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# 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=<no_default>, indicator=False, validate=None)

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

  A named Series object is treated as a DataFrame with a single named column.

  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. When performing a cross merge, no column specifications to merge on are allowed.

  Warning

  If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

  Parameters:
    right DataFrame or named Series

      Object to merge with.

    how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’, ‘left_anti’, ‘right_anti’},

      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.

      - cross: creates the cartesian product from both frames, preserves the order of the left keys.

      - left_anti: use only keys from left frame that are not in right frame, similar to SQL left anti join; preserve key order.

        Added in version 3.0.

      - right_anti: use only keys from right frame that are not in left frame, similar to SQL right anti join; preserve key order.

        Added in version 3.0.

    on Hashable or a sequence of the previous

      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 Hashable or a sequence of the previous, 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 Hashable or a sequence of the previous, 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 list-like, default is (“_x”, “_y”)

      A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

    copy bool, default False

      This keyword is now ignored; changing its value will have no impact on the method.

      Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

    indicator bool or str, default False

      If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.

    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.

  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.

  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  bar        2  bar        6
      3  baz        3  baz        7
      4  foo        5  foo        5
      5  foo        5  foo        8

  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  bar           2  bar            6
      3  baz           3  baz            7
      4  foo           5  foo            5
      5  foo           5  foo            8

  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')

      >>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
      >>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
      >>> df1
            a  b
      0   foo  1
      1   bar  2
      >>> df2
            a  c
      0   foo  3
      1   baz  4

      >>> df1.merge(df2, how='inner', on='a')
            a  b  c
      0   foo  1  3

      >>> df1.merge(df2, how='left', on='a')
            a  b  c
      0   foo  1  3.0
      1   bar  2  NaN

      >>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
      >>> df2 = pd.DataFrame({'right': [7, 8]})
      >>> df1
          left
      0   foo
      1   bar
      >>> df2
          right
      0   7
      1   8

      >>> df1.merge(df2, how='cross')
         left  right
      0   foo      7
      1   foo      8
      2   bar      7
      3   bar      8