.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   np.random.seed(123456)
   import pandas as pd
   pd.options.display.max_rows=8
   np.set_printoptions(precision=4, suppress=True)

Reshaping by pivoting DataFrame objects
---------------------------------------
.. code-block:: python

   import pandas.util.testing as tm; tm.N = 3
   def unpivot(frame):
       N, K = frame.shape
       data = {'value' : frame.values.ravel('F'),
               'variable' : np.asarray(frame.columns).repeat(N),
               'date' : np.tile(np.asarray(frame.index), K)}
       return pd.DataFrame(data, columns=['date', 'variable', 'value'])
   df = unpivot(tm.makeTimeDataFrame())

.. ipython::
   :suppress:

   In [1]: import pandas.util.testing as tm; tm.N = 3

   In [2]: def unpivot(frame):
      ...:         N, K = frame.shape
      ...:         data = {'value' : frame.values.ravel('F'),
      ...:                 'variable' : np.asarray(frame.columns).repeat(N),
      ...:                 'date' : np.tile(np.asarray(frame.index), K)}
      ...:         columns = ['date', 'variable', 'value']
      ...:         return pd.DataFrame(data, columns=columns)
      ...:

   In [3]: df = unpivot(tm.makeTimeDataFrame())

Data is often stored in CSV files or databases in so-called "stacked" or
"record" format:

.. ipython:: python

   df

To select out everything for variable ``A`` we could do:

.. ipython:: python

   df[df['variable'] == 'A']

But suppose we wish to do time series operations with the variables. A better
representation would be where the ``columns`` are the unique variables and an
``index`` of dates identifies individual observations. To reshape the data into
this form, use the ``pivot`` function:

.. ipython:: python

   df
   df.pivot(index='date', columns='variable', values='value')

If the ``values`` argument is omitted, and the input DataFrame has more than
one column of values which are not used as column or index inputs to ``pivot``,
then the resulting "pivoted" DataFrame will have :ref:`hierarchical columns
<advanced.hierarchical>` whose topmost level indicates the respective value
column:

.. ipython:: python
   
   df['value2'] = df['value'] * 2
   df
   pivoted = df.pivot('date', 'variable')
   pivoted

You of course can then select subsets from the pivoted DataFrame:

.. ipython:: python

   pivoted['value2']

Note that this returns a view on the underlying data in the case where the data
are homogeneously-typed.