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v0.18.1.txt
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.. _whatsnew_0181:
v0.18.1 (April ??, 2016)
------------------------
This is a minor bug-fix release from 0.18.0 and includes a large number of
bug fixes along several new features, enhancements, and performance improvements.
We recommend that all users upgrade to this version.
Highlights include:
- Custom business hour offset, see :ref:`here <whatsnew_0181.enhancements.custombusinesshour>`.
.. contents:: What's new in v0.18.1
:local:
:backlinks: none
.. _whatsnew_0181.new_features:
New features
~~~~~~~~~~~~
.. _whatsnew_0181.enhancements.custombusinesshour:
Custom Business Hour
^^^^^^^^^^^^^^^^^^^^
The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusinessDay`` which
allows you to specify arbitrary holidays. For details,
see :ref:`Custom Business Hour <timeseries.custombusinesshour>` (:issue:`11514`)
.. ipython:: python
from pandas.tseries.offsets import CustomBusinessHour
from pandas.tseries.holiday import USFederalHolidayCalendar
bhour_us = CustomBusinessHour(calendar=USFederalHolidayCalendar())
# Friday before MLK Day
dt = datetime(2014, 1, 17, 15)
dt + bhour_us
# Tuesday after MLK Day (Monday is skipped because it's a holiday)
dt + bhour_us * 2
.. _whatsnew_0181.enhancements:
Enhancements
~~~~~~~~~~~~
.. _whatsnew_0181.partial_string_indexing:
Partial string indexing on ``DateTimeIndex`` when part of a ``MultiIndex``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Partial string indexing now matches on ``DateTimeIndex`` when part of a ``MultiIndex`` (:issue:`10331`)
.. ipython:: python
dft2 = pd.DataFrame(np.random.randn(20, 1),
columns=['A'],
index=pd.MultiIndex.from_product([pd.date_range('20130101',
periods=10,
freq='12H'),
['a', 'b']]))
dft2
dft2.loc['2013-01-05']
idx = pd.IndexSlice
dft2 = dft2.swaplevel(0, 1).sort_index()
dft2.loc[idx[:, '2013-01-05'], :]
.. _whatsnew_0181.other:
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.sparse:
Sparse changes
~~~~~~~~~~~~~~
These changes conform sparse handling to return the correct types and work to make a smoother experience with indexing.
``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others. Also now it handles negative indexer as the same rule as ``Index`` (:issue:`10560`, :issue:`12796`)
.. ipython:: python
s = pd.SparseArray([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
s.take(0)
s.take([1, 2, 3])
- Bug in ``SparseSeries.__getitem__`` with ``Ellipsis`` raises ``KeyError`` (:issue:`9467`)
- Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`)
- Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`)
- Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`)
- Bug in ``SparseDataFrame.loc[]``, ``.iloc[]`` may results in dense ``Series``, rather than ``SparseSeries`` (:issue:`12787`)
- Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`)
- Bug in ``SparseSeries.shape`` ignores ``fill_value`` (:issue:`10452`)
- Bug in ``SparseSeries.reindex`` incorrectly handle ``fill_value`` (:issue:`12797`)
- Bug in ``SparseArray.to_frame()`` results in ``DataFrame``, rather than ``SparseDataFrame`` (:issue:`9850`)
- Bug in ``SparseArray.to_dense()`` does not preserve ``dtype`` (:issue:`10648`)
- Bug in ``SparseArray.to_dense()`` incorrectly handle ``fill_value`` (:issue:`12797`)
.. _whatsnew_0181.api:
API changes
~~~~~~~~~~~
- ``.searchsorted()`` for ``Index`` and ``TimedeltaIndex`` now accept a ``sorter`` argument to maintain compatibility with numpy's ``searchsorted`` function (:issue:`12238`)
- ``Period`` and ``PeriodIndex`` now raises ``IncompatibleFrequency`` error which inherits ``ValueError`` rather than raw ``ValueError`` (:issue:`12615`)
- ``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`)
- ``pd.concat(ignore_index=True)`` now uses ``RangeIndex`` as default (:issue:`12695`)
.. _whatsnew_0181.apply_resample:
Using ``.apply`` on groupby resampling
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Using ``apply`` on resampling groupby operations (using a ``pd.TimeGrouper``) now has the same output types as a similar ``apply`` on other groupby operations. (:issue:`11742`).
.. ipython:: python
df = pd.DataFrame({'date': pd.to_datetime(['10/10/2000', '11/10/2000']), 'value': [10, 13]})
df
Previous behavior:
.. code-block:: python
In [1]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum())
Out[1]:
...
TypeError: cannot concatenate a non-NDFrame object
# Output is a Series
In [2]: df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum())
Out[2]:
date
2000-10-31 value 10
2000-11-30 value 13
dtype: int64
New Behavior:
.. ipython:: python
# Output is a Series
df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x.value.sum())
# Output is a DataFrame
df.groupby(pd.TimeGrouper(key='date', freq='M')).apply(lambda x: x[['value']].sum())
.. _whatsnew_0181.deprecations:
Deprecations
^^^^^^^^^^^^
- The method name ``Index.sym_diff()`` is deprecated and can be replaced by ``Index.symmetric_difference()`` (:issue:`12591`)
.. _whatsnew_0181.performance:
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
.. _whatsnew_0181.bug_fixes:
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:`12713`)
- 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`)
- 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 a callable. (:issue:`12706`)
- Bug in ``GroupBy.filter`` when ``dropna=False`` and no groups fulfilled the criteria (:issue:`12768`)
- Bug in ``__name__`` of ``.cum*`` functions (:issue:`12021`)
- Bug in ``.drop()`` with a non-unique ``MultiIndex``. (:issue:`12701`)
- Bug in ``.concat`` of datetime tz-aware and naive DataFrames (:issue:`12467`)
- 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`)
- Bug in ``.quantile()`` with interpolation may coerce to ``float`` unexpectedly (:issue:`12772`)
- Bug in ``.quantile()`` with empty Series may return scalar rather than empty Series (:issue:`12772`)
- Bug in equality testing with a ``Categorical`` in a ``DataFrame`` (:issue:`12564`)
- Bug in ``GroupBy.first()``, ``.last()`` returns incorrect row when ``TimeGrouper`` is used (:issue:`7453`)
- 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`)
- Bug in ``.str`` accessor methods may raise ``ValueError`` if input has ``name`` and the result is ``DataFrame`` or ``MultiIndex`` (:issue:`12617`)
- Bug in ``CategoricalIndex.get_loc`` returns different result from regular ``Index`` (:issue:`12531`)
- Bug in ``PeriodIndex.resample`` where name not propagated (:issue:`12769`)
- Bug in ``concat`` raises ``AttributeError`` when input data contains tz-aware datetime and timedelta (:issue:`12620`)
- Bug in ``concat`` doesn't handle empty ``Series`` properly (:issue:`11082`)
- 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`)