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Python | pandas.map()
  • Difficulty Level : Hard
  • Last Updated : 04 Jul, 2018

pandas.map() is used to map values from two series having one column same. For mapping two series, the last column of the first series should be same as index column of the second series, also the values should be unique.

Syntax:

Series.map(arg, na_action=None)

Parameters:

arg : function, dict, or Series

na_action : {None, ‘ignore’} If ‘ignore’, propagate NA values, without passing them to the mapping correspondence. na_action checks the NA value and ignores it while mapping in case of ‘ignore’



Return type:

Pandas Series with same as index as caller

Example #1:
In the following example, two series are made from same data. pokemon_names column and pokemon_types index column are same and hence Pandas.map() matches the rest of two columns and returns a new series.

Note:
-> 2nd column of caller of map function must be same as index column of passed series.
-> The values of common column must be unique too.




import pandas as pd
  
#reading csv files
pokemon_names = pd.read_csv("pokemon.csv", usecols = ["Pokemon"],
                                                  squeeze = True)
  
#usecol is used to use selected columns
#index_col is used to make passed column as index
pokemon_types = pd.read_csv("pokemon.csv", index_col = "Pokemon",
                                                  squeeze = True)
  
#using pandas map function
new=pokemon_names.map(pokemon_types)
  
print (new)

Output:


Example #2:

This function works only with Series. Passing a data frame would give an Attribute error. Passing series with different length will give the output series of length same as the caller.

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