# pandas.DataFrame.merge

`DataFrame.``merge`(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

  Merge DataFrame or named Series objects with a database-style join.

  The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on.

  Parameters:

  right : DataFrame or named Series

  > Object to merge with.

  how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’

  > Type of merge to be performed.

  > - left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
  > - right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
  > - outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
  > - inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.

  on : label or list

  > Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

  left_on : label or list, or array-like

  > Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.

  right_on : label or list, or array-like

  > Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.

  left_index : bool, default False

  > Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.

  right_index : bool, default False

  > Use the index from the right DataFrame as the join key. Same caveats as left_index.

  sort : bool, default False

  > Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).

  suffixes : tuple of (str, str), default (‘_x’, ‘_y’)

  > Suffix to apply to overlapping column names in the left and right side, respectively. To raise an exception on overlapping columns use (False, False).

  copy : bool, default True

  > If False, avoid copy if possible.

  indicator : bool or str, default False

  > If True, adds a column to output DataFrame called “_merge” with information on the source of each row. If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string. Information column is Categorical-type and takes on a value of “left_only” for observations whose merge key only appears in ‘left’ DataFrame, “right_only” for observations whose merge key only appears in ‘right’ DataFrame, and “both” if the observation’s merge key is found in both.

  validate : str, optional

  > If specified, checks if merge is of specified type.

  > - “one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
  > - “one_to_many” or “1:m”: check if merge keys are unique in left dataset.
  > - “many_to_one” or “m:1”: check if merge keys are unique in right dataset.
  > - “many_to_many” or “m:m”: allowed, but does not result in checks.

  > New in version 0.21.0.

  Returns:

  DataFrame

  > A DataFrame of the two merged objects.

  See also

  `merge_ordered`
    Merge with optional filling/interpolation.
  `merge_asof`
    Merge on nearest keys.
  `DataFrame.join`
    Similar method using indices.

  Notes

  Support for specifying index levels as the on, left_on, and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0

  Examples

      >>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
      ...                     'value': [1, 2, 3, 5]})
      >>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
      ...                     'value': [5, 6, 7, 8]})
      >>> df1
          lkey value
      0   foo      1
      1   bar      2
      2   baz      3
      3   foo      5
      >>> df2
          rkey value
      0   foo      5
      1   bar      6
      2   baz      7
      3   foo      8

  Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

      >>> df1.merge(df2, left_on='lkey', right_on='rkey')
        lkey  value_x rkey  value_y
      0  foo        1  foo        5
      1  foo        1  foo        8
      2  foo        5  foo        5
      3  foo        5  foo        8
      4  bar        2  bar        6
      5  baz        3  baz        7

  Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

      >>> df1.merge(df2, left_on='lkey', right_on='rkey',
      ...           suffixes=('_left', '_right'))
        lkey  value_left rkey  value_right
      0  foo           1  foo            5
      1  foo           1  foo            8
      2  foo           5  foo            5
      3  foo           5  foo            8
      4  bar           2  bar            6
      5  baz           3  baz            7

  Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

      >>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
      Traceback (most recent call last):
      ...
      ValueError: columns overlap but no suffix specified:
          Index(['value'], dtype='object')

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