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COMPAT: compat for dateutil 2.5.0 and differing dayfirst=True & yearf…
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index f0d5bf7e046c3..863bc6f630d06 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -589,23 +589,62 @@ def test_parsers_quarter_invalid(self): self.assertRaises(ValueError, tools.parse_time_string, case) def test_parsers_dayfirst_yearfirst(self): + + # https://github.com/dateutil/dateutil/issues/217 + # this issue was closed + import dateutil + is_compat_version = dateutil.__version__ >= LooseVersion('2.5.2') + if is_compat_version: + dayfirst_yearfirst1 = datetime.datetime(2010, 12, 11) + dayfirst_yearfirst2 = datetime.datetime(2020, 12, 21) + else: + dayfirst_yearfirst1 = datetime.datetime(2010, 11, 12) + dayfirst_yearfirst2 = datetime.datetime(2020, 12, 21) + # str : dayfirst, yearfirst, expected - cases = {'10-11-12': [(False, False, datetime.datetime(2012, 10, 11)), - (True, False, datetime.datetime(2012, 11, 10)), - (False, True, datetime.datetime(2010, 11, 12)), - (True, True, datetime.datetime(2010, 11, 12))], - '20/12/21': [(False, False, datetime.datetime(2021, 12, 20)), - (True, False, datetime.datetime(2021, 12, 20)), - (False, True, datetime.datetime(2020, 12, 21)), - (True, True, datetime.datetime(2020, 12, 21))]} + cases = {'10-11-12': [(False, False, False, + datetime.datetime(2012, 10, 11)), + (True, False, False, + datetime.datetime(2012, 11, 10)), + (False, True, False, + datetime.datetime(2010, 11, 12)), + (True, True, False, dayfirst_yearfirst1)], + '20/12/21': [(False, False, False, + datetime.datetime(2021, 12, 20)), + (True, False, False, + datetime.datetime(2021, 12, 20)), + (False, True, False, + datetime.datetime(2020, 12, 21)), + (True, True, True, dayfirst_yearfirst2)]} tm._skip_if_no_dateutil() from dateutil.parser import parse for date_str, values in compat.iteritems(cases): - for dayfirst, yearfirst, expected in values: - result1, _, _ = tools.parse_time_string(date_str, - dayfirst=dayfirst, - yearfirst=yearfirst) + for dayfirst, yearfirst, is_compat, expected in values: + + f = lambda x: tools.parse_time_string(x, + dayfirst=dayfirst, + yearfirst=yearfirst) + + # we now have an invalid parse + if is_compat and is_compat_version: + self.assertRaises(tslib.DateParseError, f, date_str) + + def f(date_str): + return to_datetime(date_str, dayfirst=dayfirst, + yearfirst=yearfirst) + + self.assertRaises(ValueError, f, date_str) + + def f(date_str): + return DatetimeIndex([date_str], dayfirst=dayfirst, + yearfirst=yearfirst)[0] + + self.assertRaises(ValueError, f, date_str) + + continue + + result1, _, _ = f(date_str) result2 = to_datetime(date_str, dayfirst=dayfirst, yearfirst=yearfirst) @@ -614,7 +653,6 @@ def test_parsers_dayfirst_yearfirst(self): yearfirst=yearfirst)[0] # Timestamp doesn't support dayfirst and yearfirst - self.assertEqual(result1, expected) self.assertEqual(result2, expected) self.assertEqual(result3, expected)
closes #12730
https://api.github.com/repos/pandas-dev/pandas/pulls/12731
2016-03-29T14:32:03Z
2016-03-29T17:29:48Z
2016-03-29T17:29:48Z
2016-03-29T17:29:48Z
Fix pandas.Timedelta range
diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst index fe7ab67b7f759..490b593b4c9c2 100644 --- a/doc/source/gotchas.rst +++ b/doc/source/gotchas.rst @@ -356,27 +356,6 @@ such as ``numpy.logical_and``. See the `this old issue <https://github.com/pydata/pandas/issues/2388>`__ for a more detailed discussion. -.. _gotchas.timestamp-limits: - -Timestamp limitations ---------------------- - -Minimum and maximum timestamps -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Since pandas represents timestamps in nanosecond resolution, the timespan that -can be represented using a 64-bit integer is limited to approximately 584 years: - -.. ipython:: python - - begin = pd.Timestamp.min - begin - - end = pd.Timestamp.max - end - -See :ref:`here <timeseries.oob>` for ways to represent data outside these bound. - Parsing Dates from Text Files ----------------------------- diff --git a/doc/source/timedeltas.rst b/doc/source/timedeltas.rst index 29a75f3423cfa..fa5ffd0831706 100644 --- a/doc/source/timedeltas.rst +++ b/doc/source/timedeltas.rst @@ -109,6 +109,26 @@ The ``unit`` keyword argument specifies the unit of the Timedelta: to_timedelta(np.arange(5), unit='s') to_timedelta(np.arange(5), unit='d') +.. _timedeltas.limitations: + +Timedelta limitations +~~~~~~~~~~~~~~~~~~~~~ + +Pandas represents ``Timedeltas`` in nanosecond resolution using +64 bit integers. As such, the 64 bit integer limits determine +the ``Timedelta`` limits. + +.. ipython:: python + min_int = np.iinfo(np.int64).min + max_int = np.iinfo(np.int64).max + + # Note: the smallest integer gives a NaT + Timedelta(min_int) + Timedelta(min_int+1) == Timedelta.min + Timedelta(max_int) == Timedelta.max + + # (min_int - 1) and (max_int + 1) result in OverflowErrors + .. _timedeltas.operations: Operations diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index c912d7d8b9818..1f9c66ea62717 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -307,6 +307,27 @@ using various combinations of parameters like ``start``, ``end``, The start and end dates are strictly inclusive. So it will not generate any dates outside of those dates if specified. +.. _timeseries.timestamp-limits: + +Timestamp limitations +--------------------- + +Minimum and maximum timestamps +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Since pandas represents timestamps in nanosecond resolution, the timespan that +can be represented using a 64-bit integer is limited to approximately 584 years: + +.. ipython:: python + + begin = pd.Timestamp.min + begin + + end = pd.Timestamp.max + end + +See :ref:`here <timeseries.oob>` for ways to represent data outside these bound. + .. _timeseries.datetimeindex: DatetimeIndex @@ -1691,7 +1712,7 @@ the quarter end: Representing out-of-bounds spans -------------------------------- -If you have data that is outside of the ``Timestamp`` bounds, see :ref:`Timestamp limitations <gotchas.timestamp-limits>`, +If you have data that is outside of the ``Timestamp`` bounds, see :ref:`Timestamp limitations <timeseries.timestamp-limits>`, then you can use a ``PeriodIndex`` and/or ``Series`` of ``Periods`` to do computations. .. ipython:: python diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index e6ea9217347ea..8cff34ad7b748 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -129,6 +129,7 @@ Bug Fixes - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) +- Bug in ``Timedelta.min`` and ``Timedelta.max``, the properties now report the true minimum/maximum ``timedeltas`` as recognized by Pandas. See :ref:`documentation <timedeltas.limitations>`. (:issue:`12727`) diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 4bdd0ed462852..434c44e402461 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -1083,6 +1083,35 @@ def test_timedelta_hash_equality(self): ns_td = Timedelta(1, 'ns') self.assertNotEqual(hash(ns_td), hash(ns_td.to_pytimedelta())) + def test_implementation_limits(self): + min_td = Timedelta(Timedelta.min) + max_td = Timedelta(Timedelta.max) + + # GH 12727 + # timedelta limits correspond to int64 boundaries + self.assertTrue(min_td.value == np.iinfo(np.int64).min + 1) + self.assertTrue(max_td.value == np.iinfo(np.int64).max) + + # Beyond lower limit, a NAT before the Overflow + self.assertIsInstance(min_td - Timedelta(1, 'ns'), + pd.tslib.NaTType) + + with tm.assertRaises(OverflowError): + min_td - Timedelta(2, 'ns') + + with tm.assertRaises(OverflowError): + max_td + Timedelta(1, 'ns') + + # Same tests using the internal nanosecond values + td = Timedelta(min_td.value - 1, 'ns') + self.assertIsInstance(td, pd.tslib.NaTType) + + with tm.assertRaises(OverflowError): + Timedelta(min_td.value - 2, 'ns') + + with tm.assertRaises(OverflowError): + Timedelta(max_td.value + 1, 'ns') + class TestTimedeltaIndex(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index dc089785238d9..98e6f1d1c53f4 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -2722,6 +2722,11 @@ class Timedelta(_Timedelta): __pos__ = _op_unary_method(lambda x: x, '__pos__') __abs__ = _op_unary_method(lambda x: abs(x), '__abs__') + +# Resolution is in nanoseconds +Timedelta.min = Timedelta(np.iinfo(np.int64).min+1, 'ns') +Timedelta.max = Timedelta(np.iinfo(np.int64).max, 'ns') + cdef PyTypeObject* td_type = <PyTypeObject*> Timedelta cdef inline bint is_timedelta(object o):
- [x] closes #12727 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry _Problem_ Pandas Timedelta derives from `datetime.timedelta` and increase the resolution of the timedeltas. As such the Pandas.Timedelta object can only have a smaller range of values. _Solution_ This change modifies the properties that report the range and resolution to reflect Pandas capabilities. **Reference** https://github.com/python/cpython/blob/8d1d7e6816753248768e4cc1c0370221814e9cf1/Lib/datetime.py#L651-L654
https://api.github.com/repos/pandas-dev/pandas/pulls/12728
2016-03-29T07:28:44Z
2016-03-31T13:24:07Z
null
2016-03-31T13:24:43Z
Omit tests folders from coverage
diff --git a/.coveragerc b/.coveragerc index 5b264a626abfa..3f630aa6cf8f5 100644 --- a/.coveragerc +++ b/.coveragerc @@ -1,6 +1,7 @@ # .coveragerc to control coverage.py [run] branch = False +omit = */tests/* [report] # Regexes for lines to exclude from consideration @@ -23,4 +24,4 @@ exclude_lines = ignore_errors = False [html] -directory = coverage_html_report \ No newline at end of file +directory = coverage_html_report
xref #12634
https://api.github.com/repos/pandas-dev/pandas/pulls/12721
2016-03-27T21:27:10Z
2016-03-31T13:43:09Z
null
2016-04-10T22:35:07Z
Convert badge list into Markdown table in README.md
diff --git a/README.md b/README.md index 3b7032321fbcf..eab9dc89003ee 100644 --- a/README.md +++ b/README.md @@ -5,60 +5,18 @@ # pandas: powerful Python data analysis toolkit -<table> -<tr> - <td>Latest Release</td> - <td><img src="https://img.shields.io/pypi/v/pandas.svg" alt="latest release" /></td> -</tr> - <td></td> - <td><img src="https://anaconda.org/pandas/pandas/badges/version.svg" alt="latest release" /></td> -</tr> -<tr> - <td>Package Status</td> - <td><img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" /></td> -</tr> -<tr> - <td>License</td> - <td><img src="https://img.shields.io/pypi/l/pandas.svg" alt="license" /></td> -</tr> -<tr> - <td>Build Status</td> - <td> - <a href="https://travis-ci.org/pydata/pandas"> - <img src="https://travis-ci.org/pydata/pandas.svg?branch=master" alt="travis build status" /> - </a> - </td> -</tr> - <td></td> - <td> - <a href="https://ci.appveyor.com/project/jreback/pandas-465"> - <img src="https://ci.appveyor.com/api/projects/status/iblk29s98quexwxi/branch/master?svg=true" alt="appveyor build status" /> - </a> - </td> -</tr> -<tr> - <td>Coverage</td> - <td><img src="https://codecov.io/github/pydata/pandas/coverage.svg?branch=master" alt="coverage" /></td> -</tr> -<tr> - <td>Conda</td> - <td> - <a href="http://pandas.pydata.org"> - <img src="http://pubbadges.s3-website-us-east-1.amazonaws.com/pkgs-downloads-pandas.png" alt="conda downloads" /> - </a> - </td> -</tr> -<tr> - <td>PyPI</td> - <td> - <a href="https://pypi.python.org/pypi/pandas/"> - <img src="https://img.shields.io/pypi/dm/pandas.svg" alt="pypi downloads" /> - </a> - </td> -</tr> -</table> - -[![https://gitter.im/pydata/pandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pydata/pandas?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) +| | | +------------ | ------------- +Latest Release | [![Latest Version](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.python.org/pypi/pandas/) +Latest Release | [![Latest Version](https://anaconda.org/pandas/pandas/badges/version.svg)](https://anaconda.org/pandas/pandas/) +Package Status | [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.python.org/pypi/pandas/) +License | [![License](https://img.shields.io/pypi/l/pandas.svg)](https://pypi.python.org/pypi/pandas/) +Build Status | [![Build Status](https://travis-ci.org/pydata/pandas.svg?branch=master)](https://travis-ci.org/pydata/pandas) +Build Status | [![Build Status](https://ci.appveyor.com/api/projects/status/iblk29s98quexwxi/branch/master?svg=true)](https://ci.appveyor.com/project/jreback/pandas-465) +Coverage | [![Coverage](https://codecov.io/github/pydata/pandas/coverage.svg?branch=master)](https://codecov.io/github/pydata/pandas?branch=master) +Conda | [![Conda](http://pubbadges.s3-website-us-east-1.amazonaws.com/pkgs-downloads-pandas.png)](http://conda.anaconda.org/pandas) +PyPI | [![PyPI](https://img.shields.io/pypi/dm/pandas.svg)](https://pypi.python.org/pypi/pandas/) +Gitter | [![https://gitter.im/pydata/pandas](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/pydata/pandas) ## What is it
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Also correct some badge links
https://api.github.com/repos/pandas-dev/pandas/pulls/12719
2016-03-26T15:30:13Z
2016-04-01T13:17:54Z
null
2016-04-01T13:17:58Z
Raising TypeError on invalid window when using .rolling #12669
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index e6ea9217347ea..bbbb31ea48ed5 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -169,3 +169,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) +- Bug in ``window`` Better error message on invalid window when using .rolling (:issue:`12669`) diff --git a/pandas/core/window.py b/pandas/core/window.py index 31874a96f8111..66d35b7ef38a9 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -1354,6 +1354,8 @@ def _get_center_of_mass(com, span, halflife, alpha): def _offset(window, center): + if com.is_float(window): + raise TypeError("Window should be of type Integer, given Float") if not com.is_integer(window): window = len(window) offset = (window - 1) / 2. if center else 0 diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index fb0e2ad2ca34e..d4e51fbbdb8ca 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -761,6 +761,9 @@ def test_rolling_median(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): self._check_moment_func(mom.rolling_median, np.median, name='median') + # GH 12669 + with self.assertRaises(TypeError): + pd.DataFrame(np.arange(10)).rolling(2., center=True).median() def test_rolling_min(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
- [x] closes #12669 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12718
2016-03-26T13:29:24Z
2016-04-26T22:54:53Z
null
2016-04-26T22:54:53Z
COMPAT: make read_excel accept path objects for the filepath (#12655)
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1179a347e4c46..2888dd92d2fff 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -169,3 +169,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) +- ``read_excel`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 5656c360b3990..07078faef0266 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -13,7 +13,7 @@ from pandas.core.frame import DataFrame from pandas.io.parsers import TextParser from pandas.io.common import (_is_url, _urlopen, _validate_header_arg, - get_filepath_or_buffer, _is_s3_url) + get_filepath_or_buffer) from pandas.tseries.period import Period from pandas import json from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, @@ -82,7 +82,8 @@ def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, Parameters ---------- - io : string, file-like object, pandas ExcelFile, or xlrd workbook. + io : string, path object (pathlib.Path or py._path.local.LocalPath), + file-like object, pandas ExcelFile, or xlrd workbook. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/workbook.xlsx @@ -184,8 +185,9 @@ class ExcelFile(object): Parameters ---------- - io : string, file-like object or xlrd workbook - If a string, expected to be a path to xls or xlsx file + io : string, path object (pathlib.Path or py._path.local.LocalPath), + file-like object or xlrd workbook + If a string or path object, expected to be a path to xls or xlsx file engine: string, default None If io is not a buffer or path, this must be set to identify io. Acceptable values are None or xlrd @@ -207,21 +209,22 @@ def __init__(self, io, **kwds): if engine is not None and engine != 'xlrd': raise ValueError("Unknown engine: %s" % engine) - if isinstance(io, compat.string_types): - if _is_s3_url(io): - buffer, _, _ = get_filepath_or_buffer(io) - self.book = xlrd.open_workbook(file_contents=buffer.read()) - elif _is_url(io): - data = _urlopen(io).read() - self.book = xlrd.open_workbook(file_contents=data) - else: - self.book = xlrd.open_workbook(io) - elif engine == 'xlrd' and isinstance(io, xlrd.Book): + # If io is a url, want to keep the data as bytes so can't pass + # to get_filepath_or_buffer() + if _is_url(io): + io = _urlopen(io) + # Deal with S3 urls, path objects, etc. Will convert them to + # buffer or path string + io, _, _ = get_filepath_or_buffer(io) + + if engine == 'xlrd' and isinstance(io, xlrd.Book): self.book = io elif not isinstance(io, xlrd.Book) and hasattr(io, "read"): # N.B. xlrd.Book has a read attribute too data = io.read() self.book = xlrd.open_workbook(file_contents=data) + elif isinstance(io, compat.string_types): + self.book = xlrd.open_workbook(io) else: raise ValueError('Must explicitly set engine if not passing in' ' buffer or path for io.') diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index b8d61047a7b6d..c4fd0577f13a0 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -528,6 +528,33 @@ def test_read_from_file_url(self): tm.assert_frame_equal(url_table, local_table) + def test_read_from_pathlib_path(self): + tm._skip_if_no_pathlib() + + from pathlib import Path + + str_path = os.path.join(self.dirpath, 'test1' + self.ext) + expected = read_excel(str_path, 'Sheet1', index_col=0) + + path_obj = Path(self.dirpath, 'test1' + self.ext) + actual = read_excel(path_obj, 'Sheet1', index_col=0) + + tm.assert_frame_equal(expected, actual) + + def test_read_from_py_localpath(self): + tm._skip_if_no_localpath() + + from py.path import local as LocalPath + + str_path = os.path.join(self.dirpath, 'test1' + self.ext) + expected = read_excel(str_path, 'Sheet1', index_col=0) + + abs_dir = os.path.abspath(self.dirpath) + path_obj = LocalPath(abs_dir).join('test1' + self.ext) + actual = read_excel(path_obj, 'Sheet1', index_col=0) + + tm.assert_frame_equal(expected, actual) + def test_reader_closes_file(self): pth = os.path.join(self.dirpath, 'test1' + self.ext)
- [x] closes #12655 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fairly simple fix overall, since `get_filepath_or_buffer` is already set up to just convert the path objs to string, we can do that first and then just let the existing logic handle it. Since the libraries that contain the path objects aren't hard dependencies, I'm not sure how to typecheck against them without first using the `_IS_LIBRARY_INSTALLED` checks and then importing if they're installed. Let me know if this should be done differently.
https://api.github.com/repos/pandas-dev/pandas/pulls/12717
2016-03-26T11:35:05Z
2016-04-01T13:02:56Z
null
2016-04-01T13:03:09Z
Allow float window when using .rolling
diff --git a/pandas/core/window.py b/pandas/core/window.py index 31874a96f8111..ec20d5c006f1b 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -124,6 +124,24 @@ def _dir_additions(self): def _get_window(self, other=None): return self.window + def _prep_window(self, **kwargs): + """ provide validation for our window type, return the window """ + window = self._get_window() + + if isinstance(window, (list, tuple, np.ndarray)): + return com._asarray_tuplesafe(window).astype(float) + elif com.is_integer(window): + try: + import scipy.signal as sig + except ImportError: + raise ImportError('Please install scipy to generate window ' + 'weight') + # the below may pop from kwargs + win_type = _validate_win_type(self.win_type, kwargs) + return sig.get_window(win_type, window).astype(float) + + raise ValueError('Invalid window %s' % str(window)) + @property def _window_type(self): return self.__class__.__name__ @@ -317,24 +335,6 @@ class Window(_Window): * ``slepian`` (needs width). """ - def _prep_window(self, **kwargs): - """ provide validation for our window type, return the window """ - window = self._get_window() - - if isinstance(window, (list, tuple, np.ndarray)): - return com._asarray_tuplesafe(window).astype(float) - elif com.is_integer(window): - try: - import scipy.signal as sig - except ImportError: - raise ImportError('Please install scipy to generate window ' - 'weight') - # the below may pop from kwargs - win_type = _validate_win_type(self.win_type, kwargs) - return sig.get_window(win_type, window).astype(float) - - raise ValueError('Invalid window %s' % str(window)) - def _apply_window(self, mean=True, how=None, **kwargs): """ Applies a moving window of type ``window_type`` on the data. @@ -437,8 +437,7 @@ def _apply(self, func, window=None, center=None, check_minp=None, how=None, """ if center is None: center = self.center - if window is None: - window = self._get_window() + window = self._prep_window(**kwargs) if check_minp is None: check_minp = _use_window @@ -1354,7 +1353,9 @@ def _get_center_of_mass(com, span, halflife, alpha): def _offset(window, center): - if not com.is_integer(window): + if com.is_float(window) and int(window) == window: + window = int(window) + elif not com.is_integer(window): window = len(window) offset = (window - 1) / 2. if center else 0 try: diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index fb0e2ad2ca34e..2fff725378e07 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -3,7 +3,7 @@ import sys import warnings -from nose.tools import assert_raises +from nose.tools import assert_raises, raises from datetime import datetime from numpy.random import randn from numpy.testing.decorators import slow @@ -225,6 +225,14 @@ def b(x): result = r.aggregate([a, b]) assert_frame_equal(result, expected) + @raises(ValueError) + def test_window_rolling_float(self): + pd.DataFrame(np.arange(10)).rolling(2.).median() + + @raises(ValueError) + def test_window_rolling_center_float(self): + pd.DataFrame(np.arange(10)).rolling(2., center=True).median() + def test_preserve_metadata(self): # GH 10565 s = Series(np.arange(100), name='foo')
- [ ] closes #12669 - [ ] tests added - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry https://github.com/pydata/pandas/issues/12669
https://api.github.com/repos/pandas-dev/pandas/pulls/12714
2016-03-25T00:37:09Z
2016-04-10T17:31:05Z
null
2016-04-10T22:35:24Z
BUG: Bug in groupby.transform(..) when axis=1 is specified with a non_monotonic indexer
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c9cd09cba0b39..f82a18f50063a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -118,7 +118,7 @@ Performance Improvements Bug Fixes ~~~~~~~~~ - ``usecols`` parameter in ``pd.read_csv`` is now respected even when the lines of a CSV file are not even (:issue:`12203`) - +- Bug in ``groupby.transform(..)`` when ``axis=1`` is specified with a non-monotonic ordered index (:issue:` `) - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) - Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index f3fe5a5a2d5d8..398e37d52d7ba 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -457,19 +457,18 @@ def _set_selection_from_grouper(self): self._group_selection = ax.difference(Index(groupers)).tolist() def _set_result_index_ordered(self, result): - # set the result index on the passed values object - # return the new object - # related 8046 + # set the result index on the passed values object and + # return the new object, xref 8046 # the values/counts are repeated according to the group index - # shortcut of we have an already ordered grouper + # shortcut if we have an already ordered grouper if not self.grouper.is_monotonic: index = Index(np.concatenate( self._get_indices(self.grouper.result_index))) - result.index = index - result = result.sort_index() + result.set_axis(self.axis, index) + result = result.sort_index(axis=self.axis) - result.index = self.obj.index + result.set_axis(self.axis, self.obj._get_axis(self.axis)) return result def _dir_additions(self): diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 947daab2017d3..e823874f85cec 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -1090,6 +1090,45 @@ def test_transform_broadcast(self): for idx in gp.index: assert_fp_equal(res.xs(idx), agged[idx]) + def test_transform_axis(self): + + # make sure that we are setting the axes + # correctly when on axis=0 or 1 + # in the presence of a non-monotonic indexer + + base = self.tsframe.iloc[0:5] + r = len(base.index) + c = len(base.columns) + tso = DataFrame(np.random.randn(r, c), + index=base.index, + columns=base.columns, + dtype='float64') + # monotonic + ts = tso + grouped = ts.groupby(lambda x: x.weekday()) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: x - x.mean()) + assert_frame_equal(result, expected) + + ts = ts.T + grouped = ts.groupby(lambda x: x.weekday(), axis=1) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: (x.T - x.mean(1)).T) + assert_frame_equal(result, expected) + + # non-monotonic + ts = tso.iloc[[1, 0] + list(range(2, len(base)))] + grouped = ts.groupby(lambda x: x.weekday()) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: x - x.mean()) + assert_frame_equal(result, expected) + + ts = ts.T + grouped = ts.groupby(lambda x: x.weekday(), axis=1) + result = ts - grouped.transform('mean') + expected = grouped.apply(lambda x: (x.T - x.mean(1)).T) + assert_frame_equal(result, expected) + def test_transform_dtype(self): # GH 9807 # Check transform dtype output is preserved
https://api.github.com/repos/pandas-dev/pandas/pulls/12713
2016-03-24T21:22:04Z
2016-03-25T12:54:42Z
null
2016-03-25T12:54:42Z
Validate that float_format option is callable
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 7843cb24b7686..714a0cf19d14a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -124,6 +124,7 @@ Bug Fixes - Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) - Bugs in concatenation with a coercable dtype was too aggressive. (:issue:`12411`, :issue:`12045`, :issue:`11594`, :issue:`10571`) +- Bug in ``float_format`` option with option not being validated as callable. (:issue:`12706`) diff --git a/pandas/core/config.py b/pandas/core/config.py index 7b1e5b29f1cbb..d489a3bc2f079 100644 --- a/pandas/core/config.py +++ b/pandas/core/config.py @@ -803,3 +803,21 @@ def inner(x): is_str = is_type_factory(str) is_unicode = is_type_factory(compat.text_type) is_text = is_instance_factory((str, bytes)) + + +def is_callable_or_none(obj): + """ + + Parameters + ---------- + `obj` - the object to be checked + + Returns + ------- + validator - returns True if object is callable or None, and + raises ValueError otherwise. + + """ + if not (callable(obj) or obj is None): + raise ValueError("Value must be an callable or None") + return True diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py index f9b91db608093..5c2ef2191b031 100644 --- a/pandas/core/config_init.py +++ b/pandas/core/config_init.py @@ -13,7 +13,8 @@ import pandas.core.config as cf from pandas.core.config import (is_int, is_bool, is_text, is_instance_factory, - is_one_of_factory, get_default_val) + is_one_of_factory, get_default_val, + is_callable_or_none) from pandas.core.format import detect_console_encoding # @@ -279,7 +280,8 @@ def mpl_style_cb(key): with cf.config_prefix('display'): cf.register_option('precision', 6, pc_precision_doc, validator=is_int) - cf.register_option('float_format', None, float_format_doc) + cf.register_option('float_format', None, float_format_doc, + validator=is_callable_or_none) cf.register_option('column_space', 12, validator=is_int) cf.register_option('max_info_rows', 1690785, pc_max_info_rows_doc, validator=is_instance_factory((int, type(None)))) diff --git a/pandas/tests/test_config.py b/pandas/tests/test_config.py index 693b1d0ec71de..b0a06493ea83a 100644 --- a/pandas/tests/test_config.py +++ b/pandas/tests/test_config.py @@ -206,6 +206,12 @@ def test_validation(self): self.assertRaises(ValueError, self.cf.set_option, 'a', 'ab') self.assertRaises(ValueError, self.cf.set_option, 'b.c', 1) + self.cf.register_option('b', lambda: None, 'doc', + validator=self.cf.is_callable_or_none) + self.cf.set_option('b', '%.1f'.format) # Formatter is callable + self.cf.set_option('b', None) # Formatter is none (default) + self.assertRaises(ValueError, self.cf.set_option, 'b', '%.1f') + def test_reset_option(self): self.cf.register_option('a', 1, 'doc', validator=self.cf.is_int) self.cf.register_option('b.c', 'hullo', 'doc2',
- [x] closes #12706 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12711
2016-03-24T16:12:47Z
2016-03-26T00:32:57Z
null
2016-03-28T12:20:14Z
WIP/API: Implemented NDFrame.argsort() and NDFrame.ordering().
diff --git a/doc/source/api.rst b/doc/source/api.rst index d6402100a296f..3abbb5920c1bc 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -398,8 +398,9 @@ Reshaping, sorting .. autosummary:: :toctree: generated/ - Series.argsort Series.reorder_levels + Series.argsort + Series.ordering Series.sort_values Series.sort_index Series.sortlevel @@ -909,6 +910,8 @@ Reshaping, sorting, transposing DataFrame.pivot DataFrame.reorder_levels + DataFrame.argsort + DataFrame.ordering DataFrame.sort_values DataFrame.sort_index DataFrame.sortlevel @@ -1181,6 +1184,9 @@ Reshaping, sorting, transposing .. autosummary:: :toctree: generated/ + Panel.argsort + Panel.ordering + Panel.sort_values Panel.sort_index Panel.swaplevel Panel.transpose @@ -1271,6 +1277,15 @@ Conversion Panel4D.isnull Panel4D.notnull +Reshaping, sorting, transposing +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. autosummary:: + :toctree: generated/ + + Panel4D.argsort + Panel4D.ordering + Panel4D.sort_values + .. _api.index: Index diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 5f7111849cb87..d9730e3bdf50e 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -307,6 +307,8 @@ def _get_axis_number(self, axis): if com.is_integer(axis): if axis in self._AXIS_NAMES: return axis + elif self.ndim + axis in self._AXIS_NAMES: + return self.ndim + axis else: try: return self._AXIS_NUMBERS[axis] @@ -931,6 +933,68 @@ def to_dense(self): # compat return self + # ---------------------------------------------------------------------- + # sorting + + _shared_docs['argsort'] = """ + Returns the indices that would sort the %(klass)s. + Equivalent to ``self.values.argsort(axis, kind, order)``. + + Parameters + ---------- + %(argsort_args)s + + Returns + ------- + index_array : numpy.ndarray + Array of indices that sort the %(klass)s along the specified axis. + + See also + -------- + numpy.ndarray.argsort + """ + + _shared_doc_kwargs['argsort_args'] = """ + axis : int or axis name, default -1 + Axis along which to sort. + kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort' + Sorting algorithm. See np.sort for more information. + 'mergesort' is the only stable algorithm. + order : ignored + """ + + @Appender(_shared_docs['argsort'] % _shared_doc_kwargs) + def argsort(self, axis=-1, kind='quicksort', order=None): + return self.values.argsort(self._get_axis_number(axis), kind, order) + + _shared_docs['ordering'] = """ + Returns the order of each entry in the %(klass)s along the specified axis. + + Parameters + ---------- + %(argsort_args)s + fill_value : default -1 + Value to place in locations of NA/null values. + + Returns + ------- + ordering : %(klass)s + %(klass)s with the same shape and axes, with values equal to + the order of each entry along the specified axis. + + See also + -------- + %(klass)s.argsort + """ + + @Appender(_shared_docs['ordering'] % _shared_doc_kwargs) + def ordering(self, axis=-1, kind='quicksort', order=None, fill_value=-1): + axis = self._get_axis_number(axis) + new_values = self.argsort(axis, kind, order).argsort(axis, kind, order) + result = self._constructor(new_values, *self.axes) + result[self.isnull()] = fill_value + return result + # ---------------------------------------------------------------------- # Picklability diff --git a/pandas/core/series.py b/pandas/core/series.py index b25cd63acaff6..9b38f06ecb2c6 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1802,42 +1802,6 @@ def order(self, na_last=None, ascending=True, kind='quicksort', return self.sort_values(ascending=ascending, kind=kind, na_position=na_position, inplace=inplace) - def argsort(self, axis=0, kind='quicksort', order=None): - """ - Overrides ndarray.argsort. Argsorts the value, omitting NA/null values, - and places the result in the same locations as the non-NA values - - Parameters - ---------- - axis : int (can only be zero) - kind : {'mergesort', 'quicksort', 'heapsort'}, default 'quicksort' - Choice of sorting algorithm. See np.sort for more - information. 'mergesort' is the only stable algorithm - order : ignored - - Returns - ------- - argsorted : Series, with -1 indicated where nan values are present - - See also - -------- - numpy.ndarray.argsort - """ - values = self._values - mask = isnull(values) - - if mask.any(): - result = Series(-1, index=self.index, name=self.name, - dtype='int64') - notmask = ~mask - result[notmask] = np.argsort(values[notmask], kind=kind) - return self._constructor(result, - index=self.index).__finalize__(self) - else: - return self._constructor( - np.argsort(values, kind=kind), index=self.index, - dtype='int64').__finalize__(self) - @deprecate_kwarg('take_last', 'keep', mapping={True: 'last', False: 'first'}) def nlargest(self, n=5, keep='first'): diff --git a/pandas/tests/frame/test_sorting.py b/pandas/tests/frame/test_sorting.py index ff2159f8b6f40..667fb4a2e7571 100644 --- a/pandas/tests/frame/test_sorting.py +++ b/pandas/tests/frame/test_sorting.py @@ -93,7 +93,7 @@ def test_sort_index(self): # by column sorted_df = frame.sort_values(by='A') - indexer = frame['A'].argsort().values + indexer = frame['A'].argsort() expected = frame.ix[frame.index[indexer]] assert_frame_equal(sorted_df, expected) diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 1d15a5552a13a..a820b463ccbb4 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -11,8 +11,8 @@ import numpy as np import pandas as pd -from pandas import (Index, Series, DataFrame, isnull, notnull, bdate_range, - date_range, _np_version_under1p9) +from pandas import (Index, Series, DataFrame, Panel, Panel4D, isnull, notnull, + bdate_range, date_range, _np_version_under1p9) from pandas.core.index import MultiIndex from pandas.tseries.index import Timestamp from pandas.tseries.tdi import Timedelta @@ -262,11 +262,7 @@ def test_kurt(self): self.assertEqual(0, s.kurt()) self.assertTrue((df.kurt() == 0).all()) - def test_argsort(self): - self._check_accum_op('argsort') - argsorted = self.ts.argsort() - self.assertTrue(issubclass(argsorted.dtype.type, np.integer)) - + def test_argsort_timestamps(self): # GH 2967 (introduced bug in 0.11-dev I think) s = Series([Timestamp('201301%02d' % (i + 1)) for i in range(5)]) self.assertEqual(s.dtype, 'datetime64[ns]') @@ -275,24 +271,64 @@ def test_argsort(self): self.assertTrue(isnull(shifted[4])) result = s.argsort() - expected = Series(lrange(5), dtype='int64') + expected = np.arange(5, dtype=np.int64) assert_series_equal(result, expected) result = shifted.argsort() - expected = Series(lrange(4) + [-1], dtype='int64') + expected = np.arange(5, dtype=np.int64) assert_series_equal(result, expected) - def test_argsort_stable(self): + def test_argsort_and_ordering(self): + argsorted = self.ts.argsort() + self.assertTrue(issubclass(argsorted.dtype.type, np.integer)) + s = Series(np.random.randint(0, 100, size=10000)) - mindexer = s.argsort(kind='mergesort') - qindexer = s.argsort() + s[::21] = nan + df = DataFrame(s.values.reshape(100, 100)) + p = Panel(s.values.reshape(100, 10, 10)) + p4d = Panel4D(s.values.reshape(10, 10, 10, 10)) + + for x in [s, df, p, p4d]: + for axis in [-1] + list(x._AXIS_NAMES.keys()) + \ + list(x._AXIS_NUMBERS.keys()): + for kind in ['quicksort', 'mergesort', 'heapsort']: + + result = x.argsort(axis=axis, kind=kind) + expected = x.values.argsort(axis=x._get_axis_number(axis), + kind=kind) + self.assert_numpy_array_equal(result, expected) + + result = x.ordering(axis=axis, kind=kind) + expected = x._constructor( + expected.argsort(axis=x._get_axis_number(axis), + kind=kind), + *x.axes) + expected[x.isnull()] = -1 + self.assertEqual(result, expected) + + s = Series([1, 5, nan, 0, 4], index=list('abcde')) + result = s.argsort() + expected = np.array([3, 0, 4, 1, 2], dtype=np.int64) + self.assert_numpy_array_equal(result, expected) - mexpected = np.argsort(s.values, kind='mergesort') - qexpected = np.argsort(s.values, kind='quicksort') + result = s.ordering() + expected = Series([1, 3, -1, 0, 2], index=list('abcde')) + self.assert_series_equal(result, expected) + + df = DataFrame([[1, 5, nan, 0, 4], + [8, 2, 6, 9, 7]], + index=list('xy'), columns=list('abcde')) + result = df.argsort() + expected = np.array([[3, 0, 4, 1, 2], + [1, 2, 4, 0, 3]], + dtype=np.int64) + self.assert_numpy_array_equal(result, expected) - self.assert_numpy_array_equal(mindexer, mexpected) - self.assert_numpy_array_equal(qindexer, qexpected) - self.assertFalse(np.array_equal(qindexer, mindexer)) + result = df.ordering() + expected = DataFrame([[1, 3, -1, 0, 2], + [3, 0, 1, 4, 2]], + index=list('xy'), columns=list('abcde')) + self.assert_frame_equal(result, expected) def test_cumsum(self): self._check_accum_op('cumsum')
... still a work in progress
https://api.github.com/repos/pandas-dev/pandas/pulls/12707
2016-03-24T05:44:18Z
2016-03-24T14:56:53Z
null
2016-03-24T19:49:13Z
BUG: concatenation with a coercable dtype was too aggressive
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index e664020946baf..1ac95c3ea6195 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -122,6 +122,7 @@ Bug Fixes - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) - Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) +- Bugs in concatenation with a coercable dtype was too aggressive. (:issue:`12411`, :issue:`12045`, :issue:`11594`, :issue:`10571`) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index ced663bd62197..df257fb5fd1d0 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -45,21 +45,19 @@ class IndexingError(Exception): class _NDFrameIndexer(object): _valid_types = None _exception = KeyError + axis = None def __init__(self, obj, name): self.obj = obj self.ndim = obj.ndim self.name = name - self.axis = None - def __call__(self, *args, **kwargs): + def __call__(self, axis=None): # we need to return a copy of ourselves - self = self.__class__(self.obj, self.name) + new_self = self.__class__(self.obj, self.name) - # set the passed in values - for k, v in compat.iteritems(kwargs): - setattr(self, k, v) - return self + new_self.axis = axis + return new_self def __iter__(self): raise NotImplementedError('ix is not iterable') diff --git a/pandas/core/internals.py b/pandas/core/internals.py index d99c4ef45dcd3..a31bd347e674a 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -4820,21 +4820,23 @@ def get_reindexed_values(self, empty_dtype, upcasted_na): else: fill_value = upcasted_na - if self.is_null and not getattr(self.block, 'is_categorical', - None): - missing_arr = np.empty(self.shape, dtype=empty_dtype) - if np.prod(self.shape): - # NumPy 1.6 workaround: this statement gets strange if all - # blocks are of same dtype and some of them are empty: - # empty one are considered "null" so they must be filled, - # but no dtype upcasting happens and the dtype may not - # allow NaNs. - # - # In general, no one should get hurt when one tries to put - # incorrect values into empty array, but numpy 1.6 is - # strict about that. + if self.is_null: + if getattr(self.block, 'is_object', False): + # we want to avoid filling with np.nan if we are + # using None; we already know that we are all + # nulls + values = self.block.values.ravel(order='K') + if len(values) and values[0] is None: + fill_value = None + + if getattr(self.block, 'is_datetimetz', False): + pass + elif getattr(self.block, 'is_categorical', False): + pass + else: + missing_arr = np.empty(self.shape, dtype=empty_dtype) missing_arr.fill(fill_value) - return missing_arr + return missing_arr if not self.indexers: if not self.block._can_consolidate: diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index 89552ab776608..e5be2bb08f605 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -2481,7 +2481,7 @@ def f(): # setitem df.loc(axis=0)[:, :, ['C1', 'C3']] = -10 - def test_loc_arguments(self): + def test_loc_axis_arguments(self): index = MultiIndex.from_product([_mklbl('A', 4), _mklbl('B', 2), _mklbl('C', 4), _mklbl('D', 2)]) @@ -2532,6 +2532,41 @@ def f(): self.assertRaises(ValueError, f) + def test_loc_coerceion(self): + + # 12411 + df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), + pd.NaT]}) + expected = df.dtypes + + result = df.iloc[[0]] + assert_series_equal(result.dtypes, expected) + + result = df.iloc[[1]] + assert_series_equal(result.dtypes, expected) + + # 12045 + import datetime + df = DataFrame({'date': [datetime.datetime(2012, 1, 1), + datetime.datetime(1012, 1, 2)]}) + expected = df.dtypes + + result = df.iloc[[0]] + assert_series_equal(result.dtypes, expected) + + result = df.iloc[[1]] + assert_series_equal(result.dtypes, expected) + + # 11594 + df = DataFrame({'text': ['some words'] + [None] * 9}) + expected = df.dtypes + + result = df.iloc[0:2] + assert_series_equal(result.dtypes, expected) + + result = df.iloc[3:] + assert_series_equal(result.dtypes, expected) + def test_per_axis_per_level_setitem(self): # test index maker diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 339ab9e0da6a1..e8ad776fd5578 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -728,6 +728,37 @@ def test_to_string_truncate_multilevel(self): with option_context("display.max_rows", 7, "display.max_columns", 7): self.assertTrue(has_doubly_truncated_repr(df)) + def test_truncate_with_different_dtypes(self): + + # 11594, 12045, 12211 + # when truncated the dtypes of the splits can differ + + # 12211 + df = DataFrame({'date' : [pd.Timestamp('20130101').tz_localize('UTC')] + [pd.NaT]*5}) + + with option_context("display.max_rows", 5): + result = str(df) + self.assertTrue('2013-01-01 00:00:00+00:00' in result) + self.assertTrue('NaT' in result) + self.assertTrue('...' in result) + self.assertTrue('[6 rows x 1 columns]' in result) + + # 11594 + import datetime + s = Series([datetime.datetime(2012, 1, 1)]*10 + [datetime.datetime(1012,1,2)] + [datetime.datetime(2012, 1, 3)]*10) + + with pd.option_context('display.max_rows', 8): + result = str(s) + self.assertTrue('object' in result) + + # 12045 + df = DataFrame({'text': ['some words'] + [None]*9}) + + with pd.option_context('display.max_rows', 8, 'display.max_columns', 3): + result = str(df) + self.assertTrue('None' in result) + self.assertFalse('NaN' in result) + def test_to_html_with_col_space(self): def check_with_width(df, col_space): import re diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 82fdf0a3d3b46..016dd5ed4e56b 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -980,7 +980,9 @@ def get_result(self): if self.axis == 0: new_data = com._concat_compat([x._values for x in self.objs]) name = com._consensus_name_attr(self.objs) - return (Series(new_data, index=self.new_axes[0], name=name) + return (Series(new_data, index=self.new_axes[0], + name=name, + dtype=new_data.dtype) .__finalize__(self, method='concat')) # combine as columns in a frame diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index ddc4e7aaf1588..25e6466fac725 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -923,6 +923,41 @@ def _constructor(self): tm.assertIsInstance(result, NotADataFrame) + def test_empty_dtype_coerce(self): + + # xref to 12411 + # xref to #12045 + # xref to #11594 + # see below + + # 10571 + df1 = DataFrame(data=[[1, None], [2, None]], columns=['a', 'b']) + df2 = DataFrame(data=[[3, None], [4, None]], columns=['a', 'b']) + result = concat([df1, df2]) + expected = df1.dtypes + assert_series_equal(result.dtypes, expected) + + def test_dtype_coerceion(self): + + # 12411 + df = DataFrame({'date': [pd.Timestamp('20130101').tz_localize('UTC'), + pd.NaT]}) + + result = concat([df.iloc[[0]], df.iloc[[1]]]) + assert_series_equal(result.dtypes, df.dtypes) + + # 12045 + import datetime + df = DataFrame({'date': [datetime.datetime(2012, 1, 1), + datetime.datetime(1012, 1, 2)]}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + assert_series_equal(result.dtypes, df.dtypes) + + # 11594 + df = DataFrame({'text': ['some words'] + [None] * 9}) + result = concat([df.iloc[[0]], df.iloc[[1]]]) + assert_series_equal(result.dtypes, df.dtypes) + def test_append_dtype_coerce(self): # GH 4993
closes #12411 closes #12045 closes #11594 closes #10571
https://api.github.com/repos/pandas-dev/pandas/pulls/12702
2016-03-23T16:59:58Z
2016-03-23T17:56:45Z
null
2016-03-23T17:56:45Z
Fix #12564 for Categorical: consistent result if comparing as DataFrame
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index f20b961455ba7..dc50eb94f7120 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -207,7 +207,9 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) + - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`) - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) +- Bug in ``CategoricalBlock`` when Categoricals equality check raises ValueError in DataFrame (:issue:`12564`) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index a31bd347e674a..9f4d4f7aa2e51 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -1874,6 +1874,17 @@ def _slice(self, slicer): # return same dims as we currently have return self.values._slice(slicer) + def _try_coerce_result(self, result): + """ reverse of try_coerce_args """ + + # fix issue #12564: CategoricalBlock is 1-dim only, while category + # datarame can be more. + if ((not com.is_categorical_dtype(result)) and + isinstance(result, np.ndarray)): + result = _block_shape(result, ndim=self.ndim) + + return result + def fillna(self, value, limit=None, inplace=False, downcast=None, mgr=None): # we may need to upcast our fill to match our dtype diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py index e5be2bb08f605..3650051e11886 100644 --- a/pandas/tests/indexing/test_indexing.py +++ b/pandas/tests/indexing/test_indexing.py @@ -936,6 +936,22 @@ def check(target, indexers, value, compare_fn, expected=None): check(target=df, indexers=(df.index, df.columns), value=df, compare_fn=assert_frame_equal, expected=copy) + def test_indexing_with_category(self): + + # https://github.com/pydata/pandas/issues/12564#issuecomment-194022792 + # consistent result if comparing as Dataframe + + cat = DataFrame({'A': ['foo', 'bar', 'baz']}) + exp = DataFrame({'A': [True, False, False]}) + + res = (cat[['A']] == 'foo') + tm.assert_frame_equal(res, exp) + + cat['A'] = cat['A'].astype('category') + + res = (cat[['A']] == 'foo') + tm.assert_frame_equal(res, exp) + def test_indexing_with_datetime_tz(self): # 8260 diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py old mode 100755 new mode 100644
- [x] closes #12564 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12698
2016-03-23T01:44:16Z
2016-04-03T14:06:01Z
null
2016-04-08T12:27:52Z
Fix #12695 : RangeIndex generated with pd.concat with ignore_index as True
diff --git a/pandas/tools/merge.py b/pandas/tools/merge.py index 82fdf0a3d3b46..dd5398af7eb7f 100644 --- a/pandas/tools/merge.py +++ b/pandas/tools/merge.py @@ -992,7 +992,7 @@ def get_result(self): # names (because set via the 'key' argument in the 'concat' # function call. If that's not the case, use the series names # as column names - if (columns.equals(Index(np.arange(len(self.objs)))) and + if (columns.equals(com._default_index(len(self.objs))) and not self.ignore_index): columns = np.array([data[i].name for i in range(len(data))], @@ -1079,8 +1079,7 @@ def _get_concat_axis(self): if self.axis == 0: indexes = [x.index for x in self.objs] elif self.ignore_index: - idx = Index(np.arange(len(self.objs))) - idx.is_unique = True # arange is always unique + idx = com._default_index(len(self.objs)) return idx elif self.keys is None: names = [] @@ -1092,8 +1091,7 @@ def _get_concat_axis(self): if x.name is not None: names.append(x.name) else: - idx = Index(np.arange(len(self.objs))) - idx.is_unique = True + idx = com._default_index(len(self.objs)) return idx return Index(names) @@ -1103,8 +1101,7 @@ def _get_concat_axis(self): indexes = [x._data.axes[self.axis] for x in self.objs] if self.ignore_index: - idx = Index(np.arange(sum(len(i) for i in indexes))) - idx.is_unique = True + idx = com._default_index(sum(len(i) for i in indexes)) return idx if self.keys is None:
- [x] closes #12695 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Replaced generated indexes with RangeIndex, test still needed
https://api.github.com/repos/pandas-dev/pandas/pulls/12696
2016-03-22T20:52:36Z
2016-04-10T14:22:42Z
null
2016-08-31T00:52:25Z
Fix typos
diff --git a/pandas/core/frame.py b/pandas/core/frame.py index dc82852f4661e..b2c90bd1c38b8 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -3624,7 +3624,7 @@ def combine(self, other, func, fill_value=None, overwrite=True): other_mask = isnull(otherSeries) # don't overwrite columns unecessarily - # DO propogate if this column is not in the intersection + # DO propagate if this column is not in the intersection if not overwrite and other_mask.all(): result[col] = this[col].copy() continue diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 484cb6afa77b2..d99c4ef45dcd3 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -158,7 +158,7 @@ def array_dtype(self): def make_block(self, values, placement=None, ndim=None, **kwargs): """ - Create a new block, with type inference propogate any values that are + Create a new block, with type inference propagate any values that are not specified """ if placement is None: diff --git a/pandas/io/stata.py b/pandas/io/stata.py index a878829a60404..657cfd9777db9 100644 --- a/pandas/io/stata.py +++ b/pandas/io/stata.py @@ -1933,7 +1933,7 @@ def _check_column_names(self, data): * Variables with names that are too long When an illegal variable name is detected, it is converted, and if - dates are exported, the variable name is propogated to the date + dates are exported, the variable name is propagated to the date conversion dictionary """ converted_names = [] diff --git a/pandas/tseries/index.py b/pandas/tseries/index.py index 8381273873dcf..50171c3ae4fe3 100644 --- a/pandas/tseries/index.py +++ b/pandas/tseries/index.py @@ -1126,7 +1126,7 @@ def _can_fast_union(self, other): return (right_start == left_end + offset) or right_start in left except (ValueError): - # if we are comparing an offset that does not propogate timezones + # if we are comparing an offset that does not propagate timezones # this will raise return False diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py index 4ddfc6ac573e4..e2de3c5e01ba2 100644 --- a/pandas/tseries/tests/test_resample.py +++ b/pandas/tseries/tests/test_resample.py @@ -2029,7 +2029,7 @@ def test_resample_tz_localized(self): assert_series_equal(result, expected) # GH 6397 - # comparing an offset that doesn't propogate tz's + # comparing an offset that doesn't propagate tz's rng = date_range('1/1/2011', periods=20000, freq='H') rng = rng.tz_localize('EST') ts = DataFrame(index=rng)
https://api.github.com/repos/pandas-dev/pandas/pulls/12692
2016-03-22T15:58:55Z
2016-03-22T16:13:59Z
null
2016-03-22T16:14:03Z
Fix : 9784 Added the note for display.max_colwidth in dsintro.rst
diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index d674ee293e4ed..927cabb9cbd71 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -720,6 +720,21 @@ option: pd.DataFrame(np.random.randn(3, 12)) +Note : You can also adjust the max width of the individual columns by using `display.max_colwidth` +option: + +.. ipython:: python + + datafile={'filename':['filename_01','filename_02'], + 'path':["media/user_name/storage/folder_01/filename_01", + "media/user_name/storage/folder_02/filename_02"]} + + pd.set_option('display.max_colwidth',30) + pd.DataFrame(data=datafile) + + pd.set_option('display.max_colwidth',100) + pd.DataFrame(data=datafile) + .. ipython:: python :suppress:
- [x] closes #9784 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12682
2016-03-21T12:45:47Z
2016-04-04T16:52:39Z
null
2016-04-04T16:52:56Z
API: Index.take inconsistently handle fill_value
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 1179a347e4c46..486d7f3a067bc 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -29,7 +29,6 @@ New features Enhancements ~~~~~~~~~~~~ - .. _whatsnew_0181.partial_string_indexing: Partial string indexing on ``DateTimeIndex`` when part of a ``MultiIndex`` @@ -59,6 +58,14 @@ Other Enhancements - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). +- ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) + +.. ipython:: python + + idx = pd.Index([1., 2., 3., 4.], dtype='float') + idx.take([2, -1]) # default, allow_fill=True, fill_value=None + idx.take([2, -1], fill_value=True) + .. _whatsnew_0181.api: diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 588521e6810b3..d5e0c71087fdf 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1329,24 +1329,60 @@ def _ensure_compat_concat(indexes): return indexes - def take(self, indices, axis=0, allow_fill=True, fill_value=None): - """ - return a new Index of the values selected by the indexer + _index_shared_docs['take'] = """ + return a new Index of the values selected by the indices For internal compatibility with numpy arrays. - # filling must always be None/nan here - # but is passed thru internally + Parameters + ---------- + indices : list + Indices to be taken + axis : int, optional + The axis over which to select values, always 0. + allow_fill : bool, default True + fill_value : bool, default None + If allow_fill=True and fill_value is not None, indices specified by + -1 is regarded as NA. If Index doesn't hold NA, raise ValueError See also -------- numpy.ndarray.take """ - + @Appender(_index_shared_docs['take']) + def take(self, indices, axis=0, allow_fill=True, fill_value=None): indices = com._ensure_platform_int(indices) - taken = self.values.take(indices) + if self._can_hold_na: + taken = self._assert_take_fillable(self.values, indices, + allow_fill=allow_fill, + fill_value=fill_value, + na_value=self._na_value) + else: + if allow_fill and fill_value is not None: + msg = 'Unable to fill values because {0} cannot contain NA' + raise ValueError(msg.format(self.__class__.__name__)) + taken = self.values.take(indices) return self._shallow_copy(taken) + def _assert_take_fillable(self, values, indices, allow_fill=True, + fill_value=None, na_value=np.nan): + """ Internal method to handle NA filling of take """ + indices = com._ensure_platform_int(indices) + + # only fill if we are passing a non-None fill_value + if allow_fill and fill_value is not None: + if (indices < -1).any(): + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + raise ValueError(msg) + taken = values.take(indices) + mask = indices == -1 + if mask.any(): + taken[mask] = na_value + else: + taken = values.take(indices) + return taken + @cache_readonly def _isnan(self): """ return if each value is nan""" diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index a9390c76f26a6..5844c69c57f0e 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -459,21 +459,13 @@ def _convert_list_indexer(self, keyarr, kind=None): return None - def take(self, indexer, axis=0, allow_fill=True, fill_value=None): - """ - For internal compatibility with numpy arrays. - - # filling must always be None/nan here - # but is passed thru internally - assert isnull(fill_value) - - See also - -------- - numpy.ndarray.take - """ - - indexer = com._ensure_platform_int(indexer) - taken = self.codes.take(indexer) + @Appender(_index_shared_docs['take']) + def take(self, indices, axis=0, allow_fill=True, fill_value=None): + indices = com._ensure_platform_int(indices) + taken = self._assert_take_fillable(self.codes, indices, + allow_fill=allow_fill, + fill_value=fill_value, + na_value=-1) return self._create_from_codes(taken) def delete(self, loc): diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index 4a77282b3877a..49a9c3d622c1b 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -1,3 +1,4 @@ + # pylint: disable=E1101,E1103,W0232 import datetime import warnings @@ -11,7 +12,7 @@ from pandas.compat import range, zip, lrange, lzip, map from pandas import compat -from pandas.core.base import FrozenList +from pandas.core.base import FrozenList, FrozenNDArray import pandas.core.base as base from pandas.util.decorators import (Appender, cache_readonly, deprecate, deprecate_kwarg) @@ -1003,12 +1004,38 @@ def __getitem__(self, key): names=self.names, sortorder=sortorder, verify_integrity=False) - def take(self, indexer, axis=None): - indexer = com._ensure_platform_int(indexer) - new_labels = [lab.take(indexer) for lab in self.labels] - return MultiIndex(levels=self.levels, labels=new_labels, + @Appender(_index_shared_docs['take']) + def take(self, indices, axis=0, allow_fill=True, fill_value=None): + indices = com._ensure_platform_int(indices) + taken = self._assert_take_fillable(self.labels, indices, + allow_fill=allow_fill, + fill_value=fill_value, + na_value=-1) + return MultiIndex(levels=self.levels, labels=taken, names=self.names, verify_integrity=False) + def _assert_take_fillable(self, values, indices, allow_fill=True, + fill_value=None, na_value=None): + """ Internal method to handle NA filling of take """ + # only fill if we are passing a non-None fill_value + if allow_fill and fill_value is not None: + if (indices < -1).any(): + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + raise ValueError(msg) + taken = [lab.take(indices) for lab in self.labels] + mask = indices == -1 + if mask.any(): + masked = [] + for new_label in taken: + label_values = new_label.values() + label_values[mask] = na_value + masked.append(base.FrozenNDArray(label_values)) + taken = masked + else: + taken = [lab.take(indices) for lab in self.labels] + return taken + def append(self, other): """ Append a collection of Index options together diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 24a20f7adf624..41933247961e9 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -1240,6 +1240,34 @@ def test_nan_first_take_datetime(self): exp = Index([idx[-1], idx[0], idx[1]]) tm.assert_index_equal(res, exp) + def test_take_fill_value(self): + # GH 12631 + idx = pd.Index(list('ABC'), name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.Index(list('BAC'), name='xxx') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.Index(['B', 'A', np.nan], name='xxx') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.Index(['B', 'A', 'C'], name='xxx') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_reindex_preserves_name_if_target_is_list_or_ndarray(self): # GH6552 idx = pd.Index([0, 1, 2]) diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index d0aae9ba281f3..a8534309c115c 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -708,3 +708,100 @@ def test_fillna_categorical(self): with tm.assertRaisesRegexp(ValueError, 'fill value must be in categories'): idx.fillna(2.0) + + def test_take_fill_value(self): + # GH 12631 + + # numeric category + idx = pd.CategoricalIndex([1, 2, 3], name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.CategoricalIndex([2, 1, 3], name='xxx') + tm.assert_index_equal(result, expected) + tm.assert_categorical_equal(result.values, expected.values) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.CategoricalIndex([2, 1, np.nan], categories=[1, 2, 3], + name='xxx') + tm.assert_index_equal(result, expected) + tm.assert_categorical_equal(result.values, expected.values) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.CategoricalIndex([2, 1, 3], name='xxx') + tm.assert_index_equal(result, expected) + tm.assert_categorical_equal(result.values, expected.values) + + # object category + idx = pd.CategoricalIndex(list('CBA'), categories=list('ABC'), + ordered=True, name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'), + ordered=True, name='xxx') + tm.assert_index_equal(result, expected) + tm.assert_categorical_equal(result.values, expected.values) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.CategoricalIndex(['B', 'C', np.nan], + categories=list('ABC'), ordered=True, + name='xxx') + tm.assert_index_equal(result, expected) + tm.assert_categorical_equal(result.values, expected.values) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.CategoricalIndex(list('BCA'), categories=list('ABC'), + ordered=True, name='xxx') + tm.assert_index_equal(result, expected) + tm.assert_categorical_equal(result.values, expected.values) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + + def test_take_fill_value_datetime(self): + + # datetime category + idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + name='xxx') + idx = pd.CategoricalIndex(idx) + result = idx.take(np.array([1, 0, -1])) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx') + expected = pd.CategoricalIndex(expected) + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'], + name='xxx') + exp_cats = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01']) + expected = pd.CategoricalIndex(expected, categories=exp_cats) + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx') + expected = pd.CategoricalIndex(expected) + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index f70ea49bd4c29..f37e62e52b618 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -1514,6 +1514,46 @@ def test_take_preserve_name(self): taken = self.index.take([3, 0, 1]) self.assertEqual(taken.names, self.index.names) + def test_take_fill_value(self): + # GH 12631 + vals = [['A', 'B'], + [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]] + idx = pd.MultiIndex.from_product(vals, names=['str', 'dt']) + + result = idx.take(np.array([1, 0, -1])) + exp_vals = [('A', pd.Timestamp('2011-01-02')), + ('A', pd.Timestamp('2011-01-01')), + ('B', pd.Timestamp('2011-01-02'))] + expected = pd.MultiIndex.from_tuples(exp_vals, names=['str', 'dt']) + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + exp_vals = [('A', pd.Timestamp('2011-01-02')), + ('A', pd.Timestamp('2011-01-01')), + (np.nan, pd.NaT)] + expected = pd.MultiIndex.from_tuples(exp_vals, names=['str', 'dt']) + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + exp_vals = [('A', pd.Timestamp('2011-01-02')), + ('A', pd.Timestamp('2011-01-01')), + ('B', pd.Timestamp('2011-01-02'))] + expected = pd.MultiIndex.from_tuples(exp_vals, names=['str', 'dt']) + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_join_level(self): def _check_how(other, how): join_index, lidx, ridx = other.join(self.index, how=how, diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py index 325e0df14a07e..56e19c8b1d7d9 100644 --- a/pandas/tests/indexes/test_numeric.py +++ b/pandas/tests/indexes/test_numeric.py @@ -343,6 +343,34 @@ def test_fillna_float64(self): exp = Index([1.0, 'obj', 3.0], name='x') self.assert_index_equal(idx.fillna('obj'), exp) + def test_take_fill_value(self): + # GH 12631 + idx = pd.Float64Index([1., 2., 3.], name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.Float64Index([2., 1., 3.], name='xxx') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.Float64Index([2., 1., np.nan], name='xxx') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.Float64Index([2., 1., 3.], name='xxx') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + class TestInt64Index(Numeric, tm.TestCase): _holder = Int64Index @@ -757,6 +785,33 @@ def test_take_preserve_name(self): taken = index.take([3, 0, 1]) self.assertEqual(index.name, taken.name) + def test_take_fill_value(self): + # GH 12631 + idx = pd.Int64Index([1, 2, 3], name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.Int64Index([2, 1, 3], name='xxx') + tm.assert_index_equal(result, expected) + + # fill_value + msg = "Unable to fill values because Int64Index cannot contain NA" + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -1]), fill_value=True) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.Int64Index([2, 1, 3], name='xxx') + tm.assert_index_equal(result, expected) + + msg = "Unable to fill values because Int64Index cannot contain NA" + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_int_name_format(self): index = Index(['a', 'b', 'c'], name=0) s = Series(lrange(3), index) diff --git a/pandas/tests/indexes/test_range.py b/pandas/tests/indexes/test_range.py index 84ed1049936d3..f41c252f44d39 100644 --- a/pandas/tests/indexes/test_range.py +++ b/pandas/tests/indexes/test_range.py @@ -647,6 +647,33 @@ def test_take_preserve_name(self): taken = index.take([3, 0, 1]) self.assertEqual(index.name, taken.name) + def test_take_fill_value(self): + # GH 12631 + idx = pd.RangeIndex(1, 4, name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.Int64Index([2, 1, 3], name='xxx') + tm.assert_index_equal(result, expected) + + # fill_value + msg = "Unable to fill values because RangeIndex cannot contain NA" + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -1]), fill_value=True) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.Int64Index([2, 1, 3], name='xxx') + tm.assert_index_equal(result, expected) + + msg = "Unable to fill values because RangeIndex cannot contain NA" + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_print_unicode_columns(self): df = pd.DataFrame({u("\u05d0"): [1, 2, 3], "\u05d1": [4, 5, 6], diff --git a/pandas/tseries/base.py b/pandas/tseries/base.py index 7584b99dbdb97..48e17fd84a3b2 100644 --- a/pandas/tseries/base.py +++ b/pandas/tseries/base.py @@ -12,6 +12,7 @@ import pandas.tslib as tslib import pandas.lib as lib from pandas.core.index import Index +from pandas.indexes.base import _index_shared_docs from pandas.util.decorators import Appender, cache_readonly import pandas.tseries.frequencies as frequencies import pandas.algos as _algos @@ -258,22 +259,22 @@ def sort_values(self, return_indexer=False, ascending=True): return self._simple_new(sorted_values, **attribs) + @Appender(_index_shared_docs['take']) def take(self, indices, axis=0, allow_fill=True, fill_value=None): - """ - Analogous to ndarray.take - """ indices = com._ensure_int64(indices) + maybe_slice = lib.maybe_indices_to_slice(indices, len(self)) if isinstance(maybe_slice, slice): return self[maybe_slice] - taken = self.asi8.take(com._ensure_platform_int(indices)) - - # only fill if we are passing a non-None fill_value - if allow_fill and fill_value is not None: - mask = indices == -1 - if mask.any(): - taken[mask] = tslib.iNaT - return self._shallow_copy(taken, freq=None) + + taken = self._assert_take_fillable(self.asi8, indices, + allow_fill=allow_fill, + fill_value=fill_value, + na_value=tslib.iNaT) + + # keep freq in PeriodIndex, reset otherwise + freq = self.freq if isinstance(self, com.ABCPeriodIndex) else None + return self._shallow_copy(taken, freq=freq) def get_duplicates(self): values = Index.get_duplicates(self) diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index b34af4e62845b..e9a9796f9c48d 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -851,14 +851,6 @@ def _format_native_types(self, na_rep=u('NaT'), date_format=None, values[imask] = np.array([formatter(dt) for dt in values[imask]]) return values - def take(self, indices, axis=0, allow_fill=True, fill_value=None): - """ - Analogous to ndarray.take - """ - indices = com._ensure_platform_int(indices) - taken = self.asi8.take(indices, axis=axis) - return self._simple_new(taken, self.name, freq=self.freq) - def append(self, other): """ Append a collection of Index options together diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index e0dad2995f91c..947a200e3b63d 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -2815,6 +2815,38 @@ def test_take(self): self.assertEqual(taken.freq, index.freq) self.assertEqual(taken.name, expected.name) + def test_take_fill_value(self): + # GH 12631 + idx = pd.PeriodIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + name='xxx', freq='D') + result = idx.take(np.array([1, 0, -1])) + expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx', freq='D') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', 'NaT'], + name='xxx', freq='D') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.PeriodIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx', freq='D') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_joins(self): index = period_range('1/1/2000', '1/20/2000', freq='D') diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py index 4bdd0ed462852..e285856eca3f7 100644 --- a/pandas/tseries/tests/test_timedeltas.py +++ b/pandas/tseries/tests/test_timedeltas.py @@ -1613,6 +1613,38 @@ def test_take(self): self.assertIsNone(taken.freq) self.assertEqual(taken.name, expected.name) + def test_take_fill_value(self): + # GH 12631 + idx = pd.TimedeltaIndex(['1 days', '2 days', '3 days'], + name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.TimedeltaIndex(['2 days', '1 days', '3 days'], + name='xxx') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.TimedeltaIndex(['2 days', '1 days', 'NaT'], + name='xxx') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.TimedeltaIndex(['2 days', '1 days', '3 days'], + name='xxx') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_isin(self): index = tm.makeTimedeltaIndex(4) diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index 615390b5209b6..8ed3ef93cbdb4 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -3297,6 +3297,69 @@ def test_take(self): self.assertEqual(taken.tz, expected.tz) self.assertEqual(taken.name, expected.name) + def test_take_fill_value(self): + # GH 12631 + idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + name='xxx') + result = idx.take(np.array([1, 0, -1])) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'], + name='xxx') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + + def test_take_fill_value_with_timezone(self): + idx = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + name='xxx', tz='US/Eastern') + result = idx.take(np.array([1, 0, -1])) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx', tz='US/Eastern') + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', 'NaT'], + name='xxx', tz='US/Eastern') + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, + fill_value=True) + expected = pd.DatetimeIndex(['2011-02-01', '2011-01-01', '2011-03-01'], + name='xxx', tz='US/Eastern') + tm.assert_index_equal(result, expected) + + msg = ('When allow_fill=True and fill_value is not None, ' + 'all indices must be >= -1') + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with tm.assertRaisesRegexp(ValueError, msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + with tm.assertRaises(IndexError): + idx.take(np.array([1, -5])) + def test_map_bug_1677(self): index = DatetimeIndex(['2012-04-25 09:30:00.393000']) f = index.asof
- [x] closes #12631 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12676
2016-03-20T15:31:10Z
2016-03-31T13:38:15Z
null
2016-03-31T13:40:47Z
BUG: .describe lost CategoricalIndex info
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index e6ea9217347ea..55750fe5700c6 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -160,8 +160,6 @@ Bug Fixes - - - Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) @@ -169,3 +167,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) +- Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 7233e063afe8e..4f9fa260182f7 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4899,7 +4899,9 @@ def describe_1d(data, percentiles): for name in idxnames: if name not in names: names.append(name) + d = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1) + d.columns = self.columns._shallow_copy(values=d.columns.values) d.columns.names = data.columns.names return d diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 58828d52b60dd..588521e6810b3 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -327,8 +327,7 @@ def _simple_new(cls, values, name=None, dtype=None, **kwargs): result._reset_identity() return result - def _shallow_copy(self, values=None, **kwargs): - """ + _index_shared_docs['_shallow_copy'] = """ create a new Index with the same class as the caller, don't copy the data, use the same object attributes with passed in attributes taking precedence @@ -340,6 +339,8 @@ def _shallow_copy(self, values=None, **kwargs): values : the values to create the new Index, optional kwargs : updates the default attributes for this Index """ + @Appender(_index_shared_docs['_shallow_copy']) + def _shallow_copy(self, values=None, **kwargs): if values is None: values = self.values attributes = self._get_attributes_dict() diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index cc25b5fc6fb8c..a9390c76f26a6 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -7,7 +7,7 @@ deprecate_kwarg) from pandas.core.missing import _clean_reindex_fill_method from pandas.core.config import get_option -from pandas.indexes.base import Index +from pandas.indexes.base import Index, _index_shared_docs import pandas.core.base as base import pandas.core.common as com import pandas.indexes.base as ibase @@ -136,6 +136,19 @@ def _simple_new(cls, values, name=None, categories=None, ordered=None, result._reset_identity() return result + @Appender(_index_shared_docs['_shallow_copy']) + def _shallow_copy(self, values=None, categories=None, ordered=None, + **kwargs): + # categories and ordered can't be part of attributes, + # as these are properties + if categories is None: + categories = self.categories + if ordered is None: + ordered = self.ordered + return super(CategoricalIndex, + self)._shallow_copy(values=values, categories=categories, + ordered=ordered, **kwargs) + def _is_dtype_compat(self, other): """ *this is an internal non-public method* diff --git a/pandas/indexes/multi.py b/pandas/indexes/multi.py index d14568ceca258..4a77282b3877a 100644 --- a/pandas/indexes/multi.py +++ b/pandas/indexes/multi.py @@ -28,7 +28,8 @@ from pandas.core.config import get_option from pandas.indexes.base import (Index, _ensure_index, _ensure_frozen, - _get_na_value, InvalidIndexError) + _get_na_value, InvalidIndexError, + _index_shared_docs) import pandas.indexes.base as ibase @@ -381,6 +382,7 @@ def view(self, cls=None): def _shallow_copy_with_infer(self, values=None, **kwargs): return self._shallow_copy(values, **kwargs) + @Appender(_index_shared_docs['_shallow_copy']) def _shallow_copy(self, values=None, **kwargs): if values is not None: if 'name' in kwargs: diff --git a/pandas/indexes/range.py b/pandas/indexes/range.py index 4b06af9240436..dbee753af855c 100644 --- a/pandas/indexes/range.py +++ b/pandas/indexes/range.py @@ -6,7 +6,7 @@ from pandas import compat from pandas.compat import lrange, range -from pandas.indexes.base import Index +from pandas.indexes.base import Index, _index_shared_docs from pandas.util.decorators import Appender, cache_readonly import pandas.core.common as com import pandas.indexes.base as ibase @@ -225,9 +225,8 @@ def has_duplicates(self): def tolist(self): return lrange(self._start, self._stop, self._step) + @Appender(_index_shared_docs['_shallow_copy']) def _shallow_copy(self, values=None, **kwargs): - """ create a new Index, don't copy the data, use the same object attributes - with passed in attributes taking precedence """ if values is None: return RangeIndex(name=self.name, fastpath=True, **dict(self._get_data_as_items())) diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index d9cad6a542fb3..74682c506c769 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -257,6 +257,52 @@ def test_bool_describe_in_mixed_frame(self): index=['count', 'unique', 'top', 'freq']) assert_frame_equal(result, expected) + def test_describe_categorical_columns(self): + # GH 11558 + columns = pd.CategoricalIndex(['int1', 'int2', 'obj'], + ordered=True, name='XXX') + df = DataFrame({'int1': [10, 20, 30, 40, 50], + 'int2': [10, 20, 30, 40, 50], + 'obj': ['A', 0, None, 'X', 1]}, + columns=columns) + result = df.describe() + + exp_columns = pd.CategoricalIndex(['int1', 'int2'], + categories=['int1', 'int2', 'obj'], + ordered=True, name='XXX') + expected = DataFrame({'int1': [5, 30, df.int1.std(), + 10, 20, 30, 40, 50], + 'int2': [5, 30, df.int2.std(), + 10, 20, 30, 40, 50]}, + index=['count', 'mean', 'std', 'min', '25%', + '50%', '75%', 'max'], + columns=exp_columns) + tm.assert_frame_equal(result, expected) + tm.assert_categorical_equal(result.columns.values, + expected.columns.values) + + def test_describe_datetime_columns(self): + columns = pd.DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01'], + freq='MS', tz='US/Eastern', name='XXX') + df = DataFrame({0: [10, 20, 30, 40, 50], + 1: [10, 20, 30, 40, 50], + 2: ['A', 0, None, 'X', 1]}) + df.columns = columns + result = df.describe() + + exp_columns = pd.DatetimeIndex(['2011-01-01', '2011-02-01'], + freq='MS', tz='US/Eastern', name='XXX') + expected = DataFrame({0: [5, 30, df.iloc[:, 0].std(), + 10, 20, 30, 40, 50], + 1: [5, 30, df.iloc[:, 1].std(), + 10, 20, 30, 40, 50]}, + index=['count', 'mean', 'std', 'min', '25%', + '50%', '75%', 'max']) + expected.columns = exp_columns + tm.assert_frame_equal(result, expected) + self.assertEqual(result.columns.freq, 'MS') + self.assertEqual(result.columns.tz, expected.columns.tz) + def test_reduce_mixed_frame(self): # GH 6806 df = DataFrame({ diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index 9aeff41ee70f5..b60cbcba45dd8 100755 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -2768,7 +2768,7 @@ def test_value_counts_with_nan(self): pd.Series([2, 1, 3], index=pd.CategoricalIndex(["a", "b", np.nan]))) - with tm.assert_produces_warning(FutureWarning): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): s = pd.Series(pd.Categorical( ["a", "b", "a"], categories=["a", "b", np.nan])) tm.assert_series_equal( @@ -2779,7 +2779,7 @@ def test_value_counts_with_nan(self): pd.Series([2, 1, 0], index=pd.CategoricalIndex(["a", "b", np.nan]))) - with tm.assert_produces_warning(FutureWarning): + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): s = pd.Series(pd.Categorical( ["a", "b", None, "a", None, None], categories=["a", "b", np.nan ])) diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index ac98e377f1719..ff9fd7dfb5980 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -2821,8 +2821,8 @@ def test_non_cython_api(self): # describe expected = DataFrame(dict(B=concat( - [df.loc[[0, 1], 'B'].describe(), df.loc[[2], 'B'].describe() - ], keys=[1, 3]))) + [df.loc[[0, 1], 'B'].describe(), df.loc[[2], 'B'].describe()], + keys=[1, 3]))) expected.index.names = ['A', None] result = g.describe() assert_frame_equal(result, expected) @@ -4008,6 +4008,36 @@ def test_groupby_categorical_index(self): [0, 1, 2, 3], levels, ordered=True), name='cats') assert_frame_equal(result, expected) + def test_groupby_describe_categorical_columns(self): + # GH 11558 + cats = pd.CategoricalIndex(['qux', 'foo', 'baz', 'bar'], + categories=['foo', 'bar', 'baz', 'qux'], + ordered=True) + df = DataFrame(np.random.randn(20, 4), columns=cats) + result = df.groupby([1, 2, 3, 4] * 5).describe() + + tm.assert_index_equal(result.columns, cats) + tm.assert_categorical_equal(result.columns.values, cats.values) + + def test_groupby_unstack_categorical(self): + # GH11558 (example is taken from the original issue) + df = pd.DataFrame({'a': range(10), + 'medium': ['A', 'B'] * 5, + 'artist': list('XYXXY') * 2}) + df['medium'] = df['medium'].astype('category') + + gcat = df.groupby(['artist', 'medium'])['a'].count().unstack() + result = gcat.describe() + + exp_columns = pd.CategoricalIndex(['A', 'B'], ordered=False, + name='medium') + tm.assert_index_equal(result.columns, exp_columns) + tm.assert_categorical_equal(result.columns.values, exp_columns.values) + + result = gcat['A'] + gcat['B'] + expected = pd.Series([6, 4], index=pd.Index(['X', 'Y'], name='artist')) + tm.assert_series_equal(result, expected) + def test_groupby_groups_datetimeindex(self): # #1430 from pandas.tseries.api import DatetimeIndex
- [x] closes #11558 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry #11558 is partially fixed by #12531, this PR let `.describe()` to preserve `CategoricalIndex` info. And added explicit test cases derived from #11558.
https://api.github.com/repos/pandas-dev/pandas/pulls/12675
2016-03-20T12:06:58Z
2016-03-29T20:09:45Z
null
2016-03-29T20:23:35Z
Fixed #12651: providing links to the .expanding in .cum*
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index 1e30921e7248f..aad36fa5bbdaa 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -466,6 +466,8 @@ preserve the location of NA values: df.cumsum() +Please see :ref:`expanding <stats.moments.expanding>` + Here is a quick reference summary table of common functions. Each also takes an optional ``level`` parameter which applies only if the object has a :ref:`hierarchical index<advanced.hierarchical>`.
- [ x] closes #12561 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry #12561 : wrote the `.expanding` function int the `basics.rst`
https://api.github.com/repos/pandas-dev/pandas/pulls/12674
2016-03-20T09:22:49Z
2016-03-20T15:28:55Z
null
2016-03-20T16:43:08Z
Fixed #12651: providing links to the .cum* in .expanding
diff --git a/doc/source/computation.rst b/doc/source/computation.rst index d247f79c00a46..aff679c4b1ed7 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -578,6 +578,7 @@ they are implemented in pandas such that the following two calls are equivalent: df.expanding(min_periods=1).mean()[:5] These have a similar set of methods to ``.rolling`` methods. +Please see the :ref:`cumsum, cummax, cummin, cumprod <basics.stats>` Method Summary ~~~~~~~~~~~~~~
- [x] closes #12651 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry #12651 commit for the .cum\* in .expanding
https://api.github.com/repos/pandas-dev/pandas/pulls/12673
2016-03-20T09:10:34Z
2016-05-07T18:51:30Z
null
2016-05-07T18:51:30Z
DOC: Fix `xarray` broken link
diff --git a/doc/source/install.rst b/doc/source/install.rst index f0e6955f38612..a7abf4ce54fb9 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -244,7 +244,7 @@ Optional Dependencies * `Cython <http://www.cython.org>`__: Only necessary to build development version. Version 0.19.1 or higher. * `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions -* `xarray <http://xarray.readthedocs.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended. +* `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended. * `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended. * `SQLAlchemy <http://www.sqlalchemy.org>`__: for SQL database support. Version 0.8.1 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the `SQLAlchemy docs <http://docs.sqlalchemy.org/en/latest/dialects/index.html>`__. Some common drivers are:
[skip ci]
https://api.github.com/repos/pandas-dev/pandas/pulls/12672
2016-03-20T08:04:07Z
2016-03-20T14:48:49Z
null
2016-03-20T15:17:52Z
Fixed #12661: more clarification in the where statement
diff --git a/doc/source/10min.rst b/doc/source/10min.rst index d51290b2a983b..46f3b44055571 100644 --- a/doc/source/10min.rst +++ b/doc/source/10min.rst @@ -282,7 +282,14 @@ Using a single column's values to select data. df[df.A > 0] -A ``where`` operation for getting. +A ``where`` is an attribute of the DataFrame class which helps in getting the results +based upon the conditional statement that was passed as an argument. + +.. ipython:: python + + df.where(df>0) + +It is similar to the statement. .. ipython:: python
- [x] closes #12661 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry Improvement in the documentation w.r.t to issue #12661
https://api.github.com/repos/pandas-dev/pandas/pulls/12671
2016-03-20T06:55:34Z
2016-05-13T23:36:43Z
null
2016-05-13T23:36:43Z
ENH: xz compression in to_csv() resolves #11852
diff --git a/ci/requirements-2.7.pip b/ci/requirements-2.7.pip index 9334ca9e03cc1..cc3462dbf9ed0 100644 --- a/ci/requirements-2.7.pip +++ b/ci/requirements-2.7.pip @@ -4,4 +4,5 @@ google-api-python-client==1.2 python-gflags==2.0 oauth2client==1.5.0 pathlib +backports.lzma py diff --git a/doc/source/install.rst b/doc/source/install.rst index a7abf4ce54fb9..6bf707de5d925 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -271,6 +271,7 @@ Optional Dependencies `httplib2 <http://pypi.python.org/pypi/httplib2>`__ and `google-api-python-client <http://github.com/google/google-api-python-client>`__ : Needed for :mod:`~pandas.io.gbq` +* `Backports.lzma <https://pypi.python.org/pypi/backports.lzma/>`__: Only for Python 2, for writing to and/or reading from an xz compressed DataFrame in CSV. * One of the following combinations of libraries is needed to use the top-level :func:`~pandas.io.html.read_html` function: diff --git a/doc/source/io.rst b/doc/source/io.rst index 4abc3d722465d..d606e919e4292 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -217,14 +217,14 @@ chunksize : int, default ``None`` Quoting, Compression, and File Format +++++++++++++++++++++++++++++++++++++ -compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``None``}, default ``'infer'`` +compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``None``}, default ``'infer'`` For on-the-fly decompression of on-disk data. If 'infer', then use gzip, - bz2 or zip if filepath_or_buffer is a string ending in '.gz', '.bz2' or - '.zip', respectively, and no decompression otherwise. If using 'zip', + bz2, zip, or xz if filepath_or_buffer is a string ending in '.gz', '.bz2', + '.zip', or '.xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to ``None`` for no decompression. - .. versionadded:: 0.18.0 support for 'zip' compression. + .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression. thousands : str, default ``None`` Thousands separator. diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index e664020946baf..7cf5a0780bb7c 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -57,6 +57,7 @@ Other Enhancements ^^^^^^^^^^^^^^^^^^ - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) +- ``pd.read_csv()`` now supports opening files using xz compression when ``compression='xz'`` is specified, `xz` is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). .. _whatsnew_0181.api: diff --git a/pandas/core/frame.py b/pandas/core/frame.py index b2c90bd1c38b8..a504f91705733 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1301,7 +1301,7 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None, defaults to 'ascii' on Python 2 and 'utf-8' on Python 3. compression : string, optional a string representing the compression to use in the output file, - allowed values are 'gzip', 'bz2', + allowed values are 'gzip', 'bz2', 'xz', only used when the first argument is a filename line_terminator : string, default '\\n' The newline character or character sequence to use in the output diff --git a/pandas/io/common.py b/pandas/io/common.py index d44057178d27e..485f52f4274ff 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -375,6 +375,12 @@ def _get_handle(path, mode, encoding=None, compression=None): raise ValueError('Multiple files found in ZIP file.' ' Only one file per ZIP :{}' .format(zip_names)) + elif compression == 'xz': + if compat.PY2: + from backports import lzma + else: + import lzma + f = lzma.LZMAFile(path, mode) else: raise ValueError('Unrecognized compression type: %s' % compression) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 301b9c889f5ff..b475e316ab04e 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -158,14 +158,14 @@ class ParserWarning(Warning): information <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ on ``iterator`` and ``chunksize``. -compression : {'infer', 'gzip', 'bz2', 'zip', None}, default 'infer' +compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer', then use gzip, - bz2 or zip if filepath_or_buffer is a string ending in '.gz', '.bz2' or - '.zip', respectively, and no decompression otherwise. If using 'zip', - the ZIP file must contain only one data file to be read in. + bz2, zip or xz if filepath_or_buffer is a string ending in '.gz', '.bz2', + '.zip', or 'xz', respectively, and no decompression otherwise. If using + 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. - .. versionadded:: 0.18.0 support for 'zip' compression. + .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression. thousands : str, default None Thousands separator @@ -279,6 +279,8 @@ def _read(filepath_or_buffer, kwds): inferred_compression = 'bz2' elif filepath_or_buffer.endswith('.zip'): inferred_compression = 'zip' + elif filepath_or_buffer.endswith('.xz'): + inferred_compression = 'xz' else: inferred_compression = None else: @@ -1421,6 +1423,21 @@ def _wrap_compressed(f, compression, encoding=None): raise ValueError('Multiple files found in compressed ' 'zip file %s', str(zip_names)) + elif compression == 'xz': + + if compat.PY3: + import lzma + else: + from backports import lzma + f = lzma.LZMAFile(f) + + if compat.PY3: + from io import TextIOWrapper + + f = TextIOWrapper(f) + + return f + else: raise ValueError('do not recognize compression method %s' % compression) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 7c7b40d77e821..ea156b95ed3f7 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2808,6 +2808,38 @@ def test_bz2(self): result = self.read_csv(path, compression='infer') tm.assert_frame_equal(result, expected) + def test_xz(self): + try: + if compat.PY3: + import lzma + else: + from backports import lzma + except ImportError: + raise nose.SkipTest('need lzma to run') + + with open(self.csv1, 'rb') as data_file: + data = data_file.read() + expected = self.read_csv(self.csv1) + + with tm.ensure_clean() as path: + tmp = lzma.LZMAFile(path, mode='wb') + tmp.write(data) + tmp.close() + + result = self.read_csv(path, compression='xz') + tm.assert_frame_equal(result, expected) + + with open(path, 'rb') as f: + result = self.read_csv(f, compression='xz') + tm.assert_frame_equal(result, expected) + + with tm.ensure_clean('test.xz') as path: + tmp = lzma.LZMAFile(path, mode='wb') + tmp.write(data) + tmp.close() + result = self.read_csv(path, compression='infer') + tm.assert_frame_equal(result, expected) + def test_decompression_regex_sep(self): try: import gzip diff --git a/pandas/parser.pyx b/pandas/parser.pyx index 8bfc0ab8d6c56..a146b57cf5319 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -582,6 +582,16 @@ cdef class TextReader: else: raise ValueError('Multiple files found in compressed ' 'zip file %s', str(zip_names)) + elif self.compression == 'xz': + if PY3: + import lzma + else: + from backports import lzma + + if isinstance(source, basestring): + source = lzma.LZMAFile(source, 'rb') + else: + source = lzma.LZMAFile(filename=source) else: raise ValueError('Unrecognized compression type: %s' % self.compression) diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py index 4faf67eda6c78..5713a246e2930 100644 --- a/pandas/tests/frame/test_to_csv.py +++ b/pandas/tests/frame/test_to_csv.py @@ -985,6 +985,30 @@ def test_to_csv_compression_bz2(self): for col in df.columns: self.assertIn(col, text) + def test_to_csv_compression_xz(self): + # GH11852 + # use the compression kw in to_csv + df = DataFrame([[0.123456, 0.234567, 0.567567], + [12.32112, 123123.2, 321321.2]], + index=['A', 'B'], columns=['X', 'Y', 'Z']) + + with ensure_clean() as filename: + + df.to_csv(filename, compression="xz") + + # test the round trip - to_csv -> read_csv + rs = read_csv(filename, compression="xz", index_col=0) + assert_frame_equal(df, rs) + + # explicitly make sure file is xzipped + if compat.PY2: + from backports import lzma + else: + import lzma + f = lzma.open(filename, 'rb') + assert_frame_equal(df, read_csv(f, index_col=0)) + f.close() + def test_to_csv_compression_value_error(self): # GH7615 # use the compression kw in to_csv
- [x] closes #11852 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12668
2016-03-18T20:26:34Z
2016-03-23T20:38:17Z
null
2016-03-23T20:38:52Z
Updated docstring for copy() method
diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 24b883b90cf5d..d2379942de121 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2952,7 +2952,13 @@ def copy(self, deep=True): Parameters ---------- deep : boolean or string, default True - Make a deep copy, i.e. also copy data + Make a deep copy, including a copy of the data and the indices. + With ``deep=False`` neither the indices or the data are copied. + + Note that when ``deep=True`` data is copied unless ``dtype=object``, + in which case only the reference to the object is copied. This is + in contrast to ``copy.deepcopy`` in the Standard Library, which + recursively copies object data. Returns -------
- [x] closes #12663 Added note that a copy with `deep=True` does not copy the data of objects contained in underlying arrays with `dtype=object`.
https://api.github.com/repos/pandas-dev/pandas/pulls/12664
2016-03-18T04:48:50Z
2016-03-23T17:36:38Z
null
2016-03-23T17:36:49Z
BUG: fixed SAS7BDATReader._get_properties
diff --git a/pandas/io/sas/sas7bdat.py b/pandas/io/sas/sas7bdat.py index e068c51df585d..47552e2eee0f6 100644 --- a/pandas/io/sas/sas7bdat.py +++ b/pandas/io/sas/sas7bdat.py @@ -327,12 +327,13 @@ def _get_properties(self): _os_version_number_length) self.os_version = buf.rstrip(b'\x00 ').decode() - buf = self._read_bytes( - _os_name_offset, _os_name_length).rstrip(b'\x00 ') + buf = self._read_bytes(_os_name_offset + total_align, + _os_name_length).rstrip(b'\x00 ') if len(buf) > 0: - self.os_name = buf.rstrip(b'\x00 ').decode() + self.os_name = buf.decode() else: - buf = self._path_or_buf.read(_os_maker_offset, _os_maker_length) + buf = self._read_bytes(_os_maker_offset + total_align, + _os_maker_length) self.os_name = buf.rstrip(b'\x00 ').decode() # Read a single float of the given width (4 or 8).
this fixes a crasher I had in one of my files using SAS7BDATReader. cc: @kshedden self._path_or_buf.read is an obvious mistake also added + total_align to _os_name_offset and _os_make_offset as they are present in the original code from Jared Hobbs: https://bitbucket.org/jaredhobbs/sas7bdat/src/da1faa90d0b15c2c97a2a8eb86c91c58081bdd86/sas7bdat.py?fileviewer=file-view-default#sas7bdat.py-1450
https://api.github.com/repos/pandas-dev/pandas/pulls/12658
2016-03-17T13:30:36Z
2016-04-22T15:14:34Z
null
2016-04-22T15:14:34Z
Modest performance, address #12647
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 928fefd6ce17e..d70f3807d264d 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -248,7 +248,7 @@ Deprecations Performance Improvements ~~~~~~~~~~~~~~~~~~~~~~~~ - +- Improved speed of SAS reader (:issue:`12656`) - Improved performance of ``DataFrame.to_sql`` when checking case sensitivity for tables. Now only checks if table has been created correctly when table name is not lower case. (:issue:`12876`) @@ -281,6 +281,7 @@ Bug Fixes - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) - Bug in correctly raising a ``ValueError`` in ``.resample(..).fillna(..)`` when passing a non-string (:issue:`12952`) +- Various encoding and header processing issues in SAS reader (:issue:`12659`, :issue:`12654`, :issue:`12647`, :issue:`12809`, :issue:`12656`) - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) - Bug in ``Timedelta.min`` and ``Timedelta.max``, the properties now report the true minimum/maximum ``timedeltas`` as recognized by Pandas. See :ref:`documentation <timedeltas.limitations>`. (:issue:`12727`) diff --git a/pandas/io/sas/sas7bdat.py b/pandas/io/sas/sas7bdat.py index e068c51df585d..b75f05cf9ed7e 100644 --- a/pandas/io/sas/sas7bdat.py +++ b/pandas/io/sas/sas7bdat.py @@ -19,157 +19,8 @@ from pandas.io.common import get_filepath_or_buffer, BaseIterator import numpy as np import struct -from .saslib import (_rle_decompress, _rdc_decompress, - process_byte_array_with_data) - -_magic = (b"\x00\x00\x00\x00\x00\x00\x00\x00" + - b"\x00\x00\x00\x00\xc2\xea\x81\x60" + - b"\xb3\x14\x11\xcf\xbd\x92\x08\x00" + - b"\x09\xc7\x31\x8c\x18\x1f\x10\x11") - -_align_1_checker_value = b'3' -_align_1_offset = 32 -_align_1_length = 1 -_align_1_value = 4 -_u64_byte_checker_value = b'3' -_align_2_offset = 35 -_align_2_length = 1 -_align_2_value = 4 -_endianness_offset = 37 -_endianness_length = 1 -_platform_offset = 39 -_platform_length = 1 -_encoding_offset = 70 -_encoding_length = 1 -_dataset_offset = 92 -_dataset_length = 64 -_file_type_offset = 156 -_file_type_length = 8 -_date_created_offset = 164 -_date_created_length = 8 -_date_modified_offset = 172 -_date_modified_length = 8 -_header_size_offset = 196 -_header_size_length = 4 -_page_size_offset = 200 -_page_size_length = 4 -_page_count_offset = 204 -_page_count_length = 4 -_sas_release_offset = 216 -_sas_release_length = 8 -_sas_server_type_offset = 224 -_sas_server_type_length = 16 -_os_version_number_offset = 240 -_os_version_number_length = 16 -_os_maker_offset = 256 -_os_maker_length = 16 -_os_name_offset = 272 -_os_name_length = 16 -_page_bit_offset_x86 = 16 -_page_bit_offset_x64 = 32 -_subheader_pointer_length_x86 = 12 -_subheader_pointer_length_x64 = 24 -_page_type_offset = 0 -_page_type_length = 2 -_block_count_offset = 2 -_block_count_length = 2 -_subheader_count_offset = 4 -_subheader_count_length = 2 -_page_meta_type = 0 -_page_data_type = 256 -_page_amd_type = 1024 -_page_metc_type = 16384 -_page_comp_type = -28672 -_page_mix_types = [512, 640] -_subheader_pointers_offset = 8 -_truncated_subheader_id = 1 -_compressed_subheader_id = 4 -_compressed_subheader_type = 1 -_text_block_size_length = 2 -_row_length_offset_multiplier = 5 -_row_count_offset_multiplier = 6 -_col_count_p1_multiplier = 9 -_col_count_p2_multiplier = 10 -_row_count_on_mix_page_offset_multiplier = 15 -_column_name_pointer_length = 8 -_column_name_text_subheader_offset = 0 -_column_name_text_subheader_length = 2 -_column_name_offset_offset = 2 -_column_name_offset_length = 2 -_column_name_length_offset = 4 -_column_name_length_length = 2 -_column_data_offset_offset = 8 -_column_data_length_offset = 8 -_column_data_length_length = 4 -_column_type_offset = 14 -_column_type_length = 1 -_column_format_text_subheader_index_offset = 22 -_column_format_text_subheader_index_length = 2 -_column_format_offset_offset = 24 -_column_format_offset_length = 2 -_column_format_length_offset = 26 -_column_format_length_length = 2 -_column_label_text_subheader_index_offset = 28 -_column_label_text_subheader_index_length = 2 -_column_label_offset_offset = 30 -_column_label_offset_length = 2 -_column_label_length_offset = 32 -_column_label_length_length = 2 -_rle_compression = 'SASYZCRL' -_rdc_compression = 'SASYZCR2' - -_compression_literals = [_rle_compression, _rdc_compression] - -# Incomplete list of encodings -_encoding_names = {29: "latin1", 20: "utf-8", 33: "cyrillic", 60: "wlatin2", - 61: "wcyrillic", 62: "wlatin1", 90: "ebcdic870"} - -# Should be enum - - -class _index: - rowSizeIndex = 0 - columnSizeIndex = 1 - subheaderCountsIndex = 2 - columnTextIndex = 3 - columnNameIndex = 4 - columnAttributesIndex = 5 - formatAndLabelIndex = 6 - columnListIndex = 7 - dataSubheaderIndex = 8 - - -_subheader_signature_to_index = { - b"\xF7\xF7\xF7\xF7": _index.rowSizeIndex, - b"\x00\x00\x00\x00\xF7\xF7\xF7\xF7": _index.rowSizeIndex, - b"\xF7\xF7\xF7\xF7\x00\x00\x00\x00": _index.rowSizeIndex, - b"\xF7\xF7\xF7\xF7\xFF\xFF\xFB\xFE": _index.rowSizeIndex, - b"\xF6\xF6\xF6\xF6": _index.columnSizeIndex, - b"\x00\x00\x00\x00\xF6\xF6\xF6\xF6": _index.columnSizeIndex, - b"\xF6\xF6\xF6\xF6\x00\x00\x00\x00": _index.columnSizeIndex, - b"\xF6\xF6\xF6\xF6\xFF\xFF\xFB\xFE": _index.columnSizeIndex, - b"\x00\xFC\xFF\xFF": _index.subheaderCountsIndex, - b"\xFF\xFF\xFC\x00": _index.subheaderCountsIndex, - b"\x00\xFC\xFF\xFF\xFF\xFF\xFF\xFF": _index.subheaderCountsIndex, - b"\xFF\xFF\xFF\xFF\xFF\xFF\xFC\x00": _index.subheaderCountsIndex, - b"\xFD\xFF\xFF\xFF": _index.columnTextIndex, - b"\xFF\xFF\xFF\xFD": _index.columnTextIndex, - b"\xFD\xFF\xFF\xFF\xFF\xFF\xFF\xFF": _index.columnTextIndex, - b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFD": _index.columnTextIndex, - b"\xFF\xFF\xFF\xFF": _index.columnNameIndex, - b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF": _index.columnNameIndex, - b"\xFC\xFF\xFF\xFF": _index.columnAttributesIndex, - b"\xFF\xFF\xFF\xFC": _index.columnAttributesIndex, - b"\xFC\xFF\xFF\xFF\xFF\xFF\xFF\xFF": _index.columnAttributesIndex, - b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFC": _index.columnAttributesIndex, - b"\xFE\xFB\xFF\xFF": _index.formatAndLabelIndex, - b"\xFF\xFF\xFB\xFE": _index.formatAndLabelIndex, - b"\xFE\xFB\xFF\xFF\xFF\xFF\xFF\xFF": _index.formatAndLabelIndex, - b"\xFF\xFF\xFF\xFF\xFF\xFF\xFB\xFE": _index.formatAndLabelIndex, - b"\xFE\xFF\xFF\xFF": _index.columnListIndex, - b"\xFF\xFF\xFF\xFE": _index.columnListIndex, - b"\xFE\xFF\xFF\xFF\xFF\xFF\xFF\xFF": _index.columnListIndex, - b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFE": _index.columnListIndex} +import pandas.io.sas.sas_constants as const +from pandas.io.sas.saslib import Parser class _subheader_pointer(object): @@ -202,18 +53,27 @@ class SAS7BDATReader(BaseIterator): Return SAS7BDATReader object for iterations, returns chunks with given number of lines. encoding : string, defaults to None - String encoding. If None, text variables are left as raw bytes. + String encoding. + convert_text : bool, defaults to True + If False, text variables are left as raw bytes. + convert_header_text : bool, defaults to True + If False, header text, including column names, are left as raw + bytes. """ def __init__(self, path_or_buf, index=None, convert_dates=True, - blank_missing=True, chunksize=None, encoding=None): + blank_missing=True, chunksize=None, encoding=None, + convert_text=True, convert_header_text=True): self.index = index self.convert_dates = convert_dates self.blank_missing = blank_missing self.chunksize = chunksize self.encoding = encoding + self.convert_text = convert_text + self.convert_header_text = convert_header_text + self.default_encoding = "latin-1" self.compression = "" self.column_names_strings = [] self.column_names = [] @@ -241,44 +101,45 @@ def _get_properties(self): # Check magic number self._path_or_buf.seek(0) self._cached_page = self._path_or_buf.read(288) - if self._cached_page[0:len(_magic)] != _magic: + if self._cached_page[0:len(const.magic)] != const.magic: raise ValueError("magic number mismatch (not a SAS file?)") # Get alignment information align1, align2 = 0, 0 - buf = self._read_bytes(_align_1_offset, _align_1_length) - if buf == _u64_byte_checker_value: - align2 = _align_2_value + buf = self._read_bytes(const.align_1_offset, const.align_1_length) + if buf == const.u64_byte_checker_value: + align2 = const.align_2_value self.U64 = True self._int_length = 8 - self._page_bit_offset = _page_bit_offset_x64 - self._subheader_pointer_length = _subheader_pointer_length_x64 + self._page_bit_offset = const.page_bit_offset_x64 + self._subheader_pointer_length = const.subheader_pointer_length_x64 else: self.U64 = False - self._page_bit_offset = _page_bit_offset_x86 - self._subheader_pointer_length = _subheader_pointer_length_x86 + self._page_bit_offset = const.page_bit_offset_x86 + self._subheader_pointer_length = const.subheader_pointer_length_x86 self._int_length = 4 - buf = self._read_bytes(_align_2_offset, _align_2_length) - if buf == _align_1_checker_value: - align1 = _align_2_value + buf = self._read_bytes(const.align_2_offset, const.align_2_length) + if buf == const.align_1_checker_value: + align1 = const.align_2_value total_align = align1 + align2 # Get endianness information - buf = self._read_bytes(_endianness_offset, _endianness_length) + buf = self._read_bytes(const.endianness_offset, + const.endianness_length) if buf == b'\x01': self.byte_order = "<" else: self.byte_order = ">" # Get encoding information - buf = self._read_bytes(_encoding_offset, _encoding_length)[0] - if buf in _encoding_names: - self.file_encoding = _encoding_names[buf] + buf = self._read_bytes(const.encoding_offset, const.encoding_length)[0] + if buf in const.encoding_names: + self.file_encoding = const.encoding_names[buf] else: self.file_encoding = "unknown (code=%s)" % str(buf) # Get platform information - buf = self._read_bytes(_platform_offset, _platform_length) + buf = self._read_bytes(const.platform_offset, const.platform_length) if buf == b'1': self.platform = "unix" elif buf == b'2': @@ -286,23 +147,29 @@ def _get_properties(self): else: self.platform = "unknown" - buf = self._read_bytes(_dataset_offset, _dataset_length) - self.name = buf.rstrip(b'\x00 ').decode() + buf = self._read_bytes(const.dataset_offset, const.dataset_length) + self.name = buf.rstrip(b'\x00 ') + if self.convert_header_text: + self.name = self.name.decode( + self.encoding or self.default_encoding) - buf = self._read_bytes(_file_type_offset, _file_type_length) - self.file_type = buf.rstrip(b'\x00 ').decode() + buf = self._read_bytes(const.file_type_offset, const.file_type_length) + self.file_type = buf.rstrip(b'\x00 ') + if self.convert_header_text: + self.file_type = self.file_type.decode( + self.encoding or self.default_encoding) # Timestamp is epoch 01/01/1960 epoch = pd.datetime(1960, 1, 1) - x = self._read_float(_date_created_offset + align1, - _date_created_length) + x = self._read_float(const.date_created_offset + align1, + const.date_created_length) self.date_created = epoch + pd.to_timedelta(x, unit='s') - x = self._read_float(_date_modified_offset + align1, - _date_modified_length) + x = self._read_float(const.date_modified_offset + align1, + const.date_modified_length) self.date_modified = epoch + pd.to_timedelta(x, unit='s') - self.header_length = self._read_int(_header_size_offset + align1, - _header_size_length) + self.header_length = self._read_int(const.header_size_offset + align1, + const.header_size_length) # Read the rest of the header into cached_page. buf = self._path_or_buf.read(self.header_length - 288) @@ -310,30 +177,44 @@ def _get_properties(self): if len(self._cached_page) != self.header_length: raise ValueError("The SAS7BDAT file appears to be truncated.") - self._page_length = self._read_int(_page_size_offset + align1, - _page_size_length) - self._page_count = self._read_int(_page_count_offset + align1, - _page_count_length) - - buf = self._read_bytes(_sas_release_offset + total_align, - _sas_release_length) - self.sas_release = buf.rstrip(b'\x00 ').decode() - - buf = self._read_bytes(_sas_server_type_offset + total_align, - _sas_server_type_length) - self.server_type = buf.rstrip(b'\x00 ').decode() - - buf = self._read_bytes(_os_version_number_offset + total_align, - _os_version_number_length) - self.os_version = buf.rstrip(b'\x00 ').decode() - - buf = self._read_bytes( - _os_name_offset, _os_name_length).rstrip(b'\x00 ') + self._page_length = self._read_int(const.page_size_offset + align1, + const.page_size_length) + self._page_count = self._read_int(const.page_count_offset + align1, + const.page_count_length) + + buf = self._read_bytes(const.sas_release_offset + total_align, + const.sas_release_length) + self.sas_release = buf.rstrip(b'\x00 ') + if self.convert_header_text: + self.sas_release = self.sas_release.decode( + self.encoding or self.default_encoding) + + buf = self._read_bytes(const.sas_server_type_offset + total_align, + const.sas_server_type_length) + self.server_type = buf.rstrip(b'\x00 ') + if self.convert_header_text: + self.server_type = self.server_type.decode( + self.encoding or self.default_encoding) + + buf = self._read_bytes(const.os_version_number_offset + total_align, + const.os_version_number_length) + self.os_version = buf.rstrip(b'\x00 ') + if self.convert_header_text: + self.os_version = self.os_version.decode( + self.encoding or self.default_encoding) + + buf = self._read_bytes(const.os_name_offset + total_align, + const.os_name_length) + buf = buf.rstrip(b'\x00 ') if len(buf) > 0: - self.os_name = buf.rstrip(b'\x00 ').decode() + self.os_name = buf.decode(self.encoding or self.default_encoding) else: - buf = self._path_or_buf.read(_os_maker_offset, _os_maker_length) - self.os_name = buf.rstrip(b'\x00 ').decode() + buf = self._read_bytes(const.os_maker_offset + total_align, + const.os_maker_length) + self.os_name = buf.rstrip(b'\x00 ') + if self.convert_header_text: + self.os_name = self.os_name.decode( + self.encoding or self.default_encoding) # Read a single float of the given width (4 or 8). def _read_float(self, offset, width): @@ -378,32 +259,32 @@ def _parse_metadata(self): def _process_page_meta(self): self._read_page_header() - pt = [_page_meta_type, _page_amd_type] + _page_mix_types + pt = [const.page_meta_type, const.page_amd_type] + const.page_mix_types if self._current_page_type in pt: self._process_page_metadata() - return ((self._current_page_type in [256] + _page_mix_types) or + return ((self._current_page_type in [256] + const.page_mix_types) or (self._current_page_data_subheader_pointers is not None)) def _read_page_header(self): bit_offset = self._page_bit_offset - tx = _page_type_offset + bit_offset - self._current_page_type = self._read_int(tx, _page_type_length) - tx = _block_count_offset + bit_offset - self._current_page_block_count = self._read_int(tx, - _block_count_length) - tx = _subheader_count_offset + bit_offset + tx = const.page_type_offset + bit_offset + self._current_page_type = self._read_int(tx, const.page_type_length) + tx = const.block_count_offset + bit_offset + self._current_page_block_count = self._read_int( + tx, const.block_count_length) + tx = const.subheader_count_offset + bit_offset self._current_page_subheaders_count = ( - self._read_int(tx, _subheader_count_length)) + self._read_int(tx, const.subheader_count_length)) def _process_page_metadata(self): bit_offset = self._page_bit_offset for i in range(self._current_page_subheaders_count): pointer = self._process_subheader_pointers( - _subheader_pointers_offset + bit_offset, i) + const.subheader_pointers_offset + bit_offset, i) if pointer.length == 0: continue - if pointer.compression == _truncated_subheader_id: + if pointer.compression == const.truncated_subheader_id: continue subheader_signature = self._read_subheader_signature( pointer.offset) @@ -413,13 +294,13 @@ def _process_page_metadata(self): self._process_subheader(subheader_index, pointer) def _get_subheader_index(self, signature, compression, ptype): - index = _subheader_signature_to_index.get(signature) + index = const.subheader_signature_to_index.get(signature) if index is None: - f1 = ((compression == _compressed_subheader_id) or + f1 = ((compression == const.compressed_subheader_id) or (compression == 0)) - f2 = (ptype == _compressed_subheader_type) + f2 = (ptype == const.compressed_subheader_type) if (self.compression != "") and f1 and f2: - index = _index.dataSubheaderIndex + index = const.index.dataSubheaderIndex else: raise ValueError("Unknown subheader signature") return index @@ -457,23 +338,23 @@ def _process_subheader(self, subheader_index, pointer): offset = pointer.offset length = pointer.length - if subheader_index == _index.rowSizeIndex: + if subheader_index == const.index.rowSizeIndex: processor = self._process_rowsize_subheader - elif subheader_index == _index.columnSizeIndex: + elif subheader_index == const.index.columnSizeIndex: processor = self._process_columnsize_subheader - elif subheader_index == _index.columnTextIndex: + elif subheader_index == const.index.columnTextIndex: processor = self._process_columntext_subheader - elif subheader_index == _index.columnNameIndex: + elif subheader_index == const.index.columnNameIndex: processor = self._process_columnname_subheader - elif subheader_index == _index.columnAttributesIndex: + elif subheader_index == const.index.columnAttributesIndex: processor = self._process_columnattributes_subheader - elif subheader_index == _index.formatAndLabelIndex: + elif subheader_index == const.index.formatAndLabelIndex: processor = self._process_format_subheader - elif subheader_index == _index.columnListIndex: + elif subheader_index == const.index.columnListIndex: processor = self._process_columnlist_subheader - elif subheader_index == _index.subheaderCountsIndex: + elif subheader_index == const.index.subheaderCountsIndex: processor = self._process_subheader_counts - elif subheader_index == _index.dataSubheaderIndex: + elif subheader_index == const.index.dataSubheaderIndex: self._current_page_data_subheader_pointers.append(pointer) return else: @@ -494,14 +375,14 @@ def _process_rowsize_subheader(self, offset, length): lcp_offset += 378 self.row_length = self._read_int( - offset + _row_length_offset_multiplier * int_len, int_len) + offset + const.row_length_offset_multiplier * int_len, int_len) self.row_count = self._read_int( - offset + _row_count_offset_multiplier * int_len, int_len) + offset + const.row_count_offset_multiplier * int_len, int_len) self.col_count_p1 = self._read_int( - offset + _col_count_p1_multiplier * int_len, int_len) + offset + const.col_count_p1_multiplier * int_len, int_len) self.col_count_p2 = self._read_int( - offset + _col_count_p2_multiplier * int_len, int_len) - mx = _row_count_on_mix_page_offset_multiplier * int_len + offset + const.col_count_p2_multiplier * int_len, int_len) + mx = const.row_count_on_mix_page_offset_multiplier * int_len self._mix_page_row_count = self._read_int(offset + mx, int_len) self._lcs = self._read_int(lcs_offset, 2) self._lcp = self._read_int(lcp_offset, 2) @@ -522,17 +403,19 @@ def _process_subheader_counts(self, offset, length): def _process_columntext_subheader(self, offset, length): offset += self._int_length - text_block_size = self._read_int(offset, _text_block_size_length) + text_block_size = self._read_int(offset, const.text_block_size_length) buf = self._read_bytes(offset, text_block_size) - self.column_names_strings.append( - buf[0:text_block_size].rstrip(b"\x00 ").decode()) + cname_raw = buf[0:text_block_size].rstrip(b"\x00 ") + cname = cname_raw + if self.convert_header_text: + cname = cname.decode(self.encoding or self.default_encoding) + self.column_names_strings.append(cname) if len(self.column_names_strings) == 1: - column_name = self.column_names_strings[0] compression_literal = "" - for cl in _compression_literals: - if cl in column_name: + for cl in const.compression_literals: + if cl in cname_raw: compression_literal = cl self.compression = compression_literal offset -= self._int_length @@ -549,39 +432,43 @@ def _process_columntext_subheader(self, offset, length): if self.U64: offset1 += 4 buf = self._read_bytes(offset1, self._lcp) - self.creator_proc = buf[0:self._lcp].decode() - elif compression_literal == _rle_compression: + self.creator_proc = buf[0:self._lcp] + elif compression_literal == const.rle_compression: offset1 = offset + 40 if self.U64: offset1 += 4 buf = self._read_bytes(offset1, self._lcp) - self.creator_proc = buf[0:self._lcp].decode() + self.creator_proc = buf[0:self._lcp] elif self._lcs > 0: self._lcp = 0 offset1 = offset + 16 if self.U64: offset1 += 4 buf = self._read_bytes(offset1, self._lcs) - self.creator_proc = buf[0:self._lcp].decode() + self.creator_proc = buf[0:self._lcp] + if self.convert_header_text: + if hasattr(self, "creator_proc"): + self.creator_proc = self.creator_proc.decode( + self.encoding or self.default_encoding) def _process_columnname_subheader(self, offset, length): int_len = self._int_length offset += int_len column_name_pointers_count = (length - 2 * int_len - 12) // 8 for i in range(column_name_pointers_count): - text_subheader = offset + _column_name_pointer_length * \ - (i + 1) + _column_name_text_subheader_offset - col_name_offset = offset + _column_name_pointer_length * \ - (i + 1) + _column_name_offset_offset - col_name_length = offset + _column_name_pointer_length * \ - (i + 1) + _column_name_length_offset + text_subheader = offset + const.column_name_pointer_length * \ + (i + 1) + const.column_name_text_subheader_offset + col_name_offset = offset + const.column_name_pointer_length * \ + (i + 1) + const.column_name_offset_offset + col_name_length = offset + const.column_name_pointer_length * \ + (i + 1) + const.column_name_length_offset idx = self._read_int( - text_subheader, _column_name_text_subheader_length) + text_subheader, const.column_name_text_subheader_length) col_offset = self._read_int( - col_name_offset, _column_name_offset_length) + col_name_offset, const.column_name_offset_length) col_len = self._read_int( - col_name_length, _column_name_length_length) + col_name_length, const.column_name_length_length) name_str = self.column_names_strings[idx] self.column_names.append(name_str[col_offset:col_offset + col_len]) @@ -592,21 +479,27 @@ def _process_columnattributes_subheader(self, offset, length): length - 2 * int_len - 12) // (int_len + 8) self.column_types = np.empty( column_attributes_vectors_count, dtype=np.dtype('S1')) + self._column_data_lengths = np.empty( + column_attributes_vectors_count, dtype=np.int64) + self._column_data_offsets = np.empty( + column_attributes_vectors_count, dtype=np.int64) for i in range(column_attributes_vectors_count): col_data_offset = (offset + int_len + - _column_data_offset_offset + i * (int_len + 8)) + const.column_data_offset_offset + + i * (int_len + 8)) col_data_len = (offset + 2 * int_len + - _column_data_length_offset + i * (int_len + 8)) + const.column_data_length_offset + + i * (int_len + 8)) col_types = (offset + 2 * int_len + - _column_type_offset + i * (int_len + 8)) + const.column_type_offset + i * (int_len + 8)) - self._column_data_offsets.append( - self._read_int(col_data_offset, int_len)) + x = self._read_int(col_data_offset, int_len) + self._column_data_offsets[i] = x - x = self._read_int(col_data_len, _column_data_length_length) - self._column_data_lengths.append(x) + x = self._read_int(col_data_len, const.column_data_length_length) + self._column_data_lengths[i] = x - x = self._read_int(col_types, _column_type_length) + x = self._read_int(col_types, const.column_type_length) if x == 1: self.column_types[i] = b'd' else: @@ -618,31 +511,43 @@ def _process_columnlist_subheader(self, offset, length): def _process_format_subheader(self, offset, length): int_len = self._int_length - text_subheader_format = offset + \ - _column_format_text_subheader_index_offset + 3 * int_len - col_format_offset = offset + _column_format_offset_offset + 3 * int_len - col_format_len = offset + _column_format_length_offset + 3 * int_len - text_subheader_label = offset + \ - _column_label_text_subheader_index_offset + 3 * int_len - col_label_offset = offset + _column_label_offset_offset + 3 * int_len - col_label_len = offset + _column_label_length_offset + 3 * int_len + text_subheader_format = ( + offset + + const.column_format_text_subheader_index_offset + + 3 * int_len) + col_format_offset = (offset + + const.column_format_offset_offset + + 3 * int_len) + col_format_len = (offset + + const.column_format_length_offset + + 3 * int_len) + text_subheader_label = ( + offset + + const.column_label_text_subheader_index_offset + + 3 * int_len) + col_label_offset = (offset + + const.column_label_offset_offset + + 3 * int_len) + col_label_len = offset + const.column_label_length_offset + 3 * int_len x = self._read_int(text_subheader_format, - _column_format_text_subheader_index_length) + const.column_format_text_subheader_index_length) format_idx = min(x, len(self.column_names_strings) - 1) format_start = self._read_int( - col_format_offset, _column_format_offset_length) + col_format_offset, const.column_format_offset_length) format_len = self._read_int( - col_format_len, _column_format_length_length) + col_format_len, const.column_format_length_length) label_idx = self._read_int( - text_subheader_label, _column_label_text_subheader_index_length) + text_subheader_label, + const.column_label_text_subheader_index_length) label_idx = min(label_idx, len(self.column_names_strings) - 1) label_start = self._read_int( - col_label_offset, _column_label_offset_length) - label_len = self._read_int(col_label_len, _column_label_length_length) + col_label_offset, const.column_label_offset_length) + label_len = self._read_int(col_label_len, + const.column_label_length_length) label_names = self.column_names_strings[label_idx] column_label = label_names[label_start: label_start + label_len] @@ -678,10 +583,8 @@ def read(self, nrows=None): self._byte_chunk = np.empty((nd, 8 * nrows), dtype=np.uint8) self._current_row_in_chunk_index = 0 - for i in range(nrows): - done = self._readline() - if done: - break + p = Parser(self) + p.read(nrows) rslt = self._chunk_to_dataframe() if self.index is not None: @@ -689,78 +592,6 @@ def read(self, nrows=None): return rslt - def _readline(self): - - bit_offset = self._page_bit_offset - subheader_pointer_length = self._subheader_pointer_length - - # If there is no page, go to the end of the header and read a page. - if self._cached_page is None: - self._path_or_buf.seek(self.header_length) - done = self._read_next_page() - if done: - return True - - # Loop until a data row is read - while True: - if self._current_page_type == _page_meta_type: - flag = (self._current_row_on_page_index >= - len(self._current_page_data_subheader_pointers)) - if flag: - done = self._read_next_page() - if done: - return True - self._current_row_on_page_index = 0 - continue - current_subheader_pointer = ( - self._current_page_data_subheader_pointers[ - self._current_row_on_page_index]) - process_byte_array_with_data(self, - current_subheader_pointer.offset, - current_subheader_pointer.length, - self._byte_chunk, - self._string_chunk) - return False - elif self._current_page_type in _page_mix_types: - align_correction = (bit_offset + _subheader_pointers_offset + - self._current_page_subheaders_count * - subheader_pointer_length) - align_correction = align_correction % 8 - offset = bit_offset + align_correction - offset += _subheader_pointers_offset - offset += (self._current_page_subheaders_count * - subheader_pointer_length) - offset += self._current_row_on_page_index * self.row_length - process_byte_array_with_data(self, offset, self.row_length, - self._byte_chunk, - self._string_chunk) - mn = min(self.row_count, self._mix_page_row_count) - if self._current_row_on_page_index == mn: - done = self._read_next_page() - if done: - return True - self._current_row_on_page_index = 0 - return False - elif self._current_page_type == _page_data_type: - process_byte_array_with_data(self, - bit_offset + - _subheader_pointers_offset + - self._current_row_on_page_index * - self.row_length, - self.row_length, self._byte_chunk, - self._string_chunk) - flag = (self._current_row_on_page_index == - self._current_page_block_count) - if flag: - done = self._read_next_page() - if done: - return True - self._current_row_on_page_index = 0 - return False - else: - raise ValueError("unknown page type: %s", - self._current_page_type) - def _read_next_page(self): self._current_page_data_subheader_pointers = [] self._cached_page = self._path_or_buf.read(self._page_length) @@ -773,24 +604,15 @@ def _read_next_page(self): self._page_length)) self._read_page_header() - if self._current_page_type == _page_meta_type: + if self._current_page_type == const.page_meta_type: self._process_page_metadata() - pt = [_page_meta_type, _page_data_type] + [_page_mix_types] + pt = [const.page_meta_type, const.page_data_type] + pt += [const.page_mix_types] if self._current_page_type not in pt: return self._read_next_page() return False - def _decompress(self, row_length, page): - page = np.frombuffer(page, dtype=np.uint8) - if self.compression == _rle_compression: - return _rle_decompress(row_length, page) - elif self.compression == _rdc_compression: - return _rdc_decompress(row_length, page) - else: - raise ValueError("unknown SAS compression method: %s" % - self.compression) - def _chunk_to_dataframe(self): n = self._current_row_in_chunk_index @@ -813,10 +635,9 @@ def _chunk_to_dataframe(self): jb += 1 elif self.column_types[j] == b's': rslt[name] = self._string_chunk[js, :] - rslt[name] = rslt[name].apply(lambda x: x.rstrip(b'\x00 ')) - if self.encoding is not None: - rslt[name] = rslt[name].apply( - lambda x: x.decode(encoding=self.encoding)) + if self.convert_text and (self.encoding is not None): + rslt[name] = rslt[name].str.decode( + self.encoding or self.default_encoding) if self.blank_missing: ii = rslt[name].str.len() == 0 rslt.loc[ii, name] = np.nan diff --git a/pandas/io/sas/sas_constants.py b/pandas/io/sas/sas_constants.py new file mode 100644 index 0000000000000..65ae1e9102cb2 --- /dev/null +++ b/pandas/io/sas/sas_constants.py @@ -0,0 +1,147 @@ +magic = (b"\x00\x00\x00\x00\x00\x00\x00\x00" + + b"\x00\x00\x00\x00\xc2\xea\x81\x60" + + b"\xb3\x14\x11\xcf\xbd\x92\x08\x00" + + b"\x09\xc7\x31\x8c\x18\x1f\x10\x11") + +align_1_checker_value = b'3' +align_1_offset = 32 +align_1_length = 1 +align_1_value = 4 +u64_byte_checker_value = b'3' +align_2_offset = 35 +align_2_length = 1 +align_2_value = 4 +endianness_offset = 37 +endianness_length = 1 +platform_offset = 39 +platform_length = 1 +encoding_offset = 70 +encoding_length = 1 +dataset_offset = 92 +dataset_length = 64 +file_type_offset = 156 +file_type_length = 8 +date_created_offset = 164 +date_created_length = 8 +date_modified_offset = 172 +date_modified_length = 8 +header_size_offset = 196 +header_size_length = 4 +page_size_offset = 200 +page_size_length = 4 +page_count_offset = 204 +page_count_length = 4 +sas_release_offset = 216 +sas_release_length = 8 +sas_server_type_offset = 224 +sas_server_type_length = 16 +os_version_number_offset = 240 +os_version_number_length = 16 +os_maker_offset = 256 +os_maker_length = 16 +os_name_offset = 272 +os_name_length = 16 +page_bit_offset_x86 = 16 +page_bit_offset_x64 = 32 +subheader_pointer_length_x86 = 12 +subheader_pointer_length_x64 = 24 +page_type_offset = 0 +page_type_length = 2 +block_count_offset = 2 +block_count_length = 2 +subheader_count_offset = 4 +subheader_count_length = 2 +page_meta_type = 0 +page_data_type = 256 +page_amd_type = 1024 +page_metc_type = 16384 +page_comp_type = -28672 +page_mix_types = [512, 640] +subheader_pointers_offset = 8 +truncated_subheader_id = 1 +compressed_subheader_id = 4 +compressed_subheader_type = 1 +text_block_size_length = 2 +row_length_offset_multiplier = 5 +row_count_offset_multiplier = 6 +col_count_p1_multiplier = 9 +col_count_p2_multiplier = 10 +row_count_on_mix_page_offset_multiplier = 15 +column_name_pointer_length = 8 +column_name_text_subheader_offset = 0 +column_name_text_subheader_length = 2 +column_name_offset_offset = 2 +column_name_offset_length = 2 +column_name_length_offset = 4 +column_name_length_length = 2 +column_data_offset_offset = 8 +column_data_length_offset = 8 +column_data_length_length = 4 +column_type_offset = 14 +column_type_length = 1 +column_format_text_subheader_index_offset = 22 +column_format_text_subheader_index_length = 2 +column_format_offset_offset = 24 +column_format_offset_length = 2 +column_format_length_offset = 26 +column_format_length_length = 2 +column_label_text_subheader_index_offset = 28 +column_label_text_subheader_index_length = 2 +column_label_offset_offset = 30 +column_label_offset_length = 2 +column_label_length_offset = 32 +column_label_length_length = 2 +rle_compression = b'SASYZCRL' +rdc_compression = b'SASYZCR2' + +compression_literals = [rle_compression, rdc_compression] + +# Incomplete list of encodings, using SAS nomenclature: +# http://support.sas.com/documentation/cdl/en/nlsref/61893/HTML/default/viewer.htm#a002607278.htm +encoding_names = {29: "latin1", 20: "utf-8", 33: "cyrillic", 60: "wlatin2", + 61: "wcyrillic", 62: "wlatin1", 90: "ebcdic870"} + + +class index: + rowSizeIndex = 0 + columnSizeIndex = 1 + subheaderCountsIndex = 2 + columnTextIndex = 3 + columnNameIndex = 4 + columnAttributesIndex = 5 + formatAndLabelIndex = 6 + columnListIndex = 7 + dataSubheaderIndex = 8 + + +subheader_signature_to_index = { + b"\xF7\xF7\xF7\xF7": index.rowSizeIndex, + b"\x00\x00\x00\x00\xF7\xF7\xF7\xF7": index.rowSizeIndex, + b"\xF7\xF7\xF7\xF7\x00\x00\x00\x00": index.rowSizeIndex, + b"\xF7\xF7\xF7\xF7\xFF\xFF\xFB\xFE": index.rowSizeIndex, + b"\xF6\xF6\xF6\xF6": index.columnSizeIndex, + b"\x00\x00\x00\x00\xF6\xF6\xF6\xF6": index.columnSizeIndex, + b"\xF6\xF6\xF6\xF6\x00\x00\x00\x00": index.columnSizeIndex, + b"\xF6\xF6\xF6\xF6\xFF\xFF\xFB\xFE": index.columnSizeIndex, + b"\x00\xFC\xFF\xFF": index.subheaderCountsIndex, + b"\xFF\xFF\xFC\x00": index.subheaderCountsIndex, + b"\x00\xFC\xFF\xFF\xFF\xFF\xFF\xFF": index.subheaderCountsIndex, + b"\xFF\xFF\xFF\xFF\xFF\xFF\xFC\x00": index.subheaderCountsIndex, + b"\xFD\xFF\xFF\xFF": index.columnTextIndex, + b"\xFF\xFF\xFF\xFD": index.columnTextIndex, + b"\xFD\xFF\xFF\xFF\xFF\xFF\xFF\xFF": index.columnTextIndex, + b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFD": index.columnTextIndex, + b"\xFF\xFF\xFF\xFF": index.columnNameIndex, + b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF": index.columnNameIndex, + b"\xFC\xFF\xFF\xFF": index.columnAttributesIndex, + b"\xFF\xFF\xFF\xFC": index.columnAttributesIndex, + b"\xFC\xFF\xFF\xFF\xFF\xFF\xFF\xFF": index.columnAttributesIndex, + b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFC": index.columnAttributesIndex, + b"\xFE\xFB\xFF\xFF": index.formatAndLabelIndex, + b"\xFF\xFF\xFB\xFE": index.formatAndLabelIndex, + b"\xFE\xFB\xFF\xFF\xFF\xFF\xFF\xFF": index.formatAndLabelIndex, + b"\xFF\xFF\xFF\xFF\xFF\xFF\xFB\xFE": index.formatAndLabelIndex, + b"\xFE\xFF\xFF\xFF": index.columnListIndex, + b"\xFF\xFF\xFF\xFE": index.columnListIndex, + b"\xFE\xFF\xFF\xFF\xFF\xFF\xFF\xFF": index.columnListIndex, + b"\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFE": index.columnListIndex} diff --git a/pandas/io/sas/saslib.pyx b/pandas/io/sas/saslib.pyx index a963bf4fe25d3..6bd72a56f4743 100644 --- a/pandas/io/sas/saslib.pyx +++ b/pandas/io/sas/saslib.pyx @@ -1,22 +1,18 @@ import numpy as np cimport numpy as np -from numpy cimport uint8_t, uint16_t +from numpy cimport uint8_t, uint16_t, int8_t +import sas_constants as const # rle_decompress decompresses data using a Run Length Encoding # algorithm. It is partially documented here: # # https://cran.r-project.org/web/packages/sas7bdat/vignettes/sas7bdat.pdf -def _rle_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): +cdef np.ndarray[uint8_t, ndim=1] rle_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): - cdef uint8_t control_byte - cdef uint8_t [:] result = np.zeros(result_length, np.uint8) - - cdef int rpos = 0 - cdef int ipos = 0 - cdef int i - cdef int nbytes - cdef uint8_t x - cdef length = len(inbuff) + cdef: + uint8_t control_byte, x, end_of_first_byte + uint8_t [:] result = np.zeros(result_length, np.uint8) + int rpos = 0, ipos = 0, i, nbytes, length = len(inbuff) while ipos < length: control_byte = inbuff[ipos] & 0xF0 @@ -105,24 +101,19 @@ def _rle_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): if len(result) != result_length: print("RLE: %v != %v\n", (len(result), result_length)) - return np.asarray(result).tostring() + return np.asarray(result) # rdc_decompress decompresses data using the Ross Data Compression algorithm: # # http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/CUJ/1992/9210/ross/ross.htm -def _rdc_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): - - cdef uint8_t cmd - cdef uint16_t ctrl_bits - cdef uint16_t ctrl_mask = 0 - cdef uint16_t ofs - cdef uint16_t cnt - cdef int ipos = 0 - cdef int rpos = 0 - cdef int k +cdef np.ndarray[uint8_t, ndim=1] rdc_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): - cdef uint8_t [:] outbuff = np.zeros(result_length, dtype=np.uint8) + cdef: + uint8_t cmd + uint16_t ctrl_bits, ctrl_mask = 0, ofs, cnt + int ipos = 0, rpos = 0, k + uint8_t [:] outbuff = np.zeros(result_length, dtype=np.uint8) ii = -1 @@ -189,49 +180,205 @@ def _rdc_decompress(int result_length, np.ndarray[uint8_t, ndim=1] inbuff): if len(outbuff) != result_length: raise ValueError("RDC: %v != %v\n", len(outbuff), result_length) - return np.asarray(outbuff).tostring() - -def process_byte_array_with_data(parser, int offset, int length, np.ndarray[uint8_t, ndim=2] byte_chunk, - np.ndarray[dtype=object, ndim=2] string_chunk): - - cdef int s - cdef int j - cdef int m - cdef int start - cdef int end - cdef bytes source - cdef bytes temp - cdef int jb - cdef int js - - if (parser.compression != "") and (length < parser.row_length): - source = parser._decompress(parser.row_length, parser._cached_page[offset:offset + length]) - else: - source = parser._cached_page[offset:offset + length] - - s = 8 * parser._current_row_in_chunk_index - js = 0 - jb = 0 - for j in range(parser.column_count): - length = parser._column_data_lengths[j] - if length == 0: - break - start = parser._column_data_offsets[j] - end = start + length - temp = source[start:end] - if parser.column_types[j] == b'd': - m = 8 - length - if parser.byte_order == "<": - byte_chunk[jb, s+m:s+8] = np.frombuffer(temp, dtype=np.uint8) + return np.asarray(outbuff) + + +cdef class Parser(object): + + cdef: + int column_count + long[:] lengths + long[:] offsets + long[:] column_types + uint8_t[:, :] byte_chunk + object[:, :] string_chunk + char *cached_page + int current_row_on_page_index + int current_row_in_chunk_index + int current_row_in_file_index + int row_length + int bit_offset + int subheader_pointer_length + bint is_little_endian + np.ndarray[uint8_t, ndim=1] (*decompress)(int result_length, np.ndarray[uint8_t, ndim=1] inbuff) + object parser + + def __init__(self, object parser): + cdef: + int j + char[:] column_types + + self.current_row_on_page_index = parser._current_row_on_page_index + self.current_row_in_chunk_index = parser._current_row_in_chunk_index + self.current_row_in_file_index = parser._current_row_in_file_index + self.parser = parser + self.column_count = parser.column_count + self.lengths = parser._column_data_lengths + self.offsets = parser._column_data_offsets + self.byte_chunk = parser._byte_chunk + self.string_chunk = parser._string_chunk + self.row_length = parser.row_length + self.cached_page = <char *>parser._cached_page + self.bit_offset = self.parser._page_bit_offset + self.subheader_pointer_length = self.parser._subheader_pointer_length + self.is_little_endian = parser.byte_order == "<" + self.column_types = np.empty(self.column_count, dtype=long) + + column_types = parser.column_types + + # map column types + for j in range(self.column_count): + if column_types[j] == b'd': + self.column_types[j] = 1 + elif column_types[j] == b's': + self.column_types[j] = 2 else: - byte_chunk[jb, s:s+length] = np.frombuffer(temp, dtype=np.uint8) - jb += 1 - elif parser.column_types[j] == b's': - string_chunk[js, parser._current_row_in_chunk_index] = bytes(temp) - js += 1 + raise ValueError("unknown column type: %s" % self.parser.columns[j].ctype) + + # compression + if parser.compression == const.rle_compression: + self.decompress = rle_decompress + elif parser.compression == const.rdc_compression: + self.decompress = rdc_decompress else: - raise ValueError("unknown column type: %s" % parser.columns[j].ctype) + self.decompress = NULL + + def read(self, int nrows): + cdef: + bint done + int i + + for i in range(nrows): + done = self.readline() + if done: + break + + # update the parser + self.parser._current_row_on_page_index = self.current_row_on_page_index + self.parser._current_row_in_chunk_index = self.current_row_in_chunk_index + self.parser._current_row_in_file_index = self.current_row_in_file_index + + cdef bint read_next_page(self): + cdef done + + done = self.parser._read_next_page() + if done: + self.cached_page = NULL + else: + self.cached_page = <char *>self.parser._cached_page + self.current_row_on_page_index = 0 + return done + + cdef bint readline(self): + + cdef: + int offset, bit_offset, align_correction, subheader_pointer_length + bint done, flag + + bit_offset = self.bit_offset + subheader_pointer_length = self.subheader_pointer_length + + # If there is no page, go to the end of the header and read a page. + if self.cached_page == NULL: + self.parser._path_or_buf.seek(self.parser.header_length) + done = self.read_next_page() + if done: + return True + + # Loop until a data row is read + while True: + if self.parser._current_page_type == const.page_meta_type: + flag = (self.current_row_on_page_index >= + len(self.parser._current_page_data_subheader_pointers)) + if flag: + done = self.read_next_page() + if done: + return True + continue + current_subheader_pointer = ( + self.parser._current_page_data_subheader_pointers[ + self.current_row_on_page_index]) + self.process_byte_array_with_data(current_subheader_pointer.offset, + current_subheader_pointer.length) + return False + elif self.parser._current_page_type in const.page_mix_types: + align_correction = (bit_offset + const.subheader_pointers_offset + + self.parser._current_page_subheaders_count * + subheader_pointer_length) + align_correction = align_correction % 8 + offset = bit_offset + align_correction + offset += const.subheader_pointers_offset + offset += (self.parser._current_page_subheaders_count * + subheader_pointer_length) + offset += self.current_row_on_page_index * self.row_length + self.process_byte_array_with_data(offset, + self.row_length) + mn = min(self.parser.row_count, self.parser._mix_page_row_count) + if self.current_row_on_page_index == mn: + done = self.read_next_page() + if done: + return True + return False + elif self.parser._current_page_type == const.page_data_type: + self.process_byte_array_with_data(bit_offset + + const.subheader_pointers_offset + + self.current_row_on_page_index * + self.row_length, + self.row_length) + flag = (self.current_row_on_page_index == + self.parser._current_page_block_count) + if flag: + done = self.read_next_page() + if done: + return True + return False + else: + raise ValueError("unknown page type: %s", + self.parser._current_page_type) + + cdef void process_byte_array_with_data(self, int offset, int length): + + cdef: + long s, j, k, m, jb, js, lngt, start + np.ndarray[uint8_t, ndim=1] source + long[:] column_types + long[:] lengths + long[:] offsets + uint8_t[:, :] byte_chunk + object[:, :] string_chunk + + source = np.frombuffer(self.cached_page[offset:offset+length], dtype=np.uint8) + + if self.decompress != NULL and (length < self.row_length): + source = self.decompress(self.row_length, source) + + column_types = self.column_types + lengths = self.lengths + offsets = self.offsets + byte_chunk = self.byte_chunk + string_chunk = self.string_chunk + s = 8 * self.current_row_in_chunk_index + js = 0 + jb = 0 + for j in range(self.column_count): + lngt = lengths[j] + if lngt == 0: + break + start = offsets[j] + if column_types[j] == 1: + # decimal + if self.is_little_endian: + m = s + 8 - lngt + else: + m = s + for k in range(lngt): + byte_chunk[jb, m + k] = source[start + k] + jb += 1 + elif column_types[j] == 2: + # string + string_chunk[js, self.current_row_in_chunk_index] = source[start:(start+lngt)].tostring().rstrip() + js += 1 - parser._current_row_on_page_index += 1 - parser._current_row_in_chunk_index += 1 - parser._current_row_in_file_index += 1 + self.current_row_on_page_index += 1 + self.current_row_in_chunk_index += 1 + self.current_row_in_file_index += 1 diff --git a/pandas/io/tests/sas/data/airline.csv b/pandas/io/tests/sas/data/airline.csv new file mode 100644 index 0000000000000..a6336446003a9 --- /dev/null +++ b/pandas/io/tests/sas/data/airline.csv @@ -0,0 +1,33 @@ +YEAR,Y,W,R,L,K +1948.00000839,1.21399998665,0.243000000715,0.145400002599,1.41499996185,0.611999988556 +1949.0000084,1.35399997234,0.259999990463,0.218099996448,1.38399994373,0.559000015259 +1950.0,1.56900000572,0.277999997139,0.315699994564,1.38800001144,0.573000013828 +1951.0,1.94799995422,0.29699999094,0.393999993801,1.54999995232,0.56400001049 +1952.0,2.2650001049,0.310000002384,0.355899989605,1.80200004578,0.574000000954 +1953.0,2.73099994659,0.321999996901,0.359299987555,1.92599999905,0.711000025272 +1954.0,3.02500009537,0.335000008345,0.402500003576,1.96399998665,0.776000022888 +1955.0,3.56200003624,0.34999999404,0.396100014448,2.11599993706,0.827000021935 +1956.0,3.97900009155,0.361000001431,0.38220000267,2.43499994278,0.800000011921 +1957.00036436,4.42000007629,0.379000008106,0.30450001359,2.70700001717,0.921000003815 +1958.00096758,4.56300020218,0.391000002623,0.328399986029,2.70600008965,1.06700003147 +1959.00096116,5.38500022888,0.425999999046,0.38560000062,2.84599995613,1.08299994469 +1960.0,5.55399990082,0.441000014544,0.319299995899,3.08899998665,1.48099994659 +1961.0,5.46500015259,0.460000008345,0.307900011539,3.12199997902,1.73599994183 +1962.0,5.82499980927,0.485000014305,0.378300011158,3.18400001526,1.92599999905 +1963.0,6.87599992752,0.505999982357,0.418000012636,3.26300001144,2.04099988937 +1964.0,7.82299995422,0.537999987602,0.516300022602,3.41199994087,1.99699997902 +1965.0,9.11999988556,0.56400001049,0.587899982929,3.62299990654,2.25699996948 +1966.00082638,10.5120000839,0.586000025272,0.536899983883,4.07399988174,2.742000103 +1967.00001699,13.0200004578,0.621999979019,0.444299995899,4.71000003815,3.56399989128 +1968.00096202,15.2609996796,0.666000008583,0.305200010538,5.21700000763,4.76700019836 +1969.00023643,16.3129997253,0.731000006199,0.233199998736,5.5689997673,6.5110001564 +1970.00003926,16.0020008087,0.830999970436,0.188299998641,5.49499988556,7.62699985504 +1971.00096131,15.8760004044,0.90600001812,0.202299997211,5.33400011063,8.67300033569 +1972.0001291,16.6620006561,1.0,0.250600010157,5.34499979019,8.33100032806 +1973.00001162,17.013999939,1.05599999428,0.266799986362,5.66200017929,8.55700016022 +1974.00096128,19.3050003052,1.13100004196,0.266400009394,5.72900009155,9.50800037384 +1975.0,18.7210006714,1.24699997902,0.230100005865,5.72200012207,9.06200027466 +1976.0,19.25,1.375,0.345200002193,5.76200008392,8.26200008392 +1977.0,20.6469993591,1.54400002956,0.450800001621,5.87699985504,7.47399997711 +1978.0,22.7259998322,1.70299994946,0.587700009346,6.10799980164,7.10400009155 +1979.0,23.6189994812,1.77900004387,0.534600019455,6.85200023651,6.87400007248 diff --git a/pandas/io/tests/sas/data/airline.sas7bdat b/pandas/io/tests/sas/data/airline.sas7bdat new file mode 100644 index 0000000000000..462299bc6a952 Binary files /dev/null and b/pandas/io/tests/sas/data/airline.sas7bdat differ diff --git a/pandas/io/tests/sas/data/productsales.csv b/pandas/io/tests/sas/data/productsales.csv new file mode 100644 index 0000000000000..fea9b68912297 --- /dev/null +++ b/pandas/io/tests/sas/data/productsales.csv @@ -0,0 +1,1441 @@ +ACTUAL,PREDICT,COUNTRY,REGION,DIVISION,PRODTYPE,PRODUCT,QUARTER,YEAR,MONTH +925,850,CANADA,EAST,EDUCATION,FURNITURE,SOFA,1,1993,12054 +999,297,CANADA,EAST,EDUCATION,FURNITURE,SOFA,1,1993,12085 +608,846,CANADA,EAST,EDUCATION,FURNITURE,SOFA,1,1993,12113 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+yearmonth,useGpCo,useGpVi,useSpec,useUrge,caseGpCo,caseGpVi,caseSpec,caseUrge,expendGpCo,expendGpVi,expendSpec,expendUrge +201401,11,12,13,14,15,16,17,18,19,20,21,22 +201402,11,12,13,14,15,16,17,18,19,20,21,22 +201403,11,12,13,14,15,16,17,18,19,20,21,22 +201404,11,12,13,14,15,16,17,18,19,20,21,22 +201405,11,12,13,14,15,16,17,18,19,20,21,22 +201406,11,12,13,14,15,16,17,18,19,20,21,22 +201407,11,12,13,14,15,16,17,18,19,20,21,22 +201408,11,12,13,14,15,16,17,18,19,20,21,22 +201409,11,12,13,14,15,16,17,18,19,20,21,22 +201410,11,12,13,14,15,16,17,18,19,20,21,22 +201411,11,12,13,14,15,16,17,18,19,20,21,22 +201412,11,12,13,14,15,16,17,18,19,20,21,22 +201501,11,12,13,14,15,16,17,18,19,20,21,22 +201502,11,12,13,14,15,16,17,18,19,20,21,22 +201503,11,12,13,14,15,16,17,18,19,20,21,22 +201504,11,12,13,14,15,16,17,18,19,20,21,22 +201505,11,12,13,14,15,16,17,18,19,20,21,22 +201506,11,12,13,14,15,16,17,18,19,20,21,22 +201507,11,12,13,14,15,16,17,18,19,20,21,22 +201508,11,12,13,14,15,16,17,18,19,20,21,22 +201509,11,12,13,14,15,16,17,18,19,20,21,22 +201510,11,12,13,14,15,16,17,18,19,20,21,22 +201511,11,12,13,14,15,16,17,18,19,20,21,22 +201512,11,12,13,14,15,16,17,18,19,20,21,22 +201601,11,12,13,14,15,16,17,18,19,20,21,22 +201602,11,12,13,14,15,16,17,18,19,20,21,22 +201603,11,12,13,14,15,16,17,18,19,20,21,22 +201604,11,12,13,14,15,16,17,18,19,20,21,22 +201605,11,12,13,14,15,16,17,18,19,20,21,22 +201606,11,12,13,14,15,16,17,18,19,20,21,22 +201607,11,12,13,14,15,16,17,18,19,20,21,22 +201608,11,12,13,14,15,16,17,18,19,20,21,22 +201609,11,12,13,14,15,16,17,18,19,20,21,22 +201610,11,12,13,14,15,16,17,18,19,20,21,22 +201611,11,12,13,14,15,16,17,18,19,20,21,22 +201612,11,12,13,14,15,16,17,18,19,20,21,22 diff --git a/pandas/io/tests/sas/data/test_12659.sas7bdat b/pandas/io/tests/sas/data/test_12659.sas7bdat new file mode 100644 index 0000000000000..bf91e931aa64a Binary files /dev/null and b/pandas/io/tests/sas/data/test_12659.sas7bdat differ diff --git a/pandas/io/tests/sas/test_sas7bdat.py b/pandas/io/tests/sas/test_sas7bdat.py index a9e6ea68f3979..6661d9fee5df0 100644 --- a/pandas/io/tests/sas/test_sas7bdat.py +++ b/pandas/io/tests/sas/test_sas7bdat.py @@ -47,7 +47,7 @@ def test_from_buffer(self): byts = open(fname, 'rb').read() buf = io.BytesIO(byts) df = pd.read_sas(buf, format="sas7bdat", encoding='utf-8') - tm.assert_frame_equal(df, df0) + tm.assert_frame_equal(df, df0, check_exact=False) def test_from_iterator(self): for j in 0, 1: @@ -62,3 +62,53 @@ def test_from_iterator(self): tm.assert_frame_equal(df, df0.iloc[0:2, :]) df = rdr.read(3) tm.assert_frame_equal(df, df0.iloc[2:5, :]) + + +def test_encoding_options(): + dirpath = tm.get_data_path() + fname = os.path.join(dirpath, "test1.sas7bdat") + df1 = pd.read_sas(fname) + df2 = pd.read_sas(fname, encoding='utf-8') + for col in df1.columns: + try: + df1[col] = df1[col].str.decode('utf-8') + except AttributeError: + pass + tm.assert_frame_equal(df1, df2) + + from pandas.io.sas.sas7bdat import SAS7BDATReader + rdr = SAS7BDATReader(fname, convert_header_text=False) + df3 = rdr.read() + for x, y in zip(df1.columns, df3.columns): + assert(x == y.decode()) + + +def test_productsales(): + dirpath = tm.get_data_path() + fname = os.path.join(dirpath, "productsales.sas7bdat") + df = pd.read_sas(fname, encoding='utf-8') + fname = os.path.join(dirpath, "productsales.csv") + df0 = pd.read_csv(fname) + vn = ["ACTUAL", "PREDICT", "QUARTER", "YEAR", "MONTH"] + df0[vn] = df0[vn].astype(np.float64) + tm.assert_frame_equal(df, df0) + + +def test_12659(): + dirpath = tm.get_data_path() + fname = os.path.join(dirpath, "test_12659.sas7bdat") + df = pd.read_sas(fname) + fname = os.path.join(dirpath, "test_12659.csv") + df0 = pd.read_csv(fname) + df0 = df0.astype(np.float64) + tm.assert_frame_equal(df, df0) + + +def test_airline(): + dirpath = tm.get_data_path() + fname = os.path.join(dirpath, "airline.sas7bdat") + df = pd.read_sas(fname) + fname = os.path.join(dirpath, "airline.csv") + df0 = pd.read_csv(fname) + df0 = df0.astype(np.float64) + tm.assert_frame_equal(df, df0, check_exact=False)
closes #12659 closes #12654 closes #12647 closes #12809 Major performance improvements through use of Cython
https://api.github.com/repos/pandas-dev/pandas/pulls/12656
2016-03-17T12:55:37Z
2016-04-22T15:09:57Z
null
2016-04-22T15:11:03Z
BUG: pivot_table dropna=False drops columns/index names
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 071cf5f17fc56..6413161f77ab0 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -272,6 +272,8 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``pivot_table`` when ``dropna=False`` where table index/column names disappear (:issue:`12133`) +- Bug in ``crosstab`` when ``margins=True`` and ``dropna=False`` raises ``level`` is ``none`` failure (:issue:`12642`) - Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 06b31b5d5dc30..d7798bf1e7982 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -128,13 +128,15 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', if not dropna: try: - m = MultiIndex.from_arrays(cartesian_product(table.index.levels)) + m = MultiIndex.from_arrays(cartesian_product(table.index.levels), + names=table.index.names) table = table.reindex_axis(m, axis=0) except AttributeError: pass # it's a single level try: - m = MultiIndex.from_arrays(cartesian_product(table.columns.levels)) + m = MultiIndex.from_arrays(cartesian_product(table.columns.levels), + names=table.columns.names) table = table.reindex_axis(m, axis=1) except AttributeError: pass # it's a single level or a series diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index ae0cd67ad77e6..bff82e32dccc0 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -899,8 +899,8 @@ def test_crosstab_dropna(self): 'two', 'two', 'two'], dtype=object) c = np.array(['dull', 'dull', 'dull', 'dull', 'dull', 'shiny', 'shiny'], dtype=object) - res = crosstab(a, [b, c], rownames=['a'], - colnames=['b', 'c'], dropna=False) + res = pd.crosstab(a, [b, c], rownames=['a'], + colnames=['b', 'c'], dropna=False) m = MultiIndex.from_tuples([('one', 'dull'), ('one', 'shiny'), ('two', 'dull'), ('two', 'shiny')]) assert_equal(res.columns.values, m.values) @@ -936,7 +936,7 @@ def test_crosstab_no_overlap(self): tm.assert_frame_equal(actual, expected) - def test_margin_ignore_dropna_bug(self): + def test_margin_dropna(self): # GH 12577 # pivot_table counts null into margin ('All') # when margins=true and dropna=true @@ -965,6 +965,62 @@ def test_margin_ignore_dropna_bug(self): expected.columns = Index([3, 4, 'All'], name='b') tm.assert_frame_equal(actual, expected) + # GH 12642 + # _add_margins raises KeyError: Level None not found + # when margins=True and dropna=False + df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan], + 'b': [3, 3, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3, 4, 'All'], name='b') + tm.assert_frame_equal(actual, expected) + + df = DataFrame({'a': [1, np.nan, np.nan, np.nan, 2, np.nan], + 'b': [3, np.nan, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3.0, 4.0, 'All'], name='b') + tm.assert_frame_equal(actual, expected) + + a = np.array(['foo', 'foo', 'foo', 'bar', + 'bar', 'foo', 'foo'], dtype=object) + b = np.array(['one', 'one', 'two', 'one', + 'two', np.nan, 'two'], dtype=object) + c = np.array(['dull', 'dull', 'dull', 'dull', + 'dull', 'shiny', 'shiny'], dtype=object) + + actual = pd.crosstab(a, [b, c], rownames=['a'], + colnames=['b', 'c'], margins=True, dropna=False) + m = MultiIndex.from_arrays([['one', 'one', 'two', 'two', 'All'], + ['dull', 'shiny', 'dull', 'shiny', '']], + names=['b', 'c']) + expected = DataFrame([[1, 0, 1, 0, 2], [2, 0, 1, 1, 5], + [3, 0, 2, 1, 7]], columns=m) + expected.index = Index(['bar', 'foo', 'All'], name='a') + tm.assert_frame_equal(actual, expected) + + actual = pd.crosstab([a, b], c, rownames=['a', 'b'], + colnames=['c'], margins=True, dropna=False) + m = MultiIndex.from_arrays([['bar', 'bar', 'foo', 'foo', 'All'], + ['one', 'two', 'one', 'two', '']], + names=['a', 'b']) + expected = DataFrame([[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], + [5, 2, 7]], index=m) + expected.columns = Index(['dull', 'shiny', 'All'], name='c') + tm.assert_frame_equal(actual, expected) + + actual = pd.crosstab([a, b], c, rownames=['a', 'b'], + colnames=['c'], margins=True, dropna=True) + m = MultiIndex.from_arrays([['bar', 'bar', 'foo', 'foo', 'All'], + ['one', 'two', 'one', 'two', '']], + names=['a', 'b']) + expected = DataFrame([[1, 0, 1], [1, 0, 1], [2, 0, 2], [1, 1, 2], + [5, 1, 6]], index=m) + expected.columns = Index(['dull', 'shiny', 'All'], name='c') + tm.assert_frame_equal(actual, expected) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- [X] closes #12133 - [X] closes #12642, closes #12327 (replace PR) - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [X] whatsnew entry Bug fixed from pull request #12327.
https://api.github.com/repos/pandas-dev/pandas/pulls/12650
2016-03-17T02:29:44Z
2016-04-17T14:31:40Z
null
2016-04-17T14:31:42Z
BUG: crosstab with margins=True and dropna=False
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b7a0cf888f1a2..db58e562b7f9c 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -102,6 +102,10 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) +- Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``pivot_table`` when ``margins=True`` and ``dropna=False`` where column names result in KeyError (:issue:`12642`) + + @@ -131,4 +135,3 @@ Bug Fixes -- Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index 06b31b5d5dc30..0d2d3a29092ba 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -121,6 +121,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', agged = grouped.agg(aggfunc) table = agged + print data if table.index.nlevels > 1: to_unstack = [agged.index.names[i] or i for i in range(len(index), len(keys))] @@ -175,16 +176,18 @@ def _add_margins(table, data, values, rows, cols, aggfunc, exception_msg = 'Conflicting name "{0}" in margins'.format(margins_name) for level in table.index.names: - if margins_name in table.index.get_level_values(level): - raise ValueError(exception_msg) + if level is not None: + if margins_name in table.index.get_level_values(level): + raise ValueError(exception_msg) grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name) # could be passed a Series object with no 'columns' if hasattr(table, 'columns'): for level in table.columns.names[1:]: - if margins_name in table.columns.get_level_values(level): - raise ValueError(exception_msg) + if level is not None: + if margins_name in table.columns.get_level_values(level): + raise ValueError(exception_msg) if len(rows) > 1: key = (margins_name,) + ('',) * (len(rows) - 1) @@ -465,3 +468,12 @@ def _get_names(arrs, names, prefix='row'): names = list(names) return names + +a = np.array(['foo', 'foo', 'foo', 'bar', + 'bar', 'foo', 'foo'], dtype=object) +b = np.array(['one', 'one', 'two', 'one', + 'two', np.nan, 'two'], dtype=object) +c = np.array(['dull', 'dull', 'dull', 'dull', + 'dull', 'shiny', 'shiny'], dtype=object) + +print crosstab(a, [b, c], margins=True, dropna=False) \ No newline at end of file diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index ae0cd67ad77e6..62636f91bcbbc 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -936,7 +936,7 @@ def test_crosstab_no_overlap(self): tm.assert_frame_equal(actual, expected) - def test_margin_ignore_dropna_bug(self): + def test_margin_dropna(self): # GH 12577 # pivot_table counts null into margin ('All') # when margins=true and dropna=true @@ -965,6 +965,54 @@ def test_margin_ignore_dropna_bug(self): expected.columns = Index([3, 4, 'All'], name='b') tm.assert_frame_equal(actual, expected) + # GH 12642 + # _add_margins raises KeyError: Level None not found + # when margins=True and dropna=False + + df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan], + 'b': [3, 3, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3, 4, 'All']) + tm.assert_frame_equal(actual, expected) + + df = DataFrame({'a': [1, np.nan, np.nan, np.nan, 2, np.nan], + 'b': [3, np.nan, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3.0, 4.0, 'All']) + tm.assert_frame_equal(actual, expected) + + a = np.array(['foo', 'foo', 'foo', 'bar', + 'bar', 'foo', 'foo'], dtype=object) + b = np.array(['one', 'one', 'two', 'one', + 'two', np.nan, 'two'], dtype=object) + c = np.array(['dull', 'dull', 'dull', 'dull', + 'dull', 'shiny', 'shiny'], dtype=object) + + res = crosstab(a, [b, c], rownames=['a'], + colnames=['b', 'c'], margins=True, dropna=False) + m = MultiIndex.from_tuples([('one', 'dull'), ('one', 'shiny'), + ('two', 'dull'), ('two', 'shiny'), + ('All', '')]) + assert_equal(res.columns.values, m.values) + + res = crosstab([a, b], c, rownames=['a', 'b'], + colnames=['c'], margins=True, dropna=False) + m = MultiIndex.from_tuples([('bar', 'one'), ('bar', 'two'), + ('foo', 'one'), ('foo', 'two'), + ('All', '')]) + assert_equal(res.index.values, m.values) + + res = crosstab([a, b], c, rownames=['a', 'b'], + colnames=['c'], margins=True, dropna=True) + m = MultiIndex.from_tuples([('bar', 'one'), ('bar', 'two'), + ('foo', 'one'), ('foo', 'two'), + ('All', '')]) + assert_equal(res.index.values, m.values) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- [+] closes #12642 - [+] tests added / passed - [+] passes `git diff upstream/master | flake8 --diff` - [+] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12643
2016-03-16T15:18:46Z
2016-03-16T20:40:49Z
null
2016-03-16T21:09:48Z
CLN: removed unused parameter 'out' from Series.idxmin/idxmax
diff --git a/pandas/core/series.py b/pandas/core/series.py index d339a93a3ed9b..7962da7e2441f 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1196,7 +1196,7 @@ def drop_duplicates(self, keep='first', inplace=False): def duplicated(self, keep='first'): return super(Series, self).duplicated(keep=keep) - def idxmin(self, axis=None, out=None, skipna=True): + def idxmin(self, axis=None, skipna=True): """ Index of first occurrence of minimum of values. @@ -1223,7 +1223,7 @@ def idxmin(self, axis=None, out=None, skipna=True): return np.nan return self.index[i] - def idxmax(self, axis=None, out=None, skipna=True): + def idxmax(self, axis=None, skipna=True): """ Index of first occurrence of maximum of values.
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry It seems misleading to have the `out` parameter in the function signature when it is not used: ``` pd.Series.argmin(self, axis=None, out=None, skipna=True) pd.Series.argmax(self, axis=None, out=None, skipna=True) ``` (unlike the `numpy.argmin/argmax` methods where `out` can be used)
https://api.github.com/repos/pandas-dev/pandas/pulls/12638
2016-03-16T06:18:29Z
2016-04-07T03:38:24Z
null
2016-04-30T15:59:50Z
MAINT: PEP8 + spelling fixes in testing.py
diff --git a/pandas/util/testing.py b/pandas/util/testing.py index ba869efbc5837..ebd1f7d2c17f8 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -1,9 +1,6 @@ from __future__ import division # pylint: disable-msg=W0402 -# flake8: noqa - -import random import re import string import sys @@ -25,10 +22,10 @@ import numpy as np import pandas as pd -from pandas.core.common import (is_sequence, array_equivalent, is_list_like, is_number, - is_datetimelike_v_numeric, is_datetimelike_v_object, - is_number, pprint_thing, take_1d, - needs_i8_conversion) +from pandas.core.common import (is_sequence, array_equivalent, + is_list_like, is_datetimelike_v_numeric, + is_datetimelike_v_object, is_number, + pprint_thing, take_1d, needs_i8_conversion) import pandas.compat as compat import pandas.lib as lib @@ -50,21 +47,24 @@ K = 4 _RAISE_NETWORK_ERROR_DEFAULT = False + # set testing_mode def set_testing_mode(): # set the testing mode filters - testing_mode = os.environ.get('PANDAS_TESTING_MODE','None') + testing_mode = os.environ.get('PANDAS_TESTING_MODE', 'None') if 'deprecate' in testing_mode: warnings.simplefilter('always', DeprecationWarning) + def reset_testing_mode(): # reset the testing mode filters - testing_mode = os.environ.get('PANDAS_TESTING_MODE','None') + testing_mode = os.environ.get('PANDAS_TESTING_MODE', 'None') if 'deprecate' in testing_mode: warnings.simplefilter('ignore', DeprecationWarning) set_testing_mode() + class TestCase(unittest.TestCase): @classmethod @@ -88,19 +88,24 @@ def round_trip_pickle(self, obj, path=None): # https://docs.python.org/3/library/unittest.html#deprecated-aliases def assertEquals(self, *args, **kwargs): - return deprecate('assertEquals', self.assertEqual)(*args, **kwargs) + return deprecate('assertEquals', + self.assertEqual)(*args, **kwargs) def assertNotEquals(self, *args, **kwargs): - return deprecate('assertNotEquals', self.assertNotEqual)(*args, **kwargs) + return deprecate('assertNotEquals', + self.assertNotEqual)(*args, **kwargs) def assert_(self, *args, **kwargs): - return deprecate('assert_', self.assertTrue)(*args, **kwargs) + return deprecate('assert_', + self.assertTrue)(*args, **kwargs) def assertAlmostEquals(self, *args, **kwargs): - return deprecate('assertAlmostEquals', self.assertAlmostEqual)(*args, **kwargs) + return deprecate('assertAlmostEquals', + self.assertAlmostEqual)(*args, **kwargs) def assertNotAlmostEquals(self, *args, **kwargs): - return deprecate('assertNotAlmostEquals', self.assertNotAlmostEqual)(*args, **kwargs) + return deprecate('assertNotAlmostEquals', + self.assertNotAlmostEqual)(*args, **kwargs) def assert_almost_equal(left, right, check_exact=False, **kwargs): @@ -121,6 +126,7 @@ def assert_almost_equal(left, right, check_exact=False, **kwargs): assert_dict_equal = _testing.assert_dict_equal + def randbool(size=(), p=0.5): return rand(*size) <= p @@ -168,7 +174,7 @@ def randu(nchars): See `randu_array` if you want to create an array of random unicode strings. """ - return ''.join(choice(RANDU_CHARS, nchars)) + return ''.join(np.random.choice(RANDU_CHARS, nchars)) def close(fignum=None): @@ -186,6 +192,7 @@ def _skip_if_32bit(): if is_platform_32bit(): raise nose.SkipTest("skipping for 32 bit") + def mplskip(cls): """Skip a TestCase instance if matplotlib isn't installed""" @@ -201,13 +208,15 @@ def setUpClass(cls): cls.setUpClass = setUpClass return cls + def _skip_if_no_mpl(): try: - import matplotlib + import matplotlib # noqa except ImportError: import nose raise nose.SkipTest("matplotlib not installed") + def _skip_if_mpl_1_5(): import matplotlib v = matplotlib.__version__ @@ -215,18 +224,20 @@ def _skip_if_mpl_1_5(): import nose raise nose.SkipTest("matplotlib 1.5") + def _skip_if_no_scipy(): try: - import scipy.stats + import scipy.stats # noqa except ImportError: import nose raise nose.SkipTest("no scipy.stats module") try: - import scipy.interpolate + import scipy.interpolate # noqa except ImportError: import nose raise nose.SkipTest('scipy.interpolate missing') + def _skip_if_scipy_0_17(): import scipy v = scipy.__version__ @@ -234,6 +245,7 @@ def _skip_if_scipy_0_17(): import nose raise nose.SkipTest("scipy 0.17") + def _skip_if_no_xarray(): try: import xarray @@ -246,9 +258,10 @@ def _skip_if_no_xarray(): import nose raise nose.SkipTest("xarray not version is too low: {0}".format(v)) + def _skip_if_no_pytz(): try: - import pytz + import pytz # noqa except ImportError: import nose raise nose.SkipTest("pytz not installed") @@ -256,7 +269,7 @@ def _skip_if_no_pytz(): def _skip_if_no_dateutil(): try: - import dateutil + import dateutil # noqa except ImportError: import nose raise nose.SkipTest("dateutil not installed") @@ -267,14 +280,16 @@ def _skip_if_windows_python_3(): import nose raise nose.SkipTest("not used on python 3/win32") + def _skip_if_windows(): if is_platform_windows(): import nose raise nose.SkipTest("Running on Windows") + def _skip_if_no_pathlib(): try: - from pathlib import Path + from pathlib import Path # noqa except ImportError: import nose raise nose.SkipTest("pathlib not available") @@ -282,7 +297,7 @@ def _skip_if_no_pathlib(): def _skip_if_no_localpath(): try: - from py.path import local as LocalPath + from py.path import local as LocalPath # noqa except ImportError: import nose raise nose.SkipTest("py.path not installed") @@ -294,7 +309,7 @@ def _incompat_bottleneck_version(method): as we don't match the nansum/nanprod behavior for all-nan ops, see GH9422 """ - if method not in ['sum','prod']: + if method not in ['sum', 'prod']: return False try: import bottleneck as bn @@ -302,6 +317,7 @@ def _incompat_bottleneck_version(method): except ImportError: return False + def skip_if_no_ne(engine='numexpr'): import nose _USE_NUMEXPR = pd.computation.expressions._USE_NUMEXPR @@ -354,7 +370,7 @@ def check_output(*popenargs, **kwargs): """ if 'stdout' in kwargs: raise ValueError('stdout argument not allowed, it will be overridden.') - process = subprocess.Popen(stdout=subprocess.PIPE,stderr=subprocess.PIPE, + process = subprocess.Popen(stdout=subprocess.PIPE, stderr=subprocess.PIPE, *popenargs, **kwargs) output, unused_err = process.communicate() retcode = process.poll() @@ -410,14 +426,15 @@ def get_locales(prefix=None, normalize=True, return None try: - # raw_locales is "\n" seperated list of locales + # raw_locales is "\n" separated list of locales # it may contain non-decodable parts, so split # extract what we can and then rejoin. raw_locales = raw_locales.split(b'\n') out_locales = [] for x in raw_locales: if compat.PY3: - out_locales.append(str(x, encoding=pd.options.display.encoding)) + out_locales.append(str( + x, encoding=pd.options.display.encoding)) else: out_locales.append(str(x)) @@ -512,7 +529,7 @@ def _valid_locales(locales, normalize): return list(filter(_can_set_locale, map(normalizer, locales))) -#------------------------------------------------------------------------------ +# ----------------------------------------------------------------------------- # Console debugging tools def debug(f, *args, **kwargs): @@ -540,7 +557,7 @@ def set_trace(): from pdb import Pdb as OldPdb OldPdb().set_trace(sys._getframe().f_back) -#------------------------------------------------------------------------------ +# ----------------------------------------------------------------------------- # contextmanager to ensure the file cleanup @@ -584,13 +601,14 @@ def ensure_clean(filename=None, return_filelike=False): os.close(fd) except Exception as e: print("Couldn't close file descriptor: %d (file: %s)" % - (fd, filename)) + (fd, filename)) try: if os.path.exists(filename): os.remove(filename) except Exception as e: print("Exception on removing file: %s" % e) + def get_data_path(f=''): """Return the path of a data file, these are relative to the current test directory. @@ -600,7 +618,7 @@ def get_data_path(f=''): base_dir = os.path.abspath(os.path.dirname(filename)) return os.path.join(base_dir, 'data', f) -#------------------------------------------------------------------------------ +# ----------------------------------------------------------------------------- # Comparators @@ -611,8 +629,10 @@ def equalContents(arr1, arr2): def assert_equal(a, b, msg=""): - """asserts that a equals b, like nose's assert_equal, but allows custom message to start. - Passes a and b to format string as well. So you can use '{0}' and '{1}' to display a and b. + """asserts that a equals b, like nose's assert_equal, + but allows custom message to start. Passes a and b to + format string as well. So you can use '{0}' and '{1}' + to display a and b. Examples -------- @@ -700,8 +720,8 @@ def _get_ilevel_values(index, level): # length comparison if len(left) != len(right): raise_assert_detail(obj, '{0} length are different'.format(obj), - '{0}, {1}'.format(len(left), left), - '{0}, {1}'.format(len(right), right)) + '{0}, {1}'.format(len(left), left), + '{0}, {1}'.format(len(right), right)) # MultiIndex special comparison for little-friendly error messages if left.nlevels > 1: @@ -720,8 +740,10 @@ def _get_ilevel_values(index, level): if check_exact: if not left.equals(right): - diff = np.sum((left.values != right.values).astype(int)) * 100.0 / len(left) - msg = '{0} values are different ({1} %)'.format(obj, np.round(diff, 5)) + diff = np.sum((left.values != right.values) + .astype(int)) * 100.0 / len(left) + msg = '{0} values are different ({1} %)'\ + .format(obj, np.round(diff, 5)) raise_assert_detail(obj, msg, left, right) else: _testing.assert_almost_equal(left.values, right.values, @@ -769,7 +791,7 @@ def assert_attr_equal(attr, left, right, obj='Attributes'): return True else: raise_assert_detail(obj, 'Attribute "{0}" are different'.format(attr), - left_attr, right_attr) + left_attr, right_attr) def assert_is_valid_plot_return_object(objs): @@ -790,6 +812,7 @@ def assert_is_valid_plot_return_object(objs): def isiterable(obj): return hasattr(obj, '__iter__') + def is_sorted(seq): if isinstance(seq, (Index, Series)): seq = seq.values @@ -839,8 +862,10 @@ def assertIsInstance(obj, cls, msg=''): "%sExpected object to be of type %r, found %r instead" % ( msg, cls, type(obj))) + def assert_isinstance(obj, class_type_or_tuple, msg=''): - return deprecate('assert_isinstance', assertIsInstance)(obj, class_type_or_tuple, msg=msg) + return deprecate('assert_isinstance', assertIsInstance)( + obj, class_type_or_tuple, msg=msg) def assertNotIsInstance(obj, cls, msg=''): @@ -907,8 +932,8 @@ def assert_numpy_array_equal(left, right, right = np.array(right) if left.shape != right.shape: - raise_assert_detail(obj, '{0} shapes are different'.format(obj), - left.shape, right.shape) + raise_assert_detail(obj, '{0} shapes are different' + .format(obj), left.shape, right.shape) diff = 0 for l, r in zip(left, right): @@ -917,7 +942,8 @@ def assert_numpy_array_equal(left, right, diff += 1 diff = diff * 100.0 / left.size - msg = '{0} values are different ({1} %)'.format(obj, np.round(diff, 5)) + msg = '{0} values are different ({1} %)'\ + .format(obj, np.round(diff, 5)) raise_assert_detail(obj, msg, left, right) elif is_list_like(left): msg = "First object is iterable, second isn't" @@ -982,7 +1008,8 @@ def assert_series_equal(left, right, check_dtype=True, # index comparison assert_index_equal(left.index, right.index, exact=check_index_type, check_names=check_names, - check_less_precise=check_less_precise, check_exact=check_exact, + check_less_precise=check_less_precise, + check_exact=check_exact, obj='{0}.index'.format(obj)) if check_dtype: @@ -998,7 +1025,7 @@ def assert_series_equal(left, right, check_dtype=True, if (is_datetimelike_v_numeric(left, right) or is_datetimelike_v_object(left, right) or needs_i8_conversion(left) or - needs_i8_conversion(right)): + needs_i8_conversion(right)): # datetimelike may have different objects (e.g. datetime.datetime # vs Timestamp) but will compare equal @@ -1093,13 +1120,15 @@ def assert_frame_equal(left, right, check_dtype=True, # index comparison assert_index_equal(left.index, right.index, exact=check_index_type, check_names=check_names, - check_less_precise=check_less_precise, check_exact=check_exact, + check_less_precise=check_less_precise, + check_exact=check_exact, obj='{0}.index'.format(obj)) # column comparison assert_index_equal(left.columns, right.columns, exact=check_column_type, check_names=check_names, - check_less_precise=check_less_precise, check_exact=check_exact, + check_less_precise=check_less_precise, + check_exact=check_exact, obj='{0}.columns'.format(obj)) # compare by blocks @@ -1118,14 +1147,13 @@ def assert_frame_equal(left, right, check_dtype=True, assert col in right lcol = left.iloc[:, i] rcol = right.iloc[:, i] - assert_series_equal(lcol, rcol, - check_dtype=check_dtype, - check_index_type=check_index_type, - check_less_precise=check_less_precise, - check_exact=check_exact, - check_names=check_names, - check_datetimelike_compat=check_datetimelike_compat, - obj='DataFrame.iloc[:, {0}]'.format(i)) + assert_series_equal( + lcol, rcol, check_dtype=check_dtype, + check_index_type=check_index_type, + check_less_precise=check_less_precise, + check_exact=check_exact, check_names=check_names, + check_datetimelike_compat=check_datetimelike_compat, + obj='DataFrame.iloc[:, {0}]'.format(i)) def assert_panelnd_equal(left, right, @@ -1165,15 +1193,19 @@ def assert_contains_all(iterable, dic): def assert_copy(iter1, iter2, **eql_kwargs): """ - iter1, iter2: iterables that produce elements comparable with assert_almost_equal + iter1, iter2: iterables that produce elements + comparable with assert_almost_equal - Checks that the elements are equal, but not the same object. (Does not - check that items in sequences are also not the same object) + Checks that the elements are equal, but not + the same object. (Does not check that items + in sequences are also not the same object) """ for elem1, elem2 in zip(iter1, iter2): assert_almost_equal(elem1, elem2, **eql_kwargs) - assert elem1 is not elem2, "Expected object %r and object %r to be different objects, were same." % ( - type(elem1), type(elem2)) + assert elem1 is not elem2, ("Expected object %r and " + "object %r to be different " + "objects, were same." + % (type(elem1), type(elem2))) def getCols(k): @@ -1307,6 +1339,7 @@ def getTimeSeriesData(nper=None, freq='B'): def getPeriodData(nper=None): return dict((c, makePeriodSeries(nper)) for c in getCols(K)) + # make frame def makeTimeDataFrame(nper=None, freq='B'): data = getTimeSeriesData(nper, freq) @@ -1330,9 +1363,11 @@ def getMixedTypeDict(): return index, data + def makeMixedDataFrame(): return DataFrame(getMixedTypeDict()[1]) + def makePeriodFrame(nper=None): data = getPeriodData(nper) return DataFrame(data) @@ -1362,9 +1397,9 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None, nentries - number of entries in index nlevels - number of levels (> 1 produces multindex) prefix - a string prefix for labels - names - (Optional), bool or list of strings. if True will use default names, - if false will use no names, if a list is given, the name of each level - in the index will be taken from the list. + names - (Optional), bool or list of strings. if True will use default + names, if false will use no names, if a list is given, the name of + each level in the index will be taken from the list. ndupe_l - (Optional), list of ints, the number of rows for which the label will repeated at the corresponding level, you can specify just the first few, the rest will use the default ndupe_l of 1. @@ -1382,8 +1417,8 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None, if ndupe_l is None: ndupe_l = [1] * nlevels assert (is_sequence(ndupe_l) and len(ndupe_l) <= nlevels) - assert (names is None or names is False - or names is True or len(names) is nlevels) + assert (names is None or names is False or + names is True or len(names) is nlevels) assert idx_type is None or \ (idx_type in ('i', 'f', 's', 'u', 'dt', 'p', 'td') and nlevels == 1) @@ -1399,8 +1434,9 @@ def makeCustomIndex(nentries, nlevels, prefix='#', names=False, ndupe_l=None, names = [names] # specific 1D index type requested? - idx_func = dict(i=makeIntIndex, f=makeFloatIndex, s=makeStringIndex, - u=makeUnicodeIndex, dt=makeDateIndex, td=makeTimedeltaIndex, + idx_func = dict(i=makeIntIndex, f=makeFloatIndex, + s=makeStringIndex, u=makeUnicodeIndex, + dt=makeDateIndex, td=makeTimedeltaIndex, p=makePeriodIndex).get(idx_type) if idx_func: idx = idx_func(nentries) @@ -1457,14 +1493,15 @@ def makeCustomDataframe(nrows, ncols, c_idx_names=True, r_idx_names=True, c_idx_nlevels ==1. c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex - data_gen_f - a function f(row,col) which return the data value at that position, - the default generator used yields values of the form "RxCy" based on position. + data_gen_f - a function f(row,col) which return the data value + at that position, the default generator used yields values of the form + "RxCy" based on position. c_ndupe_l, r_ndupe_l - list of integers, determines the number - of duplicates for each label at a given level of the corresponding index. - The default `None` value produces a multiplicity of 1 across - all levels, i.e. a unique index. Will accept a partial list of - length N < idx_nlevels, for just the first N levels. If ndupe - doesn't divide nrows/ncol, the last label might have lower multiplicity. + of duplicates for each label at a given level of the corresponding + index. The default `None` value produces a multiplicity of 1 across + all levels, i.e. a unique index. Will accept a partial list of length + N < idx_nlevels, for just the first N levels. If ndupe doesn't divide + nrows/ncol, the last label might have lower multiplicity. dtype - passed to the DataFrame constructor as is, in case you wish to have more control in conjuncion with a custom `data_gen_f` r_idx_type, c_idx_type - "i"/"f"/"s"/"u"/"dt"/"td". @@ -1484,8 +1521,9 @@ def makeCustomDataframe(nrows, ncols, c_idx_names=True, r_idx_names=True, # make the data a random int between 1 and 100 >> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100)) - # 2-level multiindex on rows with each label duplicated twice on first level, - # default names on both axis, single index on both axis + # 2-level multiindex on rows with each label duplicated + # twice on first level, default names on both axis, single + # index on both axis >> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2]) # DatetimeIndex on row, index with unicode labels on columns @@ -1505,9 +1543,11 @@ def makeCustomDataframe(nrows, ncols, c_idx_names=True, r_idx_names=True, assert c_idx_nlevels > 0 assert r_idx_nlevels > 0 assert r_idx_type is None or \ - (r_idx_type in ('i', 'f', 's', 'u', 'dt', 'p', 'td') and r_idx_nlevels == 1) + (r_idx_type in ('i', 'f', 's', + 'u', 'dt', 'p', 'td') and r_idx_nlevels == 1) assert c_idx_type is None or \ - (c_idx_type in ('i', 'f', 's', 'u', 'dt', 'p', 'td') and c_idx_nlevels == 1) + (c_idx_type in ('i', 'f', 's', + 'u', 'dt', 'p', 'td') and c_idx_nlevels == 1) columns = makeCustomIndex(ncols, nlevels=c_idx_nlevels, prefix='C', names=c_idx_names, ndupe_l=c_ndupe_l, @@ -1599,12 +1639,14 @@ def add_nans(panel): dm[col][:i + j] = np.NaN return panel + def add_nans_panel4d(panel4d): for l, label in enumerate(panel4d.labels): panel = panel4d[label] add_nans(panel) return panel4d + class TestSubDict(dict): def __init__(self, *args, **kwargs): @@ -1676,6 +1718,7 @@ def skip_if_no_package(*args, **kwargs): exc_failed_check=SkipTest, *args, **kwargs) + def skip_if_no_package_deco(pkg_name, version=None, app='pandas'): from nose import SkipTest @@ -1683,11 +1726,12 @@ def deco(func): @wraps(func) def wrapper(*args, **kwargs): package_check(pkg_name, version=version, app=app, - exc_failed_import=SkipTest, exc_failed_check=SkipTest) + exc_failed_import=SkipTest, + exc_failed_check=SkipTest) return func(*args, **kwargs) return wrapper return deco - # +# # Additional tags decorators for nose # @@ -1738,13 +1782,13 @@ def dec(f): # or this e.errno/e.reason.errno _network_errno_vals = ( - 101, # Network is unreachable - 111, # Connection refused - 110, # Connection timed out - 104, # Connection reset Error - 54, # Connection reset by peer - 60, # urllib.error.URLError: [Errno 60] Connection timed out - ) + 101, # Network is unreachable + 111, # Connection refused + 110, # Connection timed out + 104, # Connection reset Error + 54, # Connection reset by peer + 60, # urllib.error.URLError: [Errno 60] Connection timed out +) # Both of the above shouldn't mask real issues such as 404's # or refused connections (changed DNS). @@ -1755,7 +1799,8 @@ def dec(f): _network_error_classes = (IOError, httplib.HTTPException) if sys.version_info >= (3, 3): - _network_error_classes += (TimeoutError,) + _network_error_classes += (TimeoutError,) # noqa + def can_connect(url, error_classes=_network_error_classes): """Try to connect to the given url. True if succeeds, False if IOError @@ -1805,8 +1850,8 @@ def network(t, url="http://www.google.com", t : callable The test requiring network connectivity. url : path - The url to test via ``pandas.io.common.urlopen`` to check for connectivity. - Defaults to 'http://www.google.com'. + The url to test via ``pandas.io.common.urlopen`` to check + for connectivity. Defaults to 'http://www.google.com'. raise_on_error : bool If True, never catches errors. check_before_test : bool @@ -1897,8 +1942,8 @@ def wrapper(*args, **kwargs): e_str = str(e) if any([m.lower() in e_str.lower() for m in _skip_on_messages]): - raise SkipTest("Skipping test because exception message is known" - " and error %s" % e) + raise SkipTest("Skipping test because exception " + "message is known and error %s" % e) if not isinstance(e, error_classes): raise @@ -2006,16 +2051,19 @@ def assertRaises(_exception, _callable=None, *args, **kwargs): else: return manager + def assertRaisesRegexp(_exception, _regexp, _callable=None, *args, **kwargs): - """ Port of assertRaisesRegexp from unittest in Python 2.7 - used in with statement. + """ Port of assertRaisesRegexp from unittest in + Python 2.7 - used in with statement. Explanation from standard library: - Like assertRaises() but also tests that regexp matches on the string - representation of the raised exception. regexp may be a regular expression - object or a string containing a regular expression suitable for use by - re.search(). + Like assertRaises() but also tests that regexp matches on the + string representation of the raised exception. regexp may be a + regular expression object or a string containing a regular + expression suitable for use by re.search(). - You can pass either a regular expression or a compiled regular expression object. + You can pass either a regular expression + or a compiled regular expression object. >>> assertRaisesRegexp(ValueError, 'invalid literal for.*XYZ', ... int, 'XYZ') >>> import re @@ -2052,7 +2100,10 @@ def assertRaisesRegexp(_exception, _regexp, _callable=None, *args, **kwargs): class _AssertRaisesContextmanager(object): - """handles the behind the scenes work for assertRaises and assertRaisesRegexp""" + """ + Handles the behind the scenes work + for assertRaises and assertRaisesRegexp + """ def __init__(self, exception, regexp=None, *args, **kwargs): self.exception = exception if regexp is not None and not hasattr(regexp, "search"): @@ -2084,6 +2135,7 @@ def handle_success(self, exc_type, exc_value, traceback): raise_with_traceback(e, traceback) return True + @contextmanager def assert_produces_warning(expected_warning=Warning, filter_level="always", clear=None, check_stacklevel=True): @@ -2119,7 +2171,7 @@ def assert_produces_warning(expected_warning=Warning, filter_level="always", # if they have happened before # to guarantee that we will catch them if not is_list_like(clear): - clear = [ clear ] + clear = [clear] for m in clear: try: m.__warningregistry__.clear() @@ -2137,11 +2189,13 @@ def assert_produces_warning(expected_warning=Warning, filter_level="always", saw_warning = True if check_stacklevel and issubclass(actual_warning.category, - (FutureWarning, DeprecationWarning)): + (FutureWarning, + DeprecationWarning)): from inspect import getframeinfo, stack caller = getframeinfo(stack()[2][0]) - msg = ("Warning not set with correct stacklevel. File were warning" - " is raised: {0} != {1}. Warning message: {2}".format( + msg = ("Warning not set with correct stacklevel. " + "File where warning is raised: {0} != {1}. " + "Warning message: {2}".format( actual_warning.filename, caller.filename, actual_warning.message)) assert actual_warning.filename == caller.filename, msg @@ -2214,12 +2268,15 @@ def test_parallel(num_threads=2, kwargs_list=None): num_threads : int, optional The number of times the function is run in parallel. kwargs_list : list of dicts, optional - The list of kwargs to update original function kwargs on different threads. + The list of kwargs to update original + function kwargs on different threads. Notes ----- This decorator does not pass the return value of the decorated function. - Original from scikit-image: https://github.com/scikit-image/scikit-image/pull/1519 + Original from scikit-image: + + https://github.com/scikit-image/scikit-image/pull/1519 """
https://api.github.com/repos/pandas-dev/pandas/pulls/12637
2016-03-16T04:55:43Z
2016-03-17T13:44:36Z
null
2016-03-17T14:59:10Z
Allow dtype='category' or dtype=None for Series with Categorical
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index a3e20879a2f58..f382efa90cbc0 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -93,3 +93,5 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) + +- Bug in ``Series`` when Series with Categorical is created with dtype specified (:issue:`12574`) diff --git a/pandas/core/series.py b/pandas/core/series.py index d339a93a3ed9b..649d5dbe8f384 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -195,9 +195,12 @@ def __init__(self, data=None, index=None, dtype=None, name=None, else: data = data.reindex(index, copy=copy) elif isinstance(data, Categorical): - if dtype is not None: + # GH12574: Allow dtype=category only, otherwise error + if ((dtype is not None) and + not is_categorical_dtype(dtype)): raise ValueError("cannot specify a dtype with a " - "Categorical") + "Categorical unless " + "dtype='category'") elif (isinstance(data, types.GeneratorType) or (compat.PY3 and isinstance(data, map))): data = list(data) diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 43e47ae121408..356267630b274 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -139,6 +139,17 @@ def test_constructor_categorical(self): res = Series(cat) self.assertTrue(res.values.equals(cat)) + # GH12574 + self.assertRaises( + ValueError, lambda: Series(pd.Categorical([1, 2, 3]), + dtype='int64')) + cat = Series(pd.Categorical([1, 2, 3]), dtype='category') + self.assertTrue(com.is_categorical_dtype(cat)) + self.assertTrue(com.is_categorical_dtype(cat.dtype)) + s = Series([1, 2, 3], dtype='category') + self.assertTrue(com.is_categorical_dtype(s)) + self.assertTrue(com.is_categorical_dtype(s.dtype)) + def test_constructor_maskedarray(self): data = ma.masked_all((3, ), dtype=float) result = Series(data)
Allows user to pass dtype='category' when creating a Series with Categorical, but produces a ValueError is any non-categorical dtype is passed. - [x] closes #12574 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12636
2016-03-16T03:03:18Z
2016-03-16T13:59:42Z
null
2016-03-16T13:59:51Z
BUG: Concat with tz-aware and timedelta raises AttributeError
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b7a0cf888f1a2..a2324a49ebb73 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -125,10 +125,13 @@ Bug Fixes - +- Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) + + + diff --git a/pandas/core/common.py b/pandas/core/common.py index 05bcb53d85dd0..379e59394b6f5 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -2713,7 +2713,7 @@ def is_nonempty(x): # these are mandated to handle empties as well if 'datetime' in typs or 'datetimetz' in typs or 'timedelta' in typs: from pandas.tseries.common import _concat_compat - return _concat_compat(to_concat, axis=axis) + return _concat_compat(to_concat, axis=axis, typs=typs) elif 'sparse' in typs: from pandas.sparse.array import _concat_compat diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index d5ddfe624e240..ddc4e7aaf1588 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1161,6 +1161,20 @@ def test_concat_tz_series(self): result = pd.concat([first, second]) self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + def test_concat_tz_series_with_datetimelike(self): + # GH 12620 + # tz and timedelta + x = [pd.Timestamp('2011-01-01', tz='US/Eastern'), + pd.Timestamp('2011-02-01', tz='US/Eastern')] + y = [pd.Timedelta('1 day'), pd.Timedelta('2 day')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + + # tz and period + y = [pd.Period('2011-03', freq='M'), pd.Period('2011-04', freq='M')] + result = concat([pd.Series(x), pd.Series(y)], ignore_index=True) + tm.assert_series_equal(result, pd.Series(x + y, dtype='object')) + def test_concat_period_series(self): x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 5c31d79dc6780..87b5b1c895038 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -238,7 +238,7 @@ class CombinedDatetimelikeProperties(DatetimeProperties, TimedeltaProperties): __doc__ = DatetimeProperties.__doc__ -def _concat_compat(to_concat, axis=0): +def _concat_compat(to_concat, axis=0, typs=None): """ provide concatenation of an datetimelike array of arrays each of which is a single M8[ns], datetimet64[ns, tz] or m8[ns] dtype @@ -272,38 +272,33 @@ def convert_to_pydatetime(x, axis): return x - typs = get_dtype_kinds(to_concat) + if typs is None: + typs = get_dtype_kinds(to_concat) - # datetimetz - if 'datetimetz' in typs: - - # if to_concat have 'datetime' or 'object' - # then we need to coerce to object - if 'datetime' in typs or 'object' in typs: - to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] - return np.concatenate(to_concat, axis=axis) + # must be single dtype + if len(typs) == 1: - # we require ALL of the same tz for datetimetz - tzs = set([getattr(x, 'tz', None) for x in to_concat]) - set([None]) - if len(tzs) == 1: - return DatetimeIndex(np.concatenate([x.tz_localize(None).asi8 - for x in to_concat]), - tz=list(tzs)[0]) + if 'datetimetz' in typs: + # datetime with no tz should be stored as "datetime" in typs, + # thus no need to care - # single dtype - if len(typs) == 1: + # we require ALL of the same tz for datetimetz + tzs = set([x.tz for x in to_concat]) + if len(tzs) == 1: + return DatetimeIndex(np.concatenate([x.tz_localize(None).asi8 + for x in to_concat]), + tz=list(tzs)[0]) - if not len(typs - set(['datetime'])): + elif 'datetime' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) return new_values.view(_NS_DTYPE) - elif not len(typs - set(['timedelta'])): + elif 'timedelta' in typs: new_values = np.concatenate([x.view(np.int64) for x in to_concat], axis=axis) return new_values.view(_TD_DTYPE) # need to coerce to object to_concat = [convert_to_pydatetime(x, axis) for x in to_concat] - return np.concatenate(to_concat, axis=axis)
- [x] closes #12620 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry @jreback 's comment on #12620: > should be handled at a slightly higher level. I added number of `dtype` check logic in `tseries/common/_concat_compat`. But it should work on `base/common/_concat_compat`. Lmk which is preferable.
https://api.github.com/repos/pandas-dev/pandas/pulls/12635
2016-03-15T21:19:19Z
2016-03-17T14:17:46Z
null
2016-03-19T20:54:13Z
BUG: #12624 where Panel.fillna() ignores inplace=True
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b7a0cf888f1a2..88cefae1d4a75 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,46 +89,17 @@ Bug Fixes ~~~~~~~~~ - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) -- Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) - - - - - - - +- Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) +- BUG: Can't get period code with frequency alias 'minute' or 'Minute' - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - - - - - - - - - - - - Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`) +- Bug in ``Panel.fillna()`` ignores ``inplace=True`` (:issue:`12633`) - - - - - - - - - - - - - - +- BUG: Crosstab with margins=True ignoring dropna=True - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 963c953154b57..59d1cf1ca45dc 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3124,13 +3124,17 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, # fill in 2d chunks result = dict([(col, s.fillna(method=method, value=value)) for col, s in self.iteritems()]) - return self._constructor.from_dict(result).__finalize__(self) + new_obj = self._constructor.\ + from_dict(result).__finalize__(self) + new_data = new_obj._data - # 2d or less - method = mis._clean_fill_method(method) - new_data = self._data.interpolate(method=method, axis=axis, - limit=limit, inplace=inplace, - coerce=True, downcast=downcast) + else: + # 2d or less + method = mis._clean_fill_method(method) + new_data = self._data.interpolate(method=method, axis=axis, + limit=limit, inplace=inplace, + coerce=True, + downcast=downcast) else: if method is not None: raise ValueError('cannot specify both a fill method and value') diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 0a1e15921dad7..dbab9a2298282 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1528,6 +1528,24 @@ def test_fillna(self): p.iloc[0:2, 0:2, 0:2] = np.nan self.assertRaises(NotImplementedError, lambda: p.fillna(999, limit=1)) + # Test in place fillNA + # Expected result + expected = Panel([[[0, 1], [2, 1]], [[10, 11], [12, 11]]], + items=['a', 'b'], minor_axis=['x', 'y'], + dtype=np.float64) + # method='ffill' + p1 = Panel([[[0, 1], [2, np.nan]], [[10, 11], [12, np.nan]]], + items=['a', 'b'], minor_axis=['x', 'y'], + dtype=np.float64) + p1.fillna(method='ffill', inplace=True) + assert_panel_equal(p1, expected) + + # method='bfill' + p2 = Panel([[[0, np.nan], [2, 1]], [[10, np.nan], [12, 11]]], + items=['a', 'b'], minor_axis=['x', 'y'], dtype=np.float64) + p2.fillna(method='bfill', inplace=True) + assert_panel_equal(p2, expected) + def test_ffill_bfill(self): assert_panel_equal(self.panel.ffill(), self.panel.fillna(method='ffill'))
Added if condition for fillna when ndim==3 (for Panel) Issue: https://github.com/pydata/pandas/issues/12624
https://api.github.com/repos/pandas-dev/pandas/pulls/12633
2016-03-15T13:51:01Z
2016-03-17T13:54:20Z
null
2016-03-17T13:54:26Z
CI: add in support for coverage via Codecov
diff --git a/.travis.yml b/.travis.yml index 0c2f1b0d0fe2d..7d31ae65d0af8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -36,26 +36,26 @@ matrix: - LOCALE_OVERRIDE="zh_CN.GB18030" - FULL_DEPS=true - JOB_TAG=_LOCALE - - BUILD_TYPE=conda - python: 2.7 env: - JOB_NAME: "27_nslow" - NOSE_ARGS="not slow and not disabled" - FULL_DEPS=true - CLIPBOARD_GUI=gtk2 + - LINT=true - python: 3.4 env: - JOB_NAME: "34_nslow" - NOSE_ARGS="not slow and not disabled" - FULL_DEPS=true - CLIPBOARD=xsel - - BUILD_TYPE=conda - python: 3.5 env: - JOB_NAME: "35_nslow" - NOSE_ARGS="not slow and not network and not disabled" - FULL_DEPS=true - CLIPBOARD=xsel + - COVERAGE=true - python: 2.7 env: - JOB_NAME: "27_slow" @@ -87,14 +87,12 @@ matrix: - JOB_NAME: "27_nslow_nnet_COMPAT" - NOSE_ARGS="not slow and not network and not disabled" - LOCALE_OVERRIDE="it_IT.UTF-8" - - BUILD_TYPE=conda - INSTALL_TEST=true - JOB_TAG=_COMPAT - python: 2.7 env: - JOB_NAME: "doc_build" - FULL_DEPS=true - - BUILD_TYPE=conda - DOC_BUILD=true # if rst files were changed, build docs in parallel with tests - JOB_TAG=_DOC_BUILD allow_failures: @@ -129,14 +127,12 @@ matrix: - JOB_NAME: "27_nslow_nnet_COMPAT" - NOSE_ARGS="not slow and not network and not disabled" - LOCALE_OVERRIDE="it_IT.UTF-8" - - BUILD_TYPE=conda - INSTALL_TEST=true - JOB_TAG=_COMPAT - python: 2.7 env: - JOB_NAME: "doc_build" - FULL_DEPS=true - - BUILD_TYPE=conda - DOC_BUILD=true - JOB_TAG=_DOC_BUILD @@ -159,6 +155,7 @@ install: - ci/submit_ccache.sh before_script: + - source activate pandas && pip install codecov - ci/install_db.sh script: @@ -166,7 +163,9 @@ script: - ci/run_build_docs.sh - ci/script.sh - ci/lint.sh -# nothing here, or failed tests won't fail travis + +after_success: + - source activate pandas && codecov after_script: - ci/install_test.sh diff --git a/ci/install_travis.sh b/ci/install_travis.sh index 996061ac29af8..07d33f56beba8 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -85,7 +85,7 @@ conda info -a || exit 1 # build deps REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.build" -time conda create -n pandas python=$TRAVIS_PYTHON_VERSION nose flake8 || exit 1 +time conda create -n pandas python=$TRAVIS_PYTHON_VERSION nose coverage flake8 || exit 1 # may have additional installation instructions for this build INSTALL="ci/install-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.sh" diff --git a/ci/lint.sh b/ci/lint.sh index 4350ecd8b11ed..56bfb4cb0c23c 100755 --- a/ci/lint.sh +++ b/ci/lint.sh @@ -5,13 +5,19 @@ echo "inside $0" source activate pandas RET=0 -for path in 'core' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' -do - echo "linting -> pandas/$path" - flake8 pandas/$path --filename '*.py' - if [ $? -ne "0" ]; then - RET=1 - fi -done + +if [ "$LINT" ]; then + echo "Linting" + for path in 'core' 'io' 'stats' 'compat' 'sparse' 'tools' 'tseries' 'tests' 'computation' 'util' + do + echo "linting -> pandas/$path" + flake8 pandas/$path --filename '*.py' + if [ $? -ne "0" ]; then + RET=1 + fi + done +else + echo "NOT Linting" +fi exit $RET diff --git a/ci/script.sh b/ci/script.sh index b2e02d27c970b..e2ba883b81883 100755 --- a/ci/script.sh +++ b/ci/script.sh @@ -19,6 +19,9 @@ fi if [ "$BUILD_TEST" ]; then echo "We are not running nosetests as this is simply a build test." +elif [ "$COVERAGE" ]; then + echo nosetests --exe -A "$NOSE_ARGS" pandas --with-coverage --with-xunit --xunit-file=/tmp/nosetests.xml + nosetests --exe -A "$NOSE_ARGS" pandas --with-coverage --cover-package=pandas --cover-tests --with-xunit --xunit-file=/tmp/nosetests.xml else echo nosetests --exe -A "$NOSE_ARGS" pandas --doctest-tests --with-xunit --xunit-file=/tmp/nosetests.xml nosetests --exe -A "$NOSE_ARGS" pandas --doctest-tests --with-xunit --xunit-file=/tmp/nosetests.xml
https://api.github.com/repos/pandas-dev/pandas/pulls/12632
2016-03-15T12:40:16Z
2016-03-15T13:09:25Z
null
2016-03-15T15:34:24Z
BUG: #12624 where Panel.fillna() ignores inplace=True
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dbe446f0a7b4f..767dd76373017 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,3 +89,5 @@ Bug Fixes ~~~~~~~~~ - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) + +- Bug in ``Panel.fillna`` was ignoring the ``inplace=True`` option. (:issue:`12624`) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 963c953154b57..50e1b21011a92 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -3122,7 +3122,8 @@ def fillna(self, value=None, method=None, axis=None, inplace=False, elif self.ndim == 3: # fill in 2d chunks - result = dict([(col, s.fillna(method=method, value=value)) + result = dict([(col, s.fillna(method=method, value=value, + inplace=inplace)) for col, s in self.iteritems()]) return self._constructor.from_dict(result).__finalize__(self) diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index 0a1e15921dad7..b037b519b485f 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -1528,6 +1528,12 @@ def test_fillna(self): p.iloc[0:2, 0:2, 0:2] = np.nan self.assertRaises(NotImplementedError, lambda: p.fillna(999, limit=1)) + def test_fillna_inplace(self): + panel = self.panel.copy() + panel.fillna(method='backfill', inplace=True) + assert_frame_equal(panel['ItemA'], + self.panel['ItemA'].fillna(method='backfill')) + def test_ffill_bfill(self): assert_panel_equal(self.panel.ffill(), self.panel.fillna(method='ffill'))
- [x] closes #12624 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Fixes #12624 Author: Pedro M Duarte pmd323@gmail.com
https://api.github.com/repos/pandas-dev/pandas/pulls/12630
2016-03-15T04:39:51Z
2016-03-15T23:49:22Z
null
2016-03-16T00:13:32Z
BUG: GH12622 where pprint of Timestamp in nested structure fails
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 2545407ce43c9..103159ae33d88 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -143,3 +143,5 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) + +- Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index ecbe2827f5447..f0d5bf7e046c3 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -453,6 +453,25 @@ def test_nat_fields(self): self.assertTrue(np.isnan(ts.daysinmonth)) self.assertTrue(np.isnan(ts.days_in_month)) + def test_pprint(self): + # GH12622 + import pprint + nested_obj = {'foo': 1, + 'bar': [{'w': {'a': Timestamp('2011-01-01')}}] * 10} + result = pprint.pformat(nested_obj, width=50) + expected = r'''{'bar': [{'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}], + 'foo': 1}''' + self.assertEqual(result, expected) + class TestDatetimeParsingWrappers(tm.TestCase): def test_does_not_convert_mixed_integer(self): diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx index 04ee609814b5b..dc089785238d9 100644 --- a/pandas/tslib.pyx +++ b/pandas/tslib.pyx @@ -316,50 +316,6 @@ class Timestamp(_Timestamp): return ts_base - def __repr__(self): - stamp = self._repr_base - zone = None - - try: - stamp += self.strftime('%z') - if self.tzinfo: - zone = _get_zone(self.tzinfo) - except ValueError: - year2000 = self.replace(year=2000) - stamp += year2000.strftime('%z') - if self.tzinfo: - zone = _get_zone(self.tzinfo) - - try: - stamp += zone.strftime(' %%Z') - except: - pass - - tz = ", tz='{0}'".format(zone) if zone is not None else "" - offset = ", offset='{0}'".format(self.offset.freqstr) if self.offset is not None else "" - - return "Timestamp('{stamp}'{tz}{offset})".format(stamp=stamp, tz=tz, offset=offset) - - @property - def _date_repr(self): - # Ideal here would be self.strftime("%Y-%m-%d"), but - # the datetime strftime() methods require year >= 1900 - return '%d-%.2d-%.2d' % (self.year, self.month, self.day) - - @property - def _time_repr(self): - result = '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second) - - if self.nanosecond != 0: - result += '.%.9d' % (self.nanosecond + 1000 * self.microsecond) - elif self.microsecond != 0: - result += '.%.6d' % self.microsecond - - return result - - @property - def _repr_base(self): - return '%s %s' % (self._date_repr, self._time_repr) def _round(self, freq, rounder): @@ -977,6 +933,30 @@ cdef class _Timestamp(datetime): self._assert_tzawareness_compat(other) return _cmp_scalar(self.value, ots.value, op) + def __repr__(self): + stamp = self._repr_base + zone = None + + try: + stamp += self.strftime('%z') + if self.tzinfo: + zone = _get_zone(self.tzinfo) + except ValueError: + year2000 = self.replace(year=2000) + stamp += year2000.strftime('%z') + if self.tzinfo: + zone = _get_zone(self.tzinfo) + + try: + stamp += zone.strftime(' %%Z') + except: + pass + + tz = ", tz='{0}'".format(zone) if zone is not None else "" + offset = ", offset='{0}'".format(self.offset.freqstr) if self.offset is not None else "" + + return "Timestamp('{stamp}'{tz}{offset})".format(stamp=stamp, tz=tz, offset=offset) + cdef bint _compare_outside_nanorange(_Timestamp self, datetime other, int op) except -1: cdef datetime dtval = self.to_datetime() @@ -1098,6 +1078,27 @@ cdef class _Timestamp(datetime): out = get_start_end_field(np.array([self.value], dtype=np.int64), field, freqstr, month_kw) return out[0] + property _repr_base: + def __get__(self): + return '%s %s' % (self._date_repr, self._time_repr) + + property _date_repr: + def __get__(self): + # Ideal here would be self.strftime("%Y-%m-%d"), but + # the datetime strftime() methods require year >= 1900 + return '%d-%.2d-%.2d' % (self.year, self.month, self.day) + + property _time_repr: + def __get__(self): + result = '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second) + + if self.nanosecond != 0: + result += '.%.9d' % (self.nanosecond + 1000 * self.microsecond) + elif self.microsecond != 0: + result += '.%.6d' % self.microsecond + + return result + property asm8: def __get__(self): return np.datetime64(self.value, 'ns')
- [x] closes #12622 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry I suspect this is a bug in Cython (I don't think `type(A).func` should be an `instancemethod`). Not sure if we should use this workaround as we encounter issues in the wild, or do this for all the functions we can think of.
https://api.github.com/repos/pandas-dev/pandas/pulls/12629
2016-03-15T02:18:14Z
2016-03-25T13:25:14Z
null
2016-03-25T13:36:27Z
BUG: .rename* not treating Series as mapping
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dbe446f0a7b4f..76d86847d3f2c 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,3 +89,8 @@ Bug Fixes ~~~~~~~~~ - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) + + +- Bug in ``Series.rename``, ``DataFrame.rename`` and ``DataFrame.rename_axis`` not treating ``Series`` as mappings to relabel (:issue:`12623`). + + diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 963c953154b57..4d62002134eb1 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -700,12 +700,9 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False): 1 2 5 2 3 6 """ - is_scalar_or_list = ( - (not com.is_sequence(mapper) and not callable(mapper)) or - (com.is_list_like(mapper) and not com.is_dict_like(mapper)) - ) - - if is_scalar_or_list: + non_mapper = lib.isscalar(mapper) or (com.is_list_like(mapper) and not + com.is_dict_like(mapper)) + if non_mapper: return self._set_axis_name(mapper, axis=axis) else: axis = self._get_axis_name(axis) diff --git a/pandas/core/series.py b/pandas/core/series.py index d339a93a3ed9b..398c62467424b 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -8,7 +8,6 @@ import types import warnings -from collections import MutableMapping from numpy import nan, ndarray import numpy as np @@ -2331,11 +2330,9 @@ def align(self, other, join='outer', axis=None, level=None, copy=True, @Appender(generic._shared_docs['rename'] % _shared_doc_kwargs) def rename(self, index=None, **kwargs): - is_scalar_or_list = ( - (not com.is_sequence(index) and not callable(index)) or - (com.is_list_like(index) and not isinstance(index, MutableMapping)) - ) - if is_scalar_or_list: + non_mapping = lib.isscalar(index) or (com.is_list_like(index) and + not com.is_dict_like(index)) + if non_mapping: return self._set_name(index, inplace=kwargs.get('inplace')) return super(Series, self).rename(index=index, **kwargs) diff --git a/pandas/tests/series/test_alter_axes.py b/pandas/tests/series/test_alter_axes.py index 0bbb96d3e1d5d..4f9a55908fe96 100644 --- a/pandas/tests/series/test_alter_axes.py +++ b/pandas/tests/series/test_alter_axes.py @@ -55,6 +55,13 @@ def test_rename(self): renamed = renamer.rename({}) self.assertEqual(renamed.index.name, renamer.index.name) + def test_rename_by_series(self): + s = Series(range(5), name='foo') + renamer = Series({1: 10, 2: 20}) + result = s.rename(renamer) + expected = Series(range(5), index=[0, 10, 20, 3, 4], name='foo') + tm.assert_series_equal(result, expected) + def test_rename_set_name(self): s = Series(range(4), index=list('abcd')) for name in ['foo', ['foo'], ('foo',)]: diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py index 1198d6b194c60..68cc74e010781 100644 --- a/pandas/tests/test_generic.py +++ b/pandas/tests/test_generic.py @@ -88,21 +88,53 @@ def _compare(self, result, expected): def test_rename(self): # single axis + idx = list('ABCD') + # relabeling values passed into self.rename + args = [ + str.lower, + {x: x.lower() for x in idx}, + Series({x: x.lower() for x in idx}), + ] + for axis in self._axes(): - kwargs = {axis: list('ABCD')} + kwargs = {axis: idx} obj = self._construct(4, **kwargs) - # no values passed - # self.assertRaises(Exception, o.rename(str.lower)) - - # rename a single axis - result = obj.rename(**{axis: str.lower}) - expected = obj.copy() - setattr(expected, axis, list('abcd')) - self._compare(result, expected) + for arg in args: + # rename a single axis + result = obj.rename(**{axis: arg}) + expected = obj.copy() + setattr(expected, axis, list('abcd')) + self._compare(result, expected) # multiple axes at once + def test_rename_axis(self): + idx = list('ABCD') + # relabeling values passed into self.rename + args = [ + str.lower, + {x: x.lower() for x in idx}, + Series({x: x.lower() for x in idx}), + ] + + for axis in self._axes(): + kwargs = {axis: idx} + obj = self._construct(4, **kwargs) + + for arg in args: + # rename a single axis + result = obj.rename_axis(arg, axis=axis) + expected = obj.copy() + setattr(expected, axis, list('abcd')) + self._compare(result, expected) + # scalar values + for arg in ['foo', None]: + result = obj.rename_axis(arg, axis=axis) + expected = obj.copy() + getattr(expected, axis).name = arg + self._compare(result, expected) + def test_get_numeric_data(self): n = 4
Closes https://github.com/pydata/pandas/issues/12623 I added com.is_dict_like in https://github.com/pydata/pandas/pull/11980 and failed to use it for the `rename` method. Using that now and did some refactoring while I was in there. Added more tests for rename.
https://api.github.com/repos/pandas-dev/pandas/pulls/12626
2016-03-14T22:14:24Z
2016-03-17T14:06:48Z
null
2017-04-05T02:06:43Z
TST: Holiday tests/docs
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 31e40c6bcbb2c..c265ebf3e7927 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -23,15 +23,16 @@ def next_monday(dt): def next_monday_or_tuesday(dt): """ For second holiday of two adjacent ones! - If holiday falls on Saturday, use following Monday instead; - if holiday falls on Sunday or Monday, use following Tuesday instead - (because Monday is already taken by adjacent holiday on the day before) + 1. The first holiday falls on Friday, the second on Saturday. The one + on Saturday moves to next Monday + 2. The first holiday falls on Saturday, the second on Sunday. The first one + moves to next Monday and the second one moves to Tuesday. + 3. The first holiday falls on Sunday and the second on Monday. The first + one moves to Tuesday. """ dow = dt.weekday() if dow == 5 or dow == 6: return dt + timedelta(2) - elif dow == 0: - return dt + timedelta(1) return dt
``` >>> from pandas.tseries.holiday import next_monday_or_tuesday >>> from pandas.tseries.holiday import AbstractHolidayCalendar >>>from pandas.tseries.holiday import Holiday >>>class WrongBoxingDay(AbstractHolidayCalendar): rules = [ Holiday('xmas', month=12, day=25, observance=next_monday_or_tuesday), Holiday('boxing day', month=12, day=26, observance=next_monday_or_tuesday), ] >>>import pandas as pd >>>cal = WrongBoxingDay() >>>cal.holidays(pd.datetime(2016,12,1), pd.datetime(2016,12,31)) DatetimeIndex(['2016-12-27', '2016-12-27'], dtype='datetime64[ns]', freq=None, tz=None) ``` The next_monday_or_tuesday function in holiday module isn't doing what it is supposed to do. In the example above, xmas should move to Dec 27 while boxing day should stay on Dec 26, but it was also moved to Dec 27. This bug still exits in the latest version. How my commit works is explained in the docstring of the function.
https://api.github.com/repos/pandas-dev/pandas/pulls/12616
2016-03-14T08:12:58Z
2016-05-07T18:49:59Z
null
2016-05-07T18:49:59Z
BUG: Mixed period cannot be displayed with ValueError
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b7a0cf888f1a2..1a3ee89562cc3 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -44,7 +44,7 @@ Enhancements API changes ~~~~~~~~~~~ - +- ``Period`` and ``PeriodIndex`` now raises ``IncompatibleFrequency`` error which inherits ``ValueError`` rather than raw ``ValueError`` (:issue:`12615`) @@ -130,5 +130,6 @@ Bug Fixes - - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) + diff --git a/pandas/core/format.py b/pandas/core/format.py index 1f1ff73869f73..16a870cbc6901 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -2235,7 +2235,13 @@ def _format_strings(self): class PeriodArrayFormatter(IntArrayFormatter): def _format_strings(self): - values = PeriodIndex(self.values).to_native_types() + from pandas.tseries.period import IncompatibleFrequency + try: + values = PeriodIndex(self.values).to_native_types() + except IncompatibleFrequency: + # periods may contains different freq + values = Index(self.values, dtype='object').to_native_types() + formatter = self.formatter or (lambda x: '%s' % x) fmt_values = [formatter(x) for x in values] return fmt_values diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index 48c017c43c0aa..33c213ac5d8df 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -452,7 +452,8 @@ def extract_ordinals(ndarray[object] values, freq): p = values[i] ordinals[i] = p.ordinal if p.freqstr != freqstr: - raise ValueError(_DIFFERENT_FREQ_INDEX.format(freqstr, p.freqstr)) + msg = _DIFFERENT_FREQ_INDEX.format(freqstr, p.freqstr) + raise IncompatibleFrequency(msg) return ordinals @@ -627,6 +628,11 @@ cdef ndarray[int64_t] localize_dt64arr_to_period(ndarray[int64_t] stamps, _DIFFERENT_FREQ = "Input has different freq={1} from Period(freq={0})" _DIFFERENT_FREQ_INDEX = "Input has different freq={1} from PeriodIndex(freq={0})" + +class IncompatibleFrequency(ValueError): + pass + + cdef class Period(object): """ Represents an period of time @@ -768,7 +774,7 @@ cdef class Period(object): from pandas.tseries.frequencies import get_freq_code as _gfc if other.freq != self.freq: msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) - raise ValueError(msg) + raise IncompatibleFrequency(msg) if self.ordinal == tslib.iNaT or other.ordinal == tslib.iNaT: return _nat_scalar_rules[op] return PyObject_RichCompareBool(self.ordinal, other.ordinal, op) @@ -809,7 +815,7 @@ cdef class Period(object): ordinal = self.ordinal + other.n return Period(ordinal=ordinal, freq=self.freq) msg = _DIFFERENT_FREQ.format(self.freqstr, other.freqstr) - raise ValueError(msg) + raise IncompatibleFrequency(msg) else: # pragma no cover return NotImplemented diff --git a/pandas/tests/test_format.py b/pandas/tests/test_format.py index 6772c1ee4b1ee..7b1138db6c08d 100644 --- a/pandas/tests/test_format.py +++ b/pandas/tests/test_format.py @@ -3151,6 +3151,20 @@ def test_to_csv_engine_kw_deprecation(self): df = DataFrame({'col1': [1], 'col2': ['a'], 'col3': [10.1]}) df.to_csv(engine='python') + def test_period(self): + # GH 12615 + df = pd.DataFrame({'A': pd.period_range('2013-01', + periods=4, freq='M'), + 'B': [pd.Period('2011-01', freq='M'), + pd.Period('2011-02-01', freq='D'), + pd.Period('2011-03-01 09:00', freq='H'), + pd.Period('2011-04', freq='M')], + 'C': list('abcd')}) + exp = (" A B C\n0 2013-01 2011-01 a\n" + "1 2013-02 2011-02-01 b\n2 2013-03 2011-03-01 09:00 c\n" + "3 2013-04 2011-04 d") + self.assertEqual(str(df), exp) + class TestSeriesFormatting(tm.TestCase): _multiprocess_can_split_ = True @@ -3481,6 +3495,27 @@ def test_mixed_datetime64(self): result = repr(df.ix[0]) self.assertTrue('2012-01-01' in result) + def test_period(self): + # GH 12615 + index = pd.period_range('2013-01', periods=6, freq='M') + s = Series(np.arange(6), index=index) + exp = ("2013-01 0\n2013-02 1\n2013-03 2\n2013-04 3\n" + "2013-05 4\n2013-06 5\nFreq: M, dtype: int64") + self.assertEqual(str(s), exp) + + s = Series(index) + exp = ("0 2013-01\n1 2013-02\n2 2013-03\n3 2013-04\n" + "4 2013-05\n5 2013-06\ndtype: object") + self.assertEqual(str(s), exp) + + # periods with mixed freq + s = Series([pd.Period('2011-01', freq='M'), + pd.Period('2011-02-01', freq='D'), + pd.Period('2011-03-01 09:00', freq='H')]) + exp = ("0 2011-01\n1 2011-02-01\n" + "2 2011-03-01 09:00\ndtype: object") + self.assertEqual(str(s), exp) + def test_max_multi_index_display(self): # GH 7101 diff --git a/pandas/tseries/common.py b/pandas/tseries/common.py index 5c31d79dc6780..aa50d0e581e99 100644 --- a/pandas/tseries/common.py +++ b/pandas/tseries/common.py @@ -6,6 +6,7 @@ from pandas.core.base import PandasDelegate, NoNewAttributesMixin from pandas.core import common as com from pandas.tseries.index import DatetimeIndex +from pandas._period import IncompatibleFrequency # flake8: noqa from pandas.tseries.period import PeriodIndex from pandas.tseries.tdi import TimedeltaIndex from pandas import tslib diff --git a/pandas/tseries/period.py b/pandas/tseries/period.py index df04984bcb582..798df0b9e31bd 100644 --- a/pandas/tseries/period.py +++ b/pandas/tseries/period.py @@ -8,13 +8,10 @@ from pandas.tseries.tools import parse_time_string import pandas.tseries.offsets as offsets -from pandas._period import Period import pandas._period as period -from pandas._period import ( - get_period_field_arr, - _validate_end_alias, - _quarter_to_myear, -) +from pandas._period import (Period, IncompatibleFrequency, + get_period_field_arr, _validate_end_alias, + _quarter_to_myear) import pandas.core.common as com from pandas.core.common import (isnull, _INT64_DTYPE, _maybe_box, @@ -69,13 +66,13 @@ def wrapper(self, other): other_base, _ = _gfc(other.freq) if other.freq != self.freq: msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) - raise ValueError(msg) + raise IncompatibleFrequency(msg) result = func(other.ordinal) elif isinstance(other, PeriodIndex): if other.freq != self.freq: msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) - raise ValueError(msg) + raise IncompatibleFrequency(msg) result = getattr(self.values, opname)(other.values) @@ -392,7 +389,7 @@ def searchsorted(self, key, side='left'): if isinstance(key, Period): if key.freq != self.freq: msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, key.freqstr) - raise ValueError(msg) + raise IncompatibleFrequency(msg) key = key.ordinal elif isinstance(key, compat.string_types): key = Period(key, freq=self.freq).ordinal @@ -573,6 +570,8 @@ def _maybe_convert_timedelta(self, other): base = frequencies.get_base_alias(freqstr) if base == self.freq.rule_code: return other.n + msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) + raise IncompatibleFrequency(msg) elif isinstance(other, np.ndarray): if com.is_integer_dtype(other): return other @@ -583,8 +582,9 @@ def _maybe_convert_timedelta(self, other): offset_nanos = tslib._delta_to_nanoseconds(offset) if (nanos % offset_nanos).all() == 0: return nanos // offset_nanos + # raise when input doesn't have freq msg = "Input has different freq from PeriodIndex(freq={0})" - raise ValueError(msg.format(self.freqstr)) + raise IncompatibleFrequency(msg.format(self.freqstr)) def _add_delta(self, other): ordinal_delta = self._maybe_convert_timedelta(other) @@ -663,8 +663,8 @@ def get_value(self, series, key): def get_indexer(self, target, method=None, limit=None, tolerance=None): if hasattr(target, 'freq') and target.freq != self.freq: - raise ValueError('target and index have different freq: ' - '(%s, %s)' % (target.freq, self.freq)) + msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, target.freqstr) + raise IncompatibleFrequency(msg) return Index.get_indexer(self, target, method, limit, tolerance) def get_loc(self, key, method=None, tolerance=None): @@ -801,7 +801,7 @@ def _assert_can_do_setop(self, other): if self.freq != other.freq: msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr) - raise ValueError(msg) + raise IncompatibleFrequency(msg) def _wrap_union_result(self, other, result): name = self.name if self.name == other.name else None diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py index 7ddf3354324f9..3c35fc8299517 100644 --- a/pandas/tseries/tests/test_base.py +++ b/pandas/tseries/tests/test_base.py @@ -6,6 +6,7 @@ PeriodIndex, TimedeltaIndex, Timedelta, timedelta_range, date_range, Float64Index) import pandas.tslib as tslib +import pandas.tseries.period as period import pandas.util.testing as tm @@ -1617,9 +1618,9 @@ def test_add_iadd(self): for o in [pd.offsets.YearBegin(2), pd.offsets.MonthBegin(1), pd.offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365), Timedelta(days=365)]: - msg = 'Input has different freq from PeriodIndex\\(freq=A-DEC\\)' - with tm.assertRaisesRegexp(ValueError, - 'Input has different freq from Period'): + msg = ('Input has different freq(=.+)? ' + 'from PeriodIndex\\(freq=A-DEC\\)') + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng + o rng = pd.period_range('2014-01', '2016-12', freq='M') @@ -1633,8 +1634,8 @@ def test_add_iadd(self): pd.offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365), Timedelta(days=365)]: rng = pd.period_range('2014-01', '2016-12', freq='M') - msg = 'Input has different freq from PeriodIndex\\(freq=M\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=M\\)' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng + o # Tick @@ -1654,8 +1655,8 @@ def test_add_iadd(self): pd.offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23), Timedelta('23:00:00')]: rng = pd.period_range('2014-05-01', '2014-05-15', freq='D') - msg = 'Input has different freq from PeriodIndex\\(freq=D\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=D\\)' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng + o offsets = [pd.offsets.Hour(2), timedelta(hours=2), @@ -1676,10 +1677,10 @@ def test_add_iadd(self): np.timedelta64(30, 's'), Timedelta(seconds=30)]: rng = pd.period_range('2014-01-01 10:00', '2014-01-05 10:00', freq='H') - msg = 'Input has different freq from PeriodIndex\\(freq=H\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=H\\)' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): result = rng + delta - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng += delta # int @@ -1745,8 +1746,9 @@ def test_sub_isub(self): pd.offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: rng = pd.period_range('2014', '2024', freq='A') - msg = 'Input has different freq from PeriodIndex\\(freq=A-DEC\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = ('Input has different freq(=.+)? ' + 'from PeriodIndex\\(freq=A-DEC\\)') + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng - o rng = pd.period_range('2014-01', '2016-12', freq='M') @@ -1760,8 +1762,8 @@ def test_sub_isub(self): pd.offsets.Minute(), np.timedelta64(365, 'D'), timedelta(365)]: rng = pd.period_range('2014-01', '2016-12', freq='M') - msg = 'Input has different freq from PeriodIndex\\(freq=M\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=M\\)' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng - o # Tick @@ -1780,8 +1782,8 @@ def test_sub_isub(self): pd.offsets.Minute(), np.timedelta64(4, 'h'), timedelta(hours=23)]: rng = pd.period_range('2014-05-01', '2014-05-15', freq='D') - msg = 'Input has different freq from PeriodIndex\\(freq=D\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=D\\)' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng - o offsets = [pd.offsets.Hour(2), timedelta(hours=2), @@ -1801,10 +1803,10 @@ def test_sub_isub(self): np.timedelta64(30, 's')]: rng = pd.period_range('2014-01-01 10:00', '2014-01-05 10:00', freq='H') - msg = 'Input has different freq from PeriodIndex\\(freq=H\\)' - with tm.assertRaisesRegexp(ValueError, msg): + msg = 'Input has different freq(=.+)? from PeriodIndex\\(freq=H\\)' + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): result = rng + delta - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): rng += delta # int diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index e8af63f3355c9..e0dad2995f91c 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -2886,12 +2886,16 @@ def test_union(self): # raise if different frequencies index = period_range('1/1/2000', '1/20/2000', freq='D') index2 = period_range('1/1/2000', '1/20/2000', freq='W-WED') - self.assertRaises(ValueError, index.union, index2) + with tm.assertRaises(period.IncompatibleFrequency): + index.union(index2) - self.assertRaises(ValueError, index.join, index.to_timestamp()) + msg = 'can only call with other PeriodIndex-ed objects' + with tm.assertRaisesRegexp(ValueError, msg): + index.join(index.to_timestamp()) index3 = period_range('1/1/2000', '1/20/2000', freq='2D') - self.assertRaises(ValueError, index.join, index3) + with tm.assertRaises(period.IncompatibleFrequency): + index.join(index3) def test_union_dataframe_index(self): rng1 = pd.period_range('1/1/1999', '1/1/2012', freq='M') @@ -2919,10 +2923,12 @@ def test_intersection(self): # raise if different frequencies index = period_range('1/1/2000', '1/20/2000', freq='D') index2 = period_range('1/1/2000', '1/20/2000', freq='W-WED') - self.assertRaises(ValueError, index.intersection, index2) + with tm.assertRaises(period.IncompatibleFrequency): + index.intersection(index2) index3 = period_range('1/1/2000', '1/20/2000', freq='2D') - self.assertRaises(ValueError, index.intersection, index3) + with tm.assertRaises(period.IncompatibleFrequency): + index.intersection(index3) def test_intersection_cases(self): base = period_range('6/1/2000', '6/30/2000', freq='D', name='idx') @@ -3213,11 +3219,11 @@ def test_searchsorted(self): self.assertEqual(pidx.searchsorted(p2), 3) msg = "Input has different freq=H from PeriodIndex" - with self.assertRaisesRegexp(ValueError, msg): + with self.assertRaisesRegexp(period.IncompatibleFrequency, msg): pidx.searchsorted(pd.Period('2014-01-01', freq='H')) msg = "Input has different freq=5D from PeriodIndex" - with self.assertRaisesRegexp(ValueError, msg): + with self.assertRaisesRegexp(period.IncompatibleFrequency, msg): pidx.searchsorted(pd.Period('2014-01-01', freq='5D')) def test_round_trip(self): @@ -3535,7 +3541,7 @@ def test_pi_ops_array(self): # incompatible freq msg = "Input has different freq from PeriodIndex\(freq=M\)" - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): idx + np.array([np.timedelta64(1, 'D')] * 4) idx = PeriodIndex(['2011-01-01 09:00', '2011-01-01 10:00', 'NaT', @@ -3551,7 +3557,7 @@ def test_pi_ops_array(self): self.assert_index_equal(result, exp) msg = "Input has different freq from PeriodIndex\(freq=H\)" - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): idx + np.array([np.timedelta64(1, 's')] * 4) idx = PeriodIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00', 'NaT', @@ -3754,7 +3760,7 @@ def test_pi_pi_comp(self): # different base freq msg = "Input has different freq=A-DEC from PeriodIndex" - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): base <= Period('2011', freq='A') with tm.assertRaisesRegexp(ValueError, msg): @@ -3763,10 +3769,10 @@ def test_pi_pi_comp(self): # different mult msg = "Input has different freq=4M from PeriodIndex" - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): base <= Period('2011', freq='4M') - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): idx = PeriodIndex(['2011', '2012', '2013', '2014'], freq='4M') base <= idx @@ -3812,9 +3818,9 @@ def test_pi_nat_comp(self): diff = PeriodIndex( ['2011-02', '2011-01', '2011-04', 'NaT'], freq='4M') msg = "Input has different freq=4M from PeriodIndex" - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): idx1 > diff - with tm.assertRaisesRegexp(ValueError, msg): + with tm.assertRaisesRegexp(period.IncompatibleFrequency, msg): idx1 == diff
- [x] closes #xxxx (not reported) - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry ``` # we can create Series contains Periods with mixed freq s = pd.Series([pd.Period('2011-01', freq='M'), pd.Period('2011-02-01', freq='D')]) # print the created Series raises ValueError print(s) # ValueError: Input has different freq=D from PeriodIndex(freq=M) ```
https://api.github.com/repos/pandas-dev/pandas/pulls/12615
2016-03-14T07:40:23Z
2016-03-17T16:56:09Z
null
2016-03-19T20:54:47Z
BUG: Crosstab margins ignoring dropna
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dbe446f0a7b4f..00d8d5c145915 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,3 +89,5 @@ Bug Fixes ~~~~~~~~~ - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) +- Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``pivot_table`` when ``margins=True`` and ``dropna=False`` where column names result in KeyError diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index d699639c6c796..55fc4a1fd1b4b 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -149,6 +149,8 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', table = table.fillna(value=fill_value, downcast='infer') if margins: + if dropna: + data = data[data.notnull().all(axis=1)] table = _add_margins(table, data, values, rows=index, cols=columns, aggfunc=aggfunc, margins_name=margins_name) @@ -181,8 +183,9 @@ def _add_margins(table, data, values, rows, cols, aggfunc, # could be passed a Series object with no 'columns' if hasattr(table, 'columns'): for level in table.columns.names[1:]: - if margins_name in table.columns.get_level_values(level): - raise ValueError(exception_msg) + if level is not None: + if margins_name in table.columns.get_level_values(level): + raise ValueError(exception_msg) if len(rows) > 1: key = (margins_name,) + ('',) * (len(rows) - 1) diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 994269d36cd85..44f1de4754e55 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -936,6 +936,59 @@ def test_crosstab_no_overlap(self): tm.assert_frame_equal(actual, expected) + def test_margin_ignore_dropna_bug(self): + # GH 12577 + # pivot_table counts null into margin ('All') + # when margins=true and dropna=true + + df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan], + 'b': [3, 3, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=True) + expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 3, 5]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3, 4, 'All'], name='b') + tm.assert_frame_equal(actual, expected) + + df = DataFrame({'a': [1, np.nan, np.nan, np.nan, 2, np.nan], + 'b': [3, np.nan, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=True) + expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3.0, 4.0, 'All'], name='b') + tm.assert_frame_equal(actual, expected) + + df = DataFrame({'a': [1, np.nan, np.nan, np.nan, np.nan, 2], + 'b': [3, 3, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=True) + expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 1, 2]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3, 4, 'All'], name='b') + tm.assert_frame_equal(actual, expected) + + df = pd.DataFrame({'a': [1, 2, 2, 2, 2, np.nan], + 'b': [3, 3, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [1, 3, 4], [2, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3, 4, 'All']) + tm.assert_frame_equal(actual, expected) + + df = DataFrame({'a': [1, np.nan, np.nan, np.nan, 2, np.nan], + 'b': [3, np.nan, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [1, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3.0, 4.0, 'All']) + tm.assert_frame_equal(actual, expected) + + df = DataFrame({'a': [1, np.nan, np.nan, np.nan, np.nan, 2], + 'b': [3, 3, 4, 4, 4, 4]}) + actual = pd.crosstab(df.a, df.b, margins=True, dropna=False) + expected = pd.DataFrame([[1, 0, 1], [0, 1, 1], [2, 4, 6]]) + expected.index = Index([1.0, 2.0, 'All'], name='a') + expected.columns = Index([3, 4, 'All']) + tm.assert_frame_equal(actual, expected) + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
To fix bug #12577 : Crosstab margins ignoring dropna
https://api.github.com/repos/pandas-dev/pandas/pulls/12614
2016-03-14T07:12:22Z
2016-03-16T13:17:21Z
null
2016-03-16T15:14:04Z
COMPAT: dateutil 2.5.0 bug fix
diff --git a/ci/install_travis.sh b/ci/install_travis.sh index 1b1eae7b44e45..996061ac29af8 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -130,7 +130,7 @@ else echo "pip installs" REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.pip" if [ -e ${REQ} ]; then - pip install -r $REQ + pip install --upgrade -r $REQ fi # remove any installed pandas package diff --git a/ci/requirements-2.7.build b/ci/requirements-2.7.build index 6c9965ac0305e..eca8460468d34 100644 --- a/ci/requirements-2.7.build +++ b/ci/requirements-2.7.build @@ -1,4 +1,4 @@ -dateutil=2.1 +python-dateutil=2.4.1 pytz=2013b numpy=1.9.3 cython=0.19.1 diff --git a/ci/requirements-2.7.pip b/ci/requirements-2.7.pip index 54596ad2a8169..9334ca9e03cc1 100644 --- a/ci/requirements-2.7.pip +++ b/ci/requirements-2.7.pip @@ -1,7 +1,7 @@ blosc httplib2 -google-api-python-client == 1.2 -python-gflags == 2.0 -oauth2client == 1.5.0 +google-api-python-client==1.2 +python-gflags==2.0 +oauth2client==1.5.0 pathlib py diff --git a/ci/requirements-2.7.run b/ci/requirements-2.7.run index 6768a75f5c285..0c1132eaa62d3 100644 --- a/ci/requirements-2.7.run +++ b/ci/requirements-2.7.run @@ -1,4 +1,4 @@ -dateutil=2.1 +python-dateutil=2.4.1 pytz=2013b numpy=1.9.3 xlwt=0.7.5 diff --git a/ci/requirements-3.5_OSX.build b/ci/requirements-3.5_OSX.build index 9558cf00ddf5c..8dbecfc9e9292 100644 --- a/ci/requirements-3.5_OSX.build +++ b/ci/requirements-3.5_OSX.build @@ -1,4 +1,2 @@ -python-dateutil -pytz numpy cython diff --git a/ci/requirements-3.5_OSX.pip b/ci/requirements-3.5_OSX.pip new file mode 100644 index 0000000000000..8a7f51f1bea9c --- /dev/null +++ b/ci/requirements-3.5_OSX.pip @@ -0,0 +1 @@ +python-dateutil>=2.5.0 diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run index 80e12ac3fed34..49c336cae40b1 100644 --- a/ci/requirements-3.5_OSX.run +++ b/ci/requirements-3.5_OSX.run @@ -1,4 +1,3 @@ -python-dateutil pytz numpy openpyxl diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py index aade3b8411bb9..24de4985d63f3 100644 --- a/pandas/compat/__init__.py +++ b/pandas/compat/__init__.py @@ -325,6 +325,10 @@ def raise_with_traceback(exc, traceback=Ellipsis): def parse_date(timestr, *args, **kwargs): timestr = bytes(timestr) return _date_parser.parse(timestr, *args, **kwargs) +elif PY2 and LooseVersion(dateutil.__version__) == '2.0': + # dateutil brokenness + raise Exception('dateutil 2.0 incompatible with Python 2.x, you must ' + 'install version 1.5 or 2.1+!') else: parse_date = _date_parser.parse diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index f32dfd37e837c..700ec3387d459 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -10,6 +10,7 @@ import re import nose import platform +from distutils.version import LooseVersion from multiprocessing.pool import ThreadPool @@ -1048,12 +1049,20 @@ def test_parse_dates_string(self): 'C': [2, 4, 5]}, idx) tm.assert_frame_equal(rs, xp) - def test_yy_format(self): + def test_yy_format_with_yearfirst(self): data = """date,time,B,C 090131,0010,1,2 090228,1020,3,4 090331,0830,5,6 """ + + # https://github.com/dateutil/dateutil/issues/217 + import dateutil + if dateutil.__version__ >= LooseVersion('2.5.0'): + raise nose.SkipTest("testing yearfirst=True not-support" + "on datetutil < 2.5.0 this works but" + "is wrong") + rs = self.read_csv(StringIO(data), index_col=0, parse_dates=[['date', 'time']]) idx = DatetimeIndex([datetime(2009, 1, 31, 0, 10, 0), diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 937a8fa340348..ecbe2827f5447 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -474,6 +474,11 @@ def test_does_not_convert_mixed_integer(self): good_date_string)) def test_parsers(self): + + # https://github.com/dateutil/dateutil/issues/217 + import dateutil + yearfirst = dateutil.__version__ >= LooseVersion('2.5.0') + cases = {'2011-01-01': datetime.datetime(2011, 1, 1), '2Q2005': datetime.datetime(2005, 4, 1), '2Q05': datetime.datetime(2005, 4, 1), @@ -527,20 +532,26 @@ def test_parsers(self): } for date_str, expected in compat.iteritems(cases): - result1, _, _ = tools.parse_time_string(date_str) - result2 = to_datetime(date_str) - result3 = to_datetime([date_str]) - result4 = to_datetime(np.array([date_str], dtype=object)) - result5 = Timestamp(date_str) - result6 = DatetimeIndex([date_str])[0] - result7 = date_range(date_str, freq='S', periods=1) + result1, _, _ = tools.parse_time_string(date_str, + yearfirst=yearfirst) + result2 = to_datetime(date_str, yearfirst=yearfirst) + result3 = to_datetime([date_str], yearfirst=yearfirst) + result4 = to_datetime(np.array([date_str], dtype=object), + yearfirst=yearfirst) + result6 = DatetimeIndex([date_str], yearfirst=yearfirst)[0] self.assertEqual(result1, expected) self.assertEqual(result2, expected) self.assertEqual(result3, expected) self.assertEqual(result4, expected) - self.assertEqual(result5, expected) self.assertEqual(result6, expected) - self.assertEqual(result7, expected) + + # these really need to have yearfist, but we don't support + if not yearfirst: + result5 = Timestamp(date_str) + self.assertEqual(result5, expected) + result7 = date_range(date_str, freq='S', periods=1, + yearfirst=yearfirst) + self.assertEqual(result7, expected) # NaT result1, _, _ = tools.parse_time_string('NaT') diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index bbcb68721a0f2..d413a4a2bf096 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -1,6 +1,4 @@ from datetime import datetime, timedelta, time -import sys - import numpy as np import pandas.lib as lib @@ -10,17 +8,6 @@ import pandas.compat as compat from pandas.util.decorators import deprecate_kwarg -try: - import dateutil - # raise exception if dateutil 2.0 install on 2.x platform - if (sys.version_info[0] == 2 and - dateutil.__version__ == '2.0'): # pragma: no cover - raise Exception('dateutil 2.0 incompatible with Python 2.x, you must ' - 'install version 1.5 or 2.1+!') -except ImportError: # pragma: no cover - print('Please install python-dateutil via easy_install or some method!') - raise # otherwise a 2nd import won't show the message - _DATEUTIL_LEXER_SPLIT = None try: # Since these are private methods from dateutil, it is safely imported
COMPAT: test with latest dateutil via pip COMPAT: dateutil 2.5.0 bugfix causes some tests to fail, skip for now xref #12611
https://api.github.com/repos/pandas-dev/pandas/pulls/12613
2016-03-14T01:48:06Z
2016-03-14T20:57:46Z
null
2016-03-14T20:57:46Z
BUG: ensure Series.name is hashable, #12610
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index e664020946baf..7701bc66b8b92 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -165,3 +165,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``Series.name`` when ``name`` attribute can be a hashable type (:issue:`12610`) \ No newline at end of file diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 24b883b90cf5d..cbcd949dd5c36 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -81,7 +81,7 @@ class NDFrame(PandasObject): copy : boolean, default False """ _internal_names = ['_data', '_cacher', '_item_cache', '_cache', 'is_copy', - '_subtyp', '_index', '_default_kind', + '_subtyp', '_name', '_index', '_default_kind', '_default_fill_value', '_metadata', '__array_struct__', '__array_interface__'] _internal_names_set = set(_internal_names) diff --git a/pandas/core/series.py b/pandas/core/series.py index b25cd63acaff6..cc58b32de999a 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -232,7 +232,7 @@ def __init__(self, data=None, index=None, dtype=None, name=None, generic.NDFrame.__init__(self, data, fastpath=True) - object.__setattr__(self, 'name', name) + self.name = name self._set_axis(0, index, fastpath=True) @classmethod @@ -301,6 +301,16 @@ def _update_inplace(self, result, **kwargs): # we want to call the generic version and not the IndexOpsMixin return generic.NDFrame._update_inplace(self, result, **kwargs) + @property + def name(self): + return self._name + + @name.setter + def name(self, value): + if value is not None and not com.is_hashable(value): + raise TypeError('Series.name must be a hashable type') + object.__setattr__(self, '_name', value) + # ndarray compatibility @property def dtype(self): diff --git a/pandas/tests/series/test_alter_axes.py b/pandas/tests/series/test_alter_axes.py index 4f9a55908fe96..ee054437e60a8 100644 --- a/pandas/tests/series/test_alter_axes.py +++ b/pandas/tests/series/test_alter_axes.py @@ -1,6 +1,8 @@ # coding=utf-8 # pylint: disable-msg=E1101,W0612 +from datetime import datetime + import numpy as np import pandas as pd @@ -64,7 +66,7 @@ def test_rename_by_series(self): def test_rename_set_name(self): s = Series(range(4), index=list('abcd')) - for name in ['foo', ['foo'], ('foo',)]: + for name in ['foo', 123, 123., datetime(2001, 11, 11), ('foo',)]: result = s.rename(name) self.assertEqual(result.name, name) self.assert_numpy_array_equal(result.index.values, s.index.values) @@ -72,12 +74,18 @@ def test_rename_set_name(self): def test_rename_set_name_inplace(self): s = Series(range(3), index=list('abc')) - for name in ['foo', ['foo'], ('foo',)]: + for name in ['foo', 123, 123., datetime(2001, 11, 11), ('foo',)]: s.rename(name, inplace=True) self.assertEqual(s.name, name) self.assert_numpy_array_equal(s.index.values, np.array(['a', 'b', 'c'])) + def test_set_name_attribute(self): + s = Series([1, 2, 3]) + for name in [7, 7., 'name', datetime(2001, 1, 1), (1,), u"\u05D0"]: + s.name = name + self.assertEqual(s.name, name) + def test_set_name(self): s = Series([1, 2, 3]) s2 = s._set_name('foo') diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py index 356267630b274..89793018b5193 100644 --- a/pandas/tests/series/test_constructors.py +++ b/pandas/tests/series/test_constructors.py @@ -725,3 +725,14 @@ def f(): self.assertEqual(s.dtype, 'timedelta64[ns]') s = Series([pd.NaT, np.nan, '1 Day']) self.assertEqual(s.dtype, 'timedelta64[ns]') + + def test_constructor_name_hashable(self): + for n in [777, 777., 'name', datetime(2001, 11, 11), (1, ), u"\u05D0"]: + for data in [[1, 2, 3], np.ones(3), {'a': 0, 'b': 1}]: + s = Series(data, name=n) + self.assertEqual(s.name, n) + + def test_constructor_name_unhashable(self): + for n in [['name_list'], np.ones(2), {1: 2}]: + for data in [['name_list'], np.ones(2), {1: 2}]: + self.assertRaises(TypeError, Series, data, name=n) diff --git a/pandas/tests/series/test_io.py b/pandas/tests/series/test_io.py index beb023215e6ce..2cdd6b22afc74 100644 --- a/pandas/tests/series/test_io.py +++ b/pandas/tests/series/test_io.py @@ -146,8 +146,9 @@ def test_timeseries_periodindex(self): self.assertEqual(new_ts.index.freq, 'M') def test_pickle_preserve_name(self): - unpickled = self._pickle_roundtrip_name(self.ts) - self.assertEqual(unpickled.name, self.ts.name) + for n in [777, 777., 'name', datetime(2001, 11, 11), (1, 2)]: + unpickled = self._pickle_roundtrip_name(tm.makeTimeSeries(name=n)) + self.assertEqual(unpickled.name, n) def _pickle_roundtrip_name(self, obj): diff --git a/pandas/tests/series/test_repr.py b/pandas/tests/series/test_repr.py index ec2efdcf40705..68ad29f1418c3 100644 --- a/pandas/tests/series/test_repr.py +++ b/pandas/tests/series/test_repr.py @@ -97,7 +97,7 @@ def test_repr(self): rep_str = repr(ser) self.assertIn("Name: 0", rep_str) - ser = Series(["a\n\r\tb"], name=["a\n\r\td"], index=["a\n\r\tf"]) + ser = Series(["a\n\r\tb"], name="a\n\r\td", index=["a\n\r\tf"]) self.assertFalse("\t" in repr(ser)) self.assertFalse("\r" in repr(ser)) self.assertFalse("a\n" in repr(ser))
- [x] closes #12610 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 and since Python 2.6 we can use the abstract base class collections.Hashable.
https://api.github.com/repos/pandas-dev/pandas/pulls/12612
2016-03-13T23:51:26Z
2016-03-25T13:30:15Z
null
2016-03-25T17:16:28Z
added pd as namespace
diff --git a/doc/source/io.rst b/doc/source/io.rst index 64967f1979807..5a42f58e69cc7 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -19,10 +19,9 @@ import matplotlib.pyplot as plt plt.close('all') - from pandas import * options.display.max_rows=15 import pandas.util.testing as tm - clipdf = DataFrame({'A':[1,2,3],'B':[4,5,6],'C':['p','q','r']}, + clipdf = pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':['p','q','r']}, index=['x','y','z']) =============================== @@ -1195,7 +1194,7 @@ class of the csv module. For this, you have to specify ``sep=None``. .. ipython:: python :suppress: - df = DataFrame(np.random.randn(10, 4)) + df = pd.DataFrame(np.random.randn(10, 4)) df.to_csv('tmp.sv', sep='|') df.to_csv('tmp2.sv', sep=':') @@ -1375,7 +1374,7 @@ Note ``NaN``'s, ``NaT``'s and ``None`` will be converted to ``null`` and ``datet .. ipython:: python - dfj = DataFrame(randn(5, 2), columns=list('AB')) + dfj = pd.DataFrame(randn(5, 2), columns=list('AB')) json = dfj.to_json() json @@ -1387,10 +1386,10 @@ file / string. Consider the following DataFrame and Series: .. ipython:: python - dfjo = DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)), + dfjo = pd.DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)), columns=list('ABC'), index=list('xyz')) dfjo - sjo = Series(dict(x=15, y=16, z=17), name='D') + sjo = pd.Series(dict(x=15, y=16, z=17), name='D') sjo **Column oriented** (the default for ``DataFrame``) serializes the data as @@ -1472,10 +1471,10 @@ Writing to a file, with a date index and a date column .. ipython:: python dfj2 = dfj.copy() - dfj2['date'] = Timestamp('20130101') + dfj2['date'] = pd.Timestamp('20130101') dfj2['ints'] = list(range(5)) dfj2['bools'] = True - dfj2.index = date_range('20130101', periods=5) + dfj2.index = pd.date_range('20130101', periods=5) dfj2.to_json('test.json') open('test.json').read() @@ -1506,7 +1505,7 @@ problems: In [141]: from datetime import timedelta - In [142]: dftd = DataFrame([timedelta(23), timedelta(seconds=5), 42]) + In [142]: dftd = pd.DataFrame([timedelta(23), timedelta(seconds=5), 42]) In [143]: dftd.to_json() @@ -1633,7 +1632,7 @@ Preserve string indices: .. ipython:: python - si = DataFrame(np.zeros((4, 4)), + si = pd.DataFrame(np.zeros((4, 4)), columns=list(range(4)), index=[str(i) for i in range(4)]) si @@ -1681,7 +1680,7 @@ data: randfloats = np.random.uniform(-100, 1000, 10000) randfloats.shape = (1000, 10) - dffloats = DataFrame(randfloats, columns=list('ABCDEFGHIJ')) + dffloats = pd.DataFrame(randfloats, columns=list('ABCDEFGHIJ')) jsonfloats = dffloats.to_json() @@ -1884,7 +1883,7 @@ Read in pandas ``to_html`` output (with some loss of floating point precision) .. code-block:: python - df = DataFrame(randn(2, 2)) + df = pd.DataFrame(randn(2, 2)) s = df.to_html(float_format='{0:.40g}'.format) dfin = read_html(s, index_col=0) @@ -1937,7 +1936,7 @@ in the method ``to_string`` described above. .. ipython:: python - df = DataFrame(randn(2, 2)) + df = pd.DataFrame(randn(2, 2)) df print(df.to_html()) # raw html @@ -2013,7 +2012,7 @@ Finally, the ``escape`` argument allows you to control whether the .. ipython:: python - df = DataFrame({'a': list('&<>'), 'b': randn(3)}) + df = pd.DataFrame({'a': list('&<>'), 'b': randn(3)}) .. ipython:: python @@ -2367,7 +2366,7 @@ Added support for Openpyxl >= 2.2 bio = BytesIO() # By setting the 'engine' in the ExcelWriter constructor. - writer = ExcelWriter(bio, engine='xlsxwriter') + writer = pd.ExcelWriter(bio, engine='xlsxwriter') df.to_excel(writer, sheet_name='Sheet1') # Save the workbook @@ -2423,7 +2422,7 @@ argument to ``to_excel`` and to ``ExcelWriter``. The built-in engines are: df.to_excel('path_to_file.xlsx', sheet_name='Sheet1', engine='xlsxwriter') # By setting the 'engine' in the ExcelWriter constructor. - writer = ExcelWriter('path_to_file.xlsx', engine='xlsxwriter') + writer = pd.ExcelWriter('path_to_file.xlsx', engine='xlsxwriter') # Or via pandas configuration. from pandas import options @@ -2559,7 +2558,7 @@ both on the writing (serialization), and reading (deserialization). .. ipython:: python - df = DataFrame(np.random.rand(5,2),columns=list('AB')) + df = pd.DataFrame(np.random.rand(5,2),columns=list('AB')) df.to_msgpack('foo.msg') pd.read_msgpack('foo.msg') s = Series(np.random.rand(5),index=date_range('20130101',periods=5)) @@ -2647,7 +2646,7 @@ for some advanced strategies .. ipython:: python - store = HDFStore('store.h5') + store = pd.HDFStore('store.h5') print(store) Objects can be written to the file just like adding key-value pairs to a @@ -2656,11 +2655,11 @@ dict: .. ipython:: python np.random.seed(1234) - index = date_range('1/1/2000', periods=8) - s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e']) - df = DataFrame(randn(8, 3), index=index, + index = pd.date_range('1/1/2000', periods=8) + s = pd.Series(randn(5), index=['a', 'b', 'c', 'd', 'e']) + df = pd.DataFrame(randn(8, 3), index=index, columns=['A', 'B', 'C']) - wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], + wp = pd.Panel(randn(2, 5, 4), items=['Item1', 'Item2'], major_axis=date_range('1/1/2000', periods=5), minor_axis=['A', 'B', 'C', 'D']) @@ -2705,7 +2704,7 @@ Closing a Store, Context Manager # Working with, and automatically closing the store with the context # manager - with HDFStore('store.h5') as store: + with pd.HDFStore('store.h5') as store: store.keys() .. ipython:: python @@ -2772,7 +2771,7 @@ This is also true for the major axis of a ``Panel``: [[np.nan, np.nan, np.nan], [np.nan,5,6]], [[np.nan, np.nan, np.nan],[np.nan,3,np.nan]]] - panel_with_major_axis_all_missing = Panel(matrix, + panel_with_major_axis_all_missing = pd.Panel(matrix, items=['Item1', 'Item2','Item3'], major_axis=[1,2], minor_axis=['A', 'B', 'C']) @@ -2816,7 +2815,7 @@ This format is specified by default when using ``put`` or ``to_hdf`` or by ``for .. code-block:: python - DataFrame(randn(10,2)).to_hdf('test_fixed.h5','df') + pd.DataFrame(randn(10,2)).to_hdf('test_fixed.h5','df') pd.read_hdf('test_fixed.h5','df',where='index>5') TypeError: cannot pass a where specification when reading a fixed format. @@ -2848,7 +2847,7 @@ enable ``put/append/to_hdf`` to by default store in the ``table`` format. .. ipython:: python - store = HDFStore('store.h5') + store = pd.HDFStore('store.h5') df1 = df[0:4] df2 = df[4:] @@ -2914,7 +2913,7 @@ defaults to `nan`. .. ipython:: python - df_mixed = DataFrame({ 'A' : randn(8), + df_mixed = pd.DataFrame({ 'A' : randn(8), 'B' : randn(8), 'C' : np.array(randn(8),dtype='float32'), 'string' :'string', @@ -2940,12 +2939,12 @@ storing/selecting from homogeneous index DataFrames. .. ipython:: python - index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], + index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two', 'three']], labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], names=['foo', 'bar']) - df_mi = DataFrame(np.random.randn(10, 3), index=index, + df_mi = pd.DataFrame(np.random.randn(10, 3), index=index, columns=['A', 'B', 'C']) df_mi @@ -3127,7 +3126,7 @@ specified in the format: ``<float>(<unit>)``, where float may be signed (and fra .. ipython:: python from datetime import timedelta - dftd = DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ])) + dftd = pd.DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ])) dftd['C'] = dftd['A']-dftd['B'] dftd store.append('dftd',dftd,data_columns=True) @@ -3163,8 +3162,8 @@ Oftentimes when appending large amounts of data to a store, it is useful to turn .. ipython:: python - df_1 = DataFrame(randn(10,2),columns=list('AB')) - df_2 = DataFrame(randn(10,2),columns=list('AB')) + df_1 = pd.DataFrame(randn(10,2),columns=list('AB')) + df_2 = pd.DataFrame(randn(10,2),columns=list('AB')) st = pd.HDFStore('appends.h5',mode='w') st.append('df', df_1, data_columns=['B'], index=False) @@ -3261,7 +3260,7 @@ chunks. .. ipython:: python - dfeq = DataFrame({'number': np.arange(1,11)}) + dfeq = pd.DataFrame({'number': np.arange(1,11)}) dfeq store.append('dfeq', dfeq, data_columns=['number']) @@ -3301,7 +3300,7 @@ Sometimes you want to get the coordinates (a.k.a the index locations) of your qu .. ipython:: python - df_coord = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000)) + df_coord = pd.DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000)) store.append('df_coord',df_coord) c = store.select_as_coordinates('df_coord','index>20020101') c.summary() @@ -3318,7 +3317,7 @@ a datetimeindex which are 5. .. ipython:: python - df_mask = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000)) + df_mask = pd.DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000)) store.append('df_mask',df_mask) c = store.select_column('df_mask','index') where = c[DatetimeIndex(c).month==5].index @@ -3366,7 +3365,7 @@ results. .. ipython:: python - df_mt = DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8), + df_mt = pd.DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8), columns=['A', 'B', 'C', 'D', 'E', 'F']) df_mt['foo'] = 'bar' df_mt.ix[1, ('A', 'B')] = np.nan @@ -3458,7 +3457,7 @@ Compression for all objects within the file .. code-block:: python - store_compressed = HDFStore('store_compressed.h5', complevel=9, complib='blosc') + store_compressed = pd.HDFStore('store_compressed.h5', complevel=9, complib='blosc') Or on-the-fly compression (this only applies to tables). You can turn off file compression for a specific table by passing ``complevel=0`` @@ -3556,7 +3555,7 @@ stored in a more efficient manner. .. ipython:: python - dfcat = DataFrame({ 'A' : Series(list('aabbcdba')).astype('category'), + dfcat = pd.DataFrame({ 'A' : Series(list('aabbcdba')).astype('category'), 'B' : np.random.randn(8) }) dfcat dfcat.dtypes @@ -3614,7 +3613,7 @@ Starting in 0.11.0, passing a ``min_itemsize`` dict will cause all passed column .. ipython:: python - dfs = DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5))) + dfs = pd.DataFrame(dict(A = 'foo', B = 'bar'),index=list(range(5))) dfs # A and B have a size of 30 @@ -3633,7 +3632,7 @@ You could inadvertently turn an actual ``nan`` value into a missing value. .. ipython:: python - dfss = DataFrame(dict(A = ['foo','bar','nan'])) + dfss = pd.DataFrame(dict(A = ['foo','bar','nan'])) dfss store.append('dfss', dfss) @@ -3667,7 +3666,7 @@ It is possible to write an ``HDFStore`` object that can easily be imported into index=range(100)) df_for_r.head() - store_export = HDFStore('export.h5') + store_export = pd.HDFStore('export.h5') store_export.append('df_for_r', df_for_r, data_columns=df_dc.columns) store_export @@ -3756,7 +3755,7 @@ number of options, please see the docstring. .. ipython:: python # a legacy store - legacy_store = HDFStore(legacy_file_path,'r') + legacy_store = pd.HDFStore(legacy_file_path,'r') legacy_store # copy (and return the new handle) @@ -3920,7 +3919,7 @@ the database using :func:`~pandas.DataFrame.to_sql`. (42, datetime.datetime(2010,10,19), 'Y', -12.5, False), (63, datetime.datetime(2010,10,20), 'Z', 5.73, True)] - data = DataFrame(d, columns=c) + data = pd.DataFrame(d, columns=c) .. ipython:: python @@ -4400,7 +4399,7 @@ into a .dta file. The format version of this file is always 115 (Stata 12). .. ipython:: python - df = DataFrame(randn(10, 2), columns=list('AB')) + df = pd.DataFrame(randn(10, 2), columns=list('AB')) df.to_stata('stata.dta') *Stata* data files have limited data type support; only strings with @@ -4625,7 +4624,7 @@ This is an informal comparison of various IO methods, using pandas 0.13.1. .. code-block:: python - In [1]: df = DataFrame(randn(1000000,2),columns=list('AB')) + In [1]: df = pd.DataFrame(randn(1000000,2),columns=list('AB')) In [2]: df.info() <class 'pandas.core.frame.DataFrame'> @@ -4699,7 +4698,7 @@ And here's the code import os from pandas.io import sql - df = DataFrame(randn(1000000,2),columns=list('AB')) + df = pd.DataFrame(randn(1000000,2),columns=list('AB')) def test_sql_write(df): if os.path.exists('test.sql'):
- [x ] closes https://github.com/pydata/pandas/pull/12268#discussion_r55924987 This is the msg: more consistent with code in the rest of the document added more namespace Generally, also numpy's randn should used as np.random.randn isolated namespace changes Note: I hope I got the rebase & merge right.
https://api.github.com/repos/pandas-dev/pandas/pulls/12608
2016-03-13T15:08:29Z
2016-05-26T16:15:03Z
null
2016-05-26T16:15:03Z
WIP: ENH: pivot/groupby index with nan
diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in index 3ce82dace40a9..9f21d6c3afb68 100644 --- a/pandas/_libs/hashtable_class_helper.pxi.in +++ b/pandas/_libs/hashtable_class_helper.pxi.in @@ -830,7 +830,7 @@ cdef class PyObjectHashTable(HashTable): val = values[i] hash(val) - if check_null and val != val or val is None: + if check_null and (val != val or val is None): labels[i] = na_sentinel continue diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index 7fab9295bb94e..f617b0d654928 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -521,7 +521,8 @@ def sort_mixed(values): return ordered, _ensure_platform_int(new_labels) -def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): +def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None, + dropna=True): """ Encode input values as an enumerated type or categorical variable @@ -534,6 +535,10 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): na_sentinel : int, default -1 Value to mark "not found" size_hint : hint to the hashtable sizer + dropna : boolean, default True + Drop NaN values + + .. versionadded:: 0.20.0 Returns ------- @@ -552,9 +557,8 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None): table = hash_klass(size_hint or len(values)) uniques = vec_klass() - check_nulls = not is_integer_dtype(original) + check_nulls = (not is_integer_dtype(original)) and dropna labels = table.get_labels(values, uniques, 0, na_sentinel, check_nulls) - labels = _ensure_platform_int(labels) uniques = uniques.to_array() diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 86978a9739ca4..78897ce539ddd 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4132,7 +4132,7 @@ def clip_lower(self, threshold, axis=None): 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, **kwargs): + group_keys=True, squeeze=False, dropna=True, **kwargs): """ Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns. @@ -4164,6 +4164,10 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, squeeze : boolean, default False reduce the dimensionality of the return type if possible, otherwise return a consistent type + dropna : boolean, default True + drop NaN in the grouping values + + .. versionadded:: 0.20.0 Examples -------- @@ -4188,7 +4192,7 @@ def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, axis = self._get_axis_number(axis) return groupby(self, by=by, axis=axis, level=level, as_index=as_index, sort=sort, group_keys=group_keys, squeeze=squeeze, - **kwargs) + dropna=dropna, **kwargs) def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None): diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 5591ce4b0d4aa..2c6500cb5042a 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -359,8 +359,8 @@ class _GroupBy(PandasObject, SelectionMixin): def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, - sort=True, group_keys=True, squeeze=False, **kwargs): - + sort=True, group_keys=True, squeeze=False, dropna=True, + **kwargs): self._selection = selection if isinstance(obj, NDFrame): @@ -380,13 +380,15 @@ def __init__(self, obj, keys=None, axis=0, level=None, self.group_keys = group_keys self.squeeze = squeeze self.mutated = kwargs.pop('mutated', False) + self.dropna = dropna if grouper is None: grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, level=level, sort=sort, - mutated=self.mutated) + mutated=self.mutated, + dropna=dropna) self.obj = obj self.axis = obj._get_axis_number(axis) @@ -968,6 +970,10 @@ class GroupBy(_GroupBy): List of columns to exclude name : string Most users should ignore this + dropna : boolean, default True + drop NaN in the grouping values + + .. versionadded:: 0.20.0 Notes ----- @@ -2324,6 +2330,10 @@ class Grouping(object): level : in_axis : if the Grouping is a column in self.obj and hence among Groupby.exclusions list + dropna : boolean, default True + drop NaN in the grouping values + + .. versionadded:: 0.20.0 Returns ------- @@ -2337,7 +2347,7 @@ class Grouping(object): """ def __init__(self, index, grouper=None, obj=None, name=None, level=None, - sort=True, in_axis=False): + sort=True, in_axis=False, dropna=True): self.name = name self.level = level @@ -2346,6 +2356,7 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, self.sort = sort self.obj = obj self.in_axis = in_axis + self.dropna = dropna # right place for this? if isinstance(grouper, (Series, Index)) and name is None: @@ -2396,7 +2407,6 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, # a passed Grouper like elif isinstance(self.grouper, Grouper): - # get the new grouper grouper = self.grouper._get_binner_for_grouping(self.obj) self.obj = self.grouper.obj @@ -2433,6 +2443,11 @@ def __init__(self, index, grouper=None, obj=None, name=None, level=None, from pandas import to_timedelta self.grouper = to_timedelta(self.grouper) + # convert None to NaN if we are going to keep them + if not dropna: + if not isinstance(self.grouper, Index): + self.grouper[np.equal(self.grouper, None)] = np.NaN + def __repr__(self): return 'Grouping({0})'.format(self.name) @@ -2466,7 +2481,7 @@ def group_index(self): def _make_labels(self): if self._labels is None or self._group_index is None: labels, uniques = algorithms.factorize( - self.grouper, sort=self.sort) + self.grouper, sort=self.sort, dropna=self.dropna) uniques = Index(uniques, name=self.name) self._labels = labels self._group_index = uniques @@ -2478,7 +2493,7 @@ def groups(self): def _get_grouper(obj, key=None, axis=0, level=None, sort=True, - mutated=False): + mutated=False, dropna=True): """ create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. @@ -2631,7 +2646,8 @@ def is_in_obj(gpr): name=name, level=level, sort=sort, - in_axis=in_axis) \ + in_axis=in_axis, + dropna=dropna) \ if not isinstance(gpr, Grouping) else gpr groupings.append(ping) @@ -3386,7 +3402,6 @@ def _post_process_cython_aggregate(self, obj): return obj def aggregate(self, arg, *args, **kwargs): - _level = kwargs.pop('_level', None) result, how = self._aggregate(arg, _level=_level, *args, **kwargs) if how is None: diff --git a/pandas/tests/groupby/test_groupby_nan.py b/pandas/tests/groupby/test_groupby_nan.py new file mode 100644 index 0000000000000..e617225042110 --- /dev/null +++ b/pandas/tests/groupby/test_groupby_nan.py @@ -0,0 +1,530 @@ +# -*- coding: utf-8 -*- +from __future__ import print_function + +import datetime + +from string import ascii_lowercase + +from numpy import nan + +from pandas import ( + Index, + MultiIndex, + DataFrame, + Series, + NaT, + date_range, +) +from pandas.util.testing import assert_frame_equal, assert_series_equal +from pandas.compat import ( + product as cart_product, +) +import numpy as np + +import pandas.util.testing as tm +import pandas as pd + +from .common import MixIn + + +class TestGroupBy(MixIn, tm.TestCase): + """ + Grouping operations when keeping NaN values in the group + with the dropna=False optione (GH 3729) + + Adapted from test_groupby.TestGroupBy + """ + + def setUp(self): + MixIn.setUp(self) + self.df_nan = DataFrame( + {'A': [np.NaN, 1, np.NaN, 1, np.NaN, 1, np.NaN, np.NaN], + 'B': [1, 1, np.NaN, 3, np.NaN, np.NaN, 1, 3], + 'C': np.random.randn(8), + 'D': np.random.randn(8)}) + + def test_basic_with_nan(self): + """ + Adapted from TestGroupBy.test_basic + """ + + def checkit(dtype): + data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype) + + index = np.arange(9) + np.random.shuffle(index) + data = data.reindex(index) + + # same test as previously but with nan replacing 0 in groupby + grouped = data.groupby(lambda x: x // 3 if x // 3 else np.nan, + dropna=False) + + for k, v in grouped: + self.assertEqual(len(v), 3) + + agged = grouped.aggregate(np.mean) + self.assertEqual(agged[1], 1) + + assert_series_equal(agged, grouped.agg(np.mean)) # shorthand + assert_series_equal(agged, grouped.mean()) + assert_series_equal(grouped.agg(np.sum), grouped.sum()) + + data_nan = data.copy() + data_nan[data_nan == 0] = np.nan + value_grouped = data.groupby(data_nan, dropna=False) + assert_series_equal(value_grouped.aggregate(np.mean), agged, + check_index_type=False) + + # complex agg + agged = grouped.aggregate([np.mean, np.std]) + agged = grouped.aggregate({'one': np.mean, 'two': np.std}) + + group_constants = {'nan': 10, '1.0': 20, '2.0': 30} + agged = grouped.agg( + lambda x: group_constants[str(x.name)] + x.mean()) + self.assertEqual(agged[1], 21) + + # corner cases + self.assertRaises(Exception, grouped.aggregate, lambda x: x * 2) + + for dtype in ['int64', 'int32', 'float64', 'float32']: + checkit(dtype) + + def test_first_last_nth_with_nan(self): + """ + Adapted from TestGroupBy.test_first_last_nth_with + """ + + grouped = self.df_nan.groupby('A', dropna=False) + first = grouped.first() + + expected = self.df_nan.ix[[1, 0], ['B', 'C', 'D']] + expected.index = Index([1, np.NaN], name='A') + + assert_frame_equal(first, expected) + + nth = grouped.nth(0) + assert_frame_equal(nth, expected) + + nth = grouped.nth(-1) + expected = self.df_nan.ix[[5, 7], ['B', 'C', 'D']] + expected.index = Index([1, np.NaN], name='A') + + assert_frame_equal(nth, expected) + + last = grouped.last() + expected.iloc[0, 0] = 3.0 # there is a bug in first/last, cf GH 8427 + assert_frame_equal(last, expected) + + nth = grouped.nth(1) + expected = self.df_nan.ix[[2, 3], ['B', 'C', 'D']].copy() + expected.index = Index([np.NaN, 1], name='A') + expected = expected.sort_index() + assert_frame_equal(nth, expected) + + def test_nth_nan(self): + """ + Adapted from TestGroupBy.test_nth + """ + + df = DataFrame([[np.NaN, np.nan], [np.NaN, 4], [5, 6]], + columns=['A', 'B']) + g = df.groupby('A', dropna=False) + + assert_frame_equal(g.nth(0), df.iloc[[2, 0]].set_index('A')) + assert_frame_equal(g.nth(1), df.iloc[[1]].set_index('A')) + assert_frame_equal(g.nth(2), df.loc[[]].set_index('A')) + assert_frame_equal(g.nth(-1), df.iloc[[2, 1]].set_index('A')) + assert_frame_equal(g.nth(-2), df.iloc[[0]].set_index('A')) + assert_frame_equal(g.nth(-3), df.loc[[]].set_index('A')) + assert_series_equal(g.B.nth(0), df.set_index('A').B.iloc[[2, 0]]) + assert_series_equal(g.B.nth(1), df.set_index('A').B.iloc[[1]]) + assert_frame_equal(g[['B']].nth(0), + df.ix[[2, 0], ['A', 'B']].set_index('A')) + + # test dropna parameter for .nth() + # this might be a border-line scenario: df is + # A B + # 0 NaN NaN + # 1 NaN 4.0 + # 2 5.0 6.0 + # line 1 is dropped, even though the NaN is in the grouping column + exp = df.set_index('A') + exp.iloc[1] = np.NaN + expected = exp.iloc[[2, 1]] + assert_frame_equal(g.nth(0, dropna='any'), expected) + assert_frame_equal(g.nth(-1, dropna='any'), exp.iloc[[2, 1]]) + + exp['B'] = np.nan + assert_frame_equal(g.nth(7, dropna='any'), exp.iloc[[2, 1]]) + assert_frame_equal(g.nth(2, dropna='any'), exp.iloc[[2, 1]]) + + def test_nth_multi_index_nan(self): + """ + Adapted from TestGroupBy.test_nth_multi_index + """ + + df = self.three_group.replace('foo', np.NaN) + grouped = df.groupby(['A', 'B'], dropna=False) + result = grouped.nth(0) + expected = grouped.first() + assert_frame_equal(result, expected) + + def test_nth_multi_index_as_expected_nan(self): + """ + Adapted from TestGroupBy.test_nth_multi_index_as_expected + """ + + three_group = DataFrame( + {'A': ['foo', 'foo', 'foo', 'foo', np.NaN, np.NaN, np.NaN, np.NaN, + 'foo', 'foo', 'foo'], + 'B': [np.NaN, np.NaN, np.NaN, 'two', np.NaN, np.NaN, np.NaN, + 'two', 'two', 'two', np.NaN], + 'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny', + 'dull', 'shiny', 'shiny', 'shiny']}) + grouped = three_group.groupby(['A', 'B'], dropna=False) + result = grouped.nth(0) + expected = DataFrame( + {'C': ['dull', 'dull', 'dull', 'dull']}, + index=MultiIndex(levels=[[nan, u'foo'], [nan, u'two']], + labels=[[0, 0, 1, 1], [0, 1, 0, 1]], + names=[u'A', u'B'])) + assert_frame_equal(result, expected) + + def test_groupby_series_dropna(self): + """ + Basic test for grouping a series with the dropna=False option + """ + + s = Series([1, 2, 3, 4]) + result = s.groupby([1, np.NaN, np.NaN, 1], dropna=False).sum() + + # expected result + expected = Series([5, 5], index=[1, np.NaN]) + + assert_series_equal(result, expected) + + def test_groupby_frame_dropna(self): + """ + Basic test for grouping a dataframe with the dropna=False option + """ + + data = [[1, 1], [np.NaN, 2], [1, 3], [np.NaN, 4]] + df = DataFrame(data, columns=['a', 'b']) + result = df.groupby('a', dropna=False).sum() + + # expected result + expected = DataFrame([[4], [6]], index=[1, np.NaN], columns=['b']) + expected.index.name = 'a' + + assert_frame_equal(result, expected) + + def test_groupby_multi_dropna(self): + """ + Basic test for grouping a dataframe with a multiindex with the + dropna=False option + """ + + data = [['a', 'b', 12, 12, 12], + ['a', np.nan, 12.3, 233., 12], + ['b', 'a', 123.23, 123, 1], + ['a', 'b', 1, 1, 1.]] + df = DataFrame(data, columns=['a', 'b', 'c', 'd', 'e']) + result = df.groupby(['a', 'b'], dropna=False).sum() + + # expected result + data = [[12.30, 233., 12.], [13., 13., 13.], [123.23, 123., 1.]] + index = MultiIndex(levels=[['a', 'b'], [np.nan, 'a', 'b']], + labels=[[0, 0, 1], [0, 2, 1]], + names=['a', 'b']) + expected = DataFrame(data, index=index, columns=['c', 'd', 'e']) + + assert_frame_equal(result, expected) + + def test_with_na(self): + """ + Compare behavior with and without the dropna=False option + """ + + index = Index(np.arange(10)) + + for dtype in ['float64', 'float32', 'int64', 'int32', 'int16', 'int8', + 'datetime64[ns]']: + values = Series(np.ones(10), index, dtype=dtype) + labels = Series([nan, 'foo', 'bar', 'bar', nan, nan, 'bar', + 'bar', nan, 'foo'], index=index) + + # this SHOULD be an int + grouped = values.groupby(labels) + agged = grouped.agg(len) + expected = Series([4, 2], index=['bar', 'foo']) + + assert_series_equal(agged, expected, check_dtype=False) + + # self.assertTrue(issubclass(agged.dtype.type, np.integer)) + + # explicity return a float from my function + def f(x): + return float(len(x)) + + agged = grouped.agg(f) + expected = Series([4, 2], index=['bar', 'foo']) + + assert_series_equal(agged, expected, check_dtype=False) + # self.assertTrue(issubclass(agged.dtype.type, np.float)) + + # GH 3729: keeping the NaNs + + # this SHOULD be an int + grouped = values.groupby(labels, dropna=False) + agged = grouped.agg(len) + expected = Series([4, 4, 2], index=[nan, 'bar', 'foo']) + + assert_series_equal(agged, expected, check_dtype=False) + # self.assertTrue(issubclass(agged.dtype.type, np.integer)) + + # explicity return a float from my function + def f(x): + return float(len(x)) + + agged = grouped.agg(f) + expected = Series([4, 4, 2], index=[nan, 'bar', 'foo']) + + assert_series_equal(agged, expected, check_dtype=False) + # self.assertTrue(issubclass(agged.dtype.type, np.float) + + def test_groupby_transform_with_nan_group(self): + # GH 9941 + df = pd.DataFrame({'a': np.arange(10, dtype='int64'), + 'b': [1, 1, 2, 3, np.nan, 4, 4, 5, 5, 5]}) + result = df.groupby(df.b)['a'].transform(max) + expected = pd.Series([1., 1., 2., 3., np.nan, 6., 6., 9., 9., 9.], + name='a', dtype='float64') + assert_series_equal(result, expected) + + result = df.groupby(df.b, dropna=False)['a'].transform(max) + expected = pd.Series([1, 1, 2, 3, 4, 6, 6, 9, 9, 9], + name='a', dtype='int64') + assert_series_equal(result, expected) + + def test__cython_agg_general_nan(self): + """ + Adapted from TestGroupBy.test__cython_agg_general + """ + + ops = [('mean', np.mean), + ('median', np.median), + ('var', np.var), + ('add', np.sum), + ('prod', np.prod), + ('min', np.min), + ('max', np.max), + ('first', lambda x: x.iloc[0]), + ('last', lambda x: x.iloc[-1]), ] + df = DataFrame(np.random.randn(1000)) + labels = np.random.randint(0, 50, size=1000).astype(float) + labels[labels == 0] = np.nan + + for op, targop in ops: + result = df.groupby(labels, dropna=False)._cython_agg_general(op) + expected = df.groupby(labels, dropna=False).agg(targop) + try: + tm.assert_frame_equal(result, expected) + except BaseException as exc: + exc.args += ('operation: %s' % op, ) + raise + + def test_ops_general_nan(self): + """ + Adapted from TestGroupBy.test_ops_general + """ + + ops = [('mean', np.mean), + ('median', np.median), + ('std', np.std), + ('var', np.var), + ('sum', np.sum), + ('prod', np.prod), + ('min', np.min), + ('max', np.max), + ('first', lambda x: x.iloc[0]), + ('last', lambda x: x.iloc[-1]), + ('count', np.size), ] + try: + from scipy.stats import sem + except ImportError: + pass + else: + ops.append(('sem', sem)) + df = DataFrame(np.random.randn(1000)) + labels = np.random.randint(0, 50, size=1000).astype(float) + labels[labels == 0] = np.nan + + for op, targop in ops: + result = getattr(df.groupby(labels, dropna=False), op)() + result = result.astype(float) + expected = df.groupby(labels, dropna=False).agg(targop) + try: + tm.assert_frame_equal(result, expected) + except BaseException as exc: + exc.args += ('operation: %s' % op, ) + raise + + def test_datetime_timedelta_nat(self): + now = datetime.datetime.now() + df = DataFrame([ + [now, now + datetime.timedelta(days=1)], + [now, now + datetime.timedelta(days=2)], + [NaT, now + datetime.timedelta(days=1)], + [NaT, NaT], + ], columns=['a', 'b']) + expected = DataFrame([ + [now + datetime.timedelta(days=1)], + [now + datetime.timedelta(days=1)], + ], index=[now, NaT], columns=['b']) + expected.index.name = 'a' + + # default behavior + result = df.groupby('a').first() + tm.assert_frame_equal( + result.sort_index(), + expected.iloc[:1], + ) + + # keeping NaT + result = df.groupby('a', dropna=False).first() + tm.assert_frame_equal( + result.sort_index(), + expected, + ) + + # now we test timedelta values + df = df.applymap(lambda _: _ - now) # workaround GH 8554 + expected = expected.applymap(lambda _: _ - now) + expected.index = expected.index - now + + # default behavior + result = df.groupby('a').first() + tm.assert_frame_equal( + result.sort_index(), + expected.iloc[:1], + ) + + # keeping NaT + result = df.groupby('a', dropna=False).first() + tm.assert_frame_equal( + result.sort_index(), + expected, + ) + + def test_strings(self): + df = DataFrame([ + ['a', 1], + ['a', 2], + [None, 1], + [nan, 2], + [nan, 3], + [None, 4], + ], columns=['x', 'y']) + + # default behavior: nans are dropped + expected = Series([1], index=['a'], name='y') + expected.index.name = 'x' + + result = df.groupby('x').y.first() + tm.assert_series_equal( + expected.sort_index(), + result.sort_index(), + ) + + result = df.groupby(df.x).y.first() + tm.assert_series_equal( + expected.sort_index(), + result.sort_index(), + ) + + result = df.groupby(Index(df.x)).y.first() + tm.assert_series_equal( + expected.sort_index(), + result.sort_index(), + ) + + # not dropping the NaNs + expected = Series([1, 1], index=['a', nan], name='y') + expected.index.name = 'x' + + result = df.groupby('x', dropna=False).y.first() + tm.assert_series_equal( + expected.sort_index(), + result.sort_index(), + ) + + result = df.groupby(df.x, dropna=False).y.first() + tm.assert_series_equal( + expected.sort_index(), + result.sort_index(), + ) + + result = df.groupby(Index(df.x), dropna=False).y.first() + tm.assert_series_equal( + expected.sort_index(), + result.sort_index(), + ) + + def test_series_groupby_nunique(self): + + def check_nunique(df, keys, as_index=True): + choices = cart_product((False, True), repeat=3) + for sort, nunique_dropna, groupby_dropna in choices: + gr = df.groupby( + keys, + as_index=as_index, + sort=sort, + dropna=groupby_dropna, + ) + left = gr['julie'].nunique( + dropna=nunique_dropna, + ) + + gr = df.groupby( + keys, + as_index=as_index, + sort=sort, + dropna=groupby_dropna, + ) + right = gr['julie'].apply( + Series.nunique, + dropna=nunique_dropna, + ) + if not as_index: + right = right.reset_index(drop=True) + + assert_series_equal( + left, + right, + check_names=False, + ) + + days = date_range('2015-08-23', periods=10) + + for n, m in cart_product(10 ** np.arange(2, 6), (10, 100, 1000)): + frame = DataFrame({ + 'jim': np.random.choice( + list(ascii_lowercase), n), + 'joe': np.random.choice(days, n), + 'julie': np.random.randint(0, m, n) + }) + + check_nunique(frame, ['jim']) + check_nunique(frame, ['jim', 'joe']) + + frame.loc[1::17, 'jim'] = None + frame.loc[3::37, 'joe'] = None + frame.loc[7::19, 'julie'] = None + frame.loc[8::19, 'julie'] = None + frame.loc[9::19, 'julie'] = None + + check_nunique(frame, ['jim']) + check_nunique(frame, ['jim', 'joe']) + check_nunique(frame, ['jim'], as_index=False) + check_nunique(frame, ['jim', 'joe'], as_index=False)
I'm working on a solution for issue #3729. I've identified where to work and put in place a first, crude solution. I need to do more tests. - [ ] closes #3729. - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry xref #9941, #5456, #6992, #443 --- 2016-08-28: here are the tests I think need to be added / copied /adapted: Basic tests: - [x] Create a new test class, in a new file - [ ] test_basic_with_nan with dtypes in object, datetimes, timedelta & datetime64 with tz - [x] test_groupby_series_dropna - [x] test_groupby_frame_dropna - [ ] test_groupby_panel_dropna - [x] test_groupby_multi_dropna Tests with data types - [x] integer, floats - [x] datetime, timedelta - [x] strings and other objects - [x] None Existing tests to copy or adapt from TestGroupBy: - [x] test_first_last_nth - [x] test_nth - [x] test_nth_multi_index_as_expected - [x] test_nth_multi_index - [ ] test_group_selection_cache - [x] test_basic - [x] test_with_na - [ ] test_series_groupby_nunique - [ ] test_groupby_level_with_nas - [ ] test_groupby_nat_exclude - [ ] test_groupby_groups_datetimeindex - [ ] test_nlargest - [ ] test_nsmallest - [ ] test_groupby_as_index_agg - [ ] test_agg_api - [ ] test_transform - [ ] test_builtins_apply - [ ] test_groupby_whitelist - [x] test__cython_agg_general - [x] test_ops_general - [x] test_groupby_transform_with_nan_group And also I will need to edit the documentation, for instance this part: - [ ] Edit http://pandas-docs.github.io/pandas-docs-travis/groupby.html
https://api.github.com/repos/pandas-dev/pandas/pulls/12607
2016-03-13T14:46:55Z
2017-07-26T23:56:24Z
null
2023-05-11T01:13:26Z
Improved docs for infer_datetime_format
diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 2604b6e0784cf..fa9a5cf12570d 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -132,8 +132,10 @@ class ParserWarning(Warning): Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : boolean, default False - If True and parse_dates is enabled for a column, attempt to infer - the datetime format to speed up the processing + If True and parse_dates is enabled, pandas will attempt to infer the format + of the datetime strings in the columns, and if it can be inferred, switch + to a faster method of parsing them. In some cases this can increase the + parsing speed by ~5-10x. keep_date_col : boolean, default False If True and parse_dates specifies combining multiple columns then keep the original columns. diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index d92cfef5280fc..f9df0d082f2ff 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -231,8 +231,10 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch (e.g. a unix timestamp), which is an integer/float number. infer_datetime_format : boolean, default False - If no `format` is given, try to infer the format based on the first - datetime string. Provides a large speed-up in many cases. + If True and no `format` is given, attempt to infer the format of the + datetime strings, and if it can be inferred, switch to a faster + method of parsing them. In some cases this can increase the parsing + speed by ~5-10x. Returns ------- @@ -273,6 +275,19 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, 99 2000-04-09 Length: 100, dtype: datetime64[ns] + Infer the format from the first entry + + >>> pd.to_datetime(df.month + '/' + df.day + '/' + df.year, + infer_datetime_format=True) + 0 2000-01-01 + 1 2000-01-02 + ... + 98 2000-04-08 + 99 2000-04-09 + + This gives the same results as omitting the `infer_datetime_format=True`, + but is much faster. + Date that does not meet timestamp limitations: >>> pd.to_datetime('13000101', format='%Y%m%d')
Fixes #12152
https://api.github.com/repos/pandas-dev/pandas/pulls/12606
2016-03-13T12:27:24Z
2016-03-14T13:02:22Z
null
2016-03-14T13:02:51Z
Chooses eval engine automatically depending on whether numexpr is installed
diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index c3300ffca468e..f5b3695d4dd62 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -131,7 +131,7 @@ def _check_for_locals(expr, stack_level, parser): raise SyntaxError(msg) -def eval(expr, parser='pandas', engine='numexpr', truediv=True, +def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=None): """Evaluate a Python expression as a string using various backends. @@ -160,7 +160,7 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, ``'python'`` parser to retain strict Python semantics. See the :ref:`enhancing performance <enhancingperf.eval>` documentation for more details. - engine : string, default 'numexpr', {'python', 'numexpr'} + engine : string, default None, {'python', 'numexpr'} The engine used to evaluate the expression. Supported engines are @@ -172,6 +172,9 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, More backends may be available in the future. + If set to None (the default) then uses ``'numexpr'`` if available, + otherwise uses ``'python'``. + truediv : bool, optional Whether to use true division, like in Python >= 3 local_dict : dict or None, optional @@ -227,6 +230,12 @@ def eval(expr, parser='pandas', engine='numexpr', truediv=True, raise ValueError("multi-line expressions are only valid in the " "context of data, use DataFrame.eval") + if engine is None: + if _NUMEXPR_INSTALLED: + engine = 'numexpr' + else: + engine = 'python' + first_expr = True for expr in exprs: expr = _convert_expression(expr)
Fixes #12008
https://api.github.com/repos/pandas-dev/pandas/pulls/12605
2016-03-13T12:20:32Z
2016-04-17T15:49:39Z
null
2016-04-17T15:49:39Z
CLN GH10559 Rename sheetname variable to sheet_name for consistency
diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 5656c360b3990..6fd7b1c362975 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -72,7 +72,7 @@ def get_writer(engine_name): raise ValueError("No Excel writer '%s'" % engine_name) -def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, +def read_excel(io, sheet_name=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, @@ -86,7 +86,7 @@ def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/workbook.xlsx - sheetname : string, int, mixed list of strings/ints, or None, default 0 + sheet_name : string, int, mixed list of strings/ints, or None, default 0 Strings are used for sheet names, Integers are used in zero-indexed sheet positions. @@ -162,14 +162,14 @@ def read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, Returns ------- parsed : DataFrame or Dict of DataFrames - DataFrame from the passed in Excel file. See notes in sheetname + DataFrame from the passed in Excel file. See notes in sheet_name argument for more information on when a Dict of Dataframes is returned. """ if not isinstance(io, ExcelFile): io = ExcelFile(io, engine=engine) return io._parse_excel( - sheetname=sheetname, header=header, skiprows=skiprows, + sheet_name=sheet_name, header=header, skiprows=skiprows, index_col=index_col, parse_cols=parse_cols, parse_dates=parse_dates, date_parser=date_parser, na_values=na_values, thousands=thousands, convert_float=convert_float, has_index_names=has_index_names, @@ -226,7 +226,7 @@ def __init__(self, io, **kwds): raise ValueError('Must explicitly set engine if not passing in' ' buffer or path for io.') - def parse(self, sheetname=0, header=0, skiprows=None, skip_footer=0, + def parse(self, sheet_name=0, header=0, skiprows=None, skip_footer=0, index_col=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, @@ -238,7 +238,7 @@ def parse(self, sheetname=0, header=0, skiprows=None, skip_footer=0, docstring for more info on accepted parameters """ - return self._parse_excel(sheetname=sheetname, header=header, + return self._parse_excel(sheet_name=sheet_name, header=header, skiprows=skiprows, index_col=index_col, has_index_names=has_index_names, @@ -285,7 +285,7 @@ def _excel2num(x): else: return i in parse_cols - def _parse_excel(self, sheetname=0, header=0, skiprows=None, skip_footer=0, + def _parse_excel(self, sheet_name=0, header=0, skiprows=None, skip_footer=0, index_col=None, has_index_names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, @@ -370,29 +370,29 @@ def _parse_cell(cell_contents, cell_typ): ret_dict = False - # Keep sheetname to maintain backwards compatibility. - if isinstance(sheetname, list): - sheets = sheetname + # Keep sheet_name to maintain backwards compatibility. + if isinstance(sheet_name, list): + sheets = sheet_name ret_dict = True - elif sheetname is None: + elif sheet_name is None: sheets = self.sheet_names ret_dict = True else: - sheets = [sheetname] + sheets = [sheet_name] # handle same-type duplicates. sheets = list(set(sheets)) output = {} - for asheetname in sheets: + for a_sheet_name in sheets: if verbose: - print("Reading sheet %s" % asheetname) + print("Reading sheet %s" % a_sheet_name) - if isinstance(asheetname, compat.string_types): - sheet = self.book.sheet_by_name(asheetname) + if isinstance(a_sheet_name, compat.string_types): + sheet = self.book.sheet_by_name(a_sheet_name) else: # assume an integer if not a string - sheet = self.book.sheet_by_index(asheetname) + sheet = self.book.sheet_by_index(a_sheet_name) data = [] should_parse = {} @@ -409,7 +409,7 @@ def _parse_cell(cell_contents, cell_typ): data.append(row) if sheet.nrows == 0: - output[asheetname] = DataFrame() + output[a_sheet_name] = DataFrame() continue if com.is_list_like(header) and len(header) == 1: @@ -461,18 +461,18 @@ def _parse_cell(cell_contents, cell_typ): squeeze=squeeze, **kwds) - output[asheetname] = parser.read() - if not squeeze or isinstance(output[asheetname], DataFrame): - output[asheetname].columns = output[ - asheetname].columns.set_names(header_names) + output[a_sheet_name] = parser.read() + if not squeeze or isinstance(output[a_sheet_name], DataFrame): + output[a_sheet_name].columns = output[ + a_sheet_name].columns.set_names(header_names) except StopIteration: # No Data, return an empty DataFrame - output[asheetname] = DataFrame() + output[a_sheet_name] = DataFrame() if ret_dict: return output else: - return output[asheetname] + return output[a_sheet_name] @property def sheet_names(self): diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py index b8d61047a7b6d..d109b0a20b698 100644 --- a/pandas/io/tests/test_excel.py +++ b/pandas/io/tests/test_excel.py @@ -360,16 +360,16 @@ def test_reader_converters(self): tm.assert_frame_equal(actual, expected) def test_reading_all_sheets(self): - # Test reading all sheetnames by setting sheetname to None, + # Test reading all sheet_names by setting sheet_name to None, # Ensure a dict is returned. # See PR #9450 basename = 'test_multisheet' - dfs = self.get_exceldf(basename, sheetname=None) + dfs = self.get_exceldf(basename, sheet_name=None) expected_keys = ['Alpha', 'Beta', 'Charlie'] tm.assert_contains_all(expected_keys, dfs.keys()) def test_reading_multiple_specific_sheets(self): - # Test reading specific sheetnames by specifying a mixed list + # Test reading specific sheet_names by specifying a mixed list # of integers and strings, and confirm that duplicated sheet # references (positions/names) are removed properly. # Ensure a dict is returned @@ -377,17 +377,17 @@ def test_reading_multiple_specific_sheets(self): basename = 'test_multisheet' # Explicitly request duplicates. Only the set should be returned. expected_keys = [2, 'Charlie', 'Charlie'] - dfs = self.get_exceldf(basename, sheetname=expected_keys) + dfs = self.get_exceldf(basename, sheet_name=expected_keys) expected_keys = list(set(expected_keys)) tm.assert_contains_all(expected_keys, dfs.keys()) assert len(expected_keys) == len(dfs.keys()) def test_reading_all_sheets_with_blank(self): - # Test reading all sheetnames by setting sheetname to None, + # Test reading all sheet_names by setting sheet_name to None, # In the case where some sheets are blank. # Issue #11711 basename = 'blank_with_header' - dfs = self.get_exceldf(basename, sheetname=None) + dfs = self.get_exceldf(basename, sheet_name=None) expected_keys = ['Sheet1', 'Sheet2', 'Sheet3'] tm.assert_contains_all(expected_keys, dfs.keys()) @@ -488,7 +488,7 @@ def test_read_xlrd_Book(self): result = read_excel(xl, "SheetA") tm.assert_frame_equal(df, result) - result = read_excel(book, sheetname="SheetA", engine="xlrd") + result = read_excel(book, sheet_name="SheetA", engine="xlrd") tm.assert_frame_equal(df, result) @tm.network @@ -546,9 +546,9 @@ def test_creating_and_reading_multiple_sheets(self): _skip_if_no_xlwt() _skip_if_no_openpyxl() - def tdf(sheetname): + def tdf(sheet_name): d, i = [11, 22, 33], [1, 2, 3] - return DataFrame(d, i, columns=[sheetname]) + return DataFrame(d, i, columns=[sheet_name]) sheets = ['AAA', 'BBB', 'CCC'] @@ -557,9 +557,9 @@ def tdf(sheetname): with ensure_clean(self.ext) as pth: with ExcelWriter(pth) as ew: - for sheetname, df in iteritems(dfs): - df.to_excel(ew, sheetname) - dfs_returned = read_excel(pth, sheetname=sheets) + for sheet_name, df in iteritems(dfs): + df.to_excel(ew, sheet_name) + dfs_returned = read_excel(pth, sheet_name=sheets) for s in sheets: tm.assert_frame_equal(dfs[s], dfs_returned[s])
- [x] closes #10559 - [x] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12604
2016-03-13T04:11:25Z
2016-05-07T18:50:37Z
null
2016-09-27T14:32:11Z
BUG: Matched Round Signature with NumPy's
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 0118dea6f8867..2acef5ca61f1e 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -90,6 +90,7 @@ Performance Improvements Bug Fixes ~~~~~~~~~ +- ``round`` now accepts an ``out`` argument to maintain compatibility with numpy's ``round`` function (:issue:`12600`) - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) - Bug in ``Series`` construction with ``Categorical`` and ``dtype='category'`` is specified (:issue:`12574`) @@ -114,6 +115,18 @@ Bug Fixes + + + + + + + + + + + + @@ -130,6 +143,7 @@ Bug Fixes + - Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 01156252fcd6d..dc82852f4661e 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -46,6 +46,7 @@ from pandas import compat from pandas.util.decorators import (deprecate, Appender, Substitution, deprecate_kwarg) +from pandas.util.validators import validate_args from pandas.tseries.period import PeriodIndex from pandas.tseries.index import DatetimeIndex @@ -4420,7 +4421,7 @@ def merge(self, right, how='inner', on=None, left_on=None, right_on=None, right_index=right_index, sort=sort, suffixes=suffixes, copy=copy, indicator=indicator) - def round(self, decimals=0, out=None): + def round(self, decimals=0, *args): """ Round a DataFrame to a variable number of decimal places. @@ -4471,6 +4472,8 @@ def round(self, decimals=0, out=None): See Also -------- numpy.around + Series.round + """ from pandas.tools.merge import concat @@ -4486,6 +4489,9 @@ def _series_round(s, decimals): return s.round(decimals) return s + validate_args(args, min_length=0, max_length=1, + msg="Inplace rounding is not supported") + if isinstance(decimals, (dict, Series)): if isinstance(decimals, Series): if not decimals.index.is_unique: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index cb972c571ef47..24b883b90cf5d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -28,6 +28,7 @@ AbstractMethodError) import pandas.core.nanops as nanops from pandas.util.decorators import Appender, Substitution, deprecate_kwarg +from pandas.util.validators import validate_kwargs from pandas.core import config # goal is to be able to define the docs close to function, while still being @@ -5231,29 +5232,13 @@ def _doc_parms(cls): %(outname)s : %(name1)s\n""" -def _validate_kwargs(fname, kwargs, *compat_args): - """ - Checks whether parameters passed to the - **kwargs argument in a 'stat' function 'fname' - are valid parameters as specified in *compat_args - - """ - list(map(kwargs.__delitem__, filter( - kwargs.__contains__, compat_args))) - if kwargs: - bad_arg = list(kwargs)[0] # first 'key' element - raise TypeError(("{fname}() got an unexpected " - "keyword argument '{arg}'". - format(fname=fname, arg=bad_arg))) - - def _make_stat_function(name, name1, name2, axis_descr, desc, f): @Substitution(outname=name, desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_num_doc) def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs): - _validate_kwargs(name, kwargs, 'out', 'dtype') + validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: @@ -5274,7 +5259,7 @@ def _make_stat_function_ddof(name, name1, name2, axis_descr, desc, f): @Appender(_num_ddof_doc) def stat_func(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs): - _validate_kwargs(name, kwargs, 'out', 'dtype') + validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: @@ -5296,7 +5281,7 @@ def _make_cum_function(name, name1, name2, axis_descr, desc, accum_func, @Appender("Return cumulative {0} over requested axis.".format(name) + _cnum_doc) def func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs): - _validate_kwargs(name, kwargs, 'out', 'dtype') + validate_kwargs(name, kwargs, 'out', 'dtype') if axis is None: axis = self._stat_axis_number else: @@ -5331,7 +5316,7 @@ def _make_logical_function(name, name1, name2, axis_descr, desc, f): @Appender(_bool_doc) def logical_func(self, axis=None, bool_only=None, skipna=None, level=None, **kwargs): - _validate_kwargs(name, kwargs, 'out', 'dtype') + validate_kwargs(name, kwargs, 'out', 'dtype') if skipna is None: skipna = True if axis is None: diff --git a/pandas/core/series.py b/pandas/core/series.py index 80154065f0c8f..734485cb99937 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -39,6 +39,7 @@ from pandas.tseries.period import PeriodIndex, Period from pandas import compat from pandas.util.terminal import get_terminal_size +from pandas.util.validators import validate_args from pandas.compat import zip, u, OrderedDict, StringIO @@ -1256,7 +1257,7 @@ def idxmax(self, axis=None, out=None, skipna=True): argmin = idxmin argmax = idxmax - def round(self, decimals=0): + def round(self, decimals=0, *args): """ Round each value in a Series to the given number of decimals. @@ -1274,8 +1275,12 @@ def round(self, decimals=0): See Also -------- numpy.around + DataFrame.round """ + validate_args(args, min_length=0, max_length=1, + msg="Inplace rounding is not supported") + result = _values_from_object(self).round(decimals) result = self._constructor(result, index=self.index).__finalize__(self) diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py index 8d0ddc678a11f..d9cad6a542fb3 100644 --- a/pandas/tests/frame/test_analytics.py +++ b/pandas/tests/frame/test_analytics.py @@ -2060,6 +2060,17 @@ def test_round(self): assert_series_equal(df.round(decimals)['col1'], expected_rounded['col1']) + def test_numpy_round(self): + # See gh-12600 + df = DataFrame([[1.53, 1.36], [0.06, 7.01]]) + out = np.round(df, decimals=0) + expected = DataFrame([[2., 1.], [0., 7.]]) + assert_frame_equal(out, expected) + + msg = "Inplace rounding is not supported" + with tm.assertRaisesRegexp(ValueError, msg): + np.round(df, decimals=0, out=df) + def test_round_mixed_type(self): # GH11885 df = DataFrame({'col1': [1.1, 2.2, 3.3, 4.4], diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 5f2512c26a499..1d15a5552a13a 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -511,6 +511,17 @@ def test_round(self): assert_series_equal(result, expected) self.assertEqual(result.name, self.ts.name) + def test_numpy_round(self): + # See gh-12600 + s = Series([1.53, 1.36, 0.06]) + out = np.round(s, decimals=0) + expected = Series([2., 1., 0.]) + assert_series_equal(out, expected) + + msg = "Inplace rounding is not supported" + with tm.assertRaisesRegexp(ValueError, msg): + np.round(s, decimals=0, out=s) + def test_built_in_round(self): if not compat.PY3: raise nose.SkipTest( diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py index e27e45a96432f..367b8d21f95d0 100644 --- a/pandas/tests/test_util.py +++ b/pandas/tests/test_util.py @@ -2,6 +2,8 @@ import nose from pandas.util.decorators import deprecate_kwarg +from pandas.util.validators import validate_args, validate_kwargs + import pandas.util.testing as tm @@ -73,6 +75,81 @@ def test_rands_array(): assert(arr.shape == (10, 10)) assert(len(arr[1, 1]) == 7) + +class TestValidateArgs(tm.TestCase): + + def test_bad_min_length(self): + msg = "'min_length' must be non-negative" + with tm.assertRaisesRegexp(ValueError, msg): + validate_args((None,), min_length=-1, max_length=5) + + def test_bad_arg_length_no_max(self): + min_length = 5 + msg = "expected at least {min_length} arguments".format( + min_length=min_length) + + with tm.assertRaisesRegexp(ValueError, msg): + validate_args((None,), min_length=min_length, max_length=None) + + def test_bad_arg_length_with_max(self): + min_length = 5 + max_length = 10 + msg = ("expected between {min_length} and {max_length}" + " arguments inclusive".format(min_length=min_length, + max_length=max_length)) + + with tm.assertRaisesRegexp(ValueError, msg): + validate_args((None,), min_length=min_length, + max_length=max_length) + + def test_bad_min_max_length(self): + msg = "'min_length' > 'max_length'" + with tm.assertRaisesRegexp(ValueError, msg): + validate_args((None,), min_length=5, max_length=2) + + def test_not_all_none(self): + msg = "All arguments must be None" + with tm.assertRaisesRegexp(ValueError, msg): + validate_args(('foo',), min_length=0, + max_length=1, msg=msg) + + with tm.assertRaisesRegexp(ValueError, msg): + validate_args(('foo', 'bar', 'baz'), min_length=2, + max_length=5, msg=msg) + + with tm.assertRaisesRegexp(ValueError, msg): + validate_args((None, 'bar', None), min_length=2, + max_length=5, msg=msg) + + def test_validation(self): + # No exceptions should be thrown + validate_args((None,), min_length=0, max_length=1) + validate_args((None, None), min_length=1, max_length=5) + + +class TestValidateKwargs(tm.TestCase): + + def test_bad_kwarg(self): + goodarg = 'f' + badarg = goodarg + 'o' + + kwargs = {goodarg: 'foo', badarg: 'bar'} + compat_args = (goodarg, badarg + 'o') + fname = 'func' + + msg = ("{fname}\(\) got an unexpected " + "keyword argument '{arg}'".format( + fname=fname, arg=badarg)) + + with tm.assertRaisesRegexp(TypeError, msg): + validate_kwargs(fname, kwargs, *compat_args) + + def test_validation(self): + # No exceptions should be thrown + compat_args = ('f', 'b', 'ba') + kwargs = {'f': 'foo', 'b': 'bar'} + validate_kwargs('func', kwargs, *compat_args) + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False) diff --git a/pandas/util/validators.py b/pandas/util/validators.py new file mode 100644 index 0000000000000..f308a04165d74 --- /dev/null +++ b/pandas/util/validators.py @@ -0,0 +1,97 @@ +""" +Module that contains many useful utilities +for validating data or function arguments +""" + + +def validate_args(args, min_length=0, max_length=None, msg=""): + """ + Checks whether the length of the `*args` argument passed into a function + has at least `min_length` arguments. If `max_length` is an integer, checks + whether `*args` has at most `max_length` arguments inclusive. Raises a + ValueError if any of the aforementioned conditions are False. + + Parameters + ---------- + args: tuple + The `*args` parameter passed into a function + + min_length: int, optional + The minimum number of arguments that should be contained in the `args`. + tuple. This number must be non-negative. The default is '0'. + + max_length: int, optional + If not `None`, the maximum number of arguments that should be contained + in the `args` parameter. This number must be at least as large as the + provided `min_length` value. The default is None. + + msg: str, optional + Error message to display when a custom check of args fails. For + example, pandas does not support a non-None argument for `out` + when rounding a `Series` or `DataFrame` object. `msg` in this + case can be "Inplace rounding is not supported". + + Raises + ------ + ValueError if `args` fails to have a length that is at least `min_length` + and at most `max_length` inclusive (provided `max_length` is not None) + + """ + length = len(args) + + if min_length < 0: + raise ValueError("'min_length' must be non-negative") + + if max_length is None: + if length < min_length: + raise ValueError(("expected at least {min_length} arguments " + "but got {length} arguments instead". + format(min_length=min_length, length=length))) + + if min_length > max_length: + raise ValueError("'min_length' > 'max_length'") + + if (length < min_length) or (length > max_length): + raise ValueError(("expected between {min_length} and {max_length} " + "arguments inclusive but got {length} arguments " + "instead".format(min_length=min_length, + length=length, + max_length=max_length))) + + # See gh-12600; this is to allow compatibility with NumPy, + # which passes in an 'out' parameter as a positional argument + if args: + args = list(filter(lambda elt: elt is not None, args)) + + if args: + raise ValueError(msg) + + +def validate_kwargs(fname, kwargs, *compat_args): + """ + Checks whether parameters passed to the **kwargs argument in a + function 'fname' are valid parameters as specified in *compat_args + + Parameters + ---------- + fname: str + The name of the function being passed the `**kwargs` parameter + + kwargs: dict + The `**kwargs` parameter passed into `fname` + + compat_args: *args + A tuple of keys that `kwargs` is allowed to have + + Raises + ------ + ValueError if `kwargs` contains keys not in `compat_args` + + """ + list(map(kwargs.__delitem__, filter( + kwargs.__contains__, compat_args))) + if kwargs: + bad_arg = list(kwargs)[0] # first 'key' element + raise TypeError(("{fname}() got an unexpected " + "keyword argument '{arg}'". + format(fname=fname, arg=bad_arg)))
Closes #12600 by re-accepting the `out` parameter as a `**kwargs` argument. Also removes the `out` parameter from the signature for `DataFrame.round` and replaces it with `**kwargs` like its `Series` counterpart. Furthermore, since `out` is a parameter that is not supported in `pandas`, checks that `out` is `None` and throws an error if it isn't.
https://api.github.com/repos/pandas-dev/pandas/pulls/12603
2016-03-12T23:38:09Z
2016-03-17T22:57:44Z
null
2016-03-17T23:16:03Z
fixes print syntax error in documentation
diff --git a/doc/source/comparison_with_r.rst b/doc/source/comparison_with_r.rst index 0841f3354d160..db813e7cf55a1 100644 --- a/doc/source/comparison_with_r.rst +++ b/doc/source/comparison_with_r.rst @@ -295,7 +295,7 @@ In ``pandas`` the equivalent expression, using the }) grouped = df.groupby(['month','week']) - print grouped['x'].agg([np.mean, np.std]) + print(grouped['x'].agg([np.mean, np.std])) For more details and examples see :ref:`the groupby documentation @@ -505,4 +505,4 @@ For more details and examples see :ref:`categorical introduction <categorical>` .. cast: http://www.inside-r.org/packages/cran/reshape2/docs/cast .. |factor| replace:: ``factor`` -.. _factor: https://stat.ethz.ch/R-manual/R-devel/library/base/html/factor.html \ No newline at end of file +.. _factor: https://stat.ethz.ch/R-manual/R-devel/library/base/html/factor.html
fixes the syntax error in the "comparison with R" docs
https://api.github.com/repos/pandas-dev/pandas/pulls/12602
2016-03-12T16:25:39Z
2016-03-12T17:31:14Z
null
2016-03-12T17:31:20Z
Include index names in column names of HTML output of DataFrame Style…
diff --git a/pandas/core/style.py b/pandas/core/style.py index f66ac7485c76e..cb8d5f9e55d65 100644 --- a/pandas/core/style.py +++ b/pandas/core/style.py @@ -179,6 +179,7 @@ def _translate(self): DATA_CLASS = "data" BLANK_CLASS = "blank" BLANK_VALUE = "" + INDEX_CLASS = COL_HEADING_CLASS cell_context = dict() @@ -201,9 +202,15 @@ def _translate(self): head = [] for r in range(n_clvls): - row_es = [{"type": "th", - "value": BLANK_VALUE, - "class": " ".join([BLANK_CLASS])}] * n_rlvls + row_es = [] + for index_name in self.data.index.names: + if index_name is not None: + val, th_class = index_name, INDEX_CLASS + else: + val, th_class = BLANK_VALUE, BLANK_CLASS + + row_es.append({"type": "th", "value": val, + "class": " ".join([th_class])}) for c in range(len(clabels[0])): cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c] cs.extend(cell_context.get(
Include index names in column names of HTML output of DataFrame Styler, if the index names are available (previous behaviour is not to include index column names and instead leave blank spaces)
https://api.github.com/repos/pandas-dev/pandas/pulls/12598
2016-03-11T17:57:17Z
2016-03-11T19:05:54Z
null
2016-03-11T21:46:30Z
Implementing rolling min/max functions that can retain the original type
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index b7a0cf888f1a2..d9e0061411843 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -132,3 +132,4 @@ Bug Fixes - Bug in ``pivot_table`` when ``margins=True`` and ``dropna=True`` where nulls still contributed to margin count (:issue:`12577`) +- Bug in ``.rolling.min`` and ``.rolling.max`` where passing columns of type float32 raised a Value error (:issue:`12373`) diff --git a/pandas/algos.pyx b/pandas/algos.pyx index 0f9ceba48e608..fb42bb9292272 100644 --- a/pandas/algos.pyx +++ b/pandas/algos.pyx @@ -1625,117 +1625,56 @@ def roll_median_c(ndarray[float64_t] arg, int win, int minp): # of its Simplified BSD license # https://github.com/kwgoodman/bottleneck -cdef struct pairs: - double value - int death - from libc cimport stdlib @cython.boundscheck(False) @cython.wraparound(False) -def roll_max(ndarray[float64_t] a, int window, int minp): - "Moving max of 1d array of dtype=float64 along axis=0 ignoring NaNs." - cdef np.float64_t ai, aold - cdef Py_ssize_t count - cdef pairs* ring - cdef pairs* minpair - cdef pairs* end - cdef pairs* last - cdef Py_ssize_t i0 - cdef np.npy_intp *dim - dim = PyArray_DIMS(a) - cdef Py_ssize_t n0 = dim[0] - cdef np.npy_intp *dims = [n0] - cdef np.ndarray[np.float64_t, ndim=1] y = PyArray_EMPTY(1, dims, - NPY_float64, 0) - - if window < 1: - raise ValueError('Invalid window size %d' - % (window)) - - if minp > window: - raise ValueError('Invalid min_periods size %d greater than window %d' - % (minp, window)) - - minp = _check_minp(window, minp, n0) - with nogil: - ring = <pairs*>stdlib.malloc(window * sizeof(pairs)) - end = ring + window - last = ring - - minpair = ring - ai = a[0] - if ai == ai: - minpair.value = ai - else: - minpair.value = MINfloat64 - minpair.death = window - - count = 0 - for i0 in range(n0): - ai = a[i0] - if ai == ai: - count += 1 - else: - ai = MINfloat64 - if i0 >= window: - aold = a[i0 - window] - if aold == aold: - count -= 1 - if minpair.death == i0: - minpair += 1 - if minpair >= end: - minpair = ring - if ai >= minpair.value: - minpair.value = ai - minpair.death = i0 + window - last = minpair - else: - while last.value <= ai: - if last == ring: - last = end - last -= 1 - last += 1 - if last == end: - last = ring - last.value = ai - last.death = i0 + window - if count >= minp: - y[i0] = minpair.value - else: - y[i0] = NaN - - for i0 in range(minp - 1): - y[i0] = NaN - - stdlib.free(ring) - return y +def roll_max(ndarray[numeric] a, int window, int minp): + """ + Moving max of 1d array of any numeric type along axis=0 ignoring NaNs. + Parameters + ---------- + a: numpy array + window: int, size of rolling window + minp: if number of observations in window + is below this, output a NaN + """ + return _roll_min_max(a, window, minp, 1) -cdef double_t _get_max(object skiplist, int nobs, int minp): - if nobs >= minp: - return <IndexableSkiplist> skiplist.get(nobs - 1) - else: - return NaN +@cython.boundscheck(False) +@cython.wraparound(False) +def roll_min(ndarray[numeric] a, int window, int minp): + """ + Moving max of 1d array of any numeric type along axis=0 ignoring NaNs. + Parameters + ---------- + a: numpy array + window: int, size of rolling window + minp: if number of observations in window + is below this, output a NaN + """ + return _roll_min_max(a, window, minp, 0) @cython.boundscheck(False) @cython.wraparound(False) -def roll_min(np.ndarray[np.float64_t, ndim=1] a, int window, int minp): - "Moving min of 1d array of dtype=float64 along axis=0 ignoring NaNs." - cdef np.float64_t ai, aold +cdef _roll_min_max(ndarray[numeric] a, int window, int minp, bint is_max): + "Moving min/max of 1d array of any numeric type along axis=0 ignoring NaNs." + cdef numeric ai, aold cdef Py_ssize_t count - cdef pairs* ring - cdef pairs* minpair - cdef pairs* end - cdef pairs* last + cdef Py_ssize_t* death + cdef numeric* ring + cdef numeric* minvalue + cdef numeric* end + cdef numeric* last cdef Py_ssize_t i0 cdef np.npy_intp *dim dim = PyArray_DIMS(a) cdef Py_ssize_t n0 = dim[0] cdef np.npy_intp *dims = [n0] - cdef np.ndarray[np.float64_t, ndim=1] y = PyArray_EMPTY(1, dims, - NPY_float64, 0) + cdef bint should_replace + cdef np.ndarray[numeric, ndim=1] y = PyArray_EMPTY(1, dims, PyArray_TYPE(a), 0) if window < 1: raise ValueError('Invalid window size %d' @@ -1747,58 +1686,85 @@ def roll_min(np.ndarray[np.float64_t, ndim=1] a, int window, int minp): minp = _check_minp(window, minp, n0) with nogil: - ring = <pairs*>stdlib.malloc(window * sizeof(pairs)) + ring = <numeric*>stdlib.malloc(window * sizeof(numeric)) + death = <Py_ssize_t*>stdlib.malloc(window * sizeof(Py_ssize_t)) end = ring + window last = ring - minpair = ring + minvalue = ring ai = a[0] - if ai == ai: - minpair.value = ai + if numeric in cython.floating: + if ai == ai: + minvalue[0] = ai + elif is_max: + minvalue[0] = MINfloat64 + else: + minvalue[0] = MAXfloat64 else: - minpair.value = MAXfloat64 - minpair.death = window + minvalue[0] = ai + death[0] = window count = 0 for i0 in range(n0): ai = a[i0] - if ai == ai: - count += 1 + if numeric in cython.floating: + if ai == ai: + count += 1 + elif is_max: + ai = MINfloat64 + else: + ai = MAXfloat64 else: - ai = MAXfloat64 + count += 1 if i0 >= window: aold = a[i0 - window] if aold == aold: count -= 1 - if minpair.death == i0: - minpair += 1 - if minpair >= end: - minpair = ring - if ai <= minpair.value: - minpair.value = ai - minpair.death = i0 + window - last = minpair + if death[minvalue-ring] == i0: + minvalue += 1 + if minvalue >= end: + minvalue = ring + should_replace = ai >= minvalue[0] if is_max else ai <= minvalue[0] + if should_replace: + minvalue[0] = ai + death[minvalue-ring] = i0 + window + last = minvalue else: - while last.value >= ai: + should_replace = last[0] <= ai if is_max else last[0] >= ai + while should_replace: if last == ring: last = end last -= 1 + should_replace = last[0] <= ai if is_max else last[0] >= ai last += 1 if last == end: last = ring - last.value = ai - last.death = i0 + window - if count >= minp: - y[i0] = minpair.value + last[0] = ai + death[last - ring] = i0 + window + if numeric in cython.floating: + if count >= minp: + y[i0] = minvalue[0] + else: + y[i0] = NaN else: - y[i0] = NaN + y[i0] = minvalue[0] for i0 in range(minp - 1): - y[i0] = NaN + if numeric in cython.floating: + y[i0] = NaN + else: + y[i0] = 0 stdlib.free(ring) + stdlib.free(death) return y +cdef double_t _get_max(object skiplist, int nobs, int minp): + if nobs >= minp: + return <IndexableSkiplist> skiplist.get(nobs - 1) + else: + return NaN + cdef double_t _get_min(object skiplist, int nobs, int minp): if nobs >= minp: return <IndexableSkiplist> skiplist.get(0) diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 0d3b7e967f3ce..fb0e2ad2ca34e 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -2697,3 +2697,19 @@ def test_rolling_median_memory_error(self): n = 20000 Series(np.random.randn(n)).rolling(window=2, center=False).median() Series(np.random.randn(n)).rolling(window=2, center=False).median() + + def test_rolling_min_max_numeric_types(self): + # GH12373 + types_test = [np.dtype("f{}".format(width)) for width in [4, 8]] + types_test.extend([np.dtype("{}{}".format(sign, width)) + for width in [1, 2, 4, 8] for sign in "ui"]) + for data_type in types_test: + # Just testing that these don't throw exceptions and that + # the return type is float64. Other tests will cover quantitative + # correctness + result = (DataFrame(np.arange(20, dtype=data_type)) + .rolling(window=5).max()) + self.assertEqual(result.dtypes[0], np.dtype("f8")) + result = (DataFrame(np.arange(20, dtype=data_type)) + .rolling(window=5).min()) + self.assertEqual(result.dtypes[0], np.dtype("f8"))
- [ ] Enhances previous bugfix to #12373 - [ ] Added test_rolling_min_max_test_types to test_windows - [ ] passes `git diff upstream/master | flake8 --diff` - [ ] Added documentation to computation.rst for the new as_float argument and updated whatsnew for v0.18.0 - Changed the rolling min/max functions in algos.pyx so that they use a cython fused type as input instead of a float64 so that the function can accept arrays of any numeric type - Merged the functionality of rolling min/max into a common function with branches based on whether or not it's running min/max - When running rolling min/max for intergral types and there are not enough minimum periods, the output values returned are zero - Added a unit test to test_moments to make sure that rolling min/max works for all integral types and float32/64 - Updated computations and whatsnew doc
https://api.github.com/repos/pandas-dev/pandas/pulls/12595
2016-03-11T15:31:51Z
2016-03-17T15:13:06Z
null
2016-06-07T12:40:26Z
API: deprecate Index.sym_diff in favor of symmetric_difference
diff --git a/doc/source/api.rst b/doc/source/api.rst index 85f1984f29291..d6402100a296f 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1374,7 +1374,7 @@ Combining / joining / set operations Index.intersection Index.union Index.difference - Index.sym_diff + Index.symmetric_difference Selecting ~~~~~~~~~ diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 7494f8ae88307..04b166dacf2b7 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -1359,7 +1359,7 @@ operators. Difference is provided via the ``.difference()`` method. a & b a.difference(b) -Also available is the ``sym_diff (^)`` operation, which returns elements +Also available is the ``symmetric_difference (^)`` operation, which returns elements that appear in either ``idx1`` or ``idx2`` but not both. This is equivalent to the Index created by ``idx1.difference(idx2).union(idx2.difference(idx1))``, with duplicates dropped. @@ -1368,7 +1368,7 @@ with duplicates dropped. idx1 = pd.Index([1, 2, 3, 4]) idx2 = pd.Index([2, 3, 4, 5]) - idx1.sym_diff(idx2) + idx1.symmetric_difference(idx2) idx1 ^ idx2 Missing values diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 70a1ad4a335ea..906312bc338eb 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -34,6 +34,8 @@ API changes Deprecations ^^^^^^^^^^^^ +- The method name ``Index.sym_diff()`` is deprecated and can be replaced by ``Index.symmetric_difference()`` (:issue:`12591`) + .. _whatsnew_0181.performance: Performance Improvements diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 852cddc456213..58828d52b60dd 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -1619,7 +1619,7 @@ def __or__(self, other): return self.union(other) def __xor__(self, other): - return self.sym_diff(other) + return self.symmetric_difference(other) def union(self, other): """ @@ -1796,7 +1796,7 @@ def difference(self, other): diff = deprecate('diff', difference) - def sym_diff(self, other, result_name=None): + def symmetric_difference(self, other, result_name=None): """ Compute the sorted symmetric difference of two Index objects. @@ -1807,11 +1807,11 @@ def sym_diff(self, other, result_name=None): Returns ------- - sym_diff : Index + symmetric_difference : Index Notes ----- - ``sym_diff`` contains elements that appear in either ``idx1`` or + ``symmetric_difference`` contains elements that appear in either ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by ``(idx1 - idx2) + (idx2 - idx1)`` with duplicates dropped. @@ -1822,7 +1822,7 @@ def sym_diff(self, other, result_name=None): -------- >>> idx1 = Index([1, 2, 3, 4]) >>> idx2 = Index([2, 3, 4, 5]) - >>> idx1.sym_diff(idx2) + >>> idx1.symmetric_difference(idx2) Int64Index([1, 5], dtype='int64') You can also use the ``^`` operator: @@ -1843,6 +1843,8 @@ def sym_diff(self, other, result_name=None): attribs['freq'] = None return self._shallow_copy_with_infer(the_diff, **attribs) + sym_diff = deprecate('sym_diff', symmetric_difference) + def get_loc(self, key, method=None, tolerance=None): """ Get integer location for requested label diff --git a/pandas/tests/indexes/common.py b/pandas/tests/indexes/common.py index f1824267d63d8..a6aaa69183f10 100644 --- a/pandas/tests/indexes/common.py +++ b/pandas/tests/indexes/common.py @@ -305,7 +305,7 @@ def test_setops_errorcases(self): # # non-iterable input cases = [0.5, 'xxx'] methods = [idx.intersection, idx.union, idx.difference, - idx.sym_diff] + idx.symmetric_difference] for method in methods: for case in cases: @@ -404,7 +404,7 @@ def test_difference_base(self): with tm.assertRaisesRegexp(TypeError, msg): result = first.difference([1, 2, 3]) - def test_symmetric_diff(self): + def test_symmetric_difference(self): for name, idx in compat.iteritems(self.indices): first = idx[1:] second = idx[:-1] @@ -412,7 +412,7 @@ def test_symmetric_diff(self): pass else: answer = idx[[0, -1]] - result = first.sym_diff(second) + result = first.symmetric_difference(second) self.assertTrue(tm.equalContents(result, answer)) # GH 10149 @@ -422,17 +422,21 @@ def test_symmetric_diff(self): if isinstance(idx, PeriodIndex): msg = "can only call with other PeriodIndex-ed objects" with tm.assertRaisesRegexp(ValueError, msg): - result = first.sym_diff(case) + result = first.symmetric_difference(case) elif isinstance(idx, CategoricalIndex): pass else: - result = first.sym_diff(case) + result = first.symmetric_difference(case) self.assertTrue(tm.equalContents(result, answer)) if isinstance(idx, MultiIndex): msg = "other must be a MultiIndex or a list of tuples" with tm.assertRaisesRegexp(TypeError, msg): - result = first.sym_diff([1, 2, 3]) + result = first.symmetric_difference([1, 2, 3]) + + # 12591 deprecated + with tm.assert_produces_warning(FutureWarning): + first.sym_diff(second) def test_insert_base(self): diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py index 465879dd62466..24a20f7adf624 100644 --- a/pandas/tests/indexes/test_base.py +++ b/pandas/tests/indexes/test_base.py @@ -641,11 +641,11 @@ def test_difference(self): self.assertEqual(len(result), 0) self.assertEqual(result.name, first.name) - def test_symmetric_diff(self): + def test_symmetric_difference(self): # smoke idx1 = Index([1, 2, 3, 4], name='idx1') idx2 = Index([2, 3, 4, 5]) - result = idx1.sym_diff(idx2) + result = idx1.symmetric_difference(idx2) expected = Index([1, 5]) self.assertTrue(tm.equalContents(result, expected)) self.assertIsNone(result.name) @@ -658,7 +658,7 @@ def test_symmetric_diff(self): # multiIndex idx1 = MultiIndex.from_tuples(self.tuples) idx2 = MultiIndex.from_tuples([('foo', 1), ('bar', 3)]) - result = idx1.sym_diff(idx2) + result = idx1.symmetric_difference(idx2) expected = MultiIndex.from_tuples([('bar', 2), ('baz', 3), ('bar', 3)]) self.assertTrue(tm.equalContents(result, expected)) @@ -667,7 +667,7 @@ def test_symmetric_diff(self): # and the correct non-nan values are there. punt on sorting. idx1 = Index([1, 2, 3, np.nan]) idx2 = Index([0, 1, np.nan]) - result = idx1.sym_diff(idx2) + result = idx1.symmetric_difference(idx2) # expected = Index([0.0, np.nan, 2.0, 3.0, np.nan]) nans = pd.isnull(result) @@ -679,11 +679,11 @@ def test_symmetric_diff(self): idx1 = Index([1, 2, 3, 4], name='idx1') idx2 = np.array([2, 3, 4, 5]) expected = Index([1, 5]) - result = idx1.sym_diff(idx2) + result = idx1.symmetric_difference(idx2) self.assertTrue(tm.equalContents(result, expected)) self.assertEqual(result.name, 'idx1') - result = idx1.sym_diff(idx2, result_name='new_name') + result = idx1.symmetric_difference(idx2, result_name='new_name') self.assertTrue(tm.equalContents(result, expected)) self.assertEqual(result.name, 'new_name')
closes #12591
https://api.github.com/repos/pandas-dev/pandas/pulls/12594
2016-03-11T14:09:02Z
2016-03-17T13:48:15Z
null
2016-03-17T13:48:23Z
BLD: allow different options for compiler warnings on windows
diff --git a/setup.py b/setup.py index 9d3b8e25c9590..e3fb5a007aad3 100755 --- a/setup.py +++ b/setup.py @@ -14,6 +14,15 @@ import platform from distutils.version import LooseVersion +def is_platform_windows(): + return sys.platform == 'win32' or sys.platform == 'cygwin' + +def is_platform_linux(): + return sys.platform == 'linux2' + +def is_platform_mac(): + return sys.platform == 'darwin' + # versioning import versioneer cmdclass = versioneer.get_cmdclass() @@ -376,7 +385,10 @@ def pxd(name): return os.path.abspath(pjoin('pandas', name + '.pxd')) # args to ignore warnings -extra_compile_args=['-Wno-unused-function'] +if is_platform_windows(): + extra_compile_args=[] +else: + extra_compile_args=['-Wno-unused-function'] lib_depends = lib_depends + ['pandas/src/numpy_helper.h', 'pandas/src/parse_helper.h'] @@ -388,7 +400,7 @@ def pxd(name): # some linux distros require it -libraries = ['m'] if 'win32' not in sys.platform else [] +libraries = ['m'] if not is_platform_windows() else [] ext_data = dict( lib={'pyxfile': 'lib',
windows doesn't like gcc/clang options, big surprise, so disable for now
https://api.github.com/repos/pandas-dev/pandas/pulls/12590
2016-03-11T02:11:23Z
2016-03-11T02:22:01Z
null
2016-03-11T02:22:01Z
BLD: suppress useless warnings
diff --git a/setup.py b/setup.py index 94fd2998a84e6..9d3b8e25c9590 100755 --- a/setup.py +++ b/setup.py @@ -375,6 +375,8 @@ def srcpath(name=None, suffix='.pyx', subdir='src'): def pxd(name): return os.path.abspath(pjoin('pandas', name + '.pxd')) +# args to ignore warnings +extra_compile_args=['-Wno-unused-function'] lib_depends = lib_depends + ['pandas/src/numpy_helper.h', 'pandas/src/parse_helper.h'] @@ -440,7 +442,7 @@ def pxd(name): sources=sources, depends=data.get('depends', []), include_dirs=include, - extra_compile_args=['-w']) + extra_compile_args=extra_compile_args) extensions.append(obj) @@ -449,7 +451,7 @@ def pxd(name): sources=[srcpath('sparse', suffix=suffix)], include_dirs=[], libraries=libraries, - extra_compile_args=['-w']) + extra_compile_args=extra_compile_args) extensions.extend([sparse_ext]) @@ -457,7 +459,7 @@ def pxd(name): sources=[srcpath('testing', suffix=suffix)], include_dirs=[], libraries=libraries, - extra_compile_args=['-w']) + extra_compile_args=extra_compile_args) extensions.extend([testing_ext]) @@ -478,7 +480,7 @@ def pxd(name): language='c++', include_dirs=['pandas/src/msgpack'] + common_include, define_macros=macros, - extra_compile_args=['-w']) + extra_compile_args=extra_compile_args) unpacker_ext = Extension('pandas.msgpack._unpacker', depends=['pandas/src/msgpack/unpack.h', 'pandas/src/msgpack/unpack_define.h', @@ -489,7 +491,7 @@ def pxd(name): language='c++', include_dirs=['pandas/src/msgpack'] + common_include, define_macros=macros, - extra_compile_args=['-w']) + extra_compile_args=extra_compile_args) extensions.append(packer_ext) extensions.append(unpacker_ext) @@ -513,7 +515,7 @@ def pxd(name): include_dirs=['pandas/src/ujson/python', 'pandas/src/ujson/lib', 'pandas/src/datetime'] + common_include, - extra_compile_args=['-D_GNU_SOURCE', '-w']) + extra_compile_args=['-D_GNU_SOURCE'] + extra_compile_args) extensions.append(ujson_ext)
https://api.github.com/repos/pandas-dev/pandas/pulls/12589
2016-03-11T01:03:54Z
2016-03-11T01:14:05Z
null
2016-03-11T01:14:05Z
DOC: Fixes LaTeX build error
diff --git a/pandas/tseries/resample.py b/pandas/tseries/resample.py index 4e1ca42710cc5..0ac10eb4fa15b 100644 --- a/pandas/tseries/resample.py +++ b/pandas/tseries/resample.py @@ -414,6 +414,8 @@ def backfill(self, limit=None): def fillna(self, method, limit=None): """ + Fill missing values + Parameters ---------- method : str, method of resampling ('ffill', 'bfill')
The PDF doc build was chocking on the `Parameters` section being the first item.
https://api.github.com/repos/pandas-dev/pandas/pulls/12586
2016-03-10T13:36:16Z
2016-03-11T14:33:31Z
null
2017-04-05T02:06:37Z
Resolve ImportError in read_gbq which appears with oauthclient >= 2.0
diff --git a/ci/requirements-2.7.pip b/ci/requirements-2.7.pip index 9bc533110cea3..54596ad2a8169 100644 --- a/ci/requirements-2.7.pip +++ b/ci/requirements-2.7.pip @@ -2,5 +2,6 @@ blosc httplib2 google-api-python-client == 1.2 python-gflags == 2.0 +oauth2client == 1.5.0 pathlib py diff --git a/ci/requirements-3.4.pip b/ci/requirements-3.4.pip index 62be867437af1..55986a0220bf0 100644 --- a/ci/requirements-3.4.pip +++ b/ci/requirements-3.4.pip @@ -2,3 +2,4 @@ python-dateutil==2.2 blosc httplib2 google-api-python-client +oauth2client diff --git a/doc/source/install.rst b/doc/source/install.rst index 11b9115aa9c81..c1950ee7bf76c 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -267,9 +267,11 @@ Optional Dependencies <http://www.vergenet.net/~conrad/software/xsel/>`__, or `xclip <http://sourceforge.net/projects/xclip/>`__: necessary to use :func:`~pandas.io.clipboard.read_clipboard`. Most package managers on Linux distributions will have ``xclip`` and/or ``xsel`` immediately available for installation. -* Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ - and `google-api-python-client <http://github.com/google/google-api-python-client>`__: Needed for :mod:`~pandas.io.gbq` -* `httplib2 <http://pypi.python.org/pypi/httplib2>`__: Needed for :mod:`~pandas.io.gbq` +* Google's `python-gflags <http://code.google.com/p/python-gflags/>`__ , + `oauth2client <https://github.com/google/oauth2client>`__ , + `httplib2 <http://pypi.python.org/pypi/httplib2>`__ + and `google-api-python-client <http://github.com/google/google-api-python-client>`__ + : Needed for :mod:`~pandas.io.gbq` * One of the following combinations of libraries is needed to use the top-level :func:`~pandas.io.html.read_html` function: diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 90ded17286c9f..acd9533165c6d 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1271,3 +1271,4 @@ Bug Fixes - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) - Bug when increasing the buffer size of CSV reader in ``read_csv`` (:issue:`12494`) - Bug when setting columns of a ``DataFrame`` with duplicate column names (:issue:`12344`) +- Resolve ImportError in ``read_gbq`` which appears with oauthclient >= 2.0.0 (:issue:`12572`) diff --git a/pandas/io/gbq.py b/pandas/io/gbq.py index c7481a953e47b..e706434f29dc5 100644 --- a/pandas/io/gbq.py +++ b/pandas/io/gbq.py @@ -50,7 +50,6 @@ def _test_google_api_imports(): from apiclient.errors import HttpError # noqa from oauth2client.client import AccessTokenRefreshError # noqa from oauth2client.client import OAuth2WebServerFlow # noqa - from oauth2client.client import SignedJwtAssertionCredentials # noqa from oauth2client.file import Storage # noqa from oauth2client.tools import run_flow, argparser # noqa except ImportError as e: @@ -179,7 +178,30 @@ def get_user_account_credentials(self): return credentials def get_service_account_credentials(self): - from oauth2client.client import SignedJwtAssertionCredentials + # Bug fix for https://github.com/pydata/pandas/issues/12572 + # We need to know that a supported version of oauth2client is installed + # Test that either of the following is installed: + # - SignedJwtAssertionCredentials from oauth2client.client + # - ServiceAccountCredentials from oauth2client.service_account + # SignedJwtAssertionCredentials is available in oauthclient < 2.0.0 + # ServiceAccountCredentials is available in oauthclient >= 2.0.0 + oauth2client_v1 = True + oauth2client_v2 = True + + try: + from oauth2client.client import SignedJwtAssertionCredentials + except ImportError: + oauth2client_v1 = False + + try: + from oauth2client.service_account import ServiceAccountCredentials + except ImportError: + oauth2client_v2 = False + + if not oauth2client_v1 and not oauth2client_v2: + raise ImportError("Missing oauth2client required for BigQuery " + "service account support") + from os.path import isfile try: @@ -197,11 +219,16 @@ def get_service_account_credentials(self): json_key['private_key'] = bytes( json_key['private_key'], 'UTF-8') - return SignedJwtAssertionCredentials( - json_key['client_email'], - json_key['private_key'], - self.scope, - ) + if oauth2client_v1: + return SignedJwtAssertionCredentials( + json_key['client_email'], + json_key['private_key'], + self.scope, + ) + else: + return ServiceAccountCredentials.from_json_keyfile_dict( + json_key, + self.scope) except (KeyError, ValueError, TypeError, AttributeError): raise InvalidPrivateKeyFormat( "Private key is missing or invalid. It should be service " diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py index 5a1c2d63af365..865b7e8d689c0 100644 --- a/pandas/io/tests/test_gbq.py +++ b/pandas/io/tests/test_gbq.py @@ -77,7 +77,6 @@ def _test_imports(): from oauth2client.client import OAuth2WebServerFlow # noqa from oauth2client.client import AccessTokenRefreshError # noqa - from oauth2client.client import SignedJwtAssertionCredentials # noqa from oauth2client.file import Storage # noqa from oauth2client.tools import run_flow # noqa @@ -115,6 +114,30 @@ def _test_imports(): raise ImportError( "pandas requires httplib2 for Google BigQuery support") + # Bug fix for https://github.com/pydata/pandas/issues/12572 + # We need to know that a supported version of oauth2client is installed + # Test that either of the following is installed: + # - SignedJwtAssertionCredentials from oauth2client.client + # - ServiceAccountCredentials from oauth2client.service_account + # SignedJwtAssertionCredentials is available in oauthclient < 2.0.0 + # ServiceAccountCredentials is available in oauthclient >= 2.0.0 + oauth2client_v1 = True + oauth2client_v2 = True + + try: + from oauth2client.client import SignedJwtAssertionCredentials # noqa + except ImportError: + oauth2client_v1 = False + + try: + from oauth2client.service_account import ServiceAccountCredentials # noqa + except ImportError: + oauth2client_v2 = False + + if not oauth2client_v1 and not oauth2client_v2: + raise ImportError("Missing oauth2client required for BigQuery " + "service account support") + def test_requirements(): try:
- [x] closes #12572 - [x] tests passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12582
2016-03-10T03:12:37Z
2016-03-11T01:39:04Z
null
2016-03-12T13:53:36Z
DOC: Remove Panel.to_json from docs.
diff --git a/doc/source/api.rst b/doc/source/api.rst index 662f8c512e8a7..85f1984f29291 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1215,7 +1215,6 @@ Serialization / IO / Conversion Panel.to_pickle Panel.to_excel Panel.to_hdf - Panel.to_json Panel.to_sparse Panel.to_frame Panel.to_xarray
- [DOC] closes #12571 - removed Panel.to_json from docs, as it is not yet implemented.
https://api.github.com/repos/pandas-dev/pandas/pulls/12581
2016-03-10T01:02:58Z
2016-03-10T12:48:49Z
null
2016-03-10T12:48:56Z
Fix #12529 / Improve to_clipboard for objects containing unicode
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 70a1ad4a335ea..417988c2f935f 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,3 +43,5 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + +- Bug in ``to_clipboard`` when called for objects containing unicode without passing an encoding (:issue:`12529`) diff --git a/pandas/io/clipboard.py b/pandas/io/clipboard.py index 2109e1c5d6d4c..956f3859a04fe 100644 --- a/pandas/io/clipboard.py +++ b/pandas/io/clipboard.py @@ -73,6 +73,9 @@ def to_clipboard(obj, excel=None, sep=None, **kwargs): # pragma: no cover - Linux: xclip, or xsel (with gtk or PyQt4 modules) - Windows: - OS X: + + If the object contains unicode and no encoding is passed as keyword + argument, the default locale will be used. """ from pandas.util.clipboard import clipboard_set if excel is None: @@ -86,6 +89,12 @@ def to_clipboard(obj, excel=None, sep=None, **kwargs): # pragma: no cover obj.to_csv(buf, sep=sep, **kwargs) clipboard_set(buf.getvalue()) return + except UnicodeEncodeError: + # try again with encoding from locale + from locale import getdefaultlocale + obj.to_csv(buf, sep=sep, encoding=getdefaultlocale()[1], **kwargs) + clipboard_set(buf.getvalue()) + return except: pass
Superceded by #14599 --- - [x] closes #12529 - [ ] tests added / passed - [ ] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry I added the whatsnew entry to 0.18.0.
https://api.github.com/repos/pandas-dev/pandas/pulls/12580
2016-03-09T23:57:00Z
2016-11-18T09:34:48Z
null
2023-05-11T01:13:26Z
DOC: clean up some doc-build warnings
diff --git a/ci/build_docs.sh b/ci/build_docs.sh index 12fb8cae4dfc7..8594cd4af34e5 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -36,6 +36,10 @@ if [ x"$DOC_BUILD" != x"" ]; then echo ./make.py ./make.py + echo ######################## + echo # Create and send docs # + echo ######################## + cd /tmp/doc/build/html git config --global user.email "pandas-docs-bot@localhost.foo" git config --global user.name "pandas-docs-bot" diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst index 599724d88bf63..d674ee293e4ed 100644 --- a/doc/source/dsintro.rst +++ b/doc/source/dsintro.rst @@ -692,6 +692,7 @@ R package): .. ipython:: python :suppress: + :okwarning: # restore GlobalPrintConfig pd.reset_option('^display\.') diff --git a/doc/source/options.rst b/doc/source/options.rst index b02765f469d38..170dfda16a467 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -438,6 +438,7 @@ For instance: .. ipython:: python :suppress: + :okwarning: pd.reset_option('^display\.') diff --git a/doc/source/release.rst b/doc/source/release.rst index 60d6b15a21a7b..e15175c33e2af 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt plt.close('all') - options.display.max_rows=15 + pd.options.display.max_rows=15 import pandas.util.testing as tm ************* diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 90ded17286c9f..2c6eb28b9b2f4 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -863,11 +863,10 @@ Previous API will work but deprecations In [7]: r.iloc[0] = 5 ValueError: .resample() is now a deferred operation - use .resample(...).mean() instead of .resample(...) - assignment will have no effect as you are working on a copy + use .resample(...).mean() instead of .resample(...) There is a situation where the new API can not perform all the operations when using original code. - This code is intending to resample every 2s, take the ``mean`` AND then take the ``min` of those results. + This code is intending to resample every 2s, take the ``mean`` AND then take the ``min`` of those results. .. code-block:: python diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 80f3d0d66ca9a..01156252fcd6d 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1627,6 +1627,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, human-readable units (base-2 representation). null_counts : boolean, default None Whether to show the non-null counts + - If None, then only show if the frame is smaller than max_info_rows and max_info_columns. - If True, always show counts. @@ -4932,6 +4933,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, 0 or 'index' for row-wise, 1 or 'columns' for column-wise interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} .. versionadded:: 0.18.0 + This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: @@ -4945,11 +4947,12 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, Returns ------- quantiles : Series or DataFrame - If ``q`` is an array, a DataFrame will be returned where the - index is ``q``, the columns are the columns of self, and the - values are the quantiles. - If ``q`` is a float, a Series will be returned where the - index is the columns of self and the values are the quantiles. + + - If ``q`` is an array, a DataFrame will be returned where the + index is ``q``, the columns are the columns of self, and the + values are the quantiles. + - If ``q`` is a float, a Series will be returned where the + index is the columns of self and the values are the quantiles. Examples -------- @@ -4965,6 +4968,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True, 0.1 1.3 3.7 0.5 2.5 55.0 """ + self._check_percentile(q) per = np.asarray(q) * 100 diff --git a/pandas/core/generic.py b/pandas/core/generic.py index bc92733e7c2fd..963c953154b57 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -2041,11 +2041,13 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False, method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. - * default: don't fill gaps - * pad / ffill: propagate last valid observation forward to next - valid - * backfill / bfill: use next valid observation to fill gap - * nearest: use nearest valid observations to fill gap + + * default: don't fill gaps + * pad / ffill: propagate last valid observation forward to next + valid + * backfill / bfill: use next valid observation to fill gap + * nearest: use nearest valid observations to fill gap + copy : boolean, default True Return a new object, even if the passed indexes are the same level : int or name @@ -2265,11 +2267,13 @@ def _reindex_multi(self, axes, copy, fill_value): axis : %(axes_single_arg)s method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional Method to use for filling holes in reindexed DataFrame: - * default: don't fill gaps - * pad / ffill: propagate last valid observation forward to next - valid - * backfill / bfill: use next valid observation to fill gap - * nearest: use nearest valid observations to fill gap + + * default: don't fill gaps + * pad / ffill: propagate last valid observation forward to next + valid + * backfill / bfill: use next valid observation to fill gap + * nearest: use nearest valid observations to fill gap + copy : boolean, default True Return a new object, even if the passed indexes are the same level : int or name diff --git a/pandas/core/series.py b/pandas/core/series.py index 5eb1ab0f14ecf..d339a93a3ed9b 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -1289,8 +1289,10 @@ def quantile(self, q=0.5, interpolation='linear'): 0 <= q <= 1, the quantile(s) to compute interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} .. versionadded:: 0.18.0 + This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: + * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. @@ -1306,15 +1308,15 @@ def quantile(self, q=0.5, interpolation='linear'): Examples -------- - >>> s = Series([1, 2, 3, 4]) >>> s.quantile(.5) - 2.5 + 2.5 >>> s.quantile([.25, .5, .75]) 0.25 1.75 0.50 2.50 0.75 3.25 dtype: float64 + """ self._check_percentile(q) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index f7b38c75a24b9..2604b6e0784cf 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -121,6 +121,7 @@ class ParserWarning(Warning): If True, skip over blank lines rather than interpreting as NaN values parse_dates : boolean or list of ints or names or list of lists or dict, \ default False + * boolean. If True -> try parsing the index. * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. @@ -128,6 +129,7 @@ class ParserWarning(Warning): a single date column. * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo' + Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : boolean, default False If True and parse_dates is enabled for a column, attempt to infer diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py index 8f127e28e28a9..d92cfef5280fc 100644 --- a/pandas/tseries/tools.py +++ b/pandas/tseries/tools.py @@ -190,6 +190,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, ---------- arg : string, datetime, list, tuple, 1-d array, or Series errors : {'ignore', 'raise', 'coerce'}, default 'raise' + - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaT - If 'ignore', then invalid parsing will return the input @@ -201,10 +202,12 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, with day first (this is a known bug, based on dateutil behavior). yearfirst : boolean, default False Specify a date parse order if `arg` is str or its list-likes. + - If True parses dates with the year first, eg 10/11/12 is parsed as 2010-11-12. - If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil). + Warning: yearfirst=True is not strict, but will prefer to parse with year first (this is a known bug, based on dateutil beahavior). @@ -214,14 +217,17 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well). box : boolean, default True + - If True returns a DatetimeIndex - If False returns ndarray of values. format : string, default None strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse all the way up to nanoseconds. exact : boolean, True by default + - If True, require an exact format match. - If False, allow the format to match anywhere in the target string. + unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch (e.g. a unix timestamp), which is an integer/float number. infer_datetime_format : boolean, default False @@ -273,6 +279,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT + """ return _to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, diff --git a/pandas/util/nosetester.py b/pandas/util/nosetester.py index a81fc5cd4b31e..1bdaaff99fd50 100644 --- a/pandas/util/nosetester.py +++ b/pandas/util/nosetester.py @@ -123,7 +123,8 @@ def _get_custom_doctester(self): return None def _test_argv(self, label, verbose, extra_argv): - ''' Generate argv for nosetest command + """ + Generate argv for nosetest command Parameters ---------- @@ -138,7 +139,8 @@ def _test_argv(self, label, verbose, extra_argv): ------- argv : list command line arguments that will be passed to nose - ''' + """ + argv = [__file__, self.package_path] if label and label != 'full': if not isinstance(label, string_types): @@ -170,6 +172,7 @@ def test(self, label='fast', verbose=1, extra_argv=None, Identifies the tests to run. This can be a string to pass to the nosetests executable with the '-A' option, or one of several special values. Special values are: + * 'fast' - the default - which corresponds to the ``nosetests -A`` option of 'not slow'. * 'full' - fast (as above) and slow tests as in the @@ -177,6 +180,7 @@ def test(self, label='fast', verbose=1, extra_argv=None, * None or '' - run all tests. * attribute_identifier - string passed directly to nosetests as '-A'. + verbose : int, optional Verbosity value for test outputs, in the range 1-10. Default is 1. extra_argv : list, optional @@ -191,14 +195,15 @@ def test(self, label='fast', verbose=1, extra_argv=None, This specifies which warnings to configure as 'raise' instead of 'warn' during the test execution. Valid strings are: - - "develop" : equals ``(DeprecationWarning, RuntimeWarning)`` - - "release" : equals ``()``, don't raise on any warnings. + - 'develop' : equals ``(DeprecationWarning, RuntimeWarning)`` + - 'release' : equals ``()``, don't raise on any warnings. Returns ------- result : object Returns the result of running the tests as a ``nose.result.TextTestResult`` object. + """ # cap verbosity at 3 because nose becomes *very* verbose beyond that diff --git a/setup.py b/setup.py index f33b01b24c165..94fd2998a84e6 100755 --- a/setup.py +++ b/setup.py @@ -439,7 +439,8 @@ def pxd(name): obj = Extension('pandas.%s' % name, sources=sources, depends=data.get('depends', []), - include_dirs=include) + include_dirs=include, + extra_compile_args=['-w']) extensions.append(obj) @@ -447,14 +448,16 @@ def pxd(name): sparse_ext = Extension('pandas._sparse', sources=[srcpath('sparse', suffix=suffix)], include_dirs=[], - libraries=libraries) + libraries=libraries, + extra_compile_args=['-w']) extensions.extend([sparse_ext]) testing_ext = Extension('pandas._testing', sources=[srcpath('testing', suffix=suffix)], include_dirs=[], - libraries=libraries) + libraries=libraries, + extra_compile_args=['-w']) extensions.extend([testing_ext]) @@ -474,7 +477,8 @@ def pxd(name): subdir='msgpack')], language='c++', include_dirs=['pandas/src/msgpack'] + common_include, - define_macros=macros) + define_macros=macros, + extra_compile_args=['-w']) unpacker_ext = Extension('pandas.msgpack._unpacker', depends=['pandas/src/msgpack/unpack.h', 'pandas/src/msgpack/unpack_define.h', @@ -484,7 +488,8 @@ def pxd(name): subdir='msgpack')], language='c++', include_dirs=['pandas/src/msgpack'] + common_include, - define_macros=macros) + define_macros=macros, + extra_compile_args=['-w']) extensions.append(packer_ext) extensions.append(unpacker_ext) @@ -508,7 +513,7 @@ def pxd(name): include_dirs=['pandas/src/ujson/python', 'pandas/src/ujson/lib', 'pandas/src/datetime'] + common_include, - extra_compile_args=['-D_GNU_SOURCE']) + extra_compile_args=['-D_GNU_SOURCE', '-w']) extensions.append(ujson_ext)
https://api.github.com/repos/pandas-dev/pandas/pulls/12579
2016-03-09T22:05:12Z
2016-03-10T18:50:52Z
null
2016-03-10T18:50:52Z
Add normalization to crosstab
diff --git a/doc/source/reshaping.rst b/doc/source/reshaping.rst index 611771a47c233..f21b936df93f6 100644 --- a/doc/source/reshaping.rst +++ b/doc/source/reshaping.rst @@ -383,9 +383,12 @@ calling ``to_string`` if you wish: Note that ``pivot_table`` is also available as an instance method on DataFrame. +.. _reshaping.crosstabulations: + Cross tabulations ~~~~~~~~~~~~~~~~~ + Use the ``crosstab`` function to compute a cross-tabulation of two (or more) factors. By default ``crosstab`` computes a frequency table of the factors unless an array of values and an aggregation function are passed. @@ -402,6 +405,9 @@ It takes a number of arguments - ``colnames``: sequence, default None, if passed, must match number of column arrays passed - ``margins``: boolean, default False, Add row/column margins (subtotals) +- ``normalize``: boolean, {'all', 'index', 'columns'}, or {0,1}, default False. + Normalize by dividing all values by the sum of values. + Any Series passed will have their name attributes used unless row or column names for the cross-tabulation are specified @@ -416,6 +422,47 @@ For example: c = np.array([dull, dull, shiny, dull, dull, shiny], dtype=object) pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c']) + +If ``crosstab`` receives only two Series, it will provide a frequency table. + +.. ipython:: python + + df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4], + 'c': [1, 1, np.nan, 1, 1]}) + df + + pd.crosstab(df.a, df.b) + +.. versionadded:: 0.18.1 + +Frequency tables can also be normalized to show percentages rather than counts +using the ``normalize`` argument: + +.. ipython:: python + + pd.crosstab(df.a, df.b, normalize=True) + +``normalize`` can also normalize values within each row or within each column: + +.. ipython:: python + + pd.crosstab(df.a, df.b, normalize='columns') + +``crosstab`` can also be passed a third Series and an aggregation function +(``aggfunc``) that will be applied to the values of the third Series within each +group defined by the first two Series: + +.. ipython:: python + + pd.crosstab(df.a, df.b, values=df.c, aggfunc=np.sum) + +And finally, one can also add margins or normalize this output. + +.. ipython:: python + + pd.crosstab(df.a, df.b, values=df.c, aggfunc=np.sum, normalize=True, + margins=True) + .. _reshaping.pivot.margins: Adding margins (partial aggregates) diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 821f093083026..1455283a0e961 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -93,6 +93,8 @@ Other Enhancements idx = pd.Index(['a|b', 'a|c', 'b|c']) idx.str.get_dummies('|') +- ``pd.crosstab()`` has gained ``normalize`` argument for normalizing frequency tables (:issue:`12569`). Examples in updated docs :ref:`here <reshaping.crosstabulations>`. + .. _whatsnew_0181.sparse: @@ -277,9 +279,9 @@ Bug Fixes - Bug in ``__name__`` of ``.cum*`` functions (:issue:`12021`) - Bug in ``.astype()`` of a ``Float64Inde/Int64Index`` to an ``Int64Index`` (:issue:`12881`) - Bug in roundtripping an integer based index in ``.to_json()/.read_json()`` when ``orient='index'`` (the default) (:issue:`12866`) - - Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`) - Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`) +- Bug in ``pd.crosstab()`` where would silently ignore ``aggfunc`` if ``values=None`` (:issue:`12569`). - Bug in ``Timestamp.__repr__`` that caused ``pprint`` to fail in nested structures (:issue:`12622`) diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index d7798bf1e7982..de79e54e22270 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -371,7 +371,7 @@ def _convert_by(by): def crosstab(index, columns, values=None, rownames=None, colnames=None, - aggfunc=None, margins=False, dropna=True): + aggfunc=None, margins=False, dropna=True, normalize=False): """ Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an @@ -384,9 +384,10 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None, columns : array-like, Series, or list of arrays/Series Values to group by in the columns values : array-like, optional - Array of values to aggregate according to the factors + Array of values to aggregate according to the factors. + Requires `aggfunc` be specified. aggfunc : function, optional - If no values array is passed, computes a frequency table + If specified, requires `values` be specified as well rownames : sequence, default None If passed, must match number of row arrays passed colnames : sequence, default None @@ -395,6 +396,16 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None, Add row/column margins (subtotals) dropna : boolean, default True Do not include columns whose entries are all NaN + normalize : boolean, {'all', 'index', 'columns'}, or {0,1}, default False + Normalize by dividing all values by the sum of values. + + - If passed 'all' or `True`, will normalize over all values. + - If passed 'index' will normalize over each row. + - If passed 'columns' will normalize over each column. + - If margins is `True`, will also normalize margin values. + + .. versionadded:: 0.18.1 + Notes ----- @@ -438,18 +449,97 @@ def crosstab(index, columns, values=None, rownames=None, colnames=None, data.update(zip(rownames, index)) data.update(zip(colnames, columns)) + if values is None and aggfunc is not None: + raise ValueError("aggfunc cannot be used without values.") + + if values is not None and aggfunc is None: + raise ValueError("values cannot be used without an aggfunc.") + if values is None: df = DataFrame(data) df['__dummy__'] = 0 table = df.pivot_table('__dummy__', index=rownames, columns=colnames, aggfunc=len, margins=margins, dropna=dropna) - return table.fillna(0).astype(np.int64) + table = table.fillna(0).astype(np.int64) + else: data['__dummy__'] = values df = DataFrame(data) table = df.pivot_table('__dummy__', index=rownames, columns=colnames, aggfunc=aggfunc, margins=margins, dropna=dropna) - return table + + # Post-process + if normalize is not False: + table = _normalize(table, normalize=normalize, margins=margins) + + return table + + +def _normalize(table, normalize, margins): + + if not isinstance(normalize, bool) and not isinstance(normalize, + compat.string_types): + axis_subs = {0: 'index', 1: 'columns'} + try: + normalize = axis_subs[normalize] + except KeyError: + raise ValueError("Not a valid normalize argument") + + if margins is False: + + # Actual Normalizations + normalizers = { + 'all': lambda x: x / x.sum(axis=1).sum(axis=0), + 'columns': lambda x: x / x.sum(), + 'index': lambda x: x.div(x.sum(axis=1), axis=0) + } + + normalizers[True] = normalizers['all'] + + try: + f = normalizers[normalize] + except KeyError: + raise ValueError("Not a valid normalize argument") + + table = f(table) + table = table.fillna(0) + + elif margins is True: + + column_margin = table.loc[:, 'All'].drop('All') + index_margin = table.loc['All', :].drop('All') + table = table.drop('All', axis=1).drop('All') + + # Normalize core + table = _normalize(table, normalize=normalize, margins=False) + + # Fix Margins + if normalize == 'columns': + column_margin = column_margin / column_margin.sum() + table = concat([table, column_margin], axis=1) + table = table.fillna(0) + + elif normalize == 'index': + index_margin = index_margin / index_margin.sum() + table = table.append(index_margin) + table = table.fillna(0) + + elif normalize == "all" or normalize is True: + column_margin = column_margin / column_margin.sum() + index_margin = index_margin / index_margin.sum() + index_margin.loc['All'] = 1 + table = concat([table, column_margin], axis=1) + table = table.append(index_margin) + + table = table.fillna(0) + + else: + raise ValueError("Not a valid normalize argument") + + else: + raise ValueError("Not a valid margins argument") + + return table def _get_names(arrs, names, prefix='row'): diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index bff82e32dccc0..5ebd2e4f693cf 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -1021,6 +1021,150 @@ def test_margin_dropna(self): expected.columns = Index(['dull', 'shiny', 'All'], name='c') tm.assert_frame_equal(actual, expected) + def test_crosstab_normalize(self): + # Issue 12578 + df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4], + 'c': [1, 1, np.nan, 1, 1]}) + + rindex = pd.Index([1, 2], name='a') + cindex = pd.Index([3, 4], name='b') + full_normal = pd.DataFrame([[0.2, 0], [0.2, 0.6]], + index=rindex, columns=cindex) + row_normal = pd.DataFrame([[1.0, 0], [0.25, 0.75]], + index=rindex, columns=cindex) + col_normal = pd.DataFrame([[0.5, 0], [0.5, 1.0]], + index=rindex, columns=cindex) + + # Check all normalize args + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='all'), + full_normal) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=True), + full_normal) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='index'), + row_normal) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='columns'), + col_normal) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=1), + pd.crosstab(df.a, df.b, normalize='columns')) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=0), + pd.crosstab(df.a, df.b, normalize='index')) + + row_normal_margins = pd.DataFrame([[1.0, 0], + [0.25, 0.75], + [0.4, 0.6]], + index=pd.Index([1, 2, 'All'], + name='a', + dtype='object'), + columns=pd.Index([3, 4], name='b')) + col_normal_margins = pd.DataFrame([[0.5, 0, 0.2], [0.5, 1.0, 0.8]], + index=pd.Index([1, 2], name='a', + dtype='object'), + columns=pd.Index([3, 4, 'All'], + name='b')) + + all_normal_margins = pd.DataFrame([[0.2, 0, 0.2], + [0.2, 0.6, 0.8], + [0.4, 0.6, 1]], + index=pd.Index([1, 2, 'All'], + name='a', + dtype='object'), + columns=pd.Index([3, 4, 'All'], + name='b')) + + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='index', + margins=True), row_normal_margins) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='columns', + margins=True), col_normal_margins) + tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize=True, + margins=True), all_normal_margins) + + # Test arrays + pd.crosstab([np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])], + np.array([1, 2, 1, 2])) + + # Test with aggfunc + norm_counts = pd.DataFrame([[0.25, 0, 0.25], + [0.25, 0.5, 0.75], + [0.5, 0.5, 1]], + index=pd.Index([1, 2, 'All'], + name='a', + dtype='object'), + columns=pd.Index([3, 4, 'All'], + name='b')) + test_case = pd.crosstab(df.a, df.b, df.c, aggfunc='count', + normalize='all', + margins=True) + tm.assert_frame_equal(test_case, norm_counts) + + df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4], + 'c': [0, 4, np.nan, 3, 3]}) + + norm_sum = pd.DataFrame([[0, 0, 0.], + [0.4, 0.6, 1], + [0.4, 0.6, 1]], + index=pd.Index([1, 2, 'All'], + name='a', + dtype='object'), + columns=pd.Index([3, 4, 'All'], + name='b', + dtype='object')) + test_case = pd.crosstab(df.a, df.b, df.c, aggfunc=np.sum, + normalize='all', + margins=True) + tm.assert_frame_equal(test_case, norm_sum) + + def test_crosstab_with_empties(self): + # Check handling of empties + df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4], + 'c': [np.nan, np.nan, np.nan, np.nan, np.nan]}) + + empty = pd.DataFrame([[0.0, 0.0], [0.0, 0.0]], + index=pd.Index([1, 2], + name='a', + dtype='int64'), + columns=pd.Index([3, 4], name='b')) + + for i in [True, 'index', 'columns']: + calculated = pd.crosstab(df.a, df.b, values=df.c, aggfunc='count', + normalize=i) + tm.assert_frame_equal(empty, calculated) + + nans = pd.DataFrame([[0.0, np.nan], [0.0, 0.0]], + index=pd.Index([1, 2], + name='a', + dtype='int64'), + columns=pd.Index([3, 4], name='b')) + + calculated = pd.crosstab(df.a, df.b, values=df.c, aggfunc='count', + normalize=False) + tm.assert_frame_equal(nans, calculated) + + def test_crosstab_errors(self): + # Issue 12578 + + df = pd.DataFrame({'a': [1, 2, 2, 2, 2], 'b': [3, 3, 4, 4, 4], + 'c': [1, 1, np.nan, 1, 1]}) + + error = 'values cannot be used without an aggfunc.' + with tm.assertRaisesRegexp(ValueError, error): + pd.crosstab(df.a, df.b, values=df.c) + + error = 'aggfunc cannot be used without values' + with tm.assertRaisesRegexp(ValueError, error): + pd.crosstab(df.a, df.b, aggfunc=np.mean) + + error = 'Not a valid normalize argument' + with tm.assertRaisesRegexp(ValueError, error): + pd.crosstab(df.a, df.b, normalize='42') + + with tm.assertRaisesRegexp(ValueError, error): + pd.crosstab(df.a, df.b, normalize=42) + + error = 'Not a valid margins argument' + with tm.assertRaisesRegexp(ValueError, error): + pd.crosstab(df.a, df.b, normalize='all', margins=42) + + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
Closes #12569 Note does NOT address #12577
https://api.github.com/repos/pandas-dev/pandas/pulls/12578
2016-03-09T21:58:21Z
2016-04-25T14:34:14Z
null
2016-04-25T14:34:16Z
BUG: value_count(normalize=True, dropna=True) counting missing in denominator, #12558
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 70a1ad4a335ea..24bd6101e96aa 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,3 +43,4 @@ Performance Improvements Bug Fixes ~~~~~~~~~ +- Bug in ``value_counts`` where normalizes over all observations including missing even when ``dropna=True`` (:issue:`12558`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index ad1efa21e8280..6661017ba1b4a 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -342,7 +342,14 @@ def value_counts(values, sort=True, ascending=False, normalize=False, result = result.sort_values(ascending=ascending) if normalize: - result = result / float(values.size) + if dropna: + # NaT's dropped above if time, so don't need to do again. + if not com.is_datetime_or_timedelta_dtype(dtype) \ + and not is_period and not is_datetimetz: + + result = result / float(Series(values).count()) + else: + result = result / float(values.size) return result diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index b9a4384603cda..20d8a3b088611 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -517,6 +517,33 @@ def test_dropna(self): pd.Series([10.3, 5., 5., None]).value_counts(dropna=False), pd.Series([2, 1, 1], index=[5., 10.3, np.nan])) + def test_normalize(self): + # Issue 12558 + tm.assert_series_equal( + pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts( + dropna=True, normalize=True), + pd.Series([0.75, 0.25], index=[10.3, 5.])) + + tm.assert_series_equal( + pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts( + dropna=True, normalize=False), + pd.Series([3, 1], index=[10.3, 5.])) + + tm.assert_series_equal( + pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts( + dropna=False, normalize=True), + pd.Series([0.6, 0.2, 0.2], index=[10.3, 5., np.nan])) + + tm.assert_series_equal( + pd.Series([5., 10.3, 10.3, 10.3, np.nan]).value_counts( + dropna=False, normalize=False), + pd.Series([3, 1, 1], index=[10.3, 5., np.nan])) + + s = pd.Series(['2015-01-03T00:00:00.000000000+0000', pd.NaT]) + tm.assert_series_equal( + s.value_counts(dropna=True, normalize=True), + pd.Series([1], index=[s.loc[0]])) + class GroupVarTestMixin(object):
Closes #12558
https://api.github.com/repos/pandas-dev/pandas/pulls/12576
2016-03-09T21:41:52Z
2016-03-14T01:20:23Z
null
2016-03-14T01:20:23Z
ENH: allow categoricals in msgpack
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index cc2269afa6e61..182562f055164 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -78,6 +78,7 @@ Other Enhancements - ``pd.read_csv()`` now supports opening ZIP files that contains a single CSV, via extension inference or explict ``compression='zip'`` (:issue:`12175`) - ``pd.read_csv()`` now supports opening files using xz compression, via extension inference or explicit ``compression='xz'`` is specified; ``xz`` compressions is also supported by ``DataFrame.to_csv`` in the same way (:issue:`11852`) - ``pd.read_msgpack()`` now always gives writeable ndarrays even when compression is used (:issue:`12359`). +- ``pd.read_msgpack()`` now supports serializing and de-serializing categoricals with msgpack (:issue:`12573`) - ``interpolate()`` now supports ``method='akima'`` (:issue:`7588`). - ``Index.take`` now handles ``allow_fill`` and ``fill_value`` consistently (:issue:`12631`) diff --git a/pandas/io/packers.py b/pandas/io/packers.py index d1cef04121fbb..f009793172e31 100644 --- a/pandas/io/packers.py +++ b/pandas/io/packers.py @@ -49,13 +49,15 @@ from pandas.compat import u, u_safe from pandas import (Timestamp, Period, Series, DataFrame, # noqa Index, MultiIndex, Float64Index, Int64Index, - Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT) + Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT, + Categorical) from pandas.tslib import NaTType from pandas.sparse.api import SparseSeries, SparseDataFrame, SparsePanel from pandas.sparse.array import BlockIndex, IntIndex from pandas.core.generic import NDFrame from pandas.core.common import ( PerformanceWarning, + is_categorical_dtype, needs_i8_conversion, pandas_dtype, ) @@ -226,6 +228,7 @@ def read(fh): # this is platform int, which we need to remap to np.int64 # for compat on windows platforms 7: np.dtype('int64'), + 'category': 'category' } @@ -262,6 +265,9 @@ def convert(values): v = values.ravel() # convert object + if is_categorical_dtype(values): + return values + if dtype == np.object_: return v.tolist() @@ -298,6 +304,9 @@ def unconvert(values, dtype, compress=None): if as_is_ext: values = values.data + if is_categorical_dtype(dtype): + return values + if dtype == np.object_: return np.array(values, dtype=object) @@ -393,6 +402,16 @@ def encode(obj): u'dtype': u(obj.dtype.name), u'data': convert(obj.values), u'compress': compressor} + + elif isinstance(obj, Categorical): + return {u'typ': u'category', + u'klass': u(obj.__class__.__name__), + u'name': getattr(obj, 'name', None), + u'codes': obj.codes, + u'categories': obj.categories, + u'ordered': obj.ordered, + u'compress': compressor} + elif isinstance(obj, Series): if isinstance(obj, SparseSeries): raise NotImplementedError( @@ -576,10 +595,18 @@ def decode(obj): result = result.tz_localize('UTC').tz_convert(tz) return result + elif typ == u'category': + from_codes = globals()[obj[u'klass']].from_codes + return from_codes(codes=obj[u'codes'], + categories=obj[u'categories'], + ordered=obj[u'ordered'], + name=obj[u'name']) + elif typ == u'series': dtype = dtype_for(obj[u'dtype']) pd_dtype = pandas_dtype(dtype) np_dtype = pandas_dtype(dtype).base + index = obj[u'index'] result = globals()[obj[u'klass']](unconvert(obj[u'data'], dtype, obj[u'compress']), diff --git a/pandas/io/tests/test_packers.py b/pandas/io/tests/test_packers.py index 276763989d7cf..4c0e71698ad79 100644 --- a/pandas/io/tests/test_packers.py +++ b/pandas/io/tests/test_packers.py @@ -9,13 +9,15 @@ from pandas import compat from pandas.compat import u from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range, - date_range, period_range, Index) + date_range, period_range, Index, Categorical) from pandas.core.common import PerformanceWarning from pandas.io.packers import to_msgpack, read_msgpack import pandas.util.testing as tm -from pandas.util.testing import (ensure_clean, assert_index_equal, - assert_series_equal, +from pandas.util.testing import (ensure_clean, + assert_categorical_equal, assert_frame_equal, + assert_index_equal, + assert_series_equal, patch) from pandas.tests.test_panel import assert_panel_equal @@ -335,7 +337,7 @@ def setUp(self): 'E': [0., 1, Timestamp('20100101'), 'foo', 2.], 'F': [Timestamp('20130102', tz='US/Eastern')] * 2 + [Timestamp('20130603', tz='CET')] * 3, - 'G': [Timestamp('20130102', tz='US/Eastern')] * 5 + 'G': [Timestamp('20130102', tz='US/Eastern')] * 5, } self.d['float'] = Series(data['A']) @@ -353,6 +355,29 @@ def test_basic(self): assert_series_equal(i, i_rec) +class TestCategorical(TestPackers): + + def setUp(self): + super(TestCategorical, self).setUp() + + self.d = {} + + self.d['plain_str'] = Categorical(['a', 'b', 'c', 'd', 'e']) + self.d['plain_str_ordered'] = Categorical(['a', 'b', 'c', 'd', 'e'], + ordered=True) + + self.d['plain_int'] = Categorical([5, 6, 7, 8]) + self.d['plain_int_ordered'] = Categorical([5, 6, 7, 8], ordered=True) + + def test_basic(self): + + # run multiple times here + for n in range(10): + for s, i in self.d.items(): + i_rec = self.encode_decode(i) + assert_categorical_equal(i, i_rec) + + class TestNDFrame(TestPackers): def setUp(self): @@ -365,7 +390,9 @@ def setUp(self): 'D': date_range('1/1/2009', periods=5), 'E': [0., 1, Timestamp('20100101'), 'foo', 2.], 'F': [Timestamp('20130102', tz='US/Eastern')] * 5, - 'G': [Timestamp('20130603', tz='CET')] * 5 + 'G': [Timestamp('20130603', tz='CET')] * 5, + 'H': Categorical(['a', 'b', 'c', 'd', 'e']), + 'I': Categorical(['a', 'b', 'c', 'd', 'e'], ordered=True), } self.frame = {
Supercedes #12191. I've made a best-effort to rebase this against master. It seems to work here.
https://api.github.com/repos/pandas-dev/pandas/pulls/12573
2016-03-09T16:59:14Z
2016-04-25T22:06:37Z
null
2016-04-25T22:06:59Z
TST: nicer output from pd.test()
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 518e5dd999391..97db171312557 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1633,8 +1633,8 @@ def test_result_types(self): self.check_result_type(np.float64, np.float64) def test_result_types2(self): - # xref https://github.com/pydata/pandas/issues/12293 - tm._skip_if_windows() + # xref https://github.com/pydata/pandas/issues/12293 + raise nose.SkipTest("unreliable tests on complex128") # Did not test complex64 because DataFrame is converting it to # complex128. Due to https://github.com/pydata/pandas/issues/10952 diff --git a/pandas/io/tests/test_data.py b/pandas/io/tests/test_data.py index c293ef5c9c2f6..d9c09fa788332 100644 --- a/pandas/io/tests/test_data.py +++ b/pandas/io/tests/test_data.py @@ -304,6 +304,7 @@ def setUpClass(cls): super(TestYahooOptions, cls).setUpClass() _skip_if_no_lxml() _skip_if_no_bs() + raise nose.SkipTest('unreliable test') # aapl has monthlies cls.aapl = web.Options('aapl', 'yahoo') @@ -370,6 +371,7 @@ def test_get_expiry_dates(self): @network def test_get_all_data(self): + try: data = self.aapl.get_all_data(put=True) except RemoteDataError as e: diff --git a/pandas/util/nosetester.py b/pandas/util/nosetester.py index 445cb79978fc1..a81fc5cd4b31e 100644 --- a/pandas/util/nosetester.py +++ b/pandas/util/nosetester.py @@ -122,6 +122,43 @@ def _get_custom_doctester(self): """ return None + def _test_argv(self, label, verbose, extra_argv): + ''' Generate argv for nosetest command + + Parameters + ---------- + label : {'fast', 'full', '', attribute identifier}, optional + see ``test`` docstring + verbose : int, optional + Verbosity value for test outputs, in the range 1-10. Default is 1. + extra_argv : list, optional + List with any extra arguments to pass to nosetests. + + Returns + ------- + argv : list + command line arguments that will be passed to nose + ''' + argv = [__file__, self.package_path] + if label and label != 'full': + if not isinstance(label, string_types): + raise TypeError('Selection label should be a string') + if label == 'fast': + label = 'not slow and not network and not disabled' + argv += ['-A', label] + argv += ['--verbosity', str(verbose)] + + # When installing with setuptools, and also in some other cases, the + # test_*.py files end up marked +x executable. Nose, by default, does + # not run files marked with +x as they might be scripts. However, in + # our case nose only looks for test_*.py files under the package + # directory, which should be safe. + argv += ['--exe'] + + if extra_argv: + argv += extra_argv + return argv + def test(self, label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None): """
TST: skip result2 test in computation/test_eval.py as unreliable TST: skip some unreliable tests in io/tests/test_data.py
https://api.github.com/repos/pandas-dev/pandas/pulls/12568
2016-03-09T01:34:48Z
2016-03-09T02:48:37Z
null
2016-03-09T02:48:37Z
CI: add osx to travis, xref #8630
diff --git a/.travis.yml b/.travis.yml index e08bd3b0413c9..0c2f1b0d0fe2d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -19,15 +19,16 @@ git: matrix: fast_finish: true include: - - python: 2.7 + - language: objective-c + os: osx + compiler: clang + osx_image: xcode6.4 env: - - JOB_NAME: "27_nslow_nnet_COMPAT" - - NOSE_ARGS="not slow and not network and not disabled" - - CLIPBOARD=xclip - - LOCALE_OVERRIDE="it_IT.UTF-8" - - BUILD_TYPE=conda - - INSTALL_TEST=true - - JOB_TAG=_COMPAT + - JOB_NAME: "35_osx" + - NOSE_ARGS="not slow and not network and not disabled" + - BUILD_TYPE=conda + - JOB_TAG=_OSX + - TRAVIS_PYTHON_VERSION=3.5 - python: 2.7 env: - JOB_NAME: "27_slow_nnet_LOCALE" @@ -75,18 +76,20 @@ matrix: - NOSE_ARGS="not slow and not disabled" - FULL_DEPS=true - BUILD_TEST=true - - python: 2.7 - env: - - JOB_NAME: "27_numpy_dev" - - JOB_TAG=_NUMPY_DEV - - NOSE_ARGS="not slow and not network and not disabled" - - PANDAS_TESTING_MODE="deprecate" - python: 3.5 env: - JOB_NAME: "35_numpy_dev" - JOB_TAG=_NUMPY_DEV - NOSE_ARGS="not slow and not network and not disabled" - PANDAS_TESTING_MODE="deprecate" + - python: 2.7 + env: + - JOB_NAME: "27_nslow_nnet_COMPAT" + - NOSE_ARGS="not slow and not network and not disabled" + - LOCALE_OVERRIDE="it_IT.UTF-8" + - BUILD_TYPE=conda + - INSTALL_TEST=true + - JOB_TAG=_COMPAT - python: 2.7 env: - JOB_NAME: "doc_build" @@ -108,12 +111,6 @@ matrix: - NOSE_ARGS="slow and not network and not disabled" - FULL_DEPS=true - CLIPBOARD=xsel - - python: 2.7 - env: - - JOB_NAME: "27_numpy_dev" - - JOB_TAG=_NUMPY_DEV - - NOSE_ARGS="not slow and not network and not disabled" - - PANDAS_TESTING_MODE="deprecate" - python: 2.7 env: - JOB_NAME: "27_build_test_conda" @@ -127,6 +124,14 @@ matrix: - JOB_TAG=_NUMPY_DEV - NOSE_ARGS="not slow and not network and not disabled" - PANDAS_TESTING_MODE="deprecate" + - python: 2.7 + env: + - JOB_NAME: "27_nslow_nnet_COMPAT" + - NOSE_ARGS="not slow and not network and not disabled" + - LOCALE_OVERRIDE="it_IT.UTF-8" + - BUILD_TYPE=conda + - INSTALL_TEST=true + - JOB_TAG=_COMPAT - python: 2.7 env: - JOB_NAME: "doc_build" @@ -139,16 +144,13 @@ before_install: - echo "before_install" - echo $VIRTUAL_ENV - export PATH="$HOME/miniconda/bin:$PATH" - - sudo apt-get install ccache - df -h - date - pwd - uname -a - python -V - ci/before_install_travis.sh - # Xvfb stuff for clipboard functionality; see the travis-ci documentation - export DISPLAY=:99.0 - - sh -e /etc/init.d/xvfb start install: - echo "install" @@ -157,8 +159,7 @@ install: - ci/submit_ccache.sh before_script: - - mysql -e 'create database pandas_nosetest;' - - psql -c 'create database pandas_nosetest;' -U postgres + - ci/install_db.sh script: - echo "script" diff --git a/ci/before_install_travis.sh b/ci/before_install_travis.sh index e4376e1bf21c2..76775ecbc78f0 100755 --- a/ci/before_install_travis.sh +++ b/ci/before_install_travis.sh @@ -8,6 +8,10 @@ echo "inside $0" # overview -sudo apt-get update $APT_ARGS # run apt-get update for all versions +if [ "${TRAVIS_OS_NAME}" == "linux" ]; then + sudo apt-get update $APT_ARGS # run apt-get update for all versions + + sh -e /etc/init.d/xvfb start +fi true # never fail because bad things happened here diff --git a/ci/build_docs.sh b/ci/build_docs.sh index 942c2f7c64f4f..12fb8cae4dfc7 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -1,5 +1,10 @@ #!/bin/bash +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not doing build_docs on non-linux" + exit 0 +fi + cd "$TRAVIS_BUILD_DIR" echo "inside $0" diff --git a/ci/install_db.sh b/ci/install_db.sh new file mode 100755 index 0000000000000..e4e6d7a5a9b85 --- /dev/null +++ b/ci/install_db.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not using dbs on non-linux" + exit 0 +fi + +echo "installing dbs" +mysql -e 'create database pandas_nosetest;' +psql -c 'create database pandas_nosetest;' -U postgres + +echo "done" +exit 0 diff --git a/ci/install_travis.sh b/ci/install_travis.sh index 335286d7d1676..1b1eae7b44e45 100755 --- a/ci/install_travis.sh +++ b/ci/install_travis.sh @@ -38,16 +38,16 @@ if [ -n "$LOCALE_OVERRIDE" ]; then # make sure the locale is available # probably useless, since you would need to relogin time sudo locale-gen "$LOCALE_OVERRIDE" -fi -# Need to enable for locale testing. The location of the locale file(s) is -# distro specific. For example, on Arch Linux all of the locales are in a -# commented file--/etc/locale.gen--that must be commented in to be used -# whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales -# based on what's in the files in that folder -time echo 'it_CH.UTF-8 UTF-8' | sudo tee -a /var/lib/locales/supported.d/it -time sudo locale-gen + # Need to enable for locale testing. The location of the locale file(s) is + # distro specific. For example, on Arch Linux all of the locales are in a + # commented file--/etc/locale.gen--that must be commented in to be used + # whereas Ubuntu looks in /var/lib/locales/supported.d/* and generates locales + # based on what's in the files in that folder + time echo 'it_CH.UTF-8 UTF-8' | sudo tee -a /var/lib/locales/supported.d/it + time sudo locale-gen +fi # install gui for clipboard testing if [ -n "$CLIPBOARD_GUI" ]; then @@ -67,7 +67,12 @@ fi python_major_version="${TRAVIS_PYTHON_VERSION:0:1}" [ "$python_major_version" == "2" ] && python_major_version="" -wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 +# install miniconda +if [ "${TRAVIS_OS_NAME}" == "osx" ]; then + wget http://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh -O miniconda.sh || exit 1 +else + wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh || exit 1 +fi bash miniconda.sh -b -p $HOME/miniconda || exit 1 conda config --set always_yes yes --set changeps1 no || exit 1 @@ -94,7 +99,7 @@ time conda install -n pandas --file=${REQ} || exit 1 source activate pandas # set the compiler cache to work -if [ "$IRON_TOKEN" ]; then +if [[ "$IRON_TOKEN" && "${TRAVIS_OS_NAME}" == "linux" ]]; then export PATH=/usr/lib/ccache:/usr/lib64/ccache:$PATH gcc=$(which gcc) echo "gcc: $gcc" @@ -113,24 +118,31 @@ if [ "$BUILD_TEST" ]; then else # build but don't install + echo "build em" time python setup.py build_ext --inplace || exit 1 # we may have run installations + echo "conda installs" REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.run" time conda install -n pandas --file=${REQ} || exit 1 # we may have additional pip installs + echo "pip installs" REQ="ci/requirements-${TRAVIS_PYTHON_VERSION}${JOB_TAG}.pip" if [ -e ${REQ} ]; then pip install -r $REQ fi # remove any installed pandas package - conda remove pandas + # w/o removing anything else + echo "removing installed pandas" + conda remove pandas --force # install our pandas + echo "running setup.py develop" python setup.py develop || exit 1 fi -true +echo "done" +exit 0 diff --git a/ci/prep_ccache.sh b/ci/prep_ccache.sh index 34e1f2520c422..7e586cc4d3085 100755 --- a/ci/prep_ccache.sh +++ b/ci/prep_ccache.sh @@ -1,5 +1,10 @@ #!/bin/bash +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not using ccache on non-linux" + exit 0 +fi + if [ "$IRON_TOKEN" ]; then home_dir=$(pwd) diff --git a/ci/requirements-2.7-64.run b/ci/requirements-2.7-64.run index c152c7160c9fc..42b5a789ae31a 100644 --- a/ci/requirements-2.7-64.run +++ b/ci/requirements-2.7-64.run @@ -13,10 +13,6 @@ scipy xlsxwriter boto bottleneck -patsy html5lib beautiful-soup jinja2=2.8 - -#pymysql=0.6.3 -#psycopg2=2.5.2 diff --git a/ci/requirements-3.4-64.run b/ci/requirements-3.4-64.run index e90722360d428..106cc5b7168ba 100644 --- a/ci/requirements-3.4-64.run +++ b/ci/requirements-3.4-64.run @@ -5,13 +5,8 @@ openpyxl xlsxwriter xlrd xlwt -html5lib -patsy -beautiful-soup scipy numexpr pytables -lxml -sqlalchemy bottleneck jinja2=2.8 diff --git a/ci/requirements-3.5-64.run b/ci/requirements-3.5-64.run index 81565f2467b69..96de21e3daa5e 100644 --- a/ci/requirements-3.5-64.run +++ b/ci/requirements-3.5-64.run @@ -5,21 +5,8 @@ openpyxl xlsxwriter xlrd xlwt -patsy scipy numexpr pytables -html5lib -lxml matplotlib -jinja2 blosc - -# currently causing some warnings -#sqlalchemy -#pymysql -#psycopg2 - -# not available from conda -#beautiful-soup -#bottleneck diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run index fdc5f3f7dc992..333641caf26c4 100644 --- a/ci/requirements-3.5.run +++ b/ci/requirements-3.5.run @@ -5,7 +5,6 @@ openpyxl xlsxwriter xlrd xlwt -patsy scipy numexpr pytables diff --git a/ci/requirements-3.5_OSX.build b/ci/requirements-3.5_OSX.build new file mode 100644 index 0000000000000..9558cf00ddf5c --- /dev/null +++ b/ci/requirements-3.5_OSX.build @@ -0,0 +1,4 @@ +python-dateutil +pytz +numpy +cython diff --git a/ci/requirements-3.5_OSX.run b/ci/requirements-3.5_OSX.run new file mode 100644 index 0000000000000..80e12ac3fed34 --- /dev/null +++ b/ci/requirements-3.5_OSX.run @@ -0,0 +1,20 @@ +python-dateutil +pytz +numpy +openpyxl +xlsxwriter +xlrd +xlwt +scipy +numexpr +pytables +html5lib +lxml +matplotlib +jinja2 +bottleneck +xarray +boto + +# incompat with conda ATM +# beautiful-soup diff --git a/ci/submit_ccache.sh b/ci/submit_ccache.sh index da421489230dd..7630bb7cc2760 100755 --- a/ci/submit_ccache.sh +++ b/ci/submit_ccache.sh @@ -1,18 +1,23 @@ #!/bin/bash -home_dir=$(pwd) -ccache -s - -MISSES=$(ccache -s | grep "cache miss" | grep -Po "\d+") -echo "MISSES: $MISSES" - -if [ x"$MISSES" == x"0" ]; then - echo "No cache misses detected, skipping upload" - exit 0 +if [ "${TRAVIS_OS_NAME}" != "linux" ]; then + echo "not using ccache on non-linux" + exit 0 fi if [ "$IRON_TOKEN" ]; then + home_dir=$(pwd) + ccache -s + + MISSES=$(ccache -s | grep "cache miss" | grep -Po "\d+") + echo "MISSES: $MISSES" + + if [ x"$MISSES" == x"0" ]; then + echo "No cache misses detected, skipping upload" + exit 0 + fi + # install the compiler cache sudo apt-get $APT_ARGS install ccache p7zip-full # iron_cache, pending py3 fixes upstream @@ -29,6 +34,6 @@ if [ "$IRON_TOKEN" ]; then split -b 500000 -d $HOME/ccache.7z $HOME/ccache. python ci/ironcache/put.py -fi; +fi exit 0 diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index 7451beed7025c..b4b79a87f898a 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -100,7 +100,7 @@ cython versions >=0.21 you can use ``%load_ext Cython``): .. ipython:: python :okwarning: - %load_ext cythonmagic + %load_ext Cython Now, let's simply copy our functions over to cython as is (the suffix diff --git a/doc/source/release.rst b/doc/source/release.rst index 04d74270ec938..60d6b15a21a7b 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -40,7 +40,7 @@ analysis / manipulation tool available in any language. pandas 0.18.0 ------------- -**Release date:** (January ??, 2016) +**Release date:** (March 13, 2016) This is a major release from 0.17.1 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 diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index e4b9b3486adf1..90ded17286c9f 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1,7 +1,7 @@ .. _whatsnew_0180: -v0.18.0 (February ??, 2016) ---------------------------- +v0.18.0 (March 13, 2016) +------------------------ This is a major release from 0.17.1 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 @@ -607,9 +607,9 @@ Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`1 Changes to msgpack ^^^^^^^^^^^^^^^^^^ -Forward incompatible changes in ``msgpack`` writing format were made over 0.17.0 and 0.18.0; older versions of pandas cannot read files packed by newer versions (:issue:`12129`, `10527`) +Forward incompatible changes in ``msgpack`` writing format were made over 0.17.0 and 0.18.0; older versions of pandas cannot read files packed by newer versions (:issue:`12129`, :issue:`10527`) -Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`). The following table describes the backward and forward compat of msgpacks. +Bugs in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`). The following table describes the backward and forward compat of msgpacks. .. warning:: @@ -745,58 +745,6 @@ You could also specify a ``how`` directly 2010-01-01 09:00:06 1.249976 1.219477 1.266330 1.224904 2010-01-01 09:00:08 1.020940 1.068634 1.146402 1.613897 -.. warning:: - - This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case: - - .. code-block:: python - - In [4]: r = df.resample('2s') - - In [6]: r*10 - pandas/tseries/resample.py:80: FutureWarning: .resample() is now a deferred operation - use .resample(...).mean() instead of .resample(...) - - Out[6]: - A B C D - 2010-01-01 09:00:00 4.857476 4.473507 3.570960 7.936154 - 2010-01-01 09:00:02 8.208011 7.943173 3.640340 5.310957 - 2010-01-01 09:00:04 4.339846 3.145823 4.241039 6.257326 - 2010-01-01 09:00:06 6.249881 6.097384 6.331650 6.124518 - 2010-01-01 09:00:08 5.104699 5.343172 5.732009 8.069486 - - However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``: - - .. code-block:: python - - In [7]: r.iloc[0] = 5 - ValueError: .resample() is now a deferred operation - use .resample(...).mean() instead of .resample(...) - assignment will have no effect as you are working on a copy - - There is a situation where the new API can not perform all the operations when using original code. - This code is intending to resample every 2s, take the ``mean`` AND then take the ``min` of those results. - - .. code-block:: python - - In [4]: df.resample('2s').min() - Out[4]: - A 0.433985 - B 0.314582 - C 0.357096 - D 0.531096 - dtype: float64 - - The new API will: - - .. ipython: python - - df.resample('2s').min() - - Good news is the return dimensions will differ (between the new API and the old API), so this should loudly raise - an exception. - - **New API**: Now, you can write ``.resample`` as a 2-stage operation like groupby, which @@ -886,6 +834,60 @@ New API In the new API, you can either downsample OR upsample. The prior implementation would allow you to pass an aggregator function (like ``mean``) even though you were upsampling, providing a bit of confusion. +Previous API will work but deprecations +''''''''''''''''''''''''''''''''''''''' + +.. warning:: + + This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case: + + .. code-block:: python + + In [4]: r = df.resample('2s') + + In [6]: r*10 + pandas/tseries/resample.py:80: FutureWarning: .resample() is now a deferred operation + use .resample(...).mean() instead of .resample(...) + + Out[6]: + A B C D + 2010-01-01 09:00:00 4.857476 4.473507 3.570960 7.936154 + 2010-01-01 09:00:02 8.208011 7.943173 3.640340 5.310957 + 2010-01-01 09:00:04 4.339846 3.145823 4.241039 6.257326 + 2010-01-01 09:00:06 6.249881 6.097384 6.331650 6.124518 + 2010-01-01 09:00:08 5.104699 5.343172 5.732009 8.069486 + + However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``: + + .. code-block:: python + + In [7]: r.iloc[0] = 5 + ValueError: .resample() is now a deferred operation + use .resample(...).mean() instead of .resample(...) + assignment will have no effect as you are working on a copy + + There is a situation where the new API can not perform all the operations when using original code. + This code is intending to resample every 2s, take the ``mean`` AND then take the ``min` of those results. + + .. code-block:: python + + In [4]: df.resample('2s').min() + Out[4]: + A 0.433985 + B 0.314582 + C 0.357096 + D 0.531096 + dtype: float64 + + The new API will: + + .. ipython:: python + + df.resample('2s').min() + + The good news is the return dimensions will differ between the new API and the old API, so this should loudly raise + an exception. + Changes to eval ^^^^^^^^^^^^^^^
CI: remove numpy_dev 2.7 build CI: remove extraneous requirements
https://api.github.com/repos/pandas-dev/pandas/pulls/12567
2016-03-09T01:33:57Z
2016-03-09T14:37:31Z
null
2016-03-09T15:23:57Z
BUG: GH12558 where nulls contributed to normalized value_counts
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 70a1ad4a335ea..5aa1d30979cfb 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -43,3 +43,5 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + +- Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py index ad1efa21e8280..de38c0c3940fd 100644 --- a/pandas/core/algorithms.py +++ b/pandas/core/algorithms.py @@ -342,7 +342,7 @@ def value_counts(values, sort=True, ascending=False, normalize=False, result = result.sort_values(ascending=ascending) if normalize: - result = result / float(values.size) + result = result / float(counts.sum()) return result diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index c8598639d9fad..f3fe5a5a2d5d8 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -11,7 +11,7 @@ callable, map ) from pandas import compat - +from pandas.compat.numpy_compat import _np_version_under1p8 from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.categorical import Categorical @@ -2949,8 +2949,18 @@ def value_counts(self, normalize=False, sort=True, ascending=False, if normalize: out = out.astype('float') - acc = rep(np.diff(np.r_[idx, len(ids)])) - out /= acc[mask] if dropna else acc + d = np.diff(np.r_[idx, len(ids)]) + if dropna: + m = ids[lab == -1] + if _np_version_under1p8: + mi, ml = algos.factorize(m) + d[ml] = d[ml] - np.bincount(mi) + else: + np.add.at(d, m, -1) + acc = rep(d)[mask] + else: + acc = rep(d) + out /= acc if sort and bins is None: cat = ids[inc][mask] if dropna else ids[inc] diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py index b9a4384603cda..5c83cdb1493dc 100644 --- a/pandas/tests/test_algos.py +++ b/pandas/tests/test_algos.py @@ -517,6 +517,22 @@ def test_dropna(self): pd.Series([10.3, 5., 5., None]).value_counts(dropna=False), pd.Series([2, 1, 1], index=[5., 10.3, np.nan])) + def test_value_counts_normalized(self): + # GH12558 + s = Series([1, 2, np.nan, np.nan, np.nan]) + dtypes = (np.float64, np.object, 'M8[ns]') + for t in dtypes: + s_typed = s.astype(t) + result = s_typed.value_counts(normalize=True, dropna=False) + expected = Series([0.6, 0.2, 0.2], + index=Series([np.nan, 2.0, 1.0], dtype=t)) + tm.assert_series_equal(result, expected) + + result = s_typed.value_counts(normalize=True, dropna=True) + expected = Series([0.5, 0.5], + index=Series([2.0, 1.0], dtype=t)) + tm.assert_series_equal(result, expected) + class GroupVarTestMixin(object):
- [x] closes #12558 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12566
2016-03-09T00:29:03Z
2016-03-12T17:46:32Z
null
2016-03-12T17:46:56Z
Update asv config + fix some broken benchmarks
diff --git a/.gitignore b/.gitignore index 06ff0b90b8345..d987bab6fd5d7 100644 --- a/.gitignore +++ b/.gitignore @@ -83,7 +83,10 @@ scikits # Performance Testing # ####################### -asv_bench/ +asv_bench/env/ +asv_bench/html/ +asv_bench/results/ +asv_bench/pandas/ # Documentation generated files # ################################# diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json index 6a739873a032f..7b9fe353df2e3 100644 --- a/asv_bench/asv.conf.json +++ b/asv_bench/asv.conf.json @@ -30,24 +30,62 @@ // 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. + // list or empty string indicates to just test against the default + // (latest) version. null indicates that the package is to not be + // installed. If the package to be tested is only available from + // PyPi, and the 'environment_type' is conda, then you can preface + // the package name by 'pip+', and the package will be installed via + // pip (with all the conda available packages installed first, + // followed by the pip installed packages). "matrix": { - // To run against multiple versions, replace with - // "numpy": ["1.7", "1.9"], "numpy": [], "Cython": [], "matplotlib": [], "sqlalchemy": [], "scipy": [], "numexpr": [], - "pytables": [], + "pytables": [null, ""], // platform dependent, see excludes below + "tables": [null, ""], + "libpython": [null, ""], "openpyxl": [], "xlsxwriter": [], "xlrd": [], "xlwt": [] }, + // Combinations of libraries/python versions can be excluded/included + // from the set to test. Each entry is a dictionary containing additional + // key-value pairs to include/exclude. + // + // An exclude entry excludes entries where all values match. The + // values are regexps that should match the whole string. + // + // An include entry adds an environment. Only the packages listed + // are installed. The 'python' key is required. The exclude rules + // do not apply to includes. + // + // In addition to package names, the following keys are available: + // + // - python + // Python version, as in the *pythons* variable above. + // - environment_type + // Environment type, as above. + // - sys_platform + // Platform, as in sys.platform. Possible values for the common + // cases: 'linux2', 'win32', 'cygwin', 'darwin'. + "exclude": [ + // On conda install pytables, otherwise tables + {"environment_type": "conda", "tables": ""}, + {"environment_type": "conda", "pytables": null}, + {"environment_type": "virtualenv", "tables": null}, + {"environment_type": "virtualenv", "pytables": ""}, + // On conda&win32, install libpython + {"sys_platform": "(?!win32).*", "libpython": ""}, + {"sys_platform": "win32", "libpython": null}, + {"environment_type": "(?!conda).*", "libpython": ""} + ], + "include": [], + // The directory (relative to the current directory) that benchmarks are // stored in. If not provided, defaults to "benchmarks" // "benchmark_dir": "benchmarks", @@ -56,7 +94,6 @@ // 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", @@ -66,5 +103,23 @@ // "html_dir": "html", // The number of characters to retain in the commit hashes. - // "hash_length": 8 + // "hash_length": 8, + + // `asv` will cache wheels of the recent builds in each + // environment, making them faster to install next time. This is + // number of builds to keep, per environment. + "wheel_cache_size": 8, + + // The commits after which the regression search in `asv publish` + // should start looking for regressions. Dictionary whose keys are + // regexps matching to benchmark names, and values corresponding to + // the commit (exclusive) after which to start looking for + // regressions. The default is to start from the first commit + // with results. If the commit is `null`, regression detection is + // skipped for the matching benchmark. + // + // "regressions_first_commits": { + // "some_benchmark": "352cdf", // Consider regressions only after this commit + // "another_benchmark": null, // Skip regression detection altogether + // } } diff --git a/asv_bench/benchmarks/eval.py b/asv_bench/benchmarks/eval.py index 719d92567a7be..d9978e0cc4595 100644 --- a/asv_bench/benchmarks/eval.py +++ b/asv_bench/benchmarks/eval.py @@ -3,192 +3,36 @@ import pandas.computation.expressions as expr -class eval_frame_add_all_threads(object): +class eval_frame(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 + params = [['numexpr', 'python'], [1, 'all']] + param_names = ['engine', 'threads'] - 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): + def setup(self, engine, threads): 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') + if threads == 1: + expr.set_numexpr_threads(1) + def time_add(self, engine, threads): + df, df2, df3, df4 = self.df, self.df2, self.df3, self.df4 + pd.eval('df + df2 + df3 + df4', engine=engine) -class eval_frame_mult_all_threads(object): - goal_time = 0.2 + def time_and(self, engine, threads): + df, df2, df3, df4 = self.df, self.df2, self.df3, self.df4 + pd.eval('(df > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)', engine=engine) - 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_chained_cmp(self, engine, threads): + df, df2, df3, df4 = self.df, self.df2, self.df3, self.df4 + pd.eval('df < df2 < df3 < df4', engine=engine) - def time_eval_frame_mult_python_one_thread(self): - pd.eval('df * df2 * df3 * df4', engine='python') + def time_mult(self, engine, threads): + df, df2, df3, df4 = self.df, self.df2, self.df3, self.df4 + pd.eval('df * df2 * df3 * df4', engine=engine) class query_datetime_index(object): @@ -203,6 +47,7 @@ def setup(self): self.df = DataFrame({'a': np.random.randn(self.N), }, index=self.index) def time_query_datetime_index(self): + ts = self.ts self.df.query('index < @ts') @@ -218,6 +63,7 @@ def setup(self): self.df = DataFrame({'dates': self.s.values, }) def time_query_datetime_series(self): + ts = self.ts self.df.query('dates < @ts') @@ -236,4 +82,5 @@ def setup(self): 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 + min_val, max_val = self.min_val, self.max_val + self.df.query('(a >= @min_val) & (a <= @max_val)') diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py index 48480041ed1bd..7279d73eb0d97 100644 --- a/asv_bench/benchmarks/groupby.py +++ b/asv_bench/benchmarks/groupby.py @@ -254,7 +254,7 @@ def setup(self): 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.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), @@ -651,7 +651,7 @@ class groupby_sum_multiindex(object): 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']) + self.df = DataFrame({'A': (list(range(self.N)) * 2), 'B': list(range((self.N * 2))), 'C': 1, }).set_index(['A', 'B']) def time_groupby_sum_multiindex(self): self.df.groupby(level=[0, 1]).sum() @@ -673,9 +673,9 @@ def setup(self): 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.security_ids = map((lambda x: hex(x)[2:10].upper()), list(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 range(self.n_dates) for _ in range(self.n_securities)], (range(self.n_securities) * self.n_dates)], + labels=[[i for i in range(self.n_dates) for _ in range(self.n_securities)], (list(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 range(1, (self.n_columns + 1))]) diff --git a/asv_bench/benchmarks/packers.py b/asv_bench/benchmarks/packers.py index 74bec72eb05e9..3f80c4c0c6338 100644 --- a/asv_bench/benchmarks/packers.py +++ b/asv_bench/benchmarks/packers.py @@ -321,7 +321,9 @@ def remove(self, f): class packers_read_sas7bdat(object): def setup(self): - self.f = 'data/test1.sas7bdat' + self.f = os.path.join(os.path.dirname(__file__), '..', '..', + 'pandas', 'io', 'tests', 'sas', 'data', + 'test1.sas7bdat') def time_packers_read_sas7bdat(self): pd.read_sas(self.f, format='sas7bdat') @@ -330,7 +332,9 @@ def time_packers_read_sas7bdat(self): class packers_read_xport(object): def setup(self): - self.f = 'data/paxraw_d_short.xpt' + self.f = os.path.join(os.path.dirname(__file__), '..', '..', + 'pandas', 'io', 'tests', 'sas', 'data', + 'paxraw_d_short.xpt') def time_packers_read_xport(self): pd.read_sas(self.f, format='xport') diff --git a/asv_bench/benchmarks/plotting.py b/asv_bench/benchmarks/plotting.py index d2ef65ed27ef3..7a4a98e2195c2 100644 --- a/asv_bench/benchmarks/plotting.py +++ b/asv_bench/benchmarks/plotting.py @@ -11,6 +11,8 @@ class plot_timeseries_period(object): goal_time = 0.2 def setup(self): + import matplotlib + matplotlib.use('Agg') 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)) @@ -18,10 +20,13 @@ def setup(self): def time_plot_timeseries_period(self): self.df.plot() + class plot_andrews_curves(object): goal_time = 0.6 def setup(self): + import matplotlib + matplotlib.use('Agg') self.N = 500 self.M = 10 data_dict = {x: np.random.randn(self.N) for x in range(self.M)} diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst index 29e7cc96dcc87..e64ff4c155132 100644 --- a/doc/source/contributing.rst +++ b/doc/source/contributing.rst @@ -547,8 +547,8 @@ with an imported pandas to run tests similarly. Running the performance test suite ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Performance matters and it is worth considering whether your code has introduced -performance regressions. *pandas* is in the process of migrating to the -`asv library <https://github.com/spacetelescope/asv>`__ +performance regressions. *pandas* is in the process of migrating to +`asv benchmarks <https://github.com/spacetelescope/asv>`__ to enable easy monitoring of the performance of critical *pandas* operations. These benchmarks are all found in the ``pandas/asv_bench`` directory. asv supports both python2 and python3. @@ -559,8 +559,9 @@ supports both python2 and python3. so many stylistic issues are likely a result of automated transformation of the code. -To use asv you will need either ``conda`` or ``virtualenv``. For more details -please check the `asv installation webpage <http://asv.readthedocs.org/en/latest/installing.html>`_. +To use all features of asv, you will need either ``conda`` or +``virtualenv``. For more details please check the `asv installation +webpage <http://asv.readthedocs.org/en/latest/installing.html>`_. To install asv:: @@ -571,6 +572,14 @@ the following if you have been developing on ``master``:: asv continuous master +This command uses ``conda`` by default for creating the benchmark +environments. If you want to use virtualenv instead, write:: + + asv continuous -E virtualenv master + +The ``-E virtualenv`` option should be added to all ``asv`` commands +that run benchmarks. The default value is defined in ``asv.conf.json``. + If you are working on another branch, either of the following can be used:: asv continuous master HEAD @@ -595,17 +604,26 @@ using ``.`` as a separator. For example:: will only run a ``groupby_agg_builtins1`` test defined in a ``groupby`` file. -It can also be useful to run tests in your current environment. You can simply do it by:: +You can also run the benchmark suite using the version of ``pandas`` +already installed in your current Python environment. This can be +useful if you do not have virtualenv or conda, or are using the +``setup.py develop`` approach discussed above; for the in-place build +you need to set ``PYTHONPATH``, e.g. +``PYTHONPATH="$PWD/.." asv [remaining arguments]``. +You can run benchmarks using an existing Python +environment by:: - asv dev + asv run -e -E existing -This command is equivalent to:: +or, to use a specific Python interpreter,:: - asv run --quick --show-stderr --python=same + asv run -e -E existing:python3.5 -This will launch every test only once, display stderr from the benchmarks, and use your local ``python`` that comes from your ``$PATH``. +This will display stderr from the benchmarks, and use your local +``python`` that comes from your ``$PATH``. -Information on how to write a benchmark can be found in the `asv documentation <http://asv.readthedocs.org/en/latest/writing_benchmarks.html>`_. +Information on how to write a benchmark and how to use asv can be found in the +`asv documentation <http://asv.readthedocs.org/en/latest/writing_benchmarks.html>`_. Running the vbench performance test suite (phasing out) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Enable platform-dependent config in asv (needs asv git version for it to do something) - Enable wheel cache in asv (in asv git version) - Fix a few easily fixed broken benchmarks
https://api.github.com/repos/pandas-dev/pandas/pulls/12563
2016-03-08T20:23:58Z
2016-04-25T14:53:27Z
null
2016-04-25T15:07:16Z
CI: fix appveyor to use fixed versioning
diff --git a/appveyor.yml b/appveyor.yml index 650137b995121..7941820204916 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,9 +8,6 @@ matrix: fast_finish: true # immediately finish build once one of the jobs fails. -# set clone depth -clone_depth: 300 - environment: global: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the @@ -23,7 +20,7 @@ environment: PYTHON_VERSION: "3.4" PYTHON_ARCH: "64" CONDA_PY: "34" - CONDA_NPY: "110" + CONDA_NPY: "19" - PYTHON: "C:\\Python27_64" PYTHON_VERSION: "2.7" @@ -49,7 +46,8 @@ init: - "ECHO %PYTHON_VERSION% %PYTHON%" install: - # this installs the appropriate Miniconda (Py2/Py3, 32/64 bit), + # this installs the appropriate Miniconda (Py2/Py3, 32/64 bit) + # updates conda & installs: conda-build jinja2 anaconda-client - powershell .\ci\install.ps1 - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PATH% - echo "install" @@ -57,6 +55,9 @@ install: - ls -ltr - git tag --sort v:refname + # this can conflict with git + - cmd: rmdir C:\cygwin /s /q + # install our build environment - cmd: conda config --set show_channel_urls yes --set always_yes yes --set changeps1 no - cmd: conda update -q conda @@ -64,21 +65,22 @@ install: - cmd: conda config --set ssl_verify false # this is now the downloaded conda... - - conda info -a + - cmd: conda info -a # build em using the local source checkout in the correct windows env - - conda install conda-build - - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q --no-test' + - cmd: '%CMD_IN_ENV% conda build ci\appveyor.recipe -q' # create our env - - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run - cmd: conda create -q -n pandas python=%PYTHON_VERSION% nose - cmd: activate pandas - - cmd: conda install -q --file=%REQ% - - ps: conda install -q (conda build ci\appveyor.recipe -q --output --no-test) + - SET REQ=ci\requirements-%PYTHON_VERSION%-%PYTHON_ARCH%.run + - cmd: echo "installing requirements from %REQ%" + - cmd: conda install -n pandas -q --file=%REQ% + - ps: conda install -n pandas (conda build ci\appveyor.recipe -q --output) test_script: # tests - cd \ - - conda list pandas - - nosetests --exe -A "not slow and not network and not disabled" pandas + - cmd: activate pandas + - cmd: conda list + - cmd: nosetests --exe -A "not slow and not network and not disabled" pandas diff --git a/ci/appveyor.recipe/meta.yaml b/ci/appveyor.recipe/meta.yaml index c497acf33b6e0..ccba8a29784b5 100644 --- a/ci/appveyor.recipe/meta.yaml +++ b/ci/appveyor.recipe/meta.yaml @@ -1,10 +1,10 @@ package: name: pandas - version: {{ environ.get('GIT_DESCRIBE_TAG','') }} + version: 0.18.0 build: - number: {{ environ.get('GIT_DESCRIBE_NUMBER',0) }} - string: np{{ environ.get('CONDA_NPY') }}py{{ environ.get('CONDA_PY') }}_{{ environ.get('GIT_BUILD_STR','') }} + number: {{environ.get('APPVEYOR_BUILD_NUMBER', 0)}} # [win] + string: np{{ environ.get('CONDA_NPY') }}py{{ environ.get('CONDA_PY') }}_{{ environ.get('APPVEYOR_BUILD_NUMBER', 0) }} # [win] source: diff --git a/ci/install.ps1 b/ci/install.ps1 index c964973c67693..16c92dc76d273 100644 --- a/ci/install.ps1 +++ b/ci/install.ps1 @@ -7,11 +7,7 @@ $MINICONDA_URL = "http://repo.continuum.io/miniconda/" function DownloadMiniconda ($python_version, $platform_suffix) { $webclient = New-Object System.Net.WebClient - if ($python_version -match "3.4") { - $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe" - } else { - $filename = "Miniconda-latest-Windows-" + $platform_suffix + ".exe" - } + $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe" $url = $MINICONDA_URL + $filename $basedir = $pwd.Path + "\" diff --git a/ci/requirements-2.7-64.run b/ci/requirements-2.7-64.run index 260ae8125e040..c152c7160c9fc 100644 --- a/ci/requirements-2.7-64.run +++ b/ci/requirements-2.7-64.run @@ -1,6 +1,6 @@ dateutil pytz -numpy +numpy=1.10* xlwt numexpr pytables diff --git a/ci/requirements-3.4-64.run b/ci/requirements-3.4-64.run index 5eb8a5666fa74..e90722360d428 100644 --- a/ci/requirements-3.4-64.run +++ b/ci/requirements-3.4-64.run @@ -1,6 +1,6 @@ python-dateutil pytz -numpy +numpy=1.9* openpyxl xlsxwriter xlrd diff --git a/ci/requirements-3.5-64.run b/ci/requirements-3.5-64.run index 6beeb2fc31369..81565f2467b69 100644 --- a/ci/requirements-3.5-64.run +++ b/ci/requirements-3.5-64.run @@ -1,6 +1,6 @@ python-dateutil pytz -numpy +numpy=1.10* openpyxl xlsxwriter xlrd
since the GIT_\* env variables are not available
https://api.github.com/repos/pandas-dev/pandas/pulls/12561
2016-03-08T17:43:06Z
2016-03-08T18:19:34Z
null
2016-03-08T18:19:34Z
BUG: SeriesGroupby.nunique raises an IndexError on empty Series #12553
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index bea62e98e4a2a..0cf01a0eba71e 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2843,6 +2843,10 @@ def true_and_notnull(x, *args, **kwargs): def nunique(self, dropna=True): """ Returns number of unique elements in the group """ ids, _, _ = self.grouper.group_info + + if len(ids) == 0: # bugfix for 12553 + return Series(name=self.name) + val = self.obj.get_values() try: diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 6659e6b106a67..7e8d2869f574c 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -6465,6 +6465,14 @@ def testit(label_list, shape): testit(label_list, shape) +def test_nunique_empty_Series(): + series = Series(name='foo') + group = series.groupby(level=0) + result = group.nunique() + expected = Series(name='foo') + assert_series_equal(result, expected) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure', '-s' ], exit=False)
Closes #12553 xref #13239 Made a new pull request to clean up from #12557
https://api.github.com/repos/pandas-dev/pandas/pulls/12560
2016-03-08T08:48:49Z
2016-12-16T23:57:36Z
null
2016-12-16T23:57:36Z
do not upcast results to float64 when float32 scalar *+/- float64 array
diff --git a/doc/source/whatsnew/v0.18.2.txt b/doc/source/whatsnew/v0.18.2.txt index dfb5ebc9379b1..6421f31c0267e 100644 --- a/doc/source/whatsnew/v0.18.2.txt +++ b/doc/source/whatsnew/v0.18.2.txt @@ -89,6 +89,7 @@ Other enhancements - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`) +- ``eval``'s upcasting rules for ``float32`` types have also been updated to be more consistent with numpy's rules. New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`) .. _whatsnew_0182.api: diff --git a/pandas/computation/expr.py b/pandas/computation/expr.py index 01d0fa664ac41..f1cf210754d12 100644 --- a/pandas/computation/expr.py +++ b/pandas/computation/expr.py @@ -5,6 +5,7 @@ import tokenize from functools import partial +import numpy as np import pandas as pd from pandas import compat @@ -356,6 +357,19 @@ def _possibly_transform_eq_ne(self, node, left=None, right=None): right) return op, op_class, left, right + def _possibly_downcast_constants(self, left, right): + f32 = np.dtype(np.float32) + if left.isscalar and not right.isscalar and right.return_type == f32: + # right is a float32 array, left is a scalar + name = self.env.add_tmp(np.float32(left.value)) + left = self.term_type(name, self.env) + if right.isscalar and not left.isscalar and left.return_type == f32: + # left is a float32 array, right is a scalar + name = self.env.add_tmp(np.float32(right.value)) + right = self.term_type(name, self.env) + + return left, right + def _possibly_eval(self, binop, eval_in_python): # eval `in` and `not in` (for now) in "partial" python space # things that can be evaluated in "eval" space will be turned into @@ -399,6 +413,7 @@ def _possibly_evaluate_binop(self, op, op_class, lhs, rhs, def visit_BinOp(self, node, **kwargs): op, op_class, left, right = self._possibly_transform_eq_ne(node) + left, right = self._possibly_downcast_constants(left, right) return self._possibly_evaluate_binop(op, op_class, left, right) def visit_Div(self, node, **kwargs): diff --git a/pandas/computation/ops.py b/pandas/computation/ops.py index 603c030dcaa6e..20976b82452dc 100644 --- a/pandas/computation/ops.py +++ b/pandas/computation/ops.py @@ -276,18 +276,25 @@ def _not_in(x, y): _binary_ops_dict.update(d) -def _cast_inplace(terms, dtype): +def _cast_inplace(terms, acceptable_dtypes, dtype): """Cast an expression inplace. + .. versionadded:: 0.18.2 + Parameters ---------- terms : Op The expression that should cast. + acceptable_dtypes : list of acceptable numpy.dtype + Will not cast if term's dtype in this list. dtype : str or numpy.dtype The dtype to cast to. """ dt = np.dtype(dtype) for term in terms: + if term.type in acceptable_dtypes: + continue + try: new_value = term.value.astype(dt) except AttributeError: @@ -452,7 +459,9 @@ def __init__(self, lhs, rhs, truediv, *args, **kwargs): rhs.return_type)) if truediv or PY3: - _cast_inplace(com.flatten(self), np.float_) + # do not upcast float32s to float64 un-necessarily + acceptable_dtypes = [np.float32, np.float_] + _cast_inplace(com.flatten(self), acceptable_dtypes, np.float_) _unary_ops_syms = '+', '-', '~', 'not' diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index 023519fd7fc20..bbcdaf857364c 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -739,6 +739,35 @@ def check_chained_cmp_op(self, lhs, cmp1, mid, cmp2, rhs): ENGINES_PARSERS = list(product(_engines, expr._parsers)) +#------------------------------------- +# typecasting rules consistency with python +# issue #12388 + +class TestTypeCasting(tm.TestCase): + + def check_binop_typecasting(self, engine, parser, op, dt): + tm.skip_if_no_ne(engine) + df = mkdf(5, 3, data_gen_f=f, dtype=dt) + s = 'df {} 3'.format(op) + res = pd.eval(s, engine=engine, parser=parser) + self.assertTrue(df.values.dtype == dt) + self.assertTrue(res.values.dtype == dt) + assert_frame_equal(res, eval(s)) + + s = '3 {} df'.format(op) + res = pd.eval(s, engine=engine, parser=parser) + self.assertTrue(df.values.dtype == dt) + self.assertTrue(res.values.dtype == dt) + assert_frame_equal(res, eval(s)) + + def test_binop_typecasting(self): + for engine, parser in ENGINES_PARSERS: + for op in ['+', '-', '*', '**', '/']: + # maybe someday... numexpr has too many upcasting rules now + #for dt in chain(*(np.sctypes[x] for x in ['uint', 'int', 'float'])): + for dt in [np.float32, np.float64]: + yield self.check_binop_typecasting, engine, parser, op, dt + #------------------------------------- # basic and complex alignment
- [x] closes #12388 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry
https://api.github.com/repos/pandas-dev/pandas/pulls/12559
2016-03-08T07:36:45Z
2016-05-30T14:39:34Z
null
2016-05-30T14:39:39Z
BUG: Fix for issue #12553
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py index 442f2132847ee..377f758926899 100644 --- a/pandas/core/groupby.py +++ b/pandas/core/groupby.py @@ -2790,6 +2790,10 @@ def true_and_notnull(x, *args, **kwargs): def nunique(self, dropna=True): """ Returns number of unique elements in the group """ ids, _, _ = self.grouper.group_info + + if len(ids) == 0: # bugfix for 12553 + return Series([]) + val = self.obj.get_values() try:
closes #12553
https://api.github.com/repos/pandas-dev/pandas/pulls/12557
2016-03-07T19:16:34Z
2016-03-08T08:43:56Z
null
2016-03-08T13:14:29Z
Added pandas logo and separator
diff --git a/README.md b/README.md index 6295e7374e1ee..54c772da4d270 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,8 @@ +<div align="center"> + <img src="http://pandas.pydata.org/_static/pandas_logo.png"><br> +</div> +----------------- + # pandas: powerful Python data analysis toolkit <table>
- Added pandas logo and separator at the top of page
https://api.github.com/repos/pandas-dev/pandas/pulls/12555
2016-03-07T17:25:53Z
2016-03-07T17:30:36Z
null
2016-03-07T17:30:36Z
BUG: Respect 'usecols' parameter even when CSV rows are uneven
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 2545407ce43c9..d9983759083ca 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -55,6 +55,12 @@ API changes +- ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) + + + + + @@ -95,6 +101,7 @@ Performance Improvements Bug Fixes ~~~~~~~~~ +- ``usecols`` parameter in ``pd.read_csv`` is now respected even when the lines of a CSV file are not even (:issue:`12203`) - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index fa9a5cf12570d..36a9abdfbca60 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -1984,7 +1984,9 @@ def _rows_to_cols(self, content): raise ValueError('skip footer cannot be negative') # Loop through rows to verify lengths are correct. - if col_len != zip_len and self.index_col is not False: + if (col_len != zip_len and + self.index_col is not False and + self.usecols is None): i = 0 for (i, l) in enumerate(content): if len(l) != col_len: diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 700ec3387d459..9f53fc1ded882 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2664,6 +2664,37 @@ def test_empty_header_read(count): for count in range(1, 101): test_empty_header_read(count) + def test_uneven_lines_with_usecols(self): + # See gh-12203 + csv = r"""a,b,c + 0,1,2 + 3,4,5,6,7 + 8,9,10 + """ + + # make sure that an error is still thrown + # when the 'usecols' parameter is not provided + msg = "Expected \d+ fields in line \d+, saw \d+" + with tm.assertRaisesRegexp(ValueError, msg): + df = self.read_csv(StringIO(csv)) + + expected = DataFrame({ + 'a': [0, 3, 8], + 'b': [1, 4, 9] + }) + + usecols = [0, 1] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['a', 1] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['a', 'b'] + df = self.read_csv(StringIO(csv), usecols=usecols) + tm.assert_frame_equal(df, expected) + class TestPythonParser(ParserTests, tm.TestCase): diff --git a/pandas/parser.pyx b/pandas/parser.pyx index f9b8d921f02d1..e2ba8d9d07ae2 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -143,6 +143,8 @@ cdef extern from "parser/tokenizer.h": int allow_embedded_newline int strict # raise exception on bad CSV */ + int usecols + int expected_fields int error_bad_lines int warn_bad_lines @@ -350,6 +352,8 @@ cdef class TextReader: self.compression = compression self.memory_map = memory_map + self.parser.usecols = (usecols is not None) + self._setup_parser_source(source) parser_set_default_options(self.parser) @@ -1208,7 +1212,7 @@ cdef class TextReader: else: return None -class CParserError(Exception): +class CParserError(ValueError): pass diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index dae15215929b7..a75ce2bde80e6 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -494,7 +494,8 @@ static int end_line(parser_t *self) { /* printf("Line: %d, Fields: %d, Ex-fields: %d\n", self->lines, fields, ex_fields); */ if (!(self->lines <= self->header_end + 1) - && (self->expected_fields < 0 && fields > ex_fields)) { + && (self->expected_fields < 0 && fields > ex_fields) + && !(self->usecols)) { // increment file line count self->file_lines++; diff --git a/pandas/src/parser/tokenizer.h b/pandas/src/parser/tokenizer.h index a2d7925df08e2..2d1b7fae58714 100644 --- a/pandas/src/parser/tokenizer.h +++ b/pandas/src/parser/tokenizer.h @@ -184,6 +184,8 @@ typedef struct parser_t { int allow_embedded_newline; int strict; /* raise exception on bad CSV */ + int usecols; // Boolean: 1: usecols provided, 0: none provided + int expected_fields; int error_bad_lines; int warn_bad_lines;
Closes #12203 by overriding the row alignment checks for both engines when the `usecols` parameter is passed into `read_csv`.
https://api.github.com/repos/pandas-dev/pandas/pulls/12551
2016-03-07T10:28:42Z
2016-03-20T15:10:03Z
null
2016-03-20T15:16:53Z
TST: Add more period tests
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py index 89ef4decfda39..d301016aa1316 100644 --- a/pandas/tests/test_groupby.py +++ b/pandas/tests/test_groupby.py @@ -3993,11 +3993,13 @@ def test_groupby_groups_datetimeindex_tz(self): df['datetime'] = df['datetime'].apply( lambda d: Timestamp(d, tz='US/Pacific')) - exp_idx1 = pd.DatetimeIndex( - ['2011-07-19 07:00:00', '2011-07-19 07:00:00', - '2011-07-19 08:00:00', '2011-07-19 08:00:00', - '2011-07-19 09:00:00', '2011-07-19 09:00:00'], - tz='US/Pacific', name='datetime') + exp_idx1 = pd.DatetimeIndex(['2011-07-19 07:00:00', + '2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00', + '2011-07-19 09:00:00'], + tz='US/Pacific', name='datetime') exp_idx2 = Index(['a', 'b'] * 3, name='label') exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], @@ -4013,9 +4015,9 @@ def test_groupby_groups_datetimeindex_tz(self): 'value2': [1, 2, 3, 1, 2, 3]}, index=didx) - exp_idx = pd.DatetimeIndex( - ['2011-07-19 07:00:00', '2011-07-19 08:00:00', - '2011-07-19 09:00:00'], tz='Asia/Tokyo') + exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00'], tz='Asia/Tokyo') expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, index=exp_idx, columns=['value1', 'value2']) @@ -4032,8 +4034,8 @@ def test_groupby_multi_timezone(self): 3,2000-01-31 16:50:00,America/Chicago 4,2000-01-01 16:50:00,America/New_York""" - df = pd.read_csv( - StringIO(data), header=None, names=['value', 'date', 'tz']) + df = pd.read_csv(StringIO(data), header=None, + names=['value', 'date', 'tz']) result = df.groupby('tz').date.apply( lambda x: pd.to_datetime(x).dt.tz_localize(x.name)) @@ -4051,14 +4053,54 @@ def test_groupby_multi_timezone(self): assert_series_equal(result, expected) tz = 'America/Chicago' - result = pd.to_datetime(df.groupby('tz').date.get_group( - tz)).dt.tz_localize(tz) - expected = pd.to_datetime(Series( - ['2000-01-28 16:47:00', '2000-01-29 16:48:00', - '2000-01-31 16:50:00'], index=[0, 1, 3 - ], name='date')).dt.tz_localize(tz) + res_values = df.groupby('tz').date.get_group(tz) + result = pd.to_datetime(res_values).dt.tz_localize(tz) + exp_values = Series(['2000-01-28 16:47:00', '2000-01-29 16:48:00', + '2000-01-31 16:50:00'], + index=[0, 1, 3], name='date') + expected = pd.to_datetime(exp_values).dt.tz_localize(tz) assert_series_equal(result, expected) + def test_groupby_groups_periods(self): + dates = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', + '2011-07-19 09:00:00', '2011-07-19 07:00:00', + '2011-07-19 08:00:00', '2011-07-19 09:00:00'] + df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'], + 'period': [pd.Period(d, freq='H') for d in dates], + 'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2] * 3}) + + exp_idx1 = pd.PeriodIndex(['2011-07-19 07:00:00', + '2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00', + '2011-07-19 09:00:00'], + freq='H', name='period') + exp_idx2 = Index(['a', 'b'] * 3, name='label') + exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2]) + expected = DataFrame({'value1': [0, 3, 1, 4, 2, 5], + 'value2': [1, 2, 2, 1, 1, 2]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(['period', 'label']).sum() + assert_frame_equal(result, expected) + + # by level + didx = pd.PeriodIndex(dates, freq='H') + df = DataFrame({'value1': np.arange(6, dtype='int64'), + 'value2': [1, 2, 3, 1, 2, 3]}, + index=didx) + + exp_idx = pd.PeriodIndex(['2011-07-19 07:00:00', + '2011-07-19 08:00:00', + '2011-07-19 09:00:00'], freq='H') + expected = DataFrame({'value1': [3, 5, 7], 'value2': [2, 4, 6]}, + index=exp_idx, columns=['value1', 'value2']) + + result = df.groupby(level=0).sum() + assert_frame_equal(result, expected) + def test_groupby_reindex_inside_function(self): from pandas.tseries.api import DatetimeIndex diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 47eea60b2b496..d5ddfe624e240 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -1031,6 +1031,36 @@ def test_merge_on_datetime64tz(self): result = pd.merge(left, right, on='key', how='outer') assert_frame_equal(result, expected) + def test_merge_on_periods(self): + left = pd.DataFrame({'key': pd.period_range('20151010', periods=2, + freq='D'), + 'value': [1, 2]}) + right = pd.DataFrame({'key': pd.period_range('20151011', periods=3, + freq='D'), + 'value': [1, 2, 3]}) + + expected = DataFrame({'key': pd.period_range('20151010', periods=4, + freq='D'), + 'value_x': [1, 2, np.nan, np.nan], + 'value_y': [np.nan, 1, 2, 3]}) + result = pd.merge(left, right, on='key', how='outer') + assert_frame_equal(result, expected) + + left = pd.DataFrame({'value': pd.period_range('20151010', periods=2, + freq='D'), + 'key': [1, 2]}) + right = pd.DataFrame({'value': pd.period_range('20151011', periods=2, + freq='D'), + 'key': [2, 3]}) + + exp_x = pd.period_range('20151010', periods=2, freq='D') + exp_y = pd.period_range('20151011', periods=2, freq='D') + expected = DataFrame({'value_x': list(exp_x) + [pd.NaT], + 'value_y': [pd.NaT] + list(exp_y), + 'key': [1., 2, 3]}) + result = pd.merge(left, right, on='key', how='outer') + assert_frame_equal(result, expected) + def test_concat_NaT_series(self): # GH 11693 # test for merging NaT series with datetime series. @@ -1131,6 +1161,39 @@ def test_concat_tz_series(self): result = pd.concat([first, second]) self.assertEqual(result[0].dtype, 'datetime64[ns, Europe/London]') + def test_concat_period_series(self): + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='D')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # different freq + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-10-01', '2016-01-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='M')) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + # non-period + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(pd.DatetimeIndex(['2015-11-01', '2015-12-01'])) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + + x = Series(pd.PeriodIndex(['2015-11-01', '2015-12-01'], freq='D')) + y = Series(['A', 'B']) + expected = Series([x[0], x[1], y[0], y[1]], dtype='object') + result = concat([x, y], ignore_index=True) + tm.assert_series_equal(result, expected) + def test_indicator(self): # PR #10054. xref #7412 and closes #8790. df1 = DataFrame({'col1': [0, 1], 'col_left': [ diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 845f50aa65d70..994269d36cd85 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -240,6 +240,39 @@ def test_pivot_with_tz(self): pv = df.pivot(index='dt1', columns='dt2', values='data1') tm.assert_frame_equal(pv, expected) + def test_pivot_periods(self): + df = DataFrame({'p1': [pd.Period('2013-01-01', 'D'), + pd.Period('2013-01-02', 'D'), + pd.Period('2013-01-01', 'D'), + pd.Period('2013-01-02', 'D')], + 'p2': [pd.Period('2013-01', 'M'), + pd.Period('2013-01', 'M'), + pd.Period('2013-02', 'M'), + pd.Period('2013-02', 'M')], + 'data1': np.arange(4, dtype='int64'), + 'data2': np.arange(4, dtype='int64')}) + + exp_col1 = Index(['data1', 'data1', 'data2', 'data2']) + exp_col2 = pd.PeriodIndex(['2013-01', '2013-02'] * 2, + name='p2', freq='M') + exp_col = pd.MultiIndex.from_arrays([exp_col1, exp_col2]) + expected = DataFrame([[0, 2, 0, 2], [1, 3, 1, 3]], + index=pd.PeriodIndex(['2013-01-01', '2013-01-02'], + name='p1', freq='D'), + columns=exp_col) + + pv = df.pivot(index='p1', columns='p2') + tm.assert_frame_equal(pv, expected) + + expected = DataFrame([[0, 2], [1, 3]], + index=pd.PeriodIndex(['2013-01-01', '2013-01-02'], + name='p1', freq='D'), + columns=pd.PeriodIndex(['2013-01', '2013-02'], + name='p2', freq='M')) + + pv = df.pivot(index='p1', columns='p2', values='data1') + tm.assert_frame_equal(pv, expected) + def test_margins(self): def _check_output(result, values_col, index=['A', 'B'], columns=['C'], diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index ee6f1a8b0e2db..95d84bba4b5db 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -2877,6 +2877,17 @@ def test_union(self): index3 = period_range('1/1/2000', '1/20/2000', freq='2D') self.assertRaises(ValueError, index.join, index3) + def test_union_dataframe_index(self): + rng1 = pd.period_range('1/1/1999', '1/1/2012', freq='M') + s1 = pd.Series(np.random.randn(len(rng1)), rng1) + + rng2 = pd.period_range('1/1/1980', '12/1/2001', freq='M') + s2 = pd.Series(np.random.randn(len(rng2)), rng2) + df = pd.DataFrame({'s1': s1, 's2': s2}) + + exp = pd.period_range('1/1/1980', '1/1/2012', freq='M') + self.assert_index_equal(df.index, exp) + def test_intersection(self): index = period_range('1/1/2000', '1/20/2000', freq='D') @@ -2897,6 +2908,63 @@ def test_intersection(self): index3 = period_range('1/1/2000', '1/20/2000', freq='2D') self.assertRaises(ValueError, index.intersection, index3) + def test_intersection_cases(self): + base = period_range('6/1/2000', '6/30/2000', freq='D', name='idx') + + # if target has the same name, it is preserved + rng2 = period_range('5/15/2000', '6/20/2000', freq='D', name='idx') + expected2 = period_range('6/1/2000', '6/20/2000', freq='D', + name='idx') + + # if target name is different, it will be reset + rng3 = period_range('5/15/2000', '6/20/2000', freq='D', name='other') + expected3 = period_range('6/1/2000', '6/20/2000', freq='D', + name=None) + + rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = PeriodIndex([], name='idx', freq='D') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, expected.freq) + + # non-monotonic + base = PeriodIndex(['2011-01-05', '2011-01-04', '2011-01-02', + '2011-01-03'], freq='D', name='idx') + + rng2 = PeriodIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + freq='D', name='idx') + expected2 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', + name='idx') + + rng3 = PeriodIndex(['2011-01-04', '2011-01-02', '2011-02-02', + '2011-02-03'], + freq='D', name='other') + expected3 = PeriodIndex(['2011-01-04', '2011-01-02'], freq='D', + name=None) + + rng4 = period_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = PeriodIndex([], freq='D', name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, 'D') + + # empty same freq + rng = date_range('6/1/2000', '6/15/2000', freq='T') + result = rng[0:0].intersection(rng) + self.assertEqual(len(result), 0) + + result = rng.intersection(rng[0:0]) + self.assertEqual(len(result), 0) + def test_fields(self): # year, month, day, hour, minute # second, weekofyear, week, dayofweek, weekday, dayofyear, quarter @@ -3734,6 +3802,86 @@ def test_pi_nat_comp(self): idx1 == diff +class TestSeriesPeriod(tm.TestCase): + + def setUp(self): + self.series = Series(period_range('2000-01-01', periods=10, freq='D')) + + def test_auto_conversion(self): + series = Series(list(period_range('2000-01-01', periods=10, freq='D'))) + self.assertEqual(series.dtype, 'object') + + def test_constructor_cant_cast_period(self): + with tm.assertRaises(TypeError): + Series(period_range('2000-01-01', periods=10, freq='D'), + dtype=float) + + def test_series_comparison_scalars(self): + val = pd.Period('2000-01-04', freq='D') + result = self.series > val + expected = np.array([x > val for x in self.series]) + self.assert_numpy_array_equal(result, expected) + + val = self.series[5] + result = self.series > val + expected = np.array([x > val for x in self.series]) + self.assert_numpy_array_equal(result, expected) + + def test_between(self): + left, right = self.series[[2, 7]] + result = self.series.between(left, right) + expected = (self.series >= left) & (self.series <= right) + assert_series_equal(result, expected) + + # --------------------------------------------------------------------- + # NaT support + + """ + # ToDo: Enable when support period dtype + def test_NaT_scalar(self): + series = Series([0, 1000, 2000, iNaT], dtype='period[D]') + + val = series[3] + self.assertTrue(com.isnull(val)) + + series[2] = val + self.assertTrue(com.isnull(series[2])) + + def test_NaT_cast(self): + result = Series([np.nan]).astype('period[D]') + expected = Series([NaT]) + assert_series_equal(result, expected) + """ + + def test_set_none_nan(self): + # currently Period is stored as object dtype, not as NaT + self.series[3] = None + self.assertIs(self.series[3], None) + + self.series[3:5] = None + self.assertIs(self.series[4], None) + + self.series[5] = np.nan + self.assertTrue(np.isnan(self.series[5])) + + self.series[5:7] = np.nan + self.assertTrue(np.isnan(self.series[6])) + + def test_intercept_astype_object(self): + expected = self.series.astype('object') + + df = DataFrame({'a': self.series, + 'b': np.random.randn(len(self.series))}) + + result = df.values.squeeze() + self.assertTrue((result[:, 0] == expected.values).all()) + + df = DataFrame({'a': self.series, 'b': ['foo'] * len(self.series)}) + + result = df.values.squeeze() + self.assertTrue((result[:, 0] == expected.values).all()) + + if __name__ == '__main__': import nose nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py index b83c51b6a3ab6..6167edd9499ab 100644 --- a/pandas/tseries/tests/test_timeseries.py +++ b/pandas/tseries/tests/test_timeseries.py @@ -1325,6 +1325,47 @@ def test_date_range_negative_freq(self): self.assert_index_equal(rng, exp) self.assertEqual(rng.freq, '-2M') + def test_date_range_bms_bug(self): + # #1645 + rng = date_range('1/1/2000', periods=10, freq='BMS') + + ex_first = Timestamp('2000-01-03') + self.assertEqual(rng[0], ex_first) + + def test_date_range_businesshour(self): + idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', + '2014-07-04 11:00', + '2014-07-04 12:00', '2014-07-04 13:00', + '2014-07-04 14:00', + '2014-07-04 15:00', '2014-07-04 16:00'], + freq='BH') + rng = date_range('2014-07-04 09:00', '2014-07-04 16:00', freq='BH') + tm.assert_index_equal(idx, rng) + + idx = DatetimeIndex( + ['2014-07-04 16:00', '2014-07-07 09:00'], freq='BH') + rng = date_range('2014-07-04 16:00', '2014-07-07 09:00', freq='BH') + tm.assert_index_equal(idx, rng) + + idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', + '2014-07-04 11:00', + '2014-07-04 12:00', '2014-07-04 13:00', + '2014-07-04 14:00', + '2014-07-04 15:00', '2014-07-04 16:00', + '2014-07-07 09:00', '2014-07-07 10:00', + '2014-07-07 11:00', + '2014-07-07 12:00', '2014-07-07 13:00', + '2014-07-07 14:00', + '2014-07-07 15:00', '2014-07-07 16:00', + '2014-07-08 09:00', '2014-07-08 10:00', + '2014-07-08 11:00', + '2014-07-08 12:00', '2014-07-08 13:00', + '2014-07-08 14:00', + '2014-07-08 15:00', '2014-07-08 16:00'], + freq='BH') + rng = date_range('2014-07-04 09:00', '2014-07-08 16:00', freq='BH') + tm.assert_index_equal(idx, rng) + def test_first_subset(self): ts = _simple_ts('1/1/2000', '1/1/2010', freq='12h') result = ts.first('10d') @@ -2716,6 +2757,26 @@ def test_union_bug_4564(self): exp = DatetimeIndex(sorted(set(list(left)) | set(list(right)))) self.assertTrue(result.equals(exp)) + def test_union_freq_both_none(self): + # GH11086 + expected = bdate_range('20150101', periods=10) + expected.freq = None + + result = expected.union(expected) + tm.assert_index_equal(result, expected) + self.assertIsNone(result.freq) + + def test_union_dataframe_index(self): + rng1 = date_range('1/1/1999', '1/1/2012', freq='MS') + s1 = Series(np.random.randn(len(rng1)), rng1) + + rng2 = date_range('1/1/1980', '12/1/2001', freq='MS') + s2 = Series(np.random.randn(len(rng2)), rng2) + df = DataFrame({'s1': s1, 's2': s2}) + + exp = pd.date_range('1/1/1980', '1/1/2012', freq='MS') + self.assert_index_equal(df.index, exp) + def test_intersection_bug_1708(self): from pandas import DateOffset index_1 = date_range('1/1/2012', periods=4, freq='12H') @@ -2724,14 +2785,80 @@ def test_intersection_bug_1708(self): result = index_1 & index_2 self.assertEqual(len(result), 0) - def test_union_freq_both_none(self): - # GH11086 - expected = bdate_range('20150101', periods=10) - expected.freq = None + def test_intersection(self): + # GH 4690 (with tz) + for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Pacific']: + base = date_range('6/1/2000', '6/30/2000', freq='D', name='idx') - result = expected.union(expected) - tm.assert_index_equal(result, expected) - self.assertIsNone(result.freq) + # if target has the same name, it is preserved + rng2 = date_range('5/15/2000', '6/20/2000', freq='D', name='idx') + expected2 = date_range('6/1/2000', '6/20/2000', freq='D', + name='idx') + + # if target name is different, it will be reset + rng3 = date_range('5/15/2000', '6/20/2000', freq='D', name='other') + expected3 = date_range('6/1/2000', '6/20/2000', freq='D', + name=None) + + rng4 = date_range('7/1/2000', '7/31/2000', freq='D', name='idx') + expected4 = DatetimeIndex([], name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertEqual(result.freq, expected.freq) + self.assertEqual(result.tz, expected.tz) + + # non-monotonic + base = DatetimeIndex(['2011-01-05', '2011-01-04', + '2011-01-02', '2011-01-03'], + tz=tz, name='idx') + + rng2 = DatetimeIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + tz=tz, name='idx') + expected2 = DatetimeIndex( + ['2011-01-04', '2011-01-02'], tz=tz, name='idx') + + rng3 = DatetimeIndex(['2011-01-04', '2011-01-02', + '2011-02-02', '2011-02-03'], + tz=tz, name='other') + expected3 = DatetimeIndex( + ['2011-01-04', '2011-01-02'], tz=tz, name=None) + + # GH 7880 + rng4 = date_range('7/1/2000', '7/31/2000', freq='D', tz=tz, + name='idx') + expected4 = DatetimeIndex([], tz=tz, name='idx') + + for (rng, expected) in [(rng2, expected2), (rng3, expected3), + (rng4, expected4)]: + result = base.intersection(rng) + self.assertTrue(result.equals(expected)) + self.assertEqual(result.name, expected.name) + self.assertIsNone(result.freq) + self.assertEqual(result.tz, expected.tz) + + # empty same freq GH2129 + rng = date_range('6/1/2000', '6/15/2000', freq='T') + result = rng[0:0].intersection(rng) + self.assertEqual(len(result), 0) + + result = rng.intersection(rng[0:0]) + self.assertEqual(len(result), 0) + + def test_string_index_series_name_converted(self): + # #1644 + df = DataFrame(np.random.randn(10, 4), + index=date_range('1/1/2000', periods=10)) + + result = df.ix['1/3/2000'] + self.assertEqual(result.name, df.index[2]) + + result = df.T['1/3/2000'] + self.assertEqual(result.name, df.index[2]) # GH 10699 def test_datetime64_with_DateOffset(self): @@ -3823,131 +3950,6 @@ def test_intercept_astype_object(self): result = df.values.squeeze() self.assertTrue((result[:, 0] == expected.values).all()) - def test_union(self): - rng1 = date_range('1/1/1999', '1/1/2012', freq='MS') - s1 = Series(np.random.randn(len(rng1)), rng1) - - rng2 = date_range('1/1/1980', '12/1/2001', freq='MS') - s2 = Series(np.random.randn(len(rng2)), rng2) - df = DataFrame({'s1': s1, 's2': s2}) - self.assertEqual(df.index.values.dtype, np.dtype('M8[ns]')) - - def test_intersection(self): - # GH 4690 (with tz) - for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Pacific']: - base = date_range('6/1/2000', '6/30/2000', freq='D', name='idx') - - # if target has the same name, it is preserved - rng2 = date_range('5/15/2000', '6/20/2000', freq='D', name='idx') - expected2 = date_range('6/1/2000', '6/20/2000', freq='D', - name='idx') - - # if target name is different, it will be reset - rng3 = date_range('5/15/2000', '6/20/2000', freq='D', name='other') - expected3 = date_range('6/1/2000', '6/20/2000', freq='D', - name=None) - - rng4 = date_range('7/1/2000', '7/31/2000', freq='D', name='idx') - expected4 = DatetimeIndex([], name='idx') - - for (rng, expected) in [(rng2, expected2), (rng3, expected3), - (rng4, expected4)]: - result = base.intersection(rng) - self.assertTrue(result.equals(expected)) - self.assertEqual(result.name, expected.name) - self.assertEqual(result.freq, expected.freq) - self.assertEqual(result.tz, expected.tz) - - # non-monotonic - base = DatetimeIndex(['2011-01-05', '2011-01-04', - '2011-01-02', '2011-01-03'], - tz=tz, name='idx') - - rng2 = DatetimeIndex(['2011-01-04', '2011-01-02', - '2011-02-02', '2011-02-03'], - tz=tz, name='idx') - expected2 = DatetimeIndex( - ['2011-01-04', '2011-01-02'], tz=tz, name='idx') - - rng3 = DatetimeIndex(['2011-01-04', '2011-01-02', - '2011-02-02', '2011-02-03'], - tz=tz, name='other') - expected3 = DatetimeIndex( - ['2011-01-04', '2011-01-02'], tz=tz, name=None) - - # GH 7880 - rng4 = date_range('7/1/2000', '7/31/2000', freq='D', tz=tz, - name='idx') - expected4 = DatetimeIndex([], tz=tz, name='idx') - - for (rng, expected) in [(rng2, expected2), (rng3, expected3), - (rng4, expected4)]: - result = base.intersection(rng) - self.assertTrue(result.equals(expected)) - self.assertEqual(result.name, expected.name) - self.assertIsNone(result.freq) - self.assertEqual(result.tz, expected.tz) - - # empty same freq GH2129 - rng = date_range('6/1/2000', '6/15/2000', freq='T') - result = rng[0:0].intersection(rng) - self.assertEqual(len(result), 0) - - result = rng.intersection(rng[0:0]) - self.assertEqual(len(result), 0) - - def test_date_range_bms_bug(self): - # #1645 - rng = date_range('1/1/2000', periods=10, freq='BMS') - - ex_first = Timestamp('2000-01-03') - self.assertEqual(rng[0], ex_first) - - def test_date_range_businesshour(self): - idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', - '2014-07-04 11:00', - '2014-07-04 12:00', '2014-07-04 13:00', - '2014-07-04 14:00', - '2014-07-04 15:00', '2014-07-04 16:00'], - freq='BH') - rng = date_range('2014-07-04 09:00', '2014-07-04 16:00', freq='BH') - tm.assert_index_equal(idx, rng) - - idx = DatetimeIndex( - ['2014-07-04 16:00', '2014-07-07 09:00'], freq='BH') - rng = date_range('2014-07-04 16:00', '2014-07-07 09:00', freq='BH') - tm.assert_index_equal(idx, rng) - - idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00', - '2014-07-04 11:00', - '2014-07-04 12:00', '2014-07-04 13:00', - '2014-07-04 14:00', - '2014-07-04 15:00', '2014-07-04 16:00', - '2014-07-07 09:00', '2014-07-07 10:00', - '2014-07-07 11:00', - '2014-07-07 12:00', '2014-07-07 13:00', - '2014-07-07 14:00', - '2014-07-07 15:00', '2014-07-07 16:00', - '2014-07-08 09:00', '2014-07-08 10:00', - '2014-07-08 11:00', - '2014-07-08 12:00', '2014-07-08 13:00', - '2014-07-08 14:00', - '2014-07-08 15:00', '2014-07-08 16:00'], - freq='BH') - rng = date_range('2014-07-04 09:00', '2014-07-08 16:00', freq='BH') - tm.assert_index_equal(idx, rng) - - def test_string_index_series_name_converted(self): - # #1644 - df = DataFrame(np.random.randn(10, 4), - index=date_range('1/1/2000', periods=10)) - - result = df.ix['1/3/2000'] - self.assertEqual(result.name, df.index[2]) - - result = df.T['1/3/2000'] - self.assertEqual(result.name, df.index[2]) - class TestTimestamp(tm.TestCase): def test_class_ops_pytz(self):
- [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - Added some `Period` related tests in preparation to add period dtype. - Moved some tests `test_timeseries` to correct test class.
https://api.github.com/repos/pandas-dev/pandas/pulls/12549
2016-03-06T23:45:36Z
2016-03-07T15:11:06Z
null
2020-11-01T22:12:55Z
(WIP)ENH: plot now supports cycler
diff --git a/ci/requirements-2.7_SLOW.run b/ci/requirements-2.7_SLOW.run index f02a7cb8a309a..cc70e96a00365 100644 --- a/ci/requirements-2.7_SLOW.run +++ b/ci/requirements-2.7_SLOW.run @@ -2,6 +2,7 @@ python-dateutil pytz numpy=1.8.2 matplotlib=1.3.1 +cycler scipy patsy statsmodels diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run index f9f226e3f1465..a53336ab85b15 100644 --- a/ci/requirements-3.4_SLOW.run +++ b/ci/requirements-3.4_SLOW.run @@ -12,6 +12,7 @@ scipy numexpr=2.4.4 pytables matplotlib +cycler lxml sqlalchemy bottleneck diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index bd19a83ce2b64..a8045e3e31291 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -2096,6 +2096,40 @@ def test_bar_bottom_left(self): result = [p.get_x() for p in ax.patches] self.assertEqual(result, [1] * 5) + @slow + def test_bar_hatches(self): + df = DataFrame(rand(4, 3)) + + ax = df.plot.bar() + result = [p._hatch for p in ax.patches] + self.assertEqual(result, [None] * 12) + tm.close() + + ax = df.plot.bar(hatch='*') + result = [p._hatch for p in ax.patches] + self.assertEqual(result, ['*'] * 12) + tm.close() + + from cycler import cycler + ax = df.plot.bar(hatch=cycler('hatch', ['*', '+', '//'])) + result = [p._hatch for p in ax.patches[:4]] + self.assertEqual(result, ['*'] * 4) + result = [p._hatch for p in ax.patches[4:8]] + self.assertEqual(result, ['+'] * 4) + result = [p._hatch for p in ax.patches[8:]] + self.assertEqual(result, ['//'] * 4) + tm.close() + + # length mismatch, loops implicitly + ax = df.plot.bar(hatch=cycler('hatch', ['*', '+'])) + result = [p._hatch for p in ax.patches[:4]] + self.assertEqual(result, ['*'] * 4) + result = [p._hatch for p in ax.patches[4:8]] + self.assertEqual(result, ['+'] * 4) + result = [p._hatch for p in ax.patches[8:]] + self.assertEqual(result, ['*'] * 4) + tm.close() + @slow def test_bar_nan(self): df = DataFrame({'A': [10, np.nan, 20], @@ -2953,6 +2987,10 @@ def test_line_colors_and_styles_subplots(self): self._check_colors(ax.get_lines(), linecolors=[c]) tm.close() + def _get_polycollection(self, ax): + from matplotlib.collections import PolyCollection + return [o for o in ax.get_children() if isinstance(o, PolyCollection)] + @slow def test_area_colors(self): from matplotlib import cm @@ -2963,7 +3001,7 @@ def test_area_colors(self): ax = df.plot.area(color=custom_colors) self._check_colors(ax.get_lines(), linecolors=custom_colors) - poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)] + poly = self._get_polycollection(ax) self._check_colors(poly, facecolors=custom_colors) handles, labels = ax.get_legend_handles_labels() @@ -2977,7 +3015,7 @@ def test_area_colors(self): ax = df.plot.area(colormap='jet') jet_colors = lmap(cm.jet, np.linspace(0, 1, len(df))) self._check_colors(ax.get_lines(), linecolors=jet_colors) - poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)] + poly = self._get_polycollection(ax) self._check_colors(poly, facecolors=jet_colors) handles, labels = ax.get_legend_handles_labels() @@ -2990,7 +3028,7 @@ def test_area_colors(self): # When stacked=False, alpha is set to 0.5 ax = df.plot.area(colormap=cm.jet, stacked=False) self._check_colors(ax.get_lines(), linecolors=jet_colors) - poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)] + poly = self._get_polycollection(ax) jet_with_alpha = [(c[0], c[1], c[2], 0.5) for c in jet_colors] self._check_colors(poly, facecolors=jet_with_alpha) @@ -3000,6 +3038,38 @@ def test_area_colors(self): for h in handles: self.assertEqual(h.get_alpha(), 0.5) + @slow + def test_area_hatches(self): + df = DataFrame(rand(4, 3)) + + ax = df.plot.area(stacked=False) + result = [x._hatch for x in self._get_polycollection(ax)] + self.assertEqual(result, [None] * 3) + tm.close() + + ax = df.plot.area(hatch='*', stacked=False) + result = [x._hatch for x in self._get_polycollection(ax)] + self.assertEqual(result, ['*'] * 3) + tm.close() + + from cycler import cycler + ax = df.plot.area(hatch=cycler('hatch', ['*', '+', '//']), + stacked=False) + poly = self._get_polycollection(ax) + self.assertEqual(poly[0]._hatch, '*') + self.assertEqual(poly[1]._hatch, '+') + self.assertEqual(poly[2]._hatch, '//') + tm.close() + + # length mismatch, loops implicitly + ax = df.plot.area(hatch=cycler('hatch', ['*', '+']), + stacked=False) + poly = self._get_polycollection(ax) + self.assertEqual(poly[0]._hatch, '*') + self.assertEqual(poly[1]._hatch, '+') + self.assertEqual(poly[2]._hatch, '*') + tm.close() + @slow def test_hist_colors(self): default_colors = self._maybe_unpack_cycler(self.plt.rcParams) diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py index b6c1926c1e7fc..bf30a46fd74e0 100644 --- a/pandas/tools/plotting.py +++ b/pandas/tools/plotting.py @@ -134,11 +134,14 @@ def _mpl_ge_1_5_0(): except ImportError: return False -if _mpl_ge_1_5_0(): +try: + _CYCLER_INSTALLED = True # Compat with mp 1.5, which uses cycler. import cycler colors = mpl_stylesheet.pop('axes.color_cycle') mpl_stylesheet['axes.prop_cycle'] = cycler.cycler('color', colors) +except ImportError: + _CYCLER_INSTALLED = False def _get_standard_kind(kind): @@ -884,7 +887,6 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None, self.by = by self.kind = kind - self.sort_columns = sort_columns self.subplots = subplots @@ -959,7 +961,9 @@ def __init__(self, data, kind=None, by=None, subplots=False, sharex=None, self.table = table - self.kwds = kwds + # init and validatecycler keyword + # ToDo: _validate_color_arg may change kwds to list + self.kwds = self._init_cyclic_option(kwds) self._validate_color_args() @@ -993,6 +997,60 @@ def _validate_color_args(self): " use one or the other or pass 'style' " "without a color symbol") + def _init_cyclic_option(self, kwds): + """ + Convert passed kwds to cycler instance + """ + if not _CYCLER_INSTALLED: + return kwds + + option = {} + for key, value in compat.iteritems(kwds): + if isinstance(value, cycler.Cycler): + cycler_keys = value.keys + if len(cycler_keys) > 1: + msg = ("cycler should only contain " + "passed keyword '{0}': {1}") + raise ValueError(msg.format(key, value)) + if key not in cycler_keys: + msg = ("cycler must contain " + "passed keyword '{0}': {1}") + raise ValueError(msg.format(key, value)) + + elif isinstance(value, list): + # instanciate cycler + # to do: check mpl kwds which should handle list as it is + cycler_value = cycler.cycler(key, value) + value = cycler_value + + option[key] = value + return option + + def _get_cyclic_option(self, kwds, num): + """ + Get num-th element of cycler contained in passed kwds. + """ + if not _CYCLER_INSTALLED: + return kwds + + option = {} + for key, value in compat.iteritems(kwds): + if isinstance(value, cycler.Cycler): + # cycler() will implicitly loop, cycler will not + # cycler 0.10 or later is required + for i, v in enumerate(value()): + if i == num: + try: + option[key] = v[key] + except KeyError: + msg = ("cycler doesn't contain required " + "key '{0}': {1}") + raise ValueError(msg.format(key, value)) + break + else: + option[key] = value + return option + def _iter_data(self, data=None, keep_index=False, fillna=None): if data is None: data = self.data @@ -1013,6 +1071,9 @@ def _iter_data(self, data=None, keep_index=False, fillna=None): @property def nseries(self): + """ + Number of columns to be plotted. If data is a Series, return 1. + """ if self.data.ndim == 1: return 1 else: @@ -1161,6 +1222,7 @@ def _post_plot_logic_common(self, ax, data): self._apply_axis_properties(ax.xaxis, rot=self.rot, fontsize=self.fontsize) self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize) + elif self.orientation == 'horizontal': if self._need_to_set_index: yticklabels = [labels.get(y, '') for y in ax.get_yticks()] @@ -1696,8 +1758,10 @@ def _make_plot(self): colors = self._get_colors() for i, (label, y) in enumerate(it): ax = self._get_ax(i) + kwds = self.kwds.copy() style, kwds = self._apply_style_colors(colors, kwds, i, label) + kwds = self._get_cyclic_option(kwds, i) errors = self._get_errorbars(label=label, index=i) kwds = dict(kwds, **errors) @@ -1829,13 +1893,20 @@ def __init__(self, data, **kwargs): if self.logy or self.loglog: raise ValueError("Log-y scales are not supported in area plot") + # kwds should not be passed to line + _fill_only_kwds = ['hatch'] + @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) + + line_kwds = kwds.copy() + for attr in cls._fill_only_kwds: + line_kwds.pop(attr, None) + lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds) # get data from the line to get coordinates for fill_between xdata, y_values = lines[0].get_data(orig=False) @@ -1939,6 +2010,8 @@ def _make_plot(self): kwds = self.kwds.copy() kwds['color'] = colors[i % ncolors] + kwds = self._get_cyclic_option(kwds, i) + errors = self._get_errorbars(label=label, index=i) kwds = dict(kwds, **errors) @@ -2064,6 +2137,7 @@ def _make_plot(self): ax = self._get_ax(i) kwds = self.kwds.copy() + kwds = self._get_cyclic_option(kwds, i) label = pprint_thing(label) kwds['label'] = label @@ -2180,6 +2254,7 @@ def _make_plot(self): ax.set_ylabel(label) kwds = self.kwds.copy() + kwds = self._get_cyclic_option(kwds, i) def blank_labeler(label, value): if value == 0: @@ -2320,6 +2395,7 @@ def _make_plot(self): for i, (label, y) in enumerate(self._iter_data()): ax = self._get_ax(i) kwds = self.kwds.copy() + kwds = self._get_cyclic_option(kwds, i) ret, bp = self._plot(ax, y, column_num=i, return_type=self.return_type, **kwds) @@ -2332,6 +2408,7 @@ def _make_plot(self): y = self.data.values.T ax = self._get_ax(0) kwds = self.kwds.copy() + kwds = self._get_cyclic_option(kwds, 0) ret, bp = self._plot(ax, y, column_num=0, return_type=self.return_type, **kwds)
- [x] closes #12354 - [ ] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [ ] whatsnew entry - [ ] plotting no try to use `cycler` regardless of mpl version (previously tried if mpl >= 1.5) - [ ] bug in area plot kwds handling ## Cyclic option handling Allows to accept some option (currently only `hatch`) as a list, each element is applied to corresponding columns. This can be more generalized, but I think it's better to refactor plotting submodule beforehand. ## Hatch example ``` df = pd.DataFrame({'A': np.random.randint(0, 10, 10), 'B': np.random.randint(0, 10, 10)}) # we cannot see hatch if alpha=1.0 df.plot.area(hatch='*', alpha=0.5); ``` ![index](https://cloud.githubusercontent.com/assets/1696302/13557491/be49bed4-e434-11e5-88d9-4746b78a4bf5.png) ``` from cycler import cycler df.plot.area(hatch=cycler('hatch', ['*', '//']), alpha=0.5); ``` ![indexx](https://cloud.githubusercontent.com/assets/1696302/13557494/c9c782c8-e434-11e5-9599-ce168f5385db.png)
https://api.github.com/repos/pandas-dev/pandas/pulls/12547
2016-03-06T22:20:43Z
2017-05-06T23:19:52Z
null
2017-05-08T09:42:56Z
DOC: fix doc build warnings
diff --git a/doc/source/basics.rst b/doc/source/basics.rst index d0469078aa3e9..1e30921e7248f 100644 --- a/doc/source/basics.rst +++ b/doc/source/basics.rst @@ -370,6 +370,7 @@ be broadcast: or it can return False if broadcasting can not be done: .. ipython:: python + :okwarning: np.array([1, 2, 3]) == np.array([1, 2]) @@ -1757,7 +1758,7 @@ but occasionally has non-dates intermixed and you want to represent as missing. 'foo', 1.0, 1, pd.Timestamp('20010104'), '20010105'], dtype='O') s - s.convert_objects(convert_dates='coerce') + pd.to_datetime(s, errors='coerce') In addition, :meth:`~DataFrame.convert_objects` will attempt the *soft* conversion of any *object* dtypes, meaning that if all the objects in a Series are of the same type, the Series will have that dtype. diff --git a/doc/source/enhancingperf.rst b/doc/source/enhancingperf.rst index 9503675af8681..7451beed7025c 100644 --- a/doc/source/enhancingperf.rst +++ b/doc/source/enhancingperf.rst @@ -98,6 +98,7 @@ First we're going to need to import the cython magic function to ipython (for cython versions >=0.21 you can use ``%load_ext Cython``): .. ipython:: python + :okwarning: %load_ext cythonmagic diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 6fa58bf620005..98bc50bae9260 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -1625,6 +1625,7 @@ This is the correct access method This *can* work at times, but is not guaranteed, and so should be avoided .. ipython:: python + :okwarning: dfc = dfc.copy() dfc['A'][0] = 111 diff --git a/doc/source/options.rst b/doc/source/options.rst index be1543f20a461..b02765f469d38 100644 --- a/doc/source/options.rst +++ b/doc/source/options.rst @@ -107,6 +107,7 @@ All options also have a default value, and you can use ``reset_option`` to do ju It's also possible to reset multiple options at once (using a regex): .. ipython:: python + :okwarning: pd.reset_option("^display") @@ -499,5 +500,3 @@ Enabling ``display.unicode.ambiguous_as_wide`` lets pandas to figure these chara pd.set_option('display.unicode.east_asian_width', False) pd.set_option('display.unicode.ambiguous_as_wide', False) - - diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst index 74cdc5a526585..71d3bbed223e5 100644 --- a/doc/source/r_interface.rst +++ b/doc/source/r_interface.rst @@ -136,6 +136,7 @@ DataFrames into the equivalent R object (that is, **data.frame**): .. ipython:: python + import pandas.rpy.common as com df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]}, index=["one", "two", "three"]) r_dataframe = com.convert_to_r_dataframe(df) @@ -154,6 +155,7 @@ R matrices bear no information on the data type): .. ipython:: python + import pandas.rpy.common as com r_matrix = com.convert_to_r_matrix(df) print(type(r_matrix)) diff --git a/doc/source/text.rst b/doc/source/text.rst index 42ecfef35e543..655df5c5e566c 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -9,7 +9,7 @@ randn = np.random.randn np.set_printoptions(precision=4, suppress=True) from pandas.compat import lrange - options.display.max_rows=15 + pd.options.display.max_rows=15 ====================== Working with Text Data @@ -375,53 +375,54 @@ Method Summary .. csv-table:: :header: "Method", "Description" :widths: 20, 80 - - :meth:`~Series.str.cat`,Concatenate strings - :meth:`~Series.str.split`,Split strings on delimiter - :meth:`~Series.str.rsplit`,Split strings on delimiter working from the end of the string - :meth:`~Series.str.get`,Index into each element (retrieve i-th element) - :meth:`~Series.str.join`,Join strings in each element of the Series with passed separator - :meth:`~Series.str.get_dummies`,Split strings on delimiter, returning DataFrame of dummy variables - :meth:`~Series.str.contains`,Return boolean array if each string contains pattern/regex - :meth:`~Series.str.replace`,Replace occurrences of pattern/regex with some other string - :meth:`~Series.str.repeat`,Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) - :meth:`~Series.str.pad`,"Add whitespace to left, right, or both sides of strings" - :meth:`~Series.str.center`,Equivalent to ``str.center`` - :meth:`~Series.str.ljust`,Equivalent to ``str.ljust`` - :meth:`~Series.str.rjust`,Equivalent to ``str.rjust`` - :meth:`~Series.str.zfill`,Equivalent to ``str.zfill`` - :meth:`~Series.str.wrap`,Split long strings into lines with length less than a given width - :meth:`~Series.str.slice`,Slice each string in the Series - :meth:`~Series.str.slice_replace`,Replace slice in each string with passed value - :meth:`~Series.str.count`,Count occurrences of pattern - :meth:`~Series.str.startswith`,Equivalent to ``str.startswith(pat)`` for each element - :meth:`~Series.str.endswith`,Equivalent to ``str.endswith(pat)`` for each element - :meth:`~Series.str.findall`,Compute list of all occurrences of pattern/regex for each string - :meth:`~Series.str.match`,"Call ``re.match`` on each element, returning matched groups as list" - :meth:`~Series.str.extract`,"Call ``re.search`` on each element, returning DataFrame with one row for each element and one column for each regex capture group" - :meth:`~Series.str.extractall`,"Call ``re.findall`` on each element, returning DataFrame with one row for each match and one column for each regex capture group" - :meth:`~Series.str.len`,Compute string lengths - :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`` - :meth:`~Series.str.rfind`,Equivalent to ``str.rfind`` - :meth:`~Series.str.index`,Equivalent to ``str.index`` - :meth:`~Series.str.rindex`,Equivalent to ``str.rindex`` - :meth:`~Series.str.capitalize`,Equivalent to ``str.capitalize`` - :meth:`~Series.str.swapcase`,Equivalent to ``str.swapcase`` - :meth:`~Series.str.normalize`,Return Unicode normal form. Equivalent to ``unicodedata.normalize`` - :meth:`~Series.str.translate`,Equivalent to ``str.translate`` - :meth:`~Series.str.isalnum`,Equivalent to ``str.isalnum`` - :meth:`~Series.str.isalpha`,Equivalent to ``str.isalpha`` - :meth:`~Series.str.isdigit`,Equivalent to ``str.isdigit`` - :meth:`~Series.str.isspace`,Equivalent to ``str.isspace`` - :meth:`~Series.str.islower`,Equivalent to ``str.islower`` - :meth:`~Series.str.isupper`,Equivalent to ``str.isupper`` - :meth:`~Series.str.istitle`,Equivalent to ``str.istitle`` - :meth:`~Series.str.isnumeric`,Equivalent to ``str.isnumeric`` - :meth:`~Series.str.isdecimal`,Equivalent to ``str.isdecimal`` + :delim: ; + + :meth:`~Series.str.cat`;Concatenate strings + :meth:`~Series.str.split`;Split strings on delimiter + :meth:`~Series.str.rsplit`;Split strings on delimiter working from the end of the string + :meth:`~Series.str.get`;Index into each element (retrieve i-th element) + :meth:`~Series.str.join`;Join strings in each element of the Series with passed separator + :meth:`~Series.str.get_dummies`;Split strings on the delimiter returning DataFrame of dummy variables + :meth:`~Series.str.contains`;Return boolean array if each string contains pattern/regex + :meth:`~Series.str.replace`;Replace occurrences of pattern/regex with some other string + :meth:`~Series.str.repeat`;Duplicate values (``s.str.repeat(3)`` equivalent to ``x * 3``) + :meth:`~Series.str.pad`;"Add whitespace to left, right, or both sides of strings" + :meth:`~Series.str.center`;Equivalent to ``str.center`` + :meth:`~Series.str.ljust`;Equivalent to ``str.ljust`` + :meth:`~Series.str.rjust`;Equivalent to ``str.rjust`` + :meth:`~Series.str.zfill`;Equivalent to ``str.zfill`` + :meth:`~Series.str.wrap`;Split long strings into lines with length less than a given width + :meth:`~Series.str.slice`;Slice each string in the Series + :meth:`~Series.str.slice_replace`;Replace slice in each string with passed value + :meth:`~Series.str.count`;Count occurrences of pattern + :meth:`~Series.str.startswith`;Equivalent to ``str.startswith(pat)`` for each element + :meth:`~Series.str.endswith`;Equivalent to ``str.endswith(pat)`` for each element + :meth:`~Series.str.findall`;Compute list of all occurrences of pattern/regex for each string + :meth:`~Series.str.match`;"Call ``re.match`` on each element, returning matched groups as list" + :meth:`~Series.str.extract`;"Call ``re.search`` on each element, returning DataFrame with one row for each element and one column for each regex capture group" + :meth:`~Series.str.extractall`;"Call ``re.findall`` on each element, returning DataFrame with one row for each match and one column for each regex capture group" + :meth:`~Series.str.len`;Compute string lengths + :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`` + :meth:`~Series.str.rfind`;Equivalent to ``str.rfind`` + :meth:`~Series.str.index`;Equivalent to ``str.index`` + :meth:`~Series.str.rindex`;Equivalent to ``str.rindex`` + :meth:`~Series.str.capitalize`;Equivalent to ``str.capitalize`` + :meth:`~Series.str.swapcase`;Equivalent to ``str.swapcase`` + :meth:`~Series.str.normalize`;Return Unicode normal form. Equivalent to ``unicodedata.normalize`` + :meth:`~Series.str.translate`;Equivalent to ``str.translate`` + :meth:`~Series.str.isalnum`;Equivalent to ``str.isalnum`` + :meth:`~Series.str.isalpha`;Equivalent to ``str.isalpha`` + :meth:`~Series.str.isdigit`;Equivalent to ``str.isdigit`` + :meth:`~Series.str.isspace`;Equivalent to ``str.isspace`` + :meth:`~Series.str.islower`;Equivalent to ``str.islower`` + :meth:`~Series.str.isupper`;Equivalent to ``str.isupper`` + :meth:`~Series.str.istitle`;Equivalent to ``str.istitle`` + :meth:`~Series.str.isnumeric`;Equivalent to ``str.isnumeric`` + :meth:`~Series.str.isdecimal`;Equivalent to ``str.isdecimal`` diff --git a/doc/source/whatsnew/v0.10.0.txt b/doc/source/whatsnew/v0.10.0.txt index 48ce09f32b12b..f409be7dd0f41 100644 --- a/doc/source/whatsnew/v0.10.0.txt +++ b/doc/source/whatsnew/v0.10.0.txt @@ -292,6 +292,7 @@ Updated PyTables Support store.select('df') .. ipython:: python + :okwarning: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'], major_axis=date_range('1/1/2000', periods=5), diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt index 9651c1efeff4a..3d992206cb426 100644 --- a/doc/source/whatsnew/v0.15.0.txt +++ b/doc/source/whatsnew/v0.15.0.txt @@ -420,7 +420,7 @@ Rolling/Expanding Moments improvements New behavior - .. ipython:: python + .. code-block:: python In [10]: pd.rolling_window(s, window=3, win_type='triang', center=True) Out[10]: diff --git a/doc/source/whatsnew/v0.15.1.txt b/doc/source/whatsnew/v0.15.1.txt index bd878db08a3ed..79efa2b278ae7 100644 --- a/doc/source/whatsnew/v0.15.1.txt +++ b/doc/source/whatsnew/v0.15.1.txt @@ -110,19 +110,18 @@ API changes .. code-block:: python - In [8]: s.loc[3.5:1.5] - KeyError: 3.5 + In [8]: s.loc[3.5:1.5] + KeyError: 3.5 current behavior: .. ipython:: python - s.loc[3.5:1.5] - + s.loc[3.5:1.5] - ``io.data.Options`` has been fixed for a change in the format of the Yahoo Options page (:issue:`8612`), (:issue:`8741`) - .. note:: + .. note:: As a result of a change in Yahoo's option page layout, when an expiry date is given, ``Options`` methods now return data for a single expiry date. Previously, methods returned all @@ -146,6 +145,7 @@ API changes Current behavior: .. ipython:: python + :okwarning: from pandas.io.data import Options aapl = Options('aapl','yahoo') @@ -274,4 +274,3 @@ Bug Fixes - Bug in Setting by indexer to a scalar value with a mixed-dtype `Panel4d` was failing (:issue:`8702`) - Bug where ``DataReader``'s would fail if one of the symbols passed was invalid. Now returns data for valid symbols and np.nan for invalid (:issue:`8494`) - Bug in ``get_quote_yahoo`` that wouldn't allow non-float return values (:issue:`5229`). - diff --git a/doc/source/whatsnew/v0.17.0.txt b/doc/source/whatsnew/v0.17.0.txt index 9f943fa68e639..92eafdac387fa 100644 --- a/doc/source/whatsnew/v0.17.0.txt +++ b/doc/source/whatsnew/v0.17.0.txt @@ -723,6 +723,7 @@ be broadcast: or it can return False if broadcasting can not be done: .. ipython:: python + :okwarning: np.array([1, 2, 3]) == np.array([1, 2]) diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 40c61b041babf..bdc20d964a06a 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -192,6 +192,7 @@ In v0.18.0, the ``expand`` argument was added to Currently the default is ``expand=None`` which gives a ``FutureWarning`` and uses ``expand=False``. To avoid this warning, please explicitly specify ``expand``. .. ipython:: python + :okwarning: pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None) @@ -608,12 +609,10 @@ Changes to msgpack Forward incompatible changes in ``msgpack`` writing format were made over 0.17.0 and 0.18.0; older versions of pandas cannot read files packed by newer versions (:issue:`12129`, `10527`) -Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`) +Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`). The following table describes the backward and forward compat of msgpacks. .. warning:: - As a result of a number of issues: - +----------------------+------------------------+ | Packed with | Can be unpacked with | +======================+========================+ @@ -621,13 +620,14 @@ Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.1 +----------------------+------------------------+ | pre-0.17 / Python 3 | any | +----------------------+------------------------+ - | 0.17 / Python 2 | - 0.17 / Python 2 | + | 0.17 / Python 2 | - ==0.17 / Python 2 | | | - >=0.18 / any Python | +----------------------+------------------------+ | 0.17 / Python 3 | >=0.18 / any Python | +----------------------+------------------------+ | 0.18 | >= 0.18 | - +======================+========================+ + +----------------------+------------------------+ + 0.18.0 is backward-compatible for reading files packed by older versions, except for files packed with 0.17 in Python 2, in which case only they can only be unpacked in Python 2. @@ -780,7 +780,7 @@ Now, you can write ``.resample`` as a 2-stage operation like groupby, which yields a ``Resampler``. .. ipython:: python - + :okwarning: r = df.resample('2s') r @@ -872,9 +872,28 @@ in an inplace change to the ``DataFrame``. (:issue:`9297`) .. ipython:: python df = pd.DataFrame({'a': np.linspace(0, 10, 5), 'b': range(5)}) - df.eval('c = a + b') df +.. ipython:: python + :suppress: + + df.eval('c = a + b', inplace=True) + +.. code-block:: python + + In [12]: df.eval('c = a + b') + FutureWarning: eval expressions containing an assignment currentlydefault to operating inplace. + This will change in a future version of pandas, use inplace=True to avoid this warning. + + In [13]: df + Out[13]: + a b c + 0 0.0 0 0.0 + 1 2.5 1 3.5 + 2 5.0 2 7.0 + 3 7.5 3 10.5 + 4 10.0 4 14.0 + In version 0.18.0, a new ``inplace`` keyword was added to choose whether the assignment should be done inplace or return a copy. @@ -941,7 +960,7 @@ Other API Changes - ``DataFrame.to_latex()`` now supports non-ascii encodings (eg utf-8) in Python 2 with the parameter ``encoding`` (:issue:`7061`) - ``pandas.merge()`` and ``DataFrame.merge()`` will show a specific error message when trying to merge with an object that is not of type ``DataFrame`` or a subclass (:issue:`12081`) - ``DataFrame.unstack`` and ``Series.unstack`` now take ``fill_value`` keyword to allow direct replacement of missing values when an unstack results in missing values in the resulting ``DataFrame``. As an added benefit, specifying ``fill_value`` will preserve the data type of the original stacked data. (:issue:`9746`) -- As part of the new API for :ref:`window functions <whatsnew_0180.enhancements.moments>` and :ref:`resampling <whatsnew_0180.breaking.resample>`, aggregation functions have been clarified, raising more informative error messages on invalid aggregations. (:issue:`9052`). A full set of examples are presented in :ref:`groupby <groupby.aggregation>`. +- As part of the new API for :ref:`window functions <whatsnew_0180.enhancements.moments>` and :ref:`resampling <whatsnew_0180.breaking.resample>`, aggregation functions have been clarified, raising more informative error messages on invalid aggregations. (:issue:`9052`). A full set of examples are presented in :ref:`groupby <groupby.aggregate>`. - Statistical functions for ``NDFrame`` objects will now raise if non-numpy-compatible arguments are passed in for ``**kwargs`` (:issue:`12301`) - ``.to_latex`` and ``.to_html`` gain a ``decimal`` parameter like ``.to_csv``; the default is ``'.'`` (:issue:`12031`) - More helpful error message when constructing a ``DataFrame`` with empty data but with indices (:issue:`8020`)
https://api.github.com/repos/pandas-dev/pandas/pulls/12545
2016-03-06T21:22:53Z
2016-03-08T14:26:14Z
null
2016-03-08T18:33:52Z
BLD: Install texlive packages in Travis CI
diff --git a/ci/build_docs.sh b/ci/build_docs.sh index c0843593f85ff..942c2f7c64f4f 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -19,7 +19,7 @@ if [ x"$DOC_BUILD" != x"" ]; then source activate pandas conda install -n pandas -c r r rpy2 --yes - time sudo apt-get $APT_ARGS install dvipng + time sudo apt-get $APT_ARGS install dvipng texlive-latex-base texlive-latex-extra mv "$TRAVIS_BUILD_DIR"/doc /tmp cd /tmp/doc
- Closes #12543 - `texlive-latex-base` needed for `latex` - `texlive-latex-extra` needed for `utf8x.def`
https://api.github.com/repos/pandas-dev/pandas/pulls/12544
2016-03-06T20:37:22Z
2016-03-06T22:42:23Z
null
2016-03-21T15:03:32Z
BUG: Can't get period code with frequency alias 'minute' or 'Minute'
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dbe446f0a7b4f..834e608bfaf7a 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -88,4 +88,12 @@ Performance Improvements Bug Fixes ~~~~~~~~~ + - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) + + + +- Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if + ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in + v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`) + diff --git a/pandas/src/period.pyx b/pandas/src/period.pyx index b325ccbb581d1..48c017c43c0aa 100644 --- a/pandas/src/period.pyx +++ b/pandas/src/period.pyx @@ -657,6 +657,7 @@ cdef class Period(object): if isinstance(freq, compat.string_types): from pandas.tseries.frequencies import _period_alias_dict + freq = freq.upper() freq = _period_alias_dict.get(freq, freq) elif isinstance(freq, (int, tuple)): from pandas.tseries.frequencies import get_freq_code as _gfc diff --git a/pandas/tseries/frequencies.py b/pandas/tseries/frequencies.py index 90acd99c738cc..058a8db9ead08 100644 --- a/pandas/tseries/frequencies.py +++ b/pandas/tseries/frequencies.py @@ -385,7 +385,8 @@ def get_period_alias(offset_str): 'Min': 'T', 'min': 'T', 'ms': 'L', - 'us': 'U' + 'us': 'U', + 'ns': 'N' } # TODO: Can this be killed? @@ -683,7 +684,7 @@ def _period_alias_dictionary(): alias_dict = {} M_aliases = ["M", "MTH", "MONTH", "MONTHLY"] - B_aliases = ["B", "BUS", "BUSINESS", "BUSINESSLY", 'WEEKDAY'] + B_aliases = ["B", "BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY"] D_aliases = ["D", "DAY", "DLY", "DAILY"] H_aliases = ["H", "HR", "HOUR", "HRLY", "HOURLY"] T_aliases = ["T", "MIN", "MINUTE", "MINUTELY"] @@ -705,7 +706,7 @@ def _period_alias_dictionary(): alias_dict[k] = 'H' for k in T_aliases: - alias_dict[k] = 'Min' + alias_dict[k] = 'T' for k in S_aliases: alias_dict[k] = 'S' diff --git a/pandas/tseries/tests/test_frequencies.py b/pandas/tseries/tests/test_frequencies.py index 653d92a6148e6..876f95c1b27d7 100644 --- a/pandas/tseries/tests/test_frequencies.py +++ b/pandas/tseries/tests/test_frequencies.py @@ -170,6 +170,47 @@ def test_get_rule_month(): assert (result == 'MAY') +def test_period_str_to_code(): + assert (frequencies._period_str_to_code('A') == 1000) + assert (frequencies._period_str_to_code('A-DEC') == 1000) + assert (frequencies._period_str_to_code('A-JAN') == 1001) + assert (frequencies._period_str_to_code('Q') == 2000) + assert (frequencies._period_str_to_code('Q-DEC') == 2000) + assert (frequencies._period_str_to_code('Q-FEB') == 2002) + + def _assert_depr(freq, expected, aliases): + assert isinstance(aliases, list) + assert (frequencies._period_str_to_code(freq) == expected) + + for alias in aliases: + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + assert (frequencies._period_str_to_code(alias) == expected) + + _assert_depr("M", 3000, ["MTH", "MONTH", "MONTHLY"]) + + assert (frequencies._period_str_to_code('W') == 4000) + assert (frequencies._period_str_to_code('W-SUN') == 4000) + assert (frequencies._period_str_to_code('W-FRI') == 4005) + + _assert_depr("B", 5000, ["BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY"]) + _assert_depr("D", 6000, ["DAY", "DLY", "DAILY"]) + _assert_depr("H", 7000, ["HR", "HOUR", "HRLY", "HOURLY"]) + + _assert_depr("T", 8000, ["minute", "MINUTE", "MINUTELY"]) + assert (frequencies._period_str_to_code('Min') == 8000) + + _assert_depr("S", 9000, ["sec", "SEC", "SECOND", "SECONDLY"]) + _assert_depr("L", 10000, ["MILLISECOND", "MILLISECONDLY"]) + assert (frequencies._period_str_to_code('ms') == 10000) + + _assert_depr("U", 11000, ["MICROSECOND", "MICROSECONDLY"]) + assert (frequencies._period_str_to_code('US') == 11000) + + _assert_depr("N", 12000, ["NANOSECOND", "NANOSECONDLY"]) + assert (frequencies._period_str_to_code('NS') == 12000) + + class TestFrequencyCode(tm.TestCase): def test_freq_code(self): self.assertEqual(frequencies.get_freq('A'), 1000) diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py index 95d84bba4b5db..e8af63f3355c9 100644 --- a/pandas/tseries/tests/test_period.py +++ b/pandas/tseries/tests/test_period.py @@ -22,7 +22,7 @@ import pandas as pd import numpy as np from numpy.random import randn -from pandas.compat import range, lrange, lmap, zip, text_type, PY3 +from pandas.compat import range, lrange, lmap, zip, text_type, PY3, iteritems from pandas.compat.numpy_compat import np_datetime64_compat from pandas import (Series, DataFrame, @@ -36,8 +36,6 @@ class TestPeriodProperties(tm.TestCase): "Test properties such as year, month, weekday, etc...." - # - def test_quarterly_negative_ordinals(self): p = Period(ordinal=-1, freq='Q-DEC') self.assertEqual(p.year, 1969) @@ -440,6 +438,24 @@ def test_freq_str(self): self.assertEqual(i1.freq, offsets.Minute()) self.assertEqual(i1.freqstr, 'T') + def test_period_deprecated_freq(self): + cases = {"M": ["MTH", "MONTH", "MONTHLY", "Mth", "month", "monthly"], + "B": ["BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY", "bus"], + "D": ["DAY", "DLY", "DAILY", "Day", "Dly", "Daily"], + "H": ["HR", "HOUR", "HRLY", "HOURLY", "hr", "Hour", "HRly"], + "T": ["minute", "MINUTE", "MINUTELY", "minutely"], + "S": ["sec", "SEC", "SECOND", "SECONDLY", "second"], + "L": ["MILLISECOND", "MILLISECONDLY", "millisecond"], + "U": ["MICROSECOND", "MICROSECONDLY", "microsecond"], + "N": ["NANOSECOND", "NANOSECONDLY", "nanosecond"]} + for exp, freqs in iteritems(cases): + for freq in freqs: + + with tm.assert_produces_warning(FutureWarning, + check_stacklevel=False): + res = pd.Period('2016-03-01 09:00', freq=freq) + self.assertEqual(res, Period('2016-03-01 09:00', freq=exp)) + def test_repr(self): p = Period('Jan-2000') self.assertIn('2000-01', repr(p))
- [x] closes #11854 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Also, added `NS` to normal alias not to show warnings (as the same as `MS` and `NS`)
https://api.github.com/repos/pandas-dev/pandas/pulls/12540
2016-03-06T02:13:43Z
2016-03-15T14:22:14Z
null
2016-03-15T19:57:26Z
ENH: Allow where/mask/Indexers to accept callable
diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst index 5afe69791bbdf..6227b08587790 100644 --- a/doc/source/indexing.rst +++ b/doc/source/indexing.rst @@ -79,6 +79,10 @@ of multi-axis indexing. - A slice object with labels ``'a':'f'``, (note that contrary to usual python slices, **both** the start and the stop are included!) - A boolean array + - A ``callable`` function with one argument (the calling Series, DataFrame or Panel) and + that returns valid output for indexing (one of the above) + + .. versionadded:: 0.18.1 See more at :ref:`Selection by Label <indexing.label>` @@ -93,6 +97,10 @@ of multi-axis indexing. - A list or array of integers ``[4, 3, 0]`` - A slice object with ints ``1:7`` - A boolean array + - A ``callable`` function with one argument (the calling Series, DataFrame or Panel) and + that returns valid output for indexing (one of the above) + + .. versionadded:: 0.18.1 See more at :ref:`Selection by Position <indexing.integer>` @@ -110,6 +118,8 @@ of multi-axis indexing. See more at :ref:`Advanced Indexing <advanced>` and :ref:`Advanced Hierarchical <advanced.advanced_hierarchical>`. +- ``.loc``, ``.iloc``, ``.ix`` and also ``[]`` indexing can accept a ``callable`` as indexer. See more at :ref:`Selection By Callable <indexing.callable>`. + Getting values from an object with multi-axes selection uses the following notation (using ``.loc`` as an example, but applies to ``.iloc`` and ``.ix`` as well). Any of the axes accessors may be the null slice ``:``. Axes left out of @@ -317,6 +327,7 @@ The ``.loc`` attribute is the primary access method. The following are valid inp - A list or array of labels ``['a', 'b', 'c']`` - A slice object with labels ``'a':'f'`` (note that contrary to usual python slices, **both** the start and the stop are included!) - A boolean array +- A ``callable``, see :ref:`Selection By Callable <indexing.callable>` .. ipython:: python @@ -340,13 +351,13 @@ With a DataFrame index=list('abcdef'), columns=list('ABCD')) df1 - df1.loc[['a','b','d'],:] + df1.loc[['a', 'b', 'd'], :] Accessing via label slices .. ipython:: python - df1.loc['d':,'A':'C'] + df1.loc['d':, 'A':'C'] For getting a cross section using a label (equiv to ``df.xs('a')``) @@ -358,15 +369,15 @@ For getting values with a boolean array .. ipython:: python - df1.loc['a']>0 - df1.loc[:,df1.loc['a']>0] + df1.loc['a'] > 0 + df1.loc[:, df1.loc['a'] > 0] For getting a value explicitly (equiv to deprecated ``df.get_value('a','A')``) .. ipython:: python # this is also equivalent to ``df1.at['a','A']`` - df1.loc['a','A'] + df1.loc['a', 'A'] .. _indexing.integer: @@ -387,6 +398,7 @@ The ``.iloc`` attribute is the primary access method. The following are valid in - A list or array of integers ``[4, 3, 0]`` - A slice object with ints ``1:7`` - A boolean array +- A ``callable``, see :ref:`Selection By Callable <indexing.callable>` .. ipython:: python @@ -416,26 +428,26 @@ Select via integer slicing .. ipython:: python df1.iloc[:3] - df1.iloc[1:5,2:4] + df1.iloc[1:5, 2:4] Select via integer list .. ipython:: python - df1.iloc[[1,3,5],[1,3]] + df1.iloc[[1, 3, 5], [1, 3]] .. ipython:: python - df1.iloc[1:3,:] + df1.iloc[1:3, :] .. ipython:: python - df1.iloc[:,1:3] + df1.iloc[:, 1:3] .. ipython:: python # this is also equivalent to ``df1.iat[1,1]`` - df1.iloc[1,1] + df1.iloc[1, 1] For getting a cross section using an integer position (equiv to ``df.xs(1)``) @@ -471,8 +483,8 @@ returned) dfl = pd.DataFrame(np.random.randn(5,2), columns=list('AB')) dfl - dfl.iloc[:,2:3] - dfl.iloc[:,1:3] + dfl.iloc[:, 2:3] + dfl.iloc[:, 1:3] dfl.iloc[4:6] A single indexer that is out of bounds will raise an ``IndexError``. @@ -481,12 +493,52 @@ A list of indexers where any element is out of bounds will raise an .. code-block:: python - dfl.iloc[[4,5,6]] + dfl.iloc[[4, 5, 6]] IndexError: positional indexers are out-of-bounds - dfl.iloc[:,4] + dfl.iloc[:, 4] IndexError: single positional indexer is out-of-bounds +.. _indexing.callable: + +Selection By Callable +--------------------- + +.. versionadded:: 0.18.1 + +``.loc``, ``.iloc``, ``.ix`` and also ``[]`` indexing can accept a ``callable`` as indexer. +The ``callable`` must be a function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing. + +.. ipython:: python + + df1 = pd.DataFrame(np.random.randn(6, 4), + index=list('abcdef'), + columns=list('ABCD')) + df1 + + df1.loc[lambda df: df.A > 0, :] + df1.loc[:, lambda df: ['A', 'B']] + + df1.iloc[:, lambda df: [0, 1]] + + df1[lambda df: df.columns[0]] + + +You can use callable indexing in ``Series``. + +.. ipython:: python + + df1.A.loc[lambda s: s > 0] + +Using these methods / indexers, you can chain data selection operations +without using temporary variable. + +.. ipython:: python + + bb = pd.read_csv('data/baseball.csv', index_col='id') + (bb.groupby(['year', 'team']).sum() + .loc[lambda df: df.r > 100]) + .. _indexing.basics.partial_setting: Selecting Random Samples @@ -848,6 +900,19 @@ This is equivalent (but faster than) the following. df2 = df.copy() df.apply(lambda x, y: x.where(x>0,y), y=df['A']) +.. versionadded:: 0.18.1 + +Where can accept a callable as condition and ``other`` arguments. The function must +be with one argument (the calling Series or DataFrame) and that returns valid output +as condition and ``other`` argument. + +.. ipython:: python + + df3 = pd.DataFrame({'A': [1, 2, 3], + 'B': [4, 5, 6], + 'C': [7, 8, 9]}) + df3.where(lambda x: x > 4, lambda x: x + 10) + **mask** ``mask`` is the inverse boolean operation of ``where``. diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dfe5eaa66df01..1b8f0112348eb 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -13,6 +13,8 @@ Highlights include: - ``pd.to_datetime()`` has gained the ability to assemble dates from a ``DataFrame``, see :ref:`here <whatsnew_0181.enhancements.assembling>` - Custom business hour offset, see :ref:`here <whatsnew_0181.enhancements.custombusinesshour>`. - Many bug fixes in the handling of ``sparse``, see :ref:`here <whatsnew_0181.sparse>` +- Method chaining improvements, see :ref:`here <whatsnew_0181.enhancements.method_chain>`. + .. contents:: What's new in v0.18.1 :local: @@ -94,6 +96,66 @@ Now you can do: df.groupby('group').resample('1D').ffill() +.. _whatsnew_0181.enhancements.method_chain: + +Method chaininng improvements +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The following methods / indexers now accept ``callable``. It is intended to make +these more useful in method chains, see :ref:`Selection By Callable <indexing.callable>`. +(:issue:`11485`, :issue:`12533`) + +- ``.where()`` and ``.mask()`` +- ``.loc[]``, ``iloc[]`` and ``.ix[]`` +- ``[]`` indexing + +``.where()`` and ``.mask()`` +"""""""""""""""""""""""""""" + +These can accept a callable as condition and ``other`` +arguments. + +.. ipython:: python + + df = pd.DataFrame({'A': [1, 2, 3], + 'B': [4, 5, 6], + 'C': [7, 8, 9]}) + df.where(lambda x: x > 4, lambda x: x + 10) + +``.loc[]``, ``.iloc[]``, ``.ix[]`` +"""""""""""""""""""""""""""""""""" + +These can accept a callable, and tuple of callable as a slicer. The callable +can return valid ``bool`` indexer or anything which is valid for these indexer's input. + +.. ipython:: python + + # callable returns bool indexer + df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10] + + # callable returns list of labels + df.loc[lambda x: [1, 2], lambda x: ['A', 'B']] + +``[]`` indexing +""""""""""""""" + +Finally, you can use a callable in ``[]`` indexing of Series, DataFrame and Panel. +The callable must return valid input for ``[]`` indexing depending on its +class and index type. + +.. ipython:: python + + df[lambda x: 'A'] + +Using these methods / indexers, you can chain data selection operations +without using temporary variable. + +.. ipython:: python + + bb = pd.read_csv('data/baseball.csv', index_col='id') + (bb.groupby(['year', 'team']).sum() + .loc[lambda df: df.r > 100]) + .. _whatsnew_0181.partial_string_indexing: Partial string indexing on ``DateTimeIndex`` when part of a ``MultiIndex`` diff --git a/pandas/core/common.py b/pandas/core/common.py index 14c95e01882a2..d41d49c895599 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -1843,6 +1843,16 @@ def _get_callable_name(obj): return None +def _apply_if_callable(maybe_callable, obj, **kwargs): + """ + Evaluate possibly callable input using obj and kwargs if it is callable, + otherwise return as it is + """ + if callable(maybe_callable): + return maybe_callable(obj, **kwargs) + return maybe_callable + + _string_dtypes = frozenset(map(_get_dtype_from_object, (compat.binary_type, compat.text_type))) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 9c87d1c887361..1ec5b05aa7eef 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1970,6 +1970,7 @@ def iget_value(self, i, j): return self.iat[i, j] def __getitem__(self, key): + key = com._apply_if_callable(key, self) # shortcut if we are an actual column is_mi_columns = isinstance(self.columns, MultiIndex) @@ -2138,6 +2139,9 @@ def query(self, expr, inplace=False, **kwargs): >>> df.query('a > b') >>> df[df.a > df.b] # same result as the previous expression """ + if not isinstance(expr, compat.string_types): + msg = "expr must be a string to be evaluated, {0} given" + raise ValueError(msg.format(type(expr))) kwargs['level'] = kwargs.pop('level', 0) + 1 kwargs['target'] = None res = self.eval(expr, **kwargs) @@ -2336,6 +2340,7 @@ def _box_col_values(self, values, items): name=items, fastpath=True) def __setitem__(self, key, value): + key = com._apply_if_callable(key, self) # see if we can slice the rows indexer = convert_to_index_sliceable(self, key) @@ -2454,8 +2459,9 @@ def assign(self, **kwargs): kwargs : keyword, value pairs keywords are the column names. If the values are callable, they are computed on the DataFrame and - assigned to the new columns. If the values are - not callable, (e.g. a Series, scalar, or array), + assigned to the new columns. The callable must not + change input DataFrame (though pandas doesn't check it). + If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned. Returns @@ -2513,11 +2519,7 @@ def assign(self, **kwargs): # do all calculations first... results = {} for k, v in kwargs.items(): - - if callable(v): - results[k] = v(data) - else: - results[k] = v + results[k] = com._apply_if_callable(v, data) # ... and then assign for k, v in sorted(results.items()): diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 788a564e3dee3..68c1e98c9957d 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4283,8 +4283,26 @@ def _align_series(self, other, join='outer', axis=None, level=None, Parameters ---------- - cond : boolean %(klass)s or array - other : scalar or %(klass)s + cond : boolean %(klass)s, array or callable + If cond is callable, it is computed on the %(klass)s and + should return boolean %(klass)s or array. + The callable must not change input %(klass)s + (though pandas doesn't check it). + + .. versionadded:: 0.18.1 + + A callable can be used as cond. + + other : scalar, %(klass)s, or callable + If other is callable, it is computed on the %(klass)s and + should return scalar or %(klass)s. + The callable must not change input %(klass)s + (though pandas doesn't check it). + + .. versionadded:: 0.18.1 + + A callable can be used as other. + inplace : boolean, default False Whether to perform the operation in place on the data axis : alignment axis if needed, default None @@ -4304,6 +4322,9 @@ def _align_series(self, other, join='outer', axis=None, level=None, def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, try_cast=False, raise_on_error=True): + cond = com._apply_if_callable(cond, self) + other = com._apply_if_callable(other, self) + if isinstance(cond, NDFrame): cond, _ = cond.align(self, join='right', broadcast_axis=1) else: @@ -4461,6 +4482,9 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None, @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): + + cond = com._apply_if_callable(cond, self) + return self.where(~cond, other=other, inplace=inplace, axis=axis, level=level, try_cast=try_cast, raise_on_error=raise_on_error) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index df257fb5fd1d0..acb0675247a78 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -64,6 +64,7 @@ def __iter__(self): def __getitem__(self, key): if type(key) is tuple: + key = tuple(com._apply_if_callable(x, self.obj) for x in key) try: values = self.obj.get_value(*key) if lib.isscalar(values): @@ -73,6 +74,7 @@ def __getitem__(self, key): return self._getitem_tuple(key) else: + key = com._apply_if_callable(key, self.obj) return self._getitem_axis(key, axis=0) def _get_label(self, label, axis=0): @@ -122,6 +124,10 @@ def _get_setitem_indexer(self, key): raise IndexingError(key) def __setitem__(self, key, value): + if isinstance(key, tuple): + key = tuple(com._apply_if_callable(x, self.obj) for x in key) + else: + key = com._apply_if_callable(key, self.obj) indexer = self._get_setitem_indexer(key) self._setitem_with_indexer(indexer, value) @@ -1278,6 +1284,12 @@ class _LocationIndexer(_NDFrameIndexer): _exception = Exception def __getitem__(self, key): + if isinstance(key, tuple): + key = tuple(com._apply_if_callable(x, self.obj) for x in key) + else: + # scalar callable may return tuple + key = com._apply_if_callable(key, self.obj) + if type(key) is tuple: return self._getitem_tuple(key) else: @@ -1326,6 +1338,8 @@ class _LocIndexer(_LocationIndexer): - A slice object with labels, e.g. ``'a':'f'`` (note that contrary to usual python slices, **both** the start and the stop are included!). - A boolean array. + - A ``callable`` function with one argument (the calling Series, DataFrame + or Panel) and that returns valid output for indexing (one of the above) ``.loc`` will raise a ``KeyError`` when the items are not found. @@ -1466,6 +1480,8 @@ class _iLocIndexer(_LocationIndexer): - A list or array of integers, e.g. ``[4, 3, 0]``. - A slice object with ints, e.g. ``1:7``. - A boolean array. + - A ``callable`` function with one argument (the calling Series, DataFrame + or Panel) and that returns valid output for indexing (one of the above) ``.iloc`` will raise ``IndexError`` if a requested indexer is out-of-bounds, except *slice* indexers which allow out-of-bounds @@ -1633,6 +1649,12 @@ def __getitem__(self, key): return self.obj.get_value(*key, takeable=self._takeable) def __setitem__(self, key, value): + if isinstance(key, tuple): + key = tuple(com._apply_if_callable(x, self.obj) for x in key) + else: + # scalar callable may return tuple + key = com._apply_if_callable(key, self.obj) + if not isinstance(key, tuple): key = self._tuplify(key) if len(key) != self.obj.ndim: diff --git a/pandas/core/panel.py b/pandas/core/panel.py index b84079ffc4ffd..ea88c9f7223a9 100644 --- a/pandas/core/panel.py +++ b/pandas/core/panel.py @@ -268,6 +268,8 @@ def from_dict(cls, data, intersect=False, orient='items', dtype=None): return cls(**d) def __getitem__(self, key): + key = com._apply_if_callable(key, self) + if isinstance(self._info_axis, MultiIndex): return self._getitem_multilevel(key) if not (is_list_like(key) or isinstance(key, slice)): @@ -567,6 +569,7 @@ def _box_item_values(self, key, values): return self._constructor_sliced(values, **d) def __setitem__(self, key, value): + key = com._apply_if_callable(key, self) shape = tuple(self.shape) if isinstance(value, self._constructor_sliced): value = value.reindex( diff --git a/pandas/core/series.py b/pandas/core/series.py index a33d5598be7cd..d8e99f2bddc81 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -577,6 +577,7 @@ def _slice(self, slobj, axis=0, kind=None): return self._get_values(slobj) def __getitem__(self, key): + key = com._apply_if_callable(key, self) try: result = self.index.get_value(self, key) @@ -692,6 +693,8 @@ def _get_values(self, indexer): return self._values[indexer] def __setitem__(self, key, value): + key = com._apply_if_callable(key, self) + def setitem(key, value): try: self._set_with_engine(key, value) diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py index 2006905fe034d..a6e46b7d0c756 100644 --- a/pandas/tests/frame/test_indexing.py +++ b/pandas/tests/frame/test_indexing.py @@ -119,6 +119,18 @@ def test_getitem_list(self): assert_frame_equal(result, expected) self.assertEqual(result.columns.names, ['sth', 'sth2']) + def test_getitem_callable(self): + # GH 12533 + result = self.frame[lambda x: 'A'] + tm.assert_series_equal(result, self.frame.loc[:, 'A']) + + result = self.frame[lambda x: ['A', 'B']] + tm.assert_frame_equal(result, self.frame.loc[:, ['A', 'B']]) + + df = self.frame[:3] + result = df[lambda x: [True, False, True]] + tm.assert_frame_equal(result, self.frame.iloc[[0, 2], :]) + def test_setitem_list(self): self.frame['E'] = 'foo' @@ -187,6 +199,14 @@ def test_setitem_mulit_index(self): df[('joe', 'last')] = df[('jolie', 'first')].loc[i, j] assert_frame_equal(df[('joe', 'last')], df[('jolie', 'first')]) + def test_setitem_callable(self): + # GH 12533 + df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}) + df[lambda x: 'A'] = [11, 12, 13, 14] + + exp = pd.DataFrame({'A': [11, 12, 13, 14], 'B': [5, 6, 7, 8]}) + tm.assert_frame_equal(df, exp) + def test_getitem_boolean(self): # boolean indexing d = self.tsframe.index[10] @@ -2545,6 +2565,27 @@ def test_where_axis(self): result.where(mask, d2, inplace=True, axis='columns') assert_frame_equal(result, expected) + def test_where_callable(self): + # GH 12533 + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + result = df.where(lambda x: x > 4, lambda x: x + 1) + exp = DataFrame([[2, 3, 4], [5, 5, 6], [7, 8, 9]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.where(df > 4, df + 1)) + + # return ndarray and scalar + result = df.where(lambda x: (x % 2 == 0).values, lambda x: 99) + exp = DataFrame([[99, 2, 99], [4, 99, 6], [99, 8, 99]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.where(df % 2 == 0, 99)) + + # chain + result = (df + 2).where(lambda x: x > 8, lambda x: x + 10) + exp = DataFrame([[13, 14, 15], [16, 17, 18], [9, 10, 11]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, + (df + 2).where((df + 2) > 8, (df + 2) + 10)) + def test_mask(self): df = DataFrame(np.random.randn(5, 3)) cond = df > 0 @@ -2581,6 +2622,27 @@ def test_mask_edge_case_1xN_frame(self): expec = DataFrame([[nan, 2]]) assert_frame_equal(res, expec) + def test_mask_callable(self): + # GH 12533 + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + result = df.mask(lambda x: x > 4, lambda x: x + 1) + exp = DataFrame([[1, 2, 3], [4, 6, 7], [8, 9, 10]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.mask(df > 4, df + 1)) + + # return ndarray and scalar + result = df.mask(lambda x: (x % 2 == 0).values, lambda x: 99) + exp = DataFrame([[1, 99, 3], [99, 5, 99], [7, 99, 9]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.mask(df % 2 == 0, 99)) + + # chain + result = (df + 2).mask(lambda x: x > 8, lambda x: x + 10) + exp = DataFrame([[3, 4, 5], [6, 7, 8], [19, 20, 21]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, + (df + 2).mask((df + 2) > 8, (df + 2) + 10)) + def test_head_tail(self): assert_frame_equal(self.frame.head(), self.frame[:5]) assert_frame_equal(self.frame.tail(), self.frame[-5:]) diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index 9f863bc4f62f3..49b0ce66999d8 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -136,6 +136,17 @@ def test_ops(self): result = (1 - np.isnan(df)).iloc[0:25] assert_frame_equal(result, expected) + def test_query_non_str(self): + # GH 11485 + df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'b']}) + + msg = "expr must be a string to be evaluated" + with tm.assertRaisesRegexp(ValueError, msg): + df.query(lambda x: x.B == "b") + + with tm.assertRaisesRegexp(ValueError, msg): + df.query(111) + class TestDataFrameQueryWithMultiIndex(tm.TestCase): diff --git a/pandas/tests/indexing/test_callable.py b/pandas/tests/indexing/test_callable.py new file mode 100644 index 0000000000000..3465d776bfa85 --- /dev/null +++ b/pandas/tests/indexing/test_callable.py @@ -0,0 +1,275 @@ +# -*- coding: utf-8 -*- +# pylint: disable-msg=W0612,E1101 +import nose + +import numpy as np +import pandas as pd +import pandas.util.testing as tm + + +class TestIndexingCallable(tm.TestCase): + + _multiprocess_can_split_ = True + + def test_frame_loc_ix_callable(self): + # GH 11485 + df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': list('aabb'), + 'C': [1, 2, 3, 4]}) + # iloc cannot use boolean Series (see GH3635) + + # return bool indexer + res = df.loc[lambda x: x.A > 2] + tm.assert_frame_equal(res, df.loc[df.A > 2]) + + res = df.ix[lambda x: x.A > 2] + tm.assert_frame_equal(res, df.ix[df.A > 2]) + + res = df.loc[lambda x: x.A > 2, ] + tm.assert_frame_equal(res, df.loc[df.A > 2, ]) + + res = df.ix[lambda x: x.A > 2, ] + tm.assert_frame_equal(res, df.ix[df.A > 2, ]) + + res = df.loc[lambda x: x.B == 'b', :] + tm.assert_frame_equal(res, df.loc[df.B == 'b', :]) + + res = df.ix[lambda x: x.B == 'b', :] + tm.assert_frame_equal(res, df.ix[df.B == 'b', :]) + + res = df.loc[lambda x: x.A > 2, lambda x: x.columns == 'B'] + tm.assert_frame_equal(res, df.loc[df.A > 2, [False, True, False]]) + + res = df.ix[lambda x: x.A > 2, lambda x: x.columns == 'B'] + tm.assert_frame_equal(res, df.ix[df.A > 2, [False, True, False]]) + + res = df.loc[lambda x: x.A > 2, lambda x: 'B'] + tm.assert_series_equal(res, df.loc[df.A > 2, 'B']) + + res = df.ix[lambda x: x.A > 2, lambda x: 'B'] + tm.assert_series_equal(res, df.ix[df.A > 2, 'B']) + + res = df.loc[lambda x: x.A > 2, lambda x: ['A', 'B']] + tm.assert_frame_equal(res, df.loc[df.A > 2, ['A', 'B']]) + + res = df.ix[lambda x: x.A > 2, lambda x: ['A', 'B']] + tm.assert_frame_equal(res, df.ix[df.A > 2, ['A', 'B']]) + + res = df.loc[lambda x: x.A == 2, lambda x: ['A', 'B']] + tm.assert_frame_equal(res, df.loc[df.A == 2, ['A', 'B']]) + + res = df.ix[lambda x: x.A == 2, lambda x: ['A', 'B']] + tm.assert_frame_equal(res, df.ix[df.A == 2, ['A', 'B']]) + + # scalar + res = df.loc[lambda x: 1, lambda x: 'A'] + self.assertEqual(res, df.loc[1, 'A']) + + res = df.ix[lambda x: 1, lambda x: 'A'] + self.assertEqual(res, df.ix[1, 'A']) + + def test_frame_loc_ix_callable_mixture(self): + # GH 11485 + df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': list('aabb'), + 'C': [1, 2, 3, 4]}) + + res = df.loc[lambda x: x.A > 2, ['A', 'B']] + tm.assert_frame_equal(res, df.loc[df.A > 2, ['A', 'B']]) + + res = df.ix[lambda x: x.A > 2, ['A', 'B']] + tm.assert_frame_equal(res, df.ix[df.A > 2, ['A', 'B']]) + + res = df.loc[[2, 3], lambda x: ['A', 'B']] + tm.assert_frame_equal(res, df.loc[[2, 3], ['A', 'B']]) + + res = df.ix[[2, 3], lambda x: ['A', 'B']] + tm.assert_frame_equal(res, df.ix[[2, 3], ['A', 'B']]) + + res = df.loc[3, lambda x: ['A', 'B']] + tm.assert_series_equal(res, df.loc[3, ['A', 'B']]) + + res = df.ix[3, lambda x: ['A', 'B']] + tm.assert_series_equal(res, df.ix[3, ['A', 'B']]) + + def test_frame_loc_callable(self): + # GH 11485 + df = pd.DataFrame({'X': [1, 2, 3, 4], + 'Y': list('aabb')}, + index=list('ABCD')) + + # return label + res = df.loc[lambda x: ['A', 'C']] + tm.assert_frame_equal(res, df.loc[['A', 'C']]) + + res = df.loc[lambda x: ['A', 'C'], ] + tm.assert_frame_equal(res, df.loc[['A', 'C'], ]) + + res = df.loc[lambda x: ['A', 'C'], :] + tm.assert_frame_equal(res, df.loc[['A', 'C'], :]) + + res = df.loc[lambda x: ['A', 'C'], lambda x: 'X'] + tm.assert_series_equal(res, df.loc[['A', 'C'], 'X']) + + res = df.loc[lambda x: ['A', 'C'], lambda x: ['X']] + tm.assert_frame_equal(res, df.loc[['A', 'C'], ['X']]) + + # mixture + res = df.loc[['A', 'C'], lambda x: 'X'] + tm.assert_series_equal(res, df.loc[['A', 'C'], 'X']) + + res = df.loc[['A', 'C'], lambda x: ['X']] + tm.assert_frame_equal(res, df.loc[['A', 'C'], ['X']]) + + res = df.loc[lambda x: ['A', 'C'], 'X'] + tm.assert_series_equal(res, df.loc[['A', 'C'], 'X']) + + res = df.loc[lambda x: ['A', 'C'], ['X']] + tm.assert_frame_equal(res, df.loc[['A', 'C'], ['X']]) + + def test_frame_loc_callable_setitem(self): + # GH 11485 + df = pd.DataFrame({'X': [1, 2, 3, 4], + 'Y': list('aabb')}, + index=list('ABCD')) + + # return label + res = df.copy() + res.loc[lambda x: ['A', 'C']] = -20 + exp = df.copy() + exp.loc[['A', 'C']] = -20 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ['A', 'C'], :] = 20 + exp = df.copy() + exp.loc[['A', 'C'], :] = 20 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ['A', 'C'], lambda x: 'X'] = -1 + exp = df.copy() + exp.loc[['A', 'C'], 'X'] = -1 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ['A', 'C'], lambda x: ['X']] = [5, 10] + exp = df.copy() + exp.loc[['A', 'C'], ['X']] = [5, 10] + tm.assert_frame_equal(res, exp) + + # mixture + res = df.copy() + res.loc[['A', 'C'], lambda x: 'X'] = np.array([-1, -2]) + exp = df.copy() + exp.loc[['A', 'C'], 'X'] = np.array([-1, -2]) + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[['A', 'C'], lambda x: ['X']] = 10 + exp = df.copy() + exp.loc[['A', 'C'], ['X']] = 10 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ['A', 'C'], 'X'] = -2 + exp = df.copy() + exp.loc[['A', 'C'], 'X'] = -2 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ['A', 'C'], ['X']] = -4 + exp = df.copy() + exp.loc[['A', 'C'], ['X']] = -4 + tm.assert_frame_equal(res, exp) + + def test_frame_iloc_callable(self): + # GH 11485 + df = pd.DataFrame({'X': [1, 2, 3, 4], + 'Y': list('aabb')}, + index=list('ABCD')) + + # return location + res = df.iloc[lambda x: [1, 3]] + tm.assert_frame_equal(res, df.iloc[[1, 3]]) + + res = df.iloc[lambda x: [1, 3], :] + tm.assert_frame_equal(res, df.iloc[[1, 3], :]) + + res = df.iloc[lambda x: [1, 3], lambda x: 0] + tm.assert_series_equal(res, df.iloc[[1, 3], 0]) + + res = df.iloc[lambda x: [1, 3], lambda x: [0]] + tm.assert_frame_equal(res, df.iloc[[1, 3], [0]]) + + # mixture + res = df.iloc[[1, 3], lambda x: 0] + tm.assert_series_equal(res, df.iloc[[1, 3], 0]) + + res = df.iloc[[1, 3], lambda x: [0]] + tm.assert_frame_equal(res, df.iloc[[1, 3], [0]]) + + res = df.iloc[lambda x: [1, 3], 0] + tm.assert_series_equal(res, df.iloc[[1, 3], 0]) + + res = df.iloc[lambda x: [1, 3], [0]] + tm.assert_frame_equal(res, df.iloc[[1, 3], [0]]) + + def test_frame_iloc_callable_setitem(self): + # GH 11485 + df = pd.DataFrame({'X': [1, 2, 3, 4], + 'Y': list('aabb')}, + index=list('ABCD')) + + # return location + res = df.copy() + res.iloc[lambda x: [1, 3]] = 0 + exp = df.copy() + exp.iloc[[1, 3]] = 0 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], :] = -1 + exp = df.copy() + exp.iloc[[1, 3], :] = -1 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], lambda x: 0] = 5 + exp = df.copy() + exp.iloc[[1, 3], 0] = 5 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], lambda x: [0]] = 25 + exp = df.copy() + exp.iloc[[1, 3], [0]] = 25 + tm.assert_frame_equal(res, exp) + + # mixture + res = df.copy() + res.iloc[[1, 3], lambda x: 0] = -3 + exp = df.copy() + exp.iloc[[1, 3], 0] = -3 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[[1, 3], lambda x: [0]] = -5 + exp = df.copy() + exp.iloc[[1, 3], [0]] = -5 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], 0] = 10 + exp = df.copy() + exp.iloc[[1, 3], 0] = 10 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], [0]] = [-5, -5] + exp = df.copy() + exp.iloc[[1, 3], [0]] = [-5, -5] + tm.assert_frame_equal(res, exp) + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py index 058fb430b9c87..5ed3fda7d0b8f 100644 --- a/pandas/tests/series/test_indexing.py +++ b/pandas/tests/series/test_indexing.py @@ -389,6 +389,18 @@ def test_getitem_dataframe(self): df = pd.DataFrame(rng, index=rng) self.assertRaises(TypeError, s.__getitem__, df > 5) + def test_getitem_callable(self): + # GH 12533 + s = pd.Series(4, index=list('ABCD')) + result = s[lambda x: 'A'] + self.assertEqual(result, s.loc['A']) + + result = s[lambda x: ['A', 'B']] + tm.assert_series_equal(result, s.loc[['A', 'B']]) + + result = s[lambda x: [True, False, True, True]] + tm.assert_series_equal(result, s.iloc[[0, 2, 3]]) + def test_setitem_ambiguous_keyerror(self): s = Series(lrange(10), index=lrange(0, 20, 2)) @@ -413,6 +425,12 @@ def test_setitem_float_labels(self): assert_series_equal(s, tmp) + def test_setitem_callable(self): + # GH 12533 + s = pd.Series([1, 2, 3, 4], index=list('ABCD')) + s[lambda x: 'A'] = -1 + tm.assert_series_equal(s, pd.Series([-1, 2, 3, 4], index=list('ABCD'))) + def test_slice(self): numSlice = self.series[10:20] numSliceEnd = self.series[-10:] diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py index ffefd46d20376..a6516614e9965 100644 --- a/pandas/tests/test_panel.py +++ b/pandas/tests/test_panel.py @@ -713,6 +713,14 @@ def test_getitem_fancy_xs_check_view(self): self._check_view((item, NS, 'C'), comp) self._check_view((NS, date, 'C'), comp) + def test_getitem_callable(self): + p = self.panel + # GH 12533 + + assert_frame_equal(p[lambda x: 'ItemB'], p.loc['ItemB']) + assert_panel_equal(p[lambda x: ['ItemB', 'ItemC']], + p.loc[['ItemB', 'ItemC']]) + def test_ix_setitem_slice_dataframe(self): a = Panel(items=[1, 2, 3], major_axis=[11, 22, 33], minor_axis=[111, 222, 333])
- [x] closes #12533 - [x] closes #11485 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Current impl is very simple like `assign`. Another idea is applying some restriction to return value from `callable` (for example, only allow aligned `DataFrame` or scalar).
https://api.github.com/repos/pandas-dev/pandas/pulls/12539
2016-03-06T02:02:31Z
2016-04-29T17:05:49Z
null
2016-04-29T17:06:56Z
BUG: Series.map may raise TypeError in Categorical or DatetimeTz
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index c6642c5216262..5acf880a54c58 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -129,6 +129,7 @@ API changes - ``Period`` and ``PeriodIndex`` now raises ``IncompatibleFrequency`` error which inherits ``ValueError`` rather than raw ``ValueError`` (:issue:`12615`) +- ``Series.apply`` for category dtype now applies passed function to each ``.categories`` (not ``.codes``), and returns "category" dtype if possible (:issue:`12473`) - The default for ``.query()/.eval()`` is now ``engine=None``, which will use ``numexpr`` if it's installed; otherwise it will fallback to the ``python`` engine. This mimics the pre-0.18.1 behavior if ``numexpr`` is installed (and which Previously, if numexpr was not installed, ``.query()/.eval()`` would raise). (:issue:`12749`) @@ -324,4 +325,8 @@ Bug Fixes - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`) - ``pd.read_excel()`` now accepts column names associated with keyword argument ``names``(:issue `12870`) + - Bug in ``fill_value`` is ignored if the argument to a binary operator is a constant (:issue `12723`) + + +- Bug in ``Series.map`` raises ``TypeError`` if its dtype is ``category`` or tz-aware ``datetime`` (:issue:`12473`) \ No newline at end of file diff --git a/pandas/core/categorical.py b/pandas/core/categorical.py index 986f7ad55361a..863d68a7c60e5 100644 --- a/pandas/core/categorical.py +++ b/pandas/core/categorical.py @@ -883,6 +883,30 @@ def remove_unused_categories(self, inplace=False): if not inplace: return cat + def map(self, mapper): + """ + Apply mapper function to its categories (not codes). + + Parameters + ---------- + mapper : callable + Function to be applied. When all categories are mapped + to different categories, the result will be Categorical which has + the same order property as the original. Otherwise, the result will + be np.ndarray. + + Returns + ------- + applied : Categorical or np.ndarray. + """ + new_categories = self.categories.map(mapper) + try: + return Categorical.from_codes(self._codes.copy(), + categories=new_categories, + ordered=self.ordered) + except ValueError: + return np.take(new_categories, self._codes) + __eq__ = _cat_compare_op('__eq__') __ne__ = _cat_compare_op('__ne__') __lt__ = _cat_compare_op('__lt__') diff --git a/pandas/core/common.py b/pandas/core/common.py index c0f47a48a46a8..f75b1bbce668f 100644 --- a/pandas/core/common.py +++ b/pandas/core/common.py @@ -705,7 +705,7 @@ def _maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): copy : if True always make a copy even if no upcast is required """ - if is_internal_type(values): + if is_extension_type(values): if copy: values = values.copy() else: @@ -1714,7 +1714,7 @@ def is_datetimetz(array): is_datetime64tz_dtype(array)) -def is_internal_type(value): +def is_extension_type(value): """ if we are a klass that is preserved by the internals these are internal klasses that we represent (and don't use a np.array) diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 96a2b87a1bdb7..c598a2b719f82 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -27,7 +27,7 @@ isnull, notnull, PandasError, _try_sort, _default_index, _maybe_upcast, is_sequence, _infer_dtype_from_scalar, _values_from_object, is_list_like, _maybe_box_datetimelike, is_categorical_dtype, is_object_dtype, - is_internal_type, is_datetimetz, _possibly_infer_to_datetimelike, + is_extension_type, is_datetimetz, _possibly_infer_to_datetimelike, _dict_compat) from pandas.core.generic import NDFrame, _shared_docs from pandas.core.index import Index, MultiIndex, _ensure_index @@ -2594,7 +2594,7 @@ def reindexer(value): value = com._possibly_cast_to_datetime(value, dtype) # return internal types directly - if is_internal_type(value): + if is_extension_type(value): return value # broadcast across multiple columns if necessary @@ -4094,7 +4094,7 @@ def _apply_standard(self, func, axis, ignore_failures=False, reduce=True): # we cannot reduce using non-numpy dtypes, # as demonstrated in gh-12244 - if not is_internal_type(values): + if not is_extension_type(values): # Create a dummy Series from an empty array index = self._get_axis(axis) empty_arr = np.empty(len(index), dtype=values.dtype) diff --git a/pandas/core/internals.py b/pandas/core/internals.py index 463a2da529b5d..a74d2fb45cdbc 100644 --- a/pandas/core/internals.py +++ b/pandas/core/internals.py @@ -20,7 +20,7 @@ _maybe_convert_string_to_object, _maybe_convert_scalar, is_categorical, is_datetimelike_v_numeric, - is_numeric_v_string_like, is_internal_type) + is_numeric_v_string_like, is_extension_type) import pandas.core.algorithms as algos from pandas.types.api import DatetimeTZDtype @@ -1765,7 +1765,7 @@ def should_store(self, value): return not (issubclass(value.dtype.type, (np.integer, np.floating, np.complexfloating, np.datetime64, np.bool_)) or - is_internal_type(value)) + is_extension_type(value)) def replace(self, to_replace, value, inplace=False, filter=None, regex=False, convert=True, mgr=None): @@ -3388,10 +3388,10 @@ def set(self, item, value, check=False): # FIXME: refactor, clearly separate broadcasting & zip-like assignment # can prob also fix the various if tests for sparse/categorical - value_is_internal_type = is_internal_type(value) + value_is_extension_type = is_extension_type(value) # categorical/spares/datetimetz - if value_is_internal_type: + if value_is_extension_type: def value_getitem(placement): return value @@ -3463,7 +3463,7 @@ def value_getitem(placement): unfit_count = len(unfit_mgr_locs) new_blocks = [] - if value_is_internal_type: + if value_is_extension_type: # This code (ab-)uses the fact that sparse blocks contain only # one item. new_blocks.extend( diff --git a/pandas/core/series.py b/pandas/core/series.py index bf20c5d740133..9fc1bc0dbe969 100644 --- a/pandas/core/series.py +++ b/pandas/core/series.py @@ -20,7 +20,7 @@ is_categorical_dtype, _possibly_cast_to_datetime, _possibly_castable, _possibly_convert_platform, - _try_sort, is_internal_type, is_datetimetz, + _try_sort, is_extension_type, is_datetimetz, _maybe_match_name, ABCSparseArray, _coerce_to_dtype, SettingWithCopyError, _maybe_box_datetimelike, ABCDataFrame, @@ -2063,15 +2063,21 @@ def map(self, arg, na_action=None): y : Series same index as caller """ - values = self.asobject - if na_action == 'ignore': - mask = isnull(values) - - def map_f(values, f): - return lib.map_infer_mask(values, f, mask.view(np.uint8)) + if is_extension_type(self.dtype): + values = self._values + if na_action is not None: + raise NotImplementedError + map_f = lambda values, f: values.map(f) else: - map_f = lib.map_infer + values = self.asobject + + if na_action == 'ignore': + def map_f(values, f): + return lib.map_infer_mask(values, f, + isnull(values).view(np.uint8)) + else: + map_f = lib.map_infer if isinstance(arg, (dict, Series)): if isinstance(arg, dict): @@ -2079,12 +2085,11 @@ def map_f(values, f): indexer = arg.index.get_indexer(values) new_values = algos.take_1d(arg._values, indexer) - return self._constructor(new_values, - index=self.index).__finalize__(self) else: - mapped = map_f(values, arg) - return self._constructor(mapped, - index=self.index).__finalize__(self) + new_values = map_f(values, arg) + + return self._constructor(new_values, + index=self.index).__finalize__(self) def apply(self, func, convert_dtype=True, args=(), **kwds): """ @@ -2193,7 +2198,12 @@ def apply(self, func, convert_dtype=True, args=(), **kwds): if isinstance(f, np.ufunc): return f(self) - mapped = lib.map_infer(self.asobject, f, convert=convert_dtype) + if is_extension_type(self.dtype): + mapped = self._values.map(f) + else: + values = self.asobject + mapped = lib.map_infer(values, f, convert=convert_dtype) + if len(mapped) and isinstance(mapped[0], Series): from pandas.core.frame import DataFrame return DataFrame(mapped.tolist(), index=self.index) @@ -2779,7 +2789,7 @@ def _try_cast(arr, take_fast_path): try: subarr = _possibly_cast_to_datetime(arr, dtype) - if not is_internal_type(subarr): + if not is_extension_type(subarr): subarr = np.array(subarr, dtype=dtype, copy=copy) except (ValueError, TypeError): if is_categorical_dtype(dtype): diff --git a/pandas/indexes/base.py b/pandas/indexes/base.py index 644b6720dfaac..f606f4a649047 100644 --- a/pandas/indexes/base.py +++ b/pandas/indexes/base.py @@ -2194,11 +2194,22 @@ def groupby(self, to_groupby): ------- groups : dict {group name -> group labels} - """ return self._groupby(self.values, _values_from_object(to_groupby)) def map(self, mapper): + """ + Apply mapper function to its values. + + Parameters + ---------- + mapper : callable + Function to be applied. + + Returns + ------- + applied : array + """ return self._arrmap(self.values, mapper) def isin(self, values, level=None): diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 16b8fd8df4e2a..98cb028aefae8 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -468,6 +468,24 @@ def take(self, indices, axis=0, allow_fill=True, fill_value=None): na_value=-1) return self._create_from_codes(taken) + def map(self, mapper): + """ + Apply mapper function to its categories (not codes). + + Parameters + ---------- + mapper : callable + Function to be applied. When all categories are mapped + to different categories, the result will be Categorical which has + the same order property as the original. Otherwise, the result will + be np.ndarray. + + Returns + ------- + applied : Categorical or np.ndarray. + """ + return self.values.map(mapper) + def delete(self, loc): """ Make new Index with passed location(-s) deleted diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index a8534309c115c..fa8f6a291c677 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -201,6 +201,33 @@ def test_min_max(self): self.assertEqual(ci.min(), 'c') self.assertEqual(ci.max(), 'b') + def test_map(self): + ci = pd.CategoricalIndex(list('ABABC'), categories=list('CBA'), + ordered=True) + result = ci.map(lambda x: x.lower()) + exp = pd.Categorical(list('ababc'), categories=list('cba'), + ordered=True) + tm.assert_categorical_equal(result, exp) + + ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'), + ordered=False, name='XXX') + result = ci.map(lambda x: x.lower()) + exp = pd.Categorical(list('ababc'), categories=list('bac'), + ordered=False) + tm.assert_categorical_equal(result, exp) + + tm.assert_numpy_array_equal(ci.map(lambda x: 1), np.array([1] * 5)) + + # change categories dtype + ci = pd.CategoricalIndex(list('ABABC'), categories=list('BAC'), + ordered=False) + def f(x): + return {'A': 10, 'B': 20, 'C': 30}.get(x) + result = ci.map(f) + exp = pd.Categorical([10, 20, 10, 20, 30], categories=[20, 10, 30], + ordered=False) + tm.assert_categorical_equal(result, exp) + def test_append(self): ci = self.create_index() diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py index 9182b16d1f5b5..af648d34637df 100644 --- a/pandas/tests/series/test_analytics.py +++ b/pandas/tests/series/test_analytics.py @@ -1567,6 +1567,25 @@ def test_sortlevel(self): res = s.sortlevel(['A', 'B'], sort_remaining=False) assert_series_equal(s, res) + def test_apply_categorical(self): + values = pd.Categorical(list('ABBABCD'), categories=list('DCBA'), + ordered=True) + s = pd.Series(values, name='XX', index=list('abcdefg')) + result = s.apply(lambda x: x.lower()) + + # should be categorical dtype when the number of categories are + # the same + values = pd.Categorical(list('abbabcd'), categories=list('dcba'), + ordered=True) + exp = pd.Series(values, name='XX', index=list('abcdefg')) + tm.assert_series_equal(result, exp) + tm.assert_categorical_equal(result.values, exp.values) + + result = s.apply(lambda x: 'A') + exp = pd.Series(['A'] * 7, name='XX', index=list('abcdefg')) + tm.assert_series_equal(result, exp) + self.assertEqual(result.dtype, np.object) + def test_shift_int(self): ts = self.ts.astype(int) shifted = ts.shift(1) diff --git a/pandas/tests/series/test_apply.py b/pandas/tests/series/test_apply.py index 87369a0e6ef90..154837fc2a3b1 100644 --- a/pandas/tests/series/test_apply.py +++ b/pandas/tests/series/test_apply.py @@ -110,6 +110,32 @@ def test_apply_box(self): exp = pd.Series(['Period_M', 'Period_M']) tm.assert_series_equal(res, exp) + def test_apply_datetimetz(self): + values = pd.date_range('2011-01-01', '2011-01-02', + freq='H').tz_localize('Asia/Tokyo') + s = pd.Series(values, name='XX') + + result = s.apply(lambda x: x + pd.offsets.Day()) + exp_values = pd.date_range('2011-01-02', '2011-01-03', + freq='H').tz_localize('Asia/Tokyo') + exp = pd.Series(exp_values, name='XX') + tm.assert_series_equal(result, exp) + + # change dtype + result = s.apply(lambda x: x.hour) + exp = pd.Series(list(range(24)) + [0], name='XX', dtype=np.int32) + tm.assert_series_equal(result, exp) + + # not vectorized + def f(x): + if not isinstance(x, pd.Timestamp): + raise ValueError + return str(x.tz) + + result = s.map(f) + exp = pd.Series(['Asia/Tokyo'] * 25, name='XX') + tm.assert_series_equal(result, exp) + class TestSeriesMap(TestData, tm.TestCase): @@ -255,3 +281,53 @@ def test_map_box(self): x.freqstr)) exp = pd.Series(['Period_M', 'Period_M']) tm.assert_series_equal(res, exp) + + def test_map_categorical(self): + values = pd.Categorical(list('ABBABCD'), categories=list('DCBA'), + ordered=True) + s = pd.Series(values, name='XX', index=list('abcdefg')) + + result = s.map(lambda x: x.lower()) + exp_values = pd.Categorical(list('abbabcd'), categories=list('dcba'), + ordered=True) + exp = pd.Series(exp_values, name='XX', index=list('abcdefg')) + tm.assert_series_equal(result, exp) + tm.assert_categorical_equal(result.values, exp_values) + + result = s.map(lambda x: 'A') + exp = pd.Series(['A'] * 7, name='XX', index=list('abcdefg')) + tm.assert_series_equal(result, exp) + self.assertEqual(result.dtype, np.object) + + with tm.assertRaises(NotImplementedError): + s.map(lambda x: x, na_action='ignore') + + def test_map_datetimetz(self): + values = pd.date_range('2011-01-01', '2011-01-02', + freq='H').tz_localize('Asia/Tokyo') + s = pd.Series(values, name='XX') + + # keep tz + result = s.map(lambda x: x + pd.offsets.Day()) + exp_values = pd.date_range('2011-01-02', '2011-01-03', + freq='H').tz_localize('Asia/Tokyo') + exp = pd.Series(exp_values, name='XX') + tm.assert_series_equal(result, exp) + + # change dtype + result = s.map(lambda x: x.hour) + exp = pd.Series(list(range(24)) + [0], name='XX', dtype=np.int32) + tm.assert_series_equal(result, exp) + + with tm.assertRaises(NotImplementedError): + s.map(lambda x: x, na_action='ignore') + + # not vectorized + def f(x): + if not isinstance(x, pd.Timestamp): + raise ValueError + return str(x.tz) + + result = s.map(f) + exp = pd.Series(['Asia/Tokyo'] * 25, name='XX') + tm.assert_series_equal(result, exp) diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py index a0e6241383289..a1cc05b0c9873 100644 --- a/pandas/tests/test_categorical.py +++ b/pandas/tests/test_categorical.py @@ -1551,6 +1551,24 @@ def test_comparison_with_unknown_scalars(self): self.assert_numpy_array_equal(cat == 4, [False, False, False]) self.assert_numpy_array_equal(cat != 4, [True, True, True]) + def test_map(self): + c = pd.Categorical(list('ABABC'), categories=list('CBA'), + ordered=True) + result = c.map(lambda x: x.lower()) + exp = pd.Categorical(list('ababc'), categories=list('cba'), + ordered=True) + tm.assert_categorical_equal(result, exp) + + c = pd.Categorical(list('ABABC'), categories=list('ABC'), + ordered=False) + result = c.map(lambda x: x.lower()) + exp = pd.Categorical(list('ababc'), categories=list('abc'), + ordered=False) + tm.assert_categorical_equal(result, exp) + + result = c.map(lambda x: 1) + tm.assert_numpy_array_equal(result, np.array([1] * 5)) + class TestCategoricalAsBlock(tm.TestCase): _multiprocess_can_split_ = True diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 89200ef79dac9..7a604d0e7341b 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -888,11 +888,11 @@ def assertNotIsInstance(obj, cls, msg=''): def assert_categorical_equal(res, exp): + assertIsInstance(res, pd.Categorical, '[Categorical] ') + assertIsInstance(exp, pd.Categorical, '[Categorical] ') + + assert_index_equal(res.categories, exp.categories) - if not array_equivalent(res.categories, exp.categories): - raise AssertionError( - 'categories not equivalent: {0} vs {1}.'.format(res.categories, - exp.categories)) if not array_equivalent(res.codes, exp.codes): raise AssertionError( 'codes not equivalent: {0} vs {1}.'.format(res.codes, exp.codes))
- [x] closes #12473 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Needs to decide what categorical dtype `map` should return. Even though `apply` returns `object` dtype, but returning `category` is more consistent?
https://api.github.com/repos/pandas-dev/pandas/pulls/12532
2016-03-05T15:07:41Z
2016-04-18T17:17:44Z
null
2016-05-14T10:56:00Z
BUG: CategoricalIndex.get_loc returns array even if it is unique
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index dbe446f0a7b4f..7e01690958457 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -89,3 +89,6 @@ Bug Fixes ~~~~~~~~~ - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) + +- Bug in ``CategoricalIndex.get_loc`` returns different result from + normal ``Index`` (:issue:`12531`) diff --git a/pandas/indexes/category.py b/pandas/indexes/category.py index 4ead02e5bd022..cc25b5fc6fb8c 100644 --- a/pandas/indexes/category.py +++ b/pandas/indexes/category.py @@ -287,11 +287,7 @@ def get_loc(self, key, method=None): 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 + return self._engine.get_loc(codes) def _can_reindex(self, indexer): """ always allow reindexing """ diff --git a/pandas/tests/indexes/test_category.py b/pandas/tests/indexes/test_category.py index 78016c0f0b5f7..d929c5df99e4a 100644 --- a/pandas/tests/indexes/test_category.py +++ b/pandas/tests/indexes/test_category.py @@ -363,6 +363,50 @@ def test_get_indexer(self): self.assertRaises(NotImplementedError, lambda: idx2.get_indexer(idx1, method='nearest')) + def test_get_loc(self): + # GH 12531 + cidx1 = CategoricalIndex(list('abcde'), categories=list('edabc')) + idx1 = Index(list('abcde')) + self.assertEqual(cidx1.get_loc('a'), idx1.get_loc('a')) + self.assertEqual(cidx1.get_loc('e'), idx1.get_loc('e')) + + for i in [cidx1, idx1]: + with tm.assertRaises(KeyError): + i.get_loc('NOT-EXIST') + + # non-unique + cidx2 = CategoricalIndex(list('aacded'), categories=list('edabc')) + idx2 = Index(list('aacded')) + # results in bool array + res = cidx2.get_loc('d') + self.assert_numpy_array_equal(res, idx2.get_loc('d')) + self.assert_numpy_array_equal(res, np.array([False, False, False, + True, False, True])) + # unique element results in scalar + res = cidx2.get_loc('e') + self.assertEqual(res, idx2.get_loc('e')) + self.assertEqual(res, 4) + + for i in [cidx2, idx2]: + with tm.assertRaises(KeyError): + i.get_loc('NOT-EXIST') + + # non-unique, slicable + cidx3 = CategoricalIndex(list('aabbb'), categories=list('abc')) + idx3 = Index(list('aabbb')) + # results in slice + res = cidx3.get_loc('a') + self.assertEqual(res, idx3.get_loc('a')) + self.assertEqual(res, slice(0, 2, None)) + + res = cidx3.get_loc('b') + self.assertEqual(res, idx3.get_loc('b')) + self.assertEqual(res, slice(2, 5, None)) + + for i in [cidx3, idx3]: + with tm.assertRaises(KeyError): + i.get_loc('c') + def test_repr_roundtrip(self): ci = CategoricalIndex(['a', 'b'], categories=['a', 'b'], ordered=True) diff --git a/pandas/tests/indexing/test_categorical.py b/pandas/tests/indexing/test_categorical.py index 4e31fb350f6ee..5abff801fccec 100644 --- a/pandas/tests/indexing/test_categorical.py +++ b/pandas/tests/indexing/test_categorical.py @@ -180,6 +180,50 @@ def test_loc_listlike_dtypes(self): 'that are in the categories'): df.loc[['a', 'x']] + def test_ix_categorical_index(self): + df = pd.DataFrame(np.random.randn(3, 3), + index=list('ABC'), columns=list('XYZ')) + cdf = df.copy() + cdf.index = pd.CategoricalIndex(df.index) + cdf.columns = pd.CategoricalIndex(df.columns) + + expect = pd.Series(df.ix['A', :], index=cdf.columns, name='A') + assert_series_equal(cdf.ix['A', :], expect) + + expect = pd.Series(df.ix[:, 'X'], index=cdf.index, name='X') + assert_series_equal(cdf.ix[:, 'X'], expect) + + expect = pd.DataFrame(df.ix[['A', 'B'], :], columns=cdf.columns, + index=pd.CategoricalIndex(list('AB'))) + assert_frame_equal(cdf.ix[['A', 'B'], :], expect) + + expect = pd.DataFrame(df.ix[:, ['X', 'Y']], index=cdf.index, + columns=pd.CategoricalIndex(list('XY'))) + assert_frame_equal(cdf.ix[:, ['X', 'Y']], expect) + + # non-unique + df = pd.DataFrame(np.random.randn(3, 3), + index=list('ABA'), columns=list('XYX')) + cdf = df.copy() + cdf.index = pd.CategoricalIndex(df.index) + cdf.columns = pd.CategoricalIndex(df.columns) + + expect = pd.DataFrame(df.ix['A', :], columns=cdf.columns, + index=pd.CategoricalIndex(list('AA'))) + assert_frame_equal(cdf.ix['A', :], expect) + + expect = pd.DataFrame(df.ix[:, 'X'], index=cdf.index, + columns=pd.CategoricalIndex(list('XX'))) + assert_frame_equal(cdf.ix[:, 'X'], expect) + + expect = pd.DataFrame(df.ix[['A', 'B'], :], columns=cdf.columns, + index=pd.CategoricalIndex(list('AAB'))) + assert_frame_equal(cdf.ix[['A', 'B'], :], expect) + + expect = pd.DataFrame(df.ix[:, ['X', 'Y']], index=cdf.index, + columns=pd.CategoricalIndex(list('XXY'))) + assert_frame_equal(cdf.ix[:, ['X', 'Y']], expect) + def test_read_only_source(self): # GH 10043 rw_array = np.eye(10)
- [x] related to #11558 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Made `CategoricalIndex.get_loc` to return the same result as normal `Index`. ``` data2 = pd.DataFrame([[1,2,3],[3,4,5]], index=pd.Categorical(['one', 'two'])) # in current master, it returns DataFrame with 1 row data2.ix['one'] # 0 1 2 # one 1 2 3 # after this PR, it returns Series data2.ix['one'] #0 1 #1 2 #2 3 # Name: one, dtype: int64 ```
https://api.github.com/repos/pandas-dev/pandas/pulls/12531
2016-03-05T14:29:49Z
2016-03-15T13:39:17Z
null
2016-03-15T19:58:00Z
ENH: Partial string matching for timestamps with multiindex
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst index a986c3e1cb065..c262a1c505673 100644 --- a/doc/source/timeseries.rst +++ b/doc/source/timeseries.rst @@ -422,6 +422,20 @@ We are stopping on the included end-point as it is part of the index dft.loc['2013-1-15 12:30:00'] +DatetimeIndex Partial String Indexing also works on DataFrames with hierarchical indexing (``MultiIndex``). For +instance: + +.. ipython:: python + + dft2 = pd.DataFrame(randn(20,1), + columns=['A'], + index=pd.MultiIndex.from_product([date_range('20130101', + periods=10, + freq='12H'), + ['a', 'b']])) + dft2.loc['2013-01-05'] + dft2 = dft2.swaplevel(0, 1).sort_index() + dft2.loc[pd.IndexSlice[:,'2013-01-05'],:] Datetime Indexing ~~~~~~~~~~~~~~~~~ diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index d9983759083ca..cdf38e95fd9e1 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -30,6 +30,24 @@ Enhancements ~~~~~~~~~~~~ +Partial string matches on ``DateTimeIndex`` when part of a ``MultiIndex`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Partial string matches on ``DateTimeIndex`` now work when part of a ``MultiIndex`` (:issue:`10331`) + +For example: + +.. ipython:: python + + dft2 = pd.DataFrame(randn(20,1), + columns=['A'], + index=pd.MultiIndex.from_product([date_range('20130101', + periods=10, + freq='12H'), + ['a', 'b']])) + dft2.loc['2013-01-05'] + dft2 = dft2.swaplevel(0, 1).sort_index() + dft2.loc[pd.IndexSlice[:,'2013-01-05'],:] + diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index b0dd2596fccd5..23de660fd23f4 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -1392,8 +1392,33 @@ def error(): return True + def _get_partial_string_timestamp_match_key(self, key, labels): + """Translate any partial string timestamp matches in key, returning the + new key (GH 10331)""" + if isinstance(labels, MultiIndex): + if isinstance(key, compat.string_types) and \ + labels.levels[0].is_all_dates: + # Convert key '2016-01-01' to + # ('2016-01-01'[, slice(None, None, None)]+) + key = tuple([key] + [slice(None)] * (len(labels.levels) - 1)) + + if isinstance(key, tuple): + # Convert (..., '2016-01-01', ...) in tuple to + # (..., slice('2016-01-01', '2016-01-01', None), ...) + new_key = [] + for i, component in enumerate(key): + if isinstance(component, compat.string_types) and \ + labels.levels[i].is_all_dates: + new_key.append(slice(component, component, None)) + else: + new_key.append(component) + key = tuple(new_key) + + return key + def _getitem_axis(self, key, axis=0): labels = self.obj._get_axis(axis) + key = self._get_partial_string_timestamp_match_key(key, labels) if isinstance(key, slice): self._has_valid_type(key, axis) diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py index 2c3c75cfa0431..f70ea49bd4c29 100644 --- a/pandas/tests/indexes/test_multi.py +++ b/pandas/tests/indexes/test_multi.py @@ -14,8 +14,8 @@ import numpy as np -from pandas.util.testing import (assert_almost_equal, assertRaisesRegexp, - assert_copy) +from pandas.util.testing import (assert_almost_equal, assertRaises, + assertRaisesRegexp, assert_copy) import pandas.util.testing as tm @@ -1970,3 +1970,82 @@ def test_index_name_retained(self): def test_equals_operator(self): # GH9785 self.assertTrue((self.index == self.index).all()) + + def test_partial_string_timestamp_multiindex(self): + # GH10331 + dr = pd.date_range('2016-01-01', '2016-01-03', freq='12H') + abc = ['a', 'b', 'c'] + ix = pd.MultiIndex.from_product([dr, abc]) + df = pd.DataFrame({'c1': range(0, 15)}, index=ix) + idx = pd.IndexSlice + + # c1 + # 2016-01-01 00:00:00 a 0 + # b 1 + # c 2 + # 2016-01-01 12:00:00 a 3 + # b 4 + # c 5 + # 2016-01-02 00:00:00 a 6 + # b 7 + # c 8 + # 2016-01-02 12:00:00 a 9 + # b 10 + # c 11 + # 2016-01-03 00:00:00 a 12 + # b 13 + # c 14 + + # partial string matching on a single index + df_swap = df.swaplevel(0, 1).sort_index() + just_a = df_swap.loc['a'] + result = just_a.loc['2016-01-01'] + expected = df.loc[idx[:, 'a'], :].iloc[0:2] + expected.index = expected.index.droplevel(1) + tm.assert_frame_equal(result, expected) + + # indexing with IndexSlice + result = df.loc[idx['2016-01-01':'2016-02-01', :], :] + expected = df + tm.assert_frame_equal(result, expected) + + # match on secondary index + result = df_swap.loc[idx[:, '2016-01-01':'2016-01-01'], :] + expected = df_swap.iloc[[0, 1, 5, 6, 10, 11]] + tm.assert_frame_equal(result, expected) + + # Even though this syntax works on a single index, this is somewhat + # ambiguous and we don't want to extend this behavior forward to work + # in multi-indexes. This would amount to selecting a scalar from a + # column. + with assertRaises(KeyError): + df['2016-01-01'] + + # partial string match on year only + result = df.loc['2016'] + expected = df + tm.assert_frame_equal(result, expected) + + # partial string match on date + result = df.loc['2016-01-01'] + expected = df.iloc[0:6] + tm.assert_frame_equal(result, expected) + + # partial string match on date and hour, from middle + result = df.loc['2016-01-02 12'] + expected = df.iloc[9:12] + tm.assert_frame_equal(result, expected) + + # partial string match on secondary index + result = df_swap.loc[idx[:, '2016-01-02'], :] + expected = df_swap.iloc[[2, 3, 7, 8, 12, 13]] + tm.assert_frame_equal(result, expected) + + # tuple selector with partial string match on date + result = df.loc[('2016-01-01', 'a'), :] + expected = df.iloc[[0, 3]] + tm.assert_frame_equal(result, expected) + + # Slicing date on first level should break (of course) + with assertRaises(KeyError): + df_swap.loc['2016-01-01']
- [x] closes #10331 - [x] tests added / passed - [x] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Includes implementation, unit tests, documentation fixes and whatsnew.
https://api.github.com/repos/pandas-dev/pandas/pulls/12530
2016-03-05T04:24:30Z
2016-03-21T12:27:56Z
null
2016-03-22T02:06:26Z
BUG: Add exception when different number of fields present in file lines
diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index dae15215929b7..1cf0b32d54f0b 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -537,9 +537,13 @@ static int end_line(parser_t *self) { } while (fields < ex_fields){ - end_field(self); - /* printf("Prior word: %s\n", self->words[self->words_len - 2]); */ - fields++; + self->error_msg = (char*) malloc(100); + sprintf(self->error_msg, "Expected %d fields in line %d, saw %d\n", + ex_fields, self->file_lines, fields); + + TRACE(("Error at line %d, %d fields\n", self->file_lines, fields)); + + return -1; } }
Addresses issue in #12519 by raising exception when 'filepath_or_buffer' in 'read_csv' contains different number of fields in input lines. However, according to tests the behaviour reported in bug is expected (lines 376-390): https://github.com/pydata/pandas/blob/master/pandas/io/tests/test_cparser.py#L376 So should those tests be removed?
https://api.github.com/repos/pandas-dev/pandas/pulls/12526
2016-03-04T15:50:49Z
2016-05-07T17:48:56Z
null
2016-05-07T17:48:56Z
TST: Rework style test skip
diff --git a/pandas/core/style.py b/pandas/core/style.py index 76460fdf224b2..f66ac7485c76e 100644 --- a/pandas/core/style.py +++ b/pandas/core/style.py @@ -7,8 +7,7 @@ from contextlib import contextmanager from uuid import uuid1 import copy -from collections import defaultdict -from collections.abc import MutableMapping +from collections import defaultdict, MutableMapping try: from jinja2 import Template @@ -273,7 +272,7 @@ def _translate(self): caption=caption, table_attributes=self.table_attributes) def format(self, formatter, subset=None): - ''' + """ Format the text display value of cells. .. versionadded:: 0.18.0 @@ -282,6 +281,8 @@ def format(self, formatter, subset=None): ---------- formatter: str, callable, or dict subset: IndexSlice + An argument to ``DataFrame.loc`` that restricts which elements + ``formatter`` is applied to. Returns ------- @@ -292,8 +293,9 @@ def format(self, formatter, subset=None): ``formatter`` is either an ``a`` or a dict ``{column name: a}`` where ``a`` is one of - - str: this will be wrapped in: ``a.format(x)`` - - callable: called with the value of an individual cell + + - str: this will be wrapped in: ``a.format(x)`` + - callable: called with the value of an individual cell The default display value for numeric values is the "general" (``g``) format with ``pd.options.display.precision`` precision. @@ -305,7 +307,7 @@ def format(self, formatter, subset=None): >>> df.style.format("{:.2%}") >>> df['c'] = ['a', 'b', 'c', 'd'] >>> df.style.format({'C': str.upper}) - ''' + """ if subset is None: row_locs = range(len(self.data)) col_locs = range(len(self.data.columns)) @@ -853,11 +855,11 @@ def _highlight_extrema(data, color='yellow', max_=True): def _maybe_wrap_formatter(formatter): - if not (callable(formatter) or com.is_string_like(formatter)): + if com.is_string_like(formatter): + return lambda x: formatter.format(x) + elif callable(formatter): + return formatter + else: msg = "Expected a template string or callable, got {} instead".format( formatter) raise TypeError(msg) - if not callable(formatter): - return lambda x: formatter.format(x) - else: - return formatter diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py index fb008c2d9a6f5..bfabaab8ad2f5 100644 --- a/pandas/tests/test_style.py +++ b/pandas/tests/test_style.py @@ -17,9 +17,12 @@ if job_name == '27_slow_nnet_LOCALE': raise SkipTest("No jinja") try: - from pandas.core.style import Styler + # Do try except on just jinja, so the only reason + # We skip is if jinja can't import, not something else + import jinja2 # noqa except ImportError: raise SkipTest("No Jinja2") +from pandas.core.style import Styler # noqa class TestStyler(TestCase):
Also redoes some of the changes that were inadvertently reverted in https://github.com/pydata/pandas/pull/12260. That PR had two commits, one of which was an older version of what was eventually merged in https://github.com/pydata/pandas/pull/12162. The stale commit incorrectly merged was a15248ae7. It should have been a3c38fe82. For the most part the changes were just style, but there was a python2 import error, which our tests didn't fail on because I was trying to catch a jinja ImportError, and instead caught all ImportErrors.
https://api.github.com/repos/pandas-dev/pandas/pulls/12525
2016-03-04T15:22:30Z
2016-03-05T15:13:14Z
null
2016-11-03T12:38:55Z
Revert #12260 "BUG: Make Styler ignore None index names to match to_html..."
diff --git a/doc/source/html-styling.ipynb b/doc/source/html-styling.ipynb index 77813a03c704a..1881727001546 100644 --- a/doc/source/html-styling.ipynb +++ b/doc/source/html-styling.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -93,7 +93,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" None>\n", + " <table id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -114,242 +114,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a458ec1c_c56b_11e5_9977_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_352a7df4_8d9b_11e5_9cf0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -358,10 +462,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x10b1ba8d0>" + "<pandas.core.style.Styler at 0x111c2c320>" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -381,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -393,7 +497,7 @@ " ' <style type=\"text/css\" >',\n", " ' ',\n", " ' ',\n", - " ' #T_a45fea9e_c56b_11e5_893a_a45e60bd97fbrow0_col2 {',\n", + " ' #T_3530213a_8d9b_11e5_b80c_a45e60bd97fbrow0_col2 {',\n", " ' ',\n", " ' background-color: red;',\n", " ' ',\n", @@ -401,7 +505,7 @@ " ' ']" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -428,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -454,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -466,301 +570,301 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col0 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col1 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col2 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col3 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col4 {\n", + " #T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -768,7 +872,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" None>\n", + " <table id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -789,242 +893,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a46567a8_c56b_11e5_8eb9_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_3534999c_8d9b_11e5_99d2_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -1033,10 +1241,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111d9dc50>" + "<pandas.core.style.Styler at 0x111c351d0>" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1066,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -1082,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -1094,31 +1302,31 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col4 {\n", + " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col2 {\n", + " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col1 {\n", + " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col3 {\n", + " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col0 {\n", + " #T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: yellow;\n", " \n", @@ -1126,7 +1334,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" None>\n", + " <table id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -1147,242 +1355,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a46aed36_c56b_11e5_91aa_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_3539fde2_8d9b_11e5_a76c_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -1391,10 +1703,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd4898>" + "<pandas.core.style.Styler at 0x111c35160>" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1412,7 +1724,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -1424,7 +1736,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", @@ -1432,7 +1744,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", @@ -1440,7 +1752,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", @@ -1448,7 +1760,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", @@ -1456,7 +1768,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", @@ -1464,7 +1776,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", @@ -1472,7 +1784,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", @@ -1480,7 +1792,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", @@ -1488,7 +1800,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", @@ -1496,7 +1808,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", @@ -1504,7 +1816,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", @@ -1512,7 +1824,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", @@ -1520,7 +1832,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", @@ -1528,7 +1840,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", @@ -1536,7 +1848,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", @@ -1544,7 +1856,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0 {\n", " \n", " color: black;\n", " \n", @@ -1552,7 +1864,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", @@ -1560,7 +1872,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2 {\n", " \n", " color: black;\n", " \n", @@ -1568,7 +1880,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", @@ -1576,7 +1888,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4 {\n", " \n", " color: black;\n", " \n", @@ -1584,7 +1896,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0 {\n", " \n", " color: black;\n", " \n", @@ -1592,7 +1904,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", @@ -1600,7 +1912,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2 {\n", " \n", " color: black;\n", " \n", @@ -1608,7 +1920,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", @@ -1616,7 +1928,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4 {\n", " \n", " color: black;\n", " \n", @@ -1624,7 +1936,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", @@ -1632,7 +1944,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", @@ -1640,7 +1952,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", @@ -1648,7 +1960,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -1656,7 +1968,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", @@ -1664,7 +1976,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", @@ -1672,7 +1984,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", @@ -1680,7 +1992,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", @@ -1688,7 +2000,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", @@ -1696,7 +2008,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", @@ -1704,7 +2016,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", @@ -1712,7 +2024,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", @@ -1720,7 +2032,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", @@ -1728,7 +2040,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", @@ -1736,7 +2048,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", @@ -1744,7 +2056,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", @@ -1752,7 +2064,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", @@ -1760,7 +2072,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", @@ -1768,7 +2080,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", @@ -1776,7 +2088,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", @@ -1784,7 +2096,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col0 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", @@ -1792,7 +2104,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col1 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", @@ -1800,7 +2112,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col2 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", @@ -1808,7 +2120,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col3 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", @@ -1816,7 +2128,7 @@ " \n", " }\n", " \n", - " #T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col4 {\n", + " #T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", @@ -1826,7 +2138,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" None>\n", + " <table id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -1847,242 +2159,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a470ad0c_c56b_11e5_8784_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_353f5768_8d9b_11e5_ae49_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -2091,10 +2507,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dc0dd8>" + "<pandas.core.style.Styler at 0x111c2ce48>" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2121,7 +2537,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -2143,7 +2559,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -2155,7 +2571,7 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col0 {\n", + " #T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0 {\n", " \n", " background-color: darkorange;\n", " \n", @@ -2163,7 +2579,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" None>\n", + " <table id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -2184,242 +2600,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - 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" <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a4761e18_c56b_11e5_b6a8_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_3543ed0c_8d9b_11e5_8aed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -2428,10 +2948,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd4eb8>" + "<pandas.core.style.Styler at 0x111c7d278>" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2479,7 +2999,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -2491,19 +3011,19 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col2 {\n", + " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col1 {\n", + " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1 {\n", " \n", " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col3 {\n", + " #T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3 {\n", " \n", " background-color: yellow;\n", " \n", @@ -2511,7 +3031,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" None>\n", + " <table id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -2532,242 +3052,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - 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" -0.063328\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a47c364a_c56b_11e5_bac0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_35471252_8d9b_11e5_8a7b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -2776,10 +3400,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd4ac8>" + "<pandas.core.style.Styler at 0x111c7d438>" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -2797,7 +3421,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -2809,49 +3433,49 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col1 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3 {\n", " \n", " color: black;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col1 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", " }\n", " \n", - " #T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col3 {\n", + " #T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", @@ -2859,7 +3483,7 @@ " \n", " </style>\n", "\n", - " <table id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" None>\n", + " <table id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -2880,242 +3504,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - 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" <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - 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" <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a480b42c_c56b_11e5_9c67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_3549a288_8d9b_11e5_a3ac_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -3124,10 +3852,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111daa668>" + "<pandas.core.style.Styler at 0x111c7d4e0>" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -3154,15 +3882,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Finer Control: Display Values\n", - "\n", - "We distinguish the *display* value from the *actual* value in `Styler`.\n", - "To control the display value, the text is printed in each cell, use `Styler.format`. Cells can be formatted according to a [format spec string](https://docs.python.org/3/library/string.html#format-specification-mini-language) or a callable that takes a single value and returns a string." + "## Builtin Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -3174,9 +3906,15 @@ " <style type=\"text/css\" >\n", " \n", " \n", + " #T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2 {\n", + " \n", + " background-color: red;\n", + " \n", + " }\n", + " \n", " </style>\n", "\n", - " <table id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" None>\n", + " <table id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -3197,242 +3935,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 100.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 132.92%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -31.63%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -99.08%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 200.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -107.08%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -143.87%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 56.44%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 29.57%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 300.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -162.64%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 21.96%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 67.88%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 188.93%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 400.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 96.15%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 10.40%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -48.12%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 85.02%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 500.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 145.34%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 105.77%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 16.56%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 51.50%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 600.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -133.69%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 56.29%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 139.29%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -6.33%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 700.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 12.17%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 120.76%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.20%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 162.78%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 800.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 35.45%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 103.75%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -38.57%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 51.98%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 900.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 168.66%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -132.60%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 142.90%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -208.94%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 1000.00%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -12.98%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 63.15%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -58.65%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a4850694_c56b_11e5_8e97_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 29.07%\n", + " <td id=\"T_354d50b6_8d9b_11e5_9f67_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -3441,28 +4283,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd45f8>" + "<pandas.core.style.Styler at 0x111c35a90>" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.format(\"{:.2%}\")" + "df.style.highlight_null(null_color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Use a dictionary to format specific columns." + "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -3474,565 +4316,1058 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " </style>\n", - "\n", - " <table id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" None>\n", - " \n", - "\n", - " <thead>\n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " background-color: #e5ffe5;\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1 {\n", " \n", - " </thead>\n", - " <tbody>\n", + " background-color: #188d18;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1000\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.32\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -100\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " +0.56\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", - " \n", - " </tr>\n", + " background-color: #e5ffe5;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -200\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " +0.68\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 1000\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.48\n", - " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", + " background-color: #c7eec7;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4 {\n", + " \n", + " background-color: #a6dca6;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0 {\n", + " \n", + " background-color: #ccf1cc;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1 {\n", + " \n", + " background-color: #c0eac0;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2 {\n", + " \n", + " background-color: #e5ffe5;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3 {\n", + " \n", + " background-color: #62b662;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4 {\n", + " \n", + " background-color: #5cb35c;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0 {\n", + " \n", + " background-color: #b3e3b3;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1 {\n", + " \n", + " background-color: #e5ffe5;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2 {\n", + " \n", + " background-color: #56af56;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3 {\n", + " \n", + " background-color: #56af56;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4 {\n", + " \n", + " background-color: #008000;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0 {\n", + " \n", + " background-color: #99d599;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1 {\n", + " \n", + " background-color: #329c32;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2 {\n", + " \n", + " background-color: #5fb55f;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3 {\n", + " \n", + " background-color: #daf9da;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4 {\n", + " \n", + " background-color: #3ba13b;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0 {\n", + " \n", + " background-color: #80c780;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1 {\n", + " \n", + " background-color: #108910;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2 {\n", + " \n", + " background-color: #0d870d;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3 {\n", + " \n", + " background-color: #90d090;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4 {\n", + " \n", + " background-color: #4fac4f;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col0 {\n", + " \n", + " background-color: #66b866;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col1 {\n", + " \n", + " background-color: #d2f4d2;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col2 {\n", + " \n", + " background-color: #389f38;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col3 {\n", + " \n", + " background-color: #048204;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow5_col4 {\n", + " \n", + " background-color: #70be70;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col0 {\n", + " \n", + " background-color: #4daa4d;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col1 {\n", + " \n", + " background-color: #6cbc6c;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col2 {\n", + " \n", + " background-color: #008000;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col3 {\n", + " \n", + " background-color: #a3daa3;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow6_col4 {\n", + " \n", + " background-color: #0e880e;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col0 {\n", + " \n", + " background-color: #329c32;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col1 {\n", + " \n", + " background-color: #5cb35c;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col2 {\n", + " \n", + " background-color: #0e880e;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col3 {\n", + " \n", + " background-color: #cff3cf;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow7_col4 {\n", + " \n", + " background-color: #4fac4f;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col0 {\n", + " \n", + " background-color: #198e19;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col1 {\n", + " \n", + " background-color: #008000;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col2 {\n", + " \n", + " background-color: #dcfadc;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col3 {\n", + " \n", + " background-color: #008000;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow8_col4 {\n", + " \n", + " background-color: #e5ffe5;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col0 {\n", + " \n", + " background-color: #008000;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1 {\n", + " \n", + " background-color: #7ec67e;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2 {\n", + " \n", + " background-color: #319b31;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3 {\n", + " \n", + " background-color: #e5ffe5;\n", + " \n", + " }\n", + " \n", + " #T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4 {\n", + " \n", + " background-color: #5cb35c;\n", + " \n", + " }\n", + " \n", + " </style>\n", + "\n", + " <table id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\">\n", + " \n", + "\n", + " <thead>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <th class=\"col_heading level0 col0\">A\n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1000\n", + " <th class=\"col_heading level0 col1\">B\n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <th class=\"col_heading level0 col2\">C\n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " +0.17\n", + " <th class=\"col_heading level0 col3\">D\n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <th class=\"col_heading level0 col4\">E\n", " \n", " </tr>\n", " \n", + " </thead>\n", + " <tbody>\n", + " \n", " <tr>\n", " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -100\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " +1.39\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0000\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0000\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.39\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 2000\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " +1.43\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -000\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.59\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a488d49a_c56b_11e5_b766_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x111dd4588>" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.style.format({'B': \"{:0<4.0f}\", 'D': '{:+.2f}'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or pass in a callable (or dictionary of callables) for more flexible handling." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style 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id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_355202f4_8d9b_11e5_88b7_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " ±0.96\n", - " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", - " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x111c35828>" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "cm = sns.light_palette(\"green\", as_cmap=True)\n", + "\n", + "s = df.style.background_gradient(cmap=cm)\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", + " \n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0 {\n", + " \n", + " background-color: #440154;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1 {\n", + " \n", + " background-color: #e5e419;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2 {\n", + " \n", + " background-color: #440154;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3 {\n", + " \n", + " background-color: #46327e;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4 {\n", + " \n", + " background-color: #440154;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0 {\n", + " \n", + " background-color: #3b528b;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1 {\n", + " \n", + " background-color: #433e85;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2 {\n", + " \n", + " background-color: #440154;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3 {\n", + " \n", + " background-color: #bddf26;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4 {\n", + " \n", + " background-color: #25838e;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0 {\n", + " \n", + " background-color: #21918c;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1 {\n", + " \n", + " background-color: #440154;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2 {\n", + " \n", + " background-color: #35b779;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0 {\n", + " \n", + " background-color: #5ec962;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1 {\n", + " \n", + " background-color: #95d840;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2 {\n", + " \n", + " background-color: #26ad81;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3 {\n", + " \n", + " background-color: #440154;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4 {\n", + " \n", + " background-color: #2cb17e;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3 {\n", + " \n", + " background-color: #1f9e89;\n", + " \n", + " }\n", + " \n", + " #T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4 {\n", + " \n", + " background-color: #1f958b;\n", + " \n", + " }\n", + " \n", + " </style>\n", + "\n", + " <table id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\">\n", + " \n", + "\n", + " <thead>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <th class=\"col_heading level0 col0\">A\n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " ±1.45\n", + " <th class=\"col_heading level0 col1\">B\n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <th class=\"col_heading level0 col2\">C\n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <th class=\"col_heading level0 col3\">D\n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <th class=\"col_heading level0 col4\">E\n", " \n", " </tr>\n", " \n", + " </thead>\n", + " <tbody>\n", + " \n", " <tr>\n", " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " ±1.34\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " ±0.12\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " ±0.35\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " ±1.69\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " ±0.13\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a48ce100_c56b_11e5_b7e0_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_35564a4c_8d9b_11e5_a0be_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", @@ -4041,35 +5376,22 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd4c50>" + "<pandas.core.style.Styler at 0x111c354a8>" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.format({\"B\": lambda x: \"±{:.2f}\".format(abs(x))})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Builtin Styles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we expect certain styling functions to be common enough that we've included a few \"built-in\" to the `Styler`, so you don't have to write them yourself." + "# Uses the full color range\n", + "df.loc[:4].style.background_gradient(cmap='viridis')" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -4081,15 +5403,209 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col2 {\n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0 {\n", + " \n", + " background-color: #31688e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1 {\n", + " \n", + " background-color: #efe51c;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2 {\n", + " \n", + " background-color: #440154;\n", " \n", " background-color: red;\n", " \n", " }\n", " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3 {\n", + " \n", + " background-color: #277f8e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4 {\n", + " \n", + " background-color: #31688e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0 {\n", + " \n", + " background-color: #21918c;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1 {\n", + " \n", + " background-color: #25858e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2 {\n", + " \n", + " background-color: #31688e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3 {\n", + " \n", + " background-color: #d5e21a;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4 {\n", + " \n", + " background-color: #29af7f;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0 {\n", + " \n", + " background-color: #35b779;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1 {\n", + " \n", + " background-color: #31688e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2 {\n", + " \n", + " background-color: #6ccd5a;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0 {\n", + " \n", + " background-color: #90d743;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1 {\n", + " \n", + " background-color: #b8de29;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2 {\n", + " \n", + " background-color: #5ac864;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3 {\n", + " \n", + " background-color: #31688e;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4 {\n", + " \n", + " background-color: #63cb5f;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2 {\n", + " \n", + " background-color: #fde725;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3 {\n", + " \n", + " background-color: #46c06f;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4 {\n", + " \n", + " background-color: #3bbb75;\n", + " \n", + " : ;\n", + " \n", + " }\n", + " \n", " </style>\n", "\n", - " <table id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" None>\n", + " <table id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -4110,242 +5626,176 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", - " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a49772ca_c56b_11e5_9b91_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_355acf74_8d9b_11e5_8c18_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", @@ -4354,28 +5804,32 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd4d68>" + "<pandas.core.style.Styler at 0x111c2c400>" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.highlight_null(null_color='red')" + "# Compreess the color range\n", + "(df.loc[:4]\n", + " .style\n", + " .background_gradient(cmap='viridis', low=.5, high=0)\n", + " .highlight_null('red'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can create \"heatmaps\" with the `background_gradient` method. These require matplotlib, and we'll use [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) to get a nice colormap." + "You can include \"bar charts\" in your DataFrame." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -4387,309 +5841,209 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #e5ffe5;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col1 {\n", + " height: 80%;\n", " \n", - " background-color: #188d18;\n", + " background: linear-gradient(90deg,#d65f5f 0.0%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col2 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #e5ffe5;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col3 {\n", - 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" #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1 {\n", " \n", - " background-color: #5cb35c;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col2 {\n", + " height: 80%;\n", " \n", - " background-color: #0e880e;\n", + " background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col3 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0 {\n", " \n", - " background-color: #cff3cf;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col4 {\n", + " height: 80%;\n", " \n", - " background-color: #4fac4f;\n", + " background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col0 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1 {\n", " \n", - " background-color: #198e19;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col1 {\n", + " height: 80%;\n", " \n", - " background-color: #008000;\n", + " background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col2 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0 {\n", " \n", - " background-color: #dcfadc;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col3 {\n", + " height: 80%;\n", " \n", - " background-color: #008000;\n", + " background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col4 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #e5ffe5;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col0 {\n", + " height: 80%;\n", " \n", - " background-color: #008000;\n", + " background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col1 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #7ec67e;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col2 {\n", + " height: 80%;\n", " \n", - " background-color: #319b31;\n", + " background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n", " \n", " }\n", " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col3 {\n", + " #T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #e5ffe5;\n", + " width: 10em;\n", " \n", - " }\n", - " \n", - " #T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col4 {\n", + " height: 80%;\n", " \n", - " background-color: #5cb35c;\n", + " background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" None>\n", + " <table id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -4710,242 +6064,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a49c8878_c56b_11e5_8ffb_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_355de9d2_8d9b_11e5_ac6b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -4954,33 +6412,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dc0a58>" + "<pandas.core.style.Styler at 0x111c35dd8>" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import seaborn as sns\n", - "\n", - "cm = sns.light_palette(\"green\", as_cmap=True)\n", - "\n", - "s = df.style.background_gradient(cmap=cm)\n", - "s" + "df.style.bar(subset=['A', 'B'], color='#d65f5f')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "`Styler.background_gradient` takes the keyword arguments `low` and `high`. Roughly speaking these extend the range of your data by `low` and `high` percent so that when we convert the colors, the colormap's entire range isn't used. This is useful so that you can actually read the text still." + "There's also `.highlight_min` and `.highlight_max`." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -4992,159 +6445,466 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col0 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow2_col4 {\n", " \n", - " background-color: #440154;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col1 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow6_col2 {\n", " \n", - " background-color: #e5e419;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col2 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #440154;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col3 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #46327e;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow0_col4 {\n", + " #T_35620402_8d9b_11e5_8913_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #440154;\n", + " background-color: yellow;\n", " \n", " }\n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col0 {\n", - " \n", - " background-color: #3b528b;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\">\n", " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col1 {\n", + "\n", + " <thead>\n", " \n", - " background-color: #433e85;\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col2 {\n", + " </thead>\n", + " <tbody>\n", " \n", - " background-color: #440154;\n", + " <tr>\n", + " \n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col3 {\n", - " \n", - " background-color: #bddf26;\n", - " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col4 {\n", - " \n", - " background-color: #25838e;\n", - " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col0 {\n", - " \n", - " background-color: #21918c;\n", - " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col1 {\n", - " \n", - " background-color: #440154;\n", - " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col2 {\n", - " \n", - " background-color: #35b779;\n", - " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col3 {\n", - " \n", - " background-color: #fde725;\n", - " \n", - " }\n", - " \n", - " #T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col4 {\n", - " \n", - " background-color: #fde725;\n", + " <tr>\n", + " \n", + " <th id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", + " \n", + " <td id=\"T_35620402_8d9b_11e5_8913_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", + " \n", + " </tr>\n", " \n", - 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" <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a4a280a4_c56b_11e5_9421_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", + " \n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", + " \n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", + " \n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", + " \n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", + " \n", + " <td id=\"T_356663c8_8d9b_11e5_8b10_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -5299,22 +7273,28 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111d9dcc0>" + "<pandas.core.style.Styler at 0x111c35d30>" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Uses the full color range\n", - "df.loc[:4].style.background_gradient(cmap='viridis')" + "df.style.highlight_min(axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `Styler.set_properties` when the style doesn't actually depend on the values." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "collapsed": false }, @@ -5326,599 +7306,509 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: #31688e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: #efe51c;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: #440154;\n", + " color: lawngreen;\n", " \n", - " background-color: red;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: #277f8e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4 {\n", " \n", - " background-color: #31688e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #21918c;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1 {\n", " \n", - " background-color: #25858e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #31688e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3 {\n", " \n", - " background-color: #d5e21a;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #29af7f;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0 {\n", " \n", - " background-color: #35b779;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: #31688e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2 {\n", " \n", - " background-color: #6ccd5a;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: #fde725;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4 {\n", " \n", - " background-color: #fde725;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: #90d743;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1 {\n", " \n", - " background-color: #b8de29;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: #5ac864;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3 {\n", " \n", - " background-color: #31688e;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: #63cb5f;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0 {\n", " \n", - " background-color: #fde725;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: #fde725;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col2 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2 {\n", " \n", - " background-color: #fde725;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col3 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: #46c06f;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col4 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4 {\n", " \n", - " background-color: #3bbb75;\n", + " color: lawngreen;\n", " \n", - " : ;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " </style>\n", - "\n", - " <table id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" None>\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0 {\n", + " \n", + " color: lawngreen;\n", + " \n", + " background-color: black;\n", + " \n", + " border-color: white;\n", + " \n", + " }\n", " \n", - "\n", - " <thead>\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " color: lawngreen;\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", + " background-color: black;\n", " \n", - " </thead>\n", - " <tbody>\n", + " border-color: white;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", - " \n", - " </tr>\n", + " color: lawngreen;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", + " background-color: black;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", + " border-color: white;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", - " \n", - " <td id=\"T_a4aab036_c56b_11e5_b597_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3 {\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x111daaba8>" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compreess the color range\n", - "(df.loc[:4]\n", - " .style\n", - " .background_gradient(cmap='viridis', low=.5, high=0)\n", - " .highlight_null('red'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can include \"bar charts\" in your DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style type=\"text/css\" >\n", + " color: lawngreen;\n", + " \n", + " background-color: black;\n", + " \n", + " border-color: white;\n", + " \n", + " }\n", " \n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4 {\n", + " \n", + " color: lawngreen;\n", + " \n", + " background-color: black;\n", + " \n", + " border-color: white;\n", + " \n", + " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", + " \n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 89.21303639960456%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 11.11111111111111%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 16.77000113307442%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - 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" #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 55.55555555555556%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 8.737388253449494%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 66.66666666666667%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 52.764243600289866%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 77.77777777777777%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 59.79187201238315%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 88.88888888888889%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 100.00000000000001%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col0 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 100.0%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", - " #T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col1 {\n", + " #T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4 {\n", " \n", - " width: 10em;\n", + " color: lawngreen;\n", " \n", - " height: 80%;\n", + " background-color: black;\n", " \n", - " background: linear-gradient(90deg,#d65f5f 45.17326030334935%, transparent 0%);\n", + " border-color: white;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" None>\n", + " <table id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -5939,242 +7829,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a4aead8c_c56b_11e5_8acb_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_356a46dc_8d9b_11e5_ac9a_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -6183,28 +8177,37 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dc0d68>" + "<pandas.core.style.Styler at 0x111c2cf60>" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.bar(subset=['A', 'B'], color='#d65f5f')" + "df.style.set_properties(**{'background-color': 'black',\n", + " 'color': 'lawngreen',\n", + " 'border-color': 'white'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "There's also `.highlight_min` and `.highlight_max`." + "## Sharing Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -6216,618 +8219,669 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col4 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0 {\n", " \n", - " background-color: yellow;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col2 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: yellow;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col1 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2 {\n", " \n", - " background-color: yellow;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col3 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: yellow;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow9_col0 {\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4 {\n", " \n", - " background-color: yellow;\n", + " color: red;\n", " \n", " }\n", " \n", - " </style>\n", - "\n", - " <table id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" None>\n", - " \n", - "\n", - " <thead>\n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " color: black;\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1 {\n", " \n", - " </thead>\n", - " <tbody>\n", + " color: red;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", - " \n", - " </tr>\n", + " color: red;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", - " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1 {\n", + " \n", + " color: red;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3 {\n", + " 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#T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow8_col4 {\n", + " \n", + " color: red;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col0 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col1 {\n", + " \n", + " color: red;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col2 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col3 {\n", + " \n", + " color: red;\n", + " \n", + " }\n", + " \n", + " #T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow9_col4 {\n", + " \n", + " color: black;\n", + " \n", + " }\n", + " \n", + " </style>\n", + "\n", + " <table id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\">\n", + " \n", + "\n", + " <thead>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <th class=\"col_heading level0 col0\">A\n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <th class=\"col_heading level0 col1\">B\n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <th class=\"col_heading level0 col2\">C\n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <th class=\"col_heading level0 col3\">D\n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <th class=\"col_heading level0 col4\">E\n", " \n", " </tr>\n", " \n", + " </thead>\n", + " <tbody>\n", + " \n", " <tr>\n", " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_356e3ac6_8d9b_11e5_a29c_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a4b6e3da_c56b_11e5_a44b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td 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24, "metadata": { "collapsed": false }, @@ -6869,509 +8918,309 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col0 {\n", - " \n", - " background-color: black;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col0 {\n", " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col1 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col1 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col2 {\n", " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col2 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col3 {\n", " \n", - " background-color: black;\n", + " color: black;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col4 {\n", " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col3 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col1 {\n", " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col4 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: black;\n", + " color: black;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col3 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col0 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col0 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col1 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col1 {\n", " \n", - " background-color: black;\n", + " color: black;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col2 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col2 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col3 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col4 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col3 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col0 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col1 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col4 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col2 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col3 {\n", " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col0 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col4 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col0 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col1 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col2 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col3 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1 {\n", " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col4 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2 {\n", " \n", - " background-color: black;\n", + " color: red;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col0 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4 {\n", " \n", - " background-color: black;\n", + " color: black;\n", " \n", - " border-color: white;\n", + " }\n", + " \n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " 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border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col2 {\n", - " \n", - " background-color: black;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2 {\n", " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col3 {\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col4 {\n", - " \n", - " background-color: black;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4 {\n", " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col0 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col1 {\n", - " \n", - " background-color: black;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1 {\n", " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col2 {\n", - " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2 {\n", " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col3 {\n", - " \n", - " background-color: black;\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3 {\n", " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: black;\n", " \n", " }\n", " \n", - " #T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col4 {\n", + " #T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: black;\n", - " \n", - " border-color: white;\n", - " \n", - " color: lawngreen;\n", + " color: red;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" None>\n", + " <table id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -7392,242 +9241,346 @@ " \n", " </tr>\n", " \n", + " </thead>\n", + " <tbody>\n", + " \n", " <tr>\n", " \n", - " <th class=\"col_heading level2 col0\">None\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <th class=\"blank\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " -1.0\n", + " \n", " \n", - " <th class=\"blank\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " -1.329212\n", + " \n", " \n", - " <th class=\"blank\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <th class=\"blank\">\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " 0.31628\n", + " \n", " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", - " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", - " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " 0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " -2.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " 1.070816\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " 1.438713\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " -0.564417\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " -0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " -3.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " 1.626404\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " -0.219565\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " -0.678805\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " -1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " -4.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " -0.961538\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " -0.104011\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " 0.481165\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " -0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " -5.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " -1.453425\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " -1.057737\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " -0.165562\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " -0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " -6.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " 1.336936\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " -0.562861\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " -1.392855\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " 0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " -7.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " -0.121668\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " -1.207603\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " 0.00204\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " -1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " -8.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " -0.354493\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " -1.037528\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " 0.385684\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " -0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " -9.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " -1.686583\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " 1.325963\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " -1.428984\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " 2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " -10.0\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " 0.12982\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " -0.631523\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " 0.586538\n", + " \n", " \n", - " <td id=\"T_a4c625a2_c56b_11e5_850b_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_35724a12_8d9b_11e5_a933_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " -0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -7636,37 +9589,65 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dd42b0>" + "<pandas.core.style.Styler at 0x111c7d198>" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.style.set_properties(**{'background-color': 'black',\n", - " 'color': 'lawngreen',\n", - " 'border-color': 'white'})" + "style2 = df2.style\n", + "style2.use(style1.export())\n", + "style2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Sharing Styles" + "Notice that you're able share the styles even though they're data aware. The styles are re-evaluated on the new DataFrame they've been `use`d upon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Say you have a lovely style built up for a DataFrame, and now you want to apply the same style to a second DataFrame. Export the style with `df1.style.export`, and import it on the second DataFrame with `df1.style.set`" + "## Other options\n", + "\n", + "You've seen a few methods for data-driven styling.\n", + "`Styler` also provides a few other options for styles that don't depend on the data.\n", + "\n", + "- precision\n", + "- captions\n", + "- table-wide styles\n", + "\n", + "Each of these can be specified in two ways:\n", + "\n", + "- A keyword argument to `pandas.core.Styler`\n", + "- A call to one of the `.set_` methods, e.g. `.set_caption`\n", + "\n", + "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Precision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can control the precision of floats using pandas' regular `display.precision` option." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -7678,309 +9659,409 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - 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" #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col0 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col1 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col2 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col3 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col4 {\n", + " #T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" None>\n", + " <table id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -8001,242 +10082,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.33\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.32\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.07\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.44\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.56\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.3\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.63\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.22\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.68\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.89\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.96\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.1\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.48\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.85\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.45\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.06\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.17\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.52\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.34\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.56\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.39\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.06\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.12\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.21\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.63\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.35\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.04\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.39\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.52\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.69\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.33\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.43\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.09\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.13\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.63\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.59\n", + " \n", " \n", - " <td id=\"T_a4cc7842_c56b_11e5_acef_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", + " <td id=\"T_357ab47a_8d9b_11e5_ba1e_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29\n", + " \n", " \n", " </tr>\n", " \n", @@ -8245,23 +10430,32 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111daa828>" + "<pandas.core.style.Styler at 0x111c7dbe0>" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df2 = -df\n", - "style1 = df.style.applymap(color_negative_red)\n", - "style1" + "with pd.option_context('display.precision', 2):\n", + " html = (df.style\n", + " .applymap(color_negative_red)\n", + " .apply(highlight_max))\n", + "html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or through a `set_precision` method." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -8273,309 +10467,409 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col0 {\n", - " \n", - " color: red;\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0 {\n", " \n", - " }\n", - " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col1 {\n", + " color: black;\n", " \n", - " color: red;\n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - 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" #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0 {\n", " \n", - " color: red;\n", + " color: black;\n", + " \n", + " : ;\n", " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col0 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col1 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0 {\n", " \n", " color: black;\n", " \n", + " background-color: yellow;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col2 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col3 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2 {\n", " \n", " color: black;\n", " \n", + " : ;\n", + " \n", " }\n", " \n", - " #T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col4 {\n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3 {\n", " \n", " color: red;\n", " \n", + " : ;\n", + " \n", + " }\n", + " \n", + " #T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4 {\n", + " \n", + " color: black;\n", + " \n", + " : ;\n", + " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" None>\n", + " <table id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\">\n", " \n", "\n", " <thead>\n", @@ -8596,242 +10890,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " -1\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " -1.32921\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.33\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " 0.31628\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.32\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " 0.99081\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " -2\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " 1.07082\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.07\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " 1.43871\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.44\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " -0.564417\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.56\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " -0.295722\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.3\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " -3\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " 1.6264\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.63\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " -0.219565\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.22\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " -0.678805\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.68\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " -1.88927\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.89\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " -4\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " -0.961538\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.96\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " -0.104011\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.1\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " 0.481165\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.48\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " -0.850229\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.85\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " -5\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " -1.45342\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.45\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " -1.05774\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.06\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " -0.165562\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.17\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " -0.515018\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.52\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " -6\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " 1.33694\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.34\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " -0.562861\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.56\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " -1.39285\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.39\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " 0.063328\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.06\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " -7\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " -0.121668\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.12\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " -1.2076\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.21\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " 0.00204021\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " -1.6278\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.63\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " -8\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " -0.354493\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.35\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " -1.03753\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.04\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " 0.385684\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.39\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " -0.519818\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.52\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " -9\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " -1.68658\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.69\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " 1.32596\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.33\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " -1.42898\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.43\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " 2.08935\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.09\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " -10\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " 0.12982\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.13\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " -0.631523\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.63\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " 0.586538\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.59\n", + " \n", " \n", - " <td id=\"T_a4d33e4a_c56b_11e5_b6a5_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " -0.29072\n", + " <td id=\"T_358c8026_8d9b_11e5_a659_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29\n", + " \n", " \n", " </tr>\n", " \n", @@ -8840,65 +11238,45 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111dc0390>" + "<pandas.core.style.Styler at 0x111c7dc18>" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "style2 = df2.style\n", - "style2.use(style1.export())\n", - "style2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that you're able share the styles even though they're data aware. The styles are re-evaluated on the new DataFrame they've been `use`d upon." + "df.style\\\n", + " .applymap(color_negative_red)\\\n", + " .apply(highlight_max)\\\n", + " .set_precision(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Other options\n", - "\n", - "You've seen a few methods for data-driven styling.\n", - "`Styler` also provides a few other options for styles that don't depend on the data.\n", - "\n", - "- precision\n", - "- captions\n", - "- table-wide styles\n", - "\n", - "Each of these can be specified in two ways:\n", - "\n", - "- A keyword argument to `pandas.core.Styler`\n", - "- A call to one of the `.set_` methods, e.g. `.set_caption`\n", - "\n", - "The best method to use depends on the context. Use the `Styler` constructor when building many styled DataFrames that should all share the same properties. For interactive use, the`.set_` methods are more convenient." + "Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use `df.round(2).style` if you'd prefer to round from the start." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Precision" + "### Captions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can control the precision of floats using pandas' regular `display.precision` option." + "Regular table captions can be added in a few ways." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "collapsed": false }, @@ -8910,409 +11288,311 @@ " <style type=\"text/css\" >\n", " \n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #188d18;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #c7eec7;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #a6dca6;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #ccf1cc;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #c0eac0;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #62b662;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #b3e3b3;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #56af56;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #56af56;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4 {\n", " \n", - " color: black;\n", - " \n", - " background-color: yellow;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #99d599;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #329c32;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #5fb55f;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #daf9da;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #3ba13b;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #80c780;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #108910;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #0d870d;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #90d090;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #4fac4f;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #66b866;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #d2f4d2;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #389f38;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #048204;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #70be70;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #4daa4d;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #6cbc6c;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2 {\n", " \n", - " color: black;\n", - " \n", - " background-color: yellow;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #a3daa3;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #0e880e;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #329c32;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #0e880e;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #cff3cf;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #4fac4f;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #198e19;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1 {\n", " \n", - " color: black;\n", - " \n", - " background-color: yellow;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #dcfadc;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3 {\n", " \n", - " color: black;\n", - " \n", - " background-color: yellow;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col0 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0 {\n", " \n", - " color: black;\n", - " \n", - " background-color: yellow;\n", + " background-color: #008000;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col1 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #7ec67e;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col2 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #319b31;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col3 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3 {\n", " \n", - " color: red;\n", - " \n", - " : ;\n", + " background-color: #e5ffe5;\n", " \n", " }\n", " \n", - " #T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col4 {\n", + " #T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4 {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #5cb35c;\n", " \n", " }\n", " \n", " </style>\n", "\n", - " <table id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" None>\n", + " <table id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\">\n", + " \n", + " <caption>Colormaps, with a caption.</caption>\n", " \n", "\n", " <thead>\n", @@ -9333,242 +11613,346 @@ " \n", " </tr>\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.3\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.32\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.1\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.4\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.56\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.3\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.22\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.68\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.9\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.96\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.1\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.48\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.85\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.5\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.1\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.17\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.52\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.3\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.56\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.4\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.12\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.002\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.35\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.39\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.52\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.7\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.3\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.4\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.1\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.13\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.63\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.59\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a4defce4_c56b_11e5_80dc_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29\n", + " <td id=\"T_3595c942_8d9b_11e5_8058_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -9577,32 +11961,38 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111e0a160>" + "<pandas.core.style.Styler at 0x111c7d828>" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "with pd.option_context('display.precision', 2):\n", - " html = (df.style\n", - " .applymap(color_negative_red)\n", - " .apply(highlight_max))\n", - "html" + "df.style.set_caption('Colormaps, with a caption.')\\\n", + " .background_gradient(cmap=cm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or through a `set_precision` method." + "### Table Styles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next option you have are \"table styles\".\n", + "These are styles that apply to the table as a whole, but don't look at the data.\n", + "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { "collapsed": false }, @@ -9613,410 +12003,795 @@ "\n", " <style type=\"text/css\" >\n", " \n", - " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col0 {\n", + " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb tr:hover {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " background-color: #ffff99;\n", " \n", " }\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col1 {\n", + " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb th {\n", " \n", - " color: black;\n", + " font-size: 150%;\n", " \n", - " : ;\n", + " text-align: center;\n", " \n", " }\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col2 {\n", + " #T_359bea52_8d9b_11e5_be48_a45e60bd97fb caption {\n", " \n", - " color: black;\n", - " \n", - " : ;\n", + " caption-side: bottom;\n", " \n", " }\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col3 {\n", - " \n", - " color: red;\n", - " \n", - " : ;\n", - " \n", - " }\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col4 {\n", - " \n", - " color: red;\n", - " \n", - " : ;\n", - " \n", - " }\n", + " </style>\n", + "\n", + " <table id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\">\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col0 {\n", - " \n", - " color: black;\n", - " \n", - " : ;\n", - " \n", - " }\n", + " <caption>Hover to highlight.</caption>\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col1 {\n", - " \n", - " color: red;\n", - " \n", - " : ;\n", + "\n", + " <thead>\n", " \n", - " }\n", - " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col2 {\n", + " <tr>\n", + " \n", + " <th class=\"blank\">\n", + " \n", + " <th class=\"col_heading level0 col0\">A\n", + " \n", + " <th class=\"col_heading level0 col1\">B\n", + " \n", + " <th class=\"col_heading level0 col2\">C\n", + " \n", + " <th class=\"col_heading level0 col3\">D\n", + " \n", + " <th class=\"col_heading level0 col4\">E\n", + " \n", + " </tr>\n", " \n", - " color: red;\n", + " </thead>\n", + " <tbody>\n", " \n", - " : ;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row0\">\n", + " \n", + " 0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 1.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col3 {\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", + " \n", + " 1\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " 2.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " -1.070816\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.438713\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " 0.564417\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", + " \n", + " </tr>\n", " \n", - " color: black;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", + " \n", + " </tr>\n", " \n", - " : ;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col4 {\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", + " \n", + " </tr>\n", " \n", - " color: black;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", + " \n", + " </tr>\n", " \n", - " : ;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", + " \n", + " </tr>\n", " \n", - " }\n", - " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col0 {\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", + " \n", + " </tr>\n", " \n", - " color: black;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", + " \n", + " </tr>\n", " \n", - " : ;\n", + " <tr>\n", + " \n", + " <th id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", + " \n", + " <td id=\"T_359bea52_8d9b_11e5_be48_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", + " \n", + " </tr>\n", " \n", - " }\n", + " </tbody>\n", + " </table>\n", + " " + ], + "text/plain": [ + "<pandas.core.style.Styler at 0x114c42710>" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import HTML\n", + "\n", + "def hover(hover_color=\"#ffff99\"):\n", + " return dict(selector=\"tr:hover\",\n", + " props=[(\"background-color\", \"%s\" % hover_color)])\n", + "\n", + "styles = [\n", + " hover(),\n", + " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n", + " (\"text-align\", \"center\")]),\n", + " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n", + "]\n", + "html = (df.style.set_table_styles(styles)\n", + " .set_caption(\"Hover to highlight.\"))\n", + "html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`table_styles` should be a list of dictionaries.\n", + "Each dictionary should have the `selector` and `props` keys.\n", + "The value for `selector` should be a valid CSS selector.\n", + "Recall that all the styles are already attached to an `id`, unique to\n", + "each `Styler`. This selector is in addition to that `id`.\n", + "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n", + "\n", + "`table_styles` are extremely flexible, but not as fun to type out by hand.\n", + "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Limitations\n", + "\n", + "- DataFrame only `(use Series.to_frame().style)`\n", + "- The index and columns must be unique\n", + "- No large repr, and performance isn't great; this is intended for summary DataFrames\n", + "- You can only style the *values*, not the index or columns\n", + "- You can only apply styles, you can't insert new HTML entities\n", + "\n", + "Some of these will be addressed in the future.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Terms\n", + "\n", + "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n", + "- Builtin style functions: style functions that are methods on `Styler`\n", + "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fun stuff\n", + "\n", + "Here are a few interesting examples.\n", + "\n", + "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <style type=\"text/css\" >\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col1 {\n", - " \n", - " color: red;\n", + " \n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col0 {\n", " \n", - " : ;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col2 {\n", - " \n", - " color: black;\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1 {\n", " \n", - " : ;\n", + " background-color: #779894;\n", " \n", " }\n", " \n", - " #T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col3 {\n", - " \n", - " color: black;\n", + " #T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2 {\n", " \n", - " : ;\n", + " background-color: #557e79;\n", " \n", " }\n", " \n", - 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" <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.3\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.329212\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " nan\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.32\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.31628\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.99081\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - 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" <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.3\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 0.295722\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", + " \n", + " 2\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " 3.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " -1.626404\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.22\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " 0.219565\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.68\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.678805\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.9\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 1.889273\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", + " \n", + " 3\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " 4.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.96\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 0.961538\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.1\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " 0.104011\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.48\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " -0.481165\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.85\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 0.850229\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", + " \n", + " 4\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " 5.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.5\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 1.453425\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.1\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " 1.057737\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.17\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " 0.165562\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.52\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 0.515018\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", + " \n", + " 5\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", + " \n", + " 6.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.3\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", + " \n", + " -1.336936\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.56\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", + " \n", + " 0.562861\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.4\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", + " \n", + " 1.392855\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", + " \n", + " -0.063328\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", + " \n", + " 6\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " 7.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.12\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 0.121668\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " 1.207603\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.002\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -0.00204\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 1.627796\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", + " \n", + " 7\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", + " \n", + " 8.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.35\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", + " \n", + " 0.354493\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", + " \n", + " 1.037528\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.39\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", + " \n", + " -0.385684\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.52\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", + " \n", + " 0.519818\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", + " \n", + " 8\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 9.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.7\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 1.686583\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.3\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", + " \n", + " -1.325963\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.4\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", + " \n", + " 1.428984\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.1\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", + " \n", + " -2.089354\n", + " \n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", + " <th id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", + " \n", + " 9\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 10.0\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.13\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " -0.12982\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.63\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " 0.631523\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.59\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.586538\n", + " \n", " \n", - " <td id=\"T_a4e922d2_c56b_11e5_a1a2_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29\n", + " <td id=\"T_35adc664_8d9b_11e5_afed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 0.29072\n", + " \n", " \n", " </tr>\n", " \n", @@ -10281,45 +13160,47 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111e0aac8>" + "<pandas.core.style.Styler at 0x113487dd8>" ] }, - "execution_count": 27, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df.style\\\n", - " .applymap(color_negative_red)\\\n", - " .apply(highlight_max)\\\n", - " .set_precision(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting the precision only affects the printed number; the full-precision values are always passed to your style functions. You can always use `df.round(2).style` if you'd prefer to round from the start." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Captions" + "from IPython.html import widgets\n", + "@widgets.interact\n", + "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n", + " return df.style.background_gradient(\n", + " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n", + " as_cmap=True)\n", + " )" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ - "Regular table captions can be added in a few ways." + "def magnify():\n", + " return [dict(selector=\"th\",\n", + " props=[(\"font-size\", \"4pt\")]),\n", + " dict(selector=\"td\",\n", + " props=[('padding', \"0em 0em\")]),\n", + " dict(selector=\"th:hover\",\n", + " props=[(\"font-size\", \"12pt\")]),\n", + " dict(selector=\"tr:hover td:hover\",\n", + " props=[('max-width', '200px'),\n", + " ('font-size', '12pt')])\n", + "]" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": { "collapsed": false }, @@ -10330,1648 +13211,836 @@ "\n", " <style type=\"text/css\" >\n", " \n", - " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th {\n", " \n", - " background-color: #e5ffe5;\n", + " font-size: 4pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb td {\n", " \n", - " background-color: #188d18;\n", + " padding: 0em 0em;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb th:hover {\n", " \n", - " background-color: #e5ffe5;\n", + " font-size: 12pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb tr:hover td:hover {\n", " \n", - " background-color: #c7eec7;\n", + " max-width: 200px;\n", + " \n", + " font-size: 12pt;\n", " \n", " }\n", " \n", - 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" #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20 {\n", " \n", - " background-color: #198e19;\n", + " background-color: #6590c7;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21 {\n", " \n", - " background-color: #008000;\n", + " background-color: #d73c5b;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22 {\n", " \n", - " background-color: #dcfadc;\n", + " background-color: #4479bb;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23 {\n", " \n", - " background-color: #008000;\n", + " background-color: #cfddee;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24 {\n", " \n", - " background-color: #e5ffe5;\n", + " background-color: #e58094;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0 {\n", " \n", - " background-color: #008000;\n", + " background-color: #e4798e;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1 {\n", " \n", - " background-color: #7ec67e;\n", + " background-color: #aec5e1;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2 {\n", " \n", - " background-color: #319b31;\n", + " background-color: #eb9ead;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3 {\n", " \n", - " background-color: #e5ffe5;\n", + " background-color: #ec9faf;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4 {\n", " \n", - " background-color: #5cb35c;\n", + " background-color: #cbdaec;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " </style>\n", - "\n", - " <table id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" None>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5 {\n", + " \n", + " background-color: #f9e0e5;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", " \n", - " <caption>Colormaps, with a caption.</caption>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6 {\n", + " \n", + " background-color: #db4f6b;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", " \n", - "\n", - " <thead>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " background-color: #4479bb;\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " </thead>\n", - " <tbody>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", - " \n", - " </tr>\n", + " background-color: #e57f93;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", - " \n", - " </tr>\n", + " background-color: #bdd0e7;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", - " \n", - " <td id=\"T_a4f2d070_c56b_11e5_88f7_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", - " \n", - " </tr>\n", + " background-color: #f3c2cc;\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x111e0add8>" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.style.set_caption('Colormaps, with a caption.')\\\n", - " .background_gradient(cmap=cm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table Styles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next option you have are \"table styles\".\n", - "These are styles that apply to the table as a whole, but don't look at the data.\n", - "Certain sytlings, including pseudo-selectors like `:hover` can only be used this way." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style type=\"text/css\" >\n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", " \n", - " #T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb tr:hover {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11 {\n", " \n", - " background-color: #ffff99;\n", + " background-color: #f8dfe4;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb th {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12 {\n", " \n", - " font-size: 150%;\n", + " background-color: #b0c6e2;\n", " \n", - " text-align: center;\n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb caption {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13 {\n", " \n", - " caption-side: bottom;\n", + " background-color: #ec9faf;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14 {\n", + " \n", + " background-color: #e27389;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", " \n", - " </style>\n", - "\n", - " <table id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" None>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15 {\n", + " \n", + " background-color: #eb9dac;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", " \n", - " <caption>Hover to highlight.</caption>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16 {\n", + " \n", + " background-color: #f1b8c3;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", " \n", - "\n", - " <thead>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17 {\n", " \n", - " <tr>\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"col_heading level0 col0\">A\n", - " \n", - " <th class=\"col_heading level0 col1\">B\n", - " \n", - " <th class=\"col_heading level0 col2\">C\n", - " \n", - " <th class=\"col_heading level0 col3\">D\n", - " \n", - " <th class=\"col_heading level0 col4\">E\n", - " \n", - " </tr>\n", + " background-color: #efb1be;\n", " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " </thead>\n", - " <tbody>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 1\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1.32921\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " nan\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.31628\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.99081\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " 2\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " -1.07082\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.43871\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " 0.564417\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 0.295722\n", - " \n", - " </tr>\n", + " background-color: #f2f2f2;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " 3\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " -1.6264\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " 0.219565\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", - " \n", - " </tr>\n", + " background-color: #f8dce2;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", - " \n", - " <td id=\"T_a4f780fe_c56b_11e5_9d9f_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", - " \n", - " </tr>\n", + " background-color: #a0bbdc;\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x111e12f28>" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import HTML\n", - "\n", - "def hover(hover_color=\"#ffff99\"):\n", - " return dict(selector=\"tr:hover\",\n", - " props=[(\"background-color\", \"%s\" % hover_color)])\n", - "\n", - "styles = [\n", - " hover(),\n", - " dict(selector=\"th\", props=[(\"font-size\", \"150%\"),\n", - " (\"text-align\", \"center\")]),\n", - " dict(selector=\"caption\", props=[(\"caption-side\", \"bottom\")])\n", - "]\n", - "html = (df.style.set_table_styles(styles)\n", - " .set_caption(\"Hover to highlight.\"))\n", - "html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`table_styles` should be a list of dictionaries.\n", - "Each dictionary should have the `selector` and `props` keys.\n", - "The value for `selector` should be a valid CSS selector.\n", - "Recall that all the styles are already attached to an `id`, unique to\n", - "each `Styler`. This selector is in addition to that `id`.\n", - "The value for `props` should be a list of tuples of `('attribute', 'value')`.\n", - "\n", - "`table_styles` are extremely flexible, but not as fun to type out by hand.\n", - "We hope to collect some useful ones either in pandas, or preferable in a new package that [builds on top](#Extensibility) the tools here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Limitations\n", - "\n", - "- DataFrame only `(use Series.to_frame().style)`\n", - "- The index and columns must be unique\n", - "- No large repr, and performance isn't great; this is intended for summary DataFrames\n", - "- You can only style the *values*, not the index or columns\n", - "- You can only apply styles, you can't insert new HTML entities\n", - "\n", - "Some of these will be addressed in the future.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Terms\n", - "\n", - "- Style function: a function that's passed into `Styler.apply` or `Styler.applymap` and returns values like `'css attribute: value'`\n", - "- Builtin style functions: style functions that are methods on `Styler`\n", - "- table style: a dictionary with the two keys `selector` and `props`. `selector` is the CSS selector that `props` will apply to. `props` is a list of `(attribute, value)` tuples. A list of table styles passed into `Styler`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fun stuff\n", - "\n", - "Here are a few interesting examples.\n", - "\n", - "`Styler` interacts pretty well with widgets. If you're viewing this online instead of running the notebook yourself, you're missing out on interactively adjusting the color palette." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style type=\"text/css\" >\n", - " \n", - " \n", - " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col0 {\n", + " max-width: 80px;\n", " \n", - " background-color: #557e79;\n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21 {\n", " \n", - " background-color: #779894;\n", + " background-color: #d73c5b;\n", " \n", - " }\n", - " \n", - " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col2 {\n", + " max-width: 80px;\n", " \n", - " background-color: #557e79;\n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow0_col3 {\n", + " 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" \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.678805\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 1.88927\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 0.961538\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " 0.104011\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " -0.481165\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 0.850229\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row4\">\n", - " 4\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " 5\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 1.45342\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " 1.05774\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " 0.165562\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 0.515018\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row5\">\n", - " 5\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " 6\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " -1.33694\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " 0.562861\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " 1.39285\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " -0.063328\n", - " \n", - " </tr>\n", + " background-color: #5384c0;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row6\">\n", - " 6\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " 7\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 0.121668\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " 1.2076\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -0.00204021\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 1.6278\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row7\">\n", - " 7\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " 8\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 0.354493\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " 1.03753\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.385684\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 0.519818\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row8\">\n", - " 8\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 9\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 1.68658\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -1.32596\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " 1.42898\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " -2.08935\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fb\" class=\"row_heading level4 row9\">\n", - " 9\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 10\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " -0.12982\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " 0.631523\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.586538\n", - " \n", - " <td id=\"T_a5057b46_c56b_11e5_b239_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 0.29072\n", - " \n", - " </tr>\n", + " background-color: #f1bbc6;\n", " \n", - " </tbody>\n", - " </table>\n", - " " - ], - "text/plain": [ - "<pandas.core.style.Styler at 0x111dc08d0>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.html import widgets\n", - "@widgets.interact\n", - "def f(h_neg=(0, 359, 1), h_pos=(0, 359), s=(0., 99.9), l=(0., 99.9)):\n", - " return df.style.background_gradient(\n", - " cmap=sns.palettes.diverging_palette(h_neg=h_neg, h_pos=h_pos, s=s, l=l,\n", - " as_cmap=True)\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def magnify():\n", - " return [dict(selector=\"th\",\n", - " props=[(\"font-size\", \"4pt\")]),\n", - " dict(selector=\"td\",\n", - " props=[('padding', \"0em 0em\")]),\n", - " dict(selector=\"th:hover\",\n", - " props=[(\"font-size\", \"12pt\")]),\n", - " dict(selector=\"tr:hover td:hover\",\n", - " props=[('max-width', '200px'),\n", - " ('font-size', '12pt')])\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " <style type=\"text/css\" >\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb th {\n", + " max-width: 80px;\n", " \n", - " font-size: 4pt;\n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb td {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3 {\n", " \n", - " padding: 0em 0em;\n", + " background-color: #c7d7eb;\n", " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb th:hover {\n", + " max-width: 80px;\n", " \n", - " font-size: 12pt;\n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb tr:hover td:hover {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4 {\n", " \n", - " max-width: 200px;\n", + " background-color: #4479bb;\n", " \n", - " font-size: 12pt;\n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", " \n", " }\n", " \n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5 {\n", " \n", - " background-color: #ecf2f8;\n", + " background-color: #e7eef6;\n", " \n", " max-width: 80px;\n", " \n", @@ -11979,9 +14048,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6 {\n", " \n", - " background-color: #b8cce5;\n", + " background-color: #e58195;\n", " \n", " max-width: 80px;\n", " \n", @@ -11989,9 +14058,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7 {\n", " \n", - " background-color: #efb1be;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -11999,9 +14068,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12009,9 +14078,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9 {\n", " \n", - " background-color: #eeaab7;\n", + " background-color: #b1c7e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12019,9 +14088,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col10 {\n", " \n", - " background-color: #f8dce2;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -12029,9 +14098,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col11 {\n", " \n", - " background-color: #f2c1cb;\n", + " background-color: #c1d3e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12039,9 +14108,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col12 {\n", " \n", - " background-color: #83a6d2;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12049,9 +14118,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col13 {\n", " \n", - " background-color: #de5f79;\n", + " background-color: #cedced;\n", " \n", " max-width: 80px;\n", " \n", @@ -12059,9 +14128,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col14 {\n", " \n", - " background-color: #c3d4e9;\n", + " background-color: #e7899c;\n", " \n", " max-width: 80px;\n", " \n", @@ -12069,7 +14138,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col15 {\n", " \n", " background-color: #eeacba;\n", " \n", @@ -12079,9 +14148,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col16 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #e2eaf4;\n", " \n", " max-width: 80px;\n", " \n", @@ -12089,9 +14158,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col17 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -12099,9 +14168,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col18 {\n", " \n", - " background-color: #e890a1;\n", + " background-color: #a8c0df;\n", " \n", " max-width: 80px;\n", " \n", @@ -12109,9 +14178,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col19 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -12119,9 +14188,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col20 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #b7cbe5;\n", " \n", " max-width: 80px;\n", " \n", @@ -12129,9 +14198,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col21 {\n", " \n", - " background-color: #adc4e1;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -12139,9 +14208,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col22 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #739acc;\n", " \n", " max-width: 80px;\n", " \n", @@ -12149,9 +14218,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col23 {\n", " \n", - " background-color: #b7cbe5;\n", + " background-color: #8dadd5;\n", " \n", " max-width: 80px;\n", " \n", @@ -12159,9 +14228,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #cddbed;\n", " \n", " max-width: 80px;\n", " \n", @@ -12169,9 +14238,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -12179,9 +14248,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #5887c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12189,9 +14258,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12199,9 +14268,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3 {\n", " \n", - " background-color: #cfddee;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12209,9 +14278,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4 {\n", " \n", - " background-color: #e58094;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12219,9 +14288,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5 {\n", " \n", - " background-color: #e4798e;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -12229,9 +14298,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6 {\n", " \n", - " background-color: #aec5e1;\n", + " background-color: #e78c9e;\n", " \n", " max-width: 80px;\n", " \n", @@ -12239,9 +14308,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #5182bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -12249,9 +14318,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8 {\n", " \n", - " background-color: #ec9faf;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12259,9 +14328,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #c1d3e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12269,9 +14338,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10 {\n", " \n", - " background-color: #f9e0e5;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -12279,9 +14348,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11 {\n", " \n", - " background-color: #db4f6b;\n", + " background-color: #c9d8eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12289,9 +14358,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -12299,9 +14368,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col13 {\n", " \n", - " background-color: #e57f93;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12309,9 +14378,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col14 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #e47c91;\n", " \n", " max-width: 80px;\n", " \n", @@ -12319,9 +14388,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col15 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #eda8b6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12329,9 +14398,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col16 {\n", " \n", - " background-color: #f8dfe4;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -12339,9 +14408,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col17 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -12349,9 +14418,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col18 {\n", " \n", - " background-color: #ec9faf;\n", + " background-color: #b3c9e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -12359,9 +14428,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col19 {\n", " \n", - " background-color: #e27389;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -12369,9 +14438,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col20 {\n", " \n", - " background-color: #eb9dac;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -12379,9 +14448,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col21 {\n", " \n", - " background-color: #f1b8c3;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -12389,9 +14458,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col22 {\n", " \n", - " background-color: #efb1be;\n", + " background-color: #7da2cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -12399,9 +14468,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col23 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12409,9 +14478,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col24 {\n", " \n", - " background-color: #f8dce2;\n", + " background-color: #a2bcdd;\n", " \n", " max-width: 80px;\n", " \n", @@ -12419,9 +14488,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col0 {\n", " \n", - " background-color: #a0bbdc;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -12429,9 +14498,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6490c6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12439,9 +14508,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col2 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12449,9 +14518,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col3 {\n", " \n", - " background-color: #d9e4f1;\n", + " background-color: #f3c6cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -12459,9 +14528,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col4 {\n", " \n", - " background-color: #ecf2f8;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12469,9 +14538,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col5 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -12479,9 +14548,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col6 {\n", " \n", - " background-color: #5887c2;\n", + " background-color: #e68598;\n", " \n", " max-width: 80px;\n", " \n", @@ -12489,9 +14558,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col7 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #6d96ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -12499,9 +14568,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8 {\n", " \n", - " background-color: #c7d7eb;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -12509,9 +14578,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9 {\n", " \n", - " background-color: #82a5d1;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12519,9 +14588,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -12529,9 +14598,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11 {\n", " \n", - " background-color: #e78a9d;\n", + " background-color: #e0e9f4;\n", " \n", " max-width: 80px;\n", " \n", @@ -12539,9 +14608,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12549,9 +14618,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13 {\n", " \n", - " background-color: #f1bbc6;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12559,9 +14628,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14 {\n", " \n", - " background-color: #779dcd;\n", + " background-color: #e47a90;\n", " \n", " max-width: 80px;\n", " \n", @@ -12569,9 +14638,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -12579,9 +14648,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -12589,9 +14658,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17 {\n", " \n", - " background-color: #e0e9f4;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -12599,9 +14668,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -12609,9 +14678,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19 {\n", " \n", - " background-color: #e78a9d;\n", + " background-color: #f6d2d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -12619,9 +14688,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -12629,9 +14698,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -12639,9 +14708,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12649,9 +14718,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23 {\n", " \n", - " background-color: #b9cde6;\n", + " background-color: #a3bddd;\n", " \n", " max-width: 80px;\n", " \n", @@ -12659,9 +14728,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #7199cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12669,9 +14738,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0 {\n", " \n", - " background-color: #a0bbdc;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12679,9 +14748,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #4e80be;\n", " \n", " max-width: 80px;\n", " \n", @@ -12689,9 +14758,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2 {\n", " \n", - " background-color: #618ec5;\n", + " background-color: #f8dee3;\n", " \n", " max-width: 80px;\n", " \n", @@ -12699,9 +14768,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12709,9 +14778,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4 {\n", " \n", - " background-color: #c3d4e9;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -12719,9 +14788,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -12729,9 +14798,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -12739,9 +14808,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7 {\n", " \n", - " background-color: #f1bbc6;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -12749,9 +14818,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8 {\n", " \n", - " background-color: #c7d7eb;\n", + " background-color: #f2c1cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12759,9 +14828,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12769,9 +14838,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10 {\n", " \n", - " background-color: #e7eef6;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -12779,9 +14848,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col11 {\n", " \n", - " background-color: #e58195;\n", + " background-color: #c5d5ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -12789,9 +14858,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col12 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -12799,9 +14868,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col13 {\n", " \n", - " background-color: #f2c1cb;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -12809,9 +14878,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col14 {\n", " \n", - " background-color: #b1c7e2;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -12819,9 +14888,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col15 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -12829,9 +14898,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col16 {\n", " \n", - " background-color: #c1d3e8;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -12839,9 +14908,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col17 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12849,9 +14918,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col18 {\n", " \n", - " background-color: #cedced;\n", + " background-color: #d8e3f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -12859,9 +14928,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col19 {\n", " \n", - " background-color: #e7899c;\n", + " background-color: #eb9bab;\n", " \n", " max-width: 80px;\n", " \n", @@ -12869,9 +14938,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col20 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #a3bddd;\n", " \n", " max-width: 80px;\n", " \n", @@ -12879,9 +14948,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col21 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -12889,9 +14958,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col22 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #81a4d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -12899,9 +14968,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col23 {\n", " \n", - " background-color: #a8c0df;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12909,9 +14978,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col24 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -12919,9 +14988,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col0 {\n", " \n", - " background-color: #b7cbe5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12929,9 +14998,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -12939,9 +15008,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col2 {\n", " \n", - " background-color: #739acc;\n", + " background-color: #f2bdc8;\n", " \n", " max-width: 80px;\n", " \n", @@ -12949,9 +15018,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col3 {\n", " \n", - " background-color: #8dadd5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12959,9 +15028,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col4 {\n", " \n", - " background-color: #cddbed;\n", + " background-color: #5686c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -12969,9 +15038,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col5 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #f0b5c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -12979,9 +15048,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6 {\n", " \n", - " background-color: #5887c2;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -12989,9 +15058,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #628fc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -12999,9 +15068,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #e68396;\n", " \n", " max-width: 80px;\n", " \n", @@ -13009,9 +15078,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #eda7b5;\n", " \n", " max-width: 80px;\n", " \n", @@ -13019,9 +15088,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -13029,9 +15098,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11 {\n", " \n", - " background-color: #e78c9e;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13039,9 +15108,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12 {\n", " \n", - " background-color: #5182bf;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13049,9 +15118,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -13059,9 +15128,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14 {\n", " \n", - " background-color: #c1d3e8;\n", + " background-color: #de5c76;\n", " \n", " max-width: 80px;\n", " \n", @@ -13069,9 +15138,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13079,9 +15148,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16 {\n", " \n", - " background-color: #c9d8eb;\n", + " background-color: #dd5671;\n", " \n", " max-width: 80px;\n", " \n", @@ -13089,9 +15158,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #e993a4;\n", " \n", " max-width: 80px;\n", " \n", @@ -13099,9 +15168,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13109,9 +15178,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19 {\n", " \n", - " background-color: #e47c91;\n", + " background-color: #e991a3;\n", " \n", " max-width: 80px;\n", " \n", @@ -13119,9 +15188,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20 {\n", " \n", - " background-color: #eda8b6;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13129,9 +15198,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -13139,9 +15208,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #a0bbdc;\n", " \n", " max-width: 80px;\n", " \n", @@ -13149,9 +15218,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23 {\n", " \n", - " background-color: #b3c9e3;\n", + " background-color: #96b4d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -13159,9 +15228,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -13169,9 +15238,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #d3dfef;\n", " \n", " max-width: 80px;\n", " \n", @@ -13179,9 +15248,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #487cbc;\n", " \n", " max-width: 80px;\n", " \n", @@ -13189,9 +15258,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col2 {\n", " \n", - " background-color: #7da2cf;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -13199,9 +15268,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col3 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -13209,9 +15278,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col4 {\n", " \n", - " background-color: #a2bcdd;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -13219,9 +15288,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col5 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -13229,9 +15298,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col6 {\n", " \n", - " background-color: #6490c6;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -13239,9 +15308,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col7 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #4d7fbe;\n", " \n", " max-width: 80px;\n", " \n", @@ -13249,9 +15318,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col8 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #e994a5;\n", " \n", " max-width: 80px;\n", " \n", @@ -13259,9 +15328,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -13269,9 +15338,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #f5ccd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -13279,9 +15348,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11 {\n", " \n", - " background-color: #e68598;\n", + " background-color: #d5e1f0;\n", " \n", " max-width: 80px;\n", " \n", @@ -13289,9 +15358,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12 {\n", " \n", - " background-color: #6d96ca;\n", + " background-color: #f0b4c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -13299,9 +15368,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13309,9 +15378,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #da4966;\n", " \n", " max-width: 80px;\n", " \n", @@ -13319,9 +15388,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13329,9 +15398,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16 {\n", " \n", - " background-color: #e0e9f4;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -13339,9 +15408,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13349,9 +15418,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #ecf2f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -13359,9 +15428,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19 {\n", " \n", - " background-color: #e47a90;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -13369,9 +15438,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20 {\n", " \n", - " background-color: #fbeaed;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -13379,9 +15448,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #dc516d;\n", " \n", " max-width: 80px;\n", " \n", @@ -13389,9 +15458,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #91b0d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13399,9 +15468,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #a8c0df;\n", " \n", " max-width: 80px;\n", " \n", @@ -13409,9 +15478,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -13419,9 +15488,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13429,9 +15498,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6590c7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13439,9 +15508,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #ea99a9;\n", " \n", " max-width: 80px;\n", " \n", @@ -13449,9 +15518,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3 {\n", " \n", - " background-color: #a3bddd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13459,9 +15528,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4 {\n", " \n", - " background-color: #7199cb;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -13469,9 +15538,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -13479,9 +15548,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6 {\n", " \n", - " background-color: #4e80be;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13489,9 +15558,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -13499,9 +15568,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -13509,9 +15578,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -13519,9 +15588,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13529,9 +15598,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -13539,9 +15608,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -13549,9 +15618,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13 {\n", " \n", - " background-color: #f2c1cb;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13559,9 +15628,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #e27389;\n", " \n", " max-width: 80px;\n", " \n", @@ -13569,9 +15638,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -13579,9 +15648,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16 {\n", " \n", - " background-color: #c5d5ea;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -13589,9 +15658,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #ea9aaa;\n", " \n", " max-width: 80px;\n", " \n", @@ -13599,9 +15668,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13609,9 +15678,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col19 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #e993a4;\n", " \n", " max-width: 80px;\n", " \n", @@ -13619,9 +15688,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col20 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #b9cde6;\n", " \n", " max-width: 80px;\n", " \n", @@ -13629,9 +15698,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col21 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #de5f79;\n", " \n", " max-width: 80px;\n", " \n", @@ -13639,9 +15708,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col22 {\n", " \n", - " background-color: #f6d1d8;\n", + " background-color: #b3c9e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -13649,9 +15718,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col23 {\n", " \n", - " background-color: #d8e3f1;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -13659,9 +15728,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col24 {\n", " \n", - " background-color: #eb9bab;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -13669,9 +15738,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col0 {\n", " \n", - " background-color: #a3bddd;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -13679,9 +15748,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6f98ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -13689,9 +15758,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col2 {\n", " \n", - " background-color: #81a4d1;\n", + " background-color: #ea96a7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13699,9 +15768,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col3 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -13709,7 +15778,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -13719,9 +15788,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col5 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f0b7c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13729,9 +15798,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col6 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #fae4e9;\n", " \n", " max-width: 80px;\n", " \n", @@ -13739,9 +15808,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col7 {\n", " \n", - " background-color: #f2bdc8;\n", + " background-color: #759ccd;\n", " \n", " max-width: 80px;\n", " \n", @@ -13749,9 +15818,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f2bdc8;\n", " \n", " max-width: 80px;\n", " \n", @@ -13759,9 +15828,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col9 {\n", " \n", - " background-color: #5686c1;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -13769,9 +15838,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col10 {\n", " \n", - " background-color: #f0b5c1;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -13779,9 +15848,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col11 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -13789,9 +15858,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col12 {\n", " \n", - " background-color: #628fc6;\n", + " background-color: #efb1be;\n", " \n", " max-width: 80px;\n", " \n", @@ -13799,9 +15868,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col13 {\n", " \n", - " background-color: #e68396;\n", + " background-color: #eaf0f7;\n", " \n", " max-width: 80px;\n", " \n", @@ -13809,9 +15878,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14 {\n", " \n", - " background-color: #eda7b5;\n", + " background-color: #e0657d;\n", " \n", " max-width: 80px;\n", " \n", @@ -13819,9 +15888,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #eca1b0;\n", " \n", " max-width: 80px;\n", " \n", @@ -13829,9 +15898,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -13839,9 +15908,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #e27087;\n", " \n", " max-width: 80px;\n", " \n", @@ -13849,9 +15918,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -13859,9 +15928,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19 {\n", " \n", - " background-color: #de5c76;\n", + " background-color: #e68699;\n", " \n", " max-width: 80px;\n", " \n", @@ -13869,9 +15938,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -13879,9 +15948,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21 {\n", " \n", - " background-color: #dd5671;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -13889,9 +15958,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22 {\n", " \n", - " background-color: #e993a4;\n", + " background-color: #d1deee;\n", " \n", " max-width: 80px;\n", " \n", @@ -13899,9 +15968,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #82a5d1;\n", " \n", " max-width: 80px;\n", " \n", @@ -13909,9 +15978,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24 {\n", " \n", - " background-color: #e991a3;\n", + " background-color: #7099cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -13919,9 +15988,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #a9c1e0;\n", " \n", " max-width: 80px;\n", " \n", @@ -13929,9 +15998,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6892c8;\n", " \n", " max-width: 80px;\n", " \n", @@ -13939,9 +16008,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2 {\n", " \n", - " background-color: #a0bbdc;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -13949,9 +16018,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3 {\n", " \n", - " background-color: #96b4d9;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -13959,7 +16028,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4 {\n", " \n", " background-color: #4479bb;\n", " \n", @@ -13969,9 +16038,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5 {\n", " \n", - " background-color: #d3dfef;\n", + " background-color: #e4ecf5;\n", " \n", " max-width: 80px;\n", " \n", @@ -13979,9 +16048,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6 {\n", " \n", - " background-color: #487cbc;\n", + " background-color: #d8e3f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -13989,9 +16058,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #477bbc;\n", " \n", " max-width: 80px;\n", " \n", @@ -13999,9 +16068,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14009,9 +16078,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #e7eef6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14019,9 +16088,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14029,9 +16098,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #a6bfde;\n", " \n", " max-width: 80px;\n", " \n", @@ -14039,9 +16108,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12 {\n", " \n", - " background-color: #4d7fbe;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14049,9 +16118,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13 {\n", " \n", - " background-color: #e994a5;\n", + " background-color: #a9c1e0;\n", " \n", " max-width: 80px;\n", " \n", @@ -14059,9 +16128,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -14069,9 +16138,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15 {\n", " \n", - " background-color: #f5ccd4;\n", + " background-color: #ea99a9;\n", " \n", " max-width: 80px;\n", " \n", @@ -14079,9 +16148,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16 {\n", " \n", - " background-color: #d5e1f0;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -14089,9 +16158,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17 {\n", " \n", - " background-color: #f0b4c0;\n", + " background-color: #f0b7c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14099,9 +16168,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f6d5db;\n", " \n", " max-width: 80px;\n", " \n", @@ -14109,9 +16178,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19 {\n", " \n", - " background-color: #da4966;\n", + " background-color: #eb9ead;\n", " \n", " max-width: 80px;\n", " \n", @@ -14119,7 +16188,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -14129,9 +16198,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #d8415f;\n", " \n", " max-width: 80px;\n", " \n", @@ -14139,9 +16208,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14149,9 +16218,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23 {\n", " \n", - " background-color: #ecf2f8;\n", + " background-color: #5182bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14159,9 +16228,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #457abb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14169,9 +16238,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #92b1d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14179,9 +16248,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1 {\n", " \n", - " background-color: #dc516d;\n", + " background-color: #7ba0cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14189,9 +16258,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2 {\n", " \n", - " background-color: #91b0d7;\n", + " background-color: #f3c5ce;\n", " \n", " max-width: 80px;\n", " \n", @@ -14199,9 +16268,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3 {\n", " \n", - " background-color: #a8c0df;\n", + " background-color: #f7d7de;\n", " \n", " max-width: 80px;\n", " \n", @@ -14209,9 +16278,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #5485c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14219,9 +16288,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #f5cfd7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14229,9 +16298,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -14239,9 +16308,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7 {\n", " \n", - " background-color: #ea99a9;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14249,9 +16318,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14259,9 +16328,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14269,9 +16338,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #b0c6e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14279,9 +16348,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #9fbadc;\n", " \n", " max-width: 80px;\n", " \n", @@ -14289,9 +16358,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12 {\n", " \n", - " background-color: #6a94c9;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14299,9 +16368,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #cad9ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14309,9 +16378,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14 {\n", " \n", - " background-color: #ebf1f8;\n", + " background-color: #e991a3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14319,9 +16388,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14329,9 +16398,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #de5c76;\n", " \n", " max-width: 80px;\n", " \n", @@ -14339,9 +16408,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14349,9 +16418,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f7dae0;\n", " \n", " max-width: 80px;\n", " \n", @@ -14359,9 +16428,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19 {\n", " \n", - " background-color: #e27389;\n", + " background-color: #eb9dac;\n", " \n", " max-width: 80px;\n", " \n", @@ -14369,9 +16438,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14379,7 +16448,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14389,9 +16458,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22 {\n", " \n", - " background-color: #ea9aaa;\n", + " background-color: #acc3e1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14399,9 +16468,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #497dbd;\n", " \n", " max-width: 80px;\n", " \n", @@ -14409,9 +16478,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24 {\n", " \n", - " background-color: #e993a4;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14419,9 +16488,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col0 {\n", " \n", - " background-color: #b9cde6;\n", + " background-color: #bccfe7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14429,9 +16498,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col1 {\n", " \n", - " background-color: #de5f79;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14439,9 +16508,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col2 {\n", " \n", - " background-color: #b3c9e3;\n", + " background-color: #eda6b4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14449,9 +16518,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col3 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #f5ced6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14459,9 +16528,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col4 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #5c8ac4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14469,9 +16538,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col5 {\n", " \n", - " background-color: #c6d6ea;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14479,9 +16548,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6 {\n", " \n", - " background-color: #6f98ca;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14489,9 +16558,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7 {\n", " \n", - " background-color: #ea96a7;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14499,9 +16568,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #f3c2cc;\n", " \n", " max-width: 80px;\n", " \n", @@ -14509,9 +16578,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #fae8ec;\n", " \n", " max-width: 80px;\n", " \n", @@ -14519,9 +16588,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10 {\n", " \n", - " background-color: #f0b7c2;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14529,9 +16598,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11 {\n", " \n", - " background-color: #fae4e9;\n", + " background-color: #bbcee6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14539,9 +16608,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12 {\n", " \n", - " background-color: #759ccd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14549,9 +16618,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13 {\n", " \n", - " background-color: #f2bdc8;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14559,9 +16628,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #e78a9d;\n", " \n", " max-width: 80px;\n", " \n", @@ -14569,9 +16638,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15 {\n", " \n", - " background-color: #fae7eb;\n", + " background-color: #eda7b5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14579,9 +16648,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #dc546f;\n", " \n", " max-width: 80px;\n", " \n", @@ -14589,9 +16658,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17 {\n", " \n", - " background-color: #efb1be;\n", + " background-color: #eca3b1;\n", " \n", " max-width: 80px;\n", " \n", @@ -14599,9 +16668,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18 {\n", " \n", - " background-color: #eaf0f7;\n", + " background-color: #e6edf6;\n", " \n", " max-width: 80px;\n", " \n", @@ -14609,9 +16678,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19 {\n", " \n", - " background-color: #e0657d;\n", + " background-color: #eeaab7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14619,9 +16688,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20 {\n", " \n", - " background-color: #eca1b0;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14629,7 +16698,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14639,9 +16708,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22 {\n", " \n", - " background-color: #e27087;\n", + " background-color: #b8cce5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14649,9 +16718,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23 {\n", " \n", - " background-color: #f9e2e6;\n", + " background-color: #7099cb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14659,9 +16728,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24 {\n", " \n", - " background-color: #e68699;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14669,9 +16738,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #91b0d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14679,9 +16748,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14689,9 +16758,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2 {\n", " \n", - " background-color: #d1deee;\n", + " background-color: #efb2bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14699,9 +16768,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3 {\n", " \n", - " background-color: #82a5d1;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14709,9 +16778,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4 {\n", " \n", - " background-color: #7099cb;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14719,9 +16788,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5 {\n", " \n", - " background-color: #a9c1e0;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -14729,9 +16798,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6 {\n", " \n", - " background-color: #6892c8;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -14739,9 +16808,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #6b95c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -14749,9 +16818,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #f3c6cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -14759,9 +16828,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #e8eff7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14769,9 +16838,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10 {\n", " \n", - " background-color: #e4ecf5;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14779,9 +16848,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11 {\n", " \n", - " background-color: #d8e3f1;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14789,9 +16858,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12 {\n", " \n", - " background-color: #477bbc;\n", + " background-color: #95b3d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -14799,9 +16868,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14809,9 +16878,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14 {\n", " \n", - " background-color: #e7eef6;\n", + " background-color: #eeabb8;\n", " \n", " max-width: 80px;\n", " \n", @@ -14819,9 +16888,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15 {\n", " \n", - " background-color: #cbdaec;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -14829,9 +16898,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16 {\n", " \n", - " background-color: #a6bfde;\n", + " background-color: #e3748a;\n", " \n", " max-width: 80px;\n", " \n", @@ -14839,9 +16908,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #eca4b3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14849,9 +16918,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18 {\n", " \n", - " background-color: #a9c1e0;\n", + " background-color: #f7d6dd;\n", " \n", " max-width: 80px;\n", " \n", @@ -14859,9 +16928,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #f6d2d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -14869,9 +16938,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20 {\n", " \n", - " background-color: #ea99a9;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14879,7 +16948,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -14889,9 +16958,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22 {\n", " \n", - " background-color: #f0b7c2;\n", + " background-color: #9bb7da;\n", " \n", " max-width: 80px;\n", " \n", @@ -14899,9 +16968,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23 {\n", " \n", - " background-color: #f6d5db;\n", + " background-color: #618ec5;\n", " \n", " max-width: 80px;\n", " \n", @@ -14909,9 +16978,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24 {\n", " \n", - " background-color: #eb9ead;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -14919,9 +16988,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #5787c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14929,9 +16998,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1 {\n", " \n", - " background-color: #d8415f;\n", + " background-color: #5e8bc4;\n", " \n", " max-width: 80px;\n", " \n", @@ -14939,9 +17008,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2 {\n", " \n", - " background-color: #b5cae4;\n", + " background-color: #f5cfd7;\n", " \n", " max-width: 80px;\n", " \n", @@ -14949,9 +17018,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3 {\n", " \n", - " background-color: #5182bf;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14959,9 +17028,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4 {\n", " \n", - " background-color: #457abb;\n", + " background-color: #5384c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -14969,9 +17038,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5 {\n", " \n", - " background-color: #92b1d7;\n", + " background-color: #f8dee3;\n", " \n", " max-width: 80px;\n", " \n", @@ -14979,9 +17048,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6 {\n", " \n", - " background-color: #7ba0cf;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14989,9 +17058,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7 {\n", " \n", - " background-color: #f3c5ce;\n", + " background-color: #5787c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -14999,9 +17068,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #f9e3e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15009,9 +17078,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9 {\n", " \n", - " background-color: #5485c1;\n", + " background-color: #cedced;\n", " \n", " max-width: 80px;\n", " \n", @@ -15019,9 +17088,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10 {\n", " \n", - " background-color: #f5cfd7;\n", + " background-color: #dde7f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15029,9 +17098,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11 {\n", " \n", - " background-color: #d4e0ef;\n", + " background-color: #9cb8db;\n", " \n", " max-width: 80px;\n", " \n", @@ -15039,9 +17108,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #749bcc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15049,9 +17118,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #b2c8e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15059,9 +17128,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #f8dfe4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15069,9 +17138,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15079,9 +17148,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16 {\n", " \n", - " background-color: #9fbadc;\n", + " background-color: #eeabb8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15089,9 +17158,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #f3c6cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15099,9 +17168,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18 {\n", " \n", - " background-color: #cad9ec;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15109,9 +17178,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19 {\n", " \n", - " background-color: #e991a3;\n", + " background-color: #e2eaf4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15119,9 +17188,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #dfe8f3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15129,9 +17198,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21 {\n", " \n", - " background-color: #de5c76;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15139,9 +17208,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #94b2d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15149,9 +17218,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23 {\n", " \n", - " background-color: #f7dae0;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15159,9 +17228,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24 {\n", " \n", - " background-color: #eb9dac;\n", + " background-color: #5384c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -15169,9 +17238,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #6993c8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15179,9 +17248,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6590c7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15189,9 +17258,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2 {\n", " \n", - " background-color: #acc3e1;\n", + " background-color: #e2eaf4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15199,9 +17268,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3 {\n", " \n", - " background-color: #497dbd;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15209,9 +17278,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #457abb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15219,9 +17288,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5 {\n", " \n", - " background-color: #bccfe7;\n", + " background-color: #e3ebf5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15229,9 +17298,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6 {\n", " \n", - " background-color: #8faed6;\n", + " background-color: #bdd0e7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15239,9 +17308,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7 {\n", " \n", - " background-color: #eda6b4;\n", + " background-color: #5384c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -15249,9 +17318,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8 {\n", " \n", - " background-color: #f5ced6;\n", + " background-color: #f7d7de;\n", " \n", " max-width: 80px;\n", " \n", @@ -15259,9 +17328,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9 {\n", " \n", - " background-color: #5c8ac4;\n", + " background-color: #96b4d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15269,9 +17338,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #b0c6e2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15279,9 +17348,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11 {\n", " \n", - " background-color: #f4cad3;\n", + " background-color: #b2c8e3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15289,9 +17358,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #4a7ebd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15299,9 +17368,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13 {\n", " \n", - " background-color: #f3c2cc;\n", + " background-color: #92b1d7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15309,9 +17378,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14 {\n", " \n", - " background-color: #fae8ec;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15319,9 +17388,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15329,9 +17398,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16 {\n", " \n", - " background-color: #bbcee6;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15339,9 +17408,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #ebf1f8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15349,9 +17418,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #dce6f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15359,9 +17428,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19 {\n", " \n", - " background-color: #e78a9d;\n", + " background-color: #c9d8eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15369,9 +17438,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20 {\n", " \n", - " background-color: #eda7b5;\n", + " background-color: #bfd1e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15379,9 +17448,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21 {\n", " \n", - " background-color: #dc546f;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15389,9 +17458,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22 {\n", " \n", - " background-color: #eca3b1;\n", + " background-color: #8faed6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15399,9 +17468,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23 {\n", " \n", - " background-color: #e6edf6;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15409,9 +17478,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24 {\n", " \n", - " background-color: #eeaab7;\n", + " background-color: #5a88c3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15419,9 +17488,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #628fc6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15429,9 +17498,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #749bcc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15439,9 +17508,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2 {\n", " \n", - " background-color: #b8cce5;\n", + " background-color: #f9e2e6;\n", " \n", " max-width: 80px;\n", " \n", @@ -15449,9 +17518,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3 {\n", " \n", - " background-color: #7099cb;\n", + " background-color: #f8dee3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15459,9 +17528,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #5182bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15469,9 +17538,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5 {\n", " \n", - " background-color: #91b0d7;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15479,9 +17548,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6 {\n", " \n", - " background-color: #86a8d3;\n", + " background-color: #d4e0ef;\n", " \n", " max-width: 80px;\n", " \n", @@ -15489,9 +17558,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7 {\n", " \n", - " background-color: #efb2bf;\n", + " background-color: #5182bf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15499,9 +17568,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #f4cad3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15509,9 +17578,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #85a7d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15519,9 +17588,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10 {\n", " \n", - " background-color: #f2bfca;\n", + " background-color: #cbdaec;\n", " \n", " max-width: 80px;\n", " \n", @@ -15529,9 +17598,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11 {\n", " \n", - " background-color: #fae6ea;\n", + " background-color: #bccfe7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15539,9 +17608,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12 {\n", " \n", - " background-color: #6b95c9;\n", + " background-color: #5f8cc5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15549,9 +17618,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #a2bcdd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15559,9 +17628,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14 {\n", " \n", - " background-color: #e8eff7;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15569,7 +17638,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -15579,9 +17648,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #f3c6cf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15589,9 +17658,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17 {\n", " \n", - " background-color: #95b3d8;\n", + " background-color: #fae7eb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15599,9 +17668,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18 {\n", " \n", - " background-color: #dae5f2;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -15609,9 +17678,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19 {\n", " \n", - " background-color: #eeabb8;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15619,9 +17688,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20 {\n", " \n", - " background-color: #eeacba;\n", + " background-color: #b7cbe5;\n", " \n", " max-width: 80px;\n", " \n", @@ -15629,9 +17698,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21 {\n", " \n", - " background-color: #e3748a;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15639,9 +17708,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22 {\n", " \n", - " background-color: #eca4b3;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15649,9 +17718,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23 {\n", " \n", - " background-color: #f7d6dd;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15659,9 +17728,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24 {\n", " \n", - " background-color: #f6d2d9;\n", + " background-color: #739acc;\n", " \n", " max-width: 80px;\n", " \n", @@ -15669,9 +17738,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #6a94c9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15679,9 +17748,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #6d96ca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15689,9 +17758,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2 {\n", " \n", - " background-color: #9bb7da;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15699,9 +17768,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3 {\n", " \n", - " background-color: #618ec5;\n", + " background-color: #eeaebb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15709,9 +17778,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #5384c0;\n", " \n", " max-width: 80px;\n", " \n", @@ -15719,9 +17788,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15729,9 +17798,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6 {\n", " \n", - " background-color: #5e8bc4;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15739,9 +17808,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7 {\n", " \n", - " background-color: #f5cfd7;\n", + " background-color: #4479bb;\n", " \n", " max-width: 80px;\n", " \n", @@ -15749,9 +17818,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #ec9faf;\n", " \n", " max-width: 80px;\n", " \n", @@ -15759,9 +17828,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #c6d6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -15769,9 +17838,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10 {\n", " \n", - " background-color: #f8dee3;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15779,9 +17848,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11 {\n", " \n", - " background-color: #dce6f2;\n", + " background-color: #dae5f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15789,9 +17858,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12 {\n", " \n", - " background-color: #5787c2;\n", + " background-color: #4c7ebd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15799,9 +17868,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13 {\n", " \n", - " background-color: #f9e3e7;\n", + " background-color: #d1deee;\n", " \n", " max-width: 80px;\n", " \n", @@ -15809,9 +17878,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14 {\n", " \n", - " background-color: #cedced;\n", + " background-color: #fae6ea;\n", " \n", " max-width: 80px;\n", " \n", @@ -15819,9 +17888,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15 {\n", " \n", - " background-color: #dde7f3;\n", + " background-color: #f7d9df;\n", " \n", " max-width: 80px;\n", " \n", @@ -15829,9 +17898,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16 {\n", " \n", - " background-color: #9cb8db;\n", + " background-color: #eeacba;\n", " \n", " max-width: 80px;\n", " \n", @@ -15839,9 +17908,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17 {\n", " \n", - " background-color: #749bcc;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -15849,9 +17918,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18 {\n", " \n", - " background-color: #b2c8e3;\n", + " background-color: #f6d2d9;\n", " \n", " max-width: 80px;\n", " \n", @@ -15859,9 +17928,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19 {\n", " \n", - " background-color: #f8dfe4;\n", + " background-color: #f4c9d2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15869,9 +17938,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20 {\n", " \n", - " background-color: #f4c9d2;\n", + " background-color: #bccfe7;\n", " \n", " max-width: 80px;\n", " \n", @@ -15879,9 +17948,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col16 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21 {\n", " \n", - " background-color: #eeabb8;\n", + " background-color: #d73c5b;\n", " \n", " max-width: 80px;\n", " \n", @@ -15889,9 +17958,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col17 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22 {\n", " \n", - " background-color: #f3c6cf;\n", + " background-color: #9eb9db;\n", " \n", " max-width: 80px;\n", " \n", @@ -15899,9 +17968,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col18 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #5485c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -15909,9 +17978,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col19 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #8babd4;\n", " \n", " max-width: 80px;\n", " \n", @@ -15919,9 +17988,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col20 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0 {\n", " \n", - " background-color: #dfe8f3;\n", + " background-color: #86a8d3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15929,9 +17998,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1 {\n", " \n", - " background-color: #d73c5b;\n", + " background-color: #5b89c3;\n", " \n", " max-width: 80px;\n", " \n", @@ -15939,9 +18008,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col22 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2 {\n", " \n", - " background-color: #94b2d8;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15949,9 +18018,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col23 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3 {\n", " \n", - " background-color: #4479bb;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15959,9 +18028,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col24 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #497dbd;\n", " \n", " max-width: 80px;\n", " \n", @@ -15969,9 +18038,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col0 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5 {\n", " \n", - " background-color: #6993c8;\n", + " background-color: #f2bfca;\n", " \n", " max-width: 80px;\n", " \n", @@ -15979,9 +18048,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col1 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6 {\n", " \n", - " background-color: #6590c7;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -15989,9 +18058,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col2 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7 {\n", " \n", - " background-color: #e2eaf4;\n", + " background-color: #5686c1;\n", " \n", " max-width: 80px;\n", " \n", @@ -15999,9 +18068,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col3 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8 {\n", " \n", - " background-color: #f2f2f2;\n", + " background-color: #eda8b6;\n", " \n", " max-width: 80px;\n", " \n", @@ -16009,9 +18078,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col4 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9 {\n", " \n", - " background-color: #457abb;\n", + " background-color: #d9e4f1;\n", " \n", " max-width: 80px;\n", " \n", @@ -16019,9 +18088,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col5 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10 {\n", " \n", - " background-color: #e3ebf5;\n", + " background-color: #d5e1f0;\n", " \n", " max-width: 80px;\n", " \n", @@ -16029,9 +18098,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col6 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11 {\n", " \n", - " background-color: #bdd0e7;\n", + " background-color: #bfd1e8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16039,9 +18108,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col7 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12 {\n", " \n", - " background-color: #5384c0;\n", + " background-color: #5787c2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16049,9 +18118,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col8 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13 {\n", " \n", - " background-color: #f7d7de;\n", + " background-color: #fbeaed;\n", " \n", " max-width: 80px;\n", " \n", @@ -16059,9 +18128,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col9 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14 {\n", " \n", - " background-color: #96b4d9;\n", + " background-color: #f8dee3;\n", " \n", " max-width: 80px;\n", " \n", @@ -16069,9 +18138,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col10 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15 {\n", " \n", - " background-color: #b0c6e2;\n", + " background-color: #f2f2f2;\n", " \n", " max-width: 80px;\n", " \n", @@ -16079,9 +18148,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col11 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16 {\n", " \n", - " background-color: #b2c8e3;\n", + " background-color: #eeaab7;\n", " \n", " max-width: 80px;\n", " \n", @@ -16089,9 +18158,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col12 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17 {\n", " \n", - " background-color: #4a7ebd;\n", + " background-color: #f6d1d8;\n", " \n", " max-width: 80px;\n", " \n", @@ -16099,9 +18168,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col13 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18 {\n", " \n", - " background-color: #92b1d7;\n", + " background-color: #f7d7de;\n", " \n", " max-width: 80px;\n", " \n", @@ -16109,7 +18178,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col14 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19 {\n", " \n", " background-color: #f2f2f2;\n", " \n", @@ -16119,59 +18188,9 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col15 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20 {\n", " \n", - " background-color: #f2f2f2;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col16 {\n", - " \n", - " background-color: #f4cad3;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col17 {\n", - " \n", - " background-color: #ebf1f8;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col18 {\n", - " \n", - " background-color: #dce6f2;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col19 {\n", - " \n", - " background-color: #c9d8eb;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col20 {\n", - " \n", - " background-color: #bfd1e8;\n", + " background-color: #b5cae4;\n", " \n", " max-width: 80px;\n", " \n", @@ -16179,7 +18198,7 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col21 {\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21 {\n", " \n", " background-color: #d73c5b;\n", " \n", @@ -16189,2547 +18208,2781 @@ " \n", " }\n", " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col22 {\n", - " \n", - " background-color: #8faed6;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col23 {\n", - " \n", - " background-color: #4479bb;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col24 {\n", - " \n", - " background-color: #5a88c3;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col0 {\n", - " \n", - " background-color: #628fc6;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col1 {\n", - " \n", - " background-color: #749bcc;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col2 {\n", - " \n", - " background-color: #f9e2e6;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col3 {\n", - " \n", - " background-color: #f8dee3;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col4 {\n", - " \n", - " background-color: #5182bf;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col5 {\n", - " \n", - " background-color: #f2f2f2;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col6 {\n", - " \n", - " background-color: #d4e0ef;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col7 {\n", - " \n", - " background-color: #5182bf;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col8 {\n", - " \n", - " background-color: #f4cad3;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col9 {\n", - " \n", - " background-color: #85a7d2;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col10 {\n", - " \n", - " background-color: #cbdaec;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col11 {\n", - " \n", - " background-color: #bccfe7;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " #T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col12 {\n", - " \n", - " background-color: #5f8cc5;\n", - " \n", - " max-width: 80px;\n", - " \n", - " font-size: 1pt;\n", - " \n", - " }\n", - " \n", - " 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class=\"col_heading level0 col6\">6\n", - " \n", - " <th class=\"col_heading level0 col7\">7\n", - " \n", - " <th class=\"col_heading level0 col8\">8\n", - " \n", - " <th class=\"col_heading level0 col9\">9\n", - " \n", - " <th class=\"col_heading level0 col10\">10\n", - " \n", - " <th class=\"col_heading level0 col11\">11\n", - " \n", - " <th class=\"col_heading level0 col12\">12\n", - " \n", - " <th class=\"col_heading level0 col13\">13\n", - " \n", - " <th class=\"col_heading level0 col14\">14\n", - " \n", - " <th class=\"col_heading level0 col15\">15\n", - " \n", - " <th class=\"col_heading level0 col16\">16\n", - " \n", - " <th class=\"col_heading level0 col17\">17\n", - " \n", - " <th class=\"col_heading level0 col18\">18\n", - " \n", - " <th class=\"col_heading level0 col19\">19\n", - " \n", - " <th class=\"col_heading level0 col20\">20\n", - " \n", - " <th class=\"col_heading level0 col21\">21\n", - " \n", - " <th class=\"col_heading level0 col22\">22\n", - " \n", - " <th class=\"col_heading level0 col23\">23\n", - " \n", - " <th class=\"col_heading level0 col24\">24\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th class=\"col_heading level2 col0\">None\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " <th class=\"blank\">\n", - " \n", - " </tr>\n", - " \n", - " </thead>\n", - " <tbody>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level0 row0\">\n", - " 0\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", - " 0.23\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", - " 1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", - " -0.84\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", - " -0.59\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", - " -0.96\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n", - " -0.22\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n", - " -0.62\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n", - " 1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n", - " -2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n", - " 0.87\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n", - " -0.92\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n", - " -0.23\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n", - " 2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n", - " -1.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n", - " 0.076\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n", - " -1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n", - " 1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n", - " -1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n", - " 1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n", - " -0.42\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n", - " 2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n", - " 2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n", - " 0.68\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n", - " -1.6\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row1\">\n", - " 1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", - " 1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", - " 1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n", - " 0.0037\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n", - " -2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n", - " 3.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n", - " 1.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n", - " -0.52\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n", - " -0.015\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n", - " 1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n", - " -1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n", - " -0.68\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n", - " -0.81\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n", - " 0.35\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n", - " -0.055\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n", - " 1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n", - " -2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n", - " 2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n", - " 0.78\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n", - " 0.44\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row2\">\n", - " 2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", - " -0.65\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", - " 3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", - " 0.52\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", - " 2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n", - " -0.37\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n", - " -3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n", - " 3.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n", - " -1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n", - " 2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n", - " 0.21\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n", - " -0.24\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n", - " -0.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n", - " -0.78\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n", - " -3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n", - " -0.82\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n", - " -0.21\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n", - " -0.23\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n", - " 0.86\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n", - " -0.68\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n", - " 1.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n", - " -4.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n", - " 3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n", - " 1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n", - " 0.61\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row3\">\n", - " 3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", - " -1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", - " 3.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", - " 0.43\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", - " 4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n", - " -0.43\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n", - " 4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n", - " -2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col9\" class=\"data row3 col9\">\n", - " 1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col10\" class=\"data row3 col10\">\n", - " 0.12\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col11\" class=\"data row3 col11\">\n", - " 0.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col12\" class=\"data row3 col12\">\n", - " -0.89\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col13\" class=\"data row3 col13\">\n", - " 0.27\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col14\" class=\"data row3 col14\">\n", - " -3.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col15\" class=\"data row3 col15\">\n", - " -2.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col16\" class=\"data row3 col16\">\n", - " -0.31\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col17\" class=\"data row3 col17\">\n", - " -1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col18\" class=\"data row3 col18\">\n", - " 1.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col19\" class=\"data row3 col19\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col20\" class=\"data row3 col20\">\n", - " 0.91\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col21\" class=\"data row3 col21\">\n", - " -5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col22\" class=\"data row3 col22\">\n", - " 2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col23\" class=\"data row3 col23\">\n", - " 2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n", - " 0.28\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row4\">\n", - " 4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", - " -3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", - " 4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", - " -1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", - " 5.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n", - " -1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n", - " -3.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n", - " 4.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n", - " -0.72\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n", - " 1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n", - " -0.18\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n", - " 0.83\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col12\" class=\"data row4 col12\">\n", - " -0.22\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col13\" class=\"data row4 col13\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col14\" class=\"data row4 col14\">\n", - " -4.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col15\" class=\"data row4 col15\">\n", - " -2.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col16\" class=\"data row4 col16\">\n", - " -0.97\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col17\" class=\"data row4 col17\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col18\" class=\"data row4 col18\">\n", - " 1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col19\" class=\"data row4 col19\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col20\" class=\"data row4 col20\">\n", - " 2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col21\" class=\"data row4 col21\">\n", - " -6.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col22\" class=\"data row4 col22\">\n", - " 3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col23\" class=\"data row4 col23\">\n", - " 2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow4_col24\" class=\"data row4 col24\">\n", - " 2.1\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row5\">\n", - " 5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col0\" class=\"data row5 col0\">\n", - " -0.84\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col1\" class=\"data row5 col1\">\n", - " 4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col2\" class=\"data row5 col2\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col3\" class=\"data row5 col3\">\n", - " -2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col4\" class=\"data row5 col4\">\n", - " 5.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col5\" class=\"data row5 col5\">\n", - " -0.99\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col6\" class=\"data row5 col6\">\n", - " -4.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col7\" class=\"data row5 col7\">\n", - " 3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n", - " -0.94\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n", - " 1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n", - " 0.087\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n", - " -0.11\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n", - " -0.85\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n", - " -2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n", - " -1.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n", - " 0.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n", - " -1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n", - " 1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n", - " -6.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n", - " 2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n", - " 2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n", - " 3.8\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row6\">\n", - " 6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", - " -0.74\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", - " 5.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", - " -2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", - " 4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n", - " 3.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n", - " -1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col10\" class=\"data row6 col10\">\n", - " 0.34\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col11\" class=\"data row6 col11\">\n", - " 0.57\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col12\" class=\"data row6 col12\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col13\" class=\"data row6 col13\">\n", - " 0.54\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col14\" class=\"data row6 col14\">\n", - " -4.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col15\" class=\"data row6 col15\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col16\" class=\"data row6 col16\">\n", - " -4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col17\" class=\"data row6 col17\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col18\" class=\"data row6 col18\">\n", - " -0.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col19\" class=\"data row6 col19\">\n", - " -4.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col20\" class=\"data row6 col20\">\n", - " 1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col21\" class=\"data row6 col21\">\n", - " -8.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col22\" class=\"data row6 col22\">\n", - " 3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col23\" class=\"data row6 col23\">\n", - " 2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow6_col24\" class=\"data row6 col24\">\n", - " 5.8\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row7\">\n", - " 7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col0\" class=\"data row7 col0\">\n", - " -0.44\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col1\" class=\"data row7 col1\">\n", - " 4.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col2\" class=\"data row7 col2\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col3\" class=\"data row7 col3\">\n", - " -0.21\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col4\" class=\"data row7 col4\">\n", - " 5.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col5\" class=\"data row7 col5\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n", - " 5.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n", - " 0.18\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n", - " 0.11\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n", - " 0.036\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n", - " -6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n", - " -0.45\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n", - " -6.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n", - " 0.71\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n", - " 0.67\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n", - " -7.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n", - " 3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n", - " 3.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n", - " 6.7\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row8\">\n", - " 8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", - " 0.92\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", - " 5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col2\" class=\"data row8 col2\">\n", - " -3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col3\" class=\"data row8 col3\">\n", - " -0.65\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col4\" class=\"data row8 col4\">\n", - " 6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col5\" class=\"data row8 col5\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col6\" class=\"data row8 col6\">\n", - " -1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col7\" class=\"data row8 col7\">\n", - " 5.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col8\" class=\"data row8 col8\">\n", - " -3.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n", - " -1.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n", - " -1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n", - " 0.82\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n", - " -2.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n", - " -0.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n", - " -6.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n", - " -0.52\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n", - " -6.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n", - " -3.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n", - " -0.043\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n", - " -4.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n", - " 0.51\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n", - " -5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n", - " 3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n", - " 2.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n", - " 5.1\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row9\">\n", - " 9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", - " 0.38\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", - " 5.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", - " -0.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", - " 7.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n", - " -0.44\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n", - " 5.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n", - " -2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n", - " -0.33\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n", - " -0.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n", - " 0.26\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n", - " -3.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n", - " -0.82\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n", - " -6.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n", - " -8.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n", - " 0.41\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col19\" class=\"data row9 col19\">\n", - " -4.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col20\" class=\"data row9 col20\">\n", - " 1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col21\" class=\"data row9 col21\">\n", - " -6.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col22\" class=\"data row9 col22\">\n", - " 2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col23\" class=\"data row9 col23\">\n", - " 3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow9_col24\" class=\"data row9 col24\">\n", - " 5.2\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row10\">\n", - " 10\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col0\" class=\"data row10 col0\">\n", - " 2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col1\" class=\"data row10 col1\">\n", - " 5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col2\" class=\"data row10 col2\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col3\" class=\"data row10 col3\">\n", - " -0.98\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col4\" class=\"data row10 col4\">\n", - " 7.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col5\" class=\"data row10 col5\">\n", - " -2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col6\" class=\"data row10 col6\">\n", - " -0.59\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col7\" class=\"data row10 col7\">\n", - " 5.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col8\" class=\"data row10 col8\">\n", - " -2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col9\" class=\"data row10 col9\">\n", - " -0.71\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col10\" class=\"data row10 col10\">\n", - " -0.46\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col11\" class=\"data row10 col11\">\n", - " 1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col12\" class=\"data row10 col12\">\n", - " -2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col13\" class=\"data row10 col13\">\n", - " 0.48\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n", - " -6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n", - " -3.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n", - " -7.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n", - " -5.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n", - " -0.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n", - " -4.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n", - " -0.52\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n", - " -7.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n", - " 1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n", - " 5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n", - " 5.8\n", - " \n", - " </tr>\n", - " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row11\">\n", - " 11\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n", - " 1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n", - " 4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n", - " -2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n", - " -1.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n", - " 5.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n", - " -0.49\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n", - " 0.017\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n", - " 5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n", - " -1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n", - " -0.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n", - " 0.49\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n", - " 2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n", - " -1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n", - " 1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n", - " -5.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n", - " -8.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n", - " -3.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n", - " -2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n", - " -4.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n", - " -1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n", - " -7.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n", - " 1.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n", - " 5.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n", - " 5.8\n", - " \n", - " </tr>\n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row12\">\n", - " 12\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n", - " 3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n", - " 4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n", - " -3.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n", - " 5.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n", - " 0.33\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n", - " 6.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n", - " -2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n", - " -0.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n", - " 1.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n", - " 2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n", - " -1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n", - " 0.75\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n", - " -5.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n", - " -7.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n", - " -2.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n", - " -2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n", - " -4.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n", - " -9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n", - " 2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n", - " 6.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n", - " 5.6\n", - " \n", - " </tr>\n", + " background-color: #9eb9db;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row13\">\n", - " 13\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col0\" class=\"data row13 col0\">\n", - " 2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col1\" class=\"data row13 col1\">\n", - " 4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col2\" class=\"data row13 col2\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col3\" class=\"data row13 col3\">\n", - " -2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col4\" class=\"data row13 col4\">\n", - " 6.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col5\" class=\"data row13 col5\">\n", - " -3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n", - " -2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n", - " 8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n", - " -0.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n", - " 0.71\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n", - " 2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n", - " -0.16\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n", - " -0.46\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n", - " -5.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n", - " -3.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n", - " -7.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n", - " -4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n", - " 0.33\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n", - " -3.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n", - " -1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n", - " -8.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n", - " 2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n", - " 5.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n", - " 6.7\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row14\">\n", - " 14\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n", - " 3.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n", - " 4.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n", - " -1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n", - " 6.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n", - " -1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n", - " 5.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n", - " -2.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n", - " -0.33\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n", - " -0.97\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n", - " 1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n", - " 3.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n", - " 0.29\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n", - " -4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n", - " -4.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n", - " -6.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n", - " -4.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n", - " -2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n", - " -2.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n", - " -1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n", - " -9.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n", - " 3.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n", - " 6.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n", - " 7.5\n", - " \n", - " </tr>\n", + " font-size: 1pt;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row15\">\n", - " 15\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n", - " 5.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n", - " 5.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n", - " -4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n", - " 5.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n", - " -3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n", - " -1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n", - " 5.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n", - " -3.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n", - " -0.33\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n", - " -1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n", - " 2.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n", - " 4.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n", - " 1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n", - " -4.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n", - " -5.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n", - " -4.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n", - " -1.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n", - " -1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n", - " -11\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n", - " 2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n", - " 6.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n", - " 5.9\n", - " \n", - " </tr>\n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23 {\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row16\">\n", - " 16\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n", - " 4.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n", - " 4.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n", - " -2.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n", - " -3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n", - " 6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n", - " -2.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n", - " -0.47\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n", - " 5.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n", - " -4.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n", - " 1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n", - " 0.23\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n", - " 0.099\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n", - " 5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n", - " 1.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n", - " -3.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n", - " -3.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n", - " -5.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n", - " -3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n", - " -2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n", - " -1.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n", - " -0.56\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n", - " -13\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n", - " 2.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n", - " 6.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n", - " 4.9\n", - " \n", - " </tr>\n", + " background-color: #4479bb;\n", " \n", - " <tr>\n", - " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row17\">\n", - " 17\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n", - " 5.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n", - " 4.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n", - " -3.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n", - " -3.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n", - " 6.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n", - " -0.75\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n", - " 6.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n", - " -4.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n", - " 3.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n", - " -0.29\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n", - " 0.56\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n", - " 5.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n", - " 2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n", - " -2.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n", - " -5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n", - " -3.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n", - " -3.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n", - " -2.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n", - " 0.84\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n", - " -13\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n", - " 3.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n", - " 7.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n", - " 4.7\n", - " \n", - " </tr>\n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " #T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24 {\n", + " \n", + " background-color: #89aad4;\n", + " \n", + " max-width: 80px;\n", + " \n", + " font-size: 1pt;\n", + " \n", + " }\n", + " \n", + " </style>\n", + "\n", + " <table id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\">\n", + " \n", + " <caption>Hover to magify</caption>\n", + " \n", + "\n", + " <thead>\n", " \n", " <tr>\n", " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row18\">\n", - " 18\n", + " <th class=\"blank\">\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n", - " 6\n", + " <th class=\"col_heading level0 col0\">0\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n", - " 5.8\n", + " <th class=\"col_heading level0 col1\">1\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n", - " -2.8\n", + " <th class=\"col_heading level0 col2\">2\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n", - " -4.2\n", + " <th class=\"col_heading level0 col3\">3\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n", - " 7.1\n", + " <th class=\"col_heading level0 col4\">4\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n", - " -3.3\n", + " <th class=\"col_heading level0 col5\">5\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n", - " -1.2\n", + " <th class=\"col_heading level0 col6\">6\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n", - " 7.9\n", + " <th class=\"col_heading level0 col7\">7\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n", - " -4.9\n", + " <th class=\"col_heading level0 col8\">8\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n", - " 1.4\n", + " <th class=\"col_heading level0 col9\">9\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n", - " -0.63\n", + " <th class=\"col_heading level0 col10\">10\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n", - " 0.35\n", + " <th class=\"col_heading level0 col11\">11\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n", - " 7.5\n", + " <th class=\"col_heading level0 col12\">12\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n", - " 0.87\n", + " <th class=\"col_heading level0 col13\">13\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n", - " -1.5\n", + " <th class=\"col_heading level0 col14\">14\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n", - " -2.1\n", + " <th class=\"col_heading level0 col15\">15\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n", - " -4.2\n", + " <th class=\"col_heading level0 col16\">16\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n", - " -2.5\n", + " <th class=\"col_heading level0 col17\">17\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n", - " -2.5\n", + " <th class=\"col_heading level0 col18\">18\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n", - " -2.9\n", + " <th class=\"col_heading level0 col19\">19\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n", - " 1.9\n", + " <th class=\"col_heading level0 col20\">20\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n", - " -9.7\n", + " <th class=\"col_heading level0 col21\">21\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n", - " 3.4\n", + " <th class=\"col_heading level0 col22\">22\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n", - " 7.1\n", + " <th class=\"col_heading level0 col23\">23\n", " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n", - " 4.4\n", + " <th class=\"col_heading level0 col24\">24\n", " \n", " </tr>\n", " \n", + " </thead>\n", + " <tbody>\n", + " \n", " <tr>\n", " \n", - " <th id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fb\" class=\"row_heading level24 row19\">\n", - " 19\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n", - " 4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n", - " 6.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n", - " -4.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n", - " -4.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n", - " 7.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n", - " -4.1\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n", - " -1.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n", - " 6.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n", - " -5.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n", - " -0.24\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n", - " 0.0072\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n", - " 1.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n", - " 6.4\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n", - " -2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n", - " -2.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n", - " -5.2\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n", - " -3.3\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n", - " -2.9\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n", - " -1.7\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n", - " 1.6\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n", - " -11\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n", - " 2.8\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n", - " 7.5\n", - " \n", - " <td id=\"T_a52c7836_c56b_11e5_a1b1_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n", - " 3.9\n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row0\">\n", + " \n", + " 0\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col0\" class=\"data row0 col0\">\n", + " \n", + " 0.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col1\" class=\"data row0 col1\">\n", + " \n", + " 1.03\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col2\" class=\"data row0 col2\">\n", + " \n", + " -0.84\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col3\" class=\"data row0 col3\">\n", + " \n", + " -0.59\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col4\" class=\"data row0 col4\">\n", + " \n", + " -0.96\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col5\" class=\"data row0 col5\">\n", + " \n", + " -0.22\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col6\" class=\"data row0 col6\">\n", + " \n", + " -0.62\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col7\" class=\"data row0 col7\">\n", + " \n", + " 1.84\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col8\" class=\"data row0 col8\">\n", + " \n", + " -2.05\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col9\" class=\"data row0 col9\">\n", + " \n", + " 0.87\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col10\" class=\"data row0 col10\">\n", + " \n", + " -0.92\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col11\" class=\"data row0 col11\">\n", + " \n", + " -0.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col12\" class=\"data row0 col12\">\n", + " \n", + " 2.15\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col13\" class=\"data row0 col13\">\n", + " \n", + " -1.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col14\" class=\"data row0 col14\">\n", + " \n", + " 0.08\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col15\" class=\"data row0 col15\">\n", + " \n", + " -1.25\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col16\" class=\"data row0 col16\">\n", + " \n", + " 1.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col17\" class=\"data row0 col17\">\n", + " \n", + " -1.05\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col18\" class=\"data row0 col18\">\n", + " \n", + " 1.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col19\" class=\"data row0 col19\">\n", + " \n", + " -0.42\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col20\" class=\"data row0 col20\">\n", + " \n", + " 2.29\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col21\" class=\"data row0 col21\">\n", + " \n", + " -2.59\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col22\" class=\"data row0 col22\">\n", + " \n", + " 2.82\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col23\" class=\"data row0 col23\">\n", + " \n", + " 0.68\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow0_col24\" class=\"data row0 col24\">\n", + " \n", + " -1.58\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row1\">\n", + " \n", + " 1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col0\" class=\"data row1 col0\">\n", + " \n", + " -1.75\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col1\" class=\"data row1 col1\">\n", + " \n", + " 1.56\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col2\" class=\"data row1 col2\">\n", + " \n", + " -1.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col3\" class=\"data row1 col3\">\n", + " \n", + " -1.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col4\" class=\"data row1 col4\">\n", + " \n", + " 1.03\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col5\" class=\"data row1 col5\">\n", + " \n", + " 0.0\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col6\" class=\"data row1 col6\">\n", + " \n", + " -2.46\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col7\" class=\"data row1 col7\">\n", + " \n", + " 3.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col8\" class=\"data row1 col8\">\n", + " \n", + " -1.66\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col9\" class=\"data row1 col9\">\n", + " \n", + " 1.27\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col10\" class=\"data row1 col10\">\n", + " \n", + " -0.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col11\" class=\"data row1 col11\">\n", + " \n", + " -0.02\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col12\" class=\"data row1 col12\">\n", + " \n", + " 1.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col13\" class=\"data row1 col13\">\n", + " \n", + " -1.09\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col14\" class=\"data row1 col14\">\n", + " \n", + " -1.86\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col15\" class=\"data row1 col15\">\n", + " \n", + " -1.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col16\" class=\"data row1 col16\">\n", + " \n", + " -0.68\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col17\" class=\"data row1 col17\">\n", + " \n", + " -0.81\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col18\" class=\"data row1 col18\">\n", + " \n", + " 0.35\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col19\" class=\"data row1 col19\">\n", + " \n", + " -0.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col20\" class=\"data row1 col20\">\n", + " \n", + " 1.79\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col21\" class=\"data row1 col21\">\n", + " \n", + " -2.82\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col22\" class=\"data row1 col22\">\n", + " \n", + " 2.26\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col23\" class=\"data row1 col23\">\n", + " \n", + " 0.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow1_col24\" class=\"data row1 col24\">\n", + " \n", + " 0.44\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row2\">\n", + " \n", + " 2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col0\" class=\"data row2 col0\">\n", + " \n", + " -0.65\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col1\" class=\"data row2 col1\">\n", + " \n", + " 3.22\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col2\" class=\"data row2 col2\">\n", + " \n", + " -1.76\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col3\" class=\"data row2 col3\">\n", + " \n", + " 0.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col4\" class=\"data row2 col4\">\n", + " \n", + " 2.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col5\" class=\"data row2 col5\">\n", + " \n", + " -0.37\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col6\" class=\"data row2 col6\">\n", + " \n", + " -3.0\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col7\" class=\"data row2 col7\">\n", + " \n", + " 3.73\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col8\" class=\"data row2 col8\">\n", + " \n", + " -1.87\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col9\" class=\"data row2 col9\">\n", + " \n", + " 2.46\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col10\" class=\"data row2 col10\">\n", + " \n", + " 0.21\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col11\" class=\"data row2 col11\">\n", + " \n", + " -0.24\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col12\" class=\"data row2 col12\">\n", + " \n", + " -0.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col13\" class=\"data row2 col13\">\n", + " \n", + " -0.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col14\" class=\"data row2 col14\">\n", + " \n", + " -3.02\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col15\" class=\"data row2 col15\">\n", + " \n", + " -0.82\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col16\" class=\"data row2 col16\">\n", + " \n", + " -0.21\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col17\" class=\"data row2 col17\">\n", + " \n", + " -0.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col18\" class=\"data row2 col18\">\n", + " \n", + " 0.86\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col19\" class=\"data row2 col19\">\n", + " \n", + " -0.68\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col20\" class=\"data row2 col20\">\n", + " \n", + " 1.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col21\" class=\"data row2 col21\">\n", + " \n", + " -4.89\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col22\" class=\"data row2 col22\">\n", + " \n", + " 3.03\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col23\" class=\"data row2 col23\">\n", + " \n", + " 1.91\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow2_col24\" class=\"data row2 col24\">\n", + " \n", + " 0.61\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row3\">\n", + " \n", + " 3\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col0\" class=\"data row3 col0\">\n", + " \n", + " -1.62\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col1\" class=\"data row3 col1\">\n", + " \n", + " 3.71\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col2\" class=\"data row3 col2\">\n", + " \n", + " -2.31\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col3\" class=\"data row3 col3\">\n", + " \n", + " 0.43\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col4\" class=\"data row3 col4\">\n", + " \n", + " 4.17\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col5\" class=\"data row3 col5\">\n", + " \n", + " -0.43\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col6\" class=\"data row3 col6\">\n", + " \n", + " -3.86\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col7\" class=\"data row3 col7\">\n", + " \n", + " 4.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col8\" class=\"data row3 col8\">\n", + " \n", + " -2.15\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col9\" 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow3_col24\" class=\"data row3 col24\">\n", + " \n", + " 0.28\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row4\">\n", + " \n", + " 4\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col0\" class=\"data row4 col0\">\n", + " \n", + " -3.35\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col1\" class=\"data row4 col1\">\n", + " \n", + " 4.48\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col2\" class=\"data row4 col2\">\n", + " \n", + " -1.86\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col3\" class=\"data row4 col3\">\n", + " \n", + " -1.7\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col4\" class=\"data row4 col4\">\n", + " \n", + " 5.19\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col5\" class=\"data row4 col5\">\n", + " \n", + " -1.02\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col6\" class=\"data row4 col6\">\n", + " \n", + " -3.81\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col7\" class=\"data row4 col7\">\n", + " \n", + " 4.72\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col8\" class=\"data row4 col8\">\n", + " \n", + " -0.72\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col9\" class=\"data row4 col9\">\n", + " \n", + " 1.08\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col10\" class=\"data row4 col10\">\n", + " \n", + " -0.18\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col11\" class=\"data row4 col11\">\n", + " \n", + " 0.83\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow4_col12\" 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col8\" class=\"data row5 col8\">\n", + " \n", + " -1.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col9\" class=\"data row5 col9\">\n", + " \n", + " -0.94\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col10\" class=\"data row5 col10\">\n", + " \n", + " 1.24\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col11\" class=\"data row5 col11\">\n", + " \n", + " 0.09\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col12\" class=\"data row5 col12\">\n", + " \n", + " -1.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col13\" class=\"data row5 col13\">\n", + " \n", + " -0.11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col14\" class=\"data row5 col14\">\n", + " \n", + " -4.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col15\" class=\"data row5 col15\">\n", + " \n", + " -0.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col16\" class=\"data row5 col16\">\n", + " \n", + " -2.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col17\" class=\"data row5 col17\">\n", + " \n", + " -1.35\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col18\" class=\"data row5 col18\">\n", + " \n", + " 0.8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col19\" class=\"data row5 col19\">\n", + " \n", + " -1.63\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col20\" class=\"data row5 col20\">\n", + " \n", + " 1.54\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col21\" class=\"data row5 col21\">\n", + " \n", + " -6.51\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col22\" class=\"data row5 col22\">\n", + " \n", + " 2.8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col23\" class=\"data row5 col23\">\n", + " \n", + " 2.14\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow5_col24\" class=\"data row5 col24\">\n", + " \n", + " 3.77\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row6\">\n", + " \n", + " 6\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col0\" class=\"data row6 col0\">\n", + " \n", + " -0.74\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col1\" class=\"data row6 col1\">\n", + " \n", + " 5.35\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col2\" class=\"data row6 col2\">\n", + " \n", + " -2.11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col3\" class=\"data row6 col3\">\n", + " \n", + " -1.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col4\" class=\"data row6 col4\">\n", + " \n", + " 4.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col5\" class=\"data row6 col5\">\n", + " \n", + " -1.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col6\" class=\"data row6 col6\">\n", + " \n", + " -3.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col7\" class=\"data row6 col7\">\n", + " \n", + " 3.76\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col8\" class=\"data row6 col8\">\n", + " \n", + " -3.22\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col9\" class=\"data row6 col9\">\n", + " \n", + " -1.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow6_col10\" 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col6\" class=\"data row7 col6\">\n", + " \n", + " -1.83\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col7\" class=\"data row7 col7\">\n", + " \n", + " 5.46\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col8\" class=\"data row7 col8\">\n", + " \n", + " -4.5\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col9\" class=\"data row7 col9\">\n", + " \n", + " -3.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col10\" class=\"data row7 col10\">\n", + " \n", + " -1.73\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col11\" class=\"data row7 col11\">\n", + " \n", + " 0.18\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col12\" class=\"data row7 col12\">\n", + " \n", + " 0.11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col13\" class=\"data row7 col13\">\n", + " \n", + " 0.04\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col14\" class=\"data row7 col14\">\n", + " \n", + " -5.99\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col15\" class=\"data row7 col15\">\n", + " \n", + " -0.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col16\" class=\"data row7 col16\">\n", + " \n", + " -6.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col17\" class=\"data row7 col17\">\n", + " \n", + " -3.89\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col18\" class=\"data row7 col18\">\n", + " \n", + " 0.71\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col19\" class=\"data row7 col19\">\n", + " \n", + " -3.95\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col20\" class=\"data row7 col20\">\n", + " \n", + " 0.67\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col21\" class=\"data row7 col21\">\n", + " \n", + " -7.26\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col22\" class=\"data row7 col22\">\n", + " \n", + " 2.97\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col23\" class=\"data row7 col23\">\n", + " \n", + " 3.39\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow7_col24\" class=\"data row7 col24\">\n", + " \n", + " 6.66\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row8\">\n", + " \n", + " 8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col0\" class=\"data row8 col0\">\n", + " \n", + " 0.92\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col1\" class=\"data row8 col1\">\n", + " \n", + " 5.8\n", + " \n", 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col9\" class=\"data row8 col9\">\n", + " \n", + " -1.3\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col10\" class=\"data row8 col10\">\n", + " \n", + " -1.61\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col11\" class=\"data row8 col11\">\n", + " \n", + " 0.82\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col12\" class=\"data row8 col12\">\n", + " \n", + " -2.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col13\" class=\"data row8 col13\">\n", + " \n", + " -0.4\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col14\" class=\"data row8 col14\">\n", + " \n", + " -6.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col15\" class=\"data row8 col15\">\n", + " \n", + " -0.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col16\" class=\"data row8 col16\">\n", + " \n", + " -6.6\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col17\" class=\"data row8 col17\">\n", + " \n", + " -3.48\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col18\" class=\"data row8 col18\">\n", + " \n", + " -0.04\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col19\" class=\"data row8 col19\">\n", + " \n", + " -4.6\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col20\" class=\"data row8 col20\">\n", + " \n", + " 0.51\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col21\" class=\"data row8 col21\">\n", + " \n", + " -5.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col22\" class=\"data row8 col22\">\n", + " \n", + " 3.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col23\" class=\"data row8 col23\">\n", + " \n", + " 2.4\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow8_col24\" class=\"data row8 col24\">\n", + " \n", + " 5.08\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row9\">\n", + " \n", + " 9\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col0\" class=\"data row9 col0\">\n", + " \n", + " 0.38\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col1\" class=\"data row9 col1\">\n", + " \n", + " 5.54\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col2\" class=\"data row9 col2\">\n", + " \n", + " -4.49\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col3\" class=\"data row9 col3\">\n", + " \n", + " -0.8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col4\" class=\"data row9 col4\">\n", + " \n", + " 7.05\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col5\" class=\"data row9 col5\">\n", + " \n", + " -2.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col6\" class=\"data row9 col6\">\n", + " \n", + " -0.44\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col7\" class=\"data row9 col7\">\n", + " \n", + " 5.35\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col8\" class=\"data row9 col8\">\n", + " \n", + " -1.96\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col9\" class=\"data row9 col9\">\n", + " \n", + " -0.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col10\" class=\"data row9 col10\">\n", + " \n", + " -0.8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col11\" class=\"data row9 col11\">\n", + " \n", + " 0.26\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col12\" class=\"data row9 col12\">\n", + " \n", + " -3.37\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col13\" class=\"data row9 col13\">\n", + " \n", + " -0.82\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col14\" class=\"data row9 col14\">\n", + " \n", + " -6.05\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col15\" class=\"data row9 col15\">\n", + " \n", + " -2.61\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col16\" class=\"data row9 col16\">\n", + " \n", + " -8.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col17\" class=\"data row9 col17\">\n", + " \n", + " -4.45\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow9_col18\" class=\"data row9 col18\">\n", + " \n", + " 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col14\" class=\"data row10 col14\">\n", + " \n", + " -5.97\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col15\" class=\"data row10 col15\">\n", + " \n", + " -3.44\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col16\" class=\"data row10 col16\">\n", + " \n", + " -7.77\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col17\" class=\"data row10 col17\">\n", + " \n", + " -5.49\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col18\" class=\"data row10 col18\">\n", + " \n", + " -0.7\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col19\" class=\"data row10 col19\">\n", + " \n", + " -4.61\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col20\" class=\"data row10 col20\">\n", + " \n", + " -0.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col21\" class=\"data row10 col21\">\n", + " \n", + " -7.72\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col22\" class=\"data row10 col22\">\n", + " \n", + " 1.54\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col23\" class=\"data row10 col23\">\n", + " \n", + " 5.02\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow10_col24\" class=\"data row10 col24\">\n", + " \n", + " 5.81\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row11\">\n", + " \n", + " 11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col0\" class=\"data row11 col0\">\n", + " \n", + " 1.86\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col1\" class=\"data row11 col1\">\n", + " \n", + " 4.47\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col2\" class=\"data row11 col2\">\n", + " \n", + " -2.17\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col3\" class=\"data row11 col3\">\n", + " \n", + " -1.38\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col4\" class=\"data row11 col4\">\n", + " \n", + " 5.9\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col5\" class=\"data row11 col5\">\n", + " \n", + " -0.49\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col6\" class=\"data row11 col6\">\n", + " \n", + " 0.02\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col7\" class=\"data row11 col7\">\n", + " \n", + " 5.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col8\" class=\"data row11 col8\">\n", + " \n", + " -1.04\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col9\" class=\"data row11 col9\">\n", + " \n", + " -0.6\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col10\" class=\"data row11 col10\">\n", + " \n", + " 0.49\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col11\" class=\"data row11 col11\">\n", + " \n", + " 1.96\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col12\" class=\"data row11 col12\">\n", + " \n", + " -1.47\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col13\" class=\"data row11 col13\">\n", + " \n", + " 1.88\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col14\" class=\"data row11 col14\">\n", + " \n", + " -5.92\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col15\" class=\"data row11 col15\">\n", + " \n", + " -4.55\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col16\" class=\"data row11 col16\">\n", + " \n", + " -8.15\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col17\" class=\"data row11 col17\">\n", + " \n", + " -3.42\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col18\" class=\"data row11 col18\">\n", + " \n", + " -2.24\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col19\" class=\"data row11 col19\">\n", + " \n", + " -4.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col20\" class=\"data row11 col20\">\n", + " \n", + " -1.17\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col21\" class=\"data row11 col21\">\n", + " \n", + " -7.9\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col22\" class=\"data row11 col22\">\n", + " \n", + " 1.36\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col23\" class=\"data row11 col23\">\n", + " \n", + " 5.31\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow11_col24\" class=\"data row11 col24\">\n", + " \n", + " 5.83\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row12\">\n", + " \n", + " 12\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col0\" class=\"data row12 col0\">\n", + " \n", + " 3.19\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col1\" class=\"data row12 col1\">\n", + " \n", + " 4.22\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col2\" class=\"data row12 col2\">\n", + " \n", + " -3.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col3\" class=\"data row12 col3\">\n", + " \n", + " -2.27\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col4\" class=\"data row12 col4\">\n", + " \n", + " 5.93\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col5\" class=\"data row12 col5\">\n", + " \n", + " -2.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col6\" class=\"data row12 col6\">\n", + " \n", + " 0.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col7\" class=\"data row12 col7\">\n", + " \n", + " 6.72\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col8\" class=\"data row12 col8\">\n", + " \n", + " -2.84\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col9\" class=\"data row12 col9\">\n", + " \n", + " -0.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col10\" class=\"data row12 col10\">\n", + " \n", + " 1.89\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col11\" class=\"data row12 col11\">\n", + " \n", + " 2.63\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col12\" class=\"data row12 col12\">\n", + " \n", + " -1.53\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col13\" class=\"data row12 col13\">\n", + " \n", + " 0.75\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col14\" class=\"data row12 col14\">\n", + " \n", + " -5.27\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col15\" class=\"data row12 col15\">\n", + " \n", + " -4.53\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col16\" class=\"data row12 col16\">\n", + " \n", + " -7.57\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col17\" class=\"data row12 col17\">\n", + " \n", + " -2.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col18\" class=\"data row12 col18\">\n", + " \n", + " -2.17\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col19\" class=\"data row12 col19\">\n", + " \n", + " -4.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col20\" class=\"data row12 col20\">\n", + " \n", + " -1.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col21\" class=\"data row12 col21\">\n", + " \n", + " -8.99\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col22\" class=\"data row12 col22\">\n", + " \n", + " 2.11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col23\" class=\"data row12 col23\">\n", + " \n", + " 6.42\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow12_col24\" class=\"data row12 col24\">\n", + " \n", + " 5.6\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + 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id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col6\" class=\"data row13 col6\">\n", + " \n", + " -2.17\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col7\" class=\"data row13 col7\">\n", + " \n", + " 7.99\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col8\" class=\"data row13 col8\">\n", + " \n", + " -2.56\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col9\" class=\"data row13 col9\">\n", + " \n", + " -0.8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col10\" class=\"data row13 col10\">\n", + " \n", + " 0.71\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col11\" class=\"data row13 col11\">\n", + " \n", + " 2.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col12\" class=\"data row13 col12\">\n", + " \n", + " -0.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col13\" class=\"data row13 col13\">\n", + " \n", + " -0.46\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col14\" class=\"data row13 col14\">\n", + " \n", + " -5.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col15\" class=\"data row13 col15\">\n", + " \n", + " -3.79\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col16\" class=\"data row13 col16\">\n", + " \n", + " -7.58\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col17\" class=\"data row13 col17\">\n", + " \n", + " -4.0\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col18\" class=\"data row13 col18\">\n", + " \n", + " 0.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col19\" class=\"data row13 col19\">\n", + " \n", + " -3.67\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col20\" class=\"data row13 col20\">\n", + " \n", + " -1.05\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col21\" class=\"data row13 col21\">\n", + " \n", + " -8.71\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col22\" class=\"data row13 col22\">\n", + " \n", + " 2.47\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col23\" class=\"data row13 col23\">\n", + " \n", + " 5.87\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow13_col24\" class=\"data row13 col24\">\n", + " \n", + " 6.71\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row14\">\n", + " \n", + " 14\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col0\" class=\"data row14 col0\">\n", + " \n", + " 3.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col1\" class=\"data row14 col1\">\n", + " \n", + " 4.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col2\" class=\"data row14 col2\">\n", + " \n", + " -3.88\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col3\" class=\"data row14 col3\">\n", + " \n", + " -1.58\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col4\" class=\"data row14 col4\">\n", + " \n", + " 6.22\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col5\" class=\"data row14 col5\">\n", + " \n", + " -3.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col6\" class=\"data row14 col6\">\n", + " \n", + " -1.46\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col7\" class=\"data row14 col7\">\n", + " \n", + " 5.57\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col8\" class=\"data row14 col8\">\n", + " \n", + " -2.93\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col9\" class=\"data row14 col9\">\n", + " \n", + " -0.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col10\" class=\"data row14 col10\">\n", + " \n", + " -0.97\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col11\" class=\"data row14 col11\">\n", + " \n", + " 1.72\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col12\" class=\"data row14 col12\">\n", + " \n", + " 3.61\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col13\" class=\"data row14 col13\">\n", + " \n", + " 0.29\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col14\" class=\"data row14 col14\">\n", + " \n", + " -4.21\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col15\" class=\"data row14 col15\">\n", + " \n", + " -4.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col16\" class=\"data row14 col16\">\n", + " \n", + " -6.68\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col17\" class=\"data row14 col17\">\n", + " \n", + " -4.5\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col18\" class=\"data row14 col18\">\n", + " \n", + " -2.19\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col19\" class=\"data row14 col19\">\n", + " \n", + " -2.43\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col20\" class=\"data row14 col20\">\n", + " \n", + " -1.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col21\" class=\"data row14 col21\">\n", + " \n", + " -9.36\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col22\" class=\"data row14 col22\">\n", + " \n", + " 3.36\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col23\" class=\"data row14 col23\">\n", + " \n", + " 6.11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow14_col24\" class=\"data row14 col24\">\n", + " \n", + " 7.53\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row15\">\n", + " \n", + " 15\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col0\" class=\"data row15 col0\">\n", + " \n", + " 5.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col1\" class=\"data row15 col1\">\n", + " \n", + " 5.31\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col2\" class=\"data row15 col2\">\n", + " \n", + " -3.98\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col3\" class=\"data row15 col3\">\n", + " \n", + " -2.26\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col4\" class=\"data row15 col4\">\n", + " \n", + " 5.91\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col5\" class=\"data row15 col5\">\n", + " \n", + " -3.3\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col6\" class=\"data row15 col6\">\n", + " \n", + " -1.03\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col7\" class=\"data row15 col7\">\n", + " \n", + " 5.68\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col8\" class=\"data row15 col8\">\n", + " \n", + " -3.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col9\" class=\"data row15 col9\">\n", + " \n", + " -0.33\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col10\" class=\"data row15 col10\">\n", + " \n", + " -1.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col11\" class=\"data row15 col11\">\n", + " \n", + " 2.19\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col12\" class=\"data row15 col12\">\n", + " \n", + " 4.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col13\" class=\"data row15 col13\">\n", + " \n", + " 1.01\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col14\" class=\"data row15 col14\">\n", + " \n", + " -3.22\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col15\" class=\"data row15 col15\">\n", + " \n", + " -4.31\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col16\" class=\"data row15 col16\">\n", + " \n", + " -5.74\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col17\" class=\"data row15 col17\">\n", + " \n", + " -4.44\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col18\" class=\"data row15 col18\">\n", + " \n", + " -2.3\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col19\" class=\"data row15 col19\">\n", + " \n", + " -1.36\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col20\" class=\"data row15 col20\">\n", + " \n", + " -1.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col21\" class=\"data row15 col21\">\n", + " \n", + " -11.27\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col22\" class=\"data row15 col22\">\n", + " \n", + " 2.59\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col23\" class=\"data row15 col23\">\n", + " \n", + " 6.69\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow15_col24\" class=\"data row15 col24\">\n", + " \n", + " 5.91\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row16\">\n", + " \n", + " 16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col0\" class=\"data row16 col0\">\n", + " \n", + " 4.08\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col1\" class=\"data row16 col1\">\n", + " \n", + " 4.34\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col2\" class=\"data row16 col2\">\n", + " \n", + " -2.44\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col3\" class=\"data row16 col3\">\n", + " \n", + " -3.3\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col4\" class=\"data row16 col4\">\n", + " \n", + " 6.04\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col5\" class=\"data row16 col5\">\n", + " \n", + " -2.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col6\" class=\"data row16 col6\">\n", + " \n", + " -0.47\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col7\" class=\"data row16 col7\">\n", + " \n", + " 5.28\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col8\" class=\"data row16 col8\">\n", + " \n", + " -4.84\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col9\" class=\"data row16 col9\">\n", + " \n", + " 1.58\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col10\" class=\"data row16 col10\">\n", + " \n", + " 0.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col11\" class=\"data row16 col11\">\n", + " \n", + " 0.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col12\" class=\"data row16 col12\">\n", + " \n", + " 5.79\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col13\" class=\"data row16 col13\">\n", + " \n", + " 1.8\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col14\" class=\"data row16 col14\">\n", + " \n", + " -3.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col15\" class=\"data row16 col15\">\n", + " \n", + " -3.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col16\" class=\"data row16 col16\">\n", + " \n", + " -5.53\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col17\" class=\"data row16 col17\">\n", + " \n", + " -2.97\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col18\" class=\"data row16 col18\">\n", + " \n", + " -2.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col19\" class=\"data row16 col19\">\n", + " \n", + " -1.15\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col20\" class=\"data row16 col20\">\n", + " \n", + " -0.56\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col21\" class=\"data row16 col21\">\n", + " \n", + " -13.13\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col22\" class=\"data row16 col22\">\n", + " \n", + " 2.07\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col23\" class=\"data row16 col23\">\n", + " \n", + " 6.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow16_col24\" class=\"data row16 col24\">\n", + " \n", + " 4.94\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row17\">\n", + " \n", + " 17\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col0\" class=\"data row17 col0\">\n", + " \n", + " 5.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col1\" class=\"data row17 col1\">\n", + " \n", + " 4.57\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col2\" class=\"data row17 col2\">\n", + " \n", + " -3.53\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col3\" class=\"data row17 col3\">\n", + " \n", + " -3.76\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col4\" class=\"data row17 col4\">\n", + " \n", + " 6.58\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col5\" class=\"data row17 col5\">\n", + " \n", + " -2.58\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col6\" class=\"data row17 col6\">\n", + " \n", + " -0.75\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col7\" class=\"data row17 col7\">\n", + " \n", + " 6.58\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col8\" class=\"data row17 col8\">\n", + " \n", + " -4.78\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col9\" class=\"data row17 col9\">\n", + " \n", + " 3.63\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col10\" class=\"data row17 col10\">\n", + " \n", + " -0.29\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col11\" class=\"data row17 col11\">\n", + " \n", + " 0.56\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col12\" class=\"data row17 col12\">\n", + " \n", + " 5.76\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col13\" class=\"data row17 col13\">\n", + " \n", + " 2.05\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col14\" class=\"data row17 col14\">\n", + " \n", + " -2.27\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col15\" class=\"data row17 col15\">\n", + " \n", + " -2.31\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col16\" class=\"data row17 col16\">\n", + " \n", + " -4.95\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col17\" class=\"data row17 col17\">\n", + " \n", + " -3.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col18\" class=\"data row17 col18\">\n", + " \n", + " -3.06\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col19\" class=\"data row17 col19\">\n", + " \n", + " -2.43\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col20\" class=\"data row17 col20\">\n", + " \n", + " 0.84\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col21\" class=\"data row17 col21\">\n", + " \n", + " -12.57\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col22\" class=\"data row17 col22\">\n", + " \n", + " 3.56\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col23\" class=\"data row17 col23\">\n", + " \n", + " 7.36\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow17_col24\" class=\"data row17 col24\">\n", + " \n", + " 4.7\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row18\">\n", + " \n", + " 18\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col0\" class=\"data row18 col0\">\n", + " \n", + " 5.99\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col1\" class=\"data row18 col1\">\n", + " \n", + " 5.82\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col2\" class=\"data row18 col2\">\n", + " \n", + " -2.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col3\" class=\"data row18 col3\">\n", + " \n", + " -4.15\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col4\" class=\"data row18 col4\">\n", + " \n", + " 7.12\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col5\" class=\"data row18 col5\">\n", + " \n", + " -3.32\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col6\" class=\"data row18 col6\">\n", + " \n", + " -1.21\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col7\" class=\"data row18 col7\">\n", + " \n", + " 7.93\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col8\" class=\"data row18 col8\">\n", + " \n", + " -4.85\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col9\" class=\"data row18 col9\">\n", + " \n", + " 1.44\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col10\" class=\"data row18 col10\">\n", + " \n", + " -0.63\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col11\" class=\"data row18 col11\">\n", + " \n", + " 0.35\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col12\" class=\"data row18 col12\">\n", + " \n", + " 7.47\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col13\" class=\"data row18 col13\">\n", + " \n", + " 0.87\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col14\" class=\"data row18 col14\">\n", + " \n", + " -1.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col15\" class=\"data row18 col15\">\n", + " \n", + " -2.09\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col16\" class=\"data row18 col16\">\n", + " \n", + " -4.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col17\" class=\"data row18 col17\">\n", + " \n", + " -2.55\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col18\" class=\"data row18 col18\">\n", + " \n", + " -2.46\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col19\" class=\"data row18 col19\">\n", + " \n", + " -2.89\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col20\" class=\"data row18 col20\">\n", + " \n", + " 1.9\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col21\" class=\"data row18 col21\">\n", + " \n", + " -9.74\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col22\" class=\"data row18 col22\">\n", + " \n", + " 3.43\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col23\" class=\"data row18 col23\">\n", + " \n", + " 7.07\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow18_col24\" class=\"data row18 col24\">\n", + " \n", + " 4.39\n", + " \n", + " \n", + " </tr>\n", + " \n", + " <tr>\n", + " \n", + " <th id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fb\" class=\"row_heading level24 row19\">\n", + " \n", + " 19\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col0\" class=\"data row19 col0\">\n", + " \n", + " 4.03\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col1\" class=\"data row19 col1\">\n", + " \n", + " 6.23\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col2\" class=\"data row19 col2\">\n", + " \n", + " -4.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col3\" class=\"data row19 col3\">\n", + " \n", + " -4.11\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col4\" class=\"data row19 col4\">\n", + " \n", + " 7.19\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col5\" class=\"data row19 col5\">\n", + " \n", + " -4.1\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col6\" class=\"data row19 col6\">\n", + " \n", + " -1.52\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col7\" class=\"data row19 col7\">\n", + " \n", + " 6.53\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col8\" class=\"data row19 col8\">\n", + " \n", + " -5.21\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col9\" class=\"data row19 col9\">\n", + " \n", + " -0.24\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col10\" class=\"data row19 col10\">\n", + " \n", + " 0.01\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col11\" class=\"data row19 col11\">\n", + " \n", + " 1.16\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col12\" class=\"data row19 col12\">\n", + " \n", + " 6.43\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col13\" class=\"data row19 col13\">\n", + " \n", + " -1.97\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col14\" class=\"data row19 col14\">\n", + " \n", + " -2.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col15\" class=\"data row19 col15\">\n", + " \n", + " -1.66\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col16\" class=\"data row19 col16\">\n", + " \n", + " -5.2\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col17\" class=\"data row19 col17\">\n", + " \n", + " -3.25\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col18\" class=\"data row19 col18\">\n", + " \n", + " -2.87\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col19\" class=\"data row19 col19\">\n", + " \n", + " -1.65\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col20\" class=\"data row19 col20\">\n", + " \n", + " 1.64\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col21\" class=\"data row19 col21\">\n", + " \n", + " -10.66\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col22\" class=\"data row19 col22\">\n", + " \n", + " 2.83\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col23\" class=\"data row19 col23\">\n", + " \n", + " 7.48\n", + " \n", + " \n", + " <td id=\"T_35e396c0_8d9b_11e5_80ed_a45e60bd97fbrow19_col24\" class=\"data row19 col24\">\n", + " \n", + " 3.94\n", + " \n", " \n", " </tr>\n", " \n", @@ -18738,10 +20991,10 @@ " " ], "text/plain": [ - "<pandas.core.style.Styler at 0x111c7ea58>" + "<pandas.core.style.Styler at 0x114b9b1d0>" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } diff --git a/pandas/core/style.py b/pandas/core/style.py index 76460fdf224b2..9a44109266957 100644 --- a/pandas/core/style.py +++ b/pandas/core/style.py @@ -7,8 +7,7 @@ from contextlib import contextmanager from uuid import uuid1 import copy -from collections import defaultdict -from collections.abc import MutableMapping +from collections import defaultdict, MutableMapping try: from jinja2 import Template @@ -216,7 +215,7 @@ def _translate(self): "class": " ".join(cs)}) head.append(row_es) - if self.data.index.names and self.data.index.names != [None]: + if self.data.index.names: index_header_row = [] for c, name in enumerate(self.data.index.names): @@ -273,7 +272,7 @@ def _translate(self): caption=caption, table_attributes=self.table_attributes) def format(self, formatter, subset=None): - ''' + """ Format the text display value of cells. .. versionadded:: 0.18.0 @@ -282,6 +281,8 @@ def format(self, formatter, subset=None): ---------- formatter: str, callable, or dict subset: IndexSlice + A argument to DataFrame.loc that restricts which elements + ``formatter`` is applied to. Returns ------- @@ -292,8 +293,9 @@ def format(self, formatter, subset=None): ``formatter`` is either an ``a`` or a dict ``{column name: a}`` where ``a`` is one of - - str: this will be wrapped in: ``a.format(x)`` - - callable: called with the value of an individual cell + + - str: this will be wrapped in: ``a.format(x)`` + - callable: called with the value of an individual cell The default display value for numeric values is the "general" (``g``) format with ``pd.options.display.precision`` precision. @@ -305,7 +307,7 @@ def format(self, formatter, subset=None): >>> df.style.format("{:.2%}") >>> df['c'] = ['a', 'b', 'c', 'd'] >>> df.style.format({'C': str.upper}) - ''' + """ if subset is None: row_locs = range(len(self.data)) col_locs = range(len(self.data.columns)) @@ -853,11 +855,11 @@ def _highlight_extrema(data, color='yellow', max_=True): def _maybe_wrap_formatter(formatter): - if not (callable(formatter) or com.is_string_like(formatter)): + if com.is_string_like(formatter): + return lambda x: formatter.format(x) + elif callable(formatter): + return formatter + else: msg = "Expected a template string or callable, got {} instead".format( formatter) raise TypeError(msg) - if not callable(formatter): - return lambda x: formatter.format(x) - else: - return formatter diff --git a/pandas/tests/test_style.py b/pandas/tests/test_style.py index fb008c2d9a6f5..ef5a966d65545 100644 --- a/pandas/tests/test_style.py +++ b/pandas/tests/test_style.py @@ -129,27 +129,6 @@ def test_set_properties_subset(self): expected = {(0, 0): ['color: white']} self.assertEqual(result, expected) - def test_empty_index_name_doesnt_display(self): - # https://github.com/pydata/pandas/pull/12090#issuecomment-180695902 - df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}) - result = df.style._translate() - - expected = [[{'class': 'blank', 'type': 'th', 'value': ''}, - {'class': 'col_heading level0 col0', - 'display_value': 'A', - 'type': 'th', - 'value': 'A'}, - {'class': 'col_heading level0 col1', - 'display_value': 'B', - 'type': 'th', - 'value': 'B'}, - {'class': 'col_heading level0 col2', - 'display_value': 'C', - 'type': 'th', - 'value': 'C'}]] - - self.assertEqual(result['head'], expected) - def test_index_name(self): # https://github.com/pydata/pandas/issues/11655 df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
Master started failing on https://github.com/pydata/pandas/pull/12260 [travis](https://travis-ci.org/pydata/pandas/builds/113480877). Not entirely sure why yet, but was able to reproduce one of the failures locally, and fix it with the revert.
https://api.github.com/repos/pandas-dev/pandas/pulls/12524
2016-03-04T14:04:13Z
2016-03-05T02:12:19Z
null
2016-11-03T12:38:54Z
Fix typo
diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst index a28eb39f36917..beb803282ebe3 100644 --- a/doc/source/advanced.rst +++ b/doc/source/advanced.rst @@ -281,7 +281,7 @@ As usual, **both sides** of the slicers are included as this is label indexing. .. warning:: You should specify all axes in the ``.loc`` specifier, meaning the indexer for the **index** and - for the **columns**. Their are some ambiguous cases where the passed indexer could be mis-interpreted + for the **columns**. There are some ambiguous cases where the passed indexer could be mis-interpreted as indexing *both* axes, rather than into say the MuliIndex for the rows. You should do this:
https://api.github.com/repos/pandas-dev/pandas/pulls/12523
2016-03-04T12:56:59Z
2016-03-05T18:02:40Z
null
2017-09-03T11:47:50Z
DOC: Examples of monotonic/non-monotonic label-based indexing
diff --git a/doc/source/gotchas.rst b/doc/source/gotchas.rst index fe7ab67b7f759..39044662b5d13 100644 --- a/doc/source/gotchas.rst +++ b/doc/source/gotchas.rst @@ -242,6 +242,40 @@ Label-based slicing conventions Non-monotonic indexes require exact matches ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If the index of a ``Series`` or ``DataFrame`` is monotonically increasing or decreasing, then the bounds +of a label-based slice can be outside the range of the index, much like slice indexing a +normal Python ``list``. Monotonicity of an index can be tested with the ``is_monotonic_increasing`` and +``is_monotonic_decreasing`` attributes. + +.. ipython:: python + + df = pd.DataFrame(index=[2,3,3,4,5], columns=['data'], data=range(5)) + df.index.is_monotonic_increasing + # no rows 0 or 1, but still returns rows 2, 3 (both of them), and 4: + df.loc[0:4, :] + # slice is are outside the index, so empty DataFrame is returned + df.loc[13:15, :] + +On the other hand, if the index is not monotonic, then both slice bounds must be +*unique* members of the index. + +.. ipython:: python + + df = pd.DataFrame(index=[2,3,1,4,3,5], columns=['data'], data=range(6)) + df.index.is_monotonic_increasing + # OK because 2 and 4 are in the index + df.loc[2:4, :] + # 0 is not in the index + try: + df.loc[0:4, :] + except Exception as e: + print e.__class__.__name__ + ': ' + str(e) + # 3 is not a unique label + try: + df.loc[2:3, :] + except Exception as e: + print e.__class__.__name__ + ': ' + str(e) + Endpoints are inclusive ~~~~~~~~~~~~~~~~~~~~~~~
Just ran into this gotcha and almost filed it as a bug report. Added a few examples to fill in the section header.
https://api.github.com/repos/pandas-dev/pandas/pulls/12522
2016-03-03T23:46:57Z
2016-04-17T16:03:30Z
null
2016-04-17T16:03:39Z
TST: Fix NOQA for flake8 checking in datetools.py
diff --git a/pandas/core/datetools.py b/pandas/core/datetools.py index 91b33d30004b6..f44f762b4398f 100644 --- a/pandas/core/datetools.py +++ b/pandas/core/datetools.py @@ -1,8 +1,8 @@ """A collection of random tools for dealing with dates in Python""" -from pandas.tseries.tools import * # noqa -from pandas.tseries.offsets import * # noqa -from pandas.tseries.frequencies import * # noqa +from pandas.tseries.tools import * # flake8: noqa +from pandas.tseries.offsets import * # flake8: noqa +from pandas.tseries.frequencies import * # flake8: noqa day = DateOffset() bday = BDay()
Recent changes to pyflakes (see <a href="https://github.com/pyflakes/pyflakes/blob/0532189b34086740d7fe553bd87c0a6cf682a93b/pyflakes/checker.py#L544-L546">here</a>) suddenly started raising errors in `datetools.py` due to the star imports at the top, which the `#noqa` was not preventing. The correct syntax is `# flake8: noqa` <br><br><b>PLEASE MERGE ASAP</b>
https://api.github.com/repos/pandas-dev/pandas/pulls/12517
2016-03-03T02:40:30Z
2016-03-03T02:55:40Z
null
2016-03-03T03:05:22Z
Add a UK England and Wales holiday calendar
diff --git a/pandas/tseries/holiday.py b/pandas/tseries/holiday.py index 31e40c6bcbb2c..a7fbdb0e35371 100644 --- a/pandas/tseries/holiday.py +++ b/pandas/tseries/holiday.py @@ -500,6 +500,23 @@ class USFederalHolidayCalendar(AbstractHolidayCalendar): ] +class EnglandAndWalesHolidayCalendar(AbstractHolidayCalendar): + rules = [ + Holiday('New Years Day', month=1, day=1, observance=next_monday), + GoodFriday, + EasterMonday, + Holiday('Early May bank holiday', + month=5, day=1, offset=DateOffset(weekday=MO(1))), + Holiday('Spring bank holiday', + month=5, day=31, offset=DateOffset(weekday=MO(-1))), + Holiday('Summer bank holiday', + month=8, day=31, offset=DateOffset(weekday=MO(-1))), + Holiday('Christmas Day', month=12, day=25, observance=next_monday), + Holiday('Boxing Day', + month=12, day=26, observance=next_monday_or_tuesday) + ] + + def HolidayCalendarFactory(name, base, other, base_class=AbstractHolidayCalendar): rules = AbstractHolidayCalendar.merge_class(base, other) diff --git a/pandas/tseries/tests/test_holiday.py b/pandas/tseries/tests/test_holiday.py index 62446e8e637c6..066b9871b325e 100644 --- a/pandas/tseries/tests/test_holiday.py +++ b/pandas/tseries/tests/test_holiday.py @@ -13,7 +13,8 @@ EasterMonday, GoodFriday, after_nearest_workday, weekend_to_monday, USLaborDay, USColumbusDay, - USMartinLutherKingJr, USPresidentsDay) + USMartinLutherKingJr, USPresidentsDay, + EnglandAndWalesHolidayCalendar) from pytz import utc import nose @@ -387,6 +388,55 @@ def test_both_offset_observance_raises(self): observance=next_monday) +class TestEnglandAndWalesCalendar(tm.TestCase): + KNOWN_HOLIDAYS = [ + # https://www.gov.uk/bank-holidays + datetime(2014, 1, 1), + datetime(2014, 4, 18), + datetime(2014, 4, 21), + datetime(2014, 5, 5), + datetime(2014, 5, 26), + datetime(2014, 8, 25), + datetime(2014, 12, 25), + datetime(2014, 12, 26), + + datetime(2015, 1, 1), + datetime(2015, 4, 3), + datetime(2015, 4, 6), + datetime(2015, 5, 4), + datetime(2015, 5, 25), + datetime(2015, 8, 31), + datetime(2015, 12, 25), + datetime(2015, 12, 28), + + datetime(2016, 1, 1), + datetime(2016, 3, 25), + datetime(2016, 3, 28), + datetime(2016, 5, 2), + datetime(2016, 5, 30), + datetime(2016, 8, 29), + datetime(2016, 12, 26), + datetime(2016, 12, 27), + + datetime(2017, 1, 2), + datetime(2017, 4, 14), + datetime(2017, 4, 17), + datetime(2017, 5, 1), + datetime(2017, 5, 29), + datetime(2017, 8, 28), + datetime(2017, 12, 25), + datetime(2017, 12, 26), + ] + + def test_calendar(self): + cal = EnglandAndWalesHolidayCalendar() + holidays = cal.holidays( + start=datetime(2014, 1, 1), + end=datetime(2017, 12, 31) + ) + self.assertEqual(list(holidays.to_pydatetime()), self.KNOWN_HOLIDAYS) + + if __name__ == '__main__': nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], exit=False)
The rules are described at: https://en.wikipedia.org/wiki/Public_holidays_in_the_United_Kingdom#England.2C_Northern_Ireland_and_Wales Sample data used to check those rules (which is also included in tests) is from the government's website: https://www.gov.uk/bank-holidays
https://api.github.com/repos/pandas-dev/pandas/pulls/12514
2016-03-02T17:13:07Z
2016-03-03T02:13:09Z
null
2023-05-11T01:13:25Z
BUG: Fix parse_dates processing with usecols and C engine
diff --git a/doc/source/io.rst b/doc/source/io.rst index a78222dd748ad..6b287a2eea532 100644 --- a/doc/source/io.rst +++ b/doc/source/io.rst @@ -120,8 +120,12 @@ index_col : int or sequence or ``False``, default ``None`` each line, you might consider ``index_col=False`` to force pandas to *not* use the first column as the index (row names). usecols : array-like, default ``None`` - Return a subset of the columns. Results in much faster parsing time and lower - memory usage + Return a subset of the columns. All elements in this array must either + be positional (i.e. integer indices into the document columns) or strings + that correspond to column names provided either by the user in `names` or + inferred from the document header row(s). For example, a valid `usecols` + parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Using this parameter + results in much faster parsing time and lower memory usage. squeeze : boolean, default ``False`` If the parsed data only contains one column then return a Series. prefix : str, default ``None`` diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index ecb3ff5139ad0..f991be3dc3e10 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -101,7 +101,7 @@ API changes - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) - +- ``read_csv`` no longer allows a combination of strings and integers for the ``usecols`` parameter (:issue:`12678`) - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`) - Provide a proper ``__name__`` and ``__qualname__`` attributes for generic functions (:issue:`12021`) @@ -211,6 +211,7 @@ Bug Fixes - Bug in ``value_counts`` when ``normalize=True`` and ``dropna=True`` where nulls still contributed to the normalized count (:issue:`12558`) - Bug in ``Panel.fillna()`` ignoring ``inplace=True`` (:issue:`12633`) +- Bug in ``read_csv`` when specifying ``names``, ```usecols``, and ``parse_dates`` simultaneously with the C engine (:issue:`9755`) - Bug in ``Series.rename``, ``DataFrame.rename`` and ``DataFrame.rename_axis`` not treating ``Series`` as mappings to relabel (:issue:`12623`). - Clean in ``.rolling.min`` and ``.rolling.max`` to enhance dtype handling (:issue:`12373`) diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 7bd8a593661c5..bd14862df4e8e 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -75,8 +75,12 @@ class ParserWarning(Warning): of each line, you might consider index_col=False to force pandas to _not_ use the first column as the index (row names) usecols : array-like, default None - Return a subset of the columns. - Results in much faster parsing time and lower memory usage. + Return a subset of the columns. All elements in this array must either + be positional (i.e. integer indices into the document columns) or strings + that correspond to column names provided either by the user in `names` or + inferred from the document header row(s). For example, a valid `usecols` + parameter would be [0, 1, 2] or ['foo', 'bar', 'baz']. Using this parameter + results in much faster parsing time and lower memory usage. squeeze : boolean, default False If the parsed data only contains one column then return a Series prefix : str, default None @@ -801,6 +805,26 @@ def _is_index_col(col): return col is not None and col is not False +def _validate_usecols_arg(usecols): + """ + Check whether or not the 'usecols' parameter + contains all integers (column selection by index) + or strings (column by name). Raises a ValueError + if that is not the case. + """ + # gh-12678 + if usecols is not None: + usecols_dtype = lib.infer_dtype(usecols) + if usecols_dtype not in ('integer', 'string'): + raise ValueError(("The elements of 'usecols' " + "must either be all strings " + "or all integers")) + + # validation has succeeded, so + # return the argument for assignment + return usecols + + class ParserBase(object): def __init__(self, kwds): @@ -1132,7 +1156,7 @@ def __init__(self, src, **kwds): self._reader = _parser.TextReader(src, **kwds) # XXX - self.usecols = self._reader.usecols + self.usecols = _validate_usecols_arg(self._reader.usecols) passed_names = self.names is None @@ -1157,18 +1181,21 @@ def __init__(self, src, **kwds): else: self.names = lrange(self._reader.table_width) - # If the names were inferred (not passed by user) and usedcols is - # defined, then ensure names refers to the used columns, not the - # document's columns. - if self.usecols and passed_names: - col_indices = [] - for u in self.usecols: - if isinstance(u, string_types): - col_indices.append(self.names.index(u)) - else: - col_indices.append(u) - self.names = [n for i, n in enumerate(self.names) - if i in col_indices] + # gh-9755 + # + # need to set orig_names here first + # so that proper indexing can be done + # with _set_noconvert_columns + # + # once names has been filtered, we will + # then set orig_names again to names + self.orig_names = self.names[:] + + if self.usecols: + if len(self.names) > len(self.usecols): + self.names = [n for i, n in enumerate(self.names) + if (i in self.usecols or n in self.usecols)] + if len(self.names) < len(self.usecols): raise ValueError("Usecols do not match names.") @@ -1194,13 +1221,17 @@ def __init__(self, src, **kwds): self._implicit_index = self._reader.leading_cols > 0 def _set_noconvert_columns(self): - names = self.names + names = self.orig_names + usecols = self.usecols def _set(x): - if com.is_integer(x): - self._reader.set_noconvert(x) - else: - self._reader.set_noconvert(names.index(x)) + if usecols and com.is_integer(x): + x = list(usecols)[x] + + if not com.is_integer(x): + x = names.index(x) + + self._reader.set_noconvert(x) if isinstance(self.parse_dates, list): for val in self.parse_dates: @@ -1472,7 +1503,7 @@ def __init__(self, f, **kwds): self.lineterminator = kwds['lineterminator'] self.quoting = kwds['quoting'] self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True) - self.usecols = kwds['usecols'] + self.usecols = _validate_usecols_arg(kwds['usecols']) self.skip_blank_lines = kwds['skip_blank_lines'] self.names_passed = kwds['names'] or None diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 7f523cf3aa54d..2d56275279453 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2682,12 +2682,118 @@ def test_uneven_lines_with_usecols(self): df = self.read_csv(StringIO(csv), usecols=usecols) tm.assert_frame_equal(df, expected) - usecols = ['a', 1] + usecols = ['a', 'b'] df = self.read_csv(StringIO(csv), usecols=usecols) tm.assert_frame_equal(df, expected) - usecols = ['a', 'b'] - df = self.read_csv(StringIO(csv), usecols=usecols) + def test_usecols_with_parse_dates(self): + # See gh-9755 + s = """a,b,c,d,e + 0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + + cols = { + 'a' : [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates_and_full_names(self): + # See gh-9755 + s = """0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + names = list('abcde') + + cols = { + 'a' : [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), names=names, + usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), names=names, + usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_usecols_with_parse_dates_and_usecol_names(self): + # See gh-9755 + s = """0,1,20140101,0900,4 + 0,1,20140102,1000,4""" + parse_dates = [[1, 2]] + names = list('acd') + + cols = { + 'a' : [0, 0], + 'c_d': [ + Timestamp('2014-01-01 09:00:00'), + Timestamp('2014-01-02 10:00:00') + ] + } + expected = DataFrame(cols, columns=['c_d', 'a']) + + df = self.read_csv(StringIO(s), names=names, + usecols=[0, 2, 3], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + df = self.read_csv(StringIO(s), names=names, + usecols=[3, 0, 2], + parse_dates=parse_dates) + tm.assert_frame_equal(df, expected) + + def test_mixed_dtype_usecols(self): + # See gh-12678 + data = """a,b,c + 1000,2000,3000 + 4000,5000,6000 + """ + msg = ("The elements of \'usecols\' " + "must either be all strings " + "or all integers") + usecols = [0, 'b', 2] + + with tm.assertRaisesRegexp(ValueError, msg): + df = self.read_csv(StringIO(data), usecols=usecols) + + def test_usecols_with_integer_like_header(self): + data = """2,0,1 + 1000,2000,3000 + 4000,5000,6000 + """ + + usecols = [0, 1] # column selection by index + expected = DataFrame(data=[[1000, 2000], + [4000, 5000]], + columns=['2', '0']) + df = self.read_csv(StringIO(data), usecols=usecols) + tm.assert_frame_equal(df, expected) + + usecols = ['0', '1'] # column selection by name + expected = DataFrame(data=[[2000, 3000], + [5000, 6000]], + columns=['0', '1']) + df = self.read_csv(StringIO(data), usecols=usecols) tm.assert_frame_equal(df, expected)
closes #9755 closes #12678 Continuing on my conquest of `read_csv` bugs, this PR fixes a bug brought up in #9755 in processing `parse_dates` with the C engine in which the wrong indices (those of the filtered column names) were being used to determine the date columns to not be dtype-parsed by the C engine. The correct indices are those of the original column names, as they are used later on in the actual data processing.
https://api.github.com/repos/pandas-dev/pandas/pulls/12512
2016-03-02T15:14:52Z
2016-04-06T19:17:26Z
null
2016-04-06T19:29:40Z
COMPAT: blacklist numexpr=2.4.4
diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run index f0101d34204a3..f9f226e3f1465 100644 --- a/ci/requirements-3.4_SLOW.run +++ b/ci/requirements-3.4_SLOW.run @@ -9,7 +9,7 @@ html5lib patsy beautiful-soup scipy -numexpr +numexpr=2.4.4 pytables matplotlib lxml diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run index 4ba3b473b3edd..fdc5f3f7dc992 100644 --- a/ci/requirements-3.5.run +++ b/ci/requirements-3.5.run @@ -18,6 +18,7 @@ sqlalchemy pymysql psycopg2 xarray +boto # incompat with conda ATM # beautiful-soup diff --git a/doc/source/install.rst b/doc/source/install.rst index 3836180af520f..11b9115aa9c81 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -225,7 +225,7 @@ Recommended Dependencies * `numexpr <https://github.com/pydata/numexpr>`__: for accelerating certain numerical operations. ``numexpr`` uses multiple cores as well as smart chunking and caching to achieve large speedups. - If installed, must be Version 2.1 or higher. Version 2.4.6 or higher on Windows is highly recommended. + If installed, must be Version 2.1 or higher (excluding a buggy 2.4.4). Version 2.4.6 or higher is highly recommended. * `bottleneck <http://berkeleyanalytics.com/bottleneck>`__: for accelerating certain types of ``nan`` evaluations. ``bottleneck`` uses specialized cython routines to achieve large speedups. diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 34e024def332b..8d36d323f48f9 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -12,6 +12,10 @@ users upgrade to this version. pandas >= 0.18.0 no longer supports compatibility with Python version 2.6 and 3.3 (:issue:`7718`, :issue:`11273`) +.. warning:: + + ``numexpr`` version 2.4.4 will now show a warning and not be used as a computation back-end for pandas because of some buggy behavior. This does not affect other versions (>= 2.1 and >= 2.4.6). (:issue:`12489`) + Highlights include: - Moving and expanding window functions are now methods on Series and DataFrame, @@ -1121,7 +1125,7 @@ Bug Fixes - Bug in ``Series.resample`` using a frequency of ``Nano`` when the index is a ``DatetimeIndex`` and contains non-zero nanosecond parts (:issue:`12037`) - Bug in resampling with ``.nunique`` and a sparse index (:issue:`12352`) - Removed some compiler warnings (:issue:`12471`) - +- Work around compat issues with ``boto`` in python 3.5 (:issue:`11915`) - Bug in ``NaT`` subtraction from ``Timestamp`` or ``DatetimeIndex`` with timezones (:issue:`11718`) - Bug in subtraction of ``Series`` of a single tz-aware ``Timestamp`` (:issue:`12290`) diff --git a/pandas/computation/__init__.py b/pandas/computation/__init__.py index e69de29bb2d1d..9e94215eecf62 100644 --- a/pandas/computation/__init__.py +++ b/pandas/computation/__init__.py @@ -0,0 +1,30 @@ + +import warnings +from distutils.version import LooseVersion + +_NUMEXPR_INSTALLED = False + +try: + import numexpr as ne + ver = ne.__version__ + _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') + + # we specifically disallow 2.4.4 as + # has some hard-to-diagnose bugs + if ver == LooseVersion('2.4.4'): + _NUMEXPR_INSTALLED = False + warnings.warn( + "The installed version of numexpr {ver} is not supported " + "in pandas and will be not be used\n".format(ver=ver), + UserWarning) + + elif not _NUMEXPR_INSTALLED: + warnings.warn( + "The installed version of numexpr {ver} is not supported " + "in pandas and will be not be used\nThe minimum supported " + "version is 2.1\n".format(ver=ver), UserWarning) + +except ImportError: # pragma: no cover + pass + +__all__ = ['_NUMEXPR_INSTALLED'] diff --git a/pandas/computation/eval.py b/pandas/computation/eval.py index d2d16acc27fb6..c3300ffca468e 100644 --- a/pandas/computation/eval.py +++ b/pandas/computation/eval.py @@ -6,11 +6,11 @@ import warnings import tokenize from pandas.core import common as com +from pandas.computation import _NUMEXPR_INSTALLED from pandas.computation.expr import Expr, _parsers, tokenize_string from pandas.computation.scope import _ensure_scope from pandas.compat import string_types from pandas.computation.engines import _engines -from distutils.version import LooseVersion def _check_engine(engine): @@ -35,17 +35,11 @@ def _check_engine(engine): # that won't necessarily be import-able) # Could potentially be done on engine instantiation if engine == 'numexpr': - try: - import numexpr - except ImportError: - raise ImportError("'numexpr' not found. Cannot use " + if not _NUMEXPR_INSTALLED: + raise ImportError("'numexpr' is not installed or an " + "unsupported version. Cannot use " "engine='numexpr' for query/eval " "if 'numexpr' is not installed") - else: - ne_version = numexpr.__version__ - if ne_version < LooseVersion('2.1'): - raise ImportError("'numexpr' version is %s, " - "must be >= 2.1" % ne_version) def _check_parser(parser): diff --git a/pandas/computation/expressions.py b/pandas/computation/expressions.py index 6e33250010c2b..086e92dbde1a0 100644 --- a/pandas/computation/expressions.py +++ b/pandas/computation/expressions.py @@ -9,20 +9,10 @@ import warnings import numpy as np from pandas.core.common import _values_from_object -from distutils.version import LooseVersion +from pandas.computation import _NUMEXPR_INSTALLED -try: +if _NUMEXPR_INSTALLED: import numexpr as ne - ver = ne.__version__ - _NUMEXPR_INSTALLED = ver >= LooseVersion('2.1') - if not _NUMEXPR_INSTALLED: - warnings.warn( - "The installed version of numexpr {ver} is not supported " - "in pandas and will be not be used\nThe minimum supported " - "version is 2.1\n".format(ver=ver), UserWarning) - -except ImportError: # pragma: no cover - _NUMEXPR_INSTALLED = False _TEST_MODE = None _TEST_RESULT = None diff --git a/pandas/computation/tests/test_compat.py b/pandas/computation/tests/test_compat.py new file mode 100644 index 0000000000000..80b415739c647 --- /dev/null +++ b/pandas/computation/tests/test_compat.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python + +# flake8: noqa + +import nose +from itertools import product +from distutils.version import LooseVersion + +import pandas as pd +from pandas.util import testing as tm + +from pandas.computation.engines import _engines +import pandas.computation.expr as expr + +ENGINES_PARSERS = list(product(_engines, expr._parsers)) + + +def test_compat(): + # test we have compat with our version of nu + + from pandas.computation import _NUMEXPR_INSTALLED + try: + import numexpr as ne + ver = ne.__version__ + if ver == LooseVersion('2.4.4'): + assert not _NUMEXPR_INSTALLED + elif ver < LooseVersion('2.1'): + with tm.assert_produces_warning(UserWarning, + check_stacklevel=False): + assert not _NUMEXPR_INSTALLED + else: + assert _NUMEXPR_INSTALLED + + except ImportError: + raise nose.SkipTest("not testing numexpr version compat") + + +def test_invalid_numexpr_version(): + for engine, parser in ENGINES_PARSERS: + yield check_invalid_numexpr_version, engine, parser + + +def check_invalid_numexpr_version(engine, parser): + def testit(): + a, b = 1, 2 + res = pd.eval('a + b', engine=engine, parser=parser) + tm.assert_equal(res, 3) + + if engine == 'numexpr': + try: + import numexpr as ne + except ImportError: + raise nose.SkipTest("no numexpr") + else: + if ne.__version__ < LooseVersion('2.1'): + with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " + ".+, must be >= 2.1"): + testit() + elif ne.__version__ == LooseVersion('2.4.4'): + raise nose.SkipTest("numexpr version==2.4.4") + else: + testit() + else: + testit() + + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py index b70252ed9f35b..518e5dd999391 100644 --- a/pandas/computation/tests/test_eval.py +++ b/pandas/computation/tests/test_eval.py @@ -1782,33 +1782,6 @@ def test_name_error_exprs(): yield check_name_error_exprs, engine, parser -def check_invalid_numexpr_version(engine, parser): - def testit(): - a, b = 1, 2 - res = pd.eval('a + b', engine=engine, parser=parser) - tm.assert_equal(res, 3) - - if engine == 'numexpr': - try: - import numexpr as ne - except ImportError: - raise nose.SkipTest("no numexpr") - else: - if ne.__version__ < LooseVersion('2.1'): - with tm.assertRaisesRegexp(ImportError, "'numexpr' version is " - ".+, must be >= 2.1"): - testit() - else: - testit() - else: - testit() - - -def test_invalid_numexpr_version(): - for engine, parser in ENGINES_PARSERS: - yield check_invalid_numexpr_version, engine, parser - - def check_invalid_local_variable_reference(engine, parser): tm.skip_if_no_ne(engine) diff --git a/pandas/core/datetools.py b/pandas/core/datetools.py index 91b33d30004b6..79718c79f9bdd 100644 --- a/pandas/core/datetools.py +++ b/pandas/core/datetools.py @@ -1,8 +1,10 @@ """A collection of random tools for dealing with dates in Python""" -from pandas.tseries.tools import * # noqa -from pandas.tseries.offsets import * # noqa -from pandas.tseries.frequencies import * # noqa +# flake8: noqa + +from pandas.tseries.tools import * +from pandas.tseries.offsets import * +from pandas.tseries.frequencies import * day = DateOffset() bday = BDay() diff --git a/pandas/core/format.py b/pandas/core/format.py index 101a5e64b65b5..1f1ff73869f73 100644 --- a/pandas/core/format.py +++ b/pandas/core/format.py @@ -10,7 +10,7 @@ from pandas.core.index import Index, MultiIndex, _ensure_index from pandas import compat from pandas.compat import (StringIO, lzip, range, map, zip, reduce, u, - OrderedDict) + OrderedDict, unichr) from pandas.util.terminal import get_terminal_size from pandas.core.config import get_option, set_option from pandas.io.common import _get_handle, UnicodeWriter, _expand_user diff --git a/pandas/io/common.py b/pandas/io/common.py index 8c9c348b9a11c..be8c3ccfe08e6 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -146,6 +146,10 @@ def readline(self): except ImportError: # boto is only needed for reading from S3. pass +except TypeError: + # boto/boto3 issues + # GH11915 + pass def _is_url(url): diff --git a/pandas/util/clipboard.py b/pandas/util/clipboard.py index 026f13aad0bf3..02da0d5b8159f 100644 --- a/pandas/util/clipboard.py +++ b/pandas/util/clipboard.py @@ -45,6 +45,7 @@ # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# flake8: noqa import platform import os diff --git a/pandas/util/print_versions.py b/pandas/util/print_versions.py index 80c10b53d37b5..c972caad5d74c 100644 --- a/pandas/util/print_versions.py +++ b/pandas/util/print_versions.py @@ -91,7 +91,8 @@ def show_versions(as_json=False): ("sqlalchemy", lambda mod: mod.__version__), ("pymysql", lambda mod: mod.__version__), ("psycopg2", lambda mod: mod.__version__), - ("jinja2", lambda mod: mod.__version__) + ("jinja2", lambda mod: mod.__version__), + ("boto", lambda mod: mod.__version__) ] deps_blob = list()
close #12489 xref #11915
https://api.github.com/repos/pandas-dev/pandas/pulls/12511
2016-03-02T13:37:13Z
2016-03-03T12:11:02Z
null
2016-03-03T12:11:02Z
BUG: Respect usecols even with empty data
diff --git a/doc/source/whatsnew/v0.18.1.txt b/doc/source/whatsnew/v0.18.1.txt index 071cf5f17fc56..4387a51db8df3 100644 --- a/doc/source/whatsnew/v0.18.1.txt +++ b/doc/source/whatsnew/v0.18.1.txt @@ -179,6 +179,45 @@ New Behavior: # Output is a DataFrame df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum()) +.. _whatsnew_0181.read_csv_exceptions: + +Change in ``read_csv`` exceptions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In order to standardize the ``read_csv`` API for both the C and Python engines, both will now raise an +``EmptyDataError``, a subclass of ``ValueError``, in response to empty columns or header (:issue:`12506`) + +Previous behaviour: + +.. code-block:: python + + In [1]: df = pd.read_csv(StringIO(''), engine='c') + ... + ValueError: No columns to parse from file + + In [2]: df = pd.read_csv(StringIO(''), engine='python') + ... + StopIteration + +New behaviour: + +.. code-block:: python + + In [1]: df = pd.read_csv(StringIO(''), engine='c') + ... + pandas.io.common.EmptyDataError: No columns to parse from file + + In [2]: df = pd.read_csv(StringIO(''), engine='python') + ... + pandas.io.common.EmptyDataError: No columns to parse from file + +In addition to this error change, several others have been made as well: + +- ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`) +- A ``CParserError`` is now raised instead of a generic ``Exception`` in ``read_csv`` when the C engine cannot parse a column +- A ``ValueError`` is now raised instead of a generic ``Exception`` in ``read_csv`` when the C engine encounters a ``NaN`` value in an integer column +- A ``ValueError`` is now raised instead of a generic ``Exception`` in ``read_csv`` when ``true_values`` is specified, and the C engine encounters an element in a column containing unencodable bytes +- ``pandas.parser.OverflowError`` exception has been removed and has been replaced with Python's built-in ``OverflowError`` exception .. _whatsnew_0181.deprecations: diff --git a/pandas/io/common.py b/pandas/io/common.py index e644f3a5f5090..8319e4c586e3b 100644 --- a/pandas/io/common.py +++ b/pandas/io/common.py @@ -56,7 +56,37 @@ def urlopen(*args, **kwargs): _VALID_URLS.discard('') +class CParserError(ValueError): + """ + Exception that is thrown by the C engine when it encounters + a parsing error in `pd.read_csv` + """ + pass + + class DtypeWarning(Warning): + """ + Warning that is raised whenever `pd.read_csv` encounters non- + uniform dtypes in a column(s) of a given CSV file + """ + pass + + +class EmptyDataError(ValueError): + """ + Exception that is thrown in `pd.read_csv` (by both the C and + Python engines) when empty data or header is encountered + """ + pass + + +class ParserWarning(Warning): + """ + Warning that is raised in `pd.read_csv` whenever it is necessary + to change parsers (generally from 'c' to 'python') contrary to the + one specified by the user due to lack of support or functionality for + parsing particular attributes of a CSV file with the requsted engine + """ pass diff --git a/pandas/io/excel.py b/pandas/io/excel.py index 0261e825d56e2..882f23a83f29d 100644 --- a/pandas/io/excel.py +++ b/pandas/io/excel.py @@ -13,7 +13,7 @@ from pandas.core.frame import DataFrame from pandas.io.parsers import TextParser from pandas.io.common import (_is_url, _urlopen, _validate_header_arg, - get_filepath_or_buffer) + EmptyDataError, get_filepath_or_buffer) from pandas.tseries.period import Period from pandas import json from pandas.compat import (map, zip, reduce, range, lrange, u, add_metaclass, @@ -468,7 +468,7 @@ def _parse_cell(cell_contents, cell_typ): if not squeeze or isinstance(output[asheetname], DataFrame): output[asheetname].columns = output[ asheetname].columns.set_names(header_names) - except StopIteration: + except EmptyDataError: # No Data, return an empty DataFrame output[asheetname] = DataFrame() diff --git a/pandas/io/html.py b/pandas/io/html.py index af4ecb2484797..90bbeb161442f 100644 --- a/pandas/io/html.py +++ b/pandas/io/html.py @@ -12,7 +12,8 @@ import numpy as np -from pandas.io.common import _is_url, urlopen, parse_url, _validate_header_arg +from pandas.io.common import (EmptyDataError, _is_url, urlopen, + parse_url, _validate_header_arg) from pandas.io.parsers import TextParser from pandas.compat import (lrange, lmap, u, string_types, iteritems, raise_with_traceback, binary_type) @@ -742,7 +743,7 @@ def _parse(flavor, io, match, header, index_col, skiprows, parse_dates=parse_dates, tupleize_cols=tupleize_cols, thousands=thousands)) - except StopIteration: # empty table + except EmptyDataError: # empty table continue return ret diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py index 18670e9afa65f..360025836cbd0 100755 --- a/pandas/io/parsers.py +++ b/pandas/io/parsers.py @@ -20,7 +20,8 @@ from pandas.io.date_converters import generic_parser from pandas.io.common import (get_filepath_or_buffer, _validate_header_arg, _get_handle, UnicodeReader, UTF8Recoder, - BaseIterator) + BaseIterator, CParserError, EmptyDataError, + ParserWarning) from pandas.tseries import tools from pandas.util.decorators import Appender @@ -36,10 +37,6 @@ 'N/A', 'NA', '#NA', 'NULL', 'NaN', '-NaN', 'nan', '-nan', '' ]) - -class ParserWarning(Warning): - pass - _parser_params = """Also supports optionally iterating or breaking of the file into chunks. @@ -936,7 +933,7 @@ def tostr(x): # long for n in range(len(columns[0])): if all(['Unnamed' in tostr(c[n]) for c in columns]): - raise _parser.CParserError( + raise CParserError( "Passed header=[%s] are too many rows for this " "multi_index of columns" % ','.join([str(x) for x in self.header]) @@ -1255,10 +1252,19 @@ def read(self, nrows=None): except StopIteration: if self._first_chunk: self._first_chunk = False - return _get_empty_meta(self.orig_names, - self.index_col, - self.index_names, - dtype=self.kwds.get('dtype')) + + index, columns, col_dict = _get_empty_meta( + self.orig_names, self.index_col, + self.index_names, dtype=self.kwds.get('dtype')) + + if self.usecols is not None: + columns = self._filter_usecols(columns) + + col_dict = dict(filter(lambda item: item[0] in columns, + col_dict.items())) + + return index, columns, col_dict + else: raise @@ -1750,10 +1756,26 @@ def _infer_columns(self): columns = [] for level, hr in enumerate(header): - line = self._buffered_line() + try: + line = self._buffered_line() + + while self.line_pos <= hr: + line = self._next_line() - while self.line_pos <= hr: - line = self._next_line() + except StopIteration: + if self.line_pos < hr: + raise ValueError( + 'Passed header=%s but only %d lines in file' + % (hr, self.line_pos + 1)) + + # We have an empty file, so check + # if columns are provided. That will + # serve as the 'line' for parsing + if not self.names: + raise EmptyDataError( + "No columns to parse from file") + + line = self.names[:] unnamed_count = 0 this_columns = [] @@ -1818,10 +1840,19 @@ def _infer_columns(self): else: columns = self._handle_usecols(columns, columns[0]) else: - # header is None - line = self._buffered_line() + try: + line = self._buffered_line() + + except StopIteration: + if not names: + raise EmptyDataError( + "No columns to parse from file") + + line = names[:] + ncols = len(line) num_original_columns = ncols + if not names: if self.prefix: columns = [['%s%d' % (self.prefix, i) diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py index 9a18da7d57648..cb625a26e40f9 100644 --- a/pandas/io/tests/test_html.py +++ b/pandas/io/tests/test_html.py @@ -804,3 +804,7 @@ def test_same_ordering(): dfs_lxml = read_html(filename, index_col=0, flavor=['lxml']) dfs_bs4 = read_html(filename, index_col=0, flavor=['bs4']) assert_framelist_equal(dfs_lxml, dfs_bs4) + +if __name__ == '__main__': + nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'], + exit=False) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index 2d56275279453..8f57b08ee9817 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -16,7 +16,6 @@ import nose import numpy as np import pandas.lib as lib -import pandas.parser from numpy import nan from numpy.testing.decorators import slow from pandas.lib import Timestamp @@ -32,7 +31,8 @@ ) from pandas.compat import parse_date from pandas.core.common import AbstractMethodError -from pandas.io.common import DtypeWarning, URLError +from pandas.io.common import (CParserError, DtypeWarning, + EmptyDataError, URLError) from pandas.io.parsers import (read_csv, read_table, read_fwf, TextFileReader, TextParser) from pandas.tseries.index import date_range @@ -1209,7 +1209,7 @@ def test_read_table_wrong_num_columns(self): 6,7,8,9,10,11,12 11,12,13,14,15,16 """ - self.assertRaises(Exception, self.read_csv, StringIO(data)) + self.assertRaises(ValueError, self.read_csv, StringIO(data)) def test_read_table_duplicate_index(self): data = """index,A,B,C,D @@ -1740,7 +1740,7 @@ def test_read_table_buglet_4x_multiindex(self): # Temporarily copied to TestPythonParser. # Here test that CParserError is raised: - with tm.assertRaises(pandas.parser.CParserError): + with tm.assertRaises(CParserError): text = """ A B C D E one two three four a b 10.0032 5 -0.5109 -2.3358 -0.4645 0.05076 0.3640 @@ -1840,7 +1840,7 @@ def test_parse_dates_custom_euroformat(self): tm.assert_frame_equal(df, expected) parser = lambda d: parse_date(d, day_first=True) - self.assertRaises(Exception, self.read_csv, + self.assertRaises(TypeError, self.read_csv, StringIO(text), skiprows=[0], names=['time', 'Q', 'NTU'], index_col=0, parse_dates=True, date_parser=parser, @@ -2014,7 +2014,7 @@ def test_bool_na_values(self): def test_nonexistent_path(self): # don't segfault pls #2428 path = '%s.csv' % tm.rands(10) - self.assertRaises(Exception, self.read_csv, path) + self.assertRaises(IOError, self.read_csv, path) def test_missing_trailing_delimiters(self): data = """A,B,C,D @@ -2358,7 +2358,7 @@ def test_catch_too_many_names(self): 4,,6 7,8,9 10,11,12\n""" - tm.assertRaises(Exception, read_csv, StringIO(data), + tm.assertRaises(ValueError, read_csv, StringIO(data), header=0, names=['a', 'b', 'c', 'd']) def test_ignore_leading_whitespace(self): @@ -2525,9 +2525,8 @@ def test_int64_overflow(self): result = self.read_csv(StringIO(data)) self.assertTrue(result['ID'].dtype == object) - self.assertRaises((OverflowError, pandas.parser.OverflowError), - self.read_csv, StringIO(data), - converters={'ID': np.int64}) + self.assertRaises(OverflowError, self.read_csv, + StringIO(data), converters={'ID': np.int64}) # Just inside int64 range: parse as integer i_max = np.iinfo(np.int64).max @@ -2774,7 +2773,7 @@ def test_mixed_dtype_usecols(self): usecols = [0, 'b', 2] with tm.assertRaisesRegexp(ValueError, msg): - df = self.read_csv(StringIO(data), usecols=usecols) + self.read_csv(StringIO(data), usecols=usecols) def test_usecols_with_integer_like_header(self): data = """2,0,1 @@ -2796,6 +2795,37 @@ def test_usecols_with_integer_like_header(self): df = self.read_csv(StringIO(data), usecols=usecols) tm.assert_frame_equal(df, expected) + def test_read_empty_with_usecols(self): + # See gh-12493 + names = ['Dummy', 'X', 'Dummy_2'] + usecols = names[1:2] # ['X'] + + # first, check to see that the response of + # parser when faced with no provided columns + # throws the correct error, with or without usecols + errmsg = "No columns to parse from file" + + with tm.assertRaisesRegexp(EmptyDataError, errmsg): + self.read_csv(StringIO('')) + + with tm.assertRaisesRegexp(EmptyDataError, errmsg): + self.read_csv(StringIO(''), usecols=usecols) + + expected = DataFrame(columns=usecols, index=[0], dtype=np.float64) + df = self.read_csv(StringIO(',,'), names=names, usecols=usecols) + tm.assert_frame_equal(df, expected) + + expected = DataFrame(columns=usecols) + df = self.read_csv(StringIO(''), names=names, usecols=usecols) + tm.assert_frame_equal(df, expected) + + def test_read_with_bad_header(self): + errmsg = "but only \d+ lines in file" + + with tm.assertRaisesRegexp(ValueError, errmsg): + s = StringIO(',,') + self.read_csv(s, header=[10]) + class CompressionTests(object): def test_zip(self): @@ -4399,7 +4429,7 @@ def test_raise_on_passed_int_dtype_with_nas(self): 2001,106380451,10 2001,,11 2001,106380451,67""" - self.assertRaises(Exception, read_csv, StringIO(data), sep=",", + self.assertRaises(ValueError, read_csv, StringIO(data), sep=",", skipinitialspace=True, dtype={'DOY': np.int64}) diff --git a/pandas/parser.pyx b/pandas/parser.pyx index 743d2ea0c3bb8..8369fda3e9b00 100644 --- a/pandas/parser.pyx +++ b/pandas/parser.pyx @@ -10,7 +10,7 @@ import warnings from cpython cimport (PyObject, PyBytes_FromString, PyBytes_AsString, PyBytes_Check, PyUnicode_Check, PyUnicode_AsUTF8String) -from io.common import DtypeWarning +from io.common import CParserError, DtypeWarning, EmptyDataError cdef extern from "Python.h": @@ -519,7 +519,7 @@ cdef class TextReader: self.header, self.table_width = self._get_header() if not self.table_width: - raise ValueError("No columns to parse from file") + raise EmptyDataError("No columns to parse from file") # compute buffer_lines as function of table width heuristic = 2**20 // self.table_width @@ -1009,7 +1009,7 @@ cdef class TextReader: col_res = downcast_int64(col_res, self.use_unsigned) if col_res is None: - raise Exception('Unable to parse column %d' % i) + raise CParserError('Unable to parse column %d' % i) results[i] = col_res @@ -1097,7 +1097,7 @@ cdef class TextReader: na_filter, na_hashset) if user_dtype and na_count is not None: if na_count > 0: - raise Exception("Integer column has NA values in " + raise ValueError("Integer column has NA values in " "column {column}".format(column=i)) if result is not None and dtype[1:] != 'i8': @@ -1235,13 +1235,6 @@ cdef class TextReader: else: return None -class CParserError(ValueError): - pass - - -class OverflowError(ValueError): - pass - cdef object _true_values = [b'True', b'TRUE', b'true'] cdef object _false_values = [b'False', b'FALSE', b'false'] @@ -1815,7 +1808,7 @@ cdef kh_str_t* kset_from_list(list values) except NULL: # None creeps in sometimes, which isn't possible here if not PyBytes_Check(val): - raise Exception('Must be all encoded bytes') + raise ValueError('Must be all encoded bytes') k = kh_put_str(table, PyBytes_AsString(val), &ret)
closes #12493 in which the `usecols` argument was not being respected for empty data. This is because no filtering was applied when the first (and only) chunk was being read.
https://api.github.com/repos/pandas-dev/pandas/pulls/12506
2016-03-02T01:45:47Z
2016-04-13T01:30:28Z
null
2016-04-13T01:32:31Z
CLN: Removed pandas.util.testing.choice
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 1e07e38c23970..f05d37c7607a3 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1195,3 +1195,4 @@ Bug Fixes - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) +- Removed pandas.util.testing.choice(). Should use np.random.choice(), instead. (:issue:`12386`) diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index 6db507f0e4151..a52cb018c7bae 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -96,8 +96,8 @@ class TestDataFrameQueryWithMultiIndex(tm.TestCase): def check_query_with_named_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) - b = tm.choice(['eggs', 'ham'], size=10) + a = np.random.choice(['red', 'green'], size=10) + b = np.random.choice(['eggs', 'ham'], size=10) index = MultiIndex.from_arrays([a, b], names=['color', 'food']) df = DataFrame(randn(10, 2), index=index) ind = Series(df.index.get_level_values('color').values, index=index, @@ -149,8 +149,8 @@ def test_query_with_named_multiindex(self): def check_query_with_unnamed_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) - b = tm.choice(['eggs', 'ham'], size=10) + a = np.random.choice(['red', 'green'], size=10) + b = np.random.choice(['eggs', 'ham'], size=10) index = MultiIndex.from_arrays([a, b]) df = DataFrame(randn(10, 2), index=index) ind = Series(df.index.get_level_values(0).values, index=index) @@ -243,7 +243,7 @@ def test_query_with_unnamed_multiindex(self): def check_query_with_partially_named_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) + a = np.random.choice(['red', 'green'], size=10) b = np.arange(10) index = MultiIndex.from_arrays([a, b]) index.names = [None, 'rating'] @@ -975,7 +975,7 @@ def check_query_lex_compare_strings(self, parser, engine): tm.skip_if_no_ne(engine=engine) import operator as opr - a = Series(tm.choice(list('abcde'), 20)) + a = Series(np.random.choice(list('abcde'), 20)) b = Series(np.arange(a.size)) df = DataFrame({'X': a, 'Y': b}) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index b339d25cd6c45..2fdfc7ccc37ef 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -60,8 +60,8 @@ def setUp(self): n = 100 with tm.RNGContext(42): - gender = tm.choice(['Male', 'Female'], size=n) - classroom = tm.choice(['A', 'B', 'C'], size=n) + gender = np.random.choice(['Male', 'Female'], size=n) + classroom = np.random.choice(['A', 'B', 'C'], size=n) self.hist_df = DataFrame({'gender': gender, 'classroom': classroom, @@ -3861,7 +3861,7 @@ def test_series_groupby_plotting_nominally_works(self): 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) + gender = np.random.choice(['male', 'female'], size=n) weight.groupby(gender).plot() tm.close() diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py index 983d0c310f71d..b032ce196c113 100644 --- a/pandas/tests/test_graphics_others.py +++ b/pandas/tests/test_graphics_others.py @@ -641,7 +641,7 @@ def test_grouped_plot_fignums(self): 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) + gender = np.random.choice(['male', 'female'], size=n) df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) gb = df.groupby('gender') @@ -715,7 +715,7 @@ def test_grouped_hist_legacy2(self): 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) + gender_int = np.random.choice([0, 1], size=n) df_int = DataFrame({'height': height, 'weight': weight, 'gender': gender_int}) gb = df_int.groupby('gender') diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 046d2322165b5..69994615beec1 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -236,27 +236,27 @@ def test_join_on(self): def test_join_on_fails_with_different_right_index(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}, index=tm.makeCustomIndex(10, 2)) merge(df, df2, left_on='a', right_index=True) def test_join_on_fails_with_different_left_index(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}, index=tm.makeCustomIndex(10, 2)) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}) merge(df, df2, right_on='b', left_index=True) def test_join_on_fails_with_different_column_counts(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}, index=tm.makeCustomIndex(10, 2)) merge(df, df2, right_on='a', left_on=['a', 'b']) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 35a615db444e9..c32239daaaf12 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -133,7 +133,7 @@ def randbool(size=(), p=0.5): def rands_array(nchars, size, dtype='O'): """Generate an array of byte strings.""" - retval = (choice(RANDS_CHARS, size=nchars * np.prod(size)) + retval = (np.random.choice(RANDS_CHARS, size=nchars * np.prod(size)) .view((np.str_, nchars)).reshape(size)) if dtype is None: return retval @@ -143,7 +143,7 @@ def rands_array(nchars, size, dtype='O'): def randu_array(nchars, size, dtype='O'): """Generate an array of unicode strings.""" - retval = (choice(RANDU_CHARS, size=nchars * np.prod(size)) + retval = (np.random.choice(RANDU_CHARS, size=nchars * np.prod(size)) .view((np.unicode_, nchars)).reshape(size)) if dtype is None: return retval @@ -158,7 +158,7 @@ def rands(nchars): See `rands_array` if you want to create an array of random strings. """ - return ''.join(choice(RANDS_CHARS, nchars)) + return ''.join(np.random.choice(RANDS_CHARS, nchars)) def randu(nchars): @@ -171,14 +171,6 @@ def randu(nchars): return ''.join(choice(RANDU_CHARS, nchars)) -def choice(x, size=10): - """sample with replacement; uniform over the input""" - try: - return np.random.choice(x, size=size) - except AttributeError: - return np.random.randint(len(x), size=size).choose(x) - - def close(fignum=None): from matplotlib.pyplot import get_fignums, close as _close diff --git a/vb_suite/groupby.py b/vb_suite/groupby.py index bc21372225322..268d71f864823 100644 --- a/vb_suite/groupby.py +++ b/vb_suite/groupby.py @@ -143,7 +143,7 @@ def f(): value2 = np.random.randn(n) value2[np.random.rand(n) > 0.5] = np.nan -obj = tm.choice(list('ab'), size=n).astype(object) +obj = np.random.choice(list('ab'), size=n).astype(object) obj[np.random.randn(n) > 0.5] = np.nan df = DataFrame({'key1': np.random.randint(0, 500, size=n),
- [x] closes #12386 - [x] tests passed - [x] whatsnew entry Removed pandas.util.testing.choice() method. Replaced all references to it with calls to np.random.choice(). Added entry to whatsnew. #12386
https://api.github.com/repos/pandas-dev/pandas/pulls/12505
2016-03-02T00:44:41Z
2016-03-02T12:46:00Z
null
2016-03-03T14:43:57Z
BUG: Fixed grow_buffer to grow when capacity is reached
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 8d36d323f48f9..537dfde4a024f 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1199,3 +1199,4 @@ Bug Fixes - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) +- Bug when increasing the buffer size of CSV reader in ``read_csv`` (:issue:`12494`) diff --git a/pandas/io/tests/test_parsers.py b/pandas/io/tests/test_parsers.py index d3020e337322b..f32dfd37e837c 100755 --- a/pandas/io/tests/test_parsers.py +++ b/pandas/io/tests/test_parsers.py @@ -2635,6 +2635,26 @@ def test_eof_states(self): self.assertRaises(Exception, self.read_csv, StringIO(data), escapechar='\\') + def test_grow_boundary_at_cap(self): + # See gh-12494 + # + # Cause of error was the fact that pandas + # was not increasing the buffer size when + # the desired space would fill the buffer + # to capacity, which later would cause a + # buffer overflow error when checking the + # EOF terminator of the CSV stream + def test_empty_header_read(count): + s = StringIO(',' * count) + expected = DataFrame(columns=[ + 'Unnamed: {i}'.format(i=i) + for i in range(count + 1)]) + df = read_csv(s) + tm.assert_frame_equal(df, expected) + + for count in range(1, 101): + test_empty_header_read(count) + class TestPythonParser(ParserTests, tm.TestCase): diff --git a/pandas/src/parser/tokenizer.c b/pandas/src/parser/tokenizer.c index a19930a5cef30..dae15215929b7 100644 --- a/pandas/src/parser/tokenizer.c +++ b/pandas/src/parser/tokenizer.c @@ -111,7 +111,7 @@ static void *grow_buffer(void *buffer, int length, int *capacity, void *newbuffer = buffer; // Can we fit potentially nbytes tokens (+ null terminators) in the stream? - while ( (length + space > cap) && (newbuffer != NULL) ){ + while ( (length + space >= cap) && (newbuffer != NULL) ){ cap = cap? cap << 1 : 2; buffer = newbuffer; newbuffer = safe_realloc(newbuffer, elsize * cap);
Addresses issue in #12494 by allowing `grow_buffer` to grow the size of the parser buffer when buffer capacity is achieved. Previously, you had to exceed capacity for this to occur, but that was inconsistent with the `end_field` check later on when handling the EOF terminator, where reached capacity was considered a buffer overflow.
https://api.github.com/repos/pandas-dev/pandas/pulls/12504
2016-03-01T16:59:03Z
2016-03-03T21:55:35Z
null
2016-03-03T22:21:48Z
BUG: Allow assignment by indexing with duplicate column names
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 77c8a5a585b51..49e6945bf4700 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1201,3 +1201,4 @@ Bug Fixes - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) - Bug when increasing the buffer size of CSV reader in ``read_csv`` (:issue:`12494`) +- Bug when setting columns of a ``DataFrame`` with duplicate column names (:issue:`12344`) diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py index f0f5507bc3e85..03fa072db83da 100644 --- a/pandas/core/indexing.py +++ b/pandas/core/indexing.py @@ -541,7 +541,7 @@ def can_do_equal_len(): if (len(indexer) > info_axis and is_integer(indexer[info_axis]) and all(is_null_slice(idx) for i, idx in enumerate(indexer) - if i != info_axis)): + if i != info_axis) and item_labels.is_unique): self.obj[item_labels[indexer[info_axis]]] = value return diff --git a/pandas/tests/frame/test_nonunique_indexes.py b/pandas/tests/frame/test_nonunique_indexes.py index 1b24e829088f2..77974718714f8 100644 --- a/pandas/tests/frame/test_nonunique_indexes.py +++ b/pandas/tests/frame/test_nonunique_indexes.py @@ -452,3 +452,19 @@ def test_as_matrix_duplicates(self): dtype=object) self.assertTrue(np.array_equal(result, expected)) + + def test_set_value_by_index(self): + # See gh-12344 + df = DataFrame(np.arange(9).reshape(3, 3).T) + df.columns = list('AAA') + expected = df.iloc[:, 2] + + df.iloc[:, 0] = 3 + assert_series_equal(df.iloc[:, 2], expected) + + df = DataFrame(np.arange(9).reshape(3, 3).T) + df.columns = [2, float(2), str(2)] + expected = df.iloc[:, 1] + + df.iloc[:, 0] = 3 + assert_series_equal(df.iloc[:, 1], expected)
closes #12344 in which assignment to columns in `DataFrame` with duplicate column names caused all columns with the same name to be reassigned. The bug was located <a href="https://github.com/pydata/pandas/blob/master/pandas/core/indexing.py#L545">here</a>. When you try to index into the DataFrame using `.iloc`, `pandas` will find the corresponding column name (or key) first before setting that key with the given value. Unfortunately, since all of your columns have the same key name, `pandas` ends up choosing all of the columns corresponding to that name. This PR introduces a `_set_item_by_index` method for `DataFrame` objects that allows you to bypass that issue by using the indices of the columns to set the columns themselves whenever there are duplicates involved.
https://api.github.com/repos/pandas-dev/pandas/pulls/12498
2016-03-01T02:11:13Z
2016-03-06T15:28:43Z
null
2016-03-06T15:43:07Z
Extract doc fix
diff --git a/doc/source/text.rst b/doc/source/text.rst index 53567ea25aeac..42ecfef35e543 100644 --- a/doc/source/text.rst +++ b/doc/source/text.rst @@ -256,7 +256,7 @@ It raises ``ValueError`` if ``expand=False``. .. code-block:: python >>> s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False) - ValueError: This pattern contains no groups to capture. + ValueError: only one regex group is supported with Index The table below summarizes the behavior of ``extract(expand=False)`` (input subject in first column, number of groups in regex in
A fix for a small problem with the docs for extract. Also the first code block on http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180-enhancements-extract should show a warning but it does not. any ideas how to fix that?
https://api.github.com/repos/pandas-dev/pandas/pulls/12496
2016-02-29T19:50:12Z
2016-03-01T12:44:42Z
null
2016-03-01T12:44:51Z
ENH: Allow exponentially weighted functions to specify alpha directly
diff --git a/doc/source/computation.rst b/doc/source/computation.rst index 2b8cf7e41431b..a495020d704f1 100644 --- a/doc/source/computation.rst +++ b/doc/source/computation.rst @@ -733,24 +733,29 @@ therefore there is an assumption that :math:`x_0` is not an ordinary value but rather an exponentially weighted moment of the infinite series up to that point. -One must have :math:`0 < \alpha \leq 1`, but rather than pass :math:`\alpha` -directly, it's easier to think about either the **span**, **center of mass -(com)** or **halflife** of an EW moment: +One must have :math:`0 < \alpha \leq 1`, and while since version 0.18.0 +it has been possible to pass :math:`\alpha` directly, it's often easier +to think about either the **span**, **center of mass (com)** or **half-life** +of an EW moment: .. math:: \alpha = \begin{cases} - \frac{2}{s + 1}, & s = \text{span}\\ - \frac{1}{1 + c}, & c = \text{center of mass}\\ - 1 - \exp^{\frac{\log 0.5}{h}}, & h = \text{half life} + \frac{2}{s + 1}, & \text{for span}\ s \geq 1\\ + \frac{1}{1 + c}, & \text{for center of mass}\ c \geq 0\\ + 1 - \exp^{\frac{\log 0.5}{h}}, & \text{for half-life}\ h > 0 \end{cases} -One must specify precisely one of the three to the EW functions. **Span** -corresponds to what is commonly called a "20-day EW moving average" for -example. **Center of mass** has a more physical interpretation. For example, -**span** = 20 corresponds to **com** = 9.5. **Halflife** is the period of -time for the exponential weight to reduce to one half. +One must specify precisely one of **span**, **center of mass**, **half-life** +and **alpha** to the EW functions: + +- **Span** corresponds to what is commonly called an "N-day EW moving average". +- **Center of mass** has a more physical interpretation and can be thought of + in terms of span: :math:`c = (s - 1) / 2`. +- **Half-life** is the period of time for the exponential weight to reduce to + one half. +- **Alpha** specifies the smoothing factor directly. Here is an example for a univariate time series: diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 77c8a5a585b51..c0a6a8c598ea7 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -947,6 +947,7 @@ Other API Changes - More helpful error message when constructing a ``DataFrame`` with empty data but with indices (:issue:`8020`) - ``.describe()`` will now properly handle bool dtype as a categorical (:issue:`6625`) - More helpful error message invalid ``.transform`` with user defined input (:issue:`10165`) +- Exponentially weighted functions now allow specifying alpha directly (:issue:`10789`) and raise ``ValueError`` if parameters violate ``0 < alpha <= 1`` (:issue:`12492`) .. _whatsnew_0180.deprecations: diff --git a/pandas/core/generic.py b/pandas/core/generic.py index c9473900d1d5e..1684768eec2c4 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -5165,11 +5165,12 @@ def expanding(self, min_periods=1, freq=None, center=False, axis=0): cls.expanding = expanding @Appender(rwindow.ewm.__doc__) - def ewm(self, com=None, span=None, halflife=None, min_periods=0, - freq=None, adjust=True, ignore_na=False, axis=0): + def ewm(self, com=None, span=None, halflife=None, alpha=None, + min_periods=0, freq=None, adjust=True, ignore_na=False, + axis=0): axis = self._get_axis_number(axis) return rwindow.ewm(self, com=com, span=span, halflife=halflife, - min_periods=min_periods, freq=freq, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, ignore_na=ignore_na, axis=axis) cls.ewm = ewm diff --git a/pandas/core/window.py b/pandas/core/window.py index 167599678d166..31874a96f8111 100644 --- a/pandas/core/window.py +++ b/pandas/core/window.py @@ -1038,13 +1038,21 @@ class EWM(_Rolling): Parameters ---------- - com : float. optional - Center of mass: :math:`\alpha = 1 / (1 + com)`, + com : float, optional + Specify decay in terms of center of mass, + :math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0` span : float, optional - Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)` + Specify decay in terms of span, + :math:`\alpha = 2 / (span + 1),\text{ for } span \geq 1` halflife : float, optional - Specify decay in terms of halflife, - :math:`\alpha = 1 - exp(log(0.5) / halflife)` + Specify decay in terms of half-life, + :math:`\alpha = 1 - exp(log(0.5) / halflife),\text{ for } halflife > 0` + alpha : float, optional + Specify smoothing factor :math:`\alpha` directly, + :math:`0 < \alpha \leq 1` + + .. versionadded:: 0.18.0 + min_periods : int, default 0 Minimum number of observations in window required to have a value (otherwise result is NA). @@ -1063,16 +1071,10 @@ class EWM(_Rolling): Notes ----- - Either center of mass, span or halflife must be specified - - EWMA is sometimes specified using a "span" parameter `s`, we have that the - decay parameter :math:`\alpha` is related to the span as - :math:`\alpha = 2 / (s + 1) = 1 / (1 + c)` - - where `c` is the center of mass. Given a span, the associated center of - mass is :math:`c = (s - 1) / 2` - - So a "20-day EWMA" would have center 9.5. + Exactly one of center of mass, span, half-life, and alpha must be provided. + Allowed values and relationship between the parameters are specified in the + parameter descriptions above; see the link at the end of this section for + a detailed explanation. The `freq` keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters @@ -1096,14 +1098,15 @@ class EWM(_Rolling): (if adjust is True), and 1-alpha and alpha (if adjust is False). More details can be found at - http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions + http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows """ _attributes = ['com', 'min_periods', 'freq', 'adjust', 'ignore_na', 'axis'] - def __init__(self, obj, com=None, span=None, halflife=None, min_periods=0, - freq=None, adjust=True, ignore_na=False, axis=0): + def __init__(self, obj, com=None, span=None, halflife=None, alpha=None, + min_periods=0, freq=None, adjust=True, ignore_na=False, + axis=0): self.obj = obj - self.com = _get_center_of_mass(com, span, halflife) + self.com = _get_center_of_mass(com, span, halflife, alpha) self.min_periods = min_periods self.freq = freq self.adjust = adjust @@ -1320,20 +1323,32 @@ def dataframe_from_int_dict(data, frame_template): return _flex_binary_moment(arg2, arg1, f) -def _get_center_of_mass(com, span, halflife): - valid_count = len([x for x in [com, span, halflife] if x is not None]) +def _get_center_of_mass(com, span, halflife, alpha): + valid_count = len([x for x in [com, span, halflife, alpha] + if x is not None]) if valid_count > 1: - raise Exception("com, span, and halflife are mutually exclusive") - - if span is not None: - # convert span to center of mass + raise ValueError("com, span, halflife, and alpha " + "are mutually exclusive") + + # Convert to center of mass; domain checks ensure 0 < alpha <= 1 + if com is not None: + if com < 0: + raise ValueError("com must satisfy: com >= 0") + elif span is not None: + if span < 1: + raise ValueError("span must satisfy: span >= 1") com = (span - 1) / 2. elif halflife is not None: - # convert halflife to center of mass + if halflife <= 0: + raise ValueError("halflife must satisfy: halflife > 0") decay = 1 - np.exp(np.log(0.5) / halflife) com = 1 / decay - 1 - elif com is None: - raise Exception("Must pass one of com, span, or halflife") + elif alpha is not None: + if alpha <= 0 or alpha > 1: + raise ValueError("alpha must satisfy: 0 < alpha <= 1") + com = (1.0 - alpha) / alpha + else: + raise ValueError("Must pass one of com, span, halflife, or alpha") return float(com) diff --git a/pandas/stats/moments.py b/pandas/stats/moments.py index c875a9d49039b..46d30ab7fe313 100644 --- a/pandas/stats/moments.py +++ b/pandas/stats/moments.py @@ -67,13 +67,21 @@ """ -_ewm_kw = r"""com : float. optional - Center of mass: :math:`\alpha = 1 / (1 + com)`, +_ewm_kw = r"""com : float, optional + Specify decay in terms of center of mass, + :math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0` span : float, optional - Specify decay in terms of span, :math:`\alpha = 2 / (span + 1)` + Specify decay in terms of span, + :math:`\alpha = 2 / (span + 1),\text{ for } span \geq 1` halflife : float, optional - Specify decay in terms of halflife, - :math:`\alpha = 1 - exp(log(0.5) / halflife)` + Specify decay in terms of half-life, + :math:`\alpha = 1 - exp(log(0.5) / halflife),\text{ for } halflife > 0` +alpha : float, optional + Specify smoothing factor :math:`\alpha` directly, + :math:`0 < \alpha \leq 1` + + .. versionadded:: 0.18.0 + min_periods : int, default 0 Minimum number of observations in window required to have a value (otherwise result is NA). @@ -92,16 +100,10 @@ _ewm_notes = r""" Notes ----- -Either center of mass, span or halflife must be specified - -EWMA is sometimes specified using a "span" parameter `s`, we have that the -decay parameter :math:`\alpha` is related to the span as -:math:`\alpha = 2 / (s + 1) = 1 / (1 + c)` - -where `c` is the center of mass. Given a span, the associated center of mass is -:math:`c = (s - 1) / 2` - -So a "20-day EWMA" would have center 9.5. +Exactly one of center of mass, span, half-life, and alpha must be provided. +Allowed values and relationship between the parameters are specified in the +parameter descriptions above; see the link at the end of this section for +a detailed explanation. When adjust is True (default), weighted averages are calculated using weights (1-alpha)**(n-1), (1-alpha)**(n-2), ..., 1-alpha, 1. @@ -121,7 +123,7 @@ True), and 1-alpha and alpha (if adjust is False). More details can be found at -http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-moment-functions +http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows """ _expanding_kw = """min_periods : int, default None @@ -323,14 +325,15 @@ def rolling_corr(arg1, arg2=None, window=None, pairwise=None, **kwargs): @Substitution("Exponentially-weighted moving average", _unary_arg, _ewm_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewma(arg, com=None, span=None, halflife=None, min_periods=0, freq=None, - adjust=True, how=None, ignore_na=False): +def ewma(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, + freq=None, adjust=True, how=None, ignore_na=False): return ensure_compat('ewm', 'mean', arg, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, @@ -341,14 +344,15 @@ def ewma(arg, com=None, span=None, halflife=None, min_periods=0, freq=None, @Substitution("Exponentially-weighted moving variance", _unary_arg, _ewm_kw + _bias_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, - freq=None, how=None, ignore_na=False, adjust=True): +def ewmvar(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, + bias=False, freq=None, how=None, ignore_na=False, adjust=True): return ensure_compat('ewm', 'var', arg, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, @@ -361,14 +365,15 @@ def ewmvar(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving std", _unary_arg, _ewm_kw + _bias_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, - freq=None, how=None, ignore_na=False, adjust=True): +def ewmstd(arg, com=None, span=None, halflife=None, alpha=None, min_periods=0, + bias=False, freq=None, how=None, ignore_na=False, adjust=True): return ensure_compat('ewm', 'std', arg, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, adjust=adjust, @@ -383,9 +388,9 @@ def ewmstd(arg, com=None, span=None, halflife=None, min_periods=0, bias=False, @Substitution("Exponentially-weighted moving covariance", _binary_arg_flex, _ewm_kw + _pairwise_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, - bias=False, freq=None, pairwise=None, how=None, ignore_na=False, - adjust=True): +def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, alpha=None, + min_periods=0, bias=False, freq=None, pairwise=None, how=None, + ignore_na=False, adjust=True): if arg2 is None: arg2 = arg1 pairwise = True if pairwise is None else pairwise @@ -401,6 +406,7 @@ def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, bias=bias, freq=freq, @@ -414,8 +420,9 @@ def ewmcov(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, @Substitution("Exponentially-weighted moving correlation", _binary_arg_flex, _ewm_kw + _pairwise_kw, _type_of_input_retval, _ewm_notes) @Appender(_doc_template) -def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, - freq=None, pairwise=None, how=None, ignore_na=False, adjust=True): +def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, alpha=None, + min_periods=0, freq=None, pairwise=None, how=None, ignore_na=False, + adjust=True): if arg2 is None: arg2 = arg1 pairwise = True if pairwise is None else pairwise @@ -430,6 +437,7 @@ def ewmcorr(arg1, arg2=None, com=None, span=None, halflife=None, min_periods=0, com=com, span=span, halflife=halflife, + alpha=alpha, min_periods=min_periods, freq=freq, how=how, diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py index 1b3351ae903bc..0d3b7e967f3ce 100644 --- a/pandas/tests/test_window.py +++ b/pandas/tests/test_window.py @@ -15,7 +15,7 @@ notnull, concat) from pandas.util.testing import (assert_almost_equal, assert_series_equal, assert_frame_equal, assert_panel_equal, - assert_index_equal) + assert_index_equal, assert_numpy_array_equal) import pandas.core.datetools as datetools import pandas.stats.moments as mom import pandas.core.window as rwindow @@ -1249,8 +1249,8 @@ def test_ewma_span_com_args(self): B = mom.ewma(self.arr, span=20) assert_almost_equal(A, B) - self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20) - self.assertRaises(Exception, mom.ewma, self.arr) + self.assertRaises(ValueError, mom.ewma, self.arr, com=9.5, span=20) + self.assertRaises(ValueError, mom.ewma, self.arr) def test_ewma_halflife_arg(self): with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): @@ -1258,13 +1258,78 @@ def test_ewma_halflife_arg(self): B = mom.ewma(self.arr, halflife=10.0) assert_almost_equal(A, B) - self.assertRaises(Exception, mom.ewma, self.arr, span=20, + self.assertRaises(ValueError, mom.ewma, self.arr, span=20, halflife=50) - self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, + self.assertRaises(ValueError, mom.ewma, self.arr, com=9.5, halflife=50) - self.assertRaises(Exception, mom.ewma, self.arr, com=9.5, span=20, + self.assertRaises(ValueError, mom.ewma, self.arr, com=9.5, span=20, halflife=50) - self.assertRaises(Exception, mom.ewma, self.arr) + self.assertRaises(ValueError, mom.ewma, self.arr) + + def test_ewma_alpha_old_api(self): + # GH 10789 + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + a = mom.ewma(self.arr, alpha=0.61722699889169674) + b = mom.ewma(self.arr, com=0.62014947789973052) + c = mom.ewma(self.arr, span=2.240298955799461) + d = mom.ewma(self.arr, halflife=0.721792864318) + assert_numpy_array_equal(a, b) + assert_numpy_array_equal(a, c) + assert_numpy_array_equal(a, d) + + def test_ewma_alpha_arg_old_api(self): + # GH 10789 + with tm.assert_produces_warning(FutureWarning, check_stacklevel=False): + self.assertRaises(ValueError, mom.ewma, self.arr) + self.assertRaises(ValueError, mom.ewma, self.arr, + com=10.0, alpha=0.5) + self.assertRaises(ValueError, mom.ewma, self.arr, + span=10.0, alpha=0.5) + self.assertRaises(ValueError, mom.ewma, self.arr, + halflife=10.0, alpha=0.5) + + def test_ewm_alpha(self): + # GH 10789 + s = Series(self.arr) + a = s.ewm(alpha=0.61722699889169674).mean() + b = s.ewm(com=0.62014947789973052).mean() + c = s.ewm(span=2.240298955799461).mean() + d = s.ewm(halflife=0.721792864318).mean() + assert_series_equal(a, b) + assert_series_equal(a, c) + assert_series_equal(a, d) + + def test_ewm_alpha_arg(self): + # GH 10789 + s = Series(self.arr) + self.assertRaises(ValueError, s.ewm) + self.assertRaises(ValueError, s.ewm, com=10.0, alpha=0.5) + self.assertRaises(ValueError, s.ewm, span=10.0, alpha=0.5) + self.assertRaises(ValueError, s.ewm, halflife=10.0, alpha=0.5) + + def test_ewm_domain_checks(self): + # GH 12492 + s = Series(self.arr) + # com must satisfy: com >= 0 + self.assertRaises(ValueError, s.ewm, com=-0.1) + s.ewm(com=0.0) + s.ewm(com=0.1) + # span must satisfy: span >= 1 + self.assertRaises(ValueError, s.ewm, span=-0.1) + self.assertRaises(ValueError, s.ewm, span=0.0) + self.assertRaises(ValueError, s.ewm, span=0.9) + s.ewm(span=1.0) + s.ewm(span=1.1) + # halflife must satisfy: halflife > 0 + self.assertRaises(ValueError, s.ewm, halflife=-0.1) + self.assertRaises(ValueError, s.ewm, halflife=0.0) + s.ewm(halflife=0.1) + # alpha must satisfy: 0 < alpha <= 1 + self.assertRaises(ValueError, s.ewm, alpha=-0.1) + self.assertRaises(ValueError, s.ewm, alpha=0.0) + s.ewm(alpha=0.1) + s.ewm(alpha=1.0) + self.assertRaises(ValueError, s.ewm, alpha=1.1) def test_ew_empty_arrays(self): arr = np.array([], dtype=np.float64)
- Closes #10789 - Passes Travis CI, nosetests, and flake8 - Covered by new tests in test_window.py:TestMoments - Documented in computation.rst and v0.18.0.txt
https://api.github.com/repos/pandas-dev/pandas/pulls/12492
2016-02-29T03:01:48Z
2016-03-06T14:57:59Z
null
2016-03-21T15:03:38Z
DOC: fixed references to DataFrameGroupBy methods in comparison_with_…
diff --git a/doc/source/api.rst b/doc/source/api.rst index 59f0f0a82a892..662f8c512e8a7 100644 --- a/doc/source/api.rst +++ b/doc/source/api.rst @@ -1694,16 +1694,18 @@ application to columns of a specific data type. .. autosummary:: :toctree: generated/ + DataFrameGroupBy.agg + DataFrameGroupBy.all + DataFrameGroupBy.any DataFrameGroupBy.bfill + DataFrameGroupBy.corr + DataFrameGroupBy.count + DataFrameGroupBy.cov DataFrameGroupBy.cummax DataFrameGroupBy.cummin DataFrameGroupBy.cumprod DataFrameGroupBy.cumsum DataFrameGroupBy.describe - DataFrameGroupBy.all - DataFrameGroupBy.any - DataFrameGroupBy.corr - DataFrameGroupBy.cov DataFrameGroupBy.diff DataFrameGroupBy.ffill DataFrameGroupBy.fillna @@ -1717,6 +1719,7 @@ application to columns of a specific data type. DataFrameGroupBy.rank DataFrameGroupBy.resample DataFrameGroupBy.shift + DataFrameGroupBy.size DataFrameGroupBy.skew DataFrameGroupBy.take DataFrameGroupBy.tshift diff --git a/doc/source/comparison_with_sql.rst b/doc/source/comparison_with_sql.rst index 5dc083db7d147..26e76e8c5a4f6 100644 --- a/doc/source/comparison_with_sql.rst +++ b/doc/source/comparison_with_sql.rst @@ -138,7 +138,7 @@ Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series. GROUP BY -------- -In pandas, SQL's GROUP BY operations performed using the similarly named +In pandas, SQL's GROUP BY operations are performed using the similarly named :meth:`~pandas.DataFrame.groupby` method. :meth:`~pandas.DataFrame.groupby` typically refers to a process where we'd like to split a dataset into groups, apply some function (typically aggregation) , and then combine the groups together. @@ -163,23 +163,24 @@ The pandas equivalent would be: tips.groupby('sex').size() -Notice that in the pandas code we used :meth:`~pandas.DataFrameGroupBy.size` and not -:meth:`~pandas.DataFrameGroupBy.count`. This is because :meth:`~pandas.DataFrameGroupBy.count` -applies the function to each column, returning the number of ``not null`` records within each. +Notice that in the pandas code we used :meth:`~pandas.core.groupby.DataFrameGroupBy.size` and not +:meth:`~pandas.core.groupby.DataFrameGroupBy.count`. This is because +:meth:`~pandas.core.groupby.DataFrameGroupBy.count` applies the function to each column, returning +the number of ``not null`` records within each. .. ipython:: python tips.groupby('sex').count() -Alternatively, we could have applied the :meth:`~pandas.DataFrameGroupBy.count` method to an -individual column: +Alternatively, we could have applied the :meth:`~pandas.core.groupby.DataFrameGroupBy.count` method +to an individual column: .. ipython:: python tips.groupby('sex')['total_bill'].count() Multiple functions can also be applied at once. For instance, say we'd like to see how tip amount -differs by day of the week - :meth:`~pandas.DataFrameGroupBy.agg` allows you to pass a dictionary +differs by day of the week - :meth:`~pandas.core.groupby.DataFrameGroupBy.agg` allows you to pass a dictionary to your grouped DataFrame, indicating which functions to apply to specific columns. .. code-block:: sql
…sql.rst
https://api.github.com/repos/pandas-dev/pandas/pulls/12491
2016-02-29T01:04:27Z
2016-03-01T02:56:15Z
null
2016-03-01T02:56:33Z
TST: Eliminating references to pandas.util.testing.choice
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 1e07e38c23970..eced8515ed17f 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1195,3 +1195,5 @@ Bug Fixes - Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`) - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) + +- Removed pandas.util.testing.choice(). Should use np.random.choice(), instead. (:issue:`12386`) diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py index 6db507f0e4151..a52cb018c7bae 100644 --- a/pandas/tests/frame/test_query_eval.py +++ b/pandas/tests/frame/test_query_eval.py @@ -96,8 +96,8 @@ class TestDataFrameQueryWithMultiIndex(tm.TestCase): def check_query_with_named_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) - b = tm.choice(['eggs', 'ham'], size=10) + a = np.random.choice(['red', 'green'], size=10) + b = np.random.choice(['eggs', 'ham'], size=10) index = MultiIndex.from_arrays([a, b], names=['color', 'food']) df = DataFrame(randn(10, 2), index=index) ind = Series(df.index.get_level_values('color').values, index=index, @@ -149,8 +149,8 @@ def test_query_with_named_multiindex(self): def check_query_with_unnamed_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) - b = tm.choice(['eggs', 'ham'], size=10) + a = np.random.choice(['red', 'green'], size=10) + b = np.random.choice(['eggs', 'ham'], size=10) index = MultiIndex.from_arrays([a, b]) df = DataFrame(randn(10, 2), index=index) ind = Series(df.index.get_level_values(0).values, index=index) @@ -243,7 +243,7 @@ def test_query_with_unnamed_multiindex(self): def check_query_with_partially_named_multiindex(self, parser, engine): tm.skip_if_no_ne(engine) - a = tm.choice(['red', 'green'], size=10) + a = np.random.choice(['red', 'green'], size=10) b = np.arange(10) index = MultiIndex.from_arrays([a, b]) index.names = [None, 'rating'] @@ -975,7 +975,7 @@ def check_query_lex_compare_strings(self, parser, engine): tm.skip_if_no_ne(engine=engine) import operator as opr - a = Series(tm.choice(list('abcde'), 20)) + a = Series(np.random.choice(list('abcde'), 20)) b = Series(np.arange(a.size)) df = DataFrame({'X': a, 'Y': b}) diff --git a/pandas/tests/test_graphics.py b/pandas/tests/test_graphics.py index b339d25cd6c45..2fdfc7ccc37ef 100644 --- a/pandas/tests/test_graphics.py +++ b/pandas/tests/test_graphics.py @@ -60,8 +60,8 @@ def setUp(self): n = 100 with tm.RNGContext(42): - gender = tm.choice(['Male', 'Female'], size=n) - classroom = tm.choice(['A', 'B', 'C'], size=n) + gender = np.random.choice(['Male', 'Female'], size=n) + classroom = np.random.choice(['A', 'B', 'C'], size=n) self.hist_df = DataFrame({'gender': gender, 'classroom': classroom, @@ -3861,7 +3861,7 @@ def test_series_groupby_plotting_nominally_works(self): 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) + gender = np.random.choice(['male', 'female'], size=n) weight.groupby(gender).plot() tm.close() diff --git a/pandas/tests/test_graphics_others.py b/pandas/tests/test_graphics_others.py index 983d0c310f71d..b032ce196c113 100644 --- a/pandas/tests/test_graphics_others.py +++ b/pandas/tests/test_graphics_others.py @@ -641,7 +641,7 @@ def test_grouped_plot_fignums(self): 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) + gender = np.random.choice(['male', 'female'], size=n) df = DataFrame({'height': height, 'weight': weight, 'gender': gender}) gb = df.groupby('gender') @@ -715,7 +715,7 @@ def test_grouped_hist_legacy2(self): 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) + gender_int = np.random.choice([0, 1], size=n) df_int = DataFrame({'height': height, 'weight': weight, 'gender': gender_int}) gb = df_int.groupby('gender') diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py index 046d2322165b5..69994615beec1 100644 --- a/pandas/tools/tests/test_merge.py +++ b/pandas/tools/tests/test_merge.py @@ -236,27 +236,27 @@ def test_join_on(self): def test_join_on_fails_with_different_right_index(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}, index=tm.makeCustomIndex(10, 2)) merge(df, df2, left_on='a', right_index=True) def test_join_on_fails_with_different_left_index(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}, index=tm.makeCustomIndex(10, 2)) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}) merge(df, df2, right_on='b', left_index=True) def test_join_on_fails_with_different_column_counts(self): with tm.assertRaises(ValueError): - df = DataFrame({'a': tm.choice(['m', 'f'], size=3), + df = DataFrame({'a': np.random.choice(['m', 'f'], size=3), 'b': np.random.randn(3)}) - df2 = DataFrame({'a': tm.choice(['m', 'f'], size=10), + df2 = DataFrame({'a': np.random.choice(['m', 'f'], size=10), 'b': np.random.randn(10)}, index=tm.makeCustomIndex(10, 2)) merge(df, df2, right_on='a', left_on=['a', 'b']) diff --git a/pandas/util/testing.py b/pandas/util/testing.py index 35a615db444e9..c32239daaaf12 100644 --- a/pandas/util/testing.py +++ b/pandas/util/testing.py @@ -133,7 +133,7 @@ def randbool(size=(), p=0.5): def rands_array(nchars, size, dtype='O'): """Generate an array of byte strings.""" - retval = (choice(RANDS_CHARS, size=nchars * np.prod(size)) + retval = (np.random.choice(RANDS_CHARS, size=nchars * np.prod(size)) .view((np.str_, nchars)).reshape(size)) if dtype is None: return retval @@ -143,7 +143,7 @@ def rands_array(nchars, size, dtype='O'): def randu_array(nchars, size, dtype='O'): """Generate an array of unicode strings.""" - retval = (choice(RANDU_CHARS, size=nchars * np.prod(size)) + retval = (np.random.choice(RANDU_CHARS, size=nchars * np.prod(size)) .view((np.unicode_, nchars)).reshape(size)) if dtype is None: return retval @@ -158,7 +158,7 @@ def rands(nchars): See `rands_array` if you want to create an array of random strings. """ - return ''.join(choice(RANDS_CHARS, nchars)) + return ''.join(np.random.choice(RANDS_CHARS, nchars)) def randu(nchars): @@ -171,14 +171,6 @@ def randu(nchars): return ''.join(choice(RANDU_CHARS, nchars)) -def choice(x, size=10): - """sample with replacement; uniform over the input""" - try: - return np.random.choice(x, size=size) - except AttributeError: - return np.random.randint(len(x), size=size).choose(x) - - def close(fignum=None): from matplotlib.pyplot import get_fignums, close as _close diff --git a/vb_suite/groupby.py b/vb_suite/groupby.py index bc21372225322..268d71f864823 100644 --- a/vb_suite/groupby.py +++ b/vb_suite/groupby.py @@ -143,7 +143,7 @@ def f(): value2 = np.random.randn(n) value2[np.random.rand(n) > 0.5] = np.nan -obj = tm.choice(list('ab'), size=n).astype(object) +obj = np.random.choice(list('ab'), size=n).astype(object) obj[np.random.randn(n) > 0.5] = np.nan df = DataFrame({'key1': np.random.randint(0, 500, size=n),
- [x] closes #12386 - [x] tests passed - [x] passes `git diff upstream/master | flake8 --diff` Want to remove pandas.util.testing.choice. First eliminating all references to it. Doing this in more than one commit as there are a lot of them. #12386
https://api.github.com/repos/pandas-dev/pandas/pulls/12490
2016-02-28T23:07:54Z
2016-03-01T22:27:12Z
null
2016-03-01T22:27:12Z
typo
diff --git a/pandas/core/style.py b/pandas/core/style.py index 15fcec118e7d4..9a44109266957 100644 --- a/pandas/core/style.py +++ b/pandas/core/style.py @@ -352,7 +352,7 @@ def render(self): ``Styler`` objects have defined the ``_repr_html_`` method which automatically calls ``self.render()`` when it's the last item in a Notebook cell. When calling ``Styler.render()`` - directly, wrap the resul in ``IPython.display.HTML`` to view + directly, wrap the result in ``IPython.display.HTML`` to view the rendered HTML in the notebook. """ self._compute()
fix typo in comment
https://api.github.com/repos/pandas-dev/pandas/pulls/12488
2016-02-28T15:56:09Z
2016-02-28T23:11:31Z
null
2016-02-28T23:11:36Z
CLN: cleanup strings._wrap_result
diff --git a/pandas/core/strings.py b/pandas/core/strings.py index c1ab46956c25f..a7ed1ba0c0be0 100644 --- a/pandas/core/strings.py +++ b/pandas/core/strings.py @@ -604,7 +604,7 @@ def str_extract(arr, pat, flags=0, expand=None): return _str_extract_frame(arr._orig, pat, flags=flags) else: result, name = _str_extract_noexpand(arr._data, pat, flags=flags) - return arr._wrap_result(result, name=name) + return arr._wrap_result(result, name=name, expand=expand) def str_extractall(arr, pat, flags=0): @@ -1292,7 +1292,10 @@ def __iter__(self): i += 1 g = self.get(i) - def _wrap_result(self, result, use_codes=True, name=None): + def _wrap_result(self, result, use_codes=True, + name=None, expand=None): + + from pandas.core.index import Index, MultiIndex # for category, we do the stuff on the categories, so blow it up # to the full series again @@ -1302,48 +1305,42 @@ def _wrap_result(self, result, use_codes=True, name=None): if use_codes and self._is_categorical: result = take_1d(result, self._orig.cat.codes) - # 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 - - if not hasattr(result, 'ndim'): + if not hasattr(result, 'ndim') or not hasattr(result, 'dtype'): return result + assert result.ndim < 3 - if result.ndim == 1: - # Wait until we are sure result is a Series or Index before - # checking attributes (GH 12180) - name = name or getattr(result, 'name', None) or self._orig.name - if isinstance(self._orig, 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) - return Series(result, index=self._orig.index, name=name) - else: - assert result.ndim < 3 - return DataFrame(result, index=self._orig.index) + if expand is None: + # infer from ndim if expand is not specified + expand = False if result.ndim == 1 else True + + elif expand is True and not isinstance(self._orig, Index): + # required when expand=True is explicitly specified + # not needed when infered + + def cons_row(x): + if is_list_like(x): + return x + else: + return [x] + + result = [cons_row(x) for x in result] - def _wrap_result_expand(self, result, expand=False): if not isinstance(expand, bool): raise ValueError("expand must be True or False") - # for category, we do the stuff on the categories, so blow it up - # to the full series again - if self._is_categorical: - result = take_1d(result, self._orig.cat.codes) - - from pandas.core.index import Index, MultiIndex - if not hasattr(result, 'ndim'): - return result + if name is None: + name = getattr(result, 'name', None) + if name is None: + # do not use logical or, _orig may be a DataFrame + # which has "name" column + name = self._orig.name + # Wait until we are sure result is a Series or Index before + # checking attributes (GH 12180) if isinstance(self._orig, 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): + if is_bool_dtype(result): return result if expand: @@ -1354,18 +1351,10 @@ def _wrap_result_expand(self, result, expand=False): else: index = self._orig.index if expand: - - def cons_row(x): - if is_list_like(x): - return x - else: - return [x] - cons = self._orig._constructor_expanddim - data = [cons_row(x) for x in result] - return cons(data, index=index) + return cons(result, index=index) else: - name = getattr(result, 'name', None) + # Must a Series cons = self._orig._constructor return cons(result, name=name, index=index) @@ -1380,12 +1369,12 @@ def cat(self, others=None, sep=None, na_rep=None): @copy(str_split) def split(self, pat=None, n=-1, expand=False): result = str_split(self._data, pat, n=n) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) @copy(str_rsplit) def rsplit(self, pat=None, n=-1, expand=False): result = str_rsplit(self._data, pat, n=n) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) _shared_docs['str_partition'] = (""" Split the string at the %(side)s occurrence of `sep`, and return 3 elements @@ -1440,7 +1429,7 @@ def rsplit(self, pat=None, n=-1, expand=False): def partition(self, pat=' ', expand=True): f = lambda x: x.partition(pat) result = _na_map(f, self._data) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) @Appender(_shared_docs['str_partition'] % { 'side': 'last', @@ -1451,7 +1440,7 @@ def partition(self, pat=' ', expand=True): def rpartition(self, pat=' ', expand=True): f = lambda x: x.rpartition(pat) result = _na_map(f, self._data) - return self._wrap_result_expand(result, expand=expand) + return self._wrap_result(result, expand=expand) @copy(str_get) def get(self, i): @@ -1597,7 +1586,8 @@ def get_dummies(self, sep='|'): # methods available for making the dummies... data = self._orig.astype(str) if self._is_categorical else self._data result = str_get_dummies(data, sep) - return self._wrap_result(result, use_codes=(not self._is_categorical)) + return self._wrap_result(result, use_codes=(not self._is_categorical), + expand=True) @copy(str_translate) def translate(self, table, deletechars=None):
- Merged `strings._wrap_result` and `strings._wrap_result_expand` for cleanup.
https://api.github.com/repos/pandas-dev/pandas/pulls/12487
2016-02-28T08:26:08Z
2016-03-02T12:43:50Z
null
2016-03-04T22:23:40Z
ENH: optional ':' separator in ISO8601 strings
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt index 7f253ae437d9f..dc74791e916cc 100644 --- a/doc/source/whatsnew/v0.18.0.txt +++ b/doc/source/whatsnew/v0.18.0.txt @@ -1200,3 +1200,5 @@ Bug Fixes - Bug when initializing categorical series with a scalar value. (:issue:`12336`) - Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`) - Bug when increasing the buffer size of CSV reader in ``read_csv`` (:issue:`12494`) + +- Bug in ``Timestamp`` constructor where microsecond resolution was lost if HHMMSS were not separated with ':' (:issue:`10041`) diff --git a/pandas/src/datetime/np_datetime_strings.c b/pandas/src/datetime/np_datetime_strings.c index 33ddc6c6e1f27..3a1d37f86cc28 100644 --- a/pandas/src/datetime/np_datetime_strings.c +++ b/pandas/src/datetime/np_datetime_strings.c @@ -355,6 +355,8 @@ convert_datetimestruct_local_to_utc(pandas_datetimestruct *out_dts_utc, * + Doesn't handle 24:00:00 as synonym for midnight (00:00:00) tomorrow * + Accepts special values "NaT" (not a time), "Today", (current * day according to local time) and "Now" (current time in UTC). + * + ':' separator between hours, minutes, and seconds is optional. When + * omitted, each component must be 2 digits if it appears. (GH-10041) * * 'str' must be a NULL-terminated string, and 'len' must be its length. * 'unit' should contain -1 if the unit is unknown, or the unit @@ -394,15 +396,21 @@ parse_iso_8601_datetime(char *str, int len, char *substr, sublen; PANDAS_DATETIMEUNIT bestunit; - /* if date components in are separated by one of valid separators - * months/days without leadings 0s will be parsed + /* If year-month-day are separated by a valid separator, + * months/days without leading zeroes will be parsed * (though not iso8601). If the components aren't separated, - * an error code will be retuned because the date is ambigous + * 4 (YYYY) or 8 (YYYYMMDD) digits are expected. 6 digits are + * forbidden here (but parsed as YYMMDD elsewhere). */ - int has_sep = 0; - char sep = '\0'; - char valid_sep[] = {'-', '.', '/', '\\', ' '}; - int valid_sep_len = 5; + int has_ymd_sep = 0; + char ymd_sep = '\0'; + char valid_ymd_sep[] = {'-', '.', '/', '\\', ' '}; + int valid_ymd_sep_len = sizeof(valid_ymd_sep); + + /* hour-minute-second may or may not separated by ':'. If not, then + * each component must be 2 digits. */ + int has_hms_sep = 0; + int hour_was_2_digits = 0; /* Initialize the output to all zeros */ memset(out, 0, sizeof(pandas_datetimestruct)); @@ -550,7 +558,7 @@ parse_iso_8601_datetime(char *str, int len, /* Check whether it's a leap-year */ year_leap = is_leapyear(out->year); - /* Next character must be a separator, start of month or end */ + /* Next character must be a separator, start of month, or end of string */ if (sublen == 0) { if (out_local != NULL) { *out_local = 0; @@ -558,59 +566,50 @@ parse_iso_8601_datetime(char *str, int len, bestunit = PANDAS_FR_Y; goto finish; } - else if (!isdigit(*substr)) { - for (i = 0; i < valid_sep_len; ++i) { - if (*substr == valid_sep[i]) { - has_sep = 1; - sep = valid_sep[i]; - ++substr; - --sublen; + + if (!isdigit(*substr)) { + for (i = 0; i < valid_ymd_sep_len; ++i) { + if (*substr == valid_ymd_sep[i]) { break; } } - if (i == valid_sep_len) { + if (i == valid_ymd_sep_len) { goto parse_error; } - } - - /* Can't have a trailing sep */ - if (sublen == 0) { - goto parse_error; - } - - - /* PARSE THE MONTH (2 digits) */ - if (has_sep && ((sublen >= 2 && isdigit(substr[0]) && !isdigit(substr[1])) - || (sublen == 1 && isdigit(substr[0])))) { - out->month = (substr[0] - '0'); - - if (out->month < 1) { - PyErr_Format(PyExc_ValueError, - "Month out of range in datetime string \"%s\"", str); - goto error; - } + has_ymd_sep = 1; + ymd_sep = valid_ymd_sep[i]; ++substr; --sublen; + /* Cannot have trailing separator */ + if (sublen == 0 || !isdigit(*substr)) { + goto parse_error; + } } - else if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->month = 10 * (substr[0] - '0') + (substr[1] - '0'); - if (out->month < 1 || out->month > 12) { - PyErr_Format(PyExc_ValueError, - "Month out of range in datetime string \"%s\"", str); - goto error; - } - substr += 2; - sublen -= 2; + /* PARSE THE MONTH */ + /* First digit required */ + out->month = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->month = 10 * out->month + (*substr - '0'); + ++substr; + --sublen; } - else { + else if (!has_ymd_sep) { goto parse_error; } + if (out->month < 1 || out->month > 12) { + PyErr_Format(PyExc_ValueError, + "Month out of range in datetime string \"%s\"", str); + goto error; + } - /* Next character must be a '-' or the end of the string */ + /* Next character must be the separator, start of day, or end of string */ if (sublen == 0) { - /* dates of form YYYYMM are not valid */ - if (!has_sep) { + /* Forbid YYYYMM. Parsed instead as YYMMDD by someone else. */ + if (!has_ymd_sep) { goto parse_error; } if (out_local != NULL) { @@ -619,47 +618,40 @@ parse_iso_8601_datetime(char *str, int len, bestunit = PANDAS_FR_M; goto finish; } - else if (has_sep && *substr == sep) { + + if (has_ymd_sep) { + /* Must have separator, but cannot be trailing */ + if (*substr != ymd_sep || sublen == 1) { + goto parse_error; + } ++substr; --sublen; } - else if (!isdigit(*substr)) { - goto parse_error; - } - /* Can't have a trailing '-' */ - if (sublen == 0) { - goto parse_error; + /* PARSE THE DAY */ + /* First digit required */ + if (!isdigit(*substr)) { + goto parse_error; } - - /* PARSE THE DAY (2 digits) */ - if (has_sep && ((sublen >= 2 && isdigit(substr[0]) && !isdigit(substr[1])) - || (sublen == 1 && isdigit(substr[0])))) { - out->day = (substr[0] - '0'); - - if (out->day < 1) { - PyErr_Format(PyExc_ValueError, - "Day out of range in datetime string \"%s\"", str); - goto error; - } + out->day = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->day = 10 * out->day + (*substr - '0'); ++substr; --sublen; } - else if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->day = 10 * (substr[0] - '0') + (substr[1] - '0'); - - if (out->day < 1 || - out->day > days_per_month_table[year_leap][out->month-1]) { - PyErr_Format(PyExc_ValueError, - "Day out of range in datetime string \"%s\"", str); - goto error; - } - substr += 2; - sublen -= 2; - } - else { + else if (!has_ymd_sep) { goto parse_error; } + if (out->day < 1 || + out->day > days_per_month_table[year_leap][out->month-1]) + { + PyErr_Format(PyExc_ValueError, + "Day out of range in datetime string \"%s\"", str); + goto error; + } /* Next character must be a 'T', ' ', or end of string */ if (sublen == 0) { @@ -669,104 +661,119 @@ parse_iso_8601_datetime(char *str, int len, bestunit = PANDAS_FR_D; goto finish; } - else if (*substr != 'T' && *substr != ' ') { + + if ((*substr != 'T' && *substr != ' ') || sublen == 1) { goto parse_error; } - else { + ++substr; + --sublen; + + /* PARSE THE HOURS */ + /* First digit required */ + if (!isdigit(*substr)) { + goto parse_error; + } + out->hour = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional */ + if (isdigit(*substr)) { + hour_was_2_digits = 1; + out->hour = 10 * out->hour + (*substr - '0'); ++substr; --sublen; - } - - /* PARSE THE HOURS (2 digits) */ - if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->hour = 10 * (substr[0] - '0') + (substr[1] - '0'); - if (out->hour >= 24) { PyErr_Format(PyExc_ValueError, "Hours out of range in datetime string \"%s\"", str); goto error; } - substr += 2; - sublen -= 2; - } - else if (sublen >= 1 && isdigit(substr[0])) { - out->hour = substr[0] - '0'; - ++substr; - --sublen; - } - else { - goto parse_error; } /* Next character must be a ':' or the end of the string */ - if (sublen > 0 && *substr == ':') { + if (sublen == 0) { + if (!hour_was_2_digits) { + goto parse_error; + } + bestunit = PANDAS_FR_h; + goto finish; + } + + if (*substr == ':') { + has_hms_sep = 1; ++substr; --sublen; + /* Cannot have a trailing separator */ + if (sublen == 0 || !isdigit(*substr)) { + goto parse_error; + } } - else { + else if (!isdigit(*substr)) { + if (!hour_was_2_digits) { + goto parse_error; + } bestunit = PANDAS_FR_h; goto parse_timezone; } - /* Can't have a trailing ':' */ - if (sublen == 0) { - goto parse_error; - } - - /* PARSE THE MINUTES (2 digits) */ - if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->min = 10 * (substr[0] - '0') + (substr[1] - '0'); - + /* PARSE THE MINUTES */ + /* First digit required */ + out->min = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->min = 10 * out->min + (*substr - '0'); + ++substr; + --sublen; if (out->min >= 60) { PyErr_Format(PyExc_ValueError, - "Minutes out of range in datetime string \"%s\"", str); + "Minutes out of range in datetime string \"%s\"", str); goto error; } - substr += 2; - sublen -= 2; - } - else if (sublen >= 1 && isdigit(substr[0])) { - out->min = substr[0] - '0'; - ++substr; - --sublen; } - else { + else if (!has_hms_sep) { goto parse_error; } - /* Next character must be a ':' or the end of the string */ - if (sublen > 0 && *substr == ':') { + if (sublen == 0) { + bestunit = PANDAS_FR_m; + goto finish; + } + + /* If we make it through this condition block, then the next + * character is a digit. */ + if (has_hms_sep && *substr == ':') { ++substr; --sublen; + /* Cannot have a trailing ':' */ + if (sublen == 0 || !isdigit(*substr)) { + goto parse_error; + } + } + else if (!has_hms_sep && isdigit(*substr)) { } else { bestunit = PANDAS_FR_m; goto parse_timezone; } - /* Can't have a trailing ':' */ - if (sublen == 0) { - goto parse_error; - } - - /* PARSE THE SECONDS (2 digits) */ - if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) { - out->sec = 10 * (substr[0] - '0') + (substr[1] - '0'); - + /* PARSE THE SECONDS */ + /* First digit required */ + out->sec = (*substr - '0'); + ++substr; + --sublen; + /* Second digit optional if there was a separator */ + if (isdigit(*substr)) { + out->sec = 10 * out->sec + (*substr - '0'); + ++substr; + --sublen; if (out->sec >= 60) { PyErr_Format(PyExc_ValueError, - "Seconds out of range in datetime string \"%s\"", str); + "Seconds out of range in datetime string \"%s\"", str); goto error; } - substr += 2; - sublen -= 2; - } - else if (sublen >= 1 && isdigit(substr[0])) { - out->sec = substr[0] - '0'; - ++substr; - --sublen; } - else { + else if (!has_hms_sep) { goto parse_error; } diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py index 381b106b17eb0..937a8fa340348 100644 --- a/pandas/tseries/tests/test_tslib.py +++ b/pandas/tseries/tests/test_tslib.py @@ -519,7 +519,12 @@ def test_parsers(self): '2014-06': datetime.datetime(2014, 6, 1), '06-2014': datetime.datetime(2014, 6, 1), '2014-6': datetime.datetime(2014, 6, 1), - '6-2014': datetime.datetime(2014, 6, 1), } + '6-2014': datetime.datetime(2014, 6, 1), + + '20010101 12': datetime.datetime(2001, 1, 1, 12), + '20010101 1234': datetime.datetime(2001, 1, 1, 12, 34), + '20010101 123456': datetime.datetime(2001, 1, 1, 12, 34, 56), + } for date_str, expected in compat.iteritems(cases): result1, _, _ = tools.parse_time_string(date_str) @@ -713,11 +718,22 @@ def test_parsers_iso8601(self): self.assertEqual(actual, exp) # seperators must all match - YYYYMM not valid - invalid_cases = ['2011-01/02', '2011^11^11', '201401', - '201111', '200101'] + invalid_cases = ['2011-01/02', '2011^11^11', + '201401', '201111', '200101', + # mixed separated and unseparated + '2005-0101', '200501-01', + '20010101 12:3456', '20010101 1234:56', + # HHMMSS must have two digits in each component + # if unseparated + '20010101 1', '20010101 123', '20010101 12345', + '20010101 12345Z', + # wrong separator for HHMMSS + '2001-01-01 12-34-56'] for date_str in invalid_cases: with tm.assertRaises(ValueError): tslib._test_parse_iso8601(date_str) + # If no ValueError raised, let me know which case failed. + raise Exception(date_str) class TestArrayToDatetime(tm.TestCase): @@ -881,6 +897,11 @@ def test_nanosecond_string_parsing(self): self.assertEqual(ts.value, expected_value + 4 * 3600 * 1000000000) self.assertIn(expected_repr, repr(ts)) + # GH 10041 + ts = Timestamp('20130501T071545.123456789') + self.assertEqual(ts.value, expected_value) + self.assertIn(expected_repr, repr(ts)) + def test_nanosecond_timestamp(self): # GH 7610 expected = 1293840000000000005
- [X] closes #10041 - [X] tests added / passed - [X] passes `git diff upstream/master | flake8 --diff` - [x] whatsnew entry Includes some refactoring in the ISO8601 parser. If more tests are desired, please let me know where to put them. Same for the whatsnew entry. This is effectively my first pull request for Pandas. cc @jreback
https://api.github.com/repos/pandas-dev/pandas/pulls/12483
2016-02-27T15:53:02Z
2016-03-06T20:44:23Z
null
2016-03-06T20:45:41Z