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pandas.Series.map
Series.map(arg, na_action=None)[source]
Map values of Series using input correspondence (which can be a dict, Series, or function)
Parameters:
arg : function, dict, or Series
na_action : {None, ‘ignore’}
If ‘ignore’, propagate NA values, without passing them to the mapping function
Returns:
y : Series
same index as caller
See also
Series.apply
For applying more complex functions on a Series
DataFrame.apply
Apply a function row-/column-wise
DataFrame.applymap
Apply a function elementwise on a whole DataFrame
Notes
When arg is a dictionary, values in Series that are not in the dictionary (as keys) are converted to NaN. However, if the dictionary is a dict subclass that defines missing (i.e. provides a method for default values), then this default is used rather than NaN:
from collections import Counter
counter = Counter()
counter[‘bar’] += 1
y.map(counter)
1 0
2 1
3 0
dtype: int64
Examples
Map inputs to outputs (both of type Series)
x = pd.Series([1,2,3], index=[‘one’, ‘two’, ‘three’])
x
one 1
two 2
three 3
dtype: int64
y = pd.Series([‘foo’, ‘bar’, ‘baz’], index=[1,2,3])
y
1 foo
2 bar
3 baz
x.map(y)
one foo
two bar
three baz
If arg is a dictionary, return a new Series with values converted according to the dictionary’s mapping:z = {1: ‘A’, 2: ‘B’, 3: ‘C’}
x.map(z)
one A
two B
three C
Use na_action to control whether NA values are affected by the mapping function.s = pd.Series([1, 2, 3, np.nan])
s2 = s.map(‘this is a string {}’.format, na_action=None)
0 this is a string 1.0
1 this is a string 2.0
2 this is a string 3.0
3 this is a string nan
dtype: object
s3 = s.map(‘this is a string {}’.format, na_action=’ignore’)
0 this is a string 1.0
1 this is a string 2.0
2 this is a string 3.0
3 NaN
dtype: object