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Copy file name to clipboardExpand all lines: doc/source/v0.11.0.txt
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@@ -13,7 +13,8 @@ API changes
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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.
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**Dtype Specification**
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Dtype Specification
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~~~~~~~~~~~~~~~~~~~
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.. ipython:: python
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df3
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df3.dtypes
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**Dtype conversion**
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Dtype Conversion
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~~~~~~~~~~~~~~~~
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.. ipython:: python
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Timestamp('20010104'), '20010105'],dtype='O')
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s.convert_objects(convert_dates='coerce')
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Dtype Gotchas
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~~~~~~~~~~~~~
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**Platform Gotchas**
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Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of ``int64`` and ``float64``,
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*regardless of platform*. This is not an apparent change from earlier versions of pandas. If you specify
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dtypes, they *WILL* be respected, however.
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dtypes, they *WILL* be respected, however (GH2837_)
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The following will all result in ``int64`` dtypes
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.. ipython:: python
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DataFrame([1,2],columns=['a']).dtypes
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DataFrame({'a' : [1,2] }.dtypes
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DataFrame({'a' : 1).dtypes
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DataFrame({'a' : [1,2] }).dtypes
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DataFrame({'a' : 1 }, index=range(2)).dtypes
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Keep in mind that ``DataFrame(np.array([1,2]))`` **WILL** result in ``int32`` on 32-bit platforms!
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casted
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casted.dtypes
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**Datetimes conversion**
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Datetimes Conversion
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~~~~~~~~~~~~~~~~~~~~
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Datetime64[ns] columns in a DataFrame (or a Series) allow the use of ``np.nan`` to indicate a nan value,
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in addition to the traditional ``NaT``, or not-a-time. This allows convenient nan setting in a generic way.
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Furthermore ``datetime64[ns]`` columns are created by default, when passed datetimelike objects (*this change was introduced in 0.10.1*)
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(GH2809_, GH2810_)
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.. ipython:: python
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@@ -139,23 +145,27 @@ New features
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**Enhancements**
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- In ``HDFStore``, provide dotted attribute access to ``get`` from stores (e.g. store.df == store['df'])
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- In ``HDFStore``, provide dotted attribute access to ``get`` from stores
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(e.g. store.df == store['df'])
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``Squeeze`` to possibly remove length 1 dimensions from an object.
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- ``Squeeze`` to possibly remove length 1 dimensions from an object.
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.. ipython:: python
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.. ipython:: python
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p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
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p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
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major_axis=date_range('20010102',periods=4),
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minor_axis=['A','B','C','D'])
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p
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p.reindex(items=['ItemA']).squeeze()
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p.reindex(items=['ItemA'],minor=['B']).squeeze()
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p
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p.reindex(items=['ItemA']).squeeze()
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p.reindex(items=['ItemA'],minor=['B']).squeeze()
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**Bug Fixes**
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See the `full release notes
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<https://github.com/pydata/pandas/blob/master/RELEASE.rst>`__ or issue tracker
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