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Merge pull request #11059 from jorisvandenbossche/whatsnew017
DOC: clean up 0.17 whatsnew
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‎doc/source/whatsnew/v0.17.0.txt

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@@ -21,9 +21,14 @@ users upgrade to this version.
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After installing pandas-datareader, you can easily change your imports:
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.. code-block:: Python
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.. code-block:: python
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from pandas.io import data, wb
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becomes
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.. code-block:: python
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from pandas.io import data, wb # becomes
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from pandas_datareader import data, wb
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Highlights include:
@@ -53,44 +58,60 @@ Check the :ref:`API Changes <whatsnew_0170.api>` and :ref:`deprecations <whatsne
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New features
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~~~~~~~~~~~~
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- ``merge`` now accepts the argument ``indicator`` which adds a Categorical-type column (by default called ``_merge``) to the output object that takes on the values (:issue:`8790`)
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.. _whatsnew_0170.tz:
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=================================== ================
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Observation Origin ``_merge`` value
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=================================== ================
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Merge key only in ``'left'`` frame ``left_only``
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Merge key only in ``'right'`` frame ``right_only``
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Merge key in both frames ``both``
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=================================== ================
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Datetime with TZ
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^^^^^^^^^^^^^^^^
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.. ipython:: python
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We are adding an implementation that natively supports datetime with timezones. A ``Series`` or a ``DataFrame`` column previously
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*could* be assigned a datetime with timezones, and would work as an ``object`` dtype. This had performance issues with a large
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number rows. See the :ref:`docs <timeseries.timezone_series>` for more details. (:issue:`8260`, :issue:`10763`, :issue:`11034`).
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df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
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df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
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pd.merge(df1, df2, on='col1', how='outer', indicator=True)
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The new implementation allows for having a single-timezone across all rows, with operations in a performant manner.
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For more, see the :ref:`updated docs <merging.indicator>`
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.. ipython:: python
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- ``DataFrame`` has gained the ``nlargest`` and ``nsmallest`` methods (:issue:`10393`)
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- SQL io functions now accept a SQLAlchemy connectable. (:issue:`7877`)
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- Enable writing complex values to HDF stores when using table format (:issue:`10447`)
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- Enable reading gzip compressed files via URL, either by explicitly setting the compression parameter or by inferring from the presence of the HTTP Content-Encoding header in the response (:issue:`8685`)
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- Add a ``limit_direction`` keyword argument that works with ``limit`` to enable ``interpolate`` to fill ``NaN`` values forward, backward, or both (:issue:`9218` and :issue:`10420`)
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df = DataFrame({'A' : date_range('20130101',periods=3),
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'B' : date_range('20130101',periods=3,tz='US/Eastern'),
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'C' : date_range('20130101',periods=3,tz='CET')})
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df
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df.dtypes
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.. ipython:: python
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.. ipython:: python
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ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13])
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ser.interpolate(limit=1, limit_direction='both')
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df.B
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df.B.dt.tz_localize(None)
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- Round DataFrame to variable number of decimal places (:issue:`10568`).
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This uses a new-dtype representation as well, that is very similar in look-and-feel to its numpy cousin ``datetime64[ns]``
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.. ipython :: python
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.. ipython:: python
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df = pd.DataFrame(np.random.random([3, 3]), columns=['A', 'B', 'C'],
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index=['first', 'second', 'third'])
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df
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df.round(2)
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df.round({'A': 0, 'C': 2})
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df['B'].dtype
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type(df['B'].dtype)
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.. note::
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There is a slightly different string repr for the underlying ``DatetimeIndex`` as a result of the dtype changes, but
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functionally these are the same.
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Previous Behavior:
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.. code-block:: python
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In [1]: pd.date_range('20130101',periods=3,tz='US/Eastern')
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Out[1]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
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'2013-01-03 00:00:00-05:00'],
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dtype='datetime64[ns]', freq='D', tz='US/Eastern')
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In [2]: pd.date_range('20130101',periods=3,tz='US/Eastern').dtype
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Out[2]: dtype('<M8[ns]')
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New Behavior:
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.. ipython:: python
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pd.date_range('20130101',periods=3,tz='US/Eastern')
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pd.date_range('20130101',periods=3,tz='US/Eastern').dtype
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.. _whatsnew_0170.gil:
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@@ -286,6 +307,46 @@ has been changed to make this keyword unnecessary - the change is shown below.
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Other enhancements
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^^^^^^^^^^^^^^^^^^
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- ``merge`` now accepts the argument ``indicator`` which adds a Categorical-type column (by default called ``_merge``) to the output object that takes on the values (:issue:`8790`)
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=================================== ================
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Observation Origin ``_merge`` value
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=================================== ================
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Merge key only in ``'left'`` frame ``left_only``
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Merge key only in ``'right'`` frame ``right_only``
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Merge key in both frames ``both``
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=================================== ================
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.. ipython:: python
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df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
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df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
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pd.merge(df1, df2, on='col1', how='outer', indicator=True)
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For more, see the :ref:`updated docs <merging.indicator>`
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- ``DataFrame`` has gained the ``nlargest`` and ``nsmallest`` methods (:issue:`10393`)
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- SQL io functions now accept a SQLAlchemy connectable. (:issue:`7877`)
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- Enable writing complex values to HDF stores when using table format (:issue:`10447`)
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- Enable reading gzip compressed files via URL, either by explicitly setting the compression parameter or by inferring from the presence of the HTTP Content-Encoding header in the response (:issue:`8685`)
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- Add a ``limit_direction`` keyword argument that works with ``limit`` to enable ``interpolate`` to fill ``NaN`` values forward, backward, or both (:issue:`9218` and :issue:`10420`)
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.. ipython:: python
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ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13])
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ser.interpolate(limit=1, limit_direction='both')
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- Round DataFrame to variable number of decimal places (:issue:`10568`).
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.. ipython :: python
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df = pd.DataFrame(np.random.random([3, 3]), columns=['A', 'B', 'C'],
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index=['first', 'second', 'third'])
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df
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df.round(2)
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df.round({'A': 0, 'C': 2})
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- ``pd.read_sql`` and ``to_sql`` can accept database URI as ``con`` parameter (:issue:`10214`)
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- Enable ``pd.read_hdf`` to be used without specifying a key when the HDF file contains a single dataset (:issue:`10443`)
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- Enable writing Excel files in :ref:`memory <_io.excel_writing_buffer>` using StringIO/BytesIO (:issue:`7074`)
@@ -321,13 +382,15 @@ Other enhancements
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Timestamp('2014')
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DatetimeIndex(['2012Q2', '2014'])
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.. note:: If you want to perform calculations based on today's date, use ``Timestamp.now()`` and ``pandas.tseries.offsets``.
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.. note::
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.. ipython:: python
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If you want to perform calculations based on today's date, use ``Timestamp.now()`` and ``pandas.tseries.offsets``.
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import pandas.tseries.offsets as offsets
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Timestamp.now()
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Timestamp.now() + offsets.DateOffset(years=1)
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.. ipython:: python
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import pandas.tseries.offsets as offsets
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Timestamp.now()
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Timestamp.now() + offsets.DateOffset(years=1)
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- ``to_datetime`` can now accept ``yearfirst`` keyword (:issue:`7599`)
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@@ -411,6 +474,9 @@ Other enhancements
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pd.concat([foo, bar, baz], 1)
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- Allow passing `kwargs` to the interpolation methods (:issue:`10378`).
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- Improved error message when concatenating an empty iterable of dataframes (:issue:`9157`)
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.. _whatsnew_0170.api:
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Furthermore, ``pd.to_timedelta`` has gained a similar API, of ``errors='raise'|'ignore'|'coerce'``, and the ``coerce`` keyword
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has been deprecated in favor of ``errors='coerce'``.
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.. _whatsnew_0170.tz:
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Datetime with TZ
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~~~~~~~~~~~~~~~~
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We are adding an implementation that natively supports datetime with timezones. A ``Series`` or a ``DataFrame`` column previously
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*could* be assigned a datetime with timezones, and would work as an ``object`` dtype. This had performance issues with a large
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number rows. See the :ref:`docs <timeseries.timezone_series>` for more details. (:issue:`8260`, :issue:`10763`, :issue:`11034`).
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The new implementation allows for having a single-timezone across all rows, with operations in a performant manner.
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.. ipython:: python
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df = DataFrame({'A' : date_range('20130101',periods=3),
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'B' : date_range('20130101',periods=3,tz='US/Eastern'),
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'C' : date_range('20130101',periods=3,tz='CET')})
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df
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df.dtypes
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.. ipython:: python
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df.B
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df.B.dt.tz_localize(None)
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This uses a new-dtype representation as well, that is very similar in look-and-feel to its numpy cousin ``datetime64[ns]``
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.. ipython:: python
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df['B'].dtype
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type(df['B'].dtype)
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.. note::
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There is a slightly different string repr for the underlying ``DatetimeIndex`` as a result of the dtype changes, but
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functionally these are the same.
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Previous Behavior:
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.. code-block:: python
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In [1]: pd.date_range('20130101',periods=3,tz='US/Eastern')
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Out[1]: DatetimeIndex(['2013-01-01 00:00:00-05:00', '2013-01-02 00:00:00-05:00',
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'2013-01-03 00:00:00-05:00'],
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dtype='datetime64[ns]', freq='D', tz='US/Eastern')
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In [2]: pd.date_range('20130101',periods=3,tz='US/Eastern').dtype
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Out[2]: dtype('<M8[ns]')
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New Behavior:
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.. ipython:: python
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pd.date_range('20130101',periods=3,tz='US/Eastern')
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pd.date_range('20130101',periods=3,tz='US/Eastern').dtype
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.. _whatsnew_0170.api_breaking.convert_objects:
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- Line and kde plot with ``subplots=True`` now uses default colors, not all black. Specify ``color='k'`` to draw all lines in black (:issue:`9894`)
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- Calling the ``.value_counts()`` method on a Series with ``categorical`` dtype now returns a Series with a ``CategoricalIndex`` (:issue:`10704`)
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- Allow passing `kwargs` to the interpolation methods (:issue:`10378`).
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- The metadata properties of subclasses of pandas objects will now be serialized (:issue:`10553`).
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- ``groupby`` using ``Categorical`` follows the same rule as ``Categorical.unique`` described above (:issue:`10508`)
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- Improved error message when concatenating an empty iterable of dataframes (:issue:`9157`)
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- When constructing ``DataFrame`` with an array of ``complex64`` dtype that meant the corresponding column was automatically promoted to the ``complex128`` dtype. Pandas will now preserve the itemsize of the input for complex data (:issue:`10952`)
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- When constructing ``DataFrame`` with an array of ``complex64`` dtype previously meant the corresponding column
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was automatically promoted to the ``complex128`` dtype. Pandas will now preserve the itemsize of the input for complex data (:issue:`10952`)
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- ``NaT``'s methods now either raise ``ValueError``, or return ``np.nan`` or ``NaT`` (:issue:`9513`)
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Deprecations
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^^^^^^^^^^^^
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.. note:: These indexing function have been deprecated in the documentation since 0.11.0.
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- For ``Series`` the following indexing functions are deprecated (:issue:`10177`).
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===================== =================================
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``.icol(j)`` ``.iloc[:, j]``
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===================== =================================
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.. note:: These indexing function have been deprecated in the documentation since 0.11.0.
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- ``Categorical.name`` was deprecated to make ``Categorical`` more ``numpy.ndarray`` like. Use ``Series(cat, name="whatever")`` instead (:issue:`10482`).
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- Setting missing values (NaN) in a ``Categorical``'s ``categories`` will issue a warning (:issue:`10748`). You can still have missing values in the ``values``.
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- ``drop_duplicates`` and ``duplicated``'s ``take_last`` keyword was deprecated in favor of ``keep``. (:issue:`6511`, :issue:`8505`)
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Removal of prior version deprecations/changes
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- Remove use of some deprecated numpy comparison operations, mainly in tests. (:issue:`10569`)
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- Removal of ``na_last`` parameters from ``Series.order()`` and ``Series.sort()``, in favor of ``na_position``, xref (:issue:`5231`)
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- Remove of ``percentile_width`` from ``.describe()``, in favor of ``percentiles``. (:issue:`7088`)
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- Removal of ``colSpace`` parameter from ``DataFrame.to_string()``, in favor of ``col_space``, circa 0.8.0 version.
@@ -1089,3 +1099,4 @@ Bug Fixes
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- Bug in ``Index`` arithmetic may result in incorrect class (:issue:`10638`)
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- Bug in ``date_range`` results in empty if freq is negative annualy, quarterly and monthly (:issue:`11018`)
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- Bug in ``DatetimeIndex`` cannot infer negative freq (:issue:`11018`)
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- Remove use of some deprecated numpy comparison operations, mainly in tests. (:issue:`10569`)

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