pandas.Series.map

Series.map(arg, na_action=None)

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