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# pandas.merge

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

  Merge DataFrame objects by performing a database-style join operation by 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 : | left : DataFrame right : DataFrame how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’ left: use only keys from left frame (SQL: left outer join) right: use only keys from right frame (SQL: right outer join) outer: use union of keys from both frames (SQL: full outer join) inner: use intersection of keys from both frames (SQL: inner join) on : label or list Field names to join on. Must be found in both DataFrames. If on is None and not merging on indexes, then it merges on the intersection of the columns by default. left_on : label or list, or array-like Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns right_on : label or list, or array-like Field names to join on in right DataFrame or vector/list of vectors per left_on docs left_index : boolean, 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 : boolean, default False Use the index from the right DataFrame as the join key. Same caveats as left_index sort : boolean, default False Sort the join keys lexicographically in the result DataFrame suffixes : 2-length sequence (tuple, list, ...) Suffix to apply to overlapping column names in the left and right side, respectively copy : boolean, default True If False, do not copy data unnecessarily
  Returns : | merged : DataFrame

  Examples

      >>> A              >>> B
          lkey value         rkey value
      0   foo  1         0   foo  5
      1   bar  2         1   bar  6
      2   baz  3         2   qux  7
      3   foo  4         3   bar  8

      >>> merge(A, B, left_on='lkey', right_on='rkey', how='outer')
         lkey  value_x  rkey  value_y
      0  bar   2        bar   6
      1  bar   2        bar   8
      2  baz   3        NaN   NaN
      3  foo   1        foo   5
      4  foo   4        foo   5
      5  NaN   NaN      qux   7

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