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v0.20.0.txt
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.. _whatsnew_0200:
v0.20.0 (????, 2016)
--------------------
This is a major release from 0.19 and includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
users upgrade to this version.
Highlights include:
- Building pandas for development now requires ``cython >= 0.23`` (:issue:`14831`)
Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating.
.. contents:: What's new in v0.20.0
:local:
:backlinks: none
.. _whatsnew_0200.enhancements:
New features
~~~~~~~~~~~~
``dtype`` keyword for data IO
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The ``dtype`` keyword argument in the :func:`read_csv` function for specifying the types of parsed columns is now supported with the ``'python'`` engine (:issue:`14295`). See the :ref:`io docs <io.dtypes>` for more information.
.. ipython:: python
data = "a,b\n1,2\n3,4"
pd.read_csv(StringIO(data), engine='python').dtypes
pd.read_csv(StringIO(data), engine='python', dtype={'a':'float64', 'b':'object'}).dtypes
The ``dtype`` keyword argument is also now supported in the :func:`read_fwf` function for parsing
fixed-width text files, and :func:`read_excel` for parsing Excel files.
.. ipython:: python
data = "a b\n1 2\n3 4"
pd.read_fwf(StringIO(data)).dtypes
pd.read_fwf(StringIO(data), dtype={'a':'float64', 'b':'object'}).dtypes
.. _whatsnew_0200.enhancements.groupby_access:
Groupby Enhancements
^^^^^^^^^^^^^^^^^^^^
Strings passed to ``DataFrame.groupby()`` as the ``by`` parameter may now reference either column names or index level names (:issue:`5677`)
.. ipython:: python
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3],
'B': np.arange(8)},
index=index)
df
df.groupby(['second', 'A']).sum()
.. _whatsnew_0200.enhancements.compressed_urls:
Better support for compressed URLs in ``read_csv``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The compression code was refactored (:issue:`12688`). As a result, reading
dataframes from URLs in :func:`read_csv` or :func:`read_table` now supports
additional compression methods: ``xz``, ``bz2``, and ``zip`` (:issue:`14570`).
Previously, only ``gzip`` compression was supported. By default, compression of
URLs and paths are now both inferred using their file extensions. Additionally,
support for bz2 compression in the python 2 c-engine improved (:issue:`14874`).
.. ipython:: python
url = 'https://github.com/{repo}/raw/{branch}/{path}'.format(
repo = 'pandas-dev/pandas',
branch = 'master',
path = 'pandas/io/tests/parser/data/salaries.csv.bz2',
)
df = pd.read_table(url, compression='infer') # default, infer compression
df = pd.read_table(url, compression='bz2') # explicitly specify compression
df.head(2)
.. _whatsnew_0200.enhancements.other:
Other enhancements
^^^^^^^^^^^^^^^^^^
- ``Series.sort_index`` accepts parameters ``kind`` and ``na_position`` (:issue:`13589`, :issue:`14444`)
- ``pd.read_excel`` now preserves sheet order when using ``sheetname=None`` (:issue:`9930`)
- Multiple offset aliases with decimal points are now supported (e.g. '0.5min' is parsed as '30s') (:issue:`8419`)
- New ``UnsortedIndexError`` (subclass of ``KeyError``) raised when indexing/slicing into an
unsorted MultiIndex (:issue:`11897`). This allows differentiation between errors due to lack
of sorting or an incorrect key. See :ref:`here <advanced.unsorted>`
- ``pd.cut`` and ``pd.qcut`` now support datetime64 and timedelta64 dtypes (:issue:`14714`)
- ``Series`` provides a ``to_excel`` method to output Excel files (:issue:`8825`)
- The ``usecols`` argument in ``pd.read_csv`` now accepts a callable function as a value (:issue:`14154`)
- ``pd.DataFrame.plot`` now prints a title above each subplot if ``suplots=True`` and ``title`` is a list of strings (:issue:`14753`)
- ``pd.Series.interpolate`` now supports timedelta as an index type with ``method='time'`` (:issue:`6424`)
- ``pandas.io.json.json_normalize()`` gained the option ``errors='ignore'|'raise'``; the default is ``errors='raise'`` which is backward compatible. (:issue:`14583`)
.. _whatsnew_0200.api_breaking:
Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. _whatsnew.api_breaking.index_map
Map on Index types now return other Index types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- ``map`` on an ``Index`` now returns an ``Index``, not a numpy array (:issue:`12766`)
.. ipython:: python
idx = Index([1, 2])
idx
mi = MultiIndex.from_tuples([(1, 2), (2, 4)])
mi
Previous Behavior:
.. code-block:: ipython
In [5]: idx.map(lambda x: x * 2)
Out[5]: array([2, 4])
In [6]: idx.map(lambda x: (x, x * 2))
Out[6]: array([(1, 2), (2, 4)], dtype=object)
In [7]: mi.map(lambda x: x)
Out[7]: array([(1, 2), (2, 4)], dtype=object)
In [8]: mi.map(lambda x: x[0])
Out[8]: array([1, 2])
New Behavior:
.. ipython:: python
idx.map(lambda x: x * 2)
idx.map(lambda x: (x, x * 2))
mi.map(lambda x: x)
mi.map(lambda x: x[0])
- ``map`` on a Series with datetime64 values may return int64 dtypes rather than int32
.. ipython:: python
s = Series(date_range('2011-01-02T00:00', '2011-01-02T02:00', freq='H').tz_localize('Asia/Tokyo'))
s
Previous Behavior:
.. code-block:: ipython
In [9]: s.map(lambda x: x.hour)
Out[9]:
0 0
1 1
2 2
dtype: int32
New Behavior:
.. ipython:: python
s.map(lambda x: x.hour)
.. _whatsnew_0200.api:
- ``CParserError`` has been renamed to ``ParserError`` in ``pd.read_csv`` and will be removed in the future (:issue:`12665`)
- ``SparseArray.cumsum()`` and ``SparseSeries.cumsum()`` will now always return ``SparseArray`` and ``SparseSeries`` respectively (:issue:`12855`)
Other API Changes
^^^^^^^^^^^^^^^^^
- :ref:`DatetimeIndex Partial String Indexing <timeseries.partialindexing>` now works as exact match provided that string resolution coincides with index resolution (:issue:`14826`).
.. ipython:: python
df = DataFrame({'a': [1, 2, 3]}, DatetimeIndex(['2011-12-31 23:59:59',
'2012-01-01 00:00:00',
'2012-01-01 00:00:01']))
Previous Behavior:
.. code-block:: ipython
In [4]: df['2011-12-31 23:59:59']
Out[4]:
a
2011-12-31 23:59:59 1
In [5]: df['a']['2011-12-31 23:59:59']
Out[5]:
2011-12-31 23:59:59 1
Name: a, dtype: int64
New Behavior:
.. code-block:: ipython
In [4]: df['2011-12-31 23:59:59']
KeyError: '2011-12-31 23:59:59'
In [5]: df['a']['2011-12-31 23:59:59']
Out[5]: 1
.. _whatsnew_0200.deprecations:
Deprecations
^^^^^^^^^^^^
- ``Series.repeat()`` has deprecated the ``reps`` parameter in favor of ``repeats`` (:issue:`12662`)
- ``Index.repeat()`` and ``MultiIndex.repeat()`` have deprecated the ``n`` parameter in favor of ``repeats`` (:issue:`12662`)
- ``Categorical.searchsorted()`` and ``Series.searchsorted()`` have deprecated the ``v`` parameter in favor of ``value`` (:issue:`12662`)
- ``TimedeltaIndex.searchsorted()``, ``DatetimeIndex.searchsorted()``, and ``PeriodIndex.searchsorted()`` have deprecated the ``key`` parameter in favor of ``value`` (:issue:`12662`)
.. _whatsnew_0200.prior_deprecations:
Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`)
- ``SparseArray.to_dense()`` has deprecated the ``fill`` parameter, as that parameter was not being respected (:issue:`14647`)
- ``SparseSeries.to_dense()`` has deprecated the ``sparse_only`` parameter (:issue:`14647`)
.. _whatsnew_0200.performance:
Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Improved performance of ``pd.wide_to_long()`` (:issue:`14779`)
- Increased performance of ``pd.factorize()`` by releasing the GIL with ``object`` dtype when inferred as strings (:issue:`14859`)
.. _whatsnew_0200.bug_fixes:
Bug Fixes
~~~~~~~~~
- Bug in ``TimedeltaIndex`` addition where overflow was being allowed without error (:issue:`14816`)
- Bug in ``astype()`` where ``inf`` values were incorrectly converted to integers. Now raises error now with ``astype()`` for Series and DataFrames (:issue:`14265`)
- Require at least 0.23 version of cython to avoid problems with character encodings (:issue:`14699`)