.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   np.random.seed(10)
   pd.options.display.max_rows=15
   import matplotlib
   matplotlib.style.use('ggplot')
   import matplotlib.pyplot as plt

Missing data casting rules and indexing
---------------------------------------

While pandas supports storing arrays of integer and boolean type, these types
are not capable of storing missing data. Until we can switch to using a native
NA type in NumPy, we've established some "casting rules" when reindexing will
cause missing data to be introduced into, say, a Series or DataFrame. Here they
are:

.. csv-table::
    :header: "data type", "Cast to"
    :widths: 40, 40

    integer, float
    boolean, object
    float, no cast
    object, no cast

For example:

.. ipython:: python

   s = pd.Series(np.random.randn(5), index=[0, 2, 4, 6, 7])
   s
   s > 0
   (s > 0).dtype
   crit = (s > 0).reindex(list(range(8)))
   crit
   crit.dtype

Ordinarily NumPy will complain if you try to use an object array (even if it
contains boolean values) instead of a boolean array to get or set values from
an ndarray (e.g. selecting values based on some criteria). If a boolean vector
contains NAs, an exception will be generated:

.. ipython:: python
   :okexcept:

   reindexed = s.reindex(list(range(8))).fillna(0)
   reindexed[crit]

However, these can be filled in using **fillna** and it will work fine:

.. ipython:: python

   reindexed[crit.fillna(False)]
   reindexed[crit.fillna(True)]