
        
        
            
                
                
                    s = pd.Series([2,3])
s.map(lambda x: x+5)
                
            
            0   7
1   8
dtype: int64

Here, a new Series is returned, so the original s is kept intact.

Applying a mapping

We can pass a dict or Series to map each value of the source Series to another value:


        
        
            
                
                
                    s = pd.Series(["a",3])
s.map({"a":"A"})
                
            
            0     A
1   NaN
dtype: object

Notice how since 3 is not present as a a key in our dict, we get a NaN for that entry.

Specifying na_action

By default, na_action=None, which means that NaN values are also passed into the mapping function:


        
        
            
                
                
                    s = pd.Series([2,None,3])
s.map(lambda x: 5 if pd.isna(x) else 10)   # na_action=None
                
            
            0   10
1    5
2   10
dtype: int64

Setting to na_action="ignore" means that no mapping is applied to NaN values:


        
        
            
                
                
                    s = pd.Series([2,None,3])
s.map(lambda x: 5 if pd.isna(x) else 10, na_action="ignore")
                
            
            0    10.0
1     NaN
2    10.0
dtype: float64

Published by Isshin Inada

Edited by 0 others

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Official Pandas Documentation

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.map.html

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