Pandas DataFrame: merge() function
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', '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.
|
str |
optional |
Returns: DataFrame
A DataFrame of the two merged objects.
Example:
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