pandas.Series.map¶
-
Series.
map
(arg, na_action=None)[source]¶ Map values of Series using input correspondence (a dict, Series, or function).
Parameters: arg : function, dict, or Series
Mapping correspondence.
na_action : {None, ‘ignore’}
If ‘ignore’, propagate NA values, without passing them to the mapping correspondence.
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 adict
subclass that defines__missing__
(i.e. provides a method for default values), then this default is used rather thanNaN
:>>> 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