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|
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j « S )ax
|
Concatenate pandas objects along a particular axis.
|
|
Allows optional set logic along the other axes.
|
|
Can also add a layer of hierarchical indexing on the concatenation axis,
|
which may be useful if the labels are the same (or overlapping) on
|
the passed axis number.
|
|
Parameters
|
----------
|
objs : a sequence or mapping of Series or DataFrame objects
|
If a mapping 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/'index', 1/'columns'}, default 0
|
The axis to concatenate along.
|
join : {'inner', 'outer'}, default 'outer'
|
How to handle indexes on other axis (or axes).
|
ignore_index : bool, default False
|
If True, do not use the index values along 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.
|
keys : sequence, default None
|
If multiple levels passed, should contain tuples. Construct
|
hierarchical index using the passed keys as the outermost level.
|
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 : bool, default False
|
Check whether the new concatenated axis contains duplicates. This can
|
be very expensive relative to the actual data concatenation.
|
sort : bool, default False
|
Sort non-concatenation axis if it is not already aligned. One exception to
|
this is when the non-concatentation axis is a DatetimeIndex and join='outer'
|
and the axis is not already aligned. In that case, the non-concatenation
|
axis is always sorted lexicographically.
|
copy : bool, default True
|
If False, do not copy data unnecessarily.
|
|
Returns
|
-------
|
object, type of objs
|
When concatenating all ``Series`` along the index (axis=0), a
|
``Series`` is returned. When ``objs`` contains at least one
|
``DataFrame``, a ``DataFrame`` is returned. When concatenating along
|
the columns (axis=1), a ``DataFrame`` is returned.
|
|
See Also
|
--------
|
DataFrame.join : Join DataFrames using indexes.
|
DataFrame.merge : Merge DataFrames by indexes or columns.
|
|
Notes
|
-----
|
The keys, levels, and names arguments are all optional.
|
|
A walkthrough of how this method fits in with other tools for combining
|
pandas objects can be found `here
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.
|
|
It is not recommended to build DataFrames by adding single rows in a
|
for loop. Build a list of rows and make a DataFrame in a single concat.
|
|
Examples
|
--------
|
Combine two ``Series``.
|
|
>>> s1 = pd.Series(['a', 'b'])
|
>>> s2 = pd.Series(['c', 'd'])
|
>>> pd.concat([s1, s2])
|
0 a
|
1 b
|
0 c
|
1 d
|
dtype: object
|
|
Clear the existing index and reset it in the result
|
by setting the ``ignore_index`` option to ``True``.
|
|
>>> pd.concat([s1, s2], ignore_index=True)
|
0 a
|
1 b
|
2 c
|
3 d
|
dtype: object
|
|
Add a hierarchical index at the outermost level of
|
the data with the ``keys`` option.
|
|
>>> pd.concat([s1, s2], keys=['s1', 's2'])
|
s1 0 a
|
1 b
|
s2 0 c
|
1 d
|
dtype: object
|
|
Label the index keys you create with the ``names`` option.
|
|
>>> pd.concat([s1, s2], keys=['s1', 's2'],
|
... names=['Series name', 'Row ID'])
|
Series name Row ID
|
s1 0 a
|
1 b
|
s2 0 c
|
1 d
|
dtype: object
|
|
Combine two ``DataFrame`` objects with identical columns.
|
|
>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
|
... columns=['letter', 'number'])
|
>>> df1
|
letter number
|
0 a 1
|
1 b 2
|
>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
|
... columns=['letter', 'number'])
|
>>> df2
|
letter number
|
0 c 3
|
1 d 4
|
>>> pd.concat([df1, df2])
|
letter number
|
0 a 1
|
1 b 2
|
0 c 3
|
1 d 4
|
|
Combine ``DataFrame`` objects with overlapping columns
|
and return everything. Columns outside the intersection will
|
be filled with ``NaN`` values.
|
|
>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
|
... columns=['letter', 'number', 'animal'])
|
>>> df3
|
letter number animal
|
0 c 3 cat
|
1 d 4 dog
|
>>> pd.concat([df1, df3], sort=False)
|
letter number animal
|
0 a 1 NaN
|
1 b 2 NaN
|
0 c 3 cat
|
1 d 4 dog
|
|
Combine ``DataFrame`` objects with overlapping columns
|
and return only those that are shared by passing ``inner`` to
|
the ``join`` keyword argument.
|
|
>>> pd.concat([df1, df3], join="inner")
|
letter number
|
0 a 1
|
1 b 2
|
0 c 3
|
1 d 4
|
|
Combine ``DataFrame`` objects horizontally along the x axis by
|
passing in ``axis=1``.
|
|
>>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
|
... columns=['animal', 'name'])
|
>>> pd.concat([df1, df4], axis=1)
|
letter number animal name
|
0 a 1 bird polly
|
1 b 2 monkey george
|
|
Prevent the result from including duplicate index values with the
|
``verify_integrity`` option.
|
|
>>> df5 = pd.DataFrame([1], index=['a'])
|
>>> df5
|
0
|
a 1
|
>>> df6 = pd.DataFrame([2], index=['a'])
|
>>> df6
|
0
|
a 2
|
>>> pd.concat([df5, df6], verify_integrity=True)
|
Traceback (most recent call last):
|
...
|
ValueError: Indexes have overlapping values: ['a']
|
|
Append a single row to the end of a ``DataFrame`` object.
|
|
>>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
|
>>> df7
|
a b
|
0 1 2
|
>>> new_row = pd.Series({'a': 3, 'b': 4})
|
>>> new_row
|
a 3
|
b 4
|
dtype: int64
|
>>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
|
a b
|
0 1 2
|
1 3 4
|
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