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FIX: division of Decimal would crash
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 3f510ce3d44c0..f77ff67320ca0 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -132,3 +132,5 @@ Bug Fixes - Bug in unequal comparisons between a ``Series`` of dtype `"category"` and a scalar (e.g. ``Series(Categorical(list("abc"), categories=list("cba"), ordered=True)) > "b"``, which wouldn't use the order of the categories but use the lexicographical order. (:issue:`9848`) - Bug in unequal comparisons between categorical data and a scalar, which was not in the categories (e.g. ``Series(Categorical(list("abc"), ordered=True)) > "d"``. This returned ``False`` for all elements, but now raises a ``TypeError``. Equality comparisons also now return ``False`` for ``==`` and ``True`` for ``!=``. (:issue:`9848`) + +- Bug where dividing a dataframe containing values of type Decimal by another Decimal would crash. (:issue:`9787`) diff --git a/pandas/core/common.py b/pandas/core/common.py index 0fb35c2fb02fc..5587ba5f92df0 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1397,14 +1397,19 @@ def _fill_zeros(result, x, y, name, fill): mask the nan's from x """ - if fill is None or is_float_dtype(result): return result if name.startswith(('r', '__r')): x,y = y,x - if np.isscalar(y): + is_typed_variable = (hasattr(y, 'dtype') or hasattr(y,'type')) + is_scalar = lib.isscalar(y) + + if not is_typed_variable and not is_scalar: + return result + + if is_scalar: y = np.array(y) if is_integer_dtype(y): diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 70a6e2541692a..420992a3c86b7 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -5617,6 +5617,22 @@ def test_map_type_inference(self): s2 = s.map(lambda x: np.where(x == 0, 0, 1)) self.assertTrue(issubclass(s2.dtype.type, np.integer)) + def test_divide_decimal(self): + ''' resolves issue #9787 ''' + from decimal import Decimal + + expected = Series([Decimal(5)]) + + s = Series([Decimal(10)]) + s = s/Decimal(2) + + tm.assert_series_equal(expected, s) + + s = Series([Decimal(10)]) + s = s//Decimal(2) + + tm.assert_series_equal(expected, s) + def test_map_decimal(self): from decimal import Decimal
closes #9787 Decimal objects don't support dtype, so we exclude all objects from fill_zeros that do not support .dtype or .type and are not scalar.
https://api.github.com/repos/pandas-dev/pandas/pulls/9871
2015-04-13T16:20:01Z
2015-04-14T13:27:50Z
null
2015-04-14T13:27:50Z
ENH: #9847, adding a "same" and "expand" param to StringMethods.split()
diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 6d20907373014..87169a5314643 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -624,6 +624,7 @@ def str_pad(arr, width, side='left', fillchar=' '): def str_split(arr, pat=None, n=None, return_type='series'): """ + Deprecated: return_types 'series', 'index', 'frame' are now deprecated Split each string (a la re.split) in array by given pattern, propagating NA values @@ -632,9 +633,9 @@ def str_split(arr, pat=None, n=None, return_type='series'): pat : string, default None String or regular expression to split on. If None, splits on whitespace n : int, default None (all) - return_type : {'series', 'index', 'frame'}, default 'series' - If frame, returns a DataFrame (elements are strings) - If series or index, returns the same type as the original object + return_type : {'same', 'expand'}, default 'series' + If expand, returns a DataFrame (elements are strings) + If series, index or same, returns the same type as the original object (elements are lists of strings). Notes @@ -649,11 +650,14 @@ def str_split(arr, pat=None, n=None, return_type='series'): from pandas.core.frame import DataFrame from pandas.core.index import Index - if return_type not in ('series', 'index', 'frame'): - raise ValueError("return_type must be {'series', 'index', 'frame'}") - if return_type == 'frame' and isinstance(arr, Index): + if return_type not in ('series', 'index', 'frame', 'same', 'expand'): + raise ValueError("return_type must be {'series', 'index', 'frame', 'same', 'expand'}") + if return_type in ('frame', 'expand') and isinstance(arr, Index): raise ValueError("return_type='frame' is not supported for string " "methods on Index") + if return_type in ('series', 'index', 'frame'): + warnings.warn(("'series', 'index' and 'frame' are deprecated. Please use 'same' or 'expand' instead"), + FutureWarning) if pat is None: if n is None or n == 0: n = -1 @@ -668,7 +672,7 @@ def str_split(arr, pat=None, n=None, return_type='series'): n = 0 regex = re.compile(pat) f = lambda x: regex.split(x, maxsplit=n) - if return_type == 'frame': + if return_type in ('frame', 'expand'): res = DataFrame((Series(x) for x in _na_map(f, arr)), index=arr.index) else: res = _na_map(f, arr) diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index bb75b12754dca..159635da5d505 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -1220,9 +1220,12 @@ def test_str_attribute(self): tm.assert_index_equal(idx.str.split(return_type='series'), expected) # return_type 'index' is an alias for 'series' tm.assert_index_equal(idx.str.split(return_type='index'), expected) + # return_type 'same' is an alias for 'series' and 'index' + tm.assert_index_equal(idx.str.split(return_type='same'), expected) with self.assertRaisesRegexp(ValueError, 'not supported'): idx.str.split(return_type='frame') - + with self.assertRaisesRegexp(ValueError, 'not supported'): + idx.str.split(return_type='expand') # test boolean case, should return np.array instead of boolean Index idx = Index(['a1', 'a2', 'b1', 'b2']) expected = np.array([True, True, False, False])
closes #9847
https://api.github.com/repos/pandas-dev/pandas/pulls/9870
2015-04-13T15:55:29Z
2015-05-09T15:31:37Z
null
2015-05-09T15:31:37Z
Period accepts datetime64 value
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index e1e930ee21efe..dda5a34a6d7db 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -49,6 +49,7 @@ 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`) +- ``Period`` now accepts ``datetime64`` as value input. (:issue:`9054`) .. _whatsnew_0161.api: diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index cc6ad3defe4f3..b4a4930e09d68 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -710,6 +710,10 @@ cdef class Period(object): dt = value if freq is None: raise ValueError('Must supply freq for datetime value') + elif isinstance(value, np.datetime64): + dt = Timestamp(value) + if freq is None: + raise ValueError('Must supply freq for datetime value') elif isinstance(value, date): dt = datetime(year=value.year, month=value.month, day=value.day) if freq is None: diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 17edcd7504102..a471a536a20b3 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -226,7 +226,13 @@ def test_period_constructor(self): i1 = Period(date(2007, 1, 1), freq='M') i2 = Period(datetime(2007, 1, 1), freq='M') + i3 = Period(np.datetime64('2007-01-01'), freq='M') + i4 = Period(np.datetime64('2007-01-01 00:00:00Z'), freq='M') + i5 = Period(np.datetime64('2007-01-01 00:00:00.000Z'), freq='M') self.assertEqual(i1, i2) + self.assertEqual(i1, i3) + self.assertEqual(i1, i4) + self.assertEqual(i1, i5) i1 = Period('2007-01-01 09:00:00.001') expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq='L')
Added support for `datetime64` value for Period. Fixes #9054.
https://api.github.com/repos/pandas-dev/pandas/pulls/9869
2015-04-13T15:28:38Z
2015-04-14T14:26:45Z
null
2015-04-14T14:26:45Z
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
Fix for comparisons of categorical and an scalar not in categories
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index dc5e3ddcefc06..45c4c9b00c97c 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -123,3 +123,5 @@ Bug Fixes - Bug in which ``SparseDataFrame`` could not take `nan` as a column name (:issue:`8822`) - Bug in unequal comparisons between a ``Series`` of dtype `"category"` and a scalar (e.g. ``Series(Categorical(list("abc"), categories=list("cba"), ordered=True)) > "b"``, which wouldn't use the order of the categories but use the lexicographical order. (:issue:`9848`) + +- Bug in unequal comparisons between categorical data and a scalar, which was not in the categories (e.g. ``Series(Categorical(list("abc"), ordered=True)) > "d"``. This returned ``False`` for all elements, but now raises a TypeError. Equality comparisons also now return ``False`` for ``==`` and ``True`` for ``!=``. (:issue:`9848`) diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 991678a8e7d79..b79f2c9b4f6df 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -61,7 +61,14 @@ def f(self, other): i = self.categories.get_loc(other) return getattr(self._codes, op)(i) else: - return np.repeat(False, len(self)) + if op == '__eq__': + return np.repeat(False, len(self)) + elif op == '__ne__': + return np.repeat(True, len(self)) + else: + msg = "Cannot compare a Categorical for op {op} with a scalar, " \ + "which is not a category." + raise TypeError(msg.format(op=op)) else: # allow categorical vs object dtype array comparisons for equality diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 4c5678bf6633f..af48774492b11 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1087,6 +1087,20 @@ def test_reflected_comparison_with_scalars(self): self.assert_numpy_array_equal(cat > cat[0], [False, True, True]) self.assert_numpy_array_equal(cat[0] < cat, [False, True, True]) + def test_comparison_with_unknown_scalars(self): + # https://github.com/pydata/pandas/issues/9836#issuecomment-92123057 and following + # comparisons with scalars not in categories should raise for unequal comps, but not for + # equal/not equal + cat = pd.Categorical([1, 2, 3], ordered=True) + + self.assertRaises(TypeError, lambda: cat < 4) + self.assertRaises(TypeError, lambda: cat > 4) + self.assertRaises(TypeError, lambda: 4 < cat) + self.assertRaises(TypeError, lambda: 4 > cat) + + self.assert_numpy_array_equal(cat == 4 , [False, False, False]) + self.assert_numpy_array_equal(cat != 4 , [True, True, True]) + class TestCategoricalAsBlock(tm.TestCase): _multiprocess_can_split_ = True @@ -2440,6 +2454,19 @@ def f(): cat > "b" self.assertRaises(TypeError, f) + # https://github.com/pydata/pandas/issues/9836#issuecomment-92123057 and following + # comparisons with scalars not in categories should raise for unequal comps, but not for + # equal/not equal + cat = Series(Categorical(list("abc"), ordered=True)) + + self.assertRaises(TypeError, lambda: cat < "d") + self.assertRaises(TypeError, lambda: cat > "d") + self.assertRaises(TypeError, lambda: "d" < cat) + self.assertRaises(TypeError, lambda: "d" > cat) + + self.assert_series_equal(cat == "d" , Series([False, False, False])) + self.assert_series_equal(cat != "d" , Series([True, True, True])) + # And test NaN handling... cat = Series(Categorical(["a","b","c", np.nan]))
Up to now, a comparison of categorical data and a scalar, which is not in the categories would return `False` for all elements when it should raise a `TypeError`, which it now does. Also fix that `!=` comparisons would return `False` for all elements when the more logical choice would be `True`. Fixes the raised issue in https://github.com/pydata/pandas/issues/9836#issuecomment-92123057
https://api.github.com/repos/pandas-dev/pandas/pulls/9864
2015-04-12T21:45:29Z
2015-04-13T13:40:24Z
null
2015-04-13T13:40:24Z
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
BUG: secondary ax has incorrect right_ax property
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index dc5e3ddcefc06..11d5c9690f2e8 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -110,6 +110,7 @@ Bug Fixes - 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 plotting ``secondary_y`` incorrectly attaches ``right_ax`` property to secondary axes specifying itself recursively. (:issue:`9861`) - 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 7d489ce66c288..a90fbe6c25b1f 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1609,7 +1609,10 @@ def test_line_lim(self): self.assertEqual(xmax, lines[0].get_data()[0][-1]) axes = df.plot(secondary_y=True, subplots=True) + self._check_axes_shape(axes, axes_num=3, layout=(3, 1)) for ax in axes: + self.assertTrue(hasattr(ax, 'left_ax')) + self.assertFalse(hasattr(ax, 'right_ax')) xmin, xmax = ax.get_xlim() lines = ax.get_lines() self.assertEqual(xmin, lines[0].get_data()[0][0]) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 6a284e547433a..1cd2ca1bf3b85 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -927,19 +927,21 @@ def _has_plotted_object(self, ax): 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 + # secondary axes may be passed via ax kw + return self._get_ax_layer(ax) if hasattr(ax, 'right_ax'): + # if it has right_ax proparty, ``ax`` must be left axes return ax.right_ax + elif hasattr(ax, 'left_ax'): + # if it has left_ax proparty, ``ax`` must be right axes + return ax else: + # otherwise, create twin axes orig_ax, new_ax = ax, ax.twinx() new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax - new_ax.right_ax = new_ax if not self._has_plotted_object(orig_ax): # no data on left y orig_ax.get_yaxis().set_visible(False) @@ -987,9 +989,8 @@ def result(self): 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 + # if all data is plotted on secondary, return right axes + return self._get_ax_layer(self.axes[0], primary=False) else: return self.axes[0] @@ -1229,11 +1230,18 @@ def _get_index_name(self): return name + @classmethod + def _get_ax_layer(cls, ax, primary=True): + """get left (primary) or right (secondary) axes""" + if primary: + return getattr(ax, 'left_ax', ax) + else: + return getattr(ax, 'right_ax', ax) + def _get_ax(self, i): # get the twinx ax if appropriate if self.subplots: ax = self.axes[i] - ax = self._maybe_right_yaxis(ax, i) self.axes[i] = ax else: @@ -2500,8 +2508,7 @@ 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) - + ax = MPLPlot._get_ax_layer(ax) return _plot(data, kind=kind, ax=ax, figsize=figsize, use_index=use_index, title=title, grid=grid, legend=legend, @@ -3348,11 +3355,9 @@ def _flatten(axes): def _get_all_lines(ax): lines = ax.get_lines() - # check for right_ax, which can oddly sometimes point back to ax - if hasattr(ax, 'right_ax') and ax.right_ax != ax: + if hasattr(ax, 'right_ax'): lines += ax.right_ax.get_lines() - # no such risk with left_ax if hasattr(ax, 'left_ax'): lines += ax.left_ax.get_lines() diff --git a/pandas/tseries/tests/test_plotting.py b/pandas/tseries/tests/test_plotting.py index bdc0aa02f2715..c5ed8a1ac3e31 100644 --- a/pandas/tseries/tests/test_plotting.py +++ b/pandas/tseries/tests/test_plotting.py @@ -528,7 +528,9 @@ def test_secondary_y(self): ser = Series(np.random.randn(10)) ser2 = Series(np.random.randn(10)) - ax = ser.plot(secondary_y=True).right_ax + ax = ser.plot(secondary_y=True) + self.assertTrue(hasattr(ax, 'left_ax')) + self.assertFalse(hasattr(ax, 'right_ax')) fig = ax.get_figure() axes = fig.get_axes() l = ax.get_lines()[0] @@ -543,8 +545,12 @@ def test_secondary_y(self): plt.close(ax2.get_figure()) ax = ser2.plot() - ax2 = ser.plot(secondary_y=True).right_ax + ax2 = ser.plot(secondary_y=True) self.assertTrue(ax.get_yaxis().get_visible()) + self.assertFalse(hasattr(ax, 'left_ax')) + self.assertTrue(hasattr(ax, 'right_ax')) + self.assertTrue(hasattr(ax2, 'left_ax')) + self.assertFalse(hasattr(ax2, 'right_ax')) @slow def test_secondary_y_ts(self): @@ -552,7 +558,9 @@ def test_secondary_y_ts(self): idx = date_range('1/1/2000', periods=10) ser = Series(np.random.randn(10), idx) ser2 = Series(np.random.randn(10), idx) - ax = ser.plot(secondary_y=True).right_ax + ax = ser.plot(secondary_y=True) + self.assertTrue(hasattr(ax, 'left_ax')) + self.assertFalse(hasattr(ax, 'right_ax')) fig = ax.get_figure() axes = fig.get_axes() l = ax.get_lines()[0] @@ -577,7 +585,9 @@ def test_secondary_kde(self): import matplotlib.pyplot as plt ser = Series(np.random.randn(10)) - ax = ser.plot(secondary_y=True, kind='density').right_ax + ax = ser.plot(secondary_y=True, kind='density') + self.assertTrue(hasattr(ax, 'left_ax')) + self.assertFalse(hasattr(ax, 'right_ax')) fig = ax.get_figure() axes = fig.get_axes() self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right') @@ -922,7 +932,9 @@ def test_secondary_upsample(self): ax = high.plot(secondary_y=True) for l in ax.get_lines(): self.assertEqual(PeriodIndex(l.get_xdata()).freq, 'D') - for l in ax.right_ax.get_lines(): + self.assertTrue(hasattr(ax, 'left_ax')) + self.assertFalse(hasattr(ax, 'right_ax')) + for l in ax.left_ax.get_lines(): self.assertEqual(PeriodIndex(l.get_xdata()).freq, 'D') @slow
When `secondary_y` is enabled, twin axes are created as below: - Assume current axes is `ax` (left ax) - create `new_ax` (right ax) via `ax.twinx` - Let `ax.right_ax` to specify `new_ax` (right ax) - Let `new_ax.left_ax` to specify `ax` (left ax) - Let `new_ax.right_ax` to specify `new_ax` (incorrect) The last one is incorrect and confuses other logics such as `_get_all_lines`. I think it should be removed.
https://api.github.com/repos/pandas-dev/pandas/pulls/9861
2015-04-11T23:27:43Z
2015-04-14T14:25:56Z
null
2015-04-14T14:28:42Z
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
Fix for unequal comparisons of categorical and scalar
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index ab57f1fb6ea10..2cba928be5ab7 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -116,3 +116,7 @@ Bug Fixes - 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`) + +- Bug in unequal comparisons between a ``Series`` of dtype `"category"` and a scalar (e.g. + ``Series(Categorical(list("abc"), categories=list("cba"), ordered=True)) > "b"``, which + wouldn't use the order of the categories but use the lexicographical order. (:issue:`9848`) \ No newline at end of file diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 954d2c8a77326..2af9cd43faaef 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -594,20 +594,26 @@ def wrapper(self, other): mask = isnull(self) - values = self.get_values() - other = _index.convert_scalar(values,_values_from_object(other)) + if com.is_categorical_dtype(self): + # cats are a special case as get_values() would return an ndarray, which would then + # not take categories ordering into account + # we can go directly to op, as the na_op would just test again and dispatch to it. + res = op(self.values, other) + else: + values = self.get_values() + other = _index.convert_scalar(values,_values_from_object(other)) - if issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): - values = values.view('i8') + if issubclass(values.dtype.type, (np.datetime64, np.timedelta64)): + 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)) + # 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) + # always return a full value series here + res = _values_from_object(res) res = pd.Series(res, index=self.index, name=self.name, dtype='bool') diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 7f4b3fcb94dfa..4c5678bf6633f 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -114,6 +114,9 @@ def f(): Categorical([1,2], [1,2,np.nan, np.nan]) self.assertRaises(ValueError, f) + # The default should be unordered + c1 = Categorical(["a", "b", "c", "a"]) + self.assertFalse(c1.ordered) # Categorical as input c1 = Categorical(["a", "b", "c", "a"]) @@ -367,6 +370,13 @@ def f(): self.assertRaises(TypeError, lambda: a < cat) self.assertRaises(TypeError, lambda: a < cat_rev) + # Make sure that unequal comparison take the categories order in account + cat_rev = pd.Categorical(list("abc"), categories=list("cba"), ordered=True) + exp = np.array([True, False, False]) + res = cat_rev > "b" + self.assert_numpy_array_equal(res, exp) + + def test_na_flags_int_categories(self): # #1457 @@ -2390,6 +2400,18 @@ def test_comparisons(self): exp = Series([False, False, True]) tm.assert_series_equal(res, exp) + scalar = base[1] + res = cat > scalar + exp = Series([False, False, True]) + exp2 = cat.values > scalar + tm.assert_series_equal(res, exp) + tm.assert_numpy_array_equal(res.values, exp2) + res_rev = cat_rev > scalar + exp_rev = Series([True, False, False]) + exp_rev2 = cat_rev.values > scalar + tm.assert_series_equal(res_rev, exp_rev) + tm.assert_numpy_array_equal(res_rev.values, exp_rev2) + # Only categories with same categories can be compared def f(): cat > cat_rev @@ -2408,9 +2430,16 @@ def f(): self.assertRaises(TypeError, lambda: a < cat) self.assertRaises(TypeError, lambda: a < cat_rev) - # Categoricals can be compared to scalar values - res = cat_rev > base[0] - tm.assert_series_equal(res, exp) + # unequal comparison should raise for unordered cats + cat = Series(Categorical(list("abc"))) + def f(): + cat > "b" + self.assertRaises(TypeError, f) + cat = Series(Categorical(list("abc"), ordered=False)) + def f(): + cat > "b" + self.assertRaises(TypeError, f) + # And test NaN handling... cat = Series(Categorical(["a","b","c", np.nan]))
Fixes: #9836
https://api.github.com/repos/pandas-dev/pandas/pulls/9848
2015-04-10T14:30:38Z
2015-04-11T20:06:52Z
null
2015-05-11T22:21:16Z
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
BUG: DataFrame.where does not respect axis parameter when shape is symmetric
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index 14e185b5b2a26..94ae92811c4e6 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -88,6 +88,7 @@ Bug Fixes - Bug in ``ExcelReader`` when worksheet is empty (:issue:`6403`) +- Bug causing ``DataFrame.where`` to not respect the ``axis`` parameter when the frame has a symmetric shape. (:issue:`9736`) - Bug in ``Table.select_column`` where name is not preserved (:issue:`10392`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 3bf998c1fa5a7..8742abc2d48b6 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3634,19 +3634,31 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, else: other = self._constructor(other, **self._construct_axes_dict()) + if axis is None: + axis = 0 + + if self.ndim == getattr(other, 'ndim', 0): + align = True + else: + align = (self._get_axis_number(axis) == 1) + + block_axis = self._get_block_manager_axis(axis) + if inplace: # we may have different type blocks come out of putmask, so # reconstruct the block manager self._check_inplace_setting(other) - new_data = self._data.putmask(mask=cond, new=other, align=axis is None, - inplace=True) + new_data = self._data.putmask(mask=cond, new=other, align=align, + inplace=True, axis=block_axis, + transpose=self._AXIS_REVERSED) self._update_inplace(new_data) else: - new_data = self._data.where(other=other, cond=cond, align=axis is None, + new_data = self._data.where(other=other, cond=cond, align=align, raise_on_error=raise_on_error, - try_cast=try_cast) + try_cast=try_cast, axis=block_axis, + transpose=self._AXIS_REVERSED) return self._constructor(new_data).__finalize__(self) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 42d7163e7f741..196065703aa74 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -632,7 +632,8 @@ def _is_empty_indexer(indexer): return [self] - def putmask(self, mask, new, align=True, inplace=False): + def putmask(self, mask, new, align=True, inplace=False, + axis=0, transpose=False): """ putmask the data to the block; it is possible that we may create a new dtype of block @@ -644,37 +645,55 @@ def putmask(self, mask, new, align=True, inplace=False): new : a ndarray/object align : boolean, perform alignment on other/cond, default is True inplace : perform inplace modification, default is False + axis : int + transpose : boolean + Set to True if self is stored with axes reversed Returns ------- - a new block(s), the result of the putmask + a list of new blocks, the result of the putmask """ new_values = self.values if inplace else self.values.copy() - # may need to align the new if hasattr(new, 'reindex_axis'): - new = new.values.T + new = new.values - # may need to align the mask if hasattr(mask, 'reindex_axis'): - mask = mask.values.T + mask = mask.values # if we are passed a scalar None, convert it here if not is_list_like(new) and isnull(new) and not self.is_object: new = self.fill_value if self._can_hold_element(new): + if transpose: + new_values = new_values.T + 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 the default repeat behavior in np.putmask would go in the wrong + # direction, then explictly repeat and reshape new instead + if getattr(new, 'ndim', 0) >= 1: + if self.ndim - 1 == new.ndim and axis == 1: + new = np.repeat(new, new_values.shape[-1]).reshape(self.shape) np.putmask(new_values, mask, new) # maybe upcast me elif mask.any(): + if transpose: + mask = mask.T + if isinstance(new, np.ndarray): + new = new.T + axis = new_values.ndim - axis - 1 + + # Pseudo-broadcast + if getattr(new, 'ndim', 0) >= 1: + if self.ndim - 1 == new.ndim: + new_shape = list(new.shape) + new_shape.insert(axis, 1) + new = new.reshape(tuple(new_shape)) # need to go column by column new_blocks = [] @@ -685,14 +704,15 @@ def putmask(self, mask, new, align=True, inplace=False): # need a new block if m.any(): - - n = new[i] if isinstance( - new, np.ndarray) else np.array(new) + if isinstance(new, np.ndarray): + n = np.squeeze(new[i % new.shape[0]]) + else: + n = np.array(new) # type of the new block dtype, _ = com._maybe_promote(n.dtype) - # we need to exiplicty astype here to make a copy + # we need to explicitly astype here to make a copy n = n.astype(dtype) nv = _putmask_smart(v, m, n) @@ -718,8 +738,10 @@ def putmask(self, mask, new, align=True, inplace=False): if inplace: return [self] - return [make_block(new_values, - placement=self.mgr_locs, fastpath=True)] + if transpose: + new_values = new_values.T + + return [make_block(new_values, placement=self.mgr_locs, fastpath=True)] def interpolate(self, method='pad', axis=0, index=None, values=None, inplace=False, limit=None, @@ -1003,7 +1025,7 @@ def handle_error(): fastpath=True, placement=self.mgr_locs)] def where(self, other, cond, align=True, raise_on_error=True, - try_cast=False): + try_cast=False, axis=0, transpose=False): """ evaluate the block; return result block(s) from the result @@ -1014,6 +1036,9 @@ def where(self, other, cond, align=True, raise_on_error=True, 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) + axis : int + transpose : boolean + Set to True if self is stored with axes reversed Returns ------- @@ -1021,43 +1046,23 @@ def where(self, other, cond, align=True, raise_on_error=True, """ values = self.values + if transpose: + values = values.T - # see if we can align other if hasattr(other, 'reindex_axis'): other = other.values - # make sure that we can broadcast - is_transposed = False - if hasattr(other, 'ndim') and hasattr(values, 'ndim'): - if values.ndim != other.ndim or values.shape == other.shape[::-1]: - - # if its symmetric are ok, no reshaping needed (GH 7506) - if (values.shape[0] == np.array(values.shape)).all(): - pass - - # pseodo broadcast (its a 2d vs 1d say and where needs it in a - # specific direction) - elif (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 - is_transposed = True - - # see if we can align cond - if not hasattr(cond, 'shape'): - raise ValueError( - "where must have a condition that is ndarray like") - if hasattr(cond, 'reindex_axis'): cond = cond.values - # may need to undo transpose of values - if hasattr(values, 'ndim'): - if values.ndim != cond.ndim or values.shape == cond.shape[::-1]: + # If the default broadcasting would go in the wrong direction, then + # explictly reshape other instead + if getattr(other, 'ndim', 0) >= 1: + if values.ndim - 1 == other.ndim and axis == 1: + other = other.reshape(tuple(other.shape + (1,))) - values = values.T - is_transposed = not is_transposed + if not hasattr(cond, 'shape'): + raise ValueError("where must have a condition that is ndarray like") other = _maybe_convert_string_to_object(other) @@ -1090,15 +1095,14 @@ def func(c, v, o): raise TypeError('Could not compare [%s] with block values' % repr(other)) - if is_transposed: + if transpose: result = result.T # try to cast if requested if try_cast: result = self._try_cast_result(result) - return make_block(result, - ndim=self.ndim, placement=self.mgr_locs) + return make_block(result, ndim=self.ndim, placement=self.mgr_locs) # might need to separate out blocks axis = cond.ndim - 1 @@ -1733,7 +1737,8 @@ def take_nd(self, indexer, axis=0, new_mgr_locs=None, fill_tuple=None): return self.make_block_same_class(new_values, new_mgr_locs) - def putmask(self, mask, new, align=True, inplace=False): + def putmask(self, mask, new, align=True, inplace=False, + axis=0, transpose=False): """ putmask the data to the block; it is possible that we may create a new dtype of block @@ -2425,12 +2430,18 @@ def apply(self, f, axes=None, filter=None, do_integrity_check=False, **kwargs): else: kwargs['filter'] = filter_locs - if f == 'where' and kwargs.get('align', True): + if f == 'where': align_copy = True - align_keys = ['other', 'cond'] - elif f == 'putmask' and kwargs.get('align', True): + if kwargs.get('align', True): + align_keys = ['other', 'cond'] + else: + align_keys = ['cond'] + elif f == 'putmask': align_copy = False - align_keys = ['new', 'mask'] + if kwargs.get('align', True): + align_keys = ['new', 'mask'] + else: + align_keys = ['mask'] elif f == 'eval': align_copy = False align_keys = ['other'] diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index cfa0ed1a11772..6e82f20f325cb 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -10046,6 +10046,110 @@ def test_where_complex(self): df[df.abs() >= 5] = np.nan assert_frame_equal(df,expected) + def test_where_axis(self): + # GH 9736 + df = DataFrame(np.random.randn(2, 2)) + mask = DataFrame([[False, False], [False, False]]) + s = Series([0, 1]) + + expected = DataFrame([[0, 0], [1, 1]], dtype='float64') + result = df.where(mask, s, axis='index') + assert_frame_equal(result, expected) + + result = df.copy() + result.where(mask, s, axis='index', inplace=True) + assert_frame_equal(result, expected) + + expected = DataFrame([[0, 1], [0, 1]], dtype='float64') + result = df.where(mask, s, axis='columns') + assert_frame_equal(result, expected) + + result = df.copy() + result.where(mask, s, axis='columns', inplace=True) + assert_frame_equal(result, expected) + + # Upcast needed + df = DataFrame([[1, 2], [3, 4]], dtype='int64') + mask = DataFrame([[False, False], [False, False]]) + s = Series([0, np.nan]) + + expected = DataFrame([[0, 0], [np.nan, np.nan]], dtype='float64') + result = df.where(mask, s, axis='index') + assert_frame_equal(result, expected) + + result = df.copy() + result.where(mask, s, axis='index', inplace=True) + assert_frame_equal(result, expected) + + expected = DataFrame([[0, np.nan], [0, np.nan]], dtype='float64') + result = df.where(mask, s, axis='columns') + assert_frame_equal(result, expected) + + expected = DataFrame({0 : np.array([0, 0], dtype='int64'), + 1 : np.array([np.nan, np.nan], dtype='float64')}) + result = df.copy() + result.where(mask, s, axis='columns', inplace=True) + assert_frame_equal(result, expected) + + # Multiple dtypes (=> multiple Blocks) + df = pd.concat([DataFrame(np.random.randn(10, 2)), + DataFrame(np.random.randint(0, 10, size=(10, 2)))], + ignore_index=True, axis=1) + mask = DataFrame(False, columns=df.columns, index=df.index) + s1 = Series(1, index=df.columns) + s2 = Series(2, index=df.index) + + result = df.where(mask, s1, axis='columns') + expected = DataFrame(1.0, columns=df.columns, index=df.index) + expected[2] = expected[2].astype(int) + expected[3] = expected[3].astype(int) + assert_frame_equal(result, expected) + + result = df.copy() + result.where(mask, s1, axis='columns', inplace=True) + assert_frame_equal(result, expected) + + result = df.where(mask, s2, axis='index') + expected = DataFrame(2.0, columns=df.columns, index=df.index) + expected[2] = expected[2].astype(int) + expected[3] = expected[3].astype(int) + assert_frame_equal(result, expected) + + result = df.copy() + result.where(mask, s2, axis='index', inplace=True) + assert_frame_equal(result, expected) + + # DataFrame vs DataFrame + d1 = df.copy().drop(1, axis=0) + expected = df.copy() + expected.loc[1, :] = np.nan + + result = df.where(mask, d1) + assert_frame_equal(result, expected) + result = df.where(mask, d1, axis='index') + assert_frame_equal(result, expected) + result = df.copy() + result.where(mask, d1, inplace=True) + assert_frame_equal(result, expected) + result = df.copy() + result.where(mask, d1, inplace=True, axis='index') + assert_frame_equal(result, expected) + + d2 = df.copy().drop(1, axis=1) + expected = df.copy() + expected.loc[:, 1] = np.nan + + result = df.where(mask, d2) + assert_frame_equal(result, expected) + result = df.where(mask, d2, axis='columns') + assert_frame_equal(result, expected) + result = df.copy() + result.where(mask, d2, inplace=True) + assert_frame_equal(result, expected) + result = df.copy() + result.where(mask, d2, inplace=True, axis='columns') + assert_frame_equal(result, expected) + def test_mask(self): df = DataFrame(np.random.randn(5, 3)) cond = df > 0
Fixes GH #9736. The problem was that at the `Block` level, `where` and `putmask` did a lot of guessing about how the block and the other parameters needed to be transposed in order to align correctly. It guessed wrongly when the DataFrame was symmetric because the shapes match up in both directions. This change tries to make everything explicit and remove the guessing.
https://api.github.com/repos/pandas-dev/pandas/pulls/9838
2015-04-09T02:29:07Z
2015-08-18T11:20:12Z
null
2015-08-18T12:09:34Z
BUG: read_csv skips lines with initial whitespace + one non-space character
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 7166801b3fbf0..172f3f708d9a3 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -120,6 +120,7 @@ Bug Fixes - 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`) - 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`) - Bug in index equality comparisons using ``==`` failing on Index/MultiIndex type incompatibility (:issue:`9875`) @@ -164,3 +165,5 @@ Bug Fixes - Fixed latex output for multi-indexed dataframes (:issue:`9778`) - Bug causing an exception when setting an empty range using ``DataFrame.loc`` (:issue:`9596`) + +- Bug in csv parser causing lines with initial whitespace plus one non-space character to be skipped. (:issue:`9710`) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 799872d036c4f..819c49f4fb0dd 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2273,6 +2273,20 @@ def test_nrows_and_chunksize_raises_notimplemented(self): self.assertRaises(NotImplementedError, self.read_csv, StringIO(data), nrows=10, chunksize=5) + def test_single_char_leading_whitespace(self): + # GH 9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn' : list('abab')}) + + result = self.read_csv(StringIO(data), skipinitialspace=True) + tm.assert_frame_equal(result, expected) + class TestPythonParser(ParserTests, tm.TestCase): def test_negative_skipfooter_raises(self): @@ -3313,6 +3327,25 @@ def test_buffer_overflow(self): except Exception as cperr: self.assertIn('Buffer overflow caught - possible malformed input file.', str(cperr)) + def test_single_char_leading_whitespace(self): + # GH 9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn' : list('abab')}) + + result = self.read_csv(StringIO(data), delim_whitespace=True, + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), lineterminator='\n', + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + class TestCParserLowMemory(ParserTests, tm.TestCase): def read_csv(self, *args, **kwds): @@ -3734,6 +3767,25 @@ def test_buffer_overflow(self): except Exception as cperr: self.assertIn('Buffer overflow caught - possible malformed input file.', str(cperr)) + def test_single_char_leading_whitespace(self): + # GH 9710 + data = """\ +MyColumn + a + b + a + b\n""" + + expected = DataFrame({'MyColumn' : list('abab')}) + + result = self.read_csv(StringIO(data), delim_whitespace=True, + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + + result = self.read_csv(StringIO(data), lineterminator='\n', + skipinitialspace=True) + tm.assert_frame_equal(result, expected) + class TestMiscellaneous(tm.TestCase): # for tests that don't fit into any of the other classes, e.g. those that diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index 1850aab50b55a..e7b5db9c5e361 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -849,10 +849,11 @@ int tokenize_delimited(parser_t *self, size_t line_limit) ; else { // backtrack /* We have to use i + 1 because buf has been incremented but not i */ - while (i + 1 > self->datapos && *buf != '\n') { + do { --buf; --i; - } + } while (i + 1 > self->datapos && *buf != '\n'); + if (i + 1 > self->datapos) // reached a newline rather than the beginning { ++buf; // move pointer to first char after newline @@ -1073,7 +1074,7 @@ int tokenize_delim_customterm(parser_t *self, size_t line_limit) // Next character in file c = *buf++; - TRACE(("Iter: %d Char: %c Line %d field_count %d, state %d\n", + TRACE(("tokenize_delim_customterm - Iter: %d Char: %c Line %d field_count %d, state %d\n", i, c, self->file_lines + 1, self->line_fields[self->lines], self->state)); @@ -1166,10 +1167,11 @@ int tokenize_delim_customterm(parser_t *self, size_t line_limit) ; else { // backtrack /* We have to use i + 1 because buf has been incremented but not i */ - while (i + 1 > self->datapos && *buf != self->lineterminator) { + do { --buf; --i; - } + } while (i + 1 > self->datapos && *buf != self->lineterminator); + if (i + 1 > self->datapos) // reached a newline rather than the beginning { ++buf; // move pointer to first char after newline @@ -1336,7 +1338,7 @@ int tokenize_whitespace(parser_t *self, size_t line_limit) // Next character in file c = *buf++; - TRACE(("Iter: %d Char: %c Line %d field_count %d, state %d\n", + TRACE(("tokenize_whitespace - Iter: %d Char: %c Line %d field_count %d, state %d\n", i, c, self->file_lines + 1, self->line_fields[self->lines], self->state));
Fixes GH #9710
https://api.github.com/repos/pandas-dev/pandas/pulls/9837
2015-04-09T01:20:03Z
2015-04-28T01:02:18Z
null
2015-04-28T10:30:23Z
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
Fixed bug #9733 where stat functions returned a python scalar for empty series
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index 13764543ec665..6b5ffded6c553 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -701,3 +701,5 @@ Bug Fixes - Bug in ``iloc`` allowing memory outside bounds of a Series to be accessed with negative integers (:issue:`10779`) - Bug in ``read_msgpack`` where encoding is not respected (:issue:`10580`) - Bug preventing access to the first index when using ``iloc`` with a list containing the appropriate negative integer (:issue:`10547`, :issue:`10779`) + +- Bug in stat functions (``sum``, ``mean``, etc) returning a python scalar for empty series (:issue:`9733`) diff --git a/pandas/core/common.py b/pandas/core/common.py index aaa341240f538..f31bb5675f1a5 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2484,11 +2484,18 @@ def is_integer_dtype(arr_or_dtype): return (issubclass(tipo, np.integer) and not issubclass(tipo, (np.datetime64, np.timedelta64))) + def is_int64_dtype(arr_or_dtype): tipo = _get_dtype_type(arr_or_dtype) return issubclass(tipo, np.int64) +def is_unsigned_integer_dtype(arr_or_dtype): + tipo = _get_dtype_type(arr_or_dtype) + return (issubclass(tipo, np.unsignedinteger) and + not issubclass(tipo, (np.datetime64, np.timedelta64))) + + def is_int_or_datetime_dtype(arr_or_dtype): tipo = _get_dtype_type(arr_or_dtype) return (issubclass(tipo, np.integer) or diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index c70fb6339517d..ce5dee32c47fb 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -18,6 +18,7 @@ is_float, is_integer, is_complex, is_float_dtype, is_complex_dtype, is_integer_dtype, + is_unsigned_integer_dtype, is_bool_dtype, is_object_dtype, is_datetime64_dtype, is_timedelta64_dtype, is_datetime_or_timedelta_dtype, _get_dtype, @@ -67,21 +68,7 @@ def f(values, axis=None, skipna=True, **kwds): if k not in kwds: kwds[k] = v try: - if self.zero_value is not None and values.size == 0: - if values.ndim == 1: - - # wrap the 0's if needed - if is_timedelta64_dtype(values): - return lib.Timedelta(0) - return 0 - else: - result_shape = (values.shape[:axis] + - values.shape[axis + 1:]) - result = np.empty(result_shape) - result.fill(0) - return result - - if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype, + if values.size != 0 and _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype, bn_name): result = bn_func(values, axis=axis, **kwds) @@ -187,7 +174,10 @@ def _get_values(values, skipna, fill_value=None, fill_value_typ=None, # return a platform independent precision dtype dtype_max = dtype if is_integer_dtype(dtype) or is_bool_dtype(dtype): - dtype_max = np.int64 + if is_unsigned_integer_dtype(dtype): + dtype_max = np.uint64 + else: + dtype_max = np.int64 elif is_float_dtype(dtype): dtype_max = np.float64 @@ -241,14 +231,14 @@ def nanall(values, axis=None, skipna=True): @disallow('M8') -@bottleneck_switch(zero_value=0) +@bottleneck_switch() def nansum(values, axis=None, skipna=True): values, mask, dtype, dtype_max = _get_values(values, skipna, 0) dtype_sum = dtype_max if is_float_dtype(dtype): dtype_sum = dtype the_sum = values.sum(axis, dtype=dtype_sum) - the_sum = _maybe_null_out(the_sum, axis, mask) + the_sum = _maybe_null_out(the_sum, axis, mask, False) return _wrap_results(the_sum, dtype) @@ -414,7 +404,7 @@ def nanmin(values, axis=None, skipna=True): result = values.min(axis) result = _wrap_results(result, dtype) - return _maybe_null_out(result, axis, mask) + return _maybe_null_out(result, axis, mask, True) @bottleneck_switch() @@ -445,7 +435,7 @@ def nanmax(values, axis=None, skipna=True): result = values.max(axis) result = _wrap_results(result, dtype) - return _maybe_null_out(result, axis, mask) + return _maybe_null_out(result, axis, mask, True) def nanargmax(values, axis=None, skipna=True): @@ -554,7 +544,7 @@ def nanprod(values, axis=None, skipna=True): values = values.copy() values[mask] = 1 result = values.prod(axis) - return _maybe_null_out(result, axis, mask) + return _maybe_null_out(result, axis, mask, False) def _maybe_arg_null_out(result, axis, mask, skipna): @@ -588,9 +578,11 @@ def _get_counts(mask, axis, dtype=float): return np.array(count, dtype=dtype) -def _maybe_null_out(result, axis, mask): +def _maybe_null_out(result, axis, mask, null_on_empty): if axis is not None and getattr(result, 'ndim', False): null_mask = (mask.shape[axis] - mask.sum(axis)) == 0 + if not null_on_empty: + null_mask = null_mask & (mask.shape[axis] > 0) if np.any(null_mask): if np.iscomplexobj(result): result = result.astype('c16') @@ -599,9 +591,8 @@ def _maybe_null_out(result, axis, mask): result[null_mask] = np.nan else: null_mask = mask.size - mask.sum() - if null_mask == 0: - result = np.nan - + if null_mask == 0 and (mask.size > 0 or null_on_empty): + return np.nan return result diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 56146df37a27f..41613195d9575 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -3616,15 +3616,44 @@ def test_ops_consistency_on_empty(self): # GH 7869 # consistency on empty - # float - result = Series(dtype=float).sum() - self.assertEqual(result,0) + # Test type of empty Series - result = Series(dtype=float).mean() - self.assertTrue(isnull(result)) + ops = ['median', 'mean', 'sum', 'prod'] - result = Series(dtype=float).median() - self.assertTrue(isnull(result)) + # First test numpy types + # Just make sure that numpy and pandas have the same return type + for dtype in ['int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64', 'float16', 'float32', + 'float64', 'complex64', 'complex128']: + s = Series(dtype=dtype) + for op in ops: + result = getattr(s, op)() + np_type = getattr(np, dtype) + reference = getattr(np, op)(np_type([])) + if np.isnan(reference): + self.assertTrue(np.isnan(result), + msg="%s on empty %s Series: expecting nan, got %s" % (op, dtype, str(result))) + else: + self.assertEqual(result.dtype, reference.dtype, + msg="%s on empty %s Series: returned type %s, expected %s" % + (op, dtype, str(result.dtype), str(reference.dtype))) + self.assertEqual(result, reference, + msg='%s on empty %s Series: expected %s but received %s' % + (op, dtype, str(reference), str(result))) + + # Test str/unicode types + str_series = Series(dtype='str') + unicode_series = Series(dtype='unicode') + for op in ['median', 'mean', 'prod']: + # TODO: these operations should raise type errors + # self.assertRaises(TypeError, getattr(str_series, op)(), + # msg="%s on empty str Series should raise TypeError" % op) + # self.assertRaises(TypeError, getattr(unicode_series, op)(), + # msg="%s on empty unicode Series should raise TypeError" % op) + pass + + # TODO: these operations should return empty strings + # self.assertEqual('', str_series.sum()) + # self.assertEqual('', unicode_series.sum()) # timedelta64[ns] result = Series(dtype='m8[ns]').sum()
closes #9733
https://api.github.com/repos/pandas-dev/pandas/pulls/9829
2015-04-07T11:20:53Z
2015-11-10T01:21:41Z
null
2022-10-13T00:16:30Z
BUG: complex is cast to float in sum with level arg
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 6d98b3b99021b..8125f1de3ea28 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -25,7 +25,8 @@ notnull, _DATELIKE_DTYPES, is_numeric_dtype, is_timedelta64_dtype, is_datetime64_dtype, is_categorical_dtype, _values_from_object, - is_datetime_or_timedelta_dtype, is_bool_dtype) + is_datetime_or_timedelta_dtype, is_bool_dtype, + is_complex_dtype) from pandas.core.config import option_context import pandas.lib as lib from pandas.lib import Timestamp @@ -1495,7 +1496,7 @@ def aggregate(self, values, how, axis=0): values = _algos.ensure_float64(values) elif com.is_integer_dtype(values): values = values.astype('int64', copy=False) - elif is_numeric: + elif is_numeric and not is_complex_dtype(values.dtype): values = _algos.ensure_float64(values) else: values = values.astype(object)
Preserve complex dtypes when a stat_function on DataFrame is called with the optional level argument. Minimal example: ``` import pandas as pd frame = pd.DataFrame({'z': [1+2j]}) frame.sum(level=0) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9828
2015-04-07T08:21:26Z
2015-05-09T19:46:26Z
null
2015-05-09T19:46:26Z
add biweight midcorrelation as option for series correlation
diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index f45ed9eeeaeee..c3ce516508a8d 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -37,6 +37,9 @@ New features - SQL io functions now accept a SQLAlchemy connectable. (:issue:`7877`) - 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`) +- ``Series.corr()`` and ``DataFrame.corr()`` now supports `Biweight Midcorrelation`_, a median-based correlation metric, a robust alternative to Pearson's R. (:issue:`9826`) + +_Biweight Midcorrelation: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465711/ .. _whatsnew_0170.gil: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 8f7aee0cb6f15..6898abdfd6160 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -4232,10 +4232,11 @@ def corr(self, method='pearson', min_periods=1): Parameters ---------- - method : {'pearson', 'kendall', 'spearman'} + method : {'pearson', 'kendall', 'spearman', 'bicor'} * pearson : standard correlation coefficient * kendall : Kendall Tau correlation coefficient * spearman : Spearman rank correlation + * bicor : Biweight midcorrelation, a median-based similarity metric min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py index c70fb6339517d..b658d5fadb3b8 100644 --- a/pandas/core/nanops.py +++ b/pandas/core/nanops.py @@ -651,10 +651,32 @@ def _kendall(a, b): def _spearman(a, b): return spearmanr(a, b)[0] + def _biweight_midcorrelation(a, b): + a_median = np.median(a) + b_median = np.median(b) + + # Median absolute deviation + a_mad = np.median(np.abs(a - a_median)) + b_mad = np.median(np.abs(b - b_median)) + + u = (a - a_median) / (9 * a_mad) + v = (b - b_median) / (9 * b_mad) + + w_a = np.square(1 - np.square(u)) * ((1 - np.abs(u)) > 0) + w_b = np.square(1 - np.square(v)) * ((1 - np.abs(v)) > 0) + + a_item = (a - a_median) * w_a + b_item = (b - b_median) * w_b + + return (a_item * b_item).sum() / ( + np.sqrt(np.square(a_item).sum()) * + np.sqrt(np.square(b_item).sum())) + _cor_methods = { 'pearson': _pearson, 'kendall': _kendall, - 'spearman': _spearman + 'spearman': _spearman, + 'bicor': _biweight_midcorrelation } return _cor_methods[method] diff --git a/pandas/core/series.py b/pandas/core/series.py index 87fde996aaa67..3404ae7c7d598 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1284,10 +1284,11 @@ def corr(self, other, method='pearson', Parameters ---------- other : Series - method : {'pearson', 'kendall', 'spearman'} + method : {'pearson', 'kendall', 'spearman', 'bicor} * pearson : standard correlation coefficient * kendall : Kendall Tau correlation coefficient * spearman : Spearman rank correlation + * bicor : Biweight midcorrelation, a median-based similarity metric min_periods : int, optional Minimum number of observations needed to have a valid result diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py index a903b76b3ac7f..4bbb7f1a0c776 100644 --- a/pandas/tests/test_nanops.py +++ b/pandas/tests/test_nanops.py @@ -583,6 +583,103 @@ def test_nancorr_spearman(self): self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method='spearman') + def test_nancorr_bicor(self): + a = self.arr_float_2d + b = self.arr_float1_2d + a_median = np.median(a) + b_median = np.median(b) + + # Median absolute deviation + a_mad = np.median(np.abs(a - a_median)) + b_mad = np.median(np.abs(b - b_median)) + + u = (a - a_median) / (9 * a_mad) + v = (b - b_median) / (9 * b_mad) + + w_a = np.square(1 - np.square(u)) * ((1 - np.abs(u)) > 0) + w_b = np.square(1 - np.square(v)) * ((1 - np.abs(v)) > 0) + + a_item = (a - a_median) * w_a + b_item = (b - b_median) * w_b + + targ0 = (a_item * b_item).sum() / ( + np.sqrt(np.square(a_item).sum()) * + np.sqrt(np.square(b_item).sum())) + + a = self.arr_float_2d.flat + b = self.arr_float1_2d.flat + a_median = np.median(a) + b_median = np.median(b) + + # Median absolute deviation + a_mad = np.median(np.abs(a - a_median)) + b_mad = np.median(np.abs(b - b_median)) + + u = (a - a_median) / (9 * a_mad) + v = (b - b_median) / (9 * b_mad) + + w_a = np.square(1 - np.square(u)) * ((1 - np.abs(u)) > 0) + w_b = np.square(1 - np.square(v)) * ((1 - np.abs(v)) > 0) + + a_item = (a - a_median) * w_a + b_item = (b - b_median) * w_b + + targ1 = (a_item * b_item).sum() / ( + np.sqrt(np.square(a_item).sum()) * + np.sqrt(np.square(b_item).sum())) + self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, + method='bicor') + a = self.arr_float_1d + b = self.arr_float1_1d + a_median = np.median(a) + b_median = np.median(b) + + # Median absolute deviation + a_mad = np.median(np.abs(a - a_median)) + b_mad = np.median(np.abs(b - b_median)) + + u = (a - a_median) / (9 * a_mad) + v = (b - b_median) / (9 * b_mad) + + w_a = np.square(1 - np.square(u)) * ((1 - np.abs(u)) > 0) + w_b = np.square(1 - np.square(v)) * ((1 - np.abs(v)) > 0) + + a_item = (a - a_median) * w_a + b_item = (b - b_median) * w_b + + targ0 = (a_item * b_item).sum() / ( + np.sqrt(np.square(a_item).sum()) * + np.sqrt(np.square(b_item).sum())) + + a = self.arr_float_1d.flat + b = self.arr_float1_1d.flat + a_median = np.median(a) + b_median = np.median(b) + + # Median absolute deviation + a_mad = np.median(np.abs(a - a_median)) + b_mad = np.median(np.abs(b - b_median)) + + u = (a - a_median) / (9 * a_mad) + v = (b - b_median) / (9 * b_mad) + + w_a = np.square(1 - np.square(u)) * ((1 - np.abs(u)) > 0) + w_b = np.square(1 - np.square(v)) * ((1 - np.abs(v)) > 0) + + a_item = (a - a_median) * w_a + b_item = (b - b_median) * w_b + + targ1 = (a_item * b_item).sum() / ( + np.sqrt(np.square(a_item).sum()) * + np.sqrt(np.square(b_item).sum())) + self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, + method='bicor') + + # targ0 = spearmanr(self.arr_float_1d, self.arr_float1_1d) + # targ1 = spearmanr(self.arr_float_1d.flat, self.arr_float1_1d.flat) + # self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, + # method='bicor') + def test_nancov(self): targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1] targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
Add [Biweight Midcorrelation](http://download-v2.springer.com/static/pdf/95/chp%253A10.1007%252F978-3-642-39678-6_14.pdf?token2=exp=1428360548~acl=%2Fstatic%2Fpdf%2F95%2Fchp%25253A10.1007%25252F978-3-642-39678-6_14.pdf*~hmac=980c2edddba12bbb73ebbadaa682092cde9d534bc8738d65785647402b683c21), an alternative correlation method to Pearson that is robust to outliers, to the series correlation method repertoire.
https://api.github.com/repos/pandas-dev/pandas/pulls/9826
2015-04-07T01:16:54Z
2015-10-18T14:02:24Z
null
2023-05-11T01:12:55Z
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
ENC: better pandas typesetting in ipython nbconvert --to latex
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index f700d4316842c..de06044785891 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1428,7 +1428,10 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None, if buf is None: return formatter.buf.getvalue() - + + def _repr_latex_(self): + return self.to_latex() + def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None): """ Concise summary of a DataFrame.
Currently, when IPython notebooks are converted to latex, pandas `DataFrame`s are appear as raw (verbatim) text. One simple solution is to include a `_repl_latex_` function. I have tried this solution and works. See https://github.com/ipython/ipython/issues/8261
https://api.github.com/repos/pandas-dev/pandas/pulls/9821
2015-04-06T18:39:49Z
2015-10-21T07:18:03Z
null
2023-05-11T01:12:55Z
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
BUG: where behaves badly when dtype of self is datetime or timedelta, and dtype of other is not
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..3949ad4394fb1 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 ``where`` when dtype of self is datetime64 or timedelta64, but dtype of other is not \ No newline at end of file diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e05709d7a180f..e1a50f8b5200a 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3323,7 +3323,8 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, except ValueError: new_other = np.array(other) - if not (new_other == np.array(other)).all(): + matches = (new_other == np.array(other)) + if matches is False or not matches.all(): other = np.array(other) # we can't use our existing dtype diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 7a16fb2b6b0d7..3418295adabf5 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1324,13 +1324,11 @@ 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 floats - repring NaT with np.nan so nans propagate - values is always ndarray like, other may not be """ + """ Coerce values and other to float64, with null values converted to + NaN. values is always ndarray-like, other may not be """ def masker(v): mask = isnull(v) - v = v.view('i8').astype('float64') + v = v.astype('float64') v[mask] = np.nan return v @@ -1342,6 +1340,8 @@ def masker(v): other = _coerce_scalar_to_timedelta_type(other, unit='s', box=False).item() if other == tslib.iNaT: other = np.nan + elif lib.isscalar(other): + other = np.float64(other) else: other = masker(other) @@ -1807,16 +1807,20 @@ def _try_operate(self, values): return values.view('i8') 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 """ + """ Coerce values and other to dtype 'i8'. NaN and NaT convert to + the smallest i8, and will correctly round-trip to NaT if converted + back in _try_coerce_result. values is always ndarray-like, other + may not be """ values = values.view('i8') + if is_null_datelike_scalar(other): other = tslib.iNaT elif isinstance(other, datetime): other = lib.Timestamp(other).asm8.view('i8') - else: + elif hasattr(other, 'dtype') and com.is_integer_dtype(other): other = other.view('i8') + else: + other = np.array(other, dtype='i8') return values, other diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index e140ffd97051c..25487a081571a 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -1855,6 +1855,48 @@ def test_where_dups(self): expected = Series([5,11,2,5,11,2],index=[0,1,2,0,1,2]) assert_series_equal(comb, expected) + def test_where_datetime(self): + s = Series(date_range('20130102', periods=2)) + expected = Series([10, 10], dtype='datetime64[ns]') + mask = np.array([False, False]) + + rs = s.where(mask, [10, 10]) + assert_series_equal(rs, expected) + + rs = s.where(mask, 10) + assert_series_equal(rs, expected) + + rs = s.where(mask, 10.0) + assert_series_equal(rs, expected) + + rs = s.where(mask, [10.0, 10.0]) + assert_series_equal(rs, expected) + + rs = s.where(mask, [10.0, np.nan]) + expected = Series([10, None], dtype='datetime64[ns]') + assert_series_equal(rs, expected) + + def test_where_timedelta(self): + s = Series([1, 2], dtype='timedelta64[ns]') + expected = Series([10, 10], dtype='timedelta64[ns]') + mask = np.array([False, False]) + + rs = s.where(mask, [10, 10]) + assert_series_equal(rs, expected) + + rs = s.where(mask, 10) + assert_series_equal(rs, expected) + + rs = s.where(mask, 10.0) + assert_series_equal(rs, expected) + + rs = s.where(mask, [10.0, 10.0]) + assert_series_equal(rs, expected) + + rs = s.where(mask, [10.0, np.nan]) + expected = Series([10, None], dtype='timedelta64[ns]') + assert_series_equal(rs, expected) + def test_mask(self): s = Series(np.random.randn(5)) cond = s > 0
There are a few weird behaviors that this fixes: 1. If `other` is an `int` or `float`, then `where` throws an Exception while trying to call `other.view` 2. If `other` is an np.int64, it works fine 3. If `other` is an np.float64, there is no error, but the result is bizarre (it reinterprets the bits of the float as an integer, rather than casting it) 4. If `other` is list-like and has a numerical dtype, then it throws an exception while try to call `all` on the value False (for some reason, comparing an ndarray with dtype datetime64 and an ndarray with an integer dtype just returns a scalar False rather than a boolean ndarray) ``` >>> s = Series(date_range('20130102', periods=2)) >>> s.where(s.isnull(), 0) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/evanpw/Workspace/pandas/pandas/core/generic.py", line 3399, in where try_cast=try_cast) File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 2469, in where return self.apply('where', **kwargs) File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 2451, in apply applied = getattr(b, f)(**kwargs) File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 1080, in where result = func(cond, values, other) File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 1063, in func v, o = self._try_coerce_args(v, o) File "/home/evanpw/Workspace/pandas/pandas/core/internals.py", line 1819, in _try_coerce_args other = other.view('i8') AttributeError: 'int' object has no attribute 'view' >>> s.where(s.isnull(), np.int64(10)) 0 1970-01-01 00:00:00.000000010 1 1970-01-01 00:00:00.000000010 dtype: datetime64[ns] >>> s.where(s.isnull(), np.float64(10)) 0 2116-06-17 06:38:37.588971520 1 2116-06-17 06:38:37.588971520 dtype: datetime64[ns] >>> s.where(s.isnull(), [0, 1]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/evanpw/Workspace/pandas/pandas/core/generic.py", line 3326, in where if not (new_other == np.array(other)).all(): AttributeError: 'bool' object has no attribute 'all' ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9804
2015-04-03T17:18:07Z
2015-04-14T15:24:45Z
null
2015-09-19T00:38:19Z
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
Closes #9795 (Stata writer changes input frame)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..b24815fe62ba8 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -65,7 +65,7 @@ Bug Fixes - Bug in ``scatter_matrix`` draws unexpected axis ticklabels (:issue:`5662`) - +- Fixed bug in ``StataWriter`` resulting in changes to input ``DataFrame`` upon save (:issue:`9795`). - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) diff --git a/pandas/io/stata.py b/pandas/io/stata.py index 7dd32fd00a4d2..3972bad7b2d83 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -1885,6 +1885,8 @@ def _prepare_pandas(self, data): #NOTE: we might need a different API / class for pandas objects so # we can set different semantics - handle this with a PR to pandas.io + data = data.copy() + if self._write_index: data = data.reset_index() @@ -2013,7 +2015,7 @@ def _write_variable_labels(self, labels=None): self._write(_pad_bytes("", 81)) def _prepare_data(self): - data = self.data.copy() + data = self.data typlist = self.typlist convert_dates = self._convert_dates # 1. Convert dates diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py index 8b44be61d5f66..0aaf018b21584 100644 --- a/pandas/io/tests/test_stata.py +++ b/pandas/io/tests/test_stata.py @@ -290,6 +290,15 @@ def test_stata_doc_examples(self): df = DataFrame(np.random.randn(10, 2), columns=list('AB')) df.to_stata(path) + def test_write_preserves_original(self): + # 9795 + np.random.seed(423) + df = pd.DataFrame(np.random.randn(5,4), columns=list('abcd')) + df.ix[2, 'a':'c'] = np.nan + df_copy = df.copy() + df.to_stata('test.dta', write_index=False) + tm.assert_frame_equal(df, df_copy) + def test_encoding(self): # GH 4626, proper encoding handling
closes #9795 Just needed to move the data frame copy earlier in the execution path.
https://api.github.com/repos/pandas-dev/pandas/pulls/9800
2015-04-03T12:44:36Z
2015-04-07T10:28:47Z
null
2015-11-12T23:44:34Z
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
DOC: add doc/source/contributing.rst as the CONTRIBUTING.md
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5329bad1d90e4..dc7cb7f2ab0bc 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,105 +1,569 @@ -### 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. + +### 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: + +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
xref #9754 I just did `pandoc doc/source/contributing.rst -t markdown > CONTRIBUTING.md` to generate the new CONTRIBUTING file.
https://api.github.com/repos/pandas-dev/pandas/pulls/9796
2015-04-03T02:04:49Z
2015-04-03T11:07:53Z
null
2015-04-03T11:07:53Z
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: incorrectly defined Memoridal Day holiday
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 3b3542b760d6f..0d77e2412c1d7 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -407,7 +407,7 @@ def merge(self, other, inplace=False): else: return holidays -USMemorialDay = Holiday('MemorialDay', month=5, day=24, +USMemorialDay = Holiday('MemorialDay', month=5, day=25, offset=DateOffset(weekday=MO(1))) USLaborDay = Holiday('Labor Day', month=9, day=1, offset=DateOffset(weekday=MO(1)))
closes #9760 Memorial Day was incorrectly defined. It is the last Monday in May, so the earliest it could be is the 25th (not the 24th). This change fixes the problem. ``` >>> from pandas.tseries.holiday import AbstractHolidayCalendar, USMemorialDay >>> class MemorialDayCalendar(AbstractHolidayCalendar):rules=[USMemorialDay] >>> MemorialDayCalendar().holidays('2021','2022') ``` Will now return Timestamp('2021-05-31 00:00:00') instead of Timestamp('2021-05-24 00:00:00')
https://api.github.com/repos/pandas-dev/pandas/pulls/9763
2015-03-31T16:45:07Z
2015-08-12T15:14:02Z
null
2015-08-12T15:14:02Z
**BUG**: GH9759 - allow FloatArrayFormatter to format arrays with NaNs
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index ca316bbac8474..baba88b021a3e 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -76,3 +76,5 @@ Bug Fixes - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) + +- Bug in ``FloatArrayFormatter._format_strings``: could not format float arrays containing NaN values (:issue:`9759`). Caused a FloatingPointError to be raised when trying to render a DataFrame using e.g. ``DataFrame.to_html``. diff --git a/pandas/core/format.py b/pandas/core/format.py index b21ca9050ffd0..73b446d4b96f1 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -1992,7 +1992,7 @@ def _format_strings(self): too_long = maxlen > self.digits + 5 - abs_vals = np.abs(self.values) + abs_vals = np.abs(self.values[~np.isnan(self.values)]) # this is pretty arbitrary for now has_large_values = (abs_vals > 1e8).any() diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index ce32c8af99a73..43f3863e74e64 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -2981,10 +2981,12 @@ def test_misc(self): self.assertTrue(len(result) == 0) def test_format(self): - obj = fmt.FloatArrayFormatter(np.array([12, 0], dtype=np.float64)) + obj = fmt.FloatArrayFormatter(np.array([12, 0, np.nan], + dtype=np.float64)) result = obj.get_result() self.assertEqual(result[0], " 12") self.assertEqual(result[1], " 0") + self.assertEqual(result[2], "NaN") class TestRepr_timedelta64(tm.TestCase):
https://api.github.com/repos/pandas-dev/pandas/pulls/9761
2015-03-31T13:38:50Z
2015-04-04T18:34:20Z
null
2015-04-04T18:34:32Z
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: DataFrame.equals should not care about block order (GH #9330)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..52b57529fc6c2 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -70,6 +70,7 @@ Bug Fixes - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) +- Bug in ``equals`` causing false negatives when block order differed (:issue:`9330`) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 7a16fb2b6b0d7..9b2d366bfb2be 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3310,8 +3310,20 @@ def equals(self, other): return False self._consolidate_inplace() other._consolidate_inplace() + if len(self.blocks) != len(other.blocks): + return False + + # canonicalize block order, using a tuple combining the type + # name and then mgr_locs because there might be unconsolidated + # blocks (say, Categorical) which can only be distinguished by + # the iteration order + def canonicalize(block): + return (block.dtype.name, block.mgr_locs.as_array.tolist()) + + self_blocks = sorted(self.blocks, key=canonicalize) + other_blocks = sorted(other.blocks, key=canonicalize) return all(block.equals(oblock) for block, oblock in - zip(self.blocks, other.blocks)) + zip(self_blocks, other_blocks)) class SingleBlockManager(BlockManager): diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index a15149e341f4d..66b2f4d30f6b6 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -4586,9 +4586,24 @@ def test_duplicate_column_name(self): df.to_hdf(path, 'df', format='table') other = read_hdf(path, 'df') + tm.assert_frame_equal(df, other) + self.assertTrue(df.equals(other)) + self.assertTrue(other.equals(df)) + + def test_round_trip_equals(self): + # GH 9330 + df = DataFrame({"B": [1,2], "A": ["x","y"]}) + with ensure_clean_path(self.path) as path: + df.to_hdf(path, 'df', format='table') + other = read_hdf(path, 'df') + tm.assert_frame_equal(df, other) + self.assertTrue(df.equals(other)) + self.assertTrue(other.equals(df)) + + def _test_sort(obj): if isinstance(obj, DataFrame): return obj.reindex(sorted(obj.index)) diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1acad4cf978a8..0b365a3399e0b 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -5944,6 +5944,20 @@ def test_boolean_comparison(self): self.assertRaises(ValueError, lambda : df == (2,2)) self.assertRaises(ValueError, lambda : df == [2,2]) + def test_equals_different_blocks(self): + # GH 9330 + df0 = pd.DataFrame({"A": ["x","y"], "B": [1,2], + "C": ["w","z"]}) + df1 = df0.reset_index()[["A","B","C"]] + # this assert verifies that the above operations have + # induced a block rearrangement + self.assertTrue(df0._data.blocks[0].dtype != + df1._data.blocks[0].dtype) + # do the real tests + self.assert_frame_equal(df0, df1) + self.assertTrue(df0.equals(df1)) + self.assertTrue(df1.equals(df0)) + def test_to_csv_from_csv(self): pname = '__tmp_to_csv_from_csv__' diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index 45f089f5e0a53..36585abd1b98f 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -68,15 +68,15 @@ def create_block(typestr, placement, item_shape=None, num_offset=0): elif typestr in ('object', 'string', 'O'): values = np.reshape(['A%d' % i for i in mat.ravel() + num_offset], shape) - elif typestr in ('bool'): + elif typestr in ('b','bool',): values = np.ones(shape, dtype=np.bool_) elif typestr in ('datetime', 'dt', 'M8[ns]'): values = (mat * 1e9).astype('M8[ns]') elif typestr in ('timedelta', 'td', 'm8[ns]'): values = (mat * 1).astype('m8[ns]') - elif typestr in ('category'): + elif typestr in ('category',): values = Categorical([1,1,2,2,3,3,3,3,4,4]) - elif typestr in ('category2'): + elif typestr in ('category2',): values = Categorical(['a','a','a','a','b','b','c','c','c','d']) elif typestr in ('sparse', 'sparse_na'): # FIXME: doesn't support num_rows != 10 @@ -751,6 +751,25 @@ def test_equals(self): bm2 = BlockManager(bm1.blocks[::-1], bm1.axes) self.assertTrue(bm1.equals(bm2)) + def test_equals_block_order_different_dtypes(self): + # GH 9330 + + mgr_strings = [ + "a:i8;b:f8", # basic case + "a:i8;b:f8;c:c8;d:b", # many types + "a:i8;e:dt;f:td;g:string", # more types + "a:i8;b:category;c:category2;d:category2", # categories + "c:sparse;d:sparse_na;b:f8", # sparse + ] + + for mgr_string in mgr_strings: + bm = create_mgr(mgr_string) + block_perms = itertools.permutations(bm.blocks) + for bm_perm in block_perms: + bm_this = BlockManager(bm_perm, bm.axes) + self.assertTrue(bm.equals(bm_this)) + self.assertTrue(bm_this.equals(bm)) + def test_single_mgr_ctor(self): mgr = create_single_mgr('f8', num_rows=5) self.assertEqual(mgr.as_matrix().tolist(), [0., 1., 2., 3., 4.])
closes #9330 and another version of the same problem [here on SO](http://stackoverflow.com/questions/29311659/how-can-pandas-dataframes-appear-identical-but-fail-equals) by canonicalizing the block order during an `equals` comparison. Tested at the block manager level and above it at the frame level.
https://api.github.com/repos/pandas-dev/pandas/pulls/9745
2015-03-28T22:43:43Z
2015-04-05T23:10:10Z
null
2015-04-05T23:10:10Z
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
BUG: _retry_read_url retries connection even if successful
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 1e570768b5a7a..f8b34bdfcca9e 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -48,3 +48,4 @@ Bug Fixes ~~~~~~~~~ - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) +- Bug in DataReader causing connections to be retried even when successful \ No newline at end of file diff --git a/pandas/io/data.py b/pandas/io/data.py index ea635e85ed177..949f8bcb50a92 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))
https://api.github.com/repos/pandas-dev/pandas/pulls/9723
2015-03-25T11:17:27Z
2015-03-25T11:18:52Z
null
2015-03-25T11:22:46Z
Moved imports into functions that use them
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 84472802d73be..db9d970e1aba1 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -254,3 +254,5 @@ Bug Fixes - Bug in hiding ticklabels with subplots and shared axes when adding a new plot to an existing grid of axes (:issue:`9158`) - Bug in ``transform`` and ``filter`` when grouping on a categorical variable (:issue:`9921`) - Bug in ``transform`` when groups are equal in number and dtype to the input index (:issue:`9700`) +- Google BigQuery connector now imports dependencies on a per-method basis.(:issue:`9713`) +- Updated BigQuery connector to no longer use deprecated ```oauth2client.tools.run()``` (:issue:`8327`) diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index 9ecffb382e151..f1fcc822adeaf 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -13,75 +13,22 @@ from pandas.tools.merge import concat from pandas.core.common import PandasError -_IMPORTS = False -_GOOGLE_API_CLIENT_INSTALLED = False -_GOOGLE_API_CLIENT_VALID_VERSION = False -_GOOGLE_FLAGS_INSTALLED = False -_GOOGLE_FLAGS_VALID_VERSION = False -_HTTPLIB2_INSTALLED = False -_SETUPTOOLS_INSTALLED = False -def _importers(): - # import things we need - # but make this done on a first use basis - - global _IMPORTS - if _IMPORTS: - return - - _IMPORTS = True - - if not compat.PY3: - - global _GOOGLE_API_CLIENT_INSTALLED, _GOOGLE_API_CLIENT_VALID_VERSION, \ - _GOOGLE_FLAGS_INSTALLED, _GOOGLE_FLAGS_VALID_VERSION, \ - _HTTPLIB2_INSTALLED, _SETUPTOOLS_INSTALLED - - try: - import pkg_resources - _SETUPTOOLS_INSTALLED = True - except ImportError: - _SETUPTOOLS_INSTALLED = False - - if _SETUPTOOLS_INSTALLED: - try: - from apiclient.discovery import build - from apiclient.http import MediaFileUpload - from apiclient.errors import HttpError - - from oauth2client.client import OAuth2WebServerFlow - from oauth2client.client import AccessTokenRefreshError - from oauth2client.client import flow_from_clientsecrets - from oauth2client.file import Storage - from oauth2client.tools import run - _GOOGLE_API_CLIENT_INSTALLED=True - _GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution('google-api-python-client').version - - if LooseVersion(_GOOGLE_API_CLIENT_VERSION) >= '1.2.0': - _GOOGLE_API_CLIENT_VALID_VERSION = True - - except ImportError: - _GOOGLE_API_CLIENT_INSTALLED = False - - - try: - import gflags as flags - _GOOGLE_FLAGS_INSTALLED = True - - _GOOGLE_FLAGS_VERSION = pkg_resources.get_distribution('python-gflags').version +def _check_google_client_version(): + if compat.PY3: + raise NotImplementedError("Google's libraries do not support Python 3 yet") - if LooseVersion(_GOOGLE_FLAGS_VERSION) >= '2.0': - _GOOGLE_FLAGS_VALID_VERSION = True + try: + import pkg_resources - except ImportError: - _GOOGLE_FLAGS_INSTALLED = False + except ImportError: + raise ImportError('Could not import pkg_resources (setuptools).') - try: - import httplib2 - _HTTPLIB2_INSTALLED = True - except ImportError: - _HTTPLIB2_INSTALLED = False + _GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution('google-api-python-client').version + if LooseVersion(_GOOGLE_API_CLIENT_VERSION) < '1.2.0': + raise ImportError("pandas requires google-api-python-client >= 1.2.0 for Google " + "BigQuery support, current version " + _GOOGLE_API_CLIENT_VERSION) logger = logging.getLogger('pandas.io.gbq') logger.setLevel(logging.ERROR) @@ -142,6 +89,16 @@ def __init__(self, project_id, reauth=False): self.service = self.get_service(self.credentials) def get_credentials(self): + try: + from oauth2client.client import OAuth2WebServerFlow + from oauth2client.file import Storage + from oauth2client.tools import run_flow, argparser + + except ImportError: + raise ImportError('Could not import Google API Client.') + + _check_google_client_version() + flow = OAuth2WebServerFlow(client_id='495642085510-k0tmvj2m941jhre2nbqka17vqpjfddtd.apps.googleusercontent.com', client_secret='kOc9wMptUtxkcIFbtZCcrEAc', scope='https://www.googleapis.com/auth/bigquery', @@ -151,11 +108,25 @@ def get_credentials(self): credentials = storage.get() if credentials is None or credentials.invalid or self.reauth: - credentials = run(flow, storage) + credentials = run_flow(flow, storage, argparser.parse_args([])) return credentials def get_service(self, credentials): + try: + import httplib2 + + except ImportError: + raise ImportError("pandas requires httplib2 for Google BigQuery support") + + try: + from apiclient.discovery import build + + except ImportError: + raise ImportError('Could not import Google API Client.') + + _check_google_client_version() + http = httplib2.Http() http = credentials.authorize(http) bigquery_service = build('bigquery', 'v2', http=http) @@ -163,6 +134,15 @@ def get_service(self, credentials): return bigquery_service def run_query(self, query): + try: + from apiclient.errors import HttpError + from oauth2client.client import AccessTokenRefreshError + + except ImportError: + raise ImportError('Could not import Google API Client.') + + _check_google_client_version() + job_collection = self.service.jobs() job_data = { 'configuration': { @@ -313,38 +293,6 @@ def _parse_entry(field_value, field_type): return field_value == 'true' return field_value -def _test_imports(): - - _importers() - _GOOGLE_API_CLIENT_INSTALLED - _GOOGLE_API_CLIENT_VALID_VERSION - _GOOGLE_FLAGS_INSTALLED - _GOOGLE_FLAGS_VALID_VERSION - _HTTPLIB2_INSTALLED - _SETUPTOOLS_INSTALLED - - if compat.PY3: - raise NotImplementedError("Google's libraries do not support Python 3 yet") - - if not _SETUPTOOLS_INSTALLED: - raise ImportError('Could not import pkg_resources (setuptools).') - - if not _GOOGLE_API_CLIENT_INSTALLED: - raise ImportError('Could not import Google API Client.') - - if not _GOOGLE_FLAGS_INSTALLED: - raise ImportError('Could not import Google Command Line Flags Module.') - - if not _GOOGLE_API_CLIENT_VALID_VERSION: - raise ImportError("pandas requires google-api-python-client >= 1.2.0 for Google " - "BigQuery support, current version " + _GOOGLE_API_CLIENT_VERSION) - - if not _GOOGLE_FLAGS_VALID_VERSION: - raise ImportError("pandas requires python-gflags >= 2.0.0 for Google " - "BigQuery support, current version " + _GOOGLE_FLAGS_VERSION) - - if not _HTTPLIB2_INSTALLED: - raise ImportError("pandas requires httplib2 for Google BigQuery support") def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=False): """Load data from Google BigQuery. @@ -379,7 +327,6 @@ def read_gbq(query, project_id = None, index_col=None, col_order=None, reauth=Fa """ - _test_imports() if not project_id: raise TypeError("Missing required parameter: project_id") @@ -450,7 +397,6 @@ def to_gbq(dataframe, destination_table, project_id=None, chunksize=10000, if multiple accounts are used. """ - _test_imports() if not project_id: raise TypeError("Missing required parameter: project_id") diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 2f79cc8ba1826..5417842d3f863 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -12,6 +12,9 @@ import numpy as np +from distutils.version import LooseVersion +from pandas import compat + from pandas import NaT from pandas.compat import u from pandas.core.frame import DataFrame @@ -22,6 +25,12 @@ VERSION = platform.python_version() +_IMPORTS = False +_GOOGLE_API_CLIENT_INSTALLED = False +_GOOGLE_API_CLIENT_VALID_VERSION = False +_HTTPLIB2_INSTALLED = False +_SETUPTOOLS_INSTALLED = False + def missing_bq(): try: subprocess.call('bq') @@ -29,9 +38,64 @@ def missing_bq(): except OSError: return True +def _test_imports(): + if not compat.PY3: + + global _GOOGLE_API_CLIENT_INSTALLED, _GOOGLE_API_CLIENT_VALID_VERSION, \ + _HTTPLIB2_INSTALLED, _SETUPTOOLS_INSTALLED + + try: + import pkg_resources + _SETUPTOOLS_INSTALLED = True + except ImportError: + _SETUPTOOLS_INSTALLED = False + + if _SETUPTOOLS_INSTALLED: + try: + from apiclient.discovery import build + from apiclient.errors import HttpError + + from oauth2client.client import OAuth2WebServerFlow + from oauth2client.client import AccessTokenRefreshError + + from oauth2client.file import Storage + from oauth2client.tools import run_flow + _GOOGLE_API_CLIENT_INSTALLED=True + _GOOGLE_API_CLIENT_VERSION = pkg_resources.get_distribution('google-api-python-client').version + + if LooseVersion(_GOOGLE_API_CLIENT_VERSION) >= '1.2.0': + _GOOGLE_API_CLIENT_VALID_VERSION = True + + except ImportError: + _GOOGLE_API_CLIENT_INSTALLED = False + + + try: + import httplib2 + _HTTPLIB2_INSTALLED = True + except ImportError: + _HTTPLIB2_INSTALLED = False + + + if compat.PY3: + raise NotImplementedError("Google's libraries do not support Python 3 yet") + + if not _SETUPTOOLS_INSTALLED: + raise ImportError('Could not import pkg_resources (setuptools).') + + if not _GOOGLE_API_CLIENT_INSTALLED: + raise ImportError('Could not import Google API Client.') + + if not _GOOGLE_API_CLIENT_VALID_VERSION: + raise ImportError("pandas requires google-api-python-client >= 1.2.0 for Google " + "BigQuery support, current version " + _GOOGLE_API_CLIENT_VERSION) + + if not _HTTPLIB2_INSTALLED: + raise ImportError("pandas requires httplib2 for Google BigQuery support") + def test_requirements(): try: - gbq._test_imports() + _test_imports() except (ImportError, NotImplementedError) as import_exception: raise nose.SkipTest(import_exception)
closes #9713 closes #8327 Changes to be committed: modified: pandas/io/gbq.py Moved google api imports into functions that call them, removed unused imports. This fixes #9713
https://api.github.com/repos/pandas-dev/pandas/pulls/9722
2015-03-25T04:38:20Z
2015-05-01T16:45:03Z
null
2015-05-01T16:45:03Z
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
Documentation how to drop duplicate indices
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 5079b4fa8ad6f..1f50a9c85343c 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -1137,6 +1137,16 @@ should be taken instead. df2.drop_duplicates(['a','b']) df2.drop_duplicates(['a','b'], take_last=True) +An easier way to drop duplicates on the index than to temporarily forgo it is +``groupby(level=0)`` combined with ``first()`` or ``last()``. + +.. ipython:: python + + df3 = df2.set_index('b') + df3 + df3.reset_index().drop_duplicates(subset='b', take_last=False).set_index('b') + df3.groupby(level=0).first() + .. _indexing.dictionarylike: Dictionary-like :meth:`~pandas.DataFrame.get` method
fixes #9708
https://api.github.com/repos/pandas-dev/pandas/pulls/9717
2015-03-24T12:50:10Z
2015-03-25T22:57:26Z
null
2015-03-25T22:57:26Z
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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+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
DOC: try to use matplotlib style (mpl >= 1.4) in all docs
diff --git a/doc/source/10min.rst b/doc/source/10min.rst index c98f41973e1ee..1f59c38d75f93 100644 --- a/doc/source/10min.rst +++ b/doc/source/10min.rst @@ -12,7 +12,11 @@ from pandas import options import pandas as pd np.set_printoptions(precision=4, suppress=True) - options.display.mpl_style='default' + import matplotlib + try: + matplotlib.style.use('ggplot') + except AttributeError: + options.display.mpl_style = 'default' options.display.max_rows=15 #### portions of this were borrowed from the @@ -695,8 +699,6 @@ Plotting import matplotlib.pyplot as plt plt.close('all') - from pandas import options - options.display.mpl_style='default' .. ipython:: python diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index 7fe04af716cec..d03e0fb117c5c 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -13,7 +13,6 @@ from pandas import * import pandas as pd np.set_printoptions(precision=4, suppress=True) - options.display.mpl_style='default' options.display.max_rows=15 diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 759675c51b960..4b0fe39d929a9 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -10,9 +10,13 @@ import pandas.util.testing as tm randn = np.random.randn np.set_printoptions(precision=4, suppress=True) + import matplotlib + try: + matplotlib.style.use('ggplot') + except AttributeError: + options.display.mpl_style = 'default' import matplotlib.pyplot as plt plt.close('all') - options.display.mpl_style='default' options.display.max_rows=15 Computational tools diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 6a3751cf7a0b8..0e6386955a653 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -17,7 +17,12 @@ np.random.seed(123456) pd.options.display.max_rows=15 - pd.options.display.mpl_style='default' + + import matplotlib + try: + matplotlib.style.use('ggplot') + except AttributeError: + pd.options.display.mpl_style = 'default' np.set_printoptions(precision=4, suppress=True) diff --git a/doc/source/faq.rst b/doc/source/faq.rst index de88b436198dd..467ec02b55f20 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -21,7 +21,11 @@ Frequently Asked Questions (FAQ) from pandas.tseries.offsets import * import matplotlib.pyplot as plt plt.close('all') - options.display.mpl_style='default' + import matplotlib + try: + matplotlib.style.use('ggplot') + except AttributeError: + options.display.mpl_style = 'default' from pandas.compat import lrange diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst index db19e0de3d788..7ad2641dec52a 100644 --- a/doc/source/groupby.rst +++ b/doc/source/groupby.rst @@ -12,7 +12,11 @@ np.set_printoptions(precision=4, suppress=True) import matplotlib.pyplot as plt plt.close('all') - options.display.mpl_style='default' + import matplotlib + try: + matplotlib.style.use('ggplot') + except AttributeError: + options.display.mpl_style = 'default' from pandas.compat import zip ***************************** @@ -346,7 +350,7 @@ A single group can be selected using ``GroupBy.get_group()``: .. ipython:: python grouped.get_group('bar') - + Or for an object grouped on multiple columns: .. ipython:: python diff --git a/doc/source/sparse.rst b/doc/source/sparse.rst index e72ee6b709282..79def066f0710 100644 --- a/doc/source/sparse.rst +++ b/doc/source/sparse.rst @@ -10,9 +10,6 @@ import pandas.util.testing as tm randn = np.random.randn np.set_printoptions(precision=4, suppress=True) - import matplotlib.pyplot as plt - plt.close('all') - options.display.mpl_style='default' options.display.max_rows = 15 ********************** @@ -222,4 +219,3 @@ row and columns coordinates of the matrix. Note that this will consume a signifi ss_dense = SparseSeries.from_coo(A, dense_index=True) ss_dense - diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst index 852397c355361..9d4cba2e5ee8c 100644 --- a/doc/source/visualization.rst +++ b/doc/source/visualization.rst @@ -31,10 +31,14 @@ We use the standard convention for referencing the matplotlib API: import matplotlib.pyplot as plt -.. versionadded:: 0.11.0 +The plots in this document are made using matplotlib's ``ggplot`` style (new in version 1.4): -The plots in this document are made using matplotlib's ``ggplot`` style (new in version 1.4). -If your version of matplotlib is 1.3 or lower, setting the ``display.mpl_style`` to ``'default'`` +.. code-block:: python + + import matplotlib + matplotlib.style.use('ggplot') + +If your version of matplotlib is 1.3 or lower, you can set ``display.mpl_style`` to ``'default'`` with ``pd.options.display.mpl_style = 'default'`` to produce more appealing plots. When set, matplotlib's ``rcParams`` are changed (globally!) to nicer-looking settings.
now this was only done in the plotting docs itself, with as a result that the actual style used is a mixture between the two
https://api.github.com/repos/pandas-dev/pandas/pulls/9706
2015-03-23T09:12:32Z
2015-03-24T10:35:53Z
2015-03-24T10:35:53Z
2015-03-24T10:35:53Z
DOC: add DataFrame.assign to API docs
diff --git a/doc/source/api.rst b/doc/source/api.rst index 33577a5badc54..b617009fe2f13 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -861,6 +861,7 @@ Combining / joining / merging :toctree: generated/ DataFrame.append + DataFrame.assign DataFrame.join DataFrame.merge DataFrame.update
@TomAugspurger does this look like the right place to put this? @jorisvandenbossche can we possibly merge this into the docs for v0.16?
https://api.github.com/repos/pandas-dev/pandas/pulls/9705
2015-03-23T05:32:39Z
2015-03-24T10:36:05Z
2015-03-24T10:36:04Z
2015-03-25T00:49:17Z
DOC: add automatic content to 0.16.1 whatsnew file
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index bf01d3b21f3fa..f01bca17fa83a 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -3,30 +3,45 @@ v0.16.1 (April ??, 2015) ------------------------ -This is a minor bug-fix release from 0.16.0 and includes a small number of API changes, several new features, -enhancements, and performance improvements along with a large number of bug fixes. We recommend that all -users upgrade to this version. +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. + +.. contents:: What's new in v0.16.1 + :local: + :backlinks: none + + +.. _whatsnew_0161.enhancements: + +Enhancements +~~~~~~~~~~~~ + + + + -- :ref:`Enhancements <whatsnew_0161.enhancements>` -- :ref:`API Changes <whatsnew_0161.api>` -- :ref:`Performance Improvements <whatsnew_0161.performance>` -- :ref:`Bug Fixes <whatsnew_0161.bug_fixes>` .. _whatsnew_0161.api: API changes ~~~~~~~~~~~ -.. _whatsnew_0161.enhancements: -Enhancements -~~~~~~~~~~~~ + + + .. _whatsnew_0161.performance: Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ + + + + + .. _whatsnew_0161.bug_fixes: Bug Fixes
Replaced the manual contents with the automatic (like now in 0.16.0), and a small rephrase (putting the bug fixes first)
https://api.github.com/repos/pandas-dev/pandas/pulls/9702
2015-03-22T22:35:50Z
2015-03-23T09:13:06Z
2015-03-23T09:13:06Z
2015-03-23T09:13:07Z
ENH Make year and quarter dash-separatable
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index bf01d3b21f3fa..9c88815887674 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -22,6 +22,9 @@ API changes Enhancements ~~~~~~~~~~~~ +- Add support for separating years and quarters using dashes, for + example 2014-Q1. (:issue:`9688`) + .. _whatsnew_0161.performance: Performance Improvements diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py index 609ffc4c93c10..0793508b4912c 100644 --- a/pandas/tseries/tests/test_offsets.py +++ b/pandas/tseries/tests/test_offsets.py @@ -21,7 +21,7 @@ from pandas import Series from pandas.tseries.frequencies import _offset_map from pandas.tseries.index import _to_m8, DatetimeIndex, _daterange_cache, date_range -from pandas.tseries.tools import parse_time_string +from pandas.tseries.tools import parse_time_string, DateParseError import pandas.tseries.offsets as offsets from pandas.io.pickle import read_pickle @@ -3010,12 +3010,33 @@ def test_get_offset(): (name, expected, offset)) -def test_parse_time_string(): - (date, parsed, reso) = parse_time_string('4Q1984') - (date_lower, parsed_lower, reso_lower) = parse_time_string('4q1984') - assert date == date_lower - assert parsed == parsed_lower - assert reso == reso_lower +class TestParseTimeString(tm.TestCase): + + def test_parse_time_string(self): + (date, parsed, reso) = parse_time_string('4Q1984') + (date_lower, parsed_lower, reso_lower) = parse_time_string('4q1984') + self.assertEqual(date, date_lower) + self.assertEqual(parsed, parsed_lower) + self.assertEqual(reso, reso_lower) + + def test_parse_time_quarter_w_dash(self): + # https://github.com/pydata/pandas/issue/9688 + pairs = [ + ('1988-Q2', '1988Q2'), + ('2Q-1988', '2Q1988'), + ] + + for dashed, normal in pairs: + (date_dash, parsed_dash, reso_dash) = parse_time_string(dashed) + (date, parsed, reso) = parse_time_string(normal) + + self.assertEqual(date_dash, date) + self.assertEqual(parsed_dash, parsed) + self.assertEqual(reso_dash, reso) + + self.assertRaises(DateParseError, parse_time_string, "-2Q1992") + self.assertRaises(DateParseError, parse_time_string, "2-Q1992") + self.assertRaises(DateParseError, parse_time_string, "4-4Q1992") def test_get_standard_freq(): diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index db62fceaceef8..8430e0209fd78 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -383,10 +383,10 @@ def calc_with_mask(carg,mask): return None # patterns for quarters like '4Q2005', '05Q1' -qpat1full = re.compile(r'(\d)Q(\d\d\d\d)') -qpat2full = re.compile(r'(\d\d\d\d)Q(\d)') -qpat1 = re.compile(r'(\d)Q(\d\d)') -qpat2 = re.compile(r'(\d\d)Q(\d)') +qpat1full = re.compile(r'(\d)Q-?(\d\d\d\d)') +qpat2full = re.compile(r'(\d\d\d\d)-?Q(\d)') +qpat1 = re.compile(r'(\d)Q-?(\d\d)') +qpat2 = re.compile(r'(\d\d)-?Q(\d)') ypat = re.compile(r'(\d\d\d\d)$') has_time = re.compile('(.+)([\s]|T)+(.+)') @@ -424,18 +424,18 @@ def parse_time_string(arg, freq=None, dayfirst=None, yearfirst=None): second=0, microsecond=0) # special handling for possibilities eg, 2Q2005, 2Q05, 2005Q1, 05Q1 - if len(arg) in [4, 6]: + if len(arg) in [4, 5, 6, 7]: m = ypat.match(arg) if m: ret = default.replace(year=int(m.group(1))) return ret, ret, 'year' add_century = False - if len(arg) == 4: + if len(arg) > 5: + qpats = [(qpat1full, 1), (qpat2full, 0)] + else: add_century = True qpats = [(qpat1, 1), (qpat2, 0)] - else: - qpats = [(qpat1full, 1), (qpat2full, 0)] for pat, yfirst in qpats: qparse = pat.match(arg)
This closes #9688 by making years and quarters separable by dashes.
https://api.github.com/repos/pandas-dev/pandas/pulls/9701
2015-03-22T20:52:39Z
2015-03-26T09:49:51Z
2015-03-26T09:49:50Z
2015-03-26T10:43:40Z
BUG: ensure we use group sizes, not group counts, in transform (GH9697)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index bf01d3b21f3fa..5801d1b811790 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -31,3 +31,5 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + +- Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 73439fb1e535d..6d98b3b99021b 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2453,7 +2453,7 @@ def _transform_fast(self, func): if isinstance(func, compat.string_types): func = getattr(self,func) values = func().values - counts = self.count().values + counts = self.size().values values = np.repeat(values, com._ensure_platform_int(counts)) return self._set_result_index_ordered(Series(values)) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 79ebb80fc9ebb..e7001eb09f20c 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1058,6 +1058,19 @@ def test_transform_function_aliases(self): expected = self.df.groupby('A')['C'].transform(np.mean) assert_series_equal(result, expected) + def test_transform_length(self): + # GH 9697 + df = pd.DataFrame({'col1':[1,1,2,2], 'col2':[1,2,3,np.nan]}) + expected = pd.Series([3.0]*4) + def nsum(x): + return np.nansum(x) + results = [df.groupby('col1').transform(sum)['col2'], + df.groupby('col1')['col2'].transform(sum), + df.groupby('col1').transform(nsum)['col2'], + df.groupby('col1')['col2'].transform(nsum)] + for result in results: + assert_series_equal(result, expected) + def test_with_na(self): index = Index(np.arange(10))
Switch count() to size(), so that when we build the expanded values we're using the size of the groups and not simply the number of non-null values they have. Fixes #9697.
https://api.github.com/repos/pandas-dev/pandas/pulls/9699
2015-03-22T17:05:19Z
2015-03-23T10:55:36Z
2015-03-23T10:55:36Z
2015-03-23T10:55:42Z
BUG: fix for interp with axis=1 and inplace
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index bf01d3b21f3fa..b6a6d27c929f2 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -31,3 +31,6 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + + +- Fixed bug with inplace interpolace along the "columns" axis (:issue:`9687`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index e05709d7a180f..21de2fe86689d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2736,10 +2736,11 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, if self.ndim > 2: raise NotImplementedError("Interpolate has not been implemented " "on Panel and Panel 4D objects.") - + axis = self._get_axis_number(axis) if axis == 0: ax = self._info_axis_name elif axis == 1: + orig = self self = self.T ax = 1 ax = self._get_axis_number(ax) @@ -2777,7 +2778,7 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False, if inplace: if axis == 1: self._update_inplace(new_data) - self = self.T + orig._data = self.T._data else: self._update_inplace(new_data) else: diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 1acad4cf978a8..ea7468673356d 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -8683,9 +8683,6 @@ def test_replace_simple_nested_dict_with_nonexistent_value(self): result = df.replace({'col': {-1: '-', 1: 'a', 4: 'b'}}) tm.assert_frame_equal(expected, result) - def test_interpolate(self): - pass - def test_replace_value_is_none(self): self.assertRaises(TypeError, self.tsframe.replace, nan) orig_value = self.tsframe.iloc[0, 0] diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 3dd8c2594cd46..c956376c94d45 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -963,6 +963,20 @@ def test_interp_ignore_all_good(self): result = df[['B', 'D']].interpolate(downcast=None) assert_frame_equal(result, df[['B', 'D']]) + def test_interp_inplace_axis1(self): + # GH 9687 + df = DataFrame({'A': [1, 2, 3], 'B': [2, np.nan, 6], + 'C': [3, 4, 5]}) + df.interpolate(axis=1, inplace=True) + # dtypes... a bit strange here. + # df.T w/ [int, float, int] -> [float, float, float] dtypes + # this test may break if we change that in the future. + expected = DataFrame({'A': [1., 2, 3], 'B': [2., 3, 6], + 'C': [3., 4, 5]}) + assert_frame_equal(df, expected) + self.assertIs(df, df) + + def test_describe(self): desc = tm.makeDataFrame().describe() desc = tm.makeMixedDataFrame().describe()
Closes https://github.com/pydata/pandas/issues/9687 There's still some weird issues with dtypes. In my test case there's a DataFrame with dtypes [int, float, int]. After the interpolation they should be downcasts to [int, int, int] when the missing value is filled. But along the way a transpose changes things to `[float, float, float]` before filling. And then (I think) `self._update_inplace` doesn't change dtypes, so the actual result is `[float, float, float]`.
https://api.github.com/repos/pandas-dev/pandas/pulls/9695
2015-03-21T13:40:01Z
2015-05-09T16:16:26Z
null
2015-08-18T12:45:09Z
BUG: datetime/timedelta Series quantile() call
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index bf01d3b21f3fa..576103b5411b5 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -31,3 +31,5 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + +- Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) diff --git a/pandas/core/series.py b/pandas/core/series.py index 7e3b21be13525..68f3a6032402f 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -2095,7 +2095,7 @@ def _maybe_box(self, func, dropna=False): boxer = com.i8_boxer(self) if len(values) == 0: - return boxer(iNaT) + return boxer(tslib.iNaT) values = values.view('i8') result = func(values) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index ae2ed4eaca2f4..9b5e36974553b 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -6837,6 +6837,11 @@ 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))) + self.assertTrue(pd.isnull(Series([],dtype='m8[ns]').quantile(.5))) if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
Changes to be committed: modified: pandas/core/series.py modified: pandas/tests/test_series.py Fixes global reference to iNaT (should be tslib.iNaT) in series._maybe_box. Adds test `test_datetime_timedelta_quantiles` to check for proper return value in test_series.py. Issue #9675
https://api.github.com/repos/pandas-dev/pandas/pulls/9694
2015-03-21T01:39:57Z
2015-03-25T23:03:40Z
null
2015-03-25T23:03:40Z
Update vendored pyperclip to 1.5.8
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index c517f89855601..5e0b63fab5ba5 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -193,3 +193,4 @@ Bug Fixes - Bug in ``.loc`` result with duplicated key may have ``Index`` with incorrect dtype (:issue:`11497`) - Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`) +- Bug in ``pd.read_clipboard`` and ``pd.to_clipboard`` functions not supporting Unicode (:issue:`9263`) diff --git a/pandas/io/tests/test_clipboard.py b/pandas/io/tests/test_clipboard.py index 4855b715ebbe2..a056bac293cfa 100644 --- a/pandas/io/tests/test_clipboard.py +++ b/pandas/io/tests/test_clipboard.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- import numpy as np from numpy.random import randint @@ -46,6 +47,9 @@ def setUpClass(cls): cls.data['longdf'] = mkdf(max_rows+1, 3, data_gen_f=lambda *args: randint(2), c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) + # Test for non-ascii text: GH9263 + cls.data['nonascii'] = pd.DataFrame({'en': 'in English'.split(), + 'es': 'en español'.split()}) cls.data_types = list(cls.data.keys()) @classmethod diff --git a/pandas/util/clipboard.py b/pandas/util/clipboard.py index 65c372bf7cd5b..026f13aad0bf3 100644 --- a/pandas/util/clipboard.py +++ b/pandas/util/clipboard.py @@ -1,17 +1,24 @@ -# Pyperclip v1.3 -# A cross-platform clipboard module for Python. (only handles plain text for now) -# By Al Sweigart al@coffeeghost.net +# Pyperclip v1.5.15 +# A cross-platform clipboard module for Python. +# By Al Sweigart al@inventwithpython.com # Usage: # import pyperclip # pyperclip.copy('The text to be copied to the clipboard.') # spam = pyperclip.paste() -# On Mac, this module makes use of the pbcopy and pbpaste commands, which should come with the os. -# On Linux, this module makes use of the xclip command, which should come with the os. Otherwise run "sudo apt-get install xclip" +# On Windows, no additional modules are needed. +# On Mac, this module makes use of the pbcopy and pbpaste commands, which +# should come with the os. +# On Linux, this module makes use of the xclip or xsel commands, which should +# come with the os. Otherwise run "sudo apt-get install xclip" or +# "sudo apt-get install xsel" +# Otherwise on Linux, you will need the gtk or PyQt4 modules installed. +# The gtk module is not available for Python 3, and this module does not work +# with PyGObject yet. -# Copyright (c) 2010, Albert Sweigart +# Copyright (c) 2015, Albert Sweigart # All rights reserved. # # BSD-style license: @@ -38,139 +45,221 @@ # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -# Change Log: -# 1.2 Use the platform module to help determine OS. -# 1.3 Changed ctypes.windll.user32.OpenClipboard(None) to ctypes.windll.user32.OpenClipboard(0), after some people ran into some TypeError -import platform, os +import platform +import os +from subprocess import call, Popen, PIPE + +PY2 = '2' == platform.python_version_tuple()[0] +text_type = unicode if PY2 else str + class NoClipboardProgramError(OSError): pass -def winGetClipboard(): - ctypes.windll.user32.OpenClipboard(0) - pcontents = ctypes.windll.user32.GetClipboardData(1) # 1 is CF_TEXT - data = ctypes.c_char_p(pcontents).value - #ctypes.windll.kernel32.GlobalUnlock(pcontents) - ctypes.windll.user32.CloseClipboard() +def _pasteWindows(): + CF_UNICODETEXT = 13 + d = ctypes.windll + d.user32.OpenClipboard(0) + handle = d.user32.GetClipboardData(CF_UNICODETEXT) + data = ctypes.c_wchar_p(handle).value + d.user32.CloseClipboard() return data -def winSetClipboard(text): + +def _copyWindows(text): GMEM_DDESHARE = 0x2000 - ctypes.windll.user32.OpenClipboard(0) - ctypes.windll.user32.EmptyClipboard() - try: - # works on Python 2 (bytes() only takes one argument) - hCd = ctypes.windll.kernel32.GlobalAlloc(GMEM_DDESHARE, len(bytes(text))+1) - except TypeError: - # works on Python 3 (bytes() requires an encoding) - hCd = ctypes.windll.kernel32.GlobalAlloc(GMEM_DDESHARE, len(bytes(text, 'ascii'))+1) - pchData = ctypes.windll.kernel32.GlobalLock(hCd) - try: - # works on Python 2 (bytes() only takes one argument) - ctypes.cdll.msvcrt.strcpy(ctypes.c_char_p(pchData), bytes(text)) - except TypeError: - # works on Python 3 (bytes() requires an encoding) - ctypes.cdll.msvcrt.strcpy(ctypes.c_char_p(pchData), bytes(text, 'ascii')) - ctypes.windll.kernel32.GlobalUnlock(hCd) - ctypes.windll.user32.SetClipboardData(1,hCd) - ctypes.windll.user32.CloseClipboard() - -def macSetClipboard(text): - outf = os.popen('pbcopy', 'w') - outf.write(text) - outf.close() - -def macGetClipboard(): - outf = os.popen('pbpaste', 'r') - content = outf.read() - outf.close() - return content - -def gtkGetClipboard(): + CF_UNICODETEXT = 13 + d = ctypes.windll # cdll expects 4 more bytes in user32.OpenClipboard(0) + if not isinstance(text, text_type): + text = text.decode('mbcs') + + d.user32.OpenClipboard(0) + + d.user32.EmptyClipboard() + hCd = d.kernel32.GlobalAlloc(GMEM_DDESHARE, + len(text.encode('utf-16-le')) + 2) + pchData = d.kernel32.GlobalLock(hCd) + ctypes.cdll.msvcrt.wcscpy(ctypes.c_wchar_p(pchData), text) + d.kernel32.GlobalUnlock(hCd) + d.user32.SetClipboardData(CF_UNICODETEXT, hCd) + d.user32.CloseClipboard() + + +def _pasteCygwin(): + CF_UNICODETEXT = 13 + d = ctypes.cdll + d.user32.OpenClipboard(0) + handle = d.user32.GetClipboardData(CF_UNICODETEXT) + data = ctypes.c_wchar_p(handle).value + d.user32.CloseClipboard() + return data + + +def _copyCygwin(text): + GMEM_DDESHARE = 0x2000 + CF_UNICODETEXT = 13 + d = ctypes.cdll + if not isinstance(text, text_type): + text = text.decode('mbcs') + d.user32.OpenClipboard(0) + d.user32.EmptyClipboard() + hCd = d.kernel32.GlobalAlloc(GMEM_DDESHARE, + len(text.encode('utf-16-le')) + 2) + pchData = d.kernel32.GlobalLock(hCd) + ctypes.cdll.msvcrt.wcscpy(ctypes.c_wchar_p(pchData), text) + d.kernel32.GlobalUnlock(hCd) + d.user32.SetClipboardData(CF_UNICODETEXT, hCd) + d.user32.CloseClipboard() + + +def _copyOSX(text): + p = Popen(['pbcopy', 'w'], stdin=PIPE, close_fds=True) + p.communicate(input=text.encode('utf-8')) + + +def _pasteOSX(): + p = Popen(['pbpaste', 'r'], stdout=PIPE, close_fds=True) + stdout, stderr = p.communicate() + return stdout.decode('utf-8') + + +def _pasteGtk(): return gtk.Clipboard().wait_for_text() -def gtkSetClipboard(text): + +def _copyGtk(text): + global cb cb = gtk.Clipboard() cb.set_text(text) cb.store() -def qtGetClipboard(): + +def _pasteQt(): return str(cb.text()) -def qtSetClipboard(text): + +def _copyQt(text): cb.setText(text) -def xclipSetClipboard(text): - outf = os.popen('xclip -selection c', 'w') - outf.write(text) - outf.close() -def xclipGetClipboard(): - outf = os.popen('xclip -selection c -o', 'r') - content = outf.read() - outf.close() - return content +def _copyXclip(text): + p = Popen(['xclip', '-selection', 'c'], stdin=PIPE, close_fds=True) + p.communicate(input=text.encode('utf-8')) + + +def _pasteXclip(): + p = Popen(['xclip', '-selection', 'c', '-o'], stdout=PIPE, close_fds=True) + stdout, stderr = p.communicate() + return stdout.decode('utf-8') + + +def _copyXsel(text): + p = Popen(['xsel', '-b', '-i'], stdin=PIPE, close_fds=True) + p.communicate(input=text.encode('utf-8')) + -def xselSetClipboard(text): - outf = os.popen('xsel -i', 'w') - outf.write(text) - outf.close() +def _pasteXsel(): + p = Popen(['xsel', '-b', '-o'], stdout=PIPE, close_fds=True) + stdout, stderr = p.communicate() + return stdout.decode('utf-8') -def xselGetClipboard(): - outf = os.popen('xsel -o', 'r') - content = outf.read() - outf.close() - return content +def _copyKlipper(text): + p = Popen(['qdbus', 'org.kde.klipper', '/klipper', + 'setClipboardContents', text.encode('utf-8')], + stdin=PIPE, close_fds=True) + p.communicate(input=None) -if os.name == 'nt' or platform.system() == 'Windows': + +def _pasteKlipper(): + p = Popen(['qdbus', 'org.kde.klipper', '/klipper', + 'getClipboardContents'], stdout=PIPE, close_fds=True) + stdout, stderr = p.communicate() + return stdout.decode('utf-8') + + +# Determine the OS/platform and set the copy() and paste() functions +# accordingly. +if 'cygwin' in platform.system().lower(): + _functions = 'Cygwin' # for debugging import ctypes - getcb = winGetClipboard - setcb = winSetClipboard + paste = _pasteCygwin + copy = _copyCygwin +elif os.name == 'nt' or platform.system() == 'Windows': + _functions = 'Windows' # for debugging + import ctypes + paste = _pasteWindows + copy = _copyWindows elif os.name == 'mac' or platform.system() == 'Darwin': - getcb = macGetClipboard - setcb = macSetClipboard + _functions = 'OS X pbcopy/pbpaste' # for debugging + paste = _pasteOSX + copy = _copyOSX elif os.name == 'posix' or platform.system() == 'Linux': - xclipExists = os.system('which xclip > /dev/null') == 0 - if xclipExists: - getcb = xclipGetClipboard - setcb = xclipSetClipboard - else: - xselExists = os.system('which xsel > /dev/null') == 0 - if xselExists: - getcb = xselGetClipboard - setcb = xselSetClipboard - else: + # Determine which command/module is installed, if any. + xclipExists = call(['which', 'xclip'], + stdout=PIPE, stderr=PIPE) == 0 + + xselExists = call(['which', 'xsel'], + stdout=PIPE, stderr=PIPE) == 0 + + xklipperExists = ( + call(['which', 'klipper'], stdout=PIPE, stderr=PIPE) == 0 and + call(['which', 'qdbus'], stdout=PIPE, stderr=PIPE) == 0 + ) + + gtkInstalled = False + try: + # Check it gtk is installed. + import gtk + gtkInstalled = True + except ImportError: + pass + + if not gtkInstalled: + # Check for either PyQt4 or PySide + qtBindingInstalled = True + try: + from PyQt4 import QtGui + except ImportError: try: - import gtk + from PySide import QtGui except ImportError: - try: - import PyQt4 as qt4 - import PyQt4.QtCore - import PyQt4.QtGui - except ImportError: - try: - import PySide as qt4 - import PySide.QtCore - import PySide.QtGui - except ImportError: - raise NoClipboardProgramError('Pyperclip requires the' - ' gtk, PyQt4, or PySide' - ' module installed, or ' - 'either the xclip or ' - 'xsel command.') - app = qt4.QtGui.QApplication([]) - cb = qt4.QtGui.QApplication.clipboard() - getcb = qtGetClipboard - setcb = qtSetClipboard - else: - getcb = gtkGetClipboard - setcb = gtkSetClipboard -copy = setcb -paste = getcb - -## pandas aliases + qtBindingInstalled = False + + # Set one of the copy & paste functions. + if xclipExists: + _functions = 'xclip command' # for debugging + paste = _pasteXclip + copy = _copyXclip + elif xklipperExists: + _functions = '(KDE Klipper) - qdbus (external)' # for debugging + paste = _pasteKlipper + copy = _copyKlipper + elif gtkInstalled: + _functions = 'gtk module' # for debugging + paste = _pasteGtk + copy = _copyGtk + elif qtBindingInstalled: + _functions = 'PyQt4 module' # for debugging + app = QtGui.QApplication([]) + cb = QtGui.QApplication.clipboard() + paste = _pasteQt + copy = _copyQt + elif xselExists: + # TODO: xsel doesn't seem to work on Raspberry Pi (my test Linux + # environment). Putting this as the last method tried. + _functions = 'xsel command' # for debugging + paste = _pasteXsel + copy = _copyXsel + else: + raise NoClipboardProgramError('Pyperclip requires the gtk, PyQt4, or ' + 'PySide module installed, or either the ' + 'xclip or xsel command.') +else: + raise RuntimeError('pyperclip does not support your system.') + +# pandas aliases clipboard_get = paste clipboard_set = copy
Resolves #9263 Tested: - [x] windows - [x] macosx - [x] linux
https://api.github.com/repos/pandas-dev/pandas/pulls/9693
2015-03-20T20:52:05Z
2015-12-05T15:33:44Z
null
2021-12-03T12:12:48Z
DOC: further clean-up whatsnew file
diff --git a/doc/source/release.rst b/doc/source/release.rst index 5d69c29d4612f..faefdf4f6edd3 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -60,6 +60,12 @@ Highlights include: - Backwards incompatible change to ``Timedelta`` to conform the ``.seconds`` attribute with ``datetime.timedelta``, see :ref:`here <whatsnew_0160.api_breaking.timedelta>` - Changes to the ``.loc`` slicing API to conform with the behavior of ``.ix`` see :ref:`here <whatsnew_0160.api_breaking.indexing>` - Changes to the default for ordering in the ``Categorical`` constructor, see :ref:`here <whatsnew_0160.api_breaking.categorical>` +- The ``pandas.tools.rplot``, ``pandas.sandbox.qtpandas`` and ``pandas.rpy`` + modules are deprecated. We refer users to external packages like + `seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_, + `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ and + `rpy2 <http://rpy.sourceforge.net/>`_ for similar or equivalent + functionality, see :ref:`here <whatsnew_0160.deprecations>` See the :ref:`v0.16.0 Whatsnew <whatsnew_0160>` overview or the issue tracker on GitHub for an extensive list of all API changes, enhancements and bugs that have been fixed in 0.16.0. diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 2f21323a2ddd7..356d61dbe02ca 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -15,6 +15,12 @@ Highlights include: - Changes to the ``.loc`` slicing API to conform with the behavior of ``.ix`` see :ref:`here <whatsnew_0160.api_breaking.indexing>` - Changes to the default for ordering in the ``Categorical`` constructor, see :ref:`here <whatsnew_0160.api_breaking.categorical>` - Enhancement to the ``.str`` accessor to make string operations easier, see :ref:`here <whatsnew_0160.enhancements.string>` +- The ``pandas.tools.rplot``, ``pandas.sandbox.qtpandas`` and ``pandas.rpy`` + modules are deprecated. We refer users to external packages like + `seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_, + `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_ and + `rpy2 <http://rpy.sourceforge.net/>`_ for similar or equivalent + functionality, see :ref:`here <whatsnew_0160.deprecations>` Check the :ref:`API Changes <whatsnew_0160.api>` and :ref:`deprecations <whatsnew_0160.deprecations>` before updating. @@ -129,37 +135,35 @@ String Methods Enhancements - Following new methods are accesible via ``.str`` accessor to apply the function to each values. This is intended to make it more consistent with standard methods on strings. (:issue:`9282`, :issue:`9352`, :issue:`9386`, :issue:`9387`, :issue:`9439`) -============= ============= ============= =============== =============== -.. .. Methods .. .. -============= ============= ============= =============== =============== -``isalnum()`` ``isalpha()`` ``isdigit()`` ``isdigit()`` ``isspace()`` -``islower()`` ``isupper()`` ``istitle()`` ``isnumeric()`` ``isdecimal()`` -``find()`` ``rfind()`` ``ljust()`` ``rjust()`` ``zfill()`` -============= ============= ============= =============== =============== + ============= ============= ============= =============== =============== + .. .. Methods .. .. + ============= ============= ============= =============== =============== + ``isalnum()`` ``isalpha()`` ``isdigit()`` ``isdigit()`` ``isspace()`` + ``islower()`` ``isupper()`` ``istitle()`` ``isnumeric()`` ``isdecimal()`` + ``find()`` ``rfind()`` ``ljust()`` ``rjust()`` ``zfill()`` + ============= ============= ============= =============== =============== -.. ipython:: python - - s = Series(['abcd', '3456', 'EFGH']) - s.str.isalpha() - s.str.find('ab') + .. ipython:: python + s = Series(['abcd', '3456', 'EFGH']) + s.str.isalpha() + s.str.find('ab') - :meth:`Series.str.pad` and :meth:`Series.str.center` now accept ``fillchar`` option to specify filling character (:issue:`9352`) -.. ipython:: python - - s = Series(['12', '300', '25']) - s.str.pad(5, fillchar='_') + .. ipython:: python + s = Series(['12', '300', '25']) + s.str.pad(5, fillchar='_') - Added :meth:`Series.str.slice_replace`, which previously raised ``NotImplementedError`` (:issue:`8888`) -.. ipython:: python + .. ipython:: python - s = Series(['ABCD', 'EFGH', 'IJK']) - s.str.slice_replace(1, 3, 'X') - # replaced with empty char - s.str.slice_replace(0, 1) + s = Series(['ABCD', 'EFGH', 'IJK']) + s.str.slice_replace(1, 3, 'X') + # replaced with empty char + s.str.slice_replace(0, 1) .. _whatsnew_0160.enhancements.other: @@ -175,7 +179,15 @@ Other enhancements This method is also exposed by the lower level ``Index.get_indexer`` and ``Index.get_loc`` methods. -- Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>`. (issue:`9493`:) +- The ``read_excel()`` function's :ref:`sheetname <_io.specifying_sheets>` argument now accepts a list and ``None``, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (:issue:`9450`) + + .. code-block:: python + + # Returns the 1st and 4th sheet, as a dictionary of DataFrames. + pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) + + +- Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>` (:issue:`9493`:). - Paths beginning with ~ will now be expanded to begin with the user's home directory (:issue:`9066`) - Added time interval selection in ``get_data_yahoo`` (:issue:`9071`) - Added ``Timestamp.to_datetime64()`` to complement ``Timedelta.to_timedelta64()`` (:issue:`9255`) @@ -183,17 +195,9 @@ Other enhancements - Lag parameter was added to the autocorrelation method of ``Series``, defaults to lag-1 autocorrelation (:issue:`9192`) - ``Timedelta`` will now accept ``nanoseconds`` keyword in constructor (:issue:`9273`) - SQL code now safely escapes table and column names (:issue:`8986`) - - Added auto-complete for ``Series.str.<tab>``, ``Series.dt.<tab>`` and ``Series.cat.<tab>`` (:issue:`9322`) - ``Index.get_indexer`` now supports ``method='pad'`` and ``method='backfill'`` even for any target array, not just monotonic targets. These methods also work for monotonic decreasing as well as monotonic increasing indexes (:issue:`9258`). - ``Index.asof`` now works on all index types (:issue:`9258`). -- The ``read_excel()`` function's :ref:`sheetname <_io.specifying_sheets>` argument now accepts a list and ``None``, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (:issue:`9450`) - - .. code-block:: python - - # Returns the 1st and 4th sheet, as a dictionary of DataFrames. - pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) - - A ``verbose`` argument has been augmented in ``io.read_excel()``, defaults to False. Set to True to print sheet names as they are parsed. (:issue:`9450`) - Added ``days_in_month`` (compatibility alias ``daysinmonth``) property to ``Timestamp``, ``DatetimeIndex``, ``Period``, ``PeriodIndex``, and ``Series.dt`` (:issue:`9572`) - Added ``decimal`` option in ``to_csv`` to provide formatting for non-'.' decimal separators (:issue:`781`) @@ -486,15 +490,6 @@ Other API Changes To reproduce the old behavior, simply add more precision to the label (e.g., use ``2000-02-01`` instead of ``2000-02``). -- A Spurious ``SettingWithCopy`` Warning was generated when setting a new item in a frame in some cases (:issue:`8730`) - - The following would previously report a ``SettingWithCopy`` Warning. - - .. ipython:: python - - df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])}) - df2 = df1[['x']] - df2['y'] = ['g', 'h', 'i'] .. _whatsnew_0160.deprecations: @@ -505,9 +500,8 @@ Deprecations in a future version. We refer to external packages like `seaborn <http://stanford.edu/~mwaskom/software/seaborn/>`_ for similar but more refined functionality (:issue:`3445`). - The documentation includes some examples how to convert your existing code - using ``rplot`` to seaborn: - :ref:`rplot docs <rplot>` + using ``rplot`` to seaborn: :ref:`rplot docs <rplot>`. - The ``pandas.sandbox.qtpandas`` interface is deprecated and will be removed in a future version. We refer users to the external package `pandas-qt <https://github.com/datalyze-solutions/pandas-qt>`_. (:issue:`9615`) @@ -610,6 +604,9 @@ Bug Fixes - Fixed mising numeric_only option for ``DataFrame.std/var/sem`` (:issue:`9201`) - Support constructing ``Panel`` or ``Panel4D`` with scalar data (:issue:`8285`) - ``Series`` text representation disconnected from `max_rows`/`max_columns` (:issue:`7508`). + +\ + - ``Series`` number formatting inconsistent when truncated (:issue:`8532`). Previous Behavior @@ -645,3 +642,13 @@ Bug Fixes 128 1.0000 129 1.0000 dtype: float64 + +- A Spurious ``SettingWithCopy`` Warning was generated when setting a new item in a frame in some cases (:issue:`8730`) + + The following would previously report a ``SettingWithCopy`` Warning. + + .. ipython:: python + + df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])}) + df2 = df1[['x']] + df2['y'] = ['g', 'h', 'i']
Some missing spaced in the string-methods section, and added a highlight about the deprecations. @jreback you suggested to remove the highlights alltogether now we have the automatic table of contents, but I think it is not fully equivalent (eg the deprecations I added now are not directly visible in the table of contents)
https://api.github.com/repos/pandas-dev/pandas/pulls/9689
2015-03-20T14:26:00Z
2015-03-20T15:27:34Z
2015-03-20T15:27:34Z
2015-03-20T15:27:34Z
Start python3 compatibility
diff --git a/vb_suite/frame_methods.py b/vb_suite/frame_methods.py index 1d7c5e0d9acef..c9631a5d993f9 100644 --- a/vb_suite/frame_methods.py +++ b/vb_suite/frame_methods.py @@ -505,7 +505,7 @@ def get_data(n=100000): frame_from_records_generator_nrows = Benchmark('df = DataFrame.from_records(get_data(), nrows=1000)', setup, name='frame_from_records_generator_nrows', - start_date=datetime(2013,10,04)) # issue-4911 + start_date=datetime(2013,10,4)) # issue-4911 #----------------------------------------------------------------------------- # duplicated diff --git a/vb_suite/suite.py b/vb_suite/suite.py index a16d183ae62e2..683727a5659f4 100644 --- a/vb_suite/suite.py +++ b/vb_suite/suite.py @@ -1,3 +1,4 @@ +from __future__ import print_function from vbench.api import Benchmark, GitRepo from datetime import datetime @@ -128,7 +129,7 @@ def generate_rst_files(benchmarks): f.write(rst_text) with open(os.path.join(RST_BASE, 'index.rst'), 'w') as f: - print >> f, """ + print(""" Performance Benchmarks ====================== @@ -149,15 +150,15 @@ def generate_rst_files(benchmarks): .. toctree:: :hidden: :maxdepth: 3 -""" +""", file=f) for modname, mod_bmks in sorted(by_module.items()): - print >> f, ' vb_%s' % modname + print(' vb_%s' % modname, file=f) modpath = os.path.join(RST_BASE, 'vb_%s.rst' % modname) with open(modpath, 'w') as mh: header = '%s\n%s\n\n' % (modname, '=' * len(modname)) - print >> mh, header + print(header, file=mh) for bmk in mod_bmks: - print >> mh, bmk.name - print >> mh, '-' * len(bmk.name) - print >> mh, '.. include:: vbench/%s.txt\n' % bmk.name + print(bmk.name, file=mh) + print('-' * len(bmk.name), file=mh) + print('.. include:: vbench/%s.txt\n' % bmk.name, file=mh)
Start for #9660 These are changes to get vbench running under python3. There are quite a few tests that need modification, but with these changes the code will parse and the suite can run (along with [vbench#36](https://github.com/pydata/vbench/pull/36)).
https://api.github.com/repos/pandas-dev/pandas/pulls/9686
2015-03-20T02:13:43Z
2015-07-28T21:51:47Z
null
2015-07-28T21:51:47Z
Fixes groupby.apply() error when no returns #9684
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index b80e341d4156a..a2e6b59646724 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -99,11 +99,10 @@ Bug Fixes - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) - - Bug in ``equals`` causing false negatives when block order differed (:issue:`9330`) - - Bug in ``DataFrame`` slicing may not retain metadata (:issue:`9776`) - Bug where ``TimdeltaIndex`` were not properly serialized in fixed ``HDFStore`` (:issue:`9635`) +- Fixed bug in ``groupby.apply()`` that would raise an error the user passed function either returned nothing or only returned values of ``None``. (:issue:`9685`) - Bug in plotting continuously using ``secondary_y`` may not show legend properly. (:issue:`9610`, :issue:`9779`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 6d98b3b99021b..034e8298a844c 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2808,7 +2808,12 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False): # make Nones an empty object if com._count_not_none(*values) != len(values): - v = next(v for v in values if v is not None) + try: + v = next(v for v in values if v is not None) + except StopIteration: + # If all values are None, then this will throw an error. + # We'd prefer it return an empty dataframe. + return DataFrame() if v is None: return DataFrame() elif isinstance(v, NDFrame): diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index e7001eb09f20c..a7af535034852 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -5053,6 +5053,17 @@ def test_groupby_categorical_two_columns(self): "C3":[nan,nan,nan,nan, 10,100,nan,nan, nan,nan,200,34]}, index=idx) tm.assert_frame_equal(res, exp) + def test_groupby_apply_all_none(self): + # Tests to make sure no errors if apply function returns all None + # values. Issue 9684. + test_df = DataFrame({'groups': [0,0,1,1], 'random_vars': [8,7,4,5]}) + + def test_func(x): + pass + result = test_df.groupby('groups').apply(test_func) + expected = DataFrame() + tm.assert_frame_equal(result, expected) + def assert_fp_equal(a, b): assert (np.abs(a - b) < 1e-12).all()
Closes issue: #9684 Returns empty object even if all entries are missing.
https://api.github.com/repos/pandas-dev/pandas/pulls/9685
2015-03-19T22:15:41Z
2015-04-28T01:13:59Z
null
2015-04-28T01:13:59Z
Bug: Access unknown attribute of timedeltaindexed series used to raise ValueError
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..fa9eb9972d7ac 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -70,11 +70,7 @@ Bug Fixes - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) - - - - - +- Bug : Trying to access absent attributes in TimedeltaIndex use to raise ValueError instead of AttributeError(:issue:`9680`) - Bug in ``Series.quantile`` on empty Series of type ``Datetime`` or ``Timedelta`` (:issue:`9675`) diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index ed11b12871ce5..34ae7d5d87419 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -67,7 +67,7 @@ def __contains__(self, key): try: res = self.get_loc(key) return np.isscalar(res) or type(res) == slice or np.any(res) - except (KeyError, TypeError): + except (KeyError, TypeError, ValueError): return False @property diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index c42802bdb31ad..6bdff5aab3cfd 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -745,6 +745,13 @@ def test_nonunique_contains(self): ['00:01:00', '00:01:00', '00:00:01'])): tm.assertIn(idx[0], idx) + def test_unknown_attribute(self): + #GH 9680 + tdi = pd.timedelta_range(start=0,periods=10,freq='1s') + ts = pd.Series(np.random.normal(size=10),index=tdi) + self.assertNotIn('foo',ts.__dict__.keys()) + self.assertRaises(AttributeError,lambda : ts.foo) + class TestPeriodIndexOps(Ops):
closes #9680 And now it raises AttributeError.
https://api.github.com/repos/pandas-dev/pandas/pulls/9683
2015-03-19T15:10:08Z
2015-04-28T00:53:52Z
null
2015-04-28T00:53:57Z
Added itertools.groupby equivalent example.
diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst index 81fe4aac51dd7..9347fb1838bde 100644 --- a/doc/source/cookbook.rst +++ b/doc/source/cookbook.rst @@ -452,7 +452,7 @@ Replace `Using replace with backrefs <http://stackoverflow.com/questions/16818871/extracting-value-and-creating-new-column-out-of-it>`__ -.. _cookbook.grouping: +.. _cookbook.grouping docs: Grouping -------- @@ -599,7 +599,15 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to mask = df.groupby(level=0).agg('idxmax') df_count = df.loc[mask['no']].reset_index() df_count + +`Grouping like Python's itertools.groupby +<http://stackoverflow.com/q/29142487/846892>`__ + +.. ipython:: python + df = pd.DataFrame([0, 1, 0, 1, 1, 1, 0, 1, 1], columns=['A']) + df.A.groupby((df.A != df.A.shift()).cumsum()).groups + df.A.groupby((df.A != df.A.shift()).cumsum()).cumsum() Expanding Data ************** @@ -916,7 +924,7 @@ using that handle to read. `Write a multi-row index CSV without writing duplicates <http://stackoverflow.com/questions/17349574/pandas-write-multiindex-rows-with-to-csv>`__ - +grp Parsing date components in multi-columns is faster with a format .. code-block:: python
https://api.github.com/repos/pandas-dev/pandas/pulls/9682
2015-03-19T12:20:00Z
2015-03-19T20:39:49Z
null
2015-03-19T20:39:49Z
BUG: workaround PyTables 319, but not setting expected rows (GH8265, GH9676)
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 2f21323a2ddd7..557e282d9e744 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -609,6 +609,7 @@ Bug Fixes - Bug in ``Series.values_counts`` with excluding ``NaN`` for categorical type ``Series`` with ``dropna=True`` (:issue:`9443`) - Fixed mising numeric_only option for ``DataFrame.std/var/sem`` (:issue:`9201`) - Support constructing ``Panel`` or ``Panel4D`` with scalar data (:issue:`8285`) +- Work around Pytables 319 by not passing expectedrows in constructing a HDFStore (:issue:`8265`, :issue:`9676`) - ``Series`` text representation disconnected from `max_rows`/`max_columns` (:issue:`7508`). - ``Series`` number formatting inconsistent when truncated (:issue:`8532`). diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index be81993e1aab7..07f0cd7e55159 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -3509,9 +3509,8 @@ def create_description(self, complib=None, complevel=None, fletcher32=False, expectedrows=None): """ create the description of the table from the axes & values """ - # expected rows estimate - if expectedrows is None: - expectedrows = max(self.nrows_expected, 10000) + + # provided expected rows if its passed d = dict(name='table', expectedrows=expectedrows) # description from the axes & values diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index 41287ebba1ba3..c82ee351338c2 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -9,6 +9,7 @@ import numpy as np import pandas +import pandas as pd from pandas import (Series, DataFrame, Panel, MultiIndex, Categorical, bdate_range, date_range, Index, DatetimeIndex, isnull) @@ -33,6 +34,7 @@ from pandas import compat from pandas.compat import range, lrange, u from pandas.util.testing import assert_produces_warning +from numpy.testing.decorators import slow try: import tables @@ -1497,107 +1499,6 @@ def col(t,column): store.put('f2', df) self.assertRaises(TypeError, store.create_table_index, 'f2') - def test_big_table_frame(self): - raise nose.SkipTest('no big table frame') - - # create and write a big table - df = DataFrame(np.random.randn(2000 * 100, 100), index=range( - 2000 * 100), columns=['E%03d' % i for i in range(100)]) - for x in range(20): - df['String%03d' % x] = 'string%03d' % x - - import time - x = time.time() - with ensure_clean_store(self.path,mode='w') as store: - store.append('df', df) - rows = store.root.df.table.nrows - recons = store.select('df') - assert isinstance(recons, DataFrame) - - com.pprint_thing("\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 - # columns - raise nose.SkipTest('no big table2 frame') - - # create and write a big table - com.pprint_thing("\nbig_table2 start") - import time - start_time = time.time() - df = DataFrame(np.random.randn(1000 * 1000, 60), index=range(int( - 1000 * 1000)), columns=['E%03d' % i for i in range(60)]) - for x in range(20): - df['String%03d' % x] = 'string%03d' % x - for x in range(20): - df['datetime%03d' % x] = datetime.datetime(2001, 1, 2, 0, 0) - - com.pprint_thing("\nbig_table2 frame (creation of df) [rows->%s] -> %5.2f" - % (len(df.index), time.time() - start_time)) - - def f(chunksize): - with ensure_clean_store(self.path,mode='w') as store: - store.append('df', df, chunksize=chunksize) - r = store.root.df.table.nrows - return r - - for c in [10000, 50000, 250000]: - start_time = time.time() - com.pprint_thing("big_table2 frame [chunk->%s]" % c) - rows = f(c) - com.pprint_thing("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') - - com.pprint_thing("\nbig_put start") - import time - start_time = time.time() - df = DataFrame(np.random.randn(1000 * 1000, 60), index=range(int( - 1000 * 1000)), columns=['E%03d' % i for i in range(60)]) - for x in range(20): - df['String%03d' % x] = 'string%03d' % x - for x in range(20): - df['datetime%03d' % x] = datetime.datetime(2001, 1, 2, 0, 0) - - com.pprint_thing("\nbig_put frame (creation of df) [rows->%s] -> %5.2f" - % (len(df.index), time.time() - start_time)) - - with ensure_clean_store(self.path, mode='w') as store: - start_time = time.time() - store = HDFStore(self.path, mode='w') - store.put('df', df) - - com.pprint_thing(df.get_dtype_counts()) - com.pprint_thing("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') - - # create and write a big table - wp = Panel( - np.random.randn(20, 1000, 1000), items=['Item%03d' % i for i in range(20)], - major_axis=date_range('1/1/2000', periods=1000), minor_axis=['E%03d' % i for i in range(1000)]) - - wp.ix[:, 100:200, 300:400] = np.nan - - for x in range(100): - wp['String%03d'] = 'string%03d' % x - - import time - x = time.time() - - - with ensure_clean_store(self.path, mode='w') as store: - store.append('wp', wp) - rows = store.root.wp.table.nrows - recons = store.select('wp') - assert isinstance(recons, Panel) - - com.pprint_thing("\nbig_table panel [%s] -> %5.2f" % (rows, time.time() - x)) - def test_append_diff_item_order(self): wp = tm.makePanel() @@ -1751,6 +1652,43 @@ def test_pass_spec_to_storer(self): self.assertRaises(TypeError, store.select, 'df', columns=['A']) self.assertRaises(TypeError, store.select, 'df',where=[('columns=A')]) + @slow + def test_expectedrows_where_indexing(self): + + # 8265, 9676 + # seems that setting expected rows casuses odd indexing issues in some cases + # this requires a big example + + with ensure_clean_path('test.hdf') as path: + + N = 7000000 + df = pd.DataFrame({'A' : np.random.randint(-20000,-15000,size=N), 'B' : np.random.randn(N) }) + df.to_hdf(path,'df',mode='w',complib='blosc',format='table',data_columns=True,index=False) + uniques = df.A.unique() + v1 = uniques[0] + v2 = uniques[1] + selector = "A=[v1,v2]" + expected = sorted([v1,v2]) + + def compare(result): + result = sorted(result.A.unique().tolist()) + tm.assert_almost_equal(result, expected) + + result = pd.read_hdf(path,'df',where=selector) + compare(result) + + with pd.HDFStore(path) as store: + store.create_table_index('df') + + result = pd.read_hdf(path,'df',where=selector) + compare(result) + + cs = 500000 + chunks = N // cs + for i in range(chunks): + result = pd.read_hdf(path,'df',start=i*cs,stop=(i+1)*cs,where=selector) + compare(result) + def test_append_misc(self): with ensure_clean_store(self.path) as store: @@ -4436,7 +4374,7 @@ def do_copy(f = None, new_f = None, keys = None, propindexes = True, **kwargs): safe_remove(self.path) def test_legacy_table_write(self): - raise nose.SkipTest("skipping for now") + raise nose.SkipTest("cannot write legacy tables") store = HDFStore(tm.get_data_path('legacy_hdf/legacy_table_%s.h5' % pandas.__version__), 'a')
closes #8265 closes #9676
https://api.github.com/repos/pandas-dev/pandas/pulls/9681
2015-03-19T11:53:16Z
2015-03-19T20:39:15Z
null
2015-03-19T20:39:15Z
DataFrame.plot error when both 'color' and 'style' arguments are passed (GH9671)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 05c762b91b925..806f77ea56122 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -71,6 +71,7 @@ Bug Fixes - Bug in ``transform`` causing length mismatch when null entries were present and a fast aggregator was being used (:issue:`9697`) +- Bug in ``equals`` causing false negatives when block order differed (:issue:`9330`) - Bug in ``DataFrame`` slicing may not retain metadata (:issue:`9776`) - Bug where ``TimdeltaIndex`` were not properly serialized in fixed ``HDFStore`` (:issue:`9635`) @@ -80,3 +81,6 @@ 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`) + +- Fixed bug (:issue:`9671`) where ``DataFrame.plot()`` raised an error when both ``color`` and ``style`` keywords were passed and there was no color symbol in the style strings (this should be allowed). 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/core/internals.py b/pandas/core/internals.py index 7a16fb2b6b0d7..9b2d366bfb2be 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -3310,8 +3310,20 @@ def equals(self, other): return False self._consolidate_inplace() other._consolidate_inplace() + if len(self.blocks) != len(other.blocks): + return False + + # canonicalize block order, using a tuple combining the type + # name and then mgr_locs because there might be unconsolidated + # blocks (say, Categorical) which can only be distinguished by + # the iteration order + def canonicalize(block): + return (block.dtype.name, block.mgr_locs.as_array.tolist()) + + self_blocks = sorted(self.blocks, key=canonicalize) + other_blocks = sorted(other.blocks, key=canonicalize) return all(block.equals(oblock) for block, oblock in - zip(self.blocks, other.blocks)) + zip(self_blocks, other_blocks)) class SingleBlockManager(BlockManager): diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index acdc991c92efe..03e7a8eae549d 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -4584,19 +4584,33 @@ def test_duplicate_column_name(self): with ensure_clean_path(self.path) as path: self.assertRaises(ValueError, df.to_hdf, path, 'df', format='fixed') + df.to_hdf(path, 'df', format='table') + other = read_hdf(path, 'df') + + tm.assert_frame_equal(df, other) + self.assertTrue(df.equals(other)) + self.assertTrue(other.equals(df)) + + def test_round_trip_equals(self): + # GH 9330 + df = DataFrame({"B": [1,2], "A": ["x","y"]}) + + with ensure_clean_path(self.path) as path: df.to_hdf(path, 'df', format='table') other = read_hdf(path, 'df') tm.assert_frame_equal(df, other) + self.assertTrue(df.equals(other)) + self.assertTrue(other.equals(df)) def test_preserve_timedeltaindex_type(self): - # GH9635 + # GH9635 # Storing TimedeltaIndexed DataFrames in fixed stores did not preserve # the type of the index. df = DataFrame(np.random.normal(size=(10,5))) df.index = timedelta_range(start='0s',periods=10,freq='1s',name='example') with ensure_clean_store(self.path) as store: - + store['df'] = df assert_frame_equal(store['df'], df) 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): diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index cdda087b27613..3e4c16f63035f 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -5944,6 +5944,20 @@ def test_boolean_comparison(self): self.assertRaises(ValueError, lambda : df == (2,2)) self.assertRaises(ValueError, lambda : df == [2,2]) + def test_equals_different_blocks(self): + # GH 9330 + df0 = pd.DataFrame({"A": ["x","y"], "B": [1,2], + "C": ["w","z"]}) + df1 = df0.reset_index()[["A","B","C"]] + # this assert verifies that the above operations have + # induced a block rearrangement + self.assertTrue(df0._data.blocks[0].dtype != + df1._data.blocks[0].dtype) + # do the real tests + self.assert_frame_equal(df0, df1) + self.assertTrue(df0.equals(df1)) + self.assertTrue(df1.equals(df0)) + def test_to_csv_from_csv(self): pname = '__tmp_to_csv_from_csv__' diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index 3ce4e150326a2..36c19cd39f76c 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -1154,6 +1154,22 @@ def test_plot(self): self.assertEqual(len(axes), 1) self.assertIs(ax.get_axes(), axes[0]) + def test_color_and_style_arguments(self): + df = DataFrame({'x': [1, 2], 'y': [3, 4]}) + # passing both 'color' and 'style' arguments should be allowed + # if there is no color symbol in the style strings: + ax = df.plot(color = ['red', 'black'], style = ['-', '--']) + # check that the linestyles are correctly set: + linestyle = [line.get_linestyle() for line in ax.lines] + self.assertEqual(linestyle, ['-', '--']) + # check that the colors are correctly set: + color = [line.get_color() for line in ax.lines] + self.assertEqual(color, ['red', 'black']) + # passing both 'color' and 'style' arguments should not be allowed + # if there is a color symbol in the style strings: + with tm.assertRaises(ValueError): + df.plot(color = ['red', 'black'], style = ['k-', 'r--']) + def test_nonnumeric_exclude(self): df = DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]}) ax = df.plot() diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py index 45f089f5e0a53..36585abd1b98f 100644 --- a/pandas/tests/test_internals.py +++ b/pandas/tests/test_internals.py @@ -68,15 +68,15 @@ def create_block(typestr, placement, item_shape=None, num_offset=0): elif typestr in ('object', 'string', 'O'): values = np.reshape(['A%d' % i for i in mat.ravel() + num_offset], shape) - elif typestr in ('bool'): + elif typestr in ('b','bool',): values = np.ones(shape, dtype=np.bool_) elif typestr in ('datetime', 'dt', 'M8[ns]'): values = (mat * 1e9).astype('M8[ns]') elif typestr in ('timedelta', 'td', 'm8[ns]'): values = (mat * 1).astype('m8[ns]') - elif typestr in ('category'): + elif typestr in ('category',): values = Categorical([1,1,2,2,3,3,3,3,4,4]) - elif typestr in ('category2'): + elif typestr in ('category2',): values = Categorical(['a','a','a','a','b','b','c','c','c','d']) elif typestr in ('sparse', 'sparse_na'): # FIXME: doesn't support num_rows != 10 @@ -751,6 +751,25 @@ def test_equals(self): bm2 = BlockManager(bm1.blocks[::-1], bm1.axes) self.assertTrue(bm1.equals(bm2)) + def test_equals_block_order_different_dtypes(self): + # GH 9330 + + mgr_strings = [ + "a:i8;b:f8", # basic case + "a:i8;b:f8;c:c8;d:b", # many types + "a:i8;e:dt;f:td;g:string", # more types + "a:i8;b:category;c:category2;d:category2", # categories + "c:sparse;d:sparse_na;b:f8", # sparse + ] + + for mgr_string in mgr_strings: + bm = create_mgr(mgr_string) + block_perms = itertools.permutations(bm.blocks) + for bm_perm in block_perms: + bm_this = BlockManager(bm_perm, bm.axes) + self.assertTrue(bm.equals(bm_this)) + self.assertTrue(bm_this.equals(bm)) + def test_single_mgr_ctor(self): mgr = create_single_mgr('f8', num_rows=5) self.assertEqual(mgr.as_matrix().tolist(), [0., 1., 2., 3., 4.]) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index 0be030d7c2c8e..358c5b0dd5940 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -867,12 +867,17 @@ def _validate_color_args(self): "simultaneously. Using 'color'") if 'color' in self.kwds and self.style is not None: + if com.is_list_like(self.style): + styles = self.style + else: + styles = [self.style] # need only a single match - if re.match('^[a-z]+?', self.style) is not None: - raise ValueError("Cannot pass 'style' string with a color " - "symbol and 'color' keyword argument. Please" - " use one or the other or pass 'style' " - "without a color symbol") + for s in styles: + if re.match('^[a-z]+?', s) is not None: + raise ValueError("Cannot pass 'style' string with a color " + "symbol and 'color' keyword argument. Please" + " use one or the other or pass 'style' " + "without a color symbol") def _iter_data(self, data=None, keep_index=False, fillna=None): if data is None:
closes #9671 `self.style` can be a list of strings or a string. If it is a string, we make a list containing that string, so that we only have to deal with a list of strings. We check every element in that list and raise an error if it has a color symbol. The test makes sure that calling `plot()` and passing both `color` and `style` arguments is allowed if there is no color symbol in the style string(s), and that passing both `color` and `style` arguments is not allowed if there is a color symbol in the style string(s). This test is done for a dataframe and for a series.
https://api.github.com/repos/pandas-dev/pandas/pulls/9674
2015-03-18T16:12:59Z
2015-04-06T12:23:51Z
null
2015-04-06T12:23:51Z
Fix for issue #9671
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index cf9c890823f8f..fc4530450d629 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -871,12 +871,17 @@ def _validate_color_args(self): "simultaneously. Using 'color'") if 'color' in self.kwds and self.style is not None: + if com.is_list_like(self.style): + styles = self.style + else: + styles = [self.style] # need only a single match - if re.match('^[a-z]+?', self.style) is not None: - raise ValueError("Cannot pass 'style' string with a color " - "symbol and 'color' keyword argument. Please" - " use one or the other or pass 'style' " - "without a color symbol") + for s in styles: + if re.match('^[a-z]+?', s) is not None: + raise ValueError("Cannot pass 'style' string with a color " + "symbol and 'color' keyword argument. Please" + " use one or the other or pass 'style' " + "without a color symbol") def _iter_data(self, data=None, keep_index=False, fillna=None): if data is None:
closes https://github.com/pydata/pandas/issues/9671 `self.style` can be a list of strings or a string. If it is a string, we make a list containing that string, so that we only have to deal with a list of strings. We check every element in that list and raise an error if it has a color symbol.
https://api.github.com/repos/pandas-dev/pandas/pulls/9673
2015-03-17T15:40:07Z
2015-03-18T16:14:24Z
null
2015-03-18T16:14:24Z
BUG: support TimedeltaIndex serialization in fixed stores (GH9635)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 8c49e2780ed06..30195c57a0587 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -72,7 +72,7 @@ Bug Fixes - +- Bug : TimdeltaIndex serialisation wasn't supported in fixed stores (:issue:`9635`) diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index 4dc777b71dd45..458a245da6bdb 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -18,6 +18,7 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.tseries.api import PeriodIndex, DatetimeIndex +from pandas.tseries.tdi import TimedeltaIndex from pandas.core.base import StringMixin from pandas.core.common import adjoin, pprint_thing from pandas.core.algorithms import match, unique @@ -4234,6 +4235,11 @@ def _convert_index(index, encoding=None, format_type=None): freq=getattr(index, 'freq', None), tz=getattr(index, 'tz', None), index_name=index_name) + elif isinstance(index, TimedeltaIndex): + converted = index.asi8 + return IndexCol(converted, 'timedelta64', _tables().Int64Col(), + freq=getattr(index, 'freq', None), + index_name=index_name) elif isinstance(index, (Int64Index, PeriodIndex)): atom = _tables().Int64Col() return IndexCol( @@ -4253,6 +4259,11 @@ def _convert_index(index, encoding=None, format_type=None): freq=getattr(index, 'freq', None), tz=getattr(index, 'tz', None), index_name=index_name) + elif inferred_type == 'timedelta64': + converted = values.view('i8') + return IndexCol(converted, 'timedelta64', _tables().Int64Col(), + freq=getattr(index, 'freq', None), + index_name=index_name) elif inferred_type == 'datetime': converted = np.asarray([(time.mktime(v.timetuple()) + v.microsecond / 1E6) for v in values], @@ -4303,6 +4314,8 @@ def _unconvert_index(data, kind, encoding=None): kind = _ensure_decoded(kind) if kind == u('datetime64'): index = DatetimeIndex(data) + elif kind == u('timedelta64'): + index = TimedeltaIndex(data) elif kind == u('datetime'): index = np.asarray([datetime.fromtimestamp(v) for v in data], dtype=object) diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index a15149e341f4d..acdc991c92efe 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -11,7 +11,7 @@ import pandas import pandas as pd from pandas import (Series, DataFrame, Panel, MultiIndex, Categorical, bdate_range, - date_range, Index, DatetimeIndex, isnull) + date_range, timedelta_range, Index, DatetimeIndex, TimedeltaIndex, isnull) from pandas.io.pytables import _tables try: @@ -4588,6 +4588,18 @@ def test_duplicate_column_name(self): other = read_hdf(path, 'df') tm.assert_frame_equal(df, other) + def test_preserve_timedeltaindex_type(self): + # GH9635 + # Storing TimedeltaIndexed DataFrames in fixed stores did not preserve + # the type of the index. + df = DataFrame(np.random.normal(size=(10,5))) + df.index = timedelta_range(start='0s',periods=10,freq='1s',name='example') + + with ensure_clean_store(self.path) as store: + + store['df'] = df + assert_frame_equal(store['df'], df) + def _test_sort(obj): if isinstance(obj, DataFrame):
closes #9635 Hop, revival of PR9662, after some bad git moves.
https://api.github.com/repos/pandas-dev/pandas/pulls/9672
2015-03-17T13:41:37Z
2015-04-04T18:40:35Z
null
2015-04-04T18:40:35Z
ENH: add StringMethods (.str accessor) to Index, fixes #9068
diff --git a/doc/source/text.rst b/doc/source/text.rst index a98153e277fae..ee91ea3c166b6 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -17,10 +17,10 @@ Working with Text Data .. _text.string_methods: -Series is equipped with a set of string processing methods +Series and Index are equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are -accessed via the Series's ``str`` attribute and generally have names matching +accessed via the ``str`` attribute and generally have names matching the equivalent (scalar) built-in string methods: .. ipython:: python @@ -30,6 +30,13 @@ the equivalent (scalar) built-in string methods: s.str.upper() s.str.len() +.. ipython:: python + + idx = Index([' jack', 'jill ', ' jesse ', 'frank']) + idx.str.strip() + idx.str.lstrip() + idx.str.rstrip() + Splitting and Replacing Strings ------------------------------- diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index d0f7af2275812..ab57f1fb6ea10 100644 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -18,11 +18,28 @@ Enhancements ~~~~~~~~~~~~ - Added ``StringMethods.capitalize()`` and ``swapcase`` which behave as the same as standard ``str`` (:issue:`9766`) +- Added ``StringMethods`` (.str accessor) to ``Index`` (:issue:`9068`) -- ``DataFrame.mask()`` and ``Series.mask()`` now support same keywords as ``where`` (:issue:`8801`) + The `.str` accessor is now available for both `Series` and `Index`. + + .. ipython:: python + idx = Index([' jack', 'jill ', ' jesse ', 'frank']) + idx.str.strip() + One special case for the `.str` accessor on `Index` is that if a string method returns `bool`, the `.str` accessor + 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']) + s = Series(range(4), index=idx) + s + idx.str.startswith('a') + 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 diff --git a/pandas/core/base.py b/pandas/core/base.py index dde2e74132c4b..a3d3c3791e20c 100644 --- a/pandas/core/base.py +++ b/pandas/core/base.py @@ -10,6 +10,7 @@ import pandas.tslib as tslib import pandas.lib as lib from pandas.util.decorators import Appender, cache_readonly +from pandas.core.strings import StringMethods _shared_docs = dict() @@ -497,6 +498,24 @@ def searchsorted(self, key, side='left'): #### needs tests/doc-string return self.values.searchsorted(key, side=side) + # string methods + def _make_str_accessor(self): + from pandas.core.series import Series + from pandas.core.index import Index + if isinstance(self, Series) and not com.is_object_dtype(self.dtype): + # this really should exclude all series with any non-string values, + # but that isn't practical for performance reasons until we have a + # str dtype (GH 9343) + raise AttributeError("Can only use .str accessor with string " + "values, which use np.object_ dtype in " + "pandas") + elif isinstance(self, Index) and self.inferred_type != 'string': + raise AttributeError("Can only use .str accessor with string " + "values (i.e. inferred_type is 'string')") + return StringMethods(self) + + str = AccessorProperty(StringMethods, _make_str_accessor) + _shared_docs['drop_duplicates'] = ( """Return %(klass)s with duplicate values removed diff --git a/pandas/core/series.py b/pandas/core/series.py index 68f3a6032402f..b71c269468d62 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -28,7 +28,6 @@ from pandas.core import generic, base from pandas.core.internals import SingleBlockManager from pandas.core.categorical import Categorical, CategoricalAccessor -from pandas.core.strings import StringMethods from pandas.tseries.common import (maybe_to_datetimelike, CombinedDatetimelikeProperties) from pandas.tseries.index import DatetimeIndex @@ -2494,21 +2493,6 @@ def to_period(self, freq=None, copy=True): return self._constructor(new_values, index=new_index).__finalize__(self) - #------------------------------------------------------------------------------ - # string methods - - def _make_str_accessor(self): - if not com.is_object_dtype(self.dtype): - # this really should exclude all series with any non-string values, - # but that isn't practical for performance reasons until we have a - # str dtype (GH 9343) - raise AttributeError("Can only use .str accessor with string " - "values, which use np.object_ dtype in " - "pandas") - return StringMethods(self) - - str = base.AccessorProperty(StringMethods, _make_str_accessor) - #------------------------------------------------------------------------------ # Datetimelike delegation methods diff --git a/pandas/core/strings.py b/pandas/core/strings.py index 4ef341c481a60..6d20907373014 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -1,7 +1,7 @@ import numpy as np from pandas.compat import zip -from pandas.core.common import isnull, _values_from_object +from pandas.core.common import isnull, _values_from_object, is_bool_dtype import pandas.compat as compat from pandas.util.decorators import Appender import re @@ -632,9 +632,10 @@ def str_split(arr, pat=None, n=None, return_type='series'): pat : string, default None String or regular expression to split on. If None, splits on whitespace n : int, default None (all) - return_type : {'series', 'frame'}, default 'series + return_type : {'series', 'index', 'frame'}, default 'series' If frame, returns a DataFrame (elements are strings) - If series, returns an Series (elements are lists of strings). + If series or index, returns the same type as the original object + (elements are lists of strings). Notes ----- @@ -646,9 +647,13 @@ def str_split(arr, pat=None, n=None, return_type='series'): """ from pandas.core.series import Series from pandas.core.frame import DataFrame + from pandas.core.index import Index - if return_type not in ('series', 'frame'): - raise ValueError("return_type must be {'series', 'frame'}") + if return_type not in ('series', 'index', 'frame'): + raise ValueError("return_type must be {'series', 'index', 'frame'}") + if return_type == 'frame' and isinstance(arr, Index): + raise ValueError("return_type='frame' is not supported for string " + "methods on Index") if pat is None: if n is None or n == 0: n = -1 @@ -928,9 +933,9 @@ def do_copy(target): class StringMethods(object): """ - Vectorized string functions for Series. NAs stay NA unless handled - otherwise by a particular method. Patterned after Python's string methods, - with some inspiration from R's stringr package. + Vectorized string functions for Series and Index. NAs stay NA unless + handled otherwise by a particular method. Patterned after Python's string + methods, with some inspiration from R's stringr package. Examples -------- @@ -959,11 +964,18 @@ def __iter__(self): def _wrap_result(self, result): from pandas.core.series import Series from pandas.core.frame import DataFrame + from pandas.core.index import Index if not hasattr(result, 'ndim'): return result elif result.ndim == 1: name = getattr(result, 'name', None) + if isinstance(self.series, Index): + # if result is a boolean np.array, return the np.array + # instead of wrapping it into a boolean Index (GH 8875) + if is_bool_dtype(result): + return result + return Index(result, name=name or self.series.name) return Series(result, index=self.series.index, name=name or self.series.name) else: diff --git a/pandas/tests/test_index.py b/pandas/tests/test_index.py index 61cb337880c00..bb75b12754dca 100644 --- a/pandas/tests/test_index.py +++ b/pandas/tests/test_index.py @@ -1197,6 +1197,40 @@ def test_join_self(self): for kind in kinds: joined = res.join(res, how=kind) self.assertIs(res, joined) + def test_str_attribute(self): + # GH9068 + methods = ['strip', 'rstrip', 'lstrip'] + idx = Index([' jack', 'jill ', ' jesse ', 'frank']) + for method in methods: + expected = Index([getattr(str, method)(x) for x in idx.values]) + tm.assert_index_equal(getattr(Index.str, method)(idx.str), expected) + + # create a few instances that are not able to use .str accessor + indices = [Index(range(5)), + tm.makeDateIndex(10), + MultiIndex.from_tuples([('foo', '1'), ('bar', '3')]), + PeriodIndex(start='2000', end='2010', freq='A')] + for idx in indices: + with self.assertRaisesRegexp(AttributeError, 'only use .str accessor'): + idx.str.repeat(2) + + idx = Index(['a b c', 'd e', 'f']) + expected = Index([['a', 'b', 'c'], ['d', 'e'], ['f']]) + tm.assert_index_equal(idx.str.split(), expected) + tm.assert_index_equal(idx.str.split(return_type='series'), expected) + # return_type 'index' is an alias for 'series' + tm.assert_index_equal(idx.str.split(return_type='index'), expected) + with self.assertRaisesRegexp(ValueError, 'not supported'): + idx.str.split(return_type='frame') + + # test boolean case, should return np.array instead of boolean Index + idx = Index(['a1', 'a2', 'b1', 'b2']) + expected = np.array([True, True, False, False]) + self.assert_array_equal(idx.str.startswith('a'), expected) + self.assertIsInstance(idx.str.startswith('a'), np.ndarray) + s = Series(range(4), index=idx) + expected = Series(range(2), index=['a1', 'a2']) + tm.assert_series_equal(s[s.index.str.startswith('a')], expected) def test_indexing_doesnt_change_class(self): idx = Index([1, 2, 3, 'a', 'b', 'c']) diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index fec98a37b5017..70a6e2541692a 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -4933,6 +4933,19 @@ def test_to_csv_path_is_none(self): csv_str = s.to_csv(path=None) self.assertIsInstance(csv_str, str) + def test_str_attribute(self): + # GH9068 + methods = ['strip', 'rstrip', 'lstrip'] + s = Series([' jack', 'jill ', ' jesse ', 'frank']) + for method in methods: + expected = Series([getattr(str, method)(x) for x in s.values]) + assert_series_equal(getattr(Series.str, method)(s.str), expected) + + # str accessor only valid with string values + s = Series(range(5)) + with self.assertRaisesRegexp(AttributeError, 'only use .str accessor'): + s.str.repeat(2) + def test_clip(self): val = self.ts.median()
fixes #9068
https://api.github.com/repos/pandas-dev/pandas/pulls/9667
2015-03-17T03:06:25Z
2015-04-10T13:50:14Z
2015-04-10T13:50:14Z
2015-04-29T16:56:12Z
New function to sample from frames (Issue #2419 )
diff --git a/doc/source/api.rst b/doc/source/api.rst index 87e9b20f97e69..d442d8631247c 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -390,6 +390,7 @@ Reindexing / Selection / Label manipulation Series.reindex_like Series.rename Series.reset_index + Series.sample Series.select Series.take Series.tail @@ -824,6 +825,7 @@ Reindexing / Selection / Label manipulation DataFrame.reindex_like DataFrame.rename DataFrame.reset_index + DataFrame.sample DataFrame.select DataFrame.set_index DataFrame.tail @@ -1072,6 +1074,7 @@ Reindexing / Selection / Label manipulation Panel.reindex_axis Panel.reindex_like Panel.rename + Panel.sample Panel.select Panel.take Panel.truncate diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 1729a9d76cacd..bafc1386fd223 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -508,6 +508,81 @@ A list of indexers where any element is out of bounds will raise an .. _indexing.basics.partial_setting: +Selecting Random Samples +------------------------ +.. versionadded::0.16.1 + +A random selection of rows or columns from a Series, DataFrame, or Panel with the :meth:`~DataFrame.sample` method. The method will sample rows by default, and accepts a specific number of rows/columns to return, or a fraction of rows. + +.. ipython :: python + + s = Series([0,1,2,3,4,5]) + + # When no arguments are passed, returns 1 row. + s.sample() + + # One may specify either a number of rows: + s.sample(n=3) + + # Or a fraction of the rows: + s.sample(frac=0.5) + +By default, ``sample`` will return each row at most once, but one can also sample with replacement +using the ``replace`` option: + +.. ipython :: python + + s = Series([0,1,2,3,4,5]) + + # Without replacement (default): + s.sample(n=6, replace=False) + + # With replacement: + s.sample(n=6, replace=True) + + +By default, each row has an equal probability of being selected, but if you want rows +to have different probabilities, you can pass the ``sample`` function sampling weights as +``weights``. These weights can be a list, a numpy array, or a Series, but they must be of the same length as the object you are sampling. Missing values will be treated as a weight of zero, and inf values are not allowed. If weights do not sum to 1, they will be re-normalized by dividing all weights by the sum of the weights. For example: + +.. ipython :: python + + s = Series([0,1,2,3,4,5]) + example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4] + s.sample(n=3, weights=example_weights) + + # Weights will be re-normalized automatically + example_weights2 = [0.5, 0, 0, 0, 0, 0] + s.sample(n=1, weights=example_weights2) + +When applied to a DataFrame, you can use a column of the DataFrame as sampling weights +(provided you are sampling rows and not columns) by simply passing the name of the column +as a string. + +.. ipython :: python + + df2 = DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]}) + df2.sample(n = 3, weights = 'weight_column') + +``sample`` also allows users to sample columns instead of rows using the ``axis`` argument. + +.. ipython :: python + + df3 = DataFrame({'col1':[1,2,3], 'col2':[2,3,4]}) + df3.sample(n=1, axis=1) + +Finally, one can also set a seed for ``sample``'s random number generator using the ``random_state`` argument, which will accept either an integer (as a seed) or a numpy RandomState object. + +.. ipython :: python + + df4 = DataFrame({'col1':[1,2,3], 'col2':[2,3,4]}) + + # With a given seed, the sample will always draw the same rows. + df4.sample(n=2, random_state=2) + df4.sample(n=2, random_state=2) + + + Setting With Enlargement ------------------------ diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index 0e85b1d7870b2..7ec5c716023fc 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -12,11 +12,12 @@ Highlights include: - Support for a ``CategoricalIndex``, a category based index, see :ref:`here <whatsnew_0161.enhancements.categoricalindex>` - New section on how-to-contribute to *pandas*, see :ref`here <contributing>` +- New method ``sample`` for drawing random samples from Series, DataFrames and Panels. See :ref:`here <whatsnew_0161.enchancements.sample>` + .. contents:: What's new in v0.16.1 :local: :backlinks: none - .. _whatsnew_0161.enhancements: Enhancements @@ -137,6 +138,47 @@ values NOT in the categories, similarly to how you can reindex ANY pandas index. See the :ref:`documentation <advanced.categoricalindex>` for more. (:issue:`7629`) +.. _whatsnew_0161.enhancements.sample: + +Sample +^^^^^^ + +Series, DataFrames, and Panels now have a new method: :meth:`~pandas.DataFrame.sample`. +The method accepts a specific number of rows or columns to return, or a fraction of the +total number or rows or columns. It also has options for sampling with or without replacement, +for passing in a column for weights for non-uniform sampling, and for setting seed values to facilitate replication. + +.. ipython :: python + + example_series = Series([0,1,2,3,4,5]) + + # When no arguments are passed, returns 1 + example_series.sample() + + # One may specify either a number of rows: + example_series.sample(n=3) + + # Or a fraction of the rows: + example_series.sample(frac=0.5) + + # weights are accepted. + example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4] + example_series.sample(n=3, weights=example_weights) + + # weights will also be normalized if they do not sum to one, + # and missing values will be treated as zeros. + example_weights2 = [0.5, 0, 0, 0, None, np.nan] + example_series.sample(n=1, weights=example_weights2) + + +When applied to a DataFrame, one may pass the name of a column to specify sampling weights +when sampling from rows. + +.. ipython :: python + + df = DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]}) + df.sample(n=3, weights='weight_column') + .. _whatsnew_0161.api: API changes diff --git a/pandas/core/common.py b/pandas/core/common.py index 3d23aeff942dc..cb8b19bb79720 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -3319,3 +3319,31 @@ def _maybe_match_name(a, b): if a_name == b_name: return a_name return None + +def _random_state(state=None): + """ + Helper function for processing random_state arguments. + + Parameters + ---------- + state : int, np.random.RandomState, None. + If receives an int, passes to np.random.RandomState() as seed. + If receives an np.random.RandomState object, just returns object. + If receives `None`, returns an np.random.RandomState object. + If receives anything else, raises an informative ValueError. + Default None. + + Returns + ------- + np.random.RandomState + """ + + if is_integer(state): + return np.random.RandomState(state) + elif isinstance(state, np.random.RandomState): + return state + elif state is None: + return np.random.RandomState() + else: + raise ValueError("random_state must be an integer, a numpy RandomState, or None") + diff --git a/pandas/core/generic.py b/pandas/core/generic.py index bb5256f58795a..0d17420d821f7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1948,6 +1948,120 @@ def tail(self, n=5): return self return self.iloc[-n:] + + def sample(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None): + """ + Returns a random sample of items from an axis of object. + + Parameters + ---------- + n : int, optional + Number of items from axis to return. Cannot be used with `frac`. + Default = 1 if `frac` = None. + frac : float, optional + Fraction of axis items to return. Cannot be used with `n`. + replace : boolean, optional + Sample with or without replacement. Default = False. + weights : str or ndarray-like, optional + Default 'None' results in equal probability weighting. + If called on a DataFrame, will accept the name of a column + when axis = 0. + Weights must be same length as axis being sampled. + If weights do not sum to 1, they will be normalized to sum to 1. + Missing values in the weights column will be treated as zero. + inf and -inf values not allowed. + random_state : int or numpy.random.RandomState, optional + Seed for the random number generator (if int), or numpy RandomState + object. + axis : int or string, optional + Axis to sample. Accepts axis number or name. Default is stat axis + for given data type (0 for Series and DataFrames, 1 for Panels). + + Returns + ------- + Same type as caller. + """ + + ### + # Process axis argument + ### + + if axis is None: + axis = self._stat_axis_number + + axis = self._get_axis_number(axis) + + axis_length = self.shape[axis] + + ### + # Process random_state argument + ### + + rs = com._random_state(random_state) + + ### + # Process weights + ### + + # Check weights for compliance + if weights is not None: + + # Strings acceptable if a dataframe and axis = 0 + if isinstance(weights, string_types): + if isinstance(self, pd.DataFrame): + if axis == 0: + try: + weights = self[weights] + except KeyError: + raise KeyError("String passed to weights not a valid column") + else: + raise ValueError("Strings can only be passed to weights when sampling from rows on a DataFrame") + else: + raise ValueError("Strings cannot be passed as weights when sampling from a Series or Panel.") + + #normalize format of weights to Series. + weights = pd.Series(weights, dtype='float64') + + if len(weights) != axis_length: + raise ValueError("Weights and axis to be sampled must be of same length") + + if (weights == np.inf).any() or (weights == -np.inf).any(): + raise ValueError("weight vector may not include `inf` values") + + if (weights < 0).any(): + raise ValueError("weight vector many not include negative values") + + # If has nan, set to zero. + weights = weights.fillna(0) + + # Renormalize if don't sum to 1 + if weights.sum() != 1: + weights = weights / weights.sum() + + weights = weights.values + + ### + # Process n and frac arguments + ### + + # If no frac or n, default to n=1. + if n is None and frac is None: + n = 1 + elif n is not None and frac is None and n % 1 != 0: + raise ValueError("Only integers accepted as `n` values") + elif n is None and frac is not None: + n = int(round(frac * axis_length)) + elif n is not None and frac is not None: + raise ValueError('Please enter a value for `frac` OR `n`, not both') + + # Check for negative sizes + if n < 0: + raise ValueError("A negative number of rows requested. Please provide positive value.") + + locs = rs.choice(axis_length, size=n, replace=replace, p=weights) + return self.take(locs, axis=axis) + + #---------------------------------------------------------------------- # Attribute access diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py index d0ae7c9988c8d..f1c988e3f0323 100644 --- a/pandas/tests/test_common.py +++ b/pandas/tests/test_common.py @@ -524,6 +524,26 @@ def test_is_recompilable(): for f in fails: assert not com.is_re_compilable(f) +def test_random_state(): + import numpy.random as npr + # Check with seed + state = com._random_state(5) + assert_equal(state.uniform(), npr.RandomState(5).uniform()) + + # Check with random state object + state2 = npr.RandomState(10) + assert_equal(com._random_state(state2).uniform(), npr.RandomState(10).uniform()) + + # check with no arg random state + assert isinstance(com._random_state(), npr.RandomState) + + # Error for floats or strings + with tm.assertRaises(ValueError): + com._random_state('test') + + with tm.assertRaises(ValueError): + com._random_state(5.5) + class TestTake(tm.TestCase): # standard incompatible fill error diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 3dd8c2594cd46..5a1bc99593fca 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -354,6 +354,178 @@ def test_head_tail(self): self._compare(o.head(-3), o.head(7)) self._compare(o.tail(-3), o.tail(7)) + def test_sample(self): + # Fixes issue: 2419 + + o = self._construct(shape=10) + + ### + # Check behavior of random_state argument + ### + + # Check for stability when receives seed or random state -- run 10 times. + for test in range(10): + seed = np.random.randint(0,100) + self._compare(o.sample(n=4, random_state=seed), o.sample(n=4, random_state=seed)) + self._compare(o.sample(frac=0.7,random_state=seed), o.sample(frac=0.7, random_state=seed)) + + self._compare(o.sample(n=4, random_state=np.random.RandomState(test)), + o.sample(n=4, random_state=np.random.RandomState(test))) + + self._compare(o.sample(frac=0.7,random_state=np.random.RandomState(test)), + o.sample(frac=0.7, random_state=np.random.RandomState(test))) + + + # Check for error when random_state argument invalid. + with tm.assertRaises(ValueError): + o.sample(random_state='astring!') + + ### + # Check behavior of `frac` and `N` + ### + + # Giving both frac and N throws error + with tm.assertRaises(ValueError): + o.sample(n=3, frac=0.3) + + # Check that raises right error for negative lengths + with tm.assertRaises(ValueError): + o.sample(n=-3) + with tm.assertRaises(ValueError): + o.sample(frac=-0.3) + + # Make sure float values of `n` give error + with tm.assertRaises(ValueError): + o.sample(n= 3.2) + + # Check lengths are right + self.assertTrue(len(o.sample(n=4) == 4)) + self.assertTrue(len(o.sample(frac=0.34) == 3)) + self.assertTrue(len(o.sample(frac=0.36) == 4)) + + ### + # Check weights + ### + + # Weight length must be right + with tm.assertRaises(ValueError): + o.sample(n=3, weights=[0,1]) + + with tm.assertRaises(ValueError): + bad_weights = [0.5]*11 + o.sample(n=3, weights=bad_weights) + + # Check won't accept negative weights + with tm.assertRaises(ValueError): + bad_weights = [-0.1]*10 + o.sample(n=3, weights=bad_weights) + + # Check inf and -inf throw errors: + with tm.assertRaises(ValueError): + weights_with_inf = [0.1]*10 + weights_with_inf[0] = np.inf + o.sample(n=3, weights=weights_with_inf) + + with tm.assertRaises(ValueError): + weights_with_ninf = [0.1]*10 + weights_with_ninf[0] = -np.inf + o.sample(n=3, weights=weights_with_ninf) + + + # A few dataframe test with degenerate weights. + easy_weight_list = [0]*10 + easy_weight_list[5] = 1 + + df = pd.DataFrame({'col1':range(10,20), + 'col2':range(20,30), + 'colString': ['a']*10, + 'easyweights':easy_weight_list}) + sample1 = df.sample(n=1, weights='easyweights') + assert_frame_equal(sample1, df.iloc[5:6]) + + # Ensure proper error if string given as weight for Series, panel, or + # DataFrame with axis = 1. + s = Series(range(10)) + with tm.assertRaises(ValueError): + s.sample(n=3, weights='weight_column') + + panel = pd.Panel(items = [0,1,2], major_axis = [2,3,4], minor_axis = [3,4,5]) + with tm.assertRaises(ValueError): + panel.sample(n=1, weights='weight_column') + + with tm.assertRaises(ValueError): + df.sample(n=1, weights='weight_column', axis = 1) + + # Check weighting key error + with tm.assertRaises(KeyError): + df.sample(n=3, weights='not_a_real_column_name') + + # Check np.nan are replaced by zeros. + weights_with_nan = [np.nan]*10 + weights_with_nan[5] = 0.5 + self._compare(o.sample(n=1, axis=0, weights=weights_with_nan), o.iloc[5:6]) + + # Check None are also replaced by zeros. + weights_with_None = [None]*10 + weights_with_None[5] = 0.5 + self._compare(o.sample(n=1, axis=0, weights=weights_with_None), o.iloc[5:6]) + + # Check that re-normalizes weights that don't sum to one. + weights_less_than_1 = [0]*10 + weights_less_than_1[0] = 0.5 + tm.assert_frame_equal(df.sample(n=1, weights=weights_less_than_1), df.iloc[:1]) + + + ### + # Test axis argument + ### + + # Test axis argument + df = pd.DataFrame({'col1':range(10), 'col2':['a']*10}) + second_column_weight = [0,1] + assert_frame_equal(df.sample(n=1, axis=1, weights=second_column_weight), df[['col2']]) + + # Different axis arg types + assert_frame_equal(df.sample(n=1, axis='columns', weights=second_column_weight), + df[['col2']]) + + weight = [0]*10 + weight[5] = 0.5 + assert_frame_equal(df.sample(n=1, axis='rows', weights=weight), + df.iloc[5:6]) + assert_frame_equal(df.sample(n=1, axis='index', weights=weight), + df.iloc[5:6]) + + + # Check out of range axis values + with tm.assertRaises(ValueError): + df.sample(n=1, axis=2) + + with tm.assertRaises(ValueError): + df.sample(n=1, axis='not_a_name') + + with tm.assertRaises(ValueError): + s = pd.Series(range(10)) + s.sample(n=1, axis=1) + + # Test weight length compared to correct axis + with tm.assertRaises(ValueError): + df.sample(n=1, axis=1, weights=[0.5]*10) + + # Check weights with axis = 1 + easy_weight_list = [0]*3 + easy_weight_list[2] = 1 + + df = pd.DataFrame({'col1':range(10,20), + 'col2':range(20,30), + 'colString': ['a']*10}) + sample1 = df.sample(n=1, axis=1, weights=easy_weight_list) + assert_frame_equal(sample1, df[['colString']]) + + # Test default axes + p = pd.Panel(items = ['a','b','c'], major_axis=[2,4,6], minor_axis=[1,3,5]) + assert_panel_equal(p.sample(n=3, random_state=42), p.sample(n=3, axis=1, random_state=42)) + assert_frame_equal(df.sample(n=3, random_state=42), df.sample(n=3, axis=0, random_state=42)) def test_size_compat(self): # GH8846
closes #2419 Creates a function `.sample()` in generic.py to sample from pandas objects. Returns either a fixed number of rows or a share. Also includes a number of tests in `test_sample()` -- I am open to added suggestions. Input from users with panel experience appreciated!
https://api.github.com/repos/pandas-dev/pandas/pulls/9666
2015-03-16T21:05:39Z
2015-05-01T12:50:43Z
null
2015-05-01T17:39:18Z
DOC: grammar fixes, add conda install to README
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 876daa97313e8..5329bad1d90e4 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -19,7 +19,7 @@ looking for a quick way to help out. ... ``` - - Include the full version string of pandas and it's dependencies. In recent (>0.12) versions + - 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 diff --git a/README.md b/README.md index 1c01692df9685..cea7e8c6bfd72 100644 --- a/README.md +++ b/README.md @@ -87,6 +87,12 @@ or `pip`: pip install pandas ``` +or `conda`: + +```sh +conda install pandas +``` + ## Dependencies - [NumPy](http://www.numpy.org): 1.7.0 or higher - [python-dateutil](http://labix.org/python-dateutil): 1.5 or higher diff --git a/doc/source/options.rst b/doc/source/options.rst index 79145dfbf2939..7e36f369bc7e7 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -15,7 +15,7 @@ Options and Settings Overview -------- -pandas has an options system that lets you customize some aspects of it's behaviour, +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``), @@ -291,7 +291,7 @@ display.expand_frame_repr True Whether to print out the full DataFrame multiple lines, `max_columns` is still respected, but the output will wrap-around across multiple "pages" - if it's width exceeds `display.width`. + if its width exceeds `display.width`. display.float_format None The callable should accept a floating point number and return a string with the desired format of the number. diff --git a/pandas/core/index.py b/pandas/core/index.py index 195cb21d6564c..e335d00551bab 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -699,7 +699,7 @@ def f(c): if v is None or is_integer(v): return v - # warn if its a convertible float + # warn if it's a convertible float if v == int(v): warnings.warn("slice indexers when using iloc should be integers " "and not floating point",FutureWarning) diff --git a/pandas/msgpack.pyx b/pandas/msgpack.pyx index 4413e2c0986ab..625ac55ee832c 100644 --- a/pandas/msgpack.pyx +++ b/pandas/msgpack.pyx @@ -112,7 +112,7 @@ cdef class Packer(object): * *encoding* - Convert unicode to bytes with this encoding. (default: 'utf-8') * *unicode_errors* - Error handler for encoding unicode. (default: 'strict') * *use_single_float* - Use single precision float type for float. (default: False) - * *autoreset* - Reset buffer after each pack and return it's content as `bytes`. (default: True). + * *autoreset* - Reset buffer after each pack and return its content as `bytes`. (default: True). If set this to false, use `bytes()` to get content and `.reset()` to clear buffer. """ cdef msgpack_packer pk diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 17ae8c66e26dd..79ebb80fc9ebb 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1039,7 +1039,7 @@ def test_transform_select_columns(self): def test_transform_exclude_nuisance(self): # this also tests orderings in transform between - # series/frame to make sure its consistent + # series/frame to make sure it's consistent expected = {} grouped = self.df.groupby('A') expected['C'] = grouped['C'].transform(np.mean) diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index dc77ab8190e41..18baa941b814a 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -3717,7 +3717,7 @@ def random_text(nobs=100): self.assertIsNotNone(df.is_copy) df.loc[:,'letters'] = df['letters'].apply(str.lower) - # should be ok even though its a copy! + # should be ok even though it's a copy! self.assertIsNone(df.is_copy) df['letters'] = df['letters'].apply(str.lower) self.assertIsNone(df.is_copy)
https://api.github.com/repos/pandas-dev/pandas/pulls/9665
2015-03-16T19:48:28Z
2015-03-17T00:26:59Z
2015-03-17T00:26:59Z
2015-04-29T15:31:49Z
BUG: support TimedeltaIndex serialization in fixed stores (GH9635)
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 0eeee8ccfddf6..721a49f5d58ce 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -14,11 +14,13 @@ Highlights include: - Backwards incompatible change to ``Timedelta`` to conform the ``.seconds`` attribute with ``datetime.timedelta``, see :ref:`here <whatsnew_0160.api_breaking.timedelta>` - Changes to the ``.loc`` slicing API to conform with the behavior of ``.ix`` see :ref:`here <whatsnew_0160.api_breaking.indexing>` - Changes to the default for ordering in the ``Categorical`` constructor, see :ref:`here <whatsnew_0160.api_breaking.categorical>` +- Enhancement to the ``.str`` accessor to make string operations easier, see :ref:`here <whatsnew_0160.enhancements.string>` Check the :ref:`API Changes <whatsnew_0160.api>` and :ref:`deprecations <whatsnew_0160.deprecations>` before updating. .. contents:: What's new in v0.16.0 :local: + :backlinks: none .. _whatsnew_0160.enhancements: @@ -120,6 +122,45 @@ from a ``scipy.sparse.coo_matrix``: ss = SparseSeries.from_coo(A) ss +.. _whatsnew_0160.enhancements.string: + +String Methods Enhancements +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- Following new methods are accesible via ``.str`` accessor to apply the function to each values. This is intended to make it more consistent with standard methods on strings. (:issue:`9282`, :issue:`9352`, :issue:`9386`, :issue:`9387`, :issue:`9439`) + +============= ============= ============= =============== =============== +.. .. Methods .. .. +============= ============= ============= =============== =============== +``isalnum()`` ``isalpha()`` ``isdigit()`` ``isdigit()`` ``isspace()`` +``islower()`` ``isupper()`` ``istitle()`` ``isnumeric()`` ``isdecimal()`` +``find()`` ``rfind()`` ``ljust()`` ``rjust()`` ``zfill()`` +============= ============= ============= =============== =============== + +.. ipython:: python + + s = Series(['abcd', '3456', 'EFGH']) + s.str.isalpha() + s.str.find('ab') + + +- :meth:`Series.str.pad` and :meth:`Series.str.center` now accept ``fillchar`` option to specify filling character (:issue:`9352`) + +.. ipython:: python + + s = Series(['12', '300', '25']) + s.str.pad(5, fillchar='_') + + +- Added :meth:`Series.str.slice_replace`, which previously raised ``NotImplementedError`` (:issue:`8888`) + +.. ipython:: python + + s = Series(['ABCD', 'EFGH', 'IJK']) + s.str.slice_replace(1, 3, 'X') + # replaced with empty char + s.str.slice_replace(0, 1) + .. _whatsnew_0160.enhancements.other: Other enhancements @@ -137,7 +178,6 @@ Other enhancements - Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>`. (issue:`9493`:) - Paths beginning with ~ will now be expanded to begin with the user's home directory (:issue:`9066`) - Added time interval selection in ``get_data_yahoo`` (:issue:`9071`) -- Added ``Series.str.slice_replace()``, which previously raised ``NotImplementedError`` (:issue:`8888`) - Added ``Timestamp.to_datetime64()`` to complement ``Timedelta.to_timedelta64()`` (:issue:`9255`) - ``tseries.frequencies.to_offset()`` now accepts ``Timedelta`` as input (:issue:`9064`) - Lag parameter was added to the autocorrelation method of ``Series``, defaults to lag-1 autocorrelation (:issue:`9192`) @@ -145,15 +185,8 @@ Other enhancements - SQL code now safely escapes table and column names (:issue:`8986`) - Added auto-complete for ``Series.str.<tab>``, ``Series.dt.<tab>`` and ``Series.cat.<tab>`` (:issue:`9322`) -- Added ``StringMethods.isalnum()``, ``isalpha()``, ``isdigit()``, ``isspace()``, ``islower()``, - ``isupper()``, ``istitle()`` which behave as the same as standard ``str`` (:issue:`9282`) - -- Added ``StringMethods.find()`` and ``rfind()`` which behave as the same as standard ``str`` (:issue:`9386`) - - ``Index.get_indexer`` now supports ``method='pad'`` and ``method='backfill'`` even for any target array, not just monotonic targets. These methods also work for monotonic decreasing as well as monotonic increasing indexes (:issue:`9258`). - ``Index.asof`` now works on all index types (:issue:`9258`). - -- Added ``StringMethods.isnumeric`` and ``isdecimal`` which behave as the same as standard ``str`` (:issue:`9439`) - The ``read_excel()`` function's :ref:`sheetname <_io.specifying_sheets>` argument now accepts a list and ``None``, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (:issue:`9450`) .. code-block:: python @@ -162,9 +195,6 @@ Other enhancements pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) - A ``verbose`` argument has been augmented in ``io.read_excel()``, defaults to False. Set to True to print sheet names as they are parsed. (:issue:`9450`) -- Added ``StringMethods.ljust()`` and ``rjust()`` which behave as the same as standard ``str`` (:issue:`9352`) -- ``StringMethods.pad()`` and ``center()`` now accept ``fillchar`` option to specify filling character (:issue:`9352`) -- Added ``StringMethods.zfill()`` which behave as the same as standard ``str`` (:issue:`9387`) - Added ``days_in_month`` (compatibility alias ``daysinmonth``) property to ``Timestamp``, ``DatetimeIndex``, ``Period``, ``PeriodIndex``, and ``Series.dt`` (:issue:`9572`) - Added ``decimal`` option in ``to_csv`` to provide formatting for non-'.' decimal separators (:issue:`781`) - Added ``normalize`` option for ``Timestamp`` to normalized to midnight (:issue:`8794`) @@ -454,6 +484,15 @@ Other API Changes To reproduce the old behavior, simply add more precision to the label (e.g., use ``2000-02-01`` instead of ``2000-02``). +- A Spurious ``SettingWithCopy`` Warning was generated when setting a new item in a frame in some cases (:issue:`8730`) + + The following would previously report a ``SettingWithCopy`` Warning. + + .. ipython:: python + + df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])}) + df2 = df1[['x']] + df2['y'] = ['g', 'h', 'i'] .. _whatsnew_0160.deprecations: @@ -505,6 +544,7 @@ Performance Improvements - Performance improvements in ``MultiIndex.sortlevel`` (:issue:`9445`) - Performance and memory usage improvements in ``DataFrame.duplicated`` (:issue:`9398`) - Cythonized ``Period`` (:issue:`9440`) +- Decreased memory usage on ``to_hdf`` (:issue:`9648`) .. _whatsnew_0160.bug_fixes: @@ -567,3 +607,39 @@ Bug Fixes - Bug in ``Series.values_counts`` with excluding ``NaN`` for categorical type ``Series`` with ``dropna=True`` (:issue:`9443`) - Fixed mising numeric_only option for ``DataFrame.std/var/sem`` (:issue:`9201`) - Support constructing ``Panel`` or ``Panel4D`` with scalar data (:issue:`8285`) +- ``Series`` text representation disconnected from `max_rows`/`max_columns` (:issue:`7508`). +- ``Series`` number formatting inconsistent when truncated (:issue:`8532`). + + Previous Behavior + + .. code-block:: python + + In [2]: pd.options.display.max_rows = 10 + In [3]: s = pd.Series([1,1,1,1,1,1,1,1,1,1,0.9999,1,1]*10) + In [4]: s + Out[4]: + 0 1 + 1 1 + 2 1 + ... + 127 0.9999 + 128 1.0000 + 129 1.0000 + Length: 130, dtype: float64 + + New Behavior + + .. code-block:: python + + 0 1.0000 + 1 1.0000 + 2 1.0000 + 3 1.0000 + 4 1.0000 + ... + 125 1.0000 + 126 1.0000 + 127 0.9999 + 128 1.0000 + 129 1.0000 + dtype: float64 diff --git a/pandas/core/format.py b/pandas/core/format.py index 3efcfec254591..b21ca9050ffd0 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -129,45 +129,63 @@ def to_string(self): class SeriesFormatter(object): - def __init__(self, series, buf=None, header=True, length=True, - na_rep='NaN', name=False, float_format=None, dtype=True): + def __init__(self, series, buf=None, length=True, header=True, + na_rep='NaN', name=False, float_format=None, dtype=True, + max_rows=None): self.series = series self.buf = buf if buf is not None else StringIO() self.name = name self.na_rep = na_rep - self.length = length self.header = header + self.length = length + self.max_rows = max_rows if float_format is None: float_format = get_option("display.float_format") self.float_format = float_format self.dtype = dtype + self._chk_truncate() + + def _chk_truncate(self): + from pandas.tools.merge import concat + max_rows = self.max_rows + truncate_v = max_rows and (len(self.series) > max_rows) + series = self.series + if truncate_v: + if max_rows == 1: + row_num = max_rows + series = series.iloc[:max_rows] + else: + row_num = max_rows // 2 + series = concat((series.iloc[:row_num], series.iloc[-row_num:])) + self.tr_row_num = row_num + self.tr_series = series + self.truncate_v = truncate_v + def _get_footer(self): + name = self.series.name footer = u('') - if self.name: - if getattr(self.series.index, 'freq', None): - footer += 'Freq: %s' % self.series.index.freqstr + if getattr(self.series.index, 'freq', None) is not None: + footer += 'Freq: %s' % self.series.index.freqstr - if footer and self.series.name is not None: - # categories have already a comma + linebreak - if not com.is_categorical_dtype(self.series.dtype): - footer += ', ' + if self.name is not False and name is not None: + if footer: + footer += ', ' - series_name = com.pprint_thing(self.series.name, + series_name = com.pprint_thing(name, escape_chars=('\t', '\r', '\n')) footer += ("Name: %s" % - series_name) if self.series.name is not None else "" + series_name) if name is not None else "" if self.length: if footer: footer += ', ' footer += 'Length: %d' % len(self.series) - # TODO: in tidy_repr, with freq index, no dtype is shown -> also include a guard here? - if self.dtype: - name = getattr(self.series.dtype, 'name', None) + if self.dtype is not False and self.dtype is not None: + name = getattr(self.tr_series.dtype, 'name', None) if name: if footer: footer += ', ' @@ -175,8 +193,8 @@ def _get_footer(self): # level infos are added to the end and in a new line, like it is done for Categoricals # Only added when we request a name - if self.name and com.is_categorical_dtype(self.series.dtype): - level_info = self.series.values._repr_categories_info() + if name and com.is_categorical_dtype(self.tr_series.dtype): + level_info = self.tr_series.values._repr_categories_info() if footer: footer += "\n" footer += level_info @@ -184,7 +202,7 @@ def _get_footer(self): return compat.text_type(footer) def _get_formatted_index(self): - index = self.series.index + index = self.tr_series.index is_multi = isinstance(index, MultiIndex) if is_multi: @@ -196,35 +214,44 @@ def _get_formatted_index(self): return fmt_index, have_header def _get_formatted_values(self): - return format_array(self.series.get_values(), None, + return format_array(self.tr_series.get_values(), None, float_format=self.float_format, na_rep=self.na_rep) def to_string(self): - series = self.series + series = self.tr_series + footer = self._get_footer() if len(series) == 0: - return u('') + return 'Series([], ' + footer + ')' fmt_index, have_header = self._get_formatted_index() fmt_values = self._get_formatted_values() - maxlen = max(len(x) for x in fmt_index) + maxlen = max(len(x) for x in fmt_index) # max index len pad_space = min(maxlen, 60) - result = ['%s %s'] * len(fmt_values) - for i, (k, v) in enumerate(zip(fmt_index[1:], fmt_values)): - idx = k.ljust(pad_space) - result[i] = result[i] % (idx, v) + if self.truncate_v: + n_header_rows = 0 + row_num = self.tr_row_num + width = len(fmt_values[row_num-1]) + if width > 3: + dot_str = '...' + else: + dot_str = '..' + dot_str = dot_str.center(width) + fmt_values.insert(row_num + n_header_rows, dot_str) + fmt_index.insert(row_num + 1, '') + + result = adjoin(3, *[fmt_index[1:], fmt_values]) if self.header and have_header: - result.insert(0, fmt_index[0]) + result = fmt_index[0] + '\n' + result - footer = self._get_footer() if footer: - result.append(footer) + result += '\n' + footer - return compat.text_type(u('\n').join(result)) + return compat.text_type(u('').join(result)) def _strlen_func(): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 90a6cf60fa76b..e05709d7a180f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1265,6 +1265,14 @@ def _check_setitem_copy(self, stacklevel=4, t='setting', force=False): except: pass + # we might be a false positive + try: + if self.is_copy().shape == self.shape: + self.is_copy = None + return + except: + pass + # a custom message if isinstance(self.is_copy, string_types): t = self.is_copy @@ -1344,8 +1352,9 @@ def take(self, indices, axis=0, convert=True, is_copy=True): result = self._constructor(new_data).__finalize__(self) # maybe set copy if we didn't actually change the index - if is_copy and not result._get_axis(axis).equals(self._get_axis(axis)): - result._set_is_copy(self) + if is_copy: + if not result._get_axis(axis).equals(self._get_axis(axis)): + result._set_is_copy(self) return result @@ -2005,6 +2014,14 @@ def __setattr__(self, name, value): #---------------------------------------------------------------------- # Consolidation of internals + def _protect_consolidate(self, f): + """ consolidate _data. if the blocks have changed, then clear the cache """ + blocks_before = len(self._data.blocks) + result = f() + if len(self._data.blocks) != blocks_before: + self._clear_item_cache() + return result + def _consolidate_inplace(self): f = lambda: self._data.consolidate() self._data = self._protect_consolidate(f) @@ -2029,8 +2046,6 @@ def consolidate(self, inplace=False): else: f = lambda: self._data.consolidate() cons_data = self._protect_consolidate(f) - if cons_data is self._data: - cons_data = cons_data.copy() return self._constructor(cons_data).__finalize__(self) @property @@ -2066,13 +2081,6 @@ def _check_inplace_setting(self, value): return True - def _protect_consolidate(self, f): - blocks_before = len(self._data.blocks) - result = f() - if len(self._data.blocks) != blocks_before: - self._clear_item_cache() - return result - def _get_numeric_data(self): return self._constructor( self._data.get_numeric_data()).__finalize__(self) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 5cb032521d51a..7a16fb2b6b0d7 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1752,7 +1752,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, if self.is_categorical_astype(dtype): values = self.values else: - values = np.array(self.values).astype(dtype) + values = np.asarray(self.values).astype(dtype, copy=False) if copy: values = values.copy() diff --git a/pandas/core/series.py b/pandas/core/series.py index d34657f0dc256..7e3b21be13525 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -36,7 +36,7 @@ from pandas.tseries.period import PeriodIndex, Period from pandas import compat from pandas.util.terminal import get_terminal_size -from pandas.compat import zip, u, OrderedDict +from pandas.compat import zip, u, OrderedDict, StringIO import pandas.core.ops as ops from pandas.core.algorithms import select_n @@ -883,43 +883,16 @@ def __unicode__(self): Invoked by unicode(df) in py2 only. Yields a Unicode String in both py2/py3. """ + buf = StringIO(u("")) width, height = get_terminal_size() max_rows = (height if get_option("display.max_rows") == 0 else get_option("display.max_rows")) - if max_rows and len(self.index) > max_rows: - result = self._tidy_repr(min(30, max_rows - 4)) - elif len(self.index) > 0: - result = self._get_repr(print_header=True, - length=len(self) > 50, - name=True, - dtype=True) - elif self.name is None: - result = u('Series([], dtype: %s)') % (self.dtype) - else: - result = u('Series([], name: %s, dtype: %s)') % (self.name, - self.dtype) - return result - def _tidy_repr(self, max_vals=20): - """ + self.to_string(buf=buf, name=self.name, dtype=self.dtype, + max_rows=max_rows) + result = buf.getvalue() - Internal function, should always return unicode string - """ - if max_vals > 1: - num = max_vals // 2 - else: - num = 1 - max_vals = 2 - head = self.iloc[:num]._get_repr(print_header=True, length=False, - dtype=False, name=False) - tail = self.iloc[-(max_vals - num):]._get_repr(print_header=False, - length=False, - name=False, - dtype=False) - result = head + '\n...\n' + tail - result = '%s\n%s' % (result, self._repr_footer()) - - return compat.text_type(result) + return result def _repr_footer(self): @@ -948,8 +921,8 @@ def _repr_footer(self): len(self), str(self.dtype.name)) - def to_string(self, buf=None, na_rep='NaN', float_format=None, - length=False, dtype=False, name=False): + def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True, + length=False, dtype=False, name=False, max_rows=None): """ Render a string representation of the Series @@ -962,12 +935,17 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, float_format : one-parameter function, optional formatter function to apply to columns' elements if they are floats default None + header: boolean, default True + Add the Series header (index name) length : boolean, default False Add the Series length dtype : boolean, default False Add the Series dtype name : boolean, default False - Add the Series name (which may be None) + Add the Series name if not None + max_rows : int, optional + Maximum number of rows to show before truncating. If None, show + all. Returns ------- @@ -975,7 +953,8 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, """ the_repr = self._get_repr(float_format=float_format, na_rep=na_rep, - length=length, dtype=dtype, name=name) + header=header, length=length, dtype=dtype, + name=name, max_rows=max_rows) # catch contract violations if not isinstance(the_repr, compat.text_type): @@ -993,17 +972,18 @@ def to_string(self, buf=None, na_rep='NaN', float_format=None, f.write(the_repr) def _get_repr( - self, name=False, print_header=False, length=True, dtype=True, - na_rep='NaN', float_format=None): + self, name=False, header=True, length=True, dtype=True, na_rep='NaN', + float_format=None, max_rows=None): """ Internal function, should always return unicode string """ - - formatter = fmt.SeriesFormatter(self, name=name, header=print_header, - length=length, dtype=dtype, + formatter = fmt.SeriesFormatter(self, name=name, + length=length, header=header, + dtype=dtype, na_rep=na_rep, - float_format=float_format) + float_format=float_format, + max_rows=max_rows) result = formatter.to_string() # TODO: following check prob. not neces. diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index e784934ea28b2..d95465d524e27 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -18,6 +18,7 @@ from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.tseries.api import PeriodIndex, DatetimeIndex +from pandas.tseries.tdi import TimedeltaIndex from pandas.core.base import StringMixin from pandas.core.common import adjoin, pprint_thing from pandas.core.algorithms import match, unique @@ -1782,13 +1783,13 @@ def set_atom(self, block, block_items, existing_col, min_itemsize, return self.set_atom_timedelta64(block) dtype = block.dtype.name - rvalues = block.values.ravel() - inferred_type = lib.infer_dtype(rvalues) + inferred_type = lib.infer_dtype(block.values) if inferred_type == 'date': raise TypeError( "[date] is not implemented as a table column") elif inferred_type == 'datetime': + rvalues = block.values.ravel() if getattr(rvalues[0], 'tzinfo', None) is not None: # if this block has more than one timezone, raise @@ -1917,7 +1918,7 @@ def get_atom_data(self, block, kind=None): def set_atom_data(self, block): self.kind = block.dtype.name self.typ = self.get_atom_data(block) - self.set_data(block.values.astype(self.typ.type)) + self.set_data(block.values.astype(self.typ.type, copy=False)) def set_atom_categorical(self, block, items, info=None, values=None): # currently only supports a 1-D categorical @@ -2016,7 +2017,7 @@ def convert(self, values, nan_rep, encoding): index = DatetimeIndex( self.data.ravel(), tz='UTC').tz_convert(self.tz) - self.data = np.array( + self.data = np.asarray( index.tolist(), dtype=object).reshape(self.data.shape) else: @@ -2026,14 +2027,14 @@ def convert(self, values, nan_rep, encoding): self.data = np.asarray(self.data, dtype='m8[ns]') elif dtype == u('date'): try: - self.data = np.array( + self.data = np.asarray( [date.fromordinal(v) for v in self.data], dtype=object) except ValueError: - self.data = np.array( + self.data = np.asarray( [date.fromtimestamp(v) for v in self.data], dtype=object) elif dtype == u('datetime'): - self.data = np.array( + self.data = np.asarray( [datetime.fromtimestamp(v) for v in self.data], dtype=object) @@ -2048,9 +2049,9 @@ def convert(self, values, nan_rep, encoding): else: try: - self.data = self.data.astype(dtype) + self.data = self.data.astype(dtype, copy=False) except: - self.data = self.data.astype('O') + self.data = self.data.astype('O', copy=False) # convert nans / decode if _ensure_decoded(self.kind) == u('string'): @@ -2337,9 +2338,9 @@ def read_array(self, key): ret = data if dtype == u('datetime64'): - ret = np.array(ret, dtype='M8[ns]') + ret = np.asarray(ret, dtype='M8[ns]') elif dtype == u('timedelta64'): - ret = np.array(ret, dtype='m8[ns]') + ret = np.asarray(ret, dtype='m8[ns]') if transposed: return ret.T @@ -3793,7 +3794,7 @@ def write_data(self, chunksize, dropna=True): # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask mask = com.isnull(a.data).all(axis=0) - masks.append(mask.astype('u1')) + masks.append(mask.astype('u1', copy=False)) # consolidate masks mask = masks[0] @@ -3803,8 +3804,7 @@ def write_data(self, chunksize, dropna=True): else: - mask = np.empty(nrows, dtype='u1') - mask.fill(False) + mask = None # broadcast the indexes if needed indexes = [a.cvalues for a in self.index_axes] @@ -3833,12 +3833,13 @@ def write_data(self, chunksize, dropna=True): bvalues = [] for i, v in enumerate(values): new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape - bvalues.append(values[i].ravel().reshape(new_shape)) + bvalues.append(values[i].reshape(new_shape)) # write the chunks if chunksize is None: chunksize = 100000 + rows = np.empty(min(chunksize,nrows), dtype=self.dtype) chunks = int(nrows / chunksize) + 1 for i in range(chunks): start_i = i * chunksize @@ -3847,11 +3848,20 @@ def write_data(self, chunksize, dropna=True): break self.write_data_chunk( + rows, indexes=[a[start_i:end_i] for a in bindexes], - mask=mask[start_i:end_i], + mask=mask[start_i:end_i] if mask is not None else None, values=[v[start_i:end_i] for v in bvalues]) - def write_data_chunk(self, indexes, mask, values): + def write_data_chunk(self, rows, indexes, mask, values): + """ + Parameters + ---------- + rows : an empty memory space where we are putting the chunk + indexes : an array of the indexes + mask : an array of the masks + values : an array of the values + """ # 0 len for v in values: @@ -3860,7 +3870,8 @@ def write_data_chunk(self, indexes, mask, values): try: nrows = indexes[0].shape[0] - rows = np.empty(nrows, dtype=self.dtype) + if nrows != len(rows): + rows = np.empty(nrows, dtype=self.dtype) names = self.dtype.names nindexes = len(indexes) @@ -3873,7 +3884,10 @@ def write_data_chunk(self, indexes, mask, values): rows[names[i + nindexes]] = v # mask - rows = rows[~mask.ravel().astype(bool)] + if mask is not None: + m = ~mask.ravel().astype(bool, copy=False) + if not m.all(): + rows = rows[m] except Exception as detail: raise Exception("cannot create row-data -> %s" % detail) @@ -4220,6 +4234,11 @@ def _convert_index(index, encoding=None, format_type=None): freq=getattr(index, 'freq', None), tz=getattr(index, 'tz', None), index_name=index_name) + elif isinstance(index, TimedeltaIndex): + converted = index.asi8 + return IndexCol(converted, 'timedelta64', _tables().Int64Col(), + freq=getattr(index, 'freq', None), + index_name=index_name) elif isinstance(index, (Int64Index, PeriodIndex)): atom = _tables().Int64Col() return IndexCol( @@ -4239,15 +4258,20 @@ def _convert_index(index, encoding=None, format_type=None): freq=getattr(index, 'freq', None), tz=getattr(index, 'tz', None), index_name=index_name) + elif inferred_type == 'timedelta64': + converted = values.view('i8') + return IndexCol(converted, 'timedelta64', _tables().Int64Col(), + freq=getattr(index, 'freq', None), + index_name=index_name) elif inferred_type == 'datetime': - converted = np.array([(time.mktime(v.timetuple()) + - v.microsecond / 1E6) for v in values], - dtype=np.float64) + converted = np.asarray([(time.mktime(v.timetuple()) + + v.microsecond / 1E6) for v in values], + dtype=np.float64) return IndexCol(converted, 'datetime', _tables().Time64Col(), index_name=index_name) elif inferred_type == 'date': - converted = np.array([v.toordinal() for v in values], - dtype=np.int32) + converted = np.asarray([v.toordinal() for v in values], + dtype=np.int32) return IndexCol(converted, 'date', _tables().Time32Col(), index_name=index_name) elif inferred_type == 'string': @@ -4289,22 +4313,24 @@ def _unconvert_index(data, kind, encoding=None): kind = _ensure_decoded(kind) if kind == u('datetime64'): index = DatetimeIndex(data) + elif kind == u('timedelta64'): + index = TimedeltaIndex(data) elif kind == u('datetime'): - index = np.array([datetime.fromtimestamp(v) for v in data], - dtype=object) + index = np.asarray([datetime.fromtimestamp(v) for v in data], + dtype=object) elif kind == u('date'): try: - index = np.array( + index = np.asarray( [date.fromordinal(v) for v in data], dtype=object) except (ValueError): - index = np.array( + index = np.asarray( [date.fromtimestamp(v) for v in data], dtype=object) elif kind in (u('integer'), u('float')): - index = np.array(data) + index = np.asarray(data) elif kind in (u('string')): index = _unconvert_string_array(data, nan_rep=None, encoding=encoding) elif kind == u('object'): - index = np.array(data[0]) + index = np.asarray(data[0]) else: # pragma: no cover raise ValueError('unrecognized index type %s' % kind) return index @@ -4315,7 +4341,7 @@ def _unconvert_index_legacy(data, kind, legacy=False, encoding=None): if kind == u('datetime'): index = lib.time64_to_datetime(data) elif kind in (u('integer')): - index = np.array(data, dtype=object) + index = np.asarray(data, dtype=object) elif kind in (u('string')): index = _unconvert_string_array(data, nan_rep=None, encoding=encoding) else: # pragma: no cover @@ -4334,13 +4360,13 @@ def _convert_string_array(data, encoding, itemsize=None): if itemsize is None: itemsize = lib.max_len_string_array(com._ensure_object(data.ravel())) - data = np.array(data, dtype="S%d" % itemsize) + data = np.asarray(data, dtype="S%d" % itemsize) return data def _unconvert_string_array(data, nan_rep=None, encoding=None): """ deserialize a string array, possibly decoding """ shape = data.shape - data = np.array(data.ravel(), dtype=object) + data = np.asarray(data.ravel(), dtype=object) # guard against a None encoding in PY3 (because of a legacy # where the passed encoding is actually None) @@ -4353,7 +4379,7 @@ def _unconvert_string_array(data, nan_rep=None, encoding=None): dtype = "U{0}".format(itemsize) else: dtype = "S{0}".format(itemsize) - data = data.astype(dtype).astype(object) + data = data.astype(dtype, copy=False).astype(object, copy=False) except (Exception) as e: f = np.vectorize(lambda x: x.decode(encoding), otypes=[np.object]) data = f(data) @@ -4376,7 +4402,7 @@ def _maybe_convert(values, val_kind, encoding): def _get_converter(kind, encoding): kind = _ensure_decoded(kind) if kind == 'datetime64': - return lambda x: np.array(x, dtype='M8[ns]') + return lambda x: np.asarray(x, dtype='M8[ns]') elif kind == 'datetime': return lib.convert_timestamps elif kind == 'string': @@ -4421,7 +4447,7 @@ def __init__(self, table, where=None, start=None, stop=None, **kwargs): try: inferred = lib.infer_dtype(where) if inferred == 'integer' or inferred == 'boolean': - where = np.array(where) + where = np.asarray(where) if where.dtype == np.bool_: start, stop = self.start, self.stop if start is None: diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index e95d46f66f17f..ea30bb9c9ae44 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -10,7 +10,7 @@ import pandas from pandas import (Series, DataFrame, Panel, MultiIndex, Categorical, bdate_range, - date_range, Index, DatetimeIndex, isnull) + date_range, timedelta_range, Index, DatetimeIndex, TimedeltaIndex, isnull) from pandas.io.pytables import _tables try: @@ -4593,12 +4593,17 @@ def test_categorical(self): with ensure_clean_store(self.path) as store: - s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'])) - + # basic + s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'], ordered=False)) store.append('s', s, format='table') result = store.select('s') tm.assert_series_equal(s, result) + s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'], ordered=True)) + store.append('s_ordered', s, format='table') + result = store.select('s_ordered') + tm.assert_series_equal(s, result) + df = DataFrame({"s":s, "vals":[1,2,3,4,5,6]}) store.append('df', df, format='table') result = store.select('df') @@ -4639,6 +4644,10 @@ def test_categorical(self): result = store.select('df3', where = ['s in ["b","c"]']) tm.assert_frame_equal(result, expected) + expected = df[df.s.isin(['b','c'])] + result = store.select('df3', where = ['s = ["b","c"]']) + tm.assert_frame_equal(result, expected) + expected = df[df.s.isin(['d'])] result = store.select('df3', where = ['s in ["d"]']) tm.assert_frame_equal(result, expected) @@ -4678,6 +4687,18 @@ def test_duplicate_column_name(self): other = read_hdf(path, 'df') tm.assert_frame_equal(df, other) + def test_preserve_timedeltaindex_type(self): + # GH9635 + # Storing TimedeltaIndexed DataFrames in fixed stores did not preserve + # the type of the index. + df = DataFrame(np.random.normal(size=(10,5))) + df.index = timedelta_range(start='0s',periods=10,freq='1s',name='example') + + with ensure_clean_store(self.path) as store: + + store['df'] = df + assert_frame_equal(store['df'], df) + def _test_sort(obj): if isinstance(obj, DataFrame): diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index cd78fd22e64ca..7f4b3fcb94dfa 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -13,6 +13,7 @@ from pandas import Categorical, Index, Series, DataFrame, PeriodIndex, Timestamp +from pandas.core.config import option_context import pandas.core.common as com import pandas.compat as compat import pandas.util.testing as tm @@ -1559,12 +1560,12 @@ def test_repr(self): self.assertEqual(exp, a.__unicode__()) - a = pd.Series(pd.Categorical(["a","b"] *25, name="a", ordered=True)) - exp = u("".join(["%s a\n%s b\n"%(i,i+1) for i in range(0,10,2)]) + "...\n" + - "".join(["%s a\n%s b\n"%(i,i+1) for i in range(40,50,2)]) + - "Name: a, Length: 50, dtype: category\n" + - "Categories (2, object): [a < b]") - self.assertEqual(exp,a._tidy_repr()) + a = pd.Series(pd.Categorical(["a","b"] *25, name="a")) + exp = u("0 a\n1 b\n" + " ..\n" + + "48 a\n49 b\n" + + "Name: a, dtype: category\nCategories (2, object): [a, b]") + with option_context("display.max_rows", 5): + self.assertEqual(exp, repr(a)) levs = list("abcdefghijklmnopqrstuvwxyz") a = pd.Series(pd.Categorical(["a","b"], name="a", categories=levs, ordered=True)) @@ -1573,7 +1574,6 @@ def test_repr(self): "Categories (26, object): [a < b < c < d ... w < x < y < z]") self.assertEqual(exp,a.__unicode__()) - def test_info(self): # make sure it works diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index b52e4f7e3947b..94a7dd4dd9e87 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -2438,16 +2438,16 @@ def test_to_string(self): # pass float_format format = '%.4f'.__mod__ result = self.ts.to_string(float_format=format) - result = [x.split()[1] for x in result.split('\n')] + result = [x.split()[1] for x in result.split('\n')[:-1]] expected = [format(x) for x in self.ts] self.assertEqual(result, expected) # empty string result = self.ts[:0].to_string() - self.assertEqual(result, '') + self.assertEqual(result, 'Series([], Freq: B)') result = self.ts[:0].to_string(length=0) - self.assertEqual(result, '') + self.assertEqual(result, 'Series([], Freq: B)') # name and length cp = self.ts.copy() @@ -2623,7 +2623,7 @@ def test_max_multi_index_display(self): with option_context("display.max_rows", 2): self.assertEqual(len(str(s).split('\n')),5) with option_context("display.max_rows", 1): - self.assertEqual(len(str(s).split('\n')),5) + self.assertEqual(len(str(s).split('\n')),4) with option_context("display.max_rows", 0): self.assertEqual(len(str(s).split('\n')),10) @@ -2637,10 +2637,137 @@ def test_max_multi_index_display(self): with option_context("display.max_rows", 2): self.assertEqual(len(str(s).split('\n')),4) with option_context("display.max_rows", 1): - self.assertEqual(len(str(s).split('\n')),4) + self.assertEqual(len(str(s).split('\n')),3) with option_context("display.max_rows", 0): self.assertEqual(len(str(s).split('\n')),9) + # Make sure #8532 is fixed + def test_consistent_format(self): + s = pd.Series([1,1,1,1,1,1,1,1,1,1,0.9999,1,1]*10) + with option_context("display.max_rows", 10): + res = repr(s) + exp = ('0 1.0000\n1 1.0000\n2 1.0000\n3 ' + '1.0000\n4 1.0000\n ... \n125 ' + '1.0000\n126 1.0000\n127 0.9999\n128 ' + '1.0000\n129 1.0000\ndtype: float64') + self.assertEqual(res, exp) + + @staticmethod + def gen_test_series(): + s1 = pd.Series(['a']*100) + s2 = pd.Series(['ab']*100) + s3 = pd.Series(['a', 'ab', 'abc', 'abcd', 'abcde', 'abcdef']) + s4 = s3[::-1] + test_sers = {'onel': s1, 'twol': s2, 'asc': s3, 'desc': s4} + return test_sers + + def chck_ncols(self, s): + with option_context("display.max_rows", 10): + res = repr(s) + lines = res.split('\n') + lines = [line for line in repr(s).split('\n') \ + if not re.match('[^\.]*\.+', line)][:-1] + ncolsizes = len(set(len(line.strip()) for line in lines)) + self.assertEqual(ncolsizes, 1) + + def test_format_explicit(self): + test_sers = self.gen_test_series() + with option_context("display.max_rows", 4): + res = repr(test_sers['onel']) + exp = '0 a\n1 a\n ..\n98 a\n99 a\ndtype: object' + self.assertEqual(exp, res) + res = repr(test_sers['twol']) + exp = ('0 ab\n1 ab\n ..\n98 ab\n99 ab\ndtype:' + ' object') + self.assertEqual(exp, res) + res = repr(test_sers['asc']) + exp = ('0 a\n1 ab\n ... \n4 abcde\n5' + ' abcdef\ndtype: object') + self.assertEqual(exp, res) + res = repr(test_sers['desc']) + exp = ('5 abcdef\n4 abcde\n ... \n1 ab\n0' + ' a\ndtype: object') + self.assertEqual(exp, res) + + def test_ncols(self): + test_sers = self.gen_test_series() + for s in test_sers.values(): + self.chck_ncols(s) + + def test_max_rows_eq_one(self): + s = Series(range(10)) + with option_context("display.max_rows", 1): + strrepr = repr(s).split('\n') + exp1 = ['0', '0'] + res1 = strrepr[0].split() + self.assertEqual(exp1, res1) + exp2 = ['..'] + res2 = strrepr[1].split() + self.assertEqual(exp2, res2) + + def test_truncate_ndots(self): + def getndots(s): + return len(re.match('[^\.]*(\.*)', s).groups()[0]) + + s = Series([0, 2, 3, 6]) + with option_context("display.max_rows", 2): + strrepr = repr(s).replace('\n', '') + self.assertEqual(getndots(strrepr), 2) + + s = Series([0, 100, 200, 400]) + with option_context("display.max_rows", 2): + strrepr = repr(s).replace('\n', '') + self.assertEqual(getndots(strrepr), 3) + + def test_to_string_name(self): + s = Series(range(100)) + s.name = 'myser' + res = s.to_string(max_rows=2, name=True) + exp = '0 0\n ..\n99 99\nName: myser' + self.assertEqual(res, exp) + res = s.to_string(max_rows=2, name=False) + exp = '0 0\n ..\n99 99' + self.assertEqual(res, exp) + + def test_to_string_dtype(self): + s = Series(range(100)) + res = s.to_string(max_rows=2, dtype=True) + exp = '0 0\n ..\n99 99\ndtype: int64' + self.assertEqual(res, exp) + res = s.to_string(max_rows=2, dtype=False) + exp = '0 0\n ..\n99 99' + self.assertEqual(res, exp) + + def test_to_string_length(self): + s = Series(range(100)) + res = s.to_string(max_rows=2, length=True) + exp = '0 0\n ..\n99 99\nLength: 100' + self.assertEqual(res, exp) + + def test_to_string_na_rep(self): + s = pd.Series(index=range(100)) + res = s.to_string(na_rep='foo', max_rows=2) + exp = '0 foo\n ..\n99 foo' + self.assertEqual(res, exp) + + def test_to_string_float_format(self): + s = pd.Series(range(10), dtype=float) + res = s.to_string(float_format=lambda x: '{0:2.1f}'.format(x), + max_rows=2) + exp = '0 0.0\n ..\n9 9.0' + self.assertEqual(res, exp) + + def test_to_string_header(self): + s = pd.Series(range(10)) + s.index.name = 'foo' + res = s.to_string(header=True, max_rows=2) + exp = 'foo\n0 0\n ..\n9 9' + self.assertEqual(res, exp) + res = s.to_string(header=False, max_rows=2) + exp = '0 0\n ..\n9 9' + self.assertEqual(res, exp) + + class TestEngFormatter(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index 18baa941b814a..ee6140828882c 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -3751,14 +3751,6 @@ def f(): assert_series_equal(s,df.iloc[:,0].order()) assert_series_equal(s,df[0].order()) - # operating on a copy - df = pd.DataFrame({'a': list(range(4)), 'b': list('ab..'), 'c': ['a', 'b', np.nan, 'd']}) - mask = pd.isnull(df.c) - - def f(): - df[['c']][mask] = df[['b']][mask] - self.assertRaises(com.SettingWithCopyError, f) - # false positives GH6025 df = DataFrame ({'column1':['a', 'a', 'a'], 'column2': [4,8,9] }) str(df) @@ -3790,6 +3782,24 @@ def f(): df['C'][2] = 'foo' self.assertRaises(com.SettingWithCopyError, f) + def test_setting_with_copy_bug(self): + + # operating on a copy + df = pd.DataFrame({'a': list(range(4)), 'b': list('ab..'), 'c': ['a', 'b', np.nan, 'd']}) + mask = pd.isnull(df.c) + + def f(): + df[['c']][mask] = df[['b']][mask] + self.assertRaises(com.SettingWithCopyError, f) + + # invalid warning as we are returning a new object + # GH 8730 + df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])}) + df2 = df1[['x']] + + # this should not raise + df2['y'] = ['g', 'h', 'i'] + def test_detect_chained_assignment_warnings(self): # warnings diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index 7e0dbaa735456..ae2ed4eaca2f4 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -2046,7 +2046,7 @@ def test_repr(self): # with empty series (#4651) s = Series([], dtype=np.int64, name='foo') - self.assertEqual(repr(s), 'Series([], name: foo, dtype: int64)') + self.assertEqual(repr(s), 'Series([], Name: foo, dtype: int64)') s = Series([], dtype=np.int64, name=None) self.assertEqual(repr(s), 'Series([], dtype: int64)')
closes https://github.com/pydata/pandas/issues/9635 Small change to fix issue 9635. I'm a beginner contributor, feel free to crush it out.
https://api.github.com/repos/pandas-dev/pandas/pulls/9662
2015-03-16T11:49:28Z
2015-03-17T12:41:39Z
null
2015-03-17T12:41:39Z
DOC: example of pandas to R transfer of DataFrame using HDF5 file
diff --git a/doc/source/comparison_with_r.rst b/doc/source/comparison_with_r.rst index 8462bacef47d2..0841f3354d160 100644 --- a/doc/source/comparison_with_r.rst +++ b/doc/source/comparison_with_r.rst @@ -27,6 +27,10 @@ libraries, we care about the following things: This page is also here to offer a bit of a translation guide for users of these R packages. +For transfer of ``DataFrame`` objects from ``pandas`` to R, one option is to +use HDF5 files, see :ref:`io.external_compatibility` for an +example. + Base R ------ diff --git a/doc/source/io.rst b/doc/source/io.rst index 2b13131c1d576..e649287befd1a 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -3271,20 +3271,31 @@ You could inadvertently turn an actual ``nan`` value into a missing value. store.append('dfss2', dfss, nan_rep='_nan_') store.select('dfss2') +.. _io.external_compatibility: + External Compatibility ~~~~~~~~~~~~~~~~~~~~~~ -``HDFStore`` write ``table`` format objects in specific formats suitable for +``HDFStore`` writes ``table`` format objects in specific formats suitable for producing loss-less round trips to pandas objects. For external compatibility, ``HDFStore`` can read native ``PyTables`` format tables. It is possible to write an ``HDFStore`` object that can easily -be imported into ``R`` using the ``rhdf5`` library. Create a table -format store like this: +be imported into ``R`` using the +``rhdf5`` library (`Package website`_). Create a table format store like this: + +.. _package website: http://www.bioconductor.org/packages/release/bioc/html/rhdf5.html .. ipython:: python + np.random.seed(1) + df_for_r = pd.DataFrame({"first": np.random.rand(100), + "second": np.random.rand(100), + "class": np.random.randint(0, 2, (100,))}, + index=range(100)) + df_for_r.head() + store_export = HDFStore('export.h5') - store_export.append('df_dc', df_dc, data_columns=df_dc.columns) + store_export.append('df_for_r', df_for_r, data_columns=df_dc.columns) store_export .. ipython:: python @@ -3293,7 +3304,63 @@ format store like this: store_export.close() import os os.remove('export.h5') - + +In R this file can be read into a ``data.frame`` object using the ``rhdf5`` +library. The following example function reads the corresponding column names +and data values from the values and assembles them into a ``data.frame``: + + .. code-block:: R + + # Load values and column names for all datasets from corresponding nodes and + # insert them into one data.frame object. + + library(rhdf5) + + loadhdf5data <- function(h5File) { + + listing <- h5ls(h5File) + # Find all data nodes, values are stored in *_values and corresponding column + # titles in *_items + data_nodes <- grep("_values", listing$name) + name_nodes <- grep("_items", listing$name) + data_paths = paste(listing$group[data_nodes], listing$name[data_nodes], sep = "/") + name_paths = paste(listing$group[name_nodes], listing$name[name_nodes], sep = "/") + columns = list() + for (idx in seq(data_paths)) { + # NOTE: matrices returned by h5read have to be transposed to to obtain + # required Fortran order! + data <- data.frame(t(h5read(h5File, data_paths[idx]))) + names <- t(h5read(h5File, name_paths[idx])) + entry <- data.frame(data) + colnames(entry) <- names + columns <- append(columns, entry) + } + + data <- data.frame(columns) + + return(data) + } + +Now you can import the ``DataFrame`` into R: + + .. code-block:: R + + > data = loadhdf5data("transfer.hdf5") + > head(data) + first second class + 1 0.4170220047 0.3266449 0 + 2 0.7203244934 0.5270581 0 + 3 0.0001143748 0.8859421 1 + 4 0.3023325726 0.3572698 1 + 5 0.1467558908 0.9085352 1 + 6 0.0923385948 0.6233601 1 + +.. note:: + The R function lists the entire HDF5 file's contents and assembles the + ``data.frame`` object from all matching nodes, so use this only as a + starting point if you have stored multiple ``DataFrame`` objects to a + single HDF5 file. + Backwards Compatibility ~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 721a49f5d58ce..2f21323a2ddd7 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -198,7 +198,9 @@ Other enhancements - Added ``days_in_month`` (compatibility alias ``daysinmonth``) property to ``Timestamp``, ``DatetimeIndex``, ``Period``, ``PeriodIndex``, and ``Series.dt`` (:issue:`9572`) - Added ``decimal`` option in ``to_csv`` to provide formatting for non-'.' decimal separators (:issue:`781`) - Added ``normalize`` option for ``Timestamp`` to normalized to midnight (:issue:`8794`) - +- Added example for ``DataFrame`` import to R using HDF5 file and ``rhdf5`` + library. See the :ref:`documentation <io.external_compatibility>` for more + (:issue:`9636`). .. _whatsnew_0160.api:
closes #9636 I had some trouble getting the HDF5 import of DataFrame objects to work on the R side (pandas is fine). I've added an example to the documentation with a simple load function in R which assembles an R data.frame from the corresponding *_items and *_values nodes in the HDF5 file. NOTE: Any improvements to the R code are welcome, I'm just starting to work with R. :-)
https://api.github.com/repos/pandas-dev/pandas/pulls/9661
2015-03-16T10:47:12Z
2015-03-17T13:09:55Z
null
2017-09-07T06:24:32Z
API: spurious setting_with_copy warning (GH8730)
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 0eeee8ccfddf6..4790017727005 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -454,6 +454,15 @@ Other API Changes To reproduce the old behavior, simply add more precision to the label (e.g., use ``2000-02-01`` instead of ``2000-02``). +- A Spurious ``SettingWithCopy`` Warning was generated when setting a new item in a frame in some cases (:issue:`8730`) + + The following would previously report a ``SettingWithCopy`` Warning. + + .. ipython:: python + + df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])}) + df2 = df1[['x']] + df2['y'] = ['g', 'h', 'i'] .. _whatsnew_0160.deprecations: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 90a6cf60fa76b..4029aaec78a4a 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -1265,6 +1265,14 @@ def _check_setitem_copy(self, stacklevel=4, t='setting', force=False): except: pass + # we might be a false positive + try: + if self.is_copy().shape == self.shape: + self.is_copy = None + return + except: + pass + # a custom message if isinstance(self.is_copy, string_types): t = self.is_copy @@ -1344,8 +1352,9 @@ def take(self, indices, axis=0, convert=True, is_copy=True): result = self._constructor(new_data).__finalize__(self) # maybe set copy if we didn't actually change the index - if is_copy and not result._get_axis(axis).equals(self._get_axis(axis)): - result._set_is_copy(self) + if is_copy: + if not result._get_axis(axis).equals(self._get_axis(axis)): + result._set_is_copy(self) return result diff --git a/pandas/tests/test_indexing.py b/pandas/tests/test_indexing.py index dc77ab8190e41..a633524343a49 100644 --- a/pandas/tests/test_indexing.py +++ b/pandas/tests/test_indexing.py @@ -3751,14 +3751,6 @@ def f(): assert_series_equal(s,df.iloc[:,0].order()) assert_series_equal(s,df[0].order()) - # operating on a copy - df = pd.DataFrame({'a': list(range(4)), 'b': list('ab..'), 'c': ['a', 'b', np.nan, 'd']}) - mask = pd.isnull(df.c) - - def f(): - df[['c']][mask] = df[['b']][mask] - self.assertRaises(com.SettingWithCopyError, f) - # false positives GH6025 df = DataFrame ({'column1':['a', 'a', 'a'], 'column2': [4,8,9] }) str(df) @@ -3790,6 +3782,24 @@ def f(): df['C'][2] = 'foo' self.assertRaises(com.SettingWithCopyError, f) + def test_setting_with_copy_bug(self): + + # operating on a copy + df = pd.DataFrame({'a': list(range(4)), 'b': list('ab..'), 'c': ['a', 'b', np.nan, 'd']}) + mask = pd.isnull(df.c) + + def f(): + df[['c']][mask] = df[['b']][mask] + self.assertRaises(com.SettingWithCopyError, f) + + # invalid warning as we are returning a new object + # GH 8730 + df1 = DataFrame({'x': Series(['a','b','c']), 'y': Series(['d','e','f'])}) + df2 = df1[['x']] + + # this should not raise + df2['y'] = ['g', 'h', 'i'] + def test_detect_chained_assignment_warnings(self): # warnings
closes #8730 was tricky to distinguish this case from others, but I think this is a reasonably robust soln.
https://api.github.com/repos/pandas-dev/pandas/pulls/9659
2015-03-15T15:46:51Z
2015-03-15T19:52:37Z
2015-03-15T19:52:37Z
2015-03-15T19:52:37Z
CI: don't remove python-dateutil at end of install
diff --git a/ci/install_conda.sh b/ci/install_conda.sh index 2a54a038b474e..4c8a62c64979d 100755 --- a/ci/install_conda.sh +++ b/ci/install_conda.sh @@ -98,7 +98,7 @@ fi python setup.py build_ext --inplace && python setup.py develop -for package in beautifulsoup4 'python-dateutil'; do +for package in beautifulsoup4; do pip uninstall --yes $package done
puzzled why the travis ci error started happening. This seems to fix it (and I don't even remember why this was necessary in the first place).
https://api.github.com/repos/pandas-dev/pandas/pulls/9657
2015-03-14T15:13:00Z
2015-03-14T15:33:52Z
2015-03-14T15:33:52Z
2015-03-14T15:33:52Z
DOC: Remove duplicated Indexing Changes section
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 2a83db94aa44c..3273e1b85db82 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -209,72 +209,6 @@ Using ``.components`` allows the full component access t.components t.components.seconds -.. _whatsnew_0160.api_breaking.indexing: - -Indexing Changes -~~~~~~~~~~~~~~~~ - -The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:issue:`8613`). Furthermore we have improved the content of the error messages that are raised: - -- Slicing with ``.loc`` where the start and/or stop bound is not found in the index is now allowed; this previously would raise a ``KeyError``. This makes the behavior the same as ``.ix`` in this case. This change is only for slicing, not when indexing with a single label. - - .. ipython:: python - - df = DataFrame(np.random.randn(5,4), - columns=list('ABCD'), - index=date_range('20130101',periods=5)) - df - s = Series(range(5),[-2,-1,1,2,3]) - s - - Previous Behavior - - .. code-block:: python - - In [4]: df.loc['2013-01-02':'2013-01-10'] - KeyError: 'stop bound [2013-01-10] is not in the [index]' - - In [6]: s.loc[-10:3] - KeyError: 'start bound [-10] is not the [index]' - - New Behavior - - .. ipython:: python - - df.loc['2013-01-02':'2013-01-10'] - s.loc[-10:3] - -- Allow slicing with float-like values on an integer index for ``.ix``. Previously this was only enabled for ``.loc``: - - Previous Behavior - - .. code-block:: python - - In [8]: s.ix[-1.0:2] - TypeError: the slice start value [-1.0] is not a proper indexer for this index type (Int64Index) - - New Behavior - - .. ipython:: python - - s.ix[-1.0:2] - -- Provide a useful exception for indexing with an invalid type for that index when using ``.loc``. For example trying to use ``.loc`` on an index of type ``DatetimeIndex`` or ``PeriodIndex`` or ``TimedeltaIndex``, with an integer (or a float). - - Previous Behavior - - .. code-block:: python - - In [4]: df.loc[2:3] - KeyError: 'start bound [2] is not the [index]' - - New Behavior - - .. code-block:: python - - In [4]: df.loc[2:3] - TypeError: Cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with <type 'int'> keys - .. _whatsnew_0160.api: @@ -436,15 +370,16 @@ Indexing Changes The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:issue:`8613`). Furthermore we have improved the content of the error messages that are raised: -- slicing with ``.loc`` where the start and/or stop bound is not found in the index is now allowed; this previously would raise a ``KeyError``. This makes the behavior the same as ``.ix`` in this case. This change is only for slicing, not when indexing with a single label. +- Slicing with ``.loc`` where the start and/or stop bound is not found in the index is now allowed; this previously would raise a ``KeyError``. This makes the behavior the same as ``.ix`` in this case. This change is only for slicing, not when indexing with a single label. -.. ipython:: python + .. ipython:: python - df = DataFrame(np.random.randn(5,4), + df = DataFrame(np.random.randn(5, 4), columns=list('ABCD'), - index=date_range('20130101',periods=5)) + index=date_range('20130101', periods=5)) df - s = Series(range(5),[-2,-1,1,2,3]) + + s = Series(range(5), [-2,-1,1,2,3]) s Previous Behavior @@ -464,7 +399,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: df.loc['2013-01-02':'2013-01-10'] s.loc[-10:3] -- allow slicing with float-like values on an integer index for ``.ix``. Previously this was only enabled for ``.loc``: +- Allow slicing with float-like values on an integer index for ``.ix``. Previously this was only enabled for ``.loc``: Previous Behavior @@ -479,7 +414,7 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: s.ix[-1.0:2] -- provide a useful exception for indexing with an invalid type for that index when using ``.loc``. For example trying to use ``.loc`` on an index of type ``DatetimeIndex`` or ``PeriodIndex`` or ``TimedeltaIndex``, with an integer (or a float). +- Provide a useful exception for indexing with an invalid type for that index when using ``.loc``. For example trying to use ``.loc`` on an index of type ``DatetimeIndex`` or ``PeriodIndex`` or ``TimedeltaIndex``, with an integer (or a float). Previous Behavior
"Indexing Changes" section is duplicated in what's new. - http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html
https://api.github.com/repos/pandas-dev/pandas/pulls/9655
2015-03-14T01:52:50Z
2015-03-14T01:54:08Z
null
2015-03-16T11:56:11Z
DOC: organize stringmethods in what's new
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 0eeee8ccfddf6..df99148383ade 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -14,6 +14,7 @@ Highlights include: - Backwards incompatible change to ``Timedelta`` to conform the ``.seconds`` attribute with ``datetime.timedelta``, see :ref:`here <whatsnew_0160.api_breaking.timedelta>` - Changes to the ``.loc`` slicing API to conform with the behavior of ``.ix`` see :ref:`here <whatsnew_0160.api_breaking.indexing>` - Changes to the default for ordering in the ``Categorical`` constructor, see :ref:`here <whatsnew_0160.api_breaking.categorical>` +- Enhancement to the ``.str`` accessor to make string operations easier, see :ref:`here <whatsnew_0160.enhancements.string>` Check the :ref:`API Changes <whatsnew_0160.api>` and :ref:`deprecations <whatsnew_0160.deprecations>` before updating. @@ -120,6 +121,45 @@ from a ``scipy.sparse.coo_matrix``: ss = SparseSeries.from_coo(A) ss +.. _whatsnew_0160.enhancements.string: + +String Methods Enhancements +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- Following new methods are accesible via ``.str`` accessor to apply the function to each values. This is intended to make it more consistent with standard methods on strings. (:issue:`9282`, :issue:`9352`, :issue:`9386`, :issue:`9387`, :issue:`9439`) + +============= ============= ============= =============== =============== +.. .. Methods .. .. +============= ============= ============= =============== =============== +``isalnum()`` ``isalpha()`` ``isdigit()`` ``isdigit()`` ``isspace()`` +``islower()`` ``isupper()`` ``istitle()`` ``isnumeric()`` ``isdecimal()`` +``find()`` ``rfind()`` ``ljust()`` ``rjust()`` ``zfill()`` +============= ============= ============= =============== =============== + +.. ipython:: python + + s = Series(['abcd', '3456', 'EFGH']) + s.str.isalpha() + s.str.find('ab') + + +- :meth:`Series.str.pad` and :meth:`Series.str.center` now accept ``fillchar`` option to specify filling character (:issue:`9352`) + +.. ipython:: python + + s = Series(['12', '300', '25']) + s.str.pad(5, fillchar='_') + + +- Added :meth:`Series.str.slice_replace`, which previously raised ``NotImplementedError`` (:issue:`8888`) + +.. ipython:: python + + s = Series(['ABCD', 'EFGH', 'IJK']) + s.str.slice_replace(1, 3, 'X') + # replaced with empty char + s.str.slice_replace(0, 1) + .. _whatsnew_0160.enhancements.other: Other enhancements @@ -137,7 +177,6 @@ Other enhancements - Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>`. (issue:`9493`:) - Paths beginning with ~ will now be expanded to begin with the user's home directory (:issue:`9066`) - Added time interval selection in ``get_data_yahoo`` (:issue:`9071`) -- Added ``Series.str.slice_replace()``, which previously raised ``NotImplementedError`` (:issue:`8888`) - Added ``Timestamp.to_datetime64()`` to complement ``Timedelta.to_timedelta64()`` (:issue:`9255`) - ``tseries.frequencies.to_offset()`` now accepts ``Timedelta`` as input (:issue:`9064`) - Lag parameter was added to the autocorrelation method of ``Series``, defaults to lag-1 autocorrelation (:issue:`9192`) @@ -145,15 +184,8 @@ Other enhancements - SQL code now safely escapes table and column names (:issue:`8986`) - Added auto-complete for ``Series.str.<tab>``, ``Series.dt.<tab>`` and ``Series.cat.<tab>`` (:issue:`9322`) -- Added ``StringMethods.isalnum()``, ``isalpha()``, ``isdigit()``, ``isspace()``, ``islower()``, - ``isupper()``, ``istitle()`` which behave as the same as standard ``str`` (:issue:`9282`) - -- Added ``StringMethods.find()`` and ``rfind()`` which behave as the same as standard ``str`` (:issue:`9386`) - - ``Index.get_indexer`` now supports ``method='pad'`` and ``method='backfill'`` even for any target array, not just monotonic targets. These methods also work for monotonic decreasing as well as monotonic increasing indexes (:issue:`9258`). - ``Index.asof`` now works on all index types (:issue:`9258`). - -- Added ``StringMethods.isnumeric`` and ``isdecimal`` which behave as the same as standard ``str`` (:issue:`9439`) - The ``read_excel()`` function's :ref:`sheetname <_io.specifying_sheets>` argument now accepts a list and ``None``, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (:issue:`9450`) .. code-block:: python @@ -162,9 +194,6 @@ Other enhancements pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) - A ``verbose`` argument has been augmented in ``io.read_excel()``, defaults to False. Set to True to print sheet names as they are parsed. (:issue:`9450`) -- Added ``StringMethods.ljust()`` and ``rjust()`` which behave as the same as standard ``str`` (:issue:`9352`) -- ``StringMethods.pad()`` and ``center()`` now accept ``fillchar`` option to specify filling character (:issue:`9352`) -- Added ``StringMethods.zfill()`` which behave as the same as standard ``str`` (:issue:`9387`) - Added ``days_in_month`` (compatibility alias ``daysinmonth``) property to ``Timestamp``, ``DatetimeIndex``, ``Period``, ``PeriodIndex``, and ``Series.dt`` (:issue:`9572`) - Added ``decimal`` option in ``to_csv`` to provide formatting for non-'.' decimal separators (:issue:`781`) - Added ``normalize`` option for ``Timestamp`` to normalized to midnight (:issue:`8794`)
Added summarized section to the release note based on the discussion in #9386
https://api.github.com/repos/pandas-dev/pandas/pulls/9654
2015-03-14T01:47:54Z
2015-03-16T08:49:53Z
2015-03-16T08:49:53Z
2015-03-16T11:56:04Z
COMPAT: odd arrays can be correctly ordered in newer versions of numpy
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index a7f241168ea73..cd78fd22e64ca 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -86,7 +86,11 @@ def test_constructor_unsortable(self): self.assertFalse(factor.ordered) # this however will raise as cannot be sorted - self.assertRaises(TypeError, lambda : Categorical.from_array(arr, ordered=True)) + # but fixed in newer versions of numpy + if LooseVersion(np.__version__) < "1.10": + self.assertRaises(TypeError, lambda : Categorical.from_array(arr, ordered=True)) + else: + Categorical.from_array(arr, ordered=True) def test_constructor(self):
https://api.github.com/repos/pandas-dev/pandas/pulls/9652
2015-03-13T21:20:32Z
2015-03-13T22:37:33Z
2015-03-13T22:37:33Z
2015-03-13T22:37:33Z
fix crash on unicode level names
diff --git a/pandas/core/index.py b/pandas/core/index.py index 195cb21d6564c..0eddf701995c4 100644 --- a/pandas/core/index.py +++ b/pandas/core/index.py @@ -4014,7 +4014,7 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True): labels = list(self.labels) shape = list(self.levshape) - if isinstance(level, (str, int)): + if isinstance(level, (basestring, int)): level = [level] level = [self._get_level_number(lev) for lev in level]
This fixed my problem when calling DataFrame.stack(<integer level number>). Not really familiar with pandas internals, but couldn't find any reason why unicode strings won't be accepted here.
https://api.github.com/repos/pandas-dev/pandas/pulls/9650
2015-03-13T20:31:02Z
2015-04-11T18:29:26Z
null
2015-04-11T18:29:26Z
DOC: Fix heading level for assign
diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index 6eb13ce722fff..e1c14029f1cf9 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -453,7 +453,7 @@ available to insert at a particular location in the columns: .. _dsintro.chained_assignment: Assigning New Columns in Method Chains --------------------------------------- +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. versionadded:: 0.16.0
Closes https://github.com/pydata/pandas/issues/9646 I did this through the GH online editor. Let's see if it works.
https://api.github.com/repos/pandas-dev/pandas/pulls/9649
2015-03-13T19:48:05Z
2015-03-13T23:46:29Z
2015-03-13T23:46:29Z
2015-03-13T23:46:32Z
PERF: optimize memory usage for to_hdf
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index 4790017727005..54ef3c893f355 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -514,6 +514,7 @@ Performance Improvements - Performance improvements in ``MultiIndex.sortlevel`` (:issue:`9445`) - Performance and memory usage improvements in ``DataFrame.duplicated`` (:issue:`9398`) - Cythonized ``Period`` (:issue:`9440`) +- Decreased memory usage on ``to_hdf`` (:issue:`9648`) .. _whatsnew_0160.bug_fixes: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 4029aaec78a4a..e05709d7a180f 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2014,6 +2014,14 @@ def __setattr__(self, name, value): #---------------------------------------------------------------------- # Consolidation of internals + def _protect_consolidate(self, f): + """ consolidate _data. if the blocks have changed, then clear the cache """ + blocks_before = len(self._data.blocks) + result = f() + if len(self._data.blocks) != blocks_before: + self._clear_item_cache() + return result + def _consolidate_inplace(self): f = lambda: self._data.consolidate() self._data = self._protect_consolidate(f) @@ -2038,8 +2046,6 @@ def consolidate(self, inplace=False): else: f = lambda: self._data.consolidate() cons_data = self._protect_consolidate(f) - if cons_data is self._data: - cons_data = cons_data.copy() return self._constructor(cons_data).__finalize__(self) @property @@ -2075,13 +2081,6 @@ def _check_inplace_setting(self, value): return True - def _protect_consolidate(self, f): - blocks_before = len(self._data.blocks) - result = f() - if len(self._data.blocks) != blocks_before: - self._clear_item_cache() - return result - def _get_numeric_data(self): return self._constructor( self._data.get_numeric_data()).__finalize__(self) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 5cb032521d51a..7a16fb2b6b0d7 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1752,7 +1752,7 @@ def _astype(self, dtype, copy=False, raise_on_error=True, values=None, if self.is_categorical_astype(dtype): values = self.values else: - values = np.array(self.values).astype(dtype) + values = np.asarray(self.values).astype(dtype, copy=False) if copy: values = values.copy() diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py index e784934ea28b2..be81993e1aab7 100644 --- a/pandas/io/pytables.py +++ b/pandas/io/pytables.py @@ -1782,13 +1782,13 @@ def set_atom(self, block, block_items, existing_col, min_itemsize, return self.set_atom_timedelta64(block) dtype = block.dtype.name - rvalues = block.values.ravel() - inferred_type = lib.infer_dtype(rvalues) + inferred_type = lib.infer_dtype(block.values) if inferred_type == 'date': raise TypeError( "[date] is not implemented as a table column") elif inferred_type == 'datetime': + rvalues = block.values.ravel() if getattr(rvalues[0], 'tzinfo', None) is not None: # if this block has more than one timezone, raise @@ -1917,7 +1917,7 @@ def get_atom_data(self, block, kind=None): def set_atom_data(self, block): self.kind = block.dtype.name self.typ = self.get_atom_data(block) - self.set_data(block.values.astype(self.typ.type)) + self.set_data(block.values.astype(self.typ.type, copy=False)) def set_atom_categorical(self, block, items, info=None, values=None): # currently only supports a 1-D categorical @@ -2016,7 +2016,7 @@ def convert(self, values, nan_rep, encoding): index = DatetimeIndex( self.data.ravel(), tz='UTC').tz_convert(self.tz) - self.data = np.array( + self.data = np.asarray( index.tolist(), dtype=object).reshape(self.data.shape) else: @@ -2026,14 +2026,14 @@ def convert(self, values, nan_rep, encoding): self.data = np.asarray(self.data, dtype='m8[ns]') elif dtype == u('date'): try: - self.data = np.array( + self.data = np.asarray( [date.fromordinal(v) for v in self.data], dtype=object) except ValueError: - self.data = np.array( + self.data = np.asarray( [date.fromtimestamp(v) for v in self.data], dtype=object) elif dtype == u('datetime'): - self.data = np.array( + self.data = np.asarray( [datetime.fromtimestamp(v) for v in self.data], dtype=object) @@ -2048,9 +2048,9 @@ def convert(self, values, nan_rep, encoding): else: try: - self.data = self.data.astype(dtype) + self.data = self.data.astype(dtype, copy=False) except: - self.data = self.data.astype('O') + self.data = self.data.astype('O', copy=False) # convert nans / decode if _ensure_decoded(self.kind) == u('string'): @@ -2337,9 +2337,9 @@ def read_array(self, key): ret = data if dtype == u('datetime64'): - ret = np.array(ret, dtype='M8[ns]') + ret = np.asarray(ret, dtype='M8[ns]') elif dtype == u('timedelta64'): - ret = np.array(ret, dtype='m8[ns]') + ret = np.asarray(ret, dtype='m8[ns]') if transposed: return ret.T @@ -3793,7 +3793,7 @@ def write_data(self, chunksize, dropna=True): # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask mask = com.isnull(a.data).all(axis=0) - masks.append(mask.astype('u1')) + masks.append(mask.astype('u1', copy=False)) # consolidate masks mask = masks[0] @@ -3803,8 +3803,7 @@ def write_data(self, chunksize, dropna=True): else: - mask = np.empty(nrows, dtype='u1') - mask.fill(False) + mask = None # broadcast the indexes if needed indexes = [a.cvalues for a in self.index_axes] @@ -3833,12 +3832,13 @@ def write_data(self, chunksize, dropna=True): bvalues = [] for i, v in enumerate(values): new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape - bvalues.append(values[i].ravel().reshape(new_shape)) + bvalues.append(values[i].reshape(new_shape)) # write the chunks if chunksize is None: chunksize = 100000 + rows = np.empty(min(chunksize,nrows), dtype=self.dtype) chunks = int(nrows / chunksize) + 1 for i in range(chunks): start_i = i * chunksize @@ -3847,11 +3847,20 @@ def write_data(self, chunksize, dropna=True): break self.write_data_chunk( + rows, indexes=[a[start_i:end_i] for a in bindexes], - mask=mask[start_i:end_i], + mask=mask[start_i:end_i] if mask is not None else None, values=[v[start_i:end_i] for v in bvalues]) - def write_data_chunk(self, indexes, mask, values): + def write_data_chunk(self, rows, indexes, mask, values): + """ + Parameters + ---------- + rows : an empty memory space where we are putting the chunk + indexes : an array of the indexes + mask : an array of the masks + values : an array of the values + """ # 0 len for v in values: @@ -3860,7 +3869,8 @@ def write_data_chunk(self, indexes, mask, values): try: nrows = indexes[0].shape[0] - rows = np.empty(nrows, dtype=self.dtype) + if nrows != len(rows): + rows = np.empty(nrows, dtype=self.dtype) names = self.dtype.names nindexes = len(indexes) @@ -3873,7 +3883,10 @@ def write_data_chunk(self, indexes, mask, values): rows[names[i + nindexes]] = v # mask - rows = rows[~mask.ravel().astype(bool)] + if mask is not None: + m = ~mask.ravel().astype(bool, copy=False) + if not m.all(): + rows = rows[m] except Exception as detail: raise Exception("cannot create row-data -> %s" % detail) @@ -4240,14 +4253,14 @@ def _convert_index(index, encoding=None, format_type=None): tz=getattr(index, 'tz', None), index_name=index_name) elif inferred_type == 'datetime': - converted = np.array([(time.mktime(v.timetuple()) + - v.microsecond / 1E6) for v in values], - dtype=np.float64) + converted = np.asarray([(time.mktime(v.timetuple()) + + v.microsecond / 1E6) for v in values], + dtype=np.float64) return IndexCol(converted, 'datetime', _tables().Time64Col(), index_name=index_name) elif inferred_type == 'date': - converted = np.array([v.toordinal() for v in values], - dtype=np.int32) + converted = np.asarray([v.toordinal() for v in values], + dtype=np.int32) return IndexCol(converted, 'date', _tables().Time32Col(), index_name=index_name) elif inferred_type == 'string': @@ -4290,21 +4303,21 @@ def _unconvert_index(data, kind, encoding=None): if kind == u('datetime64'): index = DatetimeIndex(data) elif kind == u('datetime'): - index = np.array([datetime.fromtimestamp(v) for v in data], - dtype=object) + index = np.asarray([datetime.fromtimestamp(v) for v in data], + dtype=object) elif kind == u('date'): try: - index = np.array( + index = np.asarray( [date.fromordinal(v) for v in data], dtype=object) except (ValueError): - index = np.array( + index = np.asarray( [date.fromtimestamp(v) for v in data], dtype=object) elif kind in (u('integer'), u('float')): - index = np.array(data) + index = np.asarray(data) elif kind in (u('string')): index = _unconvert_string_array(data, nan_rep=None, encoding=encoding) elif kind == u('object'): - index = np.array(data[0]) + index = np.asarray(data[0]) else: # pragma: no cover raise ValueError('unrecognized index type %s' % kind) return index @@ -4315,7 +4328,7 @@ def _unconvert_index_legacy(data, kind, legacy=False, encoding=None): if kind == u('datetime'): index = lib.time64_to_datetime(data) elif kind in (u('integer')): - index = np.array(data, dtype=object) + index = np.asarray(data, dtype=object) elif kind in (u('string')): index = _unconvert_string_array(data, nan_rep=None, encoding=encoding) else: # pragma: no cover @@ -4334,13 +4347,13 @@ def _convert_string_array(data, encoding, itemsize=None): if itemsize is None: itemsize = lib.max_len_string_array(com._ensure_object(data.ravel())) - data = np.array(data, dtype="S%d" % itemsize) + data = np.asarray(data, dtype="S%d" % itemsize) return data def _unconvert_string_array(data, nan_rep=None, encoding=None): """ deserialize a string array, possibly decoding """ shape = data.shape - data = np.array(data.ravel(), dtype=object) + data = np.asarray(data.ravel(), dtype=object) # guard against a None encoding in PY3 (because of a legacy # where the passed encoding is actually None) @@ -4353,7 +4366,7 @@ def _unconvert_string_array(data, nan_rep=None, encoding=None): dtype = "U{0}".format(itemsize) else: dtype = "S{0}".format(itemsize) - data = data.astype(dtype).astype(object) + data = data.astype(dtype, copy=False).astype(object, copy=False) except (Exception) as e: f = np.vectorize(lambda x: x.decode(encoding), otypes=[np.object]) data = f(data) @@ -4376,7 +4389,7 @@ def _maybe_convert(values, val_kind, encoding): def _get_converter(kind, encoding): kind = _ensure_decoded(kind) if kind == 'datetime64': - return lambda x: np.array(x, dtype='M8[ns]') + return lambda x: np.asarray(x, dtype='M8[ns]') elif kind == 'datetime': return lib.convert_timestamps elif kind == 'string': @@ -4421,7 +4434,7 @@ def __init__(self, table, where=None, start=None, stop=None, **kwargs): try: inferred = lib.infer_dtype(where) if inferred == 'integer' or inferred == 'boolean': - where = np.array(where) + where = np.asarray(where) if where.dtype == np.bool_: start, stop = self.start, self.stop if start is None: diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py index e95d46f66f17f..41287ebba1ba3 100644 --- a/pandas/io/tests/test_pytables.py +++ b/pandas/io/tests/test_pytables.py @@ -4593,12 +4593,17 @@ def test_categorical(self): with ensure_clean_store(self.path) as store: - s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'])) - + # basic + s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'], ordered=False)) store.append('s', s, format='table') result = store.select('s') tm.assert_series_equal(s, result) + s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=['a','b','c','d'], ordered=True)) + store.append('s_ordered', s, format='table') + result = store.select('s_ordered') + tm.assert_series_equal(s, result) + df = DataFrame({"s":s, "vals":[1,2,3,4,5,6]}) store.append('df', df, format='table') result = store.select('df') @@ -4639,6 +4644,10 @@ def test_categorical(self): result = store.select('df3', where = ['s in ["b","c"]']) tm.assert_frame_equal(result, expected) + expected = df[df.s.isin(['b','c'])] + result = store.select('df3', where = ['s = ["b","c"]']) + tm.assert_frame_equal(result, expected) + expected = df[df.s.isin(['d'])] result = store.select('df3', where = ['s in ["d"]']) tm.assert_frame_equal(result, expected)
from [here](http://stackoverflow.com/questions/29016093/pandas-pytable-memory-overhead-when-writing-to-hdf/29035532#29035532) reduce memeory usage necessary for using `to_hdf` - was copying always in astyping - was ravelling then reshaping - was constantly allocating a new chunked buffer, now re-uses the same buffer ``` In [1]: df = pd.DataFrame(np.random.rand(1000000,500)) df.info() In [2]: df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 1000000 entries, 0 to 999999 Columns: 500 entries, 0 to 499 dtypes: float64(500) memory usage: 3.7 GB ``` Previously ``` In [3]: %memit -r 1 df.to_hdf('test.h5','df',format='table',mode='w') peak memory: 11029.49 MiB, increment: 7130.57 MiB ``` With PR ``` In [2]: memit -r 1 df.to_hdf('test.h5','df',format='table',mode='w') peak memory: 4669.21 MiB, increment: 794.57 MiB ```
https://api.github.com/repos/pandas-dev/pandas/pulls/9648
2015-03-13T18:27:16Z
2015-03-16T00:07:22Z
2015-03-16T00:07:22Z
2019-04-22T07:41:24Z
Allow clip{,_lower,_upper} to use array-like thresholds (GH 6966)
diff --git a/doc/source/whatsnew/v0.16.1.txt b/doc/source/whatsnew/v0.16.1.txt index c0408c4123eab..b99f3e2cd7059 100755 --- a/doc/source/whatsnew/v0.16.1.txt +++ b/doc/source/whatsnew/v0.16.1.txt @@ -23,6 +23,7 @@ Enhancements - Added ``StringMethods.capitalize()`` and ``swapcase`` which behave as the same as standard ``str`` (:issue:`9766`) - Added ``StringMethods`` (.str accessor) to ``Index`` (:issue:`9068`) +- 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/generic.py b/pandas/core/generic.py index 681cfc0f7a416..d7defd2b417da 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2821,37 +2821,77 @@ def notnull(self): """ return notnull(self).__finalize__(self) - def clip(self, lower=None, upper=None, out=None): + def clip(self, lower=None, upper=None, out=None, axis=None): """ Trim values at input threshold(s) Parameters ---------- - lower : float, default None - upper : float, default None + lower : float or array_like, default None + upper : float or array_like, default None + axis : int or string axis name, optional + Align object with lower and upper along the given axis. Returns ------- clipped : Series + + Examples + -------- + >>> df + 0 1 + 0 0.335232 -1.256177 + 1 -1.367855 0.746646 + 2 0.027753 -1.176076 + 3 0.230930 -0.679613 + 4 1.261967 0.570967 + >>> df.clip(-1.0, 0.5) + 0 1 + 0 0.335232 -1.000000 + 1 -1.000000 0.500000 + 2 0.027753 -1.000000 + 3 0.230930 -0.679613 + 4 0.500000 0.500000 + >>> t + 0 -0.3 + 1 -0.2 + 2 -0.1 + 3 0.0 + 4 0.1 + dtype: float64 + >>> df.clip(t, t + 1, axis=0) + 0 1 + 0 0.335232 -0.300000 + 1 -0.200000 0.746646 + 2 0.027753 -0.100000 + 3 0.230930 0.000000 + 4 1.100000 0.570967 """ if out is not None: # pragma: no cover raise Exception('out argument is not supported yet') # GH 2747 (arguments were reversed) if lower is not None and upper is not None: - lower, upper = min(lower, upper), max(lower, upper) + if lib.isscalar(lower) and lib.isscalar(upper): + lower, upper = min(lower, upper), max(lower, upper) result = self if lower is not None: - result = result.clip_lower(lower) + result = result.clip_lower(lower, axis) if upper is not None: - result = result.clip_upper(upper) + result = result.clip_upper(upper, axis) return result - def clip_upper(self, threshold): + def clip_upper(self, threshold, axis=None): """ - Return copy of input with values above given value truncated + Return copy of input with values above given value(s) truncated + + Parameters + ---------- + threshold : float or array_like + axis : int or string axis name, optional + Align object with threshold along the given axis. See also -------- @@ -2861,14 +2901,21 @@ def clip_upper(self, threshold): ------- clipped : same type as input """ - if isnull(threshold): + if np.any(isnull(threshold)): raise ValueError("Cannot use an NA value as a clip threshold") - return self.where((self <= threshold) | isnull(self), threshold) + subset = self.le(threshold, axis=axis) | isnull(self) + return self.where(subset, threshold, axis=axis) - def clip_lower(self, threshold): + def clip_lower(self, threshold, axis=None): """ - Return copy of the input with values below given value truncated + Return copy of the input with values below given value(s) truncated + + Parameters + ---------- + threshold : float or array_like + axis : int or string axis name, optional + Align object with threshold along the given axis. See also -------- @@ -2878,10 +2925,11 @@ def clip_lower(self, threshold): ------- clipped : same type as input """ - if isnull(threshold): + if np.any(isnull(threshold)): raise ValueError("Cannot use an NA value as a clip threshold") - return self.where((self >= threshold) | isnull(self), threshold) + subset = self.ge(threshold, axis=axis) | isnull(self) + return self.where(subset, threshold, axis=axis) def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False): diff --git a/pandas/core/ops.py b/pandas/core/ops.py index 2af9cd43faaef..a4c9bff3dd97f 100644 --- a/pandas/core/ops.py +++ b/pandas/core/ops.py @@ -571,7 +571,11 @@ def na_op(x, y): return result - def wrapper(self, other): + def wrapper(self, other, axis=None): + # Validate the axis parameter + if axis is not None: + self._get_axis_number(axis) + if isinstance(other, pd.Series): name = _maybe_match_name(self, other) if len(self) != len(other): diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py index 5912ccb1494fe..e88bf9da9791d 100644 --- a/pandas/tests/test_frame.py +++ b/pandas/tests/test_frame.py @@ -11394,6 +11394,39 @@ def test_dataframe_clip(self): self.assertTrue((clipped_df.values[ub_mask] == ub).all() == True) self.assertTrue((clipped_df.values[mask] == df.values[mask]).all() == True) + def test_clip_against_series(self): + # GH #6966 + + df = DataFrame(np.random.randn(1000, 2)) + lb = Series(np.random.randn(1000)) + ub = lb + 1 + + clipped_df = df.clip(lb, ub, axis=0) + + for i in range(2): + lb_mask = df.iloc[:, i] <= lb + ub_mask = df.iloc[:, i] >= ub + mask = ~lb_mask & ~ub_mask + + assert_series_equal(clipped_df.loc[lb_mask, i], lb[lb_mask]) + assert_series_equal(clipped_df.loc[ub_mask, i], ub[ub_mask]) + assert_series_equal(clipped_df.loc[mask, i], df.loc[mask, i]) + + def test_clip_against_frame(self): + df = DataFrame(np.random.randn(1000, 2)) + lb = DataFrame(np.random.randn(1000, 2)) + ub = lb + 1 + + clipped_df = df.clip(lb, ub) + + lb_mask = df <= lb + ub_mask = df >= ub + mask = ~lb_mask & ~ub_mask + + assert_frame_equal(clipped_df[lb_mask], lb[lb_mask]) + assert_frame_equal(clipped_df[ub_mask], ub[ub_mask]) + assert_frame_equal(clipped_df[mask], df[mask]) + def test_get_X_columns(self): # numeric and object columns diff --git a/pandas/tests/test_series.py b/pandas/tests/test_series.py index f1a9e23796804..aa95986be0722 100644 --- a/pandas/tests/test_series.py +++ b/pandas/tests/test_series.py @@ -5037,6 +5037,20 @@ def test_clip_types_and_nulls(self): self.assertEqual(list(isnull(s)), list(isnull(l))) self.assertEqual(list(isnull(s)), list(isnull(u))) + def test_clip_against_series(self): + # GH #6966 + + s = Series([1.0, 1.0, 4.0]) + threshold = Series([1.0, 2.0, 3.0]) + + assert_series_equal(s.clip_lower(threshold), Series([1.0, 2.0, 4.0])) + assert_series_equal(s.clip_upper(threshold), Series([1.0, 1.0, 3.0])) + + lower = Series([1.0, 2.0, 3.0]) + upper = Series([1.5, 2.5, 3.5]) + assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5])) + assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5])) + def test_valid(self): ts = self.ts.copy() ts[::2] = np.NaN
closes #6966
https://api.github.com/repos/pandas-dev/pandas/pulls/9647
2015-03-13T16:55:41Z
2015-04-28T11:56:04Z
2015-04-28T11:56:04Z
2015-09-19T00:38:04Z
DOC: reorg whatsnew file v0.16.0
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt index d60fa718ae07c..2cb1df2cef202 100644 --- a/doc/source/whatsnew/v0.16.0.txt +++ b/doc/source/whatsnew/v0.16.0.txt @@ -7,75 +7,29 @@ This is a major release from 0.15.2 and includes a small number of API changes, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. -- Highlights include: +Highlights include: - * ``DataFrame.assign`` method, see :ref:`here <whatsnew_0160.enhancements.assign>` - * ``Series.to_coo/from_coo`` methods to interact with ``scipy.sparse``, see :ref:`here <whatsnew_0160.enhancements.sparse>` - * Backwards incompatible change to ``Timedelta`` to conform the ``.seconds`` attribute with ``datetime.timedelta``, see :ref:`here <whatsnew_0160.api_breaking.timedelta>` - * Changes to the ``.loc`` slicing API to conform with the behavior of ``.ix`` see :ref:`here <whatsnew_0160.api_breaking.indexing>` - * Changes to the default for ordering in the ``Categorical`` constructor, see :ref:`here <whatsnew_0160.api_breaking.categorical>` +- ``DataFrame.assign`` method, see :ref:`here <whatsnew_0160.enhancements.assign>` +- ``Series.to_coo/from_coo`` methods to interact with ``scipy.sparse``, see :ref:`here <whatsnew_0160.enhancements.sparse>` +- Backwards incompatible change to ``Timedelta`` to conform the ``.seconds`` attribute with ``datetime.timedelta``, see :ref:`here <whatsnew_0160.api_breaking.timedelta>` +- Changes to the ``.loc`` slicing API to conform with the behavior of ``.ix`` see :ref:`here <whatsnew_0160.api_breaking.indexing>` +- Changes to the default for ordering in the ``Categorical`` constructor, see :ref:`here <whatsnew_0160.api_breaking.categorical>` -- Check the :ref:`API Changes <whatsnew_0160.api>` and :ref:`deprecations <whatsnew_0160.deprecations>` before updating +Check the :ref:`API Changes <whatsnew_0160.api>` and :ref:`deprecations <whatsnew_0160.deprecations>` before updating. -- :ref:`Other Enhancements <whatsnew_0160.enhancements>` +.. contents:: What's new in v0.16.0 + :local: -- :ref:`Performance Improvements <whatsnew_0160.performance>` - -- :ref:`Bug Fixes <whatsnew_0160.bug_fixes>` .. _whatsnew_0160.enhancements: New features ~~~~~~~~~~~~ -- Reindex now supports ``method='nearest'`` for frames or series with a monotonic increasing or decreasing index (:issue:`9258`): - - .. ipython:: python - - df = pd.DataFrame({'x': range(5)}) - df.reindex([0.2, 1.8, 3.5], method='nearest') - - This method is also exposed by the lower level ``Index.get_indexer`` and ``Index.get_loc`` methods. - -- Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>`. (issue:`9493`:) -- Paths beginning with ~ will now be expanded to begin with the user's home directory (:issue:`9066`) -- Added time interval selection in ``get_data_yahoo`` (:issue:`9071`) -- Added ``Series.str.slice_replace()``, which previously raised ``NotImplementedError`` (:issue:`8888`) -- Added ``Timestamp.to_datetime64()`` to complement ``Timedelta.to_timedelta64()`` (:issue:`9255`) -- ``tseries.frequencies.to_offset()`` now accepts ``Timedelta`` as input (:issue:`9064`) -- Lag parameter was added to the autocorrelation method of ``Series``, defaults to lag-1 autocorrelation (:issue:`9192`) -- ``Timedelta`` will now accept ``nanoseconds`` keyword in constructor (:issue:`9273`) -- SQL code now safely escapes table and column names (:issue:`8986`) - -- Added auto-complete for ``Series.str.<tab>``, ``Series.dt.<tab>`` and ``Series.cat.<tab>`` (:issue:`9322`) -- Added ``StringMethods.isalnum()``, ``isalpha()``, ``isdigit()``, ``isspace()``, ``islower()``, - ``isupper()``, ``istitle()`` which behave as the same as standard ``str`` (:issue:`9282`) - -- Added ``StringMethods.find()`` and ``rfind()`` which behave as the same as standard ``str`` (:issue:`9386`) - -- ``Index.get_indexer`` now supports ``method='pad'`` and ``method='backfill'`` even for any target array, not just monotonic targets. These methods also work for monotonic decreasing as well as monotonic increasing indexes (:issue:`9258`). -- ``Index.asof`` now works on all index types (:issue:`9258`). - -- Added ``StringMethods.isnumeric`` and ``isdecimal`` which behave as the same as standard ``str`` (:issue:`9439`) -- The ``read_excel()`` function's :ref:`sheetname <_io.specifying_sheets>` argument now accepts a list and ``None``, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (:issue:`9450`) - - .. code-block:: python - - # Returns the 1st and 4th sheet, as a dictionary of DataFrames. - pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) - -- A ``verbose`` argument has been augmented in ``io.read_excel()``, defaults to False. Set to True to print sheet names as they are parsed. (:issue:`9450`) -- Added ``StringMethods.ljust()`` and ``rjust()`` which behave as the same as standard ``str`` (:issue:`9352`) -- ``StringMethods.pad()`` and ``center()`` now accept ``fillchar`` option to specify filling character (:issue:`9352`) -- Added ``StringMethods.zfill()`` which behave as the same as standard ``str`` (:issue:`9387`) -- Added ``days_in_month`` (compatibility alias ``daysinmonth``) property to ``Timestamp``, ``DatetimeIndex``, ``Period``, ``PeriodIndex``, and ``Series.dt`` (:issue:`9572`) -- Added ``decimal`` option in ``to_csv`` to provide formatting for non-'.' decimal separators (:issue:`781`) -- Added ``normalize`` option for ``Timestamp`` to normalized to midnight (:issue:`8794`) - .. _whatsnew_0160.enhancements.assign: DataFrame Assign -~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^ Inspired by `dplyr's <http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html#mutate>`__ ``mutate`` verb, DataFrame has a new @@ -121,7 +75,7 @@ See the :ref:`documentation <dsintro.chained_assignment>` for more. (:issue:`922 .. _whatsnew_0160.enhancements.sparse: Interaction with scipy.sparse -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Added :meth:`SparseSeries.to_coo` and :meth:`SparseSeries.from_coo` methods (:issue:`8048`) for converting to and from ``scipy.sparse.coo_matrix`` instances (see :ref:`here <sparse.scipysparse>`). For example, given a SparseSeries with MultiIndex we can convert to a `scipy.sparse.coo_matrix` by specifying the row and column labels as index levels: @@ -166,12 +120,67 @@ from a ``scipy.sparse.coo_matrix``: ss = SparseSeries.from_coo(A) ss +.. _whatsnew_0160.enhancements.other: + +Other enhancements +^^^^^^^^^^^^^^^^^^ + +- Reindex now supports ``method='nearest'`` for frames or series with a monotonic increasing or decreasing index (:issue:`9258`): + + .. ipython:: python + + df = pd.DataFrame({'x': range(5)}) + df.reindex([0.2, 1.8, 3.5], method='nearest') + + This method is also exposed by the lower level ``Index.get_indexer`` and ``Index.get_loc`` methods. + +- Allow Stata files to be read incrementally with an iterator; support for long strings in Stata files. See the docs :ref:`here<io.stata_reader>`. (issue:`9493`:) +- Paths beginning with ~ will now be expanded to begin with the user's home directory (:issue:`9066`) +- Added time interval selection in ``get_data_yahoo`` (:issue:`9071`) +- Added ``Series.str.slice_replace()``, which previously raised ``NotImplementedError`` (:issue:`8888`) +- Added ``Timestamp.to_datetime64()`` to complement ``Timedelta.to_timedelta64()`` (:issue:`9255`) +- ``tseries.frequencies.to_offset()`` now accepts ``Timedelta`` as input (:issue:`9064`) +- Lag parameter was added to the autocorrelation method of ``Series``, defaults to lag-1 autocorrelation (:issue:`9192`) +- ``Timedelta`` will now accept ``nanoseconds`` keyword in constructor (:issue:`9273`) +- SQL code now safely escapes table and column names (:issue:`8986`) + +- Added auto-complete for ``Series.str.<tab>``, ``Series.dt.<tab>`` and ``Series.cat.<tab>`` (:issue:`9322`) +- Added ``StringMethods.isalnum()``, ``isalpha()``, ``isdigit()``, ``isspace()``, ``islower()``, + ``isupper()``, ``istitle()`` which behave as the same as standard ``str`` (:issue:`9282`) + +- Added ``StringMethods.find()`` and ``rfind()`` which behave as the same as standard ``str`` (:issue:`9386`) + +- ``Index.get_indexer`` now supports ``method='pad'`` and ``method='backfill'`` even for any target array, not just monotonic targets. These methods also work for monotonic decreasing as well as monotonic increasing indexes (:issue:`9258`). +- ``Index.asof`` now works on all index types (:issue:`9258`). + +- Added ``StringMethods.isnumeric`` and ``isdecimal`` which behave as the same as standard ``str`` (:issue:`9439`) +- The ``read_excel()`` function's :ref:`sheetname <_io.specifying_sheets>` argument now accepts a list and ``None``, to get multiple or all sheets respectively. If more than one sheet is specified, a dictionary is returned. (:issue:`9450`) + + .. code-block:: python + + # Returns the 1st and 4th sheet, as a dictionary of DataFrames. + pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) + +- A ``verbose`` argument has been augmented in ``io.read_excel()``, defaults to False. Set to True to print sheet names as they are parsed. (:issue:`9450`) +- Added ``StringMethods.ljust()`` and ``rjust()`` which behave as the same as standard ``str`` (:issue:`9352`) +- ``StringMethods.pad()`` and ``center()`` now accept ``fillchar`` option to specify filling character (:issue:`9352`) +- Added ``StringMethods.zfill()`` which behave as the same as standard ``str`` (:issue:`9387`) +- Added ``days_in_month`` (compatibility alias ``daysinmonth``) property to ``Timestamp``, ``DatetimeIndex``, ``Period``, ``PeriodIndex``, and ``Series.dt`` (:issue:`9572`) +- Added ``decimal`` option in ``to_csv`` to provide formatting for non-'.' decimal separators (:issue:`781`) +- Added ``normalize`` option for ``Timestamp`` to normalized to midnight (:issue:`8794`) + + +.. _whatsnew_0160.api: + +Backwards incompatible API changes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + .. _whatsnew_0160.api_breaking: .. _whatsnew_0160.api_breaking.timedelta: Changes in Timedelta -~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^ In v0.15.0 a new scalar type ``Timedelta`` was introduced, that is a sub-class of ``datetime.timedelta``. Mentioned :ref:`here <whatsnew_0150.timedeltaindex>` was a notice of an API change w.r.t. the ``.seconds`` accessor. The intent was to provide a user-friendly set of accessors that give the 'natural' value for that unit, e.g. if you had a ``Timedelta('1 day, 10:11:12')``, then ``.seconds`` would return 12. However, this is at odds with the definition of ``datetime.timedelta``, which defines ``.seconds`` as ``10 * 3600 + 11 * 60 + 12 == 36672``. @@ -212,7 +221,7 @@ Using ``.components`` allows the full component access .. _whatsnew_0160.api_breaking.indexing: Indexing Changes -~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:issue:`8613`). Furthermore we have improved the content of the error messages that are raised: @@ -276,10 +285,72 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (: TypeError: Cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with <type 'int'> keys -.. _whatsnew_0160.api: +.. _whatsnew_0160.api_breaking.categorical: + +Categorical Changes +^^^^^^^^^^^^^^^^^^^ + +In prior versions, ``Categoricals`` that had an unspecified ordering (meaning no ``ordered`` keyword was passed) were defaulted as ``ordered`` Categoricals. Going forward, the ``ordered`` keyword in the ``Categorical`` constructor will default to ``False``. Ordering must now be explicit. + +Furthermore, previously you *could* change the ``ordered`` attribute of a Categorical by just setting the attribute, e.g. ``cat.ordered=True``; This is now deprecated and you should use ``cat.as_ordered()`` or ``cat.as_unordered()``. These will by default return a **new** object and not modify the existing object. (:issue:`9347`, :issue:`9190`) + +Previous Behavior + +.. code-block:: python + + In [3]: s = Series([0,1,2], dtype='category') + + In [4]: s + Out[4]: + 0 0 + 1 1 + 2 2 + dtype: category + Categories (3, int64): [0 < 1 < 2] + + In [5]: s.cat.ordered + Out[5]: True + + In [6]: s.cat.ordered = False + + In [7]: s + Out[7]: + 0 0 + 1 1 + 2 2 + dtype: category + Categories (3, int64): [0, 1, 2] + +New Behavior + +.. ipython:: python + + s = Series([0,1,2], dtype='category') + s + s.cat.ordered + s = s.cat.as_ordered() + s + s.cat.ordered + + # you can set in the constructor of the Categorical + s = Series(Categorical([0,1,2],ordered=True)) + s + s.cat.ordered + +For ease of creation of series of categorical data, we have added the ability to pass keywords when calling ``.astype()``. These are passed directly to the constructor. + +.. ipython:: python + + s = Series(["a","b","c","a"]).astype('category',ordered=True) + s + s = Series(["a","b","c","a"]).astype('category',categories=list('abcdef'),ordered=False) + s + + +.. _whatsnew_0160.api_breaking.other: -API Changes -~~~~~~~~~~~ +Other API Changes +^^^^^^^^^^^^^^^^^ - ``Index.duplicated`` now returns ``np.array(dtype=bool)`` rather than ``Index(dtype=object)`` containing ``bool`` values. (:issue:`8875`) - ``DataFrame.to_json`` now returns accurate type serialisation for each column for frames of mixed dtype (:issue:`9037`) @@ -367,139 +438,27 @@ 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`) - -Categorical Changes -~~~~~~~~~~~~~~~~~~~ - -.. _whatsnew_0160.api_breaking.categorical: - -In prior versions, ``Categoricals`` that had an unspecified ordering (meaning no ``ordered`` keyword was passed) were defaulted as ``ordered`` Categoricals. Going forward, the ``ordered`` keyword in the ``Categorical`` constructor will default to ``False``. Ordering must now be explicit. - -Furthermore, previously you *could* change the ``ordered`` attribute of a Categorical by just setting the attribute, e.g. ``cat.ordered=True``; This is now deprecated and you should use ``cat.as_ordered()`` or ``cat.as_unordered()``. These will by default return a **new** object and not modify the existing object. (:issue:`9347`, :issue:`9190`) - -Previous Behavior - -.. code-block:: python - - In [3]: s = Series([0,1,2], dtype='category') - - In [4]: s - Out[4]: - 0 0 - 1 1 - 2 2 - dtype: category - Categories (3, int64): [0 < 1 < 2] - - In [5]: s.cat.ordered - Out[5]: True - - In [6]: s.cat.ordered = False - - In [7]: s - Out[7]: - 0 0 - 1 1 - 2 2 - dtype: category - Categories (3, int64): [0, 1, 2] - -New Behavior - -.. ipython:: python - - s = Series([0,1,2], dtype='category') - s - s.cat.ordered - s = s.cat.as_ordered() - s - s.cat.ordered - - # you can set in the constructor of the Categorical - s = Series(Categorical([0,1,2],ordered=True)) - s - s.cat.ordered - -For ease of creation of series of categorical data, we have added the ability to pass keywords when calling ``.astype()``. These are passed directly to the constructor. - -.. ipython:: python - - s = Series(["a","b","c","a"]).astype('category',ordered=True) - s - s = Series(["a","b","c","a"]).astype('category',categories=list('abcdef'),ordered=False) - s - -Indexing Changes -~~~~~~~~~~~~~~~~ - -.. _whatsnew_0160.api_breaking.indexing: - -The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:issue:`8613`). Furthermore we have improved the content of the error messages that are raised: - -- slicing with ``.loc`` where the start and/or stop bound is not found in the index is now allowed; this previously would raise a ``KeyError``. This makes the behavior the same as ``.ix`` in this case. This change is only for slicing, not when indexing with a single label. - -.. ipython:: python - - df = DataFrame(np.random.randn(5,4), - columns=list('ABCD'), - index=date_range('20130101',periods=5)) - df - s = Series(range(5),[-2,-1,1,2,3]) - s - - Previous Behavior - - .. code-block:: python - - In [4]: df.loc['2013-01-02':'2013-01-10'] - KeyError: 'stop bound [2013-01-10] is not in the [index]' - - In [6]: s.loc[-10:3] - KeyError: 'start bound [-10] is not the [index]' - - New 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`). Old behavior: .. ipython:: python + :verbatim: - df.loc['2013-01-02':'2013-01-10'] - s.loc[-10:3] - -- allow slicing with float-like values on an integer index for ``.ix``. Previously this was only enabled for ``.loc``: - - Previous Behavior - - .. code-block:: python - - In [8]: s.ix[-1.0:2] - TypeError: the slice start value [-1.0] is not a proper indexer for this index type (Int64Index) + In [4]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') + Out[4]: Timestamp('2000-01-31 00:00:00') - New Behavior + Fixed behavior: .. ipython:: python - s.ix[-1.0:2] - -- provide a useful exception for indexing with an invalid type for that index when using ``.loc``. For example trying to use ``.loc`` on an index of type ``DatetimeIndex`` or ``PeriodIndex`` or ``TimedeltaIndex``, with an integer (or a float). - - Previous Behavior - - .. code-block:: python - - In [4]: df.loc[2:3] - KeyError: 'start bound [2] is not the [index]' - - New Behavior - - .. code-block:: python + pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') - In [4]: df.loc[2:3] - TypeError: Cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with <type 'int'> keys + To reproduce the old behavior, simply add more precision to the label (e.g., use ``2000-02-01`` instead of ``2000-02``). .. _whatsnew_0160.deprecations: Deprecations -~~~~~~~~~~~~ +^^^^^^^^^^^^ - The ``rplot`` trellis plotting interface is deprecated and will be removed in a future version. We refer to external packages like @@ -518,10 +477,11 @@ Deprecations - Adding ``DatetimeIndex/PeriodIndex`` to another ``DatetimeIndex/PeriodIndex`` is being deprecated as a set-operation. This will be changed to a ``TypeError`` in a future version. ``.union()`` should be used for the union set operation. (:issue:`9094`) - Subtracting ``DatetimeIndex/PeriodIndex`` from another ``DatetimeIndex/PeriodIndex`` is being deprecated as a set-operation. This will be changed to an actual numeric subtraction yielding a ``TimeDeltaIndex`` in a future version. ``.difference()`` should be used for the differencing set operation. (:issue:`9094`) + .. _whatsnew_0160.prior_deprecations: Removal of prior version deprecations/changes -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - ``DataFrame.pivot_table`` and ``crosstab``'s ``rows`` and ``cols`` keyword arguments were removed in favor of ``index`` and ``columns`` (:issue:`6581`) @@ -531,8 +491,8 @@ Removal of prior version deprecations/changes .. _whatsnew_0160.performance: -Performance -~~~~~~~~~~~ +Performance Improvements +~~~~~~~~~~~~~~~~~~~~~~~~ - Fixed a performance regression for ``.loc`` indexing with an array or list-like (:issue:`9126`:). - ``DataFrame.to_json`` 30x performance improvement for mixed dtype frames. (:issue:`9037`) @@ -576,21 +536,6 @@ Bug Fixes - Bug in ``unstack`` with ``TimedeltaIndex`` or ``DatetimeIndex`` and nulls (:issue:`9491`). - Bug in ``rank`` where comparing floats with tolerance will cause inconsistent behaviour (:issue:`8365`). - Fixed character encoding bug in ``read_stata`` and ``StataReader`` when loading data from a URL (:issue:`9231`). -- 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: - - .. ipython:: python - :verbatim: - - In [4]: pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') - Out[4]: Timestamp('2000-01-31 00:00:00') - - Fixed behavior: - - .. ipython:: python - - pd.to_datetime(['2000-01-31', '2000-02-28']).asof('2000-02') - - To reproduce the old behavior, simply add more precision to the label (e.g., use ``2000-02-01`` instead of ``2000-02``). - Bug in adding ``offsets.Nano`` to other offets raises ``TypeError`` (:issue:`9284`) - Bug in ``DatetimeIndex`` iteration, related to (:issue:`8890`), fixed in (:issue:`9100`) - Bugs in ``resample`` around DST transitions. This required fixing offset classes so they behave correctly on DST transitions. (:issue:`5172`, :issue:`8744`, :issue:`8653`, :issue:`9173`, :issue:`9468`). @@ -620,5 +565,4 @@ Bug Fixes - Fixed bug with reading CSV files from Amazon S3 on python 3 raising a TypeError (:issue:`9452`) - Bug in the Google BigQuery reader where the 'jobComplete' key may be present but False in the query results (:issue:`8728`) - Bug in ``Series.values_counts`` with excluding ``NaN`` for categorical type ``Series`` with ``dropna=True`` (:issue:`9443`) - - Fixed mising numeric_only option for ``DataFrame.std/var/sem`` (:issue:`9201`)
No real edits, just some rearranging. I added an automatic 'contents' in the beginning, so this gives all headers automatically, and in this way restricted the list in the beginning to just the highlights (then it can be copied exactly like this to the release notes in release.rst
https://api.github.com/repos/pandas-dev/pandas/pulls/9644
2015-03-13T14:50:42Z
2015-03-14T12:05:10Z
2015-03-14T12:05:10Z
2015-03-14T12:05:10Z
DOC: fix some doc errors/warnings
diff --git a/doc/source/categorical.rst b/doc/source/categorical.rst index 6ce93326f0e16..7fe04af716cec 100644 --- a/doc/source/categorical.rst +++ b/doc/source/categorical.rst @@ -386,9 +386,9 @@ categories or a categorical with any list-like object, will raise a TypeError. .. ipython:: python - cat = Series(Categorical([1,2,3], categories=[3,2,1])) - cat_base = Series(Categorical([2,2,2], categories=[3,2,1])) - cat_base2 = Series(Categorical([2,2,2])) + cat = Series([1,2,3]).astype("category", categories=[3,2,1], ordered=True) + cat_base = Series([2,2,2]).astype("category", categories=[3,2,1], ordered=True) + cat_base2 = Series([2,2,2]).astype("category", ordered=True) cat cat_base diff --git a/doc/source/io.rst b/doc/source/io.rst index d49e88c953b27..2b13131c1d576 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -1098,7 +1098,7 @@ class of the csv module. For this, you have to specify ``sep=None``. .. ipython:: python print(open('tmp2.sv').read()) - pd.read_csv('tmp2.sv', sep=None) + pd.read_csv('tmp2.sv', sep=None, engine='python') .. _io.chunking:
- `pd.read_csv('tmp2.sv', sep=None)` was given the warning "_ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'_" Is this correct? (assuming so I just added what the warning message was saying) - with the ordered -> unordered change in the Categoricals, the example on comparing them was failing (see http://pandas-docs.github.io/pandas-docs-travis/categorical.html#comparisons)
https://api.github.com/repos/pandas-dev/pandas/pulls/9643
2015-03-13T14:07:44Z
2015-03-14T20:57:01Z
2015-03-14T20:57:01Z
2015-03-14T20:57:02Z
COMPAT: dtype fix for windows tests; ensure passing i8 to tslib/normalize
diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 3e565d5764fe2..c338bbeae79c7 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -884,7 +884,7 @@ def test_resmaple_dst_anchor(self): dti = date_range('2013-09-30', '2013-11-02', freq='30Min', tz='Europe/Paris') values = range(dti.size) - df = DataFrame({"a": values, "b": values, "c": values}, index=dti) + df = DataFrame({"a": values, "b": values, "c": values}, index=dti, dtype='int64') how = {"a": "min", "b": "max", "c": "count"} assert_frame_equal(df.resample("W-MON", how=how)[["a", "b", "c"]], diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 763c06c7d4e7c..3f04f80406fca 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -456,7 +456,7 @@ class Timestamp(_Timestamp): tz = maybe_get_tz(tz) if not isinstance(ambiguous, basestring): ambiguous = [ambiguous] - value = tz_localize_to_utc(np.array([self.value]), tz, + value = tz_localize_to_utc(np.array([self.value],dtype='i8'), tz, ambiguous=ambiguous)[0] return Timestamp(value, tz=tz) else: @@ -468,6 +468,7 @@ class Timestamp(_Timestamp): raise TypeError('Cannot localize tz-aware Timestamp, use ' 'tz_convert for conversions') + def tz_convert(self, tz): """ Convert Timestamp to another time zone or localize to requested time @@ -569,7 +570,7 @@ class Timestamp(_Timestamp): Normalize Timestamp to midnight, preserving tz information. """ - normalized_value = date_normalize(np.array([self.value]), tz=self.tz)[0] + normalized_value = date_normalize(np.array([self.value], dtype='i8'), tz=self.tz)[0] return Timestamp(normalized_value).tz_localize(self.tz) def __radd__(self, other):
https://api.github.com/repos/pandas-dev/pandas/pulls/9642
2015-03-13T13:04:12Z
2015-03-13T13:04:26Z
2015-03-13T13:04:26Z
2015-03-13T13:04:26Z