pandas 0.10.2

- Allow mixed dtypes (e.g ``float32/float64/int32/int16/int8``) to coexist in DataFrames and propogate in operations

- added ``blocks`` attribute to DataFrames, to return a dict of dtypes to homogeneously dtyped DataFrames

- added keyword ``convert_numeric`` to ``convert_objects()`` to try to convert object dtypes to numeric types

- ``convert_dates`` in ``convert_objects`` can now be ``coerce`` which will return a datetime64[ns] dtype

with non-convertibles set as ``NaT``; will preserve an all-nan object (e.g. strings)

- Do not automatically upcast numeric specified dtypes to ``int64`` or ``float64`` (GH622_ and GH797_)

- Guarantee that ``convert_objects()`` for Series/DataFrame always returns a copy

- groupby operations will respect dtypes for numeric float operations (float32/float64); other types will be operated on,

and will try to cast back to the input dtype (e.g. if an int is passed, as long as the output doesn't have nans,

then an int will be returned)

**API Changes**

- Series now automatically will try to set the correct dtype based on passed datetimelike objects (datetime/Timestamp)

- timedelta64 are returned in appropriate cases (e.g. Series - Series, when both are datetime64)

- mixed datetimes and objects (GH2751_) in a constructor witll be casted correctly

- astype on datetimes to object are now handled (as well as NaT conversions to np.nan)

**Bug fixes**

- Single element ndarrays of datetimelike objects are handled (e.g. np.array(datetime(2001,1,1,0,0))), w/o dtype being passed

- 0-dim ndarrays with a passed dtype are handled correctly (e.g. np.array(0.,dtype='float32'))

.. _whatsnew_0102:

v0.10.2 (February ??, 2013)

---------------------------

.. include:: v0.10.2.txt

MINOR = 10

MICRO = 1

ISRELEASED = True