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v0.14.0.txt
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.. _whatsnew_0140:
v0.14.0 (May ? , 2014)
----------------------
This is a major release from 0.13.1 and includes a small number of API changes, several new features,
enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
users upgrade to this version.
Highlights include:
- MultIndexing Using Slicers
- Joining a singly-indexed DataFrame with a multi-indexed DataFrame
API changes
~~~~~~~~~~~
- ``iloc`` will now accept out-of-bounds indexers, e.g. a value that exceeds the length of the object being
indexed. These will be excluded. This will make pandas conform more with pandas/numpy indexing of out-of-bounds
values. A single indexer that is out-of-bounds and drops the dimensions of the object will still raise
``IndexError`` (:issue:`6296`). This could result in an empty axis (e.g. an empty DataFrame being returned)
.. ipython:: python
df = DataFrame(np.random.randn(5,2),columns=list('AB'))
df
df.iloc[[4,5,6]]
df.iloc[4:6]
df.iloc[:,2:3]
df.iloc[:,1:3]
MultiIndexing Using Slicers
~~~~~~~~~~~~~~~~~~~~~~~~~~~
In 0.14.0 we added a new way to slice multi-indexed objects.
You can slice a multi-index by providing multiple indexers.
You can provide any of the selectors as if you are indexing by label, see :ref:`Selection by Label <indexing.label>`,
including slices, lists of labels, labels, and boolean indexers.
You can use ``slice(None)`` to select all the contents of *that* level. You do not need to specify all the
*deeper* levels, they will be implied as ``slice(None)``.
As usual, **both sides** of the slicers are included as this is label indexing.
See :ref:`the docs<indexing.mi_slicers>`
See also issues (:issue:`6134`, :issue:`4036`, :issue:`3057`, :issue:`2598`, :issue:`5641`)
.. warning::
You should specify all axes in the ``.loc`` specifier, meaning the indexer for the **index** and
for the **columns**. Their are some ambiguous cases where the passed indexer could be mis-interpreted
as indexing *both* axes, rather than into say the MuliIndex for the rows.
You should do this:
.. code-block:: python
df.loc[(slice('A1','A3'),.....),:]
rather than this:
.. code-block:: python
df.loc[(slice('A1','A3'),.....)]
.. warning::
You will need to make sure that the selection axes are fully lexsorted!
.. ipython:: python
def mklbl(prefix,n):
return ["%s%s" % (prefix,i) for i in range(n)]
index = MultiIndex.from_product([mklbl('A',4),
mklbl('B',2),
mklbl('C',4),
mklbl('D',2)])
columns = MultiIndex.from_tuples([('a','foo'),('a','bar'),
('b','foo'),('b','bah')],
names=['lvl0', 'lvl1'])
df = DataFrame(np.arange(len(index)*len(columns)).reshape((len(index),len(columns))),
index=index,
columns=columns).sortlevel().sortlevel(axis=1)
df
Basic multi-index slicing using slices, lists, and labels.
.. ipython:: python
df.loc[(slice('A1','A3'),slice(None), ['C1','C3']),:]
You can use a ``pd.IndexSlice`` to shortcut the creation of these slices
.. ipython:: python
idx = pd.IndexSlice
df.loc[idx[:,:,['C1','C3']],idx[:,'foo']]
It is possible to perform quite complicated selections using this method on multiple
axes at the same time.
.. ipython:: python
df.loc['A1',(slice(None),'foo')]
df.loc[idx[:,:,['C1','C3']],idx[:,'foo']]
Using a boolean indexer you can provide selection related to the *values*.
.. ipython:: python
mask = df[('a','foo')]>200
df.loc[idx[mask,:,['C1','C3']],idx[:,'foo']]
You can also specify the ``axis`` argument to ``.loc`` to interpret the passed
slicers on a single axis.
.. ipython:: python
df.loc(axis=0)[:,:,['C1','C3']]
Furthermore you can *set* the values using these methods
.. ipython:: python
df2 = df.copy()
df2.loc(axis=0)[:,:,['C1','C3']] = -10
df2
You can use a right-hand-side of an alignable object as well.
.. ipython:: python
df2 = df.copy()
df2.loc[idx[:,:,['C1','C3']],:] = df2*1000
df2
Prior Version Deprecations/Changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
There are no announced changes in 0.13.1 or prior that are taking effect as of 0.14.0
Deprecations
~~~~~~~~~~~~
There are no deprecations of prior behavior in 0.14.0
Enhancements
~~~~~~~~~~~~
- pd.read_clipboard will, if 'sep' is unspecified, try to detect data copied from a spreadsheet
and parse accordingly. (:issue:`6223`)
- ``plot(legend='reverse')`` will now reverse the order of legend labels for
most plot kinds. (:issue:`6014`)
- improve performance of slice indexing on Series with string keys (:issue:`6341`, :issue:`6372`)
- Hexagonal bin plots from ``DataFrame.plot`` with ``kind='hexbin'`` (:issue:`5478`)
- Joining a singly-indexed DataFrame with a multi-indexed DataFrame (:issue:`3662`)
See :ref:`the docs<merging.join_on_mi>`. Joining multi-index DataFrames on both the left and right is not yet supported ATM.
.. ipython:: python
household = DataFrame(dict(household_id = [1,2,3],
male = [0,1,0],
wealth = [196087.3,316478.7,294750]),
columns = ['household_id','male','wealth']
).set_index('household_id')
household
portfolio = DataFrame(dict(household_id = [1,2,2,3,3,3,4],
asset_id = ["nl0000301109","nl0000289783","gb00b03mlx29",
"gb00b03mlx29","lu0197800237","nl0000289965",np.nan],
name = ["ABN Amro","Robeco","Royal Dutch Shell","Royal Dutch Shell",
"AAB Eastern Europe Equity Fund","Postbank BioTech Fonds",np.nan],
share = [1.0,0.4,0.6,0.15,0.6,0.25,1.0]),
columns = ['household_id','asset_id','name','share']
).set_index(['household_id','asset_id'])
portfolio
household.join(portfolio, how='inner')
- ``quotechar``, ``doublequote``, and ``escapechar`` can now be specified when
using ``DataFrame.to_csv`` (:issue:`5414`, :issue:`4528`)
Performance
~~~~~~~~~~~
Experimental
~~~~~~~~~~~~
There are no experimental changes in 0.14.0
Bug Fixes
~~~~~~~~~
See :ref:`V0.14.0 Bug Fixes<release.bug_fixes-0.14.0>` for an extensive list of bugs that have been fixed in 0.14.0.
See the :ref:`full release notes
<release>` or issue tracker
on GitHub for a complete list of all API changes, Enhancements and Bug Fixes.