pandas.Series.map

Series.map(self, arg, na_action=None)

Map values of Series according to input correspondence.

Used for substituting each value in a Series with another value, that may be derived from a function, a dict or a Series.

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.

Returns:

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.

Examples

>>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
>>> s
0      cat
1      dog
2      NaN
3   rabbit
dtype: object

map accepts a dict or a Series. Values that are not found in the dict are converted to NaN, unless the dict has a default value (e.g. defaultdict):

>>> s.map({'cat': 'kitten', 'dog': 'puppy'})
0   kitten
1    puppy
2      NaN
3      NaN
dtype: object

It also accepts a function:

>>> s.map('I am a {}'.format)
0       I am a cat
1       I am a dog
2       I am a nan
3    I am a rabbit
dtype: object

To avoid applying the function to missing values (and keep them as NaN) na_action='ignore' can be used:

>>> s.map('I am a {}'.format, na_action='ignore')
0     I am a cat
1     I am a dog
2            NaN
3  I am a rabbit
dtype: object