dask.dataframe.Series.map
dask.dataframe.Series.map¶
- Series.map(arg, na_action=None, meta=None)[source]¶
Map values of Series according to an input mapping or function.
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 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.
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 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