Pandas map() function used to substituting each value in Series or column of DataFrames. The substituted value may be derived from a dictionary, a function or a Series. In this tutorial, You will learn how to use the map() function with examples.
Syntax:
Series.map(self, arg, na_action=None)
Parameters:
arg -function, dict, or Series. Mapping correspondence.
na_action - {None, ‘ignore’}, default None
If ‘ignore’, propagate NaN values, without passing them to the
mapping correspondence.
Examples:
In [1]:
import pandas as pd
dix = {'A':1,'B':2,'C':3,'D':4} # Defined Dict for mapping
s = pd.Series(['A','C','D','B'])
s.map(dix)
Out[1]:
0 1
1 3
2 4
3 2
dtype: int64
In [2]:
df = pd.DataFrame([[1,2], [6, 1], [9,5],[1 ,4]], columns=list('AB'))
df
Out[2]:
A B
0 1 2
1 6 1
2 9 5
3 1 4
Map values using a function.
In [3]:
def square(x): # Defined function for mapping
return x*2
In [4]: df['B'].map(square)
Out[4]:
0 4
1 2
2 10
3 8
Name: B, dtype: int64
Parameter na_action=’ignore’ used to avoid applying a function to missing values and keep them as NaN.
In [5]: s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
In [6]: s.map('I am a {}'.format)
Out[6]:
0 I am a cat
1 I am a dog
2 I am a nan
3 I am a rabbit
dtype: object
In [7]: s.map('I am a {}'.format, na_action='ignore')
Out[7]:
0 I am a cat
1 I am a dog
2 NaN
3 I am a rabbit
dtype: object
. . .