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, adict oraSeries.

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 themapping correspondence.

Returns:

Series

Same index as caller.

See also

Series.apply

For applying more complex functions on a Series.

Series.replace

Replace values given into_replace withvalue.

DataFrame.apply

Apply a function row-/column-wise.

DataFrame.map

Apply a function elementwise on a whole DataFrame.

Notes

Whenarg is a dictionary, values in Series that are not in thedictionary (as keys) are converted toNaN. However, if thedictionary is adict subclass that defines__missing__ (i.e.provides a method for default values), then this default is usedrather thanNaN.

Examples

>>>s=pd.Series(['cat','dog',np.nan,'rabbit'])>>>s0      cat1      dog2      NaN3   rabbitdtype: object

map accepts adict or aSeries. Values that are not foundin thedict are converted toNaN, unless the dict has a defaultvalue (e.g.defaultdict):

>>>s.map({'cat':'kitten','dog':'puppy'})0   kitten1    puppy2      NaN3      NaNdtype: object

It also accepts a function:

>>>s.map('I am a{}'.format)0       I am a cat1       I am a dog2       I am a nan3    I am a rabbitdtype: object

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

>>>s.map('I am a{}'.format,na_action='ignore')0     I am a cat1     I am a dog2            NaN3  I am a rabbitdtype: object