v0.11.0.txt

.. _whatsnew_0110:

v0.11.0 (March ??, 2013)
------------------------

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.

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

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 indicies 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.

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

    ``.ix`` is especially usefull when dealing with mixed positional/label based hierarchial indexes. 

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


Selection Deprecations
~~~~~~~~~~~~~~~~~~~~~~

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

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

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

Cross-sectional slices on non-hierarchical indices are now easily performed using
``.loc`` and/or ``.loc``. The methods:

  - ``xs`` (for DataFrame),
  - ``minor_xs`` and ``major_xs`` (for Panel)

now exist primarily for backward compatibility.

See the section :ref:`Selection by Label <indexing.label>` 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 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')
   df1
   df1.dtypes
   df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'), B = Series(randn(8)), C = Series(randn(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
~~~~~~~~~~~~~~~~

.. ipython:: python

   # this is lower-common-denomicator upcasting (meaning you get the dtype which can accomodate all of the types)
   df3.values.dtype

   # conversion of dtypes
   df3.astype('float32').dtypes

   # mixed type conversions
   df3['D'] = '1.'
   df3['E'] = '1'
   df3.convert_objects(convert_numeric=True).dtypes

   # same, but specific dtype conversion
   df3['D'] = df3['D'].astype('float16')
   df3['E'] = df3['E'].astype('int32')
   df3.dtypes

   # forcing date coercion
   s = Series([datetime(2001,1,1,0,0), 'foo', 1.0, 1,
               Timestamp('20010104'), '20010105'],dtype='O')
   s.convert_objects(convert_dates='coerce')

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 (GH2837_)

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

.. ipython:: python

    DataFrame([1,2],columns=['a']).dtypes
    DataFrame({'a' : [1,2] }).dtypes
    DataFrame({'a' : 1 }, index=range(2)).dtypes

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 (coming soon).

.. ipython:: python

   dfi = df3.astype('int32')
   dfi['D'] = dfi['D'].astype('int64')
   dfi
   dfi.dtypes

   casted = dfi[dfi>0]
   casted
   casted.dtypes

While float dtypes are unchanged.

.. ipython:: python

   df4 = df3.copy()
   df4['A'] = df4['A'].astype('float32')
   df4.dtypes

   casted = df4[df4>0]
   casted
   casted.dtypes

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*)
(GH2809_, GH2810_)

.. ipython:: python

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

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

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

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

.. ipython:: python

   import datetime
   s = 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


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

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

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

    .. ipython:: python

       p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
                          major_axis=date_range('20010102',periods=4),
                          minor_axis=['A','B','C','D'])
       p
       p.reindex(items=['ItemA']).squeeze()
       p.reindex(items=['ItemA'],minor=['B']).squeeze()

  - 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().

Bug Fixes
~~~~~~~~~

  - Timedeltas are now fully operational (closes GH2898_)

    .. ipython:: python

        from datetime import datetime, timedelta
        s  = Series(date_range('2012-1-1', periods=3, freq='D'))
        td = Series([ timedelta(days=i) for i in range(3) ])
        df = DataFrame(dict(A = s, B = td))
        df
        s - s.max()
        df['C'] = df['A'] + df['B']
        df
        df.dtypes

        # timedelta are representas as ``NaT``
        y = s - s.shift()
	y

	# can be set via ``np.nan``
	y[1] = np.nan
	y

        # works on lhs too
   	s.max() - s

        # some timedelta numeric operations are supported
        td - timedelta(minutes=5,seconds=5,microseconds=5)

  - Fix pretty-printing of infinite data structures (closes GH2978_)


See the `full release notes
<https://github.com/pydata/pandas/blob/master/RELEASE.rst>`__ or issue tracker
on GitHub for a complete list.

.. _GH2809: https://github.com/pydata/pandas/issues/2809
.. _GH2810: https://github.com/pydata/pandas/issues/2810
.. _GH2837: https://github.com/pydata/pandas/issues/2837
.. _GH2898: https://github.com/pydata/pandas/issues/2898
.. _GH2978: https://github.com/pydata/pandas/issues/2978