This method is used to perform an asof merge. This is similar to a left-join except that we match on nearest key rather than equal keys. Both DataFrames must be sorted by the key.
Syntax : pandas.merge_asof(left, right, on=None, left_on=None, right_on=None, left_index=False, right_index=False, by=None, left_by=None, right_by=None, suffixes=(‘_x’, ‘_y’), tolerance=None, allow_exact_matches=True, direction=’backward’, )
Parameters :
- left, right : DataFrame
- on : label, Field name to join on. Must be found in both DataFrames.
- left_on : label, Field name to join on in left DataFrame.
- right_on : label, Field name to join on in right DataFrame.
- left_index : boolean, Use the index of the left DataFrame as the join key.
- right_index : boolean, Use the index of the right DataFrame as the join key.
Below is the implementation of the above method with some examples :
Example 1 :
Python3
# importing packageimport pandas # creating dataleft = pandas.DataFrame({'a': [1, 5, 10], 'left_val': ['a', 'b', 'c']}) right = pandas.DataFrame({'a': [1, 2, 3, 6, 7], 'right_val': [1, 2, 3, 6, 7]}) # view dataprint(left)print(right) # applying merge_asof on dataprint(pandas.merge_asof(left, right, on='a'))print(pandas.merge_asof(left, right, on='a', allow_exact_matches=False)) |
Output:
Example 2 :
Python3
# importing packageimport pandas # creating dataleft = pandas.DataFrame({'a': [1, 5, 10], 'left_val': ['a', 'b', 'c']}) right = pandas.DataFrame({'a': [1, 2, 3, 6, 7], 'right_val': [1, 2, 3, 6, 7]}) # view dataprint(left)print(right) # applying merge_asof on dataprint(pandas.merge_asof(left, right, on='a', direction='forward'))print(pandas.merge_asof(left, right, on='a', direction='nearest')) |
Output :
Example 3 :
Python3
# importing packageimport pandas # creating dataleft = pandas.DataFrame({'left_val': ['a', 'b', 'c']}, index=[1, 5, 10]) right = pandas.DataFrame({'right_val': [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7]) # view dataprint(left)print(right) # applying merge_asof on dataprint(pandas.merge_asof(left, right, left_index=True, right_index=True)) |
Output:
