Missing Data
------------

pandas primarily uses the value ``np.nan`` to represent missing data. It is by
default not included in computations. See the :ref:`Missing Data section
<missing_data>`

Reindexing allows you to change/add/delete the index on a specified axis. This
returns a copy of the data.

.. ipython:: python

   df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
   df1.loc[dates[0]:dates[1],'E'] = 1
   df1

To drop any rows that have missing data.

.. ipython:: python

   df1.dropna(how='any')

Filling missing data

.. ipython:: python

   df1.fillna(value=5)

To get the boolean mask where values are ``nan``

.. ipython:: python

   pd.isnull(df1)