.. 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

Merge
-----

Concat
~~~~~~

pandas provides various facilities for easily combining together Series,
DataFrame, and Panel objects with various kinds of set logic for the indexes
and relational algebra functionality in the case of join / merge-type
operations.

See the :ref:`Merging section <merging>`

Concatenating pandas objects together with :func:`concat`:

.. ipython:: python

   df = pd.DataFrame(np.random.randn(10, 4))
   df

   # break it into pieces
   pieces = [df[:3], df[3:7], df[7:]]

   pd.concat(pieces)

Join
~~~~

SQL style merges. See the :ref:`Database style joining <merging.join>`

.. ipython:: python

   left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
   right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
   left
   right
   pd.merge(left, right, on='key')

Another example that can be given is:

.. ipython:: python

   left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
   right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
   left
   right
   pd.merge(left, right, on='key')


Append
~~~~~~

Append rows to a dataframe. See the :ref:`Appending <merging.concatenation>`

.. ipython:: python

   df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
   df
   s = df.iloc[3]
   df.append(s, ignore_index=True)