Pandas DataFrame: merge() function

Last update on August 19 2022 21:50:51 (UTC/GMT +8 hours)

DataFrame - merge() function

The merge() function is used to 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.

Syntax:

DataFrame.merge(self, 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

Parameters:

Name	Description	Type/Default Value	Required / Optional
right	Object to merge with.	DataFrame or named Series	Required
how	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.	{'left', 'right', 'outer', 'inner'} Default Value: 'inner'	Required
on	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.	label or list	Required
left_on	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.	label or list, or array-like	Required
right_on	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.	label or list, or array-like	Required
left_index	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.	bool Default Value: False	Required
right_index	Use the index from the right DataFrame as the join key. Same caveats as left_index.	bool Default Value: False	Required
sort	Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).	bool Default Value: False	Required
suffixes	Suffix to apply to overlapping column names in the left and right side, respectively. To raise an exception on overlapping columns use (False, False).	tuple of (str, str) Default Value: (‘_x’, ‘_y’)	Required
copy	If False, avoid copy if possible.	bool Default Value: True	Required
indicator	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.	bool or str Default Value: False	Required
validate	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.	str	optional

Returns: DataFrame
A DataFrame of the two merged objects.

Example:

Download the Pandas DataFrame Notebooks from here.

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