.. 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'))

Selection
---------

.. note::

   While standard Python / Numpy expressions for selecting and setting are
   intuitive and come in handy for interactive work, for production code, we
   recommend the optimized pandas data access methods, ``.at``, ``.iat``,
   ``.loc``, ``.iloc`` and ``.ix``.

See the indexing documentation :ref:`Indexing and Selecting Data <indexing>` and :ref:`MultiIndex / Advanced Indexing <advanced>`

Getting
~~~~~~~

Selecting a single column, which yields a ``Series``,
equivalent to ``df.A``

.. ipython:: python

   df
   df['A']

Selecting via ``[]``, which slices the rows.

.. ipython:: python

   df[0:3]
   df['20130102':'20130104']

Selection by Label
~~~~~~~~~~~~~~~~~~

See more in :ref:`Selection by Label <indexing.label>`

For getting a cross section using a label

.. ipython:: python

   df.loc[dates[0]]

Selecting on a multi-axis by label

.. ipython:: python

   df.loc[:,['A','B']]

Showing label slicing, both endpoints are *included*

.. ipython:: python

   df.loc['20130102':'20130104',['A','B']]

Reduction in the dimensions of the returned object

.. ipython:: python

   df.loc['20130102',['A','B']]

For getting a scalar value

.. ipython:: python

   df.loc[dates[0],'A']

For getting fast access to a scalar (equiv to the prior method)

.. ipython:: python

   df.at[dates[0],'A']

Selection by Position
~~~~~~~~~~~~~~~~~~~~~

See more in :ref:`Selection by Position <indexing.integer>`

Select via the position of the passed integers

.. ipython:: python

   df.iloc[3]

By integer slices, acting similar to numpy/python

.. ipython:: python

   df.iloc[3:5,0:2]

By lists of integer position locations, similar to the numpy/python style

.. ipython:: python

   df.iloc[[1,2,4],[0,2]]

For slicing rows explicitly

.. ipython:: python

   df.iloc[1:3,:]

For slicing columns explicitly

.. ipython:: python

   df.iloc[:,1:3]

For getting a value explicitly

.. ipython:: python

   df.iloc[1,1]

For getting fast access to a scalar (equiv to the prior method)

.. ipython:: python

   df.iat[1,1]

Boolean Indexing
~~~~~~~~~~~~~~~~

Using a single column's values to select data.

.. ipython:: python

   df[df.A > 0]

A ``where`` operation for getting.

.. ipython:: python

   df[df > 0]

Using the :func:`~Series.isin` method for filtering:

.. ipython:: python

   df2 = df.copy()
   df2['E'] = ['one', 'one','two','three','four','three']
   df2
   df2[df2['E'].isin(['two','four'])]

Setting
~~~~~~~

Setting a new column automatically aligns the data
by the indexes

.. ipython:: python

   s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
   s1
   df['F'] = s1

Setting values by label

.. ipython:: python

   df.at[dates[0],'A'] = 0

Setting values by position

.. ipython:: python

   df.iat[0,1] = 0

Setting by assigning with a numpy array

.. ipython:: python

   df.loc[:,'D'] = np.array([5] * len(df))

The result of the prior setting operations

.. ipython:: python

   df

A ``where`` operation with setting.

.. ipython:: python

   df2 = df.copy()
   df2[df2 > 0] = -df2
   df2