nodes: 0, module; 1, function_definition; 2, function_name:concat; 3, parameters; 4, block; 5, identifier:objs; 6, default_parameter; 7, default_parameter; 8, default_parameter; 9, default_parameter; 10, default_parameter; 11, default_parameter; 12, default_parameter; 13, default_parameter; 14, default_parameter; 15, default_parameter; 16, expression_statement; 17, expression_statement; 18, return_statement; 19, identifier:axis; 20, integer:0; 21, identifier:join; 22, string; 23, identifier:join_axes; 24, None; 25, identifier:ignore_index; 26, False; 27, identifier:keys; 28, None; 29, identifier:levels; 30, None; 31, identifier:names; 32, None; 33, identifier:verify_integrity; 34, False; 35, identifier:sort; 36, None; 37, identifier:copy; 38, True; 39, comment:"""
      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
      ----------
      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 (or axes).
      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 : 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 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 : 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
      --------
      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
      <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__.
  
      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']
      """; 40, assignment; 41, call; 42, string_content:outer; 43, identifier:op; 44, call; 45, attribute; 46, argument_list; 47, identifier:_Concatenator; 48, argument_list; 49, identifier:op; 50, identifier:get_result; 51, identifier:objs; 52, keyword_argument; 53, keyword_argument; 54, keyword_argument; 55, keyword_argument; 56, keyword_argument; 57, keyword_argument; 58, keyword_argument; 59, keyword_argument; 60, keyword_argument; 61, keyword_argument; 62, identifier:axis; 63, identifier:axis; 64, identifier:join_axes; 65, identifier:join_axes; 66, identifier:ignore_index; 67, identifier:ignore_index; 68, identifier:join; 69, identifier:join; 70, identifier:keys; 71, identifier:keys; 72, identifier:levels; 73, identifier:levels; 74, identifier:names; 75, identifier:names; 76, identifier:verify_integrity; 77, identifier:verify_integrity; 78, identifier:copy; 79, identifier:copy; 80, identifier:sort; 81, identifier:sort
code: 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):
      """
      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
      ----------
      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 (or axes).
      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 : 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 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 : 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
      --------
      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
      <http://pandas.pydata.org/pandas-docs/stable/merging.html>`__.
  
      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']
      """
      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()