pandas.Series.map#
- Series.map(arg, na_action=None)[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 aSeries
.- 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.
- 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 toNaN
. 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
.Examples
>>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit']) >>> s 0 cat 1 dog 2 NaN 3 rabbit dtype: object
map
accepts adict
or aSeries
. Values that are not found in thedict
are converted toNaN
, 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