Series.map(arg, na_action=None, meta='__no_default__')[source]

Map values of Series according to input correspondence.

This docstring was copied from pandas.core.series.Series.map.

Some inconsistencies with the Dask version may exist.

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

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.

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.


Same index as caller.

See also


For applying more complex functions on a Series.


Apply a function row-/column-wise.


Apply a function elementwise on a whole DataFrame.


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.


>>> 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