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)
[source]
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: |
how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’
on : label or list Column or index level names to join on. These must be found in both DataFrames. If 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 : 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. If False, the order of the join keys depends on the join type (how keyword) 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 indicator : boolean or string, 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 : string, default None If specified, checks if merge is of specified type.
New in version 0.21.0. |
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Returns: |
merged : DataFrame The output type will the be same as ‘left’, if it is a subclass of DataFrame. |
See also
Support for specifying index levels as the on
, left_on
, and right_on
parameters was added in version 0.23.0
>>> 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
>>> A.merge(B, left_on='lkey', right_on='rkey', how='outer') lkey value_x rkey value_y 0 foo 1 foo 5 1 foo 4 foo 5 2 bar 2 bar 6 3 bar 2 bar 8 4 baz 3 NaN NaN 5 NaN NaN qux 7
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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.merge.html