pandas 1: pandas.Series.map

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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

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