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‎doc/source/whatsnew/v0.21.1.txt.orig

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.. _whatsnew_0211:
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v0.21.1
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-------
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This is a minor release from 0.21.1 and includes a number of deprecations, new
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features, enhancements, and performance improvements along with a large number
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of bug fixes. We recommend that all users upgrade to this version.
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.. _whatsnew_0211.enhancements:
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New features
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~~~~~~~~~~~~
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-
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-
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-
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.. _whatsnew_0211.enhancements.other:
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Other Enhancements
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^^^^^^^^^^^^^^^^^^
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- :meth:`Timestamp.timestamp` is now available in Python 2.7. (:issue:`17329`)
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-
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-
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.. _whatsnew_0211.deprecations:
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Deprecations
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~~~~~~~~~~~~
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-
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-
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-
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.. _whatsnew_0211.performance:
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Performance Improvements
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~~~~~~~~~~~~~~~~~~~~~~~~
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- Improved performance of plotting large series/dataframes (:issue:`18236`).
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-
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-
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.. _whatsnew_0211.docs:
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Documentation Changes
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~~~~~~~~~~~~~~~~~~~~~
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-
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-
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-
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.. _whatsnew_0211.bug_fixes:
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Bug Fixes
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~~~~~~~~~
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Conversion
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^^^^^^^^^^
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- Bug in :class:`TimedeltaIndex` subtraction could incorrectly overflow when ``NaT`` is present (:issue:`17791`)
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- Bug in :class:`DatetimeIndex` subtracting datetimelike from DatetimeIndex could fail to overflow (:issue:`18020`)
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- Bug in :meth:`IntervalIndex.copy` when copying and ``IntervalIndex`` with non-default ``closed`` (:issue:`18339`)
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- Bug in :func:`DataFrame.to_dict` where columns of datetime that are tz-aware were not converted to required arrays when used with ``orient='records'``, raising``TypeError` (:issue:`18372`)
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-
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-
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Indexing
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^^^^^^^^
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- Bug in a boolean comparison of a ``datetime.datetime`` and a ``datetime64[ns]`` dtype Series (:issue:`17965`)
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- Bug where a ``MultiIndex`` with more than a million records was not raising ``AttributeError`` when trying to access a missing attribute (:issue:`18165`)
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- Bug in :class:`IntervalIndex` constructor when a list of intervals is passed with non-default ``closed`` (:issue:`18334`)
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- Bug in ``Index.putmask`` when an invalid mask passed (:issue:`18368`)
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-
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I/O
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^^^
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- Bug in class:`~pandas.io.stata.StataReader` not converting date/time columns with display formatting addressed (:issue:`17990`). Previously columns with display formatting were normally left as ordinal numbers and not converted to datetime objects.
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- Bug in :func:`read_csv` when reading a compressed UTF-16 encoded file (:issue:`18071`)
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- Bug in :func:`read_csv` for handling null values in index columns when specifying ``na_filter=False`` (:issue:`5239`)
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- Bug in :func:`read_csv` when reading numeric category fields with high cardinality (:issue:`18186`)
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- Bug in :meth:`DataFrame.to_csv` when the table had ``MultiIndex`` columns, and a list of strings was passed in for ``header`` (:issue:`5539`)
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- :func:`read_parquet` now allows to specify the columns to read from a parquet file (:issue:`18154`)
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- :func:`read_parquet` now allows to specify kwargs which are passed to the respective engine (:issue:`18216`)
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- Bug in parsing integer datetime-like columns with specified format in ``read_sql`` (:issue:`17855`).
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- Bug in :meth:`DataFrame.to_msgpack` when serializing data of the numpy.bool_ datatype (:issue:`18390`)
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Plotting
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^^^^^^^^
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-
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-
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-
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Groupby/Resample/Rolling
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^^^^^^^^^^^^^^^^^^^^^^^^
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- Bug in ``DataFrame.resample(...).apply(...)`` when there is a callable that returns different columns (:issue:`15169`)
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- Bug in ``DataFrame.resample(...)`` when there is a time change (DST) and resampling frequecy is 12h or higher (:issue:`15549`)
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- Bug in ``pd.DataFrameGroupBy.count()`` when counting over a datetimelike column (:issue:`13393`)
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<<<<<<< HEAD
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- Bug in ``rolling.var`` where calculation is inaccurate with a zero-valued array (:issue:`18430`)
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=======
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- Bug when grouping by a single column and aggregating with a class like`list` or `tuple` (:issue:`18079`)
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>>>>>>> added whatsnew
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-
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-
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Sparse
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^^^^^^
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-
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-
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-
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Reshaping
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^^^^^^^^^
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- Error message in ``pd.merge_asof()`` for key datatype mismatch now includes datatype of left and right key (:issue:`18068`)
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- Bug in ``pd.concat`` when empty and non-empty DataFrames or Series are concatenated (:issue:`18178` :issue:`18187`)
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- Bug in ``DataFrame.filter(...)`` when :class:`unicode` is passed as a condition in Python 2 (:issue:`13101`)
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-
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Numeric
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^^^^^^^
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- Bug in ``pd.Series.rolling.skew()`` and ``rolling.kurt()`` with all equal values has floating issue (:issue:`18044`)
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-
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-
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-
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Categorical
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^^^^^^^^^^^
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- Bug in :meth:`DataFrame.astype` where casting to 'category' on an empty ``DataFrame`` causes a segmentation fault (:issue:`18004`)
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- Error messages in the testing module have been improved when items have different ``CategoricalDtype`` (:issue:`18069`)
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- ``CategoricalIndex`` can now correctly take a ``pd.api.types.CategoricalDtype`` as its dtype (:issue:`18116`)
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- Bug in ``Categorical.unique()`` returning read-only ``codes`` array when all categories were ``NaN`` (:issue:`18051`)
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String
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^^^^^^
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- :meth:`Series.str.split()` will now propogate ``NaN`` values across all expanded columns instead of ``None`` (:issue:`18450`)
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Other
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^^^^^
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-
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-

‎grp.patch

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--- a/pandas/core/groupby.py
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+++ b/pandas/core/groupby.py
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@@ -3022,7 +3022,9 @@ class SeriesGroupBy(GroupBy):
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if isinstance(func_or_funcs, compat.string_types):
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return getattr(self, func_or_funcs)(*args, **kwargs)
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- if hasattr(func_or_funcs, '__iter__'):
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+ if isinstance(func_or_funcs, collections.Iterable):
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+ # Catch instances of lists / tuples
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+ # but not the class list / tuple itself.
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ret = self._aggregate_multiple_funcs(func_or_funcs,
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(_level or 0) + 1)
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else:

‎grp_test.patch

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--- a/pandas/tests/groupby/test_groupby.py
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+++ b/pandas/tests/groupby/test_groupby.py
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@@ -2725,3 +2725,12 @@ def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
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expected = f(df.groupby(tups)[field])
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for k, v in compat.iteritems(expected):
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assert (result[k] == v)
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+
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+
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+def test_tuple():
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+ df = pd.DataFrame({'A': [1, 1, 1, 3, 3, 3],
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+ 'B': [1, 1, 1, 4, 4, 4], 'C': [1, 1, 1, 3, 4, 4]})
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+
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+ result = df.groupby(['A', 'B']).aggregate(tuple)
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+ result2 = df.groupby('A').aggregate(tuple)
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+ result2 = df.groupby('A').aggregate([tuple])

‎test_agg.py

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import pandas as pd
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import numpy as np
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def f(x):
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return list(x)
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#df = pd.DataFrame({'A' : [1, 1, 3], 'B' : [1, 2, 4]})
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#result = df.groupby('A').aggregate(f)
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#df = pd.DataFrame({'A' : [1, 1, 3], 'B' : [1, 2, 4]})
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#result = df.groupby('A').aggregate(list)
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#result = df.groupby('A').agg(list)
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df = pd.DataFrame({'A' : [1, 1, 3], 'B' : [1, 1, 4], 'C' : [1, 3, 4]})
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#result = df.groupby(['A', 'B']).aggregate(pd.Series)
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#df = pd.DataFrame({'A': [1, 1, 1, 3, 3, 3],
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# 'B': [1, 1, 1, 4, 4, 4], 'C': [1, 1, 1, 3, 4, 4]})
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#print ('series ')
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result = df.groupby('A')['C'].aggregate(np.array)
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#print (result)
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#
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result = df.groupby(['A', 'B']).aggregate(np.array)
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#print (result)
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#
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# result = df.groupby('A')['C'].aggregate(list)
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# print (result)
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def f(x):
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return np.array(x)
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print ('array')
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result = df.groupby(['A', 'B']).aggregate(f)
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print (result)
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# result = df.groupby('A')['C'].aggregate(tuple)
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# expected = pd.Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name='C')
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# expected.index.name = 'A'
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