拥有不同列的DataFrame的行连接 concat 函数

from pandas import read_csv,concat,Series,DataFrame

#DataFrame
s1 = read_csv("concat_1.csv")
s2 = read_csv("concat_2.csv")
s3 = read_csv("concat_3.csv")

print("-----------s1-----------------")
print(s1)

print("------------s2----------------")
print(s2)

print("-------------s3---------------")
print(s3)


#修改列表名称
s1.columns = ["A","B","C","D"]
s2.columns = ["E","F","G","H"]
s3.columns = ["A","C","F","H"]


print("-----------s1-----------------")
print(s1)

print("------------s2----------------")
print(s2)

print("------------s3----------------")
print(s3)

print("-----------默认的合并 sort=True--------------")
print(concat([s1,s2,s3],sort=True))


print("-------------s1s2s3 join=inner---------------")
print(concat([s1,s2,s3],join="inner"))

print("-------------s1s3 join=inner---------------")
print(concat([s1,s3],join="inner"))

print("-------------s2s3---------------")
print(concat([s2,s3],join="inner"))

output

-----------s1-----------------
    A   B   C   D
0  a0  b0  c0  d0
1  a1  b1  c1  d1
2  a2  b2  c2  d2
3  a3  b3  c3  d3
------------s2----------------
    A   B   C   D
0  a4  b4  c4  d4
1  a5  b5  c5  d5
2  a6  b6  c6  d6
3  a7  b7  c7  d7
-------------s3---------------
     A    B    C    D
0   a8   b8   c8   d8
1   a9   b9   c9   d9
2  a10  b10  c10  d10
3  a11  b11  c11  d11
-----------s1-----------------
    A   B   C   D
0  a0  b0  c0  d0
1  a1  b1  c1  d1
2  a2  b2  c2  d2
3  a3  b3  c3  d3
------------s2----------------
    E   F   G   H
0  a4  b4  c4  d4
1  a5  b5  c5  d5
2  a6  b6  c6  d6
3  a7  b7  c7  d7
------------s3----------------
     A    C    F    H
0   a8   b8   c8   d8
1   a9   b9   c9   d9
2  a10  b10  c10  d10
3  a11  b11  c11  d11
-----------默认的合并 sort=True--------------
     A    B    C    D    E    F    G    H
0   a0   b0   c0   d0  NaN  NaN  NaN  NaN
1   a1   b1   c1   d1  NaN  NaN  NaN  NaN
2   a2   b2   c2   d2  NaN  NaN  NaN  NaN
3   a3   b3   c3   d3  NaN  NaN  NaN  NaN
0  NaN  NaN  NaN  NaN   a4   b4   c4   d4
1  NaN  NaN  NaN  NaN   a5   b5   c5   d5
2  NaN  NaN  NaN  NaN   a6   b6   c6   d6
3  NaN  NaN  NaN  NaN   a7   b7   c7   d7
0   a8  NaN   b8  NaN  NaN   c8  NaN   d8
1   a9  NaN   b9  NaN  NaN   c9  NaN   d9
2  a10  NaN  b10  NaN  NaN  c10  NaN  d10
3  a11  NaN  b11  NaN  NaN  c11  NaN  d11
-------------s1s2s3 join=inner---------------
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
-------------s1s3 join=inner---------------
     A    C
0   a0   c0
1   a1   c1
2   a2   c2
3   a3   c3
0   a8   b8
1   a9   b9
2  a10  b10
3  a11  b11
-------------sss---------------
     F    H
0   b4   d4
1   b5   d5
2   b6   d6
3   b7   d7
0   c8   d8
1   c9   d9
2  c10  d10
3  c11  d11

Process finished with exit code 0

源码

def concat(objs, axis=0, join=’outer’, join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False,
sort=None, copy=True):

def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
           keys=None, levels=None, names=None, verify_integrity=False,
           sort=None, copy=True):

    """

    Parameters
    ----------

    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/'index', 1/'columns'}, default 0
        The axis to concatenate along


    join : {'inner', 'outer'}, default 'outer'
        How to handle indexes on other axis(es)


    join_axes : list of Index objects
        Specific indexes to use for the other n - 1 axes instead of performing
        inner/outer set logic


   ignore_index : boolean, 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 : boolean, default False
        Check whether the new concatenated axis contains duplicates. This can
        be very expensive relative to the actual data concatenation


    sort : boolean, default None
        Sort non-concatenation axis if it is not already aligned when `join`
        is 'outer'. The current default of sorting is deprecated and will
        change to not-sorting in a future version of pandas.

        Explicitly pass ``sort=True`` to silence the warning and sort.
        Explicitly pass ``sort=False`` to silence the warning and not sort.

        This has no effect when ``join='inner'``, which already preserves
        the order of the non-concatenation axis.

        .. versionadded:: 0.23.0

    copy : boolean, default True
        If False, do not copy data unnecessarily

    Returns
    -------
    concatenated : 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.

    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
    <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__.



     See Also
    --------
    Series.append
    DataFrame.append
    DataFrame.join
    DataFrame.merge

    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


    ignore_index=True
    -------------------------------------

    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


    keys=['s1', 's2',]
    --------------------------------------

    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])
      animal letter  number
    0    NaN      a       1
    1    NaN      b       2
    0    cat      c       3
    1    dog      d       4


    join="inner"
    ------------------------------

    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



    axis=1
    ---------------------------------

    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']
    """
    op = _Concatenator(objs, axis=axis, join_axes=join_axes,
                       ignore_index=ignore_index, join=join,
                       keys=keys, levels=levels, names=names,
                       verify_integrity=verify_integrity,
                       copy=copy, sort=sort)
    return op.get_result()