.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   np.random.seed(123456)
   import pandas as pd
   pd.options.display.max_rows=15
   randn = np.random.randn
   np.set_printoptions(precision=4, suppress=True)

   import matplotlib.pyplot as plt
   plt.close('all')
   import pandas.util.doctools as doctools
   p = doctools.TablePlotter()

.. _merging.concat:

Concatenating objects
---------------------

The ``concat`` function (in the main pandas namespace) does all of the heavy
lifting of performing concatenation operations along an axis while performing
optional set logic (union or intersection) of the indexes (if any) on the other
axes. Note that I say "if any" because there is only a single possible axis of
concatenation for Series.

Before diving into all of the details of ``concat`` and what it can do, here is
a simple example:

.. ipython:: python

   df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                       'B': ['B0', 'B1', 'B2', 'B3'],
                       'C': ['C0', 'C1', 'C2', 'C3'],
                       'D': ['D0', 'D1', 'D2', 'D3']},
                       index=[0, 1, 2, 3])

   df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                       'B': ['B4', 'B5', 'B6', 'B7'],
                       'C': ['C4', 'C5', 'C6', 'C7'],
                       'D': ['D4', 'D5', 'D6', 'D7']},
                        index=[4, 5, 6, 7])

   df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
                       'B': ['B8', 'B9', 'B10', 'B11'],
                       'C': ['C8', 'C9', 'C10', 'C11'],
                       'D': ['D8', 'D9', 'D10', 'D11']},
                       index=[8, 9, 10, 11])

   frames = [df1, df2, df3]
   result = pd.concat(frames)

.. ipython:: python

   @savefig merging_concat_basic.png
   p.plot(frames, result,labels=['df1', 'df2', 'df3'], vertical=True);

Like its sibling function on ndarrays, ``numpy.concatenate``, ``pandas.concat``
takes a list or dict of homogeneously-typed objects and concatenates them with
some configurable handling of "what to do with the other axes":

::

    pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
              keys=None, levels=None, names=None, verify_integrity=False)

- ``objs``: a sequence or mapping of Series, DataFrame, or Panel objects. If a
  dict is passed, the sorted keys will be used as the `keys` argument, unless
  it is passed, in which case the values will be selected (see below). Any None
  objects will be dropped silently unless they are all None in which case a
  ValueError will be raised.
- ``axis``: {0, 1, ...}, default 0. The axis to concatenate along.
- ``join``: {'inner', 'outer'}, default 'outer'. How to handle indexes on
  other axis(es). Outer for union and inner for intersection.
- ``join_axes``: list of Index objects. Specific indexes to use for the other
  n - 1 axes instead of performing inner/outer set logic.
- ``keys``: sequence, default None. Construct hierarchical index using the
  passed keys as the outermost level. If multiple levels passed, should
  contain tuples.
- ``levels`` : list of sequences, default None. Specific levels (unique values)
  to use for constructing a MultiIndex. Otherwise they will be inferred from the
  keys.
- ``names``: list, default None. Names for the levels in the resulting
  hierarchical index.
- ``verify_integrity``: boolean, default False. Check whether the new
  concatenated axis contains duplicates. This can be very expensive relative
  to the actual data concatenation.
- ``ignore_index`` : boolean, default False. If True, do not use the index
  values on the concatenation axis. The resulting axis will be labeled 0, ...,
  n - 1. This is useful if you are concatenating objects where the
  concatenation axis does not have meaningful indexing information. Note
  the index values on the other axes are still respected in the join.
- ``copy`` : boolean, default True. If False, do not copy data unnecessarily.

Without a little bit of context and example many of these arguments don't make
much sense. Let's take the above example. Suppose we wanted to associate
specific keys with each of the pieces of the chopped up DataFrame. We can do
this using the ``keys`` argument:

.. ipython:: python

   result = pd.concat(frames, keys=['x', 'y', 'z'])

.. ipython:: python

   @savefig merging_concat_keys.png
   p.plot(frames, result,labels=['df1', 'df2', 'df3'], vertical=True)

As you can see (if you've read the rest of the documentation), the resulting
object's index has a :ref:`hierarchical index <advanced.hierarchical>`. This
means that we can now do stuff like select out each chunk by key:

.. ipython:: python

   result.ix['y']

It's not a stretch to see how this can be very useful. More detail on this
functionality below.

.. note::
   It is worth noting however, that ``concat`` (and therefore ``append``) makes
   a full copy of the data, and that constantly reusing this function can
   create a significant performance hit. If you need to use the operation over
   several datasets, use a list comprehension.

::

   frames = [ process_your_file(f) for f in files ]
   result = pd.concat(frames)


Set logic on the other axes
~~~~~~~~~~~~~~~~~~~~~~~~~~~

When gluing together multiple DataFrames (or Panels or...), for example, you
have a choice of how to handle the other axes (other than the one being
concatenated). This can be done in three ways:

- Take the (sorted) union of them all, ``join='outer'``. This is the default
  option as it results in zero information loss.
- Take the intersection, ``join='inner'``.
- Use a specific index (in the case of DataFrame) or indexes (in the case of
  Panel or future higher dimensional objects), i.e. the ``join_axes`` argument

Here is a example of each of these methods. First, the default ``join='outer'``
behavior:

.. ipython:: python

   df4 = pd.DataFrame({'B': ['B2', 'B3', 'B6', 'B7'],
                    'D': ['D2', 'D3', 'D6', 'D7'],
                    'F': ['F2', 'F3', 'F6', 'F7']},
                   index=[2, 3, 6, 7])
   result = pd.concat([df1, df4], axis=1)


.. ipython:: python

   @savefig merging_concat_axis1.png
   p.plot([df1, df4], result,labels=['df1', 'df4'], vertical=False);

Note that the row indexes have been unioned and sorted. Here is the same thing
with ``join='inner'``:

.. ipython:: python

   result = pd.concat([df1, df4], axis=1, join='inner')

.. ipython:: python

   @savefig merging_concat_axis1_inner.png
   p.plot([df1, df4], result,labels=['df1', 'df4'], vertical=False);

Lastly, suppose we just wanted to reuse the *exact index* from the original
DataFrame:

.. ipython:: python

   result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])

.. ipython:: python

   @savefig merging_concat_axis1_join_axes.png
   p.plot([df1, df4], result,labels=['df1', 'df4'], vertical=False);
   plt.close('all');

.. _merging.concatenation:

Concatenating using ``append``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A useful shortcut to ``concat`` are the ``append`` instance methods on Series
and DataFrame. These methods actually predated ``concat``. They concatenate
along ``axis=0``, namely the index:

.. ipython:: python

   result = df1.append(df2)

.. ipython:: python

   @savefig merging_append1.png
   p.plot([df1, df2], result,labels=['df1', 'df2'], vertical=True);

In the case of DataFrame, the indexes must be disjoint but the columns do not
need to be:

.. ipython:: python

   result = df1.append(df4)

.. ipython:: python

   @savefig merging_append2.png
   p.plot([df1, df4], result,labels=['df1', 'df4'], vertical=True);

``append`` may take multiple objects to concatenate:

.. ipython:: python

   result = df1.append([df2, df3])

.. ipython:: python

   @savefig merging_append3.png
   p.plot([df1, df2, df3], result,labels=['df1', 'df2', 'df3'], vertical=True);
   plt.close('all');

.. note::

   Unlike `list.append` method, which appends to the original list and
   returns nothing, ``append`` here **does not** modify ``df1`` and
   returns its copy with ``df2`` appended.

.. _merging.ignore_index:

Ignoring indexes on the concatenation axis
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For DataFrames which don't have a meaningful index, you may wish to append them
and ignore the fact that they may have overlapping indexes:

To do this, use the ``ignore_index`` argument:

.. ipython:: python

   result = pd.concat([df1, df4], ignore_index=True)

.. ipython:: python

   @savefig merging_concat_ignore_index.png
   p.plot([df1, df4], result,labels=['df1', 'df4'], vertical=True);

This is also a valid argument to ``DataFrame.append``:

.. ipython:: python

   result = df1.append(df4, ignore_index=True)

.. ipython:: python

   @savefig merging_append_ignore_index.png
   p.plot([df1, df4], result,labels=['df1', 'df4'], vertical=True);

.. _merging.mixed_ndims:

Concatenating with mixed ndims
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can concatenate a mix of Series and DataFrames. The
Series will be transformed to DataFrames with the column name as
the name of the Series.

.. ipython:: python

   s1 = pd.Series(['X0', 'X1', 'X2', 'X3'], name='X')
   result = pd.concat([df1, s1], axis=1)

.. ipython:: python

   @savefig merging_concat_mixed_ndim.png
   p.plot([df1, s1], result,labels=['df1', 's1'], vertical=False);

If unnamed Series are passed they will be numbered consecutively.

.. ipython:: python

   s2 = pd.Series(['_0', '_1', '_2', '_3'])
   result = pd.concat([df1, s2, s2, s2], axis=1)

.. ipython:: python

   @savefig merging_concat_unnamed_series.png
   p.plot([df1, s2], result,labels=['df1', 's2'], vertical=False);

Passing ``ignore_index=True`` will drop all name references.

.. ipython:: python

   result = pd.concat([df1, s1], axis=1, ignore_index=True)

.. ipython:: python

   @savefig merging_concat_series_ignore_index.png
   p.plot([df1, s1], result,labels=['df1', 's1'], vertical=False);
   plt.close('all');

More concatenating with group keys
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A fairly common use of the ``keys`` argument is to override the column names when creating a new DataFrame based on existing Series.
Notice how the default behaviour consists on letting the resulting DataFrame inherits the parent Series' name, when these existed.

.. ipython:: python

   s3 = pd.Series([0, 1, 2, 3], name='foo')
   s4 = pd.Series([0, 1, 2, 3])
   s5 = pd.Series([0, 1, 4, 5])

   pd.concat([s3, s4, s5], axis=1)

Through the ``keys`` argument we can override the existing column names.

.. ipython:: python

   pd.concat([s3, s4, s5], axis=1, keys=['red','blue','yellow'])

Let's consider now a variation on the very first example presented:

.. ipython:: python

   result = pd.concat(frames, keys=['x', 'y', 'z'])

.. ipython:: python

   @savefig merging_concat_group_keys2.png
   p.plot(frames, result,labels=['df1', 'df2', 'df3'], vertical=True);

You can also pass a dict to ``concat`` in which case the dict keys will be used
for the ``keys`` argument (unless other keys are specified):

.. ipython:: python

   pieces = {'x': df1, 'y': df2, 'z': df3}
   result = pd.concat(pieces)

.. ipython:: python

   @savefig merging_concat_dict.png
   p.plot([df1, df2, df3], result,labels=['df1', 'df2', 'df3'], vertical=True);

.. ipython:: python

   result = pd.concat(pieces, keys=['z', 'y'])

.. ipython:: python

   @savefig merging_concat_dict_keys.png
   p.plot([df1, df2, df3], result,labels=['df1', 'df2', 'df3'], vertical=True);

The MultiIndex created has levels that are constructed from the passed keys and
the index of the DataFrame pieces:

.. ipython:: python

   result.index.levels

If you wish to specify other levels (as will occasionally be the case), you can
do so using the ``levels`` argument:

.. ipython:: python

   result = pd.concat(pieces, keys=['x', 'y', 'z'],
                   levels=[['z', 'y', 'x', 'w']],
                   names=['group_key'])

.. ipython:: python

   @savefig merging_concat_dict_keys_names.png
   p.plot([df1, df2, df3], result,labels=['df1', 'df2', 'df3'], vertical=True);
   plt.close('all');

.. ipython:: python

   result.index.levels

Yes, this is fairly esoteric, but is actually necessary for implementing things
like GroupBy where the order of a categorical variable is meaningful.

.. _merging.append.row:

Appending rows to a DataFrame
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

While not especially efficient (since a new object must be created), you can
append a single row to a DataFrame by passing a Series or dict to ``append``,
which returns a new DataFrame as above.

.. ipython:: python

   s2 = pd.Series(['X0', 'X1', 'X2', 'X3'], index=['A', 'B', 'C', 'D'])
   result = df1.append(s2, ignore_index=True)

.. ipython:: python

   @savefig merging_append_series_as_row.png
   p.plot([df1, s2], result,labels=['df1', 's2'], vertical=True);

You should use ``ignore_index`` with this method to instruct DataFrame to
discard its index. If you wish to preserve the index, you should construct an
appropriately-indexed DataFrame and append or concatenate those objects.

You can also pass a list of dicts or Series:

.. ipython:: python

   dicts = [{'A': 1, 'B': 2, 'C': 3, 'X': 4},
            {'A': 5, 'B': 6, 'C': 7, 'Y': 8}]
   result = df1.append(dicts, ignore_index=True)

.. ipython:: python

   @savefig merging_append_dits.png
   p.plot([df1, pd.DataFrame(dicts)], result,labels=['df1', 'dicts'], vertical=True);
   plt.close('all');