v0.18.0.txt

.. _whatsnew_0180:

v0.18.0 (February ??, 2016)
---------------------------

This is a major release from 0.17.1 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.

.. warning::

   pandas >= 0.18.0 no longer supports compatibility with Python version 2.6
   and 3.3 (:issue:`7718`, :issue:`11273`)

.. warning::

   ``numexpr`` version 2.4.4 will now show a warning and not be used as a computation back-end for pandas because of some buggy behavior. This does not affect other versions (>= 2.1 and >= 2.4.6). (:issue:`12489`)

Highlights include:

- Moving and expanding window functions are now methods on Series and DataFrame,
  similar to ``.groupby``, see :ref:`here <whatsnew_0180.enhancements.moments>`.
- Adding support for a ``RangeIndex`` as a specialized form of the ``Int64Index``
  for memory savings, see :ref:`here <whatsnew_0180.enhancements.rangeindex>`.
- API breaking change to the ``.resample`` method to make it more ``.groupby``
  like, see :ref:`here <whatsnew_0180.breaking.resample>`.
- Removal of support for positional indexing with floats, which was deprecated
  since 0.14.0. This will now raise a ``TypeError``, see :ref:`here <whatsnew_0180.float_indexers>`.
- The ``.to_xarray()`` function has been added for compatibility with the
  `xarray package <http://xarray.pydata.org/en/stable/>`__, see :ref:`here <whatsnew_0180.enhancements.xarray>`.
- The ``read_sas`` function has been enhanced to read ``sas7bdat`` files, see :ref:`here <whatsnew_0180.enhancements.sas>`.
- Addition of the :ref:`.str.extractall() method <whatsnew_0180.enhancements.extract>`,
  and API changes to the :ref:`.str.extract() method <whatsnew_0180.enhancements.extract>`
  and :ref:`.str.cat() method <whatsnew_0180.enhancements.strcat>`.
- ``pd.test()`` top-level nose test runner is available (:issue:`4327`).

Check the :ref:`API Changes <whatsnew_0180.api_breaking>` and :ref:`deprecations <whatsnew_0180.deprecations>` before updating.

.. contents:: What's new in v0.18.0
    :local:
    :backlinks: none

.. _whatsnew_0180.enhancements:

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

.. _whatsnew_0180.enhancements.moments:

Window functions are now methods
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Window functions have been refactored to be methods on ``Series/DataFrame`` objects, rather than top-level functions, which are now deprecated. This allows these window-type functions, to have a similar API to that of ``.groupby``. See the full documentation :ref:`here <stats.moments>` (:issue:`11603`)

.. ipython:: python

   np.random.seed(1234)
   df = DataFrame({'A' : range(10), 'B' : np.random.randn(10)})
   df

Previous Behavior:

.. code-block:: python

   In [8]: pd.rolling_mean(df,window=3)
           FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with
                          DataFrame.rolling(window=3,center=False).mean()
   Out[8]:
       A         B
   0 NaN       NaN
   1 NaN       NaN
   2   1  0.237722
   3   2 -0.023640
   4   3  0.133155
   5   4 -0.048693
   6   5  0.342054
   7   6  0.370076
   8   7  0.079587
   9   8 -0.954504

New Behavior:

.. ipython:: python

   r = df.rolling(window=3)

These show a descriptive repr

.. ipython:: python

   r
with tab-completion of available methods and properties.

.. code-block:: python

   In [9]: r.
   r.A           r.agg         r.apply       r.count       r.exclusions  r.max         r.median      r.name        r.skew        r.sum
   r.B           r.aggregate   r.corr        r.cov         r.kurt        r.mean        r.min         r.quantile    r.std         r.var

The methods operate on the ``Rolling`` object itself

.. ipython:: python

   r.mean()

They provide getitem accessors

.. ipython:: python

   r['A'].mean()

And multiple aggregations

.. ipython:: python

   r.agg({'A' : ['mean','std'],
          'B' : ['mean','std']})

.. _whatsnew_0180.enhancements.rename:

Changes to rename
^^^^^^^^^^^^^^^^^

``Series.rename`` and ``NDFrame.rename_axis`` can now take a scalar or list-like
argument for altering the Series or axis *name*, in addition to their old behaviors of altering labels. (:issue:`9494`, :issue:`11965`)

.. ipython:: python

   s = pd.Series(np.random.randn(5))
   s.rename('newname')

.. ipython:: python

   df = pd.DataFrame(np.random.randn(5, 2))
   (df.rename_axis("indexname")
      .rename_axis("columns_name", axis="columns"))

The new functionality works well in method chains. Previously these methods only accepted functions or dicts mapping a *label* to a new label.
This continues to work as before for function or dict-like values.


.. _whatsnew_0180.enhancements.rangeindex:

Range Index
^^^^^^^^^^^

A ``RangeIndex`` has been added to the ``Int64Index`` sub-classes to support a memory saving alternative for common use cases. This has a similar implementation to the python ``range`` object (``xrange`` in python 2), in that it only stores the start, stop, and step values for the index. It will transparently interact with the user API, converting to ``Int64Index`` if needed.

This will now be the default constructed index for ``NDFrame`` objects, rather than previous an ``Int64Index``. (:issue:`939`, :issue:`12070`, :issue:`12071`, :issue:`12109`, :issue:`12888`)

Previous Behavior:

.. code-block:: python

   In [3]: s = Series(range(1000))

   In [4]: s.index
   Out[4]:
   Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
               ...
               990, 991, 992, 993, 994, 995, 996, 997, 998, 999], dtype='int64', length=1000)

   In [6]: s.index.nbytes
   Out[6]: 8000


New Behavior:

.. ipython:: python

   s = Series(range(1000))
   s.index
   s.index.nbytes

.. _whatsnew_0180.enhancements.extract:

Changes to str.extract
^^^^^^^^^^^^^^^^^^^^^^

The :ref:`.str.extract <text.extract>` method takes a regular
expression with capture groups, finds the first match in each subject
string, and returns the contents of the capture groups
(:issue:`11386`).

In v0.18.0, the ``expand`` argument was added to
``extract``.

- ``expand=False``: it returns a ``Series``, ``Index``, or ``DataFrame``, depending on the subject and regular expression pattern (same behavior as pre-0.18.0).
- ``expand=True``: it always returns a ``DataFrame``, which is more consistent and less confusing from the perspective of a user.

Currently the default is ``expand=None`` which gives a ``FutureWarning`` and uses ``expand=False``. To avoid this warning, please explicitly specify ``expand``.

.. ipython:: python

   pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=None)

Extracting a regular expression with one group returns a Series if
``expand=False``.

.. ipython:: python

   pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=False)

It returns a ``DataFrame`` with one column if ``expand=True``.

.. ipython:: python

   pd.Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)', expand=True)

Calling on an ``Index`` with a regex with exactly one capture group
returns  an ``Index`` if ``expand=False``.

.. ipython:: python

   s = pd.Series(["a1", "b2", "c3"], ["A11", "B22", "C33"])
   s
   s.index.str.extract("(?P<letter>[a-zA-Z])", expand=False)

It returns a ``DataFrame`` with one column if ``expand=True``.

.. ipython:: python

   s.index.str.extract("(?P<letter>[a-zA-Z])", expand=True)

Calling on an ``Index`` with a regex with more than one capture group
raises ``ValueError`` if ``expand=False``.

.. code-block:: python

    >>> s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=False)
    ValueError: only one regex group is supported with Index

It returns a ``DataFrame`` if ``expand=True``.

.. ipython:: python

   s.index.str.extract("(?P<letter>[a-zA-Z])([0-9]+)", expand=True)

In summary, ``extract(expand=True)`` always returns a ``DataFrame``
with a row for every subject string, and a column for every capture
group.

.. _whatsnew_0180.enhancements.extractall:

Addition of str.extractall
^^^^^^^^^^^^^^^^^^^^^^^^^^

The :ref:`.str.extractall <text.extractall>` method was added
(:issue:`11386`).  Unlike ``extract`` (which returns only the first
match),

.. ipython:: python

   s = pd.Series(["a1a2", "b1", "c1"], ["A", "B", "C"])
   s
   s.str.extract("(?P<letter>[ab])(?P<digit>\d)", expand=False)

the ``extractall`` method returns all matches.

.. ipython:: python

   s.str.extractall("(?P<letter>[ab])(?P<digit>\d)")

.. _whatsnew_0180.enhancements.strcat:

Changes to str.cat
^^^^^^^^^^^^^^^^^^

The method ``.str.cat()`` concatenates the members of a ``Series``. Before, if ``NaN`` values were present in the Series, calling ``.str.cat()`` on it would return ``NaN``, unlike the rest of the ``Series.str.*`` API. This behavior has been amended to ignore ``NaN`` values by default. (:issue:`11435`).

A new, friendlier ``ValueError`` is added to protect against the mistake of supplying the ``sep`` as an arg, rather than as a kwarg. (:issue:`11334`).

.. ipython:: python

    Series(['a','b',np.nan,'c']).str.cat(sep=' ')
    Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?')

.. code-block:: python

    In [2]: Series(['a','b',np.nan,'c']).str.cat(' ')
    ValueError: Did you mean to supply a `sep` keyword?


.. _whatsnew_0180.enhancements.rounding:

Datetimelike rounding
^^^^^^^^^^^^^^^^^^^^^

``DatetimeIndex``, ``Timestamp``, ``TimedeltaIndex``, ``Timedelta`` have gained the ``.round()``, ``.floor()`` and ``.ceil()`` method for datetimelike rounding, flooring and ceiling. (:issue:`4314`, :issue:`11963`)

Naive datetimes

.. ipython:: python

   dr = pd.date_range('20130101 09:12:56.1234', periods=3)
   dr
   dr.round('s')

   # Timestamp scalar
   dr[0]
   dr[0].round('10s')

Tz-aware are rounded, floored and ceiled in local times

.. ipython:: python

   dr = dr.tz_localize('US/Eastern')
   dr
   dr.round('s')

Timedeltas

.. ipython:: python

   t = timedelta_range('1 days 2 hr 13 min 45 us',periods=3,freq='d')
   t
   t.round('10min')

   # Timedelta scalar
   t[0]
   t[0].round('2h')


In addition, ``.round()``, ``.floor()`` and ``.ceil()`` will be available thru the ``.dt`` accessor of ``Series``.

.. ipython:: python

   s = Series(dr)
   s
   s.dt.round('D')

Formatting of integer in FloatIndex
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Integers in ``FloatIndex``, e.g. 1., are now formatted with a decimal point and a ``0`` digit, e.g. ``1.0`` (:issue:`11713`)
This change not only affects the display in a jupyter notebook, but also the output of IO methods like ``.to_csv`` or ``.to_html``

Previous Behavior:

.. code-block:: python

   In [2]: s = Series([1,2,3], index=np.arange(3.))

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

   In [4]: s.index
   Out[4]: Float64Index([0.0, 1.0, 2.0], dtype='float64')

   In [5]: print(s.to_csv(path=None))
   0,1
   1,2
   2,3


New Behavior:

.. ipython:: python

   s = Series([1,2,3], index=np.arange(3.))
   s
   s.index
   print(s.to_csv(path=None))

Changes to dtype assignment behaviors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

When a DataFrame's slice is updated with a new slice of the same dtype, the dtype of the DataFrame will now remain the same. (:issue:`10503`)

Previous Behavior:

.. code-block:: python

   In [5]: df = pd.DataFrame({'a': [0, 1, 1],
                              'b': pd.Series([100, 200, 300], dtype='uint32')})

   In [7]: df.dtypes
   Out[7]:
   a     int64
   b    uint32
   dtype: object

   In [8]: ix = df['a'] == 1

   In [9]: df.loc[ix, 'b'] = df.loc[ix, 'b']

   In [11]: df.dtypes
   Out[11]:
   a    int64
   b    int64
   dtype: object

New Behavior:

.. ipython:: python

   df = pd.DataFrame({'a': [0, 1, 1],
                      'b': pd.Series([100, 200, 300], dtype='uint32')})
   df.dtypes
   ix = df['a'] == 1
   df.loc[ix, 'b'] = df.loc[ix, 'b']
   df.dtypes

When a DataFrame's integer slice is partially updated with a new slice of floats that could potentially be downcasted to integer without losing precision, the dtype of the slice will be set to float instead of integer.

Previous Behavior:

.. code-block:: python

   In [4]: df = pd.DataFrame(np.array(range(1,10)).reshape(3,3),
                             columns=list('abc'),
                             index=[[4,4,8], [8,10,12]])

   In [5]: df
   Out[5]:
         a  b  c
   4 8   1  2  3
     10  4  5  6
   8 12  7  8  9

   In [7]: df.ix[4, 'c'] = np.array([0., 1.])

   In [8]: df
   Out[8]:
         a  b  c
   4 8   1  2  0
     10  4  5  1
   8 12  7  8  9

New Behavior:

.. ipython:: python

   df = pd.DataFrame(np.array(range(1,10)).reshape(3,3),
                     columns=list('abc'),
                     index=[[4,4,8], [8,10,12]])
   df
   df.ix[4, 'c'] = np.array([0., 1.])
   df

.. _whatsnew_0180.enhancements.xarray:

to_xarray
^^^^^^^^^

In a future version of pandas, we will be deprecating ``Panel`` and other > 2 ndim objects. In order to provide for continuity,
all ``NDFrame`` objects have gained the ``.to_xarray()`` method in order to convert to ``xarray`` objects, which has
a pandas-like interface for > 2 ndim.

See the `xarray full-documentation here <http://xarray.pydata.org/en/stable/>`__.

.. code-block:: python

   In [1]: p = Panel(np.arange(2*3*4).reshape(2,3,4))

   In [2]: p.to_xarray()
   Out[2]:
   <xarray.DataArray (items: 2, major_axis: 3, minor_axis: 4)>
   array([[[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]],

          [[12, 13, 14, 15],
           [16, 17, 18, 19],
           [20, 21, 22, 23]]])
   Coordinates:
     * items       (items) int64 0 1
     * major_axis  (major_axis) int64 0 1 2
     * minor_axis  (minor_axis) int64 0 1 2 3

Latex Representation
^^^^^^^^^^^^^^^^^^^^

``DataFrame`` has gained a ``._repr_latex_()`` method in order to allow for conversion to latex in a ipython/jupyter notebook using nbconvert. (:issue:`11778`)

Note that this must be activated by setting the option ``display.latex.repr`` to ``True`` (issue:`12182`)

For example, if you have a jupyter notebook you plan to convert to latex using nbconvert, place the statement ``pd.set_option('display.latex.repr', True)`` in the first cell to have the contained DataFrame output also stored as latex.

Options ``display.latex.escape`` and ``display.latex.longtable`` have also been added to the configuration and are used automatically by the ``to_latex``
method. See the :ref:`options documentation<options>` for more info.

.. _whatsnew_0180.enhancements.sas:

read_sas changes
^^^^^^^^^^^^^^^^

``read_sas`` has gained the ability to read SAS7BDAT files, including compressed files.  The files can be read in entirety, or incrementally.  For full details see :ref:`here <io.sas>`. (:issue:`4052`)

.. _whatsnew_0180.enhancements.other:

Other enhancements
^^^^^^^^^^^^^^^^^^

- Handle truncated floats in SAS xport files (:issue:`11713`)
- Added option to hide index in ``Series.to_string`` (:issue:`11729`)
- ``read_excel`` now supports s3 urls of the format ``s3://bucketname/filename`` (:issue:`11447`)
- add support for ``AWS_S3_HOST`` env variable when reading from s3 (:issue:`12198`)
- A simple version of ``Panel.round()`` is now implemented (:issue:`11763`)
- For Python 3.x, ``round(DataFrame)``, ``round(Series)``, ``round(Panel)`` will work (:issue:`11763`)
- ``sys.getsizeof(obj)`` returns the memory usage of a pandas object, including the
  values it contains (:issue:`11597`)
- ``Series`` gained an ``is_unique`` attribute (:issue:`11946`)
- ``DataFrame.quantile`` and ``Series.quantile`` now accept ``interpolation`` keyword (:issue:`10174`).
- Added ``DataFrame.style.format`` for more flexible formatting of cell values (:issue:`11692`)
- ``DataFrame.select_dtypes`` now allows the ``np.float16`` typecode (:issue:`11990`)
- ``pivot_table()`` now accepts most iterables for the ``values`` parameter (:issue:`12017`)
- Added Google ``BigQuery`` service account authentication support, which enables authentication on remote servers. (:issue:`11881`). For further details see :ref:`here <io.bigquery_authentication>`
- ``HDFStore`` is now iterable: ``for k in store`` is equivalent to ``for k in store.keys()`` (:issue:`12221`).
- Add missing methods/fields to ``.dt`` for ``Period`` (:issue:`8848`)
- The entire codebase has been ``PEP``-ified (:issue:`12096`)

.. _whatsnew_0180.api_breaking:

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

- the leading whitespaces have been removed from the output of ``.to_string(index=False)`` method (:issue:`11833`)
- the ``out`` parameter has been removed from the ``Series.round()`` method. (:issue:`11763`)
- ``DataFrame.round()`` leaves non-numeric columns unchanged in its return, rather than raises. (:issue:`11885`)
- ``DataFrame.head(0)`` and ``DataFrame.tail(0)`` return empty frames, rather than ``self``.  (:issue:`11937`)
- ``Series.head(0)`` and ``Series.tail(0)`` return empty series, rather than ``self``.  (:issue:`11937`)
- ``to_msgpack`` and ``read_msgpack`` encoding now defaults to ``'utf-8'``. (:issue:`12170`)
- the order of keyword arguments to text file parsing functions (``.read_csv()``, ``.read_table()``, ``.read_fwf()``) changed to group related arguments. (:issue:`11555`)
- ``NaTType.isoformat`` now returns the string ``'NaT`` to allow the result to
  be passed to the constructor of ``Timestamp``. (:issue:`12300`)

NaT and Timedelta operations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

``NaT`` and ``Timedelta`` have expanded arithmetic operations, which are extended to ``Series``
arithmetic where applicable.  Operations defined for ``datetime64[ns]`` or ``timedelta64[ns]``
are now also defined for ``NaT`` (:issue:`11564`).

``NaT`` now supports arithmetic operations with integers and floats.

.. ipython:: python

   pd.NaT * 1
   pd.NaT * 1.5
   pd.NaT / 2
   pd.NaT * np.nan

``NaT`` defines more arithmetic operations with ``datetime64[ns]`` and ``timedelta64[ns]``.

.. ipython:: python

   pd.NaT / pd.NaT
   pd.Timedelta('1s') / pd.NaT

``NaT`` may represent either a ``datetime64[ns]`` null or a ``timedelta64[ns]`` null.
Given the ambiguity, it is treated as a ``timedelta64[ns]``, which allows more operations
to succeed.

.. ipython:: python

   pd.NaT + pd.NaT

   # same as
   pd.Timedelta('1s') + pd.Timedelta('1s')

as opposed to

.. code-block:: python

   In [3]: pd.Timestamp('19900315') + pd.Timestamp('19900315')
   TypeError: unsupported operand type(s) for +: 'Timestamp' and 'Timestamp'

However, when wrapped in a ``Series`` whose ``dtype`` is ``datetime64[ns]`` or ``timedelta64[ns]``,
the ``dtype`` information is respected.

.. code-block:: python

   In [1]: pd.Series([pd.NaT], dtype='<M8[ns]') + pd.Series([pd.NaT], dtype='<M8[ns]')
   TypeError: can only operate on a datetimes for subtraction,
              but the operator [__add__] was passed

.. ipython:: python

   pd.Series([pd.NaT], dtype='<m8[ns]') + pd.Series([pd.NaT], dtype='<m8[ns]')

``Timedelta`` division by ``floats`` now works.

.. ipython:: python

   pd.Timedelta('1s') / 2.0

Subtraction by ``Timedelta`` in a ``Series`` by a ``Timestamp`` works (:issue:`11925`)

.. ipython:: python

   ser = pd.Series(pd.timedelta_range('1 day', periods=3))
   ser
   pd.Timestamp('2012-01-01') - ser


``NaT.isoformat()`` now returns ``'NaT'``. This change allows allows
``pd.Timestamp`` to rehydrate any timestamp like object from its isoformat
(:issue:`12300`).

Changes to msgpack
^^^^^^^^^^^^^^^^^^

Forward incompatible changes in ``msgpack`` writing format were made over 0.17.0 and 0.18.0; older versions of pandas cannot read files packed by newer versions (:issue:`12129`, `10527`)

Bug in ``to_msgpack`` and ``read_msgpack`` introduced in 0.17.0 and fixed in 0.18.0, caused files packed in Python 2 unreadable by Python 3 (:issue:`12142`)

.. warning::

   As a result of a number of issues:

   +----------------------+------------------------+
   | Packed with          | Can be unpacked with   |
   +======================+========================+
   | pre-0.17 / Python 2  | any                    |
   +----------------------+------------------------+
   | pre-0.17 / Python 3  | any                    |
   +----------------------+------------------------+
   | 0.17 / Python 2      | - 0.17 / Python 2      |
   |                      | - >=0.18 / any Python  |
   +----------------------+------------------------+
   | 0.17 / Python 3      | >=0.18 / any Python    |
   +----------------------+------------------------+
   | 0.18                 | >= 0.18                |
   +======================+========================+

   0.18.0 is backward-compatible for reading files packed by older versions, except for files packed with 0.17 in Python 2, in which case only they can only be unpacked in Python 2.

Signature change for .rank
^^^^^^^^^^^^^^^^^^^^^^^^^^

``Series.rank`` and ``DataFrame.rank`` now have the same signature (:issue:`11759`)

Previous signature

.. code-block:: python

   In [3]: pd.Series([0,1]).rank(method='average', na_option='keep',
                                 ascending=True, pct=False)
   Out[3]:
   0    1
   1    2
   dtype: float64

   In [4]: pd.DataFrame([0,1]).rank(axis=0, numeric_only=None,
                                    method='average', na_option='keep',
                                    ascending=True, pct=False)
   Out[4]:
      0
   0  1
   1  2

New signature

.. ipython:: python

   pd.Series([0,1]).rank(axis=0, method='average', numeric_only=None,
                         na_option='keep', ascending=True, pct=False)
   pd.DataFrame([0,1]).rank(axis=0, method='average', numeric_only=None,
                            na_option='keep', ascending=True, pct=False)


Bug in QuarterBegin with n=0
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In previous versions, the behavior of the QuarterBegin offset was inconsistent
depending on the date when the ``n`` parameter was 0. (:issue:`11406`)

The general semantics of anchored offsets for ``n=0`` is to not move the date
when it is an anchor point (e.g., a quarter start date), and otherwise roll
forward to the next anchor point.

.. ipython:: python

   d = pd.Timestamp('2014-02-01')
   d
   d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
   d + pd.offsets.QuarterBegin(n=0, startingMonth=1)

For the ``QuarterBegin`` offset in previous versions, the date would be rolled
*backwards* if date was in the same month as the quarter start date.

.. code-block:: python

   In [3]: d = pd.Timestamp('2014-02-15')

   In [4]: d + pd.offsets.QuarterBegin(n=0, startingMonth=2)
   Out[4]: Timestamp('2014-02-01 00:00:00')

This behavior has been corrected in version 0.18.0, which is consistent with
other anchored offsets like ``MonthBegin`` and ``YearBegin``.

.. ipython:: python

   d = pd.Timestamp('2014-02-15')
   d + pd.offsets.QuarterBegin(n=0, startingMonth=2)

.. _whatsnew_0180.breaking.resample:

Resample API
^^^^^^^^^^^^

Like the change in the window functions API :ref:`above <whatsnew_0180.enhancements.moments>`, ``.resample(...)`` is changing to have a more groupby-like API. (:issue:`11732`, :issue:`12702`, :issue:`12202`, :issue:`12332`, :issue:`12334`, :issue:`12348`).

.. ipython:: python

   np.random.seed(1234)
   df = pd.DataFrame(np.random.rand(10,4),
                     columns=list('ABCD'),
                     index=pd.date_range('2010-01-01 09:00:00', periods=10, freq='s'))
   df


**Previous API**:

You would write a resampling operation that immediately evaluates. If a ``how`` parameter was not provided, it
would default to ``how='mean'``.

.. code-block:: python

   In [6]: df.resample('2s')
   Out[6]:
                            A         B         C         D
   2010-01-01 09:00:00  0.485748  0.447351  0.357096  0.793615
   2010-01-01 09:00:02  0.820801  0.794317  0.364034  0.531096
   2010-01-01 09:00:04  0.433985  0.314582  0.424104  0.625733
   2010-01-01 09:00:06  0.624988  0.609738  0.633165  0.612452
   2010-01-01 09:00:08  0.510470  0.534317  0.573201  0.806949

You could also specify a ``how`` directly

.. code-block:: python

   In [7]: df.resample('2s',how='sum')
   Out[7]:
                            A         B         C         D
   2010-01-01 09:00:00  0.971495  0.894701  0.714192  1.587231
   2010-01-01 09:00:02  1.641602  1.588635  0.728068  1.062191
   2010-01-01 09:00:04  0.867969  0.629165  0.848208  1.251465
   2010-01-01 09:00:06  1.249976  1.219477  1.266330  1.224904
   2010-01-01 09:00:08  1.020940  1.068634  1.146402  1.613897

.. warning::

   This new API for resample includes some internal changes for the prior-to-0.18.0 API, to work with a deprecation warning in most cases, as the resample operation returns a deferred object. We can intercept operations and just do what the (pre 0.18.0) API did (with a warning). Here is a typical use case:

   .. code-block:: python

      In [4]: r = df.resample('2s')

      In [6]: r*10
      pandas/tseries/resample.py:80: FutureWarning: .resample() is now a deferred operation
      use .resample(...).mean() instead of .resample(...)

      Out[6]:
                            A         B         C         D
      2010-01-01 09:00:00  4.857476  4.473507  3.570960  7.936154
      2010-01-01 09:00:02  8.208011  7.943173  3.640340  5.310957
      2010-01-01 09:00:04  4.339846  3.145823  4.241039  6.257326
      2010-01-01 09:00:06  6.249881  6.097384  6.331650  6.124518
      2010-01-01 09:00:08  5.104699  5.343172  5.732009  8.069486

   However, getting and assignment operations directly on a ``Resampler`` will raise a ``ValueError``:

   .. code-block:: python

      In [7]: r.iloc[0] = 5
      ValueError: .resample() is now a deferred operation
            use .resample(...).mean() instead of .resample(...)
            assignment will have no effect as you are working on a copy

**New API**:

Now, you can write ``.resample`` as a 2-stage operation like groupby, which
yields a ``Resampler``.

.. ipython:: python


   r = df.resample('2s')
   r

Downsampling
''''''''''''

You can then use this object to perform operations.
These are downsampling operations (going from a lower frequency to a higher one).

.. ipython:: python

   r.mean()

.. ipython:: python

   r.sum()

Furthermore, resample now supports ``getitem`` operations to perform the resample on specific columns.

.. ipython:: python

   r[['A','C']].mean()

and ``.aggregate`` type operations.

.. ipython:: python

   r.agg({'A' : 'mean', 'B' : 'sum'})

These accessors can of course, be combined

.. ipython:: python

   r[['A','B']].agg(['mean','sum'])

Upsampling
''''''''''

.. currentmodule:: pandas.tseries.resample

Upsampling operations take you from a higher frequency to a lower frequency. These are now
performed with the ``Resampler`` objects with :meth:`~Resampler.backfill`,
:meth:`~Resampler.ffill`, :meth:`~Resampler.fillna` and :meth:`~Resampler.asfreq` methods.

.. ipython:: python

   s = pd.Series(np.arange(5,dtype='int64'),
                 index=date_range('2010-01-01', periods=5, freq='Q'))
   s

Previously

.. code-block:: python

   In [6]: s.resample('M', fill_method='ffill')
   Out[6]:
   2010-03-31    0
   2010-04-30    0
   2010-05-31    0
   2010-06-30    1
   2010-07-31    1
   2010-08-31    1
   2010-09-30    2
   2010-10-31    2
   2010-11-30    2
   2010-12-31    3
   2011-01-31    3
   2011-02-28    3
   2011-03-31    4
   Freq: M, dtype: int64

New API

.. ipython:: python

   s.resample('M').ffill()

.. note::

   In the new API, you can either downsample OR upsample. The prior implementation would allow you to pass an aggregator function (like ``mean``) even though you were upsampling, providing a bit of confusion.

Changes to eval
^^^^^^^^^^^^^^^

In prior versions, new columns assignments in an ``eval`` expression resulted
in an inplace change to the ``DataFrame``. (:issue:`9297`)

.. ipython:: python

   df = pd.DataFrame({'a': np.linspace(0, 10, 5), 'b': range(5)})
   df.eval('c = a + b')
   df

In version 0.18.0, a new ``inplace`` keyword was added to choose whether the
assignment should be done inplace or return a copy.

.. ipython:: python

   df
   df.eval('d = c - b', inplace=False)
   df
   df.eval('d = c - b', inplace=True)
   df

.. warning::

   For backwards compatability, ``inplace`` defaults to ``True`` if not specified.
   This will change in a future version of pandas. If your code depends on an
   inplace assignment you should update to explicitly set ``inplace=True``

The ``inplace`` keyword parameter was also added the ``query`` method.

.. ipython:: python

   df.query('a > 5')
   df.query('a > 5', inplace=True)
   df

.. warning::

   Note that the default value for ``inplace`` in a ``query``
   is ``False``, which is consistent with prior versions.

``eval`` has also been updated to allow multi-line expressions for multiple
assignments.  These expressions will be evaluated one at a time in order.  Only
assignments are valid for multi-line expressions.

.. ipython:: python

   df
   df.eval("""
   e = d + a
   f = e - 22
   g = f / 2.0""", inplace=True)
   df


.. _whatsnew_0180.api:

Other API Changes
^^^^^^^^^^^^^^^^^
- ``DataFrame.between_time`` and ``Series.between_time`` now only parse a fixed set of time strings. Parsing of date strings is no longer supported and raises a ``ValueError``. (:issue:`11818`)

  .. ipython:: python

     s = pd.Series(range(10), pd.date_range('2015-01-01', freq='H', periods=10))
     s.between_time("7:00am", "9:00am")

  This will now raise.

  .. code-block:: python

     In [2]: s.between_time('20150101 07:00:00','20150101 09:00:00')
     ValueError: Cannot convert arg ['20150101 07:00:00'] to a time.

- ``.memory_usage()`` now includes values in the index, as does memory_usage in ``.info`` (:issue:`11597`)
- ``DataFrame.to_latex()`` now supports non-ascii encodings (eg utf-8) in Python 2 with the parameter ``encoding`` (:issue:`7061`)
- ``pandas.merge()`` and ``DataFrame.merge()`` will show a specific error message when trying to merge with an object that is not of type ``DataFrame`` or a subclass (:issue:`12081`)
- ``DataFrame.unstack`` and ``Series.unstack`` now take ``fill_value`` keyword to allow direct replacement of missing values when an unstack results in missing values in the resulting ``DataFrame``. As an added benefit, specifying ``fill_value`` will preserve the data type of the original stacked data.  (:issue:`9746`)
- As part of the new API for :ref:`window functions <whatsnew_0180.enhancements.moments>` and :ref:`resampling <whatsnew_0180.breaking.resample>`, aggregation functions have been clarified, raising more informative error messages on invalid aggregations. (:issue:`9052`). A full set of examples are presented in :ref:`groupby <groupby.aggregation>`.
- Statistical functions for ``NDFrame`` objects will now raise if non-numpy-compatible arguments are passed in for ``**kwargs`` (:issue:`12301`)
- ``.to_latex`` and ``.to_html`` gain a ``decimal`` parameter like ``.to_csv``; the default is ``'.'`` (:issue:`12031`)
- More helpful error message when constructing a ``DataFrame`` with empty data but with indices (:issue:`8020`)
- ``.describe()`` will now properly handle bool dtype as a categorical (:issue:`6625`)
- More helpful error message invalid ``.transform`` with user defined input (:issue:`10165`)

.. _whatsnew_0180.deprecations:

Deprecations
^^^^^^^^^^^^

.. _whatsnew_0180.window_deprecations:

- The functions ``pd.rolling_*``, ``pd.expanding_*``, and ``pd.ewm*`` are deprecated and replaced by the corresponding method call. Note that
  the new suggested syntax includes all of the arguments (even if default) (:issue:`11603`)

  .. code-block:: python

     In [1]: s = pd.Series(range(3))

     In [2]: pd.rolling_mean(s,window=2,min_periods=1)
             FutureWarning: pd.rolling_mean is deprecated for Series and
                  will be removed in a future version, replace with
                  Series.rolling(min_periods=1,window=2,center=False).mean()
     Out[2]:
             0    0.0
             1    0.5
             2    1.5
             dtype: float64

     In [3]: pd.rolling_cov(s, s, window=2)
             FutureWarning: pd.rolling_cov is deprecated for Series and
                  will be removed in a future version, replace with
                  Series.rolling(window=2).cov(other=<Series>)
     Out[3]:
             0    NaN
             1    0.5
             2    0.5
             dtype: float64

- The the ``freq`` and ``how`` arguments to the ``.rolling``, ``.expanding``, and ``.ewm`` (new) functions are deprecated, and will be removed in a future version. You can simply resample the input prior to creating a window function. (:issue:`11603`).

  For example, instead of ``s.rolling(window=5,freq='D').max()`` to get the max value on a rolling 5 Day window, one could use ``s.resample('D',how='max').rolling(window=5).max()``, which first resamples the data to daily data, then provides a rolling 5 day window.

- ``pd.tseries.frequencies.get_offset_name`` function is deprecated. Use offset's ``.freqstr`` property as alternative (:issue:`11192`)
- ``pandas.stats.fama_macbeth`` routines are deprecated and will be removed in a future version (:issue:`6077`)
- ``pandas.stats.ols``, ``pandas.stats.plm`` and ``pandas.stats.var`` routines are deprecated and will be removed in a future version (:issue:`6077`)
- show a ``FutureWarning`` rather than a ``DeprecationWarning`` on using long-time deprecated syntax in ``HDFStore.select``, where the ``where`` clause is not a string-like (:issue:`12027`)

- The ``pandas.options.display.mpl_style`` configuration has been deprecated
  and will be removed in a future version of pandas. This functionality
  is better handled by matplotlib's `style sheets`_ (:issue:`11783`).


.. _style sheets: http://matplotlib.org/users/style_sheets.html

.. _whatsnew_0180.float_indexers:

Removal of deprecated float indexers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In :issue:`4892` indexing with floating point numbers on a non-``Float64Index`` was deprecated (in version 0.14.0).
In 0.18.0, this deprecation warning is removed and these will now raise a ``TypeError``. (:issue:`12165`)

.. ipython:: python

   s = pd.Series([1,2,3])
   s
   s2 = pd.Series([1, 2, 3], index=list('abc'))
   s2

Previous Behavior:

.. code-block:: python

   In [2]: s[1.0]
   FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
   Out[2]: 2

   In [3]: s.iloc[1.0]
   FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
   Out[3]: 2

   In [4]: s.loc[1.0]
   FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point
   Out[4]: 2

   # .ix would coerce 1.0 to the positional 1, and index
   In [5]: s2.ix[1.0] = 10
   FutureWarning: scalar indexers for index type Index should be integers and not floating point

   In [6]: s2
   Out[6]:
   a     1
   b    10
   c     3
   dtype: int64

New Behavior:

.. code-block:: python

   In [2]: s[1.0]
   TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'>

   In [3]: s.iloc[1.0]
   TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'>

   In [4]: s.loc[1.0]
   TypeError: cannot do label indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [1.0] of <type 'float'>

   # .ix will now cause this to be a label lookup and coerce to and Index
   In [5]: s2.ix[1.0] = 10

   In [6]: s2
   Out[3]:
   a       1
   b       2
   c       3
   1.0    10
   dtype: int64

Float indexing on a ``Float64Index`` is unchanged.

.. ipython:: python

   s = pd.Series([1,2,3],index=np.arange(3.))
   s[1.0]
   s[1.0:2.5]

.. _whatsnew_0180.prior_deprecations:

Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

- Removal of ``rolling_corr_pairwise`` in favor of ``.rolling().corr(pairwise=True)`` (:issue:`4950`)
- Removal of ``expanding_corr_pairwise`` in favor of ``.expanding().corr(pairwise=True)`` (:issue:`4950`)
- Removal of ``DataMatrix`` module. This was not imported into the pandas namespace in any event (:issue:`12111`)
- Removal of ``cols`` keyword in favor of ``subset`` in ``DataFrame.duplicated()`` and ``DataFrame.drop_duplicates()`` (:issue:`6680`)
- Removal of the ``read_frame`` and ``frame_query`` (both aliases for ``pd.read_sql``)
  and ``write_frame`` (alias of ``to_sql``) functions in the ``pd.io.sql`` namespace,
  deprecated since 0.14.0 (:issue:`6292`).
- Removal of the ``order`` keyword from ``.factorize()`` (:issue:`6930`)

.. _whatsnew_0180.performance:

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

- Improved performance of ``andrews_curves`` (:issue:`11534`)
- Improved huge ``DatetimeIndex``, ``PeriodIndex`` and ``TimedeltaIndex``'s ops performance including ``NaT`` (:issue:`10277`)
- Improved performance of ``pandas.concat`` (:issue:`11958`)
- Improved performance of ``StataReader`` (:issue:`11591`)
- Improved performance in construction of ``Categoricals`` with ``Series`` of datetimes containing ``NaT`` (:issue:`12077`)


- Improved performance of ISO 8601 date parsing for dates without separators (:issue:`11899`), leading zeros (:issue:`11871`) and with whitespace preceding the time zone (:issue:`9714`)




.. _whatsnew_0180.bug_fixes:

Bug Fixes
~~~~~~~~~

- Bug in ``GroupBy.size`` when data-frame is empty. (:issue:`11699`)
- Bug in ``Period.end_time`` when a multiple of time period is requested (:issue:`11738`)
- Regression in ``.clip`` with tz-aware datetimes (:issue:`11838`)
- Bug in ``date_range`` when the boundaries fell on the frequency (:issue:`11804`, :issue:`12409`)
- Bug in consistency of passing nested dicts to ``.groupby(...).agg(...)`` (:issue:`9052`)
- Accept unicode in ``Timedelta`` constructor (:issue:`11995`)
- Bug in value label reading for ``StataReader`` when reading incrementally (:issue:`12014`)
- Bug in vectorized ``DateOffset`` when ``n`` parameter is ``0`` (:issue:`11370`)
- Compat for numpy 1.11 w.r.t. ``NaT`` comparison changes (:issue:`12049`)
- Bug in ``read_csv`` when reading from a ``StringIO`` in threads (:issue:`11790`)
- Bug in not treating ``NaT`` as a missing value in datetimelikes when factorizing & with ``Categoricals`` (:issue:`12077`)
- Bug in getitem when the values of a ``Series`` were tz-aware (:issue:`12089`)
- Bug in ``Series.str.get_dummies`` when one of the variables was 'name' (:issue:`12180`)
- Bug in ``pd.concat`` while concatenating tz-aware NaT series. (:issue:`11693`, :issue:`11755`, :issue:`12217`)
- Bug in ``pd.read_stata`` with version <= 108 files (:issue:`12232`)
- Bug in ``Series.resample`` using a frequency of ``Nano`` when the index is a ``DatetimeIndex`` and contains non-zero nanosecond parts (:issue:`12037`)
- Bug in resampling with ``.nunique`` and a sparse index (:issue:`12352`)
- Removed some compiler warnings (:issue:`12471`)
- Work around compat issues with ``boto`` in python 3.5 (:issue:`11915`)
- Bug in ``NaT`` subtraction from ``Timestamp`` or ``DatetimeIndex`` with timezones (:issue:`11718`)
- Bug in subtraction of ``Series`` of a single tz-aware ``Timestamp`` (:issue:`12290`)

- Bug in ``Timedelta.round`` with negative values (:issue:`11690`)
- Bug in ``.loc`` against ``CategoricalIndex`` may result in normal ``Index`` (:issue:`11586`)
- Bug in ``DataFrame.info`` when duplicated column names exist (:issue:`11761`)
- Bug in ``.copy`` of datetime tz-aware objects (:issue:`11794`)
- Bug in ``Series.apply`` and ``Series.map`` where ``timedelta64`` was not boxed (:issue:`11349`)
- Bug in ``DataFrame.set_index()`` with tz-aware ``Series`` (:issue:`12358`)



- Bug in subclasses of ``DataFrame`` where ``AttributeError`` did not propagate (:issue:`11808`)
- Bug groupby on tz-aware data where selection not returning ``Timestamp`` (:issue:`11616`)
- Bug in ``pd.read_clipboard`` and ``pd.to_clipboard`` functions not supporting Unicode; upgrade included ``pyperclip`` to v1.5.15 (:issue:`9263`)
- Bug in ``DataFrame.query`` containing an assignment (:issue:`8664`)

- Bug in ``from_msgpack`` where ``__contains__()`` fails for columns of the unpacked ``DataFrame``, if the ``DataFrame`` has object columns. (:issue:`11880`)
- Bug in ``.resample`` on categorical data with ``TimedeltaIndex`` (:issue:`12169`)


- Bug in timezone info lost when broadcasting scalar datetime to ``DataFrame`` (:issue:`11682`)
- Bug in ``Index`` creation from ``Timestamp`` with mixed tz coerces to UTC (:issue:`11488`)
- Bug in ``to_numeric`` where it does not raise if input is more than one dimension (:issue:`11776`)
- Bug in parsing timezone offset strings with non-zero minutes (:issue:`11708`)
- Bug in ``df.plot`` using incorrect colors for bar plots under matplotlib 1.5+ (:issue:`11614`)
- Bug in the ``groupby`` ``plot`` method when using keyword arguments (:issue:`11805`).
- Bug in ``DataFrame.duplicated`` and ``drop_duplicates`` causing spurious matches when setting ``keep=False`` (:issue:`11864`)
- Bug in ``.loc`` result with duplicated key may have ``Index`` with incorrect dtype (:issue:`11497`)
- Bug in ``pd.rolling_median`` where memory allocation failed even with sufficient memory (:issue:`11696`)
- Bug in ``DataFrame.style`` with spurious zeros (:issue:`12134`)
- Bug in ``DataFrame.style`` with integer columns not starting at 0 (:issue:`12125`)
- Bug in ``.style.bar`` may not rendered properly using specific browser (:issue:`11678`)
- Bug in rich comparison of ``Timedelta`` with a ``numpy.array`` of ``Timedelta`` that caused an infinite recursion (:issue:`11835`)
- Bug in ``DataFrame.round`` dropping column index name (:issue:`11986`)
- Bug in ``df.replace`` while replacing value in mixed dtype ``Dataframe`` (:issue:`11698`)
- Bug in ``Index`` prevents copying name of passed ``Index``, when a new name is not provided (:issue:`11193`)
- Bug in ``read_excel`` failing to read any non-empty sheets when empty sheets exist and ``sheetname=None`` (:issue:`11711`)
- Bug in ``read_excel`` failing to raise ``NotImplemented`` error when keywords ``parse_dates`` and ``date_parser`` are provided (:issue:`11544`)
- Bug in ``read_sql`` with ``pymysql`` connections failing to return chunked data (:issue:`11522`)
- Bug in ``.to_csv`` ignoring formatting parameters ``decimal``, ``na_rep``, ``float_format`` for float indexes (:issue:`11553`)
- Bug in ``Int64Index`` and ``Float64Index`` preventing the use of the modulo operator (:issue:`9244`)
- Bug in ``MultiIndex.drop`` for not lexsorted multi-indexes (:issue:`12078`)

- Bug in ``DataFrame`` when masking an empty ``DataFrame`` (:issue:`11859`)


- Bug in ``.plot`` potentially modifying the ``colors`` input when the number of columns didn't match the number of series provided (:issue:`12039`).
- Bug in ``Series.plot`` failing when index has a ``CustomBusinessDay`` frequency (:issue:`7222`).
- Bug in ``.to_sql`` for ``datetime.time`` values with sqlite fallback (:issue:`8341`)
- Bug in ``read_excel`` failing to read data with one column when ``squeeze=True`` (:issue:`12157`)
- Bug in ``read_excel`` failing to read one empty column (:issue:`12292`, :issue:`9002`)
- Bug in ``.groupby`` where a ``KeyError`` was not raised for a wrong column if there was only one row in the dataframe (:issue:`11741`)
- Bug in ``.read_csv`` with dtype specified on empty data producing an error (:issue:`12048`)
- Bug in ``.read_csv`` where strings like ``'2E'`` are treated as valid floats (:issue:`12237`)
- Bug in building *pandas* with debugging symbols (:issue:`12123`)


- Removed ``millisecond`` property of ``DatetimeIndex``. This would always raise a ``ValueError`` (:issue:`12019`).
- Bug in ``Series`` constructor with read-only data (:issue:`11502`)
- Removed ``pandas.util.testing.choice()``.  Should use ``np.random.choice()``, instead. (:issue:`12386`)
- Bug in ``.loc`` setitem indexer preventing the use of a TZ-aware DatetimeIndex (:issue:`12050`)
- Bug in ``.style`` indexes and multi-indexes not appearing (:issue:`11655`)
- Bug in ``to_msgpack`` and ``from_msgpack`` which did not correctly serialize or deserialize ``NaT`` (:issue:`12307`).
- Bug in ``.skew`` and ``.kurt`` due to roundoff error for highly similar values (:issue:`11974`)

- Bug in ``buffer_rd_bytes`` src->buffer could be freed more than once if reading failed, causing a segfault (:issue:`12098`)

- Bug in ``crosstab`` where arguments with non-overlapping indexes would return a ``KeyError`` (:issue:`10291`)

- Bug in ``DataFrame.apply`` in which reduction was not being prevented for cases in which ``dtype`` was not a numpy dtype (:issue:`12244`)
- Bug when initializing categorical series with a scalar value. (:issue:`12336`)
- Bug when specifying a UTC ``DatetimeIndex`` by setting ``utc=True`` in ``.to_datetime`` (:issue:`11934`)