modin.pandas.Series.map¶
- Series.map(arg: Callable | Mapping | Series, na_action: Literal['ignore'] | None = None, **kwargs: Any) Series [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:
arg (function, collections.abc.Mapping subclass or Series) – Mapping correspondence. Only function is currently supported by Snowpark pandas.
na_action ({None, 'ignore'}, default None) – If ‘ignore’, propagate NULL values, without passing them to the mapping correspondence. Note that, it will not bypass NaN values in a FLOAT column in Snowflake. ‘ignore’ is currently not supported by Snowpark pandas.
- Returns:
Same index as caller.
- Return type:
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 None 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
) (Currently not supported by Snowpark pandas):>>> s.map({'cat': 'kitten', 'dog': 'puppy'}) 0 kitten 1 puppy 2 None 3 None 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 <NA> 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 (Currently not supported by Snowpark pandas):>>> s.map('I am a {}'.format, na_action='ignore') 0 I am a cat 1 I am a dog 2 None 3 I am a rabbit dtype: object
Note that in the above example, the missing value in Snowflake is NULL, it is mapped to
None
in a string/object column.