v0.21.0.txt

.. _whatsnew_0210:

v0.21.0 (???)
-------------

This is a major release from 0.20.x and includes a number of API changes, deprecations, 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:

- Integration with `Apache Parquet <https://parquet.apache.org/>`__, including a new top-level :func:`read_parquet` and :func:`DataFrame.to_parquet` method, see :ref:`here <io.parquet>`.

Check the :ref:`API Changes <whatsnew_0210.api_breaking>` and :ref:`deprecations <whatsnew_0210.deprecations>` before updating.

.. contents:: What's new in v0.21.0
    :local:
    :backlinks: none

.. _whatsnew_0210.enhancements:

New features
~~~~~~~~~~~~

- Support for `PEP 519 -- Adding a file system path protocol
  <https://www.python.org/dev/peps/pep-0519/>`_ on most readers and writers (:issue:`13823`)
- Added ``__fspath__`` method to :class:`~pandas.HDFStore`, :class:`~pandas.ExcelFile`,
  and :class:`~pandas.ExcelWriter` to work properly with the file system path protocol (:issue:`13823`)
- Added ``skipna`` parameter to :func:`~pandas.api.types.infer_dtype` to
  support type inference in the presence of missing values (:issue:`17059`).

.. _whatsnew_0210.enhancements.infer_objects:

``infer_objects`` type conversion
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :meth:`DataFrame.infer_objects` and :meth:`Series.infer_objects`
methods have been added to perform dtype inference on object columns, replacing
some of the functionality of the deprecated ``convert_objects``
method. See the documentation :ref:`here <basics.object_conversion>`
for more details. (:issue:`11221`)

This method only performs soft conversions on object columns, converting Python objects
to native types, but not any coercive conversions.  For example:

.. ipython:: python

   df = pd.DataFrame({'A': [1, 2, 3],
                      'B': np.array([1, 2, 3], dtype='object'),
                      'C': ['1', '2', '3']})
   df.dtypes
   df.infer_objects().dtypes

Note that column ``'C'`` was not converted - only scalar numeric types
will be inferred to a new type.  Other types of conversion should be accomplished
using the :func:`to_numeric` function (or :func:`to_datetime`, :func:`to_timedelta`).

.. ipython:: python

   df = df.infer_objects()
   df['C'] = pd.to_numeric(df['C'], errors='coerce')
   df.dtypes

.. _whatsnew_0210.enhancements.attribute_access:

Improved warnings when attempting to create columns
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

New users are often flummoxed by the relationship between column operations and attribute
access on ``DataFrame`` instances (:issue:`5904` & :issue:`7175`). Two specific instances
of this confusion include attempting to create a new column by setting into an attribute:

.. code-block:: ipython

  In[1]: df = pd.DataFrame({'one': [1., 2., 3.]})
  In[2]: df.two = [4, 5, 6]

This does not raise any obvious exceptions, but also does not create a new column:

.. code-block:: ipython

  In[3]: df
  Out[3]:
      one
  0  1.0
  1  2.0
  2  3.0

The second source of confusion is creating a column whose name collides with a method or
attribute already in the instance namespace:

.. code-block:: ipython

  In[4]: df['sum'] = [5., 7., 9.]

This does not permit that column to be accessed as an attribute:

.. code-block:: ipython

  In[5]: df.sum
  Out[5]:
  <bound method DataFrame.sum of    one  sum
  0  1.0  5.0
  1  2.0  7.0
  2  3.0  9.0>

Both of these now raise a ``UserWarning`` about the potential for unexpected behavior. See :ref:`Attribute Access <indexing.attribute_access>`.

.. _whatsnew_0210.enhancements.other:

Other Enhancements
^^^^^^^^^^^^^^^^^^

- The ``validate`` argument for :func:`merge` function now checks whether a merge is one-to-one, one-to-many, many-to-one, or many-to-many. If a merge is found to not be an example of specified merge type, an exception of type ``MergeError`` will be raised. For more, see :ref:`here <merging.validation>` (:issue:`16270`)
- :func:`Series.to_dict` and :func:`DataFrame.to_dict` now support an ``into`` keyword which allows you to specify the ``collections.Mapping`` subclass that you would like returned.  The default is ``dict``, which is backwards compatible. (:issue:`16122`)
- :func:`RangeIndex.append` now returns a ``RangeIndex`` object when possible (:issue:`16212`)
- :func:`Series.rename_axis` and :func:`DataFrame.rename_axis` with ``inplace=True`` now return ``None`` while renaming the axis inplace. (:issue:`15704`)
- :func:`Series.set_axis` and :func:`DataFrame.set_axis` now support the ``inplace`` parameter. (:issue:`14636`)
- :func:`Series.to_pickle` and :func:`DataFrame.to_pickle` have gained a ``protocol`` parameter (:issue:`16252`). By default, this parameter is set to `HIGHEST_PROTOCOL <https://docs.python.org/3/library/pickle.html#data-stream-format>`__
- :func:`api.types.infer_dtype` now infers decimals. (:issue:`15690`)
- :func:`read_feather` has gained the ``nthreads`` parameter for multi-threaded operations (:issue:`16359`)
- :func:`DataFrame.clip()` and :func:`Series.clip()` have gained an ``inplace`` argument. (:issue:`15388`)
- :func:`crosstab` has gained a ``margins_name`` parameter to define the name of the row / column that will contain the totals when ``margins=True``. (:issue:`15972`)
- :func:`DataFrame.select_dtypes` now accepts scalar values for include/exclude as well as list-like. (:issue:`16855`)
- :func:`date_range` now accepts 'YS' in addition to 'AS' as an alias for start of year (:issue:`9313`)
- :func:`date_range` now accepts 'Y' in addition to 'A' as an alias for end of year (:issue:`9313`)
- Integration with `Apache Parquet <https://parquet.apache.org/>`__, including a new top-level :func:`read_parquet` and :func:`DataFrame.to_parquet` method, see :ref:`here <io.parquet>`.
- :func:`DataFrame.add_prefix` and :func:`DataFrame.add_suffix` now accept strings containing the '%' character. (:issue:`17151`)

.. _whatsnew_0210.api_breaking:

Backwards incompatible API changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. _whatsnew_0210.api_breaking.pandas_eval:

Improved error handling during item assignment in pd.eval
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:func:`eval` will now raise a ``ValueError`` when item assignment malfunctions, or
inplace operations are specified, but there is no item assignment in the expression (:issue:`16732`)

.. ipython:: python

   arr = np.array([1, 2, 3])

Previously, if you attempted the following expression, you would get a not very helpful error message:

.. code-block:: ipython

  In [3]: pd.eval("a = 1 + 2", target=arr, inplace=True)
  ...
  IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`)
  and integer or boolean arrays are valid indices

This is a very long way of saying numpy arrays don't support string-item indexing. With this
change, the error message is now this:

.. code-block:: python

   In [3]: pd.eval("a = 1 + 2", target=arr, inplace=True)
   ...
   ValueError: Cannot assign expression output to target

It also used to be possible to evaluate expressions inplace, even if there was no item assignment:

.. code-block:: ipython

  In [4]: pd.eval("1 + 2", target=arr, inplace=True)
  Out[4]: 3

However, this input does not make much sense because the output is not being assigned to
the target. Now, a ``ValueError`` will be raised when such an input is passed in:

.. code-block:: ipython

   In [4]: pd.eval("1 + 2", target=arr, inplace=True)
   ...
   ValueError: Cannot operate inplace if there is no assignment

Dtype Conversions
^^^^^^^^^^^^^^^^^

- Previously assignments, ``.where()`` and ``.fillna()`` with a ``bool`` assignment, would coerce to
  same the type (e.g. int / float), or raise for datetimelikes. These will now preseve the bools with ``object`` dtypes. (:issue:`16821`).

  .. ipython:: python

     s = Series([1, 2, 3])

  .. code-block:: python

     In [5]: s[1] = True

     In [6]: s
     Out[6]:
     0    1
     1    1
     2    3
     dtype: int64

  New Behavior

  .. ipython:: python

     s[1] = True
     s

- Previously, as assignment to a datetimelike with a non-datetimelike would coerce the
  non-datetime-like item being assigned (:issue:`14145`).

  .. ipython:: python

     s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2012-01-01')])

  .. code-block:: python

     In [1]: s[1] = 1

     In [2]: s
     Out[2]:
     0   2011-01-01 00:00:00.000000000
     1   1970-01-01 00:00:00.000000001
     dtype: datetime64[ns]

  These now coerce to ``object`` dtype.

  .. ipython:: python

     s[1] = 1
     s

- Inconsistent behavior in ``.where()`` with datetimelikes which would raise rather than coerce to ``object`` (:issue:`16402`)
- Bug in assignment against ``int64`` data with ``np.ndarray`` with ``float64`` dtype may keep ``int64`` dtype (:issue:`14001`)

.. _whatsnew_0210.api.na_changes:

NA naming Changes
^^^^^^^^^^^^^^^^^

In order to promote more consistency among the pandas API, we have added additional top-level
functions :func:`isna` and :func:`notna` that are aliases for :func:`isnull` and :func:`notnull`.
The naming scheme is now more consistent with methods like ``.dropna()`` and ``.fillna()``. Furthermore
in all cases where ``.isnull()`` and ``.notnull()`` methods are defined, these have additional methods
named ``.isna()`` and ``.notna()``, these are included for classes ``Categorical``,
``Index``, ``Series``, and ``DataFrame``. (:issue:`15001`).

The configuration option ``pd.options.mode.use_inf_as_null`` is deprecated, and ``pd.options.mode.use_inf_as_na`` is added as a replacement.

.. _whatsnew_0210.api:

Other API Changes
^^^^^^^^^^^^^^^^^

- Support has been dropped for Python 3.4 (:issue:`15251`)
- Support has been dropped for bottleneck < 1.0.0 (:issue:`15214`)
- The Categorical constructor no longer accepts a scalar for the ``categories`` keyword. (:issue:`16022`)
- Accessing a non-existent attribute on a closed :class:`~pandas.HDFStore` will now
  raise an ``AttributeError`` rather than a ``ClosedFileError`` (:issue:`16301`)
- :func:`read_csv` now treats ``'null'`` strings as missing values by default (:issue:`16471`)
- :func:`read_csv` now treats ``'n/a'`` strings as missing values by default (:issue:`16078`)
- :class:`pandas.HDFStore`'s string representation is now faster and less detailed. For the previous behavior, use ``pandas.HDFStore.info()``. (:issue:`16503`).
- Compression defaults in HDF stores now follow pytable standards. Default is no compression and if ``complib`` is missing and ``complevel`` > 0 ``zlib`` is used (:issue:`15943`)
- ``Index.get_indexer_non_unique()`` now returns a ndarray indexer rather than an ``Index``; this is consistent with ``Index.get_indexer()`` (:issue:`16819`)
- Removed the ``@slow`` decorator from ``pandas.util.testing``, which caused issues for some downstream packages' test suites. Use ``@pytest.mark.slow`` instead, which achieves the same thing (:issue:`16850`)
- Moved definition of ``MergeError`` to the ``pandas.errors`` module.
- The signature of :func:`Series.set_axis` and :func:`DataFrame.set_axis` has been changed from ``set_axis(axis, labels)`` to ``set_axis(labels, axis=0)``, for consistency with the rest of the API. The old signature is deprecated and will show a ``FutureWarning`` (:issue:`14636`)


.. _whatsnew_0210.deprecations:

Deprecations
~~~~~~~~~~~~
- :func:`read_excel()` has deprecated ``sheetname`` in favor of ``sheet_name`` for consistency with ``.to_excel()`` (:issue:`10559`).

- ``pd.options.html.border`` has been deprecated in favor of ``pd.options.display.html.border`` (:issue:`15793`).

.. _whatsnew_0210.prior_deprecations:

Removal of prior version deprecations/changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- :func:`read_excel()` has dropped the ``has_index_names`` parameter (:issue:`10967`)
- The ``pd.options.display.height`` configuration has been dropped (:issue:`3663`)
- The ``pd.options.display.line_width`` configuration has been dropped (:issue:`2881`)
- The ``pd.options.display.mpl_style`` configuration has been dropped (:issue:`12190`)
- ``Index`` has dropped the ``.sym_diff()`` method in favor of ``.symmetric_difference()`` (:issue:`12591`)
- ``Categorical`` has dropped the ``.order()`` and ``.sort()`` methods in favor of ``.sort_values()`` (:issue:`12882`)
- :func:`eval` and :func:`DataFrame.eval` have changed the default of ``inplace`` from ``None`` to ``False`` (:issue:`11149`)
- The function ``get_offset_name`` has been dropped in favor of the ``.freqstr`` attribute for an offset (:issue:`11834`)


.. _whatsnew_0210.performance:

Performance Improvements
~~~~~~~~~~~~~~~~~~~~~~~~

- Improved performance of instantiating :class:`SparseDataFrame` (:issue:`16773`)


.. _whatsnew_0210.bug_fixes:

Bug Fixes
~~~~~~~~~


Conversion
^^^^^^^^^^

- Bug in assignment against datetime-like data with ``int`` may incorrectly converte to datetime-like (:issue:`14145`)
- Bug in assignment against ``int64`` data with ``np.ndarray`` with ``float64`` dtype may keep ``int64`` dtype (:issue:`14001`)


Indexing
^^^^^^^^

- When called with a null slice (e.g. ``df.iloc[:]``), the ``.iloc`` and ``.loc`` indexers return a shallow copy of the original object. Previously they returned the original object. (:issue:`13873`).
- When called on an unsorted ``MultiIndex``, the ``loc`` indexer now will raise ``UnsortedIndexError`` only if proper slicing is used on non-sorted levels (:issue:`16734`).
- Fixes regression in 0.20.3 when indexing with a string on a ``TimedeltaIndex`` (:issue:`16896`).
- Fixed :func:`TimedeltaIndex.get_loc` handling of ``np.timedelta64`` inputs (:issue:`16909`).
- Fix :func:`MultiIndex.sort_index` ordering when ``ascending`` argument is a list, but not all levels are specified, or are in a different order (:issue:`16934`).
- Fixes bug where indexing with ``np.inf`` caused an ``OverflowError`` to be raised (:issue:`16957`)
- Bug in reindexing on an empty ``CategoricalIndex`` (:issue:`16770`)
- Fixes ``DataFrame.loc`` for setting with alignment and tz-aware ``DatetimeIndex`` (:issue:`16889`)
- Avoids ``IndexError`` when passing an Index or Series to ``.iloc`` with older numpy (:issue:`17193`)

I/O
^^^

- Bug in :func:`read_csv` in which columns were not being thoroughly de-duplicated (:issue:`17060`)
- Bug in :func:`read_csv` in which specified column names were not being thoroughly de-duplicated (:issue:`17095`)
- Bug in :func:`read_csv` in which non integer values for the header argument generated an unhelpful / unrelated error message (:issue:`16338`)
- Bug in :func:`read_csv` in which memory management issues in exception handling, under certain conditions, would cause the interpreter to segfault (:issue:`14696`, :issue:`16798`).
- Bug in :func:`read_csv` when called with ``low_memory=False`` in which a CSV with at least one column > 2GB in size would incorrectly raise a ``MemoryError`` (:issue:`16798`).
- Bug in :func:`read_csv` when called with a single-element list ``header`` would return a ``DataFrame`` of all NaN values (:issue:`7757`)
- Bug in :func:`read_stata` where value labels could not be read when using an iterator (:issue:`16923`)
- Bug in :func:`read_html` where import check fails when run in multiple threads (:issue:`16928`)

Plotting
^^^^^^^^
- Bug in plotting methods using ``secondary_y`` and ``fontsize`` not setting secondary axis font size (:issue:`12565`)


Groupby/Resample/Rolling
^^^^^^^^^^^^^^^^^^^^^^^^

- Bug in ``DataFrame.resample(...).size()`` where an empty ``DataFrame`` did not return a ``Series`` (:issue:`14962`)
- Bug in :func:`infer_freq` causing indices with 2-day gaps during the working week to be wrongly inferred as business daily (:issue:`16624`)
- Bug in ``.rolling(...).quantile()`` which incorrectly used different defaults than :func:`Series.quantile()` and :func:`DataFrame.quantile()` (:issue:`9413`, :issue:`16211`)
- Bug in ``groupby.transform()`` that would coerce boolean dtypes back to float (:issue:`16875`)
- Bug in ``Series.resample(...).apply()`` where an empty ``Series`` modified the source index and did not return the name of a ``Series`` (:issue:`14313`)
- Bug in ``.rolling(...).apply(...)`` with a ``DataFrame`` with a ``DatetimeIndex``, a ``window`` of a timedelta-convertible and ``min_periods >= 1` (:issue:`15305`)


Sparse
^^^^^^

- Bug in ``SparseSeries`` raises ``AttributeError`` when a dictionary is passed in as data (:issue:`16905`)
- Bug in :func:`SparseDataFrame.fillna` not filling all NaNs when frame was instantiated from SciPy sparse matrix (:issue:`16112`)


Reshaping
^^^^^^^^^
- Joining/Merging with a non unique ``PeriodIndex`` raised a ``TypeError`` (:issue:`16871`)
- Bug in :func:`crosstab` where non-aligned series of integers were casted to float (:issue:`17005`)
- Bug in merging with categorical dtypes with datetimelikes incorrectly raised a ``TypeError`` (:issue:`16900`)
- Bug when using :func:`isin` on a large object series and large comparison array (:issue:`16012`)
- Fixes regression from 0.20, :func:`Series.aggregate` and :func:`DataFrame.aggregate` allow dictionaries as return values again (:issue:`16741`)
- Fixes dtype of result with integer dtype input, from :func:`pivot_table` when called with ``margins=True`` (:issue:`17013`)
- Bug in :func:`crosstab` where passing two ``Series`` with the same name raised a ``KeyError`` (:issue:`13279`)

Numeric
^^^^^^^
- Bug in ``.clip()`` with ``axis=1`` and a list-like for ``threshold`` is passed; previously this raised ``ValueError`` (:issue:`15390`)


Categorical
^^^^^^^^^^^
- Bug in :func:`Series.isin` when called with a categorical (:issue`16639`)


Other
^^^^^
- Bug in :func:`eval` where the ``inplace`` parameter was being incorrectly handled (:issue:`16732`)
- Bug in ``.isin()`` in which checking membership in empty ``Series`` objects raised an error (:issue:`16991`)
- Bug in :func:`unique` where checking a tuple of strings raised a ``TypeError`` (:issue:`17108`)