() def create_index(self): return Float64Index(np.arange(5,dtype='float64')) - def test_hash_error(self): - with tm.assertRaisesRegexp(TypeError, - "unhashable type: %r" % - type(self.float).__name__): - hash(self.float) - def test_repr_roundtrip(self): for ind in (self.mixed, self.float): tm.assert_index_equal(eval(repr(ind)), ind) @@ -1594,7 +1986,8 @@ class TestInt64Index(Numeric, tm.TestCase): _multiprocess_can_split_ = True def setUp(self): - self.index = Int64Index(np.arange(0, 20, 2)) + self.indices = dict(index = Int64Index(np.arange(0, 20, 2))) + self.setup_indices() def create_index(self): return Int64Index(np.arange(5,dtype='int64')) @@ -1641,18 +2034,14 @@ def test_constructor_corner(self): with tm.assertRaisesRegexp(TypeError, 'casting'): Int64Index(arr_with_floats) - def test_hash_error(self): - with tm.assertRaisesRegexp(TypeError, - "unhashable type: %r" % - type(self.index).__name__): - hash(self.index) - def test_copy(self): i = Int64Index([], name='Foo') i_copy = i.copy() self.assertEqual(i_copy.name, 'Foo') def test_view(self): + super(TestInt64Index, self).test_view() + i = Int64Index([], name='Foo') i_view = i.view() self.assertEqual(i_view.name, 'Foo') @@ -2053,6 +2442,7 @@ def test_slice_keep_name(self): class DatetimeLike(Base): def test_view(self): + super(DatetimeLike, self).test_view() i = self.create_index() @@ -2068,6 +2458,10 @@ class TestDatetimeIndex(DatetimeLike, tm.TestCase): _holder = DatetimeIndex _multiprocess_can_split_ = True + def setUp(self): + self.indices = dict(index = tm.makeDateIndex(10)) + self.setup_indices() + def create_index(self): return date_range('20130101',periods=5) @@ -2186,6 +2580,10 @@ class TestPeriodIndex(DatetimeLike, tm.TestCase): _holder = PeriodIndex _multiprocess_can_split_ = True + def setUp(self): + self.indices = dict(index = tm.makePeriodIndex(10)) + self.setup_indices() + def create_index(self): return period_range('20130101',periods=5,freq='D') @@ -2220,6 +2618,10 @@ class TestTimedeltaIndex(DatetimeLike, tm.TestCase): _holder = TimedeltaIndex _multiprocess_can_split_ = True + def setUp(self): + self.indices = dict(index = tm.makeTimedeltaIndex(10)) + self.setup_indices() + def create_index(self): return pd.to_timedelta(range(5),unit='d') + pd.offsets.Hour(1) @@ -2294,9 +2696,10 @@ def setUp(self): major_labels = np.array([0, 0, 1, 2, 3, 3]) minor_labels = np.array([0, 1, 0, 1, 0, 1]) self.index_names = ['first', 'second'] - self.index = MultiIndex(levels=[major_axis, minor_axis], - labels=[major_labels, minor_labels], - names=self.index_names, verify_integrity=False) + self.indices = dict(index = MultiIndex(levels=[major_axis, minor_axis], + labels=[major_labels, minor_labels], + names=self.index_names, verify_integrity=False)) + self.setup_indices() def create_index(self): return self.index @@ -2332,13 +2735,7 @@ def test_labels_dtypes(self): self.assertTrue((i.labels[0]>=0).all()) self.assertTrue((i.labels[1]>=0).all()) - def test_hash_error(self): - with tm.assertRaisesRegexp(TypeError, - "unhashable type: %r" % - type(self.index).__name__): - hash(self.index) - - def test_set_names_and_rename(self): + def test_set_name_methods(self): # so long as these are synonyms, we don't need to test set_names self.assertEqual(self.index.rename, self.index.set_names) new_names = [name + "SUFFIX" for name in self.index_names] @@ -3838,7 +4235,7 @@ def test_reindex_level(self): assertRaisesRegexp(TypeError, "Fill method not supported", idx.reindex, idx, method='bfill', level='first') - def test_has_duplicates(self): + def test_duplicates(self): self.assertFalse(self.index.has_duplicates) self.assertTrue(self.index.append(self.index).has_duplicates) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 5f109212add06..3872f79df7286 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -2366,6 +2366,7 @@ def test_dups_fancy_indexing(self): rows = ['C','B','E'] expected = DataFrame({'test' : [11,9,np.nan], 'test1': [7.,6,np.nan], 'other': ['d','c',np.nan]},index=rows) + result = df.ix[rows] assert_frame_equal(result, expected) @@ -4422,6 +4423,212 @@ def test_indexing_assignment_dict_already_exists(self): tm.assert_frame_equal(df, expected) + +class TestCategoricalIndex(tm.TestCase): + + def setUp(self): + + self.df = DataFrame({'A' : np.arange(6,dtype='int64'), + 'B' : Series(list('aabbca')).astype('category',categories=list('cab')) }).set_index('B') + self.df2 = DataFrame({'A' : np.arange(6,dtype='int64'), + 'B' : Series(list('aabbca')).astype('category',categories=list('cabe')) }).set_index('B') + self.df3 = DataFrame({'A' : np.arange(6,dtype='int64'), + 'B' : Series([1,1,2,1,3,2]).astype('category',categories=[3,2,1],ordered=True) }).set_index('B') + self.df4 = DataFrame({'A' : np.arange(6,dtype='int64'), + 'B' : Series([1,1,2,1,3,2]).astype('category',categories=[3,2,1],ordered=False) }).set_index('B') + + + def test_loc_scalar(self): + + result = self.df.loc['a'] + expected = DataFrame({'A' : [0,1,5], + 'B' : Series(list('aaa')).astype('category',categories=list('cab')) }).set_index('B') + assert_frame_equal(result, expected) + + + df = self.df.copy() + df.loc['a'] = 20 + expected = DataFrame({'A' : [20,20,2,3,4,20], + 'B' : Series(list('aabbca')).astype('category',categories=list('cab')) }).set_index('B') + assert_frame_equal(df, expected) + + # value not in the categories + self.assertRaises(KeyError, lambda : df.loc['d']) + + def f(): + df.loc['d'] = 10 + self.assertRaises(TypeError, f) + + def f(): + df.loc['d','A'] = 10 + self.assertRaises(TypeError, f) + + def f(): + df.loc['d','C'] = 10 + self.assertRaises(TypeError, f) + + def test_loc_listlike(self): + + # list of labels + result = self.df.loc[['c','a']] + expected = self.df.iloc[[4,0,1,5]] + assert_frame_equal(result, expected) + + result = self.df2.loc[['a','b','e']] + expected = DataFrame({'A' : [0,1,5,2,3,np.nan], + 'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B') + assert_frame_equal(result, expected) + + # element in the categories but not in the values + self.assertRaises(KeyError, lambda : self.df2.loc['e']) + + # assign is ok + df = self.df2.copy() + df.loc['e'] = 20 + result = df.loc[['a','b','e']] + expected = DataFrame({'A' : [0,1,5,2,3,20], + 'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B') + assert_frame_equal(result, expected) + + df = self.df2.copy() + result = df.loc[['a','b','e']] + expected = DataFrame({'A' : [0,1,5,2,3,np.nan], + 'B' : Series(list('aaabbe')).astype('category',categories=list('cabe')) }).set_index('B') + assert_frame_equal(result, expected) + + + # not all labels in the categories + self.assertRaises(KeyError, lambda : self.df2.loc[['a','d']]) + + def test_reindexing(self): + + # reindexing + # convert to a regular index + result = self.df2.reindex(['a','b','e']) + expected = DataFrame({'A' : [0,1,5,2,3,np.nan], + 'B' : Series(list('aaabbe')) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(['a','b']) + expected = DataFrame({'A' : [0,1,5,2,3], + 'B' : Series(list('aaabb')) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(['e']) + expected = DataFrame({'A' : [np.nan], + 'B' : Series(['e']) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(['d']) + expected = DataFrame({'A' : [np.nan], + 'B' : Series(['d']) }).set_index('B') + assert_frame_equal(result, expected) + + # since we are actually reindexing with a Categorical + # then return a Categorical + cats = list('cabe') + + result = self.df2.reindex(pd.Categorical(['a','d'],categories=cats)) + expected = DataFrame({'A' : [0,1,5,np.nan], + 'B' : Series(list('aaad')).astype('category',categories=cats) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(pd.Categorical(['a'],categories=cats)) + expected = DataFrame({'A' : [0,1,5], + 'B' : Series(list('aaa')).astype('category',categories=cats) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(['a','b','e']) + expected = DataFrame({'A' : [0,1,5,2,3,np.nan], + 'B' : Series(list('aaabbe')) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(['a','b']) + expected = DataFrame({'A' : [0,1,5,2,3], + 'B' : Series(list('aaabb')) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(['e']) + expected = DataFrame({'A' : [np.nan], + 'B' : Series(['e']) }).set_index('B') + assert_frame_equal(result, expected) + + # give back the type of categorical that we received + result = self.df2.reindex(pd.Categorical(['a','d'],categories=cats,ordered=True)) + expected = DataFrame({'A' : [0,1,5,np.nan], + 'B' : Series(list('aaad')).astype('category',categories=cats,ordered=True) }).set_index('B') + assert_frame_equal(result, expected) + + result = self.df2.reindex(pd.Categorical(['a','d'],categories=['a','d'])) + expected = DataFrame({'A' : [0,1,5,np.nan], + 'B' : Series(list('aaad')).astype('category',categories=['a','d']) }).set_index('B') + assert_frame_equal(result, expected) + + # passed duplicate indexers are not allowed + self.assertRaises(ValueError, lambda : self.df2.reindex(['a','a'])) + + # args NotImplemented ATM + self.assertRaises(NotImplementedError, lambda : self.df2.reindex(['a'],method='ffill')) + self.assertRaises(NotImplementedError, lambda : self.df2.reindex(['a'],level=1)) + self.assertRaises(NotImplementedError, lambda : self.df2.reindex(['a'],limit=2)) + + def test_loc_slice(self): + + # slicing + # not implemented ATM + # GH9748 + + self.assertRaises(TypeError, lambda : self.df.loc[1:5]) + + #result = df.loc[1:5] + #expected = df.iloc[[1,2,3,4]] + #assert_frame_equal(result, expected) + + def test_boolean_selection(self): + + df3 = self.df3 + df4 = self.df4 + + result = df3[df3.index == 'a'] + expected = df3.iloc[[]] + assert_frame_equal(result,expected) + + result = df4[df4.index == 'a'] + expected = df4.iloc[[]] + assert_frame_equal(result,expected) + + result = df3[df3.index == 1] + expected = df3.iloc[[0,1,3]] + assert_frame_equal(result,expected) + + result = df4[df4.index == 1] + expected = df4.iloc[[0,1,3]] + assert_frame_equal(result,expected) + + # since we have an ordered categorical + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=True, + # name=u'B') + result = df3[df3.index < 2] + expected = df3.iloc[[4]] + assert_frame_equal(result,expected) + + result = df3[df3.index > 1] + expected = df3.iloc[[]] + assert_frame_equal(result,expected) + + # unordered + # cannot be compared + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=False, + # name=u'B') + self.assertRaises(TypeError, lambda : df4[df4.index < 2]) + self.assertRaises(TypeError, lambda : df4[df4.index > 1]) + class TestSeriesNoneCoercion(tm.TestCase): EXPECTED_RESULTS = [ # For numeric series, we should coerce to NaN. diff --git a/pandas/util/testing.py b/pandas/util/testing.py index b4baedada46e1..ea7354a9334ff 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -25,11 +25,6 @@ import pandas as pd from pandas.core.common import is_sequence, array_equivalent, is_list_like -import pandas.core.index as index -import pandas.core.series as series -import pandas.core.frame as frame -import pandas.core.panel as panel -import pandas.core.panel4d as panel4d import pandas.compat as compat from pandas.compat import( filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter, @@ -38,24 +33,12 @@ from pandas.computation import expressions as expr -from pandas import bdate_range -from pandas.tseries.index import DatetimeIndex -from pandas.tseries.tdi import TimedeltaIndex -from pandas.tseries.period import PeriodIndex +from pandas import (bdate_range, CategoricalIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex, + Index, MultiIndex, Series, DataFrame, Panel, Panel4D) from pandas.util.decorators import deprecate - from pandas import _testing - - from pandas.io.common import urlopen -Index = index.Index -MultiIndex = index.MultiIndex -Series = series.Series -DataFrame = frame.DataFrame -Panel = panel.Panel -Panel4D = panel4d.Panel4D - N = 30 K = 4 _RAISE_NETWORK_ERROR_DEFAULT = False @@ -550,16 +533,14 @@ def assert_equal(a, b, msg=""): assert a == b, "%s: %r != %r" % (msg.format(a,b), a, b) -def assert_index_equal(left, right): +def assert_index_equal(left, right, exact=False): assert_isinstance(left, Index, '[index] ') assert_isinstance(right, Index, '[index] ') - if not left.equals(right): + if not left.equals(right) or (exact and type(left) != type(right)): raise AssertionError("[index] left [{0} {1}], right [{2} {3}]".format(left.dtype, left, right, right.dtype)) - - def assert_attr_equal(attr, left, right): """checks attributes are equal. Both objects must have attribute.""" left_attr = getattr(left, attr) @@ -627,6 +608,7 @@ def assertNotIsInstance(obj, cls, msg=''): def assert_categorical_equal(res, exp): + if not array_equivalent(res.categories, exp.categories): raise AssertionError( 'categories not equivalent: {0} vs {1}.'.format(res.categories, @@ -827,6 +809,11 @@ def makeStringIndex(k=10): def makeUnicodeIndex(k=10): return Index(randu_array(nchars=10, size=k)) +def makeCategoricalIndex(k=10, n=3): + """ make a length k index or n categories """ + x = rands_array(nchars=4, size=n) + return CategoricalIndex(np.random.choice(x,k)) + def makeBoolIndex(k=10): if k == 1: return Index([True])
closes #7629 xref #8613 xref #8074 - [x] docs / whatsnew - [x] ~~auto-create a `CategoricalIndex` when grouping by a `Categorical` (this doesn't ATM)~~ - [x] adding a value not in the Index, e.g. `df2.loc['d'] = 5` should do what? (currently will coerce to an `Index`) - [x] `pd.concat([df2,df])` should STILL have a `CategoricalIndex` (yep)? - [x] implement `min/max` - [x] fix groupby on cat column - [x] add `Categorical` wrapper methods - [x] make repr evalable / fix - [x] contains should be on values not categories A `CategoricalIndex` is essentially a drop-in replacement for `Index`, that works nicely for non-unique values. It uses a `Categorical` to represent itself. The behavior is very similar to using a duplicated Index (for say indexing). Groupby works naturally (and returns another `CategoricalIndex`). The only real departure is that `.sort_index()` works like you would expected (which is a good thing:). Clearly this will provide idempotency for `set/reset` index w.r.t. Categoricals, and thus memory savings by its representation. This doesn't change the API at all. IOW, this is not turned on by default, you have to either use `set/reset`, assign an index, or pass a `Categorical` to `Index`. ``` In [1]: df = DataFrame({'A' : np.arange(6,dtype='int64'), ...: 'B' : Series(list('aabbca')).astype('category',categories=list('cab')) }) In [2]: df Out[2]: A B 0 0 a 1 1 a 2 2 b 3 3 b 4 4 c 5 5 a In [3]: df.dtypes Out[3]: A int64 B category dtype: object In [5]: df.B.cat.categories Out[5]: Index([u'c', u'a', u'b'], dtype='object') In [6]: df2 = df.set_index('B') In [7]: df2 Out[7]: A B a 0 a 1 b 2 b 3 c 4 a 5 In [8]: df2.index Out[8]: CategoricalIndex([u'a', u'a', u'b', u'b', u'c', u'a'], dtype='category') In [9]: df2.index.categories Out[9]: Index([u'c', u'a', u'b'], dtype='object') In [10]: df2.index.codes Out[10]: array([1, 1, 2, 2, 0, 1], dtype=int8) In [11]: df2.loc['a'] Out[11]: A B a 0 a 1 a 5 In [12]: df2.loc['a'].index Out[12]: CategoricalIndex([u'a', u'a', u'a'], dtype='category') In [13]: df2.loc['a'].index.categories Out[13]: Index([u'c', u'a', u'b'], dtype='object') In [14]: df2.sort_index() Out[14]: A B c 4 a 0 a 1 a 5 b 2 b 3 In [15]: df2.groupby(level=0).sum() Out[15]: A B a 6 b 5 c 4 In [16]: df2.groupby(level=0).sum().index Out[16]: CategoricalIndex([u'a', u'b', u'c'], dtype='category') ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9741
2015-03-27T22:01:57Z
2015-04-20T11:19:57Z
2015-04-20T11:19:57Z
2015-04-20T14:35:36Z
Plotting: don't change visibility of xaxis labels and ticklabels if passing in a axis
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 3c3742c968642..c6ace8c23e064 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -31,6 +31,14 @@ API changes +- When passing in an ax to ``df.plot( ..., ax=ax)``, the `sharex` kwarg will now default to `False`. + The result is that the visibility of xlabels and xticklabels will not anymore be changed. You + have to do that by yourself for the right axes in your figure or set ``sharex=True`` explicitly + (but this changes the visible for all axes in the figure, not only the one which is passed in!). + If pandas creates the subplots itself (e.g. no passed in `ax` kwarg), then the + default is still ``sharex=True`` and the visibility changes are applied. + + - Add support for separating years and quarters using dashes, for example 2014-Q1. (:issue:`9688`) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 1cb11179b2430..0a9df941a6045 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1000,8 +1000,14 @@ def test_plot(self): _check_plot_works(df.plot, xticks=[1, 5, 10]) _check_plot_works(df.plot, ylim=(-100, 100), xlim=(-100, 100)) - axes = _check_plot_works(df.plot, subplots=True, title='blah') + _check_plot_works(df.plot, subplots=True, title='blah') + # We have to redo it here because _check_plot_works does two plots, once without an ax + # kwarg and once with an ax kwarg and the new sharex behaviour does not remove the + # visibility of the latter axis (as ax is present). + # see: https://github.com/pydata/pandas/issues/9737 + axes = df.plot(subplots=True, title='blah') self._check_axes_shape(axes, axes_num=3, layout=(3, 1)) + #axes[0].figure.savefig("test.png") for ax in axes[:2]: self._check_visible(ax.xaxis) # xaxis must be visible for grid self._check_visible(ax.get_xticklabels(), visible=False) @@ -3138,6 +3144,78 @@ def _check_errorbar_color(containers, expected, has_err='has_xerr'): self._check_has_errorbars(ax, xerr=0, yerr=1) _check_errorbar_color(ax.containers, 'green', has_err='has_yerr') + def test_sharex_and_ax(self): + # https://github.com/pydata/pandas/issues/9737 + # using gridspec, the axis in fig.get_axis() are sorted differently than pandas expected + # them, so make sure that only the right ones are removed + import matplotlib.pyplot as plt + plt.close('all') + gs, axes = _generate_4_axes_via_gridspec() + + df = DataFrame({"a":[1,2,3,4,5,6], "b":[1,2,3,4,5,6]}) + + for ax in axes: + df.plot(x="a", y="b", title="title", ax=ax, sharex=True) + + gs.tight_layout(plt.gcf()) + for ax in plt.gcf().get_axes(): + for label in ax.get_xticklabels(): + self.assertEqual(label.get_visible(), ax.is_last_row(), + "x ticklabel has wrong visiblity") + self.assertEqual(ax.xaxis.get_label().get_visible(), ax.is_last_row(), + "x label has wrong visiblity") + + plt.close('all') + gs, axes = _generate_4_axes_via_gridspec() + # without sharex, no labels should be touched! + for ax in axes: + df.plot(x="a", y="b", title="title", ax=ax) + + gs.tight_layout(plt.gcf()) + for ax in plt.gcf().get_axes(): + for label in ax.get_xticklabels(): + self.assertTrue(label.get_visible(), "x ticklabel is invisible but shouldn't") + self.assertTrue(ax.xaxis.get_label().get_visible(), + "x label is invisible but shouldn't") + + + def test_sharey_and_ax(self): + # https://github.com/pydata/pandas/issues/9737 + # using gridspec, the axis in fig.get_axis() are sorted differently than pandas expected + # them, so make sure that only the right ones are removed + import matplotlib.pyplot as plt + + plt.close('all') + gs, axes = _generate_4_axes_via_gridspec() + + df = DataFrame({"a":[1,2,3,4,5,6], "b":[1,2,3,4,5,6]}) + + for ax in axes: + df.plot(x="a", y="b", title="title", ax=ax, sharey=True) + + gs.tight_layout(plt.gcf()) + for ax in plt.gcf().get_axes(): + for label in ax.get_yticklabels(): + self.assertEqual(label.get_visible(), ax.is_first_col(), + "y ticklabel has wrong visiblity") + self.assertEqual(ax.yaxis.get_label().get_visible(), ax.is_first_col(), + "y label has wrong visiblity") + + plt.close('all') + gs, axes = _generate_4_axes_via_gridspec() + + # without sharex, no labels should be touched! + for ax in axes: + df.plot(x="a", y="b", title="title", ax=ax) + + gs.tight_layout(plt.gcf()) + for ax in plt.gcf().get_axes(): + for label in ax.get_yticklabels(): + self.assertTrue(label.get_visible(), "y ticklabel is invisible but shouldn't") + self.assertTrue(ax.yaxis.get_label().get_visible(), + "y label is invisible but shouldn't") + + @tm.mplskip class TestDataFrameGroupByPlots(TestPlotBase): @@ -3612,6 +3690,19 @@ def _check_plot_works(f, *args, **kwargs): return ret +def _generate_4_axes_via_gridspec(): + import matplotlib.pyplot as plt + import matplotlib as mpl + import matplotlib.gridspec + + gs = mpl.gridspec.GridSpec(2, 2) + ax_tl = plt.subplot(gs[0,0]) + ax_ll = plt.subplot(gs[1,0]) + ax_tr = plt.subplot(gs[0,1]) + ax_lr = plt.subplot(gs[1,1]) + + return gs, [ax_tl, ax_ll, ax_tr, ax_lr] + def curpath(): pth, _ = os.path.split(os.path.abspath(__file__)) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index cf9c890823f8f..ce000ffc3e012 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -769,7 +769,7 @@ class MPLPlot(object): _attr_defaults = {'logy': False, 'logx': False, 'loglog': False, 'mark_right': True, 'stacked': False} - def __init__(self, data, kind=None, by=None, subplots=False, sharex=True, + def __init__(self, data, kind=None, by=None, subplots=False, sharex=None, sharey=False, use_index=True, figsize=None, grid=None, legend=True, rot=None, ax=None, fig=None, title=None, xlim=None, ylim=None, @@ -786,7 +786,16 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=True, self.sort_columns = sort_columns self.subplots = subplots - self.sharex = sharex + + if sharex is None: + if ax is None: + self.sharex = True + else: + # if we get an axis, the users should do the visibility setting... + self.sharex = False + else: + self.sharex = sharex + self.sharey = sharey self.figsize = figsize self.layout = layout @@ -2350,10 +2359,14 @@ def _plot(data, x=None, y=None, subplots=False, df_ax = """ax : matplotlib axes object, default None subplots : boolean, default False Make separate subplots for each column - sharex : boolean, default True - In case subplots=True, share x axis + sharex : boolean, default True if ax is None else False + In case subplots=True, share x axis and set some x axis labels to + invisible; defaults to True if ax is None otherwise False if an ax + is passed in; Be aware, that passing in both an ax and sharex=True + will alter all x axis labels for all axis in a figure! sharey : boolean, default False - In case subplots=True, share y axis + In case subplots=True, share y axis and set some y axis labels to + invisible layout : tuple (optional) (rows, columns) for the layout of subplots""" series_ax = """ax : matplotlib axes object @@ -2465,7 +2478,7 @@ def _plot(data, x=None, y=None, subplots=False, @Appender(_shared_docs['plot'] % _shared_doc_df_kwargs) def plot_frame(data, x=None, y=None, kind='line', ax=None, # Dataframe unique - subplots=False, sharex=True, sharey=False, layout=None, # Dataframe unique + subplots=False, sharex=None, sharey=False, layout=None, # Dataframe unique figsize=None, use_index=True, title=None, grid=None, legend=True, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, @@ -2730,8 +2743,14 @@ def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, yrot : float, default None rotation of y axis labels ax : matplotlib axes object, default None - sharex : bool, if True, the X axis will be shared amongst all subplots. - sharey : bool, if True, the Y axis will be shared amongst all subplots. + sharex : boolean, default True if ax is None else False + In case subplots=True, share x axis and set some x axis labels to + invisible; defaults to True if ax is None otherwise False if an ax + is passed in; Be aware, that passing in both an ax and sharex=True + will alter all x axis labels for all subplots in a figure! + sharey : boolean, default False + In case subplots=True, share y axis and set some y axis labels to + invisible figsize : tuple The size of the figure to create in inches by default layout: (optional) a tuple (rows, columns) for the layout of the histograms @@ -3129,7 +3148,8 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, Keyword arguments: naxes : int - Number of required axes. Exceeded axes are set invisible. Default is nrows * ncols. + Number of required axes. Exceeded axes are set invisible. Default is + nrows * ncols. sharex : bool If True, the X axis will be shared amongst all subplots. @@ -3256,12 +3276,12 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, ax = fig.add_subplot(nrows, ncols, i + 1, **kwds) axarr[i] = ax - _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey) - if naxes != nplots: for ax in axarr[naxes:]: ax.set_visible(False) + _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey) + if squeeze: # Reshape the array to have the final desired dimension (nrow,ncol), # though discarding unneeded dimensions that equal 1. If we only have @@ -3276,44 +3296,64 @@ def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True, return fig, axes +def _remove_xlabels_from_axis(ax): + for label in ax.get_xticklabels(): + label.set_visible(False) + try: + # set_visible will not be effective if + # minor axis has NullLocator and NullFormattor (default) + import matplotlib.ticker as ticker + + if isinstance(ax.xaxis.get_minor_locator(), ticker.NullLocator): + ax.xaxis.set_minor_locator(ticker.AutoLocator()) + if isinstance(ax.xaxis.get_minor_formatter(), ticker.NullFormatter): + ax.xaxis.set_minor_formatter(ticker.FormatStrFormatter('')) + for label in ax.get_xticklabels(minor=True): + label.set_visible(False) + except Exception: # pragma no cover + pass + ax.xaxis.get_label().set_visible(False) + +def _remove_ylables_from_axis(ax): + for label in ax.get_yticklabels(): + label.set_visible(False) + try: + import matplotlib.ticker as ticker + if isinstance(ax.yaxis.get_minor_locator(), ticker.NullLocator): + ax.yaxis.set_minor_locator(ticker.AutoLocator()) + if isinstance(ax.yaxis.get_minor_formatter(), ticker.NullFormatter): + ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter('')) + for label in ax.get_yticklabels(minor=True): + label.set_visible(False) + except Exception: # pragma no cover + pass + ax.yaxis.get_label().set_visible(False) def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey): if nplots > 1: + # first find out the ax layout, so that we can correctly handle 'gaps" + layout = np.zeros((nrows+1,ncols+1), dtype=np.bool) + for ax in axarr: + layout[ax.rowNum, ax.colNum] = ax.get_visible() + if sharex and nrows > 1: - for ax in axarr[:naxes][:-ncols]: # only bottom row - for label in ax.get_xticklabels(): - label.set_visible(False) - try: - # set_visible will not be effective if - # minor axis has NullLocator and NullFormattor (default) - import matplotlib.ticker as ticker - - if isinstance(ax.xaxis.get_minor_locator(), ticker.NullLocator): - ax.xaxis.set_minor_locator(ticker.AutoLocator()) - if isinstance(ax.xaxis.get_minor_formatter(), ticker.NullFormatter): - ax.xaxis.set_minor_formatter(ticker.FormatStrFormatter('')) - for label in ax.get_xticklabels(minor=True): - label.set_visible(False) - except Exception: # pragma no cover - pass - ax.xaxis.get_label().set_visible(False) + for ax in axarr: + # only the last row of subplots should get x labels -> all other off + # layout handles the case that the subplot is the last in the column, + # because below is no subplot/gap. + if not layout[ax.rowNum+1, ax.colNum]: + continue + _remove_xlabels_from_axis(ax) if sharey and ncols > 1: - for i, ax in enumerate(axarr): - if (i % ncols) != 0: # only first column - for label in ax.get_yticklabels(): - label.set_visible(False) - try: - import matplotlib.ticker as ticker - if isinstance(ax.yaxis.get_minor_locator(), ticker.NullLocator): - ax.yaxis.set_minor_locator(ticker.AutoLocator()) - if isinstance(ax.yaxis.get_minor_formatter(), ticker.NullFormatter): - ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter('')) - for label in ax.get_yticklabels(minor=True): - label.set_visible(False) - except Exception: # pragma no cover - pass - ax.yaxis.get_label().set_visible(False) + for ax in axarr: + # only the first column should get y labels -> set all other to off + # as we only have labels in teh first column and we always have a subplot there, + # we can skip the layout test + if ax.is_first_col(): + continue + _remove_ylables_from_axis(ax) + def _flatten(axes):
This does two things: - fix for changing the wrong xlabels/xticklabels if a gridspec is used and the ax is passed in (not sure about using subplot without gridspec) - changes the default behaviour for sharex if an axis is passed in: before the visibility changes were applied per default, now they are only applied if explicitly requested via `sharex=True` Closes: #9737
https://api.github.com/repos/pandas-dev/pandas/pulls/9740
2015-03-27T13:50:33Z
2015-03-31T11:54:03Z
2015-03-31T11:54:03Z
2015-03-31T11:54:12Z
ENH: Add an axis parameter to DataFrame.diff
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt old mode 100644 new mode 100755 index 352f079f38e96..29368d66b2991 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -18,6 +18,7 @@ Enhancements ~~~~~~~~~~~~ - Added ``StringMethods.capitalize()`` and ``swapcase`` which behave as the same as standard ``str`` (:issue:`9766`) +- ``DataFrame.diff`` now takes an ``axis`` parameter that determines the direction of differencing (:issue:`9727`) - Added ``StringMethods`` (.str accessor) to ``Index`` (:issue:`9068`) The `.str` accessor is now available for both `Series` and `Index`. diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 19f15f58afffd..a02fa3b9e3674 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3584,7 +3584,7 @@ def unstack(self, level=-1): #---------------------------------------------------------------------- # Time series-related - def diff(self, periods=1): + def diff(self, periods=1, axis=0): """ 1st discrete difference of object @@ -3592,12 +3592,14 @@ def diff(self, periods=1): ---------- periods : int, default 1 Periods to shift for forming difference + axis : {0 or 'index', 1 or 'columns'}, default 0 Returns ------- diffed : DataFrame """ - new_data = self._data.diff(n=periods) + bm_axis = self._get_block_manager_axis(axis) + new_data = self._data.diff(n=periods, axis=bm_axis) return self._constructor(new_data) #---------------------------------------------------------------------- diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 4d0f8394fbd2a..142a565077fbf 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -869,9 +869,9 @@ def take_nd(self, indexer, axis, new_mgr_locs=None, fill_tuple=None): def get_values(self, dtype=None): return self.values - def diff(self, n): + def diff(self, n, axis=1): """ return block for the diff of the values """ - new_values = com.diff(self.values, n, axis=1) + new_values = com.diff(self.values, n, axis=axis) return [make_block(values=new_values, ndim=self.ndim, fastpath=True, placement=self.mgr_locs)] diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index c7c35e63d3d91..467f6c60ac29b 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -10047,6 +10047,12 @@ def test_diff_float_n(self): xp = self.tsframe.diff(1) assert_frame_equal(rs, xp) + def test_diff_axis(self): + # GH 9727 + df = DataFrame([[1., 2.], [3., 4.]]) + assert_frame_equal(df.diff(axis=1), DataFrame([[np.nan, 1.], [np.nan, 1.]])) + assert_frame_equal(df.diff(axis=0), DataFrame([[np.nan, np.nan], [2., 2.]])) + def test_pct_change(self): rs = self.tsframe.pct_change(fill_method=None) assert_frame_equal(rs, self.tsframe / self.tsframe.shift(1) - 1)
https://api.github.com/repos/pandas-dev/pandas/pulls/9727
2015-03-25T12:53:50Z
2015-04-29T10:45:52Z
2015-04-29T10:45:52Z
2015-06-10T13:41:27Z
TST: Fix dateutil version check
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 79adabafb7044..e452ddee9d8db 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -167,7 +167,7 @@ def test_repr(self): # dateutil zone change (only matters for repr) import dateutil - if dateutil.__version__ >= LooseVersion('2.3') and dateutil.__version__ <= LooseVersion('2.4'): + if dateutil.__version__ >= LooseVersion('2.3') and dateutil.__version__ <= LooseVersion('2.4.0'): timezones = ['UTC', 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Pacific'] else: timezones = ['UTC', 'Asia/Tokyo', 'US/Eastern', 'dateutil/America/Los_Angeles']
This allows the test to pass when using dateutil version 2.4.0 ``` In [2]: '2.4.0' <= LooseVersion('2.4') Out[2]: False In [3]: '2.4.0' <= LooseVersion('2.4.0') Out[3]: True ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9725
2015-03-25T11:44:34Z
2015-03-25T22:06:21Z
2015-03-25T22:06:21Z
2015-03-25T22:48:08Z
DOC: fix api documentation for accessors
diff --git a/doc/_templates/autosummary/accessor.rst b/doc/_templates/autosummary/accessor.rst new file mode 100644 index 0000000000000..1401121fb51c6 --- /dev/null +++ b/doc/_templates/autosummary/accessor.rst @@ -0,0 +1,6 @@ +{{ fullname }} +{{ underline }} + +.. currentmodule:: {{ module.split('.')[0] }} + +.. automethod:: {{ [module.split('.')[1], objname]|join('.') }} diff --git a/doc/_templates/autosummary/class_without_autosummary.rst b/doc/_templates/autosummary/class_without_autosummary.rst new file mode 100644 index 0000000000000..6676c672b206d --- /dev/null +++ b/doc/_templates/autosummary/class_without_autosummary.rst @@ -0,0 +1,6 @@ +{{ fullname }} +{{ underline }} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} diff --git a/doc/source/api.rst b/doc/source/api.rst index 57ae089e463c8..3f47c0380116c 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -470,6 +470,7 @@ These can be accessed like ``Series.dt.<property>``. Series.dt.microsecond Series.dt.nanosecond Series.dt.second + Series.dt.week Series.dt.weekofyear Series.dt.dayofweek Series.dt.weekday @@ -481,6 +482,10 @@ These can be accessed like ``Series.dt.<property>``. Series.dt.is_quarter_end Series.dt.is_year_start Series.dt.is_year_end + Series.dt.daysinmonth + Series.dt.days_in_month + Series.dt.tz + Series.dt.freq **Datetime Methods** @@ -575,6 +580,20 @@ strings and apply several methods to it. These can be acccessed like Series.str.isdecimal Series.str.get_dummies +.. + The following is needed to ensure the generated pages are created with the + correct template (otherwise they would be created in the Series class page) + +.. + .. autosummary:: + :toctree: generated/ + :template: autosummary/accessor.rst + + Series.str + Series.cat + Series.dt + + .. _api.categorical: Categorical @@ -582,22 +601,28 @@ Categorical If the Series is of dtype ``category``, ``Series.cat`` can be used to change the the categorical data. This accessor is similar to the ``Series.dt`` or ``Series.str`` and has the -following usable methods and properties (all available as ``Series.cat.<method_or_property>``). +following usable methods and properties: + +.. autosummary:: + :toctree: generated/ + :template: autosummary/accessor_attribute.rst + + Series.cat.categories + Series.cat.ordered + Series.cat.codes .. autosummary:: :toctree: generated/ + :template: autosummary/accessor_method.rst - Categorical.categories - Categorical.ordered - Categorical.rename_categories - Categorical.reorder_categories - Categorical.add_categories - Categorical.remove_categories - Categorical.remove_unused_categories - Categorical.set_categories - Categorical.as_ordered - Categorical.as_unordered - Categorical.codes + Series.cat.rename_categories + Series.cat.reorder_categories + Series.cat.add_categories + Series.cat.remove_categories + Series.cat.remove_unused_categories + Series.cat.set_categories + Series.cat.as_ordered + Series.cat.as_unordered To create a Series of dtype ``category``, use ``cat = s.astype("category")``. @@ -606,8 +631,13 @@ adding ordering information or special categories is need at creation time of th .. autosummary:: :toctree: generated/ + :template: autosummary/class_without_autosummary.rst Categorical + +.. autosummary:: + :toctree: generated/ + Categorical.from_codes ``np.asarray(categorical)`` works by implementing the array interface. Be aware, that this converts
Closes #9599 With this I updated the docs (assign docstring included in API, and the cat/str/dt accessors now documented correctly). The Categorical page without methods and attributes is not yet working. Will complete this for the v0.16.1 docs
https://api.github.com/repos/pandas-dev/pandas/pulls/9721
2015-03-24T20:46:41Z
2015-05-11T12:28:04Z
2015-05-11T12:28:04Z
2015-05-11T12:47:19Z
PERF: add initial asv config and vbench->asv conversion script
diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json new file mode 100644 index 0000000000000..ddb6d97de43b5 --- /dev/null +++ b/asv_bench/asv.conf.json @@ -0,0 +1,64 @@ +{ + // The version of the config file format. Do not change, unless + // you know what you are doing. + "version": 1, + + // The name of the project being benchmarked + "project": "pandas", + + // The project's homepage + "project_url": "http://pandas.pydata.org/", + + // The URL of the source code repository for the project being + // benchmarked + "repo": "..", + + // The tool to use to create environments. May be "conda", + // "virtualenv" or other value depending on the plugins in use. + // If missing or the empty string, the tool will be automatically + // determined by looking for tools on the PATH environment + // variable. + "environment_type": "conda", + + // the base URL to show a commit for the project. + "show_commit_url": "https://github.com/pydata/pandas/commit/", + + // The Pythons you'd like to test against. If not provided, defaults + // to the current version of Python used to run `asv`. + "pythons": ["2.7", "3.4"], + + // The matrix of dependencies to test. Each key is the name of a + // package (in PyPI) and the values are version numbers. An empty + // list indicates to just test against the default (latest) + // version. + "matrix": { + // To run against multiple versions, replace with + // "numpy": ["1.7", "1.9"], + "numpy": [], + "Cython": [], + "matplotlib": [], + "sqlalchemy": [], + "scipy": [], + "pytables": [], + }, + + // The directory (relative to the current directory) that benchmarks are + // stored in. If not provided, defaults to "benchmarks" + // "benchmark_dir": "benchmarks", + + // The directory (relative to the current directory) to cache the Python + // environments in. If not provided, defaults to "env" + // "env_dir": "env", + + + // The directory (relative to the current directory) that raw benchmark + // results are stored in. If not provided, defaults to "results". + // "results_dir": "results", + + // The directory (relative to the current directory) that the html tree + // should be written to. If not provided, defaults to "html". + // "html_dir": "html", + + // The number of characters to retain in the commit hashes. + // "hash_length": 8 +} diff --git a/asv_bench/benchmarks/__init__.py b/asv_bench/benchmarks/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/asv_bench/benchmarks/attrs_caching.py b/asv_bench/benchmarks/attrs_caching.py new file mode 100644 index 0000000000000..ecb91923dc663 --- /dev/null +++ b/asv_bench/benchmarks/attrs_caching.py @@ -0,0 +1,23 @@ +from pandas_vb_common import * + + +class getattr_dataframe_index(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(10, 6)) + self.cur_index = self.df.index + + def time_getattr_dataframe_index(self): + self.foo = self.df.index + + +class setattr_dataframe_index(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(10, 6)) + self.cur_index = self.df.index + + def time_setattr_dataframe_index(self): + self.df.index = self.cur_index \ No newline at end of file diff --git a/asv_bench/benchmarks/binary_ops.py b/asv_bench/benchmarks/binary_ops.py new file mode 100644 index 0000000000000..13976014ec6f1 --- /dev/null +++ b/asv_bench/benchmarks/binary_ops.py @@ -0,0 +1,236 @@ +from pandas_vb_common import * +import pandas.computation.expressions as expr + + +class frame_add(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + + def time_frame_add(self): + (self.df + self.df2) + + +class frame_add_no_ne(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + expr.set_use_numexpr(False) + + def time_frame_add_no_ne(self): + (self.df + self.df2) + + def teardown(self): + expr.set_use_numexpr(True) + + +class frame_add_st(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_frame_add_st(self): + (self.df + self.df2) + + def teardown(self): + expr.set_numexpr_threads() + + +class frame_float_div(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 1000)) + self.df2 = DataFrame(np.random.randn(1000, 1000)) + + def time_frame_float_div(self): + (self.df // self.df2) + + +class frame_float_div_by_zero(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 1000)) + + def time_frame_float_div_by_zero(self): + (self.df / 0) + + +class frame_float_floor_by_zero(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 1000)) + + def time_frame_float_floor_by_zero(self): + (self.df // 0) + + +class frame_float_mod(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 1000)) + self.df2 = DataFrame(np.random.randn(1000, 1000)) + + def time_frame_float_mod(self): + (self.df / self.df2) + + +class frame_int_div_by_zero(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000))) + + def time_frame_int_div_by_zero(self): + (self.df / 0) + + +class frame_int_mod(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000))) + self.df2 = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000))) + + def time_frame_int_mod(self): + (self.df / self.df2) + + +class frame_mult(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + + def time_frame_mult(self): + (self.df * self.df2) + + +class frame_mult_no_ne(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + expr.set_use_numexpr(False) + + def time_frame_mult_no_ne(self): + (self.df * self.df2) + + def teardown(self): + expr.set_use_numexpr(True) + + +class frame_mult_st(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_frame_mult_st(self): + (self.df * self.df2) + + def teardown(self): + expr.set_numexpr_threads() + + +class frame_multi_and(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + + def time_frame_multi_and(self): + self.df[((self.df > 0) & (self.df2 > 0))] + + +class frame_multi_and_no_ne(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + expr.set_use_numexpr(False) + + def time_frame_multi_and_no_ne(self): + self.df[((self.df > 0) & (self.df2 > 0))] + + def teardown(self): + expr.set_use_numexpr(True) + + +class frame_multi_and_st(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_frame_multi_and_st(self): + self.df[((self.df > 0) & (self.df2 > 0))] + + def teardown(self): + expr.set_numexpr_threads() + + +class series_timestamp_compare(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.halfway = ((self.N // 2) - 1) + self.s = Series(date_range('20010101', periods=self.N, freq='T')) + self.ts = self.s[self.halfway] + + def time_series_timestamp_compare(self): + (self.s <= self.ts) + + +class timestamp_ops_diff1(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.s = Series(date_range('20010101', periods=self.N, freq='s')) + + def time_timestamp_ops_diff1(self): + self.s.diff() + + +class timestamp_ops_diff2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.s = Series(date_range('20010101', periods=self.N, freq='s')) + + def time_timestamp_ops_diff2(self): + (self.s - self.s.shift()) + + +class timestamp_series_compare(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.halfway = ((self.N // 2) - 1) + self.s = Series(date_range('20010101', periods=self.N, freq='T')) + self.ts = self.s[self.halfway] + + def time_timestamp_series_compare(self): + (self.ts >= self.s) \ No newline at end of file diff --git a/asv_bench/benchmarks/categoricals.py b/asv_bench/benchmarks/categoricals.py new file mode 100644 index 0000000000000..34caef221a340 --- /dev/null +++ b/asv_bench/benchmarks/categoricals.py @@ -0,0 +1,11 @@ +from pandas_vb_common import * + + +class concat_categorical(object): + goal_time = 0.2 + + def setup(self): + self.s = pd.Series((list('aabbcd') * 1000000)).astype('category') + + def time_concat_categorical(self): + concat([self.s, self.s]) \ No newline at end of file diff --git a/asv_bench/benchmarks/ctors.py b/asv_bench/benchmarks/ctors.py new file mode 100644 index 0000000000000..b48211b3db83e --- /dev/null +++ b/asv_bench/benchmarks/ctors.py @@ -0,0 +1,52 @@ +from pandas_vb_common import * + + +class frame_constructor_ndarray(object): + goal_time = 0.2 + + def setup(self): + self.arr = np.random.randn(100, 100) + + def time_frame_constructor_ndarray(self): + DataFrame(self.arr) + + +class ctor_index_array_string(object): + goal_time = 0.2 + + def setup(self): + self.data = np.array(['foo', 'bar', 'baz'], dtype=object) + + def time_ctor_index_array_string(self): + Index(self.data) + + +class series_constructor_ndarray(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(100) + self.index = Index(np.arange(100)) + + def time_series_constructor_ndarray(self): + Series(self.data, index=self.index) + + +class dtindex_from_series_ctor(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(([Timestamp('20110101'), Timestamp('20120101'), Timestamp('20130101')] * 1000)) + + def time_dtindex_from_series_ctor(self): + DatetimeIndex(self.s) + + +class index_from_series_ctor(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(([Timestamp('20110101'), Timestamp('20120101'), Timestamp('20130101')] * 1000)) + + def time_index_from_series_ctor(self): + Index(self.s) \ No newline at end of file diff --git a/asv_bench/benchmarks/eval.py b/asv_bench/benchmarks/eval.py new file mode 100644 index 0000000000000..397312355aa47 --- /dev/null +++ b/asv_bench/benchmarks/eval.py @@ -0,0 +1,239 @@ +from pandas_vb_common import * +import pandas.computation.expressions as expr +import pandas as pd + + +class eval_frame_add_all_threads(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_add_all_threads(self): + pd.eval('df + df2 + df3 + df4') + + +class eval_frame_add_one_thread(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_eval_frame_add_one_thread(self): + pd.eval('df + df2 + df3 + df4') + + +class eval_frame_add_python(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_add_python(self): + pd.eval('df + df2 + df3 + df4', engine='python') + + +class eval_frame_add_python_one_thread(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_eval_frame_add_python_one_thread(self): + pd.eval('df + df2 + df3 + df4', engine='python') + + +class eval_frame_and_all_threads(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_and_all_threads(self): + pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)') + + +class eval_frame_and_python_one_thread(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_eval_frame_and_python_one_thread(self): + pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)', engine='python') + + +class eval_frame_and_python(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_and_python(self): + pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)', engine='python') + + +class eval_frame_chained_cmp_all_threads(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_chained_cmp_all_threads(self): + pd.eval('df < df2 < df3 < df4') + + +class eval_frame_chained_cmp_python_one_thread(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_eval_frame_chained_cmp_python_one_thread(self): + pd.eval('df < df2 < df3 < df4', engine='python') + + +class eval_frame_chained_cmp_python(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_chained_cmp_python(self): + pd.eval('df < df2 < df3 < df4', engine='python') + + +class eval_frame_mult_all_threads(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_mult_all_threads(self): + pd.eval('df * df2 * df3 * df4') + + +class eval_frame_mult_one_thread(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_eval_frame_mult_one_thread(self): + pd.eval('df * df2 * df3 * df4') + + +class eval_frame_mult_python(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + + def time_eval_frame_mult_python(self): + pd.eval('df * df2 * df3 * df4', engine='python') + + +class eval_frame_mult_python_one_thread(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(20000, 100)) + self.df2 = DataFrame(np.random.randn(20000, 100)) + self.df3 = DataFrame(np.random.randn(20000, 100)) + self.df4 = DataFrame(np.random.randn(20000, 100)) + expr.set_numexpr_threads(1) + + def time_eval_frame_mult_python_one_thread(self): + pd.eval('df * df2 * df3 * df4', engine='python') + + +class query_datetime_index(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.halfway = ((self.N // 2) - 1) + self.index = date_range('20010101', periods=self.N, freq='T') + self.s = Series(self.index) + self.ts = self.s.iloc[self.halfway] + self.df = DataFrame({'a': np.random.randn(self.N), }, index=self.index) + + def time_query_datetime_index(self): + self.df.query('index < @ts') + + +class query_datetime_series(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.halfway = ((self.N // 2) - 1) + self.index = date_range('20010101', periods=self.N, freq='T') + self.s = Series(self.index) + self.ts = self.s.iloc[self.halfway] + self.df = DataFrame({'dates': self.s.values, }) + + def time_query_datetime_series(self): + self.df.query('dates < @ts') + + +class query_with_boolean_selection(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.halfway = ((self.N // 2) - 1) + self.index = date_range('20010101', periods=self.N, freq='T') + self.s = Series(self.index) + self.ts = self.s.iloc[self.halfway] + self.N = 1000000 + self.df = DataFrame({'a': np.random.randn(self.N), }) + self.min_val = self.df['a'].min() + self.max_val = self.df['a'].max() + + def time_query_with_boolean_selection(self): + self.df.query('(a >= @min_val) & (a <= @max_val)') \ No newline at end of file diff --git a/asv_bench/benchmarks/frame_ctor.py b/asv_bench/benchmarks/frame_ctor.py new file mode 100644 index 0000000000000..2cb337e0e6b9d --- /dev/null +++ b/asv_bench/benchmarks/frame_ctor.py @@ -0,0 +1,1706 @@ +from pandas_vb_common import * +try: + from pandas.tseries.offsets import * +except: + from pandas.core.datetools import * + + +class frame_ctor_dtindex_BDayx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BDay(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BDayx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BDayx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BDay(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BDayx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BMonthBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BMonthBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BMonthBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BMonthBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BMonthBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BMonthBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BMonthEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BMonthEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BMonthEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BMonthEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BMonthEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BMonthEndx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BQuarterBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BQuarterBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BQuarterBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BQuarterBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BQuarterBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BQuarterBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BQuarterEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BQuarterEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BQuarterEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BQuarterEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BQuarterEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BQuarterEndx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BYearBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BYearBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BYearBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BYearBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BYearBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BYearBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BYearEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BYearEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BYearEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BYearEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BYearEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BYearEndx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BusinessDayx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BusinessDay(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BusinessDayx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BusinessDayx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BusinessDay(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BusinessDayx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BusinessHourx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BusinessHour(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BusinessHourx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_BusinessHourx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(BusinessHour(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_BusinessHourx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CBMonthBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CBMonthBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CBMonthBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CBMonthBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CBMonthBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CBMonthBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CBMonthEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CBMonthEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CBMonthEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CBMonthEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CBMonthEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CBMonthEndx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CDayx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CDay(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CDayx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CDayx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CDay(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CDayx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CustomBusinessDayx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CustomBusinessDay(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CustomBusinessDayx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_CustomBusinessDayx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(CustomBusinessDay(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_CustomBusinessDayx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_DateOffsetx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(DateOffset(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_DateOffsetx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_DateOffsetx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(DateOffset(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_DateOffsetx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Dayx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Day(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Dayx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Dayx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Day(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Dayx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Easterx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Easter(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Easterx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Easterx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Easter(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Easterx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253Quarterx1__variation_last(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253Quarter(1, **{'startingMonth': 1, 'qtr_with_extra_week': 1, 'weekday': 1, 'variation': 'last', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253Quarterx1__variation_last(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253Quarterx1__variation_nearest(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253Quarter(1, **{'startingMonth': 1, 'qtr_with_extra_week': 1, 'weekday': 1, 'variation': 'nearest', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253Quarterx1__variation_nearest(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253Quarterx2__variation_last(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253Quarter(2, **{'startingMonth': 1, 'qtr_with_extra_week': 1, 'weekday': 1, 'variation': 'last', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253Quarterx2__variation_last(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253Quarterx2__variation_nearest(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253Quarter(2, **{'startingMonth': 1, 'qtr_with_extra_week': 1, 'weekday': 1, 'variation': 'nearest', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253Quarterx2__variation_nearest(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253x1__variation_last(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253(1, **{'startingMonth': 1, 'weekday': 1, 'variation': 'last', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253x1__variation_last(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253x1__variation_nearest(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253(1, **{'startingMonth': 1, 'weekday': 1, 'variation': 'nearest', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253x1__variation_nearest(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253x2__variation_last(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253(2, **{'startingMonth': 1, 'weekday': 1, 'variation': 'last', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253x2__variation_last(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_FY5253x2__variation_nearest(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(FY5253(2, **{'startingMonth': 1, 'weekday': 1, 'variation': 'nearest', })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_FY5253x2__variation_nearest(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Hourx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Hour(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Hourx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Hourx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Hour(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Hourx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_LastWeekOfMonthx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(LastWeekOfMonth(1, **{'week': 1, 'weekday': 1, })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_LastWeekOfMonthx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_LastWeekOfMonthx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(LastWeekOfMonth(2, **{'week': 1, 'weekday': 1, })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_LastWeekOfMonthx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Microx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Micro(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Microx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Microx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Micro(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Microx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Millix1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Milli(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Millix1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Millix2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Milli(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Millix2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Minutex1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Minute(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Minutex1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Minutex2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Minute(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Minutex2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_MonthBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(MonthBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_MonthBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_MonthBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(MonthBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_MonthBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_MonthEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(MonthEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_MonthEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_MonthEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(MonthEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_MonthEndx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Nanox1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Nano(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Nanox1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Nanox2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Nano(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Nanox2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_QuarterBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(QuarterBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_QuarterBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_QuarterBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(QuarterBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_QuarterBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_QuarterEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(QuarterEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_QuarterEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_QuarterEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(QuarterEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_QuarterEndx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Secondx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Second(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Secondx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Secondx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Second(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Secondx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_WeekOfMonthx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(WeekOfMonth(1, **{'week': 1, 'weekday': 1, })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_WeekOfMonthx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_WeekOfMonthx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(WeekOfMonth(2, **{'week': 1, 'weekday': 1, })) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_WeekOfMonthx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Weekx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Week(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Weekx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_Weekx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(Week(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_Weekx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_YearBeginx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(YearBegin(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_YearBeginx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_YearBeginx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(YearBegin(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_YearBeginx2(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_YearEndx1(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(YearEnd(1, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_YearEndx1(self): + DataFrame(self.d) + + +class frame_ctor_dtindex_YearEndx2(object): + goal_time = 0.2 + + def setup(self): + + def get_period_count(start_date, off): + self.ten_offsets_in_days = ((start_date + (off * 10)) - start_date).days + if (self.ten_offsets_in_days == 0): + return 1000 + else: + return min((9 * ((Timestamp.max - start_date).days // self.ten_offsets_in_days)), 1000) + + def get_index_for_offset(off): + self.start_date = Timestamp('1/1/1900') + return date_range(self.start_date, periods=min(1000, get_period_count(self.start_date, off)), freq=off) + self.idx = get_index_for_offset(YearEnd(2, **{})) + self.df = DataFrame(np.random.randn(len(self.idx), 10), index=self.idx) + self.d = dict([(col, self.df[col]) for col in self.df.columns]) + + def time_frame_ctor_dtindex_YearEndx2(self): + DataFrame(self.d) + + +class frame_ctor_list_of_dict(object): + goal_time = 0.2 + + def setup(self): + (N, K) = (5000, 50) + self.index = tm.makeStringIndex(N) + self.columns = tm.makeStringIndex(K) + self.frame = DataFrame(np.random.randn(N, K), index=self.index, columns=self.columns) + try: + self.data = self.frame.to_dict() + except: + self.data = self.frame.toDict() + self.some_dict = self.data.values()[0] + self.dict_list = [dict(zip(self.columns, row)) for row in self.frame.values] + + def time_frame_ctor_list_of_dict(self): + DataFrame(self.dict_list) + + +class frame_ctor_nested_dict(object): + goal_time = 0.2 + + def setup(self): + (N, K) = (5000, 50) + self.index = tm.makeStringIndex(N) + self.columns = tm.makeStringIndex(K) + self.frame = DataFrame(np.random.randn(N, K), index=self.index, columns=self.columns) + try: + self.data = self.frame.to_dict() + except: + self.data = self.frame.toDict() + self.some_dict = self.data.values()[0] + self.dict_list = [dict(zip(self.columns, row)) for row in self.frame.values] + + def time_frame_ctor_nested_dict(self): + DataFrame(self.data) + + +class frame_ctor_nested_dict_int64(object): + goal_time = 0.2 + + def setup(self): + self.data = dict(((i, dict(((j, float(j)) for j in xrange(100)))) for i in xrange(2000))) + + def time_frame_ctor_nested_dict_int64(self): + DataFrame(self.data) + + +class frame_from_series(object): + goal_time = 0.2 + + def setup(self): + self.mi = MultiIndex.from_tuples([(x, y) for x in range(100) for y in range(100)]) + self.s = Series(randn(10000), index=self.mi) + + def time_frame_from_series(self): + DataFrame(self.s) + + +class frame_get_numeric_data(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 25)) + self.df['foo'] = 'bar' + self.df['bar'] = 'baz' + self.df = self.df.consolidate() + + def time_frame_get_numeric_data(self): + self.df._get_numeric_data() + + +class series_ctor_from_dict(object): + goal_time = 0.2 + + def setup(self): + (N, K) = (5000, 50) + self.index = tm.makeStringIndex(N) + self.columns = tm.makeStringIndex(K) + self.frame = DataFrame(np.random.randn(N, K), index=self.index, columns=self.columns) + try: + self.data = self.frame.to_dict() + except: + self.data = self.frame.toDict() + self.some_dict = self.data.values()[0] + self.dict_list = [dict(zip(self.columns, row)) for row in self.frame.values] + + def time_series_ctor_from_dict(self): + Series(self.some_dict) \ No newline at end of file diff --git a/asv_bench/benchmarks/frame_methods.py b/asv_bench/benchmarks/frame_methods.py new file mode 100644 index 0000000000000..2bd51201b45ca --- /dev/null +++ b/asv_bench/benchmarks/frame_methods.py @@ -0,0 +1,936 @@ +from pandas_vb_common import * + + +class frame_apply_axis_1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 100)) + + def time_frame_apply_axis_1(self): + self.df.apply((lambda x: (x + 1)), axis=1) + + +class frame_apply_lambda_mean(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 100)) + + def time_frame_apply_lambda_mean(self): + self.df.apply((lambda x: x.sum())) + + +class frame_apply_np_mean(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 100)) + + def time_frame_apply_np_mean(self): + self.df.apply(np.mean) + + +class frame_apply_pass_thru(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 100)) + + def time_frame_apply_pass_thru(self): + self.df.apply((lambda x: x)) + + +class frame_apply_ref_by_name(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 3), columns=list('ABC')) + + def time_frame_apply_ref_by_name(self): + self.df.apply((lambda x: (x['A'] + x['B'])), axis=1) + + +class frame_apply_user_func(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.arange(1028.0)) + self.df = DataFrame({i: self.s for i in range(1028)}) + + def time_frame_apply_user_func(self): + self.df.apply((lambda x: np.corrcoef(x, self.s)[(0, 1)])) + + +class frame_assign_timeseries_index(object): + goal_time = 0.2 + + def setup(self): + self.idx = date_range('1/1/2000', periods=100000, freq='D') + self.df = DataFrame(randn(100000, 1), columns=['A'], index=self.idx) + + def f(x): + self.x = self.x.copy() + self.x['date'] = self.x.index + + def time_frame_assign_timeseries_index(self): + f(self.df) + + +class frame_boolean_row_select(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 100)) + self.bool_arr = np.zeros(10000, dtype=bool) + self.bool_arr[:1000] = True + + def time_frame_boolean_row_select(self): + self.df[self.bool_arr] + + +class frame_count_level_axis0_mixed_dtypes_multi(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df['foo'] = 'bar' + self.df.index = MultiIndex.from_tuples(self.df.index.map((lambda x: (x, x)))) + self.df.columns = MultiIndex.from_tuples(self.df.columns.map((lambda x: (x, x)))) + + def time_frame_count_level_axis0_mixed_dtypes_multi(self): + self.df.count(axis=0, level=1) + + +class frame_count_level_axis0_multi(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df.index = MultiIndex.from_tuples(self.df.index.map((lambda x: (x, x)))) + self.df.columns = MultiIndex.from_tuples(self.df.columns.map((lambda x: (x, x)))) + + def time_frame_count_level_axis0_multi(self): + self.df.count(axis=0, level=1) + + +class frame_count_level_axis1_mixed_dtypes_multi(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df['foo'] = 'bar' + self.df.index = MultiIndex.from_tuples(self.df.index.map((lambda x: (x, x)))) + self.df.columns = MultiIndex.from_tuples(self.df.columns.map((lambda x: (x, x)))) + + def time_frame_count_level_axis1_mixed_dtypes_multi(self): + self.df.count(axis=1, level=1) + + +class frame_count_level_axis1_multi(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df.index = MultiIndex.from_tuples(self.df.index.map((lambda x: (x, x)))) + self.df.columns = MultiIndex.from_tuples(self.df.columns.map((lambda x: (x, x)))) + + def time_frame_count_level_axis1_multi(self): + self.df.count(axis=1, level=1) + + +class frame_dropna_axis0_all(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + + def time_frame_dropna_axis0_all(self): + self.df.dropna(how='all', axis=0) + + +class frame_dropna_axis0_all_mixed_dtypes(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df['foo'] = 'bar' + + def time_frame_dropna_axis0_all_mixed_dtypes(self): + self.df.dropna(how='all', axis=0) + + +class frame_dropna_axis0_any(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + + def time_frame_dropna_axis0_any(self): + self.df.dropna(how='any', axis=0) + + +class frame_dropna_axis0_any_mixed_dtypes(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df['foo'] = 'bar' + + def time_frame_dropna_axis0_any_mixed_dtypes(self): + self.df.dropna(how='any', axis=0) + + +class frame_dropna_axis1_all(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + + def time_frame_dropna_axis1_all(self): + self.df.dropna(how='all', axis=1) + + +class frame_dropna_axis1_all_mixed_dtypes(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df['foo'] = 'bar' + + def time_frame_dropna_axis1_all_mixed_dtypes(self): + self.df.dropna(how='all', axis=1) + + +class frame_dropna_axis1_any(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + + def time_frame_dropna_axis1_any(self): + self.df.dropna(how='any', axis=1) + + +class frame_dropna_axis1_any_mixed_dtypes(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(10000, 1000) + self.df = DataFrame(self.data) + self.df.ix[50:1000, 20:50] = np.nan + self.df.ix[2000:3000] = np.nan + self.df.ix[:, 60:70] = np.nan + self.df['foo'] = 'bar' + + def time_frame_dropna_axis1_any_mixed_dtypes(self): + self.df.dropna(how='any', axis=1) + + +class frame_dtypes(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 1000)) + + def time_frame_dtypes(self): + self.df.dtypes + + +class frame_duplicated(object): + goal_time = 0.2 + + def setup(self): + self.n = (1 << 20) + self.t = date_range('2015-01-01', freq='S', periods=(self.n // 64)) + self.xs = np.random.randn((self.n // 64)).round(2) + self.df = DataFrame({'a': np.random.randint(((-1) << 8), (1 << 8), self.n), 'b': np.random.choice(self.t, self.n), 'c': np.random.choice(self.xs, self.n), }) + + def time_frame_duplicated(self): + self.df.duplicated() + + +class frame_fancy_lookup(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(10000, 8), columns=list('abcdefgh')) + self.df['foo'] = 'bar' + self.row_labels = list(self.df.index[::10])[:900] + self.col_labels = (list(self.df.columns) * 100) + self.row_labels_all = np.array((list(self.df.index) * len(self.df.columns)), dtype='object') + self.col_labels_all = np.array((list(self.df.columns) * len(self.df.index)), dtype='object') + + def time_frame_fancy_lookup(self): + self.df.lookup(self.row_labels, self.col_labels) + + +class frame_fancy_lookup_all(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(10000, 8), columns=list('abcdefgh')) + self.df['foo'] = 'bar' + self.row_labels = list(self.df.index[::10])[:900] + self.col_labels = (list(self.df.columns) * 100) + self.row_labels_all = np.array((list(self.df.index) * len(self.df.columns)), dtype='object') + self.col_labels_all = np.array((list(self.df.columns) * len(self.df.index)), dtype='object') + + def time_frame_fancy_lookup_all(self): + self.df.lookup(self.row_labels_all, self.col_labels_all) + + +class frame_fillna_inplace(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 100)) + self.df.values[::2] = np.nan + + def time_frame_fillna_inplace(self): + self.df.fillna(0, inplace=True) + + +class frame_float_equal(object): + goal_time = 0.2 + + def setup(self): + + def make_pair(frame): + self.df = frame + self.df2 = self.df.copy() + self.df2.ix[((-1), (-1))] = np.nan + return (self.df, self.df2) + + def test_equal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df) + + def test_unequal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df2) + self.float_df = DataFrame(np.random.randn(1000, 1000)) + self.object_df = DataFrame(([(['foo'] * 1000)] * 1000)) + self.nonunique_cols = self.object_df.copy() + self.nonunique_cols.columns = (['A'] * len(self.nonunique_cols.columns)) + self.pairs = dict([(name, make_pair(frame)) for (name, frame) in (('float_df', self.float_df), ('object_df', self.object_df), ('nonunique_cols', self.nonunique_cols))]) + + def time_frame_float_equal(self): + test_equal('float_df') + + +class frame_float_unequal(object): + goal_time = 0.2 + + def setup(self): + + def make_pair(frame): + self.df = frame + self.df2 = self.df.copy() + self.df2.ix[((-1), (-1))] = np.nan + return (self.df, self.df2) + + def test_equal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df) + + def test_unequal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df2) + self.float_df = DataFrame(np.random.randn(1000, 1000)) + self.object_df = DataFrame(([(['foo'] * 1000)] * 1000)) + self.nonunique_cols = self.object_df.copy() + self.nonunique_cols.columns = (['A'] * len(self.nonunique_cols.columns)) + self.pairs = dict([(name, make_pair(frame)) for (name, frame) in (('float_df', self.float_df), ('object_df', self.object_df), ('nonunique_cols', self.nonunique_cols))]) + + def time_frame_float_unequal(self): + test_unequal('float_df') + + +class frame_from_records_generator(object): + goal_time = 0.2 + + def setup(self): + + def get_data(n=100000): + return ((x, (x * 20), (x * 100)) for x in xrange(n)) + + def time_frame_from_records_generator(self): + self.df = DataFrame.from_records(get_data()) + + +class frame_from_records_generator_nrows(object): + goal_time = 0.2 + + def setup(self): + + def get_data(n=100000): + return ((x, (x * 20), (x * 100)) for x in xrange(n)) + + def time_frame_from_records_generator_nrows(self): + self.df = DataFrame.from_records(get_data(), nrows=1000) + + +class frame_get_dtype_counts(object): + goal_time = 0.2 + + def setup(self): + self.df = pandas.DataFrame(np.random.randn(10, 10000)) + + def time_frame_get_dtype_counts(self): + self.df.get_dtype_counts() + + +class frame_getitem_single_column(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 1000)) + self.df2 = DataFrame(randn(3000, 1), columns=['A']) + self.df3 = DataFrame(randn(3000, 1)) + + def f(): + if hasattr(self.df, '_item_cache'): + self.df._item_cache.clear() + for (name, col) in self.df.iteritems(): + pass + + def g(): + for (name, col) in self.df.iteritems(): + pass + + def h(): + for i in xrange(10000): + self.df2['A'] + + def j(): + for i in xrange(10000): + self.df3[0] + + def time_frame_getitem_single_column(self): + h() + + +class frame_getitem_single_column2(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 1000)) + self.df2 = DataFrame(randn(3000, 1), columns=['A']) + self.df3 = DataFrame(randn(3000, 1)) + + def f(): + if hasattr(self.df, '_item_cache'): + self.df._item_cache.clear() + for (name, col) in self.df.iteritems(): + pass + + def g(): + for (name, col) in self.df.iteritems(): + pass + + def h(): + for i in xrange(10000): + self.df2['A'] + + def j(): + for i in xrange(10000): + self.df3[0] + + def time_frame_getitem_single_column2(self): + j() + + +class frame_html_repr_trunc_mi(object): + goal_time = 0.2 + + def setup(self): + self.nrows = 10000 + self.data = randn(self.nrows, 10) + self.idx = MultiIndex.from_arrays(np.tile(randn(3, (self.nrows / 100)), 100)) + self.df = DataFrame(self.data, index=self.idx) + + def time_frame_html_repr_trunc_mi(self): + self.df._repr_html_() + + +class frame_html_repr_trunc_si(object): + goal_time = 0.2 + + def setup(self): + self.nrows = 10000 + self.data = randn(self.nrows, 10) + self.idx = randn(self.nrows) + self.df = DataFrame(self.data, index=self.idx) + + def time_frame_html_repr_trunc_si(self): + self.df._repr_html_() + + +class frame_insert_100_columns_begin(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000 + + def f(K=100): + self.df = DataFrame(index=range(self.N)) + self.new_col = np.random.randn(self.N) + for i in range(K): + self.df.insert(0, i, self.new_col) + + def time_frame_insert_100_columns_begin(self): + f() + + +class frame_insert_500_columns_end(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000 + + def f(K=500): + self.df = DataFrame(index=range(self.N)) + self.new_col = np.random.randn(self.N) + for i in range(K): + self.df[i] = self.new_col + + def time_frame_insert_500_columns_end(self): + f() + + +class frame_interpolate(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 100)) + self.df.values[::2] = np.nan + + def time_frame_interpolate(self): + self.df.interpolate() + + +class frame_interpolate_some_good(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'A': np.arange(0, 10000), 'B': np.random.randint(0, 100, 10000), 'C': randn(10000), 'D': randn(10000), }) + self.df.loc[1::5, 'A'] = np.nan + self.df.loc[1::5, 'C'] = np.nan + + def time_frame_interpolate_some_good(self): + self.df.interpolate() + + +class frame_interpolate_some_good_infer(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'A': np.arange(0, 10000), 'B': np.random.randint(0, 100, 10000), 'C': randn(10000), 'D': randn(10000), }) + self.df.loc[1::5, 'A'] = np.nan + self.df.loc[1::5, 'C'] = np.nan + + def time_frame_interpolate_some_good_infer(self): + self.df.interpolate(downcast='infer') + + +class frame_isnull(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(1000, 1000) + self.df = DataFrame(self.data) + + def time_frame_isnull(self): + isnull(self.df) + + +class frame_iteritems(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 1000)) + self.df2 = DataFrame(randn(3000, 1), columns=['A']) + self.df3 = DataFrame(randn(3000, 1)) + + def f(): + if hasattr(self.df, '_item_cache'): + self.df._item_cache.clear() + for (name, col) in self.df.iteritems(): + pass + + def g(): + for (name, col) in self.df.iteritems(): + pass + + def h(): + for i in xrange(10000): + self.df2['A'] + + def j(): + for i in xrange(10000): + self.df3[0] + + def time_frame_iteritems(self): + f() + + +class frame_iteritems_cached(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 1000)) + self.df2 = DataFrame(randn(3000, 1), columns=['A']) + self.df3 = DataFrame(randn(3000, 1)) + + def f(): + if hasattr(self.df, '_item_cache'): + self.df._item_cache.clear() + for (name, col) in self.df.iteritems(): + pass + + def g(): + for (name, col) in self.df.iteritems(): + pass + + def h(): + for i in xrange(10000): + self.df2['A'] + + def j(): + for i in xrange(10000): + self.df3[0] + + def time_frame_iteritems_cached(self): + g() + + +class frame_mask_bools(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(1000, 500) + self.df = DataFrame(self.data) + self.df = self.df.where((self.df > 0)) + self.bools = (self.df > 0) + self.mask = isnull(self.df) + + def time_frame_mask_bools(self): + self.bools.mask(self.mask) + + +class frame_mask_floats(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(1000, 500) + self.df = DataFrame(self.data) + self.df = self.df.where((self.df > 0)) + self.bools = (self.df > 0) + self.mask = isnull(self.df) + + def time_frame_mask_floats(self): + self.bools.astype(float).mask(self.mask) + + +class frame_nonunique_equal(object): + goal_time = 0.2 + + def setup(self): + + def make_pair(frame): + self.df = frame + self.df2 = self.df.copy() + self.df2.ix[((-1), (-1))] = np.nan + return (self.df, self.df2) + + def test_equal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df) + + def test_unequal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df2) + self.float_df = DataFrame(np.random.randn(1000, 1000)) + self.object_df = DataFrame(([(['foo'] * 1000)] * 1000)) + self.nonunique_cols = self.object_df.copy() + self.nonunique_cols.columns = (['A'] * len(self.nonunique_cols.columns)) + self.pairs = dict([(name, make_pair(frame)) for (name, frame) in (('float_df', self.float_df), ('object_df', self.object_df), ('nonunique_cols', self.nonunique_cols))]) + + def time_frame_nonunique_equal(self): + test_equal('nonunique_cols') + + +class frame_nonunique_unequal(object): + goal_time = 0.2 + + def setup(self): + + def make_pair(frame): + self.df = frame + self.df2 = self.df.copy() + self.df2.ix[((-1), (-1))] = np.nan + return (self.df, self.df2) + + def test_equal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df) + + def test_unequal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df2) + self.float_df = DataFrame(np.random.randn(1000, 1000)) + self.object_df = DataFrame(([(['foo'] * 1000)] * 1000)) + self.nonunique_cols = self.object_df.copy() + self.nonunique_cols.columns = (['A'] * len(self.nonunique_cols.columns)) + self.pairs = dict([(name, make_pair(frame)) for (name, frame) in (('float_df', self.float_df), ('object_df', self.object_df), ('nonunique_cols', self.nonunique_cols))]) + + def time_frame_nonunique_unequal(self): + test_unequal('nonunique_cols') + + +class frame_object_equal(object): + goal_time = 0.2 + + def setup(self): + + def make_pair(frame): + self.df = frame + self.df2 = self.df.copy() + self.df2.ix[((-1), (-1))] = np.nan + return (self.df, self.df2) + + def test_equal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df) + + def test_unequal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df2) + self.float_df = DataFrame(np.random.randn(1000, 1000)) + self.object_df = DataFrame(([(['foo'] * 1000)] * 1000)) + self.nonunique_cols = self.object_df.copy() + self.nonunique_cols.columns = (['A'] * len(self.nonunique_cols.columns)) + self.pairs = dict([(name, make_pair(frame)) for (name, frame) in (('float_df', self.float_df), ('object_df', self.object_df), ('nonunique_cols', self.nonunique_cols))]) + + def time_frame_object_equal(self): + test_equal('object_df') + + +class frame_object_unequal(object): + goal_time = 0.2 + + def setup(self): + + def make_pair(frame): + self.df = frame + self.df2 = self.df.copy() + self.df2.ix[((-1), (-1))] = np.nan + return (self.df, self.df2) + + def test_equal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df) + + def test_unequal(name): + (self.df, self.df2) = pairs[name] + return self.df.equals(self.df2) + self.float_df = DataFrame(np.random.randn(1000, 1000)) + self.object_df = DataFrame(([(['foo'] * 1000)] * 1000)) + self.nonunique_cols = self.object_df.copy() + self.nonunique_cols.columns = (['A'] * len(self.nonunique_cols.columns)) + self.pairs = dict([(name, make_pair(frame)) for (name, frame) in (('float_df', self.float_df), ('object_df', self.object_df), ('nonunique_cols', self.nonunique_cols))]) + + def time_frame_object_unequal(self): + test_unequal('object_df') + + +class frame_reindex_axis0(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 10000)) + self.idx = np.arange(4000, 7000) + + def time_frame_reindex_axis0(self): + self.df.reindex(self.idx) + + +class frame_reindex_axis1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 10000)) + self.idx = np.arange(4000, 7000) + + def time_frame_reindex_axis1(self): + self.df.reindex(columns=self.idx) + + +class frame_reindex_both_axes(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 10000)) + self.idx = np.arange(4000, 7000) + + def time_frame_reindex_both_axes(self): + self.df.reindex(index=self.idx, columns=self.idx) + + +class frame_reindex_both_axes_ix(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(10000, 10000)) + self.idx = np.arange(4000, 7000) + + def time_frame_reindex_both_axes_ix(self): + self.df.ix[(self.idx, self.idx)] + + +class frame_reindex_upcast(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(dict([(c, {0: randint(0, 2, 1000).astype(np.bool_), 1: randint(0, 1000, 1000).astype(np.int16), 2: randint(0, 1000, 1000).astype(np.int32), 3: randint(0, 1000, 1000).astype(np.int64), }[randint(0, 4)]) for c in range(1000)])) + + def time_frame_reindex_upcast(self): + self.df.reindex(permutation(range(1200))) + + +class frame_repr_tall(object): + goal_time = 0.2 + + def setup(self): + self.df = pandas.DataFrame(np.random.randn(10000, 10)) + + def time_frame_repr_tall(self): + repr(self.df) + + +class frame_repr_wide(object): + goal_time = 0.2 + + def setup(self): + self.df = pandas.DataFrame(np.random.randn(10, 10000)) + + def time_frame_repr_wide(self): + repr(self.df) + + +class frame_shift_axis0(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.rand(10000, 500)) + + def time_frame_shift_axis0(self): + self.df.shift(1, axis=0) + + +class frame_shift_axis_1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.rand(10000, 500)) + + def time_frame_shift_axis_1(self): + self.df.shift(1, axis=1) + + +class frame_to_html_mixed(object): + goal_time = 0.2 + + def setup(self): + self.nrows = 500 + self.df = DataFrame(randn(self.nrows, 10)) + self.df[0] = period_range('2000', '2010', self.nrows) + self.df[1] = range(self.nrows) + + def time_frame_to_html_mixed(self): + self.df.to_html() + + +class frame_to_string_floats(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(100, 10)) + + def time_frame_to_string_floats(self): + self.df.to_string() + + +class frame_xs_col(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(1, 100000)) + + def time_frame_xs_col(self): + self.df.xs(50000, axis=1) + + +class frame_xs_row(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(randn(100000, 1)) + + def time_frame_xs_row(self): + self.df.xs(50000) + + +class series_string_vector_slice(object): + goal_time = 0.2 + + def setup(self): + self.s = Series((['abcdefg', np.nan] * 500000)) + + def time_series_string_vector_slice(self): + self.s.str[:5] \ No newline at end of file diff --git a/asv_bench/benchmarks/gil.py b/asv_bench/benchmarks/gil.py new file mode 100644 index 0000000000000..b0486617a52af --- /dev/null +++ b/asv_bench/benchmarks/gil.py @@ -0,0 +1,267 @@ +from pandas_vb_common import * +from pandas.core import common as com +from pandas.util.testing import test_parallel + + +class nogil_groupby_count_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].count() + + def time_nogil_groupby_count_2(self): + pg2() + + +class nogil_groupby_last_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].last() + + def time_nogil_groupby_last_2(self): + pg2() + + +class nogil_groupby_max_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].max() + + def time_nogil_groupby_max_2(self): + pg2() + + +class nogil_groupby_mean_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].mean() + + def time_nogil_groupby_mean_2(self): + pg2() + + +class nogil_groupby_min_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].min() + + def time_nogil_groupby_min_2(self): + pg2() + + +class nogil_groupby_prod_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].prod() + + def time_nogil_groupby_prod_2(self): + pg2() + + +class nogil_groupby_sum_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].sum() + + def time_nogil_groupby_sum_2(self): + pg2() + + +class nogil_groupby_sum_4(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + def f(): + self.df.groupby('key')['data'].sum() + + def g2(): + for i in range(2): + f() + + def g4(): + for i in range(4): + f() + + def g8(): + for i in range(8): + f() + + @test_parallel(num_threads=2) + def pg2(): + f() + + @test_parallel(num_threads=4) + def pg4(): + f() + + @test_parallel(num_threads=8) + def pg8(): + f() + + def time_nogil_groupby_sum_4(self): + pg4() + + +class nogil_groupby_sum_8(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + def f(): + self.df.groupby('key')['data'].sum() + + def g2(): + for i in range(2): + f() + + def g4(): + for i in range(4): + f() + + def g8(): + for i in range(8): + f() + + @test_parallel(num_threads=2) + def pg2(): + f() + + @test_parallel(num_threads=4) + def pg4(): + f() + + @test_parallel(num_threads=8) + def pg8(): + f() + + def time_nogil_groupby_sum_8(self): + pg8() + + +class nogil_groupby_var_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + + @test_parallel(num_threads=2) + def pg2(): + self.df.groupby('key')['data'].var() + + def time_nogil_groupby_var_2(self): + pg2() + + +class nogil_take1d_float64(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + self.N = 10000000.0 + self.df = DataFrame({'int64': np.arange(self.N, dtype='int64'), 'float64': np.arange(self.N, dtype='float64'), }) + self.indexer = np.arange(100, (len(self.df) - 100)) + + @test_parallel(num_threads=2) + def take_1d_pg2_int64(): + com.take_1d(self.df.int64.values, self.indexer) + + @test_parallel(num_threads=2) + def take_1d_pg2_float64(): + com.take_1d(self.df.float64.values, self.indexer) + + def time_nogil_take1d_float64(self): + take_1d_pg2_int64() + + +class nogil_take1d_int64(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.ngroups = 1000 + np.random.seed(1234) + self.df = DataFrame({'key': np.random.randint(0, self.ngroups, size=self.N), 'data': np.random.randn(self.N), }) + self.N = 10000000.0 + self.df = DataFrame({'int64': np.arange(self.N, dtype='int64'), 'float64': np.arange(self.N, dtype='float64'), }) + self.indexer = np.arange(100, (len(self.df) - 100)) + + @test_parallel(num_threads=2) + def take_1d_pg2_int64(): + com.take_1d(self.df.int64.values, self.indexer) + + @test_parallel(num_threads=2) + def take_1d_pg2_float64(): + com.take_1d(self.df.float64.values, self.indexer) + + def time_nogil_take1d_int64(self): + take_1d_pg2_float64() \ No newline at end of file diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py new file mode 100644 index 0000000000000..4f1f4e46b4a31 --- /dev/null +++ b/asv_bench/benchmarks/groupby.py @@ -0,0 +1,1683 @@ +from pandas_vb_common import * +from itertools import product +from string import ascii_letters, digits + + +class groupby_agg_builtins1(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(27182) + self.n = 100000 + self.df = DataFrame(np.random.randint(1, (self.n / 100), (self.n, 3)), columns=['jim', 'joe', 'jolie']) + + def time_groupby_agg_builtins1(self): + self.df.groupby('jim').agg([sum, min, max]) + + +class groupby_agg_builtins2(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(27182) + self.n = 100000 + self.df = DataFrame(np.random.randint(1, (self.n / 100), (self.n, 3)), columns=['jim', 'joe', 'jolie']) + + def time_groupby_agg_builtins2(self): + self.df.groupby(['jim', 'joe']).agg([sum, min, max]) + + +class groupby_apply_dict_return(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(1000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.f = (lambda x: {'first': x.values[0], 'last': x.values[(-1)], }) + + def time_groupby_apply_dict_return(self): + self.data.groupby(self.labels).apply(self.f) + + +class groupby_dt_size(object): + goal_time = 0.2 + + def setup(self): + self.n = 100000 + self.offsets = np.random.randint(self.n, size=self.n).astype('timedelta64[ns]') + self.dates = (np.datetime64('now') + self.offsets) + self.df = DataFrame({'key1': np.random.randint(0, 500, size=self.n), 'key2': np.random.randint(0, 100, size=self.n), 'value1': np.random.randn(self.n), 'value2': np.random.randn(self.n), 'value3': np.random.randn(self.n), 'dates': self.dates, }) + + def time_groupby_dt_size(self): + self.df.groupby(['dates']).size() + + +class groupby_dt_timegrouper_size(object): + goal_time = 0.2 + + def setup(self): + self.n = 100000 + self.offsets = np.random.randint(self.n, size=self.n).astype('timedelta64[ns]') + self.dates = (np.datetime64('now') + self.offsets) + self.df = DataFrame({'key1': np.random.randint(0, 500, size=self.n), 'key2': np.random.randint(0, 100, size=self.n), 'value1': np.random.randn(self.n), 'value2': np.random.randn(self.n), 'value3': np.random.randn(self.n), 'dates': self.dates, }) + + def time_groupby_dt_timegrouper_size(self): + self.df.groupby(TimeGrouper(key='dates', freq='M')).size() + + +class groupby_first_datetimes(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': date_range('1/1/2011', periods=100000, freq='s'), 'b': range(100000), }) + + def time_groupby_first_datetimes(self): + self.df.groupby('b').first() + + +class groupby_first_float32(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_first_float32(self): + self.data2.groupby(self.labels).first() + + +class groupby_first_float64(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_first_float64(self): + self.data.groupby(self.labels).first() + + +class groupby_first_object(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': (['foo'] * 100000), 'b': range(100000), }) + + def time_groupby_first_object(self): + self.df.groupby('b').first() + + +class groupby_frame_apply(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.labels = np.random.randint(0, 2000, size=self.N) + self.labels2 = np.random.randint(0, 3, size=self.N) + self.df = DataFrame({'key': self.labels, 'key2': self.labels2, 'value1': randn(self.N), 'value2': (['foo', 'bar', 'baz', 'qux'] * (self.N / 4)), }) + + def f(g): + return 1 + + def time_groupby_frame_apply(self): + self.df.groupby(['key', 'key2']).apply(f) + + +class groupby_frame_apply_overhead(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.labels = np.random.randint(0, 2000, size=self.N) + self.labels2 = np.random.randint(0, 3, size=self.N) + self.df = DataFrame({'key': self.labels, 'key2': self.labels2, 'value1': randn(self.N), 'value2': (['foo', 'bar', 'baz', 'qux'] * (self.N / 4)), }) + + def f(g): + return 1 + + def time_groupby_frame_apply_overhead(self): + self.df.groupby('key').apply(f) + + +class groupby_frame_cython_many_columns(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.random.randint(0, 100, size=1000) + self.df = DataFrame(randn(1000, 1000)) + + def time_groupby_frame_cython_many_columns(self): + self.df.groupby(self.labels).sum() + + +class groupby_frame_median(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(100000, 2) + self.labels = np.random.randint(0, 1000, size=100000) + self.df = DataFrame(self.data) + + def time_groupby_frame_median(self): + self.df.groupby(self.labels).median() + + +class groupby_frame_nth_any(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randint(1, 100, (10000, 2))) + + def time_groupby_frame_nth_any(self): + self.df.groupby(0).nth(0, dropna='any') + + +class groupby_frame_nth_none(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randint(1, 100, (10000, 2))) + + def time_groupby_frame_nth_none(self): + self.df.groupby(0).nth(0) + + +class groupby_frame_singlekey_integer(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(100000, 1) + self.labels = np.random.randint(0, 1000, size=100000) + self.df = DataFrame(self.data) + + def time_groupby_frame_singlekey_integer(self): + self.df.groupby(self.labels).sum() + + +class groupby_indices(object): + goal_time = 0.2 + + def setup(self): + try: + self.rng = date_range('1/1/2000', '12/31/2005', freq='H') + (year, month, day) = (self.rng.year, self.rng.month, self.rng.day) + except: + self.rng = date_range('1/1/2000', '12/31/2000', offset=datetools.Hour()) + self.year = self.rng.map((lambda x: x.year)) + self.month = self.rng.map((lambda x: x.month)) + self.day = self.rng.map((lambda x: x.day)) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + + def time_groupby_indices(self): + len(self.ts.groupby([self.year, self.month, self.day])) + + +class groupby_int64_overflow(object): + goal_time = 0.2 + + def setup(self): + self.arr = np.random.randint(((-1) << 12), (1 << 12), ((1 << 17), 5)) + self.i = np.random.choice(len(self.arr), (len(self.arr) * 5)) + self.arr = np.vstack((self.arr, self.arr[self.i])) + self.i = np.random.permutation(len(self.arr)) + self.arr = self.arr[self.i] + self.df = DataFrame(self.arr, columns=list('abcde')) + (self.df['jim'], self.df['joe']) = (np.random.randn(2, len(self.df)) * 10) + + def time_groupby_int64_overflow(self): + self.df.groupby(list('abcde')).max() + + +class groupby_int_count(object): + goal_time = 0.2 + + def setup(self): + self.n = 10000 + self.df = DataFrame({'key1': randint(0, 500, size=self.n), 'key2': randint(0, 100, size=self.n), 'ints': randint(0, 1000, size=self.n), 'ints2': randint(0, 1000, size=self.n), }) + + def time_groupby_int_count(self): + self.df.groupby(['key1', 'key2']).count() + + +class groupby_last_datetimes(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': date_range('1/1/2011', periods=100000, freq='s'), 'b': range(100000), }) + + def time_groupby_last_datetimes(self): + self.df.groupby('b').last() + + +class groupby_last_float32(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_last_float32(self): + self.data2.groupby(self.labels).last() + + +class groupby_last_float64(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_last_float64(self): + self.data.groupby(self.labels).last() + + +class groupby_last_object(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': (['foo'] * 100000), 'b': range(100000), }) + + def time_groupby_last_object(self): + self.df.groupby('b').last() + + +class groupby_multi_count(object): + goal_time = 0.2 + + def setup(self): + self.n = 10000 + self.offsets = np.random.randint(self.n, size=self.n).astype('timedelta64[ns]') + self.dates = (np.datetime64('now') + self.offsets) + self.dates[(np.random.rand(self.n) > 0.5)] = np.datetime64('nat') + self.offsets[(np.random.rand(self.n) > 0.5)] = np.timedelta64('nat') + self.value2 = np.random.randn(self.n) + self.value2[(np.random.rand(self.n) > 0.5)] = np.nan + self.obj = tm.choice(list('ab'), size=self.n).astype(object) + self.obj[(np.random.randn(self.n) > 0.5)] = np.nan + self.df = DataFrame({'key1': np.random.randint(0, 500, size=self.n), 'key2': np.random.randint(0, 100, size=self.n), 'dates': self.dates, 'value2': self.value2, 'value3': np.random.randn(self.n), 'ints': np.random.randint(0, 1000, size=self.n), 'obj': self.obj, 'offsets': self.offsets, }) + + def time_groupby_multi_count(self): + self.df.groupby(['key1', 'key2']).count() + + +class groupby_multi_cython(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.ngroups = 100 + + def get_test_data(ngroups=100, n=self.N): + self.unique_groups = range(self.ngroups) + self.arr = np.asarray(np.tile(self.unique_groups, (n / self.ngroups)), dtype=object) + if (len(self.arr) < n): + self.arr = np.asarray((list(self.arr) + self.unique_groups[:(n - len(self.arr))]), dtype=object) + random.shuffle(self.arr) + return self.arr + self.df = DataFrame({'key1': get_test_data(ngroups=self.ngroups), 'key2': get_test_data(ngroups=self.ngroups), 'data1': np.random.randn(self.N), 'data2': np.random.randn(self.N), }) + + def f(): + self.df.groupby(['key1', 'key2']).agg((lambda x: x.values.sum())) + self.simple_series = Series(np.random.randn(self.N)) + self.key1 = self.df['key1'] + + def time_groupby_multi_cython(self): + self.df.groupby(['key1', 'key2']).sum() + + +class groupby_multi_different_functions(object): + goal_time = 0.2 + + def setup(self): + self.fac1 = np.array(['A', 'B', 'C'], dtype='O') + self.fac2 = np.array(['one', 'two'], dtype='O') + self.df = DataFrame({'key1': self.fac1.take(np.random.randint(0, 3, size=100000)), 'key2': self.fac2.take(np.random.randint(0, 2, size=100000)), 'value1': np.random.randn(100000), 'value2': np.random.randn(100000), 'value3': np.random.randn(100000), }) + + def time_groupby_multi_different_functions(self): + self.df.groupby(['key1', 'key2']).agg({'value1': 'mean', 'value2': 'var', 'value3': 'sum', }) + + +class groupby_multi_different_numpy_functions(object): + goal_time = 0.2 + + def setup(self): + self.fac1 = np.array(['A', 'B', 'C'], dtype='O') + self.fac2 = np.array(['one', 'two'], dtype='O') + self.df = DataFrame({'key1': self.fac1.take(np.random.randint(0, 3, size=100000)), 'key2': self.fac2.take(np.random.randint(0, 2, size=100000)), 'value1': np.random.randn(100000), 'value2': np.random.randn(100000), 'value3': np.random.randn(100000), }) + + def time_groupby_multi_different_numpy_functions(self): + self.df.groupby(['key1', 'key2']).agg({'value1': np.mean, 'value2': np.var, 'value3': np.sum, }) + + +class groupby_multi_index(object): + goal_time = 0.2 + + def setup(self): + self.n = (((5 * 7) * 11) * (1 << 9)) + self.alpha = list(map(''.join, product((ascii_letters + digits), repeat=4))) + self.f = (lambda k: np.repeat(np.random.choice(self.alpha, (self.n // k)), k)) + self.df = DataFrame({'a': self.f(11), 'b': self.f(7), 'c': self.f(5), 'd': self.f(1), }) + self.df['joe'] = (np.random.randn(len(self.df)) * 10).round(3) + self.i = np.random.permutation(len(self.df)) + self.df = self.df.iloc[self.i].reset_index(drop=True).copy() + + def time_groupby_multi_index(self): + self.df.groupby(list('abcd')).max() + + +class groupby_multi_python(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.ngroups = 100 + + def get_test_data(ngroups=100, n=self.N): + self.unique_groups = range(self.ngroups) + self.arr = np.asarray(np.tile(self.unique_groups, (n / self.ngroups)), dtype=object) + if (len(self.arr) < n): + self.arr = np.asarray((list(self.arr) + self.unique_groups[:(n - len(self.arr))]), dtype=object) + random.shuffle(self.arr) + return self.arr + self.df = DataFrame({'key1': get_test_data(ngroups=self.ngroups), 'key2': get_test_data(ngroups=self.ngroups), 'data1': np.random.randn(self.N), 'data2': np.random.randn(self.N), }) + + def f(): + self.df.groupby(['key1', 'key2']).agg((lambda x: x.values.sum())) + self.simple_series = Series(np.random.randn(self.N)) + self.key1 = self.df['key1'] + + def time_groupby_multi_python(self): + self.df.groupby(['key1', 'key2'])['data1'].agg((lambda x: x.values.sum())) + + +class groupby_multi_series_op(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.ngroups = 100 + + def get_test_data(ngroups=100, n=self.N): + self.unique_groups = range(self.ngroups) + self.arr = np.asarray(np.tile(self.unique_groups, (n / self.ngroups)), dtype=object) + if (len(self.arr) < n): + self.arr = np.asarray((list(self.arr) + self.unique_groups[:(n - len(self.arr))]), dtype=object) + random.shuffle(self.arr) + return self.arr + self.df = DataFrame({'key1': get_test_data(ngroups=self.ngroups), 'key2': get_test_data(ngroups=self.ngroups), 'data1': np.random.randn(self.N), 'data2': np.random.randn(self.N), }) + + def f(): + self.df.groupby(['key1', 'key2']).agg((lambda x: x.values.sum())) + self.simple_series = Series(np.random.randn(self.N)) + self.key1 = self.df['key1'] + + def time_groupby_multi_series_op(self): + self.df.groupby(['key1', 'key2'])['data1'].agg(np.std) + + +class groupby_multi_size(object): + goal_time = 0.2 + + def setup(self): + self.n = 100000 + self.offsets = np.random.randint(self.n, size=self.n).astype('timedelta64[ns]') + self.dates = (np.datetime64('now') + self.offsets) + self.df = DataFrame({'key1': np.random.randint(0, 500, size=self.n), 'key2': np.random.randint(0, 100, size=self.n), 'value1': np.random.randn(self.n), 'value2': np.random.randn(self.n), 'value3': np.random.randn(self.n), 'dates': self.dates, }) + + def time_groupby_multi_size(self): + self.df.groupby(['key1', 'key2']).size() + + +class groupby_ngroups_10000_all(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_all(self): + self.df.groupby('value')['timestamp'].all() + + +class groupby_ngroups_10000_any(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_any(self): + self.df.groupby('value')['timestamp'].any() + + +class groupby_ngroups_10000_count(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_count(self): + self.df.groupby('value')['timestamp'].count() + + +class groupby_ngroups_10000_cumcount(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_cumcount(self): + self.df.groupby('value')['timestamp'].cumcount() + + +class groupby_ngroups_10000_cummax(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_cummax(self): + self.df.groupby('value')['timestamp'].cummax() + + +class groupby_ngroups_10000_cummin(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_cummin(self): + self.df.groupby('value')['timestamp'].cummin() + + +class groupby_ngroups_10000_cumprod(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_cumprod(self): + self.df.groupby('value')['timestamp'].cumprod() + + +class groupby_ngroups_10000_cumsum(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_cumsum(self): + self.df.groupby('value')['timestamp'].cumsum() + + +class groupby_ngroups_10000_describe(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_describe(self): + self.df.groupby('value')['timestamp'].describe() + + +class groupby_ngroups_10000_diff(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_diff(self): + self.df.groupby('value')['timestamp'].diff() + + +class groupby_ngroups_10000_first(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_first(self): + self.df.groupby('value')['timestamp'].first() + + +class groupby_ngroups_10000_head(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_head(self): + self.df.groupby('value')['timestamp'].head() + + +class groupby_ngroups_10000_last(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_last(self): + self.df.groupby('value')['timestamp'].last() + + +class groupby_ngroups_10000_mad(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_mad(self): + self.df.groupby('value')['timestamp'].mad() + + +class groupby_ngroups_10000_max(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_max(self): + self.df.groupby('value')['timestamp'].max() + + +class groupby_ngroups_10000_mean(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_mean(self): + self.df.groupby('value')['timestamp'].mean() + + +class groupby_ngroups_10000_median(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_median(self): + self.df.groupby('value')['timestamp'].median() + + +class groupby_ngroups_10000_min(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_min(self): + self.df.groupby('value')['timestamp'].min() + + +class groupby_ngroups_10000_nunique(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_nunique(self): + self.df.groupby('value')['timestamp'].nunique() + + +class groupby_ngroups_10000_pct_change(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_pct_change(self): + self.df.groupby('value')['timestamp'].pct_change() + + +class groupby_ngroups_10000_prod(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_prod(self): + self.df.groupby('value')['timestamp'].prod() + + +class groupby_ngroups_10000_rank(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_rank(self): + self.df.groupby('value')['timestamp'].rank() + + +class groupby_ngroups_10000_sem(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_sem(self): + self.df.groupby('value')['timestamp'].sem() + + +class groupby_ngroups_10000_size(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_size(self): + self.df.groupby('value')['timestamp'].size() + + +class groupby_ngroups_10000_skew(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_skew(self): + self.df.groupby('value')['timestamp'].skew() + + +class groupby_ngroups_10000_std(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_std(self): + self.df.groupby('value')['timestamp'].std() + + +class groupby_ngroups_10000_sum(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_sum(self): + self.df.groupby('value')['timestamp'].sum() + + +class groupby_ngroups_10000_tail(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_tail(self): + self.df.groupby('value')['timestamp'].tail() + + +class groupby_ngroups_10000_unique(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_unique(self): + self.df.groupby('value')['timestamp'].unique() + + +class groupby_ngroups_10000_value_counts(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_value_counts(self): + self.df.groupby('value')['timestamp'].value_counts() + + +class groupby_ngroups_10000_var(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 10000 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_10000_var(self): + self.df.groupby('value')['timestamp'].var() + + +class groupby_ngroups_100_all(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_all(self): + self.df.groupby('value')['timestamp'].all() + + +class groupby_ngroups_100_any(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_any(self): + self.df.groupby('value')['timestamp'].any() + + +class groupby_ngroups_100_count(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_count(self): + self.df.groupby('value')['timestamp'].count() + + +class groupby_ngroups_100_cumcount(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_cumcount(self): + self.df.groupby('value')['timestamp'].cumcount() + + +class groupby_ngroups_100_cummax(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_cummax(self): + self.df.groupby('value')['timestamp'].cummax() + + +class groupby_ngroups_100_cummin(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_cummin(self): + self.df.groupby('value')['timestamp'].cummin() + + +class groupby_ngroups_100_cumprod(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_cumprod(self): + self.df.groupby('value')['timestamp'].cumprod() + + +class groupby_ngroups_100_cumsum(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_cumsum(self): + self.df.groupby('value')['timestamp'].cumsum() + + +class groupby_ngroups_100_describe(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_describe(self): + self.df.groupby('value')['timestamp'].describe() + + +class groupby_ngroups_100_diff(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_diff(self): + self.df.groupby('value')['timestamp'].diff() + + +class groupby_ngroups_100_first(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_first(self): + self.df.groupby('value')['timestamp'].first() + + +class groupby_ngroups_100_head(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_head(self): + self.df.groupby('value')['timestamp'].head() + + +class groupby_ngroups_100_last(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_last(self): + self.df.groupby('value')['timestamp'].last() + + +class groupby_ngroups_100_mad(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_mad(self): + self.df.groupby('value')['timestamp'].mad() + + +class groupby_ngroups_100_max(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_max(self): + self.df.groupby('value')['timestamp'].max() + + +class groupby_ngroups_100_mean(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_mean(self): + self.df.groupby('value')['timestamp'].mean() + + +class groupby_ngroups_100_median(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_median(self): + self.df.groupby('value')['timestamp'].median() + + +class groupby_ngroups_100_min(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_min(self): + self.df.groupby('value')['timestamp'].min() + + +class groupby_ngroups_100_nunique(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_nunique(self): + self.df.groupby('value')['timestamp'].nunique() + + +class groupby_ngroups_100_pct_change(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_pct_change(self): + self.df.groupby('value')['timestamp'].pct_change() + + +class groupby_ngroups_100_prod(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_prod(self): + self.df.groupby('value')['timestamp'].prod() + + +class groupby_ngroups_100_rank(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_rank(self): + self.df.groupby('value')['timestamp'].rank() + + +class groupby_ngroups_100_sem(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_sem(self): + self.df.groupby('value')['timestamp'].sem() + + +class groupby_ngroups_100_size(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_size(self): + self.df.groupby('value')['timestamp'].size() + + +class groupby_ngroups_100_skew(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_skew(self): + self.df.groupby('value')['timestamp'].skew() + + +class groupby_ngroups_100_std(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_std(self): + self.df.groupby('value')['timestamp'].std() + + +class groupby_ngroups_100_sum(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_sum(self): + self.df.groupby('value')['timestamp'].sum() + + +class groupby_ngroups_100_tail(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_tail(self): + self.df.groupby('value')['timestamp'].tail() + + +class groupby_ngroups_100_unique(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_unique(self): + self.df.groupby('value')['timestamp'].unique() + + +class groupby_ngroups_100_value_counts(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_value_counts(self): + self.df.groupby('value')['timestamp'].value_counts() + + +class groupby_ngroups_100_var(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.ngroups = 100 + self.size = (self.ngroups * 2) + self.rng = np.arange(self.ngroups) + self.df = DataFrame(dict(timestamp=self.rng.take(np.random.randint(0, self.ngroups, size=self.size)), value=np.random.randint(0, self.size, size=self.size))) + + def time_groupby_ngroups_100_var(self): + self.df.groupby('value')['timestamp'].var() + + +class groupby_nth_datetimes_any(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': date_range('1/1/2011', periods=100000, freq='s'), 'b': range(100000), }) + + def time_groupby_nth_datetimes_any(self): + self.df.groupby('b').nth(0, dropna='all') + + +class groupby_nth_datetimes_none(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': date_range('1/1/2011', periods=100000, freq='s'), 'b': range(100000), }) + + def time_groupby_nth_datetimes_none(self): + self.df.groupby('b').nth(0) + + +class groupby_nth_float32_any(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_nth_float32_any(self): + self.data2.groupby(self.labels).nth(0, dropna='all') + + +class groupby_nth_float32_none(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_nth_float32_none(self): + self.data2.groupby(self.labels).nth(0) + + +class groupby_nth_float64_any(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_nth_float64_any(self): + self.data.groupby(self.labels).nth(0, dropna='all') + + +class groupby_nth_float64_none(object): + goal_time = 0.2 + + def setup(self): + self.labels = np.arange(10000).repeat(10) + self.data = Series(randn(len(self.labels))) + self.data[::3] = np.nan + self.data[1::3] = np.nan + self.data2 = Series(randn(len(self.labels)), dtype='float32') + self.data2[::3] = np.nan + self.data2[1::3] = np.nan + self.labels = self.labels.take(np.random.permutation(len(self.labels))) + + def time_groupby_nth_float64_none(self): + self.data.groupby(self.labels).nth(0) + + +class groupby_nth_object_any(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': (['foo'] * 100000), 'b': range(100000), }) + + def time_groupby_nth_object_any(self): + self.df.groupby('b').nth(0, dropna='any') + + +class groupby_nth_object_none(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'a': (['foo'] * 100000), 'b': range(100000), }) + + def time_groupby_nth_object_none(self): + self.df.groupby('b').nth(0) + + +class groupby_pivot_table(object): + goal_time = 0.2 + + def setup(self): + self.fac1 = np.array(['A', 'B', 'C'], dtype='O') + self.fac2 = np.array(['one', 'two'], dtype='O') + self.ind1 = np.random.randint(0, 3, size=100000) + self.ind2 = np.random.randint(0, 2, size=100000) + self.df = DataFrame({'key1': self.fac1.take(self.ind1), 'key2': self.fac2.take(self.ind2), 'key3': self.fac2.take(self.ind2), 'value1': np.random.randn(100000), 'value2': np.random.randn(100000), 'value3': np.random.randn(100000), }) + + def time_groupby_pivot_table(self): + self.df.pivot_table(index='key1', columns=['key2', 'key3']) + + +class groupby_series_nth_any(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randint(1, 100, (10000, 2))) + + def time_groupby_series_nth_any(self): + self.df[1].groupby(self.df[0]).nth(0, dropna='any') + + +class groupby_series_nth_none(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randint(1, 100, (10000, 2))) + + def time_groupby_series_nth_none(self): + self.df[1].groupby(self.df[0]).nth(0) + + +class groupby_series_simple_cython(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.ngroups = 100 + + def get_test_data(ngroups=100, n=self.N): + self.unique_groups = range(self.ngroups) + self.arr = np.asarray(np.tile(self.unique_groups, (n / self.ngroups)), dtype=object) + if (len(self.arr) < n): + self.arr = np.asarray((list(self.arr) + self.unique_groups[:(n - len(self.arr))]), dtype=object) + random.shuffle(self.arr) + return self.arr + self.df = DataFrame({'key1': get_test_data(ngroups=self.ngroups), 'key2': get_test_data(ngroups=self.ngroups), 'data1': np.random.randn(self.N), 'data2': np.random.randn(self.N), }) + + def f(): + self.df.groupby(['key1', 'key2']).agg((lambda x: x.values.sum())) + self.simple_series = Series(np.random.randn(self.N)) + self.key1 = self.df['key1'] + + def time_groupby_series_simple_cython(self): + self.df.groupby('key1').rank(pct=True) + + +class groupby_simple_compress_timing(object): + goal_time = 0.2 + + def setup(self): + self.data = np.random.randn(1000000, 2) + self.labels = np.random.randint(0, 1000, size=1000000) + self.df = DataFrame(self.data) + + def time_groupby_simple_compress_timing(self): + self.df.groupby(self.labels).mean() + + +class groupby_sum_booleans(object): + goal_time = 0.2 + + def setup(self): + self.N = 500 + self.df = DataFrame({'ii': range(self.N), 'bb': [True for x in range(self.N)], }) + + def time_groupby_sum_booleans(self): + self.df.groupby('ii').sum() + + +class groupby_sum_multiindex(object): + goal_time = 0.2 + + def setup(self): + self.N = 50 + self.df = DataFrame({'A': (range(self.N) * 2), 'B': range((self.N * 2)), 'C': 1, }).set_index(['A', 'B']) + + def time_groupby_sum_multiindex(self): + self.df.groupby(level=[0, 1]).sum() + + +class groupby_transform(object): + goal_time = 0.2 + + def setup(self): + self.n_dates = 400 + self.n_securities = 250 + self.n_columns = 3 + self.share_na = 0.1 + self.dates = date_range('1997-12-31', periods=self.n_dates, freq='B') + self.dates = Index(map((lambda x: (((x.year * 10000) + (x.month * 100)) + x.day)), self.dates)) + self.secid_min = int('10000000', 16) + self.secid_max = int('F0000000', 16) + self.step = ((self.secid_max - self.secid_min) // (self.n_securities - 1)) + self.security_ids = map((lambda x: hex(x)[2:10].upper()), range(self.secid_min, (self.secid_max + 1), self.step)) + self.data_index = MultiIndex(levels=[self.dates.values, self.security_ids], labels=[[i for i in xrange(self.n_dates) for _ in xrange(self.n_securities)], (range(self.n_securities) * self.n_dates)], names=['date', 'security_id']) + self.n_data = len(self.data_index) + self.columns = Index(['factor{}'.format(i) for i in xrange(1, (self.n_columns + 1))]) + self.data = DataFrame(np.random.randn(self.n_data, self.n_columns), index=self.data_index, columns=self.columns) + self.step = int((self.n_data * self.share_na)) + for column_index in xrange(self.n_columns): + self.index = column_index + while (self.index < self.n_data): + self.data.set_value(self.data_index[self.index], self.columns[column_index], np.nan) + self.index += self.step + self.f_fillna = (lambda x: x.fillna(method='pad')) + + def time_groupby_transform(self): + self.data.groupby(level='security_id').transform(self.f_fillna) + + +class groupby_transform_multi_key1(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(2718281) + self.n = 20000 + self.df = DataFrame(np.random.randint(1, self.n, (self.n, 3)), columns=['jim', 'joe', 'jolie']) + + def time_groupby_transform_multi_key1(self): + self.df.groupby(['jim', 'joe'])['jolie'].transform('max') + + +class groupby_transform_multi_key2(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(2718281) + self.n = 20000 + self.df = DataFrame(np.random.randint(1, self.n, (self.n, 3)), columns=['jim', 'joe', 'jolie']) + self.df['jim'] = self.df['joe'] + + def time_groupby_transform_multi_key2(self): + self.df.groupby(['jim', 'joe'])['jolie'].transform('max') + + +class groupby_transform_multi_key3(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(2718281) + self.n = 200000 + self.df = DataFrame(np.random.randint(1, (self.n / 10), (self.n, 3)), columns=['jim', 'joe', 'jolie']) + + def time_groupby_transform_multi_key3(self): + self.df.groupby(['jim', 'joe'])['jolie'].transform('max') + + +class groupby_transform_multi_key4(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(2718281) + self.n = 200000 + self.df = DataFrame(np.random.randint(1, (self.n / 10), (self.n, 3)), columns=['jim', 'joe', 'jolie']) + self.df['jim'] = self.df['joe'] + + def time_groupby_transform_multi_key4(self): + self.df.groupby(['jim', 'joe'])['jolie'].transform('max') + + +class groupby_transform_series(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(0) + self.N = 120000 + self.N_TRANSITIONS = 1400 + self.transition_points = np.random.permutation(np.arange(self.N))[:self.N_TRANSITIONS] + self.transition_points.sort() + self.transitions = np.zeros((self.N,), dtype=np.bool) + self.transitions[self.transition_points] = True + self.g = self.transitions.cumsum() + self.df = DataFrame({'signal': np.random.rand(self.N), }) + + def time_groupby_transform_series(self): + self.df['signal'].groupby(self.g).transform(np.mean) + + +class groupby_transform_series2(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(0) + self.df = DataFrame({'id': (np.arange(100000) / 3), 'val': np.random.randn(100000), }) + + def time_groupby_transform_series2(self): + self.df.groupby('id')['val'].transform(np.mean) + + +class groupby_transform_ufunc(object): + goal_time = 0.2 + + def setup(self): + self.n_dates = 400 + self.n_securities = 250 + self.n_columns = 3 + self.share_na = 0.1 + self.dates = date_range('1997-12-31', periods=self.n_dates, freq='B') + self.dates = Index(map((lambda x: (((x.year * 10000) + (x.month * 100)) + x.day)), self.dates)) + self.secid_min = int('10000000', 16) + self.secid_max = int('F0000000', 16) + self.step = ((self.secid_max - self.secid_min) // (self.n_securities - 1)) + self.security_ids = map((lambda x: hex(x)[2:10].upper()), range(self.secid_min, (self.secid_max + 1), self.step)) + self.data_index = MultiIndex(levels=[self.dates.values, self.security_ids], labels=[[i for i in xrange(self.n_dates) for _ in xrange(self.n_securities)], (range(self.n_securities) * self.n_dates)], names=['date', 'security_id']) + self.n_data = len(self.data_index) + self.columns = Index(['factor{}'.format(i) for i in xrange(1, (self.n_columns + 1))]) + self.data = DataFrame(np.random.randn(self.n_data, self.n_columns), index=self.data_index, columns=self.columns) + self.step = int((self.n_data * self.share_na)) + for column_index in xrange(self.n_columns): + self.index = column_index + while (self.index < self.n_data): + self.data.set_value(self.data_index[self.index], self.columns[column_index], np.nan) + self.index += self.step + self.f_fillna = (lambda x: x.fillna(method='pad')) + + def time_groupby_transform_ufunc(self): + self.data.groupby(level='date').transform(np.max) + + +class series_value_counts_int64(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.randint(0, 1000, size=100000)) + + def time_series_value_counts_int64(self): + self.s.value_counts() + + +class series_value_counts_strings(object): + goal_time = 0.2 + + def setup(self): + self.K = 1000 + self.N = 100000 + self.uniques = tm.makeStringIndex(self.K).values + self.s = Series(np.tile(self.uniques, (self.N // self.K))) + + def time_series_value_counts_strings(self): + self.s.value_counts() \ No newline at end of file diff --git a/asv_bench/benchmarks/hdfstore_bench.py b/asv_bench/benchmarks/hdfstore_bench.py new file mode 100644 index 0000000000000..9e36f735f8608 --- /dev/null +++ b/asv_bench/benchmarks/hdfstore_bench.py @@ -0,0 +1,351 @@ +from pandas_vb_common import * +import os + + +class query_store_table(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = date_range('1/1/2000', periods=25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + self.store.append('df12', self.df) + + def time_query_store_table(self): + self.store.select('df12', [('index', '>', self.df.index[10000]), ('index', '<', self.df.index[15000])]) + + def teardown(self): + self.store.close() + + +class query_store_table_wide(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = date_range('1/1/2000', periods=25000) + self.df = DataFrame(np.random.randn(25000, 100), index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + self.store.append('df11', self.df) + + def time_query_store_table_wide(self): + self.store.select('df11', [('index', '>', self.df.index[10000]), ('index', '<', self.df.index[15000])]) + + def teardown(self): + self.store.close() + + +class read_store(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + self.store.put('df1', self.df) + + def time_read_store(self): + self.store.get('df1') + + def teardown(self): + self.store.close() + + +class read_store_mixed(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), 'string1': (['foo'] * 25000), 'bool1': ([True] * 25000), 'int1': np.random.randint(0, 250000, size=25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + self.store.put('df3', self.df) + + def time_read_store_mixed(self): + self.store.get('df3') + + def teardown(self): + self.store.close() + + +class read_store_table(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + self.store.append('df7', self.df) + + def time_read_store_table(self): + self.store.select('df7') + + def teardown(self): + self.store.close() + + +class read_store_table_mixed(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 10000 + self.index = tm.makeStringIndex(self.N) + self.df = DataFrame({'float1': randn(self.N), 'float2': randn(self.N), 'string1': (['foo'] * self.N), 'bool1': ([True] * self.N), 'int1': np.random.randint(0, self.N, size=self.N), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + self.store.append('df5', self.df) + + def time_read_store_table_mixed(self): + self.store.select('df5') + + def teardown(self): + self.store.close() + + +class read_store_table_panel(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.p = Panel(randn(20, 1000, 25), items=[('Item%03d' % i) for i in xrange(20)], major_axis=date_range('1/1/2000', periods=1000), minor_axis=[('E%03d' % i) for i in xrange(25)]) + remove(self.f) + self.store = HDFStore(self.f) + self.store.append('p1', self.p) + + def time_read_store_table_panel(self): + self.store.select('p1') + + def teardown(self): + self.store.close() + + +class read_store_table_wide(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.df = DataFrame(np.random.randn(25000, 100)) + remove(self.f) + self.store = HDFStore(self.f) + self.store.append('df9', self.df) + + def time_read_store_table_wide(self): + self.store.select('df9') + + def teardown(self): + self.store.close() + + +class write_store(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store(self): + self.store.put('df2', self.df) + + def teardown(self): + self.store.close() + + +class write_store_mixed(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), 'string1': (['foo'] * 25000), 'bool1': ([True] * 25000), 'int1': np.random.randint(0, 250000, size=25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store_mixed(self): + self.store.put('df4', self.df) + + def teardown(self): + self.store.close() + + +class write_store_table(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store_table(self): + self.store.append('df8', self.df) + + def teardown(self): + self.store.close() + + +class write_store_table_dc(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.df = DataFrame(np.random.randn(10000, 10), columns=[('C%03d' % i) for i in xrange(10)]) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store_table_dc(self): + self.store.append('df15', self.df, data_columns=True) + + def teardown(self): + self.store.close() + + +class write_store_table_mixed(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.index = tm.makeStringIndex(25000) + self.df = DataFrame({'float1': randn(25000), 'float2': randn(25000), 'string1': (['foo'] * 25000), 'bool1': ([True] * 25000), 'int1': np.random.randint(0, 25000, size=25000), }, index=self.index) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store_table_mixed(self): + self.store.append('df6', self.df) + + def teardown(self): + self.store.close() + + +class write_store_table_panel(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.p = Panel(randn(20, 1000, 25), items=[('Item%03d' % i) for i in xrange(20)], major_axis=date_range('1/1/2000', periods=1000), minor_axis=[('E%03d' % i) for i in xrange(25)]) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store_table_panel(self): + self.store.append('p2', self.p) + + def teardown(self): + self.store.close() + + +class write_store_table_wide(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.h5' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.df = DataFrame(np.random.randn(25000, 100)) + remove(self.f) + self.store = HDFStore(self.f) + + def time_write_store_table_wide(self): + self.store.append('df10', self.df) + + def teardown(self): + self.store.close() \ No newline at end of file diff --git a/asv_bench/benchmarks/index_object.py b/asv_bench/benchmarks/index_object.py new file mode 100644 index 0000000000000..9c181c92195ea --- /dev/null +++ b/asv_bench/benchmarks/index_object.py @@ -0,0 +1,292 @@ +from pandas_vb_common import * + + +class datetime_index_intersection(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=10000, freq='T') + self.rng2 = self.rng[:(-1)] + + def time_datetime_index_intersection(self): + self.rng.intersection(self.rng2) + + +class datetime_index_repr(object): + goal_time = 0.2 + + def setup(self): + self.dr = pd.date_range('20000101', freq='D', periods=100000) + + def time_datetime_index_repr(self): + self.dr._is_dates_only + + +class datetime_index_union(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=10000, freq='T') + self.rng2 = self.rng[:(-1)] + + def time_datetime_index_union(self): + self.rng.union(self.rng2) + + +class index_datetime_intersection(object): + goal_time = 0.2 + + def setup(self): + self.rng = DatetimeIndex(start='1/1/2000', periods=10000, freq=datetools.Minute()) + if (self.rng.dtype == object): + self.rng = self.rng.view(Index) + else: + self.rng = self.rng.asobject + self.rng2 = self.rng[:(-1)] + + def time_index_datetime_intersection(self): + self.rng.intersection(self.rng2) + + +class index_datetime_union(object): + goal_time = 0.2 + + def setup(self): + self.rng = DatetimeIndex(start='1/1/2000', periods=10000, freq=datetools.Minute()) + if (self.rng.dtype == object): + self.rng = self.rng.view(Index) + else: + self.rng = self.rng.asobject + self.rng2 = self.rng[:(-1)] + + def time_index_datetime_union(self): + self.rng.union(self.rng2) + + +class index_float64_boolean_indexer(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_boolean_indexer(self): + self.idx[self.mask] + + +class index_float64_boolean_series_indexer(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_boolean_series_indexer(self): + self.idx[self.series_mask] + + +class index_float64_construct(object): + goal_time = 0.2 + + def setup(self): + self.baseidx = np.arange(1000000.0) + + def time_index_float64_construct(self): + Index(self.baseidx) + + +class index_float64_div(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_div(self): + (self.idx / 2) + + +class index_float64_get(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_get(self): + self.idx[1] + + +class index_float64_mul(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_mul(self): + (self.idx * 2) + + +class index_float64_slice_indexer_basic(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_slice_indexer_basic(self): + self.idx[:(-1)] + + +class index_float64_slice_indexer_even(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeFloatIndex(1000000) + self.mask = ((np.arange(self.idx.size) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_float64_slice_indexer_even(self): + self.idx[::2] + + +class index_int64_intersection(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.options = np.arange(self.N) + self.left = Index(self.options.take(np.random.permutation(self.N)[:(self.N // 2)])) + self.right = Index(self.options.take(np.random.permutation(self.N)[:(self.N // 2)])) + + def time_index_int64_intersection(self): + self.left.intersection(self.right) + + +class index_int64_union(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + self.options = np.arange(self.N) + self.left = Index(self.options.take(np.random.permutation(self.N)[:(self.N // 2)])) + self.right = Index(self.options.take(np.random.permutation(self.N)[:(self.N // 2)])) + + def time_index_int64_union(self): + self.left.union(self.right) + + +class index_str_boolean_indexer(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeStringIndex(1000000) + self.mask = ((np.arange(1000000) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_str_boolean_indexer(self): + self.idx[self.mask] + + +class index_str_boolean_series_indexer(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeStringIndex(1000000) + self.mask = ((np.arange(1000000) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_str_boolean_series_indexer(self): + self.idx[self.series_mask] + + +class index_str_slice_indexer_basic(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeStringIndex(1000000) + self.mask = ((np.arange(1000000) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_str_slice_indexer_basic(self): + self.idx[:(-1)] + + +class index_str_slice_indexer_even(object): + goal_time = 0.2 + + def setup(self): + self.idx = tm.makeStringIndex(1000000) + self.mask = ((np.arange(1000000) % 3) == 0) + self.series_mask = Series(self.mask) + + def time_index_str_slice_indexer_even(self): + self.idx[::2] + + +class multiindex_duplicated(object): + goal_time = 0.2 + + def setup(self): + (n, k) = (200, 5000) + self.levels = [np.arange(n), tm.makeStringIndex(n).values, (1000 + np.arange(n))] + self.labels = [np.random.choice(n, (k * n)) for lev in self.levels] + self.mi = MultiIndex(levels=self.levels, labels=self.labels) + + def time_multiindex_duplicated(self): + self.mi.duplicated() + + +class multiindex_from_product(object): + goal_time = 0.2 + + def setup(self): + self.iterables = [tm.makeStringIndex(10000), xrange(20)] + + def time_multiindex_from_product(self): + MultiIndex.from_product(self.iterables) + + +class multiindex_sortlevel_int64(object): + goal_time = 0.2 + + def setup(self): + self.n = ((((3 * 5) * 7) * 11) * (1 << 10)) + (low, high) = (((-1) << 12), (1 << 12)) + self.f = (lambda k: np.repeat(np.random.randint(low, high, (self.n // k)), k)) + self.i = np.random.permutation(self.n) + self.mi = MultiIndex.from_arrays([self.f(11), self.f(7), self.f(5), self.f(3), self.f(1)])[self.i] + + def time_multiindex_sortlevel_int64(self): + self.mi.sortlevel() + + +class multiindex_with_datetime_level_full(object): + goal_time = 0.2 + + def setup(self): + self.level1 = range(1000) + self.level2 = date_range(start='1/1/2012', periods=100) + self.mi = MultiIndex.from_product([self.level1, self.level2]) + + def time_multiindex_with_datetime_level_full(self): + self.mi.copy().values + + +class multiindex_with_datetime_level_sliced(object): + goal_time = 0.2 + + def setup(self): + self.level1 = range(1000) + self.level2 = date_range(start='1/1/2012', periods=100) + self.mi = MultiIndex.from_product([self.level1, self.level2]) + + def time_multiindex_with_datetime_level_sliced(self): + self.mi[:10].values \ No newline at end of file diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py new file mode 100644 index 0000000000000..e76a87ab881c9 --- /dev/null +++ b/asv_bench/benchmarks/indexing.py @@ -0,0 +1,458 @@ +from pandas_vb_common import * +import pandas.computation.expressions as expr + + +class dataframe_getitem_scalar(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(1000) + self.columns = tm.makeStringIndex(30) + self.df = DataFrame(np.random.rand(1000, 30), index=self.index, columns=self.columns) + self.idx = self.index[100] + self.col = self.columns[10] + + def time_dataframe_getitem_scalar(self): + self.df[self.col][self.idx] + + +class datamatrix_getitem_scalar(object): + goal_time = 0.2 + + def setup(self): + try: + self.klass = DataMatrix + except: + self.klass = DataFrame + self.index = tm.makeStringIndex(1000) + self.columns = tm.makeStringIndex(30) + self.df = self.klass(np.random.rand(1000, 30), index=self.index, columns=self.columns) + self.idx = self.index[100] + self.col = self.columns[10] + + def time_datamatrix_getitem_scalar(self): + self.df[self.col][self.idx] + + +class series_get_value(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(1000) + self.s = Series(np.random.rand(1000), index=self.index) + self.idx = self.index[100] + + def time_series_get_value(self): + self.s.get_value(self.idx) + + +class time_series_getitem_scalar(object): + goal_time = 0.2 + + def setup(self): + tm.N = 1000 + self.ts = tm.makeTimeSeries() + self.dt = self.ts.index[500] + + def time_time_series_getitem_scalar(self): + self.ts[self.dt] + + +class frame_iloc_big(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(dict(A=(['foo'] * 1000000))) + + def time_frame_iloc_big(self): + self.df.iloc[:100, 0] + + +class frame_iloc_dups(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'A': ([0.1] * 3000), 'B': ([1] * 3000), }) + self.idx = (np.array(range(30)) * 99) + self.df2 = DataFrame({'A': ([0.1] * 1000), 'B': ([1] * 1000), }) + self.df2 = concat([self.df2, (2 * self.df2), (3 * self.df2)]) + + def time_frame_iloc_dups(self): + self.df2.iloc[self.idx] + + +class frame_loc_dups(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'A': ([0.1] * 3000), 'B': ([1] * 3000), }) + self.idx = (np.array(range(30)) * 99) + self.df2 = DataFrame({'A': ([0.1] * 1000), 'B': ([1] * 1000), }) + self.df2 = concat([self.df2, (2 * self.df2), (3 * self.df2)]) + + def time_frame_loc_dups(self): + self.df2.loc[self.idx] + + +class frame_xs_mi_ix(object): + goal_time = 0.2 + + def setup(self): + self.mi = MultiIndex.from_tuples([(x, y) for x in range(1000) for y in range(1000)]) + self.s = Series(np.random.randn(1000000), index=self.mi) + self.df = DataFrame(self.s) + + def time_frame_xs_mi_ix(self): + self.df.ix[999] + + +class indexing_dataframe_boolean(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(50000, 100)) + self.df2 = DataFrame(np.random.randn(50000, 100)) + + def time_indexing_dataframe_boolean(self): + (self.df > self.df2) + + +class indexing_dataframe_boolean_no_ne(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(50000, 100)) + self.df2 = DataFrame(np.random.randn(50000, 100)) + expr.set_use_numexpr(False) + + def time_indexing_dataframe_boolean_no_ne(self): + (self.df > self.df2) + + def teardown(self): + expr.set_use_numexpr(True) + + +class indexing_dataframe_boolean_rows(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) + self.indexer = (self.df['B'] > 0) + self.obj_indexer = self.indexer.astype('O') + + def time_indexing_dataframe_boolean_rows(self): + self.df[self.indexer] + + +class indexing_dataframe_boolean_rows_object(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) + self.indexer = (self.df['B'] > 0) + self.obj_indexer = self.indexer.astype('O') + + def time_indexing_dataframe_boolean_rows_object(self): + self.df[self.obj_indexer] + + +class indexing_dataframe_boolean_st(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(50000, 100)) + self.df2 = DataFrame(np.random.randn(50000, 100)) + expr.set_numexpr_threads(1) + + def time_indexing_dataframe_boolean_st(self): + (self.df > self.df2) + + def teardown(self): + expr.set_numexpr_threads() + + +class indexing_frame_get_value(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(1000) + self.columns = tm.makeStringIndex(30) + self.df = DataFrame(np.random.randn(1000, 30), index=self.index, columns=self.columns) + self.idx = self.index[100] + self.col = self.columns[10] + + def time_indexing_frame_get_value(self): + self.df.get_value(self.idx, self.col) + + +class indexing_frame_get_value_ix(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(1000) + self.columns = tm.makeStringIndex(30) + self.df = DataFrame(np.random.randn(1000, 30), index=self.index, columns=self.columns) + self.idx = self.index[100] + self.col = self.columns[10] + + def time_indexing_frame_get_value_ix(self): + self.df.ix[(self.idx, self.col)] + + +class indexing_panel_subset(object): + goal_time = 0.2 + + def setup(self): + self.p = Panel(np.random.randn(100, 100, 100)) + self.inds = range(0, 100, 10) + + def time_indexing_panel_subset(self): + self.p.ix[(self.inds, self.inds, self.inds)] + + +class multiindex_slicers(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(1234) + self.idx = pd.IndexSlice + self.n = 100000 + self.mdt = pandas.DataFrame() + self.mdt['A'] = np.random.choice(range(10000, 45000, 1000), self.n) + self.mdt['B'] = np.random.choice(range(10, 400), self.n) + self.mdt['C'] = np.random.choice(range(1, 150), self.n) + self.mdt['D'] = np.random.choice(range(10000, 45000), self.n) + self.mdt['x'] = np.random.choice(range(400), self.n) + self.mdt['y'] = np.random.choice(range(25), self.n) + self.test_A = 25000 + self.test_B = 25 + self.test_C = 40 + self.test_D = 35000 + self.eps_A = 5000 + self.eps_B = 5 + self.eps_C = 5 + self.eps_D = 5000 + self.mdt2 = self.mdt.set_index(['A', 'B', 'C', 'D']).sortlevel() + + def time_multiindex_slicers(self): + self.mdt2.loc[self.idx[(self.test_A - self.eps_A):(self.test_A + self.eps_A), (self.test_B - self.eps_B):(self.test_B + self.eps_B), (self.test_C - self.eps_C):(self.test_C + self.eps_C), (self.test_D - self.eps_D):(self.test_D + self.eps_D)], :] + + +class series_getitem_array(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_getitem_array(self): + self.s[np.arange(10000)] + + +class series_getitem_label_slice(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(1000000) + self.s = Series(np.random.rand(1000000), index=self.index) + self.lbl = self.s.index[800000] + + def time_series_getitem_label_slice(self): + self.s[:self.lbl] + + +class series_getitem_list_like(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_getitem_list_like(self): + self.s[[800000]] + + +class series_getitem_pos_slice(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(1000000) + self.s = Series(np.random.rand(1000000), index=self.index) + + def time_series_getitem_pos_slice(self): + self.s[:800000] + + +class series_getitem_scalar(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_getitem_scalar(self): + self.s[800000] + + +class series_getitem_slice(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_getitem_slice(self): + self.s[:800000] + + +class series_iloc_array(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_iloc_array(self): + self.s.iloc[np.arange(10000)] + + +class series_iloc_list_like(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_iloc_list_like(self): + self.s.iloc[[800000]] + + +class series_iloc_scalar(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_iloc_scalar(self): + self.s.iloc[800000] + + +class series_iloc_slice(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_iloc_slice(self): + self.s.iloc[:800000] + + +class series_ix_array(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_ix_array(self): + self.s.ix[np.arange(10000)] + + +class series_ix_list_like(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_ix_list_like(self): + self.s.ix[[800000]] + + +class series_ix_scalar(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_ix_scalar(self): + self.s.ix[800000] + + +class series_ix_slice(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_ix_slice(self): + self.s.ix[:800000] + + +class series_loc_array(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_loc_array(self): + self.s.loc[np.arange(10000)] + + +class series_loc_list_like(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_loc_list_like(self): + self.s.loc[[800000]] + + +class series_loc_scalar(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_loc_scalar(self): + self.s.loc[800000] + + +class series_loc_slice(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.rand(1000000)) + + def time_series_loc_slice(self): + self.s.loc[:800000] + + +class series_xs_mi_ix(object): + goal_time = 0.2 + + def setup(self): + self.mi = MultiIndex.from_tuples([(x, y) for x in range(1000) for y in range(1000)]) + self.s = Series(np.random.randn(1000000), index=self.mi) + + def time_series_xs_mi_ix(self): + self.s.ix[999] + + +class sort_level_one(object): + goal_time = 0.2 + + def setup(self): + self.a = np.repeat(np.arange(100), 1000) + self.b = np.tile(np.arange(1000), 100) + self.midx = MultiIndex.from_arrays([self.a, self.b]) + self.midx = self.midx.take(np.random.permutation(np.arange(100000))) + + def time_sort_level_one(self): + self.midx.sortlevel(1) + + +class sort_level_zero(object): + goal_time = 0.2 + + def setup(self): + self.a = np.repeat(np.arange(100), 1000) + self.b = np.tile(np.arange(1000), 100) + self.midx = MultiIndex.from_arrays([self.a, self.b]) + self.midx = self.midx.take(np.random.permutation(np.arange(100000))) + + def time_sort_level_zero(self): + self.midx.sortlevel(0) \ No newline at end of file diff --git a/asv_bench/benchmarks/inference.py b/asv_bench/benchmarks/inference.py new file mode 100644 index 0000000000000..2addc810a218f --- /dev/null +++ b/asv_bench/benchmarks/inference.py @@ -0,0 +1,138 @@ +from pandas_vb_common import * +import pandas as pd + + +class dtype_infer_datetime64(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_datetime64(self): + (self.df_datetime64['A'] - self.df_datetime64['B']) + + +class dtype_infer_float32(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_float32(self): + (self.df_float32['A'] + self.df_float32['B']) + + +class dtype_infer_float64(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_float64(self): + (self.df_float64['A'] + self.df_float64['B']) + + +class dtype_infer_int32(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_int32(self): + (self.df_int32['A'] + self.df_int32['B']) + + +class dtype_infer_int64(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_int64(self): + (self.df_int64['A'] + self.df_int64['B']) + + +class dtype_infer_timedelta64_1(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_timedelta64_1(self): + (self.df_timedelta64['A'] + self.df_timedelta64['B']) + + +class dtype_infer_timedelta64_2(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_timedelta64_2(self): + (self.df_timedelta64['A'] + self.df_timedelta64['A']) + + +class dtype_infer_uint32(object): + goal_time = 0.2 + + def setup(self): + self.N = 500000 + self.df_int64 = DataFrame(dict(A=np.arange(self.N, dtype='int64'), B=np.arange(self.N, dtype='int64'))) + self.df_int32 = DataFrame(dict(A=np.arange(self.N, dtype='int32'), B=np.arange(self.N, dtype='int32'))) + self.df_uint32 = DataFrame(dict(A=np.arange(self.N, dtype='uint32'), B=np.arange(self.N, dtype='uint32'))) + self.df_float64 = DataFrame(dict(A=np.arange(self.N, dtype='float64'), B=np.arange(self.N, dtype='float64'))) + self.df_float32 = DataFrame(dict(A=np.arange(self.N, dtype='float32'), B=np.arange(self.N, dtype='float32'))) + self.df_datetime64 = DataFrame(dict(A=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'), B=pd.to_datetime(np.arange(self.N, dtype='int64'), unit='ms'))) + self.df_timedelta64 = DataFrame(dict(A=(self.df_datetime64['A'] - self.df_datetime64['B']), B=self.df_datetime64['B'])) + + def time_dtype_infer_uint32(self): + (self.df_uint32['A'] + self.df_uint32['B']) \ No newline at end of file diff --git a/asv_bench/benchmarks/io_bench.py b/asv_bench/benchmarks/io_bench.py new file mode 100644 index 0000000000000..9eee932de8b7c --- /dev/null +++ b/asv_bench/benchmarks/io_bench.py @@ -0,0 +1,135 @@ +from pandas_vb_common import * +from pandas import concat, Timestamp +from StringIO import StringIO + + +class frame_to_csv(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(3000, 30)) + + def time_frame_to_csv(self): + self.df.to_csv('__test__.csv') + + +class frame_to_csv2(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame({'A': range(50000), }) + self.df['B'] = (self.df.A + 1.0) + self.df['C'] = (self.df.A + 2.0) + self.df['D'] = (self.df.A + 3.0) + + def time_frame_to_csv2(self): + self.df.to_csv('__test__.csv') + + +class frame_to_csv_date_formatting(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=1000) + self.data = DataFrame(self.rng, index=self.rng) + + def time_frame_to_csv_date_formatting(self): + self.data.to_csv('__test__.csv', date_format='%Y%m%d') + + +class frame_to_csv_mixed(object): + goal_time = 0.2 + + def setup(self): + + def create_cols(name): + return [('%s%03d' % (name, i)) for i in xrange(5)] + self.df_float = DataFrame(np.random.randn(5000, 5), dtype='float64', columns=create_cols('float')) + self.df_int = DataFrame(np.random.randn(5000, 5), dtype='int64', columns=create_cols('int')) + self.df_bool = DataFrame(True, index=self.df_float.index, columns=create_cols('bool')) + self.df_object = DataFrame('foo', index=self.df_float.index, columns=create_cols('object')) + self.df_dt = DataFrame(Timestamp('20010101'), index=self.df_float.index, columns=create_cols('date')) + self.df_float.ix[30:500, 1:3] = np.nan + self.df = concat([self.df_float, self.df_int, self.df_bool, self.df_object, self.df_dt], axis=1) + + def time_frame_to_csv_mixed(self): + self.df.to_csv('__test__.csv') + + +class read_csv_infer_datetime_format_custom(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=1000) + self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%m/%d/%Y %H:%M:%S.%f')))) + + def time_read_csv_infer_datetime_format_custom(self): + read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'], infer_datetime_format=True) + + +class read_csv_infer_datetime_format_iso8601(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=1000) + self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%Y-%m-%d %H:%M:%S')))) + + def time_read_csv_infer_datetime_format_iso8601(self): + read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'], infer_datetime_format=True) + + +class read_csv_infer_datetime_format_ymd(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=1000) + self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%Y%m%d')))) + + def time_read_csv_infer_datetime_format_ymd(self): + read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo'], infer_datetime_format=True) + + +class read_csv_skiprows(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(20000) + self.df = DataFrame({'float1': randn(20000), 'float2': randn(20000), 'string1': (['foo'] * 20000), 'bool1': ([True] * 20000), 'int1': np.random.randint(0, 200000, size=20000), }, index=self.index) + self.df.to_csv('__test__.csv') + + def time_read_csv_skiprows(self): + read_csv('__test__.csv', skiprows=10000) + + +class read_csv_standard(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + self.df.to_csv('__test__.csv') + + def time_read_csv_standard(self): + read_csv('__test__.csv') + + +class read_parse_dates_iso8601(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=1000) + self.data = '\n'.join(self.rng.map((lambda x: x.strftime('%Y-%m-%d %H:%M:%S')))) + + def time_read_parse_dates_iso8601(self): + read_csv(StringIO(self.data), header=None, names=['foo'], parse_dates=['foo']) + + +class write_csv_standard(object): + goal_time = 0.2 + + def setup(self): + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + + def time_write_csv_standard(self): + self.df.to_csv('__test__.csv') \ No newline at end of file diff --git a/asv_bench/benchmarks/io_sql.py b/asv_bench/benchmarks/io_sql.py new file mode 100644 index 0000000000000..e75e691b61c96 --- /dev/null +++ b/asv_bench/benchmarks/io_sql.py @@ -0,0 +1,215 @@ +from pandas_vb_common import * +from sqlalchemy import create_engine +import sqlite3 +import sqlalchemy + + +class sql_datetime_read_and_parse_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df['datetime_string'] = self.df['datetime'].map(str) + self.df.to_sql('test_type', self.engine, if_exists='replace') + self.df[['float', 'datetime_string']].to_sql('test_type', self.con, if_exists='replace') + + def time_sql_datetime_read_and_parse_sqlalchemy(self): + read_sql_table('test_type', self.engine, columns=['datetime_string'], parse_dates=['datetime_string']) + + +class sql_datetime_read_as_native_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df['datetime_string'] = self.df['datetime'].map(str) + self.df.to_sql('test_type', self.engine, if_exists='replace') + self.df[['float', 'datetime_string']].to_sql('test_type', self.con, if_exists='replace') + + def time_sql_datetime_read_as_native_sqlalchemy(self): + read_sql_table('test_type', self.engine, columns=['datetime']) + + +class sql_datetime_write_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'string': (['foo'] * 10000), 'bool': ([True] * 10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df.loc[1000:3000, 'float'] = np.nan + + def time_sql_datetime_write_sqlalchemy(self): + self.df[['datetime']].to_sql('test_datetime', self.engine, if_exists='replace') + + +class sql_float_read_query_fallback(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df['datetime_string'] = self.df['datetime'].map(str) + self.df.to_sql('test_type', self.engine, if_exists='replace') + self.df[['float', 'datetime_string']].to_sql('test_type', self.con, if_exists='replace') + + def time_sql_float_read_query_fallback(self): + read_sql_query('SELECT float FROM test_type', self.con) + + +class sql_float_read_query_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df['datetime_string'] = self.df['datetime'].map(str) + self.df.to_sql('test_type', self.engine, if_exists='replace') + self.df[['float', 'datetime_string']].to_sql('test_type', self.con, if_exists='replace') + + def time_sql_float_read_query_sqlalchemy(self): + read_sql_query('SELECT float FROM test_type', self.engine) + + +class sql_float_read_table_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df['datetime_string'] = self.df['datetime'].map(str) + self.df.to_sql('test_type', self.engine, if_exists='replace') + self.df[['float', 'datetime_string']].to_sql('test_type', self.con, if_exists='replace') + + def time_sql_float_read_table_sqlalchemy(self): + read_sql_table('test_type', self.engine, columns=['float']) + + +class sql_float_write_fallback(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'string': (['foo'] * 10000), 'bool': ([True] * 10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df.loc[1000:3000, 'float'] = np.nan + + def time_sql_float_write_fallback(self): + self.df[['float']].to_sql('test_float', self.con, if_exists='replace') + + +class sql_float_write_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'string': (['foo'] * 10000), 'bool': ([True] * 10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df.loc[1000:3000, 'float'] = np.nan + + def time_sql_float_write_sqlalchemy(self): + self.df[['float']].to_sql('test_float', self.engine, if_exists='replace') + + +class sql_read_query_fallback(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + self.df.to_sql('test2', self.engine, if_exists='replace') + self.df.to_sql('test2', self.con, if_exists='replace') + + def time_sql_read_query_fallback(self): + read_sql_query('SELECT * FROM test2', self.con) + + +class sql_read_query_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + self.df.to_sql('test2', self.engine, if_exists='replace') + self.df.to_sql('test2', self.con, if_exists='replace') + + def time_sql_read_query_sqlalchemy(self): + read_sql_query('SELECT * FROM test2', self.engine) + + +class sql_read_table_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + self.df.to_sql('test2', self.engine, if_exists='replace') + self.df.to_sql('test2', self.con, if_exists='replace') + + def time_sql_read_table_sqlalchemy(self): + read_sql_table('test2', self.engine) + + +class sql_string_write_fallback(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'string': (['foo'] * 10000), 'bool': ([True] * 10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df.loc[1000:3000, 'float'] = np.nan + + def time_sql_string_write_fallback(self): + self.df[['string']].to_sql('test_string', self.con, if_exists='replace') + + +class sql_string_write_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.df = DataFrame({'float': randn(10000), 'string': (['foo'] * 10000), 'bool': ([True] * 10000), 'datetime': date_range('2000-01-01', periods=10000, freq='s'), }) + self.df.loc[1000:3000, 'float'] = np.nan + + def time_sql_string_write_sqlalchemy(self): + self.df[['string']].to_sql('test_string', self.engine, if_exists='replace') + + +class sql_write_fallback(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + + def time_sql_write_fallback(self): + self.df.to_sql('test1', self.con, if_exists='replace') + + +class sql_write_sqlalchemy(object): + goal_time = 0.2 + + def setup(self): + self.engine = create_engine('sqlite:///:memory:') + self.con = sqlite3.connect(':memory:') + self.index = tm.makeStringIndex(10000) + self.df = DataFrame({'float1': randn(10000), 'float2': randn(10000), 'string1': (['foo'] * 10000), 'bool1': ([True] * 10000), 'int1': np.random.randint(0, 100000, size=10000), }, index=self.index) + + def time_sql_write_sqlalchemy(self): + self.df.to_sql('test1', self.engine, if_exists='replace') \ No newline at end of file diff --git a/asv_bench/benchmarks/join_merge.py b/asv_bench/benchmarks/join_merge.py new file mode 100644 index 0000000000000..08ae439e8fd5d --- /dev/null +++ b/asv_bench/benchmarks/join_merge.py @@ -0,0 +1,359 @@ +from pandas_vb_common import * + + +class append_frame_single_homogenous(object): + goal_time = 0.2 + + def setup(self): + self.df1 = pd.DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) + self.df2 = self.df1.copy() + self.df2.index = np.arange(10000, 20000) + self.mdf1 = self.df1.copy() + self.mdf1['obj1'] = 'bar' + self.mdf1['obj2'] = 'bar' + self.mdf1['int1'] = 5 + try: + self.mdf1.consolidate(inplace=True) + except: + pass + self.mdf2 = self.mdf1.copy() + self.mdf2.index = self.df2.index + + def time_append_frame_single_homogenous(self): + self.df1.append(self.df2) + + +class append_frame_single_mixed(object): + goal_time = 0.2 + + def setup(self): + self.df1 = pd.DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) + self.df2 = self.df1.copy() + self.df2.index = np.arange(10000, 20000) + self.mdf1 = self.df1.copy() + self.mdf1['obj1'] = 'bar' + self.mdf1['obj2'] = 'bar' + self.mdf1['int1'] = 5 + try: + self.mdf1.consolidate(inplace=True) + except: + pass + self.mdf2 = self.mdf1.copy() + self.mdf2.index = self.df2.index + + def time_append_frame_single_mixed(self): + self.mdf1.append(self.mdf2) + + +class concat_empty_frames1(object): + goal_time = 0.2 + + def setup(self): + self.df = pd.DataFrame(dict(A=range(10000)), index=date_range('20130101', periods=10000, freq='s')) + self.empty = pd.DataFrame() + + def time_concat_empty_frames1(self): + concat([self.df, self.empty]) + + +class concat_empty_frames2(object): + goal_time = 0.2 + + def setup(self): + self.df = pd.DataFrame(dict(A=range(10000)), index=date_range('20130101', periods=10000, freq='s')) + self.empty = pd.DataFrame() + + def time_concat_empty_frames2(self): + concat([self.empty, self.df]) + + +class concat_series_axis1(object): + goal_time = 0.2 + + def setup(self): + self.n = 1000 + self.indices = tm.makeStringIndex(1000) + self.s = Series(self.n, index=self.indices) + self.pieces = [self.s[i:(- i)] for i in range(1, 10)] + self.pieces = (self.pieces * 50) + + def time_concat_series_axis1(self): + concat(self.pieces, axis=1) + + +class concat_small_frames(object): + goal_time = 0.2 + + def setup(self): + self.df = pd.DataFrame(randn(5, 4)) + + def time_concat_small_frames(self): + concat(([self.df] * 1000)) + + +class i8merge(object): + goal_time = 0.2 + + def setup(self): + (low, high, n) = (((-1) << 10), (1 << 10), (1 << 20)) + self.left = pd.DataFrame(np.random.randint(low, high, (n, 7)), columns=list('ABCDEFG')) + self.left['left'] = self.left.sum(axis=1) + self.i = np.random.permutation(len(self.left)) + self.right = self.left.iloc[self.i].copy() + self.right.columns = (self.right.columns[:(-1)].tolist() + ['right']) + self.right.index = np.arange(len(self.right)) + self.right['right'] *= (-1) + + def time_i8merge(self): + merge(self.left, self.right, how='outer') + + +class join_dataframe_index_multi(object): + goal_time = 0.2 + + def setup(self): + self.level1 = tm.makeStringIndex(10).values + self.level2 = tm.makeStringIndex(1000).values + self.label1 = np.arange(10).repeat(1000) + self.label2 = np.tile(np.arange(1000), 10) + self.key1 = np.tile(self.level1.take(self.label1), 10) + self.key2 = np.tile(self.level2.take(self.label2), 10) + self.shuf = np.arange(100000) + random.shuffle(self.shuf) + try: + self.index2 = MultiIndex(levels=[self.level1, self.level2], labels=[self.label1, self.label2]) + self.index3 = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) + except: + pass + try: + self.DataFrame = DataMatrix + except: + pass + self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) + self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) + self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) + self.df_shuf = self.df.reindex(self.df.index[self.shuf]) + + def time_join_dataframe_index_multi(self): + self.df.join(self.df_multi, on=['key1', 'key2']) + + +class join_dataframe_index_single_key_bigger(object): + goal_time = 0.2 + + def setup(self): + self.level1 = tm.makeStringIndex(10).values + self.level2 = tm.makeStringIndex(1000).values + self.label1 = np.arange(10).repeat(1000) + self.label2 = np.tile(np.arange(1000), 10) + self.key1 = np.tile(self.level1.take(self.label1), 10) + self.key2 = np.tile(self.level2.take(self.label2), 10) + self.shuf = np.arange(100000) + random.shuffle(self.shuf) + try: + self.index2 = MultiIndex(levels=[self.level1, self.level2], labels=[self.label1, self.label2]) + self.index3 = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) + except: + pass + try: + self.DataFrame = DataMatrix + except: + pass + self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) + self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) + self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) + self.df_shuf = self.df.reindex(self.df.index[self.shuf]) + + def time_join_dataframe_index_single_key_bigger(self): + self.df.join(self.df_key2, on='key2') + + +class join_dataframe_index_single_key_bigger_sort(object): + goal_time = 0.2 + + def setup(self): + self.level1 = tm.makeStringIndex(10).values + self.level2 = tm.makeStringIndex(1000).values + self.label1 = np.arange(10).repeat(1000) + self.label2 = np.tile(np.arange(1000), 10) + self.key1 = np.tile(self.level1.take(self.label1), 10) + self.key2 = np.tile(self.level2.take(self.label2), 10) + self.shuf = np.arange(100000) + random.shuffle(self.shuf) + try: + self.index2 = MultiIndex(levels=[self.level1, self.level2], labels=[self.label1, self.label2]) + self.index3 = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) + except: + pass + try: + self.DataFrame = DataMatrix + except: + pass + self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) + self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) + self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) + self.df_shuf = self.df.reindex(self.df.index[self.shuf]) + + def time_join_dataframe_index_single_key_bigger_sort(self): + self.df_shuf.join(self.df_key2, on='key2', sort=True) + + +class join_dataframe_index_single_key_small(object): + goal_time = 0.2 + + def setup(self): + self.level1 = tm.makeStringIndex(10).values + self.level2 = tm.makeStringIndex(1000).values + self.label1 = np.arange(10).repeat(1000) + self.label2 = np.tile(np.arange(1000), 10) + self.key1 = np.tile(self.level1.take(self.label1), 10) + self.key2 = np.tile(self.level2.take(self.label2), 10) + self.shuf = np.arange(100000) + random.shuffle(self.shuf) + try: + self.index2 = MultiIndex(levels=[self.level1, self.level2], labels=[self.label1, self.label2]) + self.index3 = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + self.df_multi = DataFrame(np.random.randn(len(self.index2), 4), index=self.index2, columns=['A', 'B', 'C', 'D']) + except: + pass + try: + self.DataFrame = DataMatrix + except: + pass + self.df = pd.DataFrame({'data1': np.random.randn(100000), 'data2': np.random.randn(100000), 'key1': self.key1, 'key2': self.key2, }) + self.df_key1 = pd.DataFrame(np.random.randn(len(self.level1), 4), index=self.level1, columns=['A', 'B', 'C', 'D']) + self.df_key2 = pd.DataFrame(np.random.randn(len(self.level2), 4), index=self.level2, columns=['A', 'B', 'C', 'D']) + self.df_shuf = self.df.reindex(self.df.index[self.shuf]) + + def time_join_dataframe_index_single_key_small(self): + self.df.join(self.df_key1, on='key1') + + +class join_dataframe_integer_2key(object): + goal_time = 0.2 + + def setup(self): + self.df = pd.DataFrame({'key1': np.tile(np.arange(500).repeat(10), 2), 'key2': np.tile(np.arange(250).repeat(10), 4), 'value': np.random.randn(10000), }) + self.df2 = pd.DataFrame({'key1': np.arange(500), 'value2': randn(500), }) + self.df3 = self.df[:5000] + + def time_join_dataframe_integer_2key(self): + merge(self.df, self.df3) + + +class join_dataframe_integer_key(object): + goal_time = 0.2 + + def setup(self): + self.df = pd.DataFrame({'key1': np.tile(np.arange(500).repeat(10), 2), 'key2': np.tile(np.arange(250).repeat(10), 4), 'value': np.random.randn(10000), }) + self.df2 = pd.DataFrame({'key1': np.arange(500), 'value2': randn(500), }) + self.df3 = self.df[:5000] + + def time_join_dataframe_integer_key(self): + merge(self.df, self.df2, on='key1') + + +class join_non_unique_equal(object): + goal_time = 0.2 + + def setup(self): + self.date_index = date_range('01-Jan-2013', '23-Jan-2013', freq='T') + self.daily_dates = self.date_index.to_period('D').to_timestamp('S', 'S') + self.fracofday = (self.date_index.view(np.ndarray) - self.daily_dates.view(np.ndarray)) + self.fracofday = (self.fracofday.astype('timedelta64[ns]').astype(np.float64) / 86400000000000.0) + self.fracofday = TimeSeries(self.fracofday, self.daily_dates) + self.index = date_range(self.date_index.min().to_period('A').to_timestamp('D', 'S'), self.date_index.max().to_period('A').to_timestamp('D', 'E'), freq='D') + self.temp = TimeSeries(1.0, self.index) + + def time_join_non_unique_equal(self): + (self.fracofday * self.temp[self.fracofday.index]) + + +class left_outer_join_index(object): + goal_time = 0.2 + + def setup(self): + np.random.seed(2718281) + self.n = 50000 + self.left = pd.DataFrame(np.random.randint(1, (self.n / 500), (self.n, 2)), columns=['jim', 'joe']) + self.right = pd.DataFrame(np.random.randint(1, (self.n / 500), (self.n, 2)), columns=['jolie', 'jolia']).set_index('jolie') + + def time_left_outer_join_index(self): + self.left.join(self.right, on='jim') + + +class merge_2intkey_nosort(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.indices = tm.makeStringIndex(self.N).values + self.indices2 = tm.makeStringIndex(self.N).values + self.key = np.tile(self.indices[:8000], 10) + self.key2 = np.tile(self.indices2[:8000], 10) + self.left = pd.DataFrame({'key': self.key, 'key2': self.key2, 'value': np.random.randn(80000), }) + self.right = pd.DataFrame({'key': self.indices[2000:], 'key2': self.indices2[2000:], 'value2': np.random.randn(8000), }) + + def time_merge_2intkey_nosort(self): + merge(self.left, self.right, sort=False) + + +class merge_2intkey_sort(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.indices = tm.makeStringIndex(self.N).values + self.indices2 = tm.makeStringIndex(self.N).values + self.key = np.tile(self.indices[:8000], 10) + self.key2 = np.tile(self.indices2[:8000], 10) + self.left = pd.DataFrame({'key': self.key, 'key2': self.key2, 'value': np.random.randn(80000), }) + self.right = pd.DataFrame({'key': self.indices[2000:], 'key2': self.indices2[2000:], 'value2': np.random.randn(8000), }) + + def time_merge_2intkey_sort(self): + merge(self.left, self.right, sort=True) + + +class series_align_int64_index(object): + goal_time = 0.2 + + def setup(self): + self.n = 1000000 + + def sample(values, k): + self.sampler = np.random.permutation(len(values)) + return values.take(self.sampler[:k]) + self.sz = 500000 + self.rng = np.arange(0, 10000000000000, 10000000) + self.stamps = (np.datetime64(datetime.now()).view('i8') + self.rng) + self.idx1 = np.sort(sample(self.stamps, self.sz)) + self.idx2 = np.sort(sample(self.stamps, self.sz)) + self.ts1 = Series(np.random.randn(self.sz), self.idx1) + self.ts2 = Series(np.random.randn(self.sz), self.idx2) + + def time_series_align_int64_index(self): + (self.ts1 + self.ts2) + + +class series_align_left_monotonic(object): + goal_time = 0.2 + + def setup(self): + self.n = 1000000 + + def sample(values, k): + self.sampler = np.random.permutation(len(values)) + return values.take(self.sampler[:k]) + self.sz = 500000 + self.rng = np.arange(0, 10000000000000, 10000000) + self.stamps = (np.datetime64(datetime.now()).view('i8') + self.rng) + self.idx1 = np.sort(sample(self.stamps, self.sz)) + self.idx2 = np.sort(sample(self.stamps, self.sz)) + self.ts1 = Series(np.random.randn(self.sz), self.idx1) + self.ts2 = Series(np.random.randn(self.sz), self.idx2) + + def time_series_align_left_monotonic(self): + self.ts1.align(self.ts2, join='left') \ No newline at end of file diff --git a/asv_bench/benchmarks/miscellaneous.py b/asv_bench/benchmarks/miscellaneous.py new file mode 100644 index 0000000000000..b9c02c85fb096 --- /dev/null +++ b/asv_bench/benchmarks/miscellaneous.py @@ -0,0 +1,30 @@ +from pandas_vb_common import * +from pandas.util.decorators import cache_readonly + + +class match_strings(object): + goal_time = 0.2 + + def setup(self): + self.uniques = tm.makeStringIndex(1000).values + self.all = self.uniques.repeat(10) + + def time_match_strings(self): + match(self.all, self.uniques) + + +class misc_cache_readonly(object): + goal_time = 0.2 + + def setup(self): + + + class Foo: + + @cache_readonly + def prop(self): + return 5 + self.obj = Foo() + + def time_misc_cache_readonly(self): + self.obj.prop \ No newline at end of file diff --git a/asv_bench/benchmarks/packers.py b/asv_bench/benchmarks/packers.py new file mode 100644 index 0000000000000..81fa7c2238d16 --- /dev/null +++ b/asv_bench/benchmarks/packers.py @@ -0,0 +1,857 @@ +from numpy.random import randint +import pandas as pd +from collections import OrderedDict +from pandas.compat import BytesIO +import sqlite3 +from pandas_vb_common import * +import os +from sqlalchemy import create_engine +import numpy as np +from random import randrange +from pandas.core import common as com + + +class packers_read_csv(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.to_csv(self.f) + + def time_packers_read_csv(self): + pd.read_csv(self.f) + + +class packers_read_excel(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.bio = BytesIO() + self.writer = pd.io.excel.ExcelWriter(self.bio, engine='xlsxwriter') + self.df[:2000].to_excel(self.writer) + self.writer.save() + + def time_packers_read_excel(self): + self.bio.seek(0) + pd.read_excel(self.bio) + + +class packers_read_hdf_store(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df2.to_hdf(self.f, 'df') + + def time_packers_read_hdf_store(self): + pd.read_hdf(self.f, 'df') + + +class packers_read_hdf_table(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df2.to_hdf(self.f, 'df', format='table') + + def time_packers_read_hdf_table(self): + pd.read_hdf(self.f, 'df') + + +class packers_read_json(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.to_json(self.f, orient='split') + self.df.index = np.arange(self.N) + + def time_packers_read_json(self): + pd.read_json(self.f, orient='split') + + +class packers_read_json_date_index(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.to_json(self.f, orient='split') + + def time_packers_read_json_date_index(self): + pd.read_json(self.f, orient='split') + + +class packers_read_pack(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df2.to_msgpack(self.f) + + def time_packers_read_pack(self): + pd.read_msgpack(self.f) + + +class packers_read_pickle(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df2.to_pickle(self.f) + + def time_packers_read_pickle(self): + pd.read_pickle(self.f) + + +class packers_read_sql(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.engine = create_engine('sqlite:///:memory:') + self.df2.to_sql('table', self.engine, if_exists='replace') + + def time_packers_read_sql(self): + pd.read_sql_table('table', self.engine) + + +class packers_read_stata(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.to_stata(self.f, {'index': 'tc', }) + + def time_packers_read_stata(self): + pd.read_stata(self.f) + + +class packers_read_stata_with_validation(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df['int8_'] = [randint(np.iinfo(np.int8).min, (np.iinfo(np.int8).max - 27)) for _ in range(self.N)] + self.df['int16_'] = [randint(np.iinfo(np.int16).min, (np.iinfo(np.int16).max - 27)) for _ in range(self.N)] + self.df['int32_'] = [randint(np.iinfo(np.int32).min, (np.iinfo(np.int32).max - 27)) for _ in range(self.N)] + self.df['float32_'] = np.array(randn(self.N), dtype=np.float32) + self.df.to_stata(self.f, {'index': 'tc', }) + + def time_packers_read_stata_with_validation(self): + pd.read_stata(self.f) + + +class packers_write_csv(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + + def time_packers_write_csv(self): + self.df.to_csv(self.f) + + def teardown(self): + remove(self.f) + + +class packers_write_excel_openpyxl(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.bio = BytesIO() + + def time_packers_write_excel_openpyxl(self): + self.bio.seek(0) + self.writer = pd.io.excel.ExcelWriter(self.bio, engine='openpyxl') + self.df[:2000].to_excel(self.writer) + self.writer.save() + + +class packers_write_excel_xlsxwriter(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.bio = BytesIO() + + def time_packers_write_excel_xlsxwriter(self): + self.bio.seek(0) + self.writer = pd.io.excel.ExcelWriter(self.bio, engine='xlsxwriter') + self.df[:2000].to_excel(self.writer) + self.writer.save() + + +class packers_write_excel_xlwt(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.bio = BytesIO() + + def time_packers_write_excel_xlwt(self): + self.bio.seek(0) + self.writer = pd.io.excel.ExcelWriter(self.bio, engine='xlwt') + self.df[:2000].to_excel(self.writer) + self.writer.save() + + +class packers_write_hdf_store(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + + def time_packers_write_hdf_store(self): + self.df2.to_hdf(self.f, 'df') + + def teardown(self): + remove(self.f) + + +class packers_write_hdf_table(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + + def time_packers_write_hdf_table(self): + self.df2.to_hdf(self.f, 'df', table=True) + + def teardown(self): + remove(self.f) + + +class packers_write_json(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.index = np.arange(self.N) + + def time_packers_write_json(self): + self.df.to_json(self.f, orient='split') + + def teardown(self): + remove(self.f) + + +class packers_write_json_T(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.index = np.arange(self.N) + + def time_packers_write_json_T(self): + self.df.to_json(self.f, orient='columns') + + def teardown(self): + remove(self.f) + + +class packers_write_json_date_index(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + + def time_packers_write_json_date_index(self): + self.df.to_json(self.f, orient='split') + + def teardown(self): + remove(self.f) + + +class packers_write_json_mixed_delta_int_tstamp(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.cols = [(lambda i: ('{0}_timedelta'.format(i), [pd.Timedelta(('%d seconds' % randrange(1000000.0))) for _ in range(self.N)])), (lambda i: ('{0}_int'.format(i), randint(100000000.0, size=self.N))), (lambda i: ('{0}_timestamp'.format(i), [pd.Timestamp((1418842918083256000 + randrange(1000000000.0, 1e+18, 200))) for _ in range(self.N)]))] + self.df_mixed = DataFrame(OrderedDict([self.cols[(i % len(self.cols))](i) for i in range(self.C)]), index=self.index) + + def time_packers_write_json_mixed_delta_int_tstamp(self): + self.df_mixed.to_json(self.f, orient='split') + + def teardown(self): + remove(self.f) + + +class packers_write_json_mixed_float_int(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.cols = [(lambda i: ('{0}_float'.format(i), randn(self.N))), (lambda i: ('{0}_int'.format(i), randint(100000000.0, size=self.N)))] + self.df_mixed = DataFrame(OrderedDict([self.cols[(i % len(self.cols))](i) for i in range(self.C)]), index=self.index) + + def time_packers_write_json_mixed_float_int(self): + self.df_mixed.to_json(self.f, orient='index') + + def teardown(self): + remove(self.f) + + +class packers_write_json_mixed_float_int_T(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.cols = [(lambda i: ('{0}_float'.format(i), randn(self.N))), (lambda i: ('{0}_int'.format(i), randint(100000000.0, size=self.N)))] + self.df_mixed = DataFrame(OrderedDict([self.cols[(i % len(self.cols))](i) for i in range(self.C)]), index=self.index) + + def time_packers_write_json_mixed_float_int_T(self): + self.df_mixed.to_json(self.f, orient='columns') + + def teardown(self): + remove(self.f) + + +class packers_write_json_mixed_float_int_str(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.cols = [(lambda i: ('{0}_float'.format(i), randn(self.N))), (lambda i: ('{0}_int'.format(i), randint(100000000.0, size=self.N))), (lambda i: ('{0}_str'.format(i), [('%08x' % randrange((16 ** 8))) for _ in range(self.N)]))] + self.df_mixed = DataFrame(OrderedDict([self.cols[(i % len(self.cols))](i) for i in range(self.C)]), index=self.index) + + def time_packers_write_json_mixed_float_int_str(self): + self.df_mixed.to_json(self.f, orient='split') + + def teardown(self): + remove(self.f) + + +class packers_write_pack(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + + def time_packers_write_pack(self): + self.df2.to_msgpack(self.f) + + def teardown(self): + remove(self.f) + + +class packers_write_pickle(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + + def time_packers_write_pickle(self): + self.df2.to_pickle(self.f) + + def teardown(self): + remove(self.f) + + +class packers_write_sql(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.engine = create_engine('sqlite:///:memory:') + + def time_packers_write_sql(self): + self.df2.to_sql('table', self.engine, if_exists='replace') + + +class packers_write_stata(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df.to_stata(self.f, {'index': 'tc', }) + + def time_packers_write_stata(self): + self.df.to_stata(self.f, {'index': 'tc', }) + + def teardown(self): + remove(self.f) + + +class packers_write_stata_with_validation(object): + goal_time = 0.2 + + def setup(self): + self.f = '__test__.msg' + + def remove(f): + try: + os.remove(self.f) + except: + pass + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.N = 100000 + self.C = 5 + self.index = date_range('20000101', periods=self.N, freq='H') + self.df2 = DataFrame(dict([('float{0}'.format(i), randn(self.N)) for i in range(self.C)]), index=self.index) + self.df2['object'] = [('%08x' % randrange((16 ** 8))) for _ in range(self.N)] + remove(self.f) + self.df['int8_'] = [randint(np.iinfo(np.int8).min, (np.iinfo(np.int8).max - 27)) for _ in range(self.N)] + self.df['int16_'] = [randint(np.iinfo(np.int16).min, (np.iinfo(np.int16).max - 27)) for _ in range(self.N)] + self.df['int32_'] = [randint(np.iinfo(np.int32).min, (np.iinfo(np.int32).max - 27)) for _ in range(self.N)] + self.df['float32_'] = np.array(randn(self.N), dtype=np.float32) + self.df.to_stata(self.f, {'index': 'tc', }) + + def time_packers_write_stata_with_validation(self): + self.df.to_stata(self.f, {'index': 'tc', }) + + def teardown(self): + remove(self.f) \ No newline at end of file diff --git a/asv_bench/benchmarks/pandas_vb_common.py b/asv_bench/benchmarks/pandas_vb_common.py new file mode 120000 index 0000000000000..6e2e449a4c00a --- /dev/null +++ b/asv_bench/benchmarks/pandas_vb_common.py @@ -0,0 +1 @@ +../../vb_suite/pandas_vb_common.py \ No newline at end of file diff --git a/asv_bench/benchmarks/panel_ctor.py b/asv_bench/benchmarks/panel_ctor.py new file mode 100644 index 0000000000000..c755cb122a0bf --- /dev/null +++ b/asv_bench/benchmarks/panel_ctor.py @@ -0,0 +1,64 @@ +from pandas_vb_common import * + + +class panel_from_dict_all_different_indexes(object): + goal_time = 0.2 + + def setup(self): + self.data_frames = {} + self.start = datetime(1990, 1, 1) + self.end = datetime(2012, 1, 1) + for x in xrange(100): + self.end += timedelta(days=1) + self.dr = np.asarray(date_range(self.start, self.end)) + self.df = DataFrame({'a': ([0] * len(self.dr)), 'b': ([1] * len(self.dr)), 'c': ([2] * len(self.dr)), }, index=self.dr) + self.data_frames[x] = self.df + + def time_panel_from_dict_all_different_indexes(self): + Panel.from_dict(self.data_frames) + + +class panel_from_dict_equiv_indexes(object): + goal_time = 0.2 + + def setup(self): + self.data_frames = {} + for x in xrange(100): + self.dr = np.asarray(DatetimeIndex(start=datetime(1990, 1, 1), end=datetime(2012, 1, 1), freq=datetools.Day(1))) + self.df = DataFrame({'a': ([0] * len(self.dr)), 'b': ([1] * len(self.dr)), 'c': ([2] * len(self.dr)), }, index=self.dr) + self.data_frames[x] = self.df + + def time_panel_from_dict_equiv_indexes(self): + Panel.from_dict(self.data_frames) + + +class panel_from_dict_same_index(object): + goal_time = 0.2 + + def setup(self): + self.dr = np.asarray(DatetimeIndex(start=datetime(1990, 1, 1), end=datetime(2012, 1, 1), freq=datetools.Day(1))) + self.data_frames = {} + for x in xrange(100): + self.df = DataFrame({'a': ([0] * len(self.dr)), 'b': ([1] * len(self.dr)), 'c': ([2] * len(self.dr)), }, index=self.dr) + self.data_frames[x] = self.df + + def time_panel_from_dict_same_index(self): + Panel.from_dict(self.data_frames) + + +class panel_from_dict_two_different_indexes(object): + goal_time = 0.2 + + def setup(self): + self.data_frames = {} + self.start = datetime(1990, 1, 1) + self.end = datetime(2012, 1, 1) + for x in xrange(100): + if (x == 50): + self.end += timedelta(days=1) + self.dr = np.asarray(date_range(self.start, self.end)) + self.df = DataFrame({'a': ([0] * len(self.dr)), 'b': ([1] * len(self.dr)), 'c': ([2] * len(self.dr)), }, index=self.dr) + self.data_frames[x] = self.df + + def time_panel_from_dict_two_different_indexes(self): + Panel.from_dict(self.data_frames) \ No newline at end of file diff --git a/asv_bench/benchmarks/panel_methods.py b/asv_bench/benchmarks/panel_methods.py new file mode 100644 index 0000000000000..4145b68dca997 --- /dev/null +++ b/asv_bench/benchmarks/panel_methods.py @@ -0,0 +1,56 @@ +from pandas_vb_common import * + + +class panel_pct_change_items(object): + goal_time = 0.2 + + def setup(self): + self.index = date_range(start='2000', freq='D', periods=1000) + self.panel = Panel(np.random.randn(100, len(self.index), 1000)) + + def time_panel_pct_change_items(self): + self.panel.pct_change(1, axis='items') + + +class panel_pct_change_major(object): + goal_time = 0.2 + + def setup(self): + self.index = date_range(start='2000', freq='D', periods=1000) + self.panel = Panel(np.random.randn(100, len(self.index), 1000)) + + def time_panel_pct_change_major(self): + self.panel.pct_change(1, axis='major') + + +class panel_pct_change_minor(object): + goal_time = 0.2 + + def setup(self): + self.index = date_range(start='2000', freq='D', periods=1000) + self.panel = Panel(np.random.randn(100, len(self.index), 1000)) + + def time_panel_pct_change_minor(self): + self.panel.pct_change(1, axis='minor') + + +class panel_shift(object): + goal_time = 0.2 + + def setup(self): + self.index = date_range(start='2000', freq='D', periods=1000) + self.panel = Panel(np.random.randn(100, len(self.index), 1000)) + + def time_panel_shift(self): + self.panel.shift(1) + + +class panel_shift_minor(object): + goal_time = 0.2 + + def setup(self): + self.index = date_range(start='2000', freq='D', periods=1000) + self.panel = Panel(np.random.randn(100, len(self.index), 1000)) + + def time_panel_shift_minor(self): + self.panel.shift(1, axis='minor') \ No newline at end of file diff --git a/asv_bench/benchmarks/parser_vb.py b/asv_bench/benchmarks/parser_vb.py new file mode 100644 index 0000000000000..46167dc2bb33c --- /dev/null +++ b/asv_bench/benchmarks/parser_vb.py @@ -0,0 +1,109 @@ +from cStringIO import StringIO +from pandas_vb_common import * +import os +from pandas import read_csv, read_table + + +class read_csv_comment2(object): + goal_time = 0.2 + + def setup(self): + self.data = ['A,B,C'] + self.data = (self.data + (['1,2,3 # comment'] * 100000)) + self.data = '\n'.join(self.data) + + def time_read_csv_comment2(self): + read_csv(StringIO(self.data), comment='#') + + +class read_csv_default_converter(object): + goal_time = 0.2 + + def setup(self): + self.data = '0.1213700904466425978256438611,0.0525708283766902484401839501,0.4174092731488769913994474336\n 0.4096341697147408700274695547,0.1587830198973579909349496119,0.1292545832485494372576795285\n 0.8323255650024565799327547210,0.9694902427379478160318626578,0.6295047811546814475747169126\n 0.4679375305798131323697930383,0.2963942381834381301075609371,0.5268936082160610157032465394\n 0.6685382761849776311890991564,0.6721207066140679753374342908,0.6519975277021627935170045020\n ' + self.data = (self.data * 200) + + def time_read_csv_default_converter(self): + read_csv(StringIO(self.data), sep=',', header=None, float_precision=None) + + +class read_csv_precise_converter(object): + goal_time = 0.2 + + def setup(self): + self.data = '0.1213700904466425978256438611,0.0525708283766902484401839501,0.4174092731488769913994474336\n 0.4096341697147408700274695547,0.1587830198973579909349496119,0.1292545832485494372576795285\n 0.8323255650024565799327547210,0.9694902427379478160318626578,0.6295047811546814475747169126\n 0.4679375305798131323697930383,0.2963942381834381301075609371,0.5268936082160610157032465394\n 0.6685382761849776311890991564,0.6721207066140679753374342908,0.6519975277021627935170045020\n ' + self.data = (self.data * 200) + + def time_read_csv_precise_converter(self): + read_csv(StringIO(self.data), sep=',', header=None, float_precision='high') + + +class read_csv_roundtrip_converter(object): + goal_time = 0.2 + + def setup(self): + self.data = '0.1213700904466425978256438611,0.0525708283766902484401839501,0.4174092731488769913994474336\n 0.4096341697147408700274695547,0.1587830198973579909349496119,0.1292545832485494372576795285\n 0.8323255650024565799327547210,0.9694902427379478160318626578,0.6295047811546814475747169126\n 0.4679375305798131323697930383,0.2963942381834381301075609371,0.5268936082160610157032465394\n 0.6685382761849776311890991564,0.6721207066140679753374342908,0.6519975277021627935170045020\n ' + self.data = (self.data * 200) + + def time_read_csv_roundtrip_converter(self): + read_csv(StringIO(self.data), sep=',', header=None, float_precision='round_trip') + + +class read_csv_thou_vb(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 8 + self.format = (lambda x: '{:,}'.format(x)) + self.df = DataFrame((np.random.randn(self.N, self.K) * np.random.randint(100, 10000, (self.N, self.K)))) + self.df = self.df.applymap(self.format) + self.df.to_csv('test.csv', sep='|') + + def time_read_csv_thou_vb(self): + read_csv('test.csv', sep='|', thousands=',') + + def teardown(self): + os.remove('test.csv') + + +class read_csv_vb(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 8 + self.df = DataFrame((np.random.randn(self.N, self.K) * np.random.randint(100, 10000, (self.N, self.K)))) + self.df.to_csv('test.csv', sep='|') + + def time_read_csv_vb(self): + read_csv('test.csv', sep='|') + + def teardown(self): + os.remove('test.csv') + + +class read_table_multiple_date(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 8 + self.data = 'KORD,19990127, 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000\n KORD,19990127, 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000\n KORD,19990127, 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000\n KORD,19990127, 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000\n KORD,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000\n ' + self.data = (self.data * 200) + + def time_read_table_multiple_date(self): + read_table(StringIO(self.data), sep=',', header=None, parse_dates=[[1, 2], [1, 3]]) + + +class read_table_multiple_date_baseline(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 8 + self.data = 'KORD,19990127 19:00:00, 18:56:00, 0.8100, 2.8100, 7.2000, 0.0000, 280.0000\n KORD,19990127 20:00:00, 19:56:00, 0.0100, 2.2100, 7.2000, 0.0000, 260.0000\n KORD,19990127 21:00:00, 20:56:00, -0.5900, 2.2100, 5.7000, 0.0000, 280.0000\n KORD,19990127 21:00:00, 21:18:00, -0.9900, 2.0100, 3.6000, 0.0000, 270.0000\n KORD,19990127 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000\n ' + self.data = (self.data * 200) + + def time_read_table_multiple_date_baseline(self): + read_table(StringIO(self.data), sep=',', header=None, parse_dates=[1]) \ No newline at end of file diff --git a/asv_bench/benchmarks/plotting.py b/asv_bench/benchmarks/plotting.py new file mode 100644 index 0000000000000..d1df1b429c656 --- /dev/null +++ b/asv_bench/benchmarks/plotting.py @@ -0,0 +1,19 @@ +from pandas_vb_common import * +try: + from pandas import date_range +except ImportError: + + def date_range(start=None, end=None, periods=None, freq=None): + return DatetimeIndex(start, end, periods=periods, offset=freq) + + +class plot_timeseries_period(object): + goal_time = 0.2 + + def setup(self): + self.N = 2000 + self.M = 5 + self.df = DataFrame(np.random.randn(self.N, self.M), index=date_range('1/1/1975', periods=self.N)) + + def time_plot_timeseries_period(self): + self.df.plot() \ No newline at end of file diff --git a/asv_bench/benchmarks/reindex.py b/asv_bench/benchmarks/reindex.py new file mode 100644 index 0000000000000..d6fbd0d31c389 --- /dev/null +++ b/asv_bench/benchmarks/reindex.py @@ -0,0 +1,384 @@ +from pandas_vb_common import * +from random import shuffle + + +class dataframe_reindex(object): + goal_time = 0.2 + + def setup(self): + self.rng = DatetimeIndex(start='1/1/1970', periods=10000, freq=datetools.Minute()) + self.df = DataFrame(np.random.rand(10000, 10), index=self.rng, columns=range(10)) + self.df['foo'] = 'bar' + self.rng2 = Index(self.rng[::2]) + + def time_dataframe_reindex(self): + self.df.reindex(self.rng2) + + +class frame_drop_dup_inplace(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + + def time_frame_drop_dup_inplace(self): + self.df.drop_duplicates(['key1', 'key2'], inplace=True) + + +class frame_drop_dup_na_inplace(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + self.df.ix[:10000, :] = np.nan + + def time_frame_drop_dup_na_inplace(self): + self.df.drop_duplicates(['key1', 'key2'], inplace=True) + + +class frame_drop_duplicates(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + + def time_frame_drop_duplicates(self): + self.df.drop_duplicates(['key1', 'key2']) + + +class frame_drop_duplicates_na(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + self.df.ix[:10000, :] = np.nan + + def time_frame_drop_duplicates_na(self): + self.df.drop_duplicates(['key1', 'key2']) + + +class frame_fillna_many_columns_pad(object): + goal_time = 0.2 + + def setup(self): + self.values = np.random.randn(1000, 1000) + self.values[::2] = np.nan + self.df = DataFrame(self.values) + + def time_frame_fillna_many_columns_pad(self): + self.df.fillna(method='pad') + + +class frame_reindex_columns(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(index=range(10000), data=np.random.rand(10000, 30), columns=range(30)) + + def time_frame_reindex_columns(self): + self.df.reindex(columns=self.df.columns[1:5]) + + +class frame_sort_index_by_columns(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + + def time_frame_sort_index_by_columns(self): + self.df.sort_index(by=['key1', 'key2']) + + +class lib_fast_zip(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + + def time_lib_fast_zip(self): + lib.fast_zip(self.col_array_list) + + +class lib_fast_zip_fillna(object): + goal_time = 0.2 + + def setup(self): + self.N = 10000 + self.K = 10 + self.key1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.key2 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.df = DataFrame({'key1': self.key1, 'key2': self.key2, 'value': np.random.randn((self.N * self.K)), }) + self.col_array_list = list(self.df.values.T) + self.df.ix[:10000, :] = np.nan + + def time_lib_fast_zip_fillna(self): + lib.fast_zip_fillna(self.col_array_list) + + +class reindex_daterange_backfill(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + self.ts2 = self.ts[::2] + self.ts3 = self.ts2.reindex(self.ts.index) + self.ts4 = self.ts3.astype('float32') + + def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + + def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + + def time_reindex_daterange_backfill(self): + backfill(self.ts2, self.ts.index) + + +class reindex_daterange_pad(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + self.ts2 = self.ts[::2] + self.ts3 = self.ts2.reindex(self.ts.index) + self.ts4 = self.ts3.astype('float32') + + def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + + def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + + def time_reindex_daterange_pad(self): + pad(self.ts2, self.ts.index) + + +class reindex_fillna_backfill(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + self.ts2 = self.ts[::2] + self.ts3 = self.ts2.reindex(self.ts.index) + self.ts4 = self.ts3.astype('float32') + + def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + + def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + + def time_reindex_fillna_backfill(self): + self.ts3.fillna(method='backfill') + + +class reindex_fillna_backfill_float32(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + self.ts2 = self.ts[::2] + self.ts3 = self.ts2.reindex(self.ts.index) + self.ts4 = self.ts3.astype('float32') + + def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + + def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + + def time_reindex_fillna_backfill_float32(self): + self.ts4.fillna(method='backfill') + + +class reindex_fillna_pad(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + self.ts2 = self.ts[::2] + self.ts3 = self.ts2.reindex(self.ts.index) + self.ts4 = self.ts3.astype('float32') + + def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + + def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + + def time_reindex_fillna_pad(self): + self.ts3.fillna(method='pad') + + +class reindex_fillna_pad_float32(object): + goal_time = 0.2 + + def setup(self): + self.rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + self.ts2 = self.ts[::2] + self.ts3 = self.ts2.reindex(self.ts.index) + self.ts4 = self.ts3.astype('float32') + + def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + + def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + + def time_reindex_fillna_pad_float32(self): + self.ts4.fillna(method='pad') + + +class reindex_frame_level_align(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + random.shuffle(self.index.values) + self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index) + self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1]) + + def time_reindex_frame_level_align(self): + self.df.align(self.df_level, level=1, copy=False) + + +class reindex_frame_level_reindex(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + random.shuffle(self.index.values) + self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index) + self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1]) + + def time_reindex_frame_level_reindex(self): + self.df_level.reindex(self.df.index, level=1) + + +class reindex_multiindex(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000 + self.K = 20 + self.level1 = tm.makeStringIndex(self.N).values.repeat(self.K) + self.level2 = np.tile(tm.makeStringIndex(self.K).values, self.N) + self.index = MultiIndex.from_arrays([self.level1, self.level2]) + self.s1 = Series(np.random.randn((self.N * self.K)), index=self.index) + self.s2 = self.s1[::2] + + def time_reindex_multiindex(self): + self.s1.reindex(self.s2.index) + + +class series_align_irregular_string(object): + goal_time = 0.2 + + def setup(self): + self.n = 50000 + self.indices = tm.makeStringIndex(self.n) + + def sample(values, k): + self.sampler = np.arange(len(values)) + shuffle(self.sampler) + return values.take(self.sampler[:k]) + self.subsample_size = 40000 + self.x = Series(np.random.randn(50000), self.indices) + self.y = Series(np.random.randn(self.subsample_size), index=sample(self.indices, self.subsample_size)) + + def time_series_align_irregular_string(self): + (self.x + self.y) + + +class series_drop_duplicates_int(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.randint(0, 1000, size=10000)) + self.s2 = Series(np.tile(tm.makeStringIndex(1000).values, 10)) + + def time_series_drop_duplicates_int(self): + self.s.drop_duplicates() + + +class series_drop_duplicates_string(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.randint(0, 1000, size=10000)) + self.s2 = Series(np.tile(tm.makeStringIndex(1000).values, 10)) + + def time_series_drop_duplicates_string(self): + self.s2.drop_duplicates() \ No newline at end of file diff --git a/asv_bench/benchmarks/replace.py b/asv_bench/benchmarks/replace.py new file mode 100644 index 0000000000000..9b78c287c5ad4 --- /dev/null +++ b/asv_bench/benchmarks/replace.py @@ -0,0 +1,48 @@ +from pandas_vb_common import * +from pandas.compat import range +from datetime import timedelta + + +class replace_fillna(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + try: + self.rng = date_range('1/1/2000', periods=self.N, freq='min') + except NameError: + self.rng = DatetimeIndex('1/1/2000', periods=self.N, offset=datetools.Minute()) + self.date_range = DateRange + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_replace_fillna(self): + self.ts.fillna(0.0, inplace=True) + + +class replace_large_dict(object): + goal_time = 0.2 + + def setup(self): + self.n = (10 ** 6) + self.start_value = (10 ** 5) + self.to_rep = dict(((i, (self.start_value + i)) for i in range(self.n))) + self.s = Series(np.random.randint(self.n, size=(10 ** 3))) + + def time_replace_large_dict(self): + self.s.replace(self.to_rep, inplace=True) + + +class replace_replacena(object): + goal_time = 0.2 + + def setup(self): + self.N = 1000000 + try: + self.rng = date_range('1/1/2000', periods=self.N, freq='min') + except NameError: + self.rng = DatetimeIndex('1/1/2000', periods=self.N, offset=datetools.Minute()) + self.date_range = DateRange + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_replace_replacena(self): + self.ts.replace(np.nan, 0.0, inplace=True) \ No newline at end of file diff --git a/asv_bench/benchmarks/reshape.py b/asv_bench/benchmarks/reshape.py new file mode 100644 index 0000000000000..b4081957af97b --- /dev/null +++ b/asv_bench/benchmarks/reshape.py @@ -0,0 +1,76 @@ +from pandas_vb_common import * +from pandas.core.reshape import melt + + +class melt_dataframe(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]) + self.df = DataFrame(np.random.randn(10000, 4), index=self.index) + self.df = DataFrame(np.random.randn(10000, 3), columns=['A', 'B', 'C']) + self.df['id1'] = np.random.randint(0, 10, 10000) + self.df['id2'] = np.random.randint(100, 1000, 10000) + + def time_melt_dataframe(self): + melt(self.df, id_vars=['id1', 'id2']) + + +class reshape_pivot_time_series(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]) + self.df = DataFrame(np.random.randn(10000, 4), index=self.index) + + def unpivot(frame): + (N, K) = frame.shape + self.data = {'value': frame.values.ravel('F'), 'variable': np.asarray(frame.columns).repeat(N), 'date': np.tile(np.asarray(frame.index), K), } + return DataFrame(self.data, columns=['date', 'variable', 'value']) + self.index = date_range('1/1/2000', periods=10000, freq='h') + self.df = DataFrame(randn(10000, 50), index=self.index, columns=range(50)) + self.pdf = unpivot(self.df) + self.f = (lambda : self.pdf.pivot('date', 'variable', 'value')) + + def time_reshape_pivot_time_series(self): + self.f() + + +class reshape_stack_simple(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]) + self.df = DataFrame(np.random.randn(10000, 4), index=self.index) + self.udf = self.df.unstack(1) + + def time_reshape_stack_simple(self): + self.udf.stack() + + +class reshape_unstack_simple(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]) + self.df = DataFrame(np.random.randn(10000, 4), index=self.index) + + def time_reshape_unstack_simple(self): + self.df.unstack(1) + + +class unstack_sparse_keyspace(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex.from_arrays([np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]) + self.df = DataFrame(np.random.randn(10000, 4), index=self.index) + self.NUM_ROWS = 1000 + for iter in range(10): + self.df = DataFrame({'A': np.random.randint(50, size=self.NUM_ROWS), 'B': np.random.randint(50, size=self.NUM_ROWS), 'C': np.random.randint((-10), 10, size=self.NUM_ROWS), 'D': np.random.randint((-10), 10, size=self.NUM_ROWS), 'E': np.random.randint(10, size=self.NUM_ROWS), 'F': np.random.randn(self.NUM_ROWS), }) + self.idf = self.df.set_index(['A', 'B', 'C', 'D', 'E']) + if (len(self.idf.index.unique()) == self.NUM_ROWS): + break + + def time_unstack_sparse_keyspace(self): + self.idf.unstack() \ No newline at end of file diff --git a/asv_bench/benchmarks/series_methods.py b/asv_bench/benchmarks/series_methods.py new file mode 100644 index 0000000000000..9cd61c741dae1 --- /dev/null +++ b/asv_bench/benchmarks/series_methods.py @@ -0,0 +1,74 @@ +from pandas_vb_common import * + + +class series_isin_int64(object): + goal_time = 0.2 + + def setup(self): + self.s1 = Series(np.random.randn(10000)) + self.s2 = Series(np.random.randint(1, 10, 10000)) + self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64') + self.values = [1, 2] + self.s4 = self.s3.astype('object') + + def time_series_isin_int64(self): + self.s3.isin(self.values) + + +class series_isin_object(object): + goal_time = 0.2 + + def setup(self): + self.s1 = Series(np.random.randn(10000)) + self.s2 = Series(np.random.randint(1, 10, 10000)) + self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64') + self.values = [1, 2] + self.s4 = self.s3.astype('object') + + def time_series_isin_object(self): + self.s4.isin(self.values) + + +class series_nlargest1(object): + goal_time = 0.2 + + def setup(self): + self.s1 = Series(np.random.randn(10000)) + self.s2 = Series(np.random.randint(1, 10, 10000)) + self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64') + self.values = [1, 2] + self.s4 = self.s3.astype('object') + + def time_series_nlargest1(self): + self.s1.nlargest(3, take_last=True) + self.s1.nlargest(3, take_last=False) + + +class series_nlargest2(object): + goal_time = 0.2 + + def setup(self): + self.s1 = Series(np.random.randn(10000)) + self.s2 = Series(np.random.randint(1, 10, 10000)) + self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64') + self.values = [1, 2] + self.s4 = self.s3.astype('object') + + def time_series_nlargest2(self): + self.s2.nlargest(3, take_last=True) + self.s2.nlargest(3, take_last=False) + + +class series_nsmallest2(object): + goal_time = 0.2 + + def setup(self): + self.s1 = Series(np.random.randn(10000)) + self.s2 = Series(np.random.randint(1, 10, 10000)) + self.s3 = Series(np.random.randint(1, 10, 100000)).astype('int64') + self.values = [1, 2] + self.s4 = self.s3.astype('object') + + def time_series_nsmallest2(self): + self.s2.nsmallest(3, take_last=True) + self.s2.nsmallest(3, take_last=False) \ No newline at end of file diff --git a/asv_bench/benchmarks/sparse.py b/asv_bench/benchmarks/sparse.py new file mode 100644 index 0000000000000..dbf35f5e40f55 --- /dev/null +++ b/asv_bench/benchmarks/sparse.py @@ -0,0 +1,55 @@ +from pandas_vb_common import * +import scipy.sparse +import pandas.sparse.series +from pandas.core.sparse import SparseSeries, SparseDataFrame +from pandas.core.sparse import SparseDataFrame + + +class sparse_series_to_frame(object): + goal_time = 0.2 + + def setup(self): + self.K = 50 + self.N = 50000 + self.rng = np.asarray(date_range('1/1/2000', periods=self.N, freq='T')) + self.series = {} + for i in range(1, (self.K + 1)): + self.data = np.random.randn(self.N)[:(- i)] + self.this_rng = self.rng[:(- i)] + self.data[100:] = np.nan + self.series[i] = SparseSeries(self.data, index=self.this_rng) + + def time_sparse_series_to_frame(self): + SparseDataFrame(self.series) + + +class sparse_frame_constructor(object): + goal_time = 0.2 + + def time_sparse_frame_constructor(self): + SparseDataFrame(columns=np.arange(100), index=np.arange(1000)) + + +class sparse_series_from_coo(object): + goal_time = 0.2 + + def setup(self): + self.A = scipy.sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(100, 100)) + + def time_sparse_series_from_coo(self): + self.ss = pandas.sparse.series.SparseSeries.from_coo(self.A) + + +class sparse_series_to_coo(object): + goal_time = 0.2 + + def setup(self): + self.s = pd.Series(([np.nan] * 10000)) + self.s[0] = 3.0 + self.s[100] = (-1.0) + self.s[999] = 12.1 + self.s.index = pd.MultiIndex.from_product((range(10), range(10), range(10), range(10))) + self.ss = self.s.to_sparse() + + def time_sparse_series_to_coo(self): + self.ss.to_coo(row_levels=[0, 1], column_levels=[2, 3], sort_labels=True) \ No newline at end of file diff --git a/asv_bench/benchmarks/stat_ops.py b/asv_bench/benchmarks/stat_ops.py new file mode 100644 index 0000000000000..98e2bbfce1a44 --- /dev/null +++ b/asv_bench/benchmarks/stat_ops.py @@ -0,0 +1,236 @@ +from pandas_vb_common import * + + +class stat_ops_frame_mean_float_axis_0(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_mean_float_axis_0(self): + self.df.mean() + + +class stat_ops_frame_mean_float_axis_1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_mean_float_axis_1(self): + self.df.mean(1) + + +class stat_ops_frame_mean_int_axis_0(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_mean_int_axis_0(self): + self.dfi.mean() + + +class stat_ops_frame_mean_int_axis_1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_mean_int_axis_1(self): + self.dfi.mean(1) + + +class stat_ops_frame_sum_float_axis_0(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_sum_float_axis_0(self): + self.df.sum() + + +class stat_ops_frame_sum_float_axis_1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_sum_float_axis_1(self): + self.df.sum(1) + + +class stat_ops_frame_sum_int_axis_0(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_sum_int_axis_0(self): + self.dfi.sum() + + +class stat_ops_frame_sum_int_axis_1(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(100000, 4)) + self.dfi = DataFrame(np.random.randint(1000, size=self.df.shape)) + + def time_stat_ops_frame_sum_int_axis_1(self): + self.dfi.sum(1) + + +class stat_ops_level_frame_sum(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + random.shuffle(self.index.values) + self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index) + self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1]) + + def time_stat_ops_level_frame_sum(self): + self.df.sum(level=1) + + +class stat_ops_level_frame_sum_multiple(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + random.shuffle(self.index.values) + self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index) + self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1]) + + def time_stat_ops_level_frame_sum_multiple(self): + self.df.sum(level=[0, 1]) + + +class stat_ops_level_series_sum(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + random.shuffle(self.index.values) + self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index) + self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1]) + + def time_stat_ops_level_series_sum(self): + self.df[1].sum(level=1) + + +class stat_ops_level_series_sum_multiple(object): + goal_time = 0.2 + + def setup(self): + self.index = MultiIndex(levels=[np.arange(10), np.arange(100), np.arange(100)], labels=[np.arange(10).repeat(10000), np.tile(np.arange(100).repeat(100), 10), np.tile(np.tile(np.arange(100), 100), 10)]) + random.shuffle(self.index.values) + self.df = DataFrame(np.random.randn(len(self.index), 4), index=self.index) + self.df_level = DataFrame(np.random.randn(100, 4), index=self.index.levels[1]) + + def time_stat_ops_level_series_sum_multiple(self): + self.df[1].sum(level=[0, 1]) + + +class stat_ops_series_std(object): + goal_time = 0.2 + + def setup(self): + self.s = Series(np.random.randn(100000), index=np.arange(100000)) + self.s[::2] = np.nan + + def time_stat_ops_series_std(self): + self.s.std() + + +class stats_corr_spearman(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(1000, 30)) + + def time_stats_corr_spearman(self): + self.df.corr(method='spearman') + + +class stats_rank2d_axis0_average(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(5000, 50)) + + def time_stats_rank2d_axis0_average(self): + self.df.rank() + + +class stats_rank2d_axis1_average(object): + goal_time = 0.2 + + def setup(self): + self.df = DataFrame(np.random.randn(5000, 50)) + + def time_stats_rank2d_axis1_average(self): + self.df.rank(1) + + +class stats_rank_average(object): + goal_time = 0.2 + + def setup(self): + self.values = np.concatenate([np.arange(100000), np.random.randn(100000), np.arange(100000)]) + self.s = Series(self.values) + + def time_stats_rank_average(self): + self.s.rank() + + +class stats_rank_average_int(object): + goal_time = 0.2 + + def setup(self): + self.values = np.random.randint(0, 100000, size=200000) + self.s = Series(self.values) + + def time_stats_rank_average_int(self): + self.s.rank() + + +class stats_rank_pct_average(object): + goal_time = 0.2 + + def setup(self): + self.values = np.concatenate([np.arange(100000), np.random.randn(100000), np.arange(100000)]) + self.s = Series(self.values) + + def time_stats_rank_pct_average(self): + self.s.rank(pct=True) + + +class stats_rank_pct_average_old(object): + goal_time = 0.2 + + def setup(self): + self.values = np.concatenate([np.arange(100000), np.random.randn(100000), np.arange(100000)]) + self.s = Series(self.values) + + def time_stats_rank_pct_average_old(self): + (self.s.rank() / len(self.s)) + + +class stats_rolling_mean(object): + goal_time = 0.2 + + def setup(self): + self.arr = np.random.randn(100000) + + def time_stats_rolling_mean(self): + rolling_mean(self.arr, 100) \ No newline at end of file diff --git a/asv_bench/benchmarks/strings.py b/asv_bench/benchmarks/strings.py new file mode 100644 index 0000000000000..5adfbf4c2557d --- /dev/null +++ b/asv_bench/benchmarks/strings.py @@ -0,0 +1,393 @@ +from pandas_vb_common import * +import string +import itertools as IT +import pandas.util.testing as testing + + +class strings_cat(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_cat(self): + self.many.str.cat(sep=',') + + +class strings_center(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_center(self): + self.many.str.center(100) + + +class strings_contains_few(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_contains_few(self): + self.few.str.contains('matchthis') + + +class strings_contains_few_noregex(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_contains_few_noregex(self): + self.few.str.contains('matchthis', regex=False) + + +class strings_contains_many(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_contains_many(self): + self.many.str.contains('matchthis') + + +class strings_contains_many_noregex(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_contains_many_noregex(self): + self.many.str.contains('matchthis', regex=False) + + +class strings_count(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_count(self): + self.many.str.count('matchthis') + + +class strings_encode_decode(object): + goal_time = 0.2 + + def setup(self): + self.ser = Series(testing.makeUnicodeIndex()) + + def time_strings_encode_decode(self): + self.ser.str.encode('utf-8').str.decode('utf-8') + + +class strings_endswith(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_endswith(self): + self.many.str.endswith('matchthis') + + +class strings_extract(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_extract(self): + self.many.str.extract('(\\w*)matchthis(\\w*)') + + +class strings_findall(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_findall(self): + self.many.str.findall('[A-Z]+') + + +class strings_get(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_get(self): + self.many.str.get(0) + + +class strings_get_dummies(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + self.s = make_series(string.uppercase, strlen=10, size=10000).str.join('|') + + def time_strings_get_dummies(self): + self.s.str.get_dummies('|') + + +class strings_join_split(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_join_split(self): + self.many.str.join('--').str.split('--') + + +class strings_join_split_expand(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_join_split_expand(self): + self.many.str.join('--').str.split('--', expand=True) + + +class strings_len(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_len(self): + self.many.str.len() + + +class strings_lower(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_lower(self): + self.many.str.lower() + + +class strings_lstrip(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_lstrip(self): + self.many.str.lstrip('matchthis') + + +class strings_match(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_match(self): + self.many.str.match('mat..this') + + +class strings_pad(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_pad(self): + self.many.str.pad(100, side='both') + + +class strings_repeat(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_repeat(self): + self.many.str.repeat(list(IT.islice(IT.cycle(range(1, 4)), len(self.many)))) + + +class strings_replace(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_replace(self): + self.many.str.replace('(matchthis)', '\x01\x01') + + +class strings_rstrip(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_rstrip(self): + self.many.str.rstrip('matchthis') + + +class strings_slice(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_slice(self): + self.many.str.slice(5, 15, 2) + + +class strings_startswith(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_startswith(self): + self.many.str.startswith('matchthis') + + +class strings_strip(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_strip(self): + self.many.str.strip('matchthis') + + +class strings_title(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_title(self): + self.many.str.title() + + +class strings_upper(object): + goal_time = 0.2 + + def setup(self): + + def make_series(letters, strlen, size): + return Series(np.fromiter(IT.cycle(letters), count=(size * strlen), dtype='|S1').view('|S{}'.format(strlen))) + self.many = make_series(('matchthis' + string.uppercase), strlen=19, size=10000) + self.few = make_series(('matchthis' + (string.uppercase * 42)), strlen=19, size=10000) + + def time_strings_upper(self): + self.many.str.upper() \ No newline at end of file diff --git a/asv_bench/benchmarks/timedelta.py b/asv_bench/benchmarks/timedelta.py new file mode 100644 index 0000000000000..36a0f98e3f5ef --- /dev/null +++ b/asv_bench/benchmarks/timedelta.py @@ -0,0 +1,34 @@ +from pandas_vb_common import * +from pandas import to_timedelta + + +class timedelta_convert_int(object): + goal_time = 0.2 + + def setup(self): + self.arr = np.random.randint(0, 1000, size=10000) + + def time_timedelta_convert_int(self): + to_timedelta(self.arr, unit='s') + + +class timedelta_convert_string(object): + goal_time = 0.2 + + def setup(self): + self.arr = np.random.randint(0, 1000, size=10000) + self.arr = ['{0} days'.format(i) for i in self.arr] + + def time_timedelta_convert_string(self): + to_timedelta(self.arr) + + +class timedelta_convert_string_seconds(object): + goal_time = 0.2 + + def setup(self): + self.arr = np.random.randint(0, 60, size=10000) + self.arr = ['00:00:{0:02d}'.format(i) for i in self.arr] + + def time_timedelta_convert_string_seconds(self): + to_timedelta(self.arr) \ No newline at end of file diff --git a/asv_bench/benchmarks/timeseries.py b/asv_bench/benchmarks/timeseries.py new file mode 100644 index 0000000000000..266c198de1455 --- /dev/null +++ b/asv_bench/benchmarks/timeseries.py @@ -0,0 +1,1046 @@ +from pandas.tseries.converter import DatetimeConverter +import pandas as pd +from datetime import timedelta +import datetime as dt +from pandas_vb_common import * +from pandas.tseries.frequencies import infer_freq +import pandas.tseries.holiday +import numpy as np + + +class dataframe_resample_max_numpy(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='20130101', periods=100000, freq='50L') + self.df = DataFrame(np.random.randn(100000, 2), index=self.rng) + + def time_dataframe_resample_max_numpy(self): + self.df.resample('1s', how=np.max) + + +class dataframe_resample_max_string(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='20130101', periods=100000, freq='50L') + self.df = DataFrame(np.random.randn(100000, 2), index=self.rng) + + def time_dataframe_resample_max_string(self): + self.df.resample('1s', how='max') + + +class dataframe_resample_mean_numpy(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='20130101', periods=100000, freq='50L') + self.df = DataFrame(np.random.randn(100000, 2), index=self.rng) + + def time_dataframe_resample_mean_numpy(self): + self.df.resample('1s', how=np.mean) + + +class dataframe_resample_mean_string(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='20130101', periods=100000, freq='50L') + self.df = DataFrame(np.random.randn(100000, 2), index=self.rng) + + def time_dataframe_resample_mean_string(self): + self.df.resample('1s', how='mean') + + +class dataframe_resample_min_numpy(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='20130101', periods=100000, freq='50L') + self.df = DataFrame(np.random.randn(100000, 2), index=self.rng) + + def time_dataframe_resample_min_numpy(self): + self.df.resample('1s', how=np.min) + + +class dataframe_resample_min_string(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='20130101', periods=100000, freq='50L') + self.df = DataFrame(np.random.randn(100000, 2), index=self.rng) + + def time_dataframe_resample_min_string(self): + self.df.resample('1s', how='min') + + +class datetimeindex_add_offset(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=10000, freq='T') + + def time_datetimeindex_add_offset(self): + (self.rng + timedelta(minutes=2)) + + +class datetimeindex_converter(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_datetimeindex_converter(self): + DatetimeConverter.convert(self.rng, None, None) + + +class datetimeindex_infer_dst(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.dst_rng = date_range(start='10/29/2000 1:00:00', end='10/29/2000 1:59:59', freq='S') + self.index = date_range(start='10/29/2000', end='10/29/2000 00:59:59', freq='S') + self.index = self.index.append(self.dst_rng) + self.index = self.index.append(self.dst_rng) + self.index = self.index.append(date_range(start='10/29/2000 2:00:00', end='10/29/2000 3:00:00', freq='S')) + + def time_datetimeindex_infer_dst(self): + self.index.tz_localize('US/Eastern', infer_dst=True) + + +class datetimeindex_normalize(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000 9:30', periods=10000, freq='S', tz='US/Eastern') + + def time_datetimeindex_normalize(self): + self.rng.normalize() + + +class datetimeindex_unique(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=1000, freq='T') + self.index = self.rng.repeat(10) + + def time_datetimeindex_unique(self): + self.index.unique() + + +class dti_reset_index(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=1000, freq='H') + self.df = DataFrame(np.random.randn(len(self.rng), 2), self.rng) + + def time_dti_reset_index(self): + self.df.reset_index() + + +class dti_reset_index_tz(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=1000, freq='H', tz='US/Eastern') + self.df = DataFrame(np.random.randn(len(self.rng), 2), index=self.rng) + + def time_dti_reset_index_tz(self): + self.df.reset_index() + + +class period_setitem(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = period_range(start='1/1/1990', freq='S', periods=20000) + self.df = DataFrame(index=range(len(self.rng))) + + def time_period_setitem(self): + self.df['col'] = self.rng + + +class timeseries_1min_5min_mean(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_timeseries_1min_5min_mean(self): + self.ts[:10000].resample('5min', how='mean') + + +class timeseries_1min_5min_ohlc(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_timeseries_1min_5min_ohlc(self): + self.ts[:10000].resample('5min', how='ohlc') + + +class timeseries_add_irregular(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.lindex = np.random.permutation(self.N)[:(self.N // 2)] + self.rindex = np.random.permutation(self.N)[:(self.N // 2)] + self.left = Series(self.ts.values.take(self.lindex), index=self.ts.index.take(self.lindex)) + self.right = Series(self.ts.values.take(self.rindex), index=self.ts.index.take(self.rindex)) + + def time_timeseries_add_irregular(self): + (self.left + self.right) + + +class timeseries_asof(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 10000 + self.rng = date_range(start='1/1/1990', periods=self.N, freq='53s') + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.dates = date_range(start='1/1/1990', periods=(self.N * 10), freq='5s') + + def time_timeseries_asof(self): + self.ts.asof(self.dates) + + +class timeseries_asof_nan(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 10000 + self.rng = date_range(start='1/1/1990', periods=self.N, freq='53s') + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.dates = date_range(start='1/1/1990', periods=(self.N * 10), freq='5s') + self.ts[250:5000] = np.nan + + def time_timeseries_asof_nan(self): + self.ts.asof(self.dates) + + +class timeseries_asof_single(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 10000 + self.rng = date_range(start='1/1/1990', periods=self.N, freq='53s') + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.dates = date_range(start='1/1/1990', periods=(self.N * 10), freq='5s') + + def time_timeseries_asof_single(self): + self.ts.asof(self.dates[0]) + + +class timeseries_custom_bday_apply(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_apply(self): + self.cday.apply(self.date) + + +class timeseries_custom_bday_apply_dt64(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_apply_dt64(self): + self.cday.apply(self.dt64) + + +class timeseries_custom_bday_cal_decr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_cal_decr(self): + (self.date - (1 * self.cdayh)) + + +class timeseries_custom_bday_cal_incr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_cal_incr(self): + (self.date + (1 * self.cdayh)) + + +class timeseries_custom_bday_cal_incr_n(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_cal_incr_n(self): + (self.date + (10 * self.cdayh)) + + +class timeseries_custom_bday_cal_incr_neg_n(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_cal_incr_neg_n(self): + (self.date - (10 * self.cdayh)) + + +class timeseries_custom_bday_decr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_decr(self): + (self.date - self.cday) + + +class timeseries_custom_bday_incr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bday_incr(self): + (self.date + self.cday) + + +class timeseries_custom_bmonthbegin_decr_n(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bmonthbegin_decr_n(self): + (self.date - (10 * self.cmb)) + + +class timeseries_custom_bmonthbegin_incr_n(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bmonthbegin_incr_n(self): + (self.date + (10 * self.cmb)) + + +class timeseries_custom_bmonthend_decr_n(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bmonthend_decr_n(self): + (self.date - (10 * self.cme)) + + +class timeseries_custom_bmonthend_incr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bmonthend_incr(self): + (self.date + self.cme) + + +class timeseries_custom_bmonthend_incr_n(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_custom_bmonthend_incr_n(self): + (self.date + (10 * self.cme)) + + +class timeseries_day_apply(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_day_apply(self): + self.day.apply(self.date) + + +class timeseries_day_incr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_day_incr(self): + (self.date + self.day) + + +class timeseries_infer_freq(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/1700', freq='D', periods=100000) + self.a = self.rng[:50000].append(self.rng[50002:]) + + def time_timeseries_infer_freq(self): + infer_freq(self.a) + + +class timeseries_is_month_start(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 10000 + self.rng = date_range(start='1/1/1', periods=self.N, freq='B') + + def time_timeseries_is_month_start(self): + self.rng.is_month_start + + +class timeseries_iter_datetimeindex(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 1000000 + self.M = 10000 + self.idx1 = date_range(start='20140101', freq='T', periods=self.N) + self.idx2 = period_range(start='20140101', freq='T', periods=self.N) + + def iter_n(iterable, n=None): + self.i = 0 + for _ in iterable: + self.i += 1 + if ((n is not None) and (self.i > n)): + break + + def time_timeseries_iter_datetimeindex(self): + iter_n(self.idx1) + + +class timeseries_iter_datetimeindex_preexit(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 1000000 + self.M = 10000 + self.idx1 = date_range(start='20140101', freq='T', periods=self.N) + self.idx2 = period_range(start='20140101', freq='T', periods=self.N) + + def iter_n(iterable, n=None): + self.i = 0 + for _ in iterable: + self.i += 1 + if ((n is not None) and (self.i > n)): + break + + def time_timeseries_iter_datetimeindex_preexit(self): + iter_n(self.idx1, self.M) + + +class timeseries_iter_periodindex(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 1000000 + self.M = 10000 + self.idx1 = date_range(start='20140101', freq='T', periods=self.N) + self.idx2 = period_range(start='20140101', freq='T', periods=self.N) + + def iter_n(iterable, n=None): + self.i = 0 + for _ in iterable: + self.i += 1 + if ((n is not None) and (self.i > n)): + break + + def time_timeseries_iter_periodindex(self): + iter_n(self.idx2) + + +class timeseries_iter_periodindex_preexit(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 1000000 + self.M = 10000 + self.idx1 = date_range(start='20140101', freq='T', periods=self.N) + self.idx2 = period_range(start='20140101', freq='T', periods=self.N) + + def iter_n(iterable, n=None): + self.i = 0 + for _ in iterable: + self.i += 1 + if ((n is not None) and (self.i > n)): + break + + def time_timeseries_iter_periodindex_preexit(self): + iter_n(self.idx2, self.M) + + +class timeseries_large_lookup_value(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=1500000, freq='S') + self.ts = Series(1, index=self.rng) + + def time_timeseries_large_lookup_value(self): + self.ts[self.ts.index[(len(self.ts) // 2)]] + self.ts.index._cleanup() + + +class timeseries_period_downsample_mean(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = period_range(start='1/1/2000', end='1/1/2001', freq='T') + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + + def time_timeseries_period_downsample_mean(self): + self.ts.resample('D', how='mean') + + +class timeseries_resample_datetime64(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='2000-01-01 00:00:00', end='2000-01-01 10:00:00', freq='555000U') + self.int_ts = Series(5, self.rng, dtype='int64') + self.ts = self.int_ts.astype('datetime64[ns]') + + def time_timeseries_resample_datetime64(self): + self.ts.resample('1S', how='last') + + +class timeseries_slice_minutely(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_timeseries_slice_minutely(self): + self.ts[:10000] + + +class timeseries_sort_index(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='s') + self.rng = self.rng.take(np.random.permutation(self.N)) + self.ts = Series(np.random.randn(self.N), index=self.rng) + + def time_timeseries_sort_index(self): + self.ts.sort_index() + + +class timeseries_timestamp_downsample_mean(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', end='1/1/2001', freq='T') + self.ts = Series(np.random.randn(len(self.rng)), index=self.rng) + + def time_timeseries_timestamp_downsample_mean(self): + self.ts.resample('D', how='mean') + + +class timeseries_timestamp_tzinfo_cons(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', end='3/1/2000', tz='US/Eastern') + + def time_timeseries_timestamp_tzinfo_cons(self): + self.rng[0] + + +class timeseries_to_datetime_YYYYMMDD(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=10000, freq='D') + self.strings = Series((((self.rng.year * 10000) + (self.rng.month * 100)) + self.rng.day), dtype=np.int64).apply(str) + + def time_timeseries_to_datetime_YYYYMMDD(self): + to_datetime(self.strings, format='%Y%m%d') + + +class timeseries_to_datetime_iso8601(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=20000, freq='H') + self.strings = [x.strftime('%Y-%m-%d %H:%M:%S') for x in self.rng] + + def time_timeseries_to_datetime_iso8601(self): + to_datetime(self.strings) + + +class timeseries_to_datetime_iso8601_format(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.rng = date_range(start='1/1/2000', periods=20000, freq='H') + self.strings = [x.strftime('%Y-%m-%d %H:%M:%S') for x in self.rng] + + def time_timeseries_to_datetime_iso8601_format(self): + to_datetime(self.strings, format='%Y-%m-%d %H:%M:%S') + + +class timeseries_with_format_no_exact(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.s = Series((['19MAY11', '19MAY11:00:00:00'] * 100000)) + + def time_timeseries_with_format_no_exact(self): + to_datetime(self.s, format='%d%b%y', exact=False) + + +class timeseries_with_format_replace(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.s = Series((['19MAY11', '19MAY11:00:00:00'] * 100000)) + + def time_timeseries_with_format_replace(self): + to_datetime(self.s.str.replace(':\\S+$', ''), format='%d%b%y') + + +class timeseries_year_apply(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_year_apply(self): + self.year.apply(self.date) + + +class timeseries_year_incr(object): + goal_time = 0.2 + + def setup(self): + self.N = 100000 + self.rng = date_range(start='1/1/2000', periods=self.N, freq='T') + if hasattr(Series, 'convert'): + Series.resample = Series.convert + self.ts = Series(np.random.randn(self.N), index=self.rng) + self.date = dt.datetime(2011, 1, 1) + self.dt64 = np.datetime64('2011-01-01 09:00Z') + self.hcal = pd.tseries.holiday.USFederalHolidayCalendar() + self.day = pd.offsets.Day() + self.year = pd.offsets.YearBegin() + self.cday = pd.offsets.CustomBusinessDay() + self.cmb = pd.offsets.CustomBusinessMonthBegin(calendar=self.hcal) + self.cme = pd.offsets.CustomBusinessMonthEnd(calendar=self.hcal) + self.cdayh = pd.offsets.CustomBusinessDay(calendar=self.hcal) + + def time_timeseries_year_incr(self): + (self.date + self.year) \ No newline at end of file diff --git a/asv_bench/vbench_to_asv.py b/asv_bench/vbench_to_asv.py new file mode 100644 index 0000000000000..b3980ffed1a57 --- /dev/null +++ b/asv_bench/vbench_to_asv.py @@ -0,0 +1,151 @@ +import ast +import vbench +import os +import sys +import astor +import glob + + +def vbench_to_asv_source(bench, kinds=None): + tab = ' ' * 4 + if kinds is None: + kinds = ['time'] + + output = 'class {}(object):\n'.format(bench.name) + output += tab + 'goal_time = 0.2\n\n' + + if bench.setup: + indented_setup = [tab * 2 + '{}\n'.format(x) for x in bench.setup.splitlines()] + output += tab + 'def setup(self):\n' + ''.join(indented_setup) + '\n' + + for kind in kinds: + output += tab + 'def {}_{}(self):\n'.format(kind, bench.name) + for line in bench.code.splitlines(): + output += tab * 2 + line + '\n' + output += '\n\n' + + if bench.cleanup: + output += tab + 'def teardown(self):\n' + tab * 2 + bench.cleanup + + output += '\n\n' + return output + + +class AssignToSelf(ast.NodeTransformer): + def __init__(self): + super(AssignToSelf, self).__init__() + self.transforms = {} + self.imports = [] + + self.in_class_define = False + self.in_setup = False + + def visit_ClassDef(self, node): + self.transforms = {} + self.in_class_define = True + self.generic_visit(node) + return node + + def visit_TryExcept(self, node): + if any([isinstance(x, (ast.Import, ast.ImportFrom)) for x in node.body]): + self.imports.append(node) + else: + self.generic_visit(node) + return node + + def visit_Assign(self, node): + for target in node.targets: + if isinstance(target, ast.Name) and not isinstance(target.ctx, ast.Param) and not self.in_class_define: + self.transforms[target.id] = 'self.' + target.id + self.generic_visit(node) + + return node + + def visit_Name(self, node): + new_node = node + if node.id in self.transforms: + if not isinstance(node.ctx, ast.Param): + new_node = ast.Attribute(value=ast.Name(id='self', ctx=node.ctx), attr=node.id, ctx=node.ctx) + + self.generic_visit(node) + + return ast.copy_location(new_node, node) + + def visit_Import(self, node): + self.imports.append(node) + + def visit_ImportFrom(self, node): + self.imports.append(node) + + def visit_FunctionDef(self, node): + """Delete functions that are empty due to imports being moved""" + self.in_class_define = False + + if self.in_setup: + node.col_offset -= 4 + ast.increment_lineno(node, -1) + + if node.name == 'setup': + self.in_setup = True + + self.generic_visit(node) + + if node.name == 'setup': + self.in_setup = False + + if node.body: + return node + + +def translate_module(target_module): + g_vars = {} + l_vars = {} + exec('import ' + target_module) in g_vars + + print target_module + module = eval(target_module, g_vars) + + benchmarks = [] + for obj_str in dir(module): + obj = getattr(module, obj_str) + if isinstance(obj, vbench.benchmark.Benchmark): + benchmarks.append(obj) + + if not benchmarks: + return + + rewritten_output = '' + for bench in benchmarks: + rewritten_output += vbench_to_asv_source(bench) + + with open('rewrite.py', 'w') as f: + f.write(rewritten_output) + + ast_module = ast.parse(rewritten_output) + + transformer = AssignToSelf() + transformed_module = transformer.visit(ast_module) + + unique_imports = {astor.to_source(node): node for node in transformer.imports} + + transformed_module.body = unique_imports.values() + transformed_module.body + + transformed_source = astor.to_source(transformed_module) + + with open('benchmarks/{}.py'.format(target_module), 'w') as f: + f.write(transformed_source) + + +if __name__ == '__main__': + cwd = os.getcwd() + new_dir = os.path.join(os.path.dirname(__file__), '../vb_suite') + sys.path.insert(0, new_dir) + + for module in glob.glob(os.path.join(new_dir, '*.py')): + mod = os.path.basename(module) + if mod in ['make.py', 'measure_memory_consumption.py', 'perf_HEAD.py', 'run_suite.py', 'test_perf.py', 'generate_rst_files.py', 'test.py', 'suite.py']: + continue + print + print mod + + translate_module(mod.replace('.py', '')) diff --git a/setup.py b/setup.py index 30c5d1052d9b3..9b21860a01078 100755 --- a/setup.py +++ b/setup.py @@ -269,6 +269,7 @@ class CheckSDist(sdist_class): 'pandas/index.pyx', 'pandas/algos.pyx', 'pandas/parser.pyx', + 'pandas/src/period.pyx', 'pandas/src/sparse.pyx', 'pandas/src/testing.pyx'] diff --git a/vb_suite/binary_ops.py b/vb_suite/binary_ops.py index db9a6b730064e..cd8d1ad93b6e1 100644 --- a/vb_suite/binary_ops.py +++ b/vb_suite/binary_ops.py @@ -88,7 +88,7 @@ Benchmark("df // 0", setup, name='frame_float_floor_by_zero') setup = common_setup + """ -df = DataFrame(np.random.random_integers((1000, 1000))) +df = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000))) """ frame_int_div_by_zero = \ Benchmark("df / 0", setup, name='frame_int_div_by_zero') @@ -111,8 +111,8 @@ Benchmark("df / df2", setup, name='frame_float_mod') setup = common_setup + """ -df = DataFrame(np.random.random_integers((1000, 1000))) -df2 = DataFrame(np.random.random_integers((1000, 1000))) +df = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000))) +df2 = DataFrame(np.random.random_integers(np.iinfo(np.int16).min, np.iinfo(np.int16).max, size=(1000, 1000))) """ frame_int_mod = \ Benchmark("df / df2", setup, name='frame_int_mod') diff --git a/vb_suite/frame_ctor.py b/vb_suite/frame_ctor.py index b11dd6c290ae1..8ad63fc556c2e 100644 --- a/vb_suite/frame_ctor.py +++ b/vb_suite/frame_ctor.py @@ -50,9 +50,30 @@ # offset times 1000 can easily go out of Timestamp bounds and raise errors. dynamic_benchmarks = {} n_steps = [1, 2] +offset_kwargs = {'WeekOfMonth': {'weekday': 1, 'week': 1}, + 'LastWeekOfMonth': {'weekday': 1, 'week': 1}, + 'FY5253': {'startingMonth': 1, 'weekday': 1}, + 'FY5253Quarter': {'qtr_with_extra_week': 1, 'startingMonth': 1, 'weekday': 1}} + +offset_extra_cases = {'FY5253': {'variation': ['nearest', 'last']}, + 'FY5253Quarter': {'variation': ['nearest', 'last']}} + for offset in offsets.__all__: for n in n_steps: - setup = common_setup + """ + kwargs = {} + if offset in offset_kwargs: + kwargs = offset_kwargs[offset] + + if offset in offset_extra_cases: + extras = offset_extra_cases[offset] + else: + extras = {'': ['']} + + for extra_arg in extras: + for extra in extras[extra_arg]: + if extra: + kwargs[extra_arg] = extra + setup = common_setup + """ def get_period_count(start_date, off): ten_offsets_in_days = ((start_date + off * 10) - start_date).days @@ -69,12 +90,14 @@ def get_index_for_offset(off): periods=min(1000, get_period_count(start_date, off)), freq=off) -idx = get_index_for_offset({}({})) +idx = get_index_for_offset({}({}, **{})) df = DataFrame(np.random.randn(len(idx),10), index=idx) d = dict([ (col,df[col]) for col in df.columns ]) -""".format(offset, n) - key = 'frame_ctor_dtindex_{}x{}'.format(offset, n) - dynamic_benchmarks[key] = Benchmark("DataFrame(d)", setup, name=key) +""".format(offset, n, kwargs) + key = 'frame_ctor_dtindex_{}x{}'.format(offset, n) + if extra: + key += '__{}_{}'.format(extra_arg, extra) + dynamic_benchmarks[key] = Benchmark("DataFrame(d)", setup, name=key) # Have to stuff them in globals() so vbench detects them globals().update(dynamic_benchmarks) diff --git a/vb_suite/frame_methods.py b/vb_suite/frame_methods.py index 1d7c5e0d9acef..ce5109efe8f6d 100644 --- a/vb_suite/frame_methods.py +++ b/vb_suite/frame_methods.py @@ -418,8 +418,8 @@ def f(K=100): #---------------------------------------------------------------------- # equals setup = common_setup + """ -def make_pair(name): - df = globals()[name] +def make_pair(frame): + df = frame df2 = df.copy() df2.ix[-1,-1] = np.nan return df, df2 @@ -437,8 +437,8 @@ def test_unequal(name): nonunique_cols = object_df.copy() nonunique_cols.columns = ['A']*len(nonunique_cols.columns) -pairs = dict([(name,make_pair(name)) - for name in ('float_df', 'object_df', 'nonunique_cols')]) +pairs = dict([(name, make_pair(frame)) + for name, frame in (('float_df', float_df), ('object_df', object_df), ('nonunique_cols', nonunique_cols))]) """ frame_float_equal = Benchmark('test_equal("float_df")', setup) frame_object_equal = Benchmark('test_equal("object_df")', setup) diff --git a/vb_suite/gil.py b/vb_suite/gil.py index 30f41bb3c738d..d5aec7c3e2917 100644 --- a/vb_suite/gil.py +++ b/vb_suite/gil.py @@ -94,5 +94,5 @@ def take_1d_pg2_float64(): """ -nogil_take1d_float64 = Benchmark('take_1d_pg2()_int64', setup, start_date=datetime(2015, 1, 1)) -nogil_take1d_int64 = Benchmark('take_1d_pg2()_float64', setup, start_date=datetime(2015, 1, 1)) +nogil_take1d_float64 = Benchmark('take_1d_pg2_int64()', setup, start_date=datetime(2015, 1, 1)) +nogil_take1d_int64 = Benchmark('take_1d_pg2_float64()', setup, start_date=datetime(2015, 1, 1)) diff --git a/vb_suite/groupby.py b/vb_suite/groupby.py index 73f5f19d6a626..6795b315fc517 100644 --- a/vb_suite/groupby.py +++ b/vb_suite/groupby.py @@ -212,7 +212,7 @@ def f(): 'value3' : np.random.randn(100000)}) """ -stmt = "df.pivot_table(rows='key1', cols=['key2', 'key3'])" +stmt = "df.pivot_table(index='key1', columns=['key2', 'key3'])" groupby_pivot_table = Benchmark(stmt, setup, start_date=datetime(2011, 12, 15)) @@ -243,13 +243,13 @@ def f(): """ groupby_first_float64 = Benchmark('data.groupby(labels).first()', setup, - start_date=datetime(2012, 5, 1)) + start_date=datetime(2012, 5, 1)) groupby_first_float32 = Benchmark('data2.groupby(labels).first()', setup, start_date=datetime(2013, 1, 1)) groupby_last_float64 = Benchmark('data.groupby(labels).last()', setup, - start_date=datetime(2012, 5, 1)) + start_date=datetime(2012, 5, 1)) groupby_last_float32 = Benchmark('data2.groupby(labels).last()', setup, start_date=datetime(2013, 1, 1)) @@ -259,7 +259,7 @@ def f(): groupby_nth_float32_none = Benchmark('data2.groupby(labels).nth(0)', setup, start_date=datetime(2013, 1, 1)) groupby_nth_float64_any = Benchmark('data.groupby(labels).nth(0,dropna="all")', setup, - start_date=datetime(2012, 5, 1)) + start_date=datetime(2012, 5, 1)) groupby_nth_float32_any = Benchmark('data2.groupby(labels).nth(0,dropna="all")', setup, start_date=datetime(2013, 1, 1)) @@ -269,9 +269,9 @@ def f(): """ groupby_first_datetimes = Benchmark('df.groupby("b").first()', setup, - start_date=datetime(2013, 5, 1)) + start_date=datetime(2013, 5, 1)) groupby_last_datetimes = Benchmark('df.groupby("b").last()', setup, - start_date=datetime(2013, 5, 1)) + start_date=datetime(2013, 5, 1)) groupby_nth_datetimes_none = Benchmark('df.groupby("b").nth(0)', setup, start_date=datetime(2013, 5, 1)) groupby_nth_datetimes_any = Benchmark('df.groupby("b").nth(0,dropna="all")', setup, diff --git a/vb_suite/io_bench.py b/vb_suite/io_bench.py index a70c543ca59eb..483d61387898d 100644 --- a/vb_suite/io_bench.py +++ b/vb_suite/io_bench.py @@ -2,6 +2,7 @@ from datetime import datetime common_setup = """from pandas_vb_common import * +from StringIO import StringIO """ #---------------------------------------------------------------------- diff --git a/vb_suite/join_merge.py b/vb_suite/join_merge.py index 02132acb71a33..244c6abe71b05 100644 --- a/vb_suite/join_merge.py +++ b/vb_suite/join_merge.py @@ -31,15 +31,15 @@ except: pass -df = DataFrame({'data1' : np.random.randn(100000), +df = pd.DataFrame({'data1' : np.random.randn(100000), 'data2' : np.random.randn(100000), 'key1' : key1, 'key2' : key2}) -df_key1 = DataFrame(np.random.randn(len(level1), 4), index=level1, +df_key1 = pd.DataFrame(np.random.randn(len(level1), 4), index=level1, columns=['A', 'B', 'C', 'D']) -df_key2 = DataFrame(np.random.randn(len(level2), 4), index=level2, +df_key2 = pd.DataFrame(np.random.randn(len(level2), 4), index=level2, columns=['A', 'B', 'C', 'D']) df_shuf = df.reindex(df.index[shuf]) @@ -69,10 +69,10 @@ #---------------------------------------------------------------------- # Joins on integer keys setup = common_setup + """ -df = DataFrame({'key1': np.tile(np.arange(500).repeat(10), 2), +df = pd.DataFrame({'key1': np.tile(np.arange(500).repeat(10), 2), 'key2': np.tile(np.arange(250).repeat(10), 4), 'value': np.random.randn(10000)}) -df2 = DataFrame({'key1': np.arange(500), 'value2': randn(500)}) +df2 = pd.DataFrame({'key1': np.arange(500), 'value2': randn(500)}) df3 = df[:5000] """ @@ -96,9 +96,9 @@ key = np.tile(indices[:8000], 10) key2 = np.tile(indices2[:8000], 10) -left = DataFrame({'key' : key, 'key2':key2, +left = pd.DataFrame({'key' : key, 'key2':key2, 'value' : np.random.randn(80000)}) -right = DataFrame({'key': indices[2000:], 'key2':indices2[2000:], +right = pd.DataFrame({'key': indices[2000:], 'key2':indices2[2000:], 'value2' : np.random.randn(8000)}) """ @@ -112,7 +112,7 @@ # Appending DataFrames setup = common_setup + """ -df1 = DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) +df1 = pd.DataFrame(np.random.randn(10000, 4), columns=['A', 'B', 'C', 'D']) df2 = df1.copy() df2.index = np.arange(10000, 20000) mdf1 = df1.copy() @@ -180,7 +180,7 @@ def sample(values, k): start_date=datetime(2012, 2, 27)) setup = common_setup + """ -df = DataFrame(randn(5, 4)) +df = pd.DataFrame(randn(5, 4)) """ concat_small_frames = Benchmark('concat([df] * 1000)', setup, @@ -191,8 +191,8 @@ def sample(values, k): # Concat empty setup = common_setup + """ -df = DataFrame(dict(A = range(10000)),index=date_range('20130101',periods=10000,freq='s')) -empty = DataFrame() +df = pd.DataFrame(dict(A = range(10000)),index=date_range('20130101',periods=10000,freq='s')) +empty = pd.DataFrame() """ concat_empty_frames1 = Benchmark('concat([df,empty])', setup, @@ -207,11 +207,11 @@ def sample(values, k): setup = common_setup + """ groups = tm.makeStringIndex(10).values -left = DataFrame({'group': groups.repeat(5000), +left = pd.DataFrame({'group': groups.repeat(5000), 'key' : np.tile(np.arange(0, 10000, 2), 10), 'lvalue': np.random.randn(50000)}) -right = DataFrame({'key' : np.arange(10000), +right = pd.DataFrame({'key' : np.arange(10000), 'rvalue' : np.random.randn(10000)}) """ @@ -242,10 +242,10 @@ def sample(values, k): np.random.seed(2718281) n = 50000 -left = DataFrame(np.random.randint(1, n/500, (n, 2)), +left = pd.DataFrame(np.random.randint(1, n/500, (n, 2)), columns=['jim', 'joe']) -right = DataFrame(np.random.randint(1, n/500, (n, 2)), +right = pd.DataFrame(np.random.randint(1, n/500, (n, 2)), columns=['jolie', 'jolia']).set_index('jolie') ''' @@ -255,7 +255,7 @@ def sample(values, k): setup = common_setup + """ low, high, n = -1 << 10, 1 << 10, 1 << 20 -left = DataFrame(np.random.randint(low, high, (n, 7)), +left = pd.DataFrame(np.random.randint(low, high, (n, 7)), columns=list('ABCDEFG')) left['left'] = left.sum(axis=1) diff --git a/vb_suite/packers.py b/vb_suite/packers.py index 62e0e8fc33b58..60738a62bd287 100644 --- a/vb_suite/packers.py +++ b/vb_suite/packers.py @@ -92,7 +92,7 @@ def remove(f): # hdf table setup = common_setup + """ -df2.to_hdf(f,'df',table=True) +df2.to_hdf(f,'df',format='table') """ packers_read_hdf_table = Benchmark("pd.read_hdf(f,'df')", setup, start_date=start_date) diff --git a/vb_suite/pandas_vb_common.py b/vb_suite/pandas_vb_common.py index a599301bb53fe..128e262d45d66 100644 --- a/vb_suite/pandas_vb_common.py +++ b/vb_suite/pandas_vb_common.py @@ -1,4 +1,5 @@ from pandas import * +import pandas as pd from datetime import timedelta from numpy.random import randn from numpy.random import randint @@ -7,6 +8,7 @@ import random import numpy as np +np.random.seed(1234) try: import pandas._tseries as lib except: diff --git a/vb_suite/reindex.py b/vb_suite/reindex.py index 156382f1fb13a..07f0e0f7e1bff 100644 --- a/vb_suite/reindex.py +++ b/vb_suite/reindex.py @@ -49,6 +49,18 @@ #---------------------------------------------------------------------- # Pad / backfill +def pad(source_series, target_index): + try: + source_series.reindex(target_index, method='pad') + except: + source_series.reindex(target_index, fillMethod='pad') + +def backfill(source_series, target_index): + try: + source_series.reindex(target_index, method='backfill') + except: + source_series.reindex(target_index, fillMethod='backfill') + setup = common_setup + """ rng = date_range('1/1/2000', periods=100000, freq=datetools.Minute()) @@ -57,23 +69,23 @@ ts3 = ts2.reindex(ts.index) ts4 = ts3.astype('float32') -def pad(): +def pad(source_series, target_index): try: - ts2.reindex(ts.index, method='pad') + source_series.reindex(target_index, method='pad') except: - ts2.reindex(ts.index, fillMethod='pad') -def backfill(): + source_series.reindex(target_index, fillMethod='pad') +def backfill(source_series, target_index): try: - ts2.reindex(ts.index, method='backfill') + source_series.reindex(target_index, method='backfill') except: - ts2.reindex(ts.index, fillMethod='backfill') + source_series.reindex(target_index, fillMethod='backfill') """ -statement = "pad()" +statement = "pad(ts2, ts.index)" reindex_daterange_pad = Benchmark(statement, setup, name="reindex_daterange_pad") -statement = "backfill()" +statement = "backfill(ts2, ts.index)" reindex_daterange_backfill = Benchmark(statement, setup, name="reindex_daterange_backfill") diff --git a/vb_suite/sparse.py b/vb_suite/sparse.py index e591b197d3384..5da06451fe2d1 100644 --- a/vb_suite/sparse.py +++ b/vb_suite/sparse.py @@ -40,7 +40,7 @@ setup = common_setup + """ -s = pd.Series([nan] * 10000) +s = pd.Series([np.nan] * 10000) s[0] = 3.0 s[100] = -1.0 s[999] = 12.1 @@ -59,7 +59,7 @@ A = scipy.sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(100, 100)) """ -stmt = "ss = pandas.sparse.series.from_coo(A)" +stmt = "ss = pandas.sparse.series.SparseSeries.from_coo(A)" sparse_series_from_coo = Benchmark(stmt, setup, name="sparse_series_from_coo", start_date=datetime(2015, 1, 3)) diff --git a/vb_suite/timeseries.py b/vb_suite/timeseries.py index 75147e079bb65..7f5433980271b 100644 --- a/vb_suite/timeseries.py +++ b/vb_suite/timeseries.py @@ -1,16 +1,21 @@ from vbench.api import Benchmark from datetime import datetime +from pandas import * -common_setup = """from pandas_vb_common import * -from datetime import timedelta N = 100000 - try: - rng = date_range('1/1/2000', periods=N, freq='min') + rng = date_range(start='1/1/2000', periods=N, freq='min') except NameError: - rng = DatetimeIndex('1/1/2000', periods=N, offset=datetools.Minute()) + rng = DatetimeIndex(start='1/1/2000', periods=N, freq='T') def date_range(start=None, end=None, periods=None, freq=None): - return DatetimeIndex(start, end, periods=periods, offset=freq) + return DatetimeIndex(start=start, end=end, periods=periods, offset=freq) + + +common_setup = """from pandas_vb_common import * +from datetime import timedelta +N = 100000 + +rng = date_range(start='1/1/2000', periods=N, freq='T') if hasattr(Series, 'convert'): Series.resample = Series.convert @@ -22,7 +27,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # Lookup value in large time series, hash map population setup = common_setup + """ -rng = date_range('1/1/2000', periods=1500000, freq='s') +rng = date_range(start='1/1/2000', periods=1500000, freq='S') ts = Series(1, index=rng) """ @@ -69,7 +74,7 @@ def date_range(start=None, end=None, periods=None, freq=None): setup = common_setup + """ N = 100000 -rng = date_range('1/1/2000', periods=N, freq='s') +rng = date_range(start='1/1/2000', periods=N, freq='s') rng = rng.take(np.random.permutation(N)) ts = Series(np.random.randn(N), index=rng) """ @@ -81,7 +86,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # Shifting, add offset setup = common_setup + """ -rng = date_range('1/1/2000', periods=10000, freq='T') +rng = date_range(start='1/1/2000', periods=10000, freq='T') """ datetimeindex_add_offset = Benchmark('rng + timedelta(minutes=2)', setup, @@ -89,9 +94,9 @@ def date_range(start=None, end=None, periods=None, freq=None): setup = common_setup + """ N = 10000 -rng = date_range('1/1/1990', periods=N, freq='53s') +rng = date_range(start='1/1/1990', periods=N, freq='53s') ts = Series(np.random.randn(N), index=rng) -dates = date_range('1/1/1990', periods=N * 10, freq='5s') +dates = date_range(start='1/1/1990', periods=N * 10, freq='5s') """ timeseries_asof_single = Benchmark('ts.asof(dates[0])', setup, start_date=datetime(2012, 4, 27)) @@ -108,7 +113,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # Time zone stuff setup = common_setup + """ -rng = date_range('1/1/2000', '3/1/2000', tz='US/Eastern') +rng = date_range(start='1/1/2000', end='3/1/2000', tz='US/Eastern') """ timeseries_timestamp_tzinfo_cons = \ @@ -118,7 +123,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # Resampling period setup = common_setup + """ -rng = period_range('1/1/2000', '1/1/2001', freq='T') +rng = period_range(start='1/1/2000', end='1/1/2001', freq='T') ts = Series(np.random.randn(len(rng)), index=rng) """ @@ -127,7 +132,7 @@ def date_range(start=None, end=None, periods=None, freq=None): start_date=datetime(2012, 4, 25)) setup = common_setup + """ -rng = date_range('1/1/2000', '1/1/2001', freq='T') +rng = date_range(start='1/1/2000', end='1/1/2001', freq='T') ts = Series(np.random.randn(len(rng)), index=rng) """ @@ -149,7 +154,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # to_datetime setup = common_setup + """ -rng = date_range('1/1/2000', periods=20000, freq='h') +rng = date_range(start='1/1/2000', periods=20000, freq='H') strings = [x.strftime('%Y-%m-%d %H:%M:%S') for x in rng] """ @@ -162,7 +167,7 @@ def date_range(start=None, end=None, periods=None, freq=None): start_date=datetime(2012, 7, 11)) setup = common_setup + """ -rng = date_range('1/1/2000', periods=10000, freq='D') +rng = date_range(start='1/1/2000', periods=10000, freq='D') strings = Series(rng.year*10000+rng.month*100+rng.day,dtype=np.int64).apply(str) """ @@ -183,7 +188,7 @@ def date_range(start=None, end=None, periods=None, freq=None): setup = common_setup + """ from pandas.tseries.frequencies import infer_freq -rng = date_range('1/1/1700', freq='D', periods=100000) +rng = date_range(start='1/1/1700', freq='D', periods=100000) a = rng[:50000].append(rng[50002:]) """ @@ -193,7 +198,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # setitem PeriodIndex setup = common_setup + """ -rng = period_range('1/1/1990', freq='S', periods=20000) +rng = period_range(start='1/1/1990', freq='S', periods=20000) df = DataFrame(index=range(len(rng))) """ @@ -202,7 +207,7 @@ def date_range(start=None, end=None, periods=None, freq=None): start_date=datetime(2012, 8, 1)) setup = common_setup + """ -rng = date_range('1/1/2000 9:30', periods=10000, freq='S', tz='US/Eastern') +rng = date_range(start='1/1/2000 9:30', periods=10000, freq='S', tz='US/Eastern') """ datetimeindex_normalize = \ @@ -211,7 +216,7 @@ def date_range(start=None, end=None, periods=None, freq=None): setup = common_setup + """ from pandas.tseries.offsets import Second -s1 = date_range('1/1/2000', periods=100, freq='S') +s1 = date_range(start='1/1/2000', periods=100, freq='S') curr = s1[-1] slst = [] for i in range(100): @@ -224,7 +229,7 @@ def date_range(start=None, end=None, periods=None, freq=None): setup = common_setup + """ -rng = date_range('1/1/2000', periods=1000, freq='H') +rng = date_range(start='1/1/2000', periods=1000, freq='H') df = DataFrame(np.random.randn(len(rng), 2), rng) """ @@ -232,7 +237,7 @@ def date_range(start=None, end=None, periods=None, freq=None): Benchmark('df.reset_index()', setup, start_date=datetime(2012, 9, 1)) setup = common_setup + """ -rng = date_range('1/1/2000', periods=1000, freq='H', +rng = date_range(start='1/1/2000', periods=1000, freq='H', tz='US/Eastern') df = DataFrame(np.random.randn(len(rng), 2), index=rng) """ @@ -241,7 +246,7 @@ def date_range(start=None, end=None, periods=None, freq=None): Benchmark('df.reset_index()', setup, start_date=datetime(2012, 9, 1)) setup = common_setup + """ -rng = date_range('1/1/2000', periods=1000, freq='T') +rng = date_range(start='1/1/2000', periods=1000, freq='T') index = rng.repeat(10) """ @@ -251,13 +256,13 @@ def date_range(start=None, end=None, periods=None, freq=None): # tz_localize with infer argument. This is an attempt to emulate the results # of read_csv with duplicated data. Not passing infer_dst will fail setup = common_setup + """ -dst_rng = date_range('10/29/2000 1:00:00', - '10/29/2000 1:59:59', freq='S') -index = date_range('10/29/2000', '10/29/2000 00:59:59', freq='S') +dst_rng = date_range(start='10/29/2000 1:00:00', + end='10/29/2000 1:59:59', freq='S') +index = date_range(start='10/29/2000', end='10/29/2000 00:59:59', freq='S') index = index.append(dst_rng) index = index.append(dst_rng) -index = index.append(date_range('10/29/2000 2:00:00', - '10/29/2000 3:00:00', freq='S')) +index = index.append(date_range(start='10/29/2000 2:00:00', + end='10/29/2000 3:00:00', freq='S')) """ datetimeindex_infer_dst = \ @@ -269,7 +274,7 @@ def date_range(start=None, end=None, periods=None, freq=None): # Resampling: fast-path various functions setup = common_setup + """ -rng = date_range('20130101',periods=100000,freq='50L') +rng = date_range(start='20130101',periods=100000,freq='50L') df = DataFrame(np.random.randn(100000,2),index=rng) """ @@ -376,7 +381,7 @@ def date_range(start=None, end=None, periods=None, freq=None): setup = common_setup + """ N = 10000 -rng = date_range('1/1/1', periods=N, freq='B') +rng = date_range(start='1/1/1', periods=N, freq='B') """ timeseries_is_month_start = Benchmark('rng.is_month_start', setup,
@jorisvandenbossche Here's the initial pass I was referencing in our discussion in #9660; also relevant closes #8361. A few caveats: - The `eval` suite will have to be converted by hand, as my `ast` transformer isn't parsing the string in `eval("df1 + df2")` - Lots of test failures that I haven't even looked at - `vbench` tended to swallow failing tests, so maybe this is okay? - Uses `conda` instead of `virtualenv` as I'm on OSX and `pip install` fails on several packages. - Takes a while - haven't had a chance to compare against `vbench`. It's possible `asv` is doing a lot more iterations by default. - The conversion depends on a non-stdlib package called `astor`, which does `ast -> source` conversion. Not aware of a way to do this with the stdlib. - The code size blows up as I'm not de-duplicating the setup in any way. Probably something that will just have to be fixed by hand after the switch is made. Still a work-in-progress, but hopefully good enough that you (or someone else) can give this a shot.
https://api.github.com/repos/pandas-dev/pandas/pulls/9715
2015-03-24T06:13:49Z
2015-08-19T00:32:16Z
2015-08-19T00:32:16Z
2015-08-19T00:44:21Z
SAS xport file reader
diff --git a/doc/source/api.rst b/doc/source/api.rst index 1cbe55ddbacb6..066b36bfa57b6 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -82,6 +82,15 @@ HDFStore: PyTables (HDF5) HDFStore.get HDFStore.select +SAS +~~~ + +.. autosummary:: + :toctree: generated/ + + read_sas + XportReader + SQL ~~~ diff --git a/doc/source/io.rst b/doc/source/io.rst index 38a8d4d05b807..2f2c4c7566413 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -41,6 +41,7 @@ object. * :ref:`read_html<io.read_html>` * :ref:`read_gbq<io.bigquery>` (experimental) * :ref:`read_stata<io.stata_reader>` + * :ref:`read_sas<io.sas_reader>` * :ref:`read_clipboard<io.clipboard>` * :ref:`read_pickle<io.pickle>` @@ -4120,6 +4121,46 @@ easy conversion to and from pandas. .. _xray: http://xray.readthedocs.org/ +.. _io.sas: + +SAS Format +---------- + +.. versionadded:: 0.17.0 + +The top-level function :function:`read_sas` currently can read (but +not write) SAS xport (.XPT) format files. Pandas cannot currently +handle SAS7BDAT files. + +XPORT files only contain two value types: ASCII text and double +precision numeric values. There is no automatic type conversion to +integers, dates, or categoricals. By default the whole file is read +and returned as a ``DataFrame``. + +Specify a ``chunksize`` or use ``iterator=True`` to obtain an +``XportReader`` object for incrementally reading the file. The +``XportReader`` object also has attributes that contain additional +information about the file and its variables. + +Read a SAS XPORT file: + +.. code-block:: python + + df = pd.read_sas('sas_xport.xpt') + +Obtain an iterator and read an XPORT file 100,000 lines at a time: + +.. code-block:: python + + rdr = pd.read_sas('sas_xport.xpt', chunk=100000) + for chunk in rdr: + do_something(chunk) + +The specification_ for the xport file format is available from the SAS +web site. + +.. _specification: https://support.sas.com/techsup/technote/ts140.pdf + .. _io.perf: Performance Considerations diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index de2261a79da47..1df8ddc227a51 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -20,6 +20,7 @@ Highlights include: if they are all ``NaN``, see :ref:`here <whatsnew_0170.api_breaking.hdf_dropna>` - Support for ``Series.dt.strftime`` to generate formatted strings for datetime-likes, see :ref:`here <whatsnew_0170.strftime>` - Development installed versions of pandas will now have ``PEP440`` compliant version strings (:issue:`9518`) +- Support for reading SAS xport files, see :ref:`here <whatsnew_0170.enhancements.sas_xport>` Check the :ref:`API Changes <whatsnew_0170.api>` and :ref:`deprecations <whatsnew_0170.deprecations>` before updating. @@ -37,7 +38,6 @@ New features - Enable writing complex values to HDF stores when using table format (:issue:`10447`) - Enable reading gzip compressed files via URL, either by explicitly setting the compression parameter or by inferring from the presence of the HTTP Content-Encoding header in the response (:issue:`8685`) - .. _whatsnew_0170.gil: Releasing the GIL @@ -85,6 +85,18 @@ We are now supporting a ``Series.dt.strftime`` method for datetime-likes to gene The string format is as the python standard library and details can be found `here <https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior>`_ +.. _whatsnew_0170.enhancements.sas_xport: + +Support for SAS XPORT files +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +:meth:`~pandas.io.read_sas` provides support for reading SAS XPORT format files: + + df = pd.read_sas('sas_xport.xpt') + +It is also possible to obtain an iterator and read an XPORT file +incrementally. + .. _whatsnew_0170.enhancements.other: Other enhancements diff --git a/pandas/io/api.py b/pandas/io/api.py index 5fa8c7ef60074..fedde462c74b7 100644 --- a/pandas/io/api.py +++ b/pandas/io/api.py @@ -9,6 +9,7 @@ from pandas.io.json import read_json from pandas.io.html import read_html from pandas.io.sql import read_sql, read_sql_table, read_sql_query +from pandas.io.sas import read_sas from pandas.io.stata import read_stata from pandas.io.pickle import read_pickle, to_pickle from pandas.io.packers import read_msgpack, to_msgpack diff --git a/pandas/io/sas.py b/pandas/io/sas.py new file mode 100644 index 0000000000000..5f55f861afb72 --- /dev/null +++ b/pandas/io/sas.py @@ -0,0 +1,459 @@ +""" +Tools for reading SAS XPort files into Pandas objects. + +Based on code from Jack Cushman (github.com/jcushman/xport). + +The file format is defined here: + +https://support.sas.com/techsup/technote/ts140.pdf +""" + +from datetime import datetime +import pandas as pd +from pandas.io.common import get_filepath_or_buffer +from pandas import compat +import struct +import numpy as np +from pandas.util.decorators import Appender + +_correct_line1 = "HEADER RECORD*******LIBRARY HEADER RECORD!!!!!!!000000000000000000000000000000 " +_correct_header1 = "HEADER RECORD*******MEMBER HEADER RECORD!!!!!!!000000000000000001600000000" +_correct_header2 = "HEADER RECORD*******DSCRPTR HEADER RECORD!!!!!!!000000000000000000000000000000 " +_correct_obs_header = "HEADER RECORD*******OBS HEADER RECORD!!!!!!!000000000000000000000000000000 " +_fieldkeys = ['ntype', 'nhfun', 'field_length', 'nvar0', 'name', 'label', + 'nform', 'nfl', 'num_decimals', 'nfj', 'nfill', 'niform', + 'nifl', 'nifd', 'npos', '_'] + + +# TODO: Support for 4 byte floats, see https://github.com/jcushman/xport/pull/3 +# Need a test file + + +_base_params_doc = """\ +Parameters +---------- +filepath_or_buffer : string or file-like object + Path to SAS file or object implementing binary read method.""" + +_params2_doc = """\ +index : identifier of index column + Identifier of column that should be used as index of the DataFrame. +encoding : string + Encoding for text data. +chunksize : int + Read file `chunksize` lines at a time, returns iterator.""" + +_format_params_doc = """\ +format : string + File format, only `xport` is currently supported.""" + +_iterator_doc = """\ +iterator : boolean, default False + Return XportReader object for reading file incrementally.""" + + +_read_sas_doc = """Read a SAS file into a DataFrame. + +%(_base_params_doc)s +%(_format_params_doc)s +%(_params2_doc)s +%(_iterator_doc)s + +Returns +------- +DataFrame or XportReader + +Examples +-------- +Read a SAS Xport file: + +>>> df = pandas.read_sas('filename.XPT') + +Read a Xport file in 10,000 line chunks: + +>>> itr = pandas.read_sas('filename.XPT', chunksize=10000) +>>> for chunk in itr: +>>> do_something(chunk) + +.. versionadded:: 0.17.0 +""" % {"_base_params_doc": _base_params_doc, + "_format_params_doc": _format_params_doc, + "_params2_doc": _params2_doc, + "_iterator_doc": _iterator_doc} + + +_xport_reader_doc = """\ +Class for reading SAS Xport files. + +%(_base_params_doc)s +%(_params2_doc)s + +Attributes +---------- +member_info : list + Contains information about the file +fields : list + Contains information about the variables in the file +""" % {"_base_params_doc": _base_params_doc, + "_params2_doc": _params2_doc} + + +_read_method_doc = """\ +Read observations from SAS Xport file, returning as data frame. + +Parameters +---------- +nrows : int + Number of rows to read from data file; if None, read whole + file. + +Returns +------- +A DataFrame. +""" + + +@Appender(_read_sas_doc) +def read_sas(filepath_or_buffer, format='xport', index=None, encoding='ISO-8859-1', + chunksize=None, iterator=False): + + format = format.lower() + + if format == 'xport': + reader = XportReader(filepath_or_buffer, index=index, encoding=encoding, + chunksize=chunksize) + else: + raise ValueError('only xport format is supported') + + if iterator or chunksize: + return reader + + return reader.read() + + +def _parse_date(datestr): + """ Given a date in xport format, return Python date. """ + try: + return datetime.strptime(datestr, "%d%b%y:%H:%M:%S") # e.g. "16FEB11:10:07:55" + except ValueError: + return pd.NaT + + +def _split_line(s, parts): + """ + Parameters + ---------- + s: string + Fixed-length string to split + parts: list of (name, length) pairs + Used to break up string, name '_' will be filtered from output. + + Returns + ------- + Dict of name:contents of string at given location. + """ + out = {} + start = 0 + for name, length in parts: + out[name] = s[start:start+length].strip() + start += length + del out['_'] + return out + + +def _parse_float_vec(vec): + """ + Parse a vector of 8-byte values representing IBM 8 byte floats + into native 8 byte floats. + """ + + dtype = np.dtype('>u4,>u4') + vec1 = vec.view(dtype=dtype) + + xport1 = vec1['f0'] + xport2 = vec1['f1'] + + # Start by setting first half of ieee number to first half of IBM + # number sans exponent + ieee1 = xport1 & 0x00ffffff + + # Get the second half of the ibm number into the second half of + # the ieee number + ieee2 = xport2 + + # The fraction bit to the left of the binary point in the ieee + # format was set and the number was shifted 0, 1, 2, or 3 + # places. This will tell us how to adjust the ibm exponent to be a + # power of 2 ieee exponent and how to shift the fraction bits to + # restore the correct magnitude. + shift = np.zeros(len(vec), dtype=np.uint8) + shift[np.where(xport1 & 0x00200000)] = 1 + shift[np.where(xport1 & 0x00400000)] = 2 + shift[np.where(xport1 & 0x00800000)] = 3 + + # shift the ieee number down the correct number of places then + # set the second half of the ieee number to be the second half + # of the ibm number shifted appropriately, ored with the bits + # from the first half that would have been shifted in if we + # could shift a double. All we are worried about are the low + # order 3 bits of the first half since we're only shifting by + # 1, 2, or 3. + ieee1 >>= shift + ieee2 = (xport2 >> shift) | ((xport1 & 0x00000007) << (29 + (3 - shift))) + + # clear the 1 bit to the left of the binary point + ieee1 &= 0xffefffff + + # set the exponent of the ieee number to be the actual exponent + # plus the shift count + 1023. Or this into the first half of the + # ieee number. The ibm exponent is excess 64 but is adjusted by 65 + # since during conversion to ibm format the exponent is + # incremented by 1 and the fraction bits left 4 positions to the + # right of the radix point. (had to add >> 24 because C treats & + # 0x7f as 0x7f000000 and Python doesn't) + ieee1 |= ((((((xport1 >> 24) & 0x7f) - 65) << 2) + shift + 1023) << 20) | (xport1 & 0x80000000) + + ieee = np.empty((len(ieee1),), dtype='>u4,>u4') + ieee['f0'] = ieee1 + ieee['f1'] = ieee2 + ieee = ieee.view(dtype='>f8') + ieee = ieee.astype('f8') + + return ieee + + + +class XportReader(object): + __doc__ = _xport_reader_doc + + + def __init__(self, filepath_or_buffer, index=None, encoding='ISO-8859-1', + chunksize=None): + + self._encoding = encoding + self._lines_read = 0 + self._index = index + self._chunksize = chunksize + + if isinstance(filepath_or_buffer, str): + filepath_or_buffer, encoding, compression = get_filepath_or_buffer( + filepath_or_buffer, encoding=encoding) + + if isinstance(filepath_or_buffer, (str, compat.text_type, bytes)): + self.filepath_or_buffer = open(filepath_or_buffer, 'rb') + else: + # Copy to BytesIO, and ensure no encoding + contents = filepath_or_buffer.read() + try: + contents = contents.encode(self._encoding) + except: + pass + self.filepath_or_buffer = compat.BytesIO(contents) + + self._read_header() + + + def _get_row(self): + return self.filepath_or_buffer.read(80).decode() + + + def _read_header(self): + self.filepath_or_buffer.seek(0) + + # read file header + line1 = self._get_row() + if line1 != _correct_line1: + raise ValueError("Header record is not an XPORT file.") + + line2 = self._get_row() + file_info = _split_line(line2, [ ['prefix',24], ['version',8], ['OS',8], ['_',24], ['created',16]]) + if file_info['prefix'] != "SAS SAS SASLIB": + raise ValueError("Header record has invalid prefix.") + file_info['created'] = _parse_date(file_info['created']) + self.file_info = file_info + + line3 = self._get_row() + file_info['modified'] = _parse_date(line3[:16]) + + # read member header + header1 = self._get_row() + header2 = self._get_row() + if not header1.startswith(_correct_header1) or not header2 == _correct_header2: + raise ValueError("Member header not found.") + fieldnamelength = int(header1[-5:-2]) # usually 140, could be 135 + + # member info + member_info = _split_line(self._get_row(), [['prefix',8], ['set_name',8], + ['sasdata',8],['version',8], + ['OS',8],['_',24],['created',16]]) + member_info.update( _split_line(self._get_row(), [['modified',16], ['_',16], + ['label',40],['type',8]])) + member_info['modified'] = _parse_date(member_info['modified']) + member_info['created'] = _parse_date(member_info['created']) + self.member_info = member_info + + # read field names + types = {1: 'numeric', 2: 'char'} + fieldcount = int(self._get_row()[54:58]) + datalength = fieldnamelength*fieldcount + if datalength % 80: # round up to nearest 80 + datalength += 80 - datalength%80 + fielddata = self.filepath_or_buffer.read(datalength) + fields = [] + obs_length = 0 + while len(fielddata) >= fieldnamelength: + # pull data for one field + field, fielddata = (fielddata[:fieldnamelength], fielddata[fieldnamelength:]) + + # rest at end gets ignored, so if field is short, pad out + # to match struct pattern below + field = field.ljust(140) + + fieldstruct = struct.unpack('>hhhh8s40s8shhh2s8shhl52s', field) + field = dict(zip(_fieldkeys, fieldstruct)) + del field['_'] + field['ntype'] = types[field['ntype']] + if field['ntype'] == 'numeric' and field['field_length'] != 8: + raise TypeError("Only 8-byte floats are currently implemented. Can't read field %s." % field) + + for k, v in field.items(): + try: + field[k] = v.strip() + except AttributeError: + pass + + obs_length += field['field_length'] + fields += [field] + + header = self._get_row() + if not header == _correct_obs_header: + raise ValueError("Observation header not found.") + + self.fields = fields + self.record_length = obs_length + self.record_start = self.filepath_or_buffer.tell() + + self.nobs = self._record_count() + self.columns = [x['name'].decode() for x in self.fields] + + # Setup the dtype. + dtypel = [] + for i,field in enumerate(self.fields): + ntype = field['ntype'] + if ntype == "numeric": + dtypel.append(('s' + str(i), ">u8")) + elif ntype == "char": + dtypel.append(('s' + str(i), "S" + str(field['field_length']))) + dtype = np.dtype(dtypel) + self._dtype = dtype + + + def __iter__(self): + try: + if self._chunksize: + while True: + yield self.read(self._chunksize) + else: + yield self.read() + except StopIteration: + pass + + + def _record_count(self): + """ + Get number of records in file. + + This is maybe suboptimal because we have to seek to the end of the file. + + Side effect: returns file position to record_start. + """ + + self.filepath_or_buffer.seek(0, 2) + total_records_length = self.filepath_or_buffer.tell() - self.record_start + + if total_records_length % 80 != 0: + warnings.warn("xport file may be corrupted") + + if self.record_length > 80: + self.filepath_or_buffer.seek(self.record_start) + return total_records_length // self.record_length + + self.filepath_or_buffer.seek(-80, 2) + last_card = self.filepath_or_buffer.read(80) + last_card = np.frombuffer(last_card, dtype=np.uint64) + + # 8 byte blank + ix = np.flatnonzero(last_card == 2314885530818453536) + + if len(ix) == 0: + tail_pad = 0 + else: + tail_pad = 8 * len(ix) + + self.filepath_or_buffer.seek(self.record_start) + + return (total_records_length - tail_pad) // self.record_length + + + def get_chunk(self, size=None): + """ + Reads lines from Xport file and returns as dataframe + + Parameters + ---------- + size : int, defaults to None + Number of lines to read. If None, reads whole file. + + Returns + ------- + DataFrame + """ + if size is None: + size = self._chunksize + return self.read(nrows=size) + + + def _missing_double(self, vec): + v = vec.view(dtype='u1,u1,u2,u4') + miss = (v['f1'] == 0) & (v['f2'] == 0) & (v['f3'] == 0) + miss1 = ((v['f0'] >= 0x41) & (v['f0'] <= 0x5a)) |\ + (v['f0'] == 0x5f) | (v['f0'] == 0x2e) + miss &= miss1 + return miss + + + @Appender(_read_method_doc) + def read(self, nrows=None): + + if nrows is None: + nrows = self.nobs + + read_lines = min(nrows, self.nobs - self._lines_read) + read_len = read_lines * self.record_length + if read_len <= 0: + raise StopIteration + raw = self.filepath_or_buffer.read(read_len) + data = np.frombuffer(raw, dtype=self._dtype, count=read_lines) + + df = pd.DataFrame(index=range(read_lines)) + for j,x in enumerate(self.columns): + vec = data['s%d' % j] + ntype = self.fields[j]['ntype'] + if ntype == "numeric": + miss = self._missing_double(vec) + v = _parse_float_vec(vec) + v[miss] = np.nan + elif self.fields[j]['ntype'] == 'char': + v = [y.rstrip() for y in vec] + if compat.PY3: + v = [y.decode(self._encoding) for y in v] + df[x] = v + + if self._index is None: + df.index = range(self._lines_read, self._lines_read + read_lines) + else: + df = df.set_index(self._index) + + self._lines_read += read_lines + + return df diff --git a/pandas/io/tests/data/DEMO_G.XPT b/pandas/io/tests/data/DEMO_G.XPT new file mode 100644 index 0000000000000..587bc3c4eb649 Binary files /dev/null and b/pandas/io/tests/data/DEMO_G.XPT differ diff --git a/pandas/io/tests/data/DEMO_G.csv b/pandas/io/tests/data/DEMO_G.csv new file mode 100644 index 0000000000000..db2158a532100 --- /dev/null +++ b/pandas/io/tests/data/DEMO_G.csv @@ -0,0 +1,9757 @@ 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+71894,7,2,1,11,NA,2,2,2,11,143,NA,NA,1,1,NA,5,NA,NA,NA,1,1,2,1,2,2,1,2,2,1,11113.498573,11364.131177,1,94,14,14,3.4,5,5,0,3,0,2,41,1,4,1,4 +71895,7,2,1,31,NA,5,6,1,NA,NA,2,NA,2,2,2,NA,5,1,NA,1,2,2,1,2,2,1,2,2,3,17165.91562,17929.203991,2,96,15,6,2.3,3,1,0,0,0,1,31,2,5,1,NA +71896,7,2,1,4,NA,4,4,2,4,59,NA,NA,1,1,NA,NA,NA,NA,NA,1,1,2,1,2,2,NA,NA,NA,NA,9304.437652,9588.632035,2,97,14,14,3.91,4,4,1,1,0,1,38,1,4,1,5 +71897,7,2,2,68,NA,4,4,2,NA,NA,2,NA,1,1,NA,NA,4,1,NA,1,2,2,1,2,2,1,2,2,1,12331.419303,12882.003985,2,95,77,77,NA,2,2,0,0,2,1,68,1,4,1,4 +71898,7,2,2,65,NA,1,1,1,NA,NA,2,NA,1,1,NA,NA,4,1,NA,1,2,2,1,2,2,1,2,2,1,15207.312407,15896.113669,1,92,9,9,3.97,2,2,0,0,2,2,65,1,4,1,4 +71899,7,2,2,3,NA,3,3,1,3,37,NA,NA,1,1,NA,NA,NA,NA,NA,1,1,2,1,2,2,NA,NA,NA,NA,66833.888628,73763.947124,2,98,7,7,1.61,4,4,1,1,0,1,43,NA,NA,6,NA +71900,7,1,2,26,NA,2,2,NA,NA,NA,2,NA,1,1,NA,NA,5,5,3,1,2,2,1,2,2,NA,NA,NA,NA,55675.708832,0,1,93,3,2,0.46,2,1,0,0,0,2,26,1,5,5,NA 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+71915,7,2,1,60,NA,3,3,2,NA,NA,2,NA,1,1,NA,NA,5,5,NA,1,2,2,1,2,2,1,2,2,1,88961.259215,91446.591982,3,90,10,10,5,1,1,0,0,1,1,60,1,5,5,NA +71916,7,2,1,16,NA,3,3,1,16,198,NA,NA,1,1,NA,9,NA,NA,NA,1,2,2,1,2,2,1,2,2,1,24446.632088,24751.360191,1,94,4,4,0.79,3,3,0,1,0,1,49,1,2,3,NA diff --git a/pandas/io/tests/data/DRXFCD_G.XPT b/pandas/io/tests/data/DRXFCD_G.XPT new file mode 100644 index 0000000000000..15de11e8f9f49 Binary files /dev/null and b/pandas/io/tests/data/DRXFCD_G.XPT differ diff --git a/pandas/io/tests/data/DRXFCD_G.csv b/pandas/io/tests/data/DRXFCD_G.csv new file mode 100644 index 0000000000000..3fceacd1273bd --- /dev/null +++ b/pandas/io/tests/data/DRXFCD_G.csv @@ -0,0 +1,7619 @@ +"DRXFDCD","DRXFCSD","DRXFCLD" +1.1e+07,"MILK, HUMAN","Milk, human" +11100000,"MILK, NFS","Milk, NFS" +11111000,"MILK, COW'S, FLUID, WHOLE","Milk, cow's, fluid, whole" +11111100,"MILK, COW'S, FLUID, WHOLE, LOW SODIUM","Milk, cow's, fluid, whole, low-sodium" +11111150,"MILK, CALCIUM FORTIFIED, WHOLE, COW'S, FLUID","Milk, calcium fortified, cow's, fluid, whole" +11111160,"MILK, CALCIUM FORTIFIED, COW'S, FLUID, 1% FAT","Milk, calcium fortified, cow's, fluid, 1% fat" +11111170,"MILK, CALCIUM FORTIFIED, SKIM/NONFAT, COW, FLUID","Milk, calcium fortified, cow's, fluid, skim or nonfat" +11112110,"MILK, COW'S, FLUID, 2% FAT","Milk, cow's, fluid, 2% fat" +11112120,"MILK, COW'S, FLUID, ACIDOPHILUS, 1% FAT","Milk, cow's, fluid, acidophilus, 1% fat" +11112130,"MILK, COW'S, FLUID, ACIDOPHILUS, 2% FAT","Milk, cow's, fluid, acidophilus, 2% fat" +11112210,"MILK, COW'S, FLUID, 1% FAT","Milk, cow's, fluid, 1% fat" +11113000,"MILK, COW'S, FLUID, SKIM OR NONFAT","Milk, cow's, fluid, skim or nonfat, 0.5% or less butterfat" +11114300,"MILK, LOW LACTOSE, 1% FAT","Milk, cow's, fluid, lactose reduced, 1% fat" +11114310,"MILK, LOW LACTOSE, 1% FAT, FORTIFIED WITH CALCIUM","Milk, cow's, fluid, lactose reduced, 1% fat, fortified with calcium" +11114320,"MILK, LOW LACTOSE, NONFAT","Milk, cow's, fluid, lactose reduced, nonfat" +11114321,"MILK, LOW LACTOSE, NONFAT, W/ CALCIUM","Milk, cow's, fluid, lactose reduced, nonfat, fortified with calcium" +11114330,"MILK, COW'S FL LACTOSE REDUCED 2% FAT (LACTAID)","Milk, cow's, fluid, lactose reduced, 2% fat" +11114350,"MILK, COW'S, FLUID, LACTOSE REDUCED, WHOLE","Milk, cow's, fluid, lactose reduced, whole" +11115000,"BUTTERMILK, FLUID (INCLUDE KEFIR MILK)","Buttermilk, fluid, nonfat" +11115100,"BUTTERMILK, FLUID, 1% FAT","Buttermilk, fluid, 1% fat" +11115200,"BUTTERMILK, FLUID, 2% FAT","Buttermilk, fluid, 2% fat" +11115300,"BUTTERMILK, FLUID, WHOLE","Buttermilk, fluid, whole" +11116000,"MILK, GOAT'S, FLUID, WHOLE","Milk, goat's, fluid, whole" +11120000,"MILK, DRY, RECONSTITUTED, NFS","Milk, dry, reconstituted, NFS" +11121100,"MILK, DRY, RECONSTITUTED, WHOLE","Milk, dry, reconstituted, whole" +11121210,"MILK, DRY, RECONSTITUTED, LOWFAT","Milk, dry, reconstituted, lowfat" +11121300,"MILK, DRY, RECONSTITUTED, NONFAT","Milk, dry, reconstituted, nonfat" +11210050,"MILK, EVAPORATED, NS AS TO FAT CONTENT","Milk, evaporated, NS as to fat content (formerly NS as to dilution, used in coffee or tea, assume undiluted)" +11211050,"MILK, EVAPORATED, WHOLE","Milk, evaporated, whole (formerly NS as to dilution, used in coffee or tea)" +11211400,"MILK, EVAPORATED, 2% FAT","Milk, evaporated, 2% fat (formerly NS as to dilution)" +11212050,"MILK, EVAPORATED, SKIM","Milk, evaporated, skim (formerly NS as to dilution, used in coffee or tea)" +11220000,"MILK, CONDENSED, SWEETENED","Milk, condensed, sweetened (formerly NS as to dilution)" +11320000,"MILK, SOY, READY-TO-DRINK, NOT BABY","Milk, soy, ready-to-drink, not baby's" +11320100,"MILK, SOY, LIGHT, READY-TO-DRINK, NOT BABY'S","Milk, soy, light, ready-to-drink, not baby's" +11320200,"MILK, SOY, NONFAT, READY-TO-DRINK, NOT BABY'S","Milk, soy, nonfat, ready-to-drink, not baby's" +11321000,"MILK, SOY, READY-TO-DRINK, NOT BABY'S, CHOCOLATE","Milk, soy, ready-to-drink, not baby's, chocolate" +11321100,"MILK, SOY, LIGHT, READY-TO-DRINK, NOT BABY'S, CHOCOLATE","Milk, soy, light, ready-to-drink, not baby's, chocolate" +11321200,"MILK, SOY, NONFAT, READY-TO-DRINK, NOT BABY'S, CHOCOLATE","Milk, soy, nonfat, ready-to-drink, not baby's, chocolate" +11340000,"MILK,IMITATION,FLUID,NONSOY,SWEETENED,NOT CHOCOLATE","Milk, imitation, fluid, non-soy, sweetened, flavors other than chocolate" +11350000,"MILK, ALMOND, READY-TO-DRINK","Milk, almond, ready-to-drink" +11350010,"MILK, ALMOND, READY-TO-DRINK, CHOCOLATE","Milk, almond, ready-to-drink, chocolate" +11410000,"YOGURT, NS AS TO TYPE OF MILK/FLAVOR","Yogurt, NS as to type of milk or flavor" +11411010,"YOGURT, PLAIN, NS AS TO TYPE OF MILK","Yogurt, plain, NS as to type of milk" +11411100,"YOGURT, PLAIN, WHOLE MILK","Yogurt, plain, whole milk" +11411200,"YOGURT, PLAIN, LOWFAT MILK","Yogurt, plain, lowfat milk" +11411300,"YOGURT, PLAIN, NONFAT MILK","Yogurt, plain, nonfat milk" +11420000,"YOGURT, VANILLA, LEMON, COFFEE, NS AS TO MILK TYPE","Yogurt, vanilla, lemon, or coffee flavor, NS as to type of milk" +11421000,"YOGURT, VANILLA, LEMON, COFFEE, WHOLE MILK","Yogurt, vanilla, lemon, or coffee flavor, whole milk" +11422000,"YOGURT, VANILLA, LEMON, COFFEE, LOWFAT MILK","Yogurt, vanilla, lemon, maple, or coffee flavor, lowfat milk" +11422100,"YOGURT, VANILLA, LEMON, COFFEE, LOWFAT MILK, LOW CAL SWTNR","Yogurt, vanilla, lemon, maple, or coffee flavor, lowfat milk, sweetened with low calorie sweetener" +11423000,"YOGURT, VANILLA, LEMON, COFFEE, NONFAT MILK","Yogurt, vanilla, lemon, maple, or coffee flavor, nonfat milk" +11424000,"YOGURT, VANILLA, LEMON, COFFEE, NONFAT MILK, LOW CAL SWEET","Yogurt, vanilla, lemon, maple, or coffee flavor, nonfat milk, sweetened with low calorie sweetener" +11425000,"YOGURT, CHOCOLATE, NS AS TO TYPE OF MILK","Yogurt, chocolate, NS as to type of milk" +11426000,"YOGURT, CHOCOLATE, WHOLE MILK","Yogurt, chocolate, whole milk" +11427000,"YOGURT, CHOCOLATE, NONFAT MILK","Yogurt, chocolate, nonfat milk" +11430000,"YOGURT, FRUIT VARIETY, NS AS TO MILK TYPE","Yogurt, fruit variety, NS as to type of milk" +11431000,"YOGURT, FRUIT VARIETY, WHOLE MILK","Yogurt, fruit variety, whole milk" +11432000,"YOGURT, FRUIT VARIETY, LOWFAT MILK","Yogurt, fruit variety, lowfat milk" +11432500,"YOGURT, FRUIT VARIETY, LOWFAT MILK, W/ LOW CAL SWEETENER","Yogurt, fruit variety, lowfat milk, sweetened with low-calorie sweetener" +11433000,"YOGURT, FRUIT VARIETY, NONFAT MILK","Yogurt, fruit variety, nonfat milk" +11433500,"YOGURT, FRUITED, NONFAT MILK, LOW CAL SWEETENER","Yogurt, fruit variety, nonfat milk, sweetened with low-calorie sweetener" +11446000,"FRUIT AND LOWFAT YOGURT PARFAIT","Fruit and lowfat yogurt parfait" +11459990,"YOGURT, FROZEN, NS AS TO FLAVOR, NS TO TYPE OF MILK","Yogurt, frozen, NS as to flavor, NS as to type of milk" +11460000,"YOGURT, FROZEN, NOT CHOCOLATE, TYPE OF MILK NS","Yogurt, frozen, flavors other than chocolate, NS as to type of milk" +11460100,"YOGURT, FROZEN, CHOCOLATE, TYPE OF MILK NS","Yogurt, frozen, chocolate, NS as to type of milk" +11460150,"YOGURT, FROZEN, NS AS TO FLAVOR, LOWFAT MILK","Yogurt, frozen, NS as to flavor, lowfat milk" +11460160,"YOGURT, FROZEN, CHOCOLATE, LOWFAT MILK","Yogurt, frozen, chocolate, lowfat milk" +11460170,"YOGURT, FROZEN, NOT CHOCOLATE, LOWFAT MILK","Yogurt, frozen, flavors other than chocolate, lowfat milk" +11460190,"YOGURT, FROZEN, NS AS TO FLAVOR, NONFAT MILK","Yogurt, frozen, NS as to flavor, nonfat milk" +11460200,"YOGURT, FROZEN, CHOCOLATE, NONFAT MILK","Yogurt, frozen, chocolate, nonfat milk" +11460250,"YOGURT,FROZEN,NOT CHOCOLATE,W/ SORBET/SORBET-COATED","Yogurt, frozen, flavors other than chocolate, with sorbet or sorbet-coated" +11460300,"YOGURT, FROZEN, NOT CHOCOLATE, NONFAT MILK","Yogurt, frozen, flavors other than chocolate, nonfat milk" +11460400,"YOGURT,FRZ,CHOCOLATE,NONFAT MILK,W/ LOW-CAL SWEET","Yogurt, frozen, chocolate, nonfat milk, with low-calorie sweetener" +11460410,"YOGURT,FRZ,NOT CHOC,NONFAT MILK,W/ LOW-CAL SWEET","Yogurt, frozen, flavors other than chocolate, nonfat milk, with low-calorie sweetener" +11460420,"YOGURT, FROZEN, NS AS TO FLAVOR, WHOLE MILK","Yogurt, frozen, NS as to flavor, whole milk" +11460430,"YOGURT, FROZEN, CHOCOLATE, WHOLE MILK","Yogurt, frozen, chocolate, whole milk" +11460440,"YOGURT, FROZEN, NOT CHOCOLATE, WHOLE MILK","Yogurt, frozen, flavors other than chocolate, whole milk" +11461000,"YOGURT, FROZEN, CHOCOLATE-COATED","Yogurt, frozen, chocolate-coated" +11461200,"YOGURT, FROZEN, SANDWICH","Yogurt, frozen, sandwich" +11461250,"YOGURT, FROZEN, CONE, CHOCOLATE","Yogurt, frozen, cone, chocolate" +11461260,"YOGURT, FROZEN, CONE, NOT CHOCOLATE","Yogurt, frozen, cone, flavors other than chocolate" +11461270,"YOGURT, FROZEN, CONE, NOT CHOCOLATE, LOWFAT MILK","Yogurt, frozen, cone, flavors other than chocolate, lowfat milk" +11461280,"YOGURT, FROZ, CONE, CHOCOLATE, LOWFAT MILK","Yogurt, frozen, cone, chocolate, lowfat milk" +11480010,"YOGURT, WHOLE MILK, BF","Yogurt, whole milk, baby food" +11480020,"YOGURT, WHOLE MILK, BF, W/FRUIT& MULTIGRAIN CEREAL,NFS","Yogurt, whole milk, baby food, with fruit and multigrain cereal puree, NFS" +11480030,"YOGURT, WHOLE MILK, BF, W/FRUIT&MULTIGRAIN CEREAL + IRON","Yogurt, whole milk, baby food, with fruit and multigrain cereal puree, plus iron" +11480040,"YOGURT, WHOLE MILK, BF, W/FRUIT&MULTIGRAIN CEREAL + DHA","Yogurt, whole milk, baby food, with fruit and multigrain cereal puree, plus DHA" +11511000,"MILK, CHOCOLATE, NFS","Milk, chocolate, NFS" +11511100,"MILK, CHOCOLATE, WHOLE MILK BASED","Milk, chocolate, whole milk-based" +11511200,"MILK, CHOCOLATE, RED FAT, 2%","Milk, chocolate, reduced fat milk-based, 2% (formerly ""lowfat"")" +11511300,"MILK, CHOCOLATE, SKIM MILK BASED","Milk, chocolate, skim milk-based" +11511400,"MILK, CHOCOLATE, LOWFAT MILK BASED","Milk, chocolate, lowfat milk-based" +11512000,"COCOA,HOT CHOCOLATE,NOT FROM DRY MIX, W/WHOLE MILK","Cocoa, hot chocolate, not from dry mix, made with whole milk" +11512500,"HOT CHOCOLATE, P.R., MADE W/ WHOLE MILK","Hot chocolate, Puerto Rican style, made with whole milk" +11512510,"HOT CHOCOLATE, P.R., MADE W/ LOW FAT MILK","Hot chocolate, Puerto Rican style, made with low fat milk" +11513000,"COCOA & SUGAR MIXTURE, MILK ADDED, NS TYPE MILK","Cocoa and sugar mixture, milk added, NS as to type of milk" +11513100,"COCOA & SUGAR MIXTURE, WHOLE MILK ADDED","Cocoa and sugar mixture, whole milk added" +11513150,"COCOA & SUGAR MIXTURE, REDUCED FAT MILK ADDED","Cocoa and sugar mixture, reduced fat milk added" +11513200,"COCOA & SUGAR MIXTURE, LOWFAT MILK ADDED","Cocoa and sugar mixture, lowfat milk added" +11513300,"COCOA & SUGAR MIXTURE, SKIM MILK ADDED","Cocoa and sugar mixture, skim milk added" +11513350,"COCOA AND SUGAR MIXTURE, REDUCED SUGAR, MILK ADDED, NS TYPE","Cocoa and sugar mixture, reduced sugar, milk added, NS as to type of milk" +11513355,"COCOA AND SUGAR MIXTURE, REDUCED SUGAR, WHOLE MILK ADDED","Cocoa and sugar mixture, reduced sugar, whole milk added" +11513360,"COCOA AND SUGAR MIXTURE, REDUCED SUGAR, REDUCED FAT MILK ADD","Cocoa and sugar mixture, reduced sugar, reduced fat milk added" +11513365,"COCOA AND SUGAR MIXTURE, REDUCED SUGAR, LOWFAT MILK ADDED","Cocoa and sugar mixture, reduced sugar, lowfat milk added" +11513370,"COCOA AND SUGAR MIXTURE, REDUCED SUGAR, SKIM MILK ADDED","Cocoa and sugar mixture, reduced sugar, skim milk added" +11513400,"CHOCOLATE SYRUP, MILK ADDED, NS AS TO TYPE OF MILK","Chocolate syrup, milk added, NS as to type of milk" +11513500,"CHOCOLATE SYRUP, WHOLE MILK ADDED","Chocolate syrup, whole milk added" +11513550,"CHOCOLATE SYRUP, RED FAT MILK ADDED","Chocolate syrup, reduced fat milk added" +11513600,"CHOCOLATE SYRUP, LOWFAT MILK ADDED","Chocolate syrup, lowfat milk added" +11513700,"CHOCOLATE SYRUP, SKIM MILK ADDED","Chocolate syrup, skim milk added" +11514100,"COCOA, SUGAR, & DRY MILK MIXTURE, WATER ADDED","Cocoa, sugar, and dry milk mixture, water added" +11514300,"COCOA W/ NF DRY MILK, LO CAL SWEETENER, WATER ADDED","Cocoa with nonfat dry milk and low calorie sweetener, mixture, water added" +11514500,"COCOA W/ WHEY, LO CAL SWEETNR, FORTIFD, WATER ADDED","Cocoa, whey, and low calorie sweetener, mixture, fortified, water added" +11515100,"COCOA & SUGAR W/ MILK, FORTIFIED, PUERTO RICAN","Cocoa and sugar mixture fortified with vitamins and minerals, milk added, NS as to type of milk, Puerto Rican style" +11516000,"COCOA, WHEY, LO CAL SWEETNER MIX, LOWFAT MILK ADDED","Cocoa, whey, and low-calorie sweetener mixture, lowfat milk added" +11518050,"MILK BEV W/NF DRY MILK, LO CAL SWEET,WATER,NOT CHOC","Milk beverage with nonfat dry milk and low calorie sweetener, water added, flavors other than chocolate" +11519000,"MILK BEVERAGE, NOT CHOCOLATE, W/ WHOLE MILK","Milk beverage, made with whole milk, flavors other than chocolate" +11519040,"MILK, FLAVORS OTHER THAN CHOCOLATE, NFS","Milk, flavors other than chocolate, NFS" +11519050,"MILK, NOT CHOCOLATE, WHOLE MILK BASED","Milk, flavors other than chocolate, whole milk-based" +11519105,"MILK, FLAVORS OTHER THAN CHOCOLATE, REDUCED FAT MILK-BASED","Milk, flavors other than chocolate, reduced fat milk-based" +11519200,"MILK, FLAVORS OTHER THAN CHOCOLATE, LOWFAT MILK-BASED","Milk, flavors other than chocolate, lowfat milk-based" +11519205,"MILK, FLAVORS OTHER THAN CHOCOLATE, SKIM-MILK BASED","Milk, flavors other than chocolate, skim-milk based" +11520000,"MILK, MALTED, UNFORTIFIED, FLAVOR NS","Milk, malted, unfortified, NS as to flavor, made with milk" +11521000,"MILK, MALTED, UNFORTIFIED, CHOCOLATE FLAVOR","Milk, malted, unfortified, chocolate, made with milk" +11522000,"MILK, MALTED, UNFORTIFIED, NATURAL FLAVOR","Milk, malted, unfortified, natural flavor, made with milk" +11525000,"MILK,MALTED,FORTIFIED,NATURAL FLAVOR (INCL OVALTINE","Milk, malted, fortified, natural flavor, made with milk" +11526000,"MILK, MALTED, FORTIFIED, CHOCOLATE (INCL OVALTINE)","Milk, malted, fortified, chocolate, made with milk" +11527000,"MILK, MALTED, FORTIFIED, (INCL OVALTINE)","Milk, malted, fortified, NS as to flavor, made with milk" +11531000,"EGGNOG, MADE W/ WHOLE MILK (INCLUDE EGG NOG, NFS)","Eggnog, made with whole milk" +11531500,"EGGNOG, MADE W/ 2% REDUCED FAT MILK","Eggnog, made with 2% reduced fat milk (formerly eggnog, made with ""2% lowfat"" milk)" +11541000,"MILK SHAKE, NS AS TO FLAVOR OR TYPE","Milk shake, NS as to flavor or type" +11541100,"MILK SHAKE,HOMEMADE/ FOUNTAIN-TYPE, NS AS TO FLAVOR","Milk shake, homemade or fountain-type, NS as to flavor" +11541110,"MILK SHAKE, HOMEMADE OR FOUNTAIN-TYPE, CHOCOLATE","Milk shake, homemade or fountain-type, chocolate" +11541120,"MILK SHAKE, HOMEMADE/FOUNTAIN-TYPE, NOT CHOCOLATE","Milk shake, homemade or fountain-type, flavors other than chocolate" +11541400,"MILK SHAKE WITH MALT (INCL MALTED MILK W/ICE CREAM)","Milk shake with malt" +11541500,"MILK SHAKE, MADE W/ SKIM MILK, CHOCOLATE","Milk shake, made with skim milk, chocolate" +11541510,"MILK SHAKE,MADE W/ SKIM MILK, NOT CHOCOLATE","Milk shake, made with skim milk, flavors other than chocolate" +11542000,"CARRY-OUT MILK SHAKE, NS AS TO FLAVOR","Carry-out milk shake, NS as to flavor" +11542100,"CARRY-OUT MILK SHAKE, CHOCOLATE","Carry-out milk shake, chocolate" +11542200,"CARRY-OUT MILK SHAKE, NOT CHOCOLATE","Carry-out milk shake, flavors other than chocolate" +11551050,"MILK FRUIT DRINK (INCL LICUADO)","Milk fruit drink" +11552200,"ORANGE JULIUS","Orange Julius" +11553000,"FRUIT SMOOTHIE DRINK, W/ FRUIT OR JUICE & DAIRY PRODUCTS","Fruit smoothie drink, made with fruit or fruit juice and dairy products" +11553100,"FRUIT SMOOTHIE DRINK, NFS","Fruit smoothie drink, NFS" +11560000,"CHOC-FLAVORED DRINK, WHEY-&MILK-BASED(INCL YOO-HOO)","Chocolate-flavored drink, whey- and milk-based" +11560020,"MILK DRINK, WHEY&MILK-BASE, NOT CHOC (INCL YOO-HOO)","Flavored milk drink, whey- and milk-based, flavors other than chocolate" +11561000,"CAFE CON LECHE","Cafe con leche" +11561010,"CAFE CON LECHE PREPARED W/ SUGAR","Cafe con leche prepared with sugar" +11710000,"INFANT FORMULA, NFS","Infant formula, NFS" +11710050,"SIMILAC EXPERT CARE ALIMENTUM, INFANT FORMULA, NS AS TO FORM","Similac Expert Care Alimentum, infant formula, NS as to form" +11710051,"SIMILAC EXPERT CARE ALIMENTUM, INFANT FORMULA, READY-TO-FEED","Similac Expert Care Alimentum, infant formula, ready-to-feed" +11710053,"SIMILAC EXPERT CARE ALIMENTUM,INF FORM,PREP FR PDR,WATER NFS","Similac Expert Care Alimentum, infant formula, prepared from powder, made with water, NFS" +11710054,"SIMILAC EXPERT CARE ALIMENTUM,INF FORM,PREP FR PDR,TAP WATER","Similac Expert Care Alimentum, infant formula, prepared from powder, made with tap water" +11710055,"SIMILAC EXPERT CARE ALIMENTUM, INF FORM, FR PDR, BTL WATER","Similac Expert Care Alimentum, infant formula, prepared from powder, made with plain bottled water" +11710056,"SIMILAC EXPERT CARE ALIMENTUM,INF FORM,PREP FR PDR,BABY WATR","Similac Expert Care Alimentum, infant formula, prepared from powder, made with baby water" +11710350,"SIMILAC ADVANCE, INFANT FORMULA, NS AS TO FORM","Similac Advance, infant formula, NS as to form" +11710351,"SIMILAC ADVANCE, INFANT FORMULA, READY-TO-FEED","Similac Advance, infant formula, ready-to-feed" +11710352,"SIMILAC ADVANCE, INF FORMULA, PREP FRM LIQ CONC, W/WATER,NFS","Similac Advance, infant formula, prepared from liquid concentrate, made with water, NFS" +11710353,"SIMILAC ADVANCE, INFANT FORMULA, PREP FRM PDR, W/WATER NFS","Similac Advance, infant formula, prepared from powder, made with water, NFS" +11710354,"SIMILAC ADVANCE, INF FORMULA, PREP FRM LIQ CONC, W/TAP WATER","Similac Advance, infant formula, prepared from liquid concentrate, made with tap water" +11710355,"SIMILAC ADVANCE, INF FORMULA, PREP FRM LIQ CONC, W/BOT WATER","Similac Advance, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710356,"SIMILAC ADVANCE, INF FORMULA, PREP FR LIQ CONC, W/ BABY WATR","Similac Advance, infant formula, prepared from liquid concentrate, made with baby water" +11710357,"SIMILAC ADVANCE, INFANT FORMULA, PREP FRM PDR, W/TAP WATER","Similac Advance, infant formula, prepared from powder, made with tap water" +11710358,"SIMILAC ADVANCE, INFANT FORMULA, PREP FRM PDR, W/BOT WATER","Similac Advance, infant formula, prepared from powder, made with plain bottled water" +11710359,"SIMILAC ADVANCE, INFANT FORMULA, PREP FRM PDR, W/BABY WATER","Similac Advance, infant formula, prepared from powder, made with baby water" +11710360,"SIMILAC ADVANCE ORGANIC, INFANT FORMULA, NS AS TO FORM","Similac Advance Organic, infant formula, NS as to form" +11710361,"SIMILAC ADVANCE ORGANIC, INFANT FORMULA, READY-TO-FEED","Similac Advance Organic, infant formula, ready-to-feed" +11710363,"SIMILAC ADVANCE ORGANIC,INF FORM,PREP FR PDR,W/WATER,NFS","Similac Advance Organic, infant formula, prepared from powder, made with water, NFS" +11710367,"SIMILAC ADVANCE ORGANIC,INF FORM,PREP FR PDR,W/TAP WATER","Similac Advance Organic, infant formula, prepared from powder, made with tap water" +11710368,"SIMILAC ADVANCE ORGANIC,INF FORM,PREP FR PDR,W/BOT WATER","Similac Advance Organic, infant formula, prepared from powder, made with plain bottled water" +11710369,"SIMILAC ADVANCE ORGANIC,INF FORM,PREP FR PDR,W/BABY WATER","Similac Advance Organic, infant formula, prepared from powder, made with baby water" +11710370,"SIMILAC SENSITIVE, INFANT FORMULA, NS AS TO FORM","Similac Sensitive, infant formula, NS as to form" +11710371,"SIMILAC SENSITIVE, INFANT FORMULA, READY-TO-FEED","Similac Sensitive, infant formula, ready-to-feed" +11710372,"SIMILAC SENSITIVE, INF FORM, PREP FRM LIQ CONC, W/WATER,NFS","Similac Sensitive, infant formula, prepared from liquid concentrate, made with water, NFS" +11710373,"SIMILAC SENSITIVE, INF FORM, PREP FRM PDR, W/ WATER,NFS","Similac Sensitive, infant formula, prepared from powder, made with water, NFS" +11710374,"SIMILAC SENSITIVE, INF FORM, PREP FRM LIQ CONC, W/TAP WATER","Similac Sensitive, infant formula, prepared from liquid concentrate, made with tap water" +11710375,"SIMILAC SENSITIVE, INF FORM, PREP FRM LIQ CONC, W/BOT WATER","Similac Sensitive, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710376,"SIMILAC SENSITIVE, INF FORM, PREP FRM LIQ CONC, W/BABY WATER","Similac Sensitive, infant formula, prepared from liquid concentrate, made with baby water" +11710377,"SIMILAC SENSITIVE, INF FORM, PREP FRM PDR, W/TAP WATER","Similac Sensitive, infant formula, prepared from powder, made with tap water" +11710378,"SIMILAC SENSITIVE, INF FORM, PREP FRM PDR, W/ BOT WATER","Similac Sensitive, infant formula, prepared from powder, made with plain bottled water" +11710379,"SIMILAC SENSITIVE, INF FORM, PREP FRM PDR, W/ BABY WATER","Similac Sensitive, infant formula, prepared from powder, made with baby water" +11710380,"SIMILAC SENSITIVE FOR SPIT-UP, INFANT FORMULA, NS AS TO FORM","Similac Sensitive for Spit-Up, infant formula, NS as to form" +11710381,"SIMILAC SENSITIVE FOR SPIT-UP, INFANT FORMULA, READY-TO-FEED","Similac Sensitive for Spit-Up, infant formula, ready-to-feed" +11710383,"SIMILAC SENSITIVE SPIT-UP,INF FORM, FR PDR, W/ WATER, NFS","Similac Sensitive for Spit-Up, infant formula, prepared from powder, made with water, NFS" +11710387,"SIMILAC SENSITIVE SPIT-UP,INF FORM,PREP FR PDR,W/TAP WATER","Similac Sensitive for Spit-Up, infant formula, prepared from powder, made with tap water" +11710388,"SIMILAC SENSITIVE SPIT-UP,INF FORM,PREP FR PDR,W/BOT WATER","Similac Sensitive for Spit-Up, infant formula, prepared from powder, made with plain bottled water" +11710389,"SIMILAC SENSITIVE SPIT-UP,INF FORM,PREP FR PDR,W/BABY WATER","Similac Sensitive for Spit-Up, infant formula, prepared from powder, made with baby water" +11710470,"SIMILAC EXPERT CARE NEOSURE, INFANT FORMULA, NS AS TO FORM","Similac Expert Care NeoSure, infant formula, NS as to form" +11710471,"SIMILAC EXPERT CARE NEOSURE, INFANT FORMULA, READY-TO-FEED","Similac Expert Care NeoSure, infant formula, ready-to-feed" +11710473,"SIMILAC EXPERT CARE NEOSURE,INF FORM,PREP FR PDR,W/WATER,NFS","Similac Expert Care NeoSure, infant formula, prepared from powder, made with water, NFS" +11710477,"SIMILAC EXPERT CARE NEOSURE,INF FORM,PREP FR PDR,W/TAP WATER","Similac Expert Care NeoSure, infant formula, prepared from powder, made with tap water" +11710478,"SIMILAC EXPERT CARE NEOSURE,INF FORM,PREP FR PDR,W/BOT WATER","Similac Expert Care NeoSure, infant formula, prepared from powder, made with plain bottled water" +11710479,"SIMILAC EXPERT CARE NEOSURE,INF FORM,PREP FR PDR,W/BABY WAT","Similac Expert Care NeoSure, infant formula, prepared from powder, made with baby water" +11710480,"SIMILAC GO AND GROW, INFANT FORMULA, NS AS TO FORM","Similac Go and Grow, infant formula, NS as to form" +11710481,"SIMILAC GO AND GROW,INF FORM,PREP FR PDR,W/WATER,NFS","Similac Go and Grow, infant formula, prepared from powder, made with water, NFS" +11710482,"SIMILAC GO AND GROW,INF FORM,PREP FR PDR,W/TAP WATER","Similac Go and Grow, infant formula, prepared from powder, made with tap water" +11710483,"SIMILAC GO AND GROW,INF FORM,PREP FR PDR,W/BOT WATER","Similac Go and Grow, infant formula, prepared from powder, made with plain bottled water" +11710484,"SIMILAC GO AND GROW,INF FORM,PREP FR PDR,W/BABY WATER","Similac Go and Grow, infant formula, prepared from powder, made with baby water" +11710620,"ENFAMIL PREMIUM NEWBORN, INFANT FORMULA, NS AS TO FORM","Enfamil PREMIUM Newborn, infant formula, NS as to form" +11710621,"ENFAMIL PREMIUM NEWBORN, INFANT FORMULA, READY-TO-FEED","Enfamil PREMIUM Newborn, infant formula, ready-to-feed" +11710626,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRO PDR, WATER NFS","Enfamil PREMIUM Newborn, infant formula, prepared from powder, made with water, NFS" +11710627,"ENFAMIL PREMIUM NEWBORN, INFANT FORMULA, PREP FRM PDR,TAP","Enfamil PREMIUM Newborn, infant formula, prepared from powder, made with tap water" +11710628,"ENFAMIL PREMIUM NEWBORN, INF FORM, PREP FRM PDR,BOTTLE WATER","Enfamil PREMIUM Newborn, infant formula, prepared from powder, made with plain bottled water" +11710629,"ENFAMIL PREMIUM NEWBORN, INFANT FORMULA, PREP FRM PDR, BABY","Enfamil PREMIUM Newborn, infant formula, prepared from powder, made with baby water" +11710630,"ENFAMIL PREMIUM INFANT, INFANT FORMULA, NS AS TO FORM","Enfamil PREMIUM Infant, infant formula, NS as to form" +11710631,"ENFAMIL PREMIUM INFANT, INFANT FORMULA, READY-TO-FEED","Enfamil PREMIUM Infant, infant formula, ready-to-feed" +11710632,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM LIQ CONC,WATER NF","Enfamil PREMIUM Infant, infant formula, prepared from liquid concentrate, made with water, NFS" +11710633,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM LIQ CONC,TAP WATE","Enfamil PREMIUM Infant, infant formula, prepared from liquid concentrate, made with tap water" +11710634,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM LIQ CONC,BOT WATE","Enfamil PREMIUM Infant, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710635,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM LIQ CONC, BABY","Enfamil PREMIUM Infant, infant formula, prepared from liquid concentrate, made with baby water" +11710636,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM PDR, WATER NFS","Enfamil PREMIUM Infant, infant formula, prepared from powder, made with water, NFS" +11710637,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM PDR, TAP WATER","Enfamil PREMIUM Infant, infant formula, prepared from powder, made with tap water" +11710638,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM PDR, BOT WATER","Enfamil PREMIUM Infant, infant formula, prepared from powder, made with plain bottled water" +11710639,"ENFAMIL PREMIUM INFANT, INF FORM, PREP FRM PDR, BABY WATER","Enfamil PREMIUM Infant, infant formula, prepared from powder, made with baby water" +11710640,"ENFAMIL PREMIUM LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil PREMIUM LIPIL, infant formula, NS as to form" +11710642,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR LIQ CONC,W/WATER,NFS","Enfamil PREMIUM LIPIL, infant formula, prepared from liquid concentrate, made with water, NFS" +11710643,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR PDR,W/WATER,NFS","Enfamil PREMIUM LIPIL, infant formula, prepared from powder, made with water, NFS" +11710644,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR LIQ CONC,W/TAP WATER","Enfamil PREMIUM LIPIL, infant formula, prepared from liquid concentrate, made with tap water" +11710645,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR LIQ CONC,W/BOT WATER","Enfamil PREMIUM LIPIL, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710646,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR LIQ CONC,W/BABY WATER","Enfamil PREMIUM LIPIL, infant formula, prepared from liquid concentrate, made with baby water" +11710647,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR PDR,W/TAP WATER","Enfamil PREMIUM LIPIL, infant formula, prepared from powder, made with tap water" +11710648,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR PDR,W/BOT WATER","Enfamil PREMIUM LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710649,"ENFAMIL PREMIUM LIPIL,INF FORM,PREP FR PDR,W/BABY WATER","Enfamil PREMIUM LIPIL, infant formula, prepared from powder, made with baby water" +11710650,"ENFAMIL LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil LIPIL, infant formula, NS as to form" +11710651,"ENFAMIL LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil LIPIL, infant formula, ready-to-feed" +11710652,"ENFAMIL LIPIL, INFANT FORMULA, PREP FR LIQ CONC, W/WATER,NFS","Enfamil LIPIL, infant formula, prepared from liquid concentrate, made with water, NFS" +11710653,"ENFAMIL LIPIL, INFANT FORMULA, PREP FRM PDR, W/WATER,NFS","Enfamil LIPIL, infant formula, prepared from powder, made with water, NFS" +11710654,"ENFAMIL LIPIL, INFANT FORMULA, PREP FR LIQ CONC, W/TAP WATER","Enfamil LIPIL, infant formula, prepared from liquid concentrate, made with tap water" +11710655,"ENFAMIL LIPIL, INFANT FORMULA, PREP FR LIQ CONC, W/BOT WATER","Enfamil LIPIL, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710656,"ENFAMIL LIPIL, INFANT FORMULA, PREP FR LIQ CONC, W/BABY WAT","Enfamil LIPIL, infant formula, prepared from liquid concentrate, made with baby water" +11710657,"ENFAMIL LIPIL, INFANT FORMULA, PREP FRM PDR, W/TAP WATER","Enfamil LIPIL, infant formula, prepared from powder, made with tap water" +11710658,"ENFAMIL LIPIL, INFANT FORMULA, PREP FRM PDR, W/BOT WATER","Enfamil LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710659,"ENFAMIL LIPIL, INFANT FORMULA, PREP FRM PDR, W/BABY WATER","Enfamil LIPIL, infant formula, prepared from powder, made with baby water" +11710660,"ENFAMIL A.R. LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil A.R. Lipil, infant formula, NS as to form" +11710661,"ENFAMIL A.R. LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil A.R. Lipil, infant formula, ready-to-feed" +11710663,"ENFAMIL A.R., INFANT FORMULA, PREP FR PDR, W/WATER, NFS","Enfamil A.R. LIPIL, infant formula, prepared from powder, made with water, NFS" +11710664,"ENFAMIL A.R., INFANT FORMULA, PREP FR PDR, W/TAP WATER","Enfamil A.R. LIPIL, infant formula, prepared from powder, made with tap water" +11710665,"ENFAMIL ENFACARE LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil EnfaCare LIPIL, infant formula, NS as to form" +11710666,"ENFAMIL ENFACARE LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil EnfaCare LIPIL, infant formula, ready-to-feed" +11710667,"ENFAMIL ENFACARE LIPIL, INF FORM, PREP FR PDR, W/ WATER,NFS","Enfamil EnfaCare LIPIL, infant formula, prepared from powder, made with water, NFS" +11710668,"ENFAMIL A.R., INFANT FORMULA, PREP FR PDR, W/BOT WATER","Enfamil A.R. LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710669,"ENFAMIL A.R., INFANT FORMULA, PREP FR PDR, W/BABY WATER","Enfamil A.R. LIPIL, infant formula, prepared from powder, made with baby water" +11710670,"ENFAMIL GENTLEASE LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil Gentlease LIPIL, infant formula, NS as to form" +11710671,"ENFAMIL GENTLEASE LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil Gentlease LIPIL, infant formula, ready-to-feed" +11710673,"ENFAMIL GENTLEASE LIPIL, INF FORM, PREP FRM PDR, W/WATER,NFS","Enfamil Gentlease LIPIL, infant formula, prepared from powder, made with water, NFS" +11710674,"ENFAMIL ENFACARE LIPIL, INF FORM, PREP FR PDR, W/ TAP WATER","Enfamil EnfaCare LIPIL, infant formula, prepared from powder, made with tap water" +11710675,"ENFAMIL ENFACARE LIPIL, INF FORM, PREP FR PDR, W/ BOT WATER","Enfamil EnfaCare LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710676,"ENFAMIL ENFACARE LIPIL, INF FORM, PREP FR PDR, W/BABY WATER","Enfamil EnfaCare LIPIL, infant formula, prepared from powder, made with baby water" +11710677,"ENFAMIL GENTLEASE LIPIL, INF FORM, PREP FRM PDR, W/TAP WATER","Enfamil Gentlease LIPIL, infant formula, prepared from powder, made with tap water" +11710678,"ENFAMIL GENTLEASE LIPIL, INF FORM, PREP FRM PDR, W/BOT WATER","Enfamil Gentlease LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710679,"ENFAMIL GENTLEASE LIPIL, INF FORM, PREP FRM PDR, W/ BABY WAT","Enfamil Gentlease LIPIL, infant formula, prepared from powder, made with baby water" +11710680,"ENFAMIL ENFAGROW PREM NEXT STEP LIPIL , INF FORMULA, NS FORM","Enfamil Enfagrow PREMIUM Next Step LIPIL, infant formula, NS as to form" +11710681,"ENFAMIL ENFAGROW PREM NEXT STEP, INF FORMULA, RTF","Enfamil Enfagrow PREMIUM Next Step LIPIL, infant formula, ready-to-feed" +11710683,"ENFAMIL ENFAGROW PREM NEXT STEP,INF FORMULA,PDR,W/WATER,NFS","Enfamil Enfagrow PREMIUM Next Step LIPIL, infant formula, prepared from powder, made with water, NFS" +11710687,"ENFAMIL ENFAGROW PREM NEXT STEP,INF FORMULA,PDR,W/TAP WATER","Enfamil Enfagrow PREMIUM Next Step LIPIL, infant formula, prepared from powder, made with tap water" +11710688,"ENFAMIL ENFAGROW PREM NEXT STEP,INF FORMULA,PDR,W/BOT WATER","Enfamil Enfagrow PREMIUM Next Step LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710689,"ENFAMIL ENFAGROW PREM NEXT STEP,INF FORMULA,PDR,W/BABY WATER","Enfamil Enfagrow PREMIUM Next Step LIPIL, infant formula, prepared from powder, made with baby water" +11710690,"ENFAMIL GENTLEASE NEXT STEP LIPIL, INFANT FORMULA, NS FORM","Enfamil Gentlease Next Step LIPIL, infant formula, NS as to form" +11710693,"ENFAMIL GENTLEASE NEXT STEP,INF FORM,PREP FR PDR,W/WATER,NFS","Enfamil Gentlease Next Step LIPIL, infant formula, prepared from powder, made with water, NFS" +11710697,"ENFAMIL GENTLEASE NEXT STEP,INF FORM,PREP FR PDR,W/TAP WATER","Enfamil Gentlease Next Step LIPIL, infant formula, prepared from powder, made with tap water" +11710698,"ENFAMIL GENTLEASE NEXT STEP,INF FORM,PREP FR PDR,W/BOT WATER","Enfamil Gentlease Next Step LIPIL, infant formula, prepared from powder, made with plain bottled water" +11710699,"ENFAMIL GENTLEASE NEXT STEP,INF FORM,PREP FR PDR,W/BABY WAT","Enfamil Gentlease Next Step LIPIL, infant formula, prepared from powder, made with baby water" +11710800,"PEDIASURE, INFANT FORMULA, NS AS TO FORM","Pediasure, infant formula, NS as to form" +11710801,"PEDIASURE,INFANT FORMULA, READY-TO-FEED","Pediasure, infant formula, ready-to-feed" +11710805,"PEDIASURE FIBER, INFANT FORMULA, NS AS TO FORM","Pediasure Fiber, infant formula, NS as to form" +11710806,"PEDIASURE FIBER, INFANT FORMULA, READY-TO-FEED","Pediasure Fiber, infant formula, ready-to-feed" +11710910,"GERBER GOOD START GENTLE PLUS, INF FORM, NS FORM","Gerber Good Start Gentle Plus, infant formula, NS as to form" +11710911,"GERBER GOOD START GENTLE PLUS, INFANT FORMULA, READY-TO-FEED","Gerber Good Start Gentle Plus, infant formula, ready-to-feed" +11710912,"GERBER GOOD START GENTLE PLUS,PREP FRM LIQ CONC,W/WATER,NFS","Gerber Good Start Gentle Plus, infant formula, prepared from liquid concentrate, made with water, NFS" +11710913,"GERBER GOOD START GENTLE PLUS, PREP FRM PDR,W/WATER,NFS","Gerber Good Start Gentle Plus, infant formula, prepared from powder, made with water, NFS" +11710914,"GERBER GOOD START GENTLE PLUS,PREP FRM LIQ CONC,W/TAP WATER","Gerber Good Start Gentle Plus, infant formula, prepared from liquid concentrate, made with tap water" +11710915,"GERBER GOOD START GENTLE PLUS,PREP FRM LIQ CONC,W/BOT WATER","Gerber Good Start Gentle Plus, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710916,"GERBER GOOD START GENTLE PLUS,PREP FRM LIQ CONC,W/BABY WATER","Gerber Good Start Gentle Plus, infant formula, prepared from liquid concentrate, made with baby water" +11710917,"GERBER GOOD START GENTLE PLUS, PREP FRM PDR,W/TAP WATER","Gerber Good Start Gentle Plus, infant formula, prepared from powder, made with tap water" +11710918,"GERBER GOOD START GENTLE PLUS, PREP FRM PDR,W/BOT WATER","Gerber Good Start Gentle Plus, infant formula, prepared from powder, made with plain bottled water" +11710919,"GERBER GOOD START GENTLE PLUS, PREP FRM PDR,W/BABY WATER","Gerber Good Start Gentle Plus, infant formula, prepared from powder, made with baby water" +11710920,"GERBER GOOD START PROTECT PLUS, INFANT FORMULA, NS FORM","Gerber Good Start Protect Plus, infant formula, NS as to form" +11710923,"GERBER GOOD START PROTECT PLUS, PREP FRM PDR,W/WATER,NFS","Gerber Good Start Protect Plus, infant formula, prepared from powder, made with water, NFS" +11710927,"GERBER GOOD START PROTECT PLUS, PREP FRM PDR,W/TAP WATER","Gerber Good Start Protect Plus, infant formula, prepared from powder, made with tap water" +11710928,"GERBER GOOD START PROTECT PLUS, PREP FRM PDR,W/BOT WATER","Gerber Good Start Protect Plus, infant formula, prepared from powder, made with plain bottled water" +11710929,"GERBER GOOD START PROTECT PLUS, PREP FRM PDR,W/BABY WATER","Gerber Good Start Protect Plus, infant formula, prepared from powder, made with baby water" +11710930,"GERBER GOOD START 2 GENTLE PLUS, INFANT FORMULA, NS FORM","Gerber Good Start 2 Gentle Plus, infant formula, NS as to form" +11710933,"GERBER GOOD START 2 GENTLE PLUS, PREP FRM PDR,W/WATER,NFS","Gerber Good Start 2 Gentle Plus, infant formula, prepared from powder, made with water, NFS" +11710937,"GERBER GOOD START 2 GENTLE PLUS, PREP FRM PDR,W/TAP WATER","Gerber Good Start 2 Gentle Plus, infant formula, prepared from powder, made with tap water" +11710938,"GERBER GOOD START 2 GENTLE PLUS, PREP FRM PDR,W/BOT WATER","Gerber Good Start 2 Gentle Plus, infant formula, prepared from powder, made with plain bottled water" +11710939,"GERBER GOOD START 2 GENTLE PLUS, PREP FRM PDR,W/BABY WATER","Gerber Good Start 2 Gentle Plus, infant formula, prepared from powder, made with baby water" +11710940,"GERBER GOOD START 2 PROTECT PLUS, INFANT FORMULA, NS FORM","Gerber Good Start 2 Protect Plus, infant formula, NS as to form" +11710943,"GERBER GOOD START 2 PROTECT PLUS, PREP FRM PDR,W/WATER,NFS","Gerber Good Start 2 Protect Plus, infant formula, prepared from powder, made with water, NFS" +11710947,"GERBER GOOD START 2 PROTECT PLUS, PREP FRM PDR,W/TAP WATER","Gerber Good Start 2 Protect Plus, infant formula, prepared from powder, made with tap water" +11710948,"GERBER GOOD START 2 PROTECT PLUS, PREP FRM PDR,W/BOT WATER","Gerber Good Start 2 Protect Plus, infant formula, prepared from powder, made with plain bottled water" +11710949,"GERBER GOOD START 2 PROTECT PLUS, PREP FRM PDR,W/BABY WATER","Gerber Good Start 2 Protect Plus, infant formula, prepared from powder, made with baby water" +11710960,"AMERICA'S STORE BRAND, INFANT FORMULA, NS AS TO FORM","America's Store Brand, infant formula, NS as to form" +11710961,"AMERICA'S STORE BRAND,INF FORM,PREP FRM LIQ CONC,W/WATER,NFS","America's Store Brand, infant formula, prepared from liquid concentrate, made with water, NFS" +11710962,"AMERICA'S STORE BRAND,INF FORM,PREP FRM PDR,W/ WATER, NFS","America's Store Brand, infant formula, prepared from powder, made with water, NFS" +11710963,"AMERICA'S STORE BRAND, INFANT FORMULA, READY-TO-FEED","America's Store Brand, infant formula, ready-to-feed" +11710964,"AMERICA'S STORE BRAND,INF FORM,PREP FRM LIQ CONC,W/TAP WATER","America's Store Brand, infant formula, prepared from liquid concentrate, made with tap water" +11710965,"AMERICA'S STORE BRAND,INF FORM,PREP FRM LIQ CONC,W/BOT WATER","America's Store Brand, infant formula, prepared from liquid concentrate, made with plain bottled water" +11710966,"AMERICA'S STORE BRAND,INF FORM,PREP FRM LIQ CONC,W/BABY WATR","America's Store Brand, infant formula, prepared from liquid concentrate, made with baby water" +11710967,"AMERICA'S STORE BRAND,INF FORM,PREP FRM PDR,W/ TAP WATER","America's Store Brand, infant formula, prepared from powder, made with tap water" +11710968,"AMERICA'S STORE BRAND,INF FORM,PREP FRM PDR,W/ BOT WATER","America's Store Brand, infant formula, prepared from powder, made with plain bottled water" +11710969,"AMERICA'S STORE BRAND,INF FORM,PREP FRM PDR,W/ BABY WATER","America's Store Brand, infant formula, prepared from powder, made with baby water" +11720310,"ENFAMIL PROSOBEE LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil ProSobee LIPIL, infant formula, NS as to form" +11720311,"ENFAMIL PROSOBEE LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil ProSobee Lipil, infant formula, ready-to-feed" +11720312,"ENFAMIL PROSOBEE LIPIL,INF FORM, FR LIQ CONC,W/ WATER,NFS","Enfamil ProSobee LIPIL, infant formula, prepared from liquid concentrate, made with water, NFS" +11720313,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR PDR, W/WATER, NFS","Enfamil ProSobee LIPIL, infant formula, prepared from powder, made with water, NFS" +11720314,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR LIQ CONC,W/ TAP WATE","Enfamil ProSobee LIPIL, infant formula, prepared from liquid concentrate, made with tap water" +11720315,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR LIQ CONC,W/ BOT WATE","Enfamil ProSobee LIPIL, infant formula, prepared from liquid concentrate, made with plain bottled water" +11720316,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR LIQ CONC,W/ BABY WAT","Enfamil ProSobee LIPIL, infant formula, prepared from liquid concentrate, made with baby water" +11720317,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR PDR, W/TAP WATER","Enfamil ProSobee LIPIL, infant formula, prepared from powder, made with tap water" +11720318,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR PDR, W/BOT WATER","Enfamil ProSobee LIPIL, infant formula, prepared from powder, made with plain bottled water" +11720319,"ENFAMIL PROSOBEE LIPIL,INF FORM,PREP FR PDR, W/BABY WATER","Enfamil ProSobee LIPIL, infant formula, prepared from powder, made with baby water" +11720320,"ENFAMIL ENFAGROW SOY NEXT STEP LIPIL, INF FORMULA, NS FORM","Enfamil Enfagrow Soy Next Step LIPIL, infant formula, NS as to form" +11720323,"ENFAGROW SOY NEXT STEP LIPIL, INF FOR,FR PDR,W/WATER,NFS","Enfamil Enfagrow Soy Next Step LIPIL, infant formula, prepared from powder, made with water, NFS" +11720327,"ENFAGROW SOY NEXT STEP LIPIL,INF FORM,PREP FR PDR,TAP WATER","Enfamil Enfagrow Soy Next Step LIPIL, infant formula, prepared from powder, made with tap water" +11720328,"ENFAGROW SOY NEXT STEP LIPIL,INF FORM,PREP FR PDR, BOT WATER","Enfamil Enfagrow Soy Next Step LIPIL, infant formula, prepared from powder, made with plain bottled water" +11720329,"ENFAGROW SOY NEXT STEP LIPIL,INF FORM,PREP FR PDR,BABY WATER","Enfamil Enfagrow Soy Next Step LIPIL, infant formula, prepared from powder, made with baby water" +11720410,"SIMILAC SENSITIVE ISOMIL SOY, INFANT FORMULA, NS AS TO FORM","Similac Sensitive Isomil Soy, infant formula, NS as to form" +11720411,"SIMILAC SENSITIVE ISOMIL SOY, INFANT FORMULA, READY-TO-FEED","Similac Sensitive Isomil Soy, infant formula, ready-to-feed" +11720412,"SIMILAC SENSITIVE ISOMIL SOY, PREP FR LIQ CONC,W/WATER,NFS","Similac Sensitive Isomil Soy, infant formula, prepared from liquid concentrate, made with water, NFS" +11720413,"SIMILAC SENSITIVE ISOMIL SOY,INF FORM,FR PDR,W/WATER,NFS","Similac Sensitive Isomil Soy, infant formula, prepared from powder, made with water, NFS" +11720414,"SIMILAC SENSITIVE ISOMIL SOY,PREP FR LIQ CONC,W/TAP WATER","Similac Sensitive Isomil Soy, infant formula, prepared from liquid concentrate, made with tap water" +11720415,"SIMILAC SENSITIVE ISOMIL SOY,PREP FR LIQ CONC,W/BOT WATER","Similac Sensitive Isomil Soy, infant formula, prepared from liquid concentrate, made with plain bottled water" +11720416,"SIMILAC SENSITIVE ISOMIL SOY,PREP FR LIQ CONC,W/BABY WATER","Similac Sensitive Isomil Soy, infant formula, prepared from liquid concentrate, made with baby water" +11720417,"SIMILAC SENSITIVE ISOMIL SOY,INF FORM,PREP FR PDR+TAP WATER","Similac Sensitive Isomil Soy, infant formula, prepared from powder, made with tap water" +11720418,"SIMILAC SENSITIVE ISOMIL SOY,INF FORM,PREP FR PDR,W/BOT WATE","Similac Sensitive Isomil Soy, infant formula, prepared from powder, made with plain bottled water" +11720419,"SIMILAC SENSITIVE ISOMIL SOY,INF FORM,PREP FR PDR,W/BABY WAT","Similac Sensitive Isomil Soy, infant formula, prepared from powder, made with baby water" +11720430,"SIMILAC EXPERT CARE FOR DIARRHEA, INFANT FORMULA, NS FORM","Similac Expert Care for Diarrhea, infant formula, NS as to form" +11720431,"SIMILAC EXPERT CARE FOR DIARRHEA, INFANT FORMULA, RTF","Similac Expert Care for Diarrhea, infant formula, ready-to-feed" +11720440,"SIMILAC GO AND GROW SOY, INFANT FORMULA, NS AS TO FORM","Similac Go and Grow Soy, infant formula, NS as to form" +11720443,"SIMILAC GO AND GROW SOY, INF FORM, PREP FR PDR, WATER, NFS","Similac Go and Grow Soy, infant formula, prepared from powder, made with water, NFS" +11720447,"SIMILAC GO AND GROW SOY,INF FORM,PREP FR PDR,TAP WATER","Similac Go and Grow Soy, infant formula, prepared from powder, made with tap water" +11720448,"SIMILAC GO AND GROW SOY,INF FORM,PREP FR PDR,BOT WATER","Similac Go and Grow Soy, infant formula, prepared from powder, made with plain bottled water" +11720449,"SIMILAC GO AND GROW SOY,INF FORM,PREP FR PDR,BABY WATER","Similac Go and Grow Soy, infant formula, prepared from powder, made with baby water" +11720610,"GERBER GOOD START SOY PLUS, INFANT FORMULA, NS AS TO FORM","Gerber Good Start Soy Plus, infant formula, NS as to form" +11720611,"GERBER GOOD START SOY PLUS, INFANT FORMULA, READY-TO-FEED","Gerber Good Start Soy Plus, infant formula, ready-to-feed" +11720612,"GERBER GOOD START SOY PLUS,INF FORM,PREP FR LIQ CONC,W/WATER","Gerber Good Start Soy Plus, infant formula, prepared from liquid concentrate, made with water, NFS" +11720613,"GERBER GOOD START SOY PLUS,INF FORM, PREP FR PDR,W/WATER,NFS","Gerber Good Start Soy Plus, infant formula, prepared from powder, made with water, NFS" +11720614,"GERBER GOOD START SOY PLUS,INF FORM,FR LIQ CONC,W/TAP WATER","Gerber Good Start Soy Plus, infant formula, prepared from liquid concentrate, made with tap water" +11720615,"GERBER GOOD START SOY PLUS,INF FORM,FR LIQ CONC,W/BOT WATER","Gerber Good Start Soy Plus, infant formula, prepared from liquid concentrate, made with plain bottled water" +11720616,"GERBER GOOD START SOY PLUS,INF FORM,FR LIQ CONC,W/BABY WTR","Gerber Good Start Soy Plus, infant formula, prepared from liquid concentrate, made with baby water" +11720617,"GERBER GOOD START SOY PLUS,INF FORM, PREP FR PDR,W/TAP WATER","Gerber Good Start Soy Plus, infant formula, prepared from powder, made with tap water" +11720618,"GERBER GOOD START SOY PLUS,INF FORM, PREP FR PDR,W/BOT WATER","Gerber Good Start Soy Plus, infant formula, prepared from powder, made with plain bottled water" +11720619,"GERBER GOOD START SOY PLUS,INF FORM, PREP FR PDR,W/BABY WATE","Gerber Good Start Soy Plus, infant formula, prepared from powder, made with baby water" +11720620,"GERBER GOOD START 2 SOY PLUS, INFANT FORMULA, NS AS TO FORM","Gerber Good Start 2 Soy Plus, infant formula, NS as to form" +11720623,"GERBER GOOD START 2 SOY PLUS, INF FORM,PREP FR PDR,WATER,NFS","Gerber Good Start 2 Soy Plus, infant formula, prepared from powder, made with water, NFS" +11720627,"GERBER GOOD START 2 SOY PLUS,INF FORM,PREP FR PDR,TAP WATER","Gerber Good Start 2 Soy Plus, infant formula, prepared from powder, made with tap water" +11720628,"GERBER GOOD START 2 SOY PLUS,INF FORM,PREP FR PDR,BOT WATER","Gerber Good Start 2 Soy Plus, infant formula, prepared from powder, made with plain bottled water" +11720629,"GERBER GOOD START 2 SOY PLUS,INF FORM,PREP FR PDR,BABY WATER","Gerber Good Start 2 Soy Plus, infant formula, prepared from powder, made with baby water" +11720800,"AMERICA'S STORE BRAND SOY, INFANT FORMULA, NS AS TO FORM","America's Store Brand Soy, infant formula, NS as to form" +11720801,"AMERICA'S STORE BRAND SOY, INFANT FORMULA, READY-TO-FEED","America's Store Brand Soy, infant formula, ready-to-feed" +11720802,"AMERICA'S STORE BRAND SOY,INF FORM, FR LIQ CONC,W/WATER NFS","America's Store Brand Soy, infant formula, prepared from liquid concentrate, made with water, NFS" +11720803,"AMERICA'S STORE BRAND SOY,INF FORM,FR PDR,W/WATER,NFS","America's Store Brand Soy, infant formula, prepared from powder, made with water, NFS" +11720804,"AMERICA'S STORE BRAND SOY,INF FORM, FR LIQ CONC,W/TAP WATER","America's Store Brand Soy, infant formula, prepared from liquid concentrate, made with tap water" +11720805,"AMERICA'S STORE BRAND SOY,INF FORM,FR LIQ CONC,W/BOT WATER","America's Store Brand Soy, infant formula, prepared from liquid concentrate, made with plain bottled water" +11720806,"AMERICA'S STORE BRAND SOY,INF FORM,FR LIQ CONC,W/BABY WATER","America's Store Brand Soy, infant formula, prepared from liquid concentrate, made with baby water" +11720807,"AMERICA'S STORE BRAND SOY,INF FORM,PREP FR PDR,W/TAP WATER","America's Store Brand Soy, infant formula, prepared from powder, made with tap water" +11720808,"AMERICA'S STORE BRAND SOY,INF FORM,PREP FR PDR,W/BOT WATER","America's Store Brand Soy, infant formula, prepared from powder, made with plain bottled water" +11720809,"AMERICA'S STORE BRAND SOY,INF FORM,PREP FR PDR,W/BABY WATER","America's Store Brand Soy, infant formula, prepared from powder, made with baby water" +11740310,"ENFAMIL NUTRAMIGEN LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil Nutramigen LIPIL, infant formula, NS as to form" +11740311,"ENFAMIL NUTRAMIGEN LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil Nutramigen LIPIL, infant formula, ready-to-feed" +11740312,"ENFAMIL NUTRAMIGEN LIPIL,INF FORM,PREP FR LIQ CONC,W/WAT,NFS","Enfamil Nutramigen LIPIL, infant formula, prepared from liquid concentrate, made with water, NFS" +11740313,"ENFAMIL NUTRAMIGEN LIPIL, INF FORM, PREP FR PDR, W/WATER,NFS","Enfamil Nutramigen LIPIL, infant formula, prepared from powder, made with water, NFS" +11740314,"ENFAMIL NUTRAMIGEN LIPIL,INF FORM,FR LIQ CONC,W/TAP WATER","Enfamil Nutramigen LIPIL, infant formula, prepared from liquid concentrate, made with tap water" +11740315,"ENFAMIL NUTRAMIGEN LIPIL,INF FORM,FR LIQ CONC,W/BOT WATER","Enfamil Nutramigen LIPIL, infant formula, prepared from liquid concentrate, made with plain bottled water" +11740316,"ENFAMIL NUTRAMIGEN LIPIL,INF FORM,FR LIQ CONC,W/BABY WATER","Enfamil Nutramigen LIPIL, infant formula, prepared from liquid concentrate, made with baby water" +11740317,"ENFAMIL NUTRAMIGEN LIPIL, INF FORM, PREP FR PDR, W/TAP WATER","Enfamil Nutramigen LIPIL, infant formula, prepared from powder, made with tap water" +11740318,"ENFAMIL NUTRAMIGEN LIPIL, INF FORM, PREP FR PDR, W/BOT WATER","Enfamil Nutramigen LIPIL, infant formula, prepared from powder, made with plain bottled water" +11740319,"ENFAMIL NUTRAMIGEN LIPIL, INF FORM, PREP FR PDR, W/BABY WATR","Enfamil Nutramigen LIPIL, infant formula, prepared from powder, made with baby water" +11740320,"ENFAMIL NUTRAMIGEN AA LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil Nutramigen AA LIPIL, infant formula, NS as to form" +11740323,"ENFAMIL NUTRAMIGEN AA LIPIL,INF FORM,PREP FR PDR,W/WATER,NFS","Enfamil Nutramigen AA LIPIL, infant formula, prepared from powder, made with water, NFS" +11740327,"ENFAMIL NUTRAMIGEN AA LIPIL,INF FORM,PREP FR PDR,W/TAP WATER","Enfamil Nutramigen AA LIPIL, infant formula, prepared from powder, made with tap water" +11740328,"ENFAMIL NUTRAMIGEN AA LIPIL,INF FORM,PREP FR PDR,W/BOT WATER","Enfamil Nutramigen AA LIPIL, infant formula, prepared from powder, made with plain bottled water" +11740329,"ENFAMIL NUTRAMIGEN AA LIPIL,INF FORM,PREP FR PDR,W/BABY WAT","Enfamil Nutramigen AA LIPIL, infant formula, prepared from powder, made with baby water" +11740400,"ENFAMIL PREGESTIMIL LIPIL, INFANT FORMULA, NS AS TO FORM","Enfamil Pregestimil LIPIL, infant formula, NS as to form" +11740401,"ENFAMIL PREGESTIMIL LIPIL, INFANT FORMULA, READY-TO-FEED","Enfamil Pregestimil LIPIL, infant formula, ready-to-feed" +11740403,"ENFAMIL PREGESTIMIL LIPIL,INF FORM,PREP FR PDR, W/WATER,NFS","Enfamil Pregestimil LIPIL, infant formula, prepared from powder, made with water, NFS" +11740407,"ENFMAIL PREGESTIMIL LIPIL,INF FORM,PREP FR PDR, W/TAP WATER","Enfmail Pregestimil LIPIL, infant formula, prepared from powder, made with tap water" +11740408,"ENFMAIL PREGESTIMIL LIPIL,INF FORM,PREP FR PDR, W/BOT WATER","Enfmail Pregestimil LIPIL, infant formula, prepared from powder, made with plain bottled water" +11740409,"ENFMAIL PREGESTIMIL LIPIL,INF FORM,PREP FR PDR, W/BABY WATER","Enfmail Pregestimil LIPIL, infant formula, prepared from powder, made with baby water" +11740510,"ENFAMIL PREMATURE LIPIL 20, LOW IRON, INF FORMULA, NS FORM","Enfamil Premature LIPIL 20, low iron, infant formula, NS as to form" +11740511,"ENFAMIL PREMATURE LIPIL 20, LOW IRON, INFANT FORMULA, RTF","Enfamil Premature LIPIL 20, low iron, infant formula, ready-to-feed" +11740520,"ENFAMIL PREMATURE LIPIL 20, W/IRON, INFANT FORMULA, NS FORM","Enfamil Premature LIPIL 20, with iron, infant formula, NS as to form" +11740521,"ENFAMIL PREMATURE LIPIL 20, WITH IRON, INFANT FORMULA, RTF","Enfamil Premature LIPIL 20, with iron, infant formula, ready-to-feed" +11740540,"ENFAMIL PREMATURE LIPIL 24, LOW IRON, INF FORMULA, NS FORM","Enfamil Premature LIPIL 24, low iron, infant formula, NS as to form" +11740541,"ENFAMIL PREMATURE LIPIL 24, LOW IRON, INFANT FORMULA, RTF","Enfamil Premature LIPIL 24, low iron, infant formula, ready-to-feed" +11740550,"ENFAMIL PREMATURE LIPIL 24, W/IRON, INFANT FORMULA, NS FORM","Enfamil Premature LIPIL 24, with iron, infant formula, NS as to form" +11740551,"ENFAMIL PREMATURE LIPIL 24, WITH IRON, INFANT FORMULA, RTF","Enfamil Premature LIPIL 24, with iron, infant formula, ready-to-feed" +11810000,"MILK, DRY, NOT RECONSTITUTED, NS AS TO FAT","Milk, dry, not reconstituted, NS as to whole, lowfat, or nonfat" +11811000,"MILK, DRY, WHOLE, NOT RECONSTITUTED","Milk, dry, whole, not reconstituted" +11812000,"MILK, DRY, LOWFAT, NOT RECONSTITUTED","Milk, dry, lowfat, not reconstituted" +11813000,"MILK, DRY, NONFAT, NOT RECONSTITUTED","Milk, dry, nonfat, not reconstituted" +11825000,"WHEY, SWEET, DRY","Whey, sweet, dry" +11830100,"COCOA W/DRY MILK & SUGAR, DRY MIX, NOT RECONST","Cocoa (or chocolate) with dry milk and sugar, dry mix, not reconstituted" +11830110,"COCOA POWDER W/ NFD MILK, LOW CAL SWEETENER, DRY","Cocoa powder with nonfat dry milk and low calorie sweetener, dry mix, not reconstituted" +11830120,"COCOA W/ WHEY, LO CAL SWEETENER, FORTIFIED, DRY MIX","Cocoa, whey, and low calorie sweetener, fortified, dry mix, not reconstituted" +11830140,"CHOCOLATE, INST, DRY MIX, FORTIFD, NOT RECONST,P.R.","Chocolate, instant, dry mix, fortified with vitamins and minerals, not reconstituted, Puerto Rican style" +11830150,"COCOA POWDER, NOT RECONSTITUTED (NO DRY MILK)","Cocoa powder, not reconstituted (no dry milk)" +11830160,"COCOA-FLAVORED BEVERAGE POWDER W/ SUGAR, DRY MIX","Cocoa (or chocolate) flavored beverage powder with sugar, dry mix, not reconstituted" +11830165,"COCOA FLAV BEV PDR W/ RED SUGAR,DRY MIX,NOT RECONSTITUTED","Cocoa (or chocolate) flavored beverage powder with reduced sugar, dry mix, not reconstituted" +11830170,"COCOA FLAV BEV PDR W/ LOW CAL SWTNR,DRY MIX,NOT RECONSTITUTD","Cocoa (or chocolate) flavored beverage powder with low-calorie sweetener, dry mix, not reconstituted" +11830200,"MILK, MALTED, DRY, UNFORTIFD, NOT RECONST, NOT CHOC","Milk, malted, dry mix, unfortified, not reconstituted, flavors other than chocolate" +11830210,"MILK, MALTED, DRY, FORTIFD, NOT RECONST, NOT CHOC","Milk, malted, dry mix, fortified, not reconstituted, flavors other than chocolate" +11830250,"MILK, MALTED, DRY, UNFORTIFIED, NOT RECONST, CHOC","Milk, malted, dry mix, unfortified, not reconstituted, chocolate" +11830260,"MILK, MALTED, DRY, FORTIFIED, NOT RECONST, CHOC","Milk, malted, dry mix, fortified, not reconstituted, chocolate" +11830400,"MILK BEV POWDER, DRY, NOT RECONST, NOT CHOC","Milk beverage, powder, dry mix, not reconstituted, flavors other than chocolate" +11830450,"MILK BEV MIX, W/ SUGAR,EGG WHITE, NOT RECONSTITUTED","Milk beverage with sugar, dry milk, and egg white powder, dry mix, not reconstituted" +11830500,"MILK BEV POWDER W/ NFD MILK, LOW CAL, DRY, CHOC","Milk beverage, powder, with nonfat dry milk and low calorie sweetener, dry mix, not reconstituted, chocolate" +11830550,"MILK BEV POWDER W/ NFD MILK, LOW CAL, DRY, NOT CHOC","Milk beverage, powder, with nonfat dry milk and low calorie sweetener, dry mix, not reconstituted, flavors other than chocolate" +12100100,"CREAM, FLUID, NS AS TO LIGHT, HEAVY OR HALF & HALF","Cream, NS as to light, heavy, or half and half" +12110100,"CREAM, LIGHT, FLUID (INCL COFFEE CRM, TABLE CREAM)","Cream, light, fluid" +12110300,"CREAM, LIGHT, WHIPPED, UNSWEETENED","Cream, light, whipped, unsweetened" +12120100,"CREAM, HALF & HALF","Cream, half and half" +12120105,"CREAM, HALF & HALF, LOW FAT","Cream, half and half, low fat" +12120110,"CREAM, HALF & HALF, FAT FREE","Cream, half and half, fat free" +12130100,"CREAM, HEAVY, FLUID","Cream, heavy, fluid" +12130200,"CREAM, HEAVY, WHIPPED, UNSWEETENED","Cream, heavy, whipped, unsweetened" +12140000,"CREAM, HEAVY, WHIPPED, SWEETENED","Cream, heavy, whipped, sweetened" +12140100,"CREAM, WHIPPED, PRESSURIZED CONTAINER","Cream, whipped, pressurized container" +12140105,"CREAM, WHIPPED, PRESSURIZED CONTAINER, LIGHT","Cream, whipped, pressurized container, light" +12140110,"WHIPPED TOPPING, DAIRY BASED, FAT FREE, PRESSURIZED CONTAINR","Whipped topping, dairy based, fat free, pressurized container" +12200100,"CREAM SUBSTITUTE, NS AS TO FROZEN,LIQUID OR POWDER","Cream substitute, NS as to frozen, liquid, or powdered" +12210100,"CREAM SUBSTITUTE, FROZEN","Cream substitute, frozen" +12210200,"CREAM SUBSTITUTE, LIQUID (INCLUDE COFFEE WHITNER)","Cream substitute, liquid" +12210210,"CREAM SUBSTITUTE, FLAVORED, LIQUID","Cream substitute, flavored, liquid" +12210250,"CREAM SUBSTITUTE, LIGHT, LIQUID","Cream substitute, light, liquid" +12210255,"CREAM SUBSTITUTE, LIGHT, FLAVORED, LIQUID","Cream substitute, light, flavored, liquid" +12210260,"CREAM SUBSTITUTE, FAT FREE, LIQUID","Cream substitute, fat free, liquid" +12210270,"CREAM SUBSTITUTE, FAT FREE, FLAVORED, LIQUID","Cream substitute, fat free, flavored, liquid" +12210305,"CREAM SUBSTITUTE, SUGAR FREE, LIQUID","Cream substitute, sugar free, liquid" +12210310,"CREAM SUBSTITUTE, SUGAR FREE, FLAVORED, LIQUID","Cream substitute, sugar free, flavored, liquid" +12210400,"CREAM SUBSTITUTE, POWDERED","Cream substitute, powdered" +12210410,"CREAM SUBST, LIGHT, POWDERED (INCL COFFEE MATE, CRE","Cream substitute, light, powdered" +12210420,"CREAM SUBSTITUTE, FLAVORED, POWDERED","Cream substitute, flavored, powdered" +12210430,"CREAM SUBSTITUTE, FAT FREE, POWDER","Cream substitute, fat free, powder" +12210440,"CREAM SUBSTITUTE, FAT FREE, FLAVORED, POWDER","Cream substitute, fat free, flavored, powder" +12210500,"CREAM SUBSTITUTE, SUGAR FREE, POWDER","Cream substitute, sugar free, powder" +12210505,"CREAM SUBSTITUTE, SUGAR FREE, FLAVORED, POWDER","Cream substitute, sugar free, flavored, powder" +12220000,"WHIPPED TOPPING, NONDAIRY, NS AS TO CND/FRZ/POWDER","Whipped topping, nondairy, NS as to canned, frozen, or made from powdered mix" +12220100,"WHIPPED TOPPING, NONDAIRY, PRESSURIZED CAN","Whipped topping, nondairy, pressurized can" +12220200,"WHIPPED TOPPING, NONDAIRY, FROZEN (INCL COOL WHIP)","Whipped topping, nondairy, frozen" +12220250,"WHIPPED TOPPING, NONDAIRY, FZN, LOWFAT (INCL COOL)","Whipped topping, nondairy, frozen, lowfat" +12220270,"WHIPPED TOPPING, NONDAIRY, FROZEN, FAT FREE","Whipped topping, nondairy, frozen, fat free" +12220280,"WHIPPED TOPPING, NONDAIRY, FROZEN, SUGAR FREE","Whipped topping, nondairy, frozen, sugar free" +12220300,"WHIPPED CREAM SUBST, NONDAIRY, FROM POWDERED MIX","Whipped cream substitute, nondairy, made from powdered mix" +12220400,"WHIP CREAM SUB,NONDAIRY,LOWFAT,LO SUGAR,FROM MIX","Whipped cream substitute, nondairy, lowfat, low sugar, made from powdered mix" +12310100,"SOUR CREAM (INCL W/ CHIVES)","Sour cream" +12310200,"SOUR CREAM, HALF & HALF","Sour cream, half and half" +12310300,"SOUR CREAM, REDUCED FAT","Sour cream, reduced fat" +12310350,"SOUR CREAM, LIGHT","Sour cream, light" +12310370,"SOUR CREAM, FAT FREE","Sour cream, fat free" +12320100,"SOUR CREAM, IMITATION","Sour cream, imitation (nondairy)" +12320200,"SOUR CREAM, FILLED, SOUR DRESSING, NONBUTTERFAT","Sour cream, filled, sour dressing, nonbutterfat" +12350000,"DIP, SOUR CREAM BASE (INCLUDE BUTTERMILK-TYPE DIP)","Dip, sour cream base" +12350020,"DIP, SOUR CREAM BASE, REDUCED CALORIE","Dip, sour cream base, reduced calorie" +12350100,"SPINACH DIP","Spinach dip" +12350110,"SPINACH AND ARTICHOKE DIP","Spinach and artichoke dip" +13110000,"ICE CREAM, NFS","Ice cream, NFS" +13110100,"ICE CREAM, REGULAR, NOT CHOCOLATE","Ice cream, regular, flavors other than chocolate" +13110110,"ICE CREAM, REGULAR, CHOCOLATE","Ice cream, regular, chocolate" +13110120,"ICE CREAM, RICH, FLAVORS OTHER THAN CHOCOLATE","Ice cream, rich, flavors other than chocolate" +13110130,"ICE CREAM, RICH, CHOCOLATE","Ice cream, rich, chocolate" +13110140,"ICE CREAM, RICH, NS AS TO FLAVOR","Ice cream, rich, NS as to flavor" +13110200,"ICE CREAM, SOFT SERVE, NOT CHOCOLATE","Ice cream, soft serve, flavors other than chocolate" +13110210,"ICE CREAM, SOFT SERVE, CHOCOLATE","Ice cream, soft serve, chocolate" +13110220,"ICE CREAM, SOFT SERVE, NS AS TO FLAVOR","Ice cream, soft serve, NS as to flavor" +13110310,"ICE CREAM, NO SUGAR ADDED, NS AS TO FLAVOR","Ice cream, no sugar added, NS as to flavor" +13110320,"ICE CREAM, NO SUGAR ADDED, FLAVORS OTHER THAN CHOCOLATE","Ice cream, no sugar added, flavors other than chocolate" +13110330,"ICE CREAM, NO SUGAR ADDED, CHOCOLATE","Ice cream, no sugar added, chocolate" +13120050,"ICE CREAM BAR OR STICK, NOT CHOC- OR CAKE-COVERED","Ice cream bar or stick, not chocolate covered or cake covered" +13120100,"ICE CREAM BAR/STICK, CHOCOLATE COVERED","Ice cream bar or stick, chocolate covered" +13120110,"ICE CREAM BAR, CHOCOLATE/CARAMEL COVERED, W/ NUTS","Ice cream bar or stick, chocolate or caramel covered, with nuts" +13120120,"ICE CREAM BAR,RICH CHOC ICE CREAM,THICK CHOC COVER","Ice cream bar or stick, rich chocolate ice cream, thick chocolate covering" +13120121,"ICE CREAM BAR,RICH ICE CREAM,THICK CHOC COVER","Ice cream bar or stick, rich ice cream, thick chocolate covering" +13120130,"ICE CREAM BAR/STICK,RICH ICE CREAM,CHOC COVER,W/NUT","Ice cream bar or stick, rich ice cream, chocolate covered, with nuts" +13120140,"ICE CREAM BAR/STICK, CHOC ICE CREAM, CHOC COVER","Ice cream bar or stick, chocolate ice cream, chocolate covered" +13120300,"ICE CREAM BAR, CAKE-COVERED","Ice cream bar, cake covered" +13120310,"ICE CREAM BAR, STICK OR NUGGET, WITH CRUNCH COATING","Ice cream bar, stick or nugget, with crunch coating" +13120400,"ICE CREAM BAR/STICK W/ FRUIT","Ice cream bar or stick with fruit" +13120500,"ICE CREAM SANDWICH","Ice cream sandwich" +13120550,"ICE CREAM COOKIE SANDWICH (INCLUDE CHIPWICH)","Ice cream cookie sandwich" +13120700,"ICE CREAM CONE, W/ NUTS, NOT CHOCOLATE","Ice cream cone with nuts, flavors other than chocolate" +13120710,"ICE CREAM CONE, CHOC-COVERED, W/ NUTS, NOT CHOC","Ice cream cone, chocolate covered, with nuts, flavors other than chocolate" +13120720,"ICE CREAM CONE, CHOC-COVERED OR DIPPED, NOT CHOC","Ice cream cone, chocolate covered or dipped, flavors other than chocolate" +13120730,"ICE CREAM CONE, NO TOPPING, NOT CHOCOLATE","Ice cream cone, no topping, flavors other than chocolate" +13120740,"ICE CREAM CONE, NO TOPPING, NS AS TO FLAVOR","Ice cream cone, no topping, NS as to flavor" +13120750,"ICE CREAM CONE, W/NUTS, CHOCOLATE ICE CREAM","Ice cream cone with nuts, chocolate ice cream" +13120760,"ICE CREAM CONE, CHOC-COVERED, CHOC ICE CREAM","Ice cream cone, chocolate covered or dipped, chocolate ice cream" +13120770,"ICE CREAM CONE, NO TOPPING, CHOCOLATE ICE CREAM","Ice cream cone, no topping, chocolate ice cream" +13120780,"ICE CREAM CONE, CHOC-COVERED, W/NUT, CHOC ICE CREAM","Ice cream cone, chocolate covered, with nuts, chocolate ice cream" +13120790,"ICE CREAM SUNDAE CONE (INCL DRUMSTICK, ALL FLAVORS)","Ice cream sundae cone" +13120800,"ICE CREAM SODA, NOT CHOCOLATE","Ice cream soda, flavors other than chocolate" +13120810,"ICE CREAM SODA, CHOCOLATE","Ice cream soda, chocolate" +13121000,"ICE CREAM SUNDAE, TOPPING NS, W/ WHIPPED CREAM","Ice cream sundae, NS as to topping, with whipped cream" +13121100,"ICE CREAM SUNDAE, FRUIT TOPPING, W/ WHIPPED CREAM","Ice cream sundae, fruit topping, with whipped cream" +13121200,"ICE CREAM SUNDAE, PREPACKAGED, NOT CHOCOLATE","Ice cream sundae, prepackaged type, flavors other than chocolate" +13121300,"ICE CREAM SUNDAE,CHOCOLATE TOPPING,W/ WHIPPED CREAM","Ice cream sundae, chocolate or fudge topping, with whipped cream" +13121400,"ICE CREAM SUNDAE, NOT FRUIT/ CHOC TOP,W/ WHIP CREAM","Ice cream sundae, not fruit or chocolate topping, with whipped cream" +13121500,"ICE CREAM SUNDAE, FUDGE TOPPING, W/ CAKE","Ice cream sundae, fudge topping, with cake, with whipped cream" +13122100,"ICE CREAM PIE, NO CRUST","Ice cream pie, no crust" +13122500,"ICE CREAM PIE,COOKIE CRUST,FUDGE TOPPING,WHIP CREAM","Ice cream pie, with cookie crust, fudge topping, and whipped cream" +13126000,"ICE CREAM, FRIED","Ice cream, fried" +13127000,"DIPPIN' DOTS, ICE CREAM, FLAVORS OTHER THAN CHOCOLATE","Dippin' Dots, flash frozen ice cream snacks, flavors other than chocolate" +13127010,"DIPPIN' DOTS, ICE CREAM, CHOCOLATE","Dippin' Dots, flash frozen ice cream snacks, chocolate" +13130100,"LT ICE CREAM, NS FLAV ( ICE MILK)","Light ice cream, NS as to flavor (formerly ice milk)" +13130300,"LIGHT ICE CREAM,NOT CHOCOLATE (FORMERLY ICE MILK)","Light ice cream, flavors other than chocolate (formerly ice milk)" +13130310,"LIGHT ICE CREAM,CHOCOLATE (FORMERLY ICE MILK)","Light ice cream, chocolate (formerly ice milk)" +13130320,"LIGHT ICE CREAM, NO SUGAR ADDED, NS AS TO FLAVOR","Light ice cream, no sugar added, NS as to flavor" +13130330,"LIGHT ICE CREAM, NO SUGAR ADDED, NOT CHOCOLATE","Light ice cream, no sugar added, flavors other than chocolate" +13130340,"LIGHT ICE CREAM, NO SUGAR ADDED, CHOCOLATE","Light ice cream, no sugar added, chocolate" +13130590,"LIGHT ICE CREAM,SOFT SERVE, NS FLAVOR (FORMERLY ICE MILK)","Light ice cream, soft serve, NS as to flavor (formerly ice milk)" +13130600,"LIGHT ICE CREAM,SOFT SERVE, NOT CHOC (FORMERLY ICE MILK)","Light ice cream, soft serve, flavors other than chocolate (formerly ice milk)" +13130610,"LIGHT ICE CREAM,SOFT SERVE CHOC (TASTEE FRZ, DAIRY QUEEN)","Light ice cream, soft serve, chocolate (formerly ice milk)" +13130620,"LIGHT ICE CREAM,SOFT SERVE CONE,NOT CHOC (DAIRY QUEEN)","Light ice cream, soft serve cone, flavors other than chocolate (formerly ice milk)" +13130630,"LIGHT ICE CREAM,SOFT SERVE CONE, CHOC (FORMERLY ICE MILK)","Light ice cream, soft serve cone, chocolate (formerly ice milk)" +13130640,"LIGHT ICE CREAM,SOFT SERVE CONE, NS FLAV(FORMERLY ICE MILK)","Light ice cream, soft serve cone, NS as to flavor (formerly ice milk)" +13130700,"LIGHT ICE CREAM, SOFT SERVE, BLENDED W/ CANDY OR COOKIES","Light ice cream, soft serve, blended with candy or cookies" +13135000,"ICE CREAM SANDWICH, MADE W/ LIGHT ICE CREAM, NOT CHOCOLATE","Ice cream sandwich, made with light ice cream, flavors other than chocolate" +13135010,"ICE CREAM SANDWICH, MADE W/ LIGHT CHOCOLATE ICE CREAM","Ice cream sandwich, made with light chocolate ice cream" +13136000,"ICE CREAM SANDWICH, MADE W/ LIGHT, NO SUGAR ADDED ICE CREAM","Ice cream sandwich, made with light, no sugar added ice cream" +13140100,"LIGHT ICE CREAM,BAR/STICK, CHOC-COATED (FORMERLY ICE MILK)","Light ice cream, bar or stick, chocolate-coated (formerly ice milk)" +13140110,"LIGHT ICE CREAM,BAR, CHOC COVERED,W/NUTS (FORMERLY ICE MILK)","Light ice cream, bar or stick, chocolate covered, with nuts (formerly ice milk)" +13140450,"LIGHT ICE CREAM,CONE, NFS (FORMERLY ICE MILK)","Light ice cream, cone, NFS (formerly ice milk)" +13140500,"LIGHT ICE CREAM,CONE, NOT CHOCOLATE (FORMERLY ICE MILK)","Light ice cream, cone, flavors other than chocolate (formerly ice milk)" +13140550,"LIGHT ICE CREAM,CONE, CHOCOLATE (FORMERLY ICE MILK)","Light ice cream, cone, chocolate (formerly ice milk)" +13140570,"LIGHT ICE CREAM, NO SUGAR ADDED, CONE, NS AS TO FLAVOR","Light ice cream, no sugar added, cone, NS as to flavor" +13140575,"LIGHT ICE CREAM, NO SUGAR ADDED, CONE, NOT CHOC","Light ice cream, no sugar added, cone, flavors other than chocolate" +13140580,"LIGHT ICE CREAM, NO SUGAR ADDED, CONE, CHOCOLATE","Light ice cream, no sugar added, cone, chocolate" +13140600,"LIGHT ICE CREAM,SUNDAE,SOFT SERVE,CHOC/FUDGE TOP (ICE MILK)","Light ice cream, sundae, soft serve, chocolate or fudge topping, with whipped cream (formerly ice milk)" +13140630,"LIGHT ICE CREAM,SUNDAE,SOFT SERVE,FRUIT TOPPING (ICE MILK)","Light ice cream, sundae, soft serve, fruit topping, with whipped cream (formerly ice milk)" +13140650,"LIGHT ICE CREAM,SUNDAE,SOFT SERVE,NOT FRUIT/CHOC TOPPING","Light ice cream, sundae, soft serve, not fruit or chocolate topping, with whipped cream (formerly ice milk)" +13140660,"LIGHT ICE CREAM,SUNDAE,CHOC / FUDGE TOP (W/O WHIP CREAM)","Light ice cream, sundae, soft serve, chocolate or fudge topping (without whipped cream) (formerly ice milk)" +13140670,"LIGHT ICE CREAM,SUNDAE,FRUIT TOP (W/O WHIP CREAM)(ICE MILK)","Light ice cream, sundae, soft serve, fruit topping (without whipped cream) (formerly ice milk)" +13140680,"LIGHT ICE CREAM,SUNDAE,NO FRUIT/CHOC TOP (W/O WHIP CREAM)","Light ice cream, sundae, soft serve, not fruit or chocolate topping (without whipped cream) (formerly ice milk)" +13140700,"LIGHT ICE CREAM,CREAMSICLE OR DREAMSICLE (FORMERLY ICE MILK)","Light ice cream, creamsicle or dreamsicle (formerly ice milk)" +13140710,"LIGHT ICE CREAM, CREAMSICLE OR DREAMSICLE, NO SUGAR ADDED","Light ice cream, creamsicle or dreamsicle, no sugar added" +13140900,"LIGHT ICE CREAM,FUDGESICLE (FORMERLY ICE MILK)","Light ice cream, fudgesicle (formerly ice milk)" +13142000,"MILK DESSERT BAR/STICK, FROZEN, W/ COCONUT","Milk dessert bar or stick, frozen, with coconut" +13150000,"SHERBET, ALL FLAVORS","Sherbet, all flavors" +13160150,"FAT FREE ICE CREAM, NO SUGAR ADD, CHOC","Fat free ice cream, no sugar added, chocolate" +13160160,"FAT FREE ICE CREAM, NO SUGAR ADD, FLAVORS OTHER THAN CHOC","Fat free ice cream, no sugar added, flavors other than chocolate" +13160400,"FAT FREE ICE CREAM, FLAVORS OTHER THAN CHOC","Fat free ice cream, flavors other than chocolate" +13160410,"FAT FREE ICE CREAM, CHOC","Fat free ice cream, chocolate" +13160420,"FAT FREE ICE CREAM, NS AS TO FLAVOR","Fat free ice cream, NS as to flavor" +13161000,"MILK DESSERT BAR, FROZEN, MADE FROM LOWFAT MILK","Milk dessert bar, frozen, made from lowfat milk" +13161500,"MILK DESSERT SANDWICH BAR, FROZEN, DIETARY","Milk dessert sandwich bar, frozen, made from lowfat milk" +13161520,"MILK DESSERT SANDWICH BAR,FRZ,W/LOW-CAL SWEET,LOFAT","Milk dessert sandwich bar, frozen, with low-calorie sweetener, made from lowfat milk" +13161600,"MILK DES BAR, FROZEN, LOFAT MILK&LO CAL SWEETENER","Milk dessert bar, frozen, made from lowfat milk and low calorie sweetener" +13161630,"LIGHT ICE CREAM,BAR/STICK, W/ LOW-CAL SWEETENER, CHOC COAT","Light ice cream, bar or stick, with low-calorie sweetener, chocolate-coated (formerly ice milk)" +13170000,"BAKED ALASKA","Baked Alaska" +13200110,"PUDDING, NFS","Pudding, NFS" +13210110,"PUDDING, BREAD (INCLUDE W/ RAISINS)","Pudding, bread" +13210150,"PUERTO RICAN BREAD PUDDING MADE W/ EVAP MILK & RUM","Puerto Rican bread pudding made with evaporated milk and rum (Budin de pan)" +13210160,"DIPLOMAT PUDDING, P.R. (BUDIN DIPLOMATICO)","Diplomat pudding, Puerto Rican style (Budin Diplomatico)" +13210180,"PUDDING, MEXICAN BREAD (CAPIROTADA)","Pudding, Mexican bread (Capirotada)" +13210190,"PUDDING, MEXICAN BREAD (CAPIROTADA), LOWER FAT","Pudding, Mexican bread (Capirotada), lower fat" +13210220,"PUDDING, CHOCOLATE, NS AS TO FROM DRY MIX/RTE","Pudding, chocolate, NS as to from dry mix or ready-to-eat" +13210250,"PUDDING, CHOC, LO CAL, W/ART SWTNER, NS DRY/RTE","Pudding, chocolate, low calorie, containing artificial sweetener, NS as to from dry mix or ready-to-eat" +13210260,"RICE FLOUR CREAM, P.R.STYLE (MANJAR BLANCO)","Rice flour cream, Puerto Rican style (manjar blanco)" +13210270,"CUSTARD, P.R. (MAICENA, NATILLA)","Custard, Puerto Rican style (Maicena, Natilla)" +13210280,"PUDDING, NOT CHOC, NS FROM DRY OR RTE","Pudding, flavors other than chocolate, NS as to from dry mix or ready-to-eat" +13210290,"PUDDING, NOT CHOC, LO CAL, W/ART SWTNER, NS DRY MIX OR RTE","Pudding, flavors other than chocolate, low calorie, containing artificial sweetener, NS as to from dry mix or ready-to-eat" +13210300,"CUSTARD","Custard" +13210350,"FLAN","Flan" +13210410,"PUDDING, RICE","Pudding, rice" +13210450,"PUDDING, RICE FLOUR, W/ NUTS (INDIAN DESSERT)","Pudding, rice flour, with nuts (Indian dessert)" +13210500,"PUDDING, TAPIOCA,MADE FROM HOME RECIPE, MADE W/ MILK","Pudding, tapioca, made from home recipe, made with milk" +13210520,"PUDDING, TAPIOCA,MADE FROM DRY MIX,MADE W/ MILK","Pudding, tapioca, made from dry mix, made with milk" +13210530,"PUDDING, TAPIOCA,CHOCOLATE,MADE W/ MILK","Pudding, tapioca, chocolate, made with milk" +13210610,"PUDDING, COCONUT","Pudding, coconut" +13210710,"PUDDING, INDIAN (MILK, MOLASSES, CORNMEAL-BASED)","Pudding, Indian (milk, molasses and cornmeal-based pudding)" +13210750,"PUDDING, PUMPKIN","Pudding, pumpkin" +13210810,"P.R. PUMPKIN PUDDING (FLAN DE CALABAZA)","Puerto Rican pumpkin pudding (Flan de calabaza)" +13210820,"FRESH CORN CUSTARD, PUERTO RICAN STYLE","Fresh corn custard, Puerto Rican style (Mazamorra, Mundo Nuevo)" +13220110,"PUDDING,NOT CHOCOLATE,PREPARED FROM DRY MIX,MILK ADDED","Pudding, flavors other than chocolate, prepared from dry mix, milk added" +13220120,"PUDDING,CHOCOLATE,PREPARED FROM DRY MIX,MILK ADDED","Pudding, chocolate, prepared from dry mix, milk added" +13220210,"PUDDING,NOT CHOC,FROM DRY,LOW CAL,ARTIFICIAL SWEET,W/MILK","Pudding, flavors other than chocolate, prepared from dry mix, low calorie, containing artificial sweetener, milk added" +13220220,"PUDDING,CHOC,FROM DRY,LOW CAL,ARTIFICIAL SWEET,MILK ADDED","Pudding, chocolate, prepared from dry mix, low calorie, containing artificial sweetener, milk added" +13220230,"PUDDING, RTE, CHOCOLATE, RED FAT","Pudding, ready-to-eat, chocolate, reduced fat" +13220235,"PUDDING, RTE, CHOCOLATE, FAT FREE","Pudding, ready-to-eat, chocolate, fat free" +13220240,"PUDDING, RTE, FLAVORS OTHER THAN CHOCOLATE, RED FAT","Pudding, ready-to-eat, flavors other than chocolate, reduced fat" +13220245,"PUDDING, RTE, FLAVORS OTHER THAN CHOCOLATE, FAT FREE","Pudding, ready-to-eat, flavors other than chocolate, fat free" +13230110,"PUDDING, RTE, FLAVORS OTHER THAN CHOCOLATE","Pudding, ready-to-eat, flavors other than chocolate" +13230120,"PUDDING, RTE, LOW CAL, W/ARTIFICIAL SWTNR, NOT CHOC","Pudding, ready-to-eat, low calorie, containing artificial sweetener, flavors other than chocolate" +13230130,"PUDDING, RTE, CHOCOLATE","Pudding, ready-to-eat, chocolate" +13230140,"PUDDING,RTE, LO CAL/ W ART SWTNER, CHOC","Pudding, ready-to-eat, low calorie, containing artificial sweetener, chocolate" +13230200,"PUDDING, RTE, CHOC & NON-CHOC FLAVORS COMBINED","Pudding, ready-to-eat, chocolate and non-chocolate flavors combined" +13230500,"PUDDING, READY-TO-EAT, TAPIOCA","Pudding, ready-to-eat, tapioca" +13230510,"PUDDING, READY-TO-EAT, TAPIOCA, FAT FREE","Pudding, ready-to-eat, tapioca, fat free" +13241000,"PUDDING, W/ FRUIT & VANILLA WAFERS","Pudding, with fruit and vanilla wafers" +13250000,"MOUSSE, CHOCOLATE","Mousse, chocolate" +13250100,"MOUSSE, NOT CHOCOLATE","Mousse, not chocolate" +13250200,"MOUSSE,CHOCOLATE,LOW FAT,REDUCED CAL,DRY MIX","Mousse, chocolate, lowfat, reduced calorie, prepared from dry mix, water added" +13252100,"COCONUT CUSTARD, P.R. (FLAN DE COCO)","Coconut custard, Puerto Rican style (Flan de coco)" +13252200,"MILK DESSERT OR MILK CANDY, P.R. (DULCE DE LECHE)","Milk dessert or milk candy, Puerto Rican style (Dulce de leche)" +13252500,"BARFI/BURFI,INDIAN DESSERT,FROM MILK/CREAM/RICOTTA","Barfi or Burfi, Indian dessert, made from milk and/or cream and/or Ricotta cheese" +13252600,"TIRAMISU","Tiramisu" +13310000,"CUSTARD PUDDING, NOT CHOC, BABY, NS AS TO STR OR JR","Custard pudding, flavor other than chocolate, baby food, NS as to strained or junior" +13311000,"CUSTARD PUDDING, BABY, NOT CHOCOLATE, STRAINED","Custard pudding, baby food, flavor other than chocolate, strained" +13312000,"CUSTARD PUDDING, BABY, NOT CHOCOLATE, JUNIOR","Custard pudding, baby food, flavor other than chocolate, junior" +13411000,"WHITE SAUCE, MILK SAUCE","White sauce, milk sauce" +13412000,"MILK GRAVY, QUICK GRAVY","Milk gravy, quick gravy" +14010000,"CHEESE, NFS","Cheese, NFS" +14101010,"CHEESE, BLUE OR ROQUEFORT","Cheese, Blue or Roquefort" +14102010,"CHEESE, BRICK","Cheese, Brick" +14103010,"CHEESE, CAMEMBERT","Cheese, Camembert" +14103020,"CHEESE, BRIE","Cheese, Brie" +14104100,"CHEESE, CHEDDAR","Cheese, Cheddar" +14104110,"CHEESE, CHEDDAR, REDUCED FAT","Cheese, Cheddar, reduced fat" +14104115,"CHEESE, CHEDDAR, NONFAT OR FAT FREE","Cheese, Cheddar, nonfat or fat free" +14104200,"CHEESE, COLBY","Cheese, Colby" +14104250,"CHEESE, COLBY JACK","Cheese, Colby Jack" +14104400,"CHEESE, FETA (INCLUDE GOAT CHEESE)","Cheese, Feta" +14104600,"CHEESE, FONTINA","Cheese, Fontina" +14104700,"CHEESE, GOAT","Cheese, goat" +14105010,"CHEESE, GOUDA OR EDAM","Cheese, Gouda or Edam" +14105200,"CHEESE, GRUYERE","Cheese, Gruyere" +14106010,"CHEESE, LIMBURGER","Cheese, Limburger" +14106200,"CHEESE, MONTEREY","Cheese, Monterey" +14106500,"CHEESE, MONTEREY, REDUCED FAT","Cheese, Monterey, reduced fat" +14107010,"CHEESE, MOZZARELLA, NFS (INCLUDE PIZZA CHEESE)","Cheese, Mozzarella, NFS" +14107020,"CHEESE, MOZZARELLA, WHOLE MILK","Cheese, Mozzarella, whole milk" +14107030,"CHEESE, MOZZARELLA, PART SKIM (INCL ""LOWFAT"")","Cheese, Mozzarella, part skim" +14107040,"CHEESE, MOZZARELLA, REDUCED SODIUM","Cheese, Mozzarella, reduced sodium" +14107060,"CHEESE, MOZZARELLA, NONFAT OR FAT FREE","Cheese, Mozzarella, nonfat or fat free" +14107200,"CHEESE, MUENSTER","Cheese, Muenster" +14107250,"CHEESE, MUENSTER, REDUCED FAT","Cheese, Muenster, reduced fat" +14108010,"CHEESE, PARMESAN, DRY, GRATED (INCLUDE ROMANO)","Cheese, Parmesan, dry grated" +14108015,"CHEESE, PARMESAN, DRY GRATED, REDUCED FAT","Cheese, Parmesan, dry grated, reduced fat" +14108020,"CHEESE, PARMESAN, HARD (INCLUDE ROMANO)","Cheese, Parmesan, hard" +14108060,"CHEESE, PARMESAN, DRY GRATED, FAT FREE","Cheese, Parmesan, dry grated, fat free" +14108200,"CHEESE, PORT DU SALUT","Cheese, Port du Salut" +14108400,"CHEESE, PROVOLONE","Cheese, Provolone" +14108420,"CHEESE, PROVOLONE, REDUCED FAT","Cheese, provolone, reduced fat" +14109010,"CHEESE, SWISS","Cheese, Swiss" +14109020,"CHEESE, SWISS, REDUCED SODIUM","Cheese, Swiss, reduced sodium" +14109030,"CHEESE, SWISS, REDUCED FAT","Cheese, Swiss, reduced fat" +14109040,"CHEESE, SWISS, NONFAT OR FAT FREE","Cheese, Swiss, nonfat or fat free" +14110010,"CHEESE, CHEDDAR, REDUCED SODIUM","Cheese, Cheddar, reduced sodium" +14120010,"CHEESE, MEXICAN BLEND","Cheese, Mexican blend" +14120020,"CHEESE, MEXICAN BLEND, REDUCED FAT","Cheese, Mexican blend, reduced fat" +14131000,"QUESO ANEJO (AGED MEXICAN CHEESE)","Queso Anejo (aged Mexican cheese)" +14131500,"QUESO ASADERO (INCL OAXACAN-STYLE STRING CHEESE)","Queso Asadero" +14132000,"QUESO CHIHUAHUA (INCL MENNONITE CHEESE)","Queso Chihuahua" +14133000,"QUESO FRESCO (HISPANIC-STYLE FARMER CHEESE)","Queso Fresco" +14134000,"QUESO COTIJA","Queso cotija" +14200100,"CHEESE, COTTAGE, NFS","Cheese, cottage, NFS" +14201010,"CHEESE, COTTAGE, CREAMED","Cheese, cottage, creamed, large or small curd" +14201200,"COTTAGE CHEESE, FARMER'S","Cottage cheese, farmer's" +14201500,"CHEESE, RICOTTA","Cheese, Ricotta" +14202010,"CHEESE, COTTAGE, W/ FRUIT","Cheese, cottage, with fruit" +14202020,"CHEESE, COTTAGE, W/ VEGETABLES","Cheese, cottage, with vegetables" +14203010,"CHEESE, COTTAGE, DRY CURD","Cheese, cottage, dry curd" +14203020,"CHEESE, COTTAGE, SALTED, DRY CURD","Cheese, cottage, salted, dry curd" +14203510,"P.R. WHITE CHEESE (QUESO DEL PAIS, BLANCO)","Puerto Rican white cheese (queso del pais, blanco)" +14204010,"CHEESE, COTTAGE, LOWFAT","Cheese, cottage, lowfat (1-2% fat)" +14204020,"CHEESE, COTTAGE, LOWFAT, W/ FRUIT","Cheese, cottage, lowfat, with fruit" +14204030,"CHEESE, COTTAGE, LOWFAT, W/ VEGETABLES","Cheese, cottage, lowfat, with vegetables" +14206010,"CHEESE, COTTAGE, LOWFAT, LOW SODIUM","Cheese, cottage, lowfat, low sodium" +14207010,"CHEESE, COTTAGE, LOWFAT, LACTOSE REDUCED","Cheese, cottage, lowfat, lactose reduced" +14301010,"CHEESE, CREAM","Cheese, cream" +14303010,"CHEESE, CREAM, LIGHT/LITE (FORMERLY CALLED CR CHEESE LOWFAT)","Cheese, cream, light or lite (formerly called Cream Cheese Lowfat)" +14410100,"CHEESE, AMERICAN AND SWISS BLENDS","Cheese, American and Swiss blends" +14410110,"CHEESE, AMERICAN","Cheese, American" +14410120,"CHEESE, AMERICAN, REDUCED FAT","Cheese, American, reduced fat" +14410130,"CHEESE, AMERICAN, NONFAT OR FAT FREE","Cheese, American, nonfat or fat free" +14410210,"CHEESE, AMERICAN, REDUCED SODIUM","Cheese, American, reduced sodium" +14410330,"CHEESE SPREAD, AMERICAN OR CHEDDAR CHEESE BASE, REDUCED FAT","Cheese spread, American or Cheddar cheese base, reduced fat" +14410380,"CHEESE, PROCESSED CREAM CHEESE PRODUCT, NONFAT","Cheese, processed cream cheese product, nonfat or fat free" +14410500,"CHEESE, PROCESSED, CHEESE FOOD","Cheese, processed cheese food" +14410600,"CHEESE, PROCESSED, W/VEGETABLES(INCL PEPPER CHEESE)","Cheese, processed, with vegetables" +14410620,"CHEESE, WITH WINE","Cheese, with wine" +14420100,"CHEESE SPREAD, AMERICAN OR CHEDDAR CHEESE BASE","Cheese spread, American or Cheddar cheese base" +14420160,"CHEESE SPREAD, SWISS CHEESE BASE","Cheese spread, Swiss cheese base" +14420200,"CHEESE SPRD, CREAM CHEESE, REG","Cheese spread, cream cheese, regular" +14420210,"CHEESE SPREAD, CREAM CHEESE, LIGHT OR LITE","Cheese spread, cream cheese, light or lite" +14420300,"CHEESE SPREAD, PRESSURIZED CAN","Cheese spread, pressurized can" +14502000,"IMITATION CHEESE","Imitation cheese" +14610200,"COTTAGE CHEESE, W/ GELATIN DESSERT","Cheese, cottage cheese, with gelatin dessert" +14610210,"COTTAGE CHEESE, W/ GELATIN DESSERT & FRUIT","Cheese, cottage cheese, with gelatin dessert and fruit" +14610250,"COTTAGE CHEESE W/ GELATIN DESSERT & VEGETABLES","Cheese, cottage cheese, with gelatin dessert and vegetables" +14610520,"CHEESE W/ NUTS (INCL CHEESE BALL)","Cheese with nuts" +14620100,"DIP, CREAM CHEESE BASE","Dip, cream cheese base" +14620120,"SHRIMP DIP, CREAM CHEESE BASE (INCL CLAM DIP)","Shrimp dip, cream cheese base" +14620150,"DIP, CHEESE W/ CHILI PEPPER (CHILI CON QUESO)","Dip, cheese with chili pepper (chili con queso)" +14620200,"DIP, CHEESE BASE OTHER THAN CREAM CHEESE","Dip, cheese base other than cream cheese" +14620300,"TOPPING FROM CHEESE PIZZA","Topping from cheese pizza" +14620310,"TOPPING FROM VEGETABLE PIZZA","Topping from vegetable pizza" +14620320,"TOPPING FROM MEAT PIZZA","Topping from meat pizza" +14620330,"TOPPING FROM MEAT AND VEGETABLE PIZZA","Topping from meat and vegetable pizza" +14630100,"CHEESE FONDUE","Cheese fondue" +14630200,"CHEESE SOUFFLE","Cheese souffle" +14630300,"WELSH RAREBIT","Welsh rarebit" +14640000,"CHEESE SANDWICH","Cheese sandwich" +14640100,"CHEESE SANDWICH, GRILLED","Cheese sandwich, grilled" +14640200,"CHEESE SANDWICH, HOAGIE","Cheese sandwich, hoagie" +14650100,"CHEESE SAUCE","Cheese sauce" +14650150,"CHEESE SAUCE MADE W/ LOWFAT CHEESE","Cheese sauce made with lowfat cheese" +14650160,"ALFREDO SAUCE","Alfredo sauce" +14660200,"CHEESE, NUGGETS, FRIED (INCL BANQUET BRAND)","Cheese, nuggets or pieces, breaded, baked, or fried" +14670000,"MOZZARELLA CHEESE, TOMATO, BASIL, W/ OIL, VINEGAR","Mozzarella cheese, tomato, and basil, with oil and vinegar dressing" +14710100,"CHEDDAR CHEESE SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Cheddar cheese soup, home recipe, canned or ready-to-serve" +14710200,"BEER CHEESE SOUP, MADE WITH MILK","Beer cheese soup, made with milk" +2e+07,"MEAT, NFS","Meat, NFS" +20000070,"MEAT, BABY, NS AS TO TYPE, NS AS TO STR OR JR","Meat, baby food, NS as to type, NS as to strained or junior" +20000090,"MEAT STICKS, BABY, NS AS TO TYPE OF MEAT","Meat sticks, baby food, NS as to type of meat" +20000200,"GROUND MEAT,NFS","Ground meat, NFS" +21000100,"BEEF, NS AS TO CUT, COOKED, NS AS TO FAT","Beef, NS as to cut, cooked, NS as to fat eaten" +21000110,"BEEF, NS AS TO CUT, COOKED, LEAN & FAT","Beef, NS as to cut, cooked, lean and fat eaten" +21000120,"BEEF, NS AS TO CUT, COOKED, LEAN ONLY","Beef, NS as to cut, cooked, lean only eaten" +21001000,"STEAK, NS AS TO TYPE OF MEAT, COOKED, NS AS TO FAT","Steak, NS as to type of meat, cooked, NS as to fat eaten" +21001010,"STEAK, NS AS TO TYPE OF MEAT, COOKED, LEAN & FAT","Steak, NS as to type of meat, cooked, lean and fat eaten" +21001020,"STEAK, NS AS TO TYPE OF MEAT, COOKED, LEAN ONLY","Steak, NS as to type of meat, cooked, lean only eaten" +21002000,"BEEF, PICKLED","Beef, pickled" +21003000,"BEEF, NS AS TO CUT, FRIED, NS AS TO FAT EATEN","Beef, NS as to cut, fried, NS to fat eaten" +21101000,"BEEF STEAK, NS AS TO COOKING METHOD, NS AS TO FAT","Beef steak, NS as to cooking method, NS as to fat eaten" +21101010,"BEEF STEAK, NS AS TO COOKING METHOD, LEAN & FAT","Beef steak, NS as to cooking method, lean and fat eaten" +21101020,"BEEF STEAK, NS AS TO COOKING METHOD, LEAN ONLY","Beef steak, NS as to cooking method, lean only eaten" +21101110,"BEEF STEAK, BROILED OR BAKED, NS AS TO FAT","Beef steak, broiled or baked, NS as to fat eaten" +21101120,"BEEF STEAK, BROILED OR BAKED, LEAN & FAT","Beef steak, broiled or baked, lean and fat eaten" +21101130,"BEEF STEAK, BROILED OR BAKED, LEAN ONLY","Beef steak, broiled or baked, lean only eaten" +21102110,"BEEF STEAK, FRIED, NS AS TO FAT","Beef steak, fried, NS as to fat eaten" +21102120,"BEEF STEAK, FRIED, LEAN & FAT","Beef steak, fried, lean and fat eaten" +21102130,"BEEF STEAK, FRIED, LEAN ONLY","Beef steak, fried, lean only eaten" +21103110,"BEEF STEAK,BREADED/FLOURED,BAKED/FRIED,NS AS TO FAT","Beef steak, breaded or floured, baked or fried, NS as to fat eaten" +21103120,"BEEF STEAK, BREADED/FLOURED,BAKED/FRIED, LEAN & FAT","Beef steak, breaded or floured, baked or fried, lean and fat eaten" +21103130,"BEEF STEAK, BREADED/FLOURED, BAKED/FRIED, LEAN ONLY","Beef steak, breaded or floured, baked or fried, lean only eaten" +21104110,"BEEF STEAK, BATTERED, FRIED, NS AS TO FAT","Beef steak, battered, fried, NS as to fat eaten" +21104120,"BEEF STEAK, BATTERED, FRIED, LEAN & FAT","Beef steak, battered, fried, lean and fat eaten" +21104130,"BEEF STEAK, BATTERED, FRIED, LEAN ONLY","Beef steak, battered, fried, lean only eaten" +21105110,"BEEF STEAK, BRAISED, NS AS TO FAT","Beef steak, braised, NS as to fat eaten" +21105120,"BEEF STEAK, BRAISED, LEAN & FAT","Beef steak, braised, lean and fat eaten" +21105130,"BEEF STEAK, BRAISED, LEAN ONLY","Beef steak, braised, lean only eaten" +21301000,"BEEF, OXTAILS, COOKED","Beef, oxtails, cooked" +21302000,"BEEF, NECK BONES, COOKED","Beef, neck bones, cooked" +21304000,"BEEF, SHORTRIBS, COOKED, NS AS TO FAT","Beef, shortribs, cooked, NS as to fat eaten" +21304110,"BEEF, SHORTRIBS, COOKED, LEAN & FAT","Beef, shortribs, cooked, lean and fat eaten" +21304120,"BEEF, SHORTRIBS, COOKED, LEAN ONLY","Beef, shortribs, cooked, lean only eaten" +21304200,"BEEF, SHORTRIBS, BBQ, W/ SAUCE, NS AS TO FAT","Beef, shortribs, barbecued, with sauce, NS as to fat eaten" +21304210,"BEEF, SHORTRIBS, BBQ, W/ SAUCE, LEAN & FAT","Beef, shortribs, barbecued, with sauce, lean and fat eaten" +21304220,"BEEF, SHORTRIBS, BBQ, W/ SAUCE, LEAN ONLY","Beef, shortribs, barbecued, with sauce, lean only eaten" +21305000,"BEEF, COW HEAD, COOKED","Beef, cow head, cooked" +21401000,"BEEF, ROAST, ROASTED, NS AS TO FAT","Beef, roast, roasted, NS as to fat eaten" +21401110,"BEEF, ROAST, ROASTED, LEAN & FAT","Beef, roast, roasted, lean and fat eaten" +21401120,"BEEF, ROAST, ROASTED, LEAN ONLY","Beef, roast, roasted, lean only eaten" +21401400,"BEEF, ROAST, CANNED","Beef, roast, canned" +21407000,"BEEF, POT ROAST, BRAISED OR BOILED, NS AS TO FAT","Beef, pot roast, braised or boiled, NS as to fat eaten" +21407110,"BEEF, POT ROAST, BRAISED OR BOILED, LEAN & FAT","Beef, pot roast, braised or boiled, lean and fat eaten" +21407120,"BEEF, POT ROAST, BRAISED OR BOILED, LEAN ONLY","Beef, pot roast, braised or boiled, lean only eaten" +21410000,"BEEF, STEW MEAT, COOKED, NS AS TO FAT","Beef, stew meat, cooked, NS as to fat eaten" +21410110,"BEEF, STEW MEAT, COOKED, LEAN & FAT","Beef, stew meat, cooked, lean and fat eaten" +21410120,"BEEF, STEW MEAT, COOKED, LEAN ONLY","Beef, stew meat, cooked, lean only eaten" +21416000,"CORNED BEEF, COOKED, NS AS TO FAT","Corned beef, cooked, NS as to fat eaten" +21416110,"CORNED BEEF, COOKED, LEAN & FAT","Corned beef, cooked, lean and fat eaten" +21416120,"CORNED BEEF, COOKED, LEAN ONLY","Corned beef, cooked, lean only eaten" +21416150,"CORNED BEEF, CANNED, READY TO EAT","Corned beef, canned, ready-to-eat" +21417100,"BEEF BRISKET, COOKED, NS AS TO FAT","Beef brisket, cooked, NS as to fat eaten" +21417110,"BEEF BRISKET, COOKED, LEAN & FAT","Beef brisket, cooked, lean and fat eaten" +21417120,"BEEF BRISKET, COOKED, LEAN ONLY","Beef brisket, cooked, lean only eaten" +21420100,"BEEF, SANDWICH STEAK (FLAKED,FORMED, THINLY SLICED)","Beef, sandwich steak (flaked, formed, thinly sliced)" +21500000,"GROUND BEEF, RAW","Ground beef, raw" +21500100,"GROUND BEEF OR PATTY, NS AS TO %LEAN","Ground beef or patty, cooked, NS as to percent lean (formerly NS as to regular, lean, or extra lean)" +21500200,"GROUND BEEF OR PATTY, BREADED, COOKED","Ground beef or patty, breaded, cooked" +21500300,"GROUND BEEF PATTY, COOKED (FOR FAST FOOD SANDWICHES)","Ground beef patty, cooked (for fast food sandwiches)" +21501000,"GROUND BEEF, LESS THAN 80% LEAN, COOKED","Ground beef, less than 80% lean, cooked (formerly regular)" +21501200,"GROUND BEEF, 80% - 84% LEAN, COOKED","Ground beef, 80% - 84% lean, cooked (formerly lean)" +21501300,"GROUND BEEF, 85% - 89% LEAN, COOKED","Ground beef, 85% - 89% lean, cooked (formerly extra lean)" +21501350,"GROUND BEEF, 90% - 94% LEAN, COOKED","Ground beef, 90% - 94% lean, cooked" +21501360,"GROUND BEEF, 95% OR MORE LEAN, COOKED","Ground beef, 95% or more lean, cooked" +21540100,"GROUND BEEF W/ TEXTURED VEGETABLE PROTEIN, COOKED","Ground beef with textured vegetable protein, cooked" +21601000,"BEEF, BACON, COOKED","Beef, bacon, cooked" +21601500,"BEEF BACON, FORMED, LEAN MEAT ADDED (INCL SIZZLEAN)","Beef, bacon, formed, lean meat added, cooked" +21602000,"BEEF, DRIED, CHIPPED, UNCOOKED","Beef, dried, chipped, uncooked" +21602010,"BEEF, DRIED, CHIPPED, COOKED IN FAT","Beef, dried, chipped, cooked in fat" +21602100,"BEEF JERKY","Beef jerky" +21603000,"BEEF, PASTRAMI (BEEF, SMOKED, SPICED)","Beef, pastrami (beef, smoked, spiced)" +21701000,"BEEF, BABY, NS AS TO STRAINED OR JUNIOR","Beef, baby food, NS as to strained or junior" +21701010,"BEEF, BABY, STRAINED","Beef, baby food, strained" +21701020,"BEEF, BABY, JUNIOR","Beef, baby food, junior" +22000100,"PORK, NS AS TO CUT, COOKED, NS AS TO FAT EATEN","Pork, NS as to cut, cooked, NS as to fat eaten" +22000110,"PORK, NS AS TO CUT, COOKED, LEAN & FAT EATEN","Pork, NS as to cut, cooked, lean and fat eaten" +22000120,"PORK, NS AS TO CUT, COOKED, LEAN ONLY EATEN","Pork, NS as to cut, cooked, lean only eaten" +22000200,"PORK, NS AS TO CUT, FRIED, NS AS TO FAT EATEN","Pork, NS as to cut, fried, NS as to fat eaten" +22000210,"PORK, NS AS TO CUT, FRIED, LEAN & FAT EATEN","Pork, NS as to cut, fried, lean and fat eaten" +22000220,"PORK, NS AS TO CUT, FRIED, LEAN ONLY EATEN","Pork, NS as to cut, fried, lean only eaten" +22000300,"PORK, NS AS TO CUT, BREADED, FRIED, NS AS TO FAT","Pork, NS as to cut, breaded or floured, fried, NS as to fat eaten" +22000310,"PORK, NS AS TO CUT, BREADED, FRIED, FAT EATEN","Pork, NS as to cut, breaded or floured, fried, lean and fat eaten" +22000320,"PORK, NS AS TO CUT, BREADED, FRIED, LEAN ONLY","Pork, NS as to cut, breaded or floured, fried, lean only eaten" +22001000,"PORK, PICKLED, NS AS TO CUT","Pork, pickled, NS as to cut" +22002000,"PORK, GROUND OR PATTY, COOKED","Pork, ground or patty, cooked" +22002100,"PORK, GROUND, GROUND OR PATTY, BREADED, COOKED","Pork, ground or patty, breaded, cooked" +22002800,"PORK JERKY","Pork jerky" +22101000,"PORK CHOP, NS AS TO COOKING METHOD, NS AS TO FAT","Pork chop, NS as to cooking method, NS as to fat eaten" +22101010,"PORK CHOP, NS AS TO COOKING METHOD, LEAN & FAT","Pork chop, NS as to cooking method, lean and fat eaten" +22101020,"PORK CHOP, NS AS TO COOKING METHOD, LEAN ONLY","Pork chop, NS as to cooking method, lean only eaten" +22101100,"PORK CHOP, BROILED OR BAKED, NS AS TO FAT","Pork chop, broiled or baked, NS as to fat eaten" +22101110,"PORK CHOP, BROILED OR BAKED, LEAN & FAT","Pork chop, broiled or baked, lean and fat eaten" +22101120,"PORK CHOP, BROILED OR BAKED, LEAN ONLY","Pork chop, broiled or baked, lean only eaten" +22101130,"PORK CHOP, BREADED, BROILED OR BAKED, NS AS TO FAT","Pork chop, breaded or floured, broiled or baked, NS as to fat eaten" +22101140,"PORK CHOP, BREADED, BROILED OR BAKED, LEAN & FAT","Pork chop, breaded or floured, broiled or baked, lean and fat eaten" +22101150,"PORK CHOP, BREADED, BROILED OR BAKED, LEAN ONLY","Pork chop, breaded or floured, broiled or baked, lean only eaten" +22101200,"PORK CHOP, FRIED, NS AS TO FAT","Pork chop, fried, NS as to fat eaten" +22101210,"PORK CHOP, FRIED, LEAN & FAT","Pork chop, fried, lean and fat eaten" +22101220,"PORK CHOP, FRIED, LEAN ONLY","Pork chop, fried, lean only eaten" +22101300,"PORK CHOP, BREADED, FRIED, NS AS TO FAT","Pork chop, breaded or floured, fried, NS as to fat eaten" +22101310,"PORK CHOP, BREADED, FRIED, LEAN & FAT","Pork chop, breaded or floured, fried, lean and fat eaten" +22101320,"PORK CHOP, BREADED, FRIED, LEAN ONLY","Pork chop, breaded or floured, fried, lean only eaten" +22101400,"PORK CHOP, BATTERED, FRIED, NS AS TO FAT","Pork chop, battered, fried, NS as to fat eaten" +22101410,"PORK CHOP, BATTERED, FRIED, LEAN & FAT","Pork chop, battered, fried, lean and fat eaten" +22101420,"PORK CHOP, BATTERED, FRIED, LEAN ONLY","Pork chop, battered, fried, lean only eaten" +22101500,"PORK CHOP, STEWED, NS AS TO FAT EATEN","Pork chop, stewed, NS as to fat eaten" +22101510,"PORK CHOP, STEWED, LEAN & FAT EATEN","Pork chop, stewed, lean and fat eaten" +22101520,"PORK CHOP, STEWED, LEAN ONLY EATEN","Pork chop, stewed, lean only eaten" +22107000,"PORK CHOP, SMOKED OR CURED, COOKED, NS AS TO FAT","Pork chop, smoked or cured, cooked, NS as to fat eaten" +22107010,"PORK CHOP, SMOKED OR CURED, COOKED, LEAN & FAT","Pork chop, smoked or cured, cooked, lean and fat eaten" +22107020,"PORK CHOP, SMOKED OR CURED, COOKED, LEAN ONLY","Pork chop, smoked or cured, cooked, lean only eaten" +22201000,"PORK STEAK, NS AS TO COOKING METHOD, NS AS TO FAT","Pork steak or cutlet, NS as to cooking method, NS as to fat eaten" +22201010,"PORK STEAK, NS AS TO COOKING METHOD, LEAN & FAT","Pork steak or cutlet, NS as to cooking method, lean and fat eaten" +22201020,"PORK STEAK, NS AS TO COOKING METHOD, LEAN ONLY","Pork steak or cutlet, NS as to cooking method, lean only eaten" +22201050,"PORK STEAK OR CUTLET, BATTERED, FRIED, NS AS TO FAT","Pork steak or cutlet, battered, fried, NS as to fat eaten" +22201060,"PORK STEAK OR CUTLET, BATTERED, FRIED, LEAN & FAT","Pork steak or cutlet, battered, fried, lean and fat eaten" +22201070,"PORK STEAK OR CUTLET, BATTERED, FRIED, LEAN ONLY","Pork steak or cutlet, battered, fried, lean only eaten" +22201100,"PORK STEAK OR CUTLET, BROILED OR BAKD, NS AS TO FAT","Pork steak or cutlet, broiled or baked, NS as to fat eaten" +22201110,"PORK STEAK OR CUTLET, BROILED OR BAKED, LEAN & FAT","Pork steak or cutlet, broiled or baked, lean and fat eaten" +22201120,"PORK STEAK OR CUTLET, BROILED OR BAKED, LEAN ONLY","Pork steak or cutlet, broiled or baked, lean only eaten" +22201200,"PORK STEAK OR CUTLET, FRIED, NS AS TO FAT","Pork steak or cutlet, fried, NS as to fat eaten" +22201210,"PORK STEAK OR CUTLET, FRIED, LEAN & FAT","Pork steak or cutlet, fried, lean and fat eaten" +22201220,"PORK STEAK OR CUTLET, FRIED, LEAN ONLY","Pork steak or cutlet, fried, lean only eaten" +22201300,"PORK CUTLET, BREADED, BROILED/BAKED, NS AS TO FAT","Pork steak or cutlet, breaded or floured, broiled or baked, NS as to fat eaten" +22201310,"PORK CUTLET, BREADED, BROILED/BAKED, LEAN & FAT","Pork steak or cutlet, breaded or floured, broiled or baked, lean and fat eaten" +22201320,"PORK CUTLET, BREADED, BROILED/BAKED, LEAN ONLY","Pork steak or cutlet, breaded or floured, broiled or baked, lean only eaten" +22201400,"PORK STEAK OR CUTLET, BREADED, FRIED, NS AS TO FAT","Pork steak or cutlet, breaded or floured, fried, NS as to fat eaten" +22201410,"PORK STEAK OR CUTLET, BREADED, FRIED, LEAN & FAT","Pork steak or cutlet, breaded or floured, fried, lean and fat eaten" +22201420,"PORK STEAK OR CUTLET, BREADED, FRIED, LEAN ONLY","Pork steak or cutlet, breaded or floured, fried, lean only eaten" +22210300,"PORK, TENDERLOIN, COOKED, NS AS TO METHOD","Pork, tenderloin, cooked, NS as to cooking method" +22210310,"PORK, TENDERLOIN, BREADED, FRIED","Pork, tenderloin, breaded, fried" +22210350,"PORK, TENDERLOIN, BRAISED","Pork, tenderloin, braised" +22210400,"PORK, TENDERLOIN, BAKED","Pork, tenderloin, baked" +22210450,"PORK, TENDERLOIN, BATTERED, FRIED","Pork, tenderloin, battered, fried" +22300120,"HAM, FRIED, NS AS TO FAT","Ham, fried, NS as to fat eaten" +22300130,"HAM, FRIED, LEAN & FAT","Ham, fried, lean and fat eaten" +22300140,"HAM, FRIED, LEAN ONLY","Ham, fried, lean only eaten" +22300150,"HAM, BREADED, FRIED, NS AS TO FAT","Ham, breaded or floured, fried, NS as to fat eaten" +22300160,"HAM, BREADED, FRIED, LEAN & FAT","Ham, breaded or floured, fried, lean and fat eaten" +22300170,"HAM, BREADED, FRIED, LEAN ONLY","Ham, breaded or floured, fried, lean only eaten" +22301000,"HAM, FRESH, COOKED, NS AS TO FAT","Ham, fresh, cooked, NS as to fat eaten" +22301110,"HAM, FRESH, COOKED, LEAN & FAT","Ham, fresh, cooked, lean and fat eaten" +22301120,"HAM, FRESH, COOKED, LEAN ONLY","Ham, fresh, cooked, lean only eaten" +22311000,"HAM, SMOKED OR CURED, COOKED, NS AS TO FAT","Ham, smoked or cured, cooked, NS as to fat eaten" +22311010,"HAM, SMOKED OR CURED, COOKED, LEAN & FAT","Ham, smoked or cured, cooked, lean and fat eaten" +22311020,"HAM, SMOKED OR CURED, COOKED, LEAN ONLY","Ham, smoked or cured, cooked, lean only eaten" +22311200,"HAM, SMOKED OR CURED, LOW NA, NS AS TO FAT","Ham, smoked or cured, low sodium, cooked, NS as to fat eaten" +22311210,"HAM, SMOKED OR CURED, LOW NA, LEAN & FAT","Ham, smoked or cured, low sodium, cooked, lean and fat eaten" +22311220,"HAM, SMOKED OR CURED, LOW NA, LEAN ONLY","Ham, smoked or cured, low sodium, cooked, lean only eaten" +22311450,"HAM, PROSCIUTTO","Ham, prosciutto" +22311500,"HAM, SMOKED OR CURED, CANNED, NS AS TO FAT EATEN","Ham, smoked or cured, canned, NS as to fat eaten" +22311510,"HAM, SMOKED OR CURED, CANNED, LEAN & FAT EATEN","Ham, smoked or cured, canned, lean and fat eaten" +22311520,"HAM, SMOKED OR CURED, CANNED, LEAN ONLY EATEN","Ham, smoked or cured, canned, lean only eaten" +22321110,"HAM, SMOKED OR CURED, GROUND PATTY","Ham, smoked or cured, ground patty" +22400100,"PORK ROAST, NS AS TO CUT, NS AS TO FAT","Pork roast, NS as to cut, cooked, NS as to fat eaten" +22400110,"PORK ROAST, NS AS TO CUT, COOKED, LEAN & FAT","Pork roast, NS as to cut, cooked, lean and fat eaten" +22400120,"PORK ROAST, NS AS TO CUT, COOKED, LEAN ONLY","Pork roast, NS as to cut, cooked, lean only eaten" +22401000,"PORK ROAST, LOIN, COOKED, NS AS TO FAT","Pork roast, loin, cooked, NS as to fat eaten" +22401010,"PORK ROAST, LOIN, COOKED, LEAN & FAT","Pork roast, loin, cooked, lean and fat eaten" +22401020,"PORK ROAST, LOIN, COOKED, LEAN ONLY","Pork roast, loin, cooked, lean only eaten" +22402510,"FRIED PORK CHUNKS, P.R. (CARNE DE CERDO FRITA)","Fried pork chunks, Puerto Rican style (Carne de cerdo frita, masitas fritas)" +22411000,"PORK ROAST, SHOULDER, COOKED, NS AS TO FAT","Pork roast, shoulder, cooked, NS as to fat eaten" +22411010,"PORK ROAST, SHOULDER, COOKED, LEAN & FAT","Pork roast, shoulder, cooked, lean and fat eaten" +22411020,"PORK ROAST, SHOULDER, COOKED, LEAN ONLY","Pork roast, shoulder, cooked, lean only eaten" +22421000,"PORK ROAST, SMOKED OR CURED, COOKED, NS AS TO FAT","Pork roast, smoked or cured, cooked, NS as to fat eaten" +22421010,"PORK ROAST, SMOKED OR CURED, COOKED, LEAN & FAT","Pork roast, smoked or cured, cooked, lean and fat eaten" +22421020,"PORK ROAST, SMOKED OR CURED, COOKED, LEAN ONLY","Pork roast, smoked or cured, cooked, lean only eaten" +22431000,"PORK ROLL, CURED, FRIED","Pork roll, cured, fried" +22501010,"BACON, CANADIAN, COOKED","Canadian bacon, cooked" +22600100,"BACON, NS AS TO TYPE OF MEAT, COOKED","Bacon, NS as to type of meat, cooked" +22600200,"PORK BACON, NS AS TO FRESH/SMOKED/CURED, COOKED","Pork bacon, NS as to fresh, smoked or cured, cooked" +22601000,"PORK BACON, SMOKED OR CURED, COOKED","Pork bacon, smoked or cured, cooked" +22601040,"BACON OR SIDE PORK, FRESH, COOKED","Bacon or side pork, fresh, cooked" +22602010,"PORK BACON, SMOKED OR CURED, LOWER SODIUM","Pork bacon, smoked or cured, lower sodium" +22605010,"BACON, FORMED, LEAN MEAT ADDED, COOKED","Pork bacon, formed, lean meat added, cooked" +22621000,"SALT PORK, COOKED","Salt pork, cooked" +22621100,"FAT BACK, COOKED (INCLUDE HOG JOWL)","Fat back, cooked" +22701000,"PORK, SPARERIBS, COOKED, NS AS TO FAT EATEN","Pork, spareribs, cooked, NS as to fat eaten" +22701010,"PORK, SPARERIBS, COOKED, LEAN & FAT","Pork, spareribs, cooked, lean and fat eaten" +22701020,"PORK, SPARERIBS, COOKED, LEAN ONLY","Pork, spareribs, cooked, lean only eaten" +22701030,"PORK, SPARERIBS, BBQ, W/ SAUCE, NS FAT EATEN","Pork, spareribs, barbecued, with sauce, NS as to fat eaten" +22701040,"PORK, SPARERIBS, BBQ, W/ SAUCE, LEAN & FAT EATEN","Pork, spareribs, barbecued, with sauce, lean and fat eaten" +22701050,"PORK, SPARERIBS, BBQ, W/ SAUCE, LEAN ONLY EATEN","Pork, spareribs, barbecued, with sauce, lean only eaten" +22704010,"PORK, CRACKLINGS, COOKED","Pork, cracklings, cooked" +22705010,"PORK, EARS, TAIL, HEAD, SNOUT, MISC PARTS, COOKED","Pork ears, tail, head, snout, miscellaneous parts, cooked" +22706010,"PORK, NECK BONES, COOKED","Pork, neck bones, cooked" +22707010,"PORK, PIG'S FEET, COOKED","Pork, pig's feet, cooked" +22707020,"PORK, PIG'S FEET, PICKLED","Pork, pig's feet, pickled" +22708010,"PORK, PIG'S HOCKS, COOKED","Pork, pig's hocks, cooked" +22709010,"PORK SKIN, RINDS, DEEP-FRIED","Pork skin, rinds, deep-fried" +22709110,"PORK SKIN, BOILED","Pork skin, boiled" +22810010,"HAM, BABY, STRAINED","Ham, baby food, strained" +22820000,"MEAT STICK, BABY FOOD","Meat stick, baby food" +23000100,"LAMB, NS AS TO CUT, COOKED","Lamb, NS as to cut, cooked" +23101000,"LAMB CHOP, COOKED, NS AS TO CUT & FAT","Lamb chop, NS as to cut, cooked, NS as to fat eaten" +23101010,"LAMB CHOP, NS AS TO CUT, COOKED, LEAN & FAT","Lamb chop, NS as to cut, cooked, lean and fat eaten" +23101020,"LAMB CHOP, NS AS TO CUT, COOKED, LEAN ONLY","Lamb chop, NS as to cut, cooked, lean only eaten" +23104000,"LAMB, LOIN CHOP, COOKED, NS AS TO FAT","Lamb, loin chop, cooked, NS as to fat eaten" +23104010,"LAMB, LOIN CHOP, COOKED, LEAN & FAT","Lamb, loin chop, cooked, lean and fat eaten" +23104020,"LAMB, LOIN CHOP, COOKED, LEAN ONLY","Lamb, loin chop, cooked, lean only eaten" +23107000,"LAMB, SHOULDER CHOP, COOKED, NS AS TO FAT","Lamb, shoulder chop, cooked, NS as to fat eaten" +23107010,"LAMB, SHOULDER CHOP, COOKED, LEAN & FAT","Lamb, shoulder chop, cooked, lean and fat eaten" +23107020,"LAMB, SHOULDER CHOP, COOKED, LEAN ONLY","Lamb, shoulder chop, cooked, lean only eaten" +23110000,"LAMB, RIBS, COOKED, LEAN ONLY","Lamb, ribs, cooked, lean only eaten" +23110010,"LAMB, RIBS, COOKED, NS AS TO FAT","Lamb, ribs, cooked, NS as to fat eaten" +23110050,"LAMB, RIBS, COOKED, LEAN & FAT","Lamb, ribs, cooked, lean and fat eaten" +23111010,"LAMB HOCKS, COOKED","Lamb hocks, cooked" +23120100,"LAMB, ROAST, COOKED, NS AS TO FAT EATEN","Lamb, roast, cooked, NS as to fat eaten" +23120110,"LAMB, ROAST, COOKED, LEAN & FAT EATEN","Lamb, roast, cooked, lean and fat eaten" +23120120,"LAMB, ROAST, COOKED, LEAN ONLY EATEN","Lamb, roast, cooked, lean only eaten" +23132000,"LAMB, GROUND OR PATTY, COOKED","Lamb, ground or patty, cooked" +23150100,"GOAT, BOILED","Goat, boiled" +23150200,"GOAT, FRIED","Goat, fried" +23150250,"GOAT, BAKED","Goat, baked" +23150270,"GOAT HEAD, COOKED","Goat head, cooked" +23150300,"GOAT RIBS, COOKED","Goat ribs, cooked" +23200100,"VEAL, COOKED, NS AS TO CUT & FAT","Veal, NS as to cut, cooked, NS as to fat eaten" +23200110,"VEAL, NS AS TO CUT, COOKED, LEAN & FAT","Veal, NS as to cut, cooked, lean and fat eaten" +23200120,"VEAL, NS AS TO CUT, COOKED, LEAN ONLY","Veal, NS as to cut, cooked, lean only eaten" +23201010,"VEAL CHOP, NS AS TO COOKING METHOD, NS AS TO FAT","Veal chop, NS as to cooking method, NS as to fat eaten" +23201020,"VEAL CHOP, NS AS TO COOKING METHOD, LEAN & FAT","Veal chop, NS as to cooking method, lean and fat eaten" +23201030,"VEAL CHOP, NS AS TO COOKING METHOD, LEAN ONLY","Veal chop, NS as to cooking method, lean only eaten" +23203010,"VEAL CHOP, FRIED, NS AS TO FAT","Veal chop, fried, NS as to fat eaten" +23203020,"VEAL CHOP, FRIED, LEAN & FAT","Veal chop, fried, lean and fat eaten" +23203030,"VEAL CHOP, FRIED, LEAN ONLY","Veal chop, fried, lean only eaten" +23203100,"VEAL CHOP, BROILED, NS AS TO FAT","Veal chop, broiled, NS as to fat eaten" +23203110,"VEAL CHOP, BROILED, LEAN & FAT","Veal chop, broiled, lean and fat eaten" +23203120,"VEAL CHOP, BROILED, LEAN ONLY","Veal chop, broiled, lean only eaten" +23204010,"VEAL CUTLET, NS AS TO COOKING METHOD, NS AS TO FAT","Veal cutlet or steak, NS as to cooking method, NS as to fat eaten" +23204020,"VEAL CUTLET, NS AS TO COOKING METHOD, LEAN & FAT","Veal cutlet or steak, NS as to cooking method, lean and fat eaten" +23204030,"VEAL CUTLET, NS AS TO COOKING METHOD, LEAN ONLY","Veal cutlet or steak, NS as to cooking method, lean only eaten" +23204200,"VEAL CUTLET OR STEAK, BROILED, NS AS TO FAT","Veal cutlet or steak, broiled, NS as to fat eaten" +23204210,"VEAL CUTLET OR STEAK, BROILED, LEAN & FAT","Veal cutlet or steak, broiled, lean and fat eaten" +23204220,"VEAL CUTLET OR STEAK, BROILED, LEAN ONLY","Veal cutlet or steak, broiled, lean only eaten" +23205010,"VEAL CUTLET OR STEAK, FRIED, NS AS TO FAT","Veal cutlet or steak, fried, NS as to fat eaten" +23205020,"VEAL CUTLET OR STEAK, FRIED, LEAN & FAT","Veal cutlet or steak, fried, lean and fat eaten" +23205030,"VEAL CUTLET OR STEAK, FRIED, LEAN ONLY","Veal cutlet or steak, fried, lean only eaten" +23210010,"VEAL, ROASTED, NS AS TO FAT","Veal, roasted, NS as to fat eaten" +23210020,"VEAL, ROASTED, LEAN & FAT","Veal, roasted, lean and fat eaten" +23210030,"VEAL, ROASTED, LEAN ONLY","Veal, roasted, lean only eaten" +23220010,"VEAL, GROUND OR PATTY, COOKED","Veal, ground or patty, cooked" +23220020,"MOCK CHICKEN LEGS, COOKED","Mock chicken legs, cooked" +23220030,"VEAL PATTY, BREADED, COOKED","Veal patty, breaded, cooked" +23310000,"RABBIT, NS AS TO DOMESTIC OR WILD, COOKED","Rabbit, NS as to domestic or wild, cooked" +23311100,"RABBIT, DOMESTIC, NS AS TO COOKING METHOD","Rabbit, domestic, NS as to cooking method" +23311120,"RABBIT, NS AS TO DOMESTIC OR WILD, BREADED, FRIED","Rabbit, NS as to domestic or wild, breaded, fried" +23311200,"RABBIT, WILD, COOKED","Rabbit, wild, cooked" +23321000,"VENISON/DEER, NFS","Venison/deer, NFS" +23321050,"VENISON/DEER, CURED","Venison/deer, cured" +23321100,"VENISON/DEER, ROASTED (INCLUDE ROAST ANTELOPE)","Venison/deer, roasted" +23321200,"VENISON/DEER STEAK, COOKED, NS AS TO METHOD","Venison/deer steak, cooked, NS as to cooking method" +23321250,"VENISON/DEER STEAK, BREADED OR FLOURED, COOKED","Venison/deer steak, breaded or floured, cooked, NS as to cooking method" +23321900,"VENISON/DEER JERKY","Venison/deer jerky" +23322100,"DEER BOLOGNA","Deer bologna" +23322300,"DEER CHOP, COOKED (INCLUDE VENISON CHOP)","Deer chop, cooked" +23322350,"VENISON/DEER RIBS, COOKED","Venison/deer ribs, cooked" +23322400,"VENISON/DEER, STEWED","Venison/deer, stewed" +23323100,"MOOSE, COOKED","Moose, cooked" +23323500,"BEAR, COOKED","Bear, cooked" +23324100,"CARIBOU, COOKED","Caribou, cooked" +23326100,"BISON, COOKED","Bison, cooked" +23331100,"GROUND HOG, COOKED","Ground hog, cooked" +23332100,"OPOSSUM, COOKED","Opossum, cooked" +23333100,"SQUIRREL, COOKED","Squirrel, cooked" +23334100,"BEAVER, COOKED","Beaver, cooked" +23335100,"RACCOON, COOKED","Raccoon, cooked" +23340100,"ARMADILLO, COOKED","Armadillo, cooked" +23345100,"WILD PIG, SMOKED","Wild pig, smoked" +23350100,"OSTRICH, COOKED","Ostrich, cooked" +23410010,"LAMB, BABY, STRAINED","Lamb, baby food, strained" +23420010,"VEAL, BABY, STRAINED","Veal, baby food, strained" +24100000,"CHICKEN, NS AS TO PART, NS METHOD, SKIN","Chicken, NS as to part and cooking method, NS as to skin eaten" +24100010,"CHICKEN, NS AS TO PART, NS METHOD, W/ SKIN","Chicken, NS as to part and cooking method, skin eaten" +24100020,"CHICKEN, NS AS TO PART, NS METHOD, W/O SKIN","Chicken, NS as to part and cooking method, skin not eaten" +24102000,"CHICKEN, NS PART, ROASTED/BROILED/BAKED, NS SKIN","Chicken, NS as to part, roasted, broiled, or baked, NS as to skin eaten" +24102010,"CHICKEN, NS PART, ROASTED/BROILED/BAKED, W/ SKIN","Chicken, NS as to part, roasted, broiled, or baked, skin eaten" +24102020,"CHICKEN, NS PART, ROASTED/BROILED/BAKED, W/O SKIN","Chicken, NS as to part, roasted, broiled, or baked, skin not eaten" +24103000,"CHICKEN, STEWED, NS PART, NS SKIN","Chicken, NS as to part, stewed, NS as to skin eaten" +24103010,"CHICKEN, STEWED, NS PART, W/ SKIN","Chicken, NS as to part, stewed, skin eaten" +24103020,"CHICKEN, STEWED, NS PART, W/O SKIN","Chicken, NS as to part, stewed, skin not eaten" +24104000,"CHICKEN, FRIED, NO COATING, NS PART, NS SKIN","Chicken, NS as to part, fried, no coating, NS as to skin eaten" +24104010,"CHICKEN, FRIED, NO COATING, NS PART, W/ SKIN","Chicken, NS as to part, fried, no coating, skin eaten" +24104020,"CHICKEN, FRIED, NO COATING, NS PART, W/O SKIN","Chicken, NS as to part, fried, no coating, skin not eaten" +24107000,"CHICKEN, COATED, BKD/FRD, PPD W/ SKIN, NS SKIN EATEN","Chicken, NS as to part, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24107010,"CHICKEN, COATED, BKD/FRD, PPD W/ SKIN, SKIN EATEN","Chicken, NS as to part, coated, baked or fried, prepared with skin, skin/coating eaten" +24107020,"CHICKEN, COATED, BKD/FRD, PPD W/ SKIN, SKIN NOT EATEN","Chicken, NS as to part, coated, baked or fried, prepared with skin, skin/coating not eaten" +24107040,"CHICKEN, NS PART,COATED,BKD/FRD,PREP SKINLESS,NS COAT EATEN","Chicken, NS as to part, coated, baked or fried, prepared skinless, NS as to coating eaten" +24107050,"CHICKEN, NS PART,COATED,BKD/FRD,PREP SKINLESS,COATING EATEN","Chicken, NS as to part, coated, baked or fried, prepared skinless, coating eaten" +24107060,"CHICKEN, NS PART,COATED,BKD/FRD,PREP SKINLESS,COAT NOT EATEN","Chicken, NS as to part, coated, baked or fried, prepared skinless, coating not eaten" +24120100,"CHICKEN, BREAST, NFS","Chicken, breast, NS as to cooking method, NS as to skin eaten" +24120110,"CHICKEN, BREAST, NS AS TO COOKING METHOD, W/SKIN","Chicken, breast, NS as to cooking method, skin eaten" +24120120,"CHICKEN, BREAST, NS AS TO COOKING METHOD, W/O SKIN","Chicken, breast, NS as to cooking method, skin not eaten" +24122100,"CHICKEN, BREAST, ROASTED/BROILED/BAKED, NS SKIN","Chicken, breast, roasted, broiled, or baked, NS as to skin eaten" +24122110,"CHICKEN, BREAST, ROASTED/BROILED/BAKED, W/ SKIN","Chicken, breast, roasted, broiled, or baked, skin eaten" +24122120,"CHICKEN, BREAST, ROASTED/BROILED/BAKED, W/O SKIN","Chicken, breast, roasted, broiled, or baked, skin not eaten" +24123100,"CHICKEN, BREAST, STEWED, NS AS TO SKIN","Chicken, breast, stewed, NS as to skin eaten" +24123110,"CHICKEN, BREAST, STEWED, W/ SKIN","Chicken, breast, stewed, skin eaten" +24123120,"CHICKEN, BREAST, STEWED, W/O SKIN","Chicken, breast, stewed, skin not eaten" +24124100,"CHICKEN, BREAST, FRIED, NO COATING, NS AS TO SKIN","Chicken, breast, fried, no coating, NS as to skin eaten" +24124110,"CHICKEN, BREAST, FRIED, NO COATING, W/ SKIN","Chicken, breast, fried, no coating, skin eaten" +24124120,"CHICKEN, BREAST, FRIED, NO COATING, W/O SKIN","Chicken, breast, fried, no coating, skin not eaten" +24127100,"CHICKEN, BREAST,COATED,BKD/FRD,PPD W/ SKIN,NS SKIN EATEN","Chicken, breast, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24127110,"CHICKEN, BREAST,COATED,BKD/FRD,PPD W/ SKIN, SKIN EATEN","Chicken, breast, coated, baked or fried, prepared with skin, skin/coating eaten" +24127120,"CHICKEN, BREAST,COATED,BKD/FRD,PPD W/ SKIN, SKIN NOT EATEN","Chicken, breast, coated, baked or fried, prepared with skin, skin/coating not eaten" +24127125,"CHIC, BREAST, FF, COATED/BAKED/FRIED, PREP SKIN,NS SKIN EATE","Chicken, breast, from fast food, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24127130,"CHIC, BREAST, FF, COATED, BAKED/FRIED, PREP SKIN,SKIN EATEN","Chicken, breast, from fast food, coated, baked or fried, prepared with skin, skin/coating eaten" +24127135,"CHICK,BREAST,FF,COATED, BAKED/ FRIED,PREP SKIN,NO SKIN EATEN","Chicken, breast, from fast food, coated, baked or fried, prepared with skin, skin/coating not eaten" +24127140,"CHICKEN,BREAST,COATED,BKD/FRD,PPD SKINLESS,NS COAT EATEN","Chicken, breast, coated, baked or fried, prepared skinless, NS as to coating eaten" +24127150,"CHICKEN,BREAST,COATED,BKD/FRD,PPD SKINLESS,COAT EATEN","Chicken, breast, coated, baked or fried, prepared skinless, coating eaten" +24127160,"CHICKEN,BREAST,COATED,BKD/FRD,PPD SKINLESS,COAT NOT EATEN","Chicken, breast, coated, baked or fried, prepared skinless, coating not eaten" +24130200,"CHICKEN, LEG, NFS","Chicken, leg (drumstick and thigh), NS as to cooking method, NS as to skin eaten" +24130210,"CHICKEN, LEG, NS AS TO COOKING METHOD, W/ SKIN","Chicken, leg (drumstick and thigh), NS as to cooking method, skin eaten" +24130220,"CHICKEN, LEG, NS AS TO COOKING METHOD, W/O SKIN","Chicken, leg (drumstick and thigh), NS as to cooking method, skin not eaten" +24132200,"CHICKEN, LEG, ROASTED/BROILED/BAKED, NS SKIN","Chicken, leg (drumstick and thigh), roasted, broiled, or baked, NS as to skin eaten" +24132210,"CHICKEN, LEG, ROASTED/BROILED/BAKED, W/ SKIN","Chicken, leg (drumstick and thigh), roasted, broiled, or baked, skin eaten" +24132220,"CHICKEN, LEG, ROASTED/BROILED/BAKED, W/O SKIN","Chicken, leg (drumstick and thigh), roasted, broiled, or baked, skin not eaten" +24133200,"CHICKEN, LEG, STEWED, NS AS TO SKIN","Chicken, leg (drumstick and thigh), stewed, NS as to skin eaten" +24133210,"CHICKEN, LEG, STEWED, W/ SKIN","Chicken, leg (drumstick and thigh), stewed, skin eaten" +24133220,"CHICKEN, LEG, STEWED, W/O SKIN","Chicken, leg (drumstick and thigh), stewed, skin not eaten" +24134200,"CHICKEN, LEG, FRIED, NO COATING, NS AS TO SKIN","Chicken, leg (drumstick and thigh), fried, no coating, NS as to skin eaten" +24134210,"CHICKEN, LEG, FRIED, NO COATING, W/ SKIN","Chicken, leg (drumstick and thigh), fried, no coating, skin eaten" +24134220,"CHICKEN, LEG, FRIED, NO COATING, W/O SKIN","Chicken, leg (drumstick and thigh), fried, no coating, skin not eaten" +24137200,"CHICKEN,LEG,COATED,BKD/FRD,PPD W/SKIN,NS SKIN EATEN","Chicken, leg (drumstick and thigh), coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24137210,"CHICKEN, LEG,COATED,BKD/FRD,PPD W/ SKIN, SKIN EATEN","Chicken, leg (drumstick and thigh), coated, baked or fried, prepared with skin, skin/coating eaten" +24137220,"CHICKEN, LEG,COATED,BKD/FRD,PPD W/ SKIN, SKIN NOT EATEN","Chicken, leg (drumstick and thigh), coated, baked or fried, prepared with skin, skin/coating not eaten" +24137240,"CHICKEN,LEG,COATED,BKD/FRD,PPD SKINLESS,NS COAT EATEN","Chicken, leg (drumstick and thigh), coated, baked or fried, prepared skinless, NS as to coating eaten" +24137250,"CHICKEN,LEG,COATED,BKD/FRD,PPD SKINLESS,COAT EATEN","Chicken, leg (drumstick and thigh), coated, baked or fried, prepared skinless, coating eaten" +24137260,"CHICKEN,LEG,COATED,BKD/FRD,PPD SKINLESS,COAT NOT EATEN","Chicken, leg (drumstick and thigh), coated, baked or fried, prepared skinless, coating not eaten" +24140200,"CHICKEN, DRUMSTICK, NFS","Chicken, drumstick, NS as to cooking method, NS as to skin eaten" +24140210,"CHICKEN, DRUMSTICK, NS AS TO COOKING METHOD,W/ SKIN","Chicken, drumstick, NS as to cooking method, skin eaten" +24140220,"CHICKEN, DRUMSTICK, NS COOKING METHOD, W/O SKIN","Chicken, drumstick, NS as to cooking method, skin not eaten" +24142200,"CHICKEN, DRUMSTICK, ROASTED/BROILED/BAKED, NS SKIN","Chicken, drumstick, roasted, broiled, or baked, NS as to skin eaten" +24142210,"CHICKEN, DRUMSTICK, ROASTED/BROILED/BAKED, W/ SKIN","Chicken, drumstick, roasted, broiled, or baked, skin eaten" +24142220,"CHICKEN, DRUMSTICK, ROASTED/BROILED/BAKED, W/O SKIN","Chicken, drumstick, roasted, broiled, or baked, skin not eaten" +24143200,"CHICKEN, DRUMSTICK, STEWED, NS AS TO SKIN","Chicken, drumstick, stewed, NS as to skin eaten" +24143210,"CHICKEN, DRUMSTICK, STEWED, W/ SKIN","Chicken, drumstick, stewed, skin eaten" +24143220,"CHICKEN, DRUMSTICK, STEWED, W/O SKIN","Chicken, drumstick, stewed, skin not eaten" +24144200,"CHICKEN, DRUMSTICK, FRIED, NO COATING,NS AS TO SKIN","Chicken, drumstick, fried, no coating, NS as to skin eaten" +24144210,"CHICKEN, DRUMSTICK, FRIED, NO COATING, W/ SKIN","Chicken, drumstick, fried, no coating, skin eaten" +24144220,"CHICKEN, DRUMSTICK, FRIED, NO COATING, W/O SKIN","Chicken, drumstick, fried, no coating, skin not eaten" +24147200,"CHICKEN,DRUMSTICK,COATED,BKD/FRD,PPD W/SKIN,NS SKIN EAT","Chicken, drumstick, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24147210,"CHICKEN,DRUMSTICK,COATED,BKD/FRD,PPD W/SKIN, SKIN EAT","Chicken, drumstick, coated, baked or fried, prepared with skin, skin/coating eaten" +24147220,"CHICKEN,DRUMSTICK,COATED,BKD/FRD,PPD W/SKIN, SKIN NOT EAT","Chicken, drumstick, coated, baked or fried, prepared with skin, skin/coating not eaten" +24147225,"CHICK,DRUMSTICK,FF,COATED,BAKED/FRIED,PREP SKIN,NS SKIN EAT","Chicken, drumstick, from fast food, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24147230,"CHIC, DRUMSTICK,FF,COATED, BAKED/FRIED,PREP SKIN,SKIN EATEN","Chicken, drumstick, from fast food, coated, baked or fried, prepared with skin, skin/coating eaten" +24147235,"CHICK,DRUMSTICK,FF,COATED,BAKED/FRIED,PREP SKIN,SKIN NOT EAT","Chicken, drumstick, from fast food, coated, baked or fried, prepared with skin, skin/coating not eaten" +24147240,"CHICKEN,DRUMSTICK,COATED,BKD/FRD,PPD SKINLESS,NS COAT EAT","Chicken, drumstick, coated, baked or fried, prepared skinless, NS as to coating eaten" +24147250,"CHICKEN,DRUMSTICK,COATED,BKD/FRD,PPD SKINLESS, COAT EAT","Chicken, drumstick, coated, baked or fried, prepared skinless, coating eaten" +24147260,"CHICKEN,DRUMSTICK,COATED,BKD/FRD,PPD SKINLESS, COAT NOT EAT","Chicken, drumstick, coated, baked or fried, prepared skinless, coating not eaten" +24150200,"CHICKEN, THIGH, NFS","Chicken, thigh, NS as to cooking method, NS as to skin eaten" +24150210,"CHICKEN, THIGH, NS AS TO COOKING METHOD, W/ SKIN","Chicken, thigh, NS as to cooking method, skin eaten" +24150220,"CHICKEN, THIGH, NS AS TO COOKING METHOD, W/O SKIN","Chicken, thigh, NS as to cooking method, skin not eaten" +24152200,"CHICKEN, THIGH, ROASTED/BROILED/BAKED, NS SKIN","Chicken, thigh, roasted, broiled, or baked, NS as to skin eaten" +24152210,"CHICKEN, THIGH, ROASTED/BROILED/BAKED, W/ SKIN","Chicken, thigh, roasted, broiled, or baked, skin eaten" +24152220,"CHICKEN, THIGH, ROASTED/BROILED/BAKED, W/O SKIN","Chicken, thigh, roasted, broiled, or baked, skin not eaten" +24153200,"CHICKEN, THIGH, STEWED, NS AS TO SKIN","Chicken, thigh, stewed, NS as to skin eaten" +24153210,"CHICKEN, THIGH, STEWED, W/ SKIN","Chicken, thigh, stewed, skin eaten" +24153220,"CHICKEN, THIGH, STEWED, W/O SKIN","Chicken, thigh, stewed, skin not eaten" +24154200,"CHICKEN, THIGH, FRIED, NO COATING, NS AS TO SKIN","Chicken, thigh, fried, no coating, NS as to skin eaten" +24154210,"CHICKEN, THIGH, FRIED, NO COATING, W/ SKIN","Chicken, thigh, fried, no coating, skin eaten" +24154220,"CHICKEN, THIGH, FRIED, NO COATING, W/O SKIN","Chicken, thigh, fried, no coating, skin not eaten" +24157200,"CHICKEN,THIGH,COATED,BKD/FRD,PPD W/SKIN,NS SKIN EATEN","Chicken, thigh, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24157210,"CHICKEN,THIGH,COATED,BKD/FRD,PPD W/SKIN, SKIN EATEN","Chicken, thigh, coated, baked or fried, prepared with skin, skin/coating eaten" +24157220,"CHICKEN,THIGH,COATED,BKD/FRD,PPD W/SKIN, SKIN NOT EATEN","Chicken, thigh, coated, baked or fried, prepared with skin, skin/coating not eaten" +24157225,"CHIC, THIGH, FF, COATED, BAKED/ FRIED, PREP SKIN,NS SKIN EAT","Chicken, thigh, from fast food, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24157230,"CHICK, THIGH, FF, COATED, BAKED OR FRIED, PREP SKIN,SKIN EAT","Chicken, thigh, from fast food, coated, baked or fried, prepared with skin, skin/coating eaten" +24157235,"CHICK,THIGH,FF,COATED,BAKED/BROILED,PREP SKIN,SKIN NOT EATEN","Chicken, thigh, from fast food, coated, baked or broiled, prepared with skin, skin/coating not eaten" +24157240,"CHICKEN,THIGH,COATED,BKD/FRD,PPD SKINLESS,NS COAT EATEN","Chicken, thigh, coated, baked or fried, prepared skinless, NS as to coating eaten" +24157250,"CHICKEN,THIGH,COATED,BKD/FRD,PPD SKINLESS, COAT EATEN","Chicken, thigh, coated, baked or fried, prepared skinless, coating eaten" +24157260,"CHICKEN,THIGH,COATED,BKD/FRD,PPD SKINLESS, COAT NOT EATEN","Chicken, thigh, coated, baked or fried, prepared skinless, coating not eaten" +24160100,"CHICKEN, WING, NFS","Chicken, wing, NS as to cooking method, NS as to skin eaten" +24160110,"CHICKEN, WING, NS AS TO COOKING METHOD, W/ SKIN","Chicken, wing, NS as to cooking method, skin eaten" +24160120,"CHICKEN, WING, NS AS TO COOKING METHOD, W/O SKIN","Chicken, wing, NS as to cooking method, skin not eaten" +24162100,"CHICKEN, WING, ROASTED/BROILED/BAKED, NS SKIN","Chicken, wing, roasted, broiled, or baked, NS as to skin eaten" +24162110,"CHICKEN, WING, ROASTED/BROILED/BAKED, W/ SKIN","Chicken, wing, roasted, broiled, or baked, skin eaten" +24162120,"CHICKEN, WING, ROASTED/BROILED/BAKED, W/O SKIN","Chicken, wing, roasted, broiled, or baked, skin not eaten" +24163100,"CHICKEN, WING, STEWED, NS AS TO SKIN","Chicken, wing, stewed, NS as to skin eaten" +24163110,"CHICKEN, WING, STEWED, W/ SKIN","Chicken, wing, stewed, skin eaten" +24163120,"CHICKEN, WING, STEWED, W/O SKIN","Chicken, wing, stewed, skin not eaten" +24164100,"CHICKEN, WING, FRIED, NO COATING, NS AS TO SKIN","Chicken, wing, fried, no coating, NS as to skin eaten" +24164110,"CHICKEN, WING, FRIED, NO COATING, W/ SKIN","Chicken, wing, fried, no coating, skin eaten" +24164120,"CHICKEN, WING, FRIED, NO COATING, W/O SKIN","Chicken, wing, fried, no coating, skin not eaten" +24167100,"CHICKEN,WING,COATED,BKD/FRD,PPD W/SKIN,NS SKIN EATEN","Chicken, wing, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24167110,"CHICKEN,WING,COATED,BKD/FRD,PPD W/SKIN, SKIN EATEN","Chicken, wing, coated, baked or fried, prepared with skin, skin/coating eaten" +24167120,"CHICKEN,WING,COATED,BKD/FRD,PPD W/SKIN, SKIN NOT EATEN","Chicken, wing, coated, baked or fried, prepared with skin, skin/coating not eaten" +24167125,"CHIC, WING, FF, COATED, BAKED/FRIED, PREP SKIN,NS SKIN EATEN","Chicken, wing, from fast food, coated, baked or fried, prepared with skin, NS as to skin/coating eaten" +24167130,"CHIC, WING, FF, COATED, BAKED/FRIED, PREP SKIN, SKIN EATEN","Chicken, wing, from fast food, coated, baked or fried, prepared with skin, skin/coating eaten" +24167135,"CHIC, WING, FF, COATED,BAKED/FRIED, PREP SKIN, SKIN NO EATEN","Chicken, wing, from fast food, coated, baked or fried, prepared with skin, skin/coating not eaten" +24170200,"CHICKEN, BACK","Chicken, back" +24180200,"CHICKEN, NECK OR RIBS, NFS","Chicken, neck or ribs" +24198340,"CHICKEN TAIL","Chicken, tail" +24198440,"CHICKEN SKIN","Chicken skin" +24198500,"CHICKEN FEET","Chicken feet" +24198570,"CHICKEN, CANNED, MEAT ONLY, LIGHT & DARK MEAT","Chicken, canned, meat only" +24198670,"CHICKEN ROLL, ROASTED, LIGHT & DARK MEAT","Chicken, chicken roll, roasted" +24198690,"CHICKEN PATTY, FILLET, OR TENDERS, BREADED, COOKED, FAST FD","Chicken patty, fillet, or tenders, breaded, cooked, from fast food / restaurant" +24198700,"CHICKEN PATTY/FILLET/TENDERS, BREADED, COOKED","Chicken patty, fillet, or tenders, breaded, cooked" +24198710,"CHICKEN PATTY W/ CHEESE, BREADED, COOKED","Chicken patty with cheese, breaded, cooked" +24198720,"CHICKEN, GROUND","Chicken, ground" +24198730,"CHICKEN NUGGETS, FROM FAST FOOD / RESTAURANT","Chicken nuggets, from fast food / restaurant" +24198740,"CHICKEN NUGGETS","Chicken nuggets" +24198840,"FRIED CHICKEN CHUNKS, P. R. (CHICHARRONES DE POLLO)","Fried chicken chunks, Puerto Rican style (Chicharrones de pollo)" +24201000,"TURKEY, NFS","Turkey, NFS" +24201010,"TURKEY, LIGHT MEAT, COOKED, NS AS TO SKIN","Turkey, light meat, cooked, NS as to skin eaten" +24201020,"TURKEY, LIGHT MEAT, COOKED, W/O SKIN","Turkey, light meat, cooked, skin not eaten" +24201030,"TURKEY, LIGHT MEAT, COOKED, W/ SKIN","Turkey, light meat, cooked, skin eaten" +24201050,"TURKEY, LIGHT, BREADED, BAKED/FRIED, NS AS TO SKIN","Turkey, light meat, breaded, baked or fried, NS as to skin eaten" +24201060,"TURKEY, LIGHT MEAT, BREADED, BAKED/FRIED, W/O SKIN","Turkey, light meat, breaded, baked or fried, skin not eaten" +24201070,"TURKEY, LIGHT MEAT, BREADED, BAKED/FRIED, W/ SKIN","Turkey, light meat, breaded, baked or fried, skin eaten" +24201110,"TURKEY, LIGHT MEAT, ROASTED, NS AS TO SKIN","Turkey, light meat, roasted, NS as to skin eaten" +24201120,"TURKEY, LIGHT MEAT, ROASTED, W/O SKIN","Turkey, light meat, roasted, skin not eaten" +24201130,"TURKEY, LIGHT MEAT, ROASTED, W/ SKIN","Turkey, light meat, roasted, skin eaten" +24201210,"TURKEY, DARK MEAT, ROASTED, NS AS TO SKIN","Turkey, dark meat, roasted, NS as to skin eaten" +24201220,"TURKEY, DARK MEAT, ROASTED, W/O SKIN","Turkey, dark meat, roasted, skin not eaten" +24201230,"TURKEY, DARK MEAT, ROASTED, W/ SKIN","Turkey, dark meat, roasted, skin eaten" +24201310,"TURKEY, LIGHT & DARK MEAT, ROASTED, NS AS TO SKIN","Turkey, light and dark meat, roasted, NS as to skin eaten" +24201320,"TURKEY, LIGHT & DARK MEAT, ROASTED, W/O SKIN","Turkey, light and dark meat, roasted, skin not eaten" +24201330,"TURKEY, LIGHT & DARK MEAT, ROASTED, W/ SKIN","Turkey, light and dark meat, roasted, skin eaten" +24201350,"TURKEY, LT/DK MEAT, BATTERED, FRIED, NS AS TO SKIN","Turkey, light or dark meat, battered, fried, NS as to skin eaten" +24201360,"TURKEY, LIGHT/DARK MEAT, BATTERED, FRIED, W/O SKIN","Turkey, light or dark meat, battered, fried, skin not eaten" +24201370,"TURKEY, LIGHT/DARK MEAT, BATTERED, FRIED, W/ SKIN","Turkey, light or dark meat, battered, fried, skin eaten" +24201400,"TURKEY, LIGHT/DARK MEAT, STEWED, NS AS TO SKIN","Turkey, light or dark meat, stewed, NS as to skin eaten" +24201410,"TURKEY, LIGHT/DARK MEAT, STEWED, W/O SKIN","Turkey, light or dark meat, stewed, skin not eaten" +24201420,"TURKEY, LIGHT/DARK MEAT, STEWED, W/ SKIN","Turkey light or dark meat, stewed, skin eaten" +24201500,"TURKEY, SMOKED, NS AS TO SKIN","Turkey, light or dark meat, smoked, cooked, NS as to skin eaten" +24201510,"TURKEY, SMOKED, SKIN EATEN","Turkey, light or dark meat, smoked, cooked, skin eaten" +24201520,"TURKEY, SMOKED, SKIN NOT EATEN","Turkey, light or dark meat, smoked, cooked, skin not eaten" +24202000,"TURKEY, DRUMSTICK, COOKED, NS AS TO SKIN","Turkey, drumstick, cooked, NS as to skin eaten" +24202010,"TURKEY, DRUMSTICK, COOKED, W/O SKIN","Turkey, drumstick, cooked, skin not eaten" +24202020,"TURKEY, DRUMSTICK, COOKED, W/ SKIN","Turkey, drumstick, cooked, skin eaten" +24202050,"TURKEY, DRUMSTICK, ROASTED, NS AS TO SKIN","Turkey, drumstick, roasted, NS as to skin eaten" +24202060,"TURKEY, DRUMSTICK, ROASTED, W/O SKIN","Turkey, drumstick, roasted, skin not eaten" +24202070,"TURKEY, DRUMSTICK, ROASTED, W/ SKIN","Turkey, drumstick, roasted, skin eaten" +24202120,"TURKEY, DRUMSTICK, SMOKED, SKIN EATEN","Turkey, drumstick, smoked, cooked, skin eaten" +24202450,"TURKEY, THIGH, COOKED, NS AS TO SKIN","Turkey, thigh, cooked, NS as to skin eaten" +24202460,"TURKEY, THIGH, COOKED, W/ SKIN","Turkey, thigh, cooked, skin eaten" +24202500,"TURKEY, THIGH, COOKED, W/O SKIN","Turkey, thigh, cooked, skin not eaten" +24202600,"TURKEY, NECK, COOKED","Turkey, neck, cooked" +24203000,"TURKEY, WING, COOKED, NS AS TO SKIN","Turkey, wing, cooked, NS as to skin eaten" +24203010,"TURKEY, WING, COOKED, W/O SKIN","Turkey, wing, cooked, skin not eaten" +24203020,"TURKEY, WING, COOKED, W/ SKIN","Turkey, wing, cooked, skin eaten" +24203120,"TURKEY, WING, SMOKED, COOKED, SKIN EATEN","Turkey, wing, smoked, cooked, skin eaten" +24204000,"TURKEY, ROLLED ROAST, LIGHT OR DARK MEAT, COOKED","Turkey, rolled roast, light or dark meat, cooked" +24205000,"TURKEY, TAIL, COOKED","Turkey, tail, cooked" +24205100,"TURKEY, BACK, COOKED","Turkey, back, cooked" +24206000,"TURKEY, CANNED","Turkey, canned" +24207000,"TURKEY, GROUND","Turkey, ground" +24208000,"TURKEY NUGGETS","Turkey, nuggets" +24208500,"TURKEY BACON, COOKED","Turkey bacon, cooked" +24300100,"DUCK, COOKED, NS AS TO SKIN","Duck, cooked, NS as to skin eaten" +24300110,"DUCK, COOKED, W/ SKIN","Duck, cooked, skin eaten" +24300120,"DUCK, COOKED, W/O SKIN","Duck, cooked, skin not eaten" +24301000,"DUCK, ROASTED, NS AS TO SKIN","Duck, roasted, NS as to skin eaten" +24301010,"DUCK, ROASTED, W/ SKIN","Duck, roasted, skin eaten" +24301020,"DUCK, ROASTED, W/O SKIN","Duck, roasted, skin not eaten" +24301210,"DUCK, BATTERED, FRIED","Duck, battered, fried" +24302010,"DUCK, PRESSED, CHINESE","Duck, pressed, Chinese" +24311010,"GOOSE, WILD, ROASTED","Goose, wild, roasted" +24400000,"CORNISH GAME HEN, COOKED, NS AS TO SKIN","Cornish game hen, cooked, NS as to skin eaten" +24400010,"CORNISH GAME HEN, COOKED, W/ SKIN","Cornish game hen, cooked, skin eaten" +24400020,"CORNISH GAME HEN, COOKED, W/O SKIN","Cornish game hen, cooked, skin not eaten" +24401000,"CORNISH GAME HEN, ROASTED, NS AS TO SKIN","Cornish game hen, roasted, NS as to skin eaten" +24401010,"CORNISH GAME HEN, ROASTED, W/ SKIN","Cornish game hen, roasted, skin eaten" +24401020,"CORNISH GAME HEN, ROASTED, W/O SKIN","Cornish game hen, roasted, skin not eaten" +24402100,"DOVE, COOKED, NS AS TO COOKING METHOD","Dove, cooked, NS as to cooking method" +24402110,"DOVE, FRIED","Dove, fried" +24403100,"QUAIL, COOKED","Quail, cooked" +24404100,"PHEASANT, COOKED","Pheasant, cooked" +24701000,"CHICKEN, BABY, NS AS TO STRAINED OR JUNIOR","Chicken, baby food, NS as to strained or junior" +24701010,"CHICKEN, BABY, STRAINED","Chicken, baby food, strained" +24701020,"CHICKEN, BABY, JUNIOR","Chicken, baby food, junior" +24703000,"TURKEY, BABY, NS AS TO STRAINED OR JUNIOR","Turkey, baby food, NS as to strained or junior" +24703010,"TURKEY, BABY, STRAINED","Turkey, baby food, strained" +24703020,"TURKEY, BABY, JUNIOR","Turkey, baby food, junior" +24705010,"CHICKEN STICK, BABY FOOD","Chicken stick, baby food" +24706010,"TURKEY STICK, BABY FOOD","Turkey stick, baby food" +25110120,"BEEF LIVER, BRAISED","Beef liver, braised" +25110140,"BEEF LIVER, FRIED","Beef liver, fried" +25110420,"CHICKEN LIVER, BRAISED","Chicken liver, braised" +25110450,"CHICKEN LIVER, FRIED","Chicken liver, fried" +25112200,"LIVER PASTE OR PATE, CHICKEN (INCLUDE PATE, NFS)","Liver paste or pate, chicken" +25120000,"HEART, COOKED","Heart, cooked" +25130000,"KIDNEY, COOKED","Kidney, cooked" +25140110,"SWEETBREADS, COOKED","Sweetbreads, cooked" +25150000,"BRAINS, COOKED","Brains, cooked" +25160000,"TONGUE, COOKED","Tongue, cooked" +25160110,"TONGUE, SMOKED,CURED OR PICKLED, COOKED","Tongue, smoked, cured, or pickled, cooked" +25160130,"TONGUE POT ROAST, P.R. (LENGUA AL CALDERO)","Tongue pot roast, Puerto Rican style (Lengua al caldero)" +25170110,"TRIPE, COOKED","Tripe, cooked" +25170210,"CHITTERLINGS, COOKED","Chitterlings, cooked" +25170310,"HOG MAWS (STOMACH) COOKED","Hog maws (stomach), cooked" +25170420,"GIZZARD, COOKED","Gizzard, cooked" +25210110,"FRANKFURTER, WIENER OR HOT DOG, NFS","Frankfurter, wiener, or hot dog, NFS" +25210150,"FRANKFURTER OR HOT DOG, CHEESE-FILLED","Frankfurter or hot dog, cheese-filled" +25210210,"FRANKFURTER OR HOT DOG, BEEF","Frankfurter or hot dog, beef" +25210220,"FRANKFURTER OR HOT DOG, BEEF & PORK","Frankfurter or hot dog, beef and pork" +25210240,"FRANFURTER/HOT DOG, BEEF & PORK, LIGHT","Frankfurter or hot dog, beef and pork, reduced fat or light" +25210250,"FRANKFURTER OR HOT DOG, MEAT & POULTRY, FAT FREE","Frankfurter or hot dog, meat and poultry, fat free" +25210280,"FRANKFURTER OR HOT DOG, MEAT & POULTRY","Frankfurter or hot dog, meat and poultry" +25210290,"FRANKFURTER OR HOT DOG, MEAT & POULTRY, LIGHT","Frankfurter or hot dog, meat and poultry, reduced fat or light" +25210310,"FRANKFURTER OR HOT DOG, CHICKEN","Frankfurter or hot dog, chicken" +25210410,"FRANKFURTER OR HOT DOG, TURKEY","Frankfurter or hot dog, turkey" +25210620,"FRANKFURTER OR HOT DOG, BEEF, REDUCED FAT OR LIGHT","Frankfurter or hot dog, beef, reduced fat or light" +25210750,"FRANKFURTER OR HOT DOG, REDUCED FAT OR LIGHT, NFS","Frankfurter or hot dog, reduced fat or light, NFS" +25220010,"COLD CUT, NFS","Cold cut, NFS" +25220105,"BEEF SAUSAGE","Beef sausage" +25220106,"BEEF SAUSAGE, REDUCED FAT","Beef sausage, reduced fat" +25220150,"BEEF SAUSAGE WITH CHEESE","Beef sausage with cheese" +25220210,"BLOOD SAUSAGE","Blood sausage" +25220350,"BRATWURST","Bratwurst" +25220360,"BRATWURST W/ CHEESE","Bratwurst, with cheese" +25220390,"BOLOGNA, BEEF, LOW FAT","Bologna, beef, lowfat" +25220400,"BOLOGNA, PORK AND BEEF","Bologna, pork and beef" +25220410,"BOLOGNA, NFS","Bologna, NFS" +25220420,"BOLOGNA, LEBANON","Bologna, Lebanon" +25220430,"BOLOGNA, BEEF","Bologna, beef" +25220440,"BOLOGNA, TURKEY","Bologna, turkey" +25220450,"BOLOGNA RING, SMOKED","Bologna ring, smoked" +25220460,"BOLOGNA, PORK","Bologna, pork" +25220470,"BOLOGNA, BEEF, LOWER SODIUM","Bologna, beef, lower sodium" +25220480,"BOLOGNA, CHICKEN, BEEF, & PORK","Bologna, chicken, beef, and pork" +25220490,"BOLOGNA, W/ CHEESE","Bologna, with cheese" +25220500,"BOLOGNA, BEEF & PORK, LOWFAT","Bologna, beef and pork, lowfat" +25220510,"CAPICOLA","Capicola" +25220650,"TURKEY OR CHICKEN AND BEEF SAUSAGE","Turkey or chicken and beef sausage" +25220710,"CHORIZO","Chorizo" +25220910,"HEAD CHEESE","Head cheese" +25221110,"KNOCKWURST","Knockwurst" +25221210,"MORTADELLA","Mortadella" +25221250,"PEPPERONI","Pepperoni" +25221310,"POLISH SAUSAGE","Polish sausage" +25221350,"ITALIAN SAUSAGE","Italian sausage" +25221400,"SAUSAGE (NOT COLD CUT), NFS","Sausage (not cold cut), NFS" +25221405,"PORK SAUSAGE","Pork sausage" +25221406,"PORK SAUSAGE, REDUCED FAT","Pork sausage, reduced fat" +25221450,"PORK SAUSAGE RICE LINKS","Pork sausage rice links" +25221460,"PORK & BEEF SAUSAGE","Pork and beef sausage" +25221500,"SALAMI, NFS","Salami, NFS" +25221510,"SALAMI, SOFT, COOKED","Salami, soft, cooked" +25221520,"SALAMI, DRY OR HARD","Salami, dry or hard" +25221530,"SALAMI, BEEF","Salami, beef" +25221610,"SCRAPPLE, COOKED","Scrapple, cooked" +25221710,"SOUSE","Souse" +25221810,"THURINGER (INCLUDE SUMMER SAUSAGE)","Thuringer" +25221830,"TURKEY OR CHICKEN SAUSAGE","Turkey or chicken sausage" +25221860,"TURKEY OR CHICKEN SAUSAGE, REDUCED FAT","Turkey or chicken sausage, reduced fat" +25221870,"TURKEY AND PORK SAUSAGE","Turkey or chicken and pork sausage" +25221880,"TURKEY OR CHICKEN, PORK, AND BEEF SAUSAGE, REDUCED FAT","Turkey or chicken, pork, and beef sausage, reduced fat" +25221910,"VIENNA SAUSAGE, CANNED","Vienna sausage, canned" +25221950,"PICKLED SAUSAGE","Pickled sausage" +25230110,"LUNCHEON MEAT, NFS","Luncheon meat, NFS" +25230210,"HAM, SLICED, PREPACKAGED OR DELI, LUNCHEON MEAT","Ham, sliced, prepackaged or deli, luncheon meat" +25230220,"HAM, SLICED, LOW SALT, PREPACKAGED/DELI, LUNCH MEAT","Ham, sliced, low salt, prepackaged or deli, luncheon meat" +25230230,"HAM, SLICED, EXTRA LEAN, PREPACKAGED/DELI","Ham, sliced, extra lean, prepackaged or deli, luncheon meat" +25230235,"HAM, SLICED, EXTRA LEAN, LOWER SODIUM, PREPACKAGED OR DELI","Ham, sliced, extra lean, lower sodium, prepackaged or deli, luncheon meat" +25230310,"CHICKEN/TURKEY LOAF, PREPACK/DELI, LUNCHEON MEAT","Chicken or turkey loaf, prepackaged or deli, luncheon meat" +25230410,"HAM LOAF, LUNCHEON MEAT","Ham loaf, luncheon meat" +25230430,"HAM & CHEESE LOAF","Ham and cheese loaf" +25230450,"HONEY LOAF","Honey loaf" +25230510,"HAM,LUNCH MEAT,CHOP,MINCED,PRESSD,MINCED,NOT CANNED","Ham, luncheon meat, chopped, minced, pressed, spiced, not canned" +25230520,"HAM, LUNCHEON MEAT, CHOPPED, SPICED,LOWFAT, NOT CAN","Ham, luncheon meat, chopped, minced, pressed, spiced, lowfat, not canned" +25230530,"HAM/PORK , LUNCHEON MEAT, CHOPPED, CAN (INCL SPAM)","Ham and pork, luncheon meat, chopped, minced, pressed, spiced, canned" +25230540,"HAM, PORK & CHICKEN, LUNCHEON MEAT, CHOPPED, CANNED","Ham, pork and chicken, luncheon meat, chopped, minced, pressed, spiced, canned" +25230550,"HAM, PORK & CHICKEN, LUNCHEON MEAT, CHOPPED, CAN, RED SODIUM","Ham, pork, and chicken, luncheon meat, chopped, minced, pressed, spiced, canned, reduced sodium" +25230560,"LIVERWURST","Liverwurst" +25230610,"LUNCHEON LOAF (OLIVE, PICKLE OR PIMIENTO)","Luncheon loaf (olive, pickle, or pimiento)" +25230710,"SANDWICH LOAF, LUNCHEON MEAT","Sandwich loaf, luncheon meat" +25230790,"TURKEY HAM, SLICED, XTRA LEAN, PKG'D, DELI","Turkey ham, sliced, extra lean, prepackaged or deli, luncheon meat" +25230800,"TURKEY HAM","Turkey ham" +25230810,"VEAL LOAF","Veal loaf" +25230820,"TURKEY PASTRAMI","Turkey pastrami" +25230840,"TURKEY SALAMI","Turkey salami" +25230900,"TURKEY OR CHICKEN BREAST, PKG'D/DELI, LUNCHEON MEAT","Turkey or chicken breast, prepackaged or deli, luncheon meat" +25230905,"TURKEY/CHICKEN BREAST, LOW SALT, PREPACK/DELI, LUNCHEON MEAT","Turkey or chicken breast, low salt, prepackaged or deli, luncheon meat" +25231110,"BEEF, SLICED, PREPACKAGED/DELI, LUNCHEON MEAT","Beef, sliced, prepackaged or deli, luncheon meat" +25231150,"CORNED BEEF, PRESSED","Corned beef, pressed" +25240000,"MEAT SPREAD OR POTTED MEAT, NFS","Meat spread or potted meat, NFS" +25240110,"CHICKEN SALAD SPREAD","Chicken salad spread" +25240210,"HAM, DEVILED OR POTTED","Ham, deviled or potted" +25240220,"HAM SALAD SPREAD","Ham salad spread" +25240310,"ROAST BEEF SPREAD","Roast beef spread" +25240320,"CORNED BEEF SPREAD","Corned beef spread" +26100100,"FISH, NS AS TO TYPE, RAW","Fish, NS as to type, raw" +26100110,"FISH, COOKED, NS AS TO TYPE & COOKING METHOD","Fish, NS as to type, cooked, NS as to cooking method" +26100120,"FISH, NS AS TO TYPE, BAKED OR BROILED, MADE WITH OIL","Fish, NS as to type, baked or broiled, made with oil" +26100121,"FISH, NS AS TO TYPE, BAKED OR BROILED, MADE WITH BUTTER","Fish, NS as to type, baked or broiled, made with butter" +26100122,"FISH, NS AS TO TYPE, BAKED OR BROILED, MADE WITH MARGARINE","Fish, NS as to type, baked or broiled, made with margarine" +26100123,"FISH, NS AS TO TYPE, BAKED OR BROILED, MADE WITHOUT FAT","Fish, NS as to type, baked or broiled, made without fat" +26100124,"FISH, NS AS TO TYPE, BAKED OR BROILED, MADE W/COOKING SPRAY","Fish, NS as to type, baked or broiled, made with cooking spray" +26100130,"FISH, NS AS TO TYPE, COATED, BAKED, MADE WITH OIL","Fish, NS as to type, coated, baked or broiled, made with oil" +26100131,"FISH, NS AS TO TYPE, COATED, BAKED OR BROILED,W/ BUTTER","Fish, NS as to type, coated, baked or broiled, made with butter" +26100132,"FISH, NS AS TO TYPE, COATED, BAKED OR BROILED, W/MARGARINE","Fish, NS as to type, coated, baked or broiled, made with margarine" +26100133,"FISH, NS AS TO TYPE, COATED, BAKED OR BROILED, W/OUT FAT","Fish, NS as to type, coated, baked or broiled, made without fat" +26100134,"FISH, NS AS TO TYPE, COATED, BAKED/ BROILED, W/COOKING SPRAY","Fish, NS as to type, coated, baked or broiled, made with cooking spray" +26100140,"FISH, NS AS TO TYPE, COATED, FRIED, MADE WITH OIL","Fish, NS as to type, coated, fried, made with oil" +26100141,"FISH, NS AS TO TYPE, COATED, FRIED, MADE WITH BUTTER","Fish, NS as to type, coated, fried, made with butter" +26100142,"FISH, NS AS TO TYPE, COATED, FRIED, MADE WITH MARGARINE","Fish, NS as to type, coated, fried, made with margarine" +26100143,"FISH, NS AS TO TYPE, COATED, FRIED, MADE WITHOUT FAT","Fish, NS as to type, coated, fried, made without fat" +26100144,"FISH, NS AS TO TYPE, COATED, MADE WITH COOKING SPRAY","Fish, NS as to type, coated, fried, made with cooking spray" +26100160,"FISH, NS AS TO TYPE, STEAMED","Fish, NS as to type, steamed" +26100170,"FISH, NS AS TO TYPE, DRIED","Fish, NS as to type, dried" +26100180,"FISH, NS AS TO TYPE, CANNED","Fish, NS as to type, canned" +26100190,"FISH, NS AS TO TYPE, SMOKED","Fish, NS as to type, smoked" +26100200,"FISH, NS AS TO TYPE, FROM FAST FOOD","Fish, NS as to type, from fast food" +26100260,"FISH STICK, PATTY OR NUGGET FROM FAST FOOD","Fish stick, patty or nugget from fast food" +26100270,"FISH STICK, PATTY OR NUGGET FROM RESTAURANT, HOME, OR OTHER","Fish stick, patty or nugget from restaurant, home, or other place" +26101110,"ANCHOVY, COOKED, NS AS TO COOKING METHOD","Anchovy, cooked, NS as to cooking method" +26101180,"ANCHOVY, CANNED","Anchovy, canned" +26103110,"BARRACUDA, COOKED, NS AS TO COOKING METHOD","Barracuda, cooked, NS as to cooking method" +26103120,"BARRACUDA, BAKED OR BROILED, FAT ADDED IN COOKING","Barracuda, baked or broiled, fat added in cooking" +26103121,"BARRACUDA, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Barracuda, baked or broiled, fat not added in cooking" +26103130,"BARRACUDA, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Barracuda, coated, baked or broiled, fat added in cooking" +26103131,"BARRACUDA, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKIN","Barracuda, coated, baked or broiled, fat not added in cooking" +26103140,"BARRACUDA, COATED, FRIED","Barracuda, coated, fried" +26103160,"BARRACUDA, STEAMED OR POACHED","Barracuda, steamed or poached" +26105110,"CARP, COOKED, NS AS TO COOKING METHOD","Carp, cooked, NS as to cooking method" +26105120,"CARP, BAKED OR BROILED, FAT ADDED IN COOKING","Carp, baked or broiled, fat added in cooking" +26105121,"CARP, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Carp, baked or broiled, fat not added in cooking" +26105130,"CARP, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Carp, coated, baked or broiled, fat added in cooking" +26105131,"CARP, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Carp, coated, baked or broiled, fat not added in cooking" +26105140,"CARP, COATED, FRIED","Carp, coated, fried" +26105160,"CARP, STEAMED OR POACHED","Carp, steamed or poached" +26105190,"CARP, SMOKED","Carp, smoked" +26107110,"CATFISH, COOKED, NS AS TO COOKING METHOD","Catfish, cooked, NS as to cooking method" +26107120,"CATFISH, BAKED OR BROILED, MADE WITH OIL","Catfish, baked or broiled, made with oil" +26107121,"CATFISH, BAKED OR BROILED, MADE WITH BUTTER","Catfish, baked or broiled, made with butter" +26107122,"CATFISH, BAKED OR BROILED, MADE WITH MARGARINE","Catfish, baked or broiled, made with margarine" +26107123,"CATFISH, BAKED OR BROILED, MADE WITHOUT FAT","Catfish, baked or broiled, made without fat" +26107124,"CATFISH, BAKED OR BROILED, MADE WITH COOKING SPRAY","Catfish, baked or broiled, made with cooking spray" +26107130,"CATFISH, COATED, BAKED OR BROILED, MADE WITH OIL","Catfish, coated, baked or broiled, made with oil" +26107131,"CATFISH, COATED, BAKED OR BROILED, MADE WITH BUTTER","Catfish, coated, baked or broiled, made with butter" +26107132,"CATFISH, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Catfish, coated, baked or broiled, made with margarine" +26107133,"CATFISH, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Catfish, coated, baked or broiled, made without fat" +26107134,"CATFISH, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Catfish, coated, baked or broiled, made with cooking spray" +26107140,"CATFISH, COATED, FRIED, MADE WITH OIL","Catfish, coated, fried, made with oil" +26107141,"CATFISH, COATED, FRIED, MADE WITH BUTTER","Catfish, coated, fried, made with butter" +26107142,"CATFISH, COATED, FRIED, MADE WITH MARGARINE","Catfish, coated, fried, made with margarine" +26107143,"CATFISH, COATED, FRIED, MADE WITHOUT FAT","Catfish, coated, fried, made without fat" +26107144,"CATFISH, COATED, FRIED, MADE WITH COOKING SPRAY","Catfish, coated, fried, made with cooking spray" +26107160,"CATFISH, STEAMED OR POACHED","Catfish, steamed or poached" +26109110,"COD, COOKED, NS AS TO COOKING METHOD","Cod, cooked, NS as to cooking method" +26109120,"COD, BAKED OR BROILED, MADE WITH OIL","Cod, baked or broiled, made with oil" +26109121,"COD, BAKED OR BROILED, MADE WITH BUTTER","Cod, baked or broiled, made with butter" +26109122,"COD, BAKED OR BROILED, MADE WITH MARGARINE","Cod, baked or broiled, made with margarine" +26109123,"COD, BAKED OR BROILED, MADE WITHOUT FAT","Cod, baked or broiled, made without fat" +26109124,"COD, BAKED OR BROILED, MADE WITH COOKING SPRAY","Cod, baked or broiled, made with cooking spray" +26109130,"COD, COATED, BAKED OR BROILED, MADE WITH OIL","Cod, coated, baked or broiled, made with oil" +26109131,"COD, COATED, BAKED OR BROILED, MADE WITH BUTTER","Cod, coated, baked or broiled, made with butter" +26109132,"COD, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Cod, coated, baked or broiled, made with margarine" +26109133,"COD, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Cod, coated, baked or broiled, made without fat" +26109134,"COD, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Cod, coated, baked or broiled, made with cooking spray" +26109140,"COD, COATED, FRIED, MADE WITH OIL","Cod, coated, fried, made with oil" +26109141,"COD, COATED, FRIED, MADE WITH BUTTER","Cod, coated, fried, made with butter" +26109142,"COD, COATED, FRIED, MADE WITH MARGARINE","Cod, coated, fried, made with margarine" +26109143,"COD, COATED, FRIED, MADE WITHOUT FAT","Cod, coated, fried, made without fat" +26109144,"COD, COATED, FRIED, MADE WITH COOKING SPRAY","Cod, coated, fried, made with cooking spray" +26109160,"COD, STEAMED OR POACHED","Cod, steamed or poached" +26109170,"COD, DRIED, SALTED","Cod, dried, salted" +26109180,"COD, DRIED, SALTED, SALT REMOVED IN WATER","Cod, dried, salted, salt removed in water" +26109190,"COD, SMOKED","Cod, smoked" +26111110,"CROAKER, COOKED, NS AS TO COOKING METHOD","Croaker, cooked, NS as to cooking method" +26111120,"CROAKER, BAKED OR BROILED, FAT ADDED IN COOKING","Croaker, baked or broiled, fat added in cooking" +26111121,"CROAKER, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Croaker, baked or broiled, fat not added in cooking" +26111130,"CROAKER, COATED, BAKED, FAT ADDED IN COOKING","Croaker, coated, baked or broiled, fat added in cooking" +26111131,"CROAKER, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Croaker, coated, baked or broiled, fat not added in cooking" +26111140,"CROAKER, COATED, FRIED","Croaker, coated, fried" +26111160,"CROAKER, STEAMED OR POACHED","Croaker, steamed or poached" +26113110,"EEL, COOKED, NS AS TO COOKING METHOD","Eel, cooked, NS as to cooking method" +26113160,"EEL, STEAMED OR POACHED","Eel, steamed or poached" +26113190,"EEL, SMOKED","Eel, smoked" +26115000,"FLOUNDER, RAW","Flounder, raw" +26115110,"FLOUNDER, COOKED, NS AS TO COOKING METHOD","Flounder, cooked, NS as to cooking method" +26115120,"FLOUNDER, BAKED OR BROILED, MADE WTIH OIL","Flounder, baked or broiled, made with oil" +26115121,"FLOUNDER, BAKED OR BROILED, MADE WITH BUTTER","Flounder, baked or broiled, made with butter" +26115122,"FLOUNDER, BAKED OR BROILED, MADE WITH MARGARINE","Flounder, baked or broiled, made with margarine" +26115123,"FLOUNDER, BAKED OR BROILED, MADE WITHOUT FAT","Flounder, baked or broiled, made without fat" +26115124,"FLOUNDER, BAKED OR BROILED, MADE WITH COOKING SPRAY","Flounder, baked or broiled, made with cooking spray" +26115130,"FLOUNDER, COATED, BAKED OR BROILED, MADE WITH OIL","Flounder, coated, baked or broiled, made with oil" +26115131,"FLOUNDER, COATED, BAKED OR BROILED, MADE WITH BUTTER","Flounder, coated, baked or broiled, made with butter" +26115132,"FLOUNDER, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Flounder, coated, baked or broiled, made with margarine" +26115133,"FLOUNDER, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Flounder, coated, baked or broiled, made without fat" +26115134,"FLOUNDER, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Flounder, coated, baked or broiled, made with cooking spray" +26115140,"FLOUNDER, COATED, FRIED, MADE WITH OIL","Flounder, coated, fried, made with oil" +26115141,"FLOUNDER, COATED, FRIED, MADE WITH BUTTER","Flounder, coated, fried, made with butter" +26115142,"FLOUNDER, COATED, FRIED, MADE WITH MARGARINE","Flounder, coated, fried, made with margarine" +26115143,"FLOUNDER, COATED, FRIED, MADE WITHOUT FAT","Flounder, coated, fried, made without fat" +26115144,"FLOUNDER, COATED, FRIED, MADE WITH COOKING SPRAY","Flounder, coated, fried, made with cooking spray" +26115160,"FLOUNDER, STEAMED OR POACHED","Flounder, steamed or poached" +26115190,"FLOUNDER, SMOKED","Flounder, smoked" +26117110,"HADDOCK, COOKED, NS AS TO COOKING METHOD","Haddock, cooked, NS as to cooking method" +26117120,"HADDOCK, BAKED OR BROILED, FAT ADDED IN COOKING","Haddock, baked or broiled, fat added in cooking" +26117121,"HADDOCK, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Haddock, baked or broiled, fat not added in cooking" +26117130,"HADDOCK, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Haddock, coated, baked or broiled, fat added in cooking" +26117131,"HADDOCK, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Haddock, coated, baked or broiled, fat not added in cooking" +26117140,"HADDOCK, COATED, FRIED","Haddock, coated, fried" +26117160,"HADDOCK, STEAMED OR POACHED","Haddock, steamed or poached" +26117190,"HADDOCK, SMOKED","Haddock, smoked" +26118000,"HALIBUT, RAW","Halibut, raw" +26118010,"HALIBUT, COOKED, NS AS TO COOKING METHOD","Halibut, cooked, NS as to cooking method" +26118020,"HALIBUT, BAKED OR BROILED, MADE WITH OIL","Halibut, baked or broiled, made with oil" +26118021,"HALIBUT, BAKED OR BROILED, MADE WITH BUTTER","Halibut, baked or broiled, made with butter" +26118022,"HALIBUT, BAKED OR BROILED, MADE WITH MARGARINE","Halibut, baked or broiled, made with margarine" +26118023,"HALIBUT, BAKED OR BROILED, MADE WITHOUT FAT","Halibut, baked or broiled, made without fat" +26118024,"HALIBUT, BAKED OR BROILED, MADE WITH COOKING SPRAY","Halibut, baked or broiled, made with cooking spray" +26118030,"HALIBUT, COATED, BAKED OR BROILED, MADE WITH OIL","Halibut, coated, baked or broiled, made with oil" +26118031,"HALIBUT, COATED, BAKED OR BROILED, MADE WITH BUTTER","Halibut, coated, baked or broiled, made with butter" +26118032,"HALIBUT, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Halibut, coated, baked or broiled, made with margarine" +26118033,"HALIBUT, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Halibut, coated, baked or broiled, made without fat" +26118034,"HALIBUT, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Halibut, coated, baked or broiled, made with cooking spray" +26118040,"HALIBUT, COATED, FRIED, MADE WITH OIL","Halibut, coated, fried, made with oil" +26118041,"HALIBUT, COATED, FRIED, MADE WITH BUTTER","Halibut, coated, fried, made with butter" +26118042,"HALIBUT, COATED, FRIED, MADE WITH MARGARINE","Halibut, coated, fried, made with margarine" +26118043,"HALIBUT, COATED, FRIED, MADE WITHOUT FAT","Halibut, coated, fried, made without fat" +26118044,"HALIBUT, COATED, FRIED, MADE WITH COOKING SPRAY","Halibut, coated, fried, made with cooking spray" +26118050,"HALIBUT, STEAMED OR POACHED","Halibut, steamed or poached" +26118060,"HALIBUT, SMOKED","Halibut, smoked" +26119100,"HERRING, RAW","Herring, raw" +26119110,"HERRING, COOKED, NS AS TO COOKING METHOD","Herring, cooked, NS as to cooking method" +26119120,"HERRING, BAKED OR BROILED, FAT ADDED IN COOKING","Herring, baked or broiled, fat added in cooking" +26119121,"HERRING, BAKED OR BROILED, FAT NOT USED IN PREPARATION","Herring, baked or broiled, fat not added in cooking" +26119130,"HERRING, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Herring, coated, baked or broiled, fat added in cooking" +26119131,"HERRING, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Herring, coated, baked or broiled, fat not added in cooking" +26119140,"HERRING, COATED, FRIED","Herring, coated, fried" +26119160,"HERRING, PICKLED, IN CREAM SAUCE","Herring, pickled, in cream sauce" +26119170,"HERRING, DRIED, SALTED","Herring, dried, salted" +26119180,"HERRING, PICKLED","Herring, pickled" +26119190,"HERRING, SMOKED, KIPPERED","Herring, smoked, kippered" +26121100,"MACKEREL, RAW","Mackerel, raw" +26121110,"MACKEREL, COOKED, NS AS TO COOKING METHOD","Mackerel, cooked, NS as to cooking method" +26121120,"MACKEREL, BAKED OR BROILED, FAT ADDED IN COOKING","Mackerel, baked or broiled, fat added in cooking" +26121121,"MACKEREL, BAKED OR BROILED, FAT NOT USED IN PREPARATION","Mackerel, baked or broiled, fat not added in cooking" +26121131,"MACKEREL, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Mackerel, coated, baked or broiled, fat added in cooking" +26121132,"MACKEREL, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Mackerel, coated, baked or broiled, fat not added in cooking" +26121140,"MACKEREL, COATED, FRIED","Mackerel, coated, fried" +26121160,"MACKEREL, PICKLED","Mackerel, pickled" +26121180,"MACKEREL, CANNED","Mackerel, canned" +26121190,"MACKEREL, SMOKED","Mackerel, smoked" +26123100,"MULLET, RAW","Mullet, raw" +26123110,"MULLET, COOKED, NS AS TO COOKING METHOD","Mullet, cooked, NS as to cooking method" +26123120,"MULLET, BAKED OR BROILED, FAT USED IN PREPARATION","Mullet, baked or broiled, fat added in cooking" +26123121,"MULLET, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Mullet, baked or broiled, fat not added in cooking" +26123130,"MULLET, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Mullet, coated, baked or broiled, fat added in cooking" +26123131,"MULLET, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Mullet, coated, baked or broiled, fat not added in cooking" +26123140,"MULLET, COATED, FRIED","Mullet, coated, fried" +26123160,"MULLET, STEAMED OR POACHED","Mullet, steamed or poached" +26125100,"OCEAN PERCH, RAW","Ocean perch, raw" +26125110,"OCEAN PERCH, COOKED, NS AS TO COOKING METHOD","Ocean perch, cooked, NS as to cooking method" +26125120,"OCEAN PERCH, BAKED OR BROILED, FAT USED IN PREPARATION","Ocean perch, baked or broiled, fat added in cooking" +26125121,"OCEAN PERCH, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Ocean perch, baked or broiled, fat not added in cooking" +26125130,"OCEAN PERCH, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Ocean perch, coated, baked or broiled, fat added in cooking" +26125131,"OCEAN PERCH, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOK","Ocean perch, coated, baked or broiled, fat not added in cooking" +26125140,"OCEAN PERCH, COATED, FRIED","Ocean perch, coated, fried" +26125160,"OCEAN PERCH, STEAMED OR POACHED","Ocean perch, steamed or poached" +26127110,"PERCH, COOKED, NS AS TO COOKING METHOD","Perch, cooked, NS as to cooking method" +26127120,"PERCH, BAKED OR BROILED, MADE WITH OIL","Perch, baked or broiled, made with oil" +26127121,"PERCH, BAKED OR BROILED, MADE WITH BUTTER","Perch, baked or broiled, made with butter" +26127122,"PERCH, BAKED OR BROILED, MADE WITH MARGARINE","Perch, baked or broiled, made with margarine" +26127123,"PERCH, BAKED OR BROILED, MADE WITHOUT FAT","Perch, baked or broiled, made without fat" +26127124,"PERCH, BAKED OR BROILED, MADE WITH COOKING SPRAY","Perch, baked or broiled, made with cooking spray" +26127130,"PERCH, COATED, BAKED OR BROILED, MADE WITH OIL","Perch, coated, baked or broiled, made with oil" +26127131,"PERCH, COATED, BAKED OR BROILED, MADE WITH BUTTER","Perch, coated, baked or broiled, made with butter" +26127132,"PERCH, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Perch, coated, baked or broiled, made with margarine" +26127133,"PERCH, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Perch, coated, baked or broiled, made without fat" +26127134,"PERCH, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Perch, coated, baked or broiled, made with cooking spray" +26127140,"PERCH, COATED, FRIED","Perch, coated, fried, made with oil" +26127141,"PERCH, COATED, FRIED, MADE WITH BUTTER","Perch, coated, fried, made with butter" +26127142,"PERCH, COATED, FRIED, MADE WITH MARGARINE","Perch, coated, fried, made with margarine" +26127143,"PERCH, COATED, FRIED, MADE WITHOUT FAT","Perch, coated, fried, made without fat" +26127144,"PERCH, COATED, FRIED, MADE WITH COOKING SPRAY","Perch, coated, fried, made with cooking spray" +26127160,"PERCH, STEAMED OR POACHED","Perch, steamed or poached" +26129110,"PIKE, COOKED, NS AS TO COOKING METHOD","Pike, cooked, NS as to cooking method" +26129120,"PIKE, BAKED OR BROILED, FAT ADDED IN COOKING","Pike, baked or broiled, fat added in cooking" +26129121,"PIKE, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Pike, baked or broiled, fat not added in cooking" +26129130,"PIKE, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Pike, coated, baked or broiled, fat added in cooking" +26129131,"PIKE, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Pike, coated, baked or broiled, fat not added in cooking" +26129140,"PIKE, COATED, FRIED","Pike, coated, fried" +26129160,"PIKE, STEAMED OR POACHED","Pike, steamed or poached" +26131100,"POMPANO, RAW","Pompano, raw" +26131110,"POMPANO, COOKED, NS AS TO COOKING METHOD","Pompano, cooked, NS as to cooking method" +26131120,"POMPANO, BAKED OR BROILED, FAT ADDED IN COOKING","Pompano, baked or broiled, fat added in cooking" +26131121,"POMPANO, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Pompano, baked or broiled, fat not added in cooking" +26131130,"POMPANO, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Pompano, coated, baked or broiled, fat added in cooking" +26131131,"POMPANO, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Pompano, coated, baked or broiled, fat not added in cooking" +26131140,"POMPANO, COATED, FRIED","Pompano, coated, fried" +26131160,"POMPANO, STEAMED OR POACHED","Pompano, steamed or poached" +26131190,"POMPANO, SMOKED","Pompano, smoked" +26133100,"PORGY, RAW","Porgy, raw" +26133110,"PORGY, COOKED, NS AS TO COOKING METHOD","Porgy, cooked, NS as to cooking method" +26133120,"PORGY, BAKED OR BROILED, FAT ADDED IN COOKING","Porgy, baked or broiled, fat added in cooking" +26133121,"PORGY, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Porgy, baked or broiled, fat not added in cooking" +26133130,"PORGY, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Porgy, coated, baked or broiled, fat added in cooking" +26133131,"PORGY, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Porgy, coated, baked or broiled, fat not added in cooking" +26133140,"PORGY, COATED, FRIED","Porgy, coated, fried" +26133160,"PORGY, STEAMED OR POACHED","Porgy, steamed or poached" +26135110,"RAY, COOKED, NS AS TO COOKING METHOD","Ray, cooked, NS as to cooking method" +26135120,"RAY, BAKED OR BROILED, FAT ADDED IN COOKING","Ray, baked or broiled, fat added in cooking" +26135121,"RAY, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Ray, baked or broiled, fat not added in cooking" +26135130,"RAY, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Ray, coated, baked or broiled, fat added in cooking" +26135131,"RAY, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Ray, coated, baked or broiled, fat not added in cooking" +26135140,"RAY, COATED, FRIED","Ray, coated, fried" +26135160,"RAY, STEAMED OR POACHED","Ray, steamed or poached" +26137100,"SALMON, RAW","Salmon, raw" +26137110,"SALMON, COOKED, NS AS TO COOKING METHOD","Salmon, cooked, NS as to cooking method" +26137120,"SALMON, BAKED OR BROILED, MADE WITH OIL","Salmon, baked or broiled, made with oil" +26137121,"SALMON, BAKED OR BROILED, MADE WITH BUTTER","Salmon, baked or broiled, made with butter" +26137122,"SALMON, BAKED OR BROILED, MADE WITH MARGARINE","Salmon, baked or broiled, made with margarine" +26137123,"SALMON, BAKED OR BROILED, MADE WITHOUT FAT","Salmon, baked or broiled, made without fat" +26137124,"SALMON, BAKED OR BROILED, MADE WITH COOKING SPRAY","Salmon, baked or broiled, made with cooking spray" +26137130,"SALMON, COATED, BAKED OR BROILED, MADE WITH OIL","Salmon, coated, baked or broiled, made with oil" +26137131,"SALMON, COATED, BAKED OR BROILED, MADE WITH BUTTER","Salmon, coated, baked or broiled, made with butter" +26137132,"SALMON, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Salmon, coated, baked or broiled, made with margarine" +26137133,"SALMON, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Salmon, coated, baked or broiled, made without fat" +26137134,"SALMON, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Salmon, coated, baked or broiled, made with cooking spray" +26137140,"SALMON, COATED, FRIED, MADE WITH OIL","Salmon, coated, fried, made with oil" +26137141,"SALMON, COATED, FRIED, MADE WITH BUTTER","Salmon, coated, fried, made with butter" +26137142,"SALMON, COATED, FRIED, MADE WITH MARGARINE","Salmon, coated, fried, made with margarine" +26137143,"SALMON, COATED, FRIED, MADE WITHOUT FAT","Salmon, coated, fried, made without fat" +26137144,"SALMON, COATED, FRIED, MADE WITH COOKING SPRAY","Salmon, coated, fried, made with cooking spray" +26137160,"SALMON, STEAMED OR POACHED","Salmon, steamed or poached" +26137170,"SALMON, DRIED","Salmon, dried" +26137180,"SALMON, CANNED","Salmon, canned" +26137190,"SALMON, SMOKED (INCLUDE LOX)","Salmon, smoked" +26139110,"SARDINES, COOKED","Sardines, cooked" +26139170,"SARDINE, DRIED","Sardines, dried" +26139180,"SARDINES, CANNED IN OIL","Sardines, canned in oil" +26139190,"SARDINES, SKINLESS, BONELESS, PACKED IN WATER","Sardines, skinless, boneless, packed in water" +26141110,"SEA BASS, COOKED, NS AS TO COOKING METHOD","Sea bass, cooked, NS as to cooking method" +26141120,"SEA BASS, BAKED OR BROILED, FAT ADDED IN COOKING","Sea bass, baked or broiled, fat added in cooking" +26141121,"SEA BASS, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Sea bass, baked or broiled, fat not added in cooking" +26141130,"SEA BASS, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Sea bass, coated, baked or broiled, fat added in cooking" +26141131,"SEA BASS, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Sea bass, coated, baked or broiled, fat not added in cooking" +26141140,"SEA BASS, COATED, FRIED","Sea bass, coated, fried" +26141160,"SEA BASS, STEAMED OR POACHED","Sea bass, steamed or poached" +26141180,"SEA BASS, PICKLED (MERO EN ESCABECHE)","Sea bass, pickled (Mero en escabeche)" +26143110,"SHARK, COOKED, NS AS TO COOKING METHOD","Shark, cooked, NS as to cooking method" +26143120,"SHARK, BAKED OR BROILED, FAT ADDED IN COOKING","Shark, baked or broiled, fat added in cooking" +26143121,"SHARK, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Shark, baked or broiled, fat not added in cooking" +26143130,"SHARK, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Shark, coated, baked or broiled, fat added in cooking" +26143131,"SHARK, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Shark, coated, baked or broiled, fat not added in cooking" +26143140,"SHARK, COATED, FRIED","Shark, coated, fried" +26143160,"SHARK, STEAMED OR POACHED","Shark, steamed or poached" +26147110,"STURGEON, COOKED, NS AS TO COOKING METHOD","Sturgeon, cooked, NS as to cooking method" +26147120,"STURGEON, BAKED OR BROILED, FAT ADDED IN COOKING","Sturgeon, baked or broiled, fat added in cooking" +26147130,"STURGEON, STEAMED","Sturgeon, steamed" +26147140,"STURGEON, COATED, FRIED","Sturgeon, coated, fried" +26147190,"STURGEON, SMOKED","Sturgeon, smoked" +26149110,"SWORDFISH, COOKED, NS AS TO COOKING METHOD","Swordfish, cooked, NS as to cooking method" +26149120,"SWORDFISH, BAKED OR BROILED, FAT ADDED IN COOKING","Swordfish, baked or broiled, fat added in cooking" +26149121,"SWORDFISH, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Swordfish, baked or broiled, fat not added in cooking" +26149130,"SWORDFISH, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Swordfish, coated, baked or broiled, fat added in cooking" +26149131,"SWORDFISH, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKIN","Swordfish, coated, baked or broiled, fat not added in cooking" +26149140,"SWORDFISH, COATED, FRIED","Swordfish, coated, fried" +26149160,"SWORDFISH, STEAMED OR POACHED","Swordfish, steamed or poached" +26151110,"TROUT, COOKED, NS AS TO COOKING METHOD","Trout, cooked, NS as to cooking method" +26151120,"TROUT, BAKED OR BROILED, MADE WITH OIL","Trout, baked or broiled, made with oil" +26151121,"TROUT, BAKED OR BROILED, MADE WITH BUTTER","Trout, baked or broiled, made with butter" +26151122,"TROUT, BAKED OR BROILED, MADE WITH MARGARINE","Trout, baked or broiled, made with margarine" +26151123,"TROUT, BAKED OR BROILED, MADE WITHOUT FAT","Trout, baked or broiled, made without fat" +26151124,"TROUT, BAKED OR BROILED, MADE WITH COOKING SPRAY","Trout, baked or broiled, made with cooking spray" +26151130,"TROUT, COATED, BAKED OR BROILED, MADE WITH OIL","Trout, coated, baked or broiled, made with oil" +26151131,"TROUT, COATED, BAKED OR BROILED, MADE WITH BUTTER","Trout, coated, baked or broiled, made with butter" +26151132,"TROUT, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Trout, coated, baked or broiled, made with margarine" +26151133,"TROUT, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Trout, coated, baked or broiled, made without fat" +26151134,"TROUT, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Trout, coated, baked or broiled, made with cooking spray" +26151140,"TROUT, COATED, FRIED, MADE WITH OIL","Trout, coated, fried, made with oil" +26151141,"TROUT, COATED, FRIED, MADE WITH BUTTER","Trout, coated, fried, made with butter" +26151142,"TROUT, COATED, FRIED, MADE WITH MARGARINE","Trout, coated, fried, made with margarine" +26151143,"TROUT, COATED, FRIED, MADE WITHOUT FAT","Trout, coated, fried, made without fat" +26151144,"TROUT, COATED, FRIED, MADE WITH COOKING SPRAY","Trout, coated, fried, made with cooking spray" +26151160,"TROUT, STEAMED OR POACHED","Trout, steamed or poached" +26151190,"TROUT, SMOKED","Trout, smoked" +26153100,"TUNA, FRESH, RAW","Tuna, fresh, raw" +26153110,"TUNA, FRESH, COOKED, NS AS TO COOKING METHOD","Tuna, fresh, cooked, NS as to cooking method" +26153120,"TUNA, FRESH, BAKED OR BROILED, FAT ADDED IN COOKING","Tuna, fresh, baked or broiled, fat added in cooking" +26153122,"TUNA, FRESH, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Tuna, fresh, baked or broiled, fat not added in cooking" +26153130,"TUNA, FRESH, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Tuna, fresh, coated, baked or broiled, fat added in cooking" +26153131,"TUNA, FRESH, COATED, BAKED OR BROILED, FAT NOT ADDED","Tuna, fresh, coated, baked or broiled, fat not added" +26153140,"TUNA, FRESH, COATED, FRIED","Tuna, fresh, coated, fried" +26153160,"TUNA, FRESH, STEAMED OR POACHED","Tuna, fresh, steamed or poached" +26153170,"TUNA, FRESH, DRIED","Tuna, fresh, dried" +26153190,"TUNA, FRESH, SMOKED","Tuna, fresh, smoked" +26155110,"TUNA, CANNED, NS AS TO OIL OR WATER PACK","Tuna, canned, NS as to oil or water pack" +26155180,"TUNA, CANNED, OIL PACK","Tuna, canned, oil pack" +26155190,"TUNA, CANNED, WATER PACK","Tuna, canned, water pack" +26157110,"WHITING, COOKED, NS AS TO COOKING METHOD","Whiting, cooked, NS as to cooking method" +26157120,"WHITING, BAKED OR BROILED, MADE WITH OIL","Whiting, baked or broiled, made with oil" +26157121,"WHITING, BAKED OR BROILED, MADE WITH BUTTER","Whiting, baked or broiled, made with butter" +26157122,"WHITING, BAKED OR BROILED, MADE WITH MARGARINE","Whiting, baked or broiled, made with margarine" +26157123,"WHITING, BAKED OR BROILED, MADE WITHOUT FAT","Whiting, baked or broiled, made without fat" +26157124,"WHITING, BAKED OR BROILED, MADE WITH COOKING SPRAY","Whiting, baked or broiled, made with cooking spray" +26157130,"WHITING, COATED, BAKED OR BROILED, MADE WITH OIL","Whiting, coated, baked or broiled, made with oil" +26157131,"WHITING, COATED, BAKED OR BROILED, MADE WITH BUTTER","Whiting, coated, baked or broiled, made with butter" +26157132,"WHITING, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Whiting, coated, baked or broiled, made with margarine" +26157133,"WHITING, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Whiting, coated, baked or broiled, made without fat" +26157134,"WHITING, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Whiting, coated, baked or broiled, made with cooking spray" +26157140,"WHITING, COATED, FRIED, MADE WITH OIL","Whiting, coated, fried, made with oil" +26157141,"WHITING, COATED, FRIED, MADE WITH BUTTER","Whiting, coated, fried, made with butter" +26157142,"WHITING, COATED, FRIED, MADE WITH MARGARINE","Whiting, coated, fried, made with margarine" +26157143,"WHITING, COATED, FRIED, MADE WITHOUT FAT","Whiting, coated, fried, made without fat" +26157144,"WHITING, COATED, FRIED, MADE WITH COOKING SPRAY","Whiting, coated, fried, made with cooking spray" +26157160,"WHITING, STEAMED OR POACHED","Whiting, steamed or poached" +26158000,"TILAPIA, COOKED, NS AS TO COOKING METHOD","Tilapia, cooked, NS as to cooking method" +26158010,"TILAPIA, BAKED OR BROILED, MADE WITH OIL","Tilapia, baked or broiled, made with oil" +26158011,"TILAPIA, BAKED OR BROILED, MADE WITH BUTTER","Tilapia, baked or broiled, made with butter" +26158012,"TILAPIA, BAKED OR BROILED, MADE WITH MARGARINE","Tilapia, baked or broiled, made with margarine" +26158013,"TILAPIA, BAKED OR BROILED, MADE WITHOUT FAT","Tilapia, baked or broiled, made without fat" +26158014,"TILAPIA, BAKED OR BROILED, MADE WITH COOKING SPRAY","Tilapia, baked or broiled, made with cooking spray" +26158020,"TILAPIA, COATED, BAKED OR BROILED, MADE WITH OIL","Tilapia, coated, baked or broiled, made with oil" +26158021,"TILAPIA, COATED, BAKED OR BROILED, MADE WITH BUTTER","Tilapia, coated, baked or broiled, made with butter" +26158022,"TILAPIA, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Tilapia, coated, baked or broiled, made with margarine" +26158023,"TILAPIA, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Tilapia, coated, baked or broiled, made without fat" +26158024,"TILAPIA, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Tilapia, coated, baked or broiled, made with cooking spray" +26158030,"TILAPIA, COATED, FRIED, MADE WITH OIL","Tilapia, coated, fried, made with oil" +26158031,"TILAPIA, COATED, FRIED, MADE WITH BUTTER","Tilapia, coated, fried, made with butter" +26158032,"TILAPIA, COATED, FRIED, MADE WITH MARGARINE","Tilapia, coated, fried, made with margarine" +26158033,"TILAPIA, COATED, FRIED, MADE WITHOUT FAT","Tilapia, coated, fried, made without fat" +26158034,"TILAPIA, COATED, FRIED, MADE WITH COOKING SPRAY","Tilapia, coated, fried, made with cooking spray" +26158050,"TILAPIA, STEAMED OR POACHED","Tilapia, steamed or poached" +26203110,"FROG LEGS, NS AS TO COOKING METHOD","Frog legs, NS as to cooking method" +26203160,"FROG LEGS, STEAMED","Frog legs, steamed" +26205110,"OCTOPUS, COOKED, NS AS TO COOKING METHOD","Octopus, cooked, NS as to cooking method" +26205160,"OCTOPUS, STEAMED","Octopus, steamed" +26205170,"OCTOPUS, DRIED","Octopus, dried" +26205180,"OCTOPUS, DRIED, BOILED","Octopus, dried, boiled" +26205190,"OCTOPUS, SMOKED","Octopus, smoked" +26207110,"ROE, SHAD, COOKED (INCL COD ROE)","Roe, shad, cooked" +26209100,"ROE, HERRING","Roe, herring" +26211100,"ROE, STURGEON (INCLUDE CAVIAR)","Roe, sturgeon" +26213100,"SQUID, RAW","Squid, raw" +26213120,"SQUID, BAKED, BROILED, FAT ADDED IN COOKING","Squid, baked or broiled, fat added in cooking" +26213121,"SQUID, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Squid, baked or broiled, fat not added in cooking" +26213130,"SQUID, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Squid, coated, baked or broiled, fat added in cooking" +26213131,"SQUID, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Squid, coated, baked or broiled, fat not added in cooking" +26213140,"SQUID, COATED, FRIED","Squid, coated, fried" +26213160,"SQUID, STEAMED OR BOILED","Squid, steamed or boiled" +26213170,"SQUID, DRIED","Squid, dried" +26213180,"SQUID, PICKLED","Squid, pickled" +26213190,"SQUID, CANNED","Squid, canned" +26215120,"TURTLE, COOKED, NS AS TO METHOD","Turtle (terrapin), cooked, NS as to cooking method" +26301110,"ABALONE, COOKED, NS AS TO COOKING METHOD","Abalone, cooked, NS as to cooking method" +26301140,"ABALONE, FLOURED OR BREADED, FRIED","Abalone, floured or breaded, fried" +26301160,"ABALONE, STEAMED OR POACHED","Abalone, steamed or poached" +26303100,"CLAMS, RAW","Clams, raw" +26303110,"CLAMS, COOKED, NS AS TO COOKING METHOD","Clams, cooked, NS as to cooking method" +26303120,"CLAMS, BAKED OR BROILED, FAT ADDED IN COOKING","Clams, baked or broiled, fat added in cooking" +26303121,"CLAMS, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Clams, baked or broiled, fat not added in cooking" +26303130,"CLAMS, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Clams, coated, baked or broiled, fat added in cooking" +26303131,"CLAMS ,BAKED OR BROILED, FAT NOT ADDED IN COOKING","Clams, coated, baked or broiled, fat not added in cooking" +26303140,"CLAMS,COATED, FRIED","Clams, coated, fried" +26303160,"CLAMS, STEAMED OR BOILED","Clams, steamed or boiled" +26303180,"CLAMS, CANNED","Clams, canned" +26303190,"CLAMS, SMOKED, IN OIL","Clams, smoked, in oil" +26305110,"CRAB, COOKED, NS AS TO COOKING METHOD","Crab, cooked, NS as to cooking method" +26305120,"CRAB, BAKED OR BROILED, FAT ADDED IN COOKING","Crab, baked or broiled, fat added in cooking" +26305121,"CRAB, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Crab, baked or broiled, fat not added in cooking" +26305130,"CRAB, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Crab, coated, baked or broiled, fat added in cooking" +26305131,"CRAB, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Crab, coated, baked or broiled, fat not added in cooking" +26305160,"CRAB, HARD SHELL, STEAMED","Crab, hard shell, steamed" +26305180,"CRAB, CANNED","Crab, canned" +26307140,"CRAB, SOFT SHELL, COATED, FRIED","Crab, soft shell, coated, fried" +26309140,"CRAYFISH, COATED, FRIED","Crayfish, coated, fried" +26309160,"CRAYFISH, BOILED OR STEAMED","Crayfish, boiled or steamed" +26311110,"LOBSTER, COOKED, NS AS TO METHOD","Lobster, cooked, NS as to cooking method" +26311120,"LOBSTER, BAKED OR BROILED, FAT ADDED IN COOKING","Lobster, baked or broiled, fat added in cooking" +26311121,"LOBSTER, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Lobster, baked or broiled, fat not added in cooking" +26311140,"LOBSTER, COATED, FRIED","Lobster, coated, fried" +26311160,"LOBSTER, STEAMED OR BOILED","Lobster, steamed or boiled" +26311170,"LOBSTER, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Lobster, coated, baked or broiled, fat added in cooking" +26311171,"LOBSTER, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Lobster, coated, baked or broiled, fat not added in cooking" +26311180,"LOBSTER, CANNED","Lobster, canned" +26313100,"MUSSELS, RAW","Mussels, raw" +26313110,"MUSSELS, COOKED, NS AS TO COOKING METHOD","Mussels, cooked, NS as to cooking method" +26313160,"MUSSELS, STEAMED","Mussels, steamed or poached" +26315100,"OYSTERS, RAW","Oysters, raw" +26315110,"OYSTERS, COOKED, NS AS TO COOKING METHOD","Oysters, cooked, NS as to cooking method" +26315120,"OYSTERS, BAKED OR BROILED, FAT ADDED IN COOKING","Oysters, baked or broiled, fat added in cooking" +26315121,"OYSTERS, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Oysters, baked or broiled, fat not added in cooking" +26315130,"OYSTERS, STEAMED","Oysters, steamed" +26315140,"OYSTERS, COATED, FRIED","Oysters, coated, fried" +26315160,"OYSTERS, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Oysters, coated, baked or broiled, fat added in cooking" +26315161,"OYSTERS, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Oysters, coated, baked or broiled, fat not added in cooking" +26315180,"OYSTERS, CANNED","Oysters, canned" +26315190,"OYSTERS, SMOKED","Oysters, smoked" +26317110,"SCALLOPS, COOKED, NS AS TO COOKING METHOD","Scallops, cooked, NS as to cooking method" +26317120,"SCALLOPS, BAKED OR BROILED, FAT ADDED IN COOKING","Scallops, baked or broiled, fat added in cooking" +26317121,"SCALLOPS, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Scallops, baked or broiled, fat not added in cooking" +26317130,"SCALLOPS, STEAMED OR BOILED","Scallops, steamed or boiled" +26317140,"SCALLOPS, COATED, FRIED","Scallops, coated, fried" +26317160,"SCALLOPS, COATED, BAKED OR BROILED, FAT ADDED IN COOKING","Scallops, coated, baked or broiled, fat added in cooking" +26317161,"SCALLOPS, COATED, BAKED OR BROILED, FAT NOT ADDED IN COOKING","Scallops, coated, baked or broiled, fat not added in cooking" +26319110,"SHRIMP, COOKED, NS AS TO COOKING METHOD","Shrimp, cooked, NS as to cooking method" +26319120,"SHRIMP, BAKED OR BROILED, MADE WITH OIL","Shrimp, baked or broiled, made with oil" +26319121,"SHRIMP, BAKED OR BROILED, MADE WITH BUTTER","Shrimp, baked or broiled, made with butter" +26319122,"SHRIMP, BAKED OR BROILED, MADE WITH MARGARINE","Shrimp, baked or broiled, made with margarine" +26319123,"SHRIMP, BAKED OR BROILED, MADE WITHOUT FAT","Shrimp, baked or broiled, made without fat" +26319124,"SHRIMP, BAKED OR BROILED, MADE WITH COOKING SPRAY","Shrimp, baked or broiled, made with cooking spray" +26319130,"SHRIMP, STEAMED OR BOILED","Shrimp, steamed or boiled" +26319140,"SHRIMP, COATED, FRIED, MADE WITH OIL","Shrimp, coated, fried, made with oil" +26319141,"SHRIMP, COATED, FRIED, MADE WITH BUTTER","Shrimp, coated, fried, made with butter" +26319142,"SHRIMP, COATED, FRIED, MADE WITH MARGARINE","Shrimp, coated, fried, made with margarine" +26319143,"SHRIMP, COATED, FRIED, MADE WITHOUT FAT","Shrimp, coated, fried, made without fat" +26319144,"SHRIMP, COATED, FRIED, MADE WITH COOKING SPRAY","Shrimp, coated, fried, made with cooking spray" +26319145,"SHRIMP, COATED, FRIED, FROM FAST FOOD / RESTAURANT","Shrimp, coated, fried, from fast food / restaurant" +26319160,"SHIRMP, COATED, BAKED OR BROILED, MADE WITH OIL","Shrimp, coated, baked or broiled, made with oil" +26319161,"SHRIMP, COATED, BAKED OR BROILED, MADE WITH BUTTER","Shrimp, coated, baked or broiled, made with butter" +26319162,"SHRIMP, COATED, BAKED OR BROILED, MADE WITH MARGARINE","Shrimp, coated, baked or broiled, made with margarine" +26319163,"SHRIMP, COATED, BAKED OR BROILED, MADE WITHOUT FAT","Shrimp, coated, baked or broiled, made without fat" +26319164,"SHRIMP, COATED, BAKED OR BROILED, MADE WITH COOKING SPRAY","Shrimp, coated, baked or broiled, made with cooking spray" +26319170,"SHRIMP, DRIED","Shrimp, dried" +26319180,"SHRIMP, CANNED","Shrimp, canned" +26321110,"SNAILS, COOKED, NS AS TO METHOD","Snails, cooked, NS as to cooking method" +27111000,"BEEF W/ TOMATO-BASED SAUCE (MIXTURE)","Beef with tomato-based sauce (mixture)" +27111050,"SPAGHETTI SAUCE W/ BEEF/MEAT, HOMEMADE-STYLE","Spaghetti sauce with beef or meat other than lamb or mutton, homemade-style" +27111100,"BEEF GOULASH","Beef goulash" +27111200,"BEEF BURGUNDY (BEEF BOURGUIGNONNE)","Beef burgundy (beef bourguignonne)" +27111300,"MEXICAN BEEF STEW, NO POTATOES, TOMATO SAUCE","Mexican style beef stew, no potatoes, tomato-based sauce (mixture) (Carne guisada sin papas)" +27111310,"MEXICAN BEEF STEW, NO POTATOES, W/ CHILI PEPPERS, TOMATO SCE","Mexican style beef stew, no potatoes, with chili peppers, tomato-based sauce (mixture) (Carne guisada con chile)" +27111400,"CHILI CON CARNE, NS AS TO BEANS","Chili con carne, NS as to beans" +27111410,"CHILI CON CARNE W/ BEANS","Chili con carne with beans" +27111420,"CHILI CON CARNE W/O BEANS","Chili con carne without beans" +27111430,"CHILI CON CARNE, NS AS TO BEANS, W/ CHEESE","Chili con carne, NS as to beans, with cheese" +27111440,"CHILI CON CARNE W/ BEANS & CHEESE","Chili con carne with beans and cheese" +27111500,"BEEF SLOPPY JOE (NO BUN)","Beef sloppy joe (no bun)" +27112000,"BEEF W/ GRAVY (MIXTURE) (INCLUDE COUNTRY STYLE)","Beef with gravy (mixture)" +27112010,"SALISBURY STEAK W/ GRAVY (MIXTURE)","Salisbury steak with gravy (mixture)" +27113000,"BEEF W/ CREAM OR WHITE SAUCE (MIXTURE)","Beef with cream or white sauce (mixture)" +27113100,"BEEF STROGANOFF","Beef stroganoff" +27113200,"CREAMED CHIPPED OR DRIED BEEF","Creamed chipped or dried beef" +27113300,"SWEDISH MEATBALLS W/ CREAM OR WHITE SAUCE (MIXTURE)","Swedish meatballs with cream or white sauce (mixture)" +27114000,"BEEF W/ (MUSHROOM) SOUP (MIXTURE)","Beef with (mushroom) soup (mixture)" +27115000,"BEEF W/ SOY-BASED SAUCE (MIXTURE)","Beef with soy-based sauce (mixture)" +27115100,"STEAK TERIYAKI W/ SAUCE (MIXTURE)","Steak teriyaki with sauce (mixture)" +27116100,"BEEF CURRY","Beef curry" +27116200,"BEEF W/ BARBECUE SAUCE (MIXTURE)","Beef with barbecue sauce (mixture)" +27116300,"BEEF W/ SWEET & SOUR SAUCE (MIXTURE)","Beef with sweet and sour sauce (mixture)" +27116350,"STEWED, SEASONED GROUND BEEF, MEXICAN","Stewed, seasoned, ground beef, Mexican style (Picadillo de carne de rez)" +27116400,"STEAK TARTARE (RAW GROUND BEEF & EGG)","Steak tartare (raw ground beef and egg)" +27118110,"MEATBALLS, P. R. (ALBONDIGAS GUISADAS)","Meatballs, Puerto Rican style (Albondigas guisadas)" +27118120,"STEWED,SEASONED GROUND BEEF,PUERTO RICAN STYLE","Stewed seasoned ground beef, Puerto Rican style (Picadillo guisado, picadillo de carne)" +27118130,"STEWED DRIED BEEF, P.R. (TASAJO GUISADO)","Stewed dried beef, Puerto Rican style (Tasajo guisado, carne cecina guisada)" +27118140,"STUFFED POT ROAST, P.R.,NFS(ASSUME GRAVY,STUFFING)","Stuffed pot roast, Puerto Rican style, NFS (assume with gravy and stuffing)" +27118180,"BEEF STEW, P.R., MEAT W/ GRAVY (POTATO SEPARATE)","Puerto Rican style beef stew, meat with gravy (potatoes reported separately)" +27120020,"HAM/PORK W/ GRAVY (MIXTURE)","Ham or pork with gravy (mixture)" +27120030,"HAM/PORK W/ BARBECUE SAUCE","Ham or pork with barbecue sauce (mixture)" +27120060,"SWEET & SOUR PORK","Sweet and sour pork" +27120080,"HAM STROGANOFF (INCL HAM W/ CREAM OR WHITE SAUCE)","Ham stroganoff" +27120090,"HAM/PORK W/ (MUSHROOM) SOUP-BASE SAUCE (MIXTURE)","Ham or pork with (mushroom) soup (mixture)" +27120100,"HAM/PORK W/ TOMATO-BASED SAUCE (MIXTURE)","Ham or pork with tomato-based sauce (mixture)" +27120110,"SAUSAGE W/ TOMATO-BASED SAUCE (MIXTURE)","Sausage with tomato-based sauce (mixture)" +27120120,"SAUSAGE GRAVY","Sausage gravy" +27120130,"MEXICAN STYLE PORK STEW,NO POT,TOM-BASE SCE(MIXTUR","Mexican style pork stew, no potatoes, tomato-based sauce (mixture) (cerdo guisado sin papas)" +27120150,"PORK OR HAM W/ SOY-BASED SAUCE (MIXTURE)","Pork or ham with soy-based sauce (mixture)" +27120160,"PORK CURRY","Pork curry" +27120210,"FRANKFURTER /HOT DOG,W/CHILI,NO BUN (INCL CHILI DOG,NO BUN)","Frankfurter or hot dog, with chili, no bun" +27120250,"FRANKFURTERS/HOT DOGS W/ TOM-BASED SCE (MIXTURE)","Frankfurters or hot dogs with tomato-based sauce (mixture)" +27121000,"PORK W/ CHILE & TOM (MIXTURE) (PUERCO CON CHILE)","Pork with chili and tomatoes (mixture) (Puerco con chile)" +27121010,"STEWED PORK, P.R.","Stewed pork, Puerto Rican style" +27121410,"CHILI CON CARNE W/ BEANS, MADE W/ PORK","Chili con carne with beans, made with pork" +27130010,"LAMB W/ GRAVY (MIXTURE)","Lamb or mutton with gravy (mixture)" +27130040,"SPAGHETTI SAUCE W/ LAMB, HOMEMADE-STYLE","Spaghetti sauce with lamb or mutton, homemade-style" +27130050,"LAMB GOULASH","Lamb or mutton goulash" +27130100,"LAMB OR MUTTON CURRY","Lamb or mutton curry" +27133010,"STEWED GOAT, P.R. (CABRITO EN FRICASE)","Stewed goat, Puerto Rican style (Cabrito en fricase, chilindron de chivo)" +27135010,"VEAL W/ GRAVY (MIXTURE)","Veal with gravy (mixture)" +27135020,"VEAL SCALLOPINI","Veal scallopini" +27135030,"VEAL W/ CREAM SAUCE (INCLUDE VEAL PAPRIKASH)","Veal with cream sauce (mixture)" +27135040,"VEAL W/ BUTTER SAUCE","Veal with butter sauce (mixture)" +27135050,"VEAL MARSALA","Veal Marsala" +27135110,"VEAL PARMIGIANA","Veal parmigiana" +27135150,"VEAL CORDON BLEU","Veal cordon bleu" +27136050,"VENISON/DEER W/ TOMATO-BASED SAUCE (MIXTURE)","Venison/deer with tomato-based sauce (mixture)" +27136080,"VENISON/DEER W/ GRAVY","Venison/deer with gravy (mixture)" +27136100,"CHILI CON CARNE W/ VENISON/DEER & BEANS","Chili con carne with venison/deer and beans" +27141000,"CHICKEN CACCIATORE (INCLUDE CHICKEN W/TOMATO SAUCE)","Chicken or turkey cacciatore" +27141030,"SPAGHETTI SAUCE W/ POULTRY, HOMEMADE","Spaghetti sauce with poultry, home-made style" +27141050,"STEWED CHICKEN W/ TOMATO SAUCE, MEXICAN STYLE","Stewed chicken with tomato-based sauce, Mexican style (mixture) (Pollo guisado con tomate)" +27141500,"CHILI CON CARNE W/ CHICKEN & BEANS","Chili con carne with chicken or turkey and beans" +27142000,"CHICKEN W/ GRAVY (MIXTURE)","Chicken with gravy (mixture)" +27142100,"CHICKEN FRICASSEE","Chicken or turkey fricassee" +27142200,"TURKEY W/ GRAVY (MIXTURE)","Turkey with gravy (mixture)" +27143000,"CHICKEN OR TURKEY W/ CREAM SAUCE (MIXTURE)","Chicken or turkey with cream sauce (mixture)" +27144000,"CHICKEN W/ (MUSHROOM) SOUP-BASED SAUCE (MIXTURE)","Chicken or turkey with (mushroom) soup (mixture)" +27145000,"CHICKEN TERIYAKI","Chicken or turkey teriyaki (chicken or turkey with soy-based sauce)" +27146000,"CHICKEN OR TURKEY W/ BBQ SAUCE, SKIN EATEN","Chicken or turkey with barbecue sauce, skin eaten" +27146010,"CHICKEN OR TURKEY W/ BBQ SAUCE, SKIN NOT EATEN","Chicken or turkey with barbecue sauce, skin not eaten" +27146050,"CHICKEN WING W/ HOT PEPPER SCE (INCL BUFFALO WING)","Chicken wing with hot pepper sauce" +27146100,"SWEET & SOUR CHICKEN","Sweet and sour chicken or turkey" +27146110,"SWEET AND SOUR CHICKEN OR TURKEY, WITHOUT VEGETABLES","Sweet and sour chicken or turkey, without vegetables" +27146150,"CHICKEN CURRY","Chicken curry" +27146160,"CHICKEN WITH MOLE SAUCE","Chicken with mole sauce" +27146200,"CHICKEN W/ CHEESE SAUCE (MIXTURE)","Chicken or turkey with cheese sauce (mixture)" +27146250,"CHICKEN CORDON BLEU","Chicken or turkey cordon bleu" +27146300,"CHICKEN PARMIGIANA","Chicken or turkey parmigiana" +27146350,"ORANGE CHICKEN","Orange chicken" +27146360,"SESAME CHICKEN","Sesame chicken" +27146400,"CHICKEN KIEV","Chicken kiev" +27148010,"STUFFED CHICKEN, DRUMSTICK OR BREAST, P.R.","Stuffed chicken, drumstick or breast, Puerto Rican style (Muslo de pollo o pechuga rellena)" +27150010,"FISH W/ CREAM OR WHITE SAUCE, NOT TUNA OR LOBSTER","Fish with cream or white sauce, not tuna or lobster (mixture)" +27150020,"CRAB, DEVILED","Crab, deviled" +27150030,"CRAB IMPERIAL (INCLUDE STUFFED CRAB)","Crab imperial" +27150050,"FISH TIMBALE OR MOUSSE","Fish timbale or mousse" +27150060,"LOBSTER NEWBURG (INCLUDE LOBSTER THERMIDOR)","Lobster newburg" +27150070,"LOBSTER W/ BUTTER SAUCE (INCLUDE LOBSTER NORFOLK)","Lobster with butter sauce (mixture)" +27150100,"SHRIMP CURRY","Shrimp curry" +27150110,"SHRIMP COCKTAIL (SHRIMP W/ COCKTAIL SAUCE)","Shrimp cocktail (shrimp with cocktail sauce)" +27150120,"TUNA W/ CREAM OR WHITE SAUCE (MIXTURE)","Tuna with cream or white sauce (mixture)" +27150130,"SEAFOOD NEWBURG (INCLUDE CRABMEAT THERMIDOR)","Seafood newburg" +27150140,"CLAM SAUCE, WHITE","Clam sauce, white" +27150160,"SHRIMP W/ LOBSTER SAUCE (MIXTURE)","Shrimp with lobster sauce (mixture)" +27150170,"SWEET & SOUR SHRIMP","Sweet and sour shrimp" +27150190,"LOBSTER SAUCE (BROTH-BASED)","Lobster sauce (broth-based)" +27150200,"OYSTER SCE (WHITE SCE-BASED)","Oyster sauce (white sauce-based)" +27150210,"FISH SAUCE (BAGOONG)","Fish sauce (bagoong)" +27150230,"SHRIMP SCAMPI","Shrimp scampi" +27150250,"FISH MOOCHIM (KOREAN STYLE), DRIED FISH W/ SOY SCE","Fish moochim (Korean style), dried fish with soy sauce" +27150310,"FISH W/ TOMATO-BASED SAUCE (MIXTURE)","Fish with tomato-based sauce (mixture)" +27150320,"FISH CURRY","Fish curry" +27150330,"MUSSELS W/ TOMATO-BASED SAUCE (MIXTURE)","Mussels with tomato-based sauce (mixture)" +27150350,"SARDINES W/ TOMATO-BASED SAUCE (MIXTURE)","Sardines with tomato-based sauce (mixture)" +27150370,"SARDINES W/ MUSTARD SAUCE (MIXTURE)","Sardines with mustard sauce (mixture)" +27150410,"SHRIMP TERIYAKI","Shrimp teriyaki (shrimp with soy-based sauce) (mixture)" +27150510,"SCALLOPS W/ CHEESE SAUCE (MIXTURE)","Scallops with cheese sauce (mixture)" +27151030,"MARINATED FISH (CEVICHE)","Marinated fish (Ceviche)" +27151040,"CRABS IN TOMATO-BASED SAUCE, PUERTO RICAN STYLE","Crabs in tomato-based sauce, Puerto Rican style (mixture) (Salmorejo de jueyes)" +27151050,"SHRIMP IN GARLIC SAUCE, P.R. (CAMARONES AL AJILLO)","Shrimp in garlic sauce, Puerto Rican style (mixture) (Camarones al ajillo)" +27151070,"STEWED CODFISH, PUERTO RICAN STYLE, NO POTATOES","Stewed codfish, Puerto Rican style, no potatoes (potatoes reported separately)" +27160010,"MEAT W/ BARBECUE SAUCE, NS AS TO TYPE OF MEAT","Meat with barbecue sauce, NS as to type of meat (mixture)" +27160100,"MEATBALLS, NS AS TO TYPE OF MEAT, W/ SAUCE","Meatballs, NS as to type of meat, with sauce (mixture)" +27161010,"MEAT LOAF, P.R. (ALBONDIGON)","Puerto Rican style meat loaf (Albondigon)" +27162010,"MEAT W/ TOMATO-BASED SAUCE","Meat with tomato-based sauce (mixture)" +27162050,"SPAGHETTI SAUCE W/ COMBINATION OF MEATS, HOMEMADE","Spaghetti sauce with combination of meats, homemade-style" +27162060,"SPAGHETTI SAUCE W/ MEAT & VEGETABLES, HOMEMADE-STYLE","Spaghetti sauce with meat and vegetables, homemade-style" +27162500,"STEWED SEASONED GROUND BEEF & PORK, MEXICAN","Stewed, seasoned, ground beef and pork, Mexican style (Picadillo de carne de rez y puerco)" +27163010,"MEAT W/ GRAVY, NS AS TO TYPE OF MEAT (MIXTURE)","Meat with gravy, NS as to type of meat (mixture)" +27211000,"BEEF & POTATOES, NO SAUCE (MIXTURE)","Beef and potatoes, no sauce (mixture)" +27211100,"BEEF STEW W/ POTATOES, TOMATO-BASED SAUCE","Beef stew with potatoes, tomato-based sauce (mixture)" +27211110,"MEXICAN BEEF STEW W/POT,TOM SCE (CARNE GUISADA CON)","Mexican style beef stew with potatoes, tomato-based sauce (mixture) (Carne guisada con papas)" +27211150,"BEEF GOULASH W/ POTATOES (INCL BEEF GOULASH, NFS)","Beef goulash with potatoes" +27211190,"BEEF & POTATOES W/ CREAM, WHITE, MUSHROOM SOUP SCE (MIXTURE)","Beef and potatoes with cream sauce, white sauce or mushroom soup-based sauce (mixture)" +27211200,"BEEF STEW W/ POTATOES, GRAVY","Beef stew with potatoes, gravy" +27211300,"BEEF (ROAST) HASH","Beef (roast) hash" +27211400,"CORNED BEEF HASH","Corned beef hash" +27211500,"BEEF & POTATOES W/ CHEESE SAUCE (MIXTURE)","Beef and potatoes with cheese sauce (mixture)" +27211550,"STEWED SEASONED GROUND BEEF W/ POTATOES, MEXICAN","Stewed, seasoned, ground beef with potatoes, Mexican style (Picadillo de carne de rez con papas)" +27212000,"BEEF & NOODLES, NO SAUCE","Beef and noodles, no sauce (mixture)" +27212050,"BEEF & MACARONI WITH CHEESE SAUCE (MIXTURE)","Beef and macaroni with cheese sauce (mixture)" +27212100,"BEEF & NOODLES W/ TOMATO-BASED SAUCE (MIXTURE)","Beef and noodles with tomato-based sauce (mixture)" +27212120,"CHILI CON CARNE W/ BEANS & MACARONI","Chili con carne with beans and macaroni" +27212150,"BEEF GOULASH W/ NOODLES","Beef goulash with noodles" +27212200,"BEEF & NOODLES W/ GRAVY (MIXTURE)","Beef and noodles with gravy (mixture)" +27212300,"BEEF & NOODLES W/ CREAM OR WHITE SAUCE (MIXTURE)","Beef and noodles with cream or white sauce (mixture)" +27212350,"BEEF STROGANOFF W/ NOODLES","Beef stroganoff with noodles" +27212400,"BEEF & NOODLES W/ (MUSHROOM) SOUP (MIXTURE)","Beef and noodles with (mushroom) soup (mixture)" +27212500,"BEEF AND NOODLES WITH SOY-BASED SAUCE (MIXTURE)","Beef and noodles with soy-based sauce (mixture)" +27213000,"BEEF & RICE, NO SAUCE (MIXTURE)","Beef and rice, no sauce (mixture)" +27213010,"BIRYANI WITH MEAT","Biryani with meat" +27213100,"BEEF & RICE W/ TOMATO-BASED SAUCE (MIXTURE)","Beef and rice with tomato-based sauce (mixture)" +27213120,"PORCUPINE BALLS W/ TOMATO-BASED SAUCE (MIXTURE)","Porcupine balls with tomato-based sauce (mixture)" +27213150,"CHILI CON CARNE W/ BEANS & RICE","Chili con carne with beans and rice" +27213200,"BEEF & RICE W/ GRAVY (MIXTURE)","Beef and rice with gravy (mixture)" +27213300,"BEEF & RICE W/ CREAM SAUCE (MIXTURE)","Beef and rice with cream sauce (mixture)" +27213400,"BEEF & RICE W/ (MUSHROOM) SOUP (MIXTURE)","Beef and rice with (mushroom) soup (mixture)" +27213420,"PORCUPINE BALLS W/ (MUSHROOM) SOUP (MIXTURE)","Porcupine balls with (mushroom) soup (mixture)" +27213500,"BEEF & RICE W/ SOY-BASED SAUCE (MIXTURE)","Beef and rice with soy-based sauce (mixture)" +27213600,"BEEF AND RICE WITH CHEESE SAUCE (MIXTURE)","Beef and rice with cheese sauce (mixture)" +27214100,"MEAT LOAF MADE W/ BEEF","Meat loaf made with beef" +27214110,"MEAT LOAF W/ BEEF, W/ TOMATO SAUCE","Meat loaf made with beef, with tomato-based sauce" +27214300,"BEEF WELLINGTON","Beef wellington" +27214500,"CORNED BEEF PATTY","Corned beef patty" +27214600,"CREAMED DRIED BEEF ON TOAST","Creamed dried beef on toast" +27218110,"STUFFED POT ROAST (LARDED MEAT) W/ POTATOES, P.R.","Puerto Rican style stuffed pot roast (larded meat) with potatoes (Carne mechada con papas boliche)" +27218210,"BEEF STEW, P.R. W/ POTATOES (CARNE GUISADA CON PAPAS)","Puerto Rican style beef stew with potatoes (Carne guisada con papas)" +27218310,"STEWED CORNED BEEF, P.R. (""CORNED BEEF"" GUISADO)","Stewed corned beef, Puerto Rican style (""Corned beef"" guisado)" +27220010,"MEAT LOAF MADE W/ HAM (NOT LUNCHEON MEAT)","Meat loaf made with ham (not luncheon meat)" +27220020,"HAM & NOODLES W/ CREAM OR WHITE SAUCE (MIXTURE)","Ham and noodles with cream or white sauce (mixture)" +27220030,"HAM & RICE W/ (MUSHROOM) SOUP (MIXTURE)","Ham and rice with (mushroom) soup (mixture)" +27220050,"HAM OR PORK W/ STUFFING","Ham or pork with stuffing (mixture)" +27220080,"HAM CROQUETTE","Ham croquette" +27220110,"PORK & RICE W/ TOMATO-BASED SAUCE (MIXTURE)","Pork and rice with tomato-based sauce (mixture)" +27220120,"SAUSAGE & RICE W/ TOMATO-BASED SAUCE (MIXTURE)","Sausage and rice with tomato-based sauce (mixture)" +27220150,"SAUSAGE & RICE W/ (MUSHROOM) SOUP (MIXTURE)","Sausage and rice with (mushroom) soup (mixture)" +27220170,"SAUSAGE & RICE W/ CHEESE SAUCE (MIXTURE)","Sausage and rice with cheese sauce (mixture)" +27220190,"SAUSAGE & NOODLES W/ CREAM OR WHITE SAUCE (MIXTURE)","Sausage and noodles with cream or white sauce (mixture)" +27220210,"HAM & NOODLES, NO SAUCE (MIXTURE)","Ham and noodles, no sauce (mixture)" +27220310,"HAM & RICE, NO SAUCE (MIXTURE)","Ham or pork and rice, no sauce (mixture)" +27220510,"HAM/PORK & POTATOES W/ GRAVY (MIXTURE)","Ham or pork and potatoes with gravy (mixture)" +27220520,"PORK & POTATOES W/ CHEESE SAUCE","Ham or pork and potatoes with cheese sauce (mixture)" +27221100,"STEWED PIG'S FEET, P.R. (PATITAS DE CERDO GUISADAS)","Stewed pig's feet, Puerto Rican style (Patitas de cerdo guisadas)" +27221110,"STUFFED PORK ROAST, P.R.","Stuffed pork roast, Puerto Rican style" +27221150,"MEXICAN STYLE PORK STEW W/POT,TOM-BASE SCE(MIXTURE)","Mexican style pork stew, with potatoes, tomato-based sauce (mixture) (cerdo guisado con papas)" +27230010,"LAMB LOAF","Lamb or mutton loaf" +27231000,"LAMB OR MUTTON & POTATOES W/ GRAVY (MIXTURE)","Lamb or mutton and potatoes with gravy (mixture)" +27232000,"LAMB & POTATOES W/ TOMATO-BASED SAUCE (MIXTURE)","Lamb or mutton and potatoes with tomato-based sauce (mixture)" +27233000,"LAMB OR MUTTON & NOODLES W/ GRAVY (MIXTURE)","Lamb or mutton and noodles with gravy (mixture)" +27235000,"MEAT LOAF MADE WITH VENISON/DEER","Meat loaf made with venison/deer" +27235750,"VEAL & NOODLES W/ CREAM/WHITE SCE (MIXTURE)","Veal and noodles with cream or white sauce (mixture)" +27236000,"VENISON/DEER & NOODLE MIXTURE W/ CREAM/WHITE SAUCE","Venison/deer and noodles with cream or white sauce (mixture)" +27241000,"CHICKEN OR TURKEY HASH","Chicken or turkey hash" +27241010,"CHICKEN OR TURKEY & POTATOES W/ GRAVY (MIXTURE)","Chicken or turkey and potatoes with gravy (mixture)" +27242000,"CHICKEN OR TURKEY & NOODLES, NO SAUCE (MIXTURE)","Chicken or turkey and noodles, no sauce (mixture)" +27242200,"CHICKEN OR TURKEY & NOODLES W/ GRAVY (MIXTURE)","Chicken or turkey and noodles with gravy (mixture)" +27242250,"CHICKEN OR TURKEY & NOODLES W/ (MUSHROOM) SOUP","Chicken or turkey and noodles with (mushroom) soup (mixture)" +27242300,"CHICKEN OR TURKEY & NOODLES W/ CREAM OR WHITE SAUCE","Chicken or turkey and noodles with cream or white sauce (mixture)" +27242310,"CHICKEN & NOODLES W/ CHEESE SAUCE","Chicken or turkey and noodles with cheese sauce (mixture)" +27242350,"CHICKEN OR TURKEY TETRAZZINI","Chicken or turkey tetrazzini" +27242400,"CHICKEN & NOODLES, TOMATO-BASED SAUCE (MIXTURE)","Chicken or turkey and noodles, tomato-based sauce (mixture)" +27242500,"CHICKEN OR TURKEY AND NOODLES WITH SOY-BASED SAUCE (MIXTURE)","Chicken or turkey and noodles with soy-based sauce (mixture)" +27243000,"CHICKEN & RICE, NO SAUCE (MIXTURE)","Chicken or turkey and rice, no sauce (mixture)" +27243100,"BIRYANI WITH CHICKEN","Biryani with chicken" +27243300,"CHICKEN & RICE W/ CREAM SAUCE (MIXTURE)","Chicken or turkey and rice with cream sauce (mixture)" +27243400,"CHICKEN & RICE W/ (MUSHROOM) SOUP-BASED SAUCE","Chicken or turkey and rice with (mushroom) soup (mixture)" +27243500,"CHICKEN & RICE W/ TOMATO-BASED SAUCE (MIXTURE)","Chicken or turkey and rice with tomato-based sauce (mixture)" +27243600,"CHICKEN & RICE W/ SOY-BASED SAUCE (MIXTURE)","Chicken or turkey and rice with soy-based sauce (mixture)" +27243700,"CHICKEN IN CHEESE SCE W/ SPANISH RICE","Chicken in cheese sauce with Spanish rice" +27246100,"CHICKEN W/ DUMPLINGS (MIXTURE)","Chicken or turkey with dumplings (mixture)" +27246200,"CHICKEN W/ STUFFING (MIXTURE)","Chicken or turkey with stuffing (mixture)" +27246300,"CHICKEN OR TURKEY CAKE, PATTY OR CROQUETTE","Chicken or turkey cake, patty, or croquette" +27246400,"CHICKEN SOUFFLE","Chicken or turkey souffle" +27246500,"MEAT LOAF MADE W/ CHICKEN OR TURKEY","Meat loaf made with chicken or turkey" +27246505,"MEAT LOAF W/ CHICKEN OR TURKEY, W/ TOMATO SAUCE","Meat loaf made with chicken or turkey, with tomato-based sauce" +27250020,"CLAMS, STUFFED","Clams, stuffed" +27250030,"CODFISH BALL OR CAKE","Codfish ball or cake" +27250040,"CRAB CAKE","Crab cake" +27250050,"FISH CAKE OR PATTY, NS AS TO FISH","Fish cake or patty, NS as to fish" +27250060,"GEFILTE FISH","Gefilte fish" +27250070,"SALMON CAKE OR PATTY (INCLUDE SALMON CROQUETTE)","Salmon cake or patty" +27250080,"SALMON LOAF","Salmon loaf" +27250110,"SCALLOPS & NOODLES W/ CHEESE SAUCE (MIXTURE)","Scallops and noodles with cheese sauce (mixture)" +27250120,"SHRIMP AND NOODLES, NO SAUCE (MIXTURE)","Shrimp and noodles, no sauce (mixture)" +27250122,"SHRIMP AND NOODLES WITH GRAVY (MIXTURE)","Shrimp and noodles with gravy (mixture)" +27250124,"SHRIMP AND NOODLES WITH (MUSHROOM) SOUP (MIXTURE)","Shrimp and noodles with (mushroom) soup (mixture)" +27250126,"SHRIMP AND NOODLES WITH CREAM OR WHITE SAUCE (MIXTURE)","Shrimp and noodles with cream or white sauce (mixture)" +27250128,"SHRIMP AND NOODLES WITH SOY-BASED SAUCE (MIXTURE)","Shrimp and noodles with soy-based sauce (mixture)" +27250130,"SHRIMP & NOODLES W/ CHEESE SAUCE","Shrimp and noodles with cheese sauce (mixture)" +27250132,"SHRIMP AND NOODLES WITH TOMATO SAUCE (MIXTURE)","Shrimp and noodles with tomato sauce (mixture)" +27250150,"TUNA LOAF","Tuna loaf" +27250160,"TUNA CAKE OR PATTY","Tuna cake or patty" +27250210,"CLAM CAKE OR PATTY (INCLUDE DEVILED)","Clam cake or patty" +27250220,"OYSTER FRITTER","Oyster fritter" +27250250,"FLOUNDER W/CRAB STUFFING","Flounder with crab stuffing" +27250260,"LOBSTER W/ BREAD STUFFING,BAKED","Lobster with bread stuffing, baked" +27250270,"CLAMS, CASINO","Clams Casino" +27250300,"MACKEREL CAKE OR PATTY","Mackerel cake or patty" +27250310,"HADDOCK CAKE OR PATTY","Haddock cake or patty" +27250400,"SHRIMP CAKE OR PATTY (INCL SHRIMP BURGER OR STICK)","Shrimp cake or patty" +27250410,"SHRIMP W/ CRAB STUFFING","Shrimp with crab stuffing" +27250450,"SHRIMP TOAST, FRIED","Shrimp toast, fried" +27250520,"SEAFOOD, RESTRUCTURED (INCL IMITATION CRABMEAT)","Seafood restructured" +27250550,"SEAFOOD SOUFFLE","Seafood souffle" +27250610,"TUNA NOODLE CASSEROLE W/ CREAM OR WHITE SAUCE","Tuna noodle casserole with cream or white sauce" +27250630,"TUNA NOODLE CASSEROLE W/ (MUSHROOM) SOUP","Tuna noodle casserole with (mushroom) soup" +27250710,"TUNA & RICE W/ (MUSHROOM) SOUP (MIXTURE)","Tuna and rice with (mushroom) soup (mixture)" +27250810,"FISH & RICE W/ TOMATO-BASED SAUCE","Fish and rice with tomato-based sauce" +27250820,"FISH & RICE W/ CREAM SAUCE","Fish and rice with cream sauce" +27250830,"FISH & RICE W/ (MUSHROOM) SOUP","Fish and rice with (mushroom) soup" +27250900,"FISH & NOODLES W/ (MUSHROOM) SOUP (MIXTURE)","Fish and noodles with (mushroom) soup" +27250950,"SHELLFISH & NOODLES, TOMATO-BASED SAUCE","Shellfish mixture and noodles, tomato-based sauce (mixture)" +27251010,"STEWED SALMON, P.R. (SALMON GUISADO)","Stewed salmon, Puerto Rican style (Salmon guisado)" +27260010,"MEATLOAF, NS AS TO TYPE OF MEAT","Meat loaf, NS as to type of meat" +27260050,"MEATBALLS, W/ BREADING, W/ GRAVY","Meatballs, with breading, NS as to type of meat, with gravy" +27260080,"MEAT LOAF MADE W/ BEEF & PORK","Meat loaf made with beef and pork" +27260090,"MEAT LOAF W/ BEEF, VEAL & PORK","Meat loaf made with beef, veal and pork" +27260100,"MEAT LOAF W/ BEEF & PORK, W/ TOMATO SAUCE","Meat loaf made with beef and pork, with tomato-based sauce" +27260110,"HASH, NS AS TO TYPE OF MEAT","Hash, NS as to type of meat" +27260500,"VIENNA SAUSAGES STEWED W/ POTATOES, P.R.","Vienna sausages stewed with potatoes, Puerto Rican style (Salchichas guisadas)" +27260510,"LIVER DUMPLING","Liver dumpling" +27261000,"BREADED BRAINS, P.R. (SESOS REBOSADOS)","Breaded brains, Puerto Rican style (Sesos rebosados)" +27261500,"STEWED SEASONED GROUND BEEF&PORK,W/POT, MEXICAN","Stewed, seasoned, ground beef and pork, with potatoes, Mexican style (Picadillo de carne de rez y puerco con papas)" +27311110,"BEEF, POTATOES, & VEG (W/ CAR/DK GREEN), NO SAUCE","Beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27311120,"BEEF, POTATOES, & VEG (NO CAR/DK GREEN), NO SAUCE","Beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27311210,"CORNED BEEF, POT & VEG(W/ CAR/DK GREEN), NO SAUCE","Corned beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27311220,"CORNED BEEF, POTATO & VEG (NO CAR/DK GRN), NO SAUCE","Corned beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27311310,"BEEF STEW W/ POT & VEG(W/ CAR/DK GRN), TOMATO SAUCE","Beef stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27311320,"BEEF STEW W/ POT & VEG (NO CAR/DK GREEN), TOM SAUCE","Beef stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce" +27311410,"BEEF STEW W/ POT & VEG (W/ CAR, DK GREEN), GRAVY","Beef stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy" +27311420,"BEEF STEW W/ POT & VEG (NO CAR, DK GREEN), GRAVY","Beef stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy" +27311510,"SHEPHERD'S PIE W/ BEEF","Shepherd's pie with beef" +27311600,"BEEF, POT, & VEG (INCL CAR/DK GRN), GRAVY","Beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27311605,"BEEF, POT, & VEG (NO CAR/DK GREEN), GRAVY","Beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27311610,"BEEF, POT & VEG (INCL CAR/DK GRN), CR/SOUP-BASED SAUCE","Beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27311620,"BEEF, POT & VEG (NO CAR/DK GRN), CR/SOUP-BASED SAUCE","Beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27311625,"BEEF, POT, & VEG (INCL CAR/DK GRN), TOMATO-BASED SAUCE","Beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27311630,"BEEF, POT, & VEG (NO CAR/DK GREEN), TOMATO-BASED SAUCE","Beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27311635,"BEEF, POT, & VEG (INCL CAR/DK GRN), CHEESE SAUCE","Beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), cheese sauce (mixture)" +27311640,"BEEF, POT, & VEG (NO CAR/DK GREEN), CHEESE SAUCE","Beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27311645,"BEEF, POT, & VEG (INCL CAR/DK GRN), SOY-BASED SAUCE","Beef, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), soy-based sauce (mixture)" +27311650,"BEEF, POT, & VEG (NO CAR/DK GREEN), SOY-BASED SAUCE","Beef, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), soy-based sauce (mixture)" +27313010,"BEEF, NOODLES & VEG (W/ CARROTS/DK GREEN), NO SAUCE","Beef, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27313020,"BEEF, NOODLES & VEG (NO CARROTS/DK GREEN), NO SAUCE","Beef, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27313110,"BEEF CHOW MEIN OR CHOP SUEY W/ NOODLES","Beef chow mein or chop suey with noodles" +27313150,"BEEF, NOODLES & VEG (W/ CAR/DK GREEN), SOY SAUCE","Beef, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), soy-based sauce (mixture)" +27313160,"BEEF, NOODLES & VEG (NO CAR/DK GREEN), SOY SAUCE","Beef, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), soy-based sauce (mixture)" +27313210,"BEEF, NOODLES & VEG (W/ CAR/DK GREEN), TOMATO SAUCE","Beef, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27313220,"BEEF, NOODLES & VEG (NO CAR/DK GREEN), TOMATO SAUCE","Beef, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27313310,"BEEF, NOODLES, VEG(INCL CARROTS/DK GREEN), SOUP","Beef, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), (mushroom) soup (mixture)" +27313320,"BEEF, NOODLES, VEG (NO CARROTS/DK GREEN), SOUP","Beef, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), (mushroom) soup (mixture)" +27313410,"BEEF, NOODLES, & VEG (INCL CAR/DK GRN), GRAVY","Beef, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27313420,"BEEF, NOODLES, & VEG (NO CAR/DK GRN), GRAVY","Beef, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27315010,"BEEF, RICE & VEG (W/ CARROTS/DK GREEN), NO SAUCE","Beef, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27315020,"BEEF, RICE & VEG (NO CARROTS/DK GREEN), NO SAUCE","Beef, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27315210,"BEEF, RICE & VEG (W/ CAR/DK GREEN), TOMATO SAUCE","Beef, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27315220,"BEEF, RICE & VEG (NO CAR/DK GREEN), TOMATO SAUCE","Beef, rice, and vegetables (excluding carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27315250,"STUFFED CABBAGE ROLLS W/ BEEF AND RICE","Stuffed cabbage rolls with beef and rice" +27315270,"STUFFED GRAPE LEAVES W/ BEEF & RICE","Stuffed grape leaves with beef and rice" +27315310,"BEEF, RICE & VEGETABLES (W/ CARROTS/DK GREEN), SOUP","Beef, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), (mushroom) soup (mixture)" +27315320,"BEEF, RICE & VEGETABLES (NO CARROTS/DK GREEN), SOUP","Beef, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), (mushroom) soup (mixture)" +27315330,"BEEF, RICE & VEG (INCL CAR/DK GRN), CHEESE SAUCE","Beef, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), cheese sauce (mixture)" +27315340,"BEEF, RICE & VEG (NO CAR/DK GRN), CHEESE SAUCE","Beef, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27315410,"BEEF, RICE & VEG (INCL CAR/DK GRN), GRAVY, MIXTURE","Beef, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27315420,"BEEF, RICE & VEG (NO CAR/DK GRN), GRAVY, MIXTURE","Beef, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27315510,"BEEF, RICE & VEG (INCL CAR/DK GRN), SOY-BASED SAUCE","Beef, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), soy-based sauce (mixture)" +27315520,"BEEF, RICE & VEG (NO CAR/DK GRN), SOY-BASED SAUCE","Beef, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), soy-based sauce (mixture)" +27317010,"BEEF POT PIE (INCLUDE GREEK MEAT PIE)","Beef pot pie" +27317100,"BEEF, DUMPLINGS & VEG (INCL CAR/DK GRN), GRAVY","Beef, dumplings, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27317110,"BEEF, DUMPLINGS & VEG (NO CAR/DK GRN), GRAVY","Beef, dumplings, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27319010,"STUFFED GREEN PEPPER, P.R. (PIMIENTO RELLENO)","Stuffed green pepper, Puerto Rican style (Pimiento relleno)" +27320020,"HAM POT PIE","Ham pot pie" +27320025,"HAM OR PORK, NOODLES, VEG (NO CAR, BROC, DK GRN)NO SAUCE","Ham or pork, noodles and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27320027,"HAM OR PORK, NOODLES, VEG (INCL CAR, BROC, DARK GREEN)NO SAU","Ham or pork, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27320030,"HAM/PORK, NOODLES & VEG (NO CAR/DK GR), CHEESE SCE","Ham or pork, noodles and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27320040,"PORK, POTATOES & VEG (W/ CAR, DK GREEN), NO SAUCE","Pork, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27320070,"PORK, NOODLES, VEG (INCL CAR/DK GRN), TOMATO SAUCE","Ham or pork, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27320080,"SAUSAGE, NOODLES, VEG (NO CAR/DK GRN), TOMATO SAUCE","Sausage, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce" +27320090,"SAUSAGE, NOODLES, VEG (W/ CAR/DK GRN), TOMATO SAUCE","Sausage, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27320100,"PORK, POTATOES & VEG (W/ CAR, DK GREEN), TOMATO SCE","Pork, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27320110,"PORK, POTATOES & VEG (NO CAR, DK GREEN), TOMATO SCE","Pork, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27320120,"SAUSAGE, POT, & VEG (INCL CAR/BROC/DK GREEN), GRAVY","Sausage, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27320130,"SAUSAGE, POT, & VEG (NO CAR/BROC/DK GREEN), GRAVY","Sausage, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27320140,"PORK, POT, & VEG (INCL CAR/DK GRN), GRAVY, MIXTURE","Pork, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27320150,"PORK, POT, & VEG (NO CAR/DK GRN), GRAVY, MIXTURE","Pork, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27320210,"PORK, POTATOES & VEG (NO CAR, DK GREEN), NO SAUCE","Pork, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27320310,"PORK CHOW MEIN OR CHOP SUEY W/ NOODLES","Pork chow mein or chop suey with noodles" +27320320,"PORK, RICE & VEG (INCL CAR/DK GRN), SOY-BASED SAUCE","Pork, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), soy-based sauce (mixture)" +27320330,"PORK, RICE & VEG (NO CAR/DK GRN), SOY-BASED SAUCE","Pork, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), soy-based sauce (mixture)" +27320340,"PORK, RICE & VEG (INCL CAR/DK GRN), TOMATO SAUCE","Pork, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27320350,"PORK, RICE & VEG (NO CAR/DK GRN), TOMATO SAUCE","Pork, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27320410,"HAM, POTATOES & VEG (NO CARROTS/DK GREEN), NO SAUCE","Ham, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27320450,"HAM, POTATOES & VEG (W/ CARROTS/DK GREEN), NO SAUCE","Ham, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27320500,"SWEET & SOUR PORK W/ RICE","Sweet and sour pork with rice" +27330010,"SHEPHERD'S PIE W/ LAMB","Shepherd's pie with lamb" +27330030,"LAMB STEW W/ POT & VEG (INCL CAR/DK GREEN), GRAVY","Lamb or mutton stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy" +27330050,"LAMB, RICE & VEGETABLES (NO CARROT/DK GREEN), GRAVY","Lamb or mutton, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27330060,"LAMB, RICE & VEG (INCL CAR/DK GRN), TOMATO SAUCE","Lamb or mutton, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27330080,"LAMB, RICE, & VEGETABLES (INCL CAR, DK GRN), GRAVY","Lamb or mutton, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy" +27330110,"LAMB STEW W/ POT & VEG (NO CAR/DK GREEN), GRAVY","Lamb or mutton stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy" +27330170,"STUFFED GRAPE LEAVES W/ LAMB & RICE","Stuffed grape leaves with lamb and rice" +27330210,"LAMB STEW W/ POT & VEG (INCL CAR/DK GRN), TOM SAUCE","Lamb or mutton stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27330220,"LAMB STEW W/ POT & VEG (NO CAR/DK GRN), TOMATO SCE","Lamb or mutton stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce" +27331150,"VEAL FRICASSEE, P.R. (TERNERA EN FRICASE)","Veal fricassee, Puerto Rican style (ternera en fricase)" +27332100,"VEAL STEW W/ POT, VEG (INCL CAR/DK GRN) TOM SAUCE","Veal stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27332110,"VEAL STEW W/ POT, VEG (NO CAR,DK GRN), TOMATO SAUCE","Veal stew with potatoes and vegetables (excluding carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27335100,"RABBIT STEW W/ POTATOES & VEGETABLES","Rabbit stew with potatoes and vegetables" +27335500,"STEWED RABBIT, P.R. (FRICASE DE CONEJO)","Stewed rabbit, Puerto Rican style (Fricase de conejo)" +27336100,"VENISON/DEER STEW W/ POTATO & VEG(W/ CAR/DK GRN),TOM SCE","Venison/deer stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27336150,"VENISON/DEER STEW W/ POTATO & VEG(NO CAR/DK GRN),TOM SCE","Venison/deer stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce" +27336200,"VENISON/DEER, POTATOES & VEG (INCL CAR/DK GRN), GRAVY","Venison/deer, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27336250,"VENISON/DEER, POTATOES & VEG (NO CAR/DK GRN), GRAVY","Venison/deer, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27336300,"VENISON/DEER, NOODLES & VEG (INCL CAR/DK GRN),TOM SAUCE","Venison/deer, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27336310,"VENISON/DEER, NOODLES & VEG (NO CAR/DK GRN), TOM SAUCE","Venison/deer, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27341000,"CHICKEN OR TURKEY, POTATOES, CORN, AND CHEESE, WITH GRAVY","Chicken or turkey, potatoes, corn, and cheese, with gravy" +27341010,"CHICKEN, POT & VEG (INCL CAR/DK GRN), NO SAUCE","Chicken or turkey, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27341020,"CHICKEN, POT & VEG (NO CAR/DK GRN), NO SAUCE","Chicken or turkey, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27341025,"CHICKEN, POT & VEG (INCL CAR/DK GRN), GRAVY","Chicken or turkey, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27341030,"CHICKEN, POT & VEG (NO CAR/DK GRN), GRAVY","Chicken or turkey, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27341035,"CHICKEN, POT & VEG (INCL CAR/DK GRN), CREAM/SOUP-BASED SAUCE","Chicken or turkey, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27341040,"CHICKEN, POT & VEG (NO CAR/DK GRN), CREAM/SOUP-BASED SAUCE","Chicken or turkey, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27341045,"CHICKEN, POT & VEG (INCL CAR/DK GRN), CHEESE SAUCE","Chicken or turkey, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), cheese sauce (mixture)" +27341050,"CHICKEN, POT & VEG (NO CAR/DK GRN), CHEESE SAUCE","Chicken or turkey, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27341055,"CHICKEN, POT & VEG (INCL CAR/DK GRN), TOMATO-BASED SAUCE","Chicken or turkey, potatoes, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27341060,"CHICKEN, POT & VEG (NO CAR/DK GRN), TOMATO-BASED SAUCE","Chicken or turkey, potatoes, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27341310,"CHICKEN STEW W/ POT, VEG (INCL CAR/DK GRN), GRAVY","Chicken or turkey stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy" +27341320,"CHICKEN STEW W/ POT & VEG (NO CAR/DK GRN), GRAVY","Chicken or turkey stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy" +27341510,"CHICKEN STEW W/ POT & VEG(INCL CAR/DK GRN), TOM SCE","Chicken or turkey stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce" +27341520,"CHICKEN STEW W/ POT & VEG(NO CAR/DK GRN), TOM SAUCE","Chicken or turkey stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato- based sauce" +27343010,"CHICKEN, NOODLES & VEG (INCL CAR/DK GRN), NO SAUCE","Chicken or turkey, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27343020,"CHICKEN, NOODLES & VEG (NO CAR/DK GRN), NO SAUCE","Chicken or turkey, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27343410,"CHICKEN, NOODLES & VEG (INCL CAR/DK GRN), GRAVY","Chicken or turkey, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27343420,"CHICKEN, NOODLES & VEG (NO CAR/DK GRN), GRAVY","Chicken or turkey, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27343470,"CHICKEN OR TURKEY, NOODLES, AND VEGETABLES (INCLUDING CARROT","Chicken or turkey, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27343480,"CHIX, NDL, VEG(NO CAR/DK GRN), CR/SOUP-BASED SAUCE","Chicken or turkey, noodles, and vegetables (excluding carrots, broccoli, and/or dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27343510,"CHICKEN, NOODLES, VEG (INCL CAR/DK GRN), TOMATO SCE","Chicken or turkey, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27343520,"CHICKEN, NOODLES, VEG (NO CAR/DK GRN), TOMATO SAUCE","Chicken or turkey, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27343910,"CHICKEN CHOW MEIN/CHOP SUEY W/ NOODLES","Chicken or turkey chow mein or chop suey with noodles" +27343950,"CHICKEN, NOODLES & VEG(INCL CAR/DK GRN), CHEESE SCE","Chicken or turkey, noodles, and vegetables (including carrots, broccoli, and/or dark-green leafy), cheese sauce (mixture)" +27343960,"CHICKEN, NOODLES & VEG(NO CAR/DK GRN), CHEESE SAUCE","Chicken or turkey, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27345010,"CHICKEN, RICE & VEG (INCL CAR/DK GRN), NO SAUCE","Chicken or turkey, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27345020,"CHICKEN, RICE & VEG (NO CAR/DK GRN), NO SAUCE","Chicken or turkey, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), no sauce (mixture)" +27345210,"CHICKEN, RICE & VEG (INCL CAR/DK GRN), GRAVY","Chicken or turkey, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27345220,"CHICKEN, RICE & VEG (NO CAR/DK GRN), GRAVY","Chicken or turkey, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27345230,"CHICKEN OR TURKEY, RICE, CORN, AND CHEESE WITH GRAVY","Chicken or turkey, rice, corn, and cheese, with gravy" +27345310,"CHICKEN, RICE & VEG (INCL CAR/DK GRN), SOY SAUCE","Chicken or turkey, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), soy-based sauce (mixture)" +27345320,"CHICKEN, RICE & VEG (NO CAR/DK GRN), SOY SAUCE","Chicken or turkey, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), soy-based sauce (mixture)" +27345410,"CHIX, RICE, & VEG(INCL CAR/DK GRN), CR/SOUP-BASED SAU","Chicken or turkey, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27345420,"CHIX, RICE, AND VEG(NO CAR/DK GRN), CR/SOUP-BASED SAU","Chicken or turkey, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), cream sauce, white sauce, or mushroom soup-based sauce (mixture)" +27345440,"CHICKEN, RICE & VEG (INCL CAR/DK GRN), CHEESE SAUCE","Chicken or turkey, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), cheese sauce (mixture)" +27345450,"CHICKEN, RICE, VEG (NO CAR/DK GRN), CHEESE SAUCE","Chicken or turkey, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27345510,"CHICKEN, RICE & VEG (INCL CAR/DK GRN), TOMATO SAUCE","Chicken or turkey, rice, and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-based sauce (mixture)" +27345520,"CHICKEN, RICE & VEG (NO CAR/DK GRN), TOMATO SAUCE","Chicken or turkey, rice, and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-based sauce (mixture)" +27347100,"CHICKEN OR TURKEY POT PIE","Chicken or turkey pot pie" +27347200,"CHICKEN, STUFFING & VEG (INCL CAR/DK GRN), NO SAUCE","Chicken or turkey, stuffing, and vegetables (including carrots, broccoli, and/or dark-green leafy), no sauce (mixture)" +27347210,"CHICKEN, STUFFING, VEG (NO CAR/DK GRN), NO SAUCE","Chicken or turkey,stuffing, and vegetables (excluding carrots, broccoli, and dark green leafy), no sauce (mixture)" +27347220,"CHICKEN, STUFFING & VEG (INCL CAR/DK GRN), GRAVY","Chicken or turkey, stuffing, and vegetables (including carrots, broccoli, and/or dark-green leafy), gravy (mixture)" +27347230,"CHICKEN, STUFFING & VEG (NO CAR/DK GRN), GRAVY","Chicken or turkey, stuffing, and vegetables (excluding carrots, broccoli, and dark-green leafy), gravy (mixture)" +27347240,"CHICKEN, DUMPLINGS, VEG (INCL CAR/DK GRN), GRAVY","Chicken or turkey, dumplings, and vegetables (including carrots, broccoli, and/or dark green leafy), gravy (mixture)" +27347250,"CHICKEN, DUMPLINGS, VEG (NO CAR/DK GRN), GRAVY","Chicken or turkey, dumplings, and vegetables (excluding carrots, broccoli, and dark green leafy), gravy (mixture)" +27348100,"CHICKEN FRICASSEE, P.R. (FRICASE DE POLLO)","Chicken fricassee, Puerto Rican style (Fricase de pollo)" +27350020,"PAELLA WITH SEAFOOD","Paella with seafood" +27350030,"SEAFOOD STEW W/ POT & VEG (NO CAR/DK GREEN),TOM SCE","Seafood stew with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy), tomato-base sauce" +27350050,"SHRIMP CHOW MEIN OR CHOP SUEY W/ NOODLES","Shrimp chow mein or chop suey with noodles" +27350060,"SHRIMP CREOLE W/ RICE (INCLUDE SHRIMP JAMBALAYA)","Shrimp creole, with rice" +27350070,"TUNA POT PIE","Tuna pot pie" +27350080,"TUNA NOODLE CASSEROLE W/ VEG, CREAM OR WHITE SAUCE","Tuna noodle casserole with vegetables, cream or white sauce" +27350090,"FISH, NOODLES, VEG (INCL CAR/DK GRN), CHEESE SAUCE","Fish, noodles, and vegetables (including carrots, broccoli, and/or dark green leafy), cheese sauce (mixture)" +27350100,"FISH, NOODLES, VEG (NO CAR/DK GRN), CHEESE SAUCE","Fish, noodles, and vegetables (excluding carrots, broccoli, and dark-green leafy), cheese sauce (mixture)" +27350110,"BOUILLABAISSE","Bouillabaisse" +27350200,"OYSTER PIE (INCLUDE OYSTER POT PIE)","Oyster pie" +27350310,"SEAFOOD STEW W/ POT & VEG (W/ CAR/DK GREEN),TOM SCE","Seafood stew with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy), tomato-base sauce" +27350410,"TUNA NOODLE CASSEROLE W/ VEG & (MUSHROOM) SOUP","Tuna noodle casserole with vegetables and (mushroom) soup" +27351010,"CODFISH W/ STARCHY VEG, P.R. (SERENATA DE BACALAO)","Codfish with starchy vegetables, Puerto Rican style (Serenata de bacalao) (mixture)" +27351020,"CODFISH SALAD, P.R. (GAZPACHO DE BACALAO)","Codfish salad, Puerto Rican style (Gazpacho de bacalao)" +27351030,"STEWED CODFISH, P.R. (BACALAO GUISADO)","Stewed codfish, Puerto Rican style (Bacalao guisado)" +27351040,"BISCAYNE CODFISH, P.R. (BACALAO A LA VIZCAINA)","Biscayne codfish, Puerto Rican style (Bacalao a la Vizcaina)" +27351050,"CODFISH SALAD, P.R. (ENSALADA DE BACALAO)","Codfish salad, Puerto Rican style (Ensalada de bacalao)" +27360000,"STEW, NFS","Stew, NFS" +27360010,"GOULASH, NFS","Goulash, NFS" +27360050,"MEAT PIE, NFS","Meat pie, NFS" +27360080,"CHOW MEIN, NS AS TO TYPE OF MEAT, W/ NOODLES","Chow mein or chop suey, NS as to type of meat, with noodles" +27360090,"PAELLA, NFS","Paella, NFS" +27360100,"BRUNSWICK STEW","Brunswick stew" +27360120,"CHOW MEIN/CHOP SUEY,VARIOUS MEATS, W/ NOODLES","Chow mein or chop suey, various types of meat, with noodles" +27361010,"STEWED VARIETY MEATS (MOSTLY LIVER), P.R.(GANDINGA)","Stewed variety meats, Puerto Rican style (mostly liver) (Gandinga)" +27362000,"STEWED TRIPE W/ POTATOES, P.R. (MONDONGO)","Stewed tripe, Puerto Rican style, with potatoes (Mondongo)" +27363000,"GUMBO W/ RICE (NEW ORLEANS TYPE)","Gumbo with rice (New Orleans type with shellfish, pork, and/or poultry, tomatoes, okra, rice)" +27363100,"JAMBALAYA W/ MEAT & RICE","Jambalaya with meat and rice" +27410210,"BEEF & VEG (W/ CAR/DK GREEN, NO POTATO), NO SAUCE","Beef and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), no sauce (mixture)" +27410220,"BEEF & VEG (NO CAR/DK GREEN, NO POTATO), NO SAUCE","Beef and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), no sauce (mixture)" +27410250,"BEEF SHISH KABOB W/ VEGETABLES, EXCLUDING POTATOES","Beef shish kabob with vegetables, excluding potatoes" +27411100,"BEEF & VEG(W/ CAR/DK GREEN, NO POTATO), TOMATO SCE","Beef with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27411120,"SWISS STEAK","Swiss steak" +27411150,"BEEF ROLL,STUFFED W/VEG/MEAT MIXTURE,TOM-BASE SAUCE","Beef rolls, stuffed with vegetables or meat mixture, tomato-based sauce" +27411200,"BEEF W/ VEG (NO CAR/DK GREEN, NO POTATO),TOMATO SCE","Beef with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27414100,"BEEF W/ VEG (INCL CAR/DK GRN, NO POT), SOUP","Beef with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), (mushroom) soup (mixture)" +27414200,"BEEF W/ VEG (NO CAR/DK GRN, NO POT), SOUP","Beef with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), (mushroom) soup (mixture)" +27415100,"BEEF & VEG (W/ CAR/DK GREEN, NO POTATO), SOY SAUCE","Beef and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27415110,"BEEF AND BROCCOLI","Beef and broccoli" +27415120,"BEEF, TOFU & VEG(W/ CAR/DK GRN, NO POTATO),SOY SCE","Beef, tofu, and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27415130,"SZECHUAN BEEF","Szechuan beef" +27415140,"HUNAN BEEF","Hunan beef" +27415150,"BEEF, CHOW MEIN OR CHOP SUEY, NO NOODLES","Beef chow mein or chop suey, no noodles" +27415170,"KUNG PAO BEEF","Kung Pao beef" +27415200,"BEEF & VEG (NO CAR/DK GREEN, NO POTATO), SOY SAUCE","Beef and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27415220,"BEEF, TOFU & VEG(NO CAR/DK GRN, NO POTATO), SOY SCE","Beef, tofu, and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27416100,"BEEF & VEGETABLES, HAWAIIAN STYLE (MIXTURE)","Beef and vegetables, Hawaiian style (mixture)" +27416150,"PEPPER STEAK","Pepper steak" +27416200,"BEEF, GROUND, W/ EGG & ONION (MIXTURE)","Beef, ground, with egg and onion (mixture)" +27416250,"BEEF SALAD","Beef salad" +27416300,"BEEF TACO FILLING: BEEF, CHEESE, TOMATO, TACO SAUCE","Beef taco filling: beef, cheese, tomato, taco sauce" +27416400,"SUKIYAKI (STIR FRIED BEEF & VEGS IN SOY SAUCE)","Sukiyaki (stir fried beef and vegetables in soy sauce)" +27416450,"BEEF & VEG (INCL CAR/DK GRN, NO POTATOES), GRAVY","Beef and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), gravy (mixture)" +27416500,"BEEF & VEG (NO CAR/DK GREEN, NO POT), GRAVY","Beef and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), gravy (mixture)" +27418110,"SEASONED SHREDDED SOUP MEAT","Seasoned shredded soup meat (Ropa vieja, sopa de carne ripiada)" +27418210,"BEEF STEW, P.R. W/ VEGETABLES, NO POTATO (CARNE A LA JUDIA)","Puerto Rican style beef stew with vegetables, excluding potatoes (Carne a la Judia)" +27418310,"CORNED BEEF W/ TOMATO SAUCE & ONION, P.R. STYLE","Corned beef with tomato sauce and onion, Puerto Rican style (mixture)" +27418410,"BEEF STEAK W/ ONIONS, P.R. (BIFTEC ENCEBOLLADO)","Beef steak with onions, Puerto Rican style (mixture) (Biftec encebollado)" +27420010,"CABBAGE W/ HAM HOCKS (MIXTURE)","Cabbage with ham hocks (mixture)" +27420020,"HAM OR PORK SALAD","Ham or pork salad" +27420040,"FRANKFURTERS OR HOT DOGS & SAUERKRAUT (MIXTURE)","Frankfurters or hot dogs and sauerkraut (mixture)" +27420060,"PORK & VEG (W/ CAR/DK GREEN, NO POTATO), NO SAUCE","Pork and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), no sauce (mixture)" +27420080,"GREENS W/ HAM OR PORK (MIXTURE)","Greens with ham or pork (mixture)" +27420100,"PORK, TOFU & VEG (W/ CAR/DK GRN,NO POTATO), SOY SCE","Pork, tofu, and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-base sauce (mixture)" +27420110,"PORK & VEGETABLES, HAWAIIAN STYLE (MIXTURE)","Pork and vegetables, Hawaiian style (mixture)" +27420120,"PORK & WATERCRESS W/ SOY-BASED SAUCE (MIXTURE)","Pork and watercress with soy-based sauce (mixture)" +27420150,"KUNG PAO PORK","Kung Pao pork" +27420160,"MOO SHU (MU SHI) PORK, W/O PANCAKE","Moo Shu (Mu Shi) Pork, without Chinese pancake" +27420170,"PORK AND ONIONS W/ SOY-BASED SAUCE","Pork and onions with soy-based sauce (mixture)" +27420200,"PORK HASH,HAWAIIAN--PORK,VEG(NO CAR/DK GRN),SOY SCE","Pork hash, Hawaiian style-ground pork, vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce" +27420250,"HAM & VEG (W/ CARROT/DK GREEN, NO POTATO), NO SAUCE","Ham and vegetables (including carrots, broccoli, and/or dark- green leafy (no potatoes)), no sauce (mixture)" +27420270,"HAM & VEG (NO CARROT/DK GREEN, NO POTATO), NO SAUCE","Ham and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), no sauce (mixture)" +27420350,"PORK & VEG (NO CAR/DK GREEN, NO POTATO), NO SAUCE","Pork and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), no sauce (mixture)" +27420370,"PORK,TOFU & VEG(NO CAR/DK GREEN,NO POTATO)SOY SAUCE","Pork, tofu, and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27420390,"PORK CHOW MEIN OR CHOP SUEY, NO NOODLES","Pork chow mein or chop suey, no noodles" +27420400,"PORK & VEG (INCL CAR/DK GRN, NO POT), TOMATO SAUCE","Pork and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27420410,"PORK & VEG (NO CAR/DK GRN, NO POT), TOMATO SAUCE","Pork and vegetables (excluding carrots, broccoli, and dark- green leafy (no potatoes)), tomato-based sauce (mixture)" +27420450,"SAUSAGE & VEG (INCL CAR/DK GRN)(NO POT), TOM SAUCE","Sausage and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27420460,"SAUSAGE & VEG (NO CAR/DK GRN/POT), TOMATO SAUCE","Sausage and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27420470,"SAUSAGE & PEPPERS, NO SAUCE","Sausage and peppers, no sauce (mixture)" +27420500,"PORK & VEG (INCL CAR/DK GRN), SOY-BASED SAUCE","Pork and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27420510,"PORK & VEG (NO CAR/DK GRN), SOY-BASED SAUCE","Pork and vegetables (excluding carrots, broccoli, and dark- green leafy (no potatoes)), soy-based sauce (mixture)" +27420520,"PORK SHISH KABOB WITH VEGETABLES, EXCLUDING POTATOES","Pork shish kabob with vegetables, excluding potatoes" +27421010,"STUFFED CHRISTOPHINE, P.R. (CHAYOTE RELLENO)","Stuffed christophine, Puerto Rican style (Chayote relleno)" +27422010,"PORK CHOPS STEWED W/VEG, P.R. (CHULETAS A LA JARD.)","Pork chop stewed with vegetables, Puerto Rican style (mixture) (Chuletas a la jardinera)" +27430400,"LAMB STEW W/ VEG (INCL CAR/DK GRN, NO POT), GRAVY","Lamb or mutton stew with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), gravy" +27430410,"LAMB STEW W/ VEG (NO CAR/DK GRN, NO POT), GRAVY","Lamb or mutton stew with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), gravy" +27430500,"VEAL GOULASH W/VEG(NO CAR/DK GREEN, NO POT),TOM SCE","Veal goulash with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), tomato-base sauce" +27430510,"VEAL GOULASH W/VEG(W/ CAR/DK GREEN, NO POT),TOM SCE","Veal goulash with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), tomato-base sauce" +27430580,"VEAL W/ VEG (INCL CAR/DK GRN), NO POT, CREAM SAUCE","Veal with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), cream or white sauce" +27430590,"VEAL W/ VEG (NO CAR/DK GRN), NO POT, CREAM SAUCE","Veal with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), cream or white sauce" +27430610,"LAMB SHISH KABOB W/ VEGETABLES, EXCLUDING POTATOES","Lamb shish kabob with vegetables, excluding potatoes" +27440110,"CHICK/TURK & VEG (W/ CAR/DK GRN, NO POT), NO SAUCE","Chicken or turkey and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), no sauce (mixture)" +27440120,"CHICK/TURK & VEG (NO CAR/DK GRN, NO POT), NO SAUCE","Chicken or turkey and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), no sauce (mixture)" +27440130,"CHICKEN OR TURKEY SHISH KABOB W/VEGETABLES, EXCL POTATOES","Chicken or turkey shish kabob with vegetables, excluding potatoes" +27441120,"CHICKEN CREOLE W/O RICE","Chicken or turkey creole, without rice" +27442110,"CHICKEN/TURKEY & VEG (W/ CAR/DK GREEN,NO POT),GRAVY","Chicken or turkey and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), gravy (mixture)" +27442120,"CHICKEN/TURKEY & VEG(NO CAR/DK GREEN,NO POT), GRAVY","Chicken or turkey and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), gravy (mixture)" +27443110,"CHICKEN A LA KING W/VEG(INCL CAR/DK GRN),WHITE SCE","Chicken or turkey a la king with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), cream, white, or soup-based sauce" +27443120,"CHICKEN A LA KING W/ VEG(NO CAR/DK GRN),WHITE SAUCE","Chicken or turkey a la king with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), cream, white, or soup-based sauce" +27443150,"CHICKEN DIVAN","Chicken or turkey divan" +27445110,"CHICKEN & VEG (INCL CAR/DK GRN, NO POT), SOY SAUCE","Chicken or turkey and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27445120,"CHICKEN & VEG (NO CAR/DK GRN, NO POT), SOY SAUCE","Chicken or turkey and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27445125,"CHICKEN & VEG (INCL CAR/DK GRN, NO POT), TOMATO SAUCE","Chicken or turkey and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27445130,"CHICKEN & VEG (NO CAR/DK GRN, NO POT), TOMATO SAUCE","Chicken or turkey and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27445150,"GENERAL TSO CHICKEN","General Tso chicken" +27445180,"MOO GOO GAI PAN","Moo Goo Gai Pan" +27445220,"KUNG PAO CHICKEN","Kung pao chicken" +27445250,"ALMOND CHICKEN","Almond chicken" +27446100,"CHICKEN CHOW MEIN/CHOP SUEY, NO NOODLES","Chicken or turkey chow mein or chop suey, no noodles" +27446200,"CHICKEN OR TURKEY SALAD, W/ MAYO","Chicken or turkey salad, made with mayonnaise" +27446205,"CHICKEN/TURKEY SALAD WITH NUTS AND/OR FRUITS","Chicken or turkey salad with nuts and/or fruits" +27446220,"CHICKEN SALAD W/ EGG","Chicken or turkey salad with egg" +27446225,"CHICKEN OR TURKEY SALAD, W/ LT MAYO","Chicken or turkey salad, made with light mayonnaise" +27446230,"CHICKEN OR TURKEY SALAD, W/ MAYO-TYPE DRSG","Chicken or turkey salad, made with mayonnaise-type salad dressing" +27446235,"CHICKEN OR TURKEY SALAD, MADE W/ LT MAYO-TYPE DRSG","Chicken or turkey salad, made with light mayonnaise-type salad dressing" +27446240,"CHICKEN OR TURKEY SALAD, W/CREAMY DRSG","Chicken or turkey salad, made with creamy dressing" +27446245,"CHICKEN OR TURKEY SALAD, W/ LIT CREAMY DRSG","Chicken or turkey salad, made with light creamy dressing" +27446250,"CHICKEN OR TURKEY SALAD, MADE W/ ITALIAN DRESSING","Chicken or turkey salad, made with Italian dressing" +27446255,"CHICKEN OR TURKEY SALAD, MADE W/ LT ITALIAN DRSG","Chicken or turkey salad, made with light Italian dressing" +27446260,"CHICKEN OR TURKEY SALAD, MADE W/ FAT FREE DRSG","Chicken or turkey salad, made with any type of fat free dressing" +27446300,"CHICKEN GARDEN SALAD W/ TOMATO/CARROT, NO DRESSING","Chicken or turkey garden salad (chicken and/or turkey, tomato and/or carrots, other vegetables), no dressing" +27446310,"CHICKEN GARDEN SALAD W/VEG, NO CAR/TOM, NO DRESSING","Chicken or turkey garden salad (chicken and/or turkey, other vegetables excluding tomato and carrots), no dressing" +27446315,"CHICKEN GARDEN SALAD W/ BACON,CHEESE,TOMATO/CARROT,NO DRSG","Chicken or turkey garden salad with bacon and cheese (chicken and/or turkey, bacon, cheese, lettuce and/or greens, tomato and/or carrots, other vegetables), no dressing" +27446320,"CHICKN(BRD,FRD)GARDEN SALAD W/ BACON,CHEESE,TOM/CAR,NO DRSG","Chicken or turkey (breaded, fried) garden salad with bacon and cheese (chicken and/or turkey, bacon, cheese, lettuce and/or greens, tomato and/or carrots, other vegetables), no dressing" +27446330,"CHICKN GARDEN SALAD W/ CHEESE,TOM/CAR,NO DRSG","Chicken or turkey garden salad with cheese (chicken and/or turkey, cheese, lettuce and/or greens, tomato and/or carrots, other vegetables), no dressing" +27446332,"CHICKN(BRD,FRD)GARDEN SALAD W/ CHEESE,TOM/CAR,NO DRSG","Chicken or turkey (breaded, fried) garden salad with cheese (chicken and/or turkey, cheese, lettuce and/or greens, tomato and/or carrots, other vegetables), no dressing" +27446350,"ASIAN CHICKEN/TURKEY GARDEN SALAD, NO DRESSING","Asian chicken or turkey garden salad (chicken and/or turkey, lettuce, fruit, nuts), no dressing" +27446355,"ASIAN CHICKEN GARDEN SALAD W/CRISPY NOODLES , NO DRESSING","Asian chicken or turkey garden salad with crispy noodles (chicken and/or turkey, lettuce, fruit, nuts, crispy noodles), no dressing" +27446360,"CHICKEN/TURKEY CAESAR GARDEN SALAD, NO DRESSING","Chicken or turkey caesar garden salad (chicken and/or turkey, lettuce, tomato, cheese), no dressing" +27446362,"CHICKEN/TURKEY (BREADED, FRIED) CAESAR GARDEN SALAD, NO DRSG","Chicken or turkey (breaded, fried) caesar garden salad (chicken and/or turkey, lettuce, tomatoes, cheese), no dressing" +27446400,"CHICKEN & VEG (INCL CAR/DK GRN)(NO POT), CHEESE SCE","Chicken or turkey and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), cheese sauce (mixture)" +27446410,"CHICKEN & VEG (NO CAR/DK GRN)(NO POT), CHEESE SAUCE","Chicken or turkey and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), cheese sauce (mixture)" +27448020,"CHICKEN FRICASSEE W/SAUCE,NO POT,PUERTO RICAN STYLE","Chicken or turkey fricassee, with sauce, no potatoes, Puerto Rican style (potatoes reported separately)" +27448030,"CHICKEN FRICASSEE, NO SCE OR POT,PUERTO RICAN STYLE","Chicken or turkey fricassee, no sauce, no potatoes, Puerto Rican style (sauce and potatoes reported separately)" +27450010,"CRAB SALAD","Crab salad" +27450020,"LOBSTER SALAD","Lobster salad" +27450030,"SALMON SALAD","Salmon salad" +27450040,"SHRIMP CHOW MEIN OR CHOP SUEY, NO NOODLES","Shrimp chow mein or chop suey, no noodles" +27450060,"TUNA SALAD, W/MAYO","Tuna salad, made with mayonnaise" +27450061,"TUNA SALAD, W/LT MAYO","Tuna salad, made with light mayonnaise" +27450062,"TUNA SALAD, W/ MAYO-TYPE DRESSING","Tuna salad, made with mayonnaise-type salad dressing" +27450063,"TUNA SALAD, W/ LT MAYO-TYPE DRSG","Tuna salad, made with light mayonnaise-type salad dressing" +27450064,"TUNA SALAD, W/ CREAMY DRSG","Tuna salad, made with creamy dressing" +27450065,"TUNA SALAD, W/ LT CREAMY DRSG","Tuna salad, made with light creamy dressing" +27450066,"TUNA SALAD, W/ ITALIAN DRSG","Tuna salad, made with Italian dressing" +27450067,"TUNA SALAD, W/ LT ITALIAN DRSG","Tuna salad, made with light Italian dressing" +27450068,"TUNA SALAD, W/ ANY TYPE OF FAT FREE DRSG","Tuna salad, made with any type of fat free dressing" +27450070,"SHRIMP SALAD","Shrimp salad" +27450080,"SEAFOOD SALAD","Seafood salad" +27450090,"TUNA SALAD W/ CHEESE","Tuna salad with cheese" +27450100,"TUNA SALAD W/ EGG","Tuna salad with egg" +27450110,"SHRIMP GARDEN SALAD W/ TOMATO/CARROT, NO DRESSING","Shrimp garden salad (shrimp, lettuce, eggs, tomato and/or carrots, other vegetables), no dressing" +27450120,"SHRIMP GARDEN SALAD (NO TOMATO/CARROT, NO DRESSING)","Shrimp garden salad (shrimp, lettuce, eggs, vegetables excluding tomato and carrots), no dressing" +27450130,"CRAB SALAD MADE W/ IMITATION CRAB","Crab salad made with imitation crab" +27450150,"FISH, TOFU, & VEGETABLES, TEMPURA, HAWAIIAN","Fish, tofu, and vegetables, tempura, Hawaiian style (mixture)" +27450180,"SEAFOOD GARDEN SALAD W/ VEG(NO TOM/CAR), NO DRESSING","Seafood garden salad with seafood, lettuce, vegetables excluding tomato and carrots, no dressing" +27450190,"SEAFOOD GARDEN SALAD W/ TOM/CAR, NO DRESSING","Seafood garden salad with seafood, lettuce, tomato and/or carrots, other vegetables, no dressing" +27450200,"SEAFOOD GARDEN SALAD W/ EGG, VEG, (NO CAR/TOM) NO DRESSING","Seafood garden salad with seafood, lettuce, eggs, vegetables excluding tomato and carrots, no dressing" +27450210,"SEAFOOD GARDEN SALAD W/EGG, TOM/CAR, NO DRESSING","Seafood garden salad with seafood, lettuce, eggs, tomato and/or carrots, other vegetables, no dressing" +27450250,"OYSTERS ROCKEFELLER","Oysters Rockefeller" +27450310,"LOMI SALMON","Lomi salmon" +27450400,"SHRIMP & VEG (W/ CAR/DK GREEN, NO POT), NO SAUCE","Shrimp and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), no sauce (mixture)" +27450405,"SHRIMP & VEG (NO CARROT/DK GREEN, NO POT), NO SAUCE","Shrimp and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), no sauce (mixture)" +27450410,"SHRIMP & VEG (W/ CAR/DK GREEN, NO POT), SOY SAUCE","Shrimp and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27450420,"SHRIMP & VEG (NO CARROT/DK GREEN, NO POT),SOY SAUCE","Shrimp and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27450430,"SHRIMP SHISH KABOB WITH VEGETABLES, EXCLUDING POTATOES","Shrimp shish kabob with vegetables, excluding potatoes" +27450450,"SHRIMP CREOLE, NO RICE","Shrimp creole, no rice" +27450470,"KUNG PAO SHRIMP","Kung Pao shrimp" +27450510,"TUNA CASSEROLE W/ VEG & SOUP, NO NOODLES","Tuna casserole with vegetables and (mushroom) soup, no noodles" +27450600,"SHELLFISH MIXTURE & VEG (INCL CAR/DK GRN), SOY SCE","Shellfish mixture and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce" +27450610,"SHELLFISH MIXTURE & VEG (NO CAR/DK GRN), SOY SAUCE","Shellfish mixture and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce" +27450650,"SHELLFISH & VEG(INCL CAR/DK GRN)(NO POT),SOUP SAUCE","Shellfish mixture and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), (mushroom) soup (mixture)" +27450660,"SHELLFISH & VEG(NO CAR/DK GRN/POT),SOUP-BASED SAUCE","Shellfish mixture and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), (mushroom) soup (mixture)" +27450700,"FISH & VEG (INCL CAR/DK GRN, NO POT), TOMATO SAUCE","Fish and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), tomato-based sauce (mixture)" +27450710,"FISH & VEG (NO CAR/DK GRN, NO POT), TOMATO SAUCE","Fish and vegetables (excluding carrots, broccoli, and dark- green leafy (no potatoes)), tomato-based sauce (mixture)" +27450740,"FISH & VEGETABLES (W/ CAR/DK GRN), SOY-BASED SAUCE","Fish and vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27450750,"FISH & VEGETABLES (NO CAR/DK GRN), SOY-BASED SAUCE","Fish and vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes)), soy-based sauce (mixture)" +27450760,"FISH SHISH KABOB WITH VEGETABLES, EXCLUDING POTATOES","Fish shish kabob with vegetables, excluding potatoes" +27451010,"FRIED FISH W/ SAUCE, P.R. (PESCADO FRITO CON MOJO)","Fried fish with sauce, Puerto Rican style (Pescado frito con mojo)" +27451030,"LOBSTER W/ SAUCE, P.R. (LANGOSTA A LA CRIOLLA)","Lobster with sauce, Puerto Rican style (Langosta a la criolla)" +27451060,"OCTOPUS SALAD, P.R. (ENSALADA DE PULPO)","Octopus salad, Puerto Rican style (Ensalada de pulpo)" +27451070,"CODFISH SALAD, P.R. (SERENATA)","Codfish salad, Puerto Rican style (Serenata)" +27460010,"CHOW MEIN, NS AS TO TYPE OF MEAT, NO NOODLES","Chow mein or chop suey, NS as to type of meat, no noodles" +27460100,"LAU LAU(PORK & FISH WRAPPED IN TARO/SPINACH LEAVES)","Lau lau (pork and fish wrapped in taro or spinach leaves)" +27460490,"JULIENNE SALAD (MEAT, CHEESE, EGG, VEG) NO DRESSING","Julienne salad (meat, cheese, eggs, vegetables), no dressing" +27460510,"ANTIPASTO W/ HAM, FISH, CHEESE, VEGETABLES","Antipasto with ham, fish, cheese, vegetables" +27460710,"LIVERS, CHICKEN, CHOPPED, W/ EGGS & ONION (MIXTURE)","Livers, chicken, chopped, with eggs and onion (mixture)" +27460750,"LIVER, BEEF OR CALVES, & ONIONS","Liver, beef or calves, and onions" +27461010,"STEWED SEASONED GROUND BEEF, P.R.","Stewed seasoned ground beef, Puerto Rican style (Picadillo para relleno)" +27462000,"STEWED CHITTERLINGS, P.R. (CUAJO GUISADO)","Stewed chitterlings, Puerto Rican style (cuajo guisado)" +27463000,"STEWED GIZZARDS, P.R. (MOLLEJITAS GUISADAS)","Stewed gizzards, Puerto Rican style (Mollejitas guisadas)" +27464000,"GUMBO, NO RICE (NEW ORLEANS TYPE W/MEAT, TOM, OKRA)","Gumbo, no rice (New Orleans type with shellfish, pork, and/or poultry, tomatoes, okra)" +27500050,"SANDWICH, NFS","Sandwich, NFS" +27500100,"MEAT SANDWICH, NFS","Meat sandwich, NFS" +27500200,"WRAP SANDWICH, W/ MEAT, POULTRY OR FISH, VEGETABLES & CHEESE","Wrap sandwich, filled with meat, poultry, or fish, vegetables, and cheese" +27500300,"WRAP SANDWICH, W/ MEAT, POULTRY OR FISH & VEGETABLES","Wrap sandwich, filled with meat, poultry, or fish, and vegetables" +27510000,"BEEF SANDWICH, NFS","Beef sandwich, NFS" +27510110,"BEEF BARBECUE SANDWICH OR SLOPPY JOE, ON BUN","Beef barbecue sandwich or Sloppy Joe, on bun" +27510130,"BEEF BARBECUE SUBMARINE SANDWICH, ON BUN","Beef barbecue submarine sandwich, on bun" +27510210,"CHEESEBURGER, PLAIN, ON BUN","Cheeseburger, plain, on bun" +27510220,"CHEESEBURGER, W/ MAYO, ON BUN","Cheeseburger, with mayonnaise or salad dressing, on bun" +27510230,"CHEESEBURGER, W/ MAYO & TOMATO/CATSUP, ON BUN","Cheeseburger, with mayonnaise or salad dressing, and tomato and/or catsup, on bun" +27510240,"CHEESEBURGER, 1/4 LB MEAT, PLAIN, ON BUN","Cheeseburger, 1/4 lb meat, plain, on bun" +27510250,"CHEESEBURGER, 1/4 LB MEAT, W/ MAYO, ON BUN","Cheeseburger, 1/4 lb meat, with mayonnaise or salad dressing, on bun" +27510260,"CHEESEBURGER, 1/4 LB, W/ MUSHROOM SAUCE, ON BUN","Cheeseburger, 1/4 lb meat, with mushrooms in sauce, on bun" +27510265,"DOUBLE CHEESEBURGER, PLAIN, ON MINIATURE BUN","Double cheeseburger, (2 patties, 1 oz each), plain, on miniature bun" +27510270,"DOUBLE CHEESEBURGER, PLAIN, ON BUN","Double cheeseburger (2 patties), plain, on bun" +27510280,"DOUBLE CHEESEBURGER, W/ MAYO, ON BUN","Double cheeseburger (2 patties), with mayonnaise or salad dressing, on bun" +27510290,"DOUBLE CHEESEBURGER, PLAIN, ON DOUBLE-DECKER BUN","Double cheeseburger (2 patties), plain, on double-decker bun" +27510300,"DOUBLE CHEESEBURGER, W/MAYO, ON DOUBLE-DECKER BUN","Double cheeseburger (2 patties), with mayonnaise or salad dressing, on double-decker bun" +27510310,"CHEESEBURGER W/ TOMATO & OR CATSUP, ON BUN","Cheeseburger with tomato and/or catsup, on bun" +27510311,"CHEESEBURGER, 1 OZ MEAT, PLAIN, ON MINI BUN","Cheeseburger, 1 oz meat, plain, on miniature bun" +27510320,"CHEESEBURGER, 1/4 LB MEAT,W/ TOMATO/CATSUP, BUN","Cheeseburger, 1/4 lb meat, with tomato and/or catsup, on bun" +27510330,"DOUBLE CHEESEBURGER W/TOMATO & OR CATSUP, ON BUN","Double cheeseburger (2 patties), with tomato and/or catsup, on bun" +27510340,"DOUBLE CHEESEBURGER, W/ MAYO & TOMATO, ON BUN","Double cheeseburger (2 patties), with mayonnaise or salad dressing and tomatoes and/or catsup, on bun" +27510350,"CHEESEBURGER, 1/4 LB MEAT, W/ MAYO & TOMATO/CATSUP, ON BUN","Cheeseburger, 1/4 lb meat, with mayonnaise or salad dressing, and tomato and/or catsup, on bun" +27510355,"CHEESEBURGER, 1/3 LB MEAT, W/MAYO, TOMATO, ON BUN","Cheeseburger, 1/3 lb meat, with mayonnaise or salad dressing, tomato and/or catsup on bun" +27510359,"CHEESEBURGER, 1/3 LB MEAT, W/MAYO, MUSHROOMS,ON BUN","Cheeseburger, 1/3 lb meat, with mayonnaise or salad dressing, and mushrooms, on bun" +27510360,"BACON CHEESEBURGER, W/MAYO/SALAD DRSG, TOMATO/CATSUP,ON BUN","Bacon cheeseburger, with mayonnaise or salad dressing, tomato and/or catsup, on bun" +27510370,"DOUBLE CHEESEBURGER W/ MAYONNAISE, ON BUN","Double cheeseburger (2 patties, 1/4 lb meat each), with mayonnaise or salad dressing, on bun" +27510375,"DOUBLE CHEESEBURGER(2 PATTIES,1/4 LB EA) W/TOMATO/CATSUP/BUN","Double cheeseburger (2 patties, 1/4 lb meat each), with tomato and/or catsup, on bun" +27510380,"TRIPLE CHEESEBURGER W/ MAYO, TOMATO, ON BUN","Triple cheeseburger (3 patties, 1/4 lb meat each), with mayonnaise or salad dressing and tomatoes and/or catsup, on bun" +27510385,"DOUBLE BACON CHEESEBURGER (2 PATTIES), W/ TOMATO/CATSUP","Double bacon cheeseburger (2 patties), with tomato and/or catsup, on bun" +27510390,"DOUBLE BACON CHEESEBURGER, ON BUN","Double bacon cheeseburger (2 patties, 1/4 lb meat each), on bun" +27510400,"BACON CHEESEBURGER, 1/4 LB MEAT, W/ TOMATO, ON BUN","Bacon cheeseburger, 1/4 lb meat, with tomato and/or catsup, on bun" +27510410,"CHILIBURGER, ON BUN (INCLUDE HAMBURGER W/ CHILI)","Chiliburger, on bun" +27510420,"TACO BURGER, ON BUN (INCL CHILIBURGER W/ CHEESE)","Taco burger, on bun" +27510425,"DOUBLE BACON CHEESEBURGER (2 PATTIES,1/4 LB EA), W/ MAYO/BUN","Double bacon cheeseburger (2 patties, 1/4 lb meat each), with mayonnaise or salad dressing, on bun" +27510430,"DOUBLE BACON CHEESEBURGER, W/MAYO/DRSG,TOMATO/CATSUP,ON BUN","Double bacon cheeseburger (2 patties, 1/4 lb meat each), with mayonnaise or salad dressing, and tomato and/or catsup, on bun" +27510435,"DOUBLE BACON CHEESEBURGER (2 PATTIES,1/3 LB EA) W/ MAYO/ BUN","Double bacon cheeseburger (2 patties,1/3 lb meat each), with mayonnaise or salad dressing, on bun" +27510440,"BACON CHEESEBURGER, 1/4 LB, W/MAYO/DRSG,TOMATO/CATSUP,ON BUN","Bacon cheeseburger, 1/4 lb meat, with mayonnaise or salad dressing, and tomato and/or catsup, on bun" +27510445,"BACON CHEESEBURGER, 1/3 LB MEAT, W/TOMATO +/OR CATSUP,","Bacon cheeseburger, 1/3 lb meat, with tomato and/or catsup, on bun" +27510450,"CHEESEBURGER, 1/4 LB MEAT, W/ HAM, ON BUN","Cheeseburger, 1/4 lb meat, with ham, on bun" +27510480,"CHEESEBURGER, W/ ONIONS, ON RYE BUN","Cheeseburger (hamburger with cheese sauce), 1/4 lb meat, with grilled onions, on rye bun" +27510500,"HAMBURGER, PLAIN, ON BUN","Hamburger, plain, on bun" +27510510,"HAMBURGER, W/ TOMATO & OR CATSUP, ON BUN","Hamburger, with tomato and/or catsup, on bun" +27510520,"HAMBURGER, W/ MAYO & TOMATO/CATSUP, ON BUN","Hamburger, with mayonnaise or salad dressing, and tomato and/or catsup, on bun" +27510530,"HAMBURGER, 1/4 LB MEAT, PLAIN, ON BUN","Hamburger, 1/4 lb meat, plain, on bun" +27510540,"DOUBLE HAMBURGER W/TOMATO & OR CATSUP, ON BUN","Double hamburger (2 patties), with tomato and/or catsup, on bun" +27510550,"DOUBLE HAMBURGER W/ MAYO & TOMATO, DBL-DECKER BUN","Double hamburger (2 patties), with mayonnaise or salad dressing and tomatoes, on double-decker bun" +27510560,"HAMBURGER, 1/4 LB MEAT W/ MAYO & TOMATO/CATSUP, ON BUN","Hamburger, 1/4 lb meat, with mayonnaise or salad dressing, and tomato and/or catsup, on bun" +27510570,"HAMBURGER, 2.5 OZ MEAT, W/ MAYO & TOMATO, ON BUN","Hamburger, 2-1/2 oz meat, with mayonnaise or salad dressing and tomatoes, on bun" +27510590,"HAMBURGER, W/ MAYO, ON BUN","Hamburger, with mayonnaise or salad dressing, on bun" +27510600,"HAMBURGER, 1 OZ MEAT,PLAIN, ON MINIATURE BUN","Hamburger, 1 oz meat, plain, on miniature bun" +27510610,"HAMBURGER, 1 OZ MEAT, TOMATO, ON MINIATURE BUN","Hamburger, 1 oz meat, with tomato and/or catsup, on miniature bun" +27510620,"HAMBURGER, 1/4 LB MEAT, W/ TOMATO & OR CATSUP, BUN","Hamburger, 1/4 lb meat, with tomato and/or catsup, on bun" +27510630,"HAMBURGER, 1/4 LB MEAT, W/ MAYO, ON BUN","Hamburger, 1/4 lb meat, with mayonnaise or salad dressing, on bun" +27510650,"DOUBLE HAMBURGER, PLAIN, ON BUN","Double hamburger (2 patties), plain, on bun" +27510660,"DOUBLE HAMBURGER, W/ MAYO, ON BUN","Double hamburger (2 patties), with mayonnaise or salad dressing, on bun" +27510670,"DOUBLE HAMBURGER, W/ MAYO & TOMATO, ON BUN","Double hamburger (2 patties), with mayonnaise or salad dressing and tomatoes, on bun" +27510680,"DOUBLE HAMBURGER (1/2 LB MEAT), W/ TOM/CATSUP, BUN","Double hamburger (2 patties, 1/4 lb meat each), with tomato and/or catsup, on bun" +27510690,"DOUBLE HAMBURGER,1/2 LB MEAT,W/MAYO&TOM/CATSUP,BUN","Double hamburger (2 patties, 1/4 lb meat each), with mayonnaise or salad dressing and tomatoes and/or catsup, on double-decker bun" +27510700,"MEATBALL & SPAG SAU SUB SAND","Meatball and spaghetti sauce submarine sandwich" +27510710,"PIZZABURGER (HAMBURGER, CHEESE, SAUCE), ON 1/2 BUN","Pizzaburger (hamburger, cheese, sauce) on 1/2 bun" +27510720,"PIZZABURGER (HAMBURGER, CHEESE, SAUCE), WHOLE BUN","Pizzaburger (hamburger, cheese, sauce) on whole bun" +27510910,"CORNED BEEF SANDWICH","Corned beef sandwich" +27510950,"REUBEN(CORN BEEF W/ SAUERKRAUT & CHEESE) W/ SPREAD","Reuben sandwich (corned beef sandwich with sauerkraut and cheese), with spread" +27511010,"PASTRAMI SANDWICH","Pastrami sandwich" +27513010,"ROAST BEEF SANDWICH","Roast beef sandwich" +27513020,"ROAST BEEF SANDWICH, W/ GRAVY","Roast beef sandwich, with gravy" +27513040,"ROAST BEEF SUB SAND, W/ LETT, TOM, SPRD","Roast beef submarine sandwich, with lettuce, tomato and spread" +27513041,"ROAST BEEF SUB SAND, W/ CHEESE, LETTUCE, TOMATO, SPRD","Roast beef submarine sandwich, with cheese, lettuce, tomato and spread" +27513050,"ROAST BEEF SANDWICH W/ CHEESE","Roast beef sandwich with cheese" +27513060,"ROAST BEEF SANDWICH W/ BACON & CHEESE SAUCE","Roast beef sandwich with bacon and cheese sauce" +27513070,"ROAST BEEF SUBMARINE SANDWICH, ON ROLL, AU JUS","Roast beef submarine sandwich, on roll, au jus" +27515000,"STEAK SUBMARINE SANDWICH WITH LETTUCE AND TOMATO","Steak submarine sandwich with lettuce and tomato" +27515010,"STEAK SANDWICH, PLAIN, ON ROLL","Steak sandwich, plain, on roll" +27515020,"STEAK , CHEESE SUB SAND, W/ LETT, TOM","Steak and cheese submarine sandwich, with lettuce and tomato" +27515030,"STEAK & CHEESE SANDWICH, PLAIN, ON ROLL","Steak and cheese sandwich, plain, on roll" +27515040,"STEAK & CHEESE SUBMARINE SANDWICH, PLAIN, ON ROLL","Steak and cheese submarine sandwich, plain, on roll" +27515050,"FAJITA-STYLE BEEF SAND W/ CHEESE,PITA BRD,W/LET+TOM","Fajita-style beef sandwich with cheese, on pita bread, with lettuce and tomato" +27515070,"STEAK & CHEESE SUB, FRIED PEP & ONIONS, ON ROLL","Steak and cheese submarine sandwich, with fried peppers and onions, on roll" +27515080,"STEAK SANDWICH, PLAIN, ON BISCUIT","Steak sandwich, plain, on biscuit" +27516010,"GYRO SANDWICH W/ TOMATO & SPREAD","Gyro sandwich (pita bread, beef, lamb, onion, condiments), with tomato and spread" +27517000,"WRAP SANDWICH FILLED WITH BEEF PATTY, CHEESE, LETTUCE,SPREAD","Wrap sandwich filled with beef patty, cheese and spread and/or sauce" +27517010,"WRAP SANDWICH FILLED WITH BEEF PATTY, CHEESE, TOMATO, SPREAD","Wrap sandwich filled with beef patty, cheese, tomato and/or catsup, and spread and/or sauce" +27518000,"WRAP SAND W/BEEF PATTY,BAC, CHS, TOM,SPREAD","Wrap sandwich filled with beef patty, bacon, cheese, tomato and/or catsup, and spread and/or sauce" +27520110,"BACON SANDWICH W/ SPREAD","Bacon sandwich, with spread" +27520120,"BACON & CHEESE SANDWICH, W/ SPREAD","Bacon and cheese sandwich, with spread" +27520130,"BACON, CHICK, & TOM CLUB SANDWICH W/ LETTUCE+SPREAD","Bacon, chicken, and tomato club sandwich, with lettuce and spread" +27520135,"BACON, CHICKN & TOMATO CLUB SANDWICH W/CHEESE, LETTUCE &SPRD","Bacon, chicken, and tomato club sandwich, with cheese, lettuce and spread" +27520140,"BACON & EGG SANDWICH","Bacon and egg sandwich" +27520150,"BACON, LETTUCE, & TOMATO SANDWICH W/ SPREAD","Bacon, lettuce, and tomato sandwich with spread" +27520160,"BACON,CHICK,&TOMATO CLUB SANDWICH,MULTIGR W/ SPREAD","Bacon, chicken, and tomato club sandwich, on multigrain roll with lettuce and spread" +27520165,"BACON, CHICK FILLET (BRD, FRIED),& TOM CLUB W/LETTUCE & SPRD","Bacon, chicken fillet (breaded, fried), and tomato club with lettuce and spread" +27520166,"BACON, CHICK FILLET (BRD,FRIED),&TOM CLUB W/CHS,LETTUCE&SPRD","Bacon, chicken fillet (breaded, fried), and tomato club sandwich with cheese, lettuce and spread" +27520170,"BACON ON BISCUIT","Bacon on biscuit" +27520250,"HAM ON BISCUIT","Ham on biscuit" +27520300,"HAM SANDWICH W/ SPREAD","Ham sandwich, with spread" +27520310,"HAM SANDWICH W/ LETTUCE & SPREAD","Ham sandwich with lettuce and spread" +27520320,"HAM & CHEESE SANDWICH, W/ LETTUCE & SPREAD","Ham and cheese sandwich, with lettuce and spread" +27520330,"HAM & EGG SANDWICH","Ham and egg sandwich" +27520340,"HAM SALAD SANDWICH","Ham salad sandwich" +27520350,"HAM & CHEESE SANDWICH W/ SPREAD, GRILLED","Ham and cheese sandwich, with spread, grilled" +27520360,"HAM & CHEESE SANDWICH ON BUN W/ LETTUCE & SPREAD","Ham and cheese sandwich, on bun, with lettuce and spread" +27520370,"HOT HAM & CHEESE SANDWICH, ON BUN","Hot ham and cheese sandwich, on bun" +27520380,"HAM & CHEESE ON ENGLISH MUFFIN","Ham and cheese on English muffin" +27520390,"HAM & CHEESE SUB, W/ LETTUCE, TOMATO & SPREAD","Ham and cheese submarine sandwich, with lettuce, tomato and spread" +27520410,"CUBAN SAND, P.R STYLE(SANDWICH CUBANA), W/ SPREAD","Cuban sandwich, (Sandwich cubano), with spread" +27520420,"MIDNIGHT SAND,P.R. STYLE (MEDIA NOCHE), W/ SPREAD","Midnight sandwich, (Media noche), with spread" +27520500,"PORK SANDWICH, ON WHITE ROLL, W/ ONIONS, PICKLES & BBQ SAUCE","Pork sandwich, on white roll, with onions, dill pickles and barbecue sauce" +27520510,"PORK BARBECUE SANDWICH OR SLOPPY JOE, ON BUN","Pork barbecue sandwich or Sloppy Joe, on bun" +27520520,"PORK SANDWICH","Pork sandwich" +27520530,"PORK SANDWICH W/ GRAVY","Pork sandwich, with gravy" +27520540,"HAM & TOMATO CLUB SAND, W/ SPREAD","Ham and tomato club sandwich, with lettuce and spread" +27540110,"CHICKEN SANDWICH, W/ SPREAD","Chicken sandwich, with spread" +27540111,"CHICKEN SANDWICH, W/ CHEESE & SPREAD","Chicken sandwich, with cheese and spread" +27540120,"CHICKEN SALAD OR CHICKEN SPREAD SANDWICH","Chicken salad or chicken spread sandwich" +27540130,"CHICKEN BARBECUE SANDWICH","Chicken barbecue sandwich" +27540140,"CHICKEN FILLET (BREADED, FRIED) SANDWICH","Chicken fillet (breaded, fried) sandwich" +27540145,"CHICKEN FILLET (BREADED, FRIED) SANDWICH ON BISCUIT","Chicken fillet (breaded, fried) sandwich on biscuit" +27540150,"CHICKEN FILLET(BR FRIED) SAND W/ LET, TOM & SPREAD","Chicken fillet (breaded, fried) sandwich with lettuce, tomato and spread" +27540151,"CHICKEN FILLET(BRD, FRIED) SAND W/ CHEESE, LETT, TOM & SPRD","Chicken fillet (breaded, fried) sandwich with cheese, lettuce, tomato and spread" +27540170,"CHICKEN PATTY SANDWICH, MINI, W/ SPREAD","Chicken patty sandwich, miniature, with spread" +27540180,"CHICKEN PATTY SANDWICH ON BISCUIT","Chicken patty sandwich or biscuit" +27540190,"CHICKEN PATTY SANDWICH W/ LETTUCE & SPREAD","Chicken patty sandwich, with lettuce and spread" +27540200,"FAJITA-STYLE CHICKEN SANDWICH W/ CHEESE, LETTUC,TOM","Fajita-style chicken sandwich with cheese, on pita bread, with lettuce and tomato" +27540210,"WRAP SNDWCH W/CHICK STRIPS(BREADED,FRIED),CHS,LETTUCE & SPRD","Wrap sandwich filled with chicken strips (breaded, fried), cheese, lettuce, and spread" +27540230,"CHICKEN PATTY SAND W/ CHEES,WHEAT BUN,LET,TOM, SPRE","Chicken patty sandwich with cheese, on wheat bun, with lettuce, tomato and spread" +27540235,"CHICKEN FILLET, BROILED, SANDWICH WITH LETTUCE, TOMATO, AND","Chicken fillet, broiled, sandwich with lettuce, tomato, and spread" +27540240,"CHICKEN FILLET,(BROIL) SAND W/ LET, TOM, & SPREAD","Chicken fillet, (broiled), sandwich, on whole wheat roll, with lettuce, tomato and spread" +27540250,"CHICK FILLET,BROIL,SANDWICH,W/CHEESE,WW ROLL","Chicken fillet, broiled, sandwich with cheese, on whole wheat roll, with lettuce, tomato and non-mayonnaise type spread" +27540260,"CHICK FILLET, BROILED,SANDWICH,ON OAT BRAN BUN(LTS)","Chicken fillet, broiled, sandwich, on oat bran bun, with lettuce, tomato, spread" +27540270,"CHICKEN FILLET,SANDWICH,W/LETT,TOM,&NON-MAYO SPREAD","Chicken fillet, broiled, sandwich, with lettuce, tomato, and non-mayonnaise type spread" +27540280,"CHICKEN FILLET,BROILED,SANDWICH,W/CHEESE,ON BUN","Chicken fillet, broiled, sandwich with cheese, on bun, with lettuce, tomato and spread" +27540290,"CHICKEN SUB SANDWICH, W/ LETTUCE, TOMATO & SPREAD","Chicken submarine sandwich, with lettuce, tomato, and spread" +27540291,"CHICKEN SUB SANDWICH, W/ CHEESE, LETTUCE, TOMATO & SPREAD","Chicken submarine sandwich, with cheese, lettuce, tomato, and spread" +27540300,"WRAP SNDWCH W/CHICK STRIPS (BROILED),CHS,LETTUCE & SPRD","Wrap sandwich filled with chicken strips (broiled), cheese, lettuce, and spread" +27540310,"TURKEY SANDWICH W/ SPREAD","Turkey sandwich, with spread" +27540320,"TURKEY SALAD SANDWICH","Turkey salad or turkey spread sandwich" +27540330,"TURKEY SANDWICH W/ GRAVY","Turkey sandwich, with gravy" +27540350,"TURKEY SUB SAND, W/ CHEESE, LETT, TOM, SPRD","Turkey submarine sandwich, with cheese, lettuce, tomato and spread" +27541000,"TURKEY, HAM & ROAST BEEF CLUB SANDWCH W/LETT,TOM,SPRD","Turkey, ham, and roast beef club sandwich, with lettuce, tomato and spread" +27541001,"TURKEY, HAM & ROAST BEEF CLUB SANDWCH W/CHEESE,LETT,TOM,SPRD","Turkey, ham, and roast beef club sandwich with cheese, lettuce, tomato, and spread" +27550000,"FISH SANDWICH, ON BUN, W/ SPREAD","Fish sandwich, on bun, with spread" +27550100,"FISH SANDWICH, ON BUN, W/ CHEESE AND SPREAD","Fish sandwich, on bun, with cheese and spread" +27550110,"CRAB CAKE SANDWICH, ON BUN","Crab cake sandwich, on bun" +27550510,"SARDINE SANDWICH, W/ LETTUCE & SPREAD","Sardine sandwich, with lettuce and spread" +27550710,"TUNA SALAD SANDWICH W/ LETTUCE","Tuna salad sandwich, with lettuce" +27550720,"TUNA SALAD SANDWICH","Tuna salad sandwich" +27550730,"TUNA MELT SANDWICH","Tuna melt sandwich" +27550750,"TUNA SALSUB SAND, W/ LETT & TOMATO","Tuna salad submarine sandwich, with lettuce and tomato" +27550751,"TUNA SALAD SUB SANDWCH, W/ CHEESE, LETTUCE & TOMATO","Tuna salad submarine sandwich, with cheese, lettuce and tomato" +27560000,"LUNCHEON MEAT SANDWICH, NFS, W/ SPREAD","Luncheon meat sandwich, NFS, with spread" +27560110,"BOLOGNA SANDWICH, W/ SPREAD","Bologna sandwich, with spread" +27560120,"BOLOGNA & CHEESE SANDWICH W/ SPREAD","Bologna and cheese sandwich, with spread" +27560300,"CORN DOG (FRANKFURTER/HOT DOG W/ CORNBREAD COATING)","Corn dog (frankfurter or hot dog with cornbread coating)" +27560350,"PIG IN A BLANKET (FRANKFURTER OR HOT DOG WRAPPED IN DOUGH)","Pig in a blanket (frankfurter or hot dog wrapped in dough)" +27560410,"PUERTO RICAN SANDWICH, P.R. (SANDWICH CRIOLLO)","Puerto Rican sandwich (Sandwich criollo)" +27560500,"PEPPERONI, SALAMI SUBM SANDWICH, WITH LETTUCE, TOM, SPREAD","Pepperoni and salami submarine sandwich, with lettuce, tomato, and spread" +27560510,"SALAMI SANDWICH W/ SPREAD","Salami sandwich, with spread" +27560650,"SAUSAGE ON BISCUIT(INCL JIMMY DEAN SAUSAGE BISCUIT)","Sausage on biscuit" +27560660,"SAUSAGE GRIDDLE CAKE SANDWICH","Sausage griddle cake sandwich" +27560670,"SAUSAGE & CHEESE ON ENGLISH MUFFIN","Sausage and cheese on English muffin" +27560705,"SAUSAGE BALLS (MADE W/ BISCUIT MIX & CHEESE)","Sausage balls (made with biscuit mix and cheese)" +27560710,"SAUSAGE SANDWICH","Sausage sandwich" +27560720,"SAUSAGE & SPAGH SAUCE SANDWICH","Sausage and spaghetti sauce sandwich" +27560910,"COLD CUT SUB SANDWICH, W/ CHEESE, LETTUCE, TOMATO, SPRD","Cold cut submarine sandwich, with cheese, lettuce, tomato, and spread" +27563010,"MEAT SPREAD OR POTTED MEAT SANDWICH","Meat spread or potted meat sandwich" +27564000,"FRANKFURTER OR HOT DOG, NFS, PLAIN, ON BUN","Frankfurter or hot dog sandwich, NFS, plain, on bun" +27564010,"FRANKFURTER OR HOT DOG, NFS, PLAIN, ON WHITE BREAD","Frankfurter or hot dog sandwich, NFS, plain, on white bread" +27564020,"FRANKFURTER OR HOT DOG, NFS, PLAIN, ON WHEAT BREAD","Frankfurter or hot dog sandwich, NFS, plain, on wheat bread" +27564030,"FRANKFURTER/HOT DOG, NFS, PLAIN, WHL WHT BREAD, NS TO 100%","Frankfurter or hot dog sandwich, NFS, plain, on whole wheat bread, NS as to 100%" +27564040,"FRANKFURTER OR HOT DOG, NFS, PLAIN, ON WHOLE GRAIN WHITE BRE","Frankfurter or hot dog sandwich, NFS, plain, on whole grain white bread" +27564050,"FRANKFURTER OR HOT DOG, NFS, PLAIN, ON MULTIGRAIN BREAD","Frankfurter or hot dog sandwich, NFS, plain, on multigrain bread" +27564060,"FRANKFURTER OR HOT DOG, BEEF, PLAIN, ON BUN","Frankfurter or hot dog sandwich, beef, plain,on bun" +27564070,"FRANKFURTER OR HOT DOG, PLAIN, WHITE BREAD","Frankfurter or hot dog sandwich, beef, plain, on white bread" +27564080,"FRANKFURTER OR HOT DOG, PLAIN, WHEAT BREAD","Frankfurter or hot dog sandwich, beef, plain, on wheat bread" +27564090,"FRANKFURTER/ HOT DOG, BEEF,PLAIN, WHOLE WHEAT BREAD, NS 100%","Frankfurter or hot dog sandwich, beef, plain, on whole wheat bread, NS as to 100%" +27564100,"FRANKFURTER OR HOT DOG, PLAIN, ON WHOLE GRAIN WHITE","Frankfurter or hot dog sandwich, beef, plain, on whole grain white bread" +27564110,"FRANKFURTER OR HOT DOG, BEEF, PLAIN, ON MULTIGRAIN BREAD","Frankfurter or hot dog sandwich, beef, plain, on multigrain bread" +27564120,"FRANKFURTER OR HOT DOG, BEEF/PORK, PLAIN, ON BUN","Frankfurter or hot dog sandwich, beef and pork, plain, on bun" +27564130,"FRANKFURTER OR HOT DOG, BEEF & PORK, PLAIN, ON WHITE BREAD","Frankfurter or hot dog sandwich, beef and pork, plain, on white bread" +27564140,"FRANKFURTER OR HOT DOG, BEEF/PORK, PLAIN, ON WHEAT BREAD","Frankfurter or hot dog sandwich, beef and pork, plain, on wheat bread" +27564150,"FRANKFURTER/HOT DOG, BEEF&PORK,PLAIN,ON WHL WHT, NS TO 100%","Frankfurter or hot dog sandwich, beef and pork, plain, on whole wheat bread, NS as to 100%" +27564160,"FRANKFURTER OR HOT DOG, BEEF/PORK, PLAIN, WHOLE GRAIN WHITE","Frankfurter or hot dog sandwich, beef and pork, plain, on whole grain white bread" +27564170,"FRANKFURTER OR HOT DOG, BEEF/PORK, PLAIN, MULTIGRAIN BREAD","Frankfurter or hot dog sandwich, beef and pork, plain, on multigrain bread" +27564180,"FRANKFURTER OR HOT DOG, MEAT/POULTRY, PLAIN, ON BUN","Frankfurter or hot dog sandwich, meat and poultry, plain, on bun" +27564190,"FRANKFURTER OR HOT DOG, MEAT AND POULTRY, PLAIN, ON WHITE BR","Frankfurter or hot dog sandwich, meat and poultry, plain, on white bread" +27564200,"FRANKFURTER OR HOT DOG, MEAT AND POULTRY, PLAIN, ON WHEAT BR","Frankfurter or hot dog sandwich, meat and poultry, plain, on wheat bread" +27564210,"FRANKFURTER/HOT DOG, MEAT&POULTRY,PLAIN,WHL WHT,NS TO 100%","Frankfurter or hot dog sandwich, meat and poultry, plain, on whole wheat bread, NS as to 100%" +27564220,"FRANKFURTER OR HOT DOG, MEAT AND POULTRY, PLAIN, ON WHOLE GR","Frankfurter or hot dog sandwich, meat and poultry, plain, on whole grain white bread" +27564230,"FRANKFURTER OR HOT DOG, MEAT AND POULTRY, PLAIN, ON MULTIGRA","Frankfurter or hot dog sandwich, meat and poultry, plain, on multigrain bread" +27564240,"FRANKFURTER OR HOT DOG, CHICKEN AND/OR TURKEY, PLAIN, ON BUN","Frankfurter or hot dog sandwich, chicken and/or turkey, plain, on bun" +27564250,"FRANKFURTER OR HOT DOG, CHICKEN / TURKEY, PLAIN, ON WHITE BR","Frankfurter or hot dog sandwich, chicken and/or turkey, plain, on white bread" +27564260,"FRANKFURTER OR HOT DOG, CHICKEN AND/OR TURKEY, PLAIN, ON WHE","Frankfurter or hot dog sandwich, chicken and/or turkey, plain, on wheat bread" +27564270,"FRANKFURTER/HOT DOG, CHICK/TURKEY,PLAIN,WHL WHT,NS TO 100%","Frankfurter or hot dog sandwich, chicken and/or turkey, plain, on whole wheat bread, NS as to 100%" +27564280,"FRANKFURTER OR HOT DOG, CHICKEN AND/OR TURKEY, PLAIN, ON WHO","Frankfurter or hot dog sandwich, chicken and/or turkey, plain, on whole grain white bread" +27564290,"FRANKFURTER OR HOT DOG, CHICKEN AND/OR TURKEY, PLAIN, ON MUL","Frankfurter or hot dog sandwich, chicken and/or turkey, plain, on multigrain bread" +27564300,"FRANKFURTER OR HOT DOG, REDUCED FAT OR LIGHT, PLAIN, ON BUN","Frankfurter or hot dog sandwich, reduced fat or light, plain, on bun" +27564310,"FRANKFURTER OR HOT DOG, REDUCED FAT OR LIGHT, PLAIN, ON WHIT","Frankfurter or hot dog sandwich, reduced fat or light, plain, on white bread" +27564320,"FRANKFURTER OR HOT DOG, REDUCED FAT OR LIGHT, PLAIN, ON WHEA","Frankfurter or hot dog sandwich, reduced fat or light, plain, on wheat bread" +27564330,"FRANKFURTER/HOT DOG, RED FAT/LIGHT,PLAIN,WHL WHT,NS TO 100%","Frankfurter or hot dog sandwich, reduced fat or light, plain, on whole wheat bread, NS as to 100%" +27564340,"FRANKFURTER OR HOT DOG, REDUCED FAT OR LIGHT, PLAIN, ON WHOL","Frankfurter or hot dog sandwich, reduced fat or light, plain, on whole grain white bread" +27564350,"FRANKFURTER OR HOT DOG, REDUCED FAT OR LIGHT, PLAIN, ON MULT","Frankfurter or hot dog sandwich, reduced fat or light, plain, on multigrain bread" +27564360,"FRANKFURTER OR HOT DOG, FAT FREE, PLAIN, ON BUN","Frankfurter or hot dog sandwich, fat free, plain, on bun" +27564370,"FRANKFURTER OR HOT DOG, FAT FREE, PLAIN, ON WHITE BREAD","Frankfurter or hot dog sandwich, fat free, plain, on white bread" +27564380,"FRANKFURTER OR HOT DOG, FAT FREE, PLAIN, ON WHEAT BREAD","Frankfurter or hot dog sandwich, fat free, plain, on wheat bread" +27564390,"FRANKFURTER/HOT DOG, FAT FREE, PLAIN, WHL WHT, NS TO 100%","Frankfurter or hot dog sandwich, fat free, plain, on whole wheat bread, NS as to 100%" +27564400,"FRANKFURTER OR HOT DOG, FAT FREE, PLAIN, ON WHOLE GRAIN WHIT","Frankfurter or hot dog sandwich, fat free, plain, on whole grain white bread" +27564410,"FRANKFURTER OR HOT DOG, FAT FREE, PLAIN, ON MULTIGRAIN BREAD","Frankfurter or hot dog sandwich, fat free, plain, on multigrain bread" +27564420,"FRANKFURTER OR HOT DOG, MEATLESS, PLAIN, ON BUN","Frankfurter or hot dog sandwich, meatless, plain, on bun" +27564430,"FRANKFURTER OR HOT DOG, MEATLESS, PLAIN, ON BREAD","Frankfurter or hot dog sandwich, meatless, plain, on bread" +27564440,"FRANKFURTER OR HOT DOG, WITH CHILI, ON BUN","Frankfurter or hot dog sandwich, with chili, on bun" +27564450,"FRANKFURTER OR HOT DOG, WITH CHILI, ON WHITE BREAD","Frankfurter or hot dog sandwich, with chili, on white bread" +27564460,"FRANKFURTER OR HOT DOG, WITH CHILI, ON WHEAT BREAD","Frankfurter or hot dog sandwich, with chili, on wheat bread" +27564470,"FRANKFURTER/HOT DOG, W/CHILI, ON WHL WHT BREAD, NS TO 100%","Frankfurter or hot dog sandwich, with chili, on whole wheat bread, NS as to 100%" +27564480,"FRANKFURTER OR HOT DOG, WITH CHILI, ON WHOLE GRAIN WHITE BRE","Frankfurter or hot dog sandwich, with chili, on whole grain white bread" +27564490,"FRANKFURTER OR HOT DOG, WITH CHILI, ON MULTI-GRAIN BREAD","Frankfurter or hot dog sandwich, with chili, on multi-grain bread" +27564500,"FRANKFURTER OR HOT DOG, W/ VEGETARIAN CHILI, ON BUN","Frankfurter or hot dog sandwich, with vegetarian chili, on bun" +27564510,"FRANKFURTER OR HOT DOG, W/ VEGETARIAN CHILI, ON WHITE BREAD","Frankfurter or hot dog sandwich, with vegetarian chili, on white bread" +27564520,"FRANKFURTER OR HOT DOG, W/ VEGETARIAN CHILI, ON WHEAT BREAD","Frankfurter or hot dog sandwich, with vegetarian chili, on wheat bread" +27564530,"FRANKFURTER/HOT DOG, W/MEATLESS CHILI, ON WHL WHT,NS TO 100%","Frankfurter or hot dog sandwich, with meatless chili, on whole wheat bread, NS as to 100%" +27564540,"FRANKFURTER OR HOT DOG, W/ VEGETARIAN CHILI, ON WHOLE GRAIN","Frankfurter or hot dog sandwich, with vegetarian chili, on whole grain white bread" +27564550,"FRANKFURTER OR HOT DOG, W/ VEGETARIAN CHILI, ON MULTIGRAIN B","Frankfurter or hot dog sandwich, with vegetarian chili, on multigrain bread" +27564560,"FRANKFURTER OR HOT DOG, MEATLESS, ON BUN, WITH CHILI","Frankfurter or hot dog sandwich, meatless, on bun, with vegetarian chili" +27564570,"FRANKFURTER OR HOT DOG, MEATLESS, ON BREAD, WITH CHILI","Frankfurter or hot dog sandwich, meatless, on bread, with vegetarian chili" +27570310,"HORS D'OEUVRES, W/ SPREAD","Hors d'oeuvres, with spread" +27601000,"BEEF STEW, BABY FOOD, TODDLER","Beef stew, baby food, toddler" +27610100,"BEEF & EGG NOODLES, BABY, NS AS TO STR OR JR","Beef and egg noodles, baby food, NS as to strained or junior" +27610110,"BEEF & EGG NOODLES, BABY, STRAINED","Beef and egg noodles, baby food, strained" +27610120,"BEEF & EGG NOODLES, BABY, JUNIOR","Beef and egg noodles, baby food, junior" +27610710,"BEEF W/ VEGETABLES, BABY, STRAINED","Beef with vegetables, baby food, strained" +27610730,"BEEF W/ VEGETABLES, BABY FOOD, TODDLER","Beef with vegetables, baby food, toddler" +27640050,"CHICKEN & RICE DINNER, BABY, STRAINED","Chicken and rice dinner, baby food, strained" +27640100,"CHICKEN NOODLE DINNER, BABY, NS AS TO STR OR JR","Chicken noodle dinner, baby food, NS as to strained or junior" +27640110,"CHICKEN NOODLE DINNER, BABY, STRAINED","Chicken noodle dinner, baby food, strained" +27640120,"CHICKEN NOODLE DINNER, BABY, JUNIOR","Chicken noodle dinner, baby food, junior" +27640810,"CHICKEN, NOODLES & VEGETABLES, BABY, TODDLER","Chicken, noodles, and vegetables, baby food, toddler" +27641000,"CHICKEN STEW, BABY FOOD, TODDLER","Chicken stew, baby food, toddler" +27642100,"TURKEY, RICE & VEGETABLES, BABY, NS AS TO STR OR JR","Turkey, rice and vegetables, baby food, NS as to strained or junior" +27642110,"TURKEY, RICE & VEGETABLES, BABY, STRAINED","Turkey, rice and vegetables, baby food, strained" +27642120,"TURKEY, RICE & VEGETABLES, BABY, JUNIOR","Turkey, rice and vegetables, baby food, junior" +27642130,"TURKEY, RICE, & VEGETABLES, BABY, TODDLER","Turkey, rice, and vegetables, baby food, toddler" +27644110,"CHICKEN SOUP, BABY","Chicken soup, baby food" +28101000,"FROZEN DINNER, NFS","Frozen dinner, NFS" +28110000,"BEEF DINNER, NFS (FROZEN)","Beef dinner, NFS (frozen meal)" +28110120,"BEEF W/ POTATOES (FROZEN MEAL, LARGE MEAT PORTION)","Beef with potatoes (frozen meal, large meat portion)" +28110150,"BEEF W/ VEGETABLE (DIET FROZEN MEAL)","Beef with vegetable (diet frozen meal)" +28110220,"SIRLOIN, CHOPPED, W/ GRAVY, POT, VEG (FROZEN MEAL)","Sirloin, chopped, with gravy, mashed potatoes, vegetable (frozen meal)" +28110250,"SIRLOIN TIPS W/ GRAVY, POTATOES, VEG (FROZEN MEAL)","Sirloin tips with gravy, potatoes, vegetable (frozen meal)" +28110270,"SIRLOIN BEEF W/ GRAVY, POTATOES, VEG (FROZ MEAL)","Sirloin beef with gravy, potatoes, vegetable (frozen meal)" +28110300,"SALISBURY STEAK DINNER, NFS (FROZEN)","Salisbury steak dinner, NFS (frozen meal)" +28110310,"SALISBURY STEAK W/ GRAVY, POTATOES, VEG (FROZ MEAL)","Salisbury steak with gravy, potatoes, vegetable (frozen meal)" +28110330,"SALISBURY STEAK, GRAVY, POT, VEG, DESSERT(FRZ MEAL)","Salisbury steak with gravy, whipped potatoes, vegetable, dessert (frozen meal)" +28110340,"SALISBURY STK, GRAVY,POT,VEG,SOUP,DESSERT(FRZ MEAL)","Salisbury steak with gravy, potatoes, vegetable, soup or macaroni and cheese, dessert (frozen meal)" +28110350,"SALISBURY STEAK, POT,VEG,DESSERT(FROZ MEAL,LG MEAT)","Salisbury steak with gravy, potatoes, vegetable, dessert (frozen meal, large meat portion)" +28110370,"SALISBURY STEAK, GRAVY, MAC&CHEESE, VEG (FROZ MEAL)","Salisbury steak with gravy, macaroni and cheese, vegetable (frozen meal)" +28110380,"SALISBURY STEAK W/GRAV,MACARONI & CHEESE (FRZ MEAL)","Salisbury steak with gravy, macaroni and cheese (frozen meal)" +28110390,"SALISBURY STEAK, POT, VEG, DESSERT (DIET FZN MEAL)","Salisbury steak, potatoes, vegetable, dessert (diet frozen meal)" +28110510,"BEEF, SLICED, W/ GRAVY, POTATOES, VEG (FROZEN MEAL)","Beef, sliced, with gravy, potatoes, vegetable (frozen meal)" +28110620,"SHORTRIBS W/ BBQ SAUCE, POTATOES & VEG (FROZ MEAL)","Beef short ribs, boneless, with barbecue sauce, potatoes, vegetable (frozen meal)" +28110640,"MEATBALLS, SWEDISH, IN SAUCE W/ NOODLES (FROZ MEAL)","Meatballs, Swedish, in sauce, with noodles (frozen meal)" +28110660,"MEATBALLS,SWEDISH,W/GRAVY & NOODLES (DIET FRZ MEAL)","Meatballs, Swedish, in gravy, with noodles (diet frozen meal)" +28113110,"SALISBURY STEAK W/ TOM SAUCE, VEG (DIET FROZ MEAL)","Salisbury steak, baked, with tomato sauce, vegetable (diet frozen meal)" +28113140,"BEEF W/ SPAETZLE OR RICE, VEGETABLE (FROZEN MEAL)","Beef with spaetzle or rice, vegetable (frozen meal)" +28130000,"VEAL DINNER, NFS (FROZEN)","Veal dinner, NFS (frozen meal)" +28133110,"VEAL, BREADED, W/ SPAGHETTI, TOM SAUCE (FROZ MEAL)","Veal, breaded, with spaghetti, in tomato sauce (frozen meal)" +28133340,"VEAL PARMIGIANA, VEG, FETTUCCINE,DESSERT(FROZ MEAL)","Veal parmigiana with vegetable, fettuccine alfredo, dessert (frozen meal)" +28140100,"CHICKEN DINNER, NFS (FROZEN)","Chicken dinner, NFS (frozen meal)" +28140150,"CHICKEN DIVAN (FROZEN MEAL)","Chicken divan (frozen meal)" +28140250,"CHICKEN,GRAVY,DRESS,RICE,VEG,DESSRT(Z MEAL,LG MEAT)","Chicken, boneless, with gravy, dressing, rice, vegetable, dessert (frozen meal, large meat portion)" +28140320,"CHICKEN & NOODLES W/ VEG, DESSERT (FROZEN MEAL)","Chicken and noodles with vegetable, dessert (frozen meal)" +28140710,"CHICKEN, FRIED, W/ POTATOES, VEGETABLE (FROZ MEAL)","Chicken, fried, with potatoes, vegetable (frozen meal)" +28140720,"CHICKEN PATTY, POTATOES, VEGETABLE (FROZEN MEAL)","Chicken patty, or nuggets, boneless, breaded, potatoes, vegetable (frozen meal)" +28140730,"CHICKEN PATTY, TOM SCE, FETTUCCINE, VEG (FROZ MEAL)","Chicken patty, breaded, with tomato sauce and cheese, fettuccine alfredo, vegetable (frozen meal)" +28140740,"CHICKEN PATTY/NUGGET,PASTA,FRUIT,DESSERT(FROZ MEAL)","Chicken patty, or nuggets, boneless, breaded, with pasta and tomato sauce, fruit, dessert (frozen meal)" +28140810,"CHICKEN, FRIED, W/ POT, VEG, DESSERT (FROZEN MEAL)","Chicken, fried, with potatoes, vegetable, dessert (frozen meal)" +28141010,"CHICKEN, FRIED, POT,VEG, DESSERT(FROZ MEAL,LG MEAT)","Chicken, fried, with potatoes, vegetable, dessert (frozen meal, large meat portion)" +28141050,"CHICKEN PATTY PARMIGIANA, W/ VEG (DIET FROZ MEAL)","Chicken patty parmigiana, breaded, with vegetable (diet frozen meal)" +28141201,"TERIYAKI CHICKEN W/ RICE & VEGETABLE (DIET FROZ MEAL)","Teriyaki chicken with rice and vegetable (diet frozen meal)" +28141250,"CHICKEN W/ RICE-VEGETABLE MIXTURE (DIET FROZ MEAL)","Chicken with rice-vegetable mixture (diet frozen meal)" +28141300,"CHICKEN W/RICE & VEG, REDUCED FAT&SODIUM(DIET FROZ)","Chicken with rice and vegetable, reduced fat and sodium (diet frozen meal)" +28141600,"CHICKEN A LA KING W/ RICE (FROZEN MEAL)","Chicken a la king with rice (frozen meal)" +28141610,"CHICKEN & VEGETABLES IN CREAM SCE (DIET FROZ MEAL)","Chicken and vegetables in cream or white sauce (diet frozen meal)" +28141650,"CHICKEN & VEGETABLES AU GRATIN (DIET FROZEN MEAL)","Chicken and vegetables au gratin with rice-vegetable mixture (diet frozen entree)" +28143020,"CHICKEN AND VEGETABLE W/ RICE, ASIAN (DIET FROZEN MEAL)","Chicken and vegetable entree with rice, Asian (diet frozen meal)" +28143040,"CHICKEN CHOW MEIN W/ RICE (DIET FROZEN MEAL)","Chicken chow mein with rice (diet frozen meal)" +28143050,"CHICK CHOWMEIN W/RICE,REDUCED FAT&SODIUM(DIET FROZ)","Chicken chow mein with rice, reduced fat and sodium (diet frozen meal)" +28143080,"CHICKEN W/NOODLES & CHEESE SAUCE (DIET FROZEN MEAL)","Chicken with noodles and cheese sauce (diet frozen meal)" +28143110,"CHICKEN CACCIATORE W/ NOODLES (DIET FROZEN MEAL)","Chicken cacciatore with noodles (diet frozen meal)" +28143130,"CHICKEN & VEG ENTREE W/ NOODLES (FROZEN MEAL)","Chicken and vegetable entree with noodles (frozen meal)" +28143150,"CHICK & VEG ENTREE W/ NOODLES, (DIET FROZEN MEAL)","Chicken and vegetable entree with noodles (diet frozen meal)" +28143170,"CHICKEN IN CREAM SAUCE W/ NOODLES & VEG (FROZ MEAL)","Chicken in cream sauce with noodles and vegetable (frozen meal)" +28143180,"CHICKEN,BUTTER SCE,W/POT & VEG (FRZ, DIET MEAL)","Chicken in butter sauce with potatoes and vegetable (diet frozen meal)" +28143190,"CHICKEN, MUSHROOM SAUCE, WILD RICE, VEG (FROZ MEAL)","Chicken in mushroom sauce, white and wild rice, vegetable (frozen meal)" +28143200,"CHICKEN IN SOY-BASED SAUCE,RICE&VEG (FROZEN MEAL)","Chicken in soy-based sauce, rice and vegetables (frozen meal)" +28143210,"CHICKEN IN ORANGE SAUCE W/ RICE (DIET FROZEN MEAL)","Chicken in orange sauce with almond rice (diet frozen meal)" +28143220,"CHICKEN IN BBQ SCE,W/RICE,VEG&DES,RED FAT&SODIUM,FRZ,DIET","Chicken in barbecue sauce, with rice, vegetable and dessert, reduced fat and sodium (diet frozen meal)" +28144100,"CHICKEN & VEG W/ NOODLES & CREAM SCE (FROZEN MEAL)","Chicken and vegetable entree with noodles and cream sauce (frozen meal)" +28145000,"TURKEY DINNER, NFS (FROZEN)","Turkey dinner, NFS (frozen meal)" +28145100,"TURKEY W/DRESSING, GRAVY,VEG, FRUIT (DIET FRZ MEAL)","Turkey with dressing, gravy, vegetable and fruit (diet frozen meal)" +28145110,"TURKEY W/ VEGETABLE, STUFFING (DIET FROZEN MEAL)","Turkey with vegetable, stuffing (diet frozen meal)" +28145210,"TURKEY W/ GRAVY, DRESSING, POT, VEG (FROZEN MEAL)","Turkey with gravy, dressing, potatoes, vegetable (frozen meal)" +28145610,"TURKEY, DRESSING,POT,VEG,DESSERT(FROZ MEAL,LG MEAT)","Turkey with gravy, dressing, potatoes, vegetable, dessert (frozen meal, large meat portion)" +28145710,"TURKEY TETRAZZINI (FROZEN MEAL)","Turkey tetrazzini (frozen meal)" +28150000,"FISH DINNER, NFS (FROZEN)","Fish dinner, NFS (frozen meal)" +28150210,"HADDOCK W/ CHOPPED SPINACH (DIET FROZEN MEAL)","Haddock with chopped spinach (diet frozen meal)" +28150220,"FLOUNDER W/ CHOPPED BROCCOLI (DIET FROZEN MEAL)","Flounder with chopped broccoli (diet frozen meal)" +28150510,"FISH IN LEMON SAUCE W/ STARCH ITEM, VEG (FROZ MEAL)","Fish in lemon-butter sauce with starch item, vegetable (frozen meal)" +28150650,"FISH,BREADED/FISH STICKS,W/PASTA,VEG,DES (FRZ MEAL)","Fish, breaded, or fish sticks, with pasta, vegetable and dessert (frozen meal)" +28153010,"SHRIMP & CLAMS IN TOMATO SCE, W/ NOODLES(FROZ MEAL)","Shrimp and clams in tomato-based sauce, with noodles (frozen meal)" +28154010,"SHRIMP & VEG IN SAUCE W/ NOODLES (DIET FROZEN MEAL)","Shrimp and vegetables in sauce with noodles (diet frozen meal)" +28160300,"MEAT LOAF DINNER, NFS (FROZEN)","Meat loaf dinner, NFS (frozen meal)" +28160310,"MEATLOAF W/ POTATO, VEG (FROZ MEAL)","Meat loaf with potatoes, vegetable (frozen meal)" +28160650,"STUFFED GREEN PEPPER (FROZEN MEAL)","Stuffed green pepper (frozen meal)" +28160710,"STUFFED CABBAGE, W/ MEAT & TOM SCE (DIET FROZ MEAL)","Stuffed cabbage, with meat and tomato sauce (diet frozen meal)" +28310110,"BEEF BROTH, BOUILLON OR CONSOMME (INCL BROTH, NFS)","Beef, broth, bouillon, or consomme" +28310120,"BEEF BROTH OR BOUILLON, CANNED, LOW SODIUM","Beef, broth, bouillon, or consomme, canned, low sodium" +28310150,"OXTAIL SOUP","Oxtail soup" +28310160,"BEEF BROTH, W/ TOMATO, HOME RECIPE","Beef broth, with tomato, home recipe" +28310170,"BEEF BROTH, W/O TOMATO, HOME RECIPE","Beef broth, without tomato, home recipe" +28310230,"MEATBALL SOUP, MEXICAN STYLE, HOME RECIPE (SOPA DE ALBONDIGA","Meatball soup, Mexican style, home recipe (Sopa de Albondigas)" +28310320,"BEEF NOODLE SOUP, P.R. (SOPA DE CARNE Y FIDEOS)","Beef noodle soup, Puerto Rican style (Sopa de carne y fideos)" +28310330,"MEAT AND RICE NOODLE SOUP, ASIAN STYLE (VIETNAMESE PHO BO)","Meat and rice noodle soup, Asian style (Vietnamese Pho Bo)" +28310420,"BEEF & RICE SOUP, P.R.","Beef and rice soup, Puerto Rican style" +28311010,"PEPPERPOT (TRIPE) SOUP","Pepperpot (tripe) soup" +28311020,"MENUDO SOUP, HOME RECIPE","Menudo soup, home recipe" +28311030,"MENUDO, CANNED","Menudo soup, canned, prepared with water or ready-to-serve" +28315050,"BEEF VEGETABLE SOUP W/ POTATO, PASTA OR RICE, CHUNKY STYLE","Beef vegetable soup with potato, pasta, or rice, chunky style, canned, or ready-to-serve" +28315140,"BEEF VEGETABLE SOUP, MEXICAN STYLE, HOME RECIPE, (SOPA / CAL","Beef vegetable soup, Mexican style, home recipe, (Sopa / Caldo de Res)" +28315150,"MEAT AND CORN HOMINY SOUP, MEXICAN STYLE, HOME RECIPE (POZOL","Meat and corn hominy soup, Mexican style, home recipe (Pozole)" +28315160,"ITALIAN WEDDING SOUP","Italian Wedding Soup" +28317010,"BEEF STROGANOFF SOUP, CHUNKY STYLE, HOME RECIPE, CANNED OR R","Beef stroganoff soup, chunky style, home recipe, canned or ready-to-serve" +28320130,"HAM, RICE, & POTATO SOUP, P.R.","Ham, rice, and potato soup, Puerto Rican style" +28320140,"HAM, NOODLE & VEGETABLE SOUP, P.R.","Ham, noodle, and vegetable soup, Puerto Rican style" +28320160,"PORK VEGETABLE SOUP W/ POTATO, PASTA, OR RICE, CHUNKY","Pork vegetable soup with potato, pasta, or rice, stew type, chunky style" +28320300,"PORK W/VEG (NO CAR, BROC AND/OR DK GRN)SOUP, ASIAN STYLE","Pork with vegetable (excluding carrots, broccoli and/or dark-green leafy) soup, Asian Style" +28321130,"BACON SOUP, CREAM OF, PREPARED W/ WATER","Bacon soup, cream of, prepared with water" +28330110,"SCOTCH BROTH (LAMB, VEGETABLES, BARLEY)","Scotch broth (lamb, vegetables, and barley)" +28331110,"LAMB, PASTA & VEGETABLE SOUP, P.R.","Lamb, pasta, and vegetable soup, Puerto Rican style" +28340110,"CHICKEN OR TURKEY BROTH, BOUILLON, OR CONSOMME","Chicken or turkey broth, bouillon, or consomme" +28340120,"CHICKEN OR TURKEY BROTH, WITHOUT TOMATO, HOME RECIPE","Chicken or turkey broth, without tomato, home recipe" +28340130,"CHICKEN OR TURKEY BROTH, WITH TOMATO, HOME RECIPE","Chicken or turkey broth, with tomato, home recipe" +28340150,"MEXICAN STYLE CHICKEN BROTH SOUP STOCK","Mexican style chicken broth soup stock" +28340180,"CHICKEN OR TURKEY BROTH, LESS/REDUCED SODIUM, CANNED OR RTS","Chicken or turkey broth, less or reduced sodium, canned or ready-to-serve" +28340210,"CHICKEN RICE SOUP, P.R. (SOPA DE POLLO CON ARROZ)","Chicken rice soup, Puerto Rican style (Sopa de pollo con arroz)" +28340220,"CHICKEN SOUP W/ NOODLES & POTATOES, P.R.","Chicken soup with noodles and potatoes, Puerto Rican style" +28340310,"CHICKEN OR TURKEY GUMBO SOUP","Chicken or turkey gumbo soup, home recipe, canned or ready-to-serve" +28340510,"CHICKEN OR TURKEY NOODLE SOUP, CHUNKY STYLE, CANNED OR RTS","Chicken or turkey noodle soup, chunky style, canned or ready-to-serve" +28340550,"SWEET & SOUR SOUP","Sweet and sour soup" +28340580,"CHICKEN OR TURKEY SOUP WITH VEGETABLES, ASIAN STYLE","Chicken or turkey soup with vegetables (broccoli, carrots, celery, potatoes and onions), Asian style" +28340590,"CHICKEN OR TURKEY CORN SOUP WITH NOODLES, HOME RECIPE","Chicken or turkey corn soup with noodles, home recipe" +28340600,"CHICK/TURK+VEG SOUP, CANNED","Chicken or turkey vegetable soup, canned, prepared with water or ready-to-serve" +28340610,"CHICKEN VEGETABLE SOUP, STEW TYPE (INCL CHUNKY)","Chicken or turkey vegetable soup, stew type" +28340630,"CHICKEN OR TURKEY VEGETABLE SOUP WITH RICE, STEW TYPE, CHUNK","Chicken or turkey vegetable soup with rice, stew type, chunky style" +28340640,"CHICKEN OR TURKEY VEGETABLE SOUP WITH NOODLES, STEW TYPE, CH","Chicken or turkey vegetable soup with noodles, stew type, chunky style, canned or ready-to-serve" +28340660,"CHICKEN OR TURKEY VEGETABLE SOUP, HOME RECIPE","Chicken or turkey vegetable soup, home recipe" +28340670,"CHICKEN OR TURKEY VEGETABLE SOUP WITH RICE, MEXICAN STYLE, H","Chicken or turkey vegetable soup with rice, Mexican style, home recipe (Sopa / Caldo de Pollo)" +28340680,"CHICKEN OR TURKEY AND CORN HOMINY SOUP, MEXICAN STYLE, HOME","Chicken or turkey and corn hominy soup, Mexican style, home recipe (Pozole)" +28340690,"CHICKEN OR TURKEY VEGETABLE SOUP WITH POTATO AND CHEESE, CHU","Chicken or turkey vegetable soup with potato and cheese, chunky style, canned or ready-to-serve" +28340700,"BIRD'S NEST SOUP (CHICKEN, HAM, NOODLES)","Bird's nest soup (chicken, ham, and noodles)" +28340750,"HOT & SOUR SOUP (INCLUDE HOT & SPICY CHINESE SOUP)","Hot and sour soup" +28340800,"CHICKEN OR TURKEY SOUP WITH VEGETABLES AND FRUIT, ASIAN STYL","Chicken or turkey soup with vegetables and fruit, Asian Style" +28345010,"CHICKEN/TURKEY SOUP, CM OF, CAN, RED SOD, NS W/ MILK/WATER","Chicken or turkey soup, cream of, canned, reduced sodium, NS as to made with milk or water" +28345020,"CHICKEN/TURKEY SOUP, CM OF, CAN, RED SOD, W/ MILK","Chicken or turkey soup, cream of, canned, reduced sodium, made with milk" +28345030,"CHICKEN/TURKEY SOUP, CM OF, CAN, RED SOD, W/ WATER","Chicken or turkey soup, cream of, canned, reduced sodium, made with water" +28345110,"CHICKEN SOUP, CREAM OF, NS AS TO MILK OR WATER","Chicken or turkey soup, cream of, NS as to prepared with milk or water" +28345120,"CHICKEN/TURKEY SOUP,CREAM OF, W/ MILK","Chicken or turkey soup, cream of, prepared with milk" +28345130,"CHICKEN SOUP, CREAM OF, PREPARED W/ WATER","Chicken or turkey soup, cream of, prepared with water" +28345160,"CHICKEN OR TURKEY MUSHROOM SOUP, CREAM OF, PREPARED WITH MIL","Chicken or turkey mushroom soup, cream of, prepared with milk" +28345170,"DUCK SOUP","Duck soup" +28350040,"FISH STOCK, HOME RECIPE","Fish stock, home recipe" +28350050,"FISH CHOWDER (INCL FISHERMAN'S SOUP, SEAFOOD CHOWD)","Fish chowder" +28350110,"CRAB SOUP, NS AS TO TOMATO-BASE OR CREAM","Crab soup, NS as to tomato-base or cream style" +28350120,"CRAB SOUP, TOMATO BASE","Crab soup, tomato-base" +28350210,"CLAM CHOWDER, NS AS TO MANHATTAN OR NEW ENGLAND","Clam chowder, NS as to Manhattan or New England style" +28350220,"CLAM CHOWDER, MANHATTAN (INCLUDE CHUNKY)","Clam chowder, Manhattan" +28350310,"TURTLE & VEGETABLE SOUP (INCLUDE SNAPPER SOUP)","Turtle and vegetable soup" +28351110,"FISH AND VEGETABLE SOUP, NO POTATOES (SOPA DE PESCADO Y MARI","Fish and vegetable soup, no potatoes (Sopa de pescado y mariscos)" +28351120,"FISH SOUP, WITH POTATOES (SOPA DE PESCADO Y MARISCOS)","Fish soup, with potatoes (Sopa de Pescado y Mariscos)" +28351160,"CODFISH, RICE & VEGETABLE SOUP, P.R.","Codfish, rice, and vegetable soup, Puerto Rican style" +28351170,"CODFISH SOUP W/ NOODLES, P.R.","Codfish soup with noodles, Puerto Rican style" +28355110,"CLAM CHOWDER, NEW ENG, NS AS TO MILK OR WATER ADDED","Clam chowder, New England, NS as to prepared with water or milk" +28355120,"CLAM CHOWDER, NEW ENGLAND, W/ MILK","Clam chowder, New England, prepared with milk" +28355130,"CLAM CHOWDER, NEW ENGLAND, W/ WATER","Clam chowder, New England, prepared with water" +28355140,"CLAM CHOWDER, NEW ENGLAND, REDUCED SODIUM, CANNED OR READY-T","Clam chowder, New England, reduced sodium, canned or ready-to-serve" +28355210,"CRAB SOUP, CREAM OF, W/ MILK","Crab soup, cream of, prepared with milk" +28355250,"LOBSTER BISQUE","Lobster bisque" +28355260,"LOBSTER GUMBO","Lobster gumbo" +28355310,"OYSTER STEW","Oyster stew" +28355350,"SALMON SOUP, CREAM STYLE","Salmon soup, cream style" +28355410,"SHRIMP SOUP, CREAM OF, NS AS TO MILK/WATER ADDED","Shrimp soup, cream of, NS as to prepared with milk or water" +28355420,"SHRIMP SOUP, CREAM OF, W/ MILK","Shrimp soup, cream of, prepared with milk" +28355430,"SHRIMP SOUP, CREAM OF, W/ WATER","Shrimp soup, cream of, prepared with water" +28355440,"SHRIMP GUMBO","Shrimp gumbo" +28355450,"SEAFOOD SOUP W/ POTATOES & VEGETABLES (INCL DK GREEN LEAF)","Seafood soup with potatoes and vegetables (including carrots, broccoli, and/or dark-green leafy)" +28355460,"SEAFOOD SOUP W/ POTATOES & VEGETABLES (EXCL DK GREEN LEAF)","Seafood soup with potatoes and vegetables (excluding carrots, broccoli, and dark-green leafy)" +28355470,"SEAFOOD SOUP W/ VEGETABLES (INCL DK GREEN LEAFY)","Seafood soup with vegetables (including carrots, broccoli, and/or dark-green leafy (no potatoes))" +28355480,"SEAFOOD SOUP W/ VEGETABLES (EXCL DK GREEN LEAFY)","Seafood soup with vegetables (excluding carrots, broccoli, and dark-green leafy (no potatoes))" +28360100,"MEAT BROTH, P.R. STYLE","Meat broth, Puerto Rican style" +28360210,"SPANISH VEGETABLE SOUP, P.R. (CALDO GALLEGO)","Spanish vegetable soup, Puerto Rican style (Caldo gallego)" +28500000,"GRAVY, POULTRY","Gravy, poultry" +28500010,"GRAVY, MEAT/POULTRY, W/ WINE","Gravy, meat or poultry, with wine" +28500020,"GRAVY, MEAT, W/ FRUIT (INCLUDE FRENCH SAUCE)","Gravy, meat, with fruit" +28500030,"GRAVY, POULTRY, LOW SODIUM","Gravy, poultry, low sodium" +28500040,"GRAVY, BEEF/MEAT (INCL GRAVY,NFS;BROWN GRAVY;SWISS STEAK GRV","Gravy, beef or meat" +28500050,"GRAVY, GIBLET(INCL ANY POULTRY GRAVY W/PCS OF MEAT)","Gravy, giblet" +28500060,"GRAVY, BEEF OR MEAT, LOW SODIUM","Gravy, beef or meat, low sodium" +28500070,"GRAVY, BEEF OR MEAT, HOME RECIPE","Gravy, beef or meat, home recipe" +28500080,"GRAVY, POULTRY, HOME RECIPE","Gravy, poultry, home recipe" +28500100,"GRAVY, MUSHROOM","Gravy, mushroom" +28500150,"GRAVY, REDEYE","Gravy, redeye" +28501010,"GRAVY, BEEF/MEAT, FAT FREE","Gravy, beef or meat, fat free" +28501110,"GRAVY, POULTRY, FAT FREE","Gravy, poultry, fat free" +28510010,"GRAVY/SAUCE, POULTRY FROM CHICKEN FRICASSEE, P.R.","Gravy or sauce, poultry-based from Puerto Rican-style chicken fricasse" +28510020,"GRAVY, MEAT-BASED, FROM PUERTO RICAN POT ROAST","Gravy, meat-based, from Puerto-Rican style stuffed pot roast" +28510030,"GRAVY, MEAT-BASED, FROM PUERTO RICAN BEEF STEW","Gravy, meat-based, from Puerto-Rican style beef stew" +28520000,"GRAVY/SAUCE,CHINESE(SOY SCE,STOCK/BOUILL,CRNSTRCH)","Gravy or sauce, Chinese (soy sauce, stock or bouillon, cornstarch)" +28520100,"OYSTER-FLAVORED SAUCE","Oyster-flavored sauce" +28522000,"MOLE POBLANA (SAUCE)","Mole poblano (sauce)" +28522050,"MOLE VERDE (SAUCE)","Mole verde (sauce)" +31101010,"EGGS, WHOLE, RAW","Egg, whole, raw" +31102000,"EGGS, WHOLE, COOKED, NS AS TO METHOD","Egg, whole, cooked, NS as to cooking method" +31103010,"EGG, WHOLE, BOILED OR POACHED","Egg, whole, boiled or poached" +31105010,"EGG, WHOLE, FRIED WITHOUT FAT","Egg, whole, fried without fat" +31105020,"EGG, WHOLE, FRIED WITH MARGARINE","Egg, whole, fried with margarine" +31105030,"EGG, WHOLE, FRIED WITH OIL","Egg, whole, fried with oil" +31105040,"EGG, WHOLE, FRIED WITH BUTTER","Egg, whole, fried with butter" +31105060,"EGG, WHOLE, FRIED WITH ANIMAL FAT OR MEAT DRIPPINGS","Egg, whole, fried with animal fat or meat drippings" +31105080,"EGG, WHOLE, FRIED WITH COOKING SPRAY","Egg, whole, fried with cooking spray" +31105090,"EGG, WHOLE, FRIED, FROM FAST FOOD / RESTAURANT","Egg, whole, fried, from fast food / restaurant" +31106000,"EGGS, WHOLE, BAKED, NS AS TO ADDED FAT","Egg, whole, baked, NS as to fat added in cooking" +31106010,"EGGS, WHOLE, BAKED, NO FAT ADDED","Egg, whole, baked, fat not added in cooking" +31106020,"EGGS, WHOLE, BAKED, FAT ADDED","Egg, whole, baked, fat added in cooking" +31107000,"EGGS, WHOLE, PICKLED","Egg, whole, pickled" +31108010,"EGGS, WHITE ONLY, RAW","Egg, white only, raw" +31110010,"EGG YOLK, ONLY, RAW","Egg, yolk only, raw" +31111000,"EGG, YOLK ONLY, COOKED, NS AS TO FAT ADDED IN COOKING","Egg, yolk only, cooked, NS as to fat added in cooking" +31111010,"EGG, YOLK ONLY, COOKED, NO FAT ADDED","Egg, yolk only, cooked, fat not added in cooking" +31111020,"EGG, YOLK ONLY, COOKED, FAT ADDED IN COOKING","Egg, yolk only, cooked, fat added in cooking" +31201000,"DUCK EGG, COOKED","Duck egg, cooked" +31202000,"GOOSE EGG, COOKED","Goose egg, cooked" +31203000,"QUAIL EGG, CANNED","Quail egg, canned" +32101000,"EGGS, CREAMED","Egg, creamed" +32101500,"EGGS, BENEDICT","Egg, Benedict" +32101530,"EGG CURRY","Egg curry" +32102000,"EGGS, DEVILED","Egg, deviled" +32103000,"EGG SALAD, W/ MAYO","Egg salad, made with mayonnaise" +32103015,"EGG SALAD, W/ LT MAYO","Egg salad, made with light mayonnaise" +32103020,"EGG SALAD, W/ MAYO-TYPE DRSG","Egg salad, made with mayonnaise-type salad dressing" +32103025,"EGG SALAD, W/ LIGHT MAYO-TYPE DRSG","Egg salad, made with light mayonnaise-type salad dressing" +32103030,"EGG SALAD, W/ CREAMY DRSG","Egg salad, made with creamy dressing" +32103035,"EGG SALAD,W/ LT CREAMY DRSG","Egg salad, made with light creamy dressing" +32103040,"EGG SALAD, W/ ITALIAN DRSG","Egg salad, made with Italian dressing" +32103045,"EGG SALAD, W/ LT ITALIAN DRSG","Egg salad, made with light Italian dressing" +32103050,"EGG SALAD, W/ ANY TYPE OF FAT FREE DRSG","Egg Salad, made with any type of fat free dressing" +32105180,"HUEVOS RANCHEROS","Huevos rancheros" +32105190,"EGG CASSEROLE W/ BREAD, CHEESE, MILK & MEAT","Egg casserole with bread, cheese, milk and meat" +32105200,"EGG FOO YUNG, NFS","Egg foo yung (young), NFS" +32105210,"CHICKEN EGG FOO YUNG","Chicken egg foo yung (young)" +32105220,"PORK EGG FOO YUNG","Pork egg foo yung (young)" +32105230,"SHRIMP EGG FOO YUNG","Shrimp egg foo yung (young)" +32105240,"BEEF EGG FOO YUNG","Beef egg foo yung (young)" +32105310,"RIPE PLANTAIN OMELET, P.R. (TORTILLA DE AMARILLO)","Ripe plantain omelet, Puerto Rican style (Tortilla de amarillo)" +32105330,"SCRAMBLED EGGS W/ JERKED BEEF, P.R.","Scrambled eggs with jerked beef, Puerto Rican style (Revoltillo de tasajo)" +32110100,"EGGS, A LA MALAGUENA, P.R.(HUEVOS A LA MALAGUENA)","Eggs a la Malaguena, Puerto Rican style (Huevos a la Malaguena)" +32110150,"SHRIMP-EGG PATTY (TORTA DE CAMERON SECO)","Shrimp-egg patty (Torta de Cameron seco)" +32120100,"EGG DESSERT, CUSTARD-LIKE, W/ WATER & SUGAR, P.R.","Egg dessert, custard-like, made with water and sugar, Puerto Rican style (Tocino del cielo; Heaven's delight)" +32120200,"ZABAGLIONE","Zabaglione" +32130000,"EGG OMELET OR SCRAMBLED EGG, MADE WITH MARGARINE","Egg omelet or scrambled egg, made with margarine" +32130010,"EGG OMELET OR SCRAMBLED EGG, MADE WITH OIL","Egg omelet or scrambled egg, made with oil" +32130020,"EGG OMELET OR SCRAMBLED EGG, MADE WITH BUTTER","Egg omelet or scrambled egg, made with butter" +32130040,"EGG OMELET OR SCRAMBLED EGG, MADE W/ANIMAL FAT OR MEAT DRIP","Egg omelet or scrambled egg, made with animal fat or meat drippings" +32130060,"EGG OMELET OR SCRAMBLED EGG, MADE WITH COOKING SPRAY","Egg omelet or scrambled egg, made with cooking spray" +32130070,"EGG OMELET OR SCRAMBLED EGG, MADE WITHOUT FAT","Egg omelet or scrambled egg, made without fat" +32130080,"EGG OMELET OR SCRAMBLED EGG, FROM FAST FOOD / RESTAURANT","Egg omelet or scrambled egg, from fast food / restaurant" +32130100,"EGG OMELET OR SCRAMBLED EGG, WITH CHEESE, MADE WITH MARGARIN","Egg omelet or scrambled egg, with cheese, made with margarine" +32130110,"EGG OMELET OR SCRAMBLED EGG, WITH CHEESE, MADE WITH OIL","Egg omelet or scrambled egg, with cheese, made with oil" +32130120,"EGG OMELET OR SCRAMBLED EGG, WITH CHEESE, MADE WITH BUTTER","Egg omelet or scrambled egg, with cheese, made with butter" +32130140,"EGG OMELET OR SCRAMBLED EGG, W/ CHEESE, MADE W/ ANIMAL FAT","Egg omelet or scrambled egg, with cheese, made with animal fat or meat drippings" +32130160,"EGG OMELET OR SCRAMBLED EGG, WITH CHEESE, MADE WITH COOKING","Egg omelet or scrambled egg, with cheese, made with cooking spray" +32130170,"EGG OMELET OR SCRAMBLED EGG, WITH CHEESE, MADE WITHOUT FAT","Egg omelet or scrambled egg, with cheese, made without fat" +32130200,"EGG OMELET OR SCRAMBLED EGG, WITH MEAT, MADE WITH MARGARINE","Egg omelet or scrambled egg, with meat, made with margarine" +32130210,"EGG OMELET OR SCRAMBLED EGG, WITH MEAT, MADE WITH OIL","Egg omelet or scrambled egg, with meat, made with oil" +32130220,"EGG OMELET OR SCRAMBLED EGG, WITH MEAT, MADE WITH BUTTER","Egg omelet or scrambled egg, with meat, made with butter" +32130240,"EGG OMELET OR SCRAMBLED EGG, WITH MEAT, MADE WITH ANIMAL FAT","Egg omelet or scrambled egg, with meat, made with animal fat or meat drippings" +32130260,"EGG OMELET OR SCRAMBLED EGG, WITH MEAT, MADE W/COOKING SPRAY","Egg omelet or scrambled egg, with meat, made with cooking spray" +32130270,"EGG OMELET OR SCRAMBLED EGG, WITH MEAT, MADE WITHOUT FAT","Egg omelet or scrambled egg, with meat, made without fat" +32130300,"EGG OMELET OR SCRAMBLED EGG, W/CHEESE & MEAT, MADE W/MARGARI","Egg omelet or scrambled egg, with cheese and meat, made with margarine" +32130310,"EGG OMELET OR SCRAMBLED EGG, W/CHEESE & MEAT, MADE W/OIL","Egg omelet or scrambled egg, with cheese and meat, made with oil" +32130320,"EGG OMELET OR SCRAMBLED EGG, W/ CHEESE &MEAT, MADEW/BUTTER","Egg omelet or scrambled egg, with cheese and meat, made with butter" +32130340,"EGG OMELET OR SCR EGG, WITH CHEESE & MEAT, MADE W/ANIMAL FAT","Egg omelet or scrambled egg, with cheese and meat, made with animal fat or meat drippings" +32130360,"EGG OMELET OR SCR EGG, W/CHEESE& MEAT, MADE W/COOKING SPRAY","Egg omelet or scrambled egg, with cheese and meat, made with cooking spray" +32130370,"EGG OMELET OR SCRAMBLED EGG, W/CHEESE & MEAT, MADE WO/FAT","Egg omelet or scrambled egg, with cheese and meat, made without fat" +32130400,"EGG OMELET OR SCRAMBLED EGG, WITH TOMATOES, FAT ADDED IN COO","Egg omelet or scrambled egg, with tomatoes, fat added in cooking" +32130410,"EGG OMELET OR SCRAMBLED EGG, WITH TOMATOES, FAT NOT ADDED IN","Egg omelet or scrambled egg, with tomatoes, fat not added in cooking" +32130420,"EGG OMELET OR SCRAMBLED EGG, WITH TOMATOES, NS AS TO FAT ADD","Egg omelet or scrambled egg, with tomatoes, NS as to fat added in cooking" +32130430,"EGG OMELET OR SCRAMBLED EGG, W/ DARK-GREEN VEGS, FAT ADDED","Egg omelet or scrambled egg, with dark-green vegetables, fat added in cooking" +32130440,"EGG OMELET OR SCRAMBLED EGG, W/DARK-GREEN VEGS, FAT NOT ADDE","Egg omelet or scrambled egg, with dark-green vegetables, fat not added in cooking" +32130450,"EGG OMELET OR SCRAMBLED EGG, W/ DARK-GREEN VEGS,NS AS TO FAT","Egg omelet or scrambled egg, with dark-green vegetables, NS as to fat added in cooking" +32130460,"EGG OMELET OR SCR EGG, W/TOMATOES & DK-GREEN VEGS, FAT ADDED","Egg omelet or scrambled egg, with tomatoes and dark-green vegetables, fat added in cooking" +32130470,"EGG OMELET OR SCR EGG, W/ TOMATOES & DK-GRN VEGS,FAT NOT ADD","Egg omelet or scrambled egg, with tomatoes and dark-green vegetables, fat not added in cooking" +32130480,"EGG OMELET OR SCR EGG, W/ TOMATOES & DK-GRN VEGS, NS FAT","Egg omelet or scrambled egg, with tomatoes and dark-green vegetables, NS as to fat added in cooking" +32130490,"EGG OMELET OR SCR EGG, W/ OTHER VEGS, FAT ADDED","Egg omelet or scrambled egg, with vegetables other than dark green and/or tomatoes, fat added in cooking" +32130500,"EGG OMELET OR SCRAMBLED EGG, WITH OTHER VEGS. FAT NOT ADDED","Egg omelet or scrambled egg, with vegetables other than dark green and/or tomatoes, fat not added in cooking" +32130510,"EGG OMELET OR SCRAMBLED EGG, W/ OTHER VEGS, NS FAT","Egg omelet or scrambled egg, with vegetables other than dark green and/or tomatoes, NS as to fat added in cooking" +32130600,"EGG OMELET OR SCR EGG, W/CHEESE & TOMATOES, FAT ADDED","Egg omelet or scrambled egg, with cheese and tomatoes, fat added in cooking" +32130610,"EGG OMELET OR SCR EGG, W/CHEESE & TOMATOES, FAT NOT ADDED","Egg omelet or scrambled egg, with cheese and tomatoes, fat not added in cooking" +32130620,"EGG OMELET OR SCR EGG, W/CHEESE & TOMATOES, NS FAT ADDED","Egg omelet or scrambled egg, with cheese and tomatoes, NS as to fat added in cooking" +32130630,"EGG OMELET OR SCR EGG, W/ CHEESE&DK-GRN VEGS, FAT ADDED","Egg omelet or scrambled egg, with cheese and dark-green vegetables, fat added in cooking" +32130640,"EGG OMELET OR SCR EGG, W/ CHEESE&DK-GRN VEGS, FAT NOT ADDED","Egg omelet or scrambled egg, with cheese and dark-green vegetables, fat not added in cooking" +32130650,"EGG OMELET OR SCR EGG, W/ CHEESE&DK-GRN VEGS, NS FAT ADDED","Egg omelet or scrambled egg, with cheese and dark-green vegetables, NS as to fat added in cooking" +32130660,"EGG OMELET OR SCR EGG, W/CHEESE, TOM & DK-GRN VEGS,FAT ADDED","Egg omelet or scrambled egg, with cheese, tomatoes, and dark-green vegetables, fat added in cooking" +32130670,"EGG OMELET OR SCR EGG, W/CHEESE, TOM & DK-GRN VEGS,FAT NOT A","Egg omelet or scrambled egg, with cheese, tomatoes, and dark-green vegetables, fat not added in cooking" +32130680,"EGG OMELET OR SCR EGG, W/CHEESE, TOM & DK-GRN VEGS,NS FAT AD","Egg omelet or scrambled egg, with cheese, tomatoes, and dark-green vegetables, NS as to fat added in cooking" +32130690,"EGG OMELET OR SCR EGG, W/CHEESE & OTHER VEGS, FAT ADDED","Egg omelet or scrambled egg, with cheese and vegetables other than dark green and/or tomatoes, fat added in cooking" +32130700,"EGG OMELET OR SCREGG, W/CHEESE & OTHER VEGS, FAT NOT ADDED","Egg omelet or scrambled egg, with cheese and vegetables other than dark green and/or tomatoes, fat not added in cooking" +32130710,"EGG OMELET OR SCREGG, W/CHEESE & OTHER VEGS, NS FAT ADDED","Egg omelet or scrambled egg, with cheese and vegetables other than dark green and/or tomatoes, NS as to fat added in cooking" +32130800,"EGG OMELET OR SCRAMBLED EGG, W/MEAT & TOMATOES, FAT ADDED","Egg omelet or scrambled egg, with meat and tomatoes, fat added in cooking" +32130810,"EGG OMELET OR SCRAMBLED EGG, W/MEAT & TOMATOES, FAT NOT ADDE","Egg omelet or scrambled egg, with meat and tomatoes, fat not added in cooking" +32130820,"EGG OMELET OR SCRAMBLED EGG, W/MEAT & TOMATOES, NS FAT ADDED","Egg omelet or scrambled egg, with meat and tomatoes, NS as to fat added in cooking" +32130830,"EGG OMELET OR SCR EGG, W/MEAT & DK-GRN VEGS, FAT ADDED","Egg omelet or scrambled egg, with meat and dark-green vegetables, fat added in cooking" +32130840,"EGG OMELET OR SCR EGG, W/MEAT & DK-GRN VEGS, FAT NOT ADDED","Egg omelet or scrambled egg, with meat and dark-green vegetables, fat not added in cooking" +32130850,"EGG OMELET OR SCR EGG, W/MEAT & DK-GRN VEGS, NS FAT ADDED","Egg omelet or scrambled egg, with meat and dark-green vegetables, NS as to fat added in cooking" +32130860,"EGG OMELET OR SCR EGG, W/MEAT,TOM & DK-GRN VEGS, FAT ADDED","Egg omelet or scrambled egg, with meat, tomatoes, and dark-green vegetables, fat added in cooking" +32130870,"EGG OMELET OR SCR EGG, W/MEAT,TOM & DK-GRN VEGS, FAT NOT ADD","Egg omelet or scrambled egg, with meat, tomatoes, and dark-green vegetables, fat not added in cooking" +32130880,"EGG OMELET OR SCR EGG, W/MEAT,TOM & DK-GRN VEGS, NS FAT ADDE","Egg omelet or scrambled egg, with meat, tomatoes, and dark-green vegetables, NS as to fat added in cooking" +32130890,"EGG OMELET OR SCR EGG, W/MEAT & OTHER VEGS, FAT ADDED","Egg omelet or scrambled egg, with meat and vegetables other than dark-green and/or tomatoes, fat added in cooking" +32130900,"EGG OMELET OR SCR EGG, W/MEAT & OTHER VEGS, FAT NOT ADDED","Egg omelet or scrambled egg, with meat and vegetables other than dark-green and/or tomatoes, fat not added in cooking" +32130910,"EGG OMELET OR SCR EGG, W/MEAT & OTHER VEGS, NS FAT ADDED","Egg omelet or scrambled egg, with meat and vegetables other than dark-green and/or tomatoes, NS as to fat added in cooking" +32131000,"EGG OMELET OR SCR EGG, W/ CHEESE, MEAT & TOMATOES, FAT ADDED","Egg omelet or scrambled egg, with cheese, meat, and tomatoes, fat added in cooking" +32131010,"EGG OMELET OR SCR EGG, W/ CHEESE, MEAT & TOMATOES, FAT NOT A","Egg omelet or scrambled egg, with cheese, meat, and tomatoes, fat not added in cooking" +32131020,"EGG OMELET OR SCR EGG, W/ CHEESE, MEAT & TOMATOES, NS FAT AD","Egg omelet or scrambled egg, with cheese, meat, and tomatoes, NS as to fat added in cooking" +32131030,"EGG OMELET OR SCR EGG, W/CHEESE, MEAT&DK GRN VEG,FAT ADDED","Egg omelet or scrambled egg, with cheese, meat, and dark-green vegetables, fat added in cooking" +32131040,"EGG OMELET OR SCR EGG, W/CHEESE, MEAT&DK GRN VEG,FAT NOT ADD","Egg omelet or scrambled egg, with cheese, meat, and dark-green vegetables, fat not added in cooking" +32131050,"EGG OMELET OR SCR EGG, W/CHEESE, MEAT&DK GRN VEG,NS FAT ADDE","Egg omelet or scrambled egg, with cheese, meat, and dark-green vegetables, NS as to fat added in cooking" +32131060,"EGG OMELET/SCR EGG,W/CHEESE,MEAT,TOM&DK GRN VEG,FAT ADDED","Egg omelet or scrambled egg, with cheese, meat, tomatoes, and dark-green vegetables, fat added in cooking" +32131070,"EGG OMELET/SCR EGG,W/CHEESE,MEAT,TOM&DK GRN VEG,FAT NOT ADDE","Egg omelet or scrambled egg, with cheese, meat, tomatoes, and dark-green vegetables, fat not added in cooking" +32131080,"EGG OMELET/SCR EGG,W/CHEESE,MEAT,TOM&DK GRN VEG,NS FAT ADDED","Egg omelet or scrambled egg, with cheese, meat, tomatoes, and dark-green vegetables, NS as to fat added in cooking" +32131090,"EGG OMELET OR SCR EGG, W/ CHEESE, MEAT & OTHER VEG,FAT ADDED","Egg omelet or scrambled egg, with cheese, meat, and vegetables other than dark-green and/or tomatoes, fat added in cooking" +32131100,"EGG OMELET OR SCR EGG, W/ CHEESE, MEAT & OTHER VEG,FAT NOT A","Egg omelet or scrambled egg, with cheese, meat, and vegetables other than dark-green and/or tomatoes, fat not added in cooking" +32131110,"EGG OMELET OR SCR EGG, W/ CHEESE, MEAT & OTHER VEG,NS FAT AD","Egg omelet or scrambled egg, with cheese, meat, and vegetables other than dark-green and/or tomatoes, NS as to fat added in cooking" +32131200,"EGG OMELET OR SCR EGG, W/POTATOES +/OR ONIONS, FAT ADDED","Egg omelet or scrambled egg, with potatoes and/or onions, fat added in cooking" +32131210,"EGG OMELET OR SCR EGG, W/POTATOES +/OR ONIONS, FAT NOT ADDED","Egg omelet or scrambled egg, with potatoes and/or onions, fat not added in cooking" +32131220,"EGG OMELET OR SCR EGG, W/POTATOES +/OR ONIONS, NS FAT ADDED","Egg omelet or scrambled egg, with potatoes and/or onions, NS as to fat added in cooking" +32201000,"FRIED EGG SANDWICH","Fried egg sandwich" +32202000,"EGG, CHEESE, HAM, & BACON ON BUN","Egg, cheese, ham, and bacon on bun" +32202010,"EGG, CHEESE & HAM ON ENGLISH MUFFIN","Egg, cheese, and ham on English muffin" +32202020,"EGG, CHEESE & HAM ON BISCUIT","Egg, cheese, and ham on biscuit" +32202025,"EGG, CHEESE & HAM ON BAGEL","Egg, cheese and ham on bagel" +32202030,"EGG, CHEESE & SAUSAGE ON ENGLISH MUFFIN","Egg, cheese, and sausage on English muffin" +32202035,"EGG, EXTRA CHEESE (2 SL), & EXTRA SAUSAGE (2 PATTIES) ON BUN","Egg, extra cheese (2 slices), and extra sausage (2 patties) on bun" +32202040,"EGG, CHEESE & BEEF ON ENGLISH MUFFIN","Egg, cheese, and beef on English Muffin" +32202045,"EGG, CHEESE & STEAK ON BAGEL","Egg, cheese, and steak on bagel" +32202050,"EGG, CHEESE & SAUSAGE ON BISCUIT","Egg, cheese, and sausage on biscuit" +32202055,"EGG, CHEESE & SAUSAGE GRIDDLE CAKE SANDWICH","Egg, cheese, and sausage griddle cake sandwich" +32202060,"EGG & SAUSAGE ON BISCUIT","Egg and sausage on biscuit" +32202070,"EGG, CHEESE & BACON ON BISCUIT","Egg, cheese, and bacon on biscuit" +32202075,"EGG, CHEESE & BACON GRIDDLE CAKE SANDWICH","Egg, cheese, and bacon griddle cake sandwich" +32202080,"EGG, CHEESE & BACON ON ENGLISH MUFFIN","Egg, cheese, and bacon on English muffin" +32202085,"EGG, CHEESE & BACON ON BAGEL","Egg, cheese and bacon on bagel" +32202090,"EGG & BACON ON BISCUIT","Egg and bacon on biscuit" +32202110,"EGG & HAM ON BISCUIT","Egg and ham on biscuit" +32202120,"EGG, CHEESE & SAUSAGE ON BAGEL","Egg, cheese and sausage on bagel" +32202130,"EGG & STEAK ON BISCUIT","Egg and steak on biscuit" +32202200,"EGG & CHEESE ON BISCUIT","Egg and cheese on biscuit" +32203010,"EGG SALAD SANDWICH","Egg salad sandwich" +32204010,"SCRAMBLED EGG SANDWICH","Scrambled egg sandwich" +32300100,"EGG DROP SOUP","Egg drop soup" +32301100,"GARLIC EGG SOUP, P.R. (SOPA DE AJO)","Garlic egg soup, Puerto Rican style (Sopa de ajo)" +32400060,"EGG WHITE OMELET, SCRAMBLED, OR FRIED, MADE WITH MARGARINE","Egg white omelet, scrambled, or fried, made with margarine" +32400065,"EGG WHITE OMELET, SCRAMBLED, OR FRIED, MADE WITH OIL","Egg white omelet, scrambled, or fried, made with oil" +32400070,"EGG WHITE OMELET, SCRAMBLED, OR FRIED, MADE WITH BUTTER","Egg white omelet, scrambled, or fried, made with butter" +32400075,"EGG WHITE OMELET, SCRAMBLED, OR FRIED, MADE WITH COOKING SPR","Egg white omelet, scrambled, or fried, made with cooking spray" +32400080,"EGG WHITE OMELET, SCRAMBLED, OR FRIED, MADE WITHOUT FAT","Egg white omelet, scrambled, or fried, made without fat" +32400100,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/CHEESE, FAT ADDED","Egg white, omelet, scrambled, or fried, with cheese, fat added in cooking" +32400110,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/ CHEESE, FAT NOT A","Egg white, omelet, scrambled, or fried, with cheese, fat not added in cooking" +32400120,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, WITH CHEESE, NS FAT","Egg white, omelet, scrambled, or fried, with cheese, NS as to fat added in cooking" +32400200,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/ MEAT, FAT ADDED","Egg white, omelet, scrambled, or fried, with meat, fat added in cooking" +32400210,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/ MEAT, FAT NOT ADD","Egg white, omelet, scrambled, or fried, with meat, fat not added in cooking" +32400220,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, WITH MEAT, NS FAT","Egg white, omelet, scrambled, or fried, with meat, NS as to fat added in cooking" +32400300,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/ VEGS, FAT ADDED","Egg white, omelet, scrambled, or fried, with vegetables, fat added in cooking" +32400310,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/ VEGS, FAT NOT ADD","Egg white, omelet, scrambled, or fried, with vegetables, fat not added in cooking" +32400320,"EGG WHITE, OMELET, SCRAMBLED, OR FRIED, W/ VEGS, NS FAT","Egg white, omelet, scrambled, or fried, with vegetables, NS as to fat added in cooking" +32400400,"EGG WHITE, OMELET, SCR OR FRIED, W/ CHEESE & MEAT, FAT ADDED","Egg white, omelet, scrambled, or fried, with cheese and meat, fat added in cooking" +32400410,"EGG WHITE, OMELET, SCR OR FRIED, W/ CHEESE & MEAT, FAT NOT A","Egg white, omelet, scrambled, or fried, with cheese and meat, fat not added in cooking" +32400420,"EGG WHITE, OMELET, SCR OR FRIED, W/ CHEESE & MEAT, NS FAT AD","Egg white, omelet, scrambled, or fried, with cheese and meat, NS as to fat added in cooking" +32400500,"EGG WHITE, OMELET, SCR OR FRIED, W/CHEESE & VEG, FAT ADDED","Egg white, omelet, scrambled, or fried, with cheese and vegetables, fat added in cooking" +32400510,"EGG WHITE, OMELET, SCR OR FRIED, W/CHEESE & VEG, FAT NOT ADD","Egg white, omelet, scrambled, or fried, with cheese and vegetables, fat not added in cooking" +32400520,"EGG WHITE, OMELET, SCR OR FRIED, W/CHEESE & VEG, NS FAT ADDE","Egg white, omelet, scrambled, or fried, with cheese and vegetables, NS as to fat added in cooking" +32400600,"EGG WHITE, OMELET, SCR OR FRIED, W/MEAT & VEG, FAT ADDED","Egg white, omelet, scrambled, or fried, with meat and vegetables, fat added in cooking" +32400610,"EGG WHITE, OMELET, SCR OR FRIED, W/MEAT & VEG, FAT NOT ADDED","Egg white, omelet, scrambled, or fried, with meat and vegetables, fat not added in cooking" +32400620,"EGG WHITE, OMELET, SCR OR FRIED, W/MEAT & VEG, NS FAT ADDED","Egg white, omelet, scrambled, or fried, with meat and vegetables, NS as to fat added in cooking" +32400700,"EGG WHITE,OMELET,SCR OR FRIED,W/CHEESE, MEAT&VEG,FAT ADDED","Egg white, omelet, scrambled, or fried, with cheese, meat, and vegetables, fat added in cooking" +32400710,"EGG WHITE,OMELET,SCR OR FRIED,W/CHEESE, MEAT&VEG,FAT NOT ADD","Egg white, omelet, scrambled, or fried, with cheese, meat, and vegetables, fat not added in cooking" +32400720,"EGG WHITE,OMELET,SCR OR FRIED,W/CHEESE, MEAT&VEG,NS FAT ADDE","Egg white, omelet, scrambled, or fried, with cheese, meat, and vegetables, NS as to fat added in cooking" +32401000,"MERINGUES","Meringues" +33001000,"EGG SUB, OMELET, SCR, OR FRIED, MADE W/ MARGARINE","Egg substitute, omelet, scrambled, or fried, made with margarine" +33001010,"EGG SUB, OMELET, SCR, OR FRIED, MADE W/ OIL","Egg substitute, omelet, scrambled, or fried, made with oil" +33001020,"EGG SUB, OMELET, SCR, OR FRIED, MADE W/ BUTTER","Egg substitute, omelet, scrambled, or fried, made with butter" +33001040,"EGG SUB, OMELET, SCR, OR FRIED, MADE W/ COOKING SPRAY","Egg substitute, omelet, scrambled, or fried, made with cooking spray" +33001050,"EGG SUB, OMELET, SCR, OR FRIED, MADE WO/ FAT","Egg substitute, omelet, scrambled, or fried, made without fat" +33001100,"EGG SUBSTITUTE, CHSE FLAV,OMELET,SCRM,FRIED,FAT ADDED","Egg substitute, cheese flavored, omelet, scrambled, or fried, fat added in cooking" +33001110,"EGG SUBSTITUTE, CHSE FLAV,OMELET,SCRM,FRIED,NO FAT","Egg substitute, cheese flavored, omelet, scrambled, or fried, fat not added in cooking" +33001120,"EGG SUBSTITUTE, CHSE FLAV,OMELET,SCRM,FRIED,NS AS TO FAT","Egg substitute, cheese flavored, omelet, scrambled, or fried, NS as to fat added in cooking" +33001200,"EGG SUBSTITUTE, VEG FLAV,OMELET,SCRM,FRIED,FAT ADDED","Egg substitute, vegetable flavored, omelet, scrambled, or fried, fat added in cooking" +33001210,"EGG SUBSTITUTE, VEG FLAV,OMELET,SCRM,FRIED,NO FAT","Egg substitute, vegetable flavored, omelet, scrambled, or fried, fat not added in cooking" +33001220,"EGG SUBSTITUTE, VEG FLAV,OMELET,SCRM,FRIED,NS AS TO FAT","Egg substitute, vegetable flavored, omelet, scrambled, or fried, NS as to fat added in cooking" +33401000,"EGG SUB, OMELET, SCR, OR FRIED, W/ CHEESE, FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese, fat added in cooking" +33401010,"EGG SUB, OMELET, SCR, OR FRIED, W/ CHEESE, FAT NOT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese, fat not added in cooking" +33401020,"EGG SUB, OMELET, SCR, OR FRIED, W/ CHEESE, NS FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese, NS as to fat added in cooking" +33401100,"EGG SUB, OMELET, SCR, OR FRIED, W/ MEAT, FAT ADDED","Egg substitute, omelet, scrambled, or fried, with meat, fat added in cooking" +33401110,"EGG SUB, OMELET, SCR, OR FRIED, W/ MEAT, FAT NOT ADDED","Egg substitute, omelet, scrambled, or fried, with meat, fat not added in cooking" +33401120,"EGG SUB, OMELET, SCR, OR FRIED, W/ MEAT, NS FAT ADDED","Egg substitute, omelet, scrambled, or fried, with meat, NS as to fat added in cooking" +33401200,"EGG SUB, OMELET, SCR OR FRIED, W/VEGS, FAT ADDED IN COOKING","Egg substitute, omelet, scrambled, or fried, with vegetables, fat added in cooking" +33401210,"EGG SUB, OMELET, SCR OR FRIED, W/VEGS, FAT NOT ADDED IN COOK","Egg substitute, omelet, scrambled, or fried, with vegetables, fat not added in cooking" +33401220,"EGG SUB, OMELET, SCR OR FRIED, W/VEGS, NS FAT ADDED IN COOKI","Egg substitute, omelet, scrambled, or fried, with vegetables, NS as to fat added in cooking" +33401300,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE&MEAT, FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese and meat, fat added in cooking" +33401310,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE&MEAT, FAT NOT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese and meat, fat not added in cooking" +33401320,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE&MEAT, NS FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese and meat, NS as to fat added in cooking" +33401400,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE & VEG, FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese and vegetables, fat added in cooking" +33401410,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE & VEG, FAT NOT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese and vegetables, fat not added in cooking" +33401420,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE & VEG, NS FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese and vegetables, NS as to fat added in cooking" +33401500,"EGG SUB, OMELET, SCR OR FRIED, W/ MEAT & VEG, FAT ADDED","Egg substitute, omelet, scrambled, or fried, with meat and vegetables, fat added in cooking" +33401510,"EGG SUB, OMELET, SCR OR FRIED, W/ MEAT & VEG, FAT NOT ADDED","Egg substitute, omelet, scrambled, or fried, with meat and vegetables, fat not added in cooking" +33401520,"EGG SUB, OMELET, SCR OR FRIED, W/ MEAT & VEG, NS FAT ADDED","Egg substitute, omelet, scrambled, or fried, with meat and vegetables, NS as to fat added in cooking" +33401600,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE,MEAT&VEG,FAT ADDED","Egg substitute, omelet, scrambled, or fried, with cheese, meat, and vegetables, fat added in cooking" +33401610,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE,MEAT&VEG,FAT NOT ADD","Egg substitute, omelet, scrambled, or fried, with cheese, meat, and vegetables, fat not added in cooking" +33401620,"EGG SUB, OMELET, SCR OR FRIED, W/CHEESE,MEAT&VEG,NS FAT ADDE","Egg substitute, omelet, scrambled, or fried, with cheese, meat, and vegetables, NS as to fat added in cooking" +41101000,"BEANS, DRY, COOKED, NS AS TO TYPE, NS ADDED FAT","Beans, dry, cooked, NS as to type and as to fat added in cooking" +41101010,"BEANS, DRY, COOKED, NS AS TO TYPE, ADDED FAT","Beans, dry, cooked, NS as to type, fat added in cooking" +41101020,"BEANS, DRY, COOKED, NS AS TO TYPE, NO FAT ADDED","Beans, dry, cooked, NS as to type, fat not added in cooking" +41101100,"WHITE BEAN, DRY, COOKED, NS AS TO ADDED FAT","White beans, dry, cooked, NS as to fat added in cooking" +41101110,"WHITE BEAN, DRY, COOKED, FAT ADDED","White beans, dry, cooked, fat added in cooking" +41101120,"WHITE BEAN, DRY, COOKED, NO FAT ADDED","White beans, dry, cooked, fat not added in cooking" +41101200,"WHITE BEANS, CANNED, LOW SODIUM, NS AS TO FAT ADDED","White beans, canned, low sodium, NS as to fat added in cooking" +41101210,"WHITE BEANS, CANNED, LOW SODIUM, FAT ADDED IN COOKING","White beans, canned, low sodium, fat added in cooking" +41101220,"WHITE BEANS, CANNED, LOW SODIUM, FAT NOT ADDED IN COOKING","White beans, canned, low sodium, fat not added in cooking" +41102000,"BLACK, BROWN OR BAYO BEAN, DRY, COOKED, FAT NS","Black, brown, or Bayo beans, dry, cooked, NS as to fat added in cooking" +41102010,"BLACK, BROWN OR BAYO BEAN, DRY, COOKED, FAT ADDED","Black, brown, or Bayo beans, dry, cooked, fat added in cooking" +41102020,"BLACK, BROWN OR BAYO BEAN, DRY, COOKED, NO FAT","Black, brown, or Bayo beans, dry, cooked, fat not added in cooking" +41102100,"BLACK, BROWN, OR BAYO BEANS, CANNED, LOW SODIUM, NS FAT","Black, brown, or Bayo beans, canned, low sodium, NS as to fat added in cooking" +41102110,"BLACK, BROWN, OR BAYO BEANS, CANNED, LOW SODIUM, FAT ADDED","Black, brown, or Bayo beans, canned, low sodium, fat added in cooking" +41102120,"BLACK, BROWN, OR BAYO BEANS, CANNED, LOW SODIUM, NO FAT","Black, brown, or Bayo beans, canned, low sodium, fat not added in cooking" +41102200,"FAVA BEANS, COOKED, NS AS TO ADDED FAT","Fava beans, cooked, NS as to fat added in cooking" +41102210,"FAVA BEANS, COOKED, FAT ADDED","Fava beans, cooked, fat added in cooking" +41102220,"FAVA BEANS, COOKED, NO FAT ADDED","Fava beans, cooked, fat not added in cooking" +41103000,"LIMA BEANS, DRY, COOKED, NS AS TO ADDED FAT","Lima beans, dry, cooked, NS as to fat added in cooking" +41103010,"LIMA BEANS, DRY, COOKED, FAT ADDED","Lima beans, dry, cooked, fat added in cooking" +41103020,"LIMA BEANS, DRY, COOKED, NO FAT ADDED","Lima beans, dry, cooked, fat not added in cooking" +41103050,"PINK BEANS, DRY, COOKED, NS AS TO FAT","Pink beans, dry, cooked, NS as to fat added in cooking" +41103060,"PINK BEANS, DRY, COOKED, NO FAT ADDED","Pink beans, dry, cooked, fat not added in cooking" +41103070,"PINK BEANS, DRY, COOKED, FAT ADDED","Pink beans, dry, cooked, fat added in cooking" +41104000,"PINTO, CALICO/RED/MEX BEAN, DRY, COOKED, FAT NS","Pinto, calico, or red Mexican beans, dry, cooked, NS as to fat added in cooking" +41104010,"PINTO, CALICO/RED/MEX BEAN, DRY, COOKED, FAT ADDED","Pinto, calico, or red Mexican beans, dry, cooked, fat added in cooking" +41104020,"PINTO, CALICO/RED/MEX BEAN, DRY, COOKED, NO FAT","Pinto, calico, or red Mexican beans, dry, cooked, fat not added in cooking" +41104100,"PINTO,CALICO, RED MEXICAN BEANS, CANNED, LOW SODIUM, NS FAT","Pinto, calico, or red Mexican beans, canned, low sodium, NS as to fat added in cooking" +41104110,"PINTO,CALICO,RED MEX BEANS, CANNED, LOW SODIUM, FAT ADDED","Pinto, calico, or red Mexican beans, canned, low sodium, fat added in cooking" +41104120,"PINTO,CALICO,RED MEXICAN BEANS, CANNED, LOW SODIUM, NO FAT","Pinto, calico, or red Mexican beans, canned, low sodium, fat not added in cooking" +41106000,"RED KIDNEY BEANS, DRY, COOKED, NS AS TO ADDED FAT","Red kidney beans, dry, cooked, NS as to fat added in cooking" +41106010,"RED KIDNEY BEANS, DRY, COOKED, FAT ADDED","Red kidney beans, dry, cooked, fat added in cooking" +41106020,"RED KIDNEY BEANS, DRY, COOKED, NO FAT ADDED","Red kidney beans, dry, cooked, fat not added in cooking" +41106100,"RED KIDNEY BEANS, CANNED, LOW SODIUM, FAT ADDED IN COOKING","Red kidney beans, canned, low sodium, NS as to fat added in cooking" +41106110,"RED KIDNEY BEANS, CANNED, LOW SODIUM, FAT ADDED IN COOKING","Red kidney beans, canned, low sodium, fat added in cooking" +41106120,"RED KIDNEY BEANS, CANNED, LOW SODIUM, FAT NOT ADDED","Red kidney beans, canned, low sodium, fat not added in cooking" +41107000,"SOYBEANS, COOKED, FAT NOT ADDED","Soybeans, cooked, fat not added in cooking" +41108000,"MUNG BEANS, NO FAT ADDED","Mung beans, fat not added in cooking" +41108010,"MUNG BEANS, FAT ADDED","Mung beans, fat added in cooking" +41108020,"MUNG BEANS, NS AS TO FAT ADDED","Mung beans, NS as to fat added in cooking" +41109000,"MUNGO BEANS, COOKED, NO FAT ADDED","Mungo beans, cooked, fat not added in cooking" +41201010,"BAKED BEANS, NFS","Baked beans, NFS" +41201020,"BAKED BEANS, VEGETARIAN","Baked beans, vegetarian" +41202020,"CHILI BEANS, BARBECUE BEANS, RANCH OR MEXICAN STYLE","Chili beans, barbecue beans, ranch style beans or Mexican- style beans" +41202500,"BEANS AND TOMATOES, NS AS TO FAT ADDED","Beans and tomatoes, NS as to fat added in cooking" +41202505,"BEANS AND TOMATOES, FAT NOT ADDED","Beans and tomatoes, fat not added in cooking" +41202510,"BEANS AND TOMATOES, FAT ADDED","Beans and tomatoes, fat added in cooking" +41203030,"BLACK BEAN SALAD","Black bean salad" +41204020,"BOSTON BAKED BEANS","Boston baked beans" +41205010,"REFRIED BEANS","Refried beans" +41205015,"REFRIED BEANS, FAT NOT ADDED IN COOKING","Refried beans, fat not added in cooking" +41205020,"REFRIED BEANS W/ CHEESE","Refried beans with cheese" +41205030,"REFRIED BEANS W/ MEAT","Refried beans with meat" +41205040,"REFRIED BEANS, CANNED, LOW SODIUM","Refried beans, canned, low sodium" +41205050,"BEAN DIP, W/ REFRIED BEANS","Bean dip, made with refried beans" +41205070,"HUMMUS","Hummus" +41205100,"BLACK BEAN SAUCE","Black bean sauce" +41206030,"BEANS & FRANKS","Beans and franks" +41207030,"BEANS, DRY, COOKED, W/ GROUND BEEF","Beans, dry, cooked with ground beef" +41208030,"PORK & BEANS","Pork and beans" +41208100,"BEANS, DRY, COOKED, W/ PORK","Beans, dry, cooked with pork" +41209000,"FALAFEL","Falafel" +41210000,"BEAN CAKE, JAPANESE STYLE","Bean cake" +41210090,"STEWED BEANS W/ PORK, TOMATOES, & CHILI PEPPERS, MEXICAN","Stewed beans with pork, tomatoes, and chili peppers, Mexican style (Frijoles a la charra)" +41210100,"STEWED RED BEANS, P.R.","Stewed red beans, Puerto Rican style (Habichuelas coloradas guisadas)" +41210110,"STEWED DRY LIMA BEANS, P.R","Stewed dry lima beans, Puerto Rican style" +41210120,"STEWED WHITE BEANS, P.R.","Stewed white beans, Puerto Rican style" +41210150,"STEWED PINK BEANS W/ WHITE POTATOES & HAM, P.R.","Stewed pink beans with white potatoes and ham, Puerto Rican style" +41210160,"STEWED PINK BEANS W/ PIG'S FEET, P.R","Stewed pink beans with pig's feet, Puerto Rican style" +41210170,"STEWED RED BEANS W/ PIG'S FEET, P.R.","Stewed red beans with pig's feet, Puerto Rican style" +41210180,"STEWED WHITE BEANS W/ PIG'S FEET, P.R.","Stewed white beans with pig's feet, Puerto Rican style" +41210190,"STEWED RED BEANS W/ PIGS FEET & POTATO, P.R.","Stewed red beans with pig's feet and potatoes, Puerto Rican style" +41210200,"BLACK BEANS, CUBAN","Black beans, Cuban style (Habichuelas negras guisadas a la Cubana)" +41221010,"BAKED BEANS, LOW SODIUM","Baked beans, low sodium" +41221020,"CHILI WITH BEANS, WITHOUT MEAT","Chili with beans, without meat" +41301000,"COWPEAS, DRY, COOKED, NS AS TO ADDED FAT","Cowpeas, dry, cooked, NS as to fat added in cooking" +41301010,"COWPEAS, DRY, COOKED, FAT ADDED","Cowpeas, dry, cooked, fat added in cooking" +41301020,"COWPEAS, DRY, COOKED, NO FAT ADDED","Cowpeas, dry, cooked, fat not added in cooking" +41302000,"CHICKPEAS, DRY, COOKED, NS AS TO ADDED FAT","Chickpeas, dry, cooked, NS as to fat added in cooking" +41302010,"CHICKPEAS, DRY, COOKED, FAT ADDED","Chickpeas, dry, cooked, fat added in cooking" +41302020,"CHICKPEAS, DRY, COOKED, NO FAT ADDED","Chickpeas, dry, cooked, fat not added in cooking" +41302100,"CHICKPEAS, CANNED, LOW SODIUM, NS AS TO FAT ADDED IN COOKING","Chickpeas, canned, low sodium, NS as to fat added in cooking" +41302110,"CHICKPEAS, CANNED, LOW SODIUM, FAT ADDED IN COOKING","Chickpeas, canned, low sodium, fat added in cooking" +41302120,"CHICKPEAS, CANNED, LOW SODIUM, FAT NOT ADDED IN COOKING","Chickpeas, canned, low sodium, fat not added in cooking" +41303000,"GREEN/YELLOW SPLIT PEAS, DRY, COOKED, NO FAT ADDED","Green or yellow split peas, dry, cooked, fat not added in cooking" +41303010,"GREEN OR YELLOW SPLIT PEAS, DRY, COOKED, FAT ADDED","Green or yellow split peas, dry, cooked, fat added in cooking" +41303020,"SPLIT PEAS, DRY, COOKED, NS AS TO ADDED FAT","Green or yellow split peas, dry, cooked, NS as to fat added in cooking" +41303500,"STEWED GREEN PEAS, PUERTO RICAN STYLE","Stewed green peas, Puerto Rican style" +41303550,"STEWED GREEN PEAS, W/ PIG'S FEET & POTATO, P.R.","Stewed green peas with pig's feet and potatoes, Puerto Rican style" +41304000,"WASABI PEAS","Wasabi peas" +41304030,"PEAS, DRY, COOKED W/ PORK","Peas, dry, cooked with pork" +41304130,"COWPEAS, DRY, COOKED W/ PORK","Cowpeas, dry, cooked with pork" +41304980,"LENTILS, DRY, COOKED, NS AS TO ADDED FAT","Lentils, dry, cooked, NS as to fat added in cooking" +41304990,"LENTILS, DRY, COOKED, FAT ADDED","Lentils, dry, cooked, fat added in cooking" +41305000,"LENTILS, DRY, COOKED, NO FAT ADDED","Lentils, dry, cooked, fat not added in cooking" +41306000,"LOAF, LENTIL","Loaf, lentil" +41310100,"STEWED PIGEON PEAS, P.R.","Stewed pigeon peas, Puerto Rican style (Gandules guisados, Gandur, Gandules)" +41310150,"STEWED CHICKPEAS, P.R.","Stewed chickpeas, Puerto Rican style" +41310160,"STEWED CHICKPEAS, W/ POTATOES, P.R.","Stewed chickpeas, with potatoes, Puerto Rican style" +41310200,"CHICKPEAS STEWED W/ PIG'S FEET, P.R.","Chickpeas stewed with pig's feet, Puerto Rican style (Garbanzos guisados con patitas de cerdo)" +41310210,"CHICKPEAS, W/ SPANISH SAUSAGE, P.R.","Stewed chickpeas with Spanish sausages, Puerto Rican style (Garbanzos guisados con chorizos)" +41310220,"FRIED CHICKPEAS W/ BACON, P.R.","Fried chickpeas with bacon, Puerto Rican style (Garbanzos fritos con tocineta)" +41310310,"STEWED BLACKEYE PEAS OR COWPEAS, P.R.","Stewed blackeye peas or cowpeas, Puerto Rican style" +41311000,"PAPAD(INDIAN APPETIZER),GRILLED OR BROILED","Papad (Indian appetizer), grilled or broiled" +41410010,"SOY NUTS","Soy nuts" +41410015,"SOY CHIPS","Soy chips" +41420010,"SOYBEAN CURD","Soybean curd" +41420050,"SOYBEAN CURD CHEESE","Soybean curd cheese" +41420100,"MISO SAUCE (INCLUDES AE SAUCE)","Miso sauce" +41420110,"MISO (FERMENTED SOYBEAN PASTE)","Miso (fermented soybean paste)" +41420200,"NATTO (FERMENTED SOYBEAN PRODUCT)","Natto (fermented soybean product)" +41420250,"HOISIN SAUCE","Hoisin sauce" +41420300,"SOY SAUCE","Soy sauce" +41420350,"SOY SAUCE, REDUCED SODIUM","Soy sauce, reduced sodium" +41420380,"SOY YOGURT","Soy yogurt" +41420400,"TERIYAKI SAUCE (INCLUDE ORIENTAL BARBECUE SAUCE)","Teriyaki sauce" +41420410,"TERIYAKI SAUCE, REDUCED SODIUM","Teriyaki sauce, reduced sodium" +41420450,"WORCESTERSHIRE SAUCE","Worcestershire sauce" +41421010,"SOYBEAN CURD, DEEP-FRIED","Soybean curd, deep fried" +41421020,"SOYBEAN CURD, BREADED, FRIED","Soybean curd, breaded, fried" +41422010,"SOYBEAN MEAL","Soybean meal" +41425010,"VERMICELLI, MADE FROM SOYBEANS","Vermicelli, made from soybeans" +41440000,"TEXTURED VEGETABLE PROTEIN, DRY","Textured vegetable protein, dry" +41480000,"TOFU FROZEN DESSERT, NOT CHOCOLATE (INCL TOFUTTI)","Tofu, frozen dessert, flavors other than chocolate" +41480010,"TOFU FROZEN DESSERT, CHOCOLATE (INCLUDE TOFUTTI)","Tofu, frozen dessert, chocolate" +41601010,"BEAN SOUP, NFS","Bean soup, NFS" +41601020,"BEAN WITH BACON OR HAM SOUP, CANNED OR READY-TO-SERVE","Bean with bacon or ham soup, canned or ready-to-serve" +41601030,"BLACK BEAN SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Black bean soup, home recipe, canned or ready-to-serve" +41601040,"LIMA BEAN SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Lima bean soup, home recipe, canned or ready-to-serve" +41601070,"SOYBEAN SOUP, MISO BROTH","Soybean soup, miso broth" +41601080,"PINTO BEAN SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Pinto bean soup, home recipe, canned or ready-to-serve" +41601090,"BEAN SOUP, WITH MACARONI, HOME RECIPE, CANNED, OR READY-TO-S","Bean soup, with macaroni, home recipe, canned, or ready-to-serve" +41601100,"PORTUGUESE BEAN SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Portuguese bean soup, home recipe, canned or ready-to-serve" +41601110,"BEAN AND HAM SOUP, CHUNKY STYLE, CANNED OR READY-TO-SERVE","Bean and ham soup, chunky style, canned or ready-to-serve" +41601130,"BEAN SOUP, MIXED BEANS, HOME RECIPE, CANNED OR READY-TO-SERV","Bean soup, mixed beans, home recipe, canned or ready-to-serve" +41601140,"BEAN SOUP, HOME RECIPE","Bean soup, home recipe" +41601160,"BEAN & HAM SOUP, CAN, REDUCED SODIUM, W/ WATER/RTS","Bean and ham soup, canned, reduced sodium, prepared with water or ready-to-serve" +41601180,"BEAN & HAM SOUP, HOME RECIPE","Bean and ham soup, home recipe" +41601200,"LIQUID FROM STEWED KIDNEY BEANS, P.R.","Liquid from stewed kidney beans, Puerto Rican style" +41602010,"PEA AND HAM SOUP, CHUNKY STYLE, CANNED OR READY-TO-SERVE","Pea and ham soup, chunky style, canned or ready-to-serve" +41602020,"GARBANZO BEAN OR CHICKPEA SOUP, HOME RECIPE, CANNED OR READY","Garbanzo bean or chickpea soup, home recipe, canned or ready-to-serve" +41602030,"SPLIT PEA & HAM SOUP","Split pea and ham soup" +41602050,"SPLIT PEA SOUP","Split pea soup" +41602070,"SPLIT PEA SOUP, CAN, REDUCED SODIUM, W/ WATER/RTS","Split pea soup, canned, reduced sodium, prepared with water or ready-to-serve" +41602090,"SPLIT PEA & HAM SOUP, CAN, REDUCED SODIUM, W/ WATER/RTS","Split pea and ham soup, canned, reduced sodium, prepared with water or ready-to-serve" +41603010,"LENTIL SOUP, HOME RECIPE, CANNED, OR READY-TO-SERVE","Lentil soup, home recipe, canned, or ready-to-serve" +41610100,"WHITE BEAN SOUP, P.R.","White bean soup, Puerto Rican style (Sopon de habichuelas blancas)" +41810200,"BACON STRIP, MEATLESS","Bacon strip, meatless" +41810250,"BACON BITS, MEATLESS","Bacon bits, meatless" +41810400,"BREAKFAST LINK,PATTY,/SLICE, MEATLESS","Breakfast link, pattie, or slice, meatless" +41810600,"CHICKEN, MEATLESS NFS","Chicken, meatless, NFS" +41810610,"CHICKEN, MEATLESS, BREADED, FRIED (INCL LOMA LINDA)","Chicken, meatless, breaded, fried" +41811200,"FISH STICK, MEATLESS","Fish stick, meatless" +41811400,"FRANKFURTER OR HOT DOG, MEATLESS","Frankfurter or hot dog, meatless" +41811600,"LUNCHEON SLICE,MEATLESS-BEEF,CHICKEN,SALAM / TURKEY","Luncheon slice, meatless-beef, chicken, salami or turkey" +41811800,"MEATBALL, MEATLESS","Meatball, meatless" +41811850,"SCALLOPS, MEATLESS, BREADED, FRIED","Scallops, meatless, breaded, fried (made with meat substitute)" +41811890,"VEGETARIAN BURGER OR PATTY, MEATLESS, NO BUN","Vegetarian burger or patty, meatless, no bun" +41811950,"SWISS STEAK, W/ GRAVY, MEATLESS","Swiss steak, with gravy, meatless" +41812000,"SANDWICH SPREAD, MEAT SUBSTITUTE TYPE","Sandwich spread, meat substitute type" +41812400,"VEGETARIAN POT PIE","Vegetarian pot pie" +41812450,"VEGETARIAN CHILI (MADE W/ MEAT SUBSTITUTE)","Vegetarian chili (made with meat substitute)" +41812500,"TOFU & VEG (W/ CARROT/DK GRN, NO POTATO) W/ SOY SAUCE","Tofu and vegetables (including carrots, broccoli, and/or dark-green leafy vegetables (no potatoes)), with soy-based sauce (mixture)" +41812510,"TOFU & VEG (NO CARROT/DK GRN, NO POTATO) W/ SOY SAUCE","Tofu and vegetables (excluding carrots, broccoli, and dark-green leafy vegetables (no potatoes)), with soy-based sauce (mixture)" +41812600,"VEGETARIAN FILLET","Vegetarian, fillet" +41812800,"VEGETARIAN STEW","Vegetarian stew" +41812850,"VEGETARIAN STROGANOFF (MADE W/ MEAT SUBSTITUTE)","Vegetarian stroganoff (made with meat substitute)" +41812900,"VEGETARIAN MEAT LOAF OR PATTIES","Vegetarian meat loaf or patties (meat loaf made with meat substitute)" +41813000,"VEGATARIAN BOUILLON, DRY","Vegetarian bouillon, dry" +41901020,"SOYBURGER W/ CHEESE ON BUN","Soyburger, meatless, with cheese on bun" +42100050,"NUTS, NFS","Nuts, nfs" +42100100,"ALMONDS, NFS","Almonds, NFS" +42101000,"ALMONDS, UNROASTED","Almonds, unroasted" +42101100,"ALMONDS, ROASTED","Almonds, roasted" +42101200,"ALMONDS, DRY ROASTED","Almonds, dry roasted (assume salted)" +42101210,"ALMONDS, DRY ROASTED, W/O SALT","Almonds, dry roasted, without salt" +42101350,"ALMONDS, HONEY-ROASTED","Almonds, honey-roasted" +42102000,"BRAZIL NUTS","Brazil nuts" +42104000,"CASHEW NUTS, NFS","Cashew nuts, NFS" +42104100,"CASHEW NUTS, ROASTED","Cashew nuts, roasted (assume salted)" +42104110,"CASHEW NUTS, ROASTED, W/O SALT","Cashew nuts, roasted, without salt" +42104200,"CASHEW NUTS, DRY ROASTED","Cashew nuts, dry roasted" +42104205,"CASHEW NUTS, DRY ROASTED, WITHOUT SALT","Cashew nuts, dry roasted, without salt" +42104500,"CASHEW NUTS, HONEY-ROASTED","Cashew nuts, honey-roasted" +42105000,"CHESTNUTS, ROASTED","Chestnuts, roasted" +42106000,"COCONUT MEAT, FRESH","Coconut meat, fresh" +42106020,"COCONUT MEAT, DRIED, SWEETENED, SHREDDED","Coconut meat, dried, sweetened" +42107000,"FILBERTS, HAZELNUTS","Filberts, hazelnuts" +42109000,"MACADAMIA NUTS, UNROASTED","Macadamia nuts, unroasted" +42109100,"MACADAMIA NUTS, ROASTED","Macadamia nuts, roasted" +42110000,"MIXED NUTS, NFS","Mixed nuts, NFS" +42110100,"MIXED NUTS, ROASTED, W/ PEANUTS","Mixed nuts, roasted, with peanuts" +42110150,"MIXED NUTS, ROASTED, W/O PEANUTS","Mixed nuts, roasted, without peanuts" +42110200,"MIXED NUTS, DRY ROASTED","Mixed nuts, dry roasted" +42110300,"MIXED NUTS, HONEY-ROASTED, WITH PEANUTS","Mixed nuts, honey-roasted, with peanuts" +42111000,"PEANUTS, NFS","Peanuts, NFS" +42111030,"PEANUTS, BOILED","Peanuts, boiled" +42111100,"PEANUTS, ROASTED, SALTED","Peanuts, roasted, salted" +42111110,"PEANUTS, ROASTED, W/O SALT","Peanuts, roasted, without salt" +42111200,"PEANUTS, DRY ROASTED, SALTED","Peanuts, dry roasted, salted" +42111210,"PEANUTS, DRY ROASTED, W/O SALT","Peanuts, dry roasted, without salt" +42111500,"PEANUTS, HONEY ROASTED (INCL BEERNUTS)","Peanuts, honey-roasted" +42112000,"PECANS","Pecans" +42113000,"PINE NUTS (PIGNOLIAS)","Pine nuts (Pignolias)" +42114130,"PISTACHIO NUTS","Pistachio nuts" +42116000,"WALNUTS","Walnuts" +42116100,"WALNUTS, HONEY-ROASTED","Walnuts, honey-roasted" +42200500,"ALMOND BUTTER","Almond butter" +42200600,"ALMOND PASTE (MARZIPAN PASTE)","Almond paste (Marzipan paste)" +42201000,"CASHEW BUTTER","Cashew butter" +42202000,"PEANUT BUTTER","Peanut butter" +42202010,"PEANUT BUTTER, LOW SODIUM","Peanut butter, low sodium" +42202100,"PEANUT BUTTER, REDUCED SODIUM & REDUCED SUGAR","Peanut butter, reduced sodium and reduced sugar" +42202130,"PEANUT BUTTER, REDUCED SUGAR","Peanut butter, reduced sugar" +42202150,"PEANUT BUTTER, REDUCED FAT","Peanut butter, reduced fat" +42202200,"PEANUT BUTTER, VITAMIN & MINERAL FORTIFIED","Peanut butter, vitamin and mineral fortified" +42203000,"PEANUT BUTTER & JELLY","Peanut butter and jelly" +42204050,"PEANUT SAUCE","Peanut sauce" +42204100,"BROWN NUT GRAVY ( MEATLESS)","Brown nut gravy, meatless" +42301010,"PEANUT BUTTER SANDWICH","Peanut butter sandwich" +42302010,"PEANUT BUTTER & JELLY SANDWICH","Peanut butter and jelly sandwich" +42303010,"PEANUT BUTTER & BANANA SANDWICH","Peanut butter and banana sandwich" +42401010,"COCONUT MILK","Coconut milk (liquid expressed from grated coconut meat, water added)" +42402010,"COCONUT CREAM, CANNED, SWEETENED (INCL COCO LOPEZ)","Coconut cream (liquid expressed from grated coconut meat), canned, sweetened" +42403010,"COCONUT WATER (LIQUID FROM COCONUTS)","Coconut water (liquid from coconuts)" +42404010,"COCONUT WATER, CANNED OR BOTTLED","Coconut water, canned or bottled" +42501000,"NUT MIXTURE W/ DRIED FRUIT & SEEDS","Nut mixture with dried fruit and seeds" +42501500,"NUT MIXTURE WITH DRIED FRUIT, SEEDS, AND CHOCOLATE","Nut mixture with dried fruit, seeds, and chocolate" +42502000,"NUT MIXTURE W/ SEEDS","Nut mixture with seeds" +43101000,"PUMPKIN & SQUASH SEEDS, HULLED, UNROASTED","Pumpkin and/or squash seeds, hulled, unroasted" +43101100,"PUMPKIN & SQUASH SEEDS, HULLED, ROASTED, SALTED","Pumpkin and/or squash seeds, hulled, roasted, salted" +43101150,"PUMPKIN & SQUASH SEEDS, HULLED, ROASTED, NO SALT","Pumpkin and/or squash seeds, hulled, roasted, without salt" +43102000,"SUNFLOWER SEEDS, HULLED, UNROASTED, WITHOUT SALT","Sunflower seeds, hulled, unroasted, without salt" +43102100,"SUNFLOWER SEEDS, HULLED, ROASTED, SALTED","Sunflower seeds, hulled, roasted, salted" +43102110,"SUNFLOWER SEEDS, HULLED, ROASTED, W/O SALT","Sunflower seeds, hulled, roasted, without salt" +43102200,"SUNFLOWER SEEDS, HULLED, DRY ROASTED","Sunflower seeds, hulled, dry roasted" +43103000,"SESAME SEEDS (INCLUDE TOASTED)","Sesame seeds" +43103050,"SESAME SEEDS, WHOLE SEEDS","Sesame seeds, whole seed" +43103100,"SESAME SAUCE","Sesame sauce" +43103200,"SESAME PASTE","Sesame paste (sesame butter made from whole seeds)" +43103300,"SESAME BUTTER (TAHINI) FROM KERNELS","Sesame butter (tahini) (made from kernels)" +43104000,"FLAX SEED","Flax seeds" +43107000,"MIXED SEEDS","Mixed seeds" +44101000,"CAROB POWDER OR FLOUR","Carob powder or flour" +44201000,"CAROB CHIPS","Carob chips" +44202000,"CAROB SYRUP","Carob syrup" +50010000,"FLOUR, WHITE (INCLUDE FLOUR, NFS)","Flour, white" +50020000,"FLOUR, WHOLE WHEAT","Flour, whole wheat" +50030000,"BISCUIT MIX, DRY","Biscuit mix, dry" +51000100,"BREAD, NS AS TO MAJOR FLOUR","Bread, NS as to major flour" +51000110,"BREAD, NS AS TO MAJOR FLOUR, TOASTED","Bread, NS as to major flour, toasted" +51000180,"BREAD, HOMEMADE/PURCH AT A BAKERY, NS AS TO FLOUR","Bread, made from home recipe or purchased at a bakery, NS as to major flour" +51000190,"BREAD, HOMEMADE/PURCH AT A BAKERY, TOASTD,NS FLOUR","Bread, made from home recipe or purchased at a bakery, toasted, NS as to major flour" +51000200,"ROLL, NS AS TO MAJOR FLOUR","Roll, NS as to major flour" +51000230,"ROLL, NS AS TO MAJOR FLOUR, TOASTED","Roll, NS as to major flour, toasted" +51000250,"ROLL, HOMEMADE/PURCH AT A BAKERY, NS AS TO FLOUR","Roll, made from home recipe or purchased at a bakery, NS as to major flour" +51000260,"ROLL, HOMEMADE/PURCH AT A BAKERY, TOASTD,NS FLOUR","Roll, made from home recipe or purchased at a bakery, toasted, NS as to major flour" +51000300,"ROLL, HARD, NS AS TO MAJOR FLOUR","Roll, hard, NS as to major flour" +51000400,"ROLL, BRAN, NS AS TO TYPE OF BRAN","Roll, bran, NS as to type of bran" +51101000,"BREAD, WHITE","Bread, white" +51101010,"BREAD, WHITE, TOASTED","Bread, white, toasted" +51101050,"BREAD, WHITE, HOMEMADE OR PURCHASED AT A BAKERY","Bread, white, made from home recipe or purchased at a bakery" +51101060,"BREAD, WHITE, HOMEMADE OR PURCH AT A BAKERY TOASTED","Bread, white, made from home recipe or purchased at a bakery, toasted" +51102010,"BREAD, WHITE W/ WHOLE WHEAT SWIRL","Bread, white with whole wheat swirl" +51102020,"BREAD, WHITE W/ WHOLE WHEAT SWIRL, TOASTED","Bread, white with whole wheat swirl, toasted" +51105010,"BREAD, CUBAN (INCLUDE SPANISH, PORTUGUESE)","Bread, Cuban" +51105040,"BREAD, CUBAN, TOASTED (INCLUDE SPANISH, PORTUGUESE)","Bread, Cuban, toasted" +51106010,"BREAD, NATIVE, WATER, P.R. (PAN CRIOLLO)","Bread, Native, water, Puerto Rican style (Pan Criollo)" +51106020,"BREAD, NATIVE, WATER, P.R., TOASTED (PAN CRIOLLO)","Bread, Native, water, Puerto Rican style, toasted (Pan Criollo)" +51106200,"BREAD, LARD, P.R. (PAN DE MANTECA)","Bread, lard, Puerto Rican style (Pan de manteca)" +51106210,"BREAD, LARD, P.R., TOASTED (PAN DE MANTECA)","Bread, lard, Puerto Rican style, toasted (Pan de manteca)" +51106300,"BREAD, CARESSED, P.R. (PAN SOBAO)","Bread, caressed, Puerto Rican style (Pan sobao)" +51106310,"BREAD, CARESSED, P.R., TOASTED (PAN SOBAO)","Bread, caressed, Puerto Rican style, toasted (Pan sobao)" +51107010,"BREAD, FRENCH OR VIENNA","Bread, French or Vienna" +51107040,"BREAD, FRENCH OR VIENNA, TOASTED","Bread, French or Vienna, toasted" +51108010,"FOCACCIA, ITALIAN FLATBREAD, PLAIN","Focaccia, Italian flatbread, plain" +51108100,"NAAN, INDIAN FLATBREAD","Naan, Indian flatbread" +51109010,"BREAD, ITALIAN, GRECIAN, ARMENIAN","Bread, Italian, Grecian, Armenian" +51109040,"BREAD, ITALIAN, GRECIAN, ARMENIAN, TOASTED","Bread, Italian, Grecian, Armenian, toasted" +51109100,"BREAD, PITA","Bread, pita" +51109110,"BREAD, PITA, TOASTED","Bread, pita, toasted" +51109150,"BREAD, PITA W/ FRUIT","Bread, pita with fruit" +51109200,"BREAD, PITA W/ FRUIT, TOASTED","Bread, pita with fruit, toasted" +51111010,"BREAD, CHEESE (INCLUDE ONION CHEESE)","Bread, cheese" +51111040,"BREAD, CHEESE, TOASTED (INCLUDE ONION CHEESE)","Bread, cheese, toasted" +51113010,"BREAD, CINNAMON","Bread, cinnamon" +51113100,"BREAD, CINNAMON, TOASTED","Bread, cinnamon, toasted" +51115010,"BREAD, CORNMEAL AND MOLASSES","Bread, cornmeal and molasses" +51115020,"BREAD, CORNMEAL AND MOLASSES, TOASTED","Bread, cornmeal and molasses, toasted" +51119010,"BREAD, EGG, CHALLAH","Bread, egg, Challah" +51119040,"BREAD, EGG, CHALLAH, TOASTED","Bread, egg, Challah, toasted" +51121010,"BREAD, GARLIC","Bread, garlic" +51121110,"BREAD, ONION","Bread, onion" +51121120,"BREAD, ONION, TOASTED","Bread, onion, toasted" +51122000,"BREAD, REDUCED CALORIE/HIGH FIBER","Bread, reduced calorie and/or high fiber, white or NFS" +51122010,"BREAD, REDUCED CALORIE/HIGH FIBER, TOASTED","Bread, reduced calorie and/or high fiber, white or NFS, toasted" +51122100,"BREAD, REDUCED CALORIE/ HIGH FIBER, W/ FRUIT/NUTS","Bread, reduced calorie and/or high fiber, white or NFS, with fruit and/or nuts" +51122110,"BREAD, REDUCED CALORIE/HI FIBER, W/FRUIT/NUTS,TOAST","Bread, reduced calorie and/or high fiber, white or NFS, with fruit and/or nuts, toasted" +51122300,"BREAD, WHITE, SPECIAL FORMULA, ADDED FIBER","Bread, white, special formula, added fiber" +51122310,"BREAD,WHITE,SPECIAL FORMULA,ADDED FIBER,TOASTED","Bread, white, special formula, added fiber, toasted" +51123010,"BREAD, HIGH PROTEIN","Bread, high protein" +51123020,"BREAD, HIGH PROTEIN, TOASTED","Bread, high protein, toasted" +51127010,"BREAD, POTATO","Bread, potato" +51127020,"BREAD, POTATO, TOASTED","Bread, potato, toasted" +51129010,"BREAD, RAISIN","Bread, raisin" +51129020,"BREAD, RAISIN, TOASTED","Bread, raisin, toasted" +51130510,"BREAD, WHITE, LOW SODIUM OR NO SALT","Bread, white, low sodium or no salt" +51130520,"BREAD, WHITE, LOW SODIUM/NO SALT, TOASTED","Bread, white, low sodium or no salt, toasted" +51133010,"BREAD, SOUR DOUGH","Bread, sour dough" +51133020,"BREAD, SOUR DOUGH, TOASTED","Bread, sour dough, toasted" +51134000,"BREAD, SWEET POTATO","Bread, sweet potato" +51134010,"BREAD, SWEET POTATO, TOASTED","Bread, sweet potato, toasted" +51135000,"BREAD, VEGETABLE","Bread, vegetable" +51135010,"BREAD, VEGETABLE, TOASTED","Bread, vegetable, toasted" +51136000,"BRUSCHETTA","Bruschetta" +51140100,"BREAD DOUGH, FRIED","Bread, dough, fried" +51150000,"ROLL, WHITE, SOFT","Roll, white, soft" +51150100,"ROLL, WHITE, SOFT, TOASTED","Roll, white, soft, toasted" +51151060,"ROLL, WHITE, SOFT, HOMEMADE/PURCH AT A BAKERY","Roll, white, soft, made from home recipe or purchased at a bakery" +51152000,"ROLL, WHITE, SOFT, REDUCED CALORIE/ HIGH FIBER","Roll, white, soft, reduced calorie and/or high fiber" +51152100,"ROLL, WHITE, REDUCED CALORIE/ HIGH FIBER, TOASTED","Roll, white, soft, reduced calorie and/or high fiber, toasted" +51153000,"ROLL, WHITE, HARD","Roll, white, hard" +51153010,"ROLL, WHITE, HARD, TOASTED","Roll, white, hard, toasted" +51154510,"ROLL, DIET","Roll, diet" +51154550,"ROLL, EGG BREAD","Roll, egg bread" +51154560,"ROLL, EGG BREAD, TOASTED","Roll, egg bread, toasted" +51154600,"ROLL, CHEESE","Roll, cheese" +51155000,"ROLL, FRENCH OR VIENNA","Roll, French or Vienna" +51155010,"ROLL, FRENCH OR VIENNA, TOASTED","Roll, French or Vienna, toasted" +51156500,"ROLL, GARLIC","Roll, garlic" +51157000,"ROLL, HOAGIE, SUBMARINE,","Roll, hoagie, submarine" +51157010,"ROLL, HOAGIE, SUBMARINE, TOASTED","Roll, hoagie, submarine, toasted" +51158100,"ROLL, MEXICAN, BOLILLO","Roll, Mexican, bolillo" +51159000,"ROLL, SOUR DOUGH","Roll, sour dough" +51160000,"ROLL, SWEET, NO FROSTING","Roll, sweet, no frosting" +51160100,"ROLL, SWEET, CINNAMON BUN, NO FROSTING","Roll, sweet, cinnamon bun, no frosting" +51160110,"ROLL, SWEET, CINNAMON BUN, FROSTED","Roll, sweet, cinnamon bun, frosted" +51161000,"ROLL, SWEET, W/ FRUIT, NO FROSTING","Roll, sweet, with fruit, no frosting" +51161020,"ROLL, SWEET, W/ FRUIT, FROSTED","Roll, sweet, with fruit, frosted" +51161030,"ROLL, SWEET, W/ FRUIT, FROSTED, DIET","Roll, sweet, with fruit, frosted, diet" +51161050,"ROLL, SWEET, FROSTED","Roll, sweet, frosted" +51161250,"ROLL, SWEET, NO TOPPING, MEXICAN (PAN DULCE)","Roll, sweet, no topping, Mexican (Pan Dulce)" +51161270,"ROLL, SWEET, SUGAR TOPPING, MEXICAN (PAN DULCE)","Roll, sweet, sugar topping, Mexican (Pan Dulce)" +51161280,"ROLL,SWEET,W/ RAISINS & ICING,MEXICAN (PAN DULCE)","Roll, sweet, with raisins and icing, Mexican (Pan Dulce)" +51165000,"COFFEE CAKE, YEAST TYPE","Coffee cake, yeast type" +51166000,"CROISSANT","Croissant" +51166100,"CROISSANT, CHEESE","Croissant, cheese" +51166200,"CROISSANT, CHOCOLATE","Croissant, chocolate" +51166500,"CROISSANT, FRUIT","Croissant, fruit" +51167000,"BRIOCHE","Brioche" +51168000,"COFFEE BREAD, SPANISH","Bread, Spanish coffee" +51180010,"BAGEL","Bagel" +51180020,"BAGEL, TOASTED","Bagel, toasted" +51180030,"BAGEL, W/ RAISINS","Bagel, with raisins" +51180040,"BAGEL, W/ RAISINS, TOASTED","Bagel, with raisins, toasted" +51180080,"BAGEL W/ FRUIT OTHER THAN RAISINS","Bagel, with fruit other than raisins" +51180090,"BAGEL W/ FRUIT OTHER THAN RAISINS, TOASTED","Bagel, with fruit other than raisins, toasted" +51182010,"BREAD, STUFFING (INCLUDE HOMEMADE; STUFFING, NFS)","Bread stuffing" +51182020,"BREAD STUFFING W/ EGG","Bread stuffing made with egg" +51184000,"BREAD STICK, HARD","Bread sticks, hard" +51184010,"BREAD STICK, SOFT","Bread stick, soft" +51184020,"BREAD STICK, NS AS TO HARD OR SOFT","Bread stick, NS as to hard or soft" +51184030,"BREAD STICK, SOFT, PREP W/ GARLIC & PARMESAN CHEESE","Bread stick, soft, prepared with garlic and parmesan cheese" +51184100,"BREAD STICK, HARD, LOW SODIUM","Bread stick, hard, low sodium" +51185000,"CROUTONS","Croutons" +51186010,"MUFFIN, ENGLISH (INCLUDE SOUR DOUGH)","Muffin, English" +51186020,"MUFFIN, ENGLISH, TOASTED","Muffin, English, toasted" +51186100,"MUFFIN, ENGLISH, W/ RAISINS","Muffin, English, with raisins" +51186120,"MUFFIN, ENGLISH, W/ RAISINS, TOASTED","Muffin, English, with raisins, toasted" +51186130,"MUFFIN, ENGLISH, CHEESE","Muffin, English, cheese" +51186140,"MUFFIN, ENGLISH, CHEESE, TOASTED","Muffin, English, cheese, toasted" +51186160,"MUFFIN, ENGLISH, W/ FRUIT OTHER THAN RAISINS","Muffin, English, with fruit other than raisins" +51186180,"MUFFIN, ENGLISH, W/ FRUIT OTHER THAN RAISINS, TSTD","Muffin, English, with fruit other than raisins, toasted" +51187000,"MELBA TOAST","Melba toast" +51187020,"ANISETTE TOAST","Anisette toast" +51188100,"PANNETONE (ITALIAN-STYLE SWEET BREAD)","Pannetone (Italian-style sweet bread)" +51188500,"ZWIEBACK TOAST (INCL RUSK)","Zwieback toast" +51201010,"BREAD, 100% WHOLE WHEAT","Bread, whole wheat, 100%" +51201020,"BREAD, 100% WHOLE WHEAT, TOASTED","Bread, whole wheat, 100%, toasted" +51201060,"BREAD, 100% WHOLE WHEAT, HOME-MADE","Bread, whole wheat, 100%, made from home recipe or purchased at bakery" +51201070,"BREAD, 100% WHOLE WHEAT, HOME-MADE, TOASTED","Bread, whole wheat, 100%, made from home recipe or purchased at bakery, toasted" +51201150,"BREAD, PITA, 100% WHOLE WHEAT","Bread, pita, whole wheat, 100%" +51201160,"BREAD, PITA, 100% WHOLE WHEAT, TOASTED","Bread, pita, whole wheat, 100%, toasted" +51202000,"MUFFIN, ENGLISH, 100% WHOLE WHEAT","Muffin, English, whole wheat, 100%" +51202020,"MUFFIN, ENGLISH, 100% WHOLE WHEAT, TOASTED","Muffin, English, whole wheat, 100%, toasted" +51202050,"MUFFIN, ENGLISH, 100% WHOLE WHEAT, W/ RAISINS","Muffin, English, whole wheat, 100%, with raisins" +51202060,"MUFFIN, ENGLISH, WHOLE WHEAT, W/ RAISINS, TOASTED","Muffin, English, whole wheat, 100%, with raisins, toasted" +51207010,"BREAD, SPROUTED WHEAT","Bread, sprouted wheat" +51207020,"BREAD, SPROUTED WHEAT, TOASTED","Bread, sprouted wheat, toasted" +51208000,"BAGEL, 100% WHOLE WHEAT","Bagel, whole wheat, 100%" +51208010,"BAGEL, 100% WHOLE WHEAT, TOASTED","Bagel, whole wheat, 100%, toasted" +51208100,"BAGEL, 100% WHOLE WHEAT, W/ RAISINS","Bagel, whole wheat, 100%, with raisins" +51208110,"BAGEL, 100% WHOLE WHEAT, W/ RAISINS, TOASTED","Bagel, whole wheat, 100%, with raisins, toasted" +51220000,"ROLL, 100% WHOLE WHEAT","Roll, whole wheat, 100%" +51220010,"ROLL, 100% WHOLE WHEAT, TOASTED","Roll, whole wheat, 100%, toasted" +51220030,"ROLL, 100% WHOLE WHEAT, HOME RECIPE/BAKERY","Roll, whole wheat, 100%, made from home recipe or purchased at bakery" +51220040,"ROLL, 100% WHOLE WHEAT, HOME RECIPE/BAKERY, TOASTED","Roll, whole wheat, 100%, made from home recipe or purchased at bakery, toasted" +51300050,"BREAD, WHOLE GRAIN WHITE","Bread, whole grain white" +51300060,"BREAD, WHOLE GRAIN WHITE, TOASTED","Bread, whole grain white, toasted" +51300100,"BAGEL, WHOLE GRAIN WHITE","Bagel, whole grain white" +51300110,"BREAD, WHOLE WHEAT, NS AS TO 100%","Bread, whole wheat, NS as to 100%" +51300120,"BREAD, WHOLE WHEAT, NS AS TO 100%, TOASTED","Bread, whole wheat, NS as to 100%, toasted" +51300140,"BREAD, WHOLE WHEAT, NS AS TO 100%, MADE FROM HOME RECIPE OR","Bread, whole wheat, NS as to 100%, made from home recipe or purchased at bakery" +51300150,"BREAD, WHOLE WHEAT, NS 100%, HOME RECIPE/BAKERY, TOASTED","Bread, whole wheat, NS as to 100%, made from home recipe or purchased at bakery, toasted" +51300175,"BREAD, CHAPPATTI OR ROTI (INDIAN BREAD), WHEAT","Bread, chappatti or roti (Indian bread), wheat" +51300180,"BREAD, PURI OR POORI (INDIAN PUFFED BREAD), WHEAT","Bread, puri or poori (Indian puffed bread), wheat" +51300185,"BREAD, PARATHA, (INDIAN FLAT BREAD), WHEAT","Bread, paratha, (Indian flat bread), wheat" +51300210,"BREAD, WHOLE WHEAT, WITH RAISINS","Bread, whole wheat, with raisins" +51300220,"BREAD, WHOLE WHEAT, WITH RAISINS, TOASTED","Bread, whole wheat, with raisins, toasted" +51301010,"BREAD, WHEAT OR CRACKED WHEAT","Bread, wheat or cracked wheat" +51301020,"BREAD, WHEAT OR CRACKED WHEAT, TOASTED","Bread, wheat or cracked wheat, toasted" +51301040,"BREAD, CRACKED WHEAT, HOME RECIPE/BAKERY","Bread, wheat or cracked wheat, made from home recipe or purchased at bakery" +51301050,"BREAD, CRACKED WHEAT, HOME RECIPE/BAKERY, TOASTED","Bread, wheat or cracked wheat, made from home recipe or purchased at bakery, toasted" +51301120,"BREAD, WHEAT OR CRACKED WHEAT, W/ RAISINS","Bread, wheat or cracked wheat, with raisins" +51301130,"BREAD, WHEAT OR CRACKED WHEAT, W/ RAISINS, TOASTED","Bread, wheat or cracked wheat, with raisins, toasted" +51301510,"BREAD, CRACKED WHEAT, REDUCED CALORIE/ HIGH FIBER","Bread, wheat or cracked wheat, reduced calorie and/or high fiber" +51301520,"BREAD, CRACKED WHEAT, RED CALORIE/ HI FIBER, TOAST","Bread, wheat or cracked wheat, reduced calorie and/or high fiber, toasted" +51301540,"BREAD, FRENCH OR VIENNA, WHOLE WHEAT, NS AS TO 100%","Bread, French or Vienna, whole wheat, NS as to 100%" +51301550,"BREAD, FRENCH OR VIENNA, WHOLE WHEAT, NS AS TO 100%, TOASTED","Bread, French or Vienna, whole wheat, NS as to 100%, toasted" +51301600,"BREAD, PITA, WHOLE WHEAT, NS AS TO 100%","Bread, pita, whole wheat, NS as to 100%" +51301610,"BREAD, PITA, WHOLE WHEAT, NS AS TO 100%, TOASTED","Bread, pita, whole wheat, NS as to 100%, toasted" +51301620,"BREAD, PITA, CRACKED WHEAT","Bread, pita, wheat or cracked wheat" +51301630,"BREAD, PITA, CRACKED WHEAT, TOASTED","Bread, pita, wheat or cracked wheat, toasted" +51301700,"BAGEL, WHEAT","Bagel, wheat" +51301710,"BAGEL, WHEAT, TOASTED","Bagel, wheat, toasted" +51301750,"BAGEL, WHOLE WHEAT, NS AS TO 100%","Bagel, whole wheat, NS as to 100%" +51301760,"BAGEL, WHOLE WHEAT, NS AS TO 100%, TOASTED","Bagel, whole wheat, NS as to 100%, toasted" +51301800,"BAGEL, WHEAT, W/ RAISINS","Bagel, wheat, with raisins" +51301810,"BAGEL, WHEAT, W/ RAISINS, TOASTED","Bagel, wheat, with raisins, toasted" +51301820,"BAGEL, WHEAT, W/ FRUITS & NUTS","Bagel, wheat, with fruit and nuts" +51301830,"BAGEL, WHEAT, W/ FRUITS & NUTS, TOASTED","Bagel, wheat, with fruit and nuts, toasted" +51301900,"BAGEL, WHEAT BRAN","Bagel, wheat bran" +51301910,"BAGEL, WHEAT BRAN, TOASTED","Bagel, wheat bran, toasted" +51302500,"MUFFIN, ENGLISH, WHEAT BRAN","Muffin, English, wheat bran" +51302510,"MUFFIN, ENGLISH, WHEAT BRAN, TOASTED","Muffin, English, wheat bran, toasted" +51302520,"MUFFIN, ENGLISH, WHEAT BRAN, W/ RAISINS","Muffin, English, wheat bran, with raisins" +51302530,"MUFFIN, ENGLISH, WHEAT BRAN, W/ RAISINS, TOASTED","Muffin, English, wheat bran, with raisins, toasted" +51303010,"MUFFIN, ENGLISH, WHEAT OR CRACKED WHEAT","Muffin, English, wheat or cracked wheat" +51303020,"MUFFIN, ENGLISH, WHEAT OR CRACKED WHEAT, TOASTED","Muffin, English, wheat or cracked wheat, toasted" +51303030,"MUFFIN, ENGLISH, WHOLE WHEAT, NS AS TO 100%","Muffin, English, whole wheat, NS as to 100%" +51303040,"MUFFIN, ENGLISH, WHOLE WHEAT, NS AS TO 100%, TOASTED","Muffin, English, whole wheat, NS as to 100%, toasted" +51303050,"MUFFIN, ENGLISH, WHEAT OR CRACKED WHEAT W/ RAISINS","Muffin, English, wheat or cracked wheat, with raisins" +51303060,"MUFFIN, ENGLISH, WHEAT W/ RAISINS, TOASTED","Muffin, English, wheat or cracked wheat, with raisins, toasted" +51303070,"MUFFIN, ENGLISH, WHOLE WHEAT, NS AS TO 100%, WITH RAISINS","Muffin, English, whole wheat, NS as to 100%, with raisins" +51303080,"MUFFIN, ENGLISH, WHOLE WHEAT, NS 100%, W/RAISINS, TOASTED","Muffin, English, whole wheat, NS as to 100%, with raisins, toasted" +51306000,"BREAD STICK, HARD, WHOLE WHEAT, NS AS TO 100 %","Bread stick, hard, whole wheat, NS as to 100%" +51320010,"ROLL, WHEAT OR CRACKED WHEAT","Roll, wheat or cracked wheat" +51320020,"ROLL, WHEAT OR CRACKED WHEAT, TOASTED","Roll, wheat or cracked wheat, toasted" +51320040,"ROLL, CRACKED WHEAT, HOME RECIPE/BAKERY","Roll, wheat or cracked wheat, made from home recipe or purchased at bakery" +51320050,"ROLL, CRACKED WHEAT, HOME RECIPE/BAKERY, TOASTED","Roll, wheat or cracked wheat, made from home recipe or purchased at bakery, toasted" +51320500,"ROLL, WHOLE WHEAT, NS AS TO 100%","Roll, whole wheat, NS as to 100%" +51320510,"ROLL, WHOLE WHEAT, NS AS TO 100%, TOASTED","Roll, whole wheat, NS as to 100%, toasted" +51320530,"ROLL, WHOLE WHEAT, NS 100%, MADE FROM HOMEMADE/BAKERY","Roll, whole wheat, NS as to 100%, made from home recipe or purchased at bakery" +51320540,"ROLL, WHOLE WHEAT, NS AS TO 100%, HOMEMADE/BAKERY, TOASTED","Roll, whole wheat, NS as to 100%, made from home recipe or purchased at bakery, toasted" +51401010,"BREAD, RYE","Bread, rye" +51401020,"BREAD, RYE, TOASTED","Bread, rye, toasted" +51401030,"BREAD, MARBLE RYE & PUMPERNICKEL","Bread, marble rye and pumpernickel" +51401040,"BREAD, MARBLE RYE & PUMPERNICKEL, TOASTED","Bread, marble rye and pumpernickel, toasted" +51401060,"BREAD, RYE, REDUCED CALORIE/ HIGH FIBER (INCL LESS)","Bread, rye, reduced calorie and/or high fiber" +51401070,"BREAD, RYE, REDUCED CALORIE/ HIGH FIBER, TOASTED","Bread, rye, reduced calorie and/or high fiber, toasted" +51401200,"MUFFIN, ENGLISH, RYE","Muffin, English, rye" +51401210,"MUFFIN, ENGLISH, RYE, TOASTED","Muffin, English, rye, toasted" +51404010,"BREAD, PUMPERNICKEL","Bread, pumpernickel" +51404020,"BREAD, PUMPERNICKEL, TOASTED","Bread, pumpernickel, toasted" +51404500,"BAGEL, PUMPERNICKEL","Bagel, pumpernickel" +51404510,"BAGEL, PUMPERNICKEL, TOASTED","Bagel, pumpernickel, toasted" +51404550,"MUFFIN, ENGLISH, PUMPERNICKEL","Muffin, English, pumpernickel" +51404560,"MUFFIN, ENGLISH, PUMPERNICKEL, TOASTED","Muffin, English, pumpernickel, toasted" +51407010,"BREAD, BLACK","Bread, black" +51407020,"BREAD, BLACK, TOASTED","Bread, black, toasted" +51420000,"ROLL, RYE","Roll, rye" +51421000,"ROLL, PUMPERNICKEL","Roll, pumpernickel" +51421100,"ROLL, PUMPERNICKEL, TOASTED","Roll, pumpernickel, toasted" +51501010,"BREAD, OATMEAL","Bread, oatmeal" +51501020,"BREAD, OATMEAL, TOASTED","Bread, oatmeal, toasted" +51501040,"BREAD, OAT BRAN","Bread, oat bran" +51501050,"BREAD, OAT BRAN, TOASTED","Bread, oat bran, toasted" +51501060,"BREAD, OAT BRAN, REDUCED CALORIE/ HIGH FIBER","Bread, oat bran, reduced calorie and/or high fiber" +51501070,"BREAD, OAT BRAN REDUCED CALORIE/HI FIBER, TOASTED","Bread, oat bran, reduced calorie and/or high fiber, toasted" +51501080,"BAGEL, OAT BRAN","Bagel, oat bran" +51501090,"BAGEL, OAT BRAN, TOASTED","Bagel, oat bran, toasted" +51502010,"ROLL, OATMEAL","Roll, oatmeal" +51502020,"ROLL, OATMEAL, TOASTED","Roll, oatmeal, toasted" +51502100,"ROLL, OAT BRAN","Roll, oat bran" +51502110,"ROLL, OAT BRAN, TOASTED","Roll, oat bran, toasted" +51503000,"MUFFIN, ENGLISH, OAT BRAN","Muffin, English, oat bran" +51503010,"MUFFIN, ENGLISH, OAT BRAN, TOASTED","Muffin, English, oat bran, toasted" +51503040,"MUFFIN, ENGLISH, OAT BRAN, WITH RAISINS","Muffin, English, oat bran, with raisins" +51503050,"MUFFIN, ENGLISH, OAT BRAN, W/ RAISINS, TOASTED","Muffin, English, oat bran with raisins, toasted" +51601010,"BREAD, MULTIGRAIN, TOASTED","Bread, multigrain, toasted" +51601020,"BREAD, MULTIGRAIN","Bread, multigrain" +51601210,"BREAD, MULTIGRAIN, W/ RAISINS","Bread, multigrain, with raisins" +51601220,"BREAD, MULTIGRAIN, W/ RAISINS, TOASTED","Bread, multigrain, with raisins, toasted" +51602010,"BREAD, MULTIGRAIN, REDUCED CALORIE/ HIGH FIBER","Bread, multigrain, reduced calorie and/or high fiber" +51602020,"BREAD, MULTIGRAIN, REDUCED CALORIE/ HI FIBER, TOAST","Bread, multigrain, reduced calorie and/or high fiber, toasted" +51620000,"ROLL, MULTIGRAIN","Roll, multigrain" +51620010,"ROLL, MULTIGRAIN, TOASTED","Roll, multigrain, toasted" +51630000,"BAGEL, MULTIGRAIN","Bagel, multigrain" +51630010,"BAGEL, MULTIGRAIN, TOASTED","Bagel, multigrain, toasted" +51630100,"BAGEL, MULTIGRAIN, W/ RAISINS","Bagel, multigrain, with raisins" +51630110,"BAGEL, MULTIGRAIN, W/ RAISINS, TOASTED","Bagel, multigrain, with raisins, toasted" +51630200,"MUFFIN, ENGLISH, MULTIGRAIN","Muffin, English, multigrain" +51630210,"MUFFIN, ENGLISH, MULTIGRAIN, TOASTED","Muffin, English, multigrain, toasted" +51801010,"BREAD, BARLEY","Bread, barley" +51801020,"BREAD, BARLEY, TOASTED","Bread, barley, toasted" +51804010,"BREAD, SOY","Bread, soy" +51804020,"BREAD, SOY, TOASTED","Bread, soy, toasted" +51805010,"BREAD, SUNFLOWER MEAL","Bread, sunflower meal" +51805020,"BREAD, SUNFLOWER MEAL, TOASTED","Bread, sunflower meal, toasted" +51806010,"BREAD, RICE","Bread, rice" +51806020,"BREAD, RICE, TOASTED","Bread, rice, toasted" +51807000,"INJERA (AMERICAN-STYLE ETHIOPIAN BREAD)","Injera (American-style Ethiopian bread)" +51808000,"BREAD, LOW GLUTEN","Bread, low gluten" +51808010,"BREAD, LOW GLUTEN, TOASTED","Bread, low gluten, toasted" +52101000,"BISCUIT, BAKING POWDER OR BUTTERMILK TYPE, NFS","Biscuit, baking powder or buttermilk type, NS as to made from mix, refrigerated dough, or home recipe" +52101030,"BISCUIT DOUGH, FRIED","Biscuit dough, fried" +52101040,"CRUMPET","Crumpet" +52101050,"CRUMPET, TOASTED","Crumpet, toasted" +52101100,"BISCUIT, BAKING POWDER OR BUTTERMILK, FROM MIX","Biscuit, baking powder or buttermilk type, made from mix" +52101150,"BISCUIT,BAKING PWR/BUTTER MILK,REFRIG DOUGH,LOWFAT","Biscuit, baking powder or buttermilk type, made from refrigerated dough, lowfat" +52102040,"BISCUIT, BAK POWDER OR BUTTERMILK, FROM REFRG DOUGH","Biscuit, baking powder or buttermilk type, made from refrigerated dough" +52103000,"BISCUIT, BAKING POWDER/BUTTERMILK TYPE, COMMERCIALLY BAKED","Biscuit, baking powder or buttermilk type, commercially baked" +52104010,"BISCUIT, BAKING POWDER OR BUTTERMILK, HOMEMADE","Biscuit, baking powder or buttermilk type, made from home recipe" +52104040,"BISCUIT, WHOLE WHEAT","Biscuit, whole wheat" +52104100,"BISCUIT, CHEESE","Biscuit, cheese" +52104200,"BISCUIT, CINNAMON-RAISIN","Biscuit, cinnamon-raisin" +52105100,"SCONES","Scone" +52105110,"SCONES, WHOLE WHEAT","Scone, whole wheat" +52105200,"SCONE, WITH FRUIT","Scone, with fruit" +52201000,"CORNBREAD, PREPARED FROM MIX","Cornbread, prepared from mix" +52202060,"CORNBREAD, HOMEMADE","Cornbread, made from home recipe" +52204000,"CORNBREAD STUFFING","Cornbread stuffing" +52206010,"CORNBREAD MUFFIN, STICK, ROUND","Cornbread muffin, stick, round" +52206060,"CORNBREAD MUFFIN, STICK, ROUND, HOMEMADE","Cornbread muffin, stick, round, made from home recipe" +52207010,"CORN FLOUR PATTIES OR TARTS, FRIED","Corn flour patty or tart, fried" +52208010,"CORN PONE,BAKED (INCL HOE CAKE)","Corn pone, baked" +52208020,"CORN PONE FRIED","Corn pone, fried" +52208760,"GORDITA/SOPE SHELL, PLAIN, NO FILLING","Gordita/sope shell, plain, no filling" +52209010,"HUSH PUPPY","Hush puppy" +52211010,"JOHNNYCAKE","Johnnycake" +52213010,"SPOONBREAD","Spoonbread" +52215000,"TORTILLA, NFS","Tortilla, NFS" +52215100,"TORTILLA, CORN","Tortilla, corn" +52215200,"TORTILLA, FLOUR (WHEAT)","Tortilla, flour (wheat)" +52215260,"TORTILLA, WHOLE WHEAT","Tortilla, whole wheat" +52215300,"TACO SHELL, CORN","Taco shell, corn" +52215350,"TACO SHELL; FLOUR","Taco shell, flour" +52220110,"CORNMEAL BREAD, DOMINICAN","Cornmeal bread, Dominican style (Arepa Dominicana)" +52301000,"MUFFIN, NFS","Muffin, NFS" +52302010,"MUFFIN, FRUIT","Muffin, fruit" +52302020,"MUFFIN, FRUIT, LOW FAT","Muffin, fruit, low fat" +52302500,"MUFFIN, CHOCOLATE CHIP","Muffin, chocolate chip" +52302600,"MUFFIN, CHOCOLATE","Muffin, chocolate" +52303010,"MUFFIN, WHOLE WHEAT","Muffin, whole wheat" +52303500,"MUFFIN, WHEAT","Muffin, wheat" +52304000,"MUFFIN, WHOLE GRAIN","Muffin, whole grain" +52304010,"MUFFIN, WHEAT BRAN (INCLUDE W/ RAISINS & NUTS)","Muffin, wheat bran" +52304040,"MUFFIN,BRAN,W/ FRUIT, LOWFAT","Muffin, bran with fruit, lowfat" +52304100,"MUFFIN, OATMEAL","Muffin, oatmeal" +52304150,"MUFFIN, OAT BRAN","Muffin, oat bran" +52306010,"MUFFIN, PLAIN","Muffin, plain" +52306300,"MUFFIN, CHEESE","Muffin, cheese" +52306500,"MUFFIN, PUMPKIN, W/ RAISINS","Muffin, pumpkin" +52306550,"MUFFIN, ZUCCHINI","Muffin, zucchini" +52306700,"MUFFIN, CARROT (INCL W/ RAISINS/NUTS)","Muffin, carrot" +52311010,"POPOVER","Popover" +52401000,"BREAD, BOSTON BROWN","Bread, Boston Brown" +52403000,"BREAD, NUT","Bread, nut" +52404060,"BREAD, PUMPKIN (INCLUDE W/ RAISINS)","Bread, pumpkin" +52405010,"BREAD, FRUIT","Bread, fruit" +52407000,"BREAD, ZUCCHINI (INCL SQUASH BREAD; W/ NUTS)","Bread, zucchini" +52408000,"BREAD, IRISH SODA","Bread, Irish soda" +53100050,"CAKE, BATTER, CHOCOLATE, RAW","Cake batter, raw, chocolate" +53100070,"CAKE, BATTER, RAW, NOT CHOCOLATE","Cake batter, raw, not chocolate" +53100100,"CAKE OR CUPCAKE, NS AS TO TYPE","Cake or cupcake, NS as to type" +53101100,"CAKE, ANGEL FOOD, W/O ICING","Cake, angel food, without icing or filling" +53101200,"CAKE, ANGEL FOOD, W/ ICING","Cake, angel food, with icing or filling" +53101250,"CAKE, ANGEL FOOD, W/ FRUIT & ICING/FILLING","Cake, angel food, with fruit and icing or filling" +53102100,"CAKE OR CUPCAKE,APPLESAUCE W/O ICING","Cake or cupcake, applesauce, without icing or filling" +53102200,"CAKE OR CUPCAKE,APPLESAUCE W/ ICING","Cake or cupcake, applesauce, with icing or filling" +53102600,"CAKE OR CUPCAKE,BANANA, W/O ICING","Cake or cupcake, banana, without icing or filling" +53102700,"CAKE OR CUPCAKE,BANANA, W/ ICING","Cake or cupcake, banana, with icing or filling" +53102800,"CAKE OR CUPCAKE,BLACK FOREST (CHOC-CHERRY)","Cake or cupcake, black forest (chocolate-cherry)" +53103000,"CAKE, BOSTON CREAM PIE","Cake, Boston cream pie" +53104100,"CAKE OR CUPCAKE,CARROT, NO ICING","Cake or cupcake, carrot, without icing or filling" +53104260,"CAKE OR CUPCAKE, CARROT, WITH ICING","Cake or cupcake, carrot, with icing or filling" +53104300,"CARROT CAKE, DIET","Cake, carrot, diet" +53104400,"CAKE OR CUPCAKE,COCONUT, W/ ICING","Cake or cupcake, coconut, with icing or filling" +53104500,"CHEESECAKE","Cheesecake" +53104550,"CHEESECAKE, W/ FRUIT","Cheesecake with fruit" +53104600,"CHEESECAKE, CHOCOLATE","Cheesecake, chocolate" +53105270,"CAKE OR CUPCAKE, CHOC, DEVIL'S FOOD OR FUDGE, W/ICING /FILL","Cake or cupcake, chocolate, devil's food or fudge, with icing or filling" +53105275,"CAKE OR CUPCAKE, CHOCOLATE, DEVIL'S FOOD OR FUDGE, W/O ICING","Cake or cupcake, chocolate, devil's food or fudge, without icing or filling" +53105300,"CAKE OR CUPCAKE,GERMAN CHOC, W/ ICING or FILLING","Cake or cupcake, German chocolate, with icing or filling" +53105500,"CAKE, CHOC, W/ ICING, DIET","Cake, chocolate, with icing, diet" +53106500,"CAKE, CREAM, W/O ICING OR TOPPING","Cake, cream, without icing or topping" +53108200,"SNACK CAKE, CHOCOLATE, WITH ICING OR FILLING","Snack cake, chocolate, with icing or filling" +53108220,"SNACK CAKE, CHOC, W/ICING OR FILLING, REDUCED FAT&CALORIE","Snack cake, chocolate, with icing or filling, reduced fat and calories" +53109200,"SNACK CAKE, NOT CHOCOLATE, WITH ICING OR FILLING","Snack cake, not chocolate, with icing or filling" +53109220,"SNACK CAKE, NOT CHOC, W/ ICING OR FILLING, RED FAT&CALS","Snack cake, not chocolate, with icing or filling, reduced fat and calories" +53109300,"CAKE,DOBOS TORTE(NON-CHOC CAKE W/CHOC FILL & ICING)","Cake, Dobos Torte (non-chocolate layer cake with chocolate filling and icing)" +53110000,"CAKE, FRUITCAKE, LIGHT/DARK, HOLIDAY TYPE CAKE","Cake, fruit cake, light or dark, holiday type cake" +53111000,"CAKE OR CUPCAKE, GINGERBREAD","Cake or cupcake, gingerbread" +53112000,"CAKE, ICE CREAM & CAKE ROLL, CHOCOLATE","Cake, ice cream and cake roll, chocolate" +53112100,"CAKE, ICE CREAM & CAKE ROLL, NOT CHOCOLATE","Cake, ice cream and cake roll, not chocolate" +53113000,"CAKE, JELLY ROLL","Cake, jelly roll" +53114000,"CAKE OR CUPCAKE,LEMON, W/O ICING","Cake or cupcake, lemon, without icing or filling" +53114100,"CAKE OR CUPCAKE,LEMON, W/ ICING","Cake or cupcake, lemon, with icing or filling" +53115100,"CAKE OR CUPCAKE, MARBLE, W/O ICING OR FILLING","Cake or cupcake, marble, without icing or filling" +53115200,"CAKE OR CUPCAKE, MARBLE, WITH ICING OR FILLING","Cake or cupcake, marble, with icing or filling" +53115310,"CAKE OR CUPCAKE,NUT, W/O ICING","Cake or cupcake, nut, without icing or filling" +53115320,"CAKE OR CUPCAKE,NUT, W/ ICING","Cake or cupcake, nut, with icing or filling" +53115410,"CAKE OR CUPCAKE, OATMEAL","Cake or cupcake, oatmeal" +53115450,"CAKE OR CUPCAKE, PEANUT BUTTER","Cake or cupcake, peanut butter" +53116000,"CAKE, POUND, W/O ICING","Cake, pound, without icing or filling" +53116020,"CAKE, POUND, W/ ICING","Cake, pound, with icing or filling" +53116270,"CAKE, POUND, CHOCOLATE","Cake, pound, chocolate" +53116350,"CAKE, POUND, P.R. (PONQUE)","Cake, pound, Puerto Rican style (Ponque)" +53116390,"CAKE, POUND, REDUCED FAT, NO CHOLESTEROL","Cake, pound, reduced fat, cholesterol free" +53116500,"CAKE OR CUPCAKE,PUMPKIN, W/O ICING","Cake or cupcake, pumpkin, without icing or filling" +53116510,"CAKE OR CUPCAKE,PUMPKIN,W/ ICING","Cake or cupcake, pumpkin, with icing or filling" +53116550,"CAKE OR CUPCAKE, RAISIN-NUT","Cake or cupcake, raisin-nut" +53116570,"CAKE, RAVANI (MADE W/ FARINA)","Cake, Ravani (made with farina)" +53116600,"CAKE, RICE FLOUR, W/O ICING","Cake, rice flour, without icing or filling" +53116650,"CAKE, QUEZADILLA, EL SALVADORIAN STYLE","Cake, Quezadilla, El Salvadorian style" +53117100,"CAKE OR CUPCAKE,SPICE, W/O ICING","Cake or cupcake, spice, without icing or filling" +53117200,"CAKE OR CUPCAKE,SPICE, W/ ICING","Cake or cupcake, spice, with icing or filling" +53118100,"CAKE, SPONGE, W/O ICING","Cake, sponge, without icing or filling" +53118200,"CAKE, SPONGE, W/ ICING","Cake, sponge, with icing or filling" +53118300,"CAKE, SPONGE, CHOCOLATE","Cake, sponge, chocolate" +53118410,"RUM CAKE, WITHOUT ICING (SOPA BORRACHA)","Rum cake, without icing (Sopa Borracha)" +53118500,"CAKE, TORTE","Cake, torte" +53118550,"CAKE, TRES LECHE","Cake, tres leche" +53119000,"CAKE, UPSIDE DOWN (ALL FRUITS)","Cake, upside down (all fruits)" +53120270,"CAKE OR CUPCAKE, WHITE, WITH ICING OR FILLING","Cake or cupcake, white, with icing or filling" +53120275,"CAKE OR CUPCAKE, WHITE, WITHOUT ICING OR FILLING","Cake or cupcake, white, without icing or filling" +53121270,"CAKE OR CUPCAKE, YELLOW, WITH ICING OR FILLING","Cake or cupcake, yellow, with icing or filling" +53121275,"CAKE OR CUPCAKE, YELLOW, WITHOUT ICING OR FILLING","Cake or cupcake, yellow, without icing or filling" +53122070,"CAKE, SHORTCAKE, BISCUIT, W/ WHIPPED CREAM & FRUIT","Cake, shortcake, biscuit type, with whipped cream and fruit" +53122080,"CAKE, SHORTCAKE, BISCUIT, W/ FRUIT","Cake, shortcake, biscuit type, with fruit" +53123070,"CAKE, SHORTCAKE, SPONGE, W/ WHIPPED CREAM & FRUIT","Cake, shortcake, sponge type, with whipped cream and fruit" +53123080,"CAKE, SHORTCAKE, SPONGE, W/ FRUIT","Cake, shortcake, sponge type, with fruit" +53123500,"CAKE, SHORTCAKE, W/ WHIP TOPPING & FRUIT, DIET","Cake, shortcake, with whipped topping and fruit, diet" +53124110,"CAKE OR CUPCAKE, ZUCCHINI","Cake or cupcake, zucchini" +53200100,"COOKIE, BATTER OR DOUGH, RAW","Cookie, batter or dough, raw" +53201000,"COOKIE, NFS","Cookie, NFS" +53202000,"COOKIE, ALMOND","Cookie, almond" +53203000,"COOKIE, APPLESAUCE","Cookie, applesauce" +53203500,"COOKIE, BISCOTTI","Cookie, biscotti (Italian sugar cookie)" +53204000,"COOKIE, BROWNIE, NS AS TO ICING","Cookie, brownie, NS as to icing" +53204010,"COOKIE, BROWNIE, W/O ICING","Cookie, brownie, without icing" +53204100,"COOKIE, BROWNIE, WITH ICING OR FILLING","Cookie, brownie, with icing or filling" +53204840,"COOKIE, BROWNIE, REDUCED FAT, NS AS TO ICING","Cookie, brownie, reduced fat, NS as to icing" +53204860,"COOKIE, BROWNIE, FAT FREE, NS AS TO ICING","Cookie, brownie, fat free, NS as to icing" +53205250,"COOKIE, BUTTERSCOTCH, BROWNIE","Cookie, butterscotch, brownie" +53205260,"COOKIE, BAR, WITH CHOCOLATE","Cookie, bar, with chocolate" +53206000,"COOKIE, CHOCOLATE CHIP","Cookie, chocolate chip" +53206020,"COOKIE, CHOC CHIP, HOMEMADE OR PURCHASED AT BAKERY","Cookie, chocolate chip, made from home recipe or purchased at a bakery" +53206030,"COOKIE, CHOC CHIP, REDUCED FAT","Cookie, chocolate chip, reduced fat" +53206100,"COOKIE, CHOCOLATE CHIP SANDWICH","Cookie, chocolate chip sandwich" +53206500,"COOKIE, CHOCOLATE, MADE WITH RICE CEREAL","Cookie, chocolate, made with rice cereal" +53206550,"COOKIE, CHOCOLATE, MADE W/ OATMEAL & COCONUT","Cookie, chocolate, made with oatmeal and coconut (no-bake)" +53207000,"COOKIE, CHOCOLATE FUDGE","Cookie, chocolate or fudge" +53207020,"COOKIE, CHOCOLATE OR FUDGE, REDUCED FAT","Cookie, chocolate or fudge, reduced fat" +53207050,"COOKIE, CHOCOLATE, W/ CHOC FILLING/COATING, FAT FREE","Cookie, chocolate, with chocolate filling or coating, fat free" +53208000,"COOKIE, MARSHMALLOW, CHOCOLATE-COVERED","Cookie, marshmallow, chocolate-covered" +53208200,"COOKIE, CHOCOLATE-COVERED, MARSHMALLOW PIE","Cookie, marshmallow pie, chocolate covered" +53209005,"COOKIE, CHOCOLATE, WITH ICING OR COATING","Cookie, chocolate, with icing or coating" +53209010,"COOKIE, SUGAR WAFER, CHOCOLATE-COVERED","Cookie, sugar wafer, chocolate-covered" +53209015,"COOKIE, CHOCOLATE SANDWICH","Cookie, chocolate sandwich" +53209020,"COOKIE, CHOCOLATE SANDWICH, REDUCED FAT","Cookie, chocolate sandwich, reduced fat" +53209100,"COOKIE, CHOCOLATE, SANDWICH, W/ EXTRA FILLING","Cookie, chocolate, sandwich, with extra filling" +53209500,"COOKIE, CHOCOLATE & VANILLA SANDWICH","Cookie, chocolate and vanilla sandwich" +53210000,"COOKIE, CHOCOLATE WAFER","Cookie, chocolate wafer" +53210900,"COOKIE, GRAHAM CRACKER WITH CHOCOLATE AND MARSHMALLOW","Cookie, graham cracker with chocolate and marshmallow" +53211000,"COOKIE, BAR, W/ CHOCOLATE, NUTS, & GRAHAM CRACKERS","Cookie bar, with chocolate, nuts, and graham crackers" +53215500,"COOKIE, COCONUT","Cookie, coconut" +53220000,"COOKIE, FRUIT-FILLED","Cookie, fruit-filled bar" +53220010,"COOKIE, FRUIT-FILLED BAR, FAT FREE","Cookie, fruit-filled bar, fat free" +53220030,"COOKIE, FIG BAR","Cookie, fig bar" +53220040,"COOKIE, FIG BAR, FAT FREE","Cookie, fig bar, fat free" +53222010,"COOKIE, FORTUNE","Cookie, fortune" +53222020,"COOKIE, CONE SHELL, ICE CREAM TYPE,WAFER / CAKE","Cookie, cone shell, ice cream type, wafer or cake" +53223000,"COOKIE, GINGERSNAPS","Cookie, gingersnaps" +53223100,"COOKIE, GRANOLA","Cookie, granola" +53224000,"COOKIE, LADY FINGER","Cookie, ladyfinger" +53224250,"COOKIE, LEMON BAR","Cookie, lemon bar" +53225000,"COOKIE, MACAROON","Cookie, macaroon" +53226000,"COOKIE, MARSHMALLOW, W/ COCONUT","Cookie, marshmallow, with coconut" +53226500,"COOKIE, MARSHMALLOW, W/ RICE CEREAL (NO-BAKE)","Cookie, marshmallow, with rice cereal (no-bake)" +53226550,"COOKIE, MARSHMALLOW, W/ RICE CEREAL & CHOC CHIPS","Cookie, marshmallow, with rice cereal and chocolate chips" +53226600,"COOKIE, MARSHMALLOW & PEANUT BUTTER, W/ OAT CEREAL (NO-BAKE)","Cookie, marshmallow and peanut butter, with oat cereal (no-bake)" +53228000,"COOKIE, MERINGUE","Cookie, meringue" +53230000,"COOKIE, MOLASSES","Cookie, molasses" +53231000,"COOKIE, LEBKUCHEN","Cookie, Lebkuchen" +53231400,"COOKIE, MULTIGRAIN, HIGH FIBER","Cookie, multigrain, high fiber" +53233000,"COOKIE, OATMEAL","Cookie, oatmeal" +53233010,"COOKIE, OATMEAL, W/ RAISINS OR DATES","Cookie, oatmeal, with raisins" +53233040,"COOKIE, OATMEAL, REDUCED FAT, NS AS TO RAISINS","Cookie, oatmeal, reduced fat, NS as to raisins" +53233050,"COOKIE, OATMEAL SANDWICH, W/ CREME FILLING","Cookie, oatmeal sandwich, with creme filling" +53233060,"COOKIE, OATMEAL, W/ CHOCOLATE CHIPS","Cookie, oatmeal, with chocolate chips" +53233080,"COOKIE, OATMEAL SANDWICH, W/ PEANUT BUTTER & JELLY FILLING","Cookie, oatmeal sandwich, with peanut butter and jelly filling" +53233100,"COOKIE,OATMEAL,W/ CHOC & PEANUT BUTTER (NO-BAKE)","Cookie, oatmeal, with chocolate and peanut butter (no-bake)" +53234000,"COOKIE, PEANUT BUTTER (INCLUDE PB WAFER)","Cookie, peanut butter" +53234100,"COOKIE, PEANUT BUTTER, W/ CHOCOLATE (INCL NASSAU)","Cookie, peanut butter, with chocolate" +53234250,"COOKIE, PEANUT BUTTER W/ RICE CEREAl (NO-BAKE)","Cookie, peanut butter with rice cereal (no-bake)" +53235000,"COOKIE, PEANUT BUTTER SANDWICH","Cookie, peanut butter sandwich" +53235500,"COOKIE, W/ PEANUT BUTTER FILLING, CHOCOLATE-COATED","Cookie, with peanut butter filling, chocolate-coated" +53235600,"COOKIE, PFEFFERNUSSE","Cookie, Pfeffernusse" +53236000,"COOKIE, PIZZELLE (ITALIAN STYLE WAFER)","Cookie, pizzelle (Italian style wafer)" +53236100,"COOKIE, PUMPKIN","Cookie, pumpkin" +53237000,"COOKIE, RAISIN","Cookie, raisin" +53237010,"COOKIE, RAISIN SANDWICH, CREAM-FILLED","Cookie, raisin sandwich, cream-filled" +53237500,"COOKIE, RUM BALL (NO-BAKE)","Cookie, rum ball (no-bake)" +53238000,"COOKIE, SANDWICH TYPE, NOT CHOCOLATE OR VANILLA","Cookie, sandwich-type, not chocolate or vanilla" +53239000,"COOKIE, SHORTBREAD","Cookie, shortbread" +53239010,"COOKIE, SHORTBREAD, REDUCED FAT","Cookie, shortbread, reduced fat" +53239050,"COOKIE, SHORTBREAD, WITH ICING OR FILLING","Cookie, shortbread, with icing or filling" +53240000,"COOKIE, ANIMAL","Cookie, animal" +53240010,"COOKIE, ANIMAL, WITH FROSTING OR ICING","Cookie, animal, with frosting or icing" +53241500,"COOKIE, BUTTER OR SUGAR","Cookie, butter or sugar" +53241510,"MARIE BISCUIT","Marie biscuit" +53241600,"COOKIE, BUTTER OR SUGAR, WITH FRUIT AND/OR NUTS","Cookie, butter or sugar, with fruit and/or nuts" +53242000,"COOKIE, SUGAR WAFER","Cookie, sugar wafer" +53242500,"COOKIE, TOFFEE BAR","Cookie, toffee bar" +53243000,"COOKIE, VANILLA SANDWICH","Cookie, vanilla sandwich" +53243010,"COOKIE, VANILLA SANDWICH, EXTRA FILLING","Cookie, vanilla sandwich, extra filling" +53243050,"COOKIE, VANILLA SANDWICH, REDUCED FAT","Cookie, vanilla sandwich, reduced fat" +53244010,"COOKIE, BUTTER/SUGAR, W/ CHOCOLATE ICING / FILLING","Cookie, butter or sugar, with chocolate icing or filling" +53244020,"COOKIE, BUTTER/SUGAR, W/ ICING/FILLING OTHER THAN CHOC","Cookie, butter or sugar, with icing or filling other than chocolate" +53246000,"COOKIE, TEA, JAPANESE","Cookie, tea, Japanese" +53247000,"COOKIE, VANILLA WAFER, NS AS TO TYPE","Cookie, vanilla wafer" +53247050,"COOKIE, VANILLA WAFER, REDUCED FAT","Cookie, vanilla wafer, reduced fat" +53247500,"COOKIE, VANILLA W/ CARAMEL, COCONUT, CHOC COATING","Cookie, vanilla with caramel, coconut, and chocolate coating" +53251100,"COOKIE, RUGELACH","Cookie, rugelach" +53260030,"COOKIE, CHOCOLATE CHIP, SUGAR FREE","Cookie, chocolate chip, sugar free" +53260200,"COOKIE, OATMEAL, SUGAR FREE","Cookie, oatmeal, sugar free" +53260300,"COOKIE, SANDWICH, SUGAR FREE","Cookie, sandwich, sugar free" +53260400,"COOKIE, SUGAR OR PLAIN, SUGAR FREE","Cookie, sugar or plain, sugar free" +53260500,"COOKIE, SUGAR WAFER, SUGAR FREE","Cookie, sugar wafer, sugar free" +53260600,"COOKIE, PEANUT BUTTER, SUGAR FREE","Cookie, peanut butter, sugar free" +53270100,"COOKIE, P.R. (MANTECADITOS POLVORONES)","Cookies, Puerto Rican (Mantecaditos polvorones)" +53300100,"PIE, NFS","Pie, NFS" +53300170,"PIE, INDIVIDUAL SIZE OR TART, NFS","Pie, individual size or tart, NFS" +53300180,"PIE, FRIED, NFS","Pie, fried, NFS" +53301000,"PIE, APPLE, TWO CRUST","Pie, apple, two crust" +53301070,"PIE, APPLE, INDIVIDUAL SIZE OR TART","Pie, apple, individual size or tart" +53301080,"PIE, APPLE, FRIED PIE","Pie, apple, fried pie" +53301500,"PIE, APPLE, ONE CRUST (INCL W/ CRUMB TOPPING)","Pie, apple, one crust" +53301750,"PIE, APPLE, DIET","Pie, apple, diet" +53302000,"PIE, APRICOT, TWO CRUST","Pie, apricot, two crust" +53302070,"PIE, APRICOT, INDIVIDUAL SIZE OR TART","Pie, apricot, individual size or tart" +53302080,"PIE, APRICOT, FRIED","Pie, apricot, fried pie" +53303000,"PIE, BLACKBERRY, TWO CRUST","Pie, blackberry, two crust" +53303070,"PIE, BLACKBERRY, INDIVIDUAL SIZE OR TART","Pie, blackberry, individual size or tart" +53303500,"PIE, BERRY NOT BLACK,BLUE,BOYSEN,RASP....,TWO CRUST","Pie, berry, not blackberry, blueberry, boysenberry, huckleberry, raspberry, or strawberry; two crust" +53303510,"PIE, BERRY, ONE CRUST","Pie, berry, not blackberry, blueberry, boysenberry, huckleberry, raspberry, or strawberry; one crust" +53303570,"PIE, BERRY, INDIVIDUAL SIZE OR TART","Pie, berry, not blackberry, blueberry, boysenberry, huckleberry, raspberry, or strawberry, individual size or tart" +53304000,"PIE, BLUEBERRY, TWO CRUST","Pie, blueberry, two crust" +53304050,"PIE, BLUEBERRY, ONE CRUST","Pie, blueberry, one crust" +53304070,"PIE, BLUEBERRY, INDIVIDUAL SIZE OR TART","Pie, blueberry, individual size or tart" +53305000,"PIE, CHERRY, TWO CRUST","Pie, cherry, two crust" +53305010,"PIE, CHERRY, ONE CRUST","Pie, cherry, one crust" +53305070,"PIE, CHERRY, INDIVIDUAL SIZE OR TART","Pie, cherry, individual size or tart" +53305080,"PIE, CHERRY, FRIED PIE","Pie, cherry, fried pie" +53305700,"PIE, LEMON (NOT CREAM OR MERINGUE)","Pie, lemon (not cream or meringue)" +53305720,"PIE, LEMON (NOT CREAM OR MERINGUE), INDIVIDUAL SIZE","Pie, lemon (not cream or meringue), individual size or tart" +53305750,"PIE, LEMON, FRIED","Pie, lemon, fried pie" +53306000,"PIE, MINCE, TWO CRUST","Pie, mince, two crust" +53306070,"PIE, MINCE, INDIVIDUAL SIZE OR TART","Pie, mince, individual size or tart" +53307000,"PIE, PEACH, TWO CRUST","Pie, peach, two crust" +53307050,"PIE, PEACH, ONE-CRUST","Pie, peach, one crust" +53307070,"PIE, PEACH, INDIVIDUAL SIZE OR TART","Pie, peach, individual size or tart" +53307080,"PIE, PEACH, FRIED","Pie, peach, fried pie" +53307500,"PIE, PEAR, TWO CRUST","Pie, pear, two crust" +53307570,"PIE, PEAR, INDIVIDUAL SIZE OR TART","Pie, pear, individual size or tart" +53308000,"PIE, PINEAPPLE, TWO CRUST","Pie, pineapple, two crust" +53308070,"PIE, PINEAPPLE, INDIVIDUAL SIZE OR TART","Pie, pineapple, individual size or tart" +53308300,"PIE, PLUM, TWO CRUST","Pie, plum, two crust" +53308500,"PIE, PRUNE, ONE CRUST","Pie, prune, one crust" +53309000,"PIE, RAISIN, TWO CRUST","Pie, raisin, two crust" +53309070,"PIE, RAISIN, INDIVIDUAL SIZE OR TART","Pie, raisin, individual size or tart" +53310000,"PIE, RASPBERRY, ONE CRUST","Pie, raspberry, one crust" +53310050,"PIE, RASPBERRY, TWO CRUST","Pie, raspberry, two crust" +53311000,"PIE, RHUBARB, TWO CRUST","Pie, rhubarb, two crust" +53311050,"PIE, RHUBARB, ONE CRUST","Pie, rhubarb, one crust" +53311070,"PIE, RHUBARB, INDIVIDUAL SIZE OR TART","Pie, rhubarb, individual size or tart" +53312000,"PIE, STRAWBERRY, ONE CRUST","Pie, strawberry, one crust" +53313000,"PIE, STRAWBERRY-RHUBARB, TWO CRUST","Pie, strawberry-rhubarb, two crust" +53314000,"PIE, STRAWBERRY, INDIVIDUAL SIZE OR TART","Pie, strawberry, individual size or tart" +53340000,"PIE, APPLE-SOUR CREAM","Pie, apple-sour cream" +53340500,"PIE, CHERRY, W/ CREAM CHEESE & SOUR CREAM","Pie, cherry, made with cream cheese and sour cream" +53341000,"PIE, BANANA CREAM","Pie, banana cream" +53341070,"PIE, BANANA CREAM, INDIVIDUAL SIZE OR TART","Pie, banana cream, individual size or tart" +53341500,"PIE, BUTTERMILK","Pie, buttermilk" +53341750,"PIE, CHESS (INCL LEMON CHESS PIE)","Pie, chess" +53342000,"PIE, CHOCOLATE CREAM","Pie, chocolate cream" +53342070,"PIE, CHOCOLATE CREAM, INDIVIDUAL SIZE OR TART","Pie, chocolate cream, individual size or tart" +53343000,"PIE, COCONUT CREAM","Pie, coconut cream" +53343070,"PIE, COCONUT CREAM, INDIVIDUAL SIZE OR TART","Pie, coconut cream, individual size or tart" +53344000,"PIE, CUSTARD","Pie, custard" +53344070,"PIE, CUSTARD, INDIVIDUAL SIZE OR TART","Pie, custard, individual size or tart" +53344200,"MIXED FRUIT TART FILLED WITH CUSTARD OR CREAM CHEESE","Mixed fruit tart filled with custard or cream cheese" +53344300,"DESSERT PIZZA","Dessert pizza" +53345000,"PIE, LEMON CREAM","Pie, lemon cream" +53345070,"PIE, LEMON CREAM, INDIVIDUAL SIZE OR TART","Pie, lemon cream, individual size or tart" +53346000,"PIE, PEANUT BUTTER CREAM","Pie, peanut butter cream" +53346500,"PIE, PINEAPPLE CREAM","Pie, pineapple cream" +53347000,"PIE, PUMPKIN","Pie, pumpkin" +53347070,"PIE, PUMPKIN, INDIVIDUAL SIZE OR TART","Pie, pumpkin, individual size or tart" +53347100,"PIE, RASPBERRY CREAM","Pie, raspberry cream" +53347500,"PIE, SOUR CREAM, RAISIN","Pie, sour cream, raisin" +53347600,"PIE, SQUASH","Pie, squash" +53348000,"PIE, STRAWBERRY CREAM","Pie, strawberry cream" +53348070,"PIE, STRAWBERRY CREAM, INDIVIDUAL SIZE OR TART","Pie, strawberry cream, individual size or tart" +53360000,"PIE, SWEET POTATO","Pie, sweet potato" +53365000,"PIE, VANILLA CREAM","Pie, vanilla cream" +53366000,"PIE, YOGURT, FROZEN","Pie, yogurt, frozen" +53370000,"PIE, CHIFFON, NOT CHOCOLATE","Pie, chiffon, not chocolate" +53371000,"PIE, CHIFFON, CHOCOLATE","Pie, chiffon, chocolate" +53371100,"PIE, CHIFFON, W/ LIQUEUR","Pie, chiffon, with liqueur" +53373000,"PIE, BLACK BOTTOM","Pie, black bottom" +53381000,"PIE, LEMON MERINGUE","Pie, lemon meringue" +53381070,"PIE, LEMON MERINGUE, INDIVIDUAL SIZE OR TART","Pie, lemon meringue, individual size or tart" +53382000,"PIE, CHOCOLATE-MARSHMALLOW","Pie, chocolate-marshmallow" +53385000,"PIE, PECAN","Pie, pecan" +53385070,"PIE, PECAN, INDIVIDUAL SIZE","Pie, pecan, individual size or tart" +53385500,"PIE, OATMEAL","Pie, oatmeal" +53386000,"PIE, PUDDING, NOT CHOCOLATE","Pie, pudding, flavors other than chocolate" +53386050,"PIE, PUDDING, NOT CHOC, INDIVIDUAL SIZE","Pie, pudding, flavors other than chocolate, individual size or tart" +53386250,"PIE, PUDDING, CHOC, W/ CHOC COATING, INDIVID SIZE","Pie, pudding, chocolate, with chocolate coating, individual size" +53386500,"PIE, PUDDING, NOT CHOC, CHOC-COATED, INDIVID SIZE","Pie, pudding, flavors other than chocolate, with chocolate coating, individual size" +53387000,"PIE, TOLL HOUSE CHOCOLATE CHIP","Pie, Toll house chocolate chip" +53390000,"PIE, SHOO-FLY","Pie, shoo-fly" +53390100,"PIE, TOFU W/ FRUIT","Pie, tofu with fruit" +53391000,"PIE SHELL","Pie shell" +53391100,"PIE SHELL, GRAHAM CRACKER","Pie shell, graham cracker" +53391150,"PIE SHELL, CHOCOLATE WAFER","Pie shell, chocolate wafer" +53391200,"VANILLA WAFER DESSERT BASE","Vanilla wafer dessert base" +53400200,"BLINTZ, CHEESE-FILLED","Blintz, cheese-filled" +53400300,"BLINTZ, FRUIT-FILLED","Blintz, fruit-filled" +53410100,"COBBLER, APPLE (INCLUDE FRUIT COBBLER)","Cobbler, apple" +53410200,"COBBLER, APRICOT","Cobbler, apricot" +53410300,"COBBLER, BERRY","Cobbler, berry" +53410500,"COBBLER, CHERRY","Cobbler, cherry" +53410800,"COBBLER, PEACH","Cobbler, peach" +53410850,"COBBLER, PEAR","Cobbler, pear" +53410860,"COBBLER, PINEAPPLE","Cobbler, pineapple" +53410880,"COBBLER, PLUM","Cobbler, plum" +53410900,"COBBLER, RHUBARB","Cobbler, rhubarb" +53415100,"CRISP, APPLE, APPLE DESSERT","Crisp, apple, apple dessert" +53415120,"FRITTER, APPLE","Fritter, apple" +53415200,"FRITTER, BANANA","Fritter, banana" +53415220,"FRITTER, BERRY","Fritter, berry" +53415300,"CRISP, BLUEBERRY","Crisp, blueberry" +53415400,"CRISP, CHERRY","Crisp, cherry" +53415500,"CRISP, PEACH","Crisp, peach" +53415600,"CRISP, RHUBARB","Crisp, rhubarb" +53420000,"CREAM PUFF/ECLAIR, CUSTARD/CREAM-FILLED, NS ICING","Cream puff, eclair, custard or cream filled, NS as to icing" +53420100,"CREAM PUFF/ECLAIR, CUSTARD/CREAM-FILLED, NOT ICED","Cream puff, eclair, custard or cream filled, not iced" +53420200,"CREAM PUFF/ECLAIR, CUSTARD/CREAM-FILLED, ICED","Cream puff, eclair, custard or cream filled, iced" +53420210,"CREAM PUFF/ECLAIR, CUSTARD/CREAM-FILLED, ICED, REDUCED FAT","Cream puff, eclair, custard or cream filled, iced, reduced fat" +53420250,"CREAM PUFFS, NO FILLING OR ICING","Cream puff, no filling or icing" +53420300,"AIR-FILLED FRITTER, W/O SYRUP, PUERTO RICAN STYLE","Air filled fritter or fried puff, without syrup, Puerto Rican style (Bunuelos de viento)" +53420310,"WHEAT FLOUR FRITTER, W/O SYRUP","Wheat flour fritter, without syrup" +53420400,"SOPAIPILLA W/O SYRUP OR HONEY","Sopaipilla, without syrup or honey" +53420410,"SOPAIPILLA W/ SYRUP OR HONEY","Sopaipilla with syrup or honey" +53430000,"CREPE, DESSERT TYPE, NS AS TO FILLING","Crepe, dessert type, NS as to filling" +53430100,"CREPE, DESSERT TYPE, CHOCOLATE-FILLED","Crepe, dessert type, chocolate-filled" +53430200,"CREPE, DESSERT TYPE, FRUIT-FILLED","Crepe, dessert type, fruit-filled" +53430250,"CREPE SUZETTE","Crepe suzette" +53430300,"CREPE, DESSERT TYPE, ICE CREAM-FILLED","Crepe, dessert type, ice cream-filled" +53430700,"TAMALE, SWEET","Tamale, sweet" +53430750,"TAMALE, SWEET, W/ FRUIT","Tamale, sweet, with fruit" +53440000,"STRUDEL, APPLE (INCLUDE STRUDEL, NFS)","Strudel, apple" +53440300,"STRUDEL, BERRY","Strudel, berry" +53440500,"STRUDEL, CHERRY","Strudel, cherry" +53440600,"STRUDEL, CHEESE","Strudel, cheese" +53440700,"STRUDEL, PEACH","Strudel, peach" +53440750,"STRUDEL, PINEAPPLE","Strudel, pineapple" +53440800,"STRUDEL, CHEESE & FRUIT","Strudel, cheese and fruit" +53441110,"BAKLAVA (INCLUDE KADAYIF)","Baklava" +53441210,"BASBOUSA (SEMOLINA DESSERT DISH)","Basbousa (semolina dessert dish)" +53450000,"TURNOVER OR DUMPLING, APPLE","Turnover or dumpling, apple" +53450300,"TURNOVER OR DUMPLING, BERRY","Turnover or dumpling, berry" +53450500,"TURNOVER OR DUMPLING, CHERRY","Turnover or dumpling, cherry" +53450800,"TURNOVER OR DUMPLING, LEMON","Turnover or dumpling, lemon" +53451000,"TURNOVER OR DUMPLING, PEACH","Turnover or dumpling, peach" +53451500,"TURNOVER, GUAVA","Turnover, guava" +53451750,"TURNOVER, PUMPKIN","Turnover, pumpkin" +53452100,"PASTRY, FRUIT-FILLED","Pastry, fruit-filled" +53452120,"PASTRY, ASIAN, MADE WITH BEAN OR LOTUS SEED PASTE FILLING","Pastry, Asian, made with bean or lotus seed paste filling (baked)" +53452130,"PASTRY, ASIAN, MADE WITH BEAN PASTE AND SALTED EGG YOLK FILL","Pastry, Asian, made with bean paste and salted egg yolk filling (baked)" +53452150,"PASTRY, CHINESE (INCLUDE 9-LAYER PUDDING)","Pastry, Chinese, made with rice flour" +53452170,"PASTRY, COOKIE TYPE, FRIED(INCL POLISH PACZKI)","Pastry, cookie type, fried" +53452200,"PASTRY, ITALIAN, W/ CHEESE (INCLUDE CANNOLI)","Pastry, Italian, with cheese" +53452400,"PASTRY, PUFF","Pastry, puff" +53452420,"PASTRY, PUFF, CUSTARD/CREAM FILLED, ICED/NOT ICED","Pastry, puff, custard or cream filled, iced or not iced" +53452450,"CHEESE PASTRY PUFF","Cheese pastry puffs" +53452500,"PASTRY, MAINLY FLOUR & WATER, FRIED","Pastry, mainly flour and water, fried" +53453150,"EMPANADA, MEXICAN TURNOVER, FRUIT-FILLED","Empanada, Mexican turnover, fruit-filled" +53453170,"EMPANADA, MEXICAN TURNOVER, PUMPKIN","Empanada, Mexican turnover, pumpkin" +53500100,"BREAKFAST PASTRY, NFS","Breakfast pastry, NFS" +53510000,"DANISH PASTRY, PLAIN/SPICE (INCL W/ ICING)","Danish pastry, plain or spice" +53510100,"DANISH PASTRY, W/ FRUIT","Danish pastry, with fruit" +53511000,"DANISH PASTRY, W/ CHEESE","Danish pastry, with cheese" +53520000,"DOUGHNUT, NS AS TO CAKE OR YEAST","Doughnut, NS as to cake or yeast" +53520110,"DOUGHNUT, CAKE TYPE","Doughnut, cake type" +53520120,"DOUGHNUT, CHOCOLATE, CAKE TYPE","Doughnut, chocolate, cake type" +53520140,"DOUGHNUT, CAKE TYPE, CHOCOLATE COVERED","Doughnut, cake type, chocolate covered" +53520150,"DOUGHNUT, CAKE TYPE, CHOCOLATE COVERED, W/ PEANUTS","Doughnut, cake type, chocolate covered, dipped in peanuts" +53520160,"DOUGHNUT, CHOCOLATE, CAKE TYPE, WITH CHOCOLATE ICING","Doughnut, chocolate, cake type, with chocolate icing" +53520200,"CHURROS (INCL MEXICAN CRUELLERS)","Churros" +53520500,"DOUGHNUT, ASIAN","Doughnut, Asian" +53520600,"CRULLER, NFS","Cruller, NFS" +53520700,"FRENCH CRULLER","French cruller" +53521100,"DOUGHNUT, CHOCOLATE, RAISED OR YEAST, WITH CHOCOLATE ICING","Doughnut, chocolate, raised or yeast, with chocolate icing" +53521110,"DOUGHNUT, RAISED / YEAST","Doughnut, raised or yeast" +53521120,"DOUGHNUT, CHOCOLATE, RAISED OR YEAST","Doughnut, chocolate, raised or yeast" +53521130,"DOUGHNUT, RAISED OR YEAST, CHOCOLATE COVERED","Doughnut, raised or yeast, chocolate covered" +53521140,"DOUGHNUT, JELLY","Doughnut, jelly" +53521210,"DOUGHNUT, CUSTARD-FILLED","Doughnut, custard-filled" +53521220,"DOUGHNUT, CHOCOLATE CREAM-FILLED","Doughnut, chocolate cream-filled" +53521230,"DOUGHNUT, CUSTARD-FILLED, WITH ICING","Doughnut, custard-filled, with icing" +53521250,"DOUGHNUT, WHEAT","Doughnut, wheat" +53521300,"DOUGHNUT, WHEAT, CHOCOLATE COVERED","Doughnut, wheat, chocolate covered" +53530000,"BREAKFAST TART","Breakfast tart" +53530010,"BREAKFAST TART, LOWFAT","Breakfast tart, lowfat" +53610100,"COFFEE CAKE, CRUMB OR QUICK-BREAD TYPE","Coffee cake, crumb or quick-bread type" +53610170,"COFFEE CAKE, CRUMB OR QUICK-BREAD TYPE, W/ FRUIT","Coffee cake, crumb or quick-bread type, with fruit" +53610200,"COFFEECAKE, CRUMB OR QUICK-BREAD TYPE, CHEESE FILLD","Coffee cake, crumb or quick-bread type, cheese-filled" +53710400,"FIBER ONE CHEWY BAR","Fiber One Chewy Bar" +53710500,"KELLOGG'S NUTRI-GRAIN CEREAL BAR","Kellogg's Nutri-Grain Cereal Bar" +53710502,"KELLOGG'S NUTRI-GRAIN YOGURT BAR","Kellogg's Nutri-Grain Yogurt Bar" +53710504,"KELLOGG'S NUTRI-GRAIN FRUIT AND NUT BAR","Kellogg's Nutri-Grain Fruit and Nut Bar" +53710600,"MILK 'N CEREAL BAR","Milk 'n Cereal bar" +53710700,"KELLOGG'S SPECIAL K BAR","Kellogg's Special K bar" +53710800,"KASHI GOLEAN CHEWY BARS","Kashi GOLEAN Chewy Bars" +53710802,"KASHI TLC CHEWY GRANOLA BAR","Kashi TLC Chewy Granola Bar" +53710804,"KASHI GOLEAN CRUNCHY BARS","Kashi GOLEAN Crunchy Bars" +53710806,"KASHI TLC CRUNCHY GRANOLA BAR","Kashi TLC Crunchy Granola Bar" +53710900,"NATURE VALLEY CHEWY TRAIL MIX GRANOLA BAR","Nature Valley Chewy Trail Mix Granola Bar" +53710902,"NATURE VALLEY CHEWY GRANOLA BAR WITH YOGURT COATING","Nature Valley Chewy Granola Bar with Yogurt Coating" +53710904,"NATURE VALLEY SWEET AND SALTY GRANOLA BAR","Nature Valley Sweet and Salty Granola Bar" +53710906,"NATURE VALLEY CRUNCHY GRANOLA BAR","Nature Valley Crunchy Granola Bar" +53711000,"QUAKER CHEWY GRANOLA BAR","Quaker Chewy Granola Bar" +53711002,"QUAKER CHEWY 90 CALORIE GRANOLA BAR","Quaker Chewy 90 Calorie Granola Bar" +53711004,"QUAKER CHEWY 25% LESS SUGAR GRANOLA BAR","Quaker Chewy 25% Less Sugar Granola Bar" +53711006,"QUAKER CHEWY DIPPS GRANOLA BAR","Quaker Chewy Dipps Granola Bar" +53711100,"QUAKER GRANOLA BITES","Quaker Granola Bites" +53712000,"SNACK BAR, OATMEAL","Snack bar, oatmeal" +53712100,"GRANOLA BAR, NFS","Granola bar, NFS" +53712200,"GRANOLA BAR, LOWFAT, NFS","Granola bar, lowfat, NFS" +53712210,"GRANOLA BAR, NONFAT","Granola bar, nonfat" +53713000,"GRANOLA BAR, REDUCED SUGAR, NFS","Granola bar, reduced sugar, NFS" +53713100,"GRANOLA BAR, PEANUTS , OATS, SUGAR, WHEAT GERM","Granola bar, peanuts , oats, sugar, wheat germ" +53714200,"GRANOLA BAR, CHOCOLATE-COATED, NFS","Granola bar, chocolate-coated, NFS" +53714210,"GRANOLA BAR, WITH COCONUT, CHOCOLATE-COATED","Granola bar, with coconut, chocolate-coated" +53714220,"GRANOLA BAR WITH NUTS, CHOCOLATE-COATED","Granola bar with nuts, chocolate-coated" +53714230,"GRANOLA BAR, OATS, NUTS, COATED WITH NON-CHOCOLATE COATING","Granola bar, oats, nuts, coated with non-chocolate coating" +53714250,"GRANOLA BAR, COATED WITH NON-CHOCOLATE COATING","Granola bar, coated with non-chocolate coating" +53714300,"GRANOLA BAR, HIGH FIBER, COATED W/ NON-CHOC YOGURT COATING","Granola bar, high fiber, coated with non-chocolate yogurt coating" +53714400,"GRANOLA BAR, WITH RICE CEREAL","Granola bar, with rice cereal" +53714500,"BREAKFAST BAR, NFS","Breakfast bar, NFS" +53714510,"BREAKFAST BAR, DATE, WITH YOGURT COATING","Breakfast bar, date, with yogurt coating" +53714520,"BREAKFAST BAR, CEREAL CRUST WITH FRUIT FILLING, LOWFAT","Breakfast bar, cereal crust with fruit filling, lowfat" +53720100,"BALANCE ORIGINAL BAR","Balance Original Bar" +53720200,"CLIF BAR","Clif Bar" +53720300,"POWERBAR","PowerBar" +53720400,"SLIM FAST ORIGINAL MEAL BAR","Slim Fast Original Meal Bar" +53720500,"SNICKERS MARATHON PROTEIN BAR","Snickers Marathon Protein bar" +53720510,"SNICKERS MARATHON ENERGY BAR","Snickers Marathon Energy bar" +53720600,"SOUTH BEACH LIVING MEAL BAR","South Beach Living Meal Bar" +53720610,"SOUTH BEACH LIVING HIGH PROTEIN BAR","South Beach Living High Protein Bar" +53720700,"TIGER'S MILK BAR","Tiger's Milk bar" +53720800,"ZONE PERFECT CLASSIC CRUNCH NUTRITION BAR","Zone Perfect Classic Crunch nutrition bar" +53729000,"NUTRITION BAR OR MEAL REPLACEMENT BAR, NFS","Nutrition bar or meal replacement bar, NFS" +53801000,"CEREAL BAR WITH FRUIT FILLING, BABY FOOD","Cereal bar with fruit filling, baby food" +53803050,"COOKIE, FRUIT, BABY FOOD","Cookie, fruit, baby food" +53803100,"COOKIE, BABY FOOD","Cookie, baby food" +53803250,"COOKIE, TEETHING, BABY","Cookie, teething, baby" +53803300,"COOKIE, RICE, BABY","Cookie, rice, baby" +54001000,"CRACKER, NS AS TO SWEET/NONSWEET (INCL CRACKER,NFS)","Crackers, NS as to sweet or nonsweet" +54102010,"CRACKERS, GRAHAM","Crackers, graham" +54102020,"CRACKERS, GRAHAM, CHOCOLATE COVERED","Crackers, graham, chocolate covered" +54102050,"CRACKERS, OATMEAL","Crackers, oatmeal" +54102060,"CRACKERS, CUBAN","Crackers, Cuban" +54102070,"CRACKERS, CUCA","Crackers, Cuca" +54102080,"CRACKERS, GRAHAM, W/ RAISINS","Crackers, graham, with raisins" +54102100,"CRACKERS, GRAHAM, LOWFAT","Crackers, graham, lowfat" +54102110,"CRACKERS, GRAHAM, FAT FREE","Crackers, graham, fat free" +54102200,"CRACKERS, GRAHAM, SANDWICH-TYPE, WITH FILLING","Crackers, graham, sandwich-type, with filling" +54201010,"CRACKERS, MATZO, LOW SODIUM","Crackers, matzo, low sodium" +54202010,"CRACKERS, SALTINE, LOW SODIUM","Crackers, saltine, low sodium" +54203010,"CRACKERS, TOAST THINS (RYE/WHEAT/WHITE), LOW SODIUM","Crackers, toast thins (rye, wheat, white flour), low sodium" +54204010,"CRACKER, 100% WHOLE WHEAT,LO SODIUM","Cracker, 100% whole wheat, low sodium" +54205010,"CRACKER, SNACK, LOW SODIUM","Cracker, snack, low sodium" +54205030,"CRACKER, CHEESE, LOW SODIUM","Cracker, cheese, low sodium" +54205100,"CRACKER, SNACK, LOWFAT, LOW SODIUM","Cracker, snack, lowfat, low sodium" +54206010,"PUFFED RICE CAKE W/O SALT","Puffed rice cake without salt" +54207010,"CRISPBREAD, WHEAT, LOW SODIUM","Crispbread, wheat, low sodium" +54210010,"CRACKER, MULTIGRAIN, LOW SODIUM","Cracker, multigrain, low sodium" +54222000,"CRISPBREAD, RYE, LOW SODIUM","Crispbread, rye, low sodium" +54301000,"CRACKER, SNACK","Cracker, snack" +54301100,"CRACKER, SNACK, REDUCED FAT","Cracker, snack, reduced fat" +54301200,"CRACKER, SNACK, FAT FREE","Cracker, snack, fat free" +54304000,"CRACKERS, CHEESE","Cracker, cheese" +54304100,"CRACKER, CHEESE, REDUCED FAT","Cracker, cheese, reduced fat" +54304150,"CRACKER, CHEESE, WHOLE GRAIN","Cracker, cheese, whole grain" +54304500,"CRACKER, HIGH FIBER, NO ADDED FAT","Cracker, high fiber, no added fat" +54305000,"CRISPBREAD, WHEAT, NO ADDED FAT","Crispbread, wheat, no added fat" +54307000,"CRACKERS, MATZO","Crackers, matzo" +54308000,"CRACKERS, MILK","Crackers, milk" +54309000,"CRACKERS, OAT BRAN (INCLUDE NABISCO OAT THINS)","Crackers, oat" +54313000,"CRACKERS, OYSTER","Crackers, oyster" +54318000,"CHIPS, BROWN RICE","Chips, brown rice" +54318500,"RICE CAKE, CRACKER-TYPE","Rice cake, cracker-type" +54319000,"CRACKERS, RICE","Crackers, rice" +54319010,"PUFFED RICE CAKE","Puffed rice cake" +54319020,"POPCORN CAKE (INCL PUFFED CORN & RICE CAKE)","Popcorn cake" +54319200,"PUFFED WHEAT CAKE (INCL QUAKER)","Puffed wheat cake" +54319500,"RICE PAPER","Rice paper" +54322000,"CRISPBREAD, RYE, NO ADDED FAT","Crispbread, rye, no added fat" +54325000,"CRACKERS, SALTINES","Crackers, saltine" +54325010,"CRACKERS, SALTINE, FAT FREE","Crackers, saltine, fat free" +54325050,"CRACKERS, SALTINE, WHOLE WHEAT","Crackers, saltine, whole wheat" +54326000,"CRACKERS, MULTIGRAIN","Crackers, multigrain, made with whole wheat, wheat, oat, and other flours" +54327950,"CRACKERS, CYLINDRICAL, PEANUT BUTTER-FILLED","Crackers, cylindrical, peanut-butter filled" +54328000,"CRACKER, SANDWICH-TYPE, NFS","Crackers, sandwich-type, NFS" +54328100,"CRACKER,SANDWICH-TYPE,PEANUT BUTTER FILLED","Cracker, sandwich-type, peanut butter filled" +54328110,"CRACKER, SANDWICH, PEANUT BUTTER FILLED, RED FAT","Cracker, sandwich-type, peanut butter filled, reduced fat" +54328200,"CRACKER,SANDWICH-TYPE, CHEESE-FILLED","Cracker, sandwich-type, cheese-filled" +54334000,"CRACKERS, TOAST THINS","Crackers, toast thins (rye, pumpernickel, white flour)" +54336000,"CRACKER, WATER BISCUIT","Crackers, water biscuits" +54337000,"CRACKER, 100% WHOLE WHEAT","Cracker, 100% whole wheat" +54337050,"CRACKER, 100% WHOLE WHEAT, REDUCED FAT","Cracker, 100% whole wheat, reduced fat" +54338000,"CRACKERS, WHEAT","Crackers, wheat" +54338100,"CRACKERS, WHEAT, REDUCED FAT","Crackers, wheat, reduced fat" +54339000,"CRACKER CORN (INCL STONED CORN CRACKER)","Crackers, corn" +54350000,"CRACKERS, BABY FOOD","Crackers, baby food" +54350010,"GERBER FINGER FOODS, PUFFS, BABY FOOD","Gerber Finger Foods, Puffs, baby food" +54360000,"CRUNCHY SNACKS, CORN BASED, BABY FOOD","Crunchy snacks, corn based, baby food" +54401010,"SALTY SNACKS, CORN / CORNMEAL BASE, NUT /NUG, TSTD","Salty snacks, corn or cornmeal base, nuts or nuggets, toasted" +54401020,"SALTY SNACKS, CORN OR CORNMEAL, CORN CHIPS, CHEESE","Salty snacks, corn or cornmeal base, corn chips, corn-cheese chips" +54401050,"SALTY SNACKS, CORN OR CORNMEAL, CORN PUFFS, TWISTS","Salty snacks, corn or cornmeal base, corn puffs and twists; corn-cheese puffs and twists" +54401080,"SALTY SNACKS, CORN OR CORNMEAL, TORTILLA CHIPS","Salty snacks, corn or cornmeal base, tortilla chips" +54401090,"SALTY SNACKS, CORN/CORN-CHEESE CHIPS, UNSALTED","Salty snacks, corn or cornmeal base, corn chips, corn-cheese chips, unsalted" +54401100,"SALTY SNACKS,CORN / CORNMEAL BASE,TORTILLA CHIPS LT","Salty snacks, corn or cornmeal base, tortilla chips, light (baked with less oil)" +54401120,"SALTY SNACKS, TORTILLA CHIPS, FAT FREE, W/ OLEAN","Salty snacks, corn or cornmeal base, tortilla chips, fat free, made with Olean" +54401150,"SALTY SNACKS,CORN/CORNMEAL BASE,TORTILLA,LOWFAT,BKD","Salty snacks, corn or cornmeal base, tortilla chips, lowfat, baked without fat" +54401170,"SALTY SNACKS,CORN/CORNMEAL,TORTILLA,LOWFAT,BKD,NO SALT","Salty snacks, corn or cornmeal base, tortilla chips, lowfat, baked without fat, unsalted" +54401200,"SALTY SNACKS, CORN/CORNML BASE,W/OAT BRAN,TORT CHPS","Salty snacks, corn or cornmeal base, with oat bran, tortilla chips" +54401210,"SALTY SNACKS, CORN BASED/CHEESE PUFFS & TWISTS, LOWFAT","Salty snacks, corn based puffs and twists, cheese puffs and twists, lowfat" +54402080,"TORTILLA CHIPS, UNSALTED","Salty snacks, corn or cornmeal base, tortilla chips, unsalted" +54402200,"SALTY SNACK MIXTURE,MOSTLY CORN,W/PRETZELS,W/O NUTS","Salty snack mixture, mostly corn or cornmeal based, with pretzels, without nuts" +54402300,"SALTY SNACKS, WHEAT-BASE, HIGH FIBER","Salty snacks, wheat-based, high fiber" +54402500,"SALTY SNACKS, WHEAT-AND CORN-BASED CHIPS","Salty snacks, wheat- and corn-based chips" +54402600,"SALTY SNACKS, MULTIGRAIN, WHOLE GRAIN, CHIPS","Salty snacks, multigrain, whole grain, chips (made with whole corn, whole wheat, rice flour, and whole oat flour)" +54402610,"SALTY SNACKS, MULTIGRAIN& POT CHIPS(W/RICE FL,POT,CORN FL)","Salty snacks, multigrain and potato chips (made with rice flour, dried potatoes, corn flour, and wheat starch)" +54402700,"PITA CHIPS","Pita chips" +54403000,"POPCORN, POPPED IN OIL, UNBUTTERED","Popcorn, popped in oil, unbuttered" +54403010,"POPCORN, AIR-POPPED (NO BUTTER OR OIL ADDED)","Popcorn, air-popped (no butter or no oil added)" +54403020,"POPCORN, POPPED IN OIL, BUTTERED","Popcorn, popped in oil, buttered" +54403040,"POPCORN, AIR-POPPED, BUTTERED","Popcorn, air-popped, buttered" +54403050,"POPCORN, FLAVORED (CHEESE, BBQ, SOUR CREAM, ONION)","Popcorn, flavored" +54403060,"POPCORN, POPPED IN OIL, LOWFAT, LOW SODIUM","Popcorn, popped in oil, lowfat, low sodium" +54403070,"POPCORN, POPPED IN OIL, LOWFAT","Popcorn, popped in oil, lowfat" +54403090,"POPCORN, POPPED IN OIL, UNSALTED","Popcorn, popped in oil, unsalted" +54403110,"POPCORN, SUGAR SYRUP OR CARAMEL COATED","Popcorn, sugar syrup or caramel-coated" +54403120,"POPCORN, SUGAR SYRUP OR CARAMEL COATED, W/ NUTS","Popcorn, sugar syrup or caramel-coated, with nuts" +54403150,"POPCORN, SUGAR SYRUP/CARAMEL COATED, FAT FREE","Popcorn, sugar syrup or caramel-coated, fat free" +54406010,"SNACKS, ONION-FLAVORED RINGS","Snacks, onion-flavored rings" +54406200,"SHRIMP CHIPS","Shrimp chips (tapioca base)" +54408000,"PRETZELS, NFS","Pretzels, NFS" +54408010,"PRETZELS, HARD","Pretzels, hard" +54408020,"PRETZELS, SOFT","Pretzels, soft" +54408030,"PRETZELS, HARD, UNSALTED","Pretzel, hard, unsalted" +54408040,"PRETZELS, SOFT, UNSALTED","Pretzels, soft, unsalted" +54408050,"PRETZEL, OAT BRAN, HARD","Pretzel, oatbran, hard" +54408070,"PRETZEL, HARD, MULTIGRAIN","Pretzel, hard, multigrain" +54408100,"PRETZEL, BABY FOOD","Pretzel, baby food" +54408200,"PRETZEL, HARD, CHOCOLATE COATED","Pretzel, hard, chocolate-coated" +54408250,"PRETZEL, YOGURT COVERED","Pretzel, yogurt-covered" +54408300,"PRETZELS, CHEESE-FILLED (INCL COMBOS)","Pretzels, cheese-filled" +54412110,"WHEAT STICKS, 100% WHOLE WHEAT","Wheat sticks, 100% whole wheat" +54420010,"MULTIGRAIN MIXTURE, PRETZELS, CEREAL &/ CRACKERS,NUTS","Multigrain mixture, pretzels, cereal and/or crackers, nuts" +54420100,"ORIENTAL PARTY MIX, W/ PEANUTS, SESAME STICKS, ETC","Oriental party mix, with peanuts, sesame sticks, chili rice crackers and fried green peas" +54420200,"MULTIGRAIN MIX, BREAD STICKS, SESAME NUGGETS, PRETZ","Multigrain mixture, bread sticks, sesame nuggets, pretzels, rye chips" +54430010,"YOGURT CHIPS","Yogurt chips" +54440010,"BAGEL CHIP","Bagel chip" +55101000,"PANCAKES, PLAIN (INCLUDE PANCAKES, NFS)","Pancakes, plain" +55101010,"PANCAKES, REDUCED CALORIE, HIGH FIBER","Pancakes, reduced calorie, high fiber" +55101015,"PANCAKES, PLAIN, REDUCED FAT","Pancakes, plain, reduced fat" +55101020,"PANCAKES, PLAIN, FAT FREE","Pancakes, plain, fat free" +55103000,"PANCAKES, W/ FRUIT (INCLUDE BLUEBERRY PANCAKES)","Pancakes, with fruit" +55103100,"PANCAKES W/ CHOC CHIPS","Pancakes, with chocolate chips" +55105000,"PANCAKES, BUCKWHEAT","Pancakes, buckwheat" +55105100,"PANCAKES, CORNMEAL","Pancakes, cornmeal" +55105200,"PANCAKES, WHOLE WHEAT","Pancakes, whole wheat" +55105205,"PANCAKES, WHOLE WHEAT, REDUCED FAT","Pancakes, whole wheat, reduced fat" +55105210,"PANCAKES, WHOLE WHEAT, FAT FREE","Pancakes, whole wheat, fat free" +55105300,"PANCAKES, SOURDOUGH","Pancakes, sour dough" +55105400,"PANCAKES, RYE","Pancakes, rye" +55201000,"WAFFLE, PLAIN","Waffle, plain" +55202000,"WAFFLE, WHEAT, BRAN, OR MULTIGRAIN","Waffle, wheat, bran, or multigrain" +55203000,"WAFFLE, FRUIT","Waffle, fruit" +55203500,"WAFFLE, NUT & HONEY (INCL EGGO)","Waffle, nut and honey" +55203600,"WAFFLE, CHOCOLATE CHIP","Waffle, chocolate chip" +55204000,"WAFFLE, CORNMEAL","Waffle, cornmeal" +55205000,"WAFFLE, 100% WHOLE WHEAT OR 100% WHOLE GRAIN","Waffle, 100% whole wheat or 100% whole grain" +55206000,"WAFFLE, OAT BRAN","Waffle, oat bran" +55207000,"WAFFLE, MULTI-BRAN (INCLUDE EGGO NUTRIGRAIN)","Waffle, multi-bran" +55211000,"WAFFLE, PLAIN, FAT FREE","Waffle, plain, fat free" +55211050,"WAFFLE, PLAIN, LOWFAT","Waffle, plain, lowfat" +55212000,"WAFFLE, WHOLE WHEAT, LOWFAT","Waffle, whole wheat, lowfat" +55301000,"FRENCH TOAST, PLAIN (INCLUDE ROMAN MEAL)","French toast, plain" +55301050,"FRENCH TOAST STICKS, PLAIN","French toast sticks, plain" +55310100,"BREAD FRITTERS, P.R.","Bread fritters, Puerto Rican style (Torrejas gallegas, Galician fritters)" +55401000,"CREPE, PLAIN (INCLUDE FRENCH PANCAKE)","Crepe, plain" +55501000,"FLOUR & WATER PATTY (INCLUDE CHINESE PANCAKE)","Flour and water patty" +55502000,"FLOUR AND WATER GRAVY","Flour and water gravy" +55610200,"DUMPLING, FRIED, PUERTO RICAN STYLE","Dumpling, fried, Puerto Rican style" +55610300,"DUMPLING, PLAIN","Dumpling, plain" +55701000,"CAKE MADE W/ GLUTINOUS RICE","Cake made with glutinous rice" +55702000,"CAKE OR PANCAKE MADE W/ RICE FLOUR &/OR DRIED BEANS","Cake or pancake made with rice flour and/or dried beans" +55702100,"DOSA (INDIAN)","Dosa (Indian), plain" +55703000,"CAKE MADE W/ GLUTINOUS RICE & DRIED BEANS","Cake made with glutinous rice and dried beans" +55801000,"FUNNEL CAKE WITH SUGAR","Funnel cake with sugar" +55801010,"FUNNEL CAKE WITH SUGAR AND FRUIT","Funnel cake with sugar and fruit" +56101000,"MACARONI, COOKED, NS AS TO ADDED FAT","Macaroni, cooked, NS as to fat added in cooking" +56101010,"MACARONI, COOKED, NO FAT ADDED","Macaroni, cooked, fat not added in cooking" +56101030,"MACARONI, COOKED, FAT ADDED","Macaroni, cooked, fat added in cooking" +56102000,"MACARONI, WHOLE WHEAT, COOKED, NS AS TO ADDED FAT","Macaroni, whole wheat, cooked, NS as to fat added in cooking" +56102010,"MACARONI, WHOLE WHEAT, NO FAT ADDED","Macaroni, whole wheat, cooked, fat not added in cooking" +56102020,"MACARONI, WHOLE WHEAT, FAT ADDED","Macaroni, whole wheat, cooked, fat added in cooking" +56103000,"MACARONI, SPINACH, NS AS TO ADDED FAT","Macaroni, cooked, spinach, NS as to fat added in cooking" +56103010,"MACARONI, SPINACH, NO FAT ADDED","Macaroni, cooked, spinach, fat not added in cooking" +56103020,"MACARONI, SPINACH, FAT ADDED","Macaroni, cooked, spinach, fat added in cooking" +56104000,"MACARONI,CKD,VEGETABLE,NS AS TO FAT ADDED","Macaroni, cooked, vegetable, NS as to fat added in cooking" +56104010,"MACARONI,COOKED,VEGETABLE,FAT NOT ADDED IN COOKING","Macaroni, cooked, vegetable, fat not added in cooking" +56104020,"MACARONI,COOKED,VEGETABLE, FAT ADDED IN COOKING","Macaroni, cooked, vegetable, fat added in cooking" +56112000,"NOODLES, COOKED, NS AS TO ADDED FAT","Noodles, cooked, NS as to fat added in cooking" +56112010,"NOODLES, COOKED, NO FAT ADDED","Noodles, cooked, fat not added in cooking" +56112030,"NOODLES, COOKED, FAT ADDED","Noodles, cooked, fat added in cooking" +56113000,"NOODLES, COOKED,WHOLE WHEAT,NS AS TO FAT ADDED","Noodles, cooked, whole wheat, NS as to fat added in cooking" +56113010,"NOODLES, WHOLE WHEAT, COOKED, NO FAT ADDED","Noodles, cooked, whole wheat, fat not added in cooking" +56113990,"NOODLES, COOKED, SPINACH, NS AS TO FAT","Noodles, cooked, spinach, NS as to fat added in cooking" +56114000,"NOODLES, SPINACH, COOKED, NO FAT ADDED","Noodles, cooked, spinach, fat not added in cooking" +56114020,"NOODLES, COOKED, SPINACH, FAT ADDED","Noodles, cooked, spinach, fat added in cooking" +56116000,"NOODLES, CHOW MEIN","Noodles, chow mein" +56116990,"LONG RICE NOODLES(FROM MUNG BEANS),CKD,NS FAT ADDed","Long rice noodles (made from mung beans) cooked, NS as to fat added in cooking" +56117000,"LONG RICE NOODLES, COOKED, NO FAT ADDED","Long rice noodles (made from mung beans), cooked, fat not added in cooking" +56117010,"LONG RICE NOODLES, COOKED, FAT ADDED","Long rice noodles (made from mung beans), cooked, fat added in cooking" +56117090,"CHOW FUN RICE NOODLES,COOKED,NS AS TO FAT ADDED","Chow fun rice noodles, cooked, NS as to fat added in cooking" +56117100,"CHOW FUN RICE NOODLES, COOKED, NO FAT ADDED","Chow fun rice noodles, cooked, fat not added in cooking" +56117110,"CHOW FUN RICE NOODLES, COOKED, FAT ADDED","Chow fun rice noodles, cooked, fat added in cooking" +56130000,"SPAGHETTI, COOKED, NS AS TO ADDED FAT","Spaghetti, cooked, NS as to fat added in cooking" +56130010,"SPAGHETTI, COOKED, NO FAT ADDED","Spaghetti, cooked, fat not added in cooking" +56131000,"SPAGHETTI, COOKED, FAT ADDED","Spaghetti, cooked, fat added in cooking" +56132990,"SPAGHETTI, COOKED, WHOLE WHEAT, NS AS TO ADDED FAT","Spaghetti, cooked, whole wheat, NS as to fat added in cooking" +56133000,"SPAGHETTI, COOKED, WHOLE WHEAT, NO FAT ADDED","Spaghetti, cooked, whole wheat, fat not added in cooking" +56133010,"SPAGHETTI, COOKED, WHOLE WHEAT, FAT ADDED","Spaghetti, cooked, whole wheat, fat added in cooking" +56200300,"CEREAL, COOKED, NFS","Cereal, cooked, NFS" +56200350,"CEREAL, COOKED, INSTANT, NS AS TO GRAIN","Cereal, cooked, instant, NS as to grain" +56200390,"BARLEY, COOKED, NS AS TO FAT ADDED IN COOKING","Barley, cooked, NS as to fat added in cooking" +56200400,"BARLEY, COOKED, NO FAT ADDED","Barley, cooked, fat not added in cooking" +56200490,"BUCKWHEAT GROATS, COOKED, NS AS TO FAT ADDED","Buckwheat groats, cooked, NS as to fat added in cooking" +56200500,"BUCKWHEAT GROATS, COOKED, NO FAT ADDED (INCL KASHA)","Buckwheat groats, cooked, fat not added in cooking" +56200510,"BUCKWHEAT GROATS, COOKED, FAT ADDED","Buckwheat groats, cooked, fat added in cooking" +56200990,"GRITS, COOKED,CORN/HOMINY, NS REG, QUICK, INST, NS FAT ADDED","Grits, cooked, corn or hominy, NS as to regular, quick, or instant, NS as to fat added in cooking" +56201000,"GRITS, CORN OR HOMINY, NFS, NO FAT ADDED","Grits, cooked, corn or hominy, NS as to regular, quick, or instant, fat not added in cooking" +56201010,"GRITS, CKD, CORN/HOMINY, REGULAR, NO FAT","Grits, cooked, corn or hominy, regular, fat not added in cooking" +56201020,"GRITS, COOKED, CORN/HOMINY, REGULAR, FAT ADDED","Grits, cooked, corn or hominy, regular, fat added in cooking" +56201030,"GRITS, COOKED, CORN/HOMINY, REGULAR, NS AS TO FAT","Grits, cooked, corn or hominy, regular, NS as to fat added in cooking" +56201040,"GRITS, COOKED, CORN/HOMINY, FAT ADDED","Grits, cooked, corn or hominy, NS as to regular, quick, or instant, fat added in cooking" +56201060,"GRITS,CKD,CORN/HOMINY,W/CHEESE,NS TYPE,NS FAT ADDED","Grits, cooked, corn or hominy, with cheese, NS as to regular, quick, or instant, NS as to fat added in cooking" +56201061,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,NS TYPE,FAT NOT ADDED","Grits, cooked, corn or hominy, with cheese, NS as to regular, quick, or instant, fat not added in cooking" +56201062,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,NS TYPE,FAT ADDED","Grits, cooked, corn or hominy, with cheese, NS as to regular, quick, or instant, fat added in cooking" +56201070,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,REG,NS FAT ADDED","Grits, cooked, corn or hominy, with cheese, regular, NS as to fat added in cooking" +56201071,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,REG,FAT NOT ADDED","Grits, cooked, corn or hominy, with cheese, regular, fat not added in cooking" +56201072,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,REG,FAT ADDED","Grits, cooked, corn or hominy, with cheese, regular, fat added in cooking" +56201080,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,QUICK,NS FAT ADDED","Grits, cooked, corn or hominy, with cheese, quick, NS as to fat added in cooking" +56201081,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,QUICK,FAT NOT ADDED","Grits, cooked, corn or hominy, with cheese, quick, fat not added in cooking" +56201082,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,QUICK,FAT ADDED","Grits, cooked, corn or hominy, with cheese, quick, fat added in cooking" +56201090,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,INSTANT,NS FAT ADDED","Grits, cooked, corn or hominy, with cheese, instant, NS as to fat added in cooking" +56201091,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,INSTANT,FAT NOT ADDED","Grits, cooked, corn or hominy, with cheese, instant, fat not added in cooking" +56201092,"GRITS,CKD,CORN/HOMINY,W/ CHEESE,INSTANT,FAT ADDED","Grits, cooked, corn or hominy, with cheese, instant, fat added in cooking" +56201110,"GRITS, COOKED, CORN/HOMINY, QUICK, NO FAT ADDED","Grits, cooked, corn or hominy, quick, fat not added in cooking" +56201120,"GRITS, COOKED, CORN/HOMINY, QUICK, FAT ADDED","Grits, cooked, corn or hominy, quick, fat added in cooking" +56201130,"GRITS, COOKED, CORN/HOMINY,QUICK,NS AS TO ADDED FAT","Grits, cooked, corn or hominy, quick, NS as to fat added in cooking" +56201210,"GRITS, COOKED, CORN/HOMINY, INSTANT, NO FAT ADDED","Grits, cooked, corn or hominy, instant, fat not added in cooking" +56201220,"GRITS, CORN/HOMINY, INSTANT, FAT ADDED","Grits, cooked, corn or hominy, instant, fat added in cooking" +56201230,"GRITS, CORN/HOMINY, INSTANT, COOKED, NS AS TO FAT","Grits, cooked, corn or hominy, instant, NS as to fat added in cooking" +56201240,"GRITS, FLAVORED, INSTANT, NO FAT ADDED","Grits, cooked, flavored, corn or hominy, instant, fat not added in cooking" +56201250,"GRITS, FLAVORED, INSTANT, FAT ADDED","Grits, cooked, flavored, corn or hominy, instant, fat added in cooking" +56201260,"GRITS, FLAVORED, INSTANT, NS AS TO ADDED FAT","Grits, cooked, flavored, corn or hominy, instant, NS as to fat added in cooking" +56201300,"GRITS,COOKED,CORN/HOM,NFS,MADE W/MILK, NS AS TO FAT ADDED","Grits, cooked, corn or hominy, NS as to regular, quick, or instant, made with milk, NS as to fat added in cooking" +56201510,"CORNMEAL MUSH, MADE W/ WATER","Cornmeal mush, made with water" +56201520,"CORNMEAL MUSH, FRIED","Cornmeal mush, fried" +56201530,"CORNMEAL MUSH, MADE W/ MILK","Cornmeal mush, made with milk" +56201540,"CORNMEAL, MADE W/ MILK & SUGAR, P. R. STYLE","Cornmeal, made with milk and sugar, Puerto Rican Style (Harina de maiz)" +56201550,"CORNMEAL DUMPLINGS","Cornmeal dumpling" +56201560,"CORNMEAL STICKS, BOILED (INCL CORNMEAL GUANINES)","Cornmeal sticks, boiled" +56201600,"CORNMEAL, LIME-TREATED, COOKED","Cornmeal, lime-treated, cooked (Masa harina)" +56201700,"CORNSTARCH W/ MILK, EATEN AS CEREAL","Cornstarch with milk, eaten as a cereal (2 tbsp cornstarch in 2-1/2 cups milk)" +56201750,"CORNSTARCH, DRY","Cornstarch, dry" +56201800,"CORNSTARCH, HYDROLYZED, POWDER","Cornstarch, hydrolyzed powder" +56201990,"MILLET, COOKED, NS AS TO FAT ADDED IN COOKING","Millet, cooked, NS as to fat added in cooking" +56202000,"MILLET, COOKED, NO FAT ADDED","Millet, cooked, fat not added in cooking" +56202100,"MILLET, COOKED, FAT ADDED IN COOKING","Millet, cooked, fat added in cooking" +56202960,"OATMEAL,COOKED,NS AS TO REG,QUICK/INST,NS TO FAT","Oatmeal, cooked, NS as to regular, quick or instant; NS as to fat added in cooking" +56202970,"OATMEAL, COOKED, QUICK, NS TO FAT ADDED","Oatmeal, cooked, quick (1 or 3 minutes), NS as to fat added in cooking" +56202980,"OATMEAL, COOKED, REG, NS TO FAT ADDED","Oatmeal, cooked, regular, NS as to fat added in cooking" +56203000,"OATMEAL, COOKED, NFS, NO FAT ADDED","Oatmeal, cooked, NS as to regular, quick or instant, fat not added in cooking" +56203010,"OATMEAL, COOKED, REGULAR, NO FAT ADDED","Oatmeal, cooked, regular, fat not added in cooking" +56203020,"OATMEAL, COOKED, QUICK, NO FAT ADDED","Oatmeal, cooked, quick (1 or 3 minutes), fat not added in cooking" +56203030,"OATMEAL, COOKED, INSTANT, NO FAT ADDED IN COOKING","Oatmeal, cooked, instant, fat not added in cooking" +56203040,"OATMEAL, FAT ADDED IN COOKING, NFS","Oatmeal, cooked, NS as to regular, quick, or instant, fat added in cooking" +56203050,"OATMEAL, REGULAR, FAT ADDED IN COOKING","Oatmeal, cooked, regular, fat added in cooking" +56203060,"OATMEAL, QUICK, FAT ADDED IN COOKING","Oatmeal, cooked, quick (1 or 3 minutes), fat added in cooking" +56203070,"OATMEAL, INSTANT, FAT ADDED","Oatmeal, cooked, instant, fat added in cooking" +56203080,"OATMEAL, INSTANT, NS AS TO ADDED FAT","Oatmeal, cooked, instant, NS as to fat added in cooking" +56203110,"OATMEAL, MAPLE FLAVOR, COOKED (INCL MAYPO)","Oatmeal with maple flavor, cooked" +56203200,"OATMEAL, W/ FRUIT, COOKED","Oatmeal with fruit, cooked" +56203210,"OATMEAL, NS TYPE, MADE W/ MILK, NO FAT ADDED","Oatmeal, NS as to regular, quick, or instant, made with milk, fat not added in cooking" +56203211,"OATMEAL, CKD, REG, MADE W/ MILK, FAT NOT ADDED IN COOKING","Oatmeal, cooked, regular, made with milk, fat not added in cooking" +56203212,"OATMEAL, CKD, QUICK, MADE W/ MILK, FAT NOT ADDED IN COOKING","Oatmeal, cooked, quick (1 or 3 minutes), made with milk, fat not added in cooking" +56203213,"OATMEAL, CKD, INST, MADE W/ MILK, FAT NOT ADDED IN COOKING","Oatmeal, cooked, instant, made with milk, fat not added in cooking" +56203220,"OATMEAL, NS TYPE, MADE W/ MILK, FAT ADDED","Oatmeal, NS as to regular, quick, or instant, made with milk, fat added in cooking" +56203221,"OATMEAL, CKD, REG, MADE W/ MILK, FAT ADDED IN COOKING","Oatmeal, cooked, regular, made with milk, fat added in cooking" +56203222,"OATMEAL, CKD, QUICK, MADE W/ MILK, FAT ADDED IN COOKING","Oatmeal, cooked, quick (1 or 3 minutes), made with milk, fat added in cooking" +56203223,"OATMEAL, CKD, INST, MADE W/ MILK, FAT ADDED IN COOKING","Oatmeal, cooked, instant, made with milk, fat added in cooking" +56203230,"OATMEAL, NS TYPE, MADE W/ MILK, NS AS TO ADDED FAT","Oatmeal, NS as to regular, quick, or instant, made with milk, NS as to fat added in cooking" +56203231,"OATMEAL, CKD, REG, MADE W/ MILK, NS AS TO FAT ADDED","Oatmeal, cooked, regular, made with milk, NS as to fat added in cooking" +56203232,"OATMEAL, CKD, QUICK, MADE W/ MILK, NS AS TO FAT ADDED","Oatmeal, cooked, quick (1 or 3 minutes), made with milk, NS as to fat added in cooking" +56203233,"OATMEAL, CKD, INST, MADE W/ MILK, NS AS TO FAT ADDED","Oatmeal, cooked, instant, made with milk, NS as to fat added in cooking" +56203540,"OATMEAL, MADE W/ MILK & SUGAR, P.R. STYLE","Oatmeal, made with milk and sugar, Puerto Rican style" +56203600,"OATMEAL, MULTIGRAIN, COOKED, NS FAT ADDED","Oatmeal, multigrain, cooked, NS as to fat added in cooking" +56203610,"OATMEAL, MULTIGRAIN, COOKED, FAT NOT ADDED","Oatmeal, multigrain, cooked, fat not added in cooking" +56203620,"OATMEAL, MULTIGRAIN, COOKED, FAT ADDED","Oatmeal, multigrain, cooked, fat added in cooking" +56204000,"QUINOA, COOKED, NS AS TO FAT ADDED IN COOKING","Quinoa, cooked, NS as to fat added in cooking" +56204005,"QUINOA, COOKED, FAT NOT ADDED IN COOKING","Quinoa, cooked, fat not added in cooking" +56204010,"QUINOA, COOKED, FAT ADDED IN COOKING","Quinoa, cooked, fat added in cooking" +56205000,"RICE, COOKED, NS AS TO TYPE","Rice, cooked, NFS" +56205002,"RICE, WHITE, COOKED, MADE WITH OIL","Rice, white, cooked, fat added in cooking, made with oil" +56205004,"RICE, WHITE, COOKED, MADE WITH BUTTER","Rice, white, cooked, fat added in cooking, made with butter" +56205006,"RICE, WHITE, COOKED, MADE WITH MARGARINE","Rice, white, cooked, fat added in cooking, made with margarine" +56205008,"RICE, WHITE, COOKED, FAT NOT ADDED IN COOKING","Rice, white, cooked, fat not added in cooking" +56205012,"RICE, BROWN, COOKED, MADE WITH OIL","Rice, brown, cooked, fat added in cooking, made with oil" +56205014,"RICE, BROWN, COOKED, MADE WITH BUTTER","Rice, brown, cooked, fat added in cooking, made with butter" +56205016,"RICE, BROWN, COOKED, MADE WITH MARGARINE","Rice, brown, cooked, fat added in cooking, made with margarine" +56205018,"RICE, BROWN, COOKED, FAT NOT ADDED IN COOKING","Rice, brown, cooked, fat not added in cooking" +56205050,"RICE, CREAM OF, COOKED, NO FAT ADDED","Rice, cream of, cooked, fat not added in cooking" +56205060,"RICE, COOKED, W/ MILK","Rice, cooked, with milk" +56205070,"RICE, SWEET, (RICE, COOKED, W/ HONEY)","Rice, sweet (rice, cooked, with honey)" +56205080,"RICE, CREAMED, W/ MILK & SUGAR, PUERTO RICAN","Rice, creamed, made with milk and sugar, Puerto Rican style" +56205090,"RICE, CREAM OF, COOKED, FAT ADDED IN COOKING","Rice, cream of, cooked, fat added in cooking" +56205130,"YELLOW RICE, COOKED, NS AS TO FAT ADDED IN COOKING","Yellow rice, cooked, NS as to fat added in cooking" +56205150,"YELLOW RICE, COOKED, FAT NOT ADDED IN COOKING","Yellow rice, cooked, fat not added in cooking" +56205170,"YELLOW RICE, COOKED, FAT ADDED IN COOKING","Yellow rice, cooked, fat added in cooking" +56205190,"RICE, WHITE, COOKED, GLUTINOUS (INCL STICKY RICE)","Rice, white, cooked, glutinous" +56205200,"RICE, FRZ DES,NONDAIRY,NOT CHOC (INCL RICE DREAM)","Rice, frozen dessert, nondairy, flavors other than chocolate" +56205205,"RICE, WILD, 100%, COOKED, NS AS TO FAT ADDED IN COOKING","Rice, wild, 100%, cooked, NS as to fat added in cooking" +56205210,"RICE, WILD, 100%, COOKED, NO FAT ADDED","Rice, wild, 100%, cooked, fat not added in cooking" +56205215,"RICE, WILD, 100%, COOKED, FAT ADDED IN COOKING","Rice, wild, 100%, cooked, fat added in cooking" +56205230,"RICE DESSERT BAR,FRZ,NOT CHOC,NONDAIRY,CAROB,COVER","Rice dessert bar, frozen, flavors other than chocolate, nondairy, carob covered" +56205240,"RICE DESSERT BAR,FRZ,CHOC,NONDAIRY,CHOC COVERED","Rice dessert bar, frozen, chocolate, nondairy, chocolate covered" +56205300,"RICE, WHITE & WILD, COOKED, NO FAT ADDED","Rice, white and wild, cooked, fat not added in cooking" +56205310,"RICE, BROWN & WILD, COOKED, NO FAT ADDED","Rice, brown and wild, cooked, fat not added in cooking" +56205320,"RICE, WHITE & WILD, FAT ADDED","Rice, white and wild, cooked, fat added in cooking" +56205330,"RICE, WHITE & WILD, NS AS TO ADDED FAT","Rice, white and wild, cooked, NS as to fat added in cooking" +56205340,"RICE, BROWN & WILD, FAT ADDED","Rice, brown and wild, cooked, fat added in cooking" +56205350,"RICE, BROWN & WILD, NS AS TO ADDED FAT","Rice, brown and wild, cooked, NS as to fat added in cooking" +56205410,"RICE, WHITE, COOKED W/ (FAT) OIL, PUERTO RICAN STYLE","Rice, white, cooked with (fat) oil, Puerto Rican style (Arroz blanco)" +56206970,"WHEAT, CREAM OF,COOKED,QUICK,NS AS TO ADDED FAT","Wheat, cream of, cooked, quick, NS as to fat added in cooking" +56206980,"WHEAT, CREAM OF,COOKED,REG,NS AS TO ADDED FAT","Wheat, cream of, cooked, regular, NS as to fat added in cooking" +56206990,"WHEAT, CREAM OF,COOKED,NS AS REG,QUICK,/INST","Wheat, cream of, cooked, NS as to regular, quick, or instant, NS as to fat added in cooking" +56207000,"WHEAT, CREAM OF, COOKED, NFS, NO FAT ADDED","Wheat, cream of, cooked, NS as to regular, quick, or instant, fat not added in cooking" +56207010,"WHEAT, CREAM OF, COOKED, REGULAR, NO FAT ADDED","Wheat, cream of, cooked, regular, fat not added in cooking" +56207020,"WHEAT, CREAM OF, COOKED, QUICK, NO FAT ADDED","Wheat, cream of, cooked, quick, fat not added in cooking" +56207030,"WHEAT, CREAM OF, COOKED, INSTANT, NO FAT ADDED","Wheat, cream of, cooked, instant, fat not added in cooking" +56207040,"WHEAT, CREAM OF, MADE W/ MILK","Wheat, cream of, cooked, made with milk" +56207050,"WHEAT, CREAM OF, MADE W/ MILK & SUGAR, P.R. STYLE","Wheat, cream of, cooked, made with milk and sugar, Puerto Rican style" +56207060,"WHEAT, CREAM OF, INSTANT, COOKED, FAT ADDED","Wheat, cream of, cooked, instant, fat added in cooking" +56207070,"WHEAT, CREAM OF, INSTANT,COOKED, NS AS TO ADDED FAT","Wheat, cream of, cooked, instant, NS as to fat added in cooking" +56207080,"WHEAT, CREAM OF,COOKED,NS AS TO REG,QUICK, OR INST","Wheat, cream of, cooked, NS as to regular, quick, or instant, fat added in cooking" +56207100,"WHEAT, ROLLED, COOKED, NO FAT ADDED","Wheat, rolled, cooked, fat not added in cooking" +56207110,"BULGUR, COOKED OR CANNED, NO FAT ADDED","Bulgur, cooked or canned, fat not added in cooking" +56207120,"BULGAR, COOKED OR CANNNED, FAT ADDED IN COOKING","Bulgur, cooked or canned, fat added in cooking" +56207130,"BULGUR, COOKED OR CANNED, NS AS TO ADDED FAT","Bulgur, cooked or canned, NS as to fat added in cooking" +56207140,"WHEAT ROLLED,COOKED,NS AS TO ADDED FAT","Wheat, rolled, cooked, NS as to fat added in cooking" +56207150,"COUSCOUS, PLAIN, COOKED, FAT NOT ADDED IN COOKING","Couscous, plain, cooked, fat not added in cooking" +56207160,"COUSCOUS, PLAIN, COOKED, NS AS TO ADDED FAT","Couscous, plain, cooked, NS as to fat added in cooking" +56207180,"COUSCOUS, PLAIN, COOKED, FAT ADDED IN COOKING","Couscous, plain, cooked, fat added in cooking" +56207190,"WHOLE WHEAT CEREAL, COOKED, NS AS TO ADDED FAT","Whole wheat cereal, cooked, NS as to fat added in cooking" +56207200,"WHOLE WHEAT CEREAL, COOKED, NO FAT ADDED","Whole wheat cereal, cooked, fat not added in cooking" +56207210,"WHOLE WHEAT CEREAL, COOKED, FAT ADDED","Whole wheat cereal, cooked, fat added in cooking" +56207220,"WHEAT, CREAM OF, COOKED, REGULAR, FAT ADDED","Wheat, cream of, cooked, regular, fat added in cooking" +56207230,"WHEAT, CREAM OF, COOKED,QUICK,FAT ADDED IN COOKING","Wheat, cream of, cooked, quick, fat added in cooking" +56207300,"WHOLE WHEAT CEREAL, W/ BARLEY, COOKED, NO FAT ADDED","Whole wheat cereal, wheat and barley, cooked, fat not added in cooking" +56207330,"WHOLE WHEAT CEREAL, WHEAT & BARLEY, FAT ADDED","Whole wheat cereal, wheat and barley, cooked, fat added in cooking" +56207340,"WHOLE WHEAT CEREAL, WHEAT & BARLEY, ADDED FAT NS","Whole wheat cereal, wheat and barley, cooked, NS as to fat added in cooking" +56207350,"WHEAT CEREAL, CHOC FLAVORED, COOKED W/ MILK","Wheat cereal, chocolate flavored, cooked, made with milk" +56207360,"WHEAT CEREAL, CHOC FLAVORED, COOKED, NO FAT ADDED","Wheat cereal, chocolate flavored, cooked, fat not added in cooking" +56207370,"WHEAT CEREAL, CHOC FLAV,COOKED,NS AS TO ADDED FAT","Wheat cereal, chocolate flavored, cooked, NS as to fat added in cooking" +56208500,"OAT BRAN CEREAL, COOKED, NO FAT ADDED","Oat bran cereal, cooked, fat not added in cooking" +56208510,"OAT BRAN CEREAL, COOKED, FAT ADDED","Oat bran cereal, cooked, fat added in cooking" +56208520,"OAT BRAN CEREAL, COOKED, NS AS TO ADDED FAT","Oat bran cereal, cooked, NS as to fat added in cooking" +56208530,"OAT BRAN CEREAL, MADE W/ MILK, NO FAT ADDED","Oat bran cereal, cooked, made with milk, fat not added in cooking" +56208540,"OAT BRAN CEREAL, MADE W/ MILK, FAT ADDED","Oat bran cereal, cooked, made with milk, fat added in cooking" +56208550,"OAT BRAN CEREAL, MADE W/ MILK, NS AS TO ADDED FAT","Oat bran cereal, cooked, made with milk, NS as to fat added in cooking" +56209000,"RYE, CREAM OF, COOKED","Rye, cream of, cooked" +56210000,"NESTUM, CEREAL","Nestum cereal" +5.7e+07,"CEREAL, NFS","Cereal, NFS" +57000050,"KASHI CEREAL, NS AS TO READY-TO-EAT OR COOKED","Kashi cereal, NS as to ready to eat or cooked" +57000100,"OAT CEREAL, NFS","Oat cereal, NFS" +57100100,"CEREAL, READY-TO-EAT, NFS","Cereal, ready-to-eat, NFS" +57101000,"ALL-BRAN CEREAL","All-Bran" +57102000,"ALPEN CEREAL","Alpen" +57103000,"ALPHA-BITS CEREAL","Alpha-Bits" +57103020,"ALPHA-BITS W/ MARSHMALLOWS CEREAL","Alpha-bits with marshmallows" +57103050,"AMARANTH FLAKES CEREAL","Amaranth Flakes" +57103100,"APPLE CINNAMON CHEERIOS","Apple Cinnamon Cheerios" +57104000,"APPLE JACKS CEREAL","Apple Jacks" +57106050,"BANANA NUT CRUNCH CEREAL (POST)","Banana Nut Crunch Cereal (Post)" +57106060,"BANANA NUT CHEERIOS","Banana Nut Cheerios" +57106100,"BASIC 4 (RTE CEREAL)","Basic 4" +57106250,"BERRY BERRY KIX","Berry Berry Kix" +57106260,"BERRY BURST CHEERIOS","Berry Burst Cheerios" +57106530,"BLUEBERRY MORNING, POST","Blueberry Morning, Post" +57107000,"BOOBERRY CEREAL","Booberry" +57110000,"ALL-BRAN BRAN BUDS CEREAL, KELLOGG'S (FORMERLY BRAN BUDS)","All-Bran Bran Buds, Kellogg's (formerly Bran Buds)" +57111000,"BRAN CHEX CEREAL","Bran Chex" +57117000,"CAP'N CRUNCH CEREAL","Cap'n Crunch" +57117500,"CAP'N CRUNCH'S CHRISTMAS CRUNCH CEREAL","Cap'n Crunch's Christmas Crunch" +57119000,"CAP'N CRUNCH'S CRUNCH BERRIES CEREAL","Cap'n Crunch's Crunch Berries" +57120000,"CAP'N CRUNCH'S PEANUT BUTTER CRUNCH CEREAL","Cap'n Crunch's Peanut Butter Crunch" +57123000,"CHEERIOS","Cheerios" +57124000,"CHEX CEREAL, NFS","Chex cereal, NFS" +57124050,"CHEX CINNAMON","Chex Cinnamon" +57124100,"CHOCOLATE CHEERIOS","Chocolate Cheerios" +57124200,"CHOCOLATE FLAVORED FROSTED PUFFED CORN CEREAL","Chocolate flavored frosted puffed corn cereal" +57124300,"CHOCOLATE LUCKY CHARMS","Chocolate Lucky Charms" +57125000,"CINNAMON TOAST CRUNCH CEREAL","Cinnamon Toast Crunch" +57125010,"CINNAMON TOAST CRUNCH REDUCED SUGAR","Cinnamon Toast Crunch Reduced Sugar" +57125900,"HONEY NUT CLUSTERS CEREAL","Honey Nut Clusters (formerly called Clusters)" +57126000,"COCOA KRISPIES CEREAL","Cocoa Krispies" +57127000,"COCOA PEBBLES CEREAL","Cocoa Pebbles" +57128000,"COCOA PUFFS CEREAL","Cocoa Puffs" +57128005,"COCOA PUFFS, REDUCED SUGAR","Cocoa Puffs, reduced sugar" +57130000,"COOKIE-CRISP CEREAL (INCLUDE ALL FLAVORS)","Cookie-Crisp" +57131000,"CRUNCHY CORN BRAN CEREAL, QUAKER","Crunchy Corn Bran, Quaker" +57132000,"CORN CHEX CEREAL","Corn Chex" +57134000,"CORN FLAKES, NFS (INCLUDE STORE BRANDS)","Corn flakes, NFS" +57134090,"CORN FLAKES, LOW SODIUM","Corn flakes, low sodium" +57135000,"CORN FLAKES, KELLOGG'S","Corn flakes, Kellogg's" +57137000,"CORN PUFFS CEREAL","Corn Puffs" +57139000,"COUNT CHOCULA CEREAL","Count Chocula" +57143000,"CRACKLIN' OAT BRAN CEREAL","Cracklin' Oat Bran" +57143500,"CRANBERRY ALMOND CRUNCH, POST","Cranberry Almond Crunch, Post" +57144000,"CRISP CRUNCH CEREAL","Crisp Crunch" +57148000,"CRISPIX CEREAL","Crispix" +57148500,"CRISPY BROWN RICE CEREAL","Crispy Brown Rice Cereal" +57151000,"CRISPY RICE CEREAL","Crispy Rice" +57201900,"DORA THE EXPLORER CEREAL","Dora the Explorer Cereal" +57206000,"FAMILIA CEREAL","Familia" +57206700,"FIBER ONE CEREAL","Fiber One" +57206705,"FIBER ONE CARAMEL DELIGHT","Fiber One Caramel Delight" +57206710,"FIBER ONE HONEY CLUSTERS","Fiber One Honey Clusters" +57206715,"FIBER ONE RAISIN BRAN CLUSTERS","Fiber One Raisin Bran Clusters" +57206800,"FIBER 7 FLAKES CEREAL, HEALTH VALLEY","Fiber 7 Flakes, Health Valley" +57207000,"BRAN FLAKES CEREAL, NFS (FORMERLY 40% BRAN FLAKES, NFS)","Bran Flakes, NFS (formerly 40% Bran Flakes, NFS)" +57208000,"ALL-BRAN COMPLETE WHEAT FLAKES, KELLOGG'S","All-Bran Complete Wheat Flakes, Kellogg's" +57209000,"NATURAL BRAN FLAKES CEREAL, POST","Natural Bran Flakes, Post (formerly called 40% Bran Flakes, Post)" +57211000,"FRANKENBERRY CEREAL","Frankenberry" +57213000,"FROOT LOOPS CEREAL","Froot Loops" +57213850,"FROSTED CHEERIOS CEREAL","Frosted Cheerios" +57214000,"FROSTED MINI-WHEATS CEREAL (INCL ALL FLAVORS)","Frosted Mini-Wheats" +57214100,"FROSTED WHEAT BITES","Frosted Wheat Bites" +57215000,"FROSTY O'S CEREAL","Frosty O's" +57216000,"FROSTED RICE CEREAL, NFS","Frosted rice, NFS" +57218000,"FROSTED RICE KRISPIES, KELLOGG'S","Frosted Rice Krispies, Kellogg's" +57219000,"FRUIT & FIBRE CEREAL, NFS","Fruit & Fibre (fiber), NFS" +57221000,"FRUIT & FIBRE CEREAL, W/ DATES, RAISINS, & WALNUTS","Fruit & Fibre (fiber) with dates, raisins, and walnuts" +57221650,"FRUIT HARVEST CEREAL, KELLOGG'S","Fruit Harvest cereal, Kellogg's" +57221700,"FRUIT RINGS, NFS (INCLUDE STORE BRANDS)","Fruit Rings, NFS" +57221800,"FRUIT WHIRLS CEREAL","Fruit Whirls" +57221810,"FRUITY CHEERIOS","Fruity Cheerios" +57223000,"FRUITY PEBBLES CEREAL","Fruity Pebbles" +57224000,"GOLDEN GRAHAMS CEREAL","Golden Grahams" +57227000,"GRANOLA, NFS","Granola, NFS" +57228000,"GRANOLA, HOMEMADE","Granola, homemade" +57229000,"GRANOLA, LOWFAT, KELLOGG'S","Granola, lowfat, Kellogg's" +57229500,"GRANOLA W/ RAISINS, LOWFAT, KELLOGG'S","Granola with Raisins, lowfat, Kellogg's" +57230000,"GRAPE-NUTS CEREAL","Grape-Nuts" +57231000,"GRAPE-NUTS FLAKES","Grape-Nuts Flakes" +57231100,"GRAPE-NUTS TRAIL MIX CRUNCH","Grape-Nuts Trail Mix Crunch" +57231200,"GREAT GRAINS, RAISIN, DATE, & PECAN,WHOLE GRAIN CEREAL, POST","Great Grains, Raisin, Date, and Pecan Whole Grain Cereal, Post" +57231250,"GREAT GRAINS DOUBLE PECAN WHOLE GRAIN CEREAL, POST","Great Grains Double Pecan Whole Grain Cereal, Post" +57237100,"HONEY BUNCHES OF OATS HONEY ROASTED CEREAL","Honey Bunches of Oats Honey Roasted Cereal" +57237200,"HONEY BUNCHES OF OATS WITH VANILLA CLUSTERS, POST","Honey Bunches of Oats with Vanilla Clusters, Post" +57237300,"HONEY BUNCHES OF OATS W/ ALMONDS, POST","Honey Bunches of Oats with Almonds, Post" +57237310,"HONEY BUNCHES OF OATS WITH PECAN BUNCHES","Honey Bunches of Oats with Pecan Bunches" +57237900,"HONEY BUNCHES OF OATS JUST BUNCHES","Honey Bunches of Oats Just Bunches" +57238000,"HONEYCOMB CEREAL, PLAIN","Honeycomb, plain" +57239000,"HONEYCOMB CEREAL, STRAWBERRY","Honeycomb, strawberry" +57239100,"HONEY CRUNCH CORN FLAKES CEREAL, KELLOGG'S","Honey Crunch Corn Flakes, Kellogg's" +57240100,"HONEY NUT CHEX CEREAL","Honey Nut Chex" +57241000,"HONEY NUT CHEERIOS","Honey Nut Cheerios" +57241200,"HONEY NUT SHREDDED WHEAT CEREAL, POST","Honey Nut Shredded Wheat, Post" +57243000,"HONEY SMACKS, KELLOGG'S","Honey Smacks, Kellogg's (formerly Smacks; Honey Smacks)" +57301500,"KASHI, PUFFED","Kashi, Puffed" +57301505,"KASHI AUTUMN WHEAT","Kashi Autumn Wheat" +57301510,"KASHI GOLEAN","Kashi GOLEAN" +57301511,"KASHI GOLEAN CRUNCH","Kashi GOLEAN Crunch" +57301512,"KASHI GOLEAN CRUNCH HONEY ALMOND FLAX","Kashi GOLEAN Crunch Honey Almond Flax" +57301520,"KASHI GOOD FRIENDS","Kashi Good Friends" +57301530,"KASHI HEART TO HEART HONEY TOASTED OAT","Kashi Heart to Heart Honey Toasted Oat" +57301535,"KASHI HEART TO HEART OAT FLAKES AND BLUEBERRY CLUSTERS","Kashi Heart to Heart Oat Flakes and Blueberry Clusters" +57301540,"KASHI HONEY SUNSHINE","Kashi Honey Sunshine" +57302100,"KING VITAMAN CEREAL","King Vitaman" +57303100,"KIX CEREAL","Kix" +57303105,"HONEY KIX","Honey Kix" +57304100,"LIFE CEREAL (PLAIN & CINNAMON)","Life (plain and cinnamon)" +57305100,"LUCKY CHARMS CEREAL","Lucky Charms" +57305150,"FROSTED OAT CEREAL W/ MARSHMALLOWS","Frosted oat cereal with marshmallows" +57305160,"MALT-O-MEAL BLUEBERRY MUFFIN TOPS","Malt-O-Meal Blueberry Muffin Tops" +57305165,"MALT-O-MEAL CINNAMON TOASTERS","Malt-O-Meal Cinnamon Toasters" +57305170,"MALT-O-MEAL COCO-ROOS CEREAL","Malt-O-Meal Coco-Roos" +57305174,"MALT-O-MEAL COLOSSAL CRUNCH","Malt-O-Meal Colossal Crunch" +57305175,"MALT-O-MEAL COCOA DYNO-BITES","Malt-O-Meal Cocoa Dyno-Bites" +57305180,"MALT-O-MEAL CORN BURSTS CEREAL","Malt-O-Meal Corn Bursts" +57305200,"MALT-O-MEAL CRISPY RICE CEREAL","Malt-O-Meal Crispy Rice" +57305210,"MALT-O-MEAL FROSTED FLAKES","Malt-O-Meal Frosted Flakes" +57305215,"MALT-O-MEAL FROSTED MINI SPOONERS","Malt-O-Meal Frosted Mini Spooners" +57305300,"MALT-O-MEAL FRUITY DYNO-BITES","Malt-O-Meal Fruity Dyno-Bites" +57305400,"MALT-O-MEAL HONEY GRAHAM SQUARES","Malt-O-Meal Honey Graham Squares" +57305500,"MALT-O-MEAL HONEY & NUT TOASTY O'S CEREAL","Malt-O-Meal Honey and Nut Toasty O's" +57305600,"MALT-O-MEAL MARSHMALLOW MATEYS CEREAL","Malt-O-Meal Marshmallow Mateys" +57306100,"MALT-O-MEAL PUFFED RICE CEREAL","Malt-O-Meal Puffed Rice" +57306120,"MALTO-O-MEAL PUFFED WHEAT CEREAL","Malt-O-Meal Puffed Wheat" +57306130,"MALT-O-MEAL RAISIN BRAN","Malt-O-Meal Raisin Bran" +57306500,"MALT-O-MEAL GOLDEN PUFFS CEREAL (FORMERLY SUGAR PUFFS)","Malt-O-Meal Golden Puffs (formerly Sugar Puffs)" +57306700,"MALT-O-MEAL TOASTED OAT CEREAL","Malt-O-Meal Toasted Oat Cereal" +57306800,"MALT-O-MEAL TOOTIE FRUITIES (RTE CEREAL)","Malt-O-meal Tootie Fruities" +57307010,"MAPLE PECAN CRUNCH CEREAL, POST","Maple Pecan Crunch Cereal, Post" +57307500,"MILLET, PUFFED (CEREAL)","Millet, puffed" +57307600,"MINI-SWIRLZ CINNAMON BUN CEREAL, KELLOGG'S","Mini-Swirlz Cinnamon Bun Cereal, Kellogg's" +57308150,"MUESLIX CEREAL, NFS","Mueslix cereal, NFS" +57308190,"MUESLI, DRIED FRUIT&NUTS","Muesli, dried fruit and nuts (formerly Muesli with raisins, dates, and almonds)" +57308300,"MULTI BRAN CHEX","Multi Bran Chex" +57308400,"MULTIGRAIN CHEERIOS","MultiGrain Cheerios" +57309100,"NATURE VALLEY GRANOLA, W/ FRUIT & NUTS","Nature Valley Granola, with fruit and nuts" +57316200,"NUTTY NUGGETS (RALSTON)","Nutty Nuggets, Ralston Purina" +57316300,"OAT BRAN FLAKES, HEALTH VALLEY","Oat Bran Flakes, Health Valley" +57316380,"OAT CLUSTER CHEERIOS CRUNCH","Oat Cluster Cheerios Crunch" +57316450,"OATMEAL CRISP W/ ALMONDS CEREAL","Oatmeal Crisp with Almonds" +57316500,"OATMEAL CRISP, RAISIN","Oatmeal Crisp, Raisin (formerly Oatmeal Raisin Crisp)" +57316710,"OH'S, HONEY GRAHAM CEREAL","Oh's, Honey Graham" +57319000,"100% NATURAL CEREAL, PLAIN, QUAKER","100% Natural Cereal, plain, Quaker" +57319500,"SUN COUNTRY 100% NATURAL GRANOLA, WITH ALMONDS","Sun Country 100% Natural Granola, with Almonds" +57320500,"100 % NATURAL CEREAL, W/ OATS,HONEY & RAISINS,QUAKER","100 % Natural Cereal, with oats, honey and raisins, Quaker" +57321500,"100% NATURAL WHOLEGRAIN CEREAL W/ RAISINS, LOWFAT, QUAKER","100 % Natural Wholegrain Cereal with raisins, lowfat, Quaker" +57321700,"OPTIMUM, NATURE'S PATH","Optimum, Nature's Path" +57321800,"OPTIMUM SLIM, NATURE'S PATH","Optimum Slim, Nature's Path" +57321900,"ORGANIC FLAX PLUS, NATURE'S PATH","Organic Flax Plus, Nature's Path" +57323000,"SWEET CRUNCH CEREAL, QUAKER (FORMERLY POPEYE)","Sweet Crunch, Quaker (formerly called Popeye)" +57325000,"PRODUCT 19 CEREAL","Product 19" +57326000,"PUFFINS CEREAL","Puffins Cereal" +57327450,"QUAKER OAT BRAN CEREAL","Quaker Oat Bran Cereal" +57327500,"QUAKER OATMEAL SQUARES CEREAL (FORMERLY QUAKER OAT SQUARES)","Quaker Oatmeal Squares (formerly Quaker Oat Squares)" +57328000,"QUISP CEREAL","Quisp" +57329000,"RAISIN BRAN CEREAL, NFS","Raisin bran, NFS" +57330000,"RAISIN BRAN, KELLOGG'S","Raisin Bran, Kellogg's" +57330010,"RAISIN BRAN CRUNCH, KELLOGG'S","Raisin Bran Crunch, Kellogg's" +57331000,"RAISIN BRAN CEREAL, POST","Raisin Bran, Post" +57332050,"RAISIN BRAN, TOTAL","Raisin Bran, Total" +57332100,"RAISIN NUT BRAN CEREAL","Raisin Nut Bran" +57335550,"REESE'S PEANUT BUTTER PUFFS CEREAL","Reese's Peanut Butter Puffs cereal" +57336000,"RICE CHEX CEREAL","Rice Chex" +57337000,"RICE FLAKES, NFS","Rice Flakes, NFS" +57339000,"RICE KRISPIES, KELLOGG'S","Rice Krispies, Kellogg's" +57339500,"RICE KRISPIES TREATS CEREAL, KELLOGG'S","Rice Krispies Treats Cereal, Kellogg's" +57340000,"PUFFED RICE CEREAL","Rice, puffed" +57341000,"SHREDDED WHEAT 'N BRAN CEREAL","Shredded Wheat'N Bran" +57341200,"SMART START STRONG HEART ANTIOXIDANTS CEREAL, KELLOGG'S","Smart Start Strong Heart Antioxidants Cereal, Kellogg's" +57342010,"SMORZ, KELLOGG'S","Smorz, Kellogg's" +57344000,"SPECIAL K CEREAL","Special K" +57344001,"SPECIAL K BLUEBERRY","Special K Blueberry" +57344005,"SPECIAL K CHOCOLATEY DELIGHT","Special K Chocolatey Delight" +57344007,"SPECIAL K LOW FAT GRANOLA","Special K Low Fat Granola" +57344010,"SPECIAL K RED BERRIES","Special K Red Berries" +57344015,"SPECIAL K FRUIT & YOGURT","Special K Fruit & Yogurt" +57344020,"SPECIAL K VANILLA ALMOND","Special K Vanilla Almond" +57344025,"SPECIAL K CINNAMON PECAN, KELLOGG'S","Special K Cinnamon Pecan, Kellogg's" +57346500,"OATMEAL HONEY NUT HEAVEN, QUAKER","Oatmeal Honey Nut Heaven, Quaker (formerly Toasted Oatmeal, Honey Nut)" +57347000,"CORN POPS CEREAL","Corn Pops" +57348000,"FROSTED CORN FLAKES, NFS","Frosted corn flakes, NFS" +57349000,"FROSTED FLAKES, KELLOGG'S","Frosted Flakes, Kellogg's" +57349020,"REDUCED SUGAR FROSTED FLAKES CEREAL, KELLOGG'S","Reduced Sugar Frosted Flakes Cereal, Kellogg's" +57355000,"GOLDEN CRISP CEREAL","Golden Crisp (Formerly called Super Golden Crisp)" +57401100,"TOASTED OAT CEREAL","Toasted oat cereal" +57403100,"TOASTIES, POST","Toasties, Post" +57406100,"TOTAL CEREAL","Total" +57406105,"TOTAL CRANBERRY CRUNCH","Total Cranberry Crunch" +57407100,"TRIX CEREAL","Trix" +57407110,"TRIX, REDUCED SUGAR","Trix, reduced sugar" +57408100,"UNCLE SAM CEREAL","Uncle Sam Cereal (formerly Uncle Sam's Hi Fiber Cereal)" +57409100,"WAFFLE CRISP CEREAL, POST","Waffle Crisp, Post" +57410000,"WEETABIX WHOLE WHEAT CEREAL","Weetabix Whole Wheat Cereal" +57411000,"WHEAT CHEX CEREAL","Wheat Chex" +57412000,"WHEAT GERM CEREAL, PLAIN","Wheat germ, plain" +57413000,"WHEAT GERM CEREAL, W/ SUGAR & HONEY","Wheat germ, with sugar and honey" +57416000,"PUFFED WHEAT CEREAL, PLAIN","Wheat, puffed, plain" +57416010,"WHEAT, PUFFED, PRESWEETENED W/ SUGAR","Wheat, puffed, presweetened with sugar" +57417000,"SHREDDED WHEAT, 100%","Shredded Wheat, 100%" +57418000,"WHEATIES CEREAL","Wheaties" +57419000,"YOGURT BURST CHEERIOS","Yogurt Burst Cheerios" +57601100,"WHEAT BRAN, UNPROCESSED","Wheat bran, unprocessed" +57602100,"OATS, RAW","Oats, raw" +57602500,"OAT BRAN, UNCOOKED","Oat bran, uncooked" +57603100,"RICE POLISHINGS","Rice polishings" +57603200,"RICE BRAN CEREAL, UNCOOKED","Rice bran, uncooked" +57604100,"WHOLE WHEAT, CRACKED","Whole wheat, cracked" +57801000,"BARLEY CEREAL, BABY, DRY, INSTANT","Barley cereal, baby food, dry, instant" +57803000,"MIXED CEREAL, BABY, DRY, INSTANT","Mixed cereal, baby food, dry, instant" +57804000,"OATMEAL CEREAL, BABY, DRY, INSTANT","Oatmeal cereal, baby food, dry, instant" +57805000,"RICE CEREAL, BABY, DRY, INSTANT","Rice cereal, baby food, dry, instant" +57805080,"RICE CEREAL W/ APPLES, BABY, DRY, INSTANT","Rice cereal with apples, baby food, dry, instant" +57805090,"RICE CEREAL WITH MIXED FRUITS, BABY FOOD, DRY, INSTANT","Rice cereal with mixed fruits, baby food, dry, instant" +57805100,"RICE CEREAL W/ BANANAS, BABY, DRY, INSTANT","Rice cereal with bananas, baby food, dry, instant" +57805500,"BROWN RICE CEREAL, BABY FOOD, DRY, INSTANT","Brown rice cereal, baby food, dry, instant" +57806000,"MIXED CEREAL W/ BANANAS, BABY, DRY, INSTANT","Mixed cereal with bananas, baby food, dry, instant" +57806050,"MULTIGRAIN, WHOLE GRAIN CEREAL, BABY FOOD, DRY, INSTANT","Multigrain, whole grain cereal, baby food, dry, instant" +57806100,"OATMEAL CEREAL W/ BANANAS, BABY, DRY, INSTANT","Oatmeal cereal with bananas, baby food, dry, instant" +57806200,"OATMEAL W/ FRUIT, BABY, DRY, INSTANT, TODDLER","Oatmeal cereal with fruit, baby food, dry, instant, toddler" +57807010,"WHOLE WHEAT CEREAL W/ APPLES, BABY, DRY, INSTANT","Whole wheat cereal with apples, baby food, dry, instant" +57820000,"CEREAL, BABY, JARRED, NFS","Cereal, baby food, jarred, NFS" +57820100,"RICE CEREAL, BABY FOOD, JARRED, NFS","Rice cereal, baby food, jarred, NFS" +57822000,"MIXED CEREAL W/ APPLESAUCE & BANANAS, BABY, JARRED","Mixed cereal with applesauce and bananas, baby food, jarred" +57823000,"OATMEAL W/ APPLESAUCE & BANANAS, BABY, JARRED","Oatmeal with applesauce and bananas, baby food, jarred" +57824000,"RICE CEREAL, W/ APPLESAUCE & BANANAS, BABY, JARRED","Rice cereal with applesauce and bananas, baby food, jarred" +57824500,"RICE CEREAL W/ MIXED FRUIT, BABY, JARRED","Rice cereal with mixed fruit, baby food, jarred" +57830100,"GERBER GRADUATES FINGER SNACKS CEREAL,BABY FOOD","Gerber Graduates Finger Snacks Cereal, baby food" +58100000,"BURRITO, TACO, OR QUESADILLA W/ EGG","Burrito, taco, or quesadilla with egg" +58100005,"BURRITO, TACO, OR QUESADILLA W/ EGG & POTATO","Burrito, taco, or quesadilla with egg and potato" +58100010,"BURRITO, TACO, OR QUESADILLA W/ EGG & BREAKFAST MEAT","Burrito, taco, or quesadilla with egg and breakfast meat" +58100013,"BURRITO, TACO, OR QUESADILLA WITH EGG AND BREAKFAST MEAT, FF","Burrito, taco, or quesadilla with egg and breakfast meat, from fast food" +58100015,"BURRITO, TACO, OR QUESADILLA W/EGG, POTATO, & BREAKFAST MEAT","Burrito, taco, or quesadilla with egg, potato, and breakfast meat" +58100017,"BURRITO, TACO, OR QUESADILLA WITH EGG, POTATO, BRK MEAT, FF","Burrito, taco, or quesadilla with egg, potato, and breakfast meat, from fast food" +58100020,"BURRITO, TACO, OR QUESADILLA W/EGG, BEANS,& BREAKFAST MEAT","Burrito, taco, or quesadilla with egg, beans, and breakfast meat" +58100100,"BURRITO W/ MEAT","Burrito with meat" +58100120,"BURRITO W/ MEAT & BEANS","Burrito with meat and beans" +58100125,"BURRITO WITH MEAT AND BEANS, FROM FAST FOOD","Burrito with meat and beans, from fast food" +58100135,"BURRITO W/ MEAT & SOUR CREAM","Burrito with meat and sour cream" +58100140,"BURRITO W/ MEAT, BEANS, & SOUR CREAM","Burrito with meat, beans, and sour cream" +58100145,"BURRITO WITH MEAT, BEANS, AND SOUR CREAM, FROM FAST FOOD","Burrito with meat, beans, and sour cream, from fast food" +58100160,"BURRITO W/ MEAT, BEANS, & RICE","Burrito with meat, beans, and rice" +58100165,"BURRITO W/ MEAT, BEANS, RICE, & SOUR CREAM","Burrito with meat, beans, rice, and sour cream" +58100200,"BURRITO W/ CHICKEN","Burrito with chicken" +58100220,"BURRITO W/ CHICKEN AND BEANS","Burrito with chicken and beans" +58100235,"BURRITO W/ CHICKEN & SOUR CREAM","Burrito with chicken and sour cream" +58100245,"BURRITO W/ CHICKEN, BEANS, & SOUR CREAM","Burrito with chicken, beans, and sour cream" +58100255,"BURRITO W/ CHICKEN, BEANS, & RICE","Burrito with chicken, beans, and rice" +58100260,"BURRITO W/ CHICKEN, BEANS, RICE, & SOUR CREAM","Burrito with chicken, beans, rice, and sour cream" +58100300,"BURRITO W/ BEANS & RICE, MEATLESS","Burrito with beans and rice, meatless" +58100320,"BURRITO W/ BEANS","Burrito with beans, meatless" +58100325,"BURRITO WITH BEANS, MEATLESS, FROM FAST FOOD","Burrito with beans, meatless, from fast food" +58100330,"BURRITO W/ BEANS, RICE, & SOUR CREAM, MEATLESS","Burrito with beans, rice, and sour cream, meatless" +58100360,"CHILAQUILES,TORTILLA CASSEROLE W/ SALSA,CHEESE, EGG","Chilaquiles, tortilla casserole with salsa, cheese, and egg" +58100370,"CHILAQUILES, TORTILLA CASSEROLE, NO EGG","Chilaquiles, tortilla casserole with salsa and cheese, no egg" +58100520,"ENCHILADA W/ MEAT & BEANS, RED-CHILE OR ENCHILADA SAUCE","Enchilada with meat and beans, red-chile or enchilada sauce" +58100525,"ENCHILADA WITH MEAT & BEANS, GREEN-CHILE OR ENCHILADA SAUCE","Enchilada with meat and beans, green-chile or enchilada sauce" +58100530,"ENCHILADA W/ MEAT, RED-CHILE OR ENCHILADA SAUCE","Enchilada with meat, red-chile or enchilada sauce" +58100535,"ENCHILADA WITH MEAT, GREEN-CHILE OR ENCHILADA SAUCE","Enchilada with meat, green-chile or enchilada sauce" +58100620,"ENCHILADA W/ CHICKEN & BEANS, RED-CHILE OR ENCHILADA SAUCE","Enchilada with chicken and beans, red-chile or enchilada sauce" +58100625,"ENCHILADA WITH CHIC & BEANS, GREEN-CHILE OR ENCHILADA SAUCE","Enchilada with chicken and beans, green-chile or enchilada sauce" +58100630,"ENCHILADA W/ CHICKEN, RED-CHILE OR ENCHILADA SAUCE","Enchilada with chicken, red-chile or enchilada sauce" +58100635,"ENCHILADA WITH CHICKEN, GREEN-CHILE OR ENCHILADA SAUCE","Enchilada with chicken, green-chile or enchilada sauce" +58100720,"ENCHILADA W/ BEANS, MEATLESS, RED-CHILE OR ENCHILADA SAUCE","Enchilada with beans, meatless, red-chile or enchilada sauce" +58100725,"ENCHILADA WITH BEANS, GREEN-CHILE OR ENCHILADA SAUCE","Enchilada with beans, green-chile or enchilada sauce" +58100800,"ENCHILADA, JUST CHEESE, NO BEANS, RED-CHILE OR ENCHILADA SC","Enchilada, just cheese, meatless, no beans, red-chile or enchilada sauce" +58100805,"ENCHILADA, JUST CHEESE, GREEN-CHILE OR ENCHILADA SAUCE","Enchilada, just cheese, meatless, no beans, green-chile or enchilada sauce" +58101320,"TACO OR TOSTADA W/ MEAT","Taco or tostada with meat" +58101323,"TACO OR TOSTADA WITH MEAT, FROM FAST FOOD","Taco or tostada with meat, from fast food" +58101325,"TACO OR TOSTADA W/ MEAT & SOUR CREAM","Taco or tostada with meat and sour cream" +58101345,"SOFT TACO W/ MEAT","Soft taco with meat" +58101347,"SOFT TACO WITH MEAT, FROM FAST FOOD","Soft taco with meat, from fast food" +58101350,"SOFT TACO W/ MEAT & SOUR CREAM","Soft taco with meat and sour cream" +58101357,"SOFT TACO WITH MEAT AND SOUR CREAM, FROM FAST FOOD","Soft taco with meat and sour cream, from fast food" +58101450,"SOFT TACO WITH CHICKEN","Soft taco with chicken" +58101457,"SOFT TACO WITH CHICKEN, FROM FAST FOOD","Soft taco with chicken, from fast food" +58101460,"SOFT TACO W/ CHICKEN & SOUR CREAM","Soft taco with chicken and sour cream" +58101520,"TACO OR TOSTADA W/ CHICKEN","Taco or tostada with chicken" +58101525,"TACO OR TOSTADA W/ CHICKEN & SOUR CREAM","Taco or tostada with chicken and sour cream" +58101540,"TACO OR TOSTADA W/ FISH","Taco or tostada with fish" +58101555,"SOFT TACO W/ FISH","Soft taco with fish" +58101610,"SOFT TACO W/ BEANS","Soft taco with beans" +58101615,"SOFT TACO W/ BEANS & SOUR CREAM","Soft taco with beans and sour cream" +58101620,"SOFT TACO W/ MEAT & BEANS","Soft taco with meat and beans" +58101625,"SOFT TACO W/ CHICKEN & BEANS","Soft taco with chicken and beans" +58101630,"SOFT TACO W/ MEAT, BEANS, & SOUR CREAM","Soft taco with meat, beans, and sour cream" +58101635,"SOFT TACO W/ CHICKEN, BEANS, & SOUR CREAM","Soft taco with chicken, beans, and sour cream" +58101720,"TACO OR TOSTADA W/ BEANS","Taco or tostada with beans" +58101725,"TACO OR TOSTADA W/ BEANS & SOUR CREAM","Taco or tostada with beans and sour cream" +58101730,"TACO OR TOSTADA W/ MEAT & BEANS","Taco or tostada with meat and beans" +58101733,"TACO OR TOSTADA WITH MEAT AND BEANS, FROM FAST FOOD","Taco or tostada with meat and beans, from fast food" +58101735,"TACO OR TOSTADA W/ CHICKEN & BEANS","Taco or tostada with chicken and beans" +58101745,"TACO OR TOSTADA W/ MEAT, BEANS, & SOUR CREAM","Taco or tostada with meat, beans, and sour cream" +58101750,"TACO OR TOSTADA W/ CHICKEN, BEANS, & SOUR CREAM","Taco or tostada with chicken, beans, and sour cream" +58101800,"GROUND BEEF W/ TOMATO SAUCE, ON A CORNBREAD CRUST","Ground beef with tomato sauce and taco seasonings on a cornbread crust" +58101820,"MEXICAN CASSEROLE W/ BEEF & BEANS","Mexican casserole made with ground beef, beans, tomato sauce, cheese, taco seasonings, and corn chips" +58101830,"MEXICAN CASSEROLE W/ BEEF (INCL FRITO PIE, NFS)","Mexican casserole made with ground beef, tomato sauce, cheese, taco seasonings, and corn chips" +58101930,"TACO OR TOSTADA SALAD W/ MEAT","Taco or tostada salad with meat" +58101935,"TACO OR TOSTADA SALAD WITH CHICKEN","Taco or tostada salad with chicken" +58101940,"TACO OR TOSTADA SALAD, MEATLESS","Taco or tostada salad, meatless" +58101945,"TACO SALAD W/ MEAT & SOUR CREAM","Taco or tostada salad with meat and sour cream" +58101950,"TACO OR TOSTADA SALAD W/ CHICKEN & SOUR CREAM","Taco or tostada salad with chicken and sour cream" +58101955,"TACO OR TOSTADA SALAD, MEATLESS W/ SOUR CREAM","Taco or tostada salad, meatless with sour cream" +58103120,"TAMALE WITH MEAT","Tamale with meat" +58103130,"TAMALE WITH CHICKEN","Tamale with chicken" +58103200,"TAMALE, PLAIN, MEATLESS, NO SAUCE, PR STYLE","Tamale, plain, meatless, no sauce, Puerto Rican style or Carribean Style" +58103210,"TAMALE, MEATLESS, W/ SAUCE, P.R. OR CARIBBEAN STYLE","Tamale, meatless, with sauce, Puerto Rican or Caribbean style" +58103250,"TAMALE, PLAIN, MEATLESS, NO SAUCE, MEXICAN","Tamale, plain, meatless, no sauce, Mexican style" +58103310,"TAMALE CASSEROLE W/ MEAT","Tamale casserole with meat" +58104090,"NACHOS W/ CHEESE & SOUR CREAM","Nachos with cheese and sour cream" +58104120,"NACHOS W/ CHEESE","Nachos with cheese" +58104130,"NACHOS W/ MEAT & CHEESE","Nachos with meat and cheese" +58104150,"NACHOS W/ CHICKEN & CHEESE","Nachos with chicken and cheese" +58104160,"NACHOS W/ CHILI","Nachos with chili" +58104180,"NACHOS W/ MEAT, CHEESE, & SOUR CREAM","Nachos with meat, cheese, and sour cream" +58104190,"NACHOS W/ CHICKEN, CHEESE, & SOUR CREAM","Nachos with chicken, cheese, and sour cream" +58104260,"GORDITA, SOPE, OR CHALUPA W/ BEANS","Gordita, sope, or chalupa with beans" +58104270,"GORDITA, SOPE, OR CHALUPA W/ BEANS & SOUR CREAM","Gordita, sope, or chalupa with beans and sour cream" +58104280,"GORDITA, SOPE, OR CHALUPA W/ MEAT & SOUR CREAM","Gordita, sope, or chalupa with meat and sour cream" +58104290,"GORDITA, SOPE, OR CHALUPA W/ MEAT","Gordita, sope, or chalupa with meat" +58104320,"GORDITA, SOPE, OR CHALUPA W/ CHICKEN & SOUR CREAM","Gordita, sope, or chalupa with chicken and sour cream" +58104340,"GORDITA, SOPE, OR CHALUPA W/ CHICKEN","Gordita, sope, or chalupa with chicken" +58104500,"CHIMICHANGA W/ MEAT","Chimichanga with meat" +58104520,"CHIMICHANGA, MEATLESS","Chimichanga, meatless" +58104530,"CHIMICHANGA W/ CHICKEN","Chimichanga with chicken" +58104535,"CHIMICHANGA W/ MEAT & SOUR CREAM","Chimichanga with meat and sour cream" +58104540,"CHIMICHANGA, MEATLESS, W/ SOUR CREAM","Chimichanga, meatless, with sour cream" +58104550,"CHIMICHANGA W/ CHICKEN & SOUR CREAM","Chimichanga with chicken and sour cream" +58104710,"QUESADILLA, JUST CHEESE, MEATLESS","Quesadilla, just cheese, meatless" +58104720,"QUESADILLA, JUST CHEESE, FROM FAST FOOD","Quesadilla, just cheese, from fast food" +58104730,"QUESADILLA W/ MEAT","Quesadilla with meat" +58104740,"QUESADILLA W/ CHICKEN","Quesadilla with chicken" +58104745,"QUESADILLA WITH CHICKEN, FROM FAST FOOD","Quesadilla with chicken, from fast food" +58104750,"QUESADILLA W/ VEGETABLES","Quesadilla with vegetables" +58104760,"QUESADILLA W/ VEGETABLES & MEAT","Quesadilla with vegetables and meat" +58104770,"QUESADILLA W/ VEGETABLES & CHICKEN","Quesadilla with vegetables and chicken" +58104800,"TAQUITO OR FLAUTA W/ CHEESE","Taquito or flauta with cheese" +58104820,"TAQUITO OR FLAUTA W/ MEAT","Taquito or flauta with meat" +58104825,"TAQUITO OR FLAUTA W/ MEAT & CHEESE","Taquito or flauta with meat and cheese" +58104830,"TAQUITO OR FLAUTA W/ CHICKEN","Taquito or flauta with chicken" +58104835,"TAQUITO OR FLAUTA W/ CHICKEN AND CHEESE","Taquito or flauta with chicken and cheese" +58104900,"TAQUITO OR FLAUTA W/ EGG","Taquito or flauta with egg" +58104905,"TAQUITO OR FLAUTA W/ EGG & BREAKFAST MEAT","Taquito or flauta with egg and breakfast meat" +58105000,"FAJITA W/ CHICKEN & VEGETABLES","Fajita with chicken and vegetables" +58105050,"FAJITA W/ MEAT & VEGETABLES","Fajita with meat and vegetables" +58105075,"FAJITA W/ VEGETABLES","Fajita with vegetables" +58105100,"PUPUSA, CHEESE-FILLED","Pupusa, cheese-filled" +58105105,"PUPUSA, BEAN-FILLED","Pupusa, bean-filled" +58105110,"PUPUSA, MEAT-FILLED","Pupusa, meat-filled" +58106200,"PIZZA, CHEESE, PREP FROM FROZEN, THIN CRUST","Pizza, cheese, prepared from frozen, thin crust" +58106205,"PIZZA, CHEESE, PREP FROM FROZEN, THICK CRUST","Pizza, cheese, prepared from frozen, thick crust" +58106210,"PIZZA, CHEESE,FRM REST/FF, NS AS TO TYPE OF CRUST","Pizza, cheese, from restaurant or fast food, NS as to type of crust" +58106220,"PIZZA, CHEESE, FROM RESTAURANT OR FAST FOOD, THIN CRUST","Pizza, cheese, from restaurant or fast food, thin crust" +58106225,"PIZZA, CHEESE, FROM RESTAURANT OR FAST FOOD, REGULAR CRUST","Pizza, cheese, from restaurant or fast food, regular crust" +58106230,"PIZZA, CHEESE, FROM RESTAURANT OR FAST FOOD, THICK CRUST","Pizza, cheese, from restaurant or fast food, thick crust" +58106233,"PIZZA, CHEESE, STUFFED CRUST","Pizza, cheese, stuffed crust" +58106235,"PIZZA, CHEESE, FROM SCHOOL LUNCH, THIN CRUST","Pizza, cheese, from school lunch, thin crust" +58106236,"PIZZA, CHEESE, FROM SCHOOL LUNCH, THICK CRUST","Pizza, cheese, from school lunch, thick crust" +58106240,"PIZZA, EXTRA CHEESE, NS AS TO TYPE OF CRUST","Pizza, extra cheese, NS as to type of crust" +58106250,"PIZZA, EXTRA CHEESE, THIN CRUST","Pizza, extra cheese, thin crust" +58106255,"PIZZA, EXTRA CHEESE, REGULAR CRUST","Pizza, extra cheese, regular crust" +58106260,"PIZZA, EXTRA CHEESE, THICK CRUST","Pizza, extra cheese, thick crust" +58106300,"PIZZA, CHEESE, W/ VEGETABLES, PREP FROM FROZEN, THIN CRUST","Pizza, cheese, with vegetables, prepared from frozen, thin crust" +58106305,"PIZZA, CHEESE, W/ VEGETABLES, PREP FROM FROZEN, THICK CRUST","Pizza, cheese with vegetables, prepared from frozen, thick crust" +58106310,"PIZZA, CHEESE, W/ VEG, NS AS TO TYPE OF CRUST","Pizza, cheese, with vegetables, NS as to type of crust" +58106320,"PIZZA, CHEESE, W/ VEGETABLES, THIN CRUST","Pizza, cheese, with vegetables, thin crust" +58106325,"PIZZA, CHEESE, W/ VEGETABLES, REGULAR CRUST","Pizza, cheese, with vegetables, regular crust" +58106330,"PIZZA, CHEESE, W/ VEGETABLES, THICK CRUST","Pizza, cheese, with vegetables, thick crust" +58106340,"PIZZA W/ CHEESE & EXTRA VEGETABLES, NS AS TO CRUST","Pizza, with cheese and extra vegetables, NS as to type of crust" +58106345,"PIZZA W/ CHEESE & EXTRA VEGETABLES, THIN CRUST","Pizza with cheese and extra vegetables, thin crust" +58106347,"PIZZA W/ CHEESE & EXTRA VEGETABLES, REGULAR CRUST","Pizza with cheese and extra vegetables, regular crust" +58106350,"PIZZA W/ CHEESE & EXTRA VEGETABLES, THICK CRUST","Pizza with cheese and extra vegetables, thick crust" +58106357,"PIZZA, CHEESE, W/ FRUIT, NS AS TO TYPE OF CRUST","Pizza, cheese, with fruit, NS as to type of crust" +58106358,"PIZZA, CHEESE, W/ FRUIT, THIN CRUST","Pizza, cheese, with fruit, thin crust" +58106359,"PIZZA, CHEESE, W/ FRUIT, REGULAR CRUST","Pizza, cheese, with fruit, regular crust" +58106360,"PIZZA, CHEESE, W/ FRUIT, THICK CRUST","Pizza, cheese, with fruit, thick crust" +58106500,"PIZZA W/ MEAT, PREP FROM FROZEN, THIN CRUST","Pizza with meat, prepared from frozen, thin crust" +58106505,"PIZZA W/ MEAT, PREP FROM FROZEN, THICK CRUST","Pizza with meat, prepared from frozen, thick crust" +58106540,"PIZZA W/ PEPPERONI,FRM REST/FF, NS AS TO TYPE OF CRUST","Pizza with pepperoni, from restaurant or fast food, NS as to type of crust" +58106550,"PIZZA W/PEPPERONI, FROM RESTAURANT/FAST FOOD, THIN CRUST","Pizza with pepperoni, from restaurant or fast food, thin crust" +58106555,"PIZZA W/PEPPERONI, FROM RESTAURANT/FAST FOOD, REGULAR CRUST","Pizza with pepperoni, from restaurant or fast food, regular crust" +58106560,"PIZZA W/ PEPPERONI, FROM RESTAURANT/FAST FOOD, THICK CRUST","Pizza with pepperoni, from restaurant or fast food, thick crust" +58106565,"PIZZA WITH PEPPERONI, STUFFED CRUST","Pizza with pepperoni, stuffed crust" +58106570,"PIZZA WITH PEPPERONI, FROM SCHOOL LUNCH, THIN CRUST","Pizza with pepperoni, from school lunch, thin crust" +58106580,"PIZZA WITH PEPPERONI, FROM SCHOOL LUNCH, THICK CRUST","Pizza with pepperoni, from school lunch, thick crust" +58106610,"PIZZA W/ MEAT OTHER THAN PEPP, FRM REST/FF, NS TYPE OF CRUST","Pizza with meat other than pepperoni, from restaurant or fast food, NS as to type of crust" +58106620,"PIZZA W/MEAT NOT PEPPERONI, FRM RESTAURANT/FF,THIN CRUST","Pizza with meat other than pepperoni, from restaurant or fast food, thin crust" +58106625,"PIZZA W/MEAT NOT PEPPERONI, FRM RESTAURANT/FF, REG CRUST","Pizza with meat other than pepperoni, from restaurant or fast food, regular crust" +58106630,"PIZZA W/MEAT NOT PEPPERONI, FRM RESTAURANT/FF, THICK CRUST","Pizza with meat other than pepperoni, from restaurant or fast food, thick crust" +58106633,"PIZZA, W/MEAT NOT PEPPERONI, STUFFED CRUST","Pizza, with meat other than pepperoni, stuffed crust" +58106635,"PIZZA, W/MEAT OTHER THAN PEPPERONI, FRM SCL LUNCH, THIN CRUS","Pizza, with meat other than pepperoni, from school lunch, thin crust" +58106636,"PIZZA, W/MEAT OTHER THAN PEPPERONI, FRM SCL LUNCH, THICK CRU","Pizza, with meat other than pepperoni, from school lunch, thick crust" +58106640,"PIZZA W/ EXTRA MEAT, NS AS TO TYPE OF CRUST","Pizza with extra meat, NS as to type of crust" +58106650,"PIZZA W/ EXTRA MEAT, THIN CRUST","Pizza with extra meat, thin crust" +58106655,"PIZZA W/ EXTRA MEAT, REGULAR CRUST","Pizza with extra meat, regular crust" +58106660,"PIZZA W/ EXTRA MEAT, THICK CRUST","Pizza with extra meat, thick crust" +58106700,"PIZZA W/ MEAT & VEGS, PREP FROM FROZEN, THIN CRUST","Pizza with meat and vegetables, prepared from frozen, thin crust" +58106705,"PIZZA W/ MEAT & VEGS, PREP FROM FROZEN, THICK CRUST","Pizza with meat and vegetables, prepared from frozen, thick crust" +58106710,"PIZZA W/ MEAT & VEG, NS AS TO TYPE OF CRUST","Pizza with meat and vegetables, NS as to type of crust" +58106720,"PIZZA W/ MEAT & VEGETABLES, THIN CRUST","Pizza with meat and vegetables, thin crust" +58106725,"PIZZA W/ MEAT & VEGETABLES, REGULAR CRUST","Pizza with meat and vegetables, regular crust" +58106730,"PIZZA W/ MEAT & VEGETABLES, THICK CRUST","Pizza with meat and vegetables, thick crust" +58106735,"PIZZA W/ EXTRA MEAT & EXTRA VEGS, NS AS TO TYPE OF CRUST","Pizza with extra meat and extra vegetables, NS as to type of crust" +58106736,"PIZZA W/ EXTRA MEAT & EXTRA VEGS, THIN CRUST","Pizza with extra meat and extra vegetables, thin crust" +58106737,"PIZZA W/ EXTRA MEAT & EXTRA VEGS, THICK CRUST","Pizza with extra meat and extra vegetables, thick crust" +58106738,"PIZZA W/ EXTRA MEAT & EXTRA VEGS, REGULAR CRUST","Pizza with extra meat and extra vegetables, regular crust" +58106740,"PIZZA W/ MEAT & FRUIT, NS AS TO TYPE OF CRUST","Pizza with meat and fruit, NS as to type of crust" +58106750,"PIZZA W/ MEAT & FRUIT, THIN CRUST","Pizza with meat and fruit, thin crust" +58106755,"PIZZA W/ MEAT & FRUIT, REGULAR CRUST","Pizza with meat and fruit, regular crust" +58106760,"PIZZA W/ MEAT & FRUIT, THICK CRUST","Pizza with meat and fruit, thick crust" +58106820,"PIZZA W/ BEANS & VEG, THIN CRUST (INCL TACO PIZZA)","Pizza with beans and vegetables, thin crust" +58106830,"PIZZA W/ BEANS & VEG, THICK CRUST (INCL TACO PIZZA)","Pizza with beans and vegetables, thick crust" +58107050,"PIZZA, NO CHEESE, THIN CRUST","Pizza, no cheese, thin crust" +58107100,"PIZZA, NO CHEESE, THICK CRUST","Pizza, no cheese, thick crust" +58107220,"WHITE PIZZA, THIN CRUST","White pizza, thin crust" +58107230,"WHITE PIZZA, THICK CRUST","White pizza, thick crust" +58108000,"CALZONE, W/ CHEESE, MEATLESS (INCL STROMBOLI)","Calzone, with cheese, meatless" +58108010,"CALZONE, W/ MEAT & CHEESE (INCLUDE STROMBOLI)","Calzone, with meat and cheese" +58108050,"PIZZA ROLLS (INCLUDE PIZZA BITES)","Pizza rolls" +58110110,"EGG ROLL, MEATLESS","Egg roll, meatless" +58110120,"EGG ROLL, W/ SHRIMP","Egg roll, with shrimp" +58110130,"EGG ROLL, W/ BEEF/PORK","Egg roll, with beef and/or pork" +58110170,"EGG ROLL, W/ CHICKEN","Egg roll, with chicken or turkey" +58110200,"ROLL W/MEAT&/SHRIMP,VEGETABLES&RICE PAPER(NOT FRIED","Roll with meat and/or shrimp, vegetables and rice paper (not fried)" +58111110,"WON TON (WONTON), FRIED, FILLED W/MEAT, POULTRY, OR SEAFOOD","Won ton (wonton), fried, filled with meat, poultry, or seafood" +58111120,"WON TON (WONTON), FRIED, MEATLESS","Won ton (wonton), fried, meatless" +58111130,"WON TON (WONTON), FRIED, FILLED WITH MEAT, POULTRY, OR SEAFO","Won ton (wonton), fried, filled with meat, poultry, or seafood, and vegetable" +58111200,"PUFFS, FRIED, CRAB MEAT & CREAM CHEESE FILLED","Puffs, fried, crab meat and cream cheese filled" +58112510,"DUMPLING, STEAMED, FILLED W/ MEAT OR SEAFOOD","Dumpling, steamed, filled with meat, poultry, or seafood" +58115110,"TAMALE CASSEROLE, P.R. (TAMALES EN CAZUELA)","Tamale casserole, Puerto Rican style (Tamales en cazuela)" +58115150,"TAMAL IN A LEAF, P.R. (TAMALES EN HOJA)","Tamal in a leaf, Puerto Rican style (Tamales en hoja)" +58115210,"TACO W/ CRAB MEAT, P.R. (TACOS DE JUEYES)","Taco with crab meat, Puerto Rican style (Taco de jueye)" +58116110,"MEAT TURNOVER, PUERTO RICAN STYLE","Meat turnover, Puerto Rican style (Pastelillo de carne; Empanadilla)" +58116115,"EMPANADA, MEXICAN TURNOVER, W/ CHS & VEG","Empanada, Mexican turnover, filled with cheese and vegetables" +58116120,"EMPANADA, MEXICAN TURNOVER, W/ MEAT & VEGS","Empanada, Mexican turnover, filled with meat and vegetables" +58116130,"EMPANADA, MEXICAN TURNOVER, W/ CHIC & VEG","Empanada, Mexican turnover, filled with chicken and vegetables" +58116210,"MEAT PIE, P.R. (PASTELON DE CARNE)","Meat pie, Puerto Rican style (Pastelon de carne)" +58116310,"CHEESE TURNOVER, PUERTO RICAN STYLE","Cheese turnover, Puerto Rican style (Pastelillo de queso; Empanadilla)" +58117110,"CORNMEAL FRITTER, P.R. (AREPA, P.R. AREPITAS)","Cornmeal fritter, Puerto Rican style (Arepa; P.R. arepita)" +58117210,"CORNMEAL STICK, P.R. (SORULLOS / SORULLITOS DE MAIZ)","Cornmeal stick, Puerto Rican style (Sorullos / Sorullitos de maiz)" +58117310,"KIBBY, P.R. (BEEF & BULGUR) (PLATO ARABE)","Kibby, Puerto Rican style (beef and bulgur) (Plato Arabe)" +58117410,"CODFISH FRITTER, P.R. (BACALAITOS FRITOS)","Codfish fritter, Puerto Rican style (Bacalaitos fritos)" +58117510,"HAYACAS, P.R. (HOMINY, PORK OR HAM, VEGETABLES)","Hayacas, Puerto Rican style (hominy, pork or ham, vegetables)" +58118110,"CORNSTARCH COCONUT DESSERT, P.R. (TEMBLEQUE)","Cornstarch coconut dessert, Puerto Rican style (Tembleque)" +58118210,"CORNMEAL COCONUT DESSERT, P.R.","Cornmeal coconut dessert, Puerto Rican style (Harina de maiz con coco)" +58120110,"CREPES, FILLED W/ MEAT, FISH OR POULTRY, W/ SAUCE","Crepes, filled with meat, fish, or poultry, with sauce" +58120120,"CREPE,FILLED W/ MEAT, FISH & POULTRY,NO SCE ON TOP","Crepe, filled with beef, pork, fish and/or poultry, no sauce on top" +58121510,"DUMPLING, MEAT-FILLED (INCLUDE PIEROGI, PIROSHKI)","Dumpling, meat-filled" +58121610,"DUMPLING, POTATO/CHEESE-FILLED (INCLUDE PIEROGI)","Dumpling, potato- or cheese-filled" +58121620,"DUMPLING, VEGETABLE","Dumpling, vegetable" +58122210,"GNOCCHI, CHEESE","Gnocchi, cheese" +58122220,"GNOCCHI, POTATO","Gnocchi, potato" +58122250,"KISHKE, STUFFED DERMA","Kishke, stuffed derma" +58122310,"KNISH, POTATO (PASTRY FILLED WITH POTATO)","Knish, potato (pastry filled with potato)" +58122320,"KNISH, CHEESE (PASTRY FILLED WITH CHEESE)","Knish, cheese (pastry filled with cheese)" +58122330,"KNISH, MEAT (PASTRY FILLED WITH MEAT)","Knish, meat (pastry filled with meat)" +58123110,"SWEET BREAD DOUGH, FILLED WITH MEAT, STEAMED","Sweet bread dough, filled with meat, steamed" +58123120,"SWEET BREAD DOUGH, FILLED WITH BEAN PASTE, MEATLESS, STEAMED","Sweet bread dough, filled with bean paste, meatless, steamed" +58124210,"PASTRY, CHEESE-FILLED","Pastry, cheese-filled" +58124250,"SPANAKOPITTA (INCL GREEK SPINACH-CHEESE PIE)","Spanakopitta" +58124500,"PASTRY,FILLED W/POTATOES & PEAS, FRIED","Pastry, filled with potatoes and peas, fried" +58125110,"QUICHE W/ MEAT, POULTRY OR FISH","Quiche with meat, poultry or fish" +58125120,"SPINACH QUICHE, MEATLESS","Spinach quiche, meatless" +58125180,"CHEESE QUICHE, MEATLESS","Cheese quiche, meatless" +58126000,"BIEROCK (TURNOVER W/ BEEF & CABBAGE)","Bierock (turnover filled with ground beef and cabbage mixture)" +58126110,"TURNOVER, MEAT-FILLED, NO GRAVY","Turnover, meat-filled, no gravy" +58126120,"TURNOVER, MEAT-FILLED, W/ GRAVY","Turnover, meat-filled, with gravy" +58126130,"TURNOVER, MEAT- & CHEESE-FILLED, NO GRAVY","Turnover, meat- and cheese-filled, no gravy" +58126140,"TURNOVER, MEAT- & BEAN-FILLED, NO GRAVY","Turnover, meat- and bean-filled, no gravy" +58126150,"TURNOVER, MEAT & CHEESE, TOMATO SAUCE","Turnover, meat- and cheese-filled, tomato-based sauce" +58126160,"TURNOVER, CHEESE-FILLED, TOMATO-BASED SAUCE","Turnover, cheese-filled, tomato-based sauce" +58126170,"TURNOVER, MEAT & VEG (NO POTATO), NO GRAVY","Turnover, meat-and vegetable- filled (no potatoes, no gravy)" +58126180,"TURNOVER,MEAT- POTATO- & VEGETABLE-FILLED NO GRAVY","Turnover, meat-, potato-, and vegetable-filled, no gravy" +58126270,"TURNOVER,CHICKEN/TURKEY FILLED,NO GRAVY","Turnover, chicken- or turkey-, and cheese-filled, no gravy" +58126280,"TURNOVER, CHICKEN/TURKEY- & VEG-FILLED, LOWER FAT","Turnover, chicken- or turkey-, and vegetable-filled, lower in fat" +58126290,"TURNOVER, MEAT- & CHEESE-FILLED, LOWER FAT","Turnover, meat- and cheese-filled, lower in fat" +58126300,"TURNOVER, MEAT- & CHEESE-FILLED, TOMATO SAUCE, LOWER FAT","Turnover, meat- and cheese-filled, tomato-based sauce, lower in fat" +58126310,"TURNOVER, CHICKEN, W/ GRAVY","Turnover, chicken, with gravy" +58126400,"TURNOVER, FILLED W/ EGG, MEAT & CHEESE","Turnover, filled with egg, meat and cheese" +58126410,"TURNOVER, FILLED W/ EGG, MEAT & CHEESE, LOWER IN FAT","Turnover, filled with egg, meat, and cheese, lower in fat" +58127110,"VEGETABLES IN PASTRY (INCL ALL VARIETIES)","Vegetables in pastry" +58127150,"VEGETABLES & CHEESE IN PASTRY","Vegetables and cheese in pastry" +58127200,"CROISSANT, FILLED W/ BROCCOLI & CHEESE","Croissant sandwich, filled with broccoli and cheese" +58127210,"CROISSANT, FILLED W/ HAM & CHEESE","Croissant sandwich, filled with ham and cheese" +58127220,"CROISSANT, FILLED W/CHICKEN,BROCCOLI & CHEESE SAUCE","Croissant sandwich, filled with chicken, broccoli, and cheese sauce" +58127270,"CROISSANT W/ SAUSAGE & EGG","Croissant sandwich with sausage and egg" +58127290,"CROISSANT W/ BACON & EGG","Croissant sandwich with bacon and egg" +58127310,"CROISSANT W/ HAM, EGG, & CHEESE","Croissant sandwich with ham, egg, and cheese" +58127330,"CROISSANT W/ SAUSAGE, EGG, & CHEESE","Croissant sandwich with sausage, egg, and cheese" +58127350,"CROISSANT W/ BACON, EGG, & CHEESE","Croissant sandwich with bacon, egg, and cheese" +58127500,"VEGETABLE SUBMARINE SANDWICH, W/ FAT FREE SPREAD","Vegetable submarine sandwich, with fat free spread" +58128000,"BISCUIT W/ GRAVY","Biscuit with gravy" +58128110,"CHICKEN CORNBREAD","Chicken cornbread" +58128120,"CORNMEAL DRESSING W/ CHICKEN & VEGETABLES","Cornmeal dressing with chicken or turkey and vegetables" +58128210,"DRESSING W/ OYSTERS","Dressing with oysters" +58128220,"DRESSING W/ CHICKEN/TURKEY & VEGETABLES","Dressing with chicken or turkey and vegetables" +58128250,"DRESSING W/ MEAT & VEGETABLES","Dressing with meat and vegetables" +58130011,"LASAGNA WITH MEAT","Lasagna with meat" +58130013,"LASAGNA W/ MEAT, CANNED","Lasagna with meat, canned" +58130020,"LASAGNA, W/ MEAT & SPINACH","Lasagna with meat and spinach" +58130140,"LASAGNA WITH CHICKEN OR TURKEY","Lasagna with chicken or turkey" +58130150,"LASAGNA W/ CHIC OR TURKEY, & SPINACH","Lasagna, with chicken or turkey, and spinach" +58130310,"LASAGNA, MEATLESS","Lasagna, meatless" +58130320,"LASAGNA, MEATLESS, W/ VEGETABLES","Lasagna, meatless, with vegetables" +58130610,"LASAGNA W/ MEAT, WHOLE WHEAT NOODLES","Lasagna with meat, whole wheat noodles" +58130810,"LASAGNA, MEATLESS, WHOLE WHEAT NOODLES","Lasagna, meatless, whole wheat noodles" +58130910,"LASAGNA W/ MEAT, SPINACH NOODLES","Lasagna with meat, spinach noodles" +58130950,"LASAGNA, MEATLESS, SPINACH NOODLES","Lasagna, meatless, spinach noodles" +58131100,"RAVIOLI, FILLING NS, NO SAUCE","Ravioli, NS as to filling, no sauce" +58131110,"RAVIOLI, FILLING NS, TOMATO SAUCE","Ravioli, NS as to filling, with tomato sauce" +58131120,"RAVIOLI, NS AS TO FILLING, WITH CREAM SAUCE","Ravioli, NS as to filling, with cream sauce" +58131310,"RAVIOLI, MEAT-FILLED, NO SAUCE","Ravioli, meat-filled, no sauce" +58131320,"RAVIOLI, MEAT-FILLED, W/ TOMATO OR MEAT SAUCE","Ravioli, meat-filled, with tomato sauce or meat sauce" +58131323,"RAVIOLI, MEAT-FILLED, W/ TOMATO OR MEAT SAUCE, CANNED","Ravioli, meat-filled, with tomato sauce or meat sauce, canned" +58131330,"RAVIOLI, MEAT-FILLED, WITH CREAM SAUCE","Ravioli, meat-filled, with cream sauce" +58131510,"RAVIOLI, CHEESE-FILLED, NO SAUCE","Ravioli, cheese-filled, no sauce" +58131520,"RAVIOLI, CHEESE-FILLED, W/ TOMATO SAUCE","Ravioli, cheese-filled, with tomato sauce" +58131523,"RAVIOLI, CHEESE-FILLED, W/ TOMATO SAUCE, CANNED","Ravioli, cheese-filled, with tomato sauce, canned" +58131530,"RAVIOLI, CHEESE-FILLED, W/ MEAT SAUCE","Ravioli, cheese-filled, with meat sauce" +58131535,"RAVIOLI, CHEESE-FILLED, WITH CREAM SAUCE","Ravioli, cheese-filled, with cream sauce" +58131590,"RAVIOLI, CHEESE AND SPINACH-FILLED, NO SAUCE","Ravioli, cheese and spinach-filled, no sauce" +58131600,"RAVIOLI, CHEESE&SPINACH-FILLED, W/ CREAM SAUCE","Ravioli, cheese and spinach-filled, with cream sauce" +58131610,"RAVIOLI, CHEESE AND SPINACH FILLED, WITH TOMATO SAUCE","Ravioli, cheese and spinach filled, with tomato sauce" +58132110,"SPAGHETTI W/ TOMATO SAUCE, MEATLESS","Spaghetti with tomato sauce, meatless" +58132113,"PASTA, W/ TOMATO SAUCE & CHEESE, CANNED","Pasta with tomato sauce and cheese, canned" +58132310,"SPAGHETTI W/TOMAT SAUCE & MEAT SAUCE","Spaghetti with tomato sauce and meatballs or spaghetti with meat sauce or spaghetti with meat sauce and meatballs" +58132313,"PASTA W/ TOMATO SAUCE & MEAT/MEATBALLS, CANNED","Pasta with tomato sauce and meat or meatballs, canned" +58132340,"SPAGHETTI W/ TOMATO SAUCE & VEGETABLES","Spaghetti with tomato sauce and vegetables" +58132350,"SPAGHETTI, WHOLE WHEAT, W/ TOMATO SAUCE, MEATLESS","Spaghetti with tomato sauce, meatless, whole wheat noodles" +58132360,"SPAGHETTI, WHOLE WHEAT, W/ TOMATO & MEAT SAUCE","Spaghetti with tomato sauce and meatballs, whole wheat noodles or spaghetti with meat sauce, whole wheat noodles or spaghetti with meat sauce and meatballs, whole wheat noodles" +58132450,"SPAGHETTI W/ TOM SAUCE, MEATLESS, SPINACH NOODLES","Spaghetti with tomato sauce, meatless, made with spinach noodles" +58132460,"SPAGHETTI W/ TOMATO & MEAT SAUCE, SPINACH NOODLES","Spaghetti with tomato sauce and meatballs made with spinach noodles, or spaghetti with meat sauce made with spinach noodles, or spaghetti with meat sauce and meatballs made with spinach noodles" +58132710,"SPAGHETTI W/ TOMATO SAUCE & FRANKFURTERS/HOT DOG","Spaghetti with tomato sauce and frankfurters or hot dogs" +58132713,"PASTA W/ TOMATO SAUCE & FRANKFURTERS/HOT DOGS, CANNED","Pasta with tomato sauce and frankfurters or hot dogs, canned" +58132800,"SPAGHETTI W/ CLAM SAUCE, NS AS TO RED OR WHITE","Spaghetti with clam sauce, NS as to red or white" +58132810,"SPAGHETTI W/ RED CLAM SAUCE","Spaghetti with red clam sauce" +58132820,"SPAGHETTI W/ WHITE CLAM SAUCE","Spaghetti with white clam sauce" +58132910,"SPAGHETTI WITH TOMATO SAUCE AND POULTRY","Spaghetti with tomato sauce and poultry" +58133110,"MANICOTTI, CHEESE-FILLED, NO SAUCE","Manicotti, cheese-filled, no sauce" +58133120,"MANICOTTI, CHEESE-FILLED, W/ TOMATO SAUCE, MEATLESS","Manicotti, cheese-filled, with tomato sauce, meatless" +58133130,"MANICOTTI, CHEESE-FILLED, W/ MEAT SAUCE","Manicotti, cheese-filled, with meat sauce" +58133140,"MANICOTTI, VEG- & CHEESE-FILLED, W/TOM SCE,MEATLESS","Manicotti, vegetable- and cheese-filled, with tomato sauce, meatless" +58134110,"STUFFED SHELLS, CHEESE-FILLED, NO SAUCE","Stuffed shells, cheese-filled, no sauce" +58134120,"STUFFED SHELLS, CHEESE-FILLED, W/ TOM SC, MEATLESS","Stuffed shells, cheese-filled, with tomato sauce, meatless" +58134130,"STUFFED SHELLS, CHEESE-FILLED, W/ MEAT SAUCE","Stuffed shells, cheese-filled, with meat sauce" +58134160,"STUFFED SHELLS, CHEESE AND SPINACH FILLED, NO SAUCE","Stuffed shells, cheese- and spinach- filled, no sauce" +58134210,"STUFFED SHELLS, W/ CHICKEN, W/ TOM SCE","Stuffed shells, with chicken, with tomato sauce" +58134310,"STUFFED SHELLS, W/ FISH &/OR SHELLFISH, W/ TOM SCE","Stuffed shells, with fish and/or shellfish, with tomato sauce" +58134610,"TORTELLINI, MEAT-FILLED, W/ TOMATO SAUCE","Tortellini, meat-filled, with tomato sauce" +58134613,"TORTELLINI, MEAT-FILLED, W/ TOMATO SAUCE, CANNED","Tortellini, meat-filled, with tomato sauce, canned" +58134620,"TORTELLINI, CHEESE-FILLED, MEATLESS, W/TOMATO SAUCE","Tortellini, cheese-filled, meatless, with tomato sauce" +58134623,"TORTELLINI,CHEESE-FILLED,MEATLESS,W/TOMATO SAUCE,CANNED","Tortellini, cheese-filled, meatless, with tomato sauce, canned" +58134630,"TORTELLINI, CHEESE, W/ VEGETABLES & DRESSING","Tortellini, cheese-filled, meatless, with vegetables and vinaigrette dressing" +58134640,"TORTELLINI, CHEESE-FILLED, MEATLESS, W/ VINAIGRETTE","Tortellini, cheese-filled, meatless, with vinaigrette dressing" +58134650,"TORTELLINI, MEAT-FILLED, NO SAUCE","Tortellini, meat-filled, no sauce" +58134660,"TORTELLINI, CHEESE-FILLED, W/ CREAM SAUCE","Tortellini, cheese-filled, with cream sauce" +58134680,"TORTELLINI, CHEESE-FILLED, NO SAUCE","Tortellini, cheese-filled, no sauce" +58134710,"TORTELLINI, SPINACH-FILLED, W/ TOMATO SAUCE","Tortellini, spinach-filled, with tomato sauce" +58134720,"TORTELLINI, SPINACH-FILLED, NO SAUCE","Tortellini, spinach-filled, no sauce" +58134810,"CANNELONI, CHEESE & SPINACH-FILLED, NO SAUCE","Cannelloni, cheese- and spinach-filled, no sauce" +58135110,"CHOW FUN NOODLES W/ MEAT & VEGETABLES","Chow fun noodles with meat and vegetables" +58135120,"CHOW FUN NOODLES W/ VEGETABLES, MEATLESS","Chow fun noodles with vegetables, meatless" +58136110,"LO MEIN, NFS","Lo mein, NFS" +58136120,"LO MEIN, MEATLESS","Lo mein, meatless" +58136130,"LO MEIN WITH SHRIMP","Lo mein, with shrimp" +58136140,"LO MEIN W/ PORK","Lo mein, with pork" +58136150,"LO MEIN W/ BEEF","Lo mein, with beef" +58136160,"LO MEIN W/ CHICKEN (INCL TURKEY)","Lo mein, with chicken" +58137210,"PAD THAI, NFS","Pad Thai, NFS" +58137220,"PAD THAI, MEATLESS","Pad Thai, meatless" +58137230,"PAD THAI WITH CHICKEN","Pad Thai with chicken" +58137240,"PAD THAI WITH SEAFOOD","Pad Thai with seafood" +58137250,"PAD THAI WITH MEAT","Pad Thai with meat" +58140110,"SPAGHETTI W/ CORNED BEEF, P.R.","Spaghetti with corned beef, Puerto Rican style" +58140310,"MACARONI W/ TUNA, P.R. (MACARRONES CON ATUN)","Macaroni with tuna, Puerto Rican style (Macarrones con atun)" +58145110,"MACARONI OR NOODLES W/ CHEESE","Macaroni or noodles with cheese" +58145112,"MACARONI OR NOODLES WITH CHEESE, MADE FROM PACKAGED MIX","Macaroni or noodles with cheese, made from packaged mix" +58145113,"MACARONI OR NOODLES W/ CHEESE, CANNED","Macaroni or noodles with cheese, canned" +58145117,"MACARONI OR NOODLES WITH CHEESE, EASY MAC TYPE","Macaroni or noodles with cheese, Easy Mac type" +58145119,"MACARONI OR NOODLES WITH CHEESE, MADE FRM RED FAT PACKAGE","Macaroni or noodles with cheese, made from reduced fat packaged mix" +58145120,"MACARONI OR NOODLES W/ CHEESE & TUNA","Macaroni or noodles with cheese and tuna" +58145135,"MACARONI OR NOODLES WITH CHEESE AND MEAT","Macaroni or noodles with cheese and meat" +58145136,"MACARONI OR NOODLES W/CHEESE & MEAT, FR HAMBURGER HELPER","Macaroni or noodles with cheese and meat, prepared from Hamburger Helper mix" +58145140,"MACARONI OR NOODLES W/ CHEESE & TOMATO","Macaroni or noodles with cheese and tomato" +58145160,"MACARONI/NOODLES W/ CHEESE & FRANKFURTER/HOT DOG","Macaroni or noodles with cheese and frankfurters or hot dogs" +58145170,"MACARONI OR NOODLES WITH CHEESE AND EGG","Macaroni or noodles with cheese and egg" +58145190,"MACARONI W/ CHEESE & CHICKEN","Macaroni or noodles with cheese and chicken or turkey" +58146100,"PASTA W/ TOMATO SAUCE, MEATLESS","Pasta with tomato sauce, meatless" +58146110,"PASTA W/ MEAT SAUCE (INCLUDE AMER CHOP SUEY)","Pasta with meat sauce" +58146120,"PASTA W/ CHEESE & MEAT SAUCE","Pasta with cheese and meat sauce" +58146130,"PASTA W/ CARBONARA SAUCE","Pasta with carbonara sauce" +58146150,"PASTA W/ CHEESE & TOMATO SAUCE, MEATLESS","Pasta with cheese and tomato sauce, meatless" +58146160,"PASTA WITH VEGETABLES, NO SAUCE OR DRESSING","Pasta with vegetables, no sauce or dressing" +58146200,"PASTA, MEAT-FILLED, W/ GRAVY, CANNED","Pasta, meat-filled, with gravy, canned" +58146300,"PASTA, WHOLE WHEAT, WITH MEAT SAUCE","Pasta, whole wheat, with meat sauce" +58146310,"PASTA, WHOLE WHEAT, W/ TOMATO SAUCE, MEATLESS","Pasta, whole wheat, with tomato sauce, meatless" +58147100,"PASTA W/ PESTO SAUCE","Pasta with pesto sauce" +58147110,"MACARONI OR NOODLES W/ BEANS & TOMATO SAUCE","Macaroni or noodles with beans or lentils and tomato sauce" +58147310,"MACARONI, CREAMED","Macaroni, creamed" +58147330,"MACARONI OR NOODLES, CREAMED, WITH CHEESE","Macaroni or noodles, creamed, with cheese" +58147340,"MACARONI OR NOODLES, CREAMED, WITH CHEESE AND TUNA","Macaroni or noodles, creamed, with cheese and tuna" +58147350,"MACARONI, CREAMED, W/ VEGETABLES","Macaroni, creamed, with vegetables" +58147510,"FLAVORED PASTA (INCL LIPTON BEEF, CHICKEN FLAVORS)","Flavored pasta" +58147520,"YAT GA MEIN WITH MEAT, FISH, OR POULTRY","Yat Ga Mein with meat, fish, or poultry" +58148110,"MACARONI OR PASTA SALAD, W/ MAYO","Macaroni or pasta salad, made with mayonnaise" +58148111,"MACARONI OR PASTA SALAD, W/ LT MAYO","Macaroni or pasta salad, made with light mayonnaise" +58148112,"MACARONI OR PASTA SALAD, W/ MAYO-TYPE DRSG","Macaroni or pasta salad, made with mayonnaise-type salad dressing" +58148113,"MACARONI OR PASTA SALAD, W/LT MAYO-TYPE DRSG","Macaroni or pasta salad, made with light mayonnaise-type salad dressing" +58148114,"MACARONI OR PASTA SALAD, W/ ITALIAN DRSG","Macaroni or pasta salad, made with Italian dressing" +58148115,"MACARONI OR PASTA SALAD, W/LT ITALIAN DRSG","Macaroni or pasta salad, made with light Italian dressing" +58148116,"MACARONI OR PASTA SALAD, W/ CREAMY DRSG","Macaroni or pasta salad, made with creamy dressing" +58148117,"MACARONI OR PASTA SALAD, W/ LT CREAMY DRSG","Macaroni or pasta salad, made with light creamy dressing" +58148118,"MACARONI OR PASTA SALAD, W/ ANY TYPE OF FAT FREE DRSG","Macaroni or pasta salad, made with any type of fat free dressing" +58148120,"MACARONI OR PASTA SALAD WITH EGG","Macaroni or pasta salad with egg" +58148130,"MACARONI OR PASTA SALAD WITH TUNA","Macaroni or pasta salad with tuna" +58148140,"MACARONI OR PASTA SALAD WITH CRAB MEAT","Macaroni or pasta salad with crab meat" +58148150,"MACARONI OR PASTA SALAD WITH SHRIMP","Macaroni or pasta salad with shrimp" +58148160,"MACARONI OR PASTA SALAD WITH TUNA AND EGG","Macaroni or pasta salad with tuna and egg" +58148170,"MACARONI OR PASTA SALAD WITH CHICKEN","Macaroni or pasta salad with chicken" +58148180,"MACARONI OR PASTA SALAD WITH CHEESE","Macaroni or pasta salad with cheese" +58148550,"MACARONI OR PASTA SALAD W/ MEAT","Macaroni or pasta salad with meat" +58148600,"PASTA TETRAZZINI,DRY MIX,PREPARED W/ WATER","Pasta tetrazzini, dry mix, prepared with water" +58149110,"NOODLE PUDDING (INCLUDE KUGEL)","Noodle pudding" +58149160,"NOODLE PUDDING,W/ MILK","Noodle pudding, with milk" +58149210,"SOMEN SALAD W/ NOODLE, LETTUCE, EGG, FISH, PORK","Somen salad with noodles, lettuce, egg, fish, and pork" +58150100,"BIBIMBAP (KOREAN)","Bibimbap (Korean)" +58150110,"RICE, FRIED, MEATLESS","Rice, fried, meatless" +58150310,"RICE, FRIED, NFS","Rice, fried, NFS" +58150320,"RICE, FRIED, W/ CHICKEN (INCL TURKEY)","Rice, fried, with chicken" +58150330,"RICE, FRIED, W/ PORK","Rice, fried, with pork" +58150340,"RICE, FRIED, W/ BEEF","Rice, fried, with beef" +58150510,"RICE, FRIED, W/ SHRIMP","Rice, fried, with shrimp" +58150520,"DUKBOKI / TTEOKBOKKI (KOREAN)","Dukboki / Tteokbokki (Korean)" +58151100,"SUSHI, NFS","Sushi, NFS" +58151110,"SUSHI, NO VEG, NO SEAFOOD/FISH/SHELLFISH","Sushi, no vegetables, no seafood (no fish or shellfish)" +58151120,"SUSHI, W/ VEG, NO SEAFOOD/FISH/SHELLFISH","Sushi, with vegetables, no seafood (no fish or shellfish)" +58151130,"SUSHI, W/ VEG & SEAFD","Sushi, with vegetables and seafood" +58151140,"SUSHI, W/ VEGETABLES, ROLLED IN SEAWEED","Sushi, with vegetables, rolled in seaweed" +58151150,"SUSHI, WITH SEAFOOD, NO VEGETABLES","Sushi, with seafood, no vegetables" +58151160,"SUSHI, W/ EGG, NO VEG/SEAFD/FISH/SHLFISH, ROLL IN SEAWEED","Sushi, with egg, no vegetables, no seafood (no fish or shellfish), rolled in seaweed" +58155110,"RICE W/ CHICKEN, P.R. (ARROZ CON POLLO)","Rice with chicken, Puerto Rican style (Arroz con Pollo)" +58155210,"STUFFED RICE W/ CHICKEN, DOMINICAN STYLE","Stuffed rice with chicken, Dominican style (Arroz relleno Dominicano)" +58155310,"PAELLA, VALENCIANA STYLE, W/ MEAT","Paella, Valenciana style, with meat (Paella Valenciana)" +58155320,"SEAFOOD PAELLA, PUERTO RICAN STYLE","Seafood paella, Puerto Rican style" +58155410,"SOUPY RICE W/ CHICKEN, P.R. (ASOPAO DE POLLO)","Soupy rice with chicken, Puerto Rican style (Asopao de pollo)" +58155510,"SOUPY RICE MIXTURE W/ CHICKEN & POTATOES, P.R.STYLE","Soupy rice mixture with chicken and potatoes, Puerto Rican style" +58155610,"RICE MEAL FRITTER, PUERTO RICAN (ALMOJAMBANA)","Rice meal fritter, Puerto Rican style (Almojabana)" +58155810,"STEWED RICE, P.R. (ARROZ GUISADO)","Stewed rice, Puerto Rican style (arroz guisado)" +58155910,"RICE W/ SQUID, P.R. (ARROZ CON CALAMARES)","Rice with squid, Puerto Rican style (arroz con calamares)" +58156110,"FRIED RICE, P.R. (ARROZ FRITO)","Fried rice, Puerto Rican style (arroz frito)" +58156210,"RICE W/ VIENNA SAUSAGE, P.R. (ARROZ CON SALCHICHAS)","Rice with vienna sausage, Puerto Rican style (arroz con salchichas)" +58156310,"RICE W/ SPANISH SAUSAGE, P.R.","Rice with Spanish sausage, Puerto Rican style" +58156410,"RICE W/ ONIONS, P.R. (ARROZ CON CEBOLLAS)","Rice with onions, Puerto Rican style (arroz con cebollas)" +58156510,"SOUPY RICE, FROM P.R. ASOPAO DE POLLO","Soupy rice from Puerto Rican style Asopao de Pollo (chicken parts reported separately)" +58156610,"PIGEON PEA ASOPAO (ASOPAO DE GRANDULES)","Pigeon pea asopao (Asopao de gandules)" +58156710,"RICE W/ STEWED BEANS, P.R.","Rice with stewed beans, Puerto Rican style" +58157110,"SPICEY RICE PUDDING, P.R.","Spicy rice pudding, Puerto Rican style" +58157210,"RICE PUDDING MADE W/ COCONUT MILK, P.R.","Rice pudding made with coconut milk, Puerto Rican style" +58160000,"BIRYANI WITH VEGETABLES","Biryani with vegetables" +58160110,"RICE W/ BEANS","Rice with beans" +58160120,"RICE W/ BEANS & TOMATOES","Rice with beans and tomatoes" +58160130,"RICE W/ BEANS & CHICKEN","Rice with beans and chicken" +58160135,"RICE W/ BEANS AND BEEF","Rice with beans and beef" +58160140,"RICE W/ BEANS & PORK","Rice with beans and pork" +58160150,"RED BEANS & RICE","Red beans and rice" +58160160,"HOPPING JOHN (BLACKEYE PEAS & RICE)","Hopping John (blackeye peas and rice)" +58160400,"RICE, WHITE, WITH CORN, NS AS TO FAT ADDED IN COOKING","Rice, white, with corn, NS as to fat added in cooking" +58160410,"RICE, WHITE, WITH CORN, FAT NOT ADDED IN COOKING","Rice, white, with corn, fat not added in cooking" +58160420,"RICE, WHITE, WITH CORN, FAT ADDED IN COOKING","Rice, white, with corn, fat added in cooking" +58160430,"RICE, WHITE, WITH PEAS, NS AS TO FAT ADDED IN COOKING","Rice, white, with peas, NS as to fat added in cooking" +58160440,"RICE, WHITE, WITH PEAS, FAT NOT ADDED IN COOKING","Rice, white, with peas, fat not added in cooking" +58160450,"RICE, WHITE, WITH PEAS, FAT ADDED IN COOKING","Rice, white, with peas, fat added in cooking" +58160460,"RICE, WHITE, WITH CARROTS, NS AS TO FAT ADDED IN COOKING","Rice, white, with carrots, NS as to fat added in cooking" +58160470,"RICE, WHITE, WITH CARROTS, FAT NOT ADDED IN COOKING","Rice, white, with carrots, fat not added in cooking" +58160480,"RICE, WHITE, WITH CARROTS, FAT ADDED IN COOKING","Rice, white, with carrots, fat added in cooking" +58160490,"RICE, WHITE, W/ PEAS&CARROTS, NS AS TO FAT ADDED IN COOKING","Rice, white, with peas and carrots, NS as to fat added in cooking" +58160500,"RICE, WHITE, WITH PEAS AND CARROTS, FAT NOT ADDED IN COOKING","Rice, white, with peas and carrots, fat not added in cooking" +58160510,"RICE, WHITE, WITH PEAS AND CARROTS, FAT ADDED IN COOKING","Rice, white, with peas and carrots, fat added in cooking" +58160520,"RICE, WHITE, W/TOMATOES/TOMATO BASED SAUCE, NS AS TO FAT","Rice, white, with tomatoes (and/or tomato based sauce), NS as to fat added in cooking" +58160530,"RICE, WHITE, W/TOMATOES/ TOMATO BASED SAUCE, FAT NOT ADDED","Rice, white, with tomatoes (and/or tomato based sauce), fat not added in cooking" +58160540,"RICE, WHITE, W/ TOMATOES/TOMATO BASED SAUCE, FAT ADDED","Rice, white, with tomatoes (and/or tomato based sauce), fat added in cooking" +58160550,"RICE, WHITE, WITH DARK GREEN VEGETABLES, NS AS TO FAT ADDED","Rice, white, with dark green vegetables, NS as to fat added in cooking" +58160560,"RICE, WHITE, WITH DARK GREEN VEGS, FAT NOT ADDED IN CO","Rice, white, with dark green vegetables, fat not added in cooking" +58160570,"RICE, WHITE, WITH DARK GREEN VEGETABLES, FAT ADDED IN COOKIN","Rice, white, with dark green vegetables, fat added in cooking" +58160580,"RICE, WHITE, W/ CARROTS, TOMATOES, +/OR TOM SC, NS AS TO FAT","Rice, white, with carrots and tomatoes (and/or tomato-based sauce), NS as to fat added in cooking" +58160590,"RICE, WHITE, W/CARROTS, TOMATOES+/OR TOM SC, FAT NOT ADDED","Rice, white, with carrots and tomatoes (and/or tomato-based sauce), fat not added in cooking" +58160600,"RICE, WHITE, W/ CARROTS, TOMATOES +/OR TOM SC, FAT ADDED","Rice, white, with carrots and tomatoes (and/or tomato-based sauce), fat added in cooking" +58160610,"RICE, WHITE, W/ DK GRN VEGS, TOMATOES +/OR TOM SC, NS FAT","Rice, white, with dark green vegetables and tomatoes (and/or tomato-based sauce), NS as to fat added in cooking" +58160620,"RICE, WHITE, W/ DK GRN VEGS, TOMATOES +/OR TOM SC, FAT NOT","Rice, white, with dark green vegetables and tomatoes (and/or tomato-based sauce), fat not added in cooking" +58160630,"RICE, WHITE, W/DK GRN VEGS, TOMATOES +/OR TOM SC , FAT ADDED","Rice, white, with dark green vegetables and tomatoes (and/or tomato-based sauce), fat added in cooking" +58160640,"RICE, WHITE, WITH CARROTS AND DARK GREEN VEGS, NS FAT","Rice, white, with carrots and dark green vegetables, NS as to fat added in cooking" +58160650,"RICE, WHITE, WITH CARROTS AND DARK GREEN VEGS, FAT NOT ADDED","Rice, white, with carrots and dark green vegetables, fat not added in cooking" +58160660,"RICE, WHITE, WITH CARROTS AND DARK GREEN VEGS, FAT ADDED","Rice, white, with carrots and dark green vegetables, fat added in cooking" +58160670,"RICE, WHITE, W/ CARROTS, DARK GRN VEG&TOMATO/SC, NS FAT","Rice, white, with carrots, dark green vegetables, and tomatoes (and/or tomato-based sauce), NS as to fat added in cooking" +58160680,"RICE, WHITE, W/ CARROTS, DARK GRN VEG,&TOMATOES, NO FAT","Rice, white, with carrots, dark green vegetables, and tomatoes (and/or tomato-based sauce), fat not added in cooking" +58160690,"RICE, WHITE, W/ CARROTS, DARK GRN VEG, & TOMATOES, W/ FAT","Rice, white, with carrots, dark green vegetables, and tomatoes (and/or tomato-based sauce), fat added in cooking" +58160700,"RICE, WHITE, WITH OTHER VEGS, NS AS TO FAT ADDED IN COOKING","Rice, white, with other vegetables, NS as to fat added in cooking" +58160710,"RICE, WHITE, WITH OTHER VEGS, FAT NOT ADDED IN COOKING","Rice, white, with other vegetables, fat not added in cooking" +58160720,"RICE, WHITE, WITH OTHER VEGS, FAT ADDED IN COOKING","Rice, white, with other vegetables, fat added in cooking" +58160800,"RICE, WHITE, WITH LENTILS, NS AS TO FAT ADDED IN COOKING","Rice, white, with lentils, NS as to fat added in cooking" +58160805,"RICE, WHITE, WITH LENTILS, FAT ADDED IN COOKING","Rice, white, with lentils, fat added in cooking" +58160810,"RICE, WHITE, WITH LENTILS, FAT NOT ADDED IN COOKING","Rice, white, with lentils, fat not added in cooking" +58161200,"RICE, COOKED W/ COCONUT MILK (ARROZ CON COCO)","Rice, cooked with coconut milk (Arroz con coco)" +58161320,"RICE, BROWN, W/ BEANS","Rice, brown, with beans" +58161325,"RICE, BROWN, W/ BEANS AND TOMATOES","Rice, brown, with beans and tomatoes" +58161420,"RICE, BROWN, W/ CORN, NS AS TO FAT","Rice, brown, with corn, NS as to fat added in cooking" +58161422,"RICE, BROWN, W/ CORN, FAT NOT ADDED","Rice, brown, with corn, fat not added in cooking" +58161424,"RICE, BROWN, W/ CORN, FAT ADDED","Rice, brown, with corn, fat added in cooking" +58161430,"RICE, BROWN, W/ PEAS, NS AS TO FAT","Rice, brown, with peas, NS as to fat added in cooking" +58161432,"RICE, BROWN, W/ PEAS, FAT NOT ADDED","Rice, brown, with peas, fat not added in cooking" +58161434,"RICE, BROWN, W/ PEAS, FAT ADDED","Rice, brown, with peas, fat added in cooking" +58161435,"RICE, BROWN, WITH CARROTS, NS AS TO FAT ADDED IN COOKING","Rice, brown, with carrots, NS as to fat added in cooking" +58161437,"RICE, BROWN, WITH CARROTS, FAT NOT ADDED IN COOKING","Rice, brown, with carrots, fat not added in cooking" +58161439,"RICE, BROWN, WITH CARROTS, FAT ADDED IN COOKING","Rice, brown, with carrots, fat added in cooking" +58161440,"RICE, BROWN, W/ PEAS AND CARROTS, NS AS TO FAT","Rice, brown, with peas and carrots, NS as to fat added in cooking" +58161442,"RICE, BROWN, W/ PEAS AND CARROTS, FAT NOT ADDED","Rice, brown, with peas and carrots, fat not added in cooking" +58161444,"RICE, BROWN, W/ PEAS AND CARROTS, FAT ADDED","Rice, brown, with peas and carrots, fat added in cooking" +58161460,"RICE, BROWN, WITH TOMATOES AND/OR TOMATO BASED SAUCE, NS AS","Rice, brown, with tomatoes (and/or tomato based sauce), NS as to fat added in cooking" +58161462,"RICE, BROWN, WITH TOMATOES AND/OR TOMATO BASED SAUCE, FAT NO","Rice, brown, with tomatoes (and/or tomato based sauce), fat not added in cooking" +58161464,"RICE, BROWN, WITH TOMATOES AND/OR TOMATO BASED SAUCE, FAT AD","Rice, brown, with tomatoes (and/or tomato based sauce), fat added in cooking" +58161470,"RICE, BROWN, WITH DARK GREEN VEGETABLES, NS AS TO FAT ADDED","Rice, brown, with dark green vegetables, NS as to fat added in cooking" +58161472,"RICE, BROWN, WITH DARK GREEN VEGETABLES, FAT NOT ADDED IN CO","Rice, brown, with dark green vegetables, fat not added in cooking" +58161474,"RICE, BROWN, WITH DARK GREEN VEGETABLES, FAT ADDED IN COOKIN","Rice, brown, with dark green vegetables, fat added in cooking" +58161480,"RICE, BROWN, WITH CARROTS &TOMATOES/SC, NS AS TO FAT ADDED","Rice, brown, with carrots and tomatoes (and/or tomato-based sauce), NS as to fat added in cooking" +58161482,"RICE, BROWN, WITH CARROTS & TOMATOES/SC, FAT NOT ADDED IN C","Rice, brown, with carrots and tomatoes (and/or tomato-based sauce), fat not added in cooking" +58161484,"RICE, BROWN, WITH CARROTS & TOMATOES/SC, FAT ADDED IN COOKIN","Rice, brown, with carrots and tomatoes (and/or tomato-based sauce), fat added in cooking" +58161490,"RICE, BROWN, W/ DK GRN VEGS, TOMATOES/SC , NS AS TO FAT","Rice, brown, with dark green vegetables and tomatoes (and/or tomato-based sauce) , NS as to fat added in cooking" +58161492,"RICE, BROWN, W/ DK GRN VEGS, TOMATOES/SC , FAT NOT ADDED","Rice, brown, with dark green vegetables and tomatoes (and/or tomato-based sauce), fat not added in cooking" +58161494,"RICE, BROWN, W/ DK GRN VEGS, TOMATOES/SC , FAT ADDED","Rice, brown, with dark green vegetables and tomatoes (and/or tomato-based sauce), fat added in cooking" +58161500,"RICE, BROWN, WITH CARROTS AND DARK GREEN VEGETABLES, NS FAT","Rice, brown, with carrots and dark green vegetables, NS as to fat added in cooking" +58161502,"RICE, BROWN, WITH CARROTS AND DARK GREEN VEGETABLES, FAT NOT","Rice, brown, with carrots and dark green vegetables, fat not added in cooking" +58161504,"RICE, BROWN, WITH CARROTS AND DARK GREEN VEGETABLES, FAT ADD","Rice, brown, with carrots and dark green vegetables, fat added in cooking" +58161510,"GRAPE LEAVES STUFFED W/ RICE","Grape leaves stuffed with rice" +58161520,"RICE, BROWN, W/CARROTS, DK GRN VEGS,TOMATOES/SC, NS FAT","Rice, brown, with carrots, dark green vegetables, and tomatoes (and/or tomato-based sauce), NS as to fat added in cooking" +58161522,"RICE, BROWN, W/ CARROTS, DK GRN VEGS,TOMATOES/SC, NO FAT","Rice, brown, with carrots, dark green vegetables, and tomatoes (and/or tomato-based sauce), fat not added in cooking" +58161524,"RICE, BROWN, W/CARROTS, DK GRN VEGS,TOMATOES/SC, FAT ADDED","Rice, brown, with carrots, dark green vegetables, and tomatoes (and/or tomato-based sauce), fat added in cooking" +58161530,"RICE, BROWN, W/ OTHER VEGS, NS AS TO FAT ADDED","Rice, brown, with other vegetables, NS as to fat added in cooking" +58161532,"RICE, BROWN, WITH OTHER VEGETABLES, FAT NOT ADDED IN COOKING","Rice, brown, with other vegetables, fat not added in cooking" +58161534,"RICE, BROWN, WITH OTHER VEGETABLES, FAT ADDED IN COOKING","Rice, brown, with other vegetables, fat added in cooking" +58161710,"RICE CROQUETTE","Rice croquette" +58162090,"STUFFED PEPPER W/ MEAT","Stuffed pepper, with meat" +58162110,"STUFFED PEPPER, W/ RICE & MEAT","Stuffed pepper, with rice and meat" +58162120,"STUFFED PEPPER, W/ RICE, MEATLESS","Stuffed pepper, with rice, meatless" +58162130,"STUFFED TOMATO W/ RICE & MEAT","Stuffed tomato, with rice and meat" +58162140,"STUFFED TOMATO W/ RICE, MEATLESS","Stuffed tomato, with rice, meatless" +58162310,"RICE PILAF","Rice pilaf" +58163130,"DIRTY RICE","Dirty rice" +58163310,"FLAVORED RICE MIXTURE","Flavored rice mixture" +58163330,"FLAVORED RICE MIXTURE W/ CHEESE","Flavored rice mixture with cheese" +58163360,"FLAVORED RICE, BROWN & WILD","Flavored rice, brown and wild" +58163380,"FLAVORED RICE&PASTA MIXTURE (INCL RICE-A-RONI)","Flavored rice and pasta mixture" +58163400,"FLAVORED RICE & PASTA MIXTURE, REDUCED SODIUM","Flavored rice and pasta mixture, reduced sodium" +58163410,"SPANISH RICE, FAT ADDED IN COOKING","Spanish rice, fat added in cooking" +58163420,"SPANISH RICE, FAT NOT ADDED IN COOKING","Spanish rice, fat not added in cooking" +58163430,"SPANISH RICE, NS AS TO FAT ADDED IN COOKING","Spanish rice, NS as to fat added in cooking" +58163450,"SPANISH RICE W/ GROUND BEEF","Spanish rice with ground beef" +58163510,"RICE DRESSING (INCLUDE COMBINED W/ BREAD)","Rice dressing" +58164110,"RICE W/ RAISINS","Rice with raisins" +58164210,"RICE DESSERT/SALAD W/ FRUIT","Rice dessert or salad with fruit" +58164500,"RICE, WHITE, WITH CHEESE AND/OR CREAM BASED SAUCE, NS FAT","Rice, white, with cheese and/or cream based sauce, NS as to fat added in cooking" +58164510,"RICE, WHITE, WITH CHEESE AND/OR CREAM BASED SAUCE, FAT NOT A","Rice, white, with cheese and/or cream based sauce, fat not added in cooking" +58164520,"RICE, WHITE, WITH CHEESE AND/OR CREAM BASED SAUCE, FAT ADDED","Rice, white, with cheese and/or cream based sauce, fat added in cooking" +58164530,"RICE, WHITE, WITH GRAVY, NS AS TO FAT ADDED IN COOKING","Rice, white, with gravy, NS as to fat added in cooking" +58164540,"RICE, WHITE, WITH GRAVY, FAT NOT ADDED IN COOKING","Rice, white, with gravy, fat not added in cooking" +58164550,"RICE, WHITE, WITH GRAVY, FAT ADDED IN COOKING","Rice, white, with gravy, fat added in cooking" +58164560,"RICE, WHITE, WITH SOY BASED SAUCE, NS AS TO FAT ADDED IN COO","Rice, white, with soy-based sauce, NS as to fat added in cooking" +58164570,"RICE, WHITE, WITH SOY BASED SAUCE, FAT NOT ADDED IN COOKING","Rice, white, with soy-based sauce, fat not added in cooking" +58164580,"RICE, WHITE, WITH SOY BASED SAUCE, FAT ADDED IN COOKING","Rice, white, with soy-based sauce, fat added in cooking" +58164800,"RICE, BROWN, WITH CHEESE AND/OR CREAM BASED SAUCE, NS FAT","Rice, brown, with cheese and/or cream based sauce, NS as to fat added in cooking" +58164810,"RICE, BROWN, WITH CHEESE AND/OR CREAM BASED SAUCE, FAT NOT A","Rice, brown, with cheese and/or cream based sauce, fat not added in cooking" +58164820,"RICE, BROWN, WITH CHEESE AND/OR CREAM BASED SAUCE, FAT ADDED","Rice, brown, with cheese and/or cream based sauce, fat added in cooking" +58164830,"RICE, BROWN, WITH GRAVY, NS AS TO FAT ADDED IN COOKING","Rice, brown, with gravy, NS as to fat added in cooking" +58164840,"RICE, BROWN, WITH GRAVY, FAT NOT ADDED IN COOKING","Rice, brown, with gravy, fat not added in cooking" +58164850,"RICE, BROWN, WITH GRAVY, FAT ADDED IN COOKING","Rice, brown, with gravy, fat added in cooking" +58164860,"RICE, BROWN, WITH SOY BASED SAUCE, NS AS TO FAT ADDED IN COO","Rice, brown, with soy-based sauce, NS as to fat added in cooking" +58164870,"RICE, BROWN, WITH A SOY BASED SAUCE, FAT NOT ADDED IN COOKI","Rice, brown, with soy-based sauce, fat not added in cooking" +58164880,"RICE, BROWN, WITH SOY BASED SAUCE, FAT ADDED IN COOKING","Rice, brown, with soy-based sauce, fat added in cooking" +58165000,"RICE, WHITE, WITH VEGS, CHEESE +/OR CREAM BASED SC, NS FAT","Rice, white, with vegetables, cheese and/or cream based sauce, NS as to fat added in cooking" +58165010,"RICE, WHITE, WITH VEGS, CHEESE +/OR CREAM BASED SC, FAT NOT","Rice, white, with vegetables, cheese and/or cream based sauce, fat not added in cooking" +58165020,"RICE, WHITE, WITH VEGS, CHEESE +/OR CREAM BASED SC,FAT ADDED","Rice, white, with vegetables, cheese and/or cream based sauce, fat added in cooking" +58165030,"RICE, WHITE, WITH VEGETABLES AND GRAVY, NS AS TO FAT ADDED","Rice, white, with vegetables and gravy, NS as to fat added in cooking" +58165040,"RICE, WHITE, WITH VEGETABLES AND GRAVY, FAT NOT ADDED","Rice, white, with vegetables and gravy, fat not added in cooking" +58165050,"RICE, WHITE, WITH VEGETABLES AND GRAVY, FAT ADDED IN COOKING","Rice, white, with vegetables and gravy, fat added in cooking" +58165060,"RICE, WHITE, WITH VEGETABLES, SOY-BASED SAUCE, NS AS TO FAT","Rice, white, with vegetables, soy-based sauce, NS as to fat added in cooking" +58165070,"RICE, WHITE, WITH VEGETABLES, SOY-BASED SAUCE, FAT NOT ADDED","Rice, white, with vegetables, soy-based sauce, fat not added in cooking" +58165080,"RICE, WHITE, WITH VEGETABLES, SOY-BASED SAUCE, FAT ADDED IN","Rice, white, with vegetables, soy-based sauce, fat added in cooking" +58165400,"RICE, BROWN, WITH VEGS, CHEESE +/OR CREAM BASED SC, NS FAT","Rice, brown, with vegetables, cheese and/or cream based sauce, NS as to fat added in cooking" +58165410,"RICE, BROWN, WITH VEGS, CHEESE +/OR CREAM BASED SC, FAT NOT","Rice, brown, with vegetables, cheese and/or cream based sauce, fat not added in cooking" +58165420,"RICE, BROWN, WITH VEGS, CHEESE +/OR CREAM BASED SC,FAT ADDED","Rice, brown, with vegetables, cheese and/or cream based sauce, fat added in cooking" +58165430,"RICE, BROWN, WITH VEGETABLES AND GRAVY, NS AS TO FAT ADDED I","Rice, brown, with vegetables and gravy, NS as to fat added in cooking" +58165440,"RICE, BROWN, WITH VEGETABLES AND GRAVY, FAT NOT ADDED","Rice, brown, with vegetables and gravy, fat not added in cooking" +58165450,"RICE, BROWN, WITH VEGETABLES AND GRAVY, FAT ADDED IN COOKING","Rice, brown, with vegetables and gravy, fat added in cooking" +58165460,"RICE, BROWN, WITH VEGETABLES, SOY-BASED SAUCE, NS AS TO FAT","Rice, brown, with vegetables, soy-based sauce, NS as to fat added in cooking" +58165470,"RICE, BROWN, WITH VEGETABLES, SOY-BASED SAUCE, FAT NOT ADDED","Rice, brown, with vegetables, soy-based sauce, fat not added in cooking" +58165480,"RICE, BROWN, WITH VEGETABLES, SOY-BASED SAUCE, FAT ADDED","Rice, brown, with vegetables, soy-based sauce, fat added in cooking" +58174000,"UPMA (INDIAN BREAKFAST DISH)","Upma (Indian breakfast dish)" +58175110,"TABBOULEH (INCLUDE TABBULI)","Tabbouleh (bulgar with tomatoes and parsley)" +58200100,"WRAP SANDWICH, W/ MEAT, POULTRY OR FISH, VEGETABLES & RICE","Wrap sandwich, filled with meat, poultry, or fish, vegetables, and rice" +58200200,"WRAP SANDWICH, W/ VEGETABLES & RICE","Wrap sandwich, filled with vegetables and rice" +58200250,"WRAP SANDWICH, W/ VEGETABLES","Wrap sandwich, filled with vegetables" +58200300,"WRAP SANDWICH, W/ MEAT, POULTRY, OR FISH, VEG, RICE & CHEESE","Wrap sandwich, filled with meat, poultry, or fish, vegetables, rice, and cheese" +58301020,"LASAGNA W/ CHEESE & SAUCE (DIET FROZEN MEAL)","Lasagna with cheese and sauce (diet frozen meal)" +58301030,"VEAL LASAGNA (DIET FROZEN MEAL) (INCL LEAN CUISINE)","Veal lasagna (diet frozen meal)" +58301050,"LASAGNA, W/ CHEESE & MEAT SAUCE (DIET FROZEN MEAL)","Lasagna with cheese and meat sauce (diet frozen meal)" +58301080,"LASAGNA W/CHEESE&MEAT SAU,REDUCED FAT&NA(DIET FROZ)","Lasagna with cheese and meat sauce, reduced fat and sodium (diet frozen meal)" +58301110,"VEGETABLE LASAGNA (FROZEN MEAL)","Vegetable lasagna (frozen meal)" +58301150,"ZUCCHINI LASAGNA (DIET FROZEN MEAL)","Zucchini lasagna (diet frozen meal)" +58302000,"MACARONI & CHEESE (DIET FROZEN MEAL)","Macaroni and cheese (diet frozen meal)" +58302050,"BEEF & NOODLES W/ MEAT SCE & CHEESE (DIET FRZ MEAL)","Beef and noodles with meat sauce and cheese (diet frozen meal)" +58302060,"SPAG W/ BEEF, TOM-BASED SAUCE, LOWFAT, RED SODIUM, FRZ, DIET","Spaghetti or noodles with beef in tomato-based sauce, lowfat, reduced sodium (diet frozen meal)" +58302080,"NOODLES W/ VEG, TOM-BASED SAUCE, FRZ, DIET","Noodles with vegetables in tomato-based sauce (diet frozen meal)" +58303100,"RICE W/ BROC CHEESE SCE (FRZ SIDE DISH)","Rice, with broccoli, cheese sauce (frozen side dish)" +58303200,"RICE,GREEN BEANS,WATER CHESTNUTS IN SCE (FRZ DISH)","Rice, with green beans, water chestnuts, in sherry mushroom sauce (frozen side dish)" +58304010,"SPAGHETTI & MEATBALLS DINNER, NFS (FROZEN MEAL)","Spaghetti and meatballs dinner, NFS (frozen meal)" +58304020,"SPAGHETTI,MEATBALLS,TOM SCE,APPLES,BREAD(FROZ MEAL)","Spaghetti and meatballs with tomato sauce, sliced apples, bread (frozen meal)" +58304050,"SPAGHETTI W/ MEAT & MUSHROOM SAUCE (DIET FROZ MEAL)","Spaghetti with meat and mushroom sauce (diet frozen meal)" +58304060,"SPAGHETTI W/ MEAT SAUCE (DIET FROZEN MEAL)","Spaghetti with meat sauce (diet frozen meal)" +58304200,"RAVIOLI, CHEESE-FILLED, TOMATO SCE (DIET FROZ MEAL)","Ravioli, cheese-filled, with tomato sauce (diet frozen meal)" +58304220,"RIGATONI W/ MEAT SCE & CHEESE (DIET FRZ MEAL)","Rigatoni with meat sauce and cheese (diet frozen meal)" +58304230,"RAVIOLI, CHEESE-FILLED W/ VEG & FRUIT (FZN MEAL)","Ravioli, cheese-filled, with vegetable and fruit (frozen meal)" +58304250,"MANICOTTI W/ CHEESE, TOMATO SAUCE (DIET FROZ MEAL)","Manicotti, cheese-filled, with tomato sauce (diet frozen meal)" +58304300,"CANNELLONI, CHEESE-FILLED, TOM SCE (DIET FROZ MEAL)","Cannelloni, cheese-filled, with tomato sauce (diet frozen meal)" +58304400,"LINGUINI W/ VEG & SEAFOOD IN SCE (DIET FROZEN MEAL)","Linguini with vegetables and seafood in white wine sauce (diet frozen meal)" +58305250,"PASTA,W/ VEGETABLES & CHEESE SAUCE (DIET FROZ MEAL)","Pasta with vegetable and cheese sauce (diet frozen meal)" +58306010,"BEEF ENCHILADA DINNER, NFS (FROZEN MEAL)","Beef enchilada dinner, NFS (frozen meal)" +58306020,"BEEF ENCHILADA, GRAVY, RICE, REFRIED BEANS (FROZEN)","Beef enchilada, chili gravy, rice, refried beans (frozen meal)" +58306070,"CHEESE ENCHILADA (FROZEN MEAL)","Cheese enchilada (frozen meal)" +58306100,"CHICKEN ENCHILADA ( DIET FROZEN MEAL)","Chicken enchilada (diet frozen meal)" +58306200,"CHICKEN FAJITAS (DIET FROZEN MEAL)","Chicken fajitas (diet frozen meal)" +58306500,"CHICKEN BURRITOS (DIET FROZEN MEAL)","Chicken burritos (diet frozen meal)" +58310210,"SAUSAGE & FRENCH TOAST (FROZEN MEAL)","Sausage and french toast (frozen meal)" +58310310,"PANCAKE & SAUSAGE (FROZEN MEAL)","Pancakes and sausage (frozen meal)" +58400000,"SOUP, NFS","Soup, NFS" +58400100,"NOODLE SOUP, NFS","Noodle soup, NFS" +58400200,"RICE SOUP, NFS","Rice soup, NFS" +58401010,"BARLEY SOUP, HOME RECIPE, CANNED, OR READY-TO-SERVE","Barley soup, home recipe, canned, or ready-to-serve" +58401200,"BARLEY SOUP, SWEET, WITH OR WITHOUT NUTS, ASIAN STYLE","Barley soup, sweet, with or without nuts, Asian Style" +58402010,"BEEF NOODLE SOUP, CANNED OR READY-TO-SERVE","Beef noodle soup, canned or ready-to-serve" +58402020,"BEEF DUMPLING SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Beef dumpling soup, home recipe, canned or ready-to-serve" +58402030,"BEEF RICE SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Beef rice soup, home recipe, canned or ready-to-serve" +58402100,"BEEF NOODLE SOUP, HOME RECIPE","Beef noodle soup, home recipe" +58403010,"CHICKEN OR TURKEY NOODLE SOUP, CANNED OR READY-TO-SERVE","Chicken or turkey noodle soup, canned or ready-to-serve" +58403040,"CHICKEN OR TURKEY NOODLE SOUP, HOME RECIPE","Chicken or turkey noodle soup, home recipe" +58403050,"CHICKEN OR TURKEY NOODLE SOUP, CREAM OF, HOME RECIPE, CANNED","Chicken or turkey noodle soup, cream of, home recipe, canned, or ready-to-serve" +58403060,"CHICKEN OR TURKEY NOODLE SOUP, REDUCED SODIUM,CANNED, RTF","Chicken or turkey noodle soup, reduced sodium, canned or ready-to-serve" +58403100,"NOODLE & POTATO SOUP, P.R.","Noodle and potato soup, Puerto Rican style" +58404010,"CHICKEN OR TURKEY RICE SOUP, CANNED, OR READY-TO-SERVE","Chicken or turkey rice soup, canned, or ready-to-serve" +58404030,"CHICKEN OR TURKEY RICE SOUP, HOME RECIPE","Chicken or turkey rice soup, home recipe" +58404040,"CHICKEN OR TURKEY RICE SOUP, REDUCED SODIUM, CANNED, PREPARE","Chicken or turkey rice soup, reduced sodium, canned, prepared with water or ready-to-serve" +58404050,"CHICKEN OR TURKEY RICE SOUP, REDUCED SODIUM, CANNED, PREPARE","Chicken or turkey rice soup, reduced sodium, canned, prepared with milk" +58404100,"RICE AND POTATO SOUP, P.R.","Rice and potato soup, Puerto Rican style" +58404500,"MATZO BALL SOUP","Matzo ball soup" +58404510,"CHICKEN OR TURKEY SOUP WITH DUMPLINGS AND POTATOES,","Chicken or turkey soup with dumplings and potatoes, home recipe, canned, or ready-to-serve" +58404520,"CHICKEN OR TURKEY SOUP WITH DUMPLINGS, HOME RECIPE, CANNED O","Chicken or turkey soup with dumplings, home recipe, canned or ready-to-serve" +58407010,"INSTANT SOUP, NOODLE","Instant soup, noodle" +58407030,"SOUP, MOSTLY NOODLES","Soup, mostly noodles" +58407035,"SOUP, MOSTLY NOODLES, REDUCED SODIUM","Soup, mostly noodles, reduced sodium" +58407050,"INSTANT SOUP, NOODLE W/ EGG, SHRIMP OR CHICKEN","Instant soup, noodle with egg, shrimp or chicken" +58408010,"WON TON (WONTON) SOUP","Won ton (wonton) soup" +58408500,"NOODLE SOUP WITH VEGETABLES, ASIAN STYLE","Noodle soup with vegetables, Asian style" +58409000,"NOODLE SOUP,W/ FISH BALL,SHRIMP,&DK GREEN LEAFY VEG","Noodle soup, with fish ball, shrimp, and dark green leafy vegetable" +58421000,"SOPA SECA (DRY SOUP), Mexican style, NFS","Sopa seca (dry soup), Mexican style, NFS" +58421010,"SOPA SECA DE FIDEO, MEXICAN STYLE, MADE WITH DRY NOODLES, HO","Sopa Seca de Fideo, Mexican style, made with dry noodles, home recipe" +58421020,"SOPA DE FIDEO AGUADA, MEXICAN STYLE NOODLE SOUP, HOME RECIPE","Sopa de Fideo Aguada, Mexican style noodle soup, home recipe" +58421060,"SOPA SECA DE ARROZ (DRY RICE SOUP), MEXICAN STYLE, HOME RECI","Sopa seca de arroz (dry rice soup), Mexican style, home recipe" +58421080,"SOPA DE TORTILLA, MEXICAN STYLE TORTILLA SOUP, HOME RECIPE","Sopa de tortilla, Mexican style tortilla soup, home recipe" +58503000,"MACARONI, TOMATOES & BEEF, BABY, NS STR/JR","Macaroni, tomatoes, and beef, baby food, NS as to strained or junior" +58503010,"MACARONI, TOMATOES & BEEF, BABY, STR","Macaroni, tomatoes, and beef, baby food, strained" +58503020,"MACARONI, TOMATOES & BEEF, BABY, JR","Macaroni, tomatoes, and beef, baby food, junior" +58503050,"MACARONI W/ BEEF & TOM SCE, BABY FOOD, TODDLER","Macaroni with beef and tomato sauce, baby food, toddler" +58508000,"MACARONI & CHEESE, BABY, STRAINED","Macaroni and cheese, baby food, strained" +58508300,"MACARONI & CHEESE, BABY, TODDLER","Macaroni and cheese, baby food, toddler" +58509020,"SPAGHETTI, TOMATO SAUCE & BEEF, BABY, JUNIOR","Spaghetti, tomato sauce, and beef, baby food, junior" +58509100,"RAVIOLI, CHEESE-FILLED, W/ TOM SAUCE, BABY, TODDLER","Ravioli, cheese-filled, with tomato sauce, baby food, toddler" +58509200,"MACARONI W/ VEGETABLES, BABY, STRAINED","Macaroni with vegetables, baby food, strained" +59003000,"MEAT SUBSTITUTE,CEREAL- & VEGETABLE PROTEIN-BASED","Meat substitute, cereal- and vegetable protein-based, fried" +61100500,"CALAMONDIN, RAW","Calamondin, raw" +61101010,"GRAPEFRUIT, RAW (INCLUDE GRAPEFRUIT, NFS)","Grapefruit, raw" +61101200,"GRAPEFRUIT, CANNED OR FROZEN, NS AS TO ADDED SWTNER","Grapefruit, canned or frozen, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +61101220,"GRAPEFRUIT, CANNED OR FROZEN, UNSWT, WATER PACK","Grapefruit, canned or frozen, unsweetened, water pack" +61101230,"GRAPEFRUIT, CANNED OR FROZEN, IN LIGHT SYRUP","Grapefruit, canned or frozen, in light syrup" +61104010,"GRAPEFRUIT & ORANGE SECTIONS, RAW","Grapefruit and orange sections, raw" +61104200,"GRAPEFRUIT & ORANGE SEC, CKD/CND/FRZ, NS SWEETENER","Grapefruit and orange sections, cooked, canned, or frozen, NS as to added sweetener" +61104220,"GRAPEFRUIT & ORANGE SEC, CKD/CND/FRZ, UNSWEETENED","Grapefruit and orange sections, cooked, canned, or frozen, unsweetened, water pack" +61104230,"GRAPEFRUIT & ORANGE SEC, CKD/CND/FRZ, LIGHT SYRUP","Grapefruit and orange sections, cooked, canned, or frozen, in light syrup" +61110010,"KUMQUAT, RAW","Kumquat, raw" +61110230,"KUMQUAT, COOKED OR CANNED, IN SYRUP","Kumquat, cooked or canned, in syrup" +61113010,"LEMON, RAW","Lemon, raw" +61113500,"LEMON PIE FILLING","Lemon pie filling" +61116010,"LIME, RAW","Lime, raw" +61119010,"ORANGE, RAW","Orange, raw" +61119020,"ORANGE SECTIONS, CANNED, JUICE PACK","Orange, sections, canned, juice pack" +61119100,"ORANGE PEEL","Orange peel" +61122300,"ORANGES, MANDARIN, CANNED OR FROZEN, SWEETENER NS","Orange, mandarin, canned or frozen, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +61122320,"ORANGES, MANDARIN, CANNED OR FROZEN, JUICE PACK","Orange, mandarin, canned or frozen, juice pack" +61122330,"ORANGES, MANDARIN, CANNED OR FROZEN, IN LIGHT SYRUP","Orange, mandarin, canned or frozen, in light syrup" +61122350,"ORANGES, MANDARIN, CANNED OR FROZEN, DRAINED","Orange, mandarin, canned or frozen, drained" +61125000,"TANGELO, RAW","Tangelo, raw" +61125010,"TANGERINE, RAW (INCLUDE MANDARIN ORANGE, SATSUMA)","Tangerine, raw" +61201010,"GRAPEFRUIT JUICE, FRESHLY SQUEEZED","Grapefruit juice, freshly squeezed" +61201020,"GRAPEFRUIT JUICE, NS AS TO FORM","Grapefruit juice, NS as to form" +61201220,"GRAPEFRUIT JUICE, CANNED, BOTTLED OR IN A CARTON","Grapefruit juice, canned, bottled or in a carton" +61201620,"GRAPEFRUIT JUICE, FROZEN (RECONSTITUTED WITH WATER)","Grapefruit juice, frozen (reconstituted with water)" +61204000,"LEMON JUICE, NS AS TO FORM","Lemon juice, NS as to form" +61204010,"LEMON JUICE, FRESHLY SQUEEZED","Lemon juice, freshly squeezed" +61204200,"LEMON JUICE, CANNED OR BOTTLED","Lemon juice, canned or bottled" +61204600,"LEMON JUICE, FROZEN","Lemon juice, frozen" +61207000,"LIME JUICE, NS AS TO FORM","Lime juice, NS as to form" +61207010,"LIME JUICE, FRESHLY SQUEEZED","Lime juice, freshly squeezed" +61207200,"LIME JUICE, CANNED OR BOTTLED","Lime juice, canned or bottled" +61207600,"LIME JUICE, FROZEN","Lime juice, frozen" +61210000,"ORANGE JUICE, NFS","Orange juice, NFS" +61210010,"ORANGE JUICE, FRESHLY SQUEEZED","Orange juice, freshly squeezed" +61210220,"ORANGE JUICE, CANNED, BOTTLED OR IN A CARTON","Orange juice, canned, bottled or in a carton" +61210250,"ORANGE JUICE, W/ CALCIUM, CAN/BOTTLED/CARTON","Orange juice, with calcium added, canned, bottled or in a carton" +61210620,"ORANGE JUICE, FROZEN (RECONSTITUTED WITH WATER)","Orange juice, frozen (reconstituted with water)" +61210720,"ORANGE JUICE, FROZEN, NOT RECONSTITUTED","Orange juice, frozen, not reconstituted" +61210820,"ORANGE JUICE,FROZ, W/,CALCIUM ADDED,RECON W/WATER","Orange juice, frozen, with calcium added (reconstituted with water)" +61213000,"TANGERINE JUICE, NFS","Tangerine juice, NFS" +61213220,"TANGERINE JUICE, CANNED","Tangerine juice, canned" +61213620,"TANGERINE JUICE, FROZEN (RECONSTITUTED)","Tangerine juice, frozen (reconstituted with water)" +61213800,"FRUIT JUICE BLEND, INCL CITRUS, 100% JUICE","Fruit juice blend, including citrus, 100% juice" +61213900,"FRUIT JUICE BLEND, INCL CITRUS, 100% JUICE, W/ CALCIUM","Fruit juice blend, including citrus, 100% juice, with calcium added" +62101000,"FRUIT, DRIED, NFS (ASSUME UNCOOKED)","Fruit, dried, NFS (assume uncooked)" +62101050,"FRUIT MIXTURE, DRIED","Fruit mixture, dried (mixture includes three or more of the following: apples, apricots, dates, papaya, peaches, pears, pineapples, prunes, raisins)" +62101100,"APPLE, DRIED, UNCOOKED","Apple, dried, uncooked" +62101150,"APPLE, DRIED, UNCOOKED, LOW SODIUM","Apple, dried, uncooked, low sodium" +62101200,"APPLE, DRIED, COOKED, NS AS TO ADDED SWEETENER","Apple, dried, cooked, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +62101220,"APPLE, DRIED, COOKED, UNSWEETENED","Apple, dried, cooked, unsweetened" +62101230,"APPLE, DRIED, COOKED, W/ SUGAR","Apple, dried, cooked, with sugar" +62101300,"APPLE CHIPS","Apple chips" +62104100,"APRICOT, DRIED, UNCOOKED","Apricot, dried, uncooked" +62104200,"APRICOT, DRIED, COOKED, NS AS TO ADDED SWEETENER","Apricot, dried, cooked, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +62104220,"APRICOT, DRIED, COOKED, UNSWEETENED","Apricot, dried, cooked, unsweetened" +62104230,"APRICOT, DRIED, COOKED, W/ SUGAR","Apricot, dried, cooked, with sugar" +62105000,"BLUEBERRIES, DRIED","Blueberries, dried" +62106000,"CHERRIES, DRIED","Cherries, dried" +62107100,"BANANA FLAKES, DEHYDRATED","Banana flakes, dehydrated" +62107200,"BANANA CHIPS","Banana chips" +62108100,"CURRANTS, DRIED","Currants, dried" +62109100,"CRANBERRIES, DRIED","Cranberries, dried" +62110100,"DATE","Date" +62113100,"FIG, DRIED, UNCOOKED","Fig, dried, uncooked" +62113200,"FIG, DRIED, COOKED, NS AS TO ADDED SWEETENER","Fig, dried, cooked, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +62113220,"FIG, DRIED, COOKED, UNSWEETENED","Fig, dried, cooked, unsweetened" +62113230,"FIG, DRIED, COOKED, W/ SUGAR","Fig, dried, cooked, with sugar" +62114000,"LYCHEE, DRIED (LYCHEE NUTS)","Lychee, dried (lychee nuts)" +62114050,"MANGO, DRIED","Mango, dried" +62114110,"PAPAYA, DRIED","Papaya, dried" +62116100,"PEACH, DRIED, UNCOOKED","Peach, dried, uncooked" +62116200,"PEACH, DRIED, COOKED, NS AS TO ADDED SWEETENER","Peach, dried, cooked, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +62116220,"PEACH, DRIED, COOKED, UNSWEETENED","Peach, dried, cooked, unsweetened" +62116230,"PEACH, DRIED, COOKED, W/ SUGAR","Peach, dried, cooked, with sugar" +62119100,"PEAR, DRIED, UNCOOKED","Pear, dried, uncooked" +62119200,"PEAR, DRIED, COOKED, NS AS TO SWEETENER","Pear, dried, cooked, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +62119220,"PEAR, DRIED, COOKED, UNSWEETENED","Pear, dried, cooked, unsweetened" +62119230,"PEAR, DRIED, COOKED, W/ SUGAR","Pear, dried, cooked, with sugar" +62120100,"PINEAPPLE, DRIED","Pineapple, dried" +62121100,"PLUM, ROCK SALT, DRIED","Plum, rock salt, dried" +62122100,"PRUNE, DRIED, UNCOOKED","Prune, dried, uncooked" +62122200,"PRUNE, DRIED, COOKED, NS AS TO ADDED SWEETENER","Prune, dried, cooked, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +62122220,"PRUNE, DRIED, COOKED, UNSWEETENED","Prune, dried, cooked, unsweetened" +62122230,"PRUNE, DRIED, COOKED, W/ SUGAR","Prune, dried, cooked, with sugar" +62125100,"RAISINS (INCLUDE CINNAMON-COATED RAISINS)","Raisins" +62125110,"RAISINS, COOKED","Raisins, cooked" +62126000,"TAMARIND PULP, DRIED, SWEETENED (""PULPITAS"")","Tamarind pulp, dried, sweetened (""Pulpitas"")" +63100100,"FRUIT, NS AS TO TYPE","Fruit, NS as to type" +63101000,"APPLE, RAW","Apple, raw" +63101110,"APPLESAUCE, STEWED APPLES, NS AS TO ADDED SWEETENER","Applesauce, stewed apples, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63101120,"APPLESAUCE, STEWED APPLES, UNSWEETENED (INCL FRESH)","Applesauce, stewed apples, unsweetened" +63101130,"APPLESAUCE, STEWED APPLES, W/ SUGAR","Applesauce, stewed apples, with sugar" +63101140,"APPLESAUCE, STEWED APPLES, W/ LOW CALORIE SWEETENER","Applesauce, stewed apples, sweetened with low calorie sweetener" +63101150,"APPLESAUCE / OTHER FRUITS(INCLUDE MOTT'S FRUIT PAK)","Applesauce with other fruits" +63101210,"APPLE, COOKED OR CANNED, W/ SYRUP","Apple, cooked or canned, with syrup" +63101310,"APPLE, BAKED, NS AS TO ADDED SWEETENER","Apple, baked, NS as to added sweetener" +63101320,"APPLE, BAKED, UNSWEETENED","Apple, baked, unsweetened" +63101330,"APPLE, BAKED, W/ SUGAR","Apple, baked, with sugar" +63101410,"APPLE RINGS, FRIED","Apple rings, fried" +63101420,"APPLE, PICKLED (INCLUDE SPICED)","Apple, pickled" +63101500,"APPLE, FRIED","Apple, fried" +63103010,"APRICOT, RAW","Apricot, raw" +63103110,"APRICOT, COOKED OR CANNED, NS AS TO ADDED SWEETENER","Apricot, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63103120,"APRICOT, COOKED OR CANNED, WATER PACK, UNSWEETENED","Apricot, cooked or canned, unsweetened, water pack" +63103130,"APRICOT, COOKED OR CANNED, IN HEAVY SYRUP","Apricot, cooked or canned, in heavy syrup" +63103140,"APRICOT, COOKED OR CANNED, IN LIGHT SYRUP","Apricot, cooked or canned, in light syrup" +63103150,"APRICOT, COOKED OR CANNED, DRAINED SOLIDS","Apricot, cooked or canned, drained solids" +63103170,"APRICOT, COOKED OR CANNED, JUICE PACK","Apricot, cooked or canned, juice pack" +63105010,"AVOCADO, RAW","Avocado, raw" +63107010,"BANANA, RAW","Banana, raw" +63107050,"BANANA, WHITE, RIPE (GUINEO BLANCO MADURO)","Banana, white, ripe (guineo blanco maduro)" +63107070,"BANANA, CHINESE, RAW (INCL CAVENDISH,DWARF,FINGER)","Banana, Chinese, raw" +63107080,"BANANA, RED, RIPE (INCLUDE GUINEO MORADO)","Banana, red, ripe (guineo morado)" +63107090,"BANANA, RED, FRIED","Banana, red, fried" +63107110,"BANANA, BAKED","Banana, baked" +63107210,"BANANA, RIPE, FRIED","Banana, ripe, fried" +63107310,"BANANA, RIPE, BOILED","Banana, ripe, boiled" +63107410,"BANANA, BATTER-DIPPED, FRIED","Banana, batter-dipped, fried" +63109010,"CANTALOUPE (MUSKMELON), RAW (INCLUDE MELON, NFS)","Cantaloupe (muskmelon), raw" +63109610,"CANTALOUPE, FROZEN (BALLS)","Cantaloupe, frozen (balls)" +63109700,"CARAMBOLA (STARFRUIT),RAW","Carambola (starfruit), raw" +63109750,"CARAMBOLA (STARFRUIT), COOKED, W/ SUGAR","Carambola (starfruit), cooked, with sugar" +63110010,"CASSABA MELON, RAW","Cassaba melon, raw" +63111010,"CHERRIES, MARASCHINO","Cherries, maraschino" +63113010,"CHERRIES, SOUR, RED, RAW","Cherries, sour, red, raw" +63113030,"CHERRY PIE FILLING","Cherry pie filling" +63113050,"CHERRY PIE FILLING, LOW CALORIE","Cherry pie filling, low calorie" +63113110,"CHERRIES, SOUR, RED, COOKED, UNSWEETENED","Cherries, sour, red, cooked, unsweetened" +63115010,"CHERRIES, SWEET, RAW (INCLUDE CHERRIES, FRESH, NFS)","Cherries, sweet, raw (Queen Anne, Bing)" +63115110,"CHERRIES, SWEET, COOKED/CANNED,NS AS TO ADDED SWEET","Cherries, sweet, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63115120,"CHERRIES, SWEET, COOKED, UNSWEETENED, WATER PACK","Cherries, sweet, cooked, unsweetened, water pack" +63115130,"CHERRIES, SWEET, COOKED OR CANNED, IN HEAVY SYRUP","Cherries, sweet, cooked or canned, in heavy syrup" +63115140,"CHERRIES, SWEET, COOKED OR CANNED, IN LIGHT SYRUP","Cherries, sweet, cooked or canned, in light syrup" +63115150,"CHERRIES, SWEET, COOKED OR CANNED, DRAINED SOLIDS","Cherries, sweet, cooked or canned, drained solids" +63115170,"CHERRIES, SWEET, COOKED OR CANNED, JUICE PACK","Cherries, sweet, cooked or canned, juice pack" +63115200,"CHERRIES, FROZEN","Cherries, frozen" +63117010,"CURRANTS, RAW","Currants, raw" +63119010,"FIG, RAW","Fig, raw" +63119110,"FIG, COOKED OR CANNED, NS AS TO ADDED SWEETENER","Fig, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63119120,"FIG, COOKED OR CANNED, UNSWEETENED, WATER PACK","Fig, cooked or canned, unsweetened, water pack" +63119130,"FIG, COOKED OR CANNED, IN HEAVY SYRUP","Fig, cooked or canned, in heavy syrup" +63119140,"FIGS, COOKED OR CANNED, IN LIGHT SYRUP","Figs, cooked or canned, in light syrup" +63123000,"GRAPES, RAW, NS AS TO TYPE","Grapes, raw, NS as to type" +63123010,"GRAPES, EUROPEAN TYPE,ADHERENT SKIN,RAW(INCL TOKAY)","Grapes, European type, adherent skin, raw" +63123020,"GRAPES, AMERICAN TYPE, SLIP SKIN, RAW(INCL CONCORD)","Grapes, American type, slip skin, raw" +63123110,"GRAPES, SEEDLESS, COOKED/CANNED, NS ADDED SWEETNER","Grapes, seedless, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63123120,"GRAPES, SEEDLESS, COOKED OR CANNED, UNSWEETENED","Grapes, seedless, cooked or canned, unsweetened, water pack" +63123130,"GRAPES, SEEDLESS, COOKED OR CANNED, IN HEAVY SYRUP","Grapes, seedless, cooked or canned, in heavy syrup" +63125010,"GUAVA, RAW","Guava, raw" +63125100,"GUAVA SHELL (ASSUME CANNED IN HEAVY SYRUP)","Guava shell (assume canned in heavy syrup)" +63126010,"JUNEBERRY, RAW","Juneberry, raw" +63126500,"KIWI FRUIT, RAW","Kiwi fruit, raw" +63126510,"LYCHEE, RAW","Lychee, raw" +63126600,"LYCHEE, COOKED OR CANNED, IN SUGAR OR SYRUP","Lychee, cooked or canned, in sugar or syrup" +63127010,"HONEYDEW MELON, RAW","Honeydew melon, raw" +63127610,"HONEYDEW MELON, FROZEN (BALLS)","Honeydew, frozen (balls)" +63129010,"MANGO, RAW","Mango, raw" +63129020,"MANGO, PICKLED","Mango, pickled" +63129030,"MANGO, COOKED","Mango, cooked" +63131010,"NECTARINE, RAW","Nectarine, raw" +63131110,"NECTARINE, COOKED","Nectarine, cooked" +63133010,"PAPAYA, RAW","Papaya, raw" +63133050,"PAPAYA, GREEN, COOKED","Papaya, green, cooked" +63133100,"PAPAYA, COOKED OR CANNED, IN SUGAR OR SYRUP","Papaya, cooked or canned, in sugar or syrup" +63134010,"PASSION FRUIT, RAW","Passion fruit, raw" +63135010,"PEACH, RAW","Peach, raw" +63135110,"PEACH, COOKED OR CANNED, NS AS TO SWEETENER","Peach, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63135120,"PEACH, COOKED OR CANNED, UNSWEETENED, WATER PACK","Peach, cooked or canned, unsweetened, water pack" +63135130,"PEACH, COOKED OR CANNED, IN HEAVY SYRUP","Peach, cooked or canned, in heavy syrup" +63135140,"PEACH, COOKED OR CANNED, IN LIGHT OR MEDIUM SYRUP","Peach, cooked or canned, in light or medium syrup" +63135150,"PEACH, COOKED OR CANNED, DRAINED SOLIDS","Peach, cooked or canned, drained solids" +63135170,"PEACH, COOKED OR CANNED, JUICE PACK","Peach, cooked or canned, juice pack" +63135610,"PEACH, FROZEN, NS AS TO ADDED SWEETENER","Peach, frozen, NS as to added sweetener" +63135620,"PEACH, FROZEN, UNSWEETENED","Peach, frozen, unsweetened" +63135630,"PEACH, FROZEN, W/ SUGAR","Peach, frozen, with sugar" +63135650,"PEACH, PICKLED","Peach, pickled" +63135660,"PEACH, SPICED","Peach, spiced" +63137010,"PEAR, RAW","Pear, raw" +63137050,"PEAR, JAPANESE, RAW","Pear, Japanese, raw" +63137110,"PEAR, COOKED OR CANNED, NS AS TO ADDED SWEETENER","Pear, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63137120,"PEAR, COOKED OR CANNED, UNSWEETENED, WATER PACK","Pear, cooked or canned, unsweetened, water pack" +63137130,"PEAR, COOKED OR CANNED, IN HEAVY SYRUP","Pear, cooked or canned, in heavy syrup" +63137140,"PEAR, COOKED OR CANNED, IN LIGHT SYRUP","Pear, cooked or canned, in light syrup" +63137150,"PEAR, COOKED OR CANNED, DRAINED SOLIDS","Pear, cooked or canned, drained solids" +63137170,"PEAR, COOKED OR CANNED, JUICE PACK","Pear, cooked or canned, juice pack" +63139010,"PERSIMMONS, RAW","Persimmon, raw" +63141010,"PINEAPPLE, RAW","Pineapple, raw" +63141110,"PINEAPPLE, CANNED, NS AS TO ADDED SWEETENER","Pineapple, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63141120,"PINEAPPLE, COOKED OR CANNED, UNSWEETENED, WATERPACK","Pineapple, cooked or canned, unsweetened, waterpack" +63141130,"PINEAPPLE, COOKED OR CANNED, IN HEAVY SYRUP","Pineapple, cooked or canned, in heavy syrup" +63141140,"PINEAPPLE, COOKED OR CANNED, IN LIGHT SYRUP","Pineapple, cooked or canned, in light syrup" +63141150,"PINEAPPLE, COOKED OR CANNED, DRAINED SOLIDS","Pineapple, cooked or canned, drained solids" +63141170,"PINEAPPLE, COOKED OR CANNED, JUICE PACK","Pineapple, cooked or canned, juice pack" +63143010,"PLUM, RAW","Plum, raw" +63143110,"PLUM, COOKED OR CANNED, NS AS TO ADDED SWEETENER","Plum, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63143120,"PLUM, COOKED OR CANNED, UNSWEETENED, WATER PACK","Plum, cooked or canned, unsweetened, water pack" +63143130,"PLUM, COOKED OR CANNED, IN HEAVY SYRUP","Plum, cooked or canned, in heavy syrup" +63143140,"PLUM, COOKED OR CANNED, IN LIGHT SYRUP","Plum, cooked or canned, in light syrup" +63143150,"PLUM, COOKED OR CANNED, DRAINED SOLIDS","Plum, cooked or canned, drained solids" +63143170,"PLUM, COOKED OR CANNED, JUICE PACK","Plum, cooked or canned, juice pack" +63143650,"PLUM, PICKLED","Plum, pickled" +63145010,"POMEGRANATE, RAW","Pomegranate, raw" +63147010,"RHUBARB, RAW","Rhubarb, raw" +63147110,"RHUBARB, COOKED OR CANNED, NS ADDED SWEETNER","Rhubarb, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63147120,"RHUBARB, COOKED OR CANNED, UNSWEETENED","Rhubarb, cooked or canned, unsweetened" +63147130,"RHUBARB, COOKED OR CANNED, IN HEAVY SYRUP","Rhubarb, cooked or canned, in heavy syrup" +63147140,"RHUBARB, COOKED OR CANNED, IN LIGHT SYRUP","Rhubarb, cooked or canned, in light syrup" +63147150,"RHUBARB, COOKED OR CANNED, DRAINED SOLIDS","Rhubarb, cooked or canned, drained solids" +63147600,"RHUBARB, FROZEN, NS AS TO ADDED SWEETENER","Rhubarb, frozen, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63147620,"RHUBARB, FROZEN, W/ SUGAR","Rhubarb, frozen, with sugar" +63148750,"TAMARIND, RAW","Tamarind, raw" +63149010,"WATERMELON, RAW","Watermelon, raw" +63200100,"BERRIES, RAW, NFS","Berries, raw, NFS" +63200200,"BERRIES, FROZEN, NFS","Berries, frozen, NFS" +63201010,"BLACKBERRIES, RAW","Blackberries, raw" +63201110,"BLACKBERRIES, COOKED OR CANNED, NS ADDED SWEETNER","Blackberries, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63201130,"BLACKBERRIES, COOKED OR CANNED, IN HEAVY SYRUP","Blackberries, cooked or canned, in heavy syrup" +63201600,"BLACKBERRIES, FROZEN","Blackberries, frozen" +63201800,"BLACKBERRIES, FROZEN, SWEETENED, NFS","Blackberries, frozen, sweetened, NS as to type of sweetener" +63203010,"BLUEBERRIES, RAW","Blueberries, raw" +63203110,"BLUEBERRIES, COOKED OR CANNED, NS AS TO SWEETENER","Blueberries, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63203120,"BLUEBERRIES, COOKED OR CANNED, UNSWEET, WATER PACK","Blueberries, cooked or canned, unsweetened, water pack" +63203125,"BLUEBERRIES, COOKED OR CANNED, IN LIGHT SYRUP","Blueberries, cooked or canned, in light syrup" +63203130,"BLUEBERRIES, COOKED OR CANNED, IN HEAVY SYRUP","Blueberries, cooked or canned, in heavy syrup" +63203550,"BLUEBERRIES, FROZEN, SWEETENED","Blueberries, frozen, sweetened" +63203570,"BLUEBERRIES, FROZEN, NS SWEETENED OR UNSWEETENED","Blueberries, frozen, NS as to sweetened or unsweetened" +63203600,"BLUEBERRIES, FROZEN, UNSWEETENED","Blueberries, frozen, unsweetened" +63203700,"BLUEBERRY PIE FILLING","Blueberry pie filling" +63205010,"BOYSENBERRIES, RAW","Boysenberries, raw" +63205600,"BOYSENBERRIES, FROZEN","Boysenberries, frozen" +63207000,"CRANBERRIES, NS AS TO RAW, COOKED OR CANNED","Cranberries, NS as to raw, cooked, or canned" +63207010,"CRANBERRIES, RAW","Cranberries, raw" +63207110,"CRANBERRIES, COOKED OR CANNED (INCL CRANBERRY SCE)","Cranberries, cooked or canned" +63208000,"DEWBERRIES, RAW","Dewberries, raw" +63214000,"HUCKLEBERRIES, RAW","Huckleberries, raw" +63215010,"LOGANBERRIES, RAW","Loganberries, raw" +63215600,"LOGANBERRIES, FROZEN","Loganberries, frozen" +63217010,"MULBERRIES, RAW","Mulberries, raw" +63219000,"RASPBERRIES, RAW, NS AS TO COLOR","Raspberries, raw, NS as to color" +63219010,"RASPBERRIES, BLACK, RAW","Raspberries, black, raw" +63219020,"RASPBERRIES, RED, RAW","Raspberries, red, raw" +63219110,"RASPBERRIES, COOKED OR CANNED, NS ADDED SWEETENER","Raspberries, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63219120,"RASPBERRIES, CKD OR CND, UNSWEETENED, WATER PACK","Raspberries, cooked or canned, unsweetened, water pack" +63219130,"RASPBERRIES, COOKED OR CANNED, IN HEAVY SYRUP","Raspberries, cooked or canned, in heavy syrup" +63219600,"RASPBERRIES, FROZEN, NS AS TO ADDED SWEETNER","Raspberries, frozen, NS as to added sweetener" +63219610,"RASPBERRIES, FROZEN, UNSWEETENED","Raspberries, frozen, unsweetened" +63219620,"RASPBERRIES, FROZEN, W/ SUGAR","Raspberries, frozen, with sugar" +63223020,"STRAWBERRIES, RAW","Strawberries, raw" +63223030,"STRAWBERRIES, RAW, W/ SUGAR","Strawberries, raw, with sugar" +63223110,"STRAWBERRIES, COOKED OR CANNED, NS ADDED SWEETNER","Strawberries, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63223120,"STRAWBERRIES, CKD OR CND, UNSWEETENED, WATER PACK","Strawberries, cooked or canned, unsweetened, water pack" +63223130,"STRAWBERRIES, COOKED OR CANNED, IN SYRUP","Strawberries, cooked or canned, in syrup" +63223600,"STRAWBERRIES, FROZEN, NS AS TO ADDED SWEETNER","Strawberries, frozen, NS as to added sweetener" +63223610,"STRAWBERRIES, FROZEN, UNSWEETENED","Strawberries, frozen, unsweetened" +63223620,"STRAWBERRIES, FROZEN, W/ SUGAR","Strawberries, frozen, with sugar" +63224000,"YOUNGBERRIES, RAW","Youngberries, raw" +63301010,"AMBROSIA","Ambrosia" +63307010,"CRANBERRY-ORANGE RELISH, UNCOOKED","Cranberry-orange relish, uncooked" +63307100,"CRANBERRY-RASPBERRY SAUCE","Cranberry-raspberry Sauce" +63311000,"FRUIT SALAD, FRESH OR RAW, (EXCL CITRUS), NO DRSG","Fruit salad, fresh or raw, (excluding citrus fruits), no dressing" +63311050,"FRUIT SALAD, FRESH OR RAW, (INCL CITRUS), NO DRSG","Fruit salad, fresh or raw, (including citrus fruits), no dressing" +63311080,"FRUIT COCKTAIL OR MIX, FROZEN","Fruit cocktail or mix, frozen" +63311110,"FRUIT COCKTAIL, COOKED OR CANNED, NS ADDED SWEETNER","Fruit cocktail, cooked or canned, NS as to sweetened or unsweetened; sweetened, NS as to type of sweetener" +63311120,"FRUIT COCKTAIL, CKD OR CND, UNSWEET, WATER PACK","Fruit cocktail, cooked or canned, unsweetened, water pack" +63311130,"FRUIT COCKTAIL, COOKED OR CANNED, IN HEAVY SYRUP","Fruit cocktail, cooked or canned, in heavy syrup" +63311140,"FRUIT COCKTAIL, COOKED OR CANNED, IN LIGHT SYRUP","Fruit cocktail, cooked or canned, in light syrup" +63311145,"TROPICAL FRUIT COCKTAIL, COOKED OR CANNED, IN LIGHT SYRUP","Tropical fruit cocktail, cooked or canned, in light syrup" +63311150,"FRUIT COCKTAIL, COOKED OR CANNED, DRAINED SOLIDS","Fruit cocktail, cooked or canned, drained solids" +63311170,"FRUIT COCKTAIL, COOKED OR CANNED, JUICE PACK","Fruit cocktail, cooked or canned, juice pack" +63320100,"FRUIT SALAD, P.R. STYLE (ENSALADA DE FRUTA)","Fruit salad, Puerto Rican style (Mixture includes bananas, papayas, oranges, etc.) (Ensalada de frutas tropicales)" +63401010,"APPLE SALAD W/ DRESSING (INCLUDE WALDORF SALAD)","Apple salad with dressing" +63401015,"APPLE AND GRAPE SALAD W/ YOGURT & WALNUTS","Apple and grape salad with yogurt and walnuts" +63401060,"APPLE, CANDIED (INCLUDE CARAMEL APPLES)","Apple, candied" +63401070,"FRUIT, CHOCOLATE COVERED","Fruit, chocolate covered" +63401990,"BANANA, CHOCOLATE-COVERED, W/ NUTS","Banana, chocolate-covered with nuts" +63402010,"BANANA WHIP","Banana whip" +63402030,"PRUNE WHIP","Prune whip" +63402045,"FRIED DWARF BANANA, PUERTO RICAN STYLE","Fried dwarf banana, Puerto Rican style" +63402050,"FRIED DWARF BANANA W/ CHEESE, PUERTO RICAN STYLE","Fried dwarf banana with cheese, Puerto Rican style" +63402950,"FRUIT SALAD (NO CITRUS) W/ SALAD DRESSING","Fruit salad (excluding citrus fruits) with salad dressing or mayonnaise" +63402960,"FRUIT SALAD (EXCLUDING CITRUS FRUITS) WITH WHIPPED CREAM","Fruit salad (excluding citrus fruits) with whipped cream" +63402970,"FRUIT SALAD (EXCLUDING CITRUS FRUITS) WITH NONDAIRY WHIPPED","Fruit salad (excluding citrus fruits) with nondairy whipped topping" +63402980,"FRUIT SALAD (NO CITRUS) W/ MARSHMALLOWS","Fruit salad (excluding citrus fruits) with marshmallows" +63402990,"FRUIT SALAD (W/ CITRUS) W/ PUDDING","Fruit salad (including citrus fruits) with pudding" +63403000,"FRUIT SALAD (NO CITRUS FRUITS) W/ PUDDING","Fruit salad (excluding citrus fruits) with pudding" +63403010,"FRUIT SALAD (INCL CITRUS FRUITS) W/ SALAD DRESSING","Fruit salad (including citrus fruits) with salad dressing or mayonnaise" +63403020,"FRUIT SALAD (INCLUDING CITRUS FRUIT) WITH WHIPPED CREAM","Fruit salad (including citrus fruit) with whipped cream" +63403030,"FRUIT SALAD (INCLUDING CITRUS FRUITS) WITH NONDAIRY WHIPPED","Fruit salad (including citrus fruits) with nondairy whipped topping" +63403040,"FRUIT SALAD W/ MARSHMALLOWS","Fruit salad (including citrus fruits) with marshmallows" +63403100,"FRUIT DESSERT W/ CREAM & OR PUDDING & NUTS","Fruit dessert with cream and/or pudding and nuts" +63403150,"LIME SOUFFLE (INCLUDE OTHER CITRUS FRUITS)","Lime souffle" +63409010,"GUACAMOLE","Guacamole" +63409020,"CHUTNEY","Chutney" +63411010,"CRANBERRY SALAD, CONGEALED","Cranberry salad, congealed" +63413010,"PINEAPPLE SALAD W/ DRESSING","Pineapple salad with dressing" +63415100,"SOUP, FRUIT","Soup, fruit" +63420100,"FRUIT JUICE BAR, FROZEN, ORANGE FLAVOR","Fruit juice bar, frozen, orange flavor" +63420110,"FRUIT JUICE BAR, FROZEN, FLAVOR OTHER THAN ORANGE","Fruit juice bar, frozen, flavor other than orange" +63420200,"FRUIT JUICE BAR, FROZ, LOW CAL SWEETNER, NOT ORANGE","Fruit juice bar, frozen, sweetened with low calorie sweetener, flavors other than orange" +63430100,"SORBET, FRUIT, NONCITRUS FLAVOR","Sorbet, fruit, noncitrus flavor" +63430110,"SORBET, FRUIT, CITRUS FLAVOR","Sorbet, fruit, citrus flavor" +63430500,"FRUIT JUICE BAR W/ CREAM, FROZEN","Fruit juice bar with cream, frozen" +64100100,"FRUIT JUICE, NFS","Fruit juice, NFS" +64100110,"FRUIT JUICE BLEND, 100% JUICE","Fruit juice blend, 100% juice" +64100200,"FRUIT JUICE BLEND, WITH CRANBERRY, 100% JUICE","Fruit juice blend, with cranberry, 100% juice" +64101010,"APPLE CIDER (INCLUDE CIDER, NFS)","Apple cider" +64104010,"APPLE JUICE","Apple juice" +64104600,"BLACKBERRY JUICE (INCL BOYSENBERRY JUICE)","Blackberry juice" +64105400,"CRANBERRY JUICE, 100%, NOT A BLEND","Cranberry juice, 100%, not a blend" +64116020,"GRAPE JUICE","Grape juice" +64120010,"PAPAYA JUICE","Papaya juice" +64121000,"PASSION FRUIT JUICE","Passion fruit juice" +64124020,"PINEAPPLE JUICE","Pineapple juice" +64126000,"POMEGRANATE JUICE","Pomegranate juice" +64132010,"PRUNE JUICE","Prune juice" +64132500,"STRAWBERRY JUICE","Strawberry juice" +64133100,"WATERMELON JUICE","Watermelon juice" +64134000,"FRUIT SMOOTHIE DRINK, W/ FRUIT OR JUICE ONLY (NO DAIRY)","Fruit smoothie drink, made with fruit or fruit juice only (no dairy products)" +64200100,"FRUIT NECTAR, NFS","Fruit nectar, NFS" +64201010,"APRICOT NECTAR","Apricot nectar" +64201500,"BANANA NECTAR","Banana nectar" +64202010,"CANTALOUPE NECTAR","Cantaloupe nectar" +64203020,"GUAVA NECTAR","Guava nectar" +64204010,"MANGO NECTAR","Mango nectar" +64205010,"PEACH NECTAR","Peach nectar" +64210010,"PAPAYA NECTAR","Papaya nectar" +64213010,"PASSION FRUIT NECTAR","Passion fruit nectar" +64215010,"PEAR NECTAR","Pear nectar" +64221010,"SOURSOP (GUANABANA) NECTAR","Soursop (Guanabana) nectar" +64401000,"VINEGAR","Vinegar" +67100100,"FRUIT, BABY, NFS","Fruit, baby food, NFS" +67100110,"FRUIT BAR, WITH ADDED VITAMIN C, BABY FOOD, TODDLER","Fruit bar, with added vitamin C, baby food, toddler" +67100200,"TROPICAL FRUIT MEDLEY, BABY FOOD, STRAINED","Tropical fruit medley, baby food, strained" +67100300,"APPLES, BABY, TODDLER","Apples, baby food, toddler" +67101000,"APPLE-RASPBERRY, BABY, NS AS TO STRAINED OR JUNIOR","Apple-raspberry, baby food, NS as to strained or junior" +67101010,"APPLE-RASPBERRY, BABY, STRAINED","Apple-raspberry, baby food, strained" +67101020,"APPLE-RASPBERRY, BABY, JUNIOR","Apple-raspberry, baby food, junior" +67102000,"APPLESAUCE, BABY, NS AS TO STRAINED OR JUNIOR","Applesauce, baby food, NS as to strained or junior" +67102010,"APPLESAUCE, BABY, STRAINED","Applesauce, baby food, strained" +67102020,"APPLESAUCE, BABY, JUNIOR","Applesauce, baby food, junior" +67104000,"APPLESAUCE & APRICOTS, BABY, NS AS TO STR OR JR","Applesauce and apricots, baby food, NS as to strained or junior" +67104010,"APPLESAUCE & APRICOTS, BABY, STRAINED","Applesauce and apricots, baby food, strained" +67104020,"APPLESAUCE & APRICOTS, BABY, JUNIOR","Applesauce and apricots, baby food, junior" +67104030,"APPLESAUCE W/ BANANAS, BABY, NS STRAINED/JUNIOR","Applesauce with bananas, baby food, NS as to strained or junior" +67104040,"APPLESAUCE W/ BANANAS, BABY, STRAINED","Applesauce with bananas, baby food, strained" +67104060,"APPLESAUCE W/ BANANAS, BABY, JUNIOR","Applesauce with bananas, baby food, junior" +67104070,"APPLESAUCE W/ CHERRIES, BABY, STRAINED","Applesauce with cherries, baby food, strained" +67104080,"APPLESAUCE W/ CHERRIES, BABY, JUNIOR","Applesauce with cherries, baby food, junior" +67104090,"APPLESAUCE W/ CHERRIES, BABY, NS STRAINED/JUNIOR","Applesauce with cherries, baby food, NS as to strained or junior" +67105030,"BANANAS,BABY FOOD,STRAINED","Bananas, baby food, strained" +67106010,"BANANAS W/ APPLES & PEARS, BABY, STRAINED","Bananas with apples and pears, baby food, strained" +67106050,"BANANA WITH MIXED BERRIES, BABY FOOD, STRAINED","Banana with mixed berries, baby food, strained" +67108000,"PEACHES, BABY, NS AS TO STRAINED OR JUNIOR","Peaches, baby food, NS as to strained or junior" +67108010,"PEACHES, BABY, STRAINED","Peaches, baby food, strained" +67108020,"PEACHES, BABY, JUNIOR","Peaches, baby food, junior" +67108030,"PEACHES, BABY, TODDLER","Peaches, baby food, toddler" +67109000,"PEARS, BABY, NS AS TO STRAINED OR JUNIOR","Pears, baby food, NS as to strained or junior" +67109010,"PEARS, BABY, STRAINED","Pears, baby food, strained" +67109020,"PEARS, BABY, JUNIOR","Pears, baby food, junior" +67109030,"PEARS, BABY, TODDLER","Pears, baby food, toddler" +67110000,"PRUNES, BABY, STRAINED","Prunes, baby food, strained" +67113000,"APPLES & PEARS, BABY, NS AS TO STRAINED OR JUNIOR","Apples and pears, baby food, NS as to strained or junior" +67113010,"APPLES & PEARS, BABY, STRAINED","Apples and pears, baby food, strained" +67113020,"APPLES & PEARS, BABY, JUNIOR","Apples and pears, baby food, junior" +67114000,"PEARS & PINEAPPLE, BABY, NS AS TO STR OR JR","Pears and pineapple, baby food, NS as to strained or junior" +67114010,"PEARS & PINEAPPLE, BABY, STRAINED","Pears and pineapple, baby food, strained" +67114020,"PEARS & PINEAPPLE, BABY, JUNIOR","Pears and pineapple, baby food, junior" +67202000,"APPLE JUICE, BABY","Apple juice, baby food" +67202010,"APPLE JUICE, W/ CALCIUM, BABY","Apple juice, with added calcium, baby food" +67203000,"APPLE W/ OTHER FRUIT JUICE, BABY","Apple with other fruit juice, baby food" +67203200,"APPLE-BANANA JUICE, BABY","Apple-banana juice, baby food" +67203400,"APPLE-CHERRY JUICE, BABY","Apple-cherry juice, baby food" +67203450,"APPLE-CRANBERRY JUICE, BABY","Apple-cranberry juice, baby food" +67203500,"APPLE-GRAPE JUICE, BABY","Apple-grape juice, baby food" +67203600,"APPLE-PEACH JUICE, BABY","Apple-peach juice, baby food" +67203700,"APPLE-PRUNE JUICE, BABY","Apple-prune juice, baby food" +67203800,"GRAPE JUICE, BABY","Grape juice, baby food" +67204000,"MIXED FRUIT JUICE, NOT CITRUS, BABY","Mixed fruit juice, not citrus, baby food" +67204100,"MIXED FRUIT JUICE, NOT CITRUS, W/ CALCIUM, BABY","Mixed fruit juice, not citrus, with added calcium, baby food" +67205000,"ORANGE JUICE, BABY","Orange juice, baby food" +67211000,"ORANGE-APPLE-BANANA JUICE, BABY","Orange-apple-banana juice, baby food" +67212000,"PEAR JUICE, BABY FOOD","Pear juice, baby food" +67230000,"APPLE-SWEET POTATO-JUICE,BABY FOOD","Apple-sweet potato juice, baby food" +67230500,"ORANGE-CARROT JUICE, BABY FOOD","Orange-carrot juice, baby food" +67250100,"BANANA JUICE W/ LOWFAT YOGURT, BABY FOOD","Banana juice with lowfat yogurt, baby food" +67250150,"MIXED FRUIT JUICE W/ LOWFAT YOGURT, BABY FOOD","Mixed fruit juice with lowfat yogurt, baby food" +67260000,"FRUIT JUICE DRINK, BABY, W/ HI VIT C + CA, B VITS","Fruit juice drink, baby, with high vitamin C plus added calcium and B vitamins" +67304000,"PLUMS, BABY, NS AS TO STRAINED OR JUNIOR","Plums, baby food, NS as to strained or junior" +67304010,"PLUMS, BABY, STRAINED","Plums, baby food, strained" +67304020,"PLUMS, BABY, JUNIOR","Plums, baby food, junior" +67304030,"PLUMS, BANANAS & RICE, BABY, STRAINED","Plums, bananas, and rice, baby food strained" +67304500,"PRUNES W/ OATMEAL, BABY, STRAINED","Prunes with oatmeal, baby food, strained" +67307000,"APRICOTS, BABY, NS AS TO STR OR JR","Apricots, baby food, NS as to strained or junior" +67307010,"APRICOTS, BABY, STRAINED","Apricots, baby food, strained" +67307020,"APRICOTS, BABY, JUNIOR","Apricots, baby food, junior" +67308000,"BANANAS, BABY, NS AS TO STR OR JR","Bananas, baby food, NS as to strained or junior" +67308020,"BANANAS, BABY, JUNIOR","Bananas, baby food, junior" +67309000,"BANANAS & PINEAPPLE, BABY,NS AS TO STR/JR","Bananas and pineapple, baby food, NS as to strained or junior" +67309010,"BANANAS & PINEAPPLE, BABY, STRAINED","Bananas and pineapple, baby food, strained" +67309020,"BANANAS & PINEAPPLE, BABY, JUNIOR","Bananas and pineapple, baby food, junior" +67309030,"BANANAS AND STRAWBERRY, BABY FOOD, JUNIOR","Bananas and strawberry, baby food, junior" +67404000,"FRUIT DESSERT, BABY, NS AS TO STR OR JR","Fruit dessert, baby food, NS as to strained or junior" +67404010,"FRUIT DESSERT, BABY, STRAINED","Fruit dessert, baby food, strained" +67404020,"FRUIT DESSERT, BABY, JUNIOR","Fruit dessert, baby food, junior" +67404050,"FRUIT SUPREME DESSERT, BABY, ALL FLAVORS","Fruit Supreme dessert, baby food" +67404070,"APPLE YOGURT DESSERT, BABY, STRAINED","Apple yogurt dessert, baby food, strained" +67404110,"BANANA APPLE DESSERT, BABY FOOD, STRAINED","Banana apple dessert, baby food, strained" +67404300,"BLUEBERRY YOGURT DESSERT, BABY, STRAINED","Blueberry yogurt dessert, baby food, strained" +67404500,"MIXED FRUIT YOGURT DESSERT, BABY, STRAINED","Mixed fruit yogurt dessert, baby food, strained" +67404550,"CHERRY COBBLER, BABY, JUNIOR","Cherry cobbler, baby food, junior" +67405000,"PEACH COBBLER, BABY, NS AS TO STRAINED OR JUNIOR","Peach cobbler, baby food, NS as to strained or junior" +67405010,"PEACH COBBLER, BABY, STRAINED","Peach cobbler, baby food, strained" +67405020,"PEACH COBBLER, BABY, JUNIOR","Peach cobbler, baby food, junior" +67408010,"BANANA PUDDING, BABY, STRAINED","Banana pudding, baby food, strained" +67408500,"BANANA YOGURT DESSERT, BABY, STRAINED","Banana yogurt dessert, baby food, strained" +67410000,"CHERRY VANILLA PUDDING, BABY","Cherry vanilla pudding, baby food, strained" +67412000,"DUTCH APPLE DESSERT, BABY, NS AS TO STR OR JR","Dutch apple dessert, baby food, NS as to strained or junior" +67412010,"DUTCH APPLE DESSERT, BABY, STRAINED","Dutch apple dessert, baby food, strained" +67412020,"DUTCH APPLE DESSERT, BABY, JUNIOR","Dutch apple dessert, baby food, junior" +67413700,"PEACH YOGURT DESSERT, BABY, STRAINED","Peach yogurt dessert, baby food, strained" +67414010,"PINEAPPLE DESSERT, BABY, STRAINED","Pineapple dessert, baby food, strained" +67414100,"MANGO DESSERT, BABY","Mango dessert, baby food" +67415000,"TUTTI-FRUITTI PUDDING, BABY, NS AS TO STR OR JR","Tutti-fruitti pudding, baby food, NS as to strained or junior" +67415010,"TUTTI-FRUTTI PUDDING, BABY, STRAINED","Tutti-fruitti pudding, baby food, strained" +67415020,"TUTTI-FRUITTI PUDDING, BABY, JUNIOR","Tutti-fruitti pudding, baby food, junior" +67430000,"FRUIT FLAVORED SNACK, BABY FOOD","Fruit flavored snack, baby food" +67430500,"YOGURT AND FRUIT SNACK, BABY FOOD","Yogurt and fruit snack, baby food" +67501000,"APPLES & CHICKEN, BABY FOOD, STRAINED","Apples and chicken, baby food, strained" +67501100,"APPLES W/ HAM, BABY, STRAINED","Apples with ham, baby food, strained" +67600100,"APPLES & SWEET POTATOES, BABY, STRAINED","Apples and sweet potatoes, baby food, strained" +71000100,"WHITE POTATO, NFS","White potato, NFS" +71001000,"WHITE POTATO, RAW, W/ OR W/O PEEL","White potato, raw, with or without peel (assume peel not eaten)" +71050000,"WHITE POTATO, DRY, POWDERED, NOT RECONSTITUTED","White potato, dry, powdered, not reconstituted" +71101000,"WHITE POTATO, BAKED, PEEL NOT EATEN","White potato, baked, peel not eaten" +71101100,"WHITE POT,BAKED,PEEL EATEN,NS TO FAT ADDED IN COOK","White potato, baked, peel eaten, NS as to fat added in cooking" +71101110,"WHITE POT,BAKED,PEEL EATEN,FAT NOT ADDED IN COOKING","White potato, baked, peel eaten, fat not added in cooking" +71101120,"WHITE POT, BAKED,PEEL EATEN, FAT ADDED IN COOKING","White potato, baked, peel eaten, fat added in cooking" +71101150,"WHITE POTATO SKINS, W/ ADHERING FLESH, BAKED","White potato skins, with adhering flesh, baked" +71103000,"WHITE POTATO, BOILED, W/O PEEL, NS AS TO FAT","White potato, boiled, without peel, NS as to fat added in cooking" +71103010,"WHITE POTATO, BOILED, W/O PEEL, FAT NOT ADDED","White potato, boiled, without peel, fat not added in cooking" +71103020,"WHITE POTATO, BOILED, W/O PEEL, FAT ADDED","White potato, boiled, without peel, fat added in cooking" +71103100,"WHITE POTATO, BOILED W/ PEEL, PEEL NOT EATEN, NS AS TO FAT","White potato, boiled with peel, peel not eaten, NS as to fat added in cooking" +71103110,"WHITE POTATO, BOILED W/PEEL, PEEL NOT EATEN, FAT NOT ADDED","White potato, boiled with peel, peel not eaten, fat not added in cooking" +71103120,"WHITE POTATO, BOILED W/ PEEL, PEEL NOT EATEN, FAT ADDED","White potato, boiled with peel, peel not eaten, fat added in cooking" +71103200,"WHITE POTATO, CANNED, LOW SODIUM,NS AS TO ADDED FAT","White potato, boiled, without peel, canned, low sodium, NS as to fat added in cooking" +71103210,"WHITE POTATO, CANNED, LOW SODIUM, NO FAT ADDED","White potato, boiled, without peel, canned, low sodium, fat not added in cooking" +71103220,"WHITE POTATO, CANNED, LOW SODIUM, FAT ADDED","White potato, boiled, without peel, canned, low sodium, fat added in cooking" +71104000,"WHITE POTATO, ROASTED, NS FAT ADDED","White potato, roasted, NS as to fat added in cooking" +71104010,"WHITE POTATO, ROASTED, FAT NOT ADDED","White potato, roasted, fat not added in cooking" +71104020,"WHITE POTATO, ROASTED, FAT ADDED","White potato, roasted, fat added in cooking" +71106000,"STEWED POTATOES, P.R. (PAPAS GUISADAS)","Stewed potatoes, Puerto Rican style (Papas guisadas)" +71106010,"POTATO ONLY FROM P.R. MIXED DISHES","Potato only from Puerto Rican mixed dishes, gravy and other components reported separately" +71106020,"POTATO FROM PUERTO RICAN STYLE,POT ROAST, W/ GRAVY","Potato from Puerto Rican style stuffed pot roast, with gravy" +71106050,"POTATO FROM PUERTO RICAN BEEF STEW, W/ GRAVY","Potato from Puerto Rican beef stew, with gravy" +71106070,"POTATO FROM PUERTO RICAN CHICKEN FRICASSEE, W/ SCE","Potato from Puerto Rican chicken fricassee, with sauce" +71201015,"WHITE POTATO CHIPS, REGULAR CUT","White potato chips, regular cut" +71201020,"WHITE POTATO CHIPS, RUFFLED/RIPPLED/CRINKLE CUT","White potato chips, ruffled, rippled, or crinkle cut" +71201050,"WHITE POTATO, CHIPS, REDUCED FAT","White potato, chips, reduced fat" +71201080,"WHITE POTATO, CHIPS, FAT FREE","White potato, chips, fat free" +71201090,"WHITE POTATO, CHIPS, FAT FREE, W/ OLEAN","White potato, chips, fat free, made with Olean" +71201100,"WHITE POTATO, CHIPS, RESTRUCTURED","White potato, chips, restructured" +71201200,"WHITE POTATO, CHIPS, RESTRUCTURED, RED FAT/SODIUM","White potato, chips, restructured, reduced fat and reduced sodium" +71201210,"WHITE POTATO, CHIPS, RESTRUCTURED, FAT FREE, W/ OLEAN","White potato, chips, restructured, fat free, made with Olean" +71201250,"WHITE POTATO, CHIPS, RESTRUCTURED, BAKED","White potato, chips, restructured, baked" +71201300,"POTATO-BASED SNACKS, REDUCED FAT, LOW SODIUM","Potato based snacks, reduced fat, low sodium, all flavors" +71202000,"WHITE POTATO, CHIPS, UNSALTED","White potato, chips, unsalted" +71202100,"WHITE POTATO, CHIPS, UNSALTED, REDUCED FAT","White potato, chips, unsalted, reduced fat" +71202500,"WHITE POTATO CHIPS, LIGHTLY SALTED","White potato chips, lightly salted" +71204000,"POTATO PUFFS, CHEESE-FILLED","Potato puffs, cheese-filled" +71205000,"WHITE POTATO, STICKS","White potato, sticks" +71211000,"WHITE POTATO SKINS, CHIPS","White potato skins, chips" +71220000,"VEGETABLE CHIPS","Vegetable chips" +71301000,"WHITE POTATO, COOKED, W/ SAUCE, NS AS TO SAUCE","White potato, cooked, with sauce, NS as to sauce" +71301020,"WHITE POTATO, COOKED, WITH CHEESE","White potato, cooked, with cheese" +71301120,"WHITE POTATO, COOKED, WITH HAM AND CHEESE","White potato, cooked, with ham and cheese" +71305010,"WHITE POTATO, SCALLOPED","White potato, scalloped" +71305110,"WHITE POTATO, SCALLOPED, W/ HAM","White potato, scalloped, with ham" +71401000,"WHITE POTATO, FRENCH FRIES, NS AS TO FROM FRESH/FRZ","White potato, french fries, NS as to from fresh or frozen" +71401010,"WHITE POTATO, FRENCH FRIES, FROM FRESH, DEEP-FRIED","White potato, french fries, from fresh, deep fried" +71401015,"WHITE POTATO, FRENCH FRIES, FROM FRESH, OVEN BAKED","White potato, french fries, from fresh, oven baked" +71401020,"WHITE POTATO, FRENCH FRIES, FROM FROZEN, OVEN-BAKED","White potato, french fries, from frozen, oven baked" +71401030,"WHITE POTATO, FRENCH FRIES, FRM FRZ, DEEP FRD, FF/REST","White potato, french fries, from frozen, deep fried, from fast food / restaurant" +71401035,"WHITE POTATO, FRENCH FRIES, FR FRZN, NS AS TO FRIED OR BKD","White potato, french fries, from frozen, NS as to deep fried or oven baked" +71402500,"WHITE POTATO, FRENCH FRIES, W/ CHEESE","White potato, french fries, with cheese" +71402505,"WHITE POTATO, FRENCH FRIES, W/ CHEESE AND BACON","White potato, french fries, with cheese and bacon" +71402510,"WHITE POTATO, FRENCH FRIES, W/ CHILI & CHEESE","White potato, french fries, with chili and cheese" +71402520,"WHITE POTATO, FRENCH FRIES, W/ CHILI CON CARNE","White potato, french fries, with chili con carne" +71403000,"WHITE POTATO, HOME FRIES","White potato, home fries" +71403500,"WHITE POTATO, HOME FRIES, W/ GREEN/RED PEPPERS & ONIONS","White potato, home fries, with green or red peppers and onions" +71405000,"WHITE POTATO, HASH BROWN","White potato, hash brown, NS as to from fresh, frozen, or dry mix" +71405010,"WHITE POTATO, HASH BROWN, FROM FRESH","White potato, hash brown, from fresh" +71405020,"WHITE POTATO, HASH BROWN, FROM FROZEN","White potato, hash brown, from frozen" +71405030,"WHITE POTATO, HASH BROWN, FROM DRY MIX","White potato, hash brown, from dry mix" +71405100,"WHITE POTATO, HASH BROWN W/ CHEESE","White potato, hash brown, with cheese" +71410000,"WHITE POTATO SKINS, W/ ADHERING FLESH, FRIED","White potato skins, with adhering flesh, fried" +71410500,"WHITE POTATO SKINS W/ FLESH, FRIED, W/ CHEESE","White potato skins, with adhering flesh, fried, with cheese" +71411000,"POTATO SKINS W/ ADHERING FLESH, W/ CHEESE & BACON","White potato skins, with adhering flesh, fried, with cheese and bacon" +71501000,"WHITE POTATO, MASHED, NFS","White potato, mashed, NFS" +71501010,"WHITE POTATO, FRESH, MASHED, MADE W/ MILK","White potato, from fresh, mashed, made with milk" +71501015,"WHITE POTATO, FRESH, MASHED, MADE W/ MILK,/ SOUR CRM/CHEZ","White potato, from fresh, mashed, made with milk, and sour cream and/or cream cheese" +71501020,"WHITE POTATO, FRESH, MASHED, MADE W/ MILK & FAT","White potato, from fresh, mashed, made with milk and fat" +71501025,"WHITE POTATO, FRESH, MASHED, MADE W/ MILK/SOUR CRM/CHEZ &FAT","White potato, from fresh, mashed, made with milk, and sour cream and/or cream cheese and fat" +71501030,"WHITE POTATO, FRESH, MASHED, MADE W/ FAT","White potato, from fresh, mashed, made with fat" +71501040,"WHITE POTATO, DRY, MASHED, MADE W/ MILK & FAT","White potato, from dry, mashed, made with milk and fat" +71501050,"WHITE POTATO, FRESH, MASHED, MADE W/ MILK, FAT & CHEESE","White potato, from fresh, mashed, made with milk, fat and cheese" +71501055,"WHITE POTATO, FRESH, MASHED, MADE W/ SOUR CRM/CHEZ & FAT","White potato, from fresh, mashed, made with sour cream and/or cream cheese and fat" +71501060,"WHITE POTATO, DRY, MASHED, MADE W/ MILK, FAT & EGG","White potato, from dry, mashed, made with milk, fat and egg" +71501070,"WHITE POTATO, DRY, MASHED, MADE W/ MILK, FAT, EGG & CHEESE","White potato, from dry, mashed, made with milk, fat, egg and cheese" +71501080,"WHITE POTATO, FRESH, MASHED, NOT MADE W/ MILK OR FAT","White potato, from fresh, mashed, not made with milk or fat" +71501090,"WHITE POTATO, DRY, MASHED, MADE W/ MILK, NO FAT","White potato, from dry, mashed, made with milk, no fat" +71501200,"WHITE POTATO, COMPLETE DRY MIX, MASHED, MADE W/ WATER","White potato, from complete dry mix, mashed, made with water" +71501300,"WHITE POTATO, DRY, MASHED, NS AS TO MILK OR FAT","White potato, from dry, mashed, NS as to milk or fat" +71501310,"WHITE POTATO, FRESH, MASHED, NS AS TO MILK OR FAT","White potato, from fresh, mashed, NS as to milk or fat" +71503010,"WHITE POTATO, PATTY (INCLUDE POTATO CROQUETTES)","White potato, patty" +71505000,"WHITE POTATO, PUFFS","White potato, puffs" +71507000,"WHITE POTATO, BAKED, STUFFED, PEEL NOT EATEN, NS TOPPING","White potato, stuffed, baked, peel not eaten, NS as to topping" +71507005,"WHITE POTATO,BAKED,STUFF W/ BUTTER/MARG, NO PEEL","White potato, stuffed, baked, peel not eaten, stuffed with butter or margarine" +71507010,"WHITE POTATO, BAKED, STUFFED W/SOUR CREAM, NO PEEL","White potato, stuffed, baked, peel not eaten, stuffed with sour cream" +71507020,"WHITE POT,BAKED, STUFFED W/ CHEESE, PEEL NOT EATEN","White potato, stuffed, baked, peel not eaten, stuffed with cheese" +71507030,"WHITE POT, BAKED, STUFFED W/ CHILI, PEEL NOT EATEN","White potato, stuffed, baked, peel not eaten, stuffed with chili" +71507040,"WHITE POT, BAKED, STUFFED W/BROC&CHEESE SCE,NO PEEL","White potato, stuffed, baked, peel not eaten, stuffed with broccoli and cheese sauce" +71507050,"WHITE POT, BAKED, STUFFD W/ MEAT IN CRM SC, NO PEEL","White potato, stuffed, baked, peel not eaten, stuffed with meat in cream sauce" +71507100,"WHITE POT,BAKD,STUF W/CHIC,BROC,CHEESE,PEEL NOT EAT","White potato, stuffed, baked, peel not eaten, stuffed with chicken, broccoli and cheese sauce" +71508000,"WHITE POTATO, BAKED, STUFFED, PEEL EATEN","White potato, stuffed, baked, peel eaten, NS as to topping" +71508005,"WHITE POTATO,BAKED,STUFF W/ BUTTER/MARG, PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with butter or margarine" +71508010,"WHITE POTATO, BAKED, STUFFED W/SOUR CRM, PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with sour cream" +71508020,"WHITE POTATO, BAKED, STUFFED W/ CHEESE, PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with cheese" +71508030,"WHITE POTATO, BAKED, STUFFED W/ CHILI, PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with chili" +71508040,"WHITE POT, BKD, STUFD W/BROC&CHEESE SCE,PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with broccoli and cheese sauce" +71508050,"WHITE POT,BAKED, STUFFED W/MEAT&CRM SCE,PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with meat in cream sauce" +71508060,"WHITE POT, BAKED, STUFD W/ BACON&CHEESE, PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with bacon and cheese" +71508070,"WHITE POT,STUFF,BAKED,NO PEEL,W/ BACON & CHEESE","White potato, stuffed, baked, peel not eaten, stuffed with bacon and cheese" +71508100,"WHITE POT,BAKED,STUFF W/CHIC,BROC,CHEESE,PEEL EATEN","White potato, stuffed, baked, peel eaten, stuffed with chicken, broccoli and cheese sauce" +71508120,"WHITE POT,STUFF W/HAM,BROC,&CHEESE SAUCE,BKD,W/PEEL","White potato, stuffed with ham, broccoli and cheese sauce, baked, peel eaten" +71601010,"POTATO SALAD WITH EGG, W/ MAYO","Potato salad with egg, made with mayonnaise" +71601015,"POTATO SALAD W/ EGG, MADE W/ LT MAYO","Potato salad with egg, made with light mayonnaise" +71601020,"POTATO SALAD W/ EGG, MADE W/ MAYO-TYPE DRSG","Potato salad with egg, made with mayonnaise-type salad dressing" +71601025,"POTATO SALAD W/ EGG, MADE W/ LT MAYO-TYPE DRSG","Potato salad with egg, made with light mayonnaise-type salad dressing" +71601030,"POTATO SALAD W/ EGG, MADE W/ CREAMY DRSG","Potato salad with egg, made with creamy dressing" +71601035,"POTATO SALAD W/EGG, MADE W/ LT CREAMY DRSG","Potato salad with egg, made with light creamy dressing" +71601040,"POTATO SALAD W/ EGG, MADE W/ ITALIAN DRSG","Potato salad with egg, made with Italian dressing" +71601045,"POTATO SALAD W/ EGG, MADE W/ LT ITALIAN DRSG","Potato salad with egg, made with light Italian dressing" +71601050,"POTATO SALAD W/ EGG, MADE W/ ANY TYPE OF FAT FREE DRSG","Potato salad with egg, made with any type of fat free dressing" +71602010,"POTATO SALAD, GERMAN","Potato salad, German style" +71603010,"POTATO SALAD, MADE WITH MAYONNAISE","Potato salad, made with mayonnaise" +71603015,"POTATO SALAD, W/ LT MAYO","Potato salad, made with light mayonnaise" +71603020,"POTATO SALAD, W/ MAYO-TYPE DRSG","Potato salad, made with mayonnaise-type salad dressing" +71603025,"POTATO SALAD, W/ LT MAYO-TYPE DRSG","Potato salad, made with light mayonnaise-type salad dressing" +71603030,"POTATO SALAD, W/ CREAMY DRSG","Potato salad, made with creamy dressing" +71603035,"POTATO SALAD, W/ LT CREAMY DRSG","Potato salad, made with light creamy dressing" +71603040,"POTATO SALAD, W/ ITALIAN DRESSING","Potato salad, made with Italian dressing" +71603045,"POTATO SALAD, W/ LT ITALIAN DRSG","Potato salad, made with light Italian dressing" +71603050,"POTATO SALAD, W/ ANY TYPE OF FAT FREE DRSG","Potato salad, made with any type of fat free dressing" +71701000,"POTATO PANCAKE","Potato pancake" +71701500,"NORWEGIAN LEFSE, POTATO & FLOUR PANCAKE","Norwegian Lefse, potato and flour pancake" +71702000,"POTATO PUDDING","Potato pudding" +71703000,"STEWED POTATOES, MEXICAN (PAPAS GUISADAS)","Stewed potatoes, Mexican style (Papas guisadas)" +71703040,"STEWED POT W/TOM,MEXICAN(PAPAS GUISADAS CON TOMATE)","Stewed potatoes with tomatoes, Mexican style (Papas guisadas con tomate)" +71704000,"STEWED POTATOES WITH TOMATOES","Stewed potatoes with tomatoes" +71801000,"POTATO SOUP, NS AS TO MADE W/MILK OR WATER","Potato soup, NS as to made with milk or water" +71801010,"POTATO SOUP, CREAM OF, W/ MILK","Potato soup, cream of, prepared with milk" +71801020,"POTATO SOUP, PREPARED W/ WATER","Potato soup, prepared with water" +71801100,"POTATO & CHEESE SOUP","Potato and cheese soup" +71803010,"POTATO CHOWDER (INCL CORN CHOWDER)","Potato chowder" +71851010,"PLANTAIN SOUP, P.R. (SOPA DE PLATANO)","Plantain soup, Puerto Rican style (Sopa de platano)" +71900100,"PLANTAIN, BOILED, NS AS TO GREEN OR RIPE","Plantain, boiled, NS as to green or ripe" +71900200,"PLANTAIN, FRIED, NS TO GREEN OR RIPE","Plantain, fried, NS as to green or ripe" +71901010,"GREEN PLANTAIN, BOILED OR BAKED","Green plantains, boiled" +71901110,"FRIED GREEN PLANTAIN, P.R.","Fried green plantain, Puerto Rican style" +71905000,"RIPE PLANTAIN, RAW","Ripe plantain, raw" +71905010,"RIPE PLANTAIN, BOILED (INCL BAKED RIPE PLANTAIN)","Ripe plantain, boiled" +71905110,"FRIED RIPE PLANTAIN, P.R. (PLATANO MADURO FRITO)","Fried ripe plantain, Puerto Rican style (Platano maduro frito)" +71905120,"PLANTAIN, RIPE, ROLLED IN FLOUR, FRIED","Plantain, ripe, rolled in flour, fried" +71905210,"CANDIED RIPE PLANTAIN, P.R. (PLATANO EN ALMIBAR)","Candied ripe plantain, Puerto Rican style (Platano en almibar)" +71905410,"PLANTAIN CHIPS","Plantain chips" +71910110,"GREEN BANANA (COOKED IN SALT WATER)","Green banana, cooked (in salt water)" +71910210,"GREEN BANANA, FRIED","Green banana, fried" +71910310,"PICKLED GREEN BANANA, P.R","Pickled green bananas, Puerto Rican style (Guineos verdes en escabeche)" +71930090,"CASSAVA (YUCA BLANCA), COOKED, NS AS TO ADDED FAT","Cassava (yuca blanca), cooked, NS as to fat added in cooking" +71930100,"CASSAVA (YUCA BLANCA), COOKED, NO FAT ADDED","Cassava (yuca blanca), cooked, fat not added in cooking" +71930120,"CASSAVA (YUCA BLANCA), COOKED, FAT ADDED","Cassava (yuca blanca), cooked, fat added in cooking" +71930200,"CASABE, CASSAVA BREAD","Casabe, cassava bread" +71931010,"CASSAVA W/ CREOLE SAUCE, P.R. (YUCA AL MAJO)","Cassava with creole sauce, Puerto Rican style (Yuca al mojo)" +71941110,"SWEET POTATOES, WHITE, P.R., FRIED","Sweet potatoes, white, Puerto Rican, fried" +71941120,"SWEET POTATOES, WHITE, P.R., BOILED","Sweet potatoes, white, Puerto Rican, boiled" +71941130,"SWEET POTATOES, WHITE, P.R., ROASTED OR BAKED","Sweet potatoes, white, Puerto Rican, roasted or baked" +71945010,"YAM, PUERTO RICAN, COOKED (NAME HERVIDO)","Yam, Puerto Rican, cooked (Name hervido)" +71945020,"YAM BUNS, P.R. (BUNUELOS DE NAME)","Yam buns, Puerto Rican style (Bunuelos de name)" +71950010,"TANNIER, COOKED (INCLUDE YAUTIA)","Tannier, cooked" +71961010,"CELERIAC, COOKED (INCLUDE P.R. APIO)","Celeriac, cooked" +71962010,"DASHEEN, BOILED (INCLUDE MALANGA)","Dasheen, boiled" +71962020,"DASHEEN, FRIED (INCLUDE MALANGA)","Dasheen, fried" +71962040,"TARO, BAKED","Taro, baked" +71970110,"STARCHY VEGETABLES, P.R. STYLE, NFS (VIANDAS HERVIDAS)","Starchy vegetables, Puerto Rican style, NFS (viandas hervidas)" +71970120,"STARCHY VEGETABLES, P.R., NO PLANTAINS","Starchy vegetables, Puerto Rican style, including tannier, white sweet potato and yam, with green or ripe plantains (viandas hervidas)" +71970130,"STARCHY VEGETABLES, P.R., NO PLANTAINS","Starchy vegetables, Puerto Rican style, including tannier, white sweet potato and yam, no plantain (viandas hervidas)" +71970200,"FUFU (AFRICAN)","Fufu (African)" +71980100,"POI","Poi" +71980200,"TARO CHIPS","Taro chips" +72101100,"BEET GREENS, RAW","Beet greens, raw" +72101200,"BEET GREENS, COOKED, NS AS TO ADDED FAT","Beet greens, cooked, NS as to fat added in cooking" +72101210,"BEET GREENS, COOKED, FAT NOT ADDED","Beet greens, cooked, fat not added in cooking" +72101220,"BEET GREENS, COOKED, FAT ADDED","Beet greens, cooked, fat added in cooking" +72103000,"BROCCOLI RAAB, RAW","Broccoli raab, raw" +72103010,"BROCCOLI RAAB, COOKED, NS AS TO FAT ADDED","Broccoli raab, cooked, NS as to fat added in cooking" +72103020,"BROCCOLI RAAB, COOKED, FAT NOT ADDED","Broccoli raab, cooked, fat not added in cooking" +72103030,"BROCCOLI RAAB, COOKED, FAT ADDED IN COOKING","Broccoli raab, cooked, fat added in cooking" +72104100,"CHARD, RAW","Chard, raw" +72104200,"CHARD, COOKED, NS AS TO ADDED FAT","Chard, cooked, NS as to fat added in cooking" +72104210,"CHARD, COOKED, FAT NOT ADDED","Chard, cooked, fat not added in cooking" +72104220,"CHARD, COOKED, FAT ADDED","Chard, cooked, fat added in cooking" +72107100,"COLLARDS, RAW","Collards, raw" +72107200,"COLLARDS, COOKED, NS AS TO FORM, NS AS TO ADDED FAT","Collards, cooked, NS as to form, NS as to fat added in cooking" +72107201,"COLLARDS,COOKED,FROM FRESH,NS FAT ADDED","Collards, cooked, from fresh, NS as to fat added in cooking" +72107202,"COLLARDS,COOKED,FROM FROZEN,NS FAT ADDED","Collards, cooked, from frozen, NS as to fat added in cooking" +72107203,"COLLARDS,COOKED,FROM CANNED,NS FAT ADDED","Collards, cooked, from canned, NS as to fat added in cooking" +72107210,"COLLARDS, COOKED, NS AS TO FORM, FAT NOT ADDED","Collards, cooked, NS as to form, fat not added in cooking" +72107211,"COLLARDS,COOKED,FROM FRESH,FAT NOT ADDED","Collards, cooked, from fresh, fat not added in cooking" +72107212,"COLLARDS,COOKED,FROM FROZEN,FAT NOT ADDED","Collards, cooked, from frozen, fat not added in cooking" +72107213,"COLLARDS,COOKED,FROM CANNED,FAT NOT ADDED","Collards, cooked, from canned, fat not added in cooking" +72107220,"COLLARDS, COOKED, NS AS TO FORM, FAT ADDED","Collards, cooked, NS as to form, fat added in cooking" +72107221,"COLLARDS,COOKED,FROM FRESH,FAT ADDED","Collards, cooked, from fresh, fat added in cooking" +72107222,"COLLARDS,COOKED,FROM FROZEN,FAT ADDED","Collards, cooked, from frozen, fat added in cooking" +72107223,"COLLARDS,COOKED,FROM CANNED,FAT ADDED","Collards, cooked, from canned, fat added in cooking" +72110100,"CRESS, RAW","Cress, raw" +72110200,"CRESS, COOKED, NS AS TO FORM, NS AS TO ADDED FAT","Cress, cooked, NS as to form, NS as to fat added in cooking" +72110201,"CRESS, COOKED, FROM FRESH, NS FAT ADDED","Cress, cooked, from fresh, NS as to fat added in cooking" +72110203,"CRESS, COOKED, FROM CANNED, NS FAT ADDED","Cress, cooked, from canned, NS as to fat added in cooking" +72110210,"CRESS, COOKED, NS AS TO FORM, FAT NOT ADDED","Cress, cooked, NS as to form, fat not added in cooking" +72110211,"CRESS, COOKED, FROM FRESH, FAT NOT ADDED","Cress, cooked, from fresh, fat not added in cooking" +72110213,"CRESS, COOKED, FROM CANNED, FAT NOT ADDED","Cress, cooked, from canned, fat not added in cooking" +72110220,"CRESS, COOKED, NS AS TO FORM, FAT ADDED","Cress, cooked, NS as to form, fat added in cooking" +72110221,"CRESS, COOKED, FROM FRESH, FAT ADDED","Cress, cooked, from fresh, fat added in cooking" +72110223,"CRESS, COOKED, FROM CANNED, FAT ADDED","Cress, cooked, from canned, fat added in cooking" +72113100,"DANDELION GREENS, RAW","Dandelion greens, raw" +72113200,"DANDELION GREENS, COOKED, NS AS TO ADDED FAT","Dandelion greens, cooked, NS as to fat added in cooking" +72113210,"DANDELION GREENS, COOKED, FAT NOT ADDED","Dandelion greens, cooked, fat not added in cooking" +72113220,"DANDELION GREENS, COOKED, FAT ADDED","Dandelion greens, cooked, fat added in cooking" +72116000,"ENDIVE, CHICORY, ESCAROLE OR ROMAINE LETTUCE, RAW","Endive, chicory, escarole, or romaine lettuce, raw" +72116150,"CAESAR SALAD (WITH ROMAINE), NO DRESSING","Caesar salad (with romaine), no dressing" +72116200,"ESCAROLE, COOKED, NS AS TO ADDED FAT","Escarole, cooked, NS as to fat added in cooking" +72116210,"ESCAROLE, COOKED, FAT NOT ADDED","Escarole, cooked, fat not added in cooking" +72116220,"ESCAROLE, COOKED, FAT ADDED","Escarole, cooked, fat added in cooking" +72116230,"ESCAROLE, CREAMED","Escarole, creamed" +72118200,"GREENS, COOKED, NS AS TO FORM, NS AS TO ADDED FAT","Greens, cooked, NS as to form, NS as to fat added in cooking" +72118201,"GREENS, COOKED, FROM FRESH, NS FAT ADDED","Greens, cooked, from fresh, NS as to fat added in cooking" +72118202,"GREENS, COOKED, FROM FROZ, NS FAT ADDED","Greens, cooked, from frozen, NS as to fat added in cooking" +72118203,"GREENS, COOKED, FROM CANNED, NS FAT ADDED","Greens, cooked, from canned, NS as to fat added in cooking" +72118210,"GREENS, COOKED, NS AS TO FORM, FAT NOT ADDED","Greens, cooked, NS as to form, fat not added in cooking" +72118211,"GREENS, COOKED, FROM FRESH, FAT NOT ADDED","Greens, cooked, from fresh, fat not added in cooking" +72118212,"GREENS, COOKED, FROM FROZEN, FAT NOT ADDED","Greens, cooked, from frozen, fat not added in cooking" +72118213,"GREENS, COOKED, FROM CANNED, FAT NOT ADDED","Greens, cooked, from canned, fat not added in cooking" +72118220,"GREENS, COOKED, NS AS TO FORM, FAT ADDED","Greens, cooked, NS as to form, fat added in cooking" +72118221,"GREENS, COOKED, FROM FRESH, FAT ADDED","Greens, cooked, from fresh, fat added in cooking" +72118222,"GREENS, COOKED, FROM FROZEN, FAT ADDED","Greens, cooked, from frozen, fat added in cooking" +72118223,"GREENS, COOKED, FROM CANNED, FAT ADDED","Greens, cooked, from canned, fat added in cooking" +72118300,"CHAMNAMUL (KOREAN LEAF VEGETABLE), COOKED, NS AS TO FAT","Chamnamul (Korean leaf vegetable), cooked, NS as to fat added in cooking" +72118305,"CHAMNAMUL (KOREAN LEAF VEGETABLE), COOKED, FAT NOT ADDED","Chamnamul (Korean leaf vegetable), cooked, fat not added in cooking" +72118310,"CHAMNAMUL (KOREAN LEAF VEGETABLE), COOKED, FAT ADDED","Chamnamul (Korean leaf vegetable), cooked, fat added in cooking" +72119200,"KALE, COOKED, NS AS TO FORM, NS AS TO ADDED FAT","Kale, cooked, NS as to form, NS as to fat added in cooking" +72119201,"KALE,COOKED,FROM FRESH,NS FAT ADDED","Kale, cooked, from fresh, NS as to fat added in cooking" +72119202,"KALE,COOKED,FROM FROZEN,NS FAT ADDED","Kale, cooked, from frozen, NS as to fat added in cooking" +72119203,"KALE,COOKED,FROM CANNED,NS FAT ADDED","Kale, cooked, from canned, NS as to fat added in cooking" +72119210,"KALE, COOKED, NS AS TO FORM, FAT NOT ADDED","Kale, cooked, NS as to form, fat not added in cooking" +72119211,"KALE,COOKED,FROM FRESH,FAT NOT ADDED","Kale, cooked, from fresh, fat not added in cooking" +72119212,"KALE,COOKED,FROM FROZEN,FAT NOT ADDED","Kale, cooked, from frozen, fat not added in cooking" +72119213,"KALE,COOKED,FROM CANNED,FAT NOT ADDED","Kale, cooked, from canned, fat not added in cooking" +72119220,"KALE, COOKED, NS AS TO FORM, FAT ADDED","Kale, cooked, NS as to form, fat added in cooking" +72119221,"KALE,COOKED,FROM FRESH,FAT ADDED","Kale, cooked, from fresh, fat added in cooking" +72119222,"KALE,COOKED,FROM FROZEN,FAT ADDED","Kale, cooked, from frozen, fat added in cooking" +72119223,"KALE,COOKED,FROM CANNED,FAT ADDED","Kale, cooked, from canned, fat added in cooking" +72120200,"LAMBSQUARTER, COOKED, NS AS TO ADDED FAT","Lambsquarter, cooked, NS as to fat added in cooking" +72120210,"CRESS, COOKED, FAT NOT ADDEDS","Lambsquarter, cooked, fat not added in cooking" +72120220,"LAMBSQUARTER, COOKED, FAT ADDED","Lambsquarter, cooked, fat added in cooking" +72121210,"MUSTARD CABBAGE,COOKED, FAT NOT ADDED IN COOKING","Mustard cabbage, cooked, fat not added in cooking" +72122100,"MUSTARD GREENS, RAW","Mustard greens, raw" +72122200,"MUSTARD GREENS, COOKED, NS FORM, NS FAT ADDED","Mustard greens, cooked, NS as to form, NS as to fat added in cooking" +72122201,"MUSTARD GREENS,COOKED,FROM FRESH,NS FAT ADDED","Mustard greens, cooked, from fresh, NS as to fat added in cooking" +72122202,"MUSTARD GREENS,COOKED,FROM FROZEN,NS FAT ADDED","Mustard greens, cooked, from frozen, NS as to fat added in cooking" +72122203,"MUSTARD GREENS,COOKED,FROM CANNED,NS FAT ADDED","Mustard greens, cooked, from canned, NS as to fat added in cooking" +72122210,"MUSTARD GREENS, COOKED, NS FORM, FAT NOT ADDED","Mustard greens, cooked, NS as to form, fat not added in cooking" +72122211,"MUSTARD GREENS,COOKED,FROM FRESH,FAT NOT ADDED","Mustard greens, cooked, from fresh, fat not added in cooking" +72122212,"MUSTARD GREENS,COOKED,FROM FROZEN,FAT NOT ADDED","Mustard greens, cooked, from frozen, fat not added in cooking" +72122213,"MUSTARD GREENS,COOKED,FROM CANNED,FAT NOT ADDED","Mustard greens, cooked, from canned, fat not added in cooking" +72122220,"MUSTARD GREEN, COOKED, NS FORM, FAT ADDED","Mustard greens, cooked, NS as to form, fat added in cooking" +72122221,"MUSTARD GREENS,COOKED,FROM FRESH,FAT ADDED","Mustard greens, cooked, from fresh, fat added in cooking" +72122222,"MUSTARD GREENS,COOKED,FROM FROZEN,FAT ADDED","Mustard greens, cooked, from frozen, fat added in cooking" +72122223,"MUSTARD GREENS,COOKED,FROM CANNED,FAT ADDED","Mustard greens, cooked, from canned, fat added in cooking" +72123000,"POKE GREENS, COOKED, NS AS TO ADDED FAT","Poke greens, cooked, NS as to fat added in cooking" +72123010,"POKE GREENS, COOKED, FAT NOT ADDED","Poke greens, cooked, fat not added in cooking" +72123020,"POKE GREENS, COOKED, FAT ADDED","Poke greens, cooked, fat added in cooking" +72124100,"RADICCHIO, RAW","Radicchio, raw" +72125100,"SPINACH, RAW","Spinach, raw" +72125200,"SPINACH, COOKED, NS FORM, NS AS TO ADDED FAT","Spinach, cooked, NS as to form, NS as to fat added in cooking" +72125201,"SPINACH,COOKED,FROM FRESH,NS FAT ADDED","Spinach, cooked, from fresh, NS as to fat added in cooking" +72125202,"SPINACH,COOKED,FROM FROZEN,NS FAT ADDED","Spinach, cooked, from frozen, NS as to fat added in cooking" +72125203,"SPINACH,COOKED,FROM CANNED,NS FAT ADDED","Spinach, cooked, from canned, NS as to fat added in cooking" +72125210,"SPINACH, COOKED, NS AS TO FORM, FAT NOT ADDED","Spinach, cooked, NS as to form, fat not added in cooking" +72125211,"SPINACH,COOKED,FROM FRESH,FAT NOT ADDED","Spinach, cooked, from fresh, fat not added in cooking" +72125212,"SPINACH,COOKED,FROM FROZEN,FAT NOT ADDED","Spinach, cooked, from frozen, fat not added in cooking" +72125213,"SPINACH,COOKED,FROM CANNED,FAT NOT ADDED","Spinach, cooked, from canned, fat not added in cooking" +72125220,"SPINACH, COOKED, NS AS TO FORM, FAT ADDED","Spinach, cooked, NS as to form, fat added in cooking" +72125221,"SPINACH,COOKED,FROM FRESH,FAT ADDED","Spinach, cooked, from fresh, fat added in cooking" +72125222,"SPINACH,COOKED,FROM FROZEN,FAT ADDED","Spinach, cooked, from frozen, fat added in cooking" +72125223,"SPINACH,COOKED,FROM CANNED,FAT ADDED","Spinach, cooked, from canned, fat added in cooking" +72125230,"SPINACH, NS AS TO FORM, CREAMED","Spinach, NS as to form, creamed" +72125231,"SPINACH, FROM FRESH, CREAMED","Spinach, from fresh, creamed" +72125232,"SPINACH, FROM FROZEN, CREAMED","Spinach, from frozen, creamed" +72125233,"SPINACH, FROM CANNED, CREAMED","Spinach, from canned, creamed" +72125240,"SPINACH SOUFFLE","Spinach souffle" +72125250,"SPINACH, COOKED, NS AS TO FORM, W/ CHEESE SAUCE","Spinach, cooked, NS as to form, with cheese sauce" +72125251,"SPINACH, COOKED, FROM FRESH, W/ CHEESE SAUCE","Spinach, cooked, from fresh, with cheese sauce" +72125252,"SPINACH, COOKED, FROM FROZEN, W/ CHEESE SAUCE","Spinach, cooked, from frozen, with cheese sauce" +72125253,"SPINACH, COOKED, FROM CANNED, W/ CHEESE SAUCE","Spinach, cooked, from canned, with cheese sauce" +72125260,"SPINACH & CHEESE CASSEROLE","Spinach and cheese casserole" +72125310,"SPINACH AND COTTAGE CHEESE","Palak Paneer or Saag Paneer (Indian)" +72125500,"SPINACH & CHICK PEAS, FAT ADDED","Spinach and chickpeas, fat added" +72126000,"TARO LEAVES, COOKED, FAT NOT ADDED IN COOKING","Taro leaves, cooked, fat not added in cooking" +72127000,"THISTLE LEAVES, COOKED, FAT NOT ADDED IN COOKING","Thistle leaves, cooked, fat not added in cooking" +72128200,"TURNIP GREENS, COOKED, NS FORM, NS AS TO ADDED FAT","Turnip greens, cooked, NS as to form, NS as to fat added in cooking" +72128201,"TURNIP GREENS,COOKED,FROM FRESH,NS FAT ADDED","Turnip greens, cooked, from fresh, NS as to fat added in cooking" +72128202,"TURNIP GREENS,COOKED,FROM FROZEN,NS FAT ADDED","Turnip greens, cooked, from frozen, NS as to fat added in cooking" +72128203,"TURNIP GREENS,COOKED,FROM CANNED,NS FAT ADDED","Turnip greens, cooked, from canned, NS as to fat added in cooking" +72128210,"TURNIP GREENS, NS FORM, COOKED, FAT NOT ADDED","Turnip greens, cooked, NS as to form, fat not added in cooking" +72128211,"TURNIP GREENS,COOKED,FROM FRESH,FAT NOT ADDED","Turnip greens, cooked, from fresh, fat not added in cooking" +72128212,"TURNIP GREENS,COOKED,FROM FROZEN,FAT NOT ADDED","Turnip greens, cooked, from frozen, fat not added in cooking" +72128213,"TURNIP GREENS,COOKED,FROM CANNED,FAT NOT ADDED","Turnip greens, cooked, from canned, fat not added in cooking" +72128220,"TURNIP GREENS, COOKED, NS FORM, FAT ADDED","Turnip greens, cooked, NS as to form, fat added in cooking" +72128221,"TURNIP GREENS,COOKED,FROM FRESH,FAT ADDED","Turnip greens, cooked, from fresh, fat added in cooking" +72128222,"TURNIP GREENS,COOKED,FROM FROZEN,FAT ADDED","Turnip greens, cooked, from frozen, fat added in cooking" +72128223,"TURNIP GREENS,COOKED,FROM CANNED,FAT ADDED","Turnip greens, cooked, from canned, fat added in cooking" +72128400,"TURNIP GREENS W/ ROOTS, CKD, NS FORM, NS ADDED FAT","Turnip greens with roots, cooked, NS as to form, NS as to fat added in cooking" +72128401,"TURNIP GREENS W/ ROOTS, CKD, FROM FRESH, NS ADDED FAT","Turnip greens with roots, cooked, from fresh, NS as to fat added in cooking" +72128402,"TURNIP GREENS W/ ROOTS, CKD, FROM FROZ, NS ADDED FAT","Turnip greens with roots, cooked, from frozen, NS as to fat added in cooking" +72128403,"TURNIP GREENS W/ ROOTS, CKD, FROM CAN, NS ADDED FAT","Turnip greens with roots, cooked, from canned, NS as to fat added in cooking" +72128410,"TURNIP GREENS W/ ROOTS, CKD, NS FORM, FAT NOT ADDED","Turnip greens with roots, cooked, NS as to form, fat not added in cooking" +72128411,"TURNIP GREENS W/ ROOTS, CKD, FROM FRESH, NO FAT ADDED","Turnip greens with roots, cooked, from fresh, fat not added in cooking" +72128412,"TURNIP GREENS W/ ROOTS, CKD, FROM FROZ, NO FAT ADDED","Turnip greens with roots, cooked, from frozen, fat not added in cooking" +72128413,"TURNIP GREENS W/ ROOTS, CKD, FROM CAN, NO FAT ADDED","Turnip greens with roots, cooked, from canned, fat not added in cooking" +72128420,"TURNIP GREENS W/ ROOTS, COOKED, NS FORM, FAT ADDED","Turnip greens with roots, cooked, NS as to form, fat added in cooking" +72128421,"TURNIP GREENS W/ ROOTS, CKD, FROM FRESH, FAT ADDED","Turnip greens with roots, cooked, from fresh, fat added in cooking" +72128422,"TURNIP GREENS W/ ROOTS, CKD, FROM FROZ, FAT ADDED","Turnip greens with roots, cooked, from frozen, fat added in cooking" +72128423,"TURNIP GREENS W/ ROOTS, CKD, FROM CAN, FAT ADDED","Turnip greens with roots, cooked, from canned, fat added in cooking" +72128500,"TURNIP GREENS, CANNED, LOW NA, NS AS TO ADDED FAT","Turnip greens, canned, low sodium, cooked, NS as to fat added in cooking" +72128510,"TURNIP GREENS, CANNED, LOW SODIUM, FAT NOT ADDED","Turnip greens, canned, low sodium, cooked, fat not added in cooking" +72128520,"TURNIP GREENS, CANNED, LOW SODIUM, FAT ADDED","Turnip greens, canned, low sodium, cooked, fat added in cooking" +72130100,"WATERCRESS, RAW","Watercress, raw" +72130200,"WATERCRESS, COOKED, FAT NOT ADDED IN COOKING","Watercress, cooked, fat not added in cooking" +72132200,"BITTERMELON,HORSERADISH,JUTE,RADISH LVES,CKD,NO FAT","Bitter melon leaves, horseradish leaves, jute leaves, or radish leaves, cooked, fat not added in cooking" +72133200,"SWEET POTATO,SQUASH,PUMPKIN LEAVES,CKD,FAT NOT ADDED","Sweet potato leaves, squash leaves, pumpkin leaves, chrysanthemum leaves, bean leaves, or swamp cabbage, cooked, fat not added in cooking" +72201100,"BROCCOLI, RAW","Broccoli, raw" +72201200,"BROCCOLI, CKD, NS FORM, NS FAT (INCL BROCCOLI, NFS)","Broccoli, cooked, NS as to form, NS as to fat added in cooking" +72201201,"BROCCOLI, CKD, FROM FRESH, NS FAT (INCL BROCCOLI, NFS)","Broccoli, cooked, from fresh, NS as to fat added in cooking" +72201202,"BROCCOLI, CKD, FROM FROZ, NS FAT (INCL BROCCOLI, NFS)","Broccoli, cooked, from frozen, NS as to fat added in cooking" +72201210,"BROCCOLI, COOKED, NS AS TO FORM, NO FAT ADDED","Broccoli, cooked, NS as to form, fat not added in cooking" +72201211,"BROCCOLI, COOKED, FROM FRESH, NO FAT ADDED","Broccoli, cooked, from fresh, fat not added in cooking" +72201212,"BROCCOLI, COOKED, FROM FROZ, NO FAT ADDED","Broccoli, cooked, from frozen, fat not added in cooking" +72201220,"BROCCOLI, COOKED, NS AS TO FORM, FAT ADDED","Broccoli, cooked, NS as to form, fat added in cooking" +72201221,"BROCCOLI, COOKED, FROM FRESH, FAT ADDED","Broccoli, cooked, from fresh, fat added in cooking" +72201222,"BROCCOLI, COOKED, FROM FROZ, FAT ADDED","Broccoli, cooked, from frozen, fat added in cooking" +72201230,"BROCCOLI, COOKED, NS AS TO FORM, W/ CHEESE SAUCE","Broccoli, cooked, NS as to form, with cheese sauce" +72201231,"BROCCOLI, COOKED, FROM FRESH, W/ CHEESE SAUCE","Broccoli, cooked, from fresh, with cheese sauce" +72201232,"BROCCOLI, COOKED, FROM FROZEN, W/ CHEESE SAUCE","Broccoli, cooked, from frozen, with cheese sauce" +72201240,"BROCCOLI, COOKED, NS AS TO FORM, W/ MUSHROOM SAUCE","Broccoli, cooked, NS as to form, with mushroom sauce" +72201241,"BROCCOLI, COOKED, FROM FRESH, W/ MUSHROOM SAUCE","Broccoli, cooked, from fresh, with mushroom sauce" +72201242,"BROCCOLI, COOKED, FROM FROZEN, W/ MUSHROOM SAUCE","Broccoli, cooked, from frozen, with mushroom sauce" +72201250,"BROCCOLI, COOKED, NS AS TO FORM, W/ CREAM SAUCE","Broccoli, cooked, NS as to form, with cream sauce" +72201251,"BROCCOLI, COOKED, FROM FRESH, W/ CREAM SAUCE","Broccoli, cooked, from fresh, with cream sauce" +72201252,"BROCCOLI, COOKED, FROM FROZEN, W/ CREAM SAUCE","Broccoli, cooked, from frozen, with cream sauce" +72202010,"BROCCOLI CASSEROLE (BROC, NOODLES, CREAM SAUCE)","Broccoli casserole (broccoli, noodles, and cream sauce)" +72202020,"BROCCOLI CASSEROLE (BROC,RICE,CHEESE,MUSHROOM SCE)","Broccoli casserole (broccoli, rice, cheese, and mushroom sauce)" +72202030,"BROCCOLI, BATTER-DIPPED & FRIED","Broccoli, batter-dipped and fried" +72302000,"BROCCOLI SOUP, PREPARED WITH MILK, HOME RECIPE, CANNED OR RE","Broccoli soup, prepared with milk, home recipe, canned or ready-to-serve" +72302020,"BROCCOLI SOUP, PREP W/ WATER","Broccoli soup, prepared with water, home recipe, canned, or ready-to-serve" +72302100,"BROCCOLI CHEESE SOUP, PREPARED WITH MILK, HOME RECIPE, CANNE","Broccoli cheese soup, prepared with milk, home recipe, canned, or ready-to-serve" +72306000,"WATERCRESS BROTH W/ SHRIMP","Watercress broth with shrimp" +72307000,"SPINACH SOUP","Spinach soup" +72308000,"DARK-GREEN LEAFY VEGETABLE SOUP WITH MEAT, ASIAN STYLE","Dark-green leafy vegetable soup with meat, Asian style" +72308500,"DARK-GREEN LEAFY VEGETABLE SOUP, MEATLESS, ASIAN STYLE","Dark-green leafy vegetable soup, meatless, Asian style" +73101010,"CARROTS, RAW","Carrots, raw" +73101110,"CARROTS, RAW, SALAD (INCLUDE CARROT-RAISIN SALAD)","Carrots, raw, salad" +73101210,"CARROTS, RAW, SALAD W/ APPLES","Carrots, raw, salad with apples" +73102200,"CARROTS, COOKED, NS AS TO FORM, NS FAT ADDED","Carrots, cooked, NS as to form, NS as to fat added in cooking" +73102201,"CARROTS, COOKED, FROM FRESH, NS FAT ADDED","Carrots, cooked, from fresh, NS as to fat added in cooking" +73102202,"CARROTS, COOKED, FROM FROZEN, NS FAT ADDED","Carrots, cooked, from frozen, NS as to fat added in cooking" +73102203,"CARROTS, COOKED, FROM CANNED, NS FAT ADDED","Carrots, cooked, from canned, NS as to fat added in cooking" +73102210,"CARROTS, COOKED, NS AS TO FORM, FAT NOT ADDED","Carrots, cooked, NS as to form, fat not added in cooking" +73102211,"CARROTS, COOKED, FROM FRESH, FAT NOT ADDED","Carrots, cooked, from fresh, fat not added in cooking" +73102212,"CARROTS, COOKED, FROM FROZEN, FAT NOT ADDED","Carrots, cooked, from frozen, fat not added in cooking" +73102213,"CARROTS, COOKED, FROM CANNED, FAT NOT ADDED","Carrots, cooked, from canned, fat not added in cooking" +73102220,"CARROTS, COOKED, NS AS TO FORM, FAT ADDED","Carrots, cooked, NS as to form, fat added in cooking" +73102221,"CARROTS, COOKED, FROM FRESH, FAT ADDED","Carrots, cooked, from fresh, fat added in cooking" +73102222,"CARROTS, COOKED, FROM FROZEN, FAT ADDED","Carrots, cooked, from frozen, fat added in cooking" +73102223,"CARROTS, COOKED, FROM CANNED, FAT ADDED","Carrots, cooked, from canned, fat added in cooking" +73102230,"CARROTS, COOKED, NS AS TO FORM, CREAMED","Carrots, cooked, NS as to form, creamed" +73102231,"CARROTS, COOKED, FROM FRESH, CREAMED","Carrots, cooked, from fresh, creamed" +73102232,"CARROTS, COOKED, FROM FROZEN, CREAMED","Carrots, cooked, from frozen, creamed" +73102233,"CARROTS, COOKED, FROM CANNED, CREAMED","Carrots, cooked, from canned, creamed" +73102240,"CARROTS, COOKED, NS AS TO FORM, GLAZED","Carrots, cooked, NS as to form, glazed" +73102241,"CARROTS, COOKED, FROM FRESH, GLAZED","Carrots, cooked, from fresh, glazed" +73102242,"CARROTS, COOKED, FROM FROZEN, GLAZED","Carrots, cooked, from frozen, glazed" +73102243,"CARROTS, COOKED, FROM CANNED, GLAZED","Carrots, cooked, from canned, glazed" +73102250,"CARROTS, COOKED, NS AS TO FORM, W/ CHEESE SAUCE","Carrots, cooked, NS as to form, with cheese sauce" +73102251,"CARROTS, COOKED, FROM FRESH, W/ CHEESE SAUCE","Carrots, cooked, from fresh, with cheese sauce" +73102252,"CARROTS, COOKED, FROM FROZEN, W/ CHEESE SAUCE","Carrots, cooked, from frozen, with cheese sauce" +73102253,"CARROTS, COOKED, FROM CANNED, W/ CHEESE SAUCE","Carrots, cooked, from canned, with cheese sauce" +73103000,"CARROTS, CANNED, LOW SODIUM, NS AS TO ADDED FAT","Carrots, canned, low sodium, NS as to fat added in cooking" +73103010,"CARROTS, CANNED, LOW SODIUM, NO FAT ADDED","Carrots, canned, low sodium, fat not added in cooking" +73103020,"CARROTS, CANNED, LOW SODIUM, FAT ADDED","Carrots, canned, low sodium, fat added in cooking" +73105010,"CARROT JUICE","Carrot juice" +73111030,"PEAS & CARROTS, NS AS TO FORM, CREAMED","Peas and carrots, NS as to form, creamed" +73111031,"PEAS & CARROTS, FROM FRESH, CREAMED","Peas and carrots, from fresh, creamed" +73111032,"PEAS & CARROTS, FROM FROZEN, CREAMED","Peas and carrots, from frozen, creamed" +73111033,"PEAS & CARROTS, FROM CANNED, CREAMED","Peas and carrots, from canned, creamed" +73111200,"PEAS & CARROTS, COOKED, NS FORM, NS AS TO ADDED FAT","Peas and carrots, cooked, NS as to form, NS as to fat added in cooking" +73111201,"PEAS & CARROTS, COOKED, FROM FRESH, NS FAT ADDED","Peas and carrots, cooked, from fresh, NS as to fat added in cooking" +73111202,"PEAS & CARROTS, COOKED, FROM FROZ, NS FAT ADDED","Peas and carrots, cooked, from frozen, NS as to fat added in cooking" +73111203,"PEAS & CARROTS, COOKED, FROM CANNED, NS FAT ADDED","Peas and carrots, cooked, from canned, NS as to fat added in cooking" +73111210,"PEAS & CARROTS, COOKED, NS FORM, FAT NOT ADDED","Peas and carrots, cooked, NS as to form, fat not added in cooking" +73111211,"PEAS & CARROTS, COOKED, FROM FRESH, FAT NOT ADDED","Peas and carrots, cooked, from fresh, fat not added in cooking" +73111212,"PEAS & CARROTS, COOKED, FROM FROZ, FAT NOT ADDED","Peas and carrots, cooked, from frozen, fat not added in cooking" +73111213,"PEAS & CARROTS, COOKED, FROM CANNED, FAT NOT ADDED","Peas and carrots, cooked, from canned, fat not added in cooking" +73111220,"PEAS & CARROTS, COOKED, NS AS TO FORM, FAT ADDED","Peas and carrots, cooked, NS as to form, fat added in cooking" +73111221,"PEAS & CARROTS, COOKED, FROM FRESH, FAT ADDED","Peas and carrots, cooked, from fresh, fat added in cooking" +73111222,"PEAS & CARROTS, COOKED, FROM FROZ, FAT ADDED","Peas and carrots, cooked, from frozen, fat added in cooking" +73111223,"PEAS & CARROTS, COOKED, FROM CANNED, FAT ADDED","Peas and carrots, cooked, from canned, fat added in cooking" +73111250,"PEAS & CARROTS, CANNED, LOW SODIUM, NS ADDED FAT","Peas and carrots, canned, low sodium, NS as to fat added in cooking" +73111260,"PEAS & CARROTS, CANNED, LOW SODIUM, FAT ADDED","Peas and carrots, canned, low sodium, fat added in cooking" +73111270,"PEAS & CARROTS, CANNED, LOW SODIUM, NO FAT ADDED","Peas and carrots, canned, low sodium, fat not added in cooking" +73111400,"CARROTS IN TOMATO SAUCE","Carrots in tomato sauce" +73112000,"CARROT CHIPS, DRIED","Carrot chips, dried" +73201000,"PUMPKIN, COOKED, NS AS TO FORM, NS AS TO ADDED FAT","Pumpkin, cooked, NS as to form, NS as to fat added in cooking" +73201001,"PUMPKIN, COOKED, FROM FRESH, NS AS TO ADDED FAT","Pumpkin, cooked, from fresh, NS as to fat added in cooking" +73201002,"PUMPKIN, COOKED, FROM FROZEN, NS AS TO ADDED FAT","Pumpkin, cooked, from frozen, NS as to fat added in cooking" +73201003,"PUMPKIN, COOKED, FROM CANNED, NS AS TO ADDED FAT","Pumpkin, cooked, from canned, NS as to fat added in cooking" +73201010,"PUMPKIN, COOKED, NS AS TO FORM, FAT NOT ADDED","Pumpkin, cooked, NS as to form, fat not added in cooking" +73201011,"PUMPKIN, COOKED, FROM FRESH, FAT NOT ADDED","Pumpkin, cooked, from fresh, fat not added in cooking" +73201012,"PUMPKIN, COOKED, FROM FROZEN, FAT NOT ADDED","Pumpkin, cooked, from frozen, fat not added in cooking" +73201013,"PUMPKIN, COOKED, FROM CANNED, FAT NOT ADDED","Pumpkin, cooked, from canned, fat not added in cooking" +73201020,"PUMPKIN, COOKED, NS AS TO FORM, FAT ADDED","Pumpkin, cooked, NS as to form, fat added in cooking" +73201021,"PUMPKIN, COOKED, FROM FRESH, FAT ADDED","Pumpkin, cooked, from fresh, fat added in cooking" +73201022,"PUMPKIN, COOKED, FROM FROZEN, FAT ADDED","Pumpkin, cooked, from frozen, fat added in cooking" +73201023,"PUMPKIN, COOKED, FROM CANNED, FAT ADDED","Pumpkin, cooked, from canned, fat added in cooking" +73210010,"CALABAZA (SPANISH PUMPKIN), COOKED","Calabaza (Spanish pumpkin), cooked" +73210110,"PUMPKIN FRITTERS, P.R.","Pumpkin fritters, Puerto Rican style" +73211110,"SWEET POTATO & PUMPKIN CASSEROLE, P.R","Sweet potato and pumpkin casserole, Puerto Rican style" +73301000,"SQUASH, WINTER, MASHED, NS AS TO ADDED FAT/SUGAR","Squash, winter type, mashed, NS as to fat or sugar added in cooking" +73301010,"SQUASH, WINTER, MASHED, NO FAT OR SUGAR ADDED","Squash, winter type, mashed, no fat or sugar added in cooking" +73301020,"SQUASH, WINTER, COOKED, MASHED, FAT ADDED, NO SUGAR","Squash, winter type, mashed, fat added in cooking, no sugar added in cooking" +73301030,"SQUASH, WINTER, COOKED, MASHED, FAT & SUGAR ADDED","Squash, winter type, mashed, fat and sugar added in cooking" +73302010,"SQUASH, WINTER, RAW","Squash, winter type, raw" +73303000,"SQUASH, WINTER, BAKED, NS FAT OR SUGAR ADDED","Squash, winter type, baked, NS as to fat or sugar added in cooking" +73303010,"SQUASH, WINTER, BAKED, NO FAT OR SUGAR ADDED","Squash, winter type, baked, no fat or sugar added in cooking" +73303020,"SQUASH, WINTER, BAKED, FAT ADDED, NO SUGAR","Squash, winter type, baked, fat added in cooking, no sugar added in cooking" +73303030,"SQUASH, WINTER, BAKED, FAT & SUGAR ADDED","Squash, winter type, baked, fat and sugar added in cooking" +73303040,"SQUASH, WINTER, BAKED, NO ADDED FAT, SUGAR ADDED","Squash, winter type, baked, no fat added in cooking, sugar added in cooking" +73304010,"SQUASH, FRITTER OR CAKE","Squash fritter or cake" +73305010,"SQUASH, WINTER, BAKED W/ CHEESE","Squash, winter, baked with cheese" +73305020,"SQUASH, WINTER, SOUFFLE","Squash, winter, souffle" +73401000,"SWEET POTATO, NFS","Sweet potato, NFS" +73402000,"SWEET POTATO, BAKED, PEEL EATEN, NS AS TO ADDED FAT","Sweet potato, baked, peel eaten, NS as to fat added in cooking" +73402010,"SWEET POTATO, BAKED, PEEL EATEN, NO FAT ADDED","Sweet potato, baked, peel eaten, fat not added in cooking" +73402020,"SWEET POTATO, BAKED, PEEL EATEN, FAT ADDED","Sweet potato, baked, peel eaten, fat added in cooking" +73403000,"SWEET POTATO, BAKED, PEEL NOT EATEN, NS AS TO FAT","Sweet potato, baked, peel not eaten, NS as to fat added in cooking" +73403010,"SWEET POTATO, BAKED, PEEL NOT EATEN, FAT NOT ADDED","Sweet potato, baked, peel not eaten, fat not added in cooking" +73403020,"SWEET POTATO, BAKED, PEEL NOT EATEN, FAT ADDED","Sweet potato, baked, peel not eaten, fat added in cooking" +73405000,"SWEET POTATO, BOILED, W/O PEEL, NS AS TO ADDED FAT","Sweet potato, boiled, without peel, NS as to fat added in cooking" +73405010,"SWEET POTATO, BOILED, W/O PEEL, FAT NOT ADDED","Sweet potato, boiled, without peel, fat not added in cooking" +73405020,"SWEET POTATO, BOILED, W/O PEEL, FAT ADDED","Sweet potato, boiled, without peel, fat added in cooking" +73405100,"SWEET POTATO, BOILED W/ PEEL, PEEL NOT EATEN, NS AS TO FAT","Sweet potato, boiled with peel, peel not eaten, NS as to fat added in cooking" +73405110,"SWEET POTATO, BOILED W/ PEEL, PEEL NOT EATEN, FAT NOT ADDED","Sweet potato, boiled with peel, peel not eaten, fat not added in cooking" +73405120,"SWEET POTATO, BOILED W/ PEEL, PEEL NOT EATEN, FAT ADDED","Sweet potato, boiled with peel, peel not eaten, fat added in cooking" +73406000,"SWEET POTATO, CANDIED","Sweet potato, candied" +73406010,"SWEET POTATO W/ FRUIT","Sweet potato with fruit" +73407000,"SWEET POTATO, CANNED, NS AS TO SYRUP","Sweet potato, canned, NS as to syrup" +73407010,"SWEET POTATO, CANNED, W/O SYRUP","Sweet potato, canned without syrup" +73407020,"SWEET POTATO, CANNED IN SYRUP","Sweet potato, canned in syrup" +73407030,"SWEET POTATO, CANNED IN SYRUP, W/ FAT ADDED","Sweet potato, canned in syrup, with fat added in cooking" +73409000,"SWEET POTATO, CASSEROLE OR MASHED","Sweet potato, casserole or mashed" +73410110,"SWEET POTATO, FRIED","Sweet potato, fried" +73410210,"SWEET POTATO, CHIPS","Sweet potato, chips" +73410300,"SWEET POTATO, FRENCH FRIES","Sweet potato, french fries" +73421000,"SWEET POTATO, YELLOW, P.R., COOKED","Sweet potato, yellow, Puerto Rican, cooked" +73501000,"CARROT SOUP, CREAM OF, PREPARED WITH MILK, HOME RECIPE, CANN","Carrot soup, cream of, prepared with milk, home recipe, canned or ready-to-serve" +73501010,"CARROT WITH RICE SOUP, CREAM OF, PREPARED WITH MILK, HOME RE","Carrot with rice soup, cream of, prepared with milk, home recipe, canned or ready-to-serve" +74101000,"TOMATOES, RAW","Tomatoes, raw" +74102000,"TOMATOES, GREEN, RAW","Tomatoes, green, raw" +74201000,"TOMATOES, COOKED, NS AS TO FORM, NS AS TO METHOD","Tomatoes, cooked, NS as to form, NS as to method" +74201001,"TOMATOES, COOKED, FROM FRESH, NS AS TO METHOD","Tomatoes, cooked, from fresh, NS as to method" +74201003,"TOMATOES, COOKED, FROM CANNED, NS AS TO METHOD","Tomatoes, cooked, from canned, NS as to method" +74202010,"TOMATOES, NS AS TO FORM, BROILED","Tomatoes, NS as to form, broiled" +74202011,"TOMATOES, FROM FRESH, BROILED","Tomatoes, from fresh, broiled" +74202050,"TOMATOES, RED, NS AS TO FORM, FRIED","Tomatoes, red, NS as to form, fried" +74202051,"TOMATOES, RED, FROM FRESH, FRIED","Tomatoes, red, from fresh, fried" +74203010,"TOMATOES, NS AS TO FORM, SCALLOPED","Tomatoes, NS as to form, scalloped" +74203011,"TOMATOES, FROM FRESH, SCALLOPED","Tomatoes, from fresh, scalloped" +74204010,"TOMATOES, NS AS TO FORM, STEWED","Tomatoes, NS as to form, stewed" +74204011,"TOMATOES, FROM FRESH, STEWED","Tomatoes, from fresh, stewed" +74204013,"TOMATOES, FROM CANNED, STEWED","Tomatoes, from canned, stewed" +74204500,"TOMATOES, CANNED, LOW SODIUM","Tomatoes, canned, low sodium" +74205010,"TOMATOES, GREEN, COOKED, NS AS TO FORM (INCL FRIED)","Tomatoes, green, cooked, NS as to form" +74205011,"TOMATOES, GREEN, COOKED, FROM FRESH (INCL FRIED)","Tomatoes, green, cooked, from fresh" +74205020,"TOMATOES, GREEN, PICKLED","Tomato, green, pickled" +74206000,"TOMATOES, RED, DRIED","Tomatoes, red, dried" +74301100,"TOMATO JUICE","Tomato juice" +74301150,"TOMATO JUICE, LOW SODIUM","Tomato juice, low sodium" +74302000,"TOMATO JUICE COCKTAIL","Tomato juice cocktail" +74303000,"TOMATO & VEGETABLE JUICE, MOSTLY TOMATO (INCL V-8)","Tomato and vegetable juice, mostly tomato" +74303100,"TOMATO & VEGETABLE JUICE, MOSTLY TOMATO, LOW SODIUM","Tomato and vegetable juice, mostly tomato, low sodium" +74304000,"TOMATO JUICE W/ CLAM OR BEEF JUICE","Tomato juice with clam or beef juice" +74401010,"TOMATO CATSUP","Tomato catsup" +74401110,"TOMATO CATSUP, REDUCED SODIUM","Tomato catsup, reduced sodium" +74402010,"TOMATO CHILI SAUCE (CATSUP TYPE)","Tomato chili sauce (catsup-type)" +74402100,"SALSA, NFS","Salsa, NFS" +74402110,"SALSA, PICO DE GALLO","Salsa, pico de gallo" +74402150,"SALSA, RED, COMMERCIALLY-PREPARED","Salsa, red, commercially-prepared" +74402200,"SALSA, RED, HOMEMADE","Salsa, red, homemade" +74402250,"ENCHILADA SAUCE, RED","Enchilada sauce, red" +74402260,"ENCHILADA SAUCE, GREEN","Enchilada sauce, green" +74402350,"SALSA VERDE OR SALSA, GREEN","Salsa verde or salsa, green" +74404010,"SPAGHETTI SAUCE, MEATLESS","Spaghetti sauce, meatless" +74404020,"SPAGHETTI SAUCE W/ VEGETABLES, HOMEMADE-STYLE","Spaghetti sauce with vegetables, homemade-style" +74404030,"SPAGHETTI SAUCE W/ MEAT, CANNED, NO EXTRA MEAT","Spaghetti sauce with meat, canned, no extra meat added" +74404050,"SPAGHETTI SAUCE, MEATLESS, REDUCED SODIUM","Spaghetti sauce, meatless, reduced sodium" +74404060,"SPAGHETTI SAUCE, MEATLESS, FAT FREE","Spaghetti sauce, meatless, fat free" +74404090,"VODKA FLAVORED PASTA SAUCE MADE WITH TOMATOES AND CREAM","Vodka flavored pasta sauce made with tomatoes and cream" +74405010,"TOMATO RELISH (INCLUDE TOMATO PRESERVES)","Tomato relish" +74406010,"BARBECUE SAUCE","Barbecue sauce" +74406050,"BARBECUE SAUCE, REDUCED SODIUM","Barbecue sauce, reduced sodium" +74406100,"STEAK SAUCE, TOMATO-BASE (INCLUDE A-1)","Steak sauce, tomato-base" +74406500,"COCKTAIL SAUCE","Cocktail sauce" +74410110,"PUERTO RICAN SEASONING WITH HAM","Puerto Rican seasoning with ham" +74415110,"PUERTO RICAN SEASONING W/ HAM & TOMATO SAUCE","Puerto Rican seasoning with ham and tomato sauce" +74420110,"PUERTO RICAN SEASONING WO/ HAM & TOMATO SAUCE","Puerto Rican seasoning without ham and tomato sauce" +74501010,"TOMATO ASPIC","Tomato aspic" +74503010,"TOMATO & CORN, COOKED, FAT NOT ADDED IN COOKING","Tomato and corn, cooked, fat not added in cooking" +74504000,"TOMATO & OKRA, COOKED, NS AS TO ADDED FAT","Tomato and okra, cooked, NS as to fat added in cooking" +74504010,"TOMATO & OKRA, COOKED, NO FAT ADDED","Tomato and okra, cooked, fat not added in cooking" +74504020,"TOMATO & OKRA, COOKED, FAT ADDED","Tomato and okra, cooked, fat added in cooking" +74504100,"TOMATO & ONION, COOKED, NS FAT ADDED","Tomato and onion, cooked, NS as to fat added in cooking" +74504110,"TOMATO & ONION, COOKED, FAT NOT ADDED IN COOKING","Tomato and onion, cooked, fat not added in cooking" +74504120,"TOMATO & ONION, COOKED, FAT ADDED","Tomato and onion, cooked, fat added in cooking" +74504150,"TOMATO & CELERY, COOKED, FAT NOT ADDED IN COOKING","Tomato and celery, cooked, fat not added in cooking" +74505000,"TOMATO W/ CORN & OKRA, COOKED, NS AS TO ADDED FAT","Tomato with corn and okra, cooked, NS as to fat added in cooking" +74505010,"TOMATO W/ CORN & OKRA, COOKED, NO FAT ADDED","Tomato with corn and okra, cooked, fat not added in cooking" +74505020,"TOMATO W/ CORN & OKRA, COOKED, FAT ADDED","Tomato with corn and okra, cooked, fat added in cooking" +74506000,"TOMATO & CUCUMBER SALAD W/ OIL & VINEGAR","Tomato and cucumber salad made with tomato, cucumber, oil, and vinegar" +74601000,"TOMATO SOUP, NFS","Tomato soup, NFS" +74601010,"TOMATO SOUP, CREAM OF,PREP W/ MILK","Tomato soup, cream of, prepared with milk" +74602010,"TOMATO SOUP, PREPARED WITH WATER, OR READY-TO-SERVE","Tomato soup, prepared with water, or ready-to-serve" +74602050,"TOMATO SOUP, INSTANT TYPE, PREPARED W/ WATER","Tomato soup, instant type, prepared with water" +74602200,"TOMATO SOUP, CANNED, REDUCED SODIUM, PREPARED WITH WATER, OR","Tomato soup, canned, reduced sodium, prepared with water, or ready-to-serve" +74602300,"TOMATO SOUP, CANNED, REDUCED SODIUM, PREP W/ MILK","Tomato soup, canned, reduced sodium, prepared with milk" +74603010,"TOMATO BEEF SOUP, PREPARED W/ WATER","Tomato beef soup, prepared with water" +74604010,"TOMATO BEEF NOODLE SOUP, PREPARED W/ WATER","Tomato beef noodle soup, prepared with water" +74604100,"TOMATO BEEF RICE SOUP, PREPARED W/ WATER","Tomato beef rice soup, prepared with water" +74604500,"TOMATO NOODLE SOUP, CANNED, PREPARED WITH WATER OR READY-TO-","Tomato noodle soup, canned, prepared with water or ready-to-serve" +74604600,"TOMATO NOODLE SOUP, CANNED, PREPARED WITH MILK","Tomato noodle soup, canned, prepared with milk" +74605010,"TOMATO RICE SOUP, PREPARED W/ WATER","Tomato rice soup, prepared with water" +74606010,"TOMATO VEGETABLE SOUP, PREP W/ WATER","Tomato vegetable soup, prepared with water" +74606020,"TOMATO VEGETABLE SOUP W/NOODLES, PREPARED W/ WATER","Tomato vegetable soup with noodles, prepared with water" +74701000,"TOMATO SANDWICH","Tomato sandwich" +75100250,"RAW VEGETABLE, NFS","Raw vegetable, NFS" +75100300,"SPROUTS, NFS","Sprouts, NFS" +75100500,"ALFALFA SPROUTS, RAW","Alfalfa sprouts, raw" +75100750,"ARTICHOKE, JERUSALEM, RAW (INCLUDE SUNCHOKE)","Artichoke, Jerusalem, raw" +75100800,"ASPARAGUS, RAW","Asparagus, raw" +75101000,"BEAN SPROUTS, RAW (SOYBEAN/MUNG)","Bean sprouts, raw (soybean or mung)" +75101800,"BEANS, STRING, GREEN, RAW","Beans, string, green, raw" +75102000,"BEANS, LIMA, RAW","Beans, lima, raw" +75102500,"BEETS, RAW","Beets, raw" +75102600,"BROCCOFLOWER, RAW","Broccoflower, raw" +75102750,"BRUSSELS SPROUTS, RAW","Brussels sprouts, raw" +75103000,"CABBAGE, GREEN, RAW","Cabbage, green, raw" +75104000,"CABBAGE, CHINESE, RAW","Cabbage, Chinese, raw" +75105000,"CABBAGE, RED, RAW","Cabbage, red, raw" +75105500,"CACTUS, RAW","Cactus, raw" +75107000,"CAULIFLOWER, RAW","Cauliflower, raw" +75109000,"CELERY, RAW (INCLUDE CELERY, NFS)","Celery, raw" +75109010,"FENNEL BULB, RAW","Fennel bulb, raw" +75109400,"BASIL, RAW","Basil, raw" +75109500,"CHIVES, RAW (INCLUDE CHIVES, NFS)","Chives, raw" +75109550,"CILANTRO, RAW","Cilantro, raw" +75109600,"CORN, RAW","Corn, raw" +75111000,"CUCUMBER, RAW (INCLUDE CUCUMBER, NFS)","Cucumber, raw" +75111200,"EGGPLANT, RAW","Eggplant, raw" +75111500,"GARLIC, RAW","Garlic, raw" +75111800,"JICAMA, RAW (INCLUDE YAMBEAN)","Jicama, raw" +75112000,"KOHLRABI, RAW","Kohlrabi, raw" +75112500,"LEEK, RAW","Leek, raw" +75113000,"LETTUCE, RAW","Lettuce, raw" +75113060,"LETTUCE, BOSTON, RAW","Lettuce, Boston, raw" +75113070,"LETTUCE, MANOA","Lettuce, manoa" +75113080,"LETTUCE, ARUGULA, RAW","Lettuce, arugula, raw" +75114000,"MIXED SALAD GREENS, RAW","Mixed salad greens, raw" +75115000,"MUSHROOMS, RAW","Mushrooms, raw" +75117010,"ONIONS, YOUNG GREEN, RAW","Onions, young green, raw" +75117020,"ONIONS, MATURE, RAW","Onions, mature, raw" +75119000,"PARSLEY, RAW","Parsley, raw" +75120000,"PEAS, GREEN, RAW","Peas, green, raw" +75121000,"PEPPER, HOT CHILI, RAW (INCLUDE JALAPENO)","Pepper, hot chili, raw" +75121400,"PEPPER, POBLANO, RAW","Pepper, poblano, raw" +75121500,"PEPPER, SERRANO, RAW","Pepper, Serrano, raw" +75122000,"PEPPER, RAW, NFS","Pepper, raw, NFS" +75122100,"PEPPER, SWEET, GREEN, RAW","Pepper, sweet, green, raw" +75122200,"PEPPER, SWEET, RED, RAW","Pepper, sweet, red, raw" +75124000,"PEPPER, BANANA, RAW","Pepper, banana, raw" +75125000,"RADISH, RAW","Radish, raw" +75127000,"RUTABAGA, RAW","Rutabaga, raw" +75127500,"SEAWEED, RAW (INCLUDE BLANCHED)","Seaweed, raw" +75127750,"SNOWPEA (PEA POD), RAW","Snowpeas (pea pod), raw" +75128000,"SQUASH, SUMMER, YELLOW, RAW","Squash, summer, yellow, raw" +75128010,"SQUASH, SUMMER, GREEN, RAW (INCLUDE ZUCCHINI)","Squash, summer, green, raw" +75129000,"TURNIP, RAW","Turnip, raw" +75132000,"MIXED VEGETABLE JUICE (OTHER THAN TOMATO)","Mixed vegetable juice (vegetables other than tomato)" +75132100,"CELERY JUICE","Celery juice" +75140500,"BROCCOLI SALAD W/CAULIFLOWER,CHEESE,BACON,&DRESSING","Broccoli salad with cauliflower, cheese, bacon bits, and dressing" +75140510,"BROCCOLI SLAW SALAD","Broccoli slaw salad" +75140990,"CABBAGE SALAD OR COLESLAW, FROM FAST FOOD / RESTAURANT","Cabbage salad or coleslaw, from fast food / restaurant" +75141000,"CABBAGE SALAD OR COLESLAW, MADE WITH COLESLAW DRESSING","Cabbage salad or coleslaw, made with coleslaw dressing" +75141005,"CABBAGE SALAD OR COLESLAW, MADE W/ LIGHT COLESLAW DRESSING","Cabbage salad or coleslaw, made with light coleslaw dressing" +75141020,"CABBAGE SALAD OR COLESLAW, W/ ITALIAN DRSG","Cabbage salad or coleslaw, made with Italian dressing" +75141025,"CABBAGE SALAD OR COLESLAW, W/LT ITALIAN DRSG","Cabbage salad or coleslaw, made with light Italian dressing" +75141030,"CABBAGE SALAD OR COLESLAW, W/ CREAMY DRSG","Cabbage salad or coleslaw, made with creamy dressing" +75141035,"CABBAGE SALAD OR COLESLAW, W/ LT CREAMY DRSG","Cabbage salad or coleslaw, made with light creamy dressing" +75141040,"CABBAGE SALAD OR COLESLAW, W/ FAT FREE DRSG","Cabbage salad or coleslaw, made with any type of fat free dressing" +75141100,"CABBAGE SALAD OR COLESLAW, W/APPLES/RAISINS, DRESS","Cabbage salad or coleslaw with apples and/or raisins, with dressing" +75141200,"CABBAGE SALAD OR COLESLAW, W/ PINEAPPLE, DRESSING","Cabbage salad or coleslaw with pineapple, with dressing" +75141300,"CABBAGE, CHINESE, SALAD, W/ DRESSING","Cabbage, Chinese, salad, with dressing" +75141500,"CELERY, STUFFED W/ CHEESE","Celery, stuffed with cheese" +75142000,"CUCUMBER & VEGETABLE NAMASU","Cucumber and vegetable namasu" +75142500,"CUCUMBER SALAD, MADE WITH SOUR CREAM DRESSING","Cucumber salad, made with sour cream dressing" +75142550,"CUCUMBER SALAD, W/ ITALIAN DRSG","Cucumber salad, made with Italian dressing" +75142600,"CUCUMBER SALAD MADE W/ CUCUMBER AND VINEGAR","Cucumber salad made with cucumber and vinegar" +75143000,"LETTUCE SALAD W/ ASSORTED VEGETABLES","Lettuce, salad with assorted vegetables including tomatoes and/or carrots, no dressing" +75143050,"LETTUCE SALAD, W/ ASST VEG, NO TOM OR CAR, NO DRESS","Lettuce, salad with assorted vegetables excluding tomatoes and carrots, no dressing" +75143100,"LETTUCE SALAD, W/ AVOCADO, TOMATO/CAR, NO DRESS","Lettuce, salad with avocado, tomato, and/or carrots, with or without other vegetables, no dressing" +75143200,"LETTUCE SALAD, W/ CHEESE, TOM/CAR, NO DRESSING","Lettuce, salad with cheese, tomato and/or carrots, with or without other vegetables, no dressing" +75143300,"LETTUCE SALAD, W/ EGG, TOM/CAR, NO DRESSING","Lettuce, salad with egg, tomato, and/or carrots, with or without other vegetables, no dressing" +75143350,"LETTUCE SALAD W/ EGG, CHEESE, TOM/CAR, NO DRESSING","Lettuce, salad with egg, cheese, tomato, and/or carrots, with or without other vegetables, no dressing" +75144100,"LETTUCE, WILTED, W/ BACON DRESSING","Lettuce, wilted, with bacon dressing" +75145000,"SEVEN-LAYER SALAD(LETTUCE, MAYO, CHEESE, EGG, PEAS)","Seven-layer salad (lettuce salad made with a combination of onion, celery, green pepper, peas, mayonnaise, cheese, eggs, and/or bacon)" +75146000,"GREEK SALAD, NO DRESSING","Greek Salad, no dressing" +75147000,"SPINACH SALAD, NO DRESSING","Spinach salad, no dressing" +75148010,"COBB SALAD, NO DRESSING","Cobb salad, no dressing" +75200100,"VEGETABLES, NS AS TO TYPE, NS AS TO ADDED FAT","Vegetables, NS as to type, cooked, NS as to fat added in cooking" +75200110,"VEGETABLES, NS AS TO TYPE, NO FAT ADDED","Vegetables, NS as to type, cooked, fat not added in cooking" +75200120,"VEGETABLES, NS AS TO TYPE, COOKED, FAT ADDED","Vegetables, NS as to type, cooked, fat added in cooking" +75200600,"ALGAE, DRIED (INCLUDE SPIRULINA)","Algae, dried" +75200700,"ALOE VERA JUICE","Aloe vera juice" +75201000,"ARTICHOKE, GLOBE(FRENCH), CKD, NS FORM, NS FAT ADDED","Artichoke, globe (French), cooked, NS as to form, NS as to fat added in cooking" +75201001,"ARTICHOKE, GLOBE(FRENCH), CKD, FROM FRESH, NS FAT ADDED","Artichoke, globe (French), cooked, from fresh, NS as to fat added in cooking" +75201002,"ARTICHOKE, GLOBE(FRENCH), CKD, FROM FROZ, NS FAT ADDED","Artichoke, globe (French), cooked, from frozen, NS as to fat added in cooking" +75201003,"ARTICHOKE, GLOBE(FRENCH), CKD, FROM CAN, NS FAT ADDED","Artichoke, globe (French), cooked, from canned, NS as to fat added in cooking" +75201010,"ARTICHOKE,GLOBE (FRENCH),CKD,NS FORM,FAT NOT ADDED","Artichoke, globe (French), cooked, NS as to form, fat not added in cooking" +75201011,"ARTICHOKE,GLOBE (FRENCH),CKD,FROM FRESH,FAT NOT ADDED","Artichoke, globe (French), cooked, from fresh, fat not added in cooking" +75201012,"ARTICHOKE,GLOBE (FRENCH),CKD,FROM FROZ,FAT NOT ADDED","Artichoke, globe (French), cooked, from frozen, fat not added in cooking" +75201013,"ARTICHOKE,GLOBE (FRENCH),CKD,FROM CAN,FAT NOT ADDED","Artichoke, globe (French), cooked, from canned, fat not added in cooking" +75201020,"ARTICHOKE, GLOBE (FRENCH), CKD, NS FORM, FAT ADDED","Artichoke, globe (French), cooked, NS as to form, fat added in cooking" +75201021,"ARTICHOKE, GLOBE (FRENCH), CKD, FROM FRESH, FAT ADDED","Artichoke, globe (French), cooked, from fresh, fat added in cooking" +75201022,"ARTICHOKE, GLOBE (FRENCH), CKD, FROM FROZ, FAT ADDED","Artichoke, globe (French), cooked, from frozen, fat added in cooking" +75201023,"ARTICHOKE, GLOBE (FRENCH), CKD, FROM CAN, FAT ADDED","Artichoke, globe (French), cooked, from canned, fat added in cooking" +75201030,"ARTICHOKE SALAD IN OIL","Artichoke salad in oil" +75202000,"ASPARAGUS, COOKED, NS AS TO FORM, NS FAT ADDED","Asparagus, cooked, NS as to form, NS as to fat added in cooking" +75202001,"ASPARAGUS, COOKED, FROM FRESH, NS FAT ADDED","Asparagus, cooked, from fresh, NS as to fat added in cooking" +75202002,"ASPARAGUS, COOKED, FROM FROZEN, NS FAT ADDED","Asparagus, cooked, from frozen, NS as to fat added in cooking" +75202003,"ASPARAGUS, COOKED, FROM CANNED, NS FAT ADDED","Asparagus, cooked, from canned, NS as to fat added in cooking" +75202010,"ASPARAGUS, COOKED, NS AS TO FORM, FAT NOT ADDED","Asparagus, cooked, NS as to form, fat not added in cooking" +75202011,"ASPARAGUS, COOKED, FROM FRESH, FAT NOT ADDED","Asparagus, cooked, from fresh, fat not added in cooking" +75202012,"ASPARAGUS, COOKED, FROM FROZEN, FAT NOT ADDED","Asparagus, cooked, from frozen, fat not added in cooking" +75202013,"ASPARAGUS, COOKED, FROM CANNED, FAT NOT ADDED","Asparagus, cooked, from canned, fat not added in cooking" +75202020,"ASPARAGUS, COOKED, NS AS TO FORM, FAT ADDED","Asparagus, cooked, NS as to form, fat added in cooking" +75202021,"ASPARAGUS, COOKED, FROM FRESH, FAT ADDED","Asparagus, cooked, from fresh, fat added in cooking" +75202022,"ASPARAGUS, COOKED, FROM FROZEN, FAT ADDED","Asparagus, cooked, from frozen, fat added in cooking" +75202023,"ASPARAGUS, COOKED, FROM CANNED, FAT ADDED","Asparagus, cooked, from canned, fat added in cooking" +75203000,"BAMBOO SHOOTS, COOKED, FAT NOT ADDED IN COOKING","Bamboo shoots, cooked, fat not added in cooking" +75203020,"BAMBOO SHOOTS, COOKED, FAT ADDED IN COOKING","Bamboo shoots, cooked, fat added in cooking" +75204000,"BEANS, LIMA, IMMATURE, COOKED, NS FORM, NS AS TO FAT","Beans, lima, immature, cooked, NS as to form, NS as to fat added in cooking" +75204001,"BEANS, LIMA, IMMATURE, COOKED, FROM FRESH, NS FAT ADDED","Beans, lima, immature, cooked, from fresh, NS as to fat added in cooking" +75204002,"BEANS, LIMA, IMMATURE, COOKED, FROM FROZEN, NS FAT ADDED","Beans, lima, immature, cooked, from frozen, NS as to fat added in cooking" +75204003,"BEANS, LIMA, IMMATURE, COOKED, FROM CANNED, NS FAT ADDED","Beans, lima, immature, cooked, from canned, NS as to fat added in cooking" +75204010,"BEANS, LIMA, IMMATURE, COOKED, NS FORM, NO FAT ADDED","Beans, lima, immature, cooked, NS as to form, fat not added in cooking" +75204011,"BEANS, LIMA, IMMATURE, COOKED, FROM FRESH, NO FAT ADDED","Beans, lima, immature, cooked, from fresh, fat not added in cooking" +75204012,"BEANS, LIMA, IMMATURE, COOKED, FROM FROZ, NO FAT ADDED","Beans, lima, immature, cooked, from frozen, fat not added in cooking" +75204013,"BEANS, LIMA, IMMATURE, COOKED, FROM CAN, NO FAT ADDED","Beans, lima, immature, cooked, from canned, fat not added in cooking" +75204020,"BEANS, LIMA, IMMATURE, COOKED, NS FORM, FAT ADDED","Beans, lima, immature, cooked, NS as to form, fat added in cooking" +75204021,"BEANS, LIMA, IMMATURE, COOKED, FROM FRESH, FAT ADDED","Beans, lima, immature, cooked, from fresh, fat added in cooking" +75204022,"BEANS, LIMA, IMMATURE, COOKED, FROM FROZ, FAT ADDED","Beans, lima, immature, cooked, from frozen, fat added in cooking" +75204023,"BEANS, LIMA, IMMATURE, COOKED, FROM CAN, FAT ADDED","Beans, lima, immature, cooked, from canned, fat added in cooking" +75204100,"BEANS, LIMA,IMMATURE,CANNED,LOW SODIUM,NS AS TO FAT","Beans, lima, immature, canned, low sodium, NS as to fat added in cooking" +75204110,"BEANS, LIMA,IMMATURE,CANNED,LOW SODIUM,NO FAT ADDED","Beans, lima, immature, canned, low sodium, fat not added in cooking" +75204120,"BEANS, LIMA, IMMATURE, CANNED,LOW SODIUM, FAT ADDED","Beans, lima, immature, canned, low sodium, fat added in cooking" +75204980,"BEANS, STRING, CKD, NS FORM, NS COLOR, FAT ADDED","Beans, string, cooked, NS as to form, NS as to color, fat added in cooking" +75204981,"BEANS, STRING, CKD, FROM FRESH, NS COLOR, FAT ADDED","Beans, string, cooked, from fresh, NS as to color, fat added in cooking" +75204982,"BEANS, STRING, CKD, FROM FROZ, NS COLOR, FAT ADDED","Beans, string, cooked, from frozen, NS as to color, fat added in cooking" +75204983,"BEANS, STRING, CKD, FROM CAN, NS COLOR, FAT ADDED","Beans, string, cooked, from canned, NS as to color, fat added in cooking" +75204990,"BEANS, STRING, CKD, NS FORM, NS COLOR, NO FAT ADDED","Beans, string, cooked, NS as to form, NS as to color, fat not added in cooking" +75204991,"BEANS, STRING, CKD, FROM FRESH, NS COLOR, NO FAT ADDED","Beans, string, cooked, from fresh, NS as to color, fat not added in cooking" +75204992,"BEANS, STRING, CKD, FROM FROZ, NS COLOR, NO FAT ADDED","Beans, string, cooked, from frozen, NS as to color, fat not added in cooking" +75204993,"BEANS, STRING, CKD, FROM CAN, NS COLOR, NO FAT ADDED","Beans, string, cooked, from canned, NS as to color, fat not added in cooking" +75205000,"BEANS, STRING, CKD, NS FORM, NS COLOR, NS FAT ADDED","Beans, string, cooked, NS as to form, NS as to color, NS as to fat added in cooking" +75205001,"BEANS, STRING, CKD, FROM FRESH, NS COLOR, NS FAT ADDED","Beans, string, cooked, from fresh, NS as to color, NS as to fat added in cooking" +75205002,"BEANS, STRING, CKD, FROM FROZ, NS COLOR, NS FAT ADDED","Beans, string, cooked, from frozen, NS as to color, NS as to fat added in cooking" +75205003,"BEANS, STRING, CKD, FROM CAN, NS COLOR, NS FAT ADDED","Beans, string, cooked, from canned, NS as to color, NS as to fat added in cooking" +75205010,"BEANS, STRING, GREEN, COOKED, NS FORM, NS FAT ADDED","Beans, string, green, cooked, NS as to form, NS as to fat added in cooking" +75205011,"BEANS, STRING, GREEN, COOKED, FROM FRESH, NS FAT ADDED","Beans, string, green, cooked, from fresh, NS as to fat added in cooking" +75205012,"BEANS, STRING, GREEN, COOKED, FROM FROZEN, NS FAT ADDED","Beans, string, green, cooked, from frozen, NS as to fat added in cooking" +75205013,"BEANS, STRING, GREEN, COOKED, FROM CANNED, NS FAT ADDED","Beans, string, green, cooked, from canned, NS as to fat added in cooking" +75205020,"BEANS, STRING, GREEN, COOKED, NS FORM,FAT NOT ADDED","Beans, string, green, cooked, NS as to form, fat not added in cooking" +75205021,"BEANS, STRING, GREEN, COOKED, FROM FRESH, FAT NOT ADDED","Beans, string, green, cooked, from fresh, fat not added in cooking" +75205022,"BEANS, STRING, GREEN, COOKED, FROM FROZEN, FAT NOT ADDED","Beans, string, green, cooked, from frozen, fat not added in cooking" +75205023,"BEANS, STRING, GREEN, COOKED, FROM CANNED, FAT NOT ADDED","Beans, string, green, cooked, from canned, fat not added in cooking" +75205030,"BEANS, STRING, GREEN, COOKED, NS FORM, FAT ADDED","Beans, string, green, cooked, NS as to form, fat added in cooking" +75205031,"BEANS, STRING, GREEN, COOKED, FROM FRESH, FAT ADDED","Beans, string, green, cooked, from fresh, fat added in cooking" +75205032,"BEANS, STRING, GREEN, COOKED, FROM FROZEN, FAT ADDED","Beans, string, green, cooked, from frozen, fat added in cooking" +75205033,"BEANS, STRING, GREEN, COOKED, FROM CANNED, FAT ADDED","Beans, string, green, cooked, from canned, fat added in cooking" +75205110,"BEANS, GREEN, CANNED, LO NA, NS AS TO ADDED FAT","Beans, string, green, canned, low sodium, NS as to fat added in cooking" +75205120,"BEANS, GREEN, CANNED, LO NA, FAT NOT ADDED","Beans, string, green, canned, low sodium, fat not added in cooking" +75205130,"BEANS, GREEN, CANNED, LO NA, FAT ADDED","Beans, string, green, canned, low sodium, fat added in cooking" +75206000,"BEANS, STRING, YELLOW, COOKED, NS FORM, NS ADDED FAT","Beans, string, yellow, cooked, NS as to form, NS as to fat added in cooking" +75206001,"BEANS, STRING, YELLOW, COOKED, FROM FRESH, NS ADDED FAT","Beans, string, yellow, cooked, from fresh, NS as to fat added in cooking" +75206002,"BEANS, STRING, YELLOW, COOKED, FROM FROZ, NS ADDED FAT","Beans, string, yellow, cooked, from frozen, NS as to fat added in cooking" +75206003,"BEANS, STRING, YELLOW, COOKED, FROM CANNED, NS ADDED FAT","Beans, string, yellow, cooked, from canned, NS as to fat added in cooking" +75206010,"BEANS, STRING, YELLOW, COOKED, NS FORM, NO FAT ADDED","Beans, string, yellow, cooked, NS as to form, fat not added in cooking" +75206011,"BEANS, STRING, YELLOW, COOKED, FROM FRESH, NO FAT ADDED","Beans, string, yellow, cooked, from fresh, fat not added in cooking" +75206012,"BEANS, STRING, YELLOW, COOKED, FROM FROZ, NO FAT ADDED","Beans, string, yellow, cooked, from frozen, fat not added in cooking" +75206013,"BEANS, STRING, YELLOW, COOKED, FROM CANNED, NO FAT ADDED","Beans, string, yellow, cooked, from canned, fat not added in cooking" +75206020,"BEANS, STRING, YELLOW, COOKED, NS FORM, FAT ADDED","Beans, string, yellow, cooked, NS as to form, fat added in cooking" +75206021,"BEANS, STRING, YELLOW, COOKED, FROM FRESH, FAT ADDED","Beans, string, yellow, cooked, from fresh, fat added in cooking" +75206022,"BEANS, STRING, YELLOW, COOKED, FROM FROZ, FAT ADDED","Beans, string, yellow, cooked, from frozen, fat added in cooking" +75206023,"BEANS, STRING, YELLOW, COOKED, FROM CAN, FAT ADDED","Beans, string, yellow, cooked, from canned, fat added in cooking" +75207000,"BEAN SPROUTS, COOKED, NS FORM, NS AS TO ADDED FAT","Bean sprouts, cooked, NS as to form, NS as to fat added in cooking" +75207001,"BEAN SPROUTS, COOKED, FROM FRESH, NS FAT ADDED","Bean sprouts, cooked, from fresh, NS as to fat added in cooking" +75207003,"BEAN SPROUTS, COOKED, FROM CANNED, NS FAT ADDED","Bean sprouts, cooked, from canned, NS as to fat added in cooking" +75207010,"BEAN SPROUTS, COOKED, NS AS TO FORM, FAT NOT ADDED","Bean sprouts, cooked, NS as to form, fat not added in cooking" +75207011,"BEAN SPROUTS, COOKED, FROM FRESH, FAT NOT ADDED","Bean sprouts, cooked, from fresh, fat not added in cooking" +75207013,"BEAN SPROUTS, COOKED, FROM CANNED, FAT NOT ADDED","Bean sprouts, cooked, from canned, fat not added in cooking" +75207020,"BEAN SPROUTS, COOKED, NS AS TO FORM, FAT ADDED","Bean sprouts, cooked, NS as to form, fat added in cooking" +75207021,"BEAN SPROUTS, COOKED, FROM FRESH, FAT ADDED","Bean sprouts, cooked, from fresh, fat added in cooking" +75207023,"BEAN SPROUTS, COOKED, FROM CANNED, FAT ADDED","Bean sprouts, cooked, from canned, fat added in cooking" +75208000,"BEETS, COOKED, NS AS TO FORM, NS AS TO FAT ADDED","Beets, cooked, NS as to form, NS as to fat added in cooking" +75208001,"BEETS, COOKED, FROM FRESH, NS AS TO FAT ADDED","Beets, cooked, from fresh, NS as to fat added in cooking" +75208002,"BEETS, COOKED, FROM FROZEN, NS AS TO FAT ADDED","Beets, cooked, from frozen, NS as to fat added in cooking" +75208003,"BEETS, COOKED, FROM CANNED, NS AS TO FAT ADDED","Beets, cooked, from canned, NS as to fat added in cooking" +75208010,"BEETS, COOKED, NS AS TO FORM, FAT NOT ADDED","Beets, cooked, NS as to form, fat not added in cooking" +75208011,"BEETS, COOKED, FROM FRESH, FAT NOT ADDED","Beets, cooked, from fresh, fat not added in cooking" +75208012,"BEETS, COOKED, FROM FROZEN, FAT NOT ADDED","Beets, cooked, from frozen, fat not added in cooking" +75208013,"BEETS, COOKED, FROM CANNED, FAT NOT ADDED","Beets, cooked, from canned, fat not added in cooking" +75208020,"BEETS, COOKED, NS AS TO FORM, FAT ADDED","Beets, cooked, NS as to form, fat added in cooking" +75208021,"BEETS, COOKED, FROM FRESH, FAT ADDED","Beets, cooked, from fresh, fat added in cooking" +75208022,"BEETS, COOKED, FROM FROZEN, FAT ADDED","Beets, cooked, from frozen, fat added in cooking" +75208023,"BEETS, COOKED, FROM CANNED, FAT ADDED","Beets, cooked, from canned, fat added in cooking" +75208100,"BEETS, CANNED, LOW SODIUM, NS AS TO ADDED FAT","Beets, canned, low sodium, NS as to fat added in cooking" +75208110,"BEETS, CANNED, LOW SODIUM, FAT NOT ADDED","Beets, canned, low sodium, fat not added in cooking" +75208120,"BEETS, CANNED, LOW SODIUM, FAT ADDED","Beets, canned, low sodium, fat added in cooking" +75208290,"BITTERMELON, COOKED, NS AS TO ADDED FAT","Bitter melon, cooked, NS as to fat added in cooking" +75208300,"BITTERMELON, COOKED, NO FAT ADDED(INCL BALSAM PEAR)","Bitter melon, cooked, fat not added in cooking" +75208310,"BITTERMELON, COOKED, FAT ADDED (INCL BALSAM PEAR)","Bitter melon, cooked, fat added in cooking" +75208500,"BREADFRUIT, COOKED,FAT NOT ADDED IN COOKING","Breadfruit, cooked, fat not added in cooking" +75208520,"BREADFRUIT, FRIED","Breadfruit, fried" +75208700,"BROCCOFLOWER,COOKED,NS AS TO FAT ADDED IN COOKING","Broccoflower, cooked, NS as to fat added in cooking" +75208710,"BROCCOFLOWER,COOKED,FAT NOT ADDED IN COOKING","Broccoflower, cooked, fat not added in cooking" +75208720,"BROCCOFLOWER,COOKED,FAT ADDED IN COOKING","Broccoflower, cooked, fat added in cooking" +75209000,"BRUSSELS SPROUTS, COOKED, NS FORM, NS ADDED FAT","Brussels sprouts, cooked, NS as to form, NS as to fat added in cooking" +75209001,"BRUSSELS SPROUTS, COOKED, FROM FRESH, NS ADDED FAT","Brussels sprouts, cooked, from fresh, NS as to fat added in cooking" +75209002,"BRUSSELS SPROUTS, COOKED, FROM FROZ, NS ADDED FAT","Brussels sprouts, cooked, from frozen, NS as to fat added in cooking" +75209010,"BRUSSELS SPROUTS, COOKED, NS FORM, FAT NOT ADDED","Brussels sprouts, cooked, NS as to form, fat not added in cooking" +75209011,"BRUSSELS SPROUTS, COOKED, FROM FRESH, FAT NOT ADDED","Brussels sprouts, cooked, from fresh, fat not added in cooking" +75209012,"BRUSSELS SPROUTS, COOKED, FROM FROZ, FAT NOT ADDED","Brussels sprouts, cooked, from frozen, fat not added in cooking" +75209020,"BRUSSELS SPROUTS, COOKED, NS AS TO FORM, FAT ADDED","Brussels sprouts, cooked, NS as to form, fat added in cooking" +75209021,"BRUSSELS SPROUTS, COOKED, FROM FRESH, FAT ADDED","Brussels sprouts, cooked, from fresh, fat added in cooking" +75209022,"BRUSSELS SPROUTS, COOKED, FROM FROZ, FAT ADDED","Brussels sprouts, cooked, from frozen, fat added in cooking" +75209500,"BURDOCK, COOKED, FAT NOT ADDED IN COOKING","Burdock, cooked, fat not added in cooking" +75210000,"CABBAGE, CHINESE, COOKED, NS AS TO ADDED FAT","Cabbage, Chinese, cooked, NS as to fat added in cooking" +75210010,"CABBAGE, CHINESE, COOKED, FAT NOT ADDED","Cabbage, Chinese, cooked, fat not added in cooking" +75210020,"CABBAGE, CHINESE, COOKED, FAT ADDED","Cabbage, Chinese, cooked, fat added in cooking" +75211010,"CABBAGE, GREEN, COOKED, NS FAT","Cabbage, green, cooked, NS as to fat added in cooking" +75211020,"CABBAGE, GREEN, COOKED, FAT NOT ADDED","Cabbage, green, cooked, fat not added in cooking" +75211030,"CABBAGE, GREEN, COOKED, FAT ADDED","Cabbage, green, cooked, fat added in cooking" +75212000,"CABBAGE, RED, COOKED, NS AS TO ADDED FAT","Cabbage, red, cooked, NS as to fat added in cooking" +75212010,"CABBAGE, RED, COOKED, FAT NOT ADDED","Cabbage, red, cooked, fat not added in cooking" +75212020,"CABBAGE, RED, COOKED, FAT ADDED","Cabbage, red, cooked, fat added in cooking" +75213000,"CABBAGE, SAVOY, COOKED, NS AS TO ADDED FAT","Cabbage, savoy, cooked, NS as to fat added in cooking" +75213010,"CABBAGE, SAVOY, COOKED, FAT NOT ADDED","Cabbage, savoy, cooked, fat not added in cooking" +75213020,"CABBAGE, SAVOY, COOKED, FAT ADDED","Cabbage, savoy, cooked, fat added in cooking" +75213100,"CACTUS, COOKED, NS AS TO ADDED FAT","Cactus, cooked, NS as to fat added in cooking" +75213110,"CACTUS, COOKED, FAT NOT ADDED","Cactus, cooked, fat not added in cooking" +75213120,"CACTUS, COOKED, FAT ADDED","Cactus, cooked, fat added in cooking" +75214000,"CAULIFLOWER, COOKED, NS FORM, NS FAT ADDED","Cauliflower, cooked, NS as to form, NS as to fat added in cooking" +75214001,"CAULIFLOWER, COOKED, FROM FRESH, NS FAT ADDED","Cauliflower, cooked, from fresh, NS as to fat added in cooking" +75214002,"CAULIFLOWER, COOKED, FROM FROZEN, NS FAT ADDED","Cauliflower, cooked, from frozen, NS as to fat added in cooking" +75214003,"CAULIFLOWER, COOKED, FROM CANNED, NS FAT ADDED","Cauliflower, cooked, from canned, NS as to fat added in cooking" +75214010,"CAULIFLOWER, COOKED, NS FORM, FAT NOT ADDED","Cauliflower, cooked, NS as to form, fat not added in cooking" +75214011,"CAULIFLOWER, COOKED, FROM FRESH, FAT NOT ADDED","Cauliflower, cooked, from fresh, fat not added in cooking" +75214012,"CAULIFLOWER, COOKED, FROM FROZEN, FAT NOT ADDED","Cauliflower, cooked, from frozen, fat not added in cooking" +75214013,"CAULIFLOWER, COOKED, FROM CANNED, FAT NOT ADDED","Cauliflower, cooked, from canned, fat not added in cooking" +75214020,"CAULIFLOWER, COOKED, NS AS TO FORM, FAT ADDED","Cauliflower, cooked, NS as to form, fat added in cooking" +75214021,"CAULIFLOWER, COOKED, FROM FRESH, FAT ADDED","Cauliflower, cooked, from fresh, fat added in cooking" +75214022,"CAULIFLOWER, COOKED, FROM FROZEN, FAT ADDED","Cauliflower, cooked, from frozen, fat added in cooking" +75214023,"CAULIFLOWER, COOKED, FROM CANNED, FAT ADDED","Cauliflower, cooked, from canned, fat added in cooking" +75215000,"CELERY, COOKED, NS AS TO ADDED FAT","Celery, cooked, NS as to fat added in cooking" +75215010,"CELERY, COOKED, FAT NOT ADDED","Celery, cooked, fat not added in cooking" +75215020,"CELERY, COOKED, FAT ADDED","Celery, cooked, fat added in cooking" +75215100,"FENNEL BULB, COOKED, NS AS TO FAT ADDED","Fennel bulb, cooked, NS as to fat added in cooking" +75215110,"FENNEL BULB, COOKED, FAT NOT ADDED IN COOKING","Fennel bulb, cooked, fat not added in cooking" +75215120,"FENNEL BULB, COOKED, FAT ADDED IN COOKING","Fennel bulb, cooked, fat added in cooking" +75215510,"CHRISTOPHINE, COOKED, FAT NOT ADDED IN COOKING","Christophine, cooked, fat not added in cooking" +75216000,"CORN, COOKED, NS FORM, NS COLOR. NS FAT ADDED","Corn, cooked, NS as to form, NS as to color, NS as to fat added in cooking" +75216001,"CORN, COOKED, FROM FRESH, NS COLOR. NS FAT ADDED","Corn, cooked, from fresh, NS as to color, NS as to fat added in cooking" +75216002,"CORN, COOKED, FROM FROZEN, NS COLOR. NS FAT ADDED","Corn, cooked, from frozen, NS as to color, NS as to fat added in cooking" +75216003,"CORN, COOKED, FROM CANNED, NS COLOR, NS FAT ADDED","Corn, cooked, from canned, NS as to color, NS as to fat added in cooking" +75216010,"CORN, COOKED, NS FORM, NS COLOR, FAT NOT ADDED","Corn, cooked, NS as to form, NS as to color, fat not added in cooking" +75216011,"CORN, COOKED, FROM FRESH, NS COLOR, FAT NOT ADDED","Corn, cooked, from fresh, NS as to color, fat not added in cooking" +75216012,"CORN, COOKED, FROM FROZEN, NS COLOR, FAT NOT ADDED","Corn, cooked, from frozen, NS as to color, fat not added in cooking" +75216013,"CORN, COOKED, FROM CANNED, NS COLOR, FAT NOT ADDED","Corn, cooked, from canned, NS as to color, fat not added in cooking" +75216020,"CORN, COOKED, NS FORM, NS COLOR, FAT ADDED","Corn, cooked, NS as to form, NS as to color, fat added in cooking" +75216021,"CORN, COOKED, FROM FRESH, NS COLOR, FAT ADDED","Corn, cooked, from fresh, NS as to color, fat added in cooking" +75216022,"CORN, COOKED, FROM FROZEN, NS COLOR, FAT ADDED","Corn, cooked, from frozen, NS as to color, fat added in cooking" +75216023,"CORN, COOKED, FROM CANNED, NS COLOR, FAT ADDED","Corn, cooked, from canned, NS as to color, fat added in cooking" +75216050,"CORN, NS AS TO FORM, NS AS TO COLOR, CREAM STYLE","Corn, NS as to form, NS as to color, cream style" +75216053,"CORN, FROM CANNED, NS AS TO COLOR, CREAM STYLE","Corn, from canned, NS as to color, cream style" +75216070,"CORN, DRIED, COOKED","Corn, dried, cooked" +75216100,"CORN, YELLOW, COOKED, NS FORM, NS FAT ADDED","Corn, yellow, cooked, NS as to form, NS as to fat added in cooking" +75216101,"CORN, YELLOW, COOKED, FROM FRESH, NS FAT ADDED","Corn, yellow, cooked, from fresh, NS as to fat added in cooking" +75216102,"CORN, YELLOW, COOKED, FROM FROZEN, NS FAT ADDED","Corn, yellow, cooked, from frozen, NS as to fat added in cooking" +75216103,"CORN, YELLOW, COOKED, FROM CANNED, NS FAT ADDED","Corn, yellow, cooked, from canned, NS as to fat added in cooking" +75216110,"CORN, YELLOW, COOKED, NS FORM, FAT NOT ADDED","Corn, yellow, cooked, NS as to form, fat not added in cooking" +75216111,"CORN, YELLOW, COOKED, FROM FRESH, FAT NOT ADDED","Corn, yellow, cooked, from fresh, fat not added in cooking" +75216112,"CORN, YELLOW, COOKED, FROM FROZEN, FAT NOT ADDED","Corn, yellow, cooked, from frozen, fat not added in cooking" +75216113,"CORN, YELLOW, COOKED, FROM CANNED, FAT NOT ADDED","Corn, yellow, cooked, from canned, fat not added in cooking" +75216120,"CORN, YELLOW, COOKED, NS FORM, FAT ADDED","Corn, yellow, cooked, NS as to form, fat added in cooking" +75216121,"CORN, YELLOW, COOKED, FROM FRESH, FAT ADDED","Corn, yellow, cooked, from fresh, fat added in cooking" +75216122,"CORN, YELLOW, COOKED, FROM FROZEN, FAT ADDED","Corn, yellow, cooked, from frozen, fat added in cooking" +75216123,"CORN, YELLOW, COOKED, FROM CANNED, FAT ADDED","Corn, yellow, cooked, from canned, fat added in cooking" +75216150,"CORN, YELLOW, NS AS TO FORM, CREAM STYLE","Corn, yellow, NS as to form, cream style" +75216153,"CORN, YELLOW, FROM CANNED, CREAM STYLE","Corn, yellow, from canned, cream style" +75216160,"CORN, YELLOW & WHITE, COOKED, NS FORM, NS FAT ADDED","Corn, yellow and white, cooked, NS as to form, NS as to fat added in cooking" +75216161,"CORN, YELLOW & WHITE, COOKED, FROM FRESH, NS FAT ADDED","Corn, yellow and white, cooked, from fresh, NS as to fat added in cooking" +75216162,"CORN, YELLOW & WHITE, COOKED, FROM FROZ, NS FAT ADDED","Corn, yellow and white, cooked, from frozen, NS as to fat added in cooking" +75216163,"CORN, YELLOW & WHITE, COOKED, FROM CAN, NS FAT ADDED","Corn, yellow and white, cooked, from canned, NS as to fat added in cooking" +75216170,"CORN, YELLOW & WHITE, COOKED, NS FORM, NO FAT ADDED","Corn, yellow and white, cooked, NS as to form, fat not added in cooking" +75216171,"CORN, YELLOW & WHITE, COOKED, FROM FRESH, NO FAT ADDED","Corn, yellow and white, cooked, from fresh, fat not added in cooking" +75216172,"CORN, YELLOW & WHITE, COOKED, FROM FROZ, NO FAT ADDED","Corn, yellow and white, cooked, from frozen, fat not added in cooking" +75216173,"CORN, YELLOW & WHITE, COOKED, FROM CAN, NO FAT ADDED","Corn, yellow and white, cooked, from canned, fat not added in cooking" +75216180,"CORN, YELLOW & WHITE, COOKED, NS FORM, FAT ADDED","Corn, yellow and white, cooked, NS as to form, fat added in cooking" +75216181,"CORN, YELLOW & WHITE, COOKED, FROM FRESH, FAT ADDED","Corn, yellow and white, cooked, from fresh, fat added in cooking" +75216182,"CORN, YELLOW & WHITE, COOKED, FROM FROZ, FAT ADDED","Corn, yellow and white, cooked, from frozen, fat added in cooking" +75216183,"CORN, YELLOW & WHITE, COOKED, FROM CAN, FAT ADDED","Corn, yellow and white, cooked, from canned, fat added in cooking" +75216190,"CORN, YELLOW, NS AS TO FORM, CREAM STYLE, FAT ADDED","Corn, yellow, NS as to form, cream style, fat added in cooking" +75216193,"CORN, YELLOW, FROM CANNED, CREAM STYLE, FAT ADDED","Corn, yellow, from canned, cream style, fat added in cooking" +75216200,"CORN, WHITE, COOKED, NS FORM, NS FAT ADDED","Corn, white, cooked, NS as to form, NS as to fat added in cooking" +75216201,"CORN, WHITE, COOKED, FROM FRESH, NS FAT ADDED","Corn, white, cooked, from fresh, NS as to fat added in cooking" +75216202,"CORN, WHITE, COOKED, FROM FROZEN, NS FAT ADDED","Corn, white, cooked, from frozen, NS as to fat added in cooking" +75216203,"CORN, WHITE, COOKED, FROM CANNED, NS FAT ADDED","Corn, white, cooked, from canned, NS as to fat added in cooking" +75216210,"CORN, WHITE, COOKED, NS AS TO FORM, FAT NOT ADDED","Corn, white, cooked, NS as to form, fat not added in cooking" +75216211,"CORN, WHITE, COOKED, FROM FRESH, FAT NOT ADDED","Corn, white, cooked, from fresh, fat not added in cooking" +75216212,"CORN, WHITE, COOKED, FROM FROZEN, FAT NOT ADDED","Corn, white, cooked, from frozen, fat not added in cooking" +75216213,"CORN, WHITE, COOKED, FROM CANNED, FAT NOT ADDED","Corn, white, cooked, from canned, fat not added in cooking" +75216220,"CORN, WHITE, COOKED, NS AS TO FORM, FAT ADDED","Corn, white, cooked, NS as to form, fat added in cooking" +75216221,"CORN, WHITE, COOKED, FROM FRESH, FAT ADDED","Corn, white, cooked, from fresh, fat added in cooking" +75216222,"CORN, WHITE, COOKED, FROM FROZEN, FAT ADDED","Corn, white, cooked, from frozen, fat added in cooking" +75216223,"CORN, WHITE, COOKED, FROM CANNED, FAT ADDED","Corn, white, cooked, from canned, fat added in cooking" +75216250,"CORN, WHITE, NS AS TO FORM, CREAM STYLE","Corn, white, NS as to form, cream style" +75216253,"CORN, WHITE, FROM CANNED, CREAM STYLE","Corn, white, from canned, cream style" +75216300,"CORN, YELLOW, CANNED, LO NA, NS AS TO ADDED FAT","Corn, yellow, canned, low sodium, NS as to fat added in cooking" +75216310,"CORN, YELLOW, CANNED, LOW SODIUM, FAT NOT ADDED","Corn, yellow, canned, low sodium, fat not added in cooking" +75216320,"CORN, YELLOW, CANNED, LOW SODIUM, FAT ADDED","Corn, yellow, canned, low sodium, fat added in cooking" +75216700,"CUCUMBER, COOKED, NS AS TO ADDED FAT","Cucumber, cooked, NS as to fat added in cooking" +75216710,"CUCUMBER, COOKED, FAT NOT ADDED","Cucumber, cooked, fat not added in cooking" +75216720,"CUCUMBER, COOKED, FAT ADDED","Cucumber, cooked, fat added in cooking" +75217000,"EGGPLANT, COOKED, NS AS TO ADDED FAT","Eggplant, cooked, NS as to fat added in cooking" +75217010,"EGGPLANT, COOKED, FAT NOT ADDED","Eggplant, cooked, fat not added in cooking" +75217020,"EGGPLANT, COOKED, FAT ADDED","Eggplant, cooked, fat added in cooking" +75217300,"FLOWERS / BLOSSOMS OF SESBANIA/LILY/SQUASH, NO FAT","Flowers or blossoms of sesbania, squash, or lily, fat not added in cooking" +75217400,"GARLIC, COOKED","Garlic, cooked" +75217490,"HOMINY, COOKED, NS AS TO ADDED FAT","Hominy, cooked, NS as to fat added in cooking" +75217500,"HOMINY, COOKED, NO FAT ADDED","Hominy, cooked, fat not added in cooking" +75217520,"HOMINY, COOKED, FAT ADDED","Hominy, cooked, fat added in cooking" +75218010,"KOHLRABI, COOKED,FAT NOT ADDED IN COOKING","Kohlrabi, cooked, fat not added in cooking" +75218400,"LEEK, COOKED, NS AS TO FAT ADDED IN COOKING","Leek, cooked, NS as to fat added in cooking" +75218500,"LOTUS ROOT, COOKED, FAT NOT ADDED IN COOKING","Lotus root, cooked, fat not added in cooking" +75219000,"MUSHROOMS, COOKED, NS FORM, NS AS TO ADDED FAT","Mushrooms, cooked, NS as to form, NS as to fat added in cooking" +75219001,"MUSHROOMS, COOKED, FROM FRESH, NS FAT ADDED","Mushrooms, cooked, from fresh, NS as to fat added in cooking" +75219002,"MUSHROOMS, COOKED, FROM FROZ, NS FAT ADDED","Mushrooms, cooked, from frozen, NS as to fat added in cooking" +75219003,"MUSHROOMS, COOKED, FROM CANNED, NS FAT ADDED","Mushrooms, cooked, from canned, NS as to fat added in cooking" +75219010,"MUSHROOMS, COOKED, NS AS TO FORM, FAT NOT ADDED","Mushrooms, cooked, NS as to form, fat not added in cooking" +75219011,"MUSHROOMS, COOKED, FROM FRESH, FAT NOT ADDED","Mushrooms, cooked, from fresh, fat not added in cooking" +75219012,"MUSHROOMS, COOKED, FROM FROZ, FAT NOT ADDED","Mushrooms, cooked, from frozen, fat not added in cooking" +75219013,"MUSHROOMS, COOKED, FROM CANNED, FAT NOT ADDED","Mushrooms, cooked, from canned, fat not added in cooking" +75219020,"MUSHROOMS, COOKED, NS AS TO FORM, FAT ADDED","Mushrooms, cooked, NS as to form, fat added in cooking" +75219021,"MUSHROOMS, COOKED, FROM FRESH, FAT ADDED","Mushrooms, cooked, from fresh, fat added in cooking" +75219022,"MUSHROOMS, COOKED, FROM FROZ, FAT ADDED","Mushrooms, cooked, from frozen, fat added in cooking" +75219023,"MUSHROOMS, COOKED, FROM CANNED, FAT ADDED","Mushrooms, cooked, from canned, fat added in cooking" +75219100,"MUSHROOM, ASIAN, COOKED, FROM DRIED","Mushroom, Asian, cooked, from dried" +75220000,"OKRA, COOKED, NS FORM, NS FAT ADDED","Okra, cooked, NS as to form, NS as to fat added in cooking" +75220001,"OKRA, COOKED, FROM FRESH, NS FAT ADDED","Okra, cooked, from fresh, NS as to fat added in cooking" +75220002,"OKRA, COOKED, FROM FROZ, NS FAT ADDED","Okra, cooked, from frozen, NS as to fat added in cooking" +75220003,"OKRA, COOKED, FROM CANNED, NS FAT ADDED","Okra, cooked, from canned, NS as to fat added in cooking" +75220010,"OKRA, COOKED, NS FORM, FAT NOT ADDED","Okra, cooked, NS as to form, fat not added in cooking" +75220011,"OKRA, COOKED, FROM FRESH, FAT NOT ADDED","Okra, cooked, from fresh, fat not added in cooking" +75220012,"OKRA, COOKED, FROM FROZ, FAT NOT ADDED","Okra, cooked, from frozen, fat not added in cooking" +75220013,"OKRA, COOKED, FROM CANNED, FAT NOT ADDED","Okra, cooked, from canned, fat not added in cooking" +75220020,"OKRA, COOKED, NS FORM, FAT ADDED","Okra, cooked, NS as to form, fat added in cooking" +75220021,"OKRA, COOKED, FROM FRESH, FAT ADDED","Okra, cooked, from fresh, fat added in cooking" +75220022,"OKRA, COOKED, FROM FROZ, FAT ADDED","Okra, cooked, from frozen, fat added in cooking" +75220023,"OKRA, COOKED, FROM CANNED, FAT ADDED","Okra, cooked, from canned, fat added in cooking" +75220050,"LETTUCE, COOKED, FAT NOT ADDED IN COOKING","Lettuce, cooked, fat not added in cooking" +75220100,"LUFFA (CHINESE OKRA), COOKED, NO FAT ADDED","Luffa (Chinese okra), cooked, fat not added in cooking" +75221000,"ONIONS, MATURE, COOKED, NS FORM, NS AS TO ADDED FAT","Onions, mature, cooked, NS as to form, NS as to fat added in cooking" +75221001,"ONIONS, MATURE, COOKED, FROM FRESH, NS FAT ADDED","Onions, mature, cooked, from fresh, NS as to fat added in cooking" +75221002,"ONIONS, MATURE, COOKED, FROM FROZ, NS FAT ADDED","Onions, mature, cooked, from frozen, NS as to fat added in cooking" +75221010,"ONIONS, MATURE, COOKED, NS FORM, FAT NOT ADDED","Onions, mature, cooked, NS as to form, fat not added in cooking" +75221011,"ONIONS, MATURE, COOKED, FROM FRESH, FAT NOT ADDED","Onions, mature, cooked, from fresh, fat not added in cooking" +75221012,"ONIONS, MATURE, COOKED, FROM FROZ, FAT NOT ADDED","Onions, mature, cooked, from frozen, fat not added in cooking" +75221020,"ONIONS, MATURE, COOKED, NS AS TO FORM, FAT ADDED","Onions, mature, cooked or sauteed, NS as to form, fat added in cooking" +75221021,"ONIONS, MATURE, COOKED, FROM FRESH, FAT ADDED","Onions, mature, cooked or sauteed, from fresh, fat added in cooking" +75221022,"ONIONS, MATURE, COOKED, FROM FROZ, FAT ADDED","Onions, mature, cooked or sauteed, from frozen, fat added in cooking" +75221030,"ONIONS, PEARL, COOKED, NS FORM (INCL PICKLED/COCKTAIL)","Onions, pearl, cooked, NS as to form" +75221031,"ONIONS, PEARL, COOKED, FROM FRESH (INCL PICKLED/COCKTAIL)","Onions, pearl, cooked, from fresh" +75221032,"ONIONS, PEARL, COOKED, FROM FROZ (INCL PICKLED/COCKTAIL)","Onions, pearl, cooked, from frozen" +75221033,"ONIONS, PEARL, COOKED, FROM CAN (INCL PICKLED/COCKTAIL)","Onions, pearl, cooked, from canned" +75221040,"ONIONS, YOUNG GREEN, COOKED, NS FORM, NS ADDED FAT","Onion, young green, cooked, NS as to form, NS as to fat added in cooking" +75221041,"ONIONS, YOUNG GREEN, COOKED, FROM FRESH, NS ADDED FAT","Onion, young green, cooked, from fresh, NS as to fat added in cooking" +75221050,"ONIONS, YOUNG GREEN, COOKED, NS FORM, NO FAT ADDED","Onions, young green, cooked, NS as to form, fat not added in cooking" +75221051,"ONIONS, YOUNG GREEN, COOKED, FROM FRESH, NO FAT ADDED","Onions, young green, cooked, from fresh, fat not added in cooking" +75221060,"ONIONS, YOUNG GREEN, COOKED, NS FORM, FAT ADDED","Onion, young green, cooked, NS as to form, fat added in cooking" +75221061,"ONIONS, YOUNG GREEN, COOKED, FROM FRESH, FAT ADDED","Onion, young green, cooked, from fresh, fat added in cooking" +75221100,"ONIONS, DEHYDRATED","Onion, dehydrated" +75221160,"PALM HEARTS, COOKED (ASSUME NO FAT ADDED)","Palm hearts, cooked (assume fat not added in cooking)" +75221210,"PARSLEY, COOKED (ASSUME NO FAT ADDED)","Parsley, cooked (assume fat not added in cooking)" +75222000,"PARSNIPS, COOKED, NS AS TO ADDED FAT","Parsnips, cooked, NS as to fat added in cooking" +75222010,"PARSNIPS, COOKED, FAT NOT ADDED","Parsnips, cooked, fat not added in cooking" +75222020,"PARSNIPS, COOKED, FAT ADDED","Parsnips, cooked, fat added in cooking" +75223000,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,NS FORM,NS FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, NS as to form, NS as to fat added in cooking" +75223001,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD, FROM FRESH,NS FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from fresh, NS as to fat added in cooking" +75223002,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD, FROM FROZ,NS FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from frozen, NS as to fat added in cooking" +75223003,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD, FROM CAN,NS FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from canned, NS as to fat added in cooking" +75223010,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,NS FORM,NO FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, NS as to form, fat not added in cooking" +75223011,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,FROM FRESH,NO FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from fresh, fat not added in cooking" +75223012,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,FROM FROZ,NO FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from frozen, fat not added in cooking" +75223013,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,FROM CAN,NO FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from canned, fat not added in cooking" +75223020,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,NS FORM,W/ FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, NS as to form, fat added in cooking" +75223021,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,FROM FRESH,W/ FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from fresh, fat added in cooking" +75223022,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,FROM FROZ,W/ FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from frozen, fat added in cooking" +75223023,"PEAS,COW/FIELD/BLACKEYE,NOT DRY,CKD,FROM CAN,W/ FAT","Peas, cowpeas, field peas, or blackeye peas (not dried), cooked, from canned, fat added in cooking" +75224010,"PEAS, GREEN, COOKED, NS FORM, NS AS TO ADDED FAT","Peas, green, cooked, NS as to form, NS as to fat added in cooking" +75224011,"PEAS, GREEN, COOKED, FROM FRESH, NS FAT ADDED","Peas, green, cooked, from fresh, NS as to fat added in cooking" +75224012,"PEAS, GREEN, COOKED, FROM FROZ, NS FAT ADDED","Peas, green, cooked, from frozen, NS as to fat added in cooking" +75224013,"PEAS, GREEN, COOKED, FROM CANNED, NS FAT ADDED","Peas, green, cooked, from canned, NS as to fat added in cooking" +75224020,"PEAS, GREEN, COOKED, NS AS TO FORM, FAT NOT ADDED","Peas, green, cooked, NS as to form, fat not added in cooking" +75224021,"PEAS, GREEN, COOKED, FROM FRESH, FAT NOT ADDED","Peas, green, cooked, from fresh, fat not added in cooking" +75224022,"PEAS, GREEN, COOKED, FROM FROZ, FAT NOT ADDED","Peas, green, cooked, from frozen, fat not added in cooking" +75224023,"PEAS, GREEN, COOKED, FROM CANNED, FAT NOT ADDED","Peas, green, cooked, from canned, fat not added in cooking" +75224030,"PEAS, GREEN, COOKED, NS AS TO FORM, FAT ADDED","Peas, green, cooked, NS as to form, fat added in cooking" +75224031,"PEAS, GREEN, COOKED, FROM FRESH, FAT ADDED","Peas, green, cooked, from fresh, fat added in cooking" +75224032,"PEAS, GREEN, COOKED, FROM FROZ, FAT ADDED","Peas, green, cooked, from frozen, fat added in cooking" +75224033,"PEAS, GREEN, COOKED, FROM CANNED, FAT ADDED","Peas, green, cooked, from canned, fat added in cooking" +75224110,"PEAS, GREEN, CANNED, LOW SODIUM, NS AS TO ADDED FAT","Peas, green, canned, low sodium, NS as to fat added in cooking" +75224120,"PEAS, GREEN, CANNED, LOW SODIUM, FAT NOT ADDED","Peas, green, canned, low sodium, fat not added in cooking" +75224130,"PEAS, GREEN, CANNED, LOW SODIUM, FAT ADDED","Peas, green, canned, low sodium, fat added in cooking" +75225010,"PIGEON PEAS, COOKED, NS AS TO FORM, FAT NOT ADDED","Pigeon peas, cooked, NS as to form, fat not added in cooking" +75225011,"PIGEON PEAS, COOKED, FROM FRESH, FAT NOT ADDED","Pigeon peas, cooked, from fresh, fat not added in cooking" +75225013,"PIGEON PEAS, COOKED, FROM CANNED, FAT NOT ADDED","Pigeon peas, cooked, from canned, fat not added in cooking" +75226000,"PEPPERS, GREEN, COOKED, NS AS TO FAT","Peppers, green, cooked, NS as to fat added in cooking" +75226010,"PEPPERS, GREEN, COOKED, FAT NOT ADDED","Peppers, green, cooked, fat not added in cooking" +75226020,"PEPPERS, GREEN, COOKED, FAT ADDED","Peppers, green, cooked, fat added in cooking" +75226040,"PEPPERS, RED, COOKED, NS AS TO ADDED FAT","Peppers, red, cooked, NS as to fat added in cooking" +75226050,"PEPPERS, RED, COOKED, FAT NOT ADDED","Peppers, red, cooked, fat not added in cooking" +75226060,"PEPPERS, RED, COOKED, FAT ADDED","Peppers, red, cooked, fat added in cooking" +75226090,"PEPPERS, HOT, COOKED, NS FORM, NS FAT ADDED","Peppers, hot, cooked, NS as to form, NS as to fat added in cooking" +75226091,"PEPPERS, HOT, COOKED, FROM FRESH, NS FAT ADDED","Peppers, hot, cooked, from fresh, NS as to fat added in cooking" +75226092,"PEPPERS, HOT, COOKED, FROM FROZ, NS FAT ADDED","Peppers, hot, cooked, from frozen, NS as to fat added in cooking" +75226093,"PEPPERS, HOT, COOKED, FROM CANNED, NS FAT ADDED","Peppers, hot, cooked, from canned, NS as to fat added in cooking" +75226100,"PEPPERS, HOT, COOKED, NS FORM, NO FAT ADDED","Peppers, hot, cooked, NS as to form, fat not added in cooking" +75226101,"PEPPERS, HOT, COOKED, FROM FRESH, NO FAT ADDED","Peppers, hot, cooked, from fresh, fat not added in cooking" +75226102,"PEPPERS, HOT, COOKED, FROM FROZ, NO FAT ADDED","Peppers, hot, cooked, from frozen, fat not added in cooking" +75226103,"PEPPERS, HOT, COOKED, FROM CANNED, NO FAT ADDED","Peppers, hot, cooked, from canned, fat not added in cooking" +75226110,"PEPPERS, HOT, COOKED, NS FORM, FAT ADDED","Peppers, hot, cooked, NS as to form, fat added in cooking" +75226111,"PEPPERS, HOT, COOKED, FROM FRESH, FAT ADDED","Peppers, hot, cooked, from fresh, fat added in cooking" +75226112,"PEPPERS, HOT, COOKED, FROM FROZ, FAT ADDED","Peppers, hot, cooked, from frozen, fat added in cooking" +75226113,"PEPPERS, HOT, COOKED, FROM CANNED, FAT ADDED","Peppers, hot, cooked, from canned, fat added in cooking" +75226700,"PIMIENTO","Pimiento" +75227100,"RADISH, JAPANESE (DAIKON), COOKED, NO FAT ADDED","Radish, Japanese (daikon), cooked, fat not added in cooking" +75227110,"RADISH, JAPANESE (DAIKON), COOKED, FAT ADDED","Radish, Japanese (daikon), cooked, fat added in cooking" +75228000,"RUTABAGA, COOKED, NS AS TO ADDED FAT","Rutabaga, cooked, NS as to fat added in cooking" +75228010,"RUTABAGA, COOKED, FAT NOT ADDED","Rutabaga, cooked, fat not added in cooking" +75228020,"RUTABAGA, COOKED, FAT ADDED","Rutabaga, cooked, fat added in cooking" +75229010,"SALSIFY (VEGETABLE OYSTER), COOKED, NO FAT ADDED","Salsify (vegetable oyster), cooked, fat not added in cooking" +75230000,"SAUERKRAUT, NS AS TO ADDED FAT","Sauerkraut, cooked, NS as to fat added in cooking" +75230010,"SAUERKRAUT, NO FAT ADDED","Sauerkraut, cooked, fat not added in cooking" +75230020,"SAUERKRAUT, FAT ADDED","Sauerkraut, cooked, fat added in cooking" +75230100,"SAUERKRAUT, CANNED, LO NA","Sauerkraut, canned, low sodium" +75231000,"SNOWPEA(PEA POD), COOKED, NS FORM, NS AS TO FAT","Snowpea (pea pod), cooked, NS as to form, NS as to fat added in cooking" +75231001,"SNOWPEA(PEA POD), COOKED, FROM FRESH, NS AS TO FAT","Snowpea (pea pod), cooked, from fresh, NS as to fat added in cooking" +75231002,"SNOWPEA(PEA POD), COOKED, FROM FROZEN, NS AS TO FAT","Snowpea (pea pod), cooked, from frozen, NS as to fat added in cooking" +75231010,"SNOWPEA(PEA POD), COOKED, NS FORM, NO FAT ADDED","Snowpea (pea pod), cooked, NS as to form, fat not added in cooking" +75231011,"SNOWPEA(PEA POD), COOKED, FROM FRESH, NO FAT ADDED","Snowpea (pea pod), cooked, from fresh, fat not added in cooking" +75231012,"SNOWPEA(PEA POD), COOKED, FROM FROZ, NO FAT ADDED","Snowpea (pea pod), cooked, from frozen, fat not added in cooking" +75231020,"SNOWPEA(PEA POD), COOKED, NS AS TO FORM, FAT ADDED","Snowpea (pea pod), cooked, NS as to form, fat added in cooking" +75231021,"SNOWPEA(PEA POD), COOKED, FROM FRESH, FAT ADDED","Snowpea (pea pod), cooked, from fresh, fat added in cooking" +75231022,"SNOWPEA(PEA POD), COOKED, FROM FROZ, FAT ADDED","Snowpea (pea pod), cooked, from frozen, fat added in cooking" +75232000,"SEAWEED, DRIED","Seaweed, dried" +75232050,"SEAWEED, PREPARED W/ SOY SAUCE","Seaweed, prepared with soy sauce" +75232100,"SEAWEED, COOKED, NS AS TO FAT ADDED IN COOKING","Seaweed, cooked, NS as to fat added in cooking" +75232110,"SEAWEED, COOKED, FAT NOT ADDED IN COOKING","Seaweed, cooked, fat not added in cooking" +75232120,"SEAWEED, COOKED, FAT ADDED IN COOKING","Seaweed, cooked, fat added in cooking" +75233000,"SQUASH, SUMMER, COOKED, NS FORM, NS AS TO ADDED FAT","Squash, summer, cooked, NS as to form, NS as to fat added in cooking" +75233001,"SQUASH, SUMMER, COOKED, FROM FRESH, NS FAT ADDED","Squash, summer, cooked, from fresh, NS as to fat added in cooking" +75233002,"SQUASH, SUMMER, COOKED, FROM FROZ, NS FAT ADDED","Squash, summer, cooked, from frozen, NS as to fat added in cooking" +75233003,"SQUASH, SUMMER, COOKED, FROM CANNED, NS FAT ADDED","Squash, summer, cooked, from canned, NS as to fat added in cooking" +75233010,"SQUASH, SUMMER, COOKED, NS FORM, FAT NOT ADDED","Squash, summer, cooked, NS as to form, fat not added in cooking" +75233011,"SQUASH, SUMMER,YELLOW OR GREEN, CKD, FRESH, FAT NOT ADDED","Squash, summer,yellow or green, cooked, from fresh, fat not added in cooking" +75233012,"SQUASH, SUMMER, COOKED, FROM FROZ, FAT NOT ADDED","Squash, summer, cooked, from frozen, fat not added in cooking" +75233013,"SQUASH, SUMMER, COOKED, FROM CANNED, FAT NOT ADDED","Squash, summer, cooked, from canned, fat not added in cooking" +75233020,"SQUASH, SUMMER, COOKED, NS AS TO FORM, FAT ADDED","Squash, summer, cooked, NS as to form, fat added in cooking" +75233021,"SQUASH, SUMMER, COOKED, FROM FRESH, FAT ADDED","Squash, summer, cooked, from fresh, fat added in cooking" +75233022,"SQUASH, SUMMER, COOKED, FROM FROZ, FAT ADDED","Squash, summer, cooked, from frozen, fat added in cooking" +75233023,"SQUASH, SUMMER, COOKED, FROM CANNED, FAT ADDED","Squash, summer, cooked, from canned, fat added in cooking" +75233200,"SQUASH, SPAGHETTI, NS AS TO ADDED FAT","Squash, spaghetti, cooked, NS as to fat added in cooking" +75233210,"SQUASH, SPAGHETTI, FAT ADDED","Squash, spaghetti, cooked, fat added in cooking" +75233220,"SQUASH, SPAGHETTI, NO FAT ADDED","Squash, spaghetti, cooked, fat not added in cooking" +75233510,"SEQUIN (PORTUGUESE SQUASH), COOKED, NO FAT ADDED","Sequin (Portuguese squash), cooked, fat not added in cooking" +75234000,"TURNIP, COOKED, NS AS TO FORM, NS AS TO ADDED FAT","Turnip, cooked, NS as to form, NS as to fat added in cooking" +75234001,"TURNIP, COOKED, FROM FRESH, NS AS TO ADDED FAT","Turnip, cooked, from fresh, NS as to fat added in cooking" +75234002,"TURNIP, COOKED, FROM FROZ, NS AS TO ADDED FAT","Turnip, cooked, from frozen, NS as to fat added in cooking" +75234003,"TURNIP, COOKED, FROM CAN, NS AS TO ADDED FAT","Turnip, cooked, from canned, NS as to fat added in cooking" +75234010,"TURNIP, COOKED, NS AS TO FORM, FAT NOT ADDED","Turnip, cooked, NS as to form, fat not added in cooking" +75234011,"TURNIP, COOKED, FROM FRESH, FAT NOT ADDED","Turnip, cooked, from fresh, fat not added in cooking" +75234012,"TURNIP, COOKED, FROM FROZ, FAT NOT ADDED","Turnip, cooked, from frozen, fat not added in cooking" +75234013,"TURNIP, COOKED, FROM CANNED, FAT NOT ADDED","Turnip, cooked, from canned, fat not added in cooking" +75234020,"TURNIP, COOKED, NS AS TO FORM, FAT ADDED","Turnip, cooked, NS as to form, fat added in cooking" +75234021,"TURNIP, COOKED, FROM FRESH, FAT ADDED","Turnip, cooked, from fresh, fat added in cooking" +75234022,"TURNIP, COOKED, FROM FROZ, FAT ADDED","Turnip, cooked, from frozen, fat added in cooking" +75234023,"TURNIP, COOKED, FROM CAN, FAT ADDED","Turnip, cooked, from canned, fat added in cooking" +75235000,"WATER CHESTNUT","Water chestnut" +75235750,"WINTER MELON, COOKED (INCL CHINESE MELON, TOGAN)","Winter melon, cooked" +75236000,"YEAST (INCLUDE BREWER'S YEAST)","Yeast" +75236500,"YEAST EXTRACT SPREAD (INCL VEGEMITE, MARMITE)","Yeast extract spread" +75301100,"BEANS, LIMA, & CORN (SUCCOTASH), NS AS TO ADDED FAT","Beans, lima and corn (succotash), cooked, NS as to fat added in cooking" +75301110,"BEANS, LIMA, & CORN (SUCCOTASH), NO FAT ADDED","Beans, lima and corn (succotash), cooked, fat not added in cooking" +75301120,"BEANS, LIMA, & CORN (SUCCOTASH), FAT ADDED","Beans, lima and corn (succotash), cooked, fat added in cooking" +75302010,"BEANS, STRING, GREEN, W/ TOMATOES, FAT NOT ADDED IN COOKING","Beans, string, green, with tomatoes, cooked, fat not added in cooking" +75302020,"BEANS, STRING, GREEN, W/ ONIONS, FAT NOT ADDED IN COOKING","Beans, string, green, with onions, cooked, fat not added in cooking" +75302030,"BEANS, STRING, GREEN, W/ CHICKPEAS, FAT NOT ADDED IN COOKING","Beans, string, green, with chickpeas, cooked, fat not added in cooking" +75302040,"BEANS, STRING, GREEN, W/ ALMONDS, FAT NOT ADDED IN COOKING","Beans, string, green, with almonds, cooked, fat not added in cooking" +75302045,"BEANS, STRING, GREEN, W/ ALMONDS, FAT ADDED IN COOKING","Beans, string, green, with almonds, cooked, fat added in cooking" +75302050,"BEANS, STRING, GREEN, & POTATOES, FAT NOT ADDED","Beans, string, green, and potatoes, cooked, fat not added in cooking" +75302060,"BEANS, STRING, GREEN, W/ PINTO BEANS, FAT NOT ADDED","Beans, string, green, with pinto beans, cooked, fat not added in cooking" +75302070,"BEANS, STRING, GREEN, W/ SPAETZEL, FAT NOT ADDED","Beans, string, green, with spaetzel, cooked, fat not added in cooking" +75302080,"BEAN SALAD, YELLOW &/OR GREEN STRING BEANS","Bean salad, yellow and/or green string beans" +75302200,"BEANS, STRING, GREEN, W/ ONIONS, NS AS TO FAT ADDED","Beans, string, green, with onions, NS as to fat added in cooking" +75302210,"BEANS, STRING, GREEN, W/ ONIONS, FAT ADDED IN COOKING","Beans, string, green, with onions, fat added in cooking" +75302500,"BEANS, STRING, GREEN, & POTATOES, NS AS TO FAT ADDED","Beans, string, green, and potatoes, cooked, NS as to fat added in cooking" +75302510,"BEANS, STRING, GREEN, & POTATOES, FAT ADDED","Beans, string, green, and potatoes, cooked, fat added in cooking" +75303000,"CORN W/ PEPPERS, RED OR GREEN,COOKED, NS FAT ADDED","Corn with peppers, red or green, cooked, NS as to fat added in cooking" +75303010,"CORN W/ PEPPERS, RED OR GREEN,COOKED, NO FAT ADDED","Corn with peppers, red or green, cooked, fat not added in cooking" +75303020,"CORN W/ PEPPERS, RED OR GREEN,COOKED,FAT ADDED","Corn with peppers, red or green, cooked, fat added in cooking" +75306010,"EGGPLANT IN TOM SCE, COOKED,NO FAT ADDED","Eggplant in tomato sauce, cooked, fat not added in cooking" +75307000,"GREEN PEPPERS & ONIONS, COOKED,FAT ADDED IN COOKING","Green peppers and onions, cooked, fat added in cooking" +75311000,"MIXED VEGS (CORN,LIMA,PEAS,GRBNS,CAR), NS FORM & FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, NS as to form, NS as to fat added in cooking" +75311002,"MIXED VEGETABLES (CORN,LIMA,PEAS,GRBNS,CAR), FROZ, NS FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, from frozen, NS as to fat added in cooking" +75311003,"MIXED VEGETABLES (CORN,LIMA,PEAS,GRBNS,CAR), CANNED, NS FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, from canned, NS as to fat added in cooking" +75311010,"MIXED VEGS (CORN,LIMA,PEAS,GRBN,CAR), NS FORM, NO FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, NS as to form, fat not added in cooking" +75311012,"MIXED VEGETABLES (CORN,LIMA,PEAS,GRBNS,CAR), FROZ, NO FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, from frozen, fat not added in cooking" +75311013,"MIXED VEGETABLES (CORN,LIMA,PEAS,GRBNS,CAR), CANNED, NO FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, from canned, fat not added in cooking" +75311020,"MIXED VEGS (CORN,LIMA,PEAS,GRBNS,CAR), NS FORM,W/FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, NS as to form, fat added in cooking" +75311022,"MIXED VEGETABLES (CORN,LIMA,PEAS,GRBNS,CAR), FROZ, W/ FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, from frozen, fat added in cooking" +75311023,"MIXED VEGETABLES (CORN,LIMA,PEAS,GRBNS,CAR), CANNED, W/ FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), cooked, from canned, fat added in cooking" +75311100,"MIXED VEGETABLES, CANNED, LOW SODIUM, NS ADDED FAT","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), canned, low sodium, NS as to fat added in cooking" +75311110,"MIXED VEGETABLES, CANNED, LOW SODIUM, NO FAT ADDED","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), canned, low sodium, fat not added in cooking" +75311120,"MIXED VEGETABLES, CANNED, LOW SODIUM, FAT ADDED","Mixed vegetables (corn, lima beans, peas, green beans, and carrots), canned, low sodium, fat added in cooking" +75315000,"PEAS & CORN, COOKED, NS AS TO ADDED FAT","Peas and corn, cooked, NS as to fat added in cooking" +75315010,"PEAS & CORN, COOKED, NO FAT ADDED","Peas and corn, cooked, fat not added in cooking" +75315020,"PEAS & CORN, COOKED, FAT ADDED","Peas and corn, cooked, fat added in cooking" +75315100,"PEAS & ONIONS, COOKED, NS AS TO ADDED FAT","Peas and onions, cooked, NS as to fat added in cooking" +75315110,"PEAS & ONIONS, COOKED, FAT NOT ADDED","Peas and onions, cooked, fat not added in cooking" +75315120,"PEAS & ONIONS, COOKED, FAT ADDED","Peas and onions, cooked, fat added in cooking" +75315200,"PEAS W/ MUSHROOMS, COOKED, NS AS TO FAT","Peas with mushrooms, cooked, NS as to fat added in cooking" +75315210,"PEAS W/ MUSHROOMS, COOKED, NO FAT ADDED","Peas with mushrooms, cooked, fat not added in cooking" +75315215,"PEAS W/ MUSHROOMS, COOKED, FAT ADDED","Peas with mushrooms, cooked, fat added in cooking" +75315250,"COWPEAS W/ SNAP BEANS,COOKED, NO FAT ADDED IN COOK","Cowpeas with snap beans, cooked, fat not added in cooking" +75315300,"PEAS & POTATOES, COOKED, NO FAT ADDED IN COOKING","Peas and potatoes, cooked, fat not added in cooking" +75315305,"PEAS AND POTATOES, COOKED, NS AS TO FAT","Peas and potatoes, cooked, NS as to fat added in cooking" +75315310,"PEAS AND POTATOES, COOKED, FAT ADDED","Peas and potatoes, cooked, fat added in cooking" +75316000,"SQUASH, SUMMER, & ONIONS,COOKED, NO FAT ADDED","Squash, summer, and onions, cooked, fat not added in cooking" +75316010,"ZUCCHINI W/ TOM SCE, CKD,NO FAT ADDED IN COOKING","Zucchini with tomato sauce, cooked, fat not added in cooking" +75316020,"SQUASH, SUMMER, & ONIONS, COOKED, FAT ADDED","Squash, summer, and onions, cooked, fat added in cooking" +75316050,"RATATOUILLE","Ratatouille" +75317000,"VEGETABLES,STEWTYPE(POT,CRT,ONION,CELERY)COOK,NS FA","Vegetables, stew type (including potatoes, carrots, onions, celery) cooked, NS as to fat added in cooking" +75317010,"VEGETABLES,STEWTYPE(POT,CRT,ONION,CELERY)COOK,W/FAT","Vegetables, stew type (including potatoes, carrots, onions, celery) cooked, fat added in cooking" +75317020,"VEGETABLES,STEWTYPE(POT,CRT,ONION,CELERY)COOK,NO FA","Vegetables, stew type (including potatoes, carrots, onions, celery) cooked, fat not added in cooking" +75330100,"VEG COMBINATION (INCL CAR/ DK GRN), NO SAUCE, NS FAT","Vegetable combination (including carrots, broccoli, and/or dark-green leafy), cooked, no sauce, NS as to fat added in cooking" +75330110,"VEG COMBINATION (INCL CAR/ DK GRN), NO SAUCE, FAT NOT ADDED","Vegetable combination (including carrots, broccoli, and/or dark-green leafy), cooked, no sauce, fat not added in cooking" +75330120,"VEG COMBINATION (INCL CAR/ DK GRN), NO SAUCE, FAT ADDED","Vegetable combination (including carrots, broccoli, and/or dark-green leafy), cooked, no sauce, fat added in cooking" +75330130,"VEG COMBINATION (NO CAR/ DK GRN), NO SAUCE, NS FAT","Vegetable combination (excluding carrots, broccoli, and dark-green leafy), cooked, no sauce, NS as to fat added in cooking" +75330140,"VEG COMBINATION (NO CAR/ DK GRN), NO SAUCE, FAT NOT ADDED","Vegetable combination (excluding carrots, broccoli, and dark-green leafy), cooked, no sauce, fat not added in cooking" +75330150,"VEG COMBINATION (NO CAR/ DK GRN), NO SAUCE, FAT ADDED","Vegetable combination (excluding carrots, broccoli, and dark-green leafy), cooked, no sauce, fat added in cooking" +75340000,"VEG ASIAN,ORIENTAL STYLE,CKD,NS FAT ADDED IN COOKING","Vegetable combinations, Asian style, (broccoli, green pepper, water chestnut, etc) cooked, NS as to fat added in cooking" +75340010,"VEG COMBO ASIAN STYLE, CKD, FAT, NOT ADDED","Vegetable combinations, Asian style, (broccoli, green pepper, water chestnuts, etc), cooked, fat not added in cooking" +75340020,"VEG COMBO, ASIAN STYLE, CKD, FAT ADDED","Vegetable combinations, Asian style, (broccoli, green pepper, water chestnuts, etc), cooked, fat added in cooking" +75340160,"VEG & PASTA COMBOS, W/ CREAM/CHEESE SCE, COOKED","Vegetable and pasta combinations with cream or cheese sauce (broccoli, pasta, carrots, corn, zucchini, peppers, cauliflower, peas, etc.), cooked" +75340200,"JAI, MONK'S FOOD (MSHRMS,LILY RTS,B.CURD,W.CHSTNUT)","Jai, Monk's Food (mushrooms, lily roots, bean curd, water chestnuts)" +75340300,"PINACBET (EGGPLANT W/ TOMATO, BITTERMELON, ETC)","Pinacbet (eggplant with tomatoes, bitter melon, etc.)" +75365000,"VEGETABLE MIXTURE, DRIED (INCL SALAD CRUNCHIES)","Vegetable mixture, dried" +75400500,"ARTICHOKES, STUFFED","Artichokes, stuffed" +75401010,"ASPARAGUS, NS FORM, CREAMED OR W/ CHEESE SAUCE","Asparagus, NS as to form, creamed or with cheese sauce" +75401011,"ASPARAGUS, FROM FRESH, CREAMED OR W/ CHEESE SAUCE","Asparagus, from fresh, creamed or with cheese sauce" +75401012,"ASPARAGUS, FROM FROZEN, CREAMED OR W/ CHEESE SAUCE","Asparagus, from frozen, creamed or with cheese sauce" +75401013,"ASPARAGUS, FROM CANNED, CREAMED OR W/ CHEESE SAUCE","Asparagus, from canned, creamed or with cheese sauce" +75402010,"BEANS, LIMA, IMMATURE, NS FORM, CREAMED/ CHEESE SCE","Beans, lima, immature, NS as to form, creamed or with cheese sauce" +75402011,"BEANS, LIMA, IMMATURE, FROM FRESH, CREAMED/CHEESE SCE","Beans, lima, immature, from fresh, creamed or with cheese sauce" +75402012,"BEANS, LIMA, IMMATURE, FROM FROZEN, CREAMED/CHEESE SCE","Beans, lima, immature, from frozen, creamed or with cheese sauce" +75402013,"BEANS, LIMA, IMMATURE, FROM CANNED, CREAMED/CHEESE SCE","Beans, lima, immature, from canned, creamed or with cheese sauce" +75402020,"BEANS, LIMA, IMMATURE, CKD, NS FORM, W/ MUSHROOM SCE","Beans, lima, immature, cooked, NS as to form, with mushroom sauce" +75402021,"BEANS, LIMA, IMMATURE, CKD, FROM FRESH, W/ MUSHROOM SCE","Beans, lima, immature, cooked, from fresh, with mushroom sauce" +75402022,"BEANS, LIMA, IMMATURE, CKD, FROM FROZ, W/ MUSHROOM SCE","Beans, lima, immature, cooked, from frozen, with mushroom sauce" +75402023,"BEANS, LIMA, IMMATURE, CKD, FROM CAN, W/ MUSHROOM SCE","Beans, lima, immature, cooked, from canned, with mushroom sauce" +75403010,"BEANS, STRING, GREEN, NS FORM, CREAMED/CHEESE SCE","Beans, string, green, NS as to form, creamed or with cheese sauce" +75403011,"BEANS, STRING, GREEN, FROM FRESH, CREAMED/CHEESE SCE","Beans, string, green, from fresh, creamed or with cheese sauce" +75403012,"BEANS, STRING, GREEN, FROM FROZEN, CREAMED/CHEESE SCE","Beans, string, green, from frozen, creamed or with cheese sauce" +75403013,"BEANS, STRING, GREEN, FROM CANNED, CREAMED/CHEESE SCE","Beans, string, green, from canned, creamed or with cheese sauce" +75403020,"BEANS, STRING, GREEN, CKD, NS FORM, W/ MUSHROOM SCE","Beans, string, green, cooked, NS as to form, with mushroom sauce" +75403021,"BEANS, STRING, GREEN, CKD, FROM FRESH, W/ MUSHROOM SCE","Beans, string, green, cooked, from fresh, with mushroom sauce" +75403022,"BEANS, STRING, GREEN, CKD, FROM FROZ, W/ MUSHROOM SCE","Beans, string, green, cooked, from frozen, with mushroom sauce" +75403023,"BEANS, STRING, GREEN, CKD, FROM CAN, W/ MUSHROOM SCE","Beans, string, green, cooked, from canned, with mushroom sauce" +75403200,"BEANS, STRING, GREEN, SZECHUAN-STYLE, FAT ADDED","Beans, string, green, cooked, Szechuan-style, fat added in cooking" +75404010,"BEANS, STRING, YELLOW, NS FORM, CREAMED/ CHEESE SCE","Beans, string, yellow, NS as to form, creamed or with cheese sauce" +75404011,"BEANS, STRING, YELLOW, FROM FRESH, CREAMED/ CHEESE SCE","Beans, string, yellow, from fresh, creamed or with cheese sauce" +75404012,"BEANS, STRING, YELLOW, FROM FROZ, CREAMED/ CHEESE SCE","Beans, string, yellow, from frozen, creamed or with cheese sauce" +75404013,"BEANS, STRING, YELLOW, FROM CANNED, CREAMED/ CHEESE SCE","Beans, string, yellow, from canned, creamed or with cheese sauce" +75405010,"BEETS WITH HARVARD SAUCE","Beets with Harvard sauce" +75406010,"BRUSSEL SPROUTS, NS AS TO FORM, CREAMED","Brussels sprouts, NS as to form, creamed" +75406011,"BRUSSEL SPROUTS, FROM FRESH, CREAMED","Brussels sprouts, from fresh, creamed" +75406012,"BRUSSEL SPROUTS, FROM FROZ, CREAMED","Brussels sprouts, from frozen, creamed" +75407010,"CABBAGE, CREAMED","Cabbage, creamed" +75409010,"CAULIFLOWER, NS FORM, CREAMED(INCL W/ CHEESE SAUCE)","Cauliflower, NS as to form, creamed" +75409011,"CAULIFLOWER, FROM FRESH, CREAMED(INCL W/ CHEESE SAUCE)","Cauliflower, from fresh, creamed" +75409012,"CAULIFLOWER, FROM FROZ, CREAMED(INCL W/ CHEESE SAUCE)","Cauliflower, from frozen, creamed" +75409013,"CAULIFLOWER, FROM CANNED, CREAMED(INCL W/ CHEESE SAUCE)","Cauliflower, from canned, creamed" +75409020,"CAULIFLOWER, BATTER-DIPPED, FRIED","Cauliflower, batter-dipped, fried" +75410010,"CELERY, CREAMED","Celery, creamed" +75410500,"CHILES RELLENOS, CHEESE-FILLED","Chiles rellenos, cheese-filled (stuffed chili peppers)" +75410530,"CHILES RELLENOS, FILLED W/ MEAT & CHEESE","Chiles rellenos, filled with meat and cheese (stuffed chili peppers)" +75410550,"JALAPENO PEPPER, STUFFED W/ CHEESE, BATTERED, FRIED","Jalapeno pepper, stuffed with cheese, breaded or battered, fried" +75411010,"CORN, SCALLOPED OR PUDDING (INCLUDE CORN SOUFFLE)","Corn, scalloped or pudding" +75411020,"CORN FRITTER","Corn fritter" +75411030,"CORN, COOKED, NS FORM, W/ CREAM SAUCE, MADE W/ MILK","Corn, cooked, NS as to form, with cream sauce, made with milk" +75411031,"CORN, COOKED, FROM FRESH, W/ CREAM SAUCE, MADE W/ MILK","Corn, cooked, from fresh, with cream sauce, made with milk" +75411032,"CORN, COOKED, FROM FROZ, W/ CREAM SAUCE, MADE W/ MILK","Corn, cooked, from frozen, with cream sauce, made with milk" +75411033,"CORN, COOKED, FROM CAN, W/ CREAM SAUCE, MADE W/ MILK","Corn, cooked, from canned, with cream sauce, made with milk" +75412010,"EGGPLANT, BATTER-DIPPED, FRIED","Eggplant, batter-dipped, fried" +75412030,"EGGPLANT DIP (INCL BABA GHANOUSH)","Eggplant dip" +75412060,"EGGPLANT PARMESAN CASSEROLE, REGULAR","Eggplant parmesan casserole, regular" +75412070,"EGGPLANT W/ CHEESE & TOMATO SAUCE","Eggplant with cheese and tomato sauce" +75413010,"KOHLRABI, CREAMED","Kohlrabi, creamed" +75414010,"MUSHROOMS, NS AS TO FORM, CREAMED","Mushrooms, NS as to form, creamed" +75414011,"MUSHROOMS, FROM FRESH, CREAMED","Mushrooms, from fresh, creamed" +75414012,"MUSHROOMS, FROM FROZEN, CREAMED","Mushrooms, from frozen, creamed" +75414013,"MUSHROOMS, FROM CANNED, CREAMED","Mushrooms, from canned, creamed" +75414020,"MUSHROOMS, STUFFED","Mushrooms, stuffed" +75414030,"MUSHROOM, BATTER-DIPPED, FRIED","Mushrooms, batter-dipped, fried" +75414500,"OKRA, BATTER-DIPPED, FRIED","Okra, batter-dipped, fried" +75415010,"ONIONS, NS AS TO FORM, CREAMED","Onions, NS as to form, creamed" +75415011,"ONIONS, FROM FRESH, CREAMED","Onions, from fresh, creamed" +75415020,"ONION RINGS, NS FORM, BATTER-DIPPED, BAKED/FRIED","Onion rings, NS as to form, batter-dipped, baked or fried" +75415021,"ONION RINGS, FROM FRESH, BATTERED, BAKED/FRIED","Onion rings, from fresh, batter-dipped, baked or fried" +75415022,"ONION RINGS, FROM FROZ, BATTERED, BAKED/FRIED","Onion rings, from frozen, batter-dipped, baked or fried" +75416010,"PARSNIPS, CREAMED","Parsnips, creamed" +75416500,"PEA SALAD","Pea salad" +75416600,"PEA SALAD W/ CHEESE","Pea salad with cheese" +75417010,"PEAS, NS AS TO FORM, CREAMED","Peas, NS as to form, creamed" +75417011,"PEAS, FROM FRESH, CREAMED","Peas, from fresh, creamed" +75417012,"PEAS, FROM FROZEN, CREAMED","Peas, from frozen, creamed" +75417013,"PEAS, FROM CANNED, CREAMED","Peas, from canned, creamed" +75417020,"PEAS, COOKED, NS AS TO FORM, W/ MUSHROOM SAUCE","Peas, cooked, NS as to form, with mushroom sauce" +75417021,"PEAS, COOKED, FROM FRESH, W/ MUSHROOM SAUCE","Peas, cooked, from fresh, with mushroom sauce" +75417022,"PEAS, COOKED, FROM FROZEN, W/ MUSHROOM SAUCE","Peas, cooked, from frozen, with mushroom sauce" +75417023,"PEAS, COOKED, FROM CANNED, W/ MUSHROOM SAUCE","Peas, cooked, from canned, with mushroom sauce" +75417030,"PEAS, COOKED, NS AS TO FORM, W/ TOMATO SAUCE","Peas, cooked, NS as to form, with tomato sauce" +75417031,"PEAS, COOKED, FROM FRESH, W/ TOMATO SAUCE","Peas, cooked, from fresh, with tomato sauce" +75417032,"PEAS, COOKED, FROM FROZEN, W/ TOMATO SAUCE","Peas, cooked, from frozen, with tomato sauce" +75417033,"PEAS, COOKED, FROM CANNED, W/ TOMATO SAUCE","Peas, cooked, from canned, with tomato sauce" +75418000,"SQUASH, SUMMER, BREADED, BAKED","Squash,summer, yellow or green, breaded or battered, baked" +75418010,"SQUASH, SUMMER, BREADED OR BATTERED, FRIED","Squash, summer, yellow or green, breaded or battered, fried" +75418020,"SQUASH, SUMMER, CASSEROLE, W/ TOMATO & CHEESE","Squash, summer, casserole with tomato and cheese" +75418030,"SQUASH, SUMMER, CASSEROLE, W/ RICE & TOMATO SAUCE","Squash, summer, casserole, with rice and tomato sauce" +75418040,"SQUASH, SUMMER, CASSEROLE, W/ CHEESE SAUCE","Squash, summer, casserole, with cheese sauce" +75418050,"SQUASH, SUMMER, NS AS TO FORM, CREAMED","Squash, summer, NS as to form, creamed" +75418051,"SQUASH, SUMMER, FROM FRESH, CREAMED","Squash, summer, from fresh, creamed" +75418052,"SQUASH, SUMMER, FROM FROZEN, CREAMED","Squash, summer, from frozen, creamed" +75418053,"SQUASH, SUMMER, FROM CANNED, CREAMED","Squash, summer, from canned, creamed" +75418060,"SQUASH, SUMMER, SOUFFLE","Squash, summer, souffle" +75418100,"TURNIPS, NS AS TO FORM, CREAMED","Turnips, NS as to form, creamed" +75418101,"TURNIPS, FROM FRESH, CREAMED","Turnips, from fresh, creamed" +75418102,"TURNIPS, FROM FROZEN, CREAMED","Turnips, from frozen, creamed" +75418103,"TURNIPS, FROM CANNED, CREAMED","Turnips, from canned, creamed" +75418220,"CREAMED CHRISTOPHINE, P.R. (CHAYOTE A LA CREMA)","Creamed christophine, Puerto Rican style (Chayote a la crema)" +75439010,"VEGETABLE STEW, W/O MEAT","Vegetable stew without meat" +75439500,"CHOW MEIN OR CHOP SUEY, MEATLESS, NO NOODLES","Chow mein or chop suey, meatless, no noodles" +75440100,"VEG COMBINATION (INCL CAR/ DK GRN), W/ SOY-BASE SCE","Vegetable combination (including carrots, broccoli, and/or dark-green leafy), cooked, with soy-based sauce" +75440110,"VEG COMBINATION (NO CAR/ DK GRN), W/ SOY-BASE SAUCE","Vegetable combination (excluding carrots, broccoli, and dark-green leafy), cooked, with soy-based sauce" +75440170,"VEGETABLE STICKS, BREADED(INCL CORN,CARROT,GR BEAN)","Vegetable sticks, breaded (including corn, carrots, and green beans)" +75440200,"VEGETABLE TEMPURA","Vegetable tempura" +75440300,"VEG COMBINATIONS (INCL CAR/DK GRN), W/ TOMATO SAUCE","Vegetable combinations (including carrots, broccoli, and/or dark-green leafy), cooked, with tomato sauce" +75440310,"VEG COMBINATIONS (NO CAR/DK GRN), W/ TOMATO SAUCE","Vegetable combinations (excluding carrots, broccoli, and dark-green leafy), cooked, with tomato sauce" +75440400,"VEGETABLE,IN CHICK-PEA FLOUR BATTER,(PAKORA),FRIED","Vegetables, dipped in chick-pea flour batter, (pakora), fried" +75440500,"VEG COMBINATION (INCL CAR/DK GRN), W/ CHEESE SAUCE","Vegetable combinations (including carrots, broccoli, and/or dark-green leafy), cooked, with cheese sauce" +75440510,"VEG COMBINATION (NO CAR/ DK GRN), W/ CHEESE SAUCE","Vegetable combinations (excluding carrots, broccoli, and dark-green leafy), cooked, with cheese sauce" +75440600,"VEGETABLE CURRY","Vegetable curry" +75450500,"VEG COMBINATION (INCL CAR, DK GRN), W/ CREAM SAUCE","Vegetable combination (including carrots, broccoli, and/or dark-green leafy), cooked, with cream sauce" +75450510,"VEG COMBINATION (NO CAR, DK GRN), W/ CREAM SAUCE","Vegetable combination (excluding carrots, broccoli, and dark-green leafy), cooked, with cream sauce" +75450600,"VEG COMBINATION(INCL CAR,BROC,DK GRN)W/BUTTER SAUCE","Vegetable combination (including carrots, broccoli, and/or dark-green leafy), cooked, with butter sauce" +75460700,"VEGETABLE COMBINATION (INCL CAR/DK GRN), W/ PASTA","Vegetable combinations (including carrots, broccoli, and/or dark-green leafy), cooked, with pasta" +75460710,"VEGETABLE COMBINATION (NO CAR/DK GRN), W/ PASTA","Vegetable combinations (excluding carrots, broccoli, and dark-green leafy), cooked, with pasta" +75460800,"VEGETABLE COMB(INCL CAR/DK GRN),CKD,W/ BUTTER SAUCE","Vegetable combinations (including carrots, broccoli, and/or dark-green leafy), cooked, with butter sauce and pasta" +75460810,"VEGETABLE COMB (NO CAR/DK GRN),CKD, W/ BUTTER SAUCE","Vegetable combinations (excluding carrots, broccoli, and dark-green leafy), cooked, with butter sauce and pasta" +75460900,"CHOW MEIN OR CHOP SUEY, MEATLESS, WITH NOODLES","Chow mein or chop suey, meatless, with noodles" +75500110,"BEANS, STRING, GREEN, PICKLED","Beans, string, green, pickled" +75500210,"BEETS, PICKLED (INCLUDE W/ ONIONS, BEET SALAD)","Beets, pickled" +75500510,"CELERY, PICKLED","Celery, pickled" +75501010,"CORN RELISH","Corn relish" +75502010,"CAULIFLOWER, PICKLED","Cauliflower, pickled" +75502500,"CABBAGE, FRESH, PICKLED, JAPANESE","Cabbage, fresh, pickled, Japanese style" +75502510,"CABBAGE, RED, PICKLED (INCL SWEET & SOUR CABBAGE)","Cabbage, red, pickled" +75502520,"CABBAGE, KIMCHI (KIM CHEE) STYLE","Cabbage, Kimchi (Kim Chee) style" +75502550,"CABBAGE, MUSTARD, SALTED","Cabbage, mustard, salted" +75503010,"CUCUMBER PICKLES, DILL","Cucumber pickles, dill" +75503020,"CUCUMBER PICKLES, RELISH","Cucumber pickles, relish" +75503030,"CUCUMBER PICKLES, SOUR","Cucumber pickles, sour" +75503040,"CUCUMBER PICKLES, SWEET","Cucumber pickles, sweet" +75503080,"EGGPLANT, PICKLED","Eggplant, pickled" +75503090,"HORSERADISH","Horseradish" +75503100,"MUSTARD PICKLES (INCL CHOW-CHOW, HOT DOG RELISH)","Mustard pickles" +75503110,"CUCUMBER PICKLE, DILL, REDUCED SALT","Cucumber pickles, dill, reduced salt" +75503140,"CUCUMBER PICKLE, SWEET, REDUCED SALT","Cucumber pickles, sweet, reduced salt" +75505000,"MUSHROOMS, PICKLED","Mushrooms, pickled" +75506010,"MUSTARD (INCL HORSERADISH MUSTARD, CHINESE MUSTARD)","Mustard" +75506100,"MUSTARD SAUCE","Mustard sauce" +75507000,"OKRA, PICKLED","Okra, pickled" +75510000,"OLIVES, NFS","Olives, NFS" +75510010,"OLIVES, GREEN","Olives, green" +75510020,"OLIVES, BLACK","Olives, black" +75510030,"OLIVES, GREEN, STUFFED","Olives, green, stuffed" +75511010,"HOT PEPPER SAUCE","Hot pepper sauce" +75511020,"PEPPERS, PICKLED","Peppers, pickled" +75511040,"PEPPER, HOT, PICKLED","Pepper, hot, pickled" +75511100,"PICKLES, NS AS TO VEGETABLE","Pickles, NS as to vegetable" +75511200,"PICKLES, MIXED","Pickles, mixed" +75512010,"RADISHES, PICKLED, HAWAIIAN","Radishes, pickled, Hawaiian style" +75512510,"RECAITO (P.R. LITTLE CORIANDER)","Recaito (Puerto Rican little coriander)" +75513010,"SEAWEED, PICKLED","Seaweed, pickled" +75515000,"VEGETABLES, PICKLED, HAWAIIAN","Vegetables, pickled, Hawaiian style" +75515010,"VEGETABLE RELISH","Vegetable relish" +75515100,"VEGETABLES, PICKLED (INCLUDE GIARDINIERA)","Vegetables, pickled" +75534030,"TURNIP, PICKLED","Turnip, pickled" +75534500,"TSUKEMONO, JAPANESE PICKLES","Tsukemono, Japanese pickles" +75535000,"ZUCCHINI, PICKLED","Zucchini, pickled" +75600150,"SOUP, CREAM OF, NFS","Soup, cream of, NFS" +75601000,"ASPARAGUS SOUP, CREAM OF, NS AS TO W/ MILK OR WATER","Asparagus soup, cream of, NS as to made with milk or water" +75601010,"ASPARAGUS SOUP, CREAM OF,W/ MILK","Asparagus soup, cream of, prepared with milk" +75601020,"ASPARAGUS SOUP, CREAM OF, PREPARED W/ WATER","Asparagus soup, cream of, prepared with water" +75601100,"BEET SOUP (BORSCHT)","Beet soup (borscht)" +75601200,"CABBAGE SOUP, HOME RECIPE, CANNED OR READY-TO-SERVE","Cabbage soup, home recipe, canned or ready-to-serve" +75601210,"CABBAGE WITH MEAT SOUP, HOME RECIPE, CANNED OR READY-TO-SERV","Cabbage with meat soup, home recipe, canned or ready-to-serve" +75603010,"CELERY SOUP, CREAM OF, PREPARED WITH MILK, HOME RECIPE, CANN","Celery soup, cream of, prepared with milk, home recipe, canned or ready-to-serve" +75603020,"CELERY SOUP, CREAM OF, PREPARED WITH WATER, HOME RECIPE, CAN","Celery soup, cream of, prepared with water, home recipe, canned or ready-to-serve" +75604010,"CORN SOUP, CREAM OF, W/ MILK","Corn soup, cream of, prepared with milk" +75604020,"CORN SOUP, CREAM OF, PREPARED W/ WATER","Corn soup, cream of, prepared with water" +75604600,"GAZPACHO","Gazpacho" +75605010,"LEEK SOUP, CREAM OF, PREP W/ MILK","Leek soup, cream of, prepared with milk" +75607000,"MUSHROOM SOUP, NFS","Mushroom soup, NFS" +75607010,"MUSHROOM SOUP, CREAM OF, PREP W/ MILK","Mushroom soup, cream of, prepared with milk" +75607020,"MUSHROOM SOUP, CREAM OF, PREPARED W/ WATER","Mushroom soup, cream of, prepared with water" +75607040,"MUSHROOM SOUP, W/ MEAT BROTH, PREPARED W/ WATER","Mushroom soup, with meat broth, prepared with water" +75607050,"MUSHROOM SOUP, CM OF, LOW SOD, PREP W/ WATER","Mushroom soup, cream of, low sodium, prepared with water" +75607060,"MUSHROOM SOUP, CREAM OF, NS AS TO W/ MILK OR WATER","Mushroom soup, cream of, NS as to made with milk or water" +75607080,"MUSHROOM W/ CHICKEN SOUP, CREAM OF, PREP W/ MILK","Mushroom with chicken soup, cream of, prepared with milk" +75607090,"MUSHROOM SOUP, CREAM OF, CAN, RED. SOD., NS W/ MILK/WATER","Mushroom soup, cream of, canned, reduced sodium, NS as to made with milk or water" +75607100,"MUSHROOM SOUP, CREAM OF, CAN, RED. SODIUM, PREP W/ MILK","Mushroom soup, cream of, canned, reduced sodium, prepared with milk" +75607130,"MUSHROOM SOUP, MADE FROM DRY MIX","Mushroom soup, made from dry mix" +75607140,"MUSHROOM SOUP, CM OF, CAN, RED SOD, PREP W/ WATER","Mushroom soup, cream of, canned, reduced sodium, prepared with water" +75608010,"ONION SOUP, CREAM OF, PREP W/ MILK","Onion soup, cream of, prepared with milk" +75608100,"ONION SOUP, FRENCH","Onion soup, French" +75608200,"ONION SOUP, MADE FROM DRY MIX","Onion soup, made from dry mix" +75609010,"PEA SOUP, PREPARED WITH MILK","Pea soup, prepared with milk" +75611010,"VEGETABLE SOUP, CREAM OF, PREP W/ MILK","Vegetable soup, cream of, prepared with milk" +75612010,"ZUCCHINI SOUP, CREAM OF, PREP W/ MILK","Zucchini soup, cream of, prepared with milk" +75646010,"SHAV SOUP","Shav soup" +75647000,"SEAWEED SOUP","Seaweed soup" +75649010,"VEGETABLE SOUP, CANNED, PREPARED WITH WATER OR READY-TO-SERV","Vegetable soup, canned, prepared with water or ready-to-serve" +75649050,"VEGETABLE SOUP, MADE FROM DRY MIX","Vegetable soup, made from dry mix" +75649070,"VEGETABLE SOUP, FROM DRY MIX, LOW SODIUM","Vegetable soup, made from dry mix, low sodium" +75649110,"VEGETABLE SOUP, HOME RECIPE","Vegetable soup, home recipe" +75649150,"VEGETABLE NOODLE SOUP, HOME RECIPE","Vegetable noodle soup, home recipe" +75650990,"MINESTRONE SOUP, REDUCED SODIUM, CANNED OR READY-TO-SERVE","Minestrone soup, reduced sodium, canned or ready-to-serve" +75651000,"MINESTRONE SOUP, HOME RECIPE","Minestrone soup, home recipe" +75651010,"MINESTRONE SOUP, CANNED, PREPARED WITH WATER, OR READY-TO-SE","Minestrone soup, canned, prepared with water, or ready-to-serve" +75651020,"VEGETABLE BEEF SOUP, CANNED, PREPARED WITH WATER, OR READY-T","Vegetable beef soup, canned, prepared with water, or ready-to-serve" +75651030,"VEGETABLE BEEF NOODLE SOUP, PREPARED W/ WATER","Vegetable beef noodle soup, prepared with water" +75651040,"VEGETABLE NOODLE SOUP, CANNED, PREPARED WITH WATER, OR READY","Vegetable noodle soup, canned, prepared with water, or ready-to-serve" +75651070,"VEGETABLE RICE SOUP, CANNED, PREPARED WITH WATER OR READY-TO","Vegetable rice soup, canned, prepared with water or ready-to-serve" +75651080,"VEGETABLE BEEF SOUP WITH RICE, CANNED, PREPARED WITH WATER O","Vegetable beef soup with rice, canned, prepared with water or ready-to-serve" +75651110,"VEGETABLE CHICKEN RICE SOUP, CANNED, PREP W/WATER OR RTF","Vegetable chicken rice soup, canned, prepared with water or ready-to-serve" +75651140,"VEGETABLE SOUP WITH CHICKEN BROTH, MEXICAN STYLE, HOME RECIP","Vegetable soup with chicken broth, Mexican style, home recipe (Sopa Ranchera)" +75651150,"VEGETABLE NOODLE SOUP, RED SODIUM, CAN, PREP W/ WATER/RTS","Vegetable noodle soup, reduced sodium, canned, prepared with water or ready-to-serve" +75652010,"VEGETABLE BEEF SOUP, HOME RECIPE","Vegetable beef soup, home recipe" +75652030,"VEGETABLE BEEF SOUP, CANNED, PREPARED WITH MILK","Vegetable beef soup, canned, prepared with milk" +75652040,"VEG BEEF SOUP W/ NOODLES, HOME RECIPE","Vegetable beef soup with noodles or pasta, home recipe" +75652050,"VEG BEEF SOUP W/ RICE, HOME RECIPE","Vegetable beef soup with rice, home recipe" +75654010,"VEGETARIAN VEGETABLE SOUP, PREPARED W/ WATER","Vegetarian vegetable soup, prepared with water" +75656010,"VEGETABLE SOUP, SPANISH, STEW TYPE","Vegetable soup, Spanish style, stew type" +75656020,"VEGETABLE SOUP, CHUNKY STYLE","Vegetable soup, chunky style" +75656040,"VEGETABLE SOUP W/ PASTA, CHUNKY STYLE","Vegetable soup, with pasta, chunky style" +75656060,"VEG BEEF SOUP, CHUNKY STYLE (INCL VEG W/ MEAT SOUPS","Vegetable beef soup, chunky style" +75657000,"VEGETABLE BROTH, BOUILLON (INCL POT LIQUOR)","Vegetable broth, bouillon" +76102010,"SPINACH, CREAMED, BABY, STRAINED","Spinach, creamed, baby food, strained" +76102030,"BROCCOLI, CARROTS & CHEESE, BABY, JUNIOR","Broccoli, carrots and cheese, baby food, junior" +76201000,"CARROTS, BABY, NS AS TO STRAINED OR JUNIOR","Carrots, baby food, NS as to strained or junior" +76201010,"CARROTS, BABY, STRAINED","Carrots, baby food, strained" +76201020,"CARROTS, BABY, JUNIOR","Carrots, baby food, junior" +76201030,"CARROTS, BABY FOOD, TODDLER","Carrots, baby food, toddler" +76202000,"CARROTS & PEAS, BABY, STRAINED","Carrots and peas, baby food, strained" +76205000,"SQUASH, BABY, NS AS TO STRAINED OR JUNIOR","Squash, baby food, NS as to strained or junior" +76205010,"SQUASH, BABY, STRAINED","Squash, baby food, strained" +76205020,"SQUASH, BABY, JUNIOR","Squash, baby food, junior" +76205030,"SQUASH & CORN, BABY, STRAINED","Squash and corn, baby food, strained" +76205060,"CORN AND SWEET POTATOES, BABY FOOD, STRAINED","Corn and sweet potatoes, baby food, strained" +76209000,"SWEET POTATOES, BABY, NS AS TO STRAINED OR JUNIOR","Sweet potatoes, baby food, NS as to strained or junior" +76209010,"SWEET POTATOES, BABY, STRAINED","Sweet potatoes, baby food, strained" +76209020,"SWEET POTATOES, BABY, JUNIOR","Sweet potatoes, baby food, junior" +76401000,"BEANS, GREEN STRING, BABY, NS AS TO STR OR JR","Beans, green string, baby food, NS as to strained or junior" +76401010,"BEANS, GREEN STRING, BABY, STRAINED","Beans, green string, baby food, strained" +76401020,"BEANS, GREEN STRING, BABY, JUNIOR","Beans, green string, baby food, junior" +76401060,"BEANS, GREEN STRING, BABY, TODDLER","Beans, green string, baby food, toddler" +76402000,"GREEN BEANS & POTATOES, BABY, STRAINED","Green beans and potatoes, baby food, strained" +76403010,"BEETS, BABY, STRAINED","Beets, baby food, strained" +76405000,"CORN, CREAMED, BABY, NS AS TO STRAINED OR JUNIOR","Corn, creamed, baby food, NS as to strained or junior" +76405010,"CORN, CREAMED, BABY, STRAINED","Corn, creamed, baby food, strained" +76405020,"CORN, CREAMED, BABY, JUNIOR","Corn, creamed, baby food, junior" +76407000,"MIXED VEG, GARDEN VEG, BABY, NS AS TO STR OR JR","Mixed vegetables, garden vegetables, baby food, NS as to strained or junior" +76407010,"MIXED VEGETABLES, GARDEN VEGETABLES, BABY, STRAINED","Mixed vegetables, garden vegetables, baby food, strained" +76407020,"MIXED VEGETABLES, GARDEN VEGETABLES, BABY, JUNIOR","Mixed vegetables, garden vegetables, baby food, junior" +76409000,"PEAS, BABY, NS AS TO STRAINED OR JUNIOR","Peas, baby food, NS as to strained or junior" +76409010,"PEAS, BABY, STRAINED","Peas, baby food, strained" +76409020,"PEAS, BABY, JUNIOR","Peas, baby food, junior" +76409030,"PEAS, BABY, TODDLER","Peas, baby food, toddler" +76420000,"POTATOES, BABY, TODDLER","Potatoes, baby food, toddler" +76501000,"VEGETABLES & RICE, BABY, STRAINED","Vegetables and rice, baby food, strained" +76502000,"PEAS & BROWN RICE, BABY","Peas and brown rice, baby food" +76601010,"VEGETABLE & BACON, BABY, STRAINED","Vegetable and bacon, baby food, strained" +76602000,"CARROTS & BEEF, BABY, STRAINED","Carrots and beef, baby food, strained" +76603000,"VEGETABLE & BEEF, BABY, NS AS TO STRAINED OR JUNIOR","Vegetable and beef, baby food, NS as to strained or junior" +76603010,"VEGETABLE & BEEF, BABY, STRAINED","Vegetable and beef, baby food, strained" +76603020,"VEGETABLE & BEEF, BABY, JUNIOR","Vegetable and beef, baby food, junior" +76604000,"BROCCOLI & CHICKEN, BABY, STRAINED","Broccoli and chicken, baby food, strained" +76604500,"SWEET POTATOES & CHICKEN, BABY, STRAINED","Sweet potatoes and chicken, baby food, strained" +76605000,"VEGETABLE & CHICKEN, BABY, NS AS TO STR OR JR","Vegetable and chicken, baby food, NS as to strained or junior" +76605010,"VEGETABLE & CHICKEN, BABY, STRAINED","Vegetable and chicken, baby food, strained" +76605020,"VEGETABLE & CHICKEN, BABY, JUNIOR","Vegetable and chicken, baby food, junior" +76607000,"VEGETABLE & HAM, BABY, NS AS TO STRAINED OR JUNIOR","Vegetable and ham, baby food, NS as to strained or junior" +76607010,"VEGETABLE & HAM, BABY, STRAINED","Vegetable and ham, baby food, strained" +76607020,"VEGETABLE & HAM, BABY, JUNIOR","Vegetable and ham, baby food, junior" +76607030,"POTATOES W/ CHEESE & HAM, BABY FOOD, TODDLER","Potatoes with cheese and ham, baby food, toddler" +76607100,"POTATOES WITH CHEESE AND BROCCOLI, BABY FOOD, TODDLER","Potatoes with cheese and broccoli, baby food, toddler" +76609010,"VEGETABLE & LAMB, BABY, STRAINED","Vegetable and lamb, baby food, strained" +76611000,"VEGETABLE & TURKEY, BABY, NS AS TO STR OR JR","Vegetable and turkey, baby food, NS as to strained or junior" +76611010,"VEGETABLE & TURKEY, BABY, STRAINED","Vegetable and turkey, baby food, strained" +76611020,"VEGETABLE & TURKEY, BABY, JUNIOR","Vegetable and turkey, baby food, junior" +77121010,"FRIED STUFFED POTATOES, P.R. (RELLENOS DE PAPAS)","Fried stuffed potatoes, Puerto Rican style (Rellenos de papas)" +77121110,"POTATO&HAM FRITTERS,P.R.(FRITURAS DE PAPA Y JAMON)","Potato and ham fritters, Puerto Rican style (Frituras de papa y jamon)" +77141010,"POTATO CHICKEN PIE, P.R. (PASTELON DE POLLO)","Potato chicken pie, Puerto Rican style (Pastelon de pollo)" +77201210,"GREEN PLANTAIN W/ CRACKLINGS, P.R. (MOFONGO)","Green plantain with cracklings, Puerto Rican style (Mofongo)" +77205110,"RIPE PLANTAIN FRITTERS, P.R. (PIONONO)","Ripe plantain fritters, Puerto Rican style (Pionono)" +77205610,"RIPE PLANTAIN MEAT PIE, P.R. (PINON)","Ripe plantain meat pie, Puerto Rican style (Pinon)" +77230210,"CASSAVA PASTELES, P.R. (PASTELES DE YUCA)","Cassava Pasteles, Puerto Rican style (Pasteles de yuca)" +77230510,"CASSAVA FRITTER STUFFED W/ CRAB, P.R. (EMPANADA DE YUCA)","Cassava fritter stuffed with crab meat, Puerto Rican style (Empanada de yuca y jueyes)" +77250110,"STUFFED TANNIER FRITTERS, P.R. (ALCAPURRIAS)","Stuffed tannier fritters, Puerto Rican style (Alcapurrias)" +77250710,"TANNIER FRITTERS, P.R. (FRITURAS DE YAUTIA)","Tannier fritters, Puerto Rican style (Frituras de yautia)" +77272010,"PUERTO RICAN PASTELES (PASTELES DE MASA)","Puerto Rican pasteles (Pasteles de masa)" +77316010,"STUFFED CABBAGE, W/ MEAT, P.R.(REPOLLO RELLENO CON CARNE)","Stuffed cabbage, with meat, Puerto Rican style (Repollo relleno con carne)" +77316510,"STUFFED CABBAGE, W MEAT& RICE, SYRIAN DISH, P.R. STYLE","Stuffed cabbage, with meat and rice, Syrian dish, Puerto Rican style (Repollo relleno con carne y con arroz; Arabe Mihsy Melful)" +77316600,"EGGPLANT AND MEAT CASSEROLE","Eggplant and meat casserole" +77513010,"SPANISH STEW, P.R. (COCIDO ESPANOL)","Spanish stew, Puerto Rican style (Cocido Espanol)" +77563010,"PUERTO RICAN STEW (SALCOCHO / SANCOCHO)","Puerto Rican stew (Salcocho / Sancocho)" +78101000,"VEGETABLE & FRUIT JUICE BLEND,100% JUICE,W/ HIGH VIT C,+ E,A","Vegetable and fruit juice blend, 100% juice, with high vitamin C plus added vitamin E and vitamin A" +81100000,"TABLE FAT, NFS","Table fat, NFS" +81100500,"BUTTER, NFS","Butter, NFS" +81101000,"BUTTER, STICK, SALTED","Butter, stick, salted" +81101010,"BUTTER, WHIPPED, TUB, SALTED","Butter, whipped, tub, salted" +81101020,"BUTTER, WHIPPED, STICK, SALTED","Butter, whipped, stick, salted" +81101100,"BUTTER, STICK, UNSALTED","Butter, stick, unsalted" +81101110,"BUTTER, WHIPPED, TUB, UNSALTED","Butter, whipped, tub, unsalted" +81101120,"BUTTER, WHIPPED, STICK, UNSALTED","Butter, whipped, stick, unsalted" +81101500,"LIGHT BUTTER, STICK, SALTED","Light butter, stick, salted" +81101510,"LIGHT BUTTER, STICK, UNSALTED","Light butter, stick, unsalted" +81101520,"LIGHT BUTTER, WHIPPED, TUB, SALTED","Light butter, whipped, tub, salted" +81102000,"MARGARINE, NFS","Margarine, NFS" +81102010,"MARGARINE, STICK, SALTED","Margarine, stick, salted" +81102020,"MARGARINE, TUB, SALTED","Margarine, tub, salted" +81103020,"MARGARINE, WHIPPED, TUB, SALTED","Margarine, whipped, tub, salted" +81103030,"MARGARINE, STICK, UNSALTED","Margarine, stick, unsalted" +81103040,"MARGARINE-LIKE SPREAD, STICK, SALTED","Margarine-like spread, stick, salted" +81103041,"MARGARINE-LIKE SPREAD, MADE W/ YOGURT, STICK, SALTED","Margarine-like spread, made with yogurt, stick, salted" +81103060,"MARGARINE, TUB, UNSALTED","Margarine, tub, unsalted" +81103070,"MARGARINE, WHIPPED, TUB, UNSALTED","Margarine, whipped, tub, unsalted" +81103080,"MARGARINE-LIKE SPREAD, TUB, SALTED","Margarine-like spread, tub, salted" +81103090,"MARGARINE-LIKE SPREAD, LIQUID, SALTED","Margarine-like spread, liquid, salted" +81103100,"MARGARINE-LIKE SPREAD, STICK, UNSALTED","Margarine-like spread, stick, unsalted" +81103120,"MARGARINE-LIKE SPREAD, TUB, UNSALTED","Margarine-like spread, tub, unsalted" +81103130,"MARGARINE-LIKE SPREAD, WHIPPED, TUB, SALTED","Margarine-like spread, whipped, tub, salted" +81103140,"MARGARINE-LIKE SPREAD, TUB, SWEETENED","Margarine-like spread, tub, sweetened" +81104010,"MARGARINE-LIKE SPREAD, RED CAL, 40% FAT, TUB, SALTED","Margarine-like spread, reduced calorie, about 40% fat, tub, salted" +81104011,"MARGARINE-LIKE SPREAD,RED CAL,40% FAT,MADE W/ YOGURT,TUB","Margarine-like spread, reduced calorie, about 40% fat, made with yogurt, tub, salted" +81104020,"MARGARINE-LIKE SPREAD, RED CAL, 40% FAT, STICK, SALTED","Margarine-like spread, reduced calorie, about 40% fat, stick, salted" +81104050,"MARGARINE-LIKE SPREAD, RED CAL, 20% FAT, TUB, SALTED","Margarine-like spread, reduced calorie, about 20% fat, tub, salted" +81104070,"MARGARINE-LIKE SPREAD, RED CAL, 20% FAT, TUB, UNSALTED","Margarine-like spread, reduced calorie, about 20% fat, tub, unsalted" +81104100,"MARGARINE-LIKE SPREAD, FAT FREE, TUB, SALTED","Margarine-like spread, fat free, tub, salted" +81104110,"MARGARINE-LIKE SPREAD, FAT FREE, LIQUID, SALTED","Margarine-like spread, fat free, liquid, salted" +81104500,"VEGETABLE OIL-BUTTER SPREAD, STICK, SALTED","Vegetable oil-butter spread, stick, salted" +81104510,"VEGETABLE OIL-BUTTER SPREAD, TUB, SALTED","Vegetable oil-butter spread, tub, salted" +81104550,"VEGETABLE OIL-BUTTER SPREAD, RED CAL, STICK, SALTED","Vegetable oil-butter spread, reduced calorie, stick, salted" +81104560,"VEGETABLE OIL-BUTTER SPREAD, RED CAL, TUB, SALTED","Vegetable oil-butter spread, reduced calorie, tub, salted" +81105010,"BUTTER-MARGARINE BLEND, STICK, SALTED","Butter-margarine blend, stick, salted" +81105020,"BUTTER-MARGARINE BLEND, TUB, SALTED","Butter-margarine blend, tub, salted" +81105500,"BUTTER-VEG OIL BLEND","Butter-vegetable oil blend" +81106010,"BUTTER REPLACEMENT, FAT-FREE POWDER, NOT RECONST","Butter replacement, fat-free powder" +81201000,"ANIMAL FAT OR DRIPPINGS","Animal fat or drippings" +81202000,"LARD","Lard" +81203000,"SHORTENING, NS AS TO VEGETABLE OR ANIMAL","Shortening, NS as to vegetable or animal" +81203100,"SHORTENING, VEGETABLE","Shortening, vegetable" +81203200,"SHORTENING, ANIMAL","Shortening, animal" +81204000,"GHEE, CLARIFIED BUTTER","Ghee, clarified butter" +81301000,"GARLIC SAUCE","Garlic sauce" +81301020,"LEMON-BUTTER SAUCE","Lemon-butter sauce" +81302010,"HOLLANDAISE SAUCE","Hollandaise sauce" +81302040,"SANDWICH SPREAD","Sandwich spread" +81302050,"TARTAR SAUCE","Tartar sauce" +81302060,"HORSERADISH SAUCE","Horseradish sauce" +81302070,"PESTO SAUCE","Pesto sauce" +81312000,"TARTAR SAUCE, REDUCED FAT/CALORIE","Tartar sauce, reduced fat/calorie" +81322000,"HONEY BUTTER","Honey butter" +81330210,"ADOBO FRESCO (INCL ADOBO CRIOLLO)","Adobo fresco" +82101000,"VEGETABLE OIL, NFS (INCLUDE OIL, NFS)","Vegetable oil, NFS" +82101300,"ALMOND OIL","Almond oil" +82101500,"COCONUT OIL","Coconut oil" +82102000,"CORN OIL","Corn oil" +82102500,"CORN & CANOLA OIL","Corn and canola oil" +82103000,"COTTONSEED OIL","Cottonseed oil" +82103500,"FLAXSEED OIL","Flaxseed oil" +82104000,"OLIVE OIL","Olive oil" +82105000,"PEANUT OIL","Peanut oil" +82105500,"RAPESEED OIL (INCL CANOLA OIL, PURITAN)","Rapeseed oil" +82105750,"CANOLA & SOYBEAN OIL","Canola and soybean oil" +82105800,"CANOLA, SOYBEAN & SUNFLOWER OIL","Canola, soybean and sunflower oil" +82106000,"SAFFLOWER OIL","Safflower oil" +82107000,"SESAME OIL","Sesame oil" +82108000,"SOYBEAN OIL","Soybean oil" +82108250,"SOYBEAN & SUNFLOWER OIL","Soybean and sunflower oil" +82108500,"SUNFLOWER OIL","Sunflower oil" +82108700,"WALNUT OIL","Walnut oil" +82109000,"WHEAT GERM OIL","Wheat germ oil" +83100100,"SALAD DRESSING, NFS, FOR SALADS","Salad dressing, NFS, for salads" +83100200,"SALAD DRESSING, NFS, FOR SANDWICHES","Salad dressing, NFS, for sandwiches" +83101000,"BLUE OR ROQUEFORT CHEESE DRESSING","Blue or roquefort cheese dressing" +83101500,"BACON DRESSING (HOT)","Bacon dressing (hot)" +83101600,"BACON & TOMATO DRESSING","Bacon and tomato dressing" +83102000,"CAESAR DRESSING","Caesar dressing" +83103000,"COLESLAW DRESSSING","Coleslaw dressing" +83104000,"FRENCH OR CATALINA DRESSING","French or Catalina dressing" +83105500,"HONEY MUSTARD DRESSING","Honey mustard dressing" +83106000,"ITALIAN DRESSING, W/ VINEGAR & OIL","Italian dressing, made with vinegar and oil" +83107000,"MAYONNAISE, REGULAR","Mayonnaise, regular" +83108000,"MAYONNAISE, IMITATION","Mayonnaise, imitation" +83109000,"RUSSIAN DRESSING","Russian dressing" +83110000,"MAYONNAISE-TYPE SALAD DRESSING","Mayonnaise-type salad dressing" +83112000,"AVOCADO DRESSING","Avocado dressing" +83112500,"CREAMY DRESSING","Creamy dressing" +83112950,"POPPY SEED DRESSING","Poppy seed dressing" +83112990,"SESAME DRESSING","Sesame dressing" +83114000,"THOUSAND ISLAND DRESSING","Thousand Island dressing" +83115000,"YOGURT DRESSING","Yogurt dressing" +83200100,"SALAD DRESSING, LIGHT, NFS","Salad dressing, light, NFS" +83201000,"BLUE OR ROQUEFORT CHEESE DRESSING, LIGHT","Blue or roquefort cheese dressing, light" +83201400,"COLESLAW DRESSING, LIGHT","Coleslaw dressing, light" +83202020,"FRENCH OR CATALINA DRESSING, LIGHT","French or Catalina dressing, light" +83203000,"CAESAR DRESSING, LIGHT","Caesar dressing, light" +83204000,"MAYONNAISE, LIGHT","Mayonnaise, light" +83204030,"MAYONNAISE, REGULAR, WITH OLIVE OIL","Mayonnaise, reduced fat, with olive oil" +83204050,"MAYONNAISE-TYPE SALAD DRESSING, LIGHT","Mayonnaise-type salad dressing, light" +83204500,"HONEY MUSTARD DRESSING, LIGHT","Honey mustard dressing, light" +83205450,"ITALIAN DRESSING, LIGHT","Italian dressing, light" +83206000,"RUSSIAN DRESSING, LIGHT","Russian dressing, light" +83206500,"SESAME DRESSING, LIGHT","Sesame dressing, light" +83207000,"THOUSAND ISLAND DRESSING, LIGHT","Thousand Island dressing, light" +83208500,"KOREAN DRESSING OR MARINADE","Korean dressing or marinade" +83210100,"CREAMY DRESSING, LIGHT","Creamy dressing, light" +83300100,"BLUE OR ROQUEFORT CHEESE DRESSING, FAT FREE","Blue or roquefort cheese dressing, fat free" +83300200,"CAESAR DRESSING, FAT FREE","Caesar dressing, fat free" +83300300,"CREAMY DRESSING, FAT FREE","Creamy dressing, fat free" +83300400,"FRENCH OR CATALINA DRESSING, FAT FREE","French or Catalina dressing, fat free" +83300500,"HONEY MUSTARD DRESSING, FAT FREE","Honey mustard dressing, fat free" +83300600,"ITALIAN DRESSING, FAT FREE","Italian dressing, fat free" +83300700,"MAYONNAISE, FAT FREE","Mayonnaise, fat free" +83300800,"RUSSIAN DRESSING, FAT FREE","Russian dressing, fat free" +83300900,"SALAD DRESSING, FAT FREE, NFS","Salad dressing, fat free, NFS" +83301000,"THOUSAND ISLAND DRESSING, FAT FREE","Thousand Island dressing, fat free" +91101000,"SUGAR, NFS","Sugar, NFS" +91101010,"SUGAR, WHITE, GRANULATED OR LUMP","Sugar, white, granulated or lump" +91101020,"SUGAR, WHITE, CONFECTIONER'S, POWDERED","Sugar, white, confectioner's, powdered" +91102010,"SUGAR, BROWN","Sugar, brown" +91103010,"SUGAR, MAPLE","Sugar, maple" +91104100,"SUGAR, CINNAMON","Sugar, cinnamon" +91104200,"SUGAR, RAW","Sugar, raw" +91105010,"FRUCTOSE SWEETENER, SUGAR SUBSTITUTE, DRY POWDER","Fructose sweetener, sugar substitute, dry powder" +91106000,"SUGAR SUBSTITUTE, SUGAR-ASPARTAME BLEND, DRY PWD","Sugar substitute, sugar-aspartame blend, dry powder" +91107000,"SUCRALOSE-BASED SWEETENER, SUGAR SUBSTITUTE","Sucralose-based sweetener, sugar substitute" +91108000,"SUGAR SUB, HERBAL EXTRACT SWEETENER, POWDER","Sugar substitute, herbal extract sweetener, powder" +91108010,"SUGAR SUB, HERBAL EXTRACT SWEETENER, LIQUID","Sugar substitute, herbal extract sweetener, liquid" +91109000,"BLUE AGAVE LIQUID SWEETENER, SUGAR SUBSTITUTE","Blue Agave liquid sweetener, sugar substitute" +91200000,"SUGAR SUBSTITUTE, LOW CALORIE, POWDERED, NFS","Sugar substitute, low-calorie, powdered, NFS" +91200020,"SUGAR SUBSTITUTE, SACCHARIN-BASED, DRY POWDER","Sugar substitute, saccharin-based, dry powder" +91200030,"BROWN SUGAR SUBSTITUTE, SACCHARIN-BASED, DRY POWDER","Brown sugar substitute, saccharin-based, dry powder" +91200040,"SUGAR SUBSTITUTE, SACCHARIN-BASED, DRY POWDER AND TABLETS","Sugar substitute, saccharin-based, dry powder and tablets" +91200110,"SUGAR SUBSTITUTE, SACCHARIN-BASED, LIQUID","Sugar substitute, saccharin-based, liquid" +91201010,"SUGAR SUBSTITUTE, ASPARTAME-BASED, DRY POWDER","Sugar substitute, aspartame-based, dry powder" +91300010,"SYRUP, NFS","Syrup, NFS" +91300100,"PANCAKE SYRUP, NFS","Pancake syrup, NFS" +91301020,"CANE & CORN PANCAKE SYRUP","Cane and corn pancake syrup" +91301030,"CORN SYRUP, LIGHT OR DARK","Corn syrup, light or dark" +91301040,"BUTTERED BLENDS SYRUP (INCL MRS BUTTERWORTH)","Buttered blends syrup" +91301050,"FRUIT SYRUP","Fruit syrup" +91301060,"MAPLE SYRUP(100% MAPLE)(INCLUDE MAPLE CREAM)","Maple syrup (100% maple)" +91301080,"CHOCOLATE SYRUP, THIN TYPE","Chocolate syrup, thin type" +91301081,"CHOCOLATE SYRUP, THIN TYPE, LIGHT","Chocolate syrup, thin type, light" +91301082,"CHOCOLATE SYRUP, THIN TYPE, SUGAR FREE","Chocolate syrup, thin type, sugar free" +91301090,"SORGHUM SYRUP","Sorghum syrup" +91301100,"SUGAR (WHITE) & WATER SYRUP (INCLUDE SIMPLE SYRUP)","Sugar (white) and water syrup" +91301120,"SUGAR, CARMELIZED","Sugar, carmelized" +91301130,"FRUIT FLAVORED SYRUP USED FOR MILK BEVERAGES","Fruit flavored syrup used for milk beverages" +91301200,"SUGAR (BROWN) & WATER SYRUP","Sugar (brown) and water syrup" +91301250,"MAPLE & CORN &/OR CANE PANCAKE SYRUP BLENDS","Maple and corn and/or cane pancake syrup blends (formerly Corn and maple syrup (2% maple))" +91301510,"SYRUP, PANCAKE, REDUCED CALORIE","Syrup, pancake, reduced calorie" +91302010,"HONEY (INCLUDE PEAR HONEY, RAW HONEY)","Honey" +91303000,"MOLASSES","Molasses" +91303500,"SUGAR, BROWN, LIQUID","Sugar, brown, liquid" +91303750,"CHOCOLATE GRAVY","Chocolate gravy" +91304010,"TOPPING, BUTTERSCOTCH OR CARAMEL","Topping, butterscotch or caramel" +91304020,"TOPPING, CHOCOLATE, THICK, FUDGE TYPE","Topping, chocolate, thick, fudge type" +91304030,"TOPPING, FRUIT","Topping, fruit" +91304040,"TOPPING, MARSHMALLOW","Topping, marshmallow" +91304050,"HARD SAUCE","Hard sauce" +91304060,"TOPPING, NUT (WET)","Topping, nut (wet)" +91304070,"TOPPING, PEANUT BUTTER, THICK FUDGE TYPE","Topping, peanut butter, thick, fudge type" +91304080,"TOPPING, FRUIT, UNSWEETENED","Topping, fruit, unsweetened" +91304090,"TOPPING, CHOC FLAVOR HAZELNUT SPREAD (INCL NUTELLA)","Topping, chocolate flavored hazelnut spread" +91304250,"TOPPING, MILK CHOCOLATE W/ CEREAL","Topping, milk chocolate with cereal" +91304300,"TOPPING, CHOCOLATE, HARD COATING","Topping, chocolate, hard coating" +91305010,"ICING, CHOCOLATE","Icing, chocolate" +91305020,"ICING, WHITE","Icing, white" +91351010,"SYRUP, DIETETIC","Syrup, dietetic" +91351020,"TOPPING, DIETETIC","Topping, dietetic" +91361010,"SWEET & SOUR SAUCE (INCLUDE VIETNAMESE SAUCE)","Sweet and sour sauce" +91361020,"FRUIT SAUCE (INCLUDE ALL FRUITS)","Fruit sauce" +91361040,"DESSERT SAUCE","Dessert sauce" +91361050,"DUCK SAUCE (INCLUDE CHAISNI SAUCE)","Duck sauce" +91361070,"PLUM SAUCE, ASIAN STYLE","Plum sauce, Asian style" +91401000,"JELLY, ALL FLAVORS","Jelly, all flavors" +91402000,"JAM, PRESERVES, ALL FLAVORS","Jam, preserves, all flavors" +91403000,"FRUIT BUTTER, ALL FLAVORS (INCLUDE APPLE BUTTER)","Fruit butter, all flavors" +91404000,"MARMALADE, ALL FLAVORS","Marmalade, all flavors" +91405000,"JELLY, DIETETIC, ALL FLAVORS,SWEETENED W/ ARTIFICIAL SWEETEN","Jelly, dietetic, all flavors, sweetened with artificial sweetener" +91405500,"JELLY, REDUCED SUGAR, ALL FLAVORS","Jelly, reduced sugar, all flavors" +91406000,"JAM, MARMALADES, ARTIFICIALLY SWEETENED","Jams, preserves, marmalades, dietetic, all flavors, sweetened with artificial sweetener" +91406500,"JAM PRESERVES,MARMALADES,SWEET W/ FRUIT JUICE CONC","Jams, preserves, marmalades, sweetened with fruit juice concentrates, all flavors" +91406600,"JAMS,PRESERVES,MARMALADES,LOW SUGAR (ALL FLAVORS)","Jams, preserves, marmalades, low sugar (all flavors)" +91407100,"GUAVA PASTE","Guava paste" +91407120,"SWEET POTATO PASTE","Sweet potato paste" +91407150,"BEAN PASTE, SWEETENED","Bean paste, sweetened" +91408100,"CHINESE PRESERVED SWEET VEGETABLE","Chinese preserved sweet vegetable" +91500200,"GELATIN POWDER, SWEETENED, DRY","Gelatin powder, sweetened, dry" +91501010,"GELATIN DESSERT","Gelatin dessert" +91501015,"GELATIN SNACKS","Gelatin snacks" +91501020,"GELATIN DESSERT W/ FRUIT","Gelatin dessert with fruit" +91501030,"GELATIN DESSERT W/ WHIPPED CREAM","Gelatin dessert with whipped cream" +91501040,"GELATIN DESSERT W/ FRUIT & WHIPPED CREAM","Gelatin dessert with fruit and whipped cream" +91501050,"GELATIN DESSERT W/ CREAM CHEESE","Gelatin dessert with cream cheese" +91501060,"GELATIN DESSERT W/ SOUR CREAM","Gelatin dessert with sour cream" +91501070,"GELATIN DESSERT W/ FRUIT & SOUR CREAM","Gelatin dessert with fruit and sour cream" +91501080,"GELATIN DESSERT W/ FRUIT & CREAM CHEESE","Gelatin dessert with fruit and cream cheese" +91501090,"GELATIN DESSERT W/ FRUIT, VEGETABLES, & NUTS","Gelatin dessert with fruit, vegetable, and nuts" +91501100,"GELATIN SALAD W/ VEGETABLES","Gelatin salad with vegetables" +91501110,"GELATIN DESSERT W/ FRUIT & WHIPPED TOPPING","Gelatin dessert with fruit and whipped topping" +91501120,"GELATIN DESSERT W/ FRUIT & VEGETABLES","Gelatin dessert with fruit and vegetables" +91510100,"GELATIN POWDER, DIETETIC, DRY","Gelatin powder, dietetic, sweetened with low calorie sweetener, dry" +91511010,"GELATIN DESSERT, DIETETIC, W/ LO CAL SWEETENER","Gelatin dessert, dietetic, sweetened with low calorie sweetener" +91511020,"GELATIN DESSERT, DIET, W/ FRUIT, LO CAL SWEETNER","Gelatin dessert, dietetic, with fruit, sweetened with low calorie sweetener" +91511030,"GELATIN DESSERT, DIETETIC, W/ WHIPPED TOPPING","Gelatin dessert, dietetic, with whipped topping, sweetened with low calorie sweetener" +91511050,"GELATIN DESSERT, DIETETIC, W/ CREAM CHEESE","Gelatin dessert, dietetic, with cream cheese, sweetened with low calorie sweetener" +91511060,"GELATIN DESSERT, DIETETIC, W/ SOUR CREAM","Gelatin dessert, dietetic, with sour cream, sweetened with low calorie sweetener" +91511070,"GELATIN DESSERT, DIETETIC, W/ FRUIT & SOUR CREAM","Gelatin dessert, dietetic, with fruit and sour cream, sweetened with low calorie sweetener" +91511080,"GELATIN DESSERT, DIETETIC, W/ FRUIT & CREAM CHEESE","Gelatin dessert, dietetic, with fruit and cream cheese, sweetened with low calorie sweetener" +91511090,"GELATIN DESSERT, DIETETIC, W/ FRUIT & VEGETABLES","Gelatin dessert, dietetic, with fruit and vegetable(s), sweetened with low calorie sweetener" +91511100,"GELATIN DESSERT, DIETETIC, W/ VEGETABLES","Gelatin salad, dietetic, with vegetables, sweetened with low calorie sweetener" +91511110,"GELATIN DESSERT, DIETETIC, W/ FRUIT & WHIP TOPPING","Gelatin dessert, dietetic, with fruit and whipped topping, sweetened with low calorie sweetener" +91512010,"DANISH DESSERT PUDDING","Danish dessert pudding" +91520100,"YOOKAN, JAPANESE DESSERT MADE W/ BEAN PASTE & SUGAR","Yookan (Yokan), a Japanese dessert made with bean paste and sugar" +91550100,"COCONUT CREAM CAKE, P.R. (BIEN ME SABE)","Coconut cream cake, Puerto Rican style (Bien me sabe, ""Tastes good to me"")" +91550300,"PINEAPPLE CUSTARD, P.R. (FLAN DE PINA)","Pineapple custard, Puerto Rican style (Flan de pina)" +91560100,"HAUPIA (COCONUT PUDDING)","Haupia (coconut pudding)" +91580000,"GELATIN,FROZ,WHIPPED,ON STICK(INCL JELLO GLTN POPS)","Gelatin, frozen, whipped, on a stick" +91601000,"ICE, FRUIT","Ice, fruit" +91611000,"ICE POP","Ice pop" +91611050,"ICE POP FILLED W/ ICE CREAM, ALL FLAVOR VARIETIES","Ice pop filled with ice cream, all flavor varieties" +91611100,"ICE POP, SWEETENED W/ LOW CALORIE SWEETENER","Ice pop, sweetened with low calorie sweetener" +91621000,"SNOW CONE","Snow cone" +91700010,"CANDY, NFS","Candy, NFS" +91700500,"M&M'S ALMOND CHOCOLATE CANDIES","M&M's Almond Chocolate Candies" +91701010,"ALMONDS, CHOCOLATE-COVERED","Almonds, chocolate covered" +91701020,"ALMONDS, SUGAR-COATED (INCL JORDAN ALMONDS)","Almonds, sugar-coated" +91701030,"ALMONDS, YOGURT-COVERED","Almonds, yogurt-covered" +91702010,"BUTTERSCOTCH MORSELS","Butterscotch morsels" +91703010,"CARAMEL CANDY, CHOC-FLAVOR ROLL (INCL TOOTSIE ROLL)","Caramel, chocolate-flavored roll" +91703020,"CARAMEL CANDY, NOT CHOCOLATE","Caramel, flavor other than chocolate" +91703030,"CARAMEL CANDY, W/ NUTS","Caramel, with nuts" +91703040,"CARAMEL CANDY, CHOCOLATE COVERED","Caramel candy, chocolate covered" +91703050,"CARAMEL CANDY, W/ NUTS & CEREAL, CHOCOLATE-COVERED","Caramel with nuts and cereal, chocolate covered" +91703060,"CARAMEL CANDY, W/ NUTS, CHOCOLATE-COVERED","Caramel with nuts, chocolate covered" +91703070,"ROLOS CANDY","Rolo" +91703080,"CARAMEL, ALL FLAVORS, SUGAR FREE","Caramel, all flavors, sugar free" +91703150,"TOBLERONE,MILK CHOCOLATE W/ HONEY & ALMOND NOUGAT","Toblerone, milk chocolate with honey and almond nougat" +91703200,"TWIX CARAMEL COOKIE BARS","TWIX Caramel Cookie Bars (formerly TWIX Cookie Bars)" +91703250,"TWIX CHOCOLATE FUDGE COOKIE BARS","TWIX Chocolate Fudge Cookie Bars" +91703300,"TWIX PEANUT BUTTER COOKIE BARS","TWIX Peanut Butter Cookie Bars" +91703400,"WHATCHAMACALLIT CANDY","Whatchamacallit" +91703500,"NUTS, CAROB-COATED","Nuts, carob-coated" +91703600,"ESPRESSO COFFEE BEANS, CHOCOLATE-COVERED","Espresso coffee beans, chocolate-covered" +91705010,"MILK CHOCOLATE CANDY, PLAIN","Milk chocolate candy, plain" +91705020,"MILK CHOCOLATE CANDY, WITH CEREAL","Milk chocolate candy, with cereal" +91705030,"KIT KAT CANDY BAR","Kit Kat" +91705040,"CHOCOLATE, MILK, W/ NUTS, NOT ALMONDS OR PEANUTS","Chocolate, milk, with nuts, not almond or peanuts" +91705050,"MILK CHOCOLATE CANDY, WITH FRUIT AND NUTS","Milk chocolate candy, with fruit and nuts" +91705060,"MILK CHOCOLATE CANDY, WITH ALMONDS","Milk chocolate candy, with almonds" +91705070,"CHOCOLATE, MILK, W/ PEANUTS (INCLUDE MR GOODBAR)","Chocolate, milk, with peanuts" +91705090,"CHOCOLATE CANDY WITH FONDANT AND CARAMEL","Chocolate candy with fondant and caramel" +91705200,"CHOCOLATE, SEMI-SWEET","Chocolate, semi-sweet morsel" +91705300,"CHOCOLATE CANDY, SWEET OR DARK","Chocolate, sweet or dark" +91705310,"CHOCOLATE, SWEET OR DARK, WITH ALMONDS","Chocolate, sweet or dark, with almonds" +91705400,"CHOCOLATE CANDY, WHITE","Chocolate, white" +91705410,"CHOCOLATE CANDY, WHITE, W/ ALMONDS","Chocolate, white, with almonds" +91705420,"CHOCOLATE, WHITE, W/ CEREAL, CANDY","Chocolate, white, with cereal" +91705430,"KIT KAT WHITE","Kit Kat White" +91705500,"MEXICAN CHOCOLATE (TABLET)","Mexican chocolate (tablet)" +91706000,"COCONUT CANDY, CHOCOLATE-COVERED","Coconut candy, chocolate covered" +91706100,"COCONUT CANDY, NO CHOCOLATE COVERING","Coconut candy, no chocolate covering" +91706400,"COCONUT CANDY, P.R. STYLE","Coconut candy, Puerto Rican style" +91707000,"FONDANT CANDY","Fondant" +91707010,"FONDANT CANDY, CHOCOLATE COVERED","Fondant, chocolate covered" +91708000,"FRUIT PEEL, CANDIED","Fruit peel, candied" +91708010,"FRUIT CANDY BAR","Date candy" +91708020,"SOFT FRUIT CONFECTION","Soft fruit confections" +91708030,"FRUIT LEATHER / FRUIT SNACKS CANDY","Fruit leather and fruit snacks candy" +91708040,"FUN FRUITS CREME SUPREMES CANDY","Fun Fruits Creme Supremes" +91708070,"TAMARIND CANDY","Tamarind candy" +91708100,"FRUIT SNACKS CANDY W/ HI VIT C","Fruit snacks candy, with high vitamin C" +91708150,"YOGURT COVERED FRUIT SNACKS CANDY, W/ ADDED VITAMIN C","Yogurt covered fruit snacks candy, with added vitamin C" +91708160,"YOGURT COVERED FRUIT SNACKS CANDY ROLLS, W/ HIGH VITAMIN C","Yogurt covered fruit snacks candy rolls, with high vitamin C" +91709000,"GUMDROPS, CHOCOLATE-COVERED","Gumdrops, chocolate covered" +91713010,"FUDGE, CHOCOLATE, CHOCOLATE-COATED","Fudge, chocolate, chocolate-coated" +91713020,"FUDGE, CHOCOLATE, CHOCOLATE-COATED, W/ NUTS","Fudge, chocolate, chocolate-coated, with nuts" +91713030,"FUDGE, CHOCOLATE","Fudge, chocolate" +91713040,"FUDGE, CHOCOLATE, W/ NUTS","Fudge, chocolate, with nuts" +91713050,"FUDGE, PEANUT BUTTER","Fudge, peanut butter" +91713060,"FUDGE, PEANUT BUTTER, W/ NUTS","Fudge, peanut butter, with nuts" +91713070,"FUDGE, VANILLA","Fudge, vanilla" +91713080,"FUDGE, VANILLA, W/ NUTS","Fudge, vanilla, with nuts" +91713090,"FUDGE, DIVINITY","Fudge, divinity" +91713100,"FUDGE, BROWN SUGAR (PANUCHI)","Fudge, brown sugar (penuche)" +91715000,"FUDGE, CARAMEL AND NUT, CHOCOLATE-COATED CANDY","Fudge, caramel and nut, chocolate-coated candy" +91715100,"SNICKERS CANDY BAR","SNICKERS Bar" +91715200,"BABY RUTH CANDY BAR","Baby Ruth" +91715300,"100 GRAND BAR (INCL $100,000 BAR)","100 GRAND Bar" +91716010,"HALVAH, PLAIN","Halvah, plain" +91716110,"HALVAH, CHOCOLATE-COVERED","Halvah, chocolate covered" +91718000,"HONEY-COMBED HARD CANDY, PEANUT BUTTER","Honey-combed hard candy with peanut butter" +91718050,"HONEY-COMBED CANDY, PEANUT BUTTER, CHOC-COVERED","Honey-combed hard candy with peanut butter, chocolate covered" +91718100,"BUTTERFINGER CANDY BAR","Butterfinger" +91718110,"BUTTERFINGER CRISP","Butterfinger Crisp" +91718200,"JIMMIES (INCLUDE CHOCOLATE-FLAVORED SPRINKLES)","Chocolate-flavored sprinkles" +91718300,"LADOO, ROUND BALL, ASIAN-INDIAN DESSERT","Ladoo, round ball, Asian-Indian dessert" +91721000,"LICORICE CANDY","Licorice" +91723000,"MARSHMALLOW","Marshmallow" +91723010,"MARSHMALLOW, CHOCOLATE-COVERED","Marshmallow, chocolate covered" +91723020,"MARSHMALLOW, CANDY-COATED","Marshmallow, candy-coated" +91723050,"MARSHMALLOW, COCONUT-COATED","Marshmallow, coconut-coated" +91726000,"NOUGAT CANDY, PLAIN","Nougat, plain" +91726110,"NOUGAT CANDY, W/ CARAMEL, CHOCOLATE-COVERED","Nougat, with caramel, chocolate covered" +91726130,"MILKY WAY BAR","MILKY WAY Bar" +91726140,"MILKY WAY MIDNIGHT BAR (FORMERLY MILKY WAY DARK BAR)","MILKY WAY MIDNIGHT Bar (formerly MILKY WAY DARK Bar)" +91726150,"MARS ALMOND BAR (FORMERLY MARS BAR)","MARS Almond Bar (formerly MARS bar)" +91726410,"NOUGAT CANDY, CHOCOLATE-COVERED","Nougat, chocolate covered" +91726420,"3 MUSKETEERS BAR","3 MUSKETEERS Bar" +91726425,"3 MUSKETEERS TRUFFLE CRISP BAR","3 Musketeers Truffle Crisp Bar" +91727010,"NUTS, CHOCOLATE-COVERED, NOT ALMONDS OR PEANUTS","Nuts, chocolate covered, not almonds or peanuts" +91728000,"NUT ROLL, FUDGE OR NOUGAT, CARAMEL & NUTS","Nut roll, fudge or nougat, caramel and nuts" +91728500,"SUGARED PECANS (SUGAR & EGG WHITE COATING)","Sugared pecans (sugar and egg white coating)" +91731000,"PEANUTS, CHOCOLATE-COVERED","Peanuts, chocolate covered" +91731010,"M&M'S PEANUT CANDIES","M&M's Peanut Chocolate Candies" +91731060,"M&M'S PEANUT BUTTER CHOCOLATE CANDIES","M&M's Peanut Butter Chocolate Candies" +91731100,"PEANUTS, SUGAR-COATED","Peanuts, sugar-coated" +91731150,"PEANUTS, YOGURT-COVERED","Peanuts, yogurt covered" +91732000,"PEANUT CANDY BAR","Peanut bar" +91732100,"PLANTERS PEANUT CANDY BAR","Planters Peanut Bar" +91733000,"PEANUT BRITTLE","Peanut brittle" +91733200,"PEANUT BAR, CHOCOLATE COVERED CANDY","Peanut Bar, chocolate covered candy" +91734000,"PEANUT BUTTER CANDY, CHOCOLATE-COVERED","Peanut butter, chocolate covered" +91734100,"REESE'S PEANUT BUTTER CUPS","Reese's Peanut Butter Cup" +91734200,"REESE'S PIECES CANDY","Reese's Pieces" +91734300,"REESE'S STICKS","Reese's Sticks" +91734400,"REESE'S FAST BREAK","Reese's Fast Break" +91734450,"REESE'S CRISPY CRUNCHY BAR","Reese's Crispy Crunchy Bar" +91734500,"PEANUT BUTTER MORSELS CANDY","Peanut butter morsels" +91735000,"PRALINES","Pralines" +91736000,"PINEAPPLE CANDY, P.R. STYLE","Pineapple candy, Puerto Rican style" +91739010,"RAISINS, CHOCOLATE-COVERED","Raisins, chocolate covered" +91739600,"RAISINS, YOGURT-COVERED","Raisins, yogurt covered" +91742010,"SESAME CRUNCH CANDY (SAHADI)","Sesame Crunch (Sahadi)" +91745010,"GUMDROPS","Gumdrops" +91745020,"HARD CANDY","Hard candy" +91745040,"BUTTERSCOTCH HARD CANDY","Butterscotch hard candy" +91745100,"SKITTLES CANDY","Skittles" +91746010,"SUGAR-COATED CHOCOLATE DISCS CANDY","Sugar-coated chocolate discs" +91746100,"M&M'S MILK CHOCOLATE CANDIES","M&M's Milk Chocolate Candies (formerly M&M's Plain Chocolate Candies)" +91746120,"SIXLETS CANDY","Sixlets" +91746150,"EASTER EGG, CANDY-COATED CHOCOLATE","Easter egg, candy coated chocolate" +91746200,"M&M'S PRETZEL CHOCOLATE CANDIES","M&M's Pretzel Chocolate Candies" +91750000,"TAFFY","Taffy" +91760000,"TOFFEE, PLAIN","Toffee, plain" +91760100,"TOFFEE, CHOCOLATE COVERED (INCL HEATH BAR, SKOR)","Toffee, chocolate covered" +91760200,"TOFFEE, CHOCOLATE-COATED, W/ NUTS","Toffee, chocolate-coated, with nuts" +91760500,"TRUFFLES","Truffles" +91760700,"WAX CANDY, LIQUID FILLED","Wax candy, liquid filled" +91770000,"DIETETIC OR LOW CALORIE CANDY, NFS","Dietetic or low calorie candy, NFS" +91770010,"DIETETIC OR LOW CALORIE GUMDROPS","Dietetic or low calorie gumdrops" +91770020,"DIETETIC OR LOW CALORIE HARD CANDY","Dietetic or low calorie hard candy" +91770030,"DIETETIC OR LOW CALORIE CANDY, CHOCOLATE-COVERED","Dietetic or low calorie candy, chocolate covered" +91770050,"MINTS, DIETETIC OR LOW CALORIE","Dietetic or low calorie mints" +91800100,"CHEWING GUM, NFS","Chewing gum, NFS" +91801000,"CHEWING GUM, SUGARED","Chewing gum, sugared" +91802000,"CHEWING GUM, SUGARLESS","Chewing gum, sugarless" +92100000,"COFFEE, NS AS TO TYPE","Coffee, NS as to type" +92100500,"COFFEE, REGULAR, NS GROUND/INSTANT","Coffee, regular, NS as to ground or instant" +92101000,"COFFEE, MADE FROM GROUND, REGULAR","Coffee, made from ground, regular" +92101500,"COFFEE, BREWED, EQUAL PARTS REG & DECAFFEINATED","Coffee, made from ground, equal parts regular and decaffeinated" +92101600,"COFFEE, TURKISH","Coffee, Turkish" +92101610,"COFFEE, ESPRESSO","Coffee, espresso" +92101630,"COFFEE, ESPRESSO, DECAFFEINATED","Coffee, espresso, decaffeinated" +92101640,"COFFEE, MEXICAN, REG, UNSWEETENED (NO MILK)","Coffee, Mexican, regular, unsweetened (no milk; not cafe con leche)" +92101650,"COFFEE, MEXICAN, REG, SWEETENED (NO MILK)","Coffee, Mexican, regular, sweetened (no milk; not cafe con leche)" +92101660,"COFFEE, MEXICAN, DECAF, UNSWEETENED (NO MILK)","Coffee, Mexican, decaffeinated, unsweetened (no milk; not cafe con leche)" +92101670,"COFFEE, MEXICAN, DECAF, SWEETENED (NO MILK)","Coffee, Mexican, decaffeinated, sweetened (no milk; not cafe con leche)" +92101700,"COFFEE, MADE FROM GROUND, REGULAR, FLAVORED","Coffee, made from ground, regular, flavored" +92101800,"COFFEE, CUBAN","Coffee, Cuban" +92101900,"COFFEE, LATTE","Coffee, Latte" +92101910,"COFFEE, LATTE, DECAFFEINATED","Coffee, Latte, decaffeinated" +92101920,"BLENDED COFFEE BEVERAGE, REGULAR, SWEETENED","Blended coffee beverage, made with regular coffee, milk, and ice, sweetened" +92101925,"BLENDED COFFEE BEVERAGE, REG, SWTND, W/ WHP CRM","Blended coffee beverage, made with regular coffee, milk, and ice, sweetened, with whipped cream" +92101930,"BLENDED COFFEE BEVERAGE, DECAF, SWEETENED","Blended coffee beverage, made with decaffeinated coffee, milk, and ice, sweetened" +92101935,"BLENDED COFFEE BEVERAGE, DECAF, SWTND, W/ WHP CRM","Blended coffee beverage, made with decaffeinated coffee, milk, and ice, sweetened, with whipped cream" +92101950,"COFFEE, MOCHA","Coffee, mocha" +92101960,"COFFEE, MOCHA, MADE W/ SOY MILK","Coffee, mocha, made with soy milk" +92103000,"COFFEE, MADE FROM POWDERED INSTANT, REGULAR","Coffee, made from powdered instant, regular" +92104000,"COFFEE, FROM POWDER, 50% LESS CAFFEINE","Coffee, made from powdered instant, 50% less caffeine" +92105000,"COFFEE, LIQUID CONCENTRATE, NOT RECONSTITUTED","Coffee, liquid concentrate" +92105010,"COFFEE, MADE FROM LIQUID CONCENTRATE","Coffee, made from liquid concentrate" +92106000,"COFFEE, ACID NEUTRALIZED, FROM POWDERED INSTANT","Coffee, acid neutralized, from powdered instant" +92111000,"COFFEE, DECAFFEINATED, NS AS TO GROUND OR INSTANT","Coffee, decaffeinated, NS as to ground or instant" +92111010,"COFFEE, DECAFFEINATED, MADE FROM GROUND","Coffee, decaffeinated, made from ground" +92114000,"COFFEE, DECAFFEINATED, MADE FROM POWDERED INSTANT","Coffee, decaffeinated, made from powdered instant" +92121000,"COFFEE, FROM POWDERED MIX,W/WHITENER&SUGAR, INSTANT","Coffee, made from powdered instant mix, with whitener and sugar, instant" +92121010,"COFFEE, FROM POWDER, PRESWEETENED, NO WHITENER","Coffee, made from powdered instant mix, presweetened, no whitener" +92121020,"COFFEE & COCOA (MOCHA), W/ WHITENER, PRESWEETENED","Coffee and cocoa (mocha), made from powdered instant mix, with whitener, presweetened" +92121030,"COFFEE & COCOA, FROM MIX, W/WHITENER, LOW CAL SWEET","Coffee and cocoa (mocha), made from powdered instant mix, with whitener and low calorie sweetener" +92121040,"COFFEE, FROM POWDER, W/ WHITENER & LO CAL SWEETENER","Coffee, made from powdered instant mix, with whitener and low calorie sweetener" +92121050,"COFFEE&COCOA,FROM PWDR,W/WHITE&LOW CAL SWEET,DECAF","Coffee and cocoa (mocha), made from powdered instant mix, with whitener and low calorie sweetener, decaffeinated" +92130000,"COFFEE, REG, PRESWEETENED W/SUGAR, PRE-LIGHTENED","Coffee, regular, presweetened with sugar, pre-lightened" +92130001,"COFFEE, DECAFFEINATED, PRESWEETENED W/ SUGAR, PRE-LIGHTENED","Coffee, decaffeinated, presweetened with sugar, pre-lightened" +92130005,"COFFEE, REGULAR, WITH LOW CALORIE SWEETENER, PRE-LIGHTENED","Coffee, regular, with low-calorie sweetener, pre-lightened" +92130006,"COFFEE, DECAFFEINATED,W/ LOW CALORIE SWEETENER,PRE-LIGHTENED","Coffee, decaffeinated, with low-calorie sweetener, pre-lightened" +92130010,"COFFEE, PRE-LIGHTENED, NO SUGAR","Coffee, pre-lightened, no sugar" +92130020,"COFFEE, PRESWEETENED W/ SUGAR","Coffee, presweetened with sugar" +92150000,"COFFEE & CHICORY, NS AS TO GROUND OR INSTANT","Coffee and chicory, NS as to ground or instant" +92151000,"COFFEE & CHICORY, MADE FROM POWDERED INSTANT","Coffee and chicory, made from powdered instant" +92151100,"COFFEE, DECAFFEINATED, AND CHICORY, FROM INSTANT","Coffee, decaffeinated, and chicory, made from powdered instant" +92152000,"COFFEE & CHICORY, MADE FROM GROUND","Coffee and chicory, made from ground" +92153000,"COFFEE, REGULAR, W/ CEREAL (INCLUDE W/ BARLEY)","Coffee, regular, with cereal" +92153100,"COFFEE, DECAFFEINATED, W/ CEREAL (INCLUDE W/BARLEY)","Coffee, decaffeinated, with cereal" +92161000,"CAPPUCCINO","Cappuccino" +92161005,"CAPPUCCINO, SWEETENED","Cappuccino, sweetened" +92162000,"CAPPUCCINO, DECAFFEINATED","Cappuccino, decaffeinated" +92162005,"CAPPUCCINO, DECAFFEINATED, SWEETENED","Cappuccino, decaffeinated, sweetened" +92191000,"COFFEE, DRY POWDER, NS AS TO REG OR DECAF","Coffee, dry instant powder, NS as to regular or decaffeinated" +92191100,"COFFEE, DRY POWDER, REGULAR","Coffee, dry instant powder, regular" +92191200,"COFFEE, DRY POWDER, DECAFFEINATED","Coffee, dry instant powder, decaffeinated" +92191250,"COFFEE, DRY, ACID NEUTRALIZED (INCLUDE KAVA)","Coffee, dry, acid neutralized" +92191500,"COFFEE & CHICORY, DRY POWDER","Coffee and chicory, dry instant powder" +92191520,"COFFEE, DECAFFEINATED, AND CHICORY, DRY POWDER","Coffee, decaffeinated, and chicory, dry instant powder" +92192000,"COFFEE & COCOA (MOCHA) MIX,W/WHITENER,PRESWEET, DRY","Coffee and cocoa (mocha) mix, dry instant powder with whitener, presweetened" +92192030,"COFFEE & COCOA (MOCHA) MIX,W/WHITENER, LOW CAL, DRY","Coffee and cocoa (mocha) mix, dry instant powder with whitener and low calorie sweetener" +92192040,"COFFEE&COCOA MIX,DRY,W/WHITENER&LOW CAL SWEET,DECAF","Coffee and cocoa (mocha) mix, dry instant powder, with whitener and low calorie sweetener, decaffeinated" +92193000,"COFFEE, DRY MIX, W/ WHITENER & SUGAR","Coffee, dry instant powder, with whitener and sugar" +92193020,"COFFEE, DRY MIX, W/ WHITENER & LOW CAL SWEETENER","Coffee, dry instant powder, with whitener and low calorie sweetener" +92201010,"POSTUM (COFFEE SUBSTITUTE)","Postum" +92202010,"CHICORY (COFFEE SUBSTITUTE)","Chicory" +92203000,"CEREAL, BEVERAGE (INCLUDE PERO, BREAK AWAY)","Cereal beverage" +92203110,"CEREAL BEVERAGE, W/BEET ROOTS,FROM POWDERED INSTANT","Cereal beverage with beet roots, from powdered instant" +92204000,"MATE, SWEETENED BEVERAGE FROM DRIED GREEN LEAVES","Mate, sweetened beverage made from dried green leaves" +92205000,"RICE BEVERAGE (INCL RICE TEA)","Rice beverage" +92291300,"POSTUM, DRY POWDER","Postum, dry powder" +92301000,"TEA, NS AS TO TYPE, UNSWEETENED","Tea, NS as to type, unsweetened" +92301060,"TEA, NS AS TO TYPE, PRESWEETENED W/ SUGAR","Tea, NS as to type, presweetened with sugar" +92301080,"TEA, PRESWEETENED W/ LOW CALORIE SWEETENER","Tea, NS as to type, presweetened with low calorie sweetener" +92301100,"TEA, NS AS TO TYPE, DECAFFEINATED, UNSWEETENED","Tea, NS as to type, decaffeinated, unsweetened" +92301130,"TEA, NS AS TO TYPE, PRESWEETENED, NS AS TO SWEETNER","Tea, NS as to type, presweetened, NS as to sweetener" +92301160,"TEA, DECAFFEINATED, W/ SUGAR, NFS","Tea, NS as to type, decaffeinated, presweetened with sugar" +92301180,"TEA, DECAFFEINATED, LOW CALORIE SWEETENER, NFS","Tea, NS as to type, decaffeinated, presweetened with low calorie sweetener" +92301190,"TEA, PRESWEETENED, NS SWEETENER, DECAFFEINATED","Tea, NS as to type, decaffeinated, presweetened, NS as to sweetener" +92302000,"TEA, LEAF, UNSWEETENED","Tea, leaf, unsweetened" +92302200,"TEA, LEAF, PRESWEETENED W/ SUGAR","Tea, leaf, presweetened with sugar" +92302300,"TEA, LEAF, PRESWEETENED W/ LOW CALORIE SWEETENER","Tea, leaf, presweetened with low calorie sweetener" +92302400,"TEA, LEAF, PRESWEETENED, NS AS TO SWEETENER","Tea, leaf, presweetened, NS as to sweetener" +92302500,"TEA, DECAFFEINATED, UNSWEETENED","Tea, leaf, decaffeinated, unsweetened" +92302600,"TEA, LEAF, DECAFFEINATED, PRESWEETENED W/ SUGAR","Tea, leaf, decaffeinated, presweetened with sugar" +92302700,"TEA, LEAF, DECAFFEINATED, LOW CALORIE SWEETENER","Tea, leaf, decaffeinated, presweetened with low calorie sweetener" +92302800,"TEA, LEAF, DECAFFEINATED, PRESWEETENED, NFS","Tea, leaf, decaffeinated, presweetened, NS as to sweetener" +92304000,"TEA, MADE FROM FROZEN CONCENTRATE, UNSWEETENED","Tea, made from frozen concentrate, unsweetened" +92304700,"TEA, FROM FROZ CONC, DECAF, PRESWEETND, LOW CALORIE","Tea, made from frozen concentrate, decaffeinated, presweetened with low calorie sweetener" +92305000,"TEA, MADE FROM POWDERED INSTANT, PRESWEETENED","Tea, made from powdered instant, presweetened, NS as to sweetener" +92305010,"TEA, MADE FROM POWDERED INSTANT, UNSWEETENED","Tea, made from powdered instant, unsweetened" +92305040,"TEA, MADE FROM POWDERED INSTANT,PRESWEETEND W/SUGAR","Tea, made from powdered instant, presweetened with sugar" +92305050,"TEA, FROM POWDER, DECAFFEINATED, PRESWEET W/ SUGAR","Tea, made from powdered instant, decaffeinated, presweetened with sugar" +92305090,"TEA, MADE FROM POWDERED INSTANT,W/LO CAL SWEETENER","Tea, made from powdered instant, presweetened with low calorie sweetener" +92305110,"TEA, FROM INSTANT, DECAF, PRESWEETENED, LOW CALORIE","Tea, made from powdered instant, decaffeinated, presweetened with low calorie sweetener" +92305180,"TEA ,MADE FROM POWDERED INSTANT, DECAF ,UNSWEET","Tea, made from powdered instant, decaffeinated, unsweetened" +92305800,"TEA, FROM POWDER, DECAFFEINATED, PRESWEETENED","Tea, made from powdered instant, decaffeinated, presweetened, NS as to sweetener" +92306000,"TEA, HERBAL (INCLUDE SASSAFRAS,LICORICE)","Tea, herbal" +92306020,"TEA, HERBAL, PRESWEETENED W/ SUGAR","Tea, herbal, presweetened with sugar" +92306030,"TEA, HERBAL, PRESWEETENED W/ LOW CAL SWEETENER","Tea, herbal, presweetened with low calorie sweetener" +92306040,"TEA, HERBAL, PRESWEETENED, NS AS TO SWEETENER","Tea, herbal, presweetened, NS as to sweetener" +92306050,"TEA, MADE FROM CARAWAY SEEDS","Tea, made from caraway seeds" +92306090,"TEA, HIBISCUS","Tea, hibiscus" +92306100,"CORN BEVERAGE(INCLUDE CORN TEA)","Corn beverage" +92306200,"BEAN BEVERAGE (INCLUDE BEAN TEA)","Bean beverage" +92306610,"TEA, RUSSIAN","Tea, Russian" +92306700,"TEA, CHAMOMILE","Tea, chamomile" +92307000,"TEA, POWDERED INSTANT, UNSWEETENED, DRY","Tea, powdered instant, unsweetened, dry" +92307400,"TEA, POWDERED INSTANT, SWEETENED, NS SWEETENER, DRY","Tea, powdered instant, sweetened, NS as to sweetener, dry" +92307500,"HALF AND HALF BEVERAGE, HALF ICED TEA/HALF LEMONADE","Half and Half beverage, half iced tea and half fruit juice drink (lemonade)" +92307510,"HALF&HALF BEV, HALF ICED TEA/HALF LEMONADE,LOW CAL","Half and Half beverage, half iced tea and half fruit juice drink (lemonade), low calorie" +92400000,"SOFT DRINK, NFS","Soft drink, NFS" +92400100,"SOFT DRINK, NFS, SUGAR-FREE","Soft drink, NFS, sugar-free" +92410110,"CARBONATED WATER,SWEETEND(INCL TONIC,QUININE WATER)","Carbonated water, sweetened" +92410210,"CARBONATED WATER, UNSWEETENED (INCL CLUB SODA)","Carbonated water, unsweetened" +92410250,"CARBONATED WATER, SWEETENED, WITH LOW-CALORIE OR NO-CALORIE","Carbonated water, sweetened, with low-calorie or no-calorie sweetener" +92410310,"SOFT DRINK, COLA-TYPE","Soft drink, cola-type" +92410315,"SOFT DRINK, COLA TYPE, REDUCED SUGAR","Soft drink, cola type, reduced sugar" +92410320,"SOFT DRINK, COLA-TYPE, SUGAR-FREE","Soft drink, cola-type, sugar-free" +92410330,"SOFT DRINK, COLA-TYPE, W/ HIGHER CAFFEINE (INCL JOLT)","Soft drink, cola-type, with higher caffeine" +92410340,"SOFT DRINK, COLA-TYPE, DECAFFEINATED","Soft drink, cola-type, decaffeinated" +92410350,"SOFT DRINK, COLA-TYPE, DECAFFEINATED, SUGAR-FREE","Soft drink, cola-type, decaffeinated, sugar-free" +92410360,"SOFT DRINK, PEPPER-TYPE (INCL DR. PEPPER, MR. PIBB)","Soft drink, pepper-type" +92410370,"SOFT DRINK, PEPPER-TYPE, SUGAR-FREE","Soft drink, pepper-type, sugar-free" +92410390,"SOFT DRINK, PEPPER-TYPE, DECAFFEINATED","Soft drink, pepper-type, decaffeinated" +92410400,"SOFT DRINK, PEPPER-TYPE, DECAFFEINATED, SUGAR-FREE","Soft drink, pepper-type, decaffeinated, sugar-free" +92410410,"CREAM SODA","Cream soda" +92410420,"CREAM SODA, SUGAR-FREE","Cream soda, sugar-free" +92410510,"SOFT DRINK, FRUIT-FLAVORED, CAFFEINE FREE","Soft drink, fruit-flavored, caffeine free" +92410520,"SOFT DRINK, FRUIT-FLAV, SUGAR-FREE, CAFFEINE FREE","Soft drink, fruit-flavored, sugar free, caffeine free" +92410550,"SOFT DRINK, FRUIT-FLAVORED, W/ CAFFEINE","Soft drink, fruit flavored, caffeine containing" +92410560,"SOFT DRINK, FRUIT-FLAVORED, W/ CAFFEINE, SUGAR-FREE","Soft drink, fruit flavored, caffeine containing, sugar-free" +92410610,"GINGER ALE","Ginger ale" +92410620,"GINGERALE, SUGAR-FREE","Ginger ale, sugar-free" +92410710,"ROOT BEER","Root beer" +92410720,"ROOT BEER, SUGAR-FREE","Root beer, sugar-free" +92410810,"CHOCOLATE-FLAVORED SODA","Chocolate-flavored soda" +92410820,"CHOCOLATE-FLAVORED SODA, SUGAR-FREE","Chocolate-flavored soda, sugar-free" +92411510,"COLA W/ FRUIT OR VANILLA FLAVOR","Cola with fruit or vanilla flavor" +92411520,"COLA W/ CHOCOLATE FLAVOR","Cola with chocolate flavor" +92411610,"COLA W/ FRUIT OR VANILLA FLAVOR, SUGAR-FREE","Cola with fruit or vanilla flavor, sugar-free" +92411620,"COLA W/ CHOC FLAVOR, SUGAR FREE","Cola with chocolate flavor, sugar-free" +92417010,"SOFT DRINK, ALE TYPE (INCLUDE ALE-8)","Soft drink, ale type" +92431000,"CARBONATED JUICE DRINK, NS AS TO TYPE OF JUICE","Carbonated juice drink, NS as to type of juice" +92432000,"CARBONATED CITRUS JUICE DRINK","Carbonated citrus juice drink" +92433000,"CARBONATED NONCITRUS JUICE DRINK","Carbonated noncitrus juice drink" +92510610,"FRUIT JUICE DRINK","Fruit juice drink" +92510650,"TAMARIND DRINK, P.R. (REFRESCO DE TAMARINDO)","Tamarind drink, Puerto Rican (Refresco de tamarindo)" +92510720,"FRUIT PUNCH, MADE W/ FRUIT JUICE & SODA","Fruit punch, made with fruit juice and soda" +92510730,"FRUIT PUNCH, MADE W/ SODA, FRUIT JUICE & SHERBET","Fruit punch, made with soda, fruit juice, and sherbet or ice cream" +92511000,"LEMONADE, FROZEN CONCENTRATE, NOT RECONSTITUTED","Lemonade, frozen concentrate, not reconstituted" +92511010,"FRUIT FLAVORED DRINK (FORMERLY LEMONADE)","Fruit flavored drink (formerly lemonade)" +92511250,"CITRUS FRUIT JUICE DRINK, CONTAINING 40-50% JUICE","Citrus fruit juice drink, containing 40-50% juice" +92512040,"FROZEN DAIQUIRI MIX, CONCENTRATE, NOT RECONSTITUTED","Frozen daiquiri mix, frozen concentrate, not reconstituted" +92512050,"FROZEN DAIQUIRI MIX, FROM FROZ CONC, RECONSTITUTED","Frozen daiquiri mix, from frozen concentrate, reconstituted" +92512090,"PINA COLADA, NONALCOHOLIC","Pina Colada, nonalcoholic" +92512110,"MARGARITA MIX, NONALCOHOLIC","Margarita mix, nonalcoholic" +92513000,"FRUIT FLAVORED FROZEN DRINK","Fruit flavored frozen drink" +92530410,"FRUIT FLAVORED DRINK, WITH HIGH VITAMIN C","Fruit flavored drink, with high vitamin C" +92530510,"CRANBERRY JUICE DRINK OR COCKTAIL, W/ HIGH VIT C","Cranberry juice drink or cocktail, with high vitamin C" +92530610,"FRUIT JUICE DRINK, WITH HIGH VITAMIN C","Fruit juice drink, with high vitamin C" +92530950,"VEGETABLE & FRUIT JUICE DRINK, W/ HI VIT C","Vegetable and fruit juice drink, with high vitamin C" +92531030,"FRUIT JUICE DRINK, W/ VIT B1) & HI VIT C","Fruit juice drink, with thiamin (vitamin B1) and high vitamin C" +92541010,"FRUIT FLAVORED DRINK, MADE FROM POWDERED MIX","Fruit flavored drink, made from powdered mix" +92542000,"FRUIT FLAVORED DRINK, MADE FROM POWDERED MIX, W/ HI VIT C","Fruit flavored drink, made from powdered mix,with high vitamin C" +92550030,"FRUIT JUICE DRINK, LOW CALORIE, W/ HIGH VITAMIN C","Fruit juice drink, low calorie, with high vitamin C" +92550040,"FRUIT JUICE DRINK, LOW CALORIE","Fruit juice drink, low calorie" +92550110,"CRANBERRY JUICE DRINK OR COCKTAIL, LOW CAL, W/ HIGH VIT C","Cranberry juice drink or cocktail, low calorie, with high vitamin C" +92550350,"LIGHT ORANGE JC BEVERAGE, 40-50% JC, LOWER SUGAR & CALORIES","Light orange juice beverage, 40-50% juice, lower sugar and calories, with artificial sweetener" +92550400,"VEGETABLE & FRUIT JUICE DRINK, LOW CAL, W/ HIGH VIT C","Vegetable and fruit juice drink, low calorie, with high vitamin C" +92550405,"VEGETABLE & FRUIT JUICE DRINK, LOW CAL, W/ HIGH VIT C,+E,A","Vegetable and fruit juice drink, low calorie, with high vitamin C plus added vitamin E and vitamin A" +92550610,"FRUIT FLAVORED DRINK, LOW CAL, W/ HIGH VIT C","Fruit flavored drink, low calorie, with high vitamin C" +92550620,"FRUIT FLAVORED DRINK, LOW CALORIE","Fruit flavored drink, low calorie" +92552000,"FRUIT FLAV DRINK, MADE FROM PWDR, LOW CAL, W/ HI VIT C","Fruit flavored drink, made from powdered mix, low calorie, with high vitamin C" +92552010,"FRUIT FLAVORED DRINK, MADE FROM PWDR, LOW CALORIE","Fruit flavored drink, made from powdered mix, low calorie" +92552020,"FRUIT JUICE DRINK, REDUCED SUGAR, W/ VIT B1 & HI VIT C","Fruit juice drink, reduced sugar, with thiamin (vitamin B1) and high vitamin C" +92552030,"FRUIT JUICE DRINK, REDUCED SUGAR, WITH VITAMIN E","Fruit juice drink, reduced sugar, with vitamin E" +92582100,"FRUIT JUICE DRINK, WITH HIGH VITAMIN C, PLUS ADDED CALCIUM","Fruit juice drink, with high vitamin C, plus added calcium" +92582110,"FRUIT JUICE DRINK, W/ VIT B1, HI VIT C + CALCIUM","Fruit juice drink, with thiamin (vitamin B1) and high vitamin C plus calcium" +92610010,"HORCHATA BEVERAGE, MADE W/ ALMONDS","Horchata beverage, made with almonds or other nuts and seeds" +92610110,"COCONUT BEVERAGE, P.R.","Coconut beverage, Puerto Rican" +92611010,"OATMEAL BEVERAGE, P.R.","Oatmeal beverage, Puerto Rican" +92611100,"OATMEAL BEVERAGE W/ MILK","Oatmeal beverage with milk (Atole de avena)" +92611510,"HORCHATA BEVERAGE, MADE W/ RICE","Horchata beverage, made with rice" +92611600,"HORCHATA BEVERAGE, NFS","Horchata beverage, NFS" +92612010,"SUGAR CANE BEVERAGE, P.R.","Sugar cane beverage, Puerto Rican" +92613010,"ATOLE (CORNMEAL BEVERAGE)","Atole (corn meal beverage)" +92613510,"CORN BEV W/ CHOC & MILK(CHAMPURRADO,ATOLE DE CHOC)","Corn beverage with chocolate and milk (Champurrado, Atole de Chocolate)" +92801000,"NONALCOHOLIC WINE","Wine, nonalcoholic" +92802000,"WINE, LIGHT, NONALCOHOLIC","Wine, light, nonalcoholic" +92803000,"NONALCOHOLIC MALT BEVERAGE","Nonalcoholic malt beverage" +92804000,"SHIRLEY TEMPLE","Shirley Temple" +92900100,"TANG, DRY CONCENTRATE","Tang, dry concentrate" +92900110,"FRUIT-FLAV BEVERAGE, DRY CONC, W/ SUGAR, NOT RECONSTITUTED","Fruit-flavored beverage, dry concentrate, with sugar, not reconstituted" +92900200,"FRUIT-FLAV BEV, DRY CONC,LO CAL(INCL CRYSTAL LIGHT)","Fruit-flavored beverage, dry concentrate, low calorie, not reconstituted" +92900300,"FRUIT-FLAV THIRST QUENCH BEV, DRY CONC (GATORADE)","Fruit-flavored thirst quencher beverage, dry concentrate, not reconstituted" +93101000,"BEER","Beer" +93102000,"BEER, LITE","Beer, lite" +93106000,"ALCOHOLIC MALT BEVERAGE, SWEETENED","Alcoholic malt beverage, sweetened" +93201000,"CORDIAL OR LIQUEUR","Cordial or liqueur" +93301000,"COCKTAIL, NFS","Cocktail, NFS" +93301010,"ALEXANDER","Alexander" +93301020,"BACARDI COCKTAIL","Bacardi cocktail" +93301030,"BLOODY MARY","Bloody Mary" +93301031,"CANADIAN CLUB & SODA","Canadian Club and soda" +93301032,"CAPE COD","Cape Cod" +93301040,"DAIQUIRI","Daiquiri" +93301050,"GIMLET","Gimlet" +93301060,"GIN & TONIC","Gin and Tonic" +93301070,"GRASSHOPPER","Grasshopper" +93301080,"HIGH BALL","High ball" +93301085,"KAMIKAZE","Kamikaze" +93301090,"MANHATTAN","Manhattan" +93301100,"MARGARITA","Margarita" +93301110,"MARTINI","Martini" +93301115,"MIMOSA","Mimosa" +93301120,"MINT JULEP","Mint julep" +93301125,"MOJITO","Mojito" +93301130,"OLD FASHIONED","Old fashioned" +93301135,"ROB ROY","Rob Roy" +93301136,"RUSTY NAIL","Rusty Nail" +93301139,"SALTY DOG","Salty Dog" +93301140,"SCREWDRIVER (INCLUDE HARVEY WALLBANGER, SLO-SCREW)","Screwdriver" +93301141,"SEABREEZE","Seabreeze" +93301142,"SEVEN AND SEVEN","Seven and Seven" +93301150,"TOM COLLINS (INCLUDE VODKA COLLINS)","Tom Collins" +93301160,"WHISKEY SOUR(INCL SCOTCH,VODKA,APRICOT,BRANDY SOUR)","Whiskey sour" +93301170,"BOURBON & SODA (INCLUDE SCOTCH & SODA, RUM & SODA)","Bourbon and soda" +93301180,"MIXED DRINKS (FOR RECIPE MODIFICATIONS)","Mixed Drinks (for recipe modifications)" +93301190,"RUM & COLA","Rum and cola" +93301200,"PINA COLADA","Pina Colada" +93301220,"COQUITO, P.R. (COCONUT, RUM)","Coquito, Puerto Rican (coconut, rum)" +93301230,"SLOE GIN FIZZ","Sloe gin fizz" +93301240,"BLACK RUSSIAN","Black Russian" +93301250,"WHITE RUSSIAN","White Russian" +93301270,"FRUIT PUNCH, ALCOHOLIC","Fruit punch, alcoholic" +93301280,"SINGAPORE SLING","Singapore Sling" +93301290,"STINGER","Stinger" +93301300,"GIBSON","Gibson" +93301310,"MAI TAI","Mai Tai" +93301320,"TEQUILA SUNRISE","Tequila Sunrise" +93301330,"GIN RICKEY","Gin Rickey" +93301340,"GOLDEN CADILLAC","Golden Cadillac" +93301360,"LONG ISLAND ICED TEA","Long Island iced tea" +93301370,"FUZZY NAVEL COCKTAIL","Fuzzy Navel" +93301400,"IRISH COFFEE (INCL COFFEE ROYALE)","Irish Coffee" +93301450,"LIQUEUR W/ CREAM","Liqueur with cream" +93301500,"FROZEN DAIQUIRI","Frozen daiquiri" +93301510,"FROZEN MARGARITA","Frozen margarita" +93301550,"EGGNOG, ALCOHOLIC","Eggnog, alcoholic" +93301600,"GIN FIZZ","Gin fizz" +93302000,"RUM, HOT BUTTERED","Rum, hot buttered" +93302100,"ZOMBIE","Zombie" +93401010,"WINE, TABLE, RED","Wine, table, red" +93401020,"WINE, TABLE, WHITE","Wine, table, white" +93401100,"WINE, RICE (INCLUDE SAKI)","Wine, rice" +93401300,"WINE, COOKING (ASSUME COOKED)","Wine, cooking (assume cooked)" +93402000,"WINE, DESSERT (INCLUDE MARSALA, PORT, MADEIRA)","Wine, dessert, sweet" +93403000,"WINE, LIGHT","Wine, light" +93404000,"WINE COOLER","Wine cooler" +93404500,"SANGRIA","Sangria" +93404600,"SANGRIA, PUERTO RICAN STYLE","Sangria, Puerto Rican style" +93405000,"WINE SPRITZER","Wine spritzer" +93406000,"GLUG (INCLUDE GLOGG, GLUHWEIN)","Glug" +93501000,"BRANDY","Brandy" +93502000,"WHISKEY","Whiskey" +93503000,"GIN","Gin" +93504000,"RUM","Rum" +93504100,"RUM COOLER","Rum cooler" +93505000,"VODKA","Vodka" +94000100,"WATER, TAP","Water, tap" +94100100,"WATER, BOTTLED, UNSWEETENED","Water, bottled, unsweetened" +94100200,"WATER, BOTTLED, SWEETENED, WITH LOW OR NO CALORIE SWEETENER","Water, bottled, sweetened, with low or no calorie sweetener" +94100300,"WATER, FRUIT FLAVORED, SWTND, W/ CORN SYRUP & LOWCAL SWTNR","Water, fruit flavored, sweetened, with high fructose corn syrup and low calorie sweetener" +94210100,"PROPEL WATER","Propel Water" +94210200,"GLACEAU WATER","Glaceau Water" +94210300,"SOBE LIFEWATER","SoBe Lifewater" +94220200,"GLACEAU WATER, LOW CALORIE","Glaceau Water, low calorie" +94300100,"WATER, BABY, BOTTLED, UNSWEETENED","Water, baby, bottled, unsweetened" +95101000,"BOOST, NUTRITIONAL DRINK, READY-TO-DRINK","Boost, nutritional drink, ready-to-drink" +95101010,"BOOST PLUS, NUTRITIONAL DRINK, READY-TO-DRINK","Boost Plus, nutritional drink, ready-to-drink" +95102000,"CARNATION INSTANT BREAKFAST, NUTRITIONAL DRINK, REGULAR, RTD","Carnation Instant Breakfast, nutritional drink, regular, ready-to-drink" +95102010,"CARNATION INSTANT BREAKFAST, NUTRITIONAL DRINK, SUGAR FREE,","Carnation Instant Breakfast, nutritional drink, sugar free, ready-to-drink" +95103000,"ENSURE, NUTRITIONAL SHAKE, READY-TO-DRINK","Ensure, nutritional shake, ready-to-drink" +95103010,"ENSURE PLUS, NUTRITIONAL SHAKE, READY-TO-DRINK","Ensure Plus, nutritional shake, ready-to-drink" +95104000,"GLUCERNA, NUTRITIONAL SHAKE, READY-TO-DRINK","Glucerna, nutritional shake, ready-to-drink" +95105000,"KELLOGG'S SPECIAL K PROTEIN SHAKE","Kellogg's Special K Protein Shake" +95106000,"MUSCLE MILK, READY-TO-DRINK","Muscle Milk, ready-to-drink" +95106010,"MUSCLE MILK, LIGHT, READY-TO-DRINK","Muscle Milk, light, ready-to-drink" +95110000,"SLIM FAST SHAKE, MEAL REPLACEMENT, REGULAR, READY-TO-DRINK","Slim Fast Shake, meal replacement, regular, ready-to-drink" +95110010,"SLIM FAST SHAKE, MEAL REPLACEMENT, SUGAR FREE, RTD","Slim Fast Shake, meal replacement, sugar free, ready-to-drink" +95110020,"SLIM FAST SHAKE, MEAL REPLACEMENT, HIGH PROTEIN, RTD","Slim Fast Shake, meal replacement, high protein, ready-to-drink" +95120000,"NUTRITIONAL DRINK OR MEAL REPLACEMENT, READY-TO-DRINK, NFS","Nutritional drink or meal replacement, ready-to-drink, NFS" +95120010,"NUTRITIONAL DRINK OR MEAL REPLACEMENT, HIGH PROTEIN, RTD","Nutritional drink or meal replacement, high protein, ready-to-drink, NFS" +95120020,"NUTRITIONAL DRINK OR MEAL REPLACEMENT, HI PROT, LIGHT, RTD","Nutritional drink or meal replacement, high protein, light, ready-to-drink, NFS" +95120050,"NUTRITIONAL DRINK OR MEAL REPLACEMENT, LIQUID, SOY-BASED","Nutritional drink or meal replacement, liquid, soy-based" +95201000,"CARNATION INSTANT BREAKFAST, NUTRITIONAL DRINK MIX, REG,PDR","Carnation Instant Breakfast, nutritional drink mix, regular, powder" +95201010,"CARNATION INSTANT BREAKFAST, NUTR DRINK MIX, SUGAR FREE,PDR","Carnation Instant Breakfast, nutritional drink mix, sugar free, powder" +95201200,"EAS WHEY PROTEIN POWDER","EAS Whey Protein Powder" +95201300,"EAS SOY PROTEIN POWDER","EAS Soy Protein Powder" +95201500,"HERBALIFE, NUTRITIONAL SHAKE MIX, HIGH PROTEIN, POWDER","Herbalife, nutritional shake mix, high protein, powder" +95201600,"ISOPURE PROTEIN POWDER","Isopure protein powder" +95201700,"KELLOGG'S SPECIAL K20 PROTEIN WATER MIX","Kellogg's Special K20 Protein Water Mix" +95202000,"MUSCLE MILK, REGULAR, POWDER","Muscle Milk, regular, powder" +95202010,"MUSCLE MILK, LIGHT, POWDER","Muscle Milk, light, powder" +95210000,"SLIM FAST SHAKE MIX, POWDER","Slim Fast Shake Mix, powder" +95210010,"SLIM FAST SHAKE MIX, SUGAR FREE, POWDER","Slim Fast Shake Mix, sugar free, powder" +95210020,"SLIM FAST SHAKE MIX, HIGH PROTEIN, POWDER","Slim Fast Shake Mix, high protein, powder" +95220000,"NUTRITIONAL DRINK MIX OR MEAL REPLACEMENT, POWDER, NFS","Nutritional drink mix or meal replacement, powder, NFS" +95220010,"NUTRITIONAL DRINK MIX OR MEAL REPLACEMENT, HIGH PRO, PDR,NFS","Nutritional drink mix or meal replacement, high protein, powder, NFS" +95230000,"PROTEIN POWDER, WHEY BASED, NFS","Protein powder, whey based, NFS" +95230010,"PROTEIN POWDER, SOY BASED, NFS","Protein powder, soy based, NFS" +95230020,"PROTEIN POWDER, LIGHT, NFS","Protein powder, light, NFS" +95230030,"PROTEIN POWDER, NFS","Protein powder, NFS" +95310200,"FULL THROTTLE ENERGY DRINK","Full Throttle Energy Drink" +95310400,"MONSTER ENERGY DRINK","Monster Energy Drink" +95310500,"MOUNTAIN DEW AMP ENERGY DRINK","Mountain Dew AMP Energy Drink" +95310550,"NO FEAR ENERGY DRINK","No Fear Energy Drink" +95310555,"NO FEAR MOTHERLOAD ENERGY DRINK","No Fear Motherload Energy Drink" +95310560,"NOS ENERGY DRINK","NOS Energy Drink" +95310600,"RED BULL ENERGY DRINK","Red Bull Energy Drink" +95310700,"ROCKSTAR ENERGY DRINK","Rockstar Energy Drink" +95310750,"SOBE ENERGIZE ENERGY JUICE DRINK","SoBe Energize Energy Juice Drink" +95310800,"VAULT ENERGY DRINK","Vault Energy Drink" +95311000,"ENERGY DRINK","Energy Drink" +95312400,"MONSTER ENERGY DRINK, LO CARB","Monster Energy Drink, Lo Carb" +95312500,"MOUNTAIN DEW AMP ENERGY DRINK, SUGAR-FREE","Mountain Dew AMP Energy Drink, sugar-free" +95312550,"NO FEAR ENERGY DRINK, SUGAR-FREE","No Fear Energy Drink, sugar-free" +95312555,"NOS ENERGY DRINK, SUGAR-FREE","NOS Energy Drink, sugar-free" +95312560,"CRANBERRY JUICE ENERGY DRINK, HI VIT C & B, W/LOW CAL SWTNR","Ocean Spray Cran-Energy Cranberry Energy Juice Drink" +95312600,"RED BULL ENERGY DRINK, SUGAR-FREE","Red Bull Energy Drink, sugar-free" +95312700,"ROCKSTAR ENERGY DRINK, SUGAR-FREE","Rockstar Energy Drink, sugar-free" +95312800,"VAULT ZERO ENERGY DRINK","Vault Zero Energy Drink" +95312900,"XS ENERGY DRINK","XS Energy Drink" +95312905,"XS GOLD PLUS ENERGY DRINK","XS Gold Plus Energy Drink" +95320200,"GATORADE THIRST QUENCHER SPORTS DRINK","Gatorade Thirst Quencher sports drink" +95320500,"POWERADE SPORTS DRINK","Powerade sports drink" +95321000,"FRUIT-FLAVORED THIRST QUENCHER BEVERAGE","Fruit-flavored thirst quencher beverage" +95322200,"GATORADE G2 THIRST QUENCHER SPORTS DRINK, LOW CALORIE","Gatorade G2 Thirst Quencher sports drink, low calorie" +95322500,"POWERADE ZERO SPORTS DRINK, LOW CALORIE","Powerade Zero sports drink, low calorie" +95323000,"FRUIT-FLAV SPORTS DRINK OR THIRST QUENCHER BEVERAGE, LOW CAL","Fruit-flavored sports drink or thirst quencher beverage, low calorie" +95330100,"FLUID REPLACEMENT, ELECTROLYTE SOLUTION","Fluid replacement, electrolyte solution" +95330500,"FLUID REPLACEMENT, 5% GLUCOSE IN WATER","Fluid replacement, 5% glucose in water" +95341000,"FUZE SLENDERIZE FORTIFIED LOW CALORIE FRUIT JUICE BEVERAGE","FUZE Slenderize fortified low calorie fruit juice beverage" +95342000,"MONAVIE ACAI BLEND BEVERAGE","MonaVie acai blend beverage" diff --git a/pandas/io/tests/data/SSHSV1_A.XPT b/pandas/io/tests/data/SSHSV1_A.XPT new file mode 100644 index 0000000000000..8d954ce1882cd Binary files /dev/null and b/pandas/io/tests/data/SSHSV1_A.XPT differ diff --git a/pandas/io/tests/data/SSHSV1_A.csv b/pandas/io/tests/data/SSHSV1_A.csv new file mode 100644 index 0000000000000..d34efe4b895f7 --- /dev/null +++ b/pandas/io/tests/data/SSHSV1_A.csv @@ -0,0 +1,1427 @@ +"SEQN","SSXHE1" +3,2 +8,1 +9,2 +22,1 +23,2 +30,1 +39,2 +49,1 +67,1 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+9801,2 +9827,1 +9830,2 +9831,2 +9839,1 +9841,2 +9848,1 +9856,1 +9858,1 +9859,2 +9867,2 +9870,1 +9880,1 +9885,1 +9887,2 +9899,2 +9900,2 +9901,2 +9914,2 +9918,1 +9927,1 +9928,1 +9933,2 +9939,2 +9945,2 +9964,2 diff --git a/pandas/io/tests/test_sas.py b/pandas/io/tests/test_sas.py new file mode 100644 index 0000000000000..0e08252fdce97 --- /dev/null +++ b/pandas/io/tests/test_sas.py @@ -0,0 +1,112 @@ +import pandas as pd +import pandas.util.testing as tm +from pandas import compat +from pandas.io.sas import XportReader, read_sas +import numpy as np +import os + +# CSV versions of test XPT files were obtained using the R foreign library + +# Numbers in a SAS xport file are always float64, so need to convert +# before making comparisons. +def numeric_as_float(data): + for v in data.columns: + if data[v].dtype is np.dtype('int64'): + data[v] = data[v].astype(np.float64) + + +class TestXport(tm.TestCase): + + def setUp(self): + self.dirpath = tm.get_data_path() + self.file01 = os.path.join(self.dirpath, "DEMO_G.XPT") + self.file02 = os.path.join(self.dirpath, "SSHSV1_A.XPT") + self.file03 = os.path.join(self.dirpath, "DRXFCD_G.XPT") + + + def test1(self): + # Tests with DEMO_G.XPT (all numeric file) + + # Compare to this + data_csv = pd.read_csv(self.file01.replace(".XPT", ".csv")) + numeric_as_float(data_csv) + + # Read full file + data = XportReader(self.file01).read() + tm.assert_frame_equal(data, data_csv) + + # Test incremental read with `read` method. + reader = XportReader(self.file01) + data = reader.read(10) + tm.assert_frame_equal(data, data_csv.iloc[0:10, :]) + + # Test incremental read with `get_chunk` method. + reader = XportReader(self.file01, chunksize=10) + data = reader.get_chunk() + tm.assert_frame_equal(data, data_csv.iloc[0:10, :]) + + # Read full file with `read_sas` method + data = read_sas(self.file01) + tm.assert_frame_equal(data, data_csv) + + + def test1_index(self): + # Tests with DEMO_G.XPT using index (all numeric file) + + # Compare to this + data_csv = pd.read_csv(self.file01.replace(".XPT", ".csv")) + data_csv = data_csv.set_index("SEQN") + numeric_as_float(data_csv) + + # Read full file + data = XportReader(self.file01, index="SEQN").read() + tm.assert_frame_equal(data, data_csv) + + # Test incremental read with `read` method. + reader = XportReader(self.file01, index="SEQN") + data = reader.read(10) + tm.assert_frame_equal(data, data_csv.iloc[0:10, :]) + + # Test incremental read with `get_chunk` method. + reader = XportReader(self.file01, index="SEQN", chunksize=10) + data = reader.get_chunk() + tm.assert_frame_equal(data, data_csv.iloc[0:10, :]) + + + def test1_incremental(self): + # Test with DEMO_G.XPT, reading full file incrementally + + data_csv = pd.read_csv(self.file01.replace(".XPT", ".csv")) + data_csv = data_csv.set_index("SEQN") + numeric_as_float(data_csv) + + reader = XportReader(self.file01, index="SEQN", chunksize=1000) + + all_data = [x for x in reader] + data = pd.concat(all_data, axis=0) + + tm.assert_frame_equal(data, data_csv) + + + def test2(self): + # Test with SSHSV1_A.XPT + + # Compare to this + data_csv = pd.read_csv(self.file02.replace(".XPT", ".csv")) + numeric_as_float(data_csv) + + data = XportReader(self.file02).read() + tm.assert_frame_equal(data, data_csv) + + + def test3(self): + # Test with DRXFCD_G.XPT (contains text and numeric variables) + + # Compare to this + data_csv = pd.read_csv(self.file03.replace(".XPT", ".csv")) + + data = XportReader(self.file03).read() + tm.assert_frame_equal(data, data_csv) + + data = read_sas(self.file03) + tm.assert_frame_equal(data, data_csv)
Here is an initial attempt to port Jack Cushman's xport file reader into Pandas. Partially addresses #4052 (but this only deals with xport files, not sas7bdat files)
https://api.github.com/repos/pandas-dev/pandas/pulls/9711
2015-03-23T13:45:08Z
2015-08-14T15:40:16Z
2015-08-14T15:40:16Z
2015-11-12T23:43:34Z