.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   import os
   np.random.seed(123456)
   np.set_printoptions(precision=4, suppress=True)
   import matplotlib
   matplotlib.style.use('ggplot')
   import matplotlib.pyplot as plt
   pd.options.display.max_rows = 8

Reshaping
---------

See the sections on :ref:`Hierarchical Indexing <advanced.hierarchical>` and
:ref:`Reshaping <reshaping.stacking>`.

Stack
~~~~~

.. ipython:: python

   tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
                        'foo', 'foo', 'qux', 'qux'],
                       ['one', 'two', 'one', 'two',
                        'one', 'two', 'one', 'two']]))
   index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
   df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
   df2 = df[:4]
   df2

The :meth:`~DataFrame.stack` method "compresses" a level in the DataFrame's
columns.

.. ipython:: python

   stacked = df2.stack()
   stacked

With a "stacked" DataFrame or Series (having a ``MultiIndex`` as the
``index``), the inverse operation of :meth:`~DataFrame.stack` is
:meth:`~DataFrame.unstack`, which by default unstacks the **last level**:

.. ipython:: python

   stacked.unstack()
   stacked.unstack(1)
   stacked.unstack(0)

Pivot Tables
~~~~~~~~~~~~
See the section on :ref:`Pivot Tables <reshaping.pivot>`.

.. ipython:: python

   df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
                      'B' : ['A', 'B', 'C'] * 4,
                      'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
                      'D' : np.random.randn(12),
                      'E' : np.random.randn(12)})
   df

We can produce pivot tables from this data very easily:

.. ipython:: python

   pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])