pandas.Series.map#

Series.map(arg, na_action=None, **kwargs)[source]#

Map values of Series according to an input mapping or function.

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

Parameters:

argfunction, collections.abc.Mapping subclass or Series: Mapping correspondence.
na_action{None, ‘ignore’}, default None: If ‘ignore’, propagate NaN values, without passing them to the mapping correspondence.
**kwargs: Additional keyword arguments to pass as keywords arguments to arg.

Added in version 3.0.0.

Returns:

Series: Same index as caller.

See also

Series.apply: For applying more complex functions on a Series.
Series.replace: Replace values given in to_replace with value.
DataFrame.apply: Apply a function row-/column-wise.
DataFrame.map: 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