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v0.11.0.txt
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.. _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. There are also a number of
important API changes that long-time pandas users should pay close attention
to.
API changes
~~~~~~~~~~~
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.
Dtype Specification
~~~~~~~~~~~~~~~~~~~
.. 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()
s - datetime(2011,1,1,3,5)
s + timedelta(minutes=5)
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
datetime(2011,1,1,3,5) - s
timedelta(minutes=5) + s
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