Python | pandas.map ()

Python Methods and Functions

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 the same data. The pokemon_names column and the pokemon_types index column are the same, and therefore Pandas.map () matches the other two columns and returns a new series.

Note:
- & gt; 2nd column of caller of map function must be same as index column of passed series.
- & gt;  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 the selected columns
  # index_col is used to make the passed column an ​​index

pokemon_types = pd.read_csv ( "pokemon.csv" , index_col = "Pokemon" ,

squeeze = True )

 
# using map functions pandas

new = pokemon_names. map (pokemon_types)

 

print (new)

Exit:

Example # 2:

This function only works with in series. Passing a data frame will result in an attribute error. Passing streaks with different lengths will result in an output burst of the same length as the caller.





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