.. 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

   dates = pd.date_range('20130101', periods=6)
   df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

Operations
----------

See the :ref:`Basic section on Binary Ops <basics.binop>`

Stats
~~~~~

Operations in general *exclude* missing data.

Performing a descriptive statistic

.. ipython:: python

   df.mean()

Same operation on the other axis

.. ipython:: python

   df.mean(1)

Operating with objects that have different dimensionality and need alignment.
In addition, pandas automatically broadcasts along the specified dimension.

.. ipython:: python

   s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
   s
   df.sub(s, axis='index')


Apply
~~~~~

Applying functions to the data

.. ipython:: python

   df.apply(np.cumsum)
   df.apply(lambda x: x.max() - x.min())

Histogramming
~~~~~~~~~~~~~

See more at :ref:`Histogramming and Discretization <basics.discretization>`

.. ipython:: python

   s = pd.Series(np.random.randint(0, 7, size=100))
   s
   s.value_counts()

String Methods
~~~~~~~~~~~~~~

Series is equipped with a set of string processing methods in the `str`
attribute that make it easy to operate on each element of the array, as in the
code snippet below. Note that pattern-matching in `str` generally uses `regular
expressions <https://docs.python.org/2/library/re.html>`__ by default (and in
some cases always uses them). See more at :ref:`Vectorized String Methods
<text.string_methods>`.

.. ipython:: python

   s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
   s.str.lower()