mars.dataframe.Series.map#

Series.map(arg, na_action=None, dtype=None, memory_scale=None, skip_infer=False)#

Map values of Series according to input correspondence.

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

Parameters

  • arg (function, 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.

  • dtype (np.dtype, default None) – Specify return type of the function. Must be specified when we cannot decide the return type of the function.

  • memory_scale (float) – Specify the scale of memory uses in the function versus input size.

  • skip_infer (bool, default False) – Whether infer dtypes when dtypes or output_type is not specified

Returns

Same index as caller.

Return type

Series

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 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.

Examples

>>> import mars.tensor as mt
>>> import mars.dataframe as md
>>> s = md.Series(['cat', 'dog', mt.nan, 'rabbit'])
>>> s.execute()
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'}).execute()
0   kitten
1    puppy
2      NaN
3      NaN
dtype: object

It also accepts a function:

>>> s.map('I am a {}'.format).execute()
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').execute()
0     I am a cat
1     I am a dog
2            NaN
3  I am a rabbit
dtype: object