pandas.concat¶

pandas.concat(objs: Union[Iterable[‘DataFrame’], Mapping[Optional[Hashable], ‘DataFrame’]], axis='0', join: str = "'outer'", ignore_index: bool = 'False', keys='None', levels='None', names='None', verify_integrity: bool = 'False', sort: bool = 'False', copy: bool = 'True') → ’DataFrame’[source]¶

pandas.concat(objs: Union[Iterable[FrameOrSeriesUnion], Mapping[Optional[Hashable], FrameOrSeriesUnion]], axis='0', join: str = "'outer'", ignore_index: bool = 'False', keys='None', levels='None', names='None', verify_integrity: bool = 'False', sort: bool = 'False', copy: bool = 'True') → FrameOrSeriesUnion

Concatenate pandas objects along a particular axis with 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

objsa sequence or mapping of Series or DataFrame 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/’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_indexbool, 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.
keyssequence, default None: If multiple levels passed, should contain tuples. Construct hierarchical index using the passed keys as the outermost level.
levelslist of sequences, default None: Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys.
nameslist, default None: Names for the levels in the resulting hierarchical index.
verify_integritybool, default False: Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation.
sortbool, default False: Sort non-concatenation axis if it is not already aligned when join is ‘outer’. This has no effect when join='inner', which already preserves the order of the non-concatenation axis.

New in version 0.23.0.

Changed in version 1.0.0: Changed to not sort by default.
copybool, 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

Series.append: Concatenate Series.
DataFrame.append: Concatenate DataFrames.
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.

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']

pandas.merge_asof pandas.get_dummies