.. _whatsnew_0110:

Version 0.11.0 (April 22, 2013)
-------------------------------

{{ header }}


This is a major release from 0.10.1 and includes many new features and
enhancements along with a large number of bug fixes. The methods of Selecting
Data have had quite a number of additions, and Dtype support is now full-fledged.
There are also a number of important API changes that long-time pandas users should
pay close attention to.

There is a new section in the documentation, :ref:`10 Minutes to Pandas <10min>`,
primarily geared to new users.

There is a new section in the documentation, :ref:`Cookbook <cookbook>`, a collection
of useful recipes in pandas (and that we want contributions!).

There are several libraries that are now :ref:`Recommended Dependencies <install.recommended_dependencies>`

Selection choices
~~~~~~~~~~~~~~~~~

Starting in 0.11.0, object selection has had a number of user-requested additions in
order to support more explicit location based indexing. Pandas now supports
three types of multi-axis indexing.

- ``.loc`` is strictly label based, will raise ``KeyError`` when the items are not found, allowed inputs are:

  - A single label, e.g. ``5`` or ``'a'``, (note that ``5`` is interpreted as a *label* of the index. This use is **not** an integer position along the index)
  - A list or array of labels ``['a', 'b', 'c']``
  - A slice object with labels ``'a':'f'``, (note that contrary to usual python slices, **both** the start and the stop are included!)
  - A boolean array

  See more at :ref:`Selection by Label <indexing.label>`

- ``.iloc`` is strictly integer position based (from ``0`` to ``length-1`` of the axis), will raise ``IndexError`` when the requested indices are out of bounds. Allowed inputs are:

  - An integer e.g. ``5``
  - A list or array of integers ``[4, 3, 0]``
  - A slice object with ints ``1:7``
  - A boolean array

  See more at :ref:`Selection by Position <indexing.integer>`

- ``.ix`` supports mixed integer and label based access. It is primarily label based, but will fallback to integer positional access. ``.ix`` is the most general and will support
  any of the inputs to ``.loc`` and ``.iloc``, as well as support for floating point label schemes. ``.ix`` is especially useful when dealing with mixed positional and label
  based hierarchical indexes.

  As using integer slices with ``.ix`` have different behavior depending on whether the slice
  is interpreted as position based or label based, it's usually better to be
  explicit and use ``.iloc`` or ``.loc``.

  See more at :ref:`Advanced Indexing <advanced>` and :ref:`Advanced Hierarchical <advanced.advanced_hierarchical>`.


Selection deprecations
~~~~~~~~~~~~~~~~~~~~~~

Starting in version 0.11.0, these methods *may* be deprecated in future versions.

- ``irow``
- ``icol``
- ``iget_value``

See the section :ref:`Selection by Position <indexing.integer>` for substitutes.

Dtypes
~~~~~~

Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste.

.. ipython:: python

   df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
   df1
   df1.dtypes
   df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
                       'B': pd.Series(np.random.randn(8)),
                       'C': pd.Series(range(8), dtype='uint8')})
   df2
   df2.dtypes

   # here you get some upcasting
   df3 = df1.reindex_like(df2).fillna(value=0.0) + df2
   df3
   df3.dtypes

Dtype conversion
~~~~~~~~~~~~~~~~

This is lower-common-denominator upcasting, meaning you get the dtype which can accommodate all of the types

.. ipython:: python

   df3.values.dtype

Conversion

.. ipython:: python

   df3.astype('float32').dtypes

Mixed conversion

.. code-block:: ipython

    In [12]: df3['D'] = '1.'

    In [13]: df3['E'] = '1'

    In [14]: df3.convert_objects(convert_numeric=True).dtypes
    Out[14]:
    A    float32
    B    float64
    C    float64
    D    float64
    E      int64
    dtype: object

    # same, but specific dtype conversion
    In [15]: df3['D'] = df3['D'].astype('float16')

    In [16]: df3['E'] = df3['E'].astype('int32')

    In [17]: df3.dtypes
    Out[17]:
    A    float32
    B    float64
    C    float64
    D    float16
    E      int32
    dtype: object

Forcing date coercion (and setting ``NaT`` when not datelike)

.. code-block:: ipython

    In [18]: import datetime

    In [19]: s = pd.Series([datetime.datetime(2001, 1, 1, 0, 0), 'foo', 1.0, 1,
       ....:                pd.Timestamp('20010104'), '20010105'], dtype='O')
       ....:

    In [20]: s.convert_objects(convert_dates='coerce')
    Out[20]:
    0   2001-01-01
    1          NaT
    2          NaT
    3          NaT
    4   2001-01-04
    5   2001-01-05
    dtype: datetime64[ns]

Dtype gotchas
~~~~~~~~~~~~~

**Platform gotchas**

Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of ``int64`` and ``float64``,
*regardless of platform*. This is not an apparent change from earlier versions of pandas. If you specify
dtypes, they *WILL* be respected, however (:issue:`2837`)

The following will all result in ``int64`` dtypes

.. code-block:: ipython

    In [21]: pd.DataFrame([1, 2], columns=['a']).dtypes
    Out[21]:
    a    int64
    dtype: object

    In [22]: pd.DataFrame({'a': [1, 2]}).dtypes
    Out[22]:
    a    int64
    dtype: object

    In [23]: pd.DataFrame({'a': 1}, index=range(2)).dtypes
    Out[23]:
    a    int64
    dtype: object

Keep in mind that ``DataFrame(np.array([1,2]))`` **WILL** result in ``int32`` on 32-bit platforms!


**Upcasting gotchas**

Performing indexing operations on integer type data can easily upcast the data.
The dtype of the input data will be preserved in cases where ``nans`` are not introduced.

.. code-block:: ipython

    In [24]: dfi = df3.astype('int32')

    In [25]: dfi['D'] = dfi['D'].astype('int64')

    In [26]: dfi
    Out[26]:
      A  B  C  D  E
    0  0  0  0  1  1
    1 -2  0  1  1  1
    2 -2  0  2  1  1
    3  0 -1  3  1  1
    4  1  0  4  1  1
    5  0  0  5  1  1
    6  0 -1  6  1  1
    7  0  0  7  1  1

    In [27]: dfi.dtypes
    Out[27]:
    A    int32
    B    int32
    C    int32
    D    int64
    E    int32
    dtype: object

    In [28]: casted = dfi[dfi > 0]

    In [29]: casted
    Out[29]:
        A   B    C  D  E
    0  NaN NaN  NaN  1  1
    1  NaN NaN  1.0  1  1
    2  NaN NaN  2.0  1  1
    3  NaN NaN  3.0  1  1
    4  1.0 NaN  4.0  1  1
    5  NaN NaN  5.0  1  1
    6  NaN NaN  6.0  1  1
    7  NaN NaN  7.0  1  1

    In [30]: casted.dtypes
    Out[30]:
    A    float64
    B    float64
    C    float64
    D      int64
    E      int32
    dtype: object

While float dtypes are unchanged.

.. code-block:: ipython

    In [31]: df4 = df3.copy()

    In [32]: df4['A'] = df4['A'].astype('float32')

    In [33]: df4.dtypes
    Out[33]:
    A    float32
    B    float64
    C    float64
    D    float16
    E      int32
    dtype: object

    In [34]: casted = df4[df4 > 0]

    In [35]: casted
    Out[35]:
              A         B    C    D  E
    0       NaN       NaN  NaN  1.0  1
    1       NaN  0.567020  1.0  1.0  1
    2       NaN  0.276232  2.0  1.0  1
    3       NaN       NaN  3.0  1.0  1
    4  1.933792       NaN  4.0  1.0  1
    5       NaN  0.113648  5.0  1.0  1
    6       NaN       NaN  6.0  1.0  1
    7       NaN  0.524988  7.0  1.0  1

    In [36]: casted.dtypes
    Out[36]:
    A    float32
    B    float64
    C    float64
    D    float16
    E      int32
    dtype: object

Datetimes conversion
~~~~~~~~~~~~~~~~~~~~

Datetime64[ns] columns in a DataFrame (or a Series) allow the use of ``np.nan`` to indicate a nan value,
in addition to the traditional ``NaT``, or not-a-time. This allows convenient nan setting in a generic way.
Furthermore ``datetime64[ns]`` columns are created by default, when passed datetimelike objects (*this change was introduced in 0.10.1*)
(:issue:`2809`, :issue:`2810`)

.. ipython:: python

   df = pd.DataFrame(np.random.randn(6, 2), pd.date_range('20010102', periods=6),
                     columns=['A', ' B'])
   df['timestamp'] = pd.Timestamp('20010103')
   df

   # datetime64[ns] out of the box
   df.dtypes.value_counts()

   # use the traditional nan, which is mapped to NaT internally
   df.loc[df.index[2:4], ['A', 'timestamp']] = np.nan
   df

Astype conversion on ``datetime64[ns]`` to ``object``, implicitly converts ``NaT`` to ``np.nan``

.. ipython:: python

   s = pd.Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])
   s.dtype
   s[1] = np.nan
   s
   s.dtype
   s = s.astype('O')
   s
   s.dtype


API changes
~~~~~~~~~~~

  - Added to_series() method to indices, to facilitate the creation of indexers
    (:issue:`3275`)

  - ``HDFStore``

    - added the method ``select_column`` to select a single column from a table as a Series.
    - deprecated the ``unique`` method, can be replicated by ``select_column(key,column).unique()``
    - ``min_itemsize`` parameter to ``append`` will now automatically create data_columns for passed keys

Enhancements
~~~~~~~~~~~~

  - Improved performance of df.to_csv() by up to 10x in some cases. (:issue:`3059`)

  - Numexpr is now a :ref:`Recommended Dependencies <install.recommended_dependencies>`, to accelerate certain
    types of numerical and boolean operations

  - Bottleneck is now a :ref:`Recommended Dependencies <install.recommended_dependencies>`, to accelerate certain
    types of ``nan`` operations

  - ``HDFStore``

    - support ``read_hdf/to_hdf`` API similar to ``read_csv/to_csv``

      .. ipython:: python

          df = pd.DataFrame({'A': range(5), 'B': range(5)})
          df.to_hdf('store.h5', 'table', append=True)
          pd.read_hdf('store.h5', 'table', where=['index > 2'])

      .. ipython:: python
          :suppress:
          :okexcept:

          import os

          os.remove('store.h5')

    - provide dotted attribute access to ``get`` from stores, e.g. ``store.df == store['df']``

    - new keywords ``iterator=boolean``, and ``chunksize=number_in_a_chunk`` are
      provided to support iteration on ``select`` and ``select_as_multiple`` (:issue:`3076`)

  - You can now select timestamps from an *unordered* timeseries similarly to an *ordered* timeseries (:issue:`2437`)

  - You can now select with a string from a DataFrame with a datelike index, in a similar way to a Series (:issue:`3070`)

    .. ipython:: python

        idx = pd.date_range("2001-10-1", periods=5, freq='M')
        ts = pd.Series(np.random.rand(len(idx)), index=idx)
        ts['2001']

        df = pd.DataFrame({'A': ts})
        df['2001']

  - ``Squeeze`` to possibly remove length 1 dimensions from an object.

    .. code-block:: python

       >>> p = pd.Panel(np.random.randn(3, 4, 4), items=['ItemA', 'ItemB', 'ItemC'],
       ...              major_axis=pd.date_range('20010102', periods=4),
       ...              minor_axis=['A', 'B', 'C', 'D'])
       >>> p
       <class 'pandas.core.panel.Panel'>
       Dimensions: 3 (items) x 4 (major_axis) x 4 (minor_axis)
       Items axis: ItemA to ItemC
       Major_axis axis: 2001-01-02 00:00:00 to 2001-01-05 00:00:00
       Minor_axis axis: A to D

       >>> p.reindex(items=['ItemA']).squeeze()
                          A         B         C         D
       2001-01-02  0.926089 -2.026458  0.501277 -0.204683
       2001-01-03 -0.076524  1.081161  1.141361  0.479243
       2001-01-04  0.641817 -0.185352  1.824568  0.809152
       2001-01-05  0.575237  0.669934  1.398014 -0.399338

       >>> p.reindex(items=['ItemA'], minor=['B']).squeeze()
       2001-01-02   -2.026458
       2001-01-03    1.081161
       2001-01-04   -0.185352
       2001-01-05    0.669934
       Freq: D, Name: B, dtype: float64

  - In ``pd.io.data.Options``,

    + Fix bug when trying to fetch data for the current month when already
      past expiry.
    + Now using lxml to scrape html instead of BeautifulSoup (lxml was faster).
    + New instance variables for calls and puts are automatically created
      when a method that creates them is called. This works for current month
      where the instance variables are simply ``calls`` and ``puts``. Also
      works for future expiry months and save the instance variable as
      ``callsMMYY`` or ``putsMMYY``, where ``MMYY`` are, respectively, the
      month and year of the option's expiry.
    + ``Options.get_near_stock_price`` now allows the user to specify the
      month for which to get relevant options data.
    + ``Options.get_forward_data`` now has optional kwargs ``near`` and
      ``above_below``. This allows the user to specify if they would like to
      only return forward looking data for options near the current stock
      price. This just obtains the data from Options.get_near_stock_price
      instead of Options.get_xxx_data() (:issue:`2758`).

  - Cursor coordinate information is now displayed in time-series plots.

  - added option `display.max_seq_items` to control the number of
    elements printed per sequence pprinting it.  (:issue:`2979`)

  - added option `display.chop_threshold` to control display of small numerical
    values. (:issue:`2739`)

  - added option `display.max_info_rows` to prevent verbose_info from being
    calculated for frames above 1M rows (configurable). (:issue:`2807`, :issue:`2918`)

  - value_counts() now accepts a "normalize" argument, for normalized
    histograms. (:issue:`2710`).

  - DataFrame.from_records now accepts not only dicts but any instance of
    the collections.Mapping ABC.

  - added option `display.mpl_style` providing a sleeker visual style
    for plots. Based on https://gist.github.com/huyng/816622 (:issue:`3075`).

  - Treat boolean values as integers (values 1 and 0) for numeric
    operations. (:issue:`2641`)

  - to_html() now accepts an optional "escape" argument to control reserved
    HTML character escaping (enabled by default) and escapes ``&``, in addition
    to ``<`` and ``>``.  (:issue:`2919`)

See the :ref:`full release notes
<release>` or issue tracker
on GitHub for a complete list.


.. _whatsnew_0.11.0.contributors:

Contributors
~~~~~~~~~~~~

.. contributors:: v0.10.1..v0.11.0
