pandas.Series.map

Series.map(func=None, na_action=None, engine=None, **kwargs)

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 a Series.

Parameters:

funcfunction, collections.abc.Mapping subclass or Series

Function or mapping correspondence.

na_action{None, ‘ignore’}, default None

If ‘ignore’, propagate NaN values, without passing them to the mapping correspondence.

enginedecorator, optional

Choose the execution engine to use to run the function. Only used for functions. If map is called with a mapping or Series, an exception will be raised. If engine is not provided the function will be executed by the regular Python interpreter.

Options include JIT compilers such as Numba, Bodo or Blosc2, which in some cases can speed up the execution. To use an executor you can provide the decorators numba.jit, numba.njit, bodo.jit or blosc2.jit. You can also provide the decorator with parameters, like numba.jit(nogit=True).

Not all functions can be executed with all execution engines. In general, JIT compilers will require type stability in the function (no variable should change data type during the execution). And not all pandas and NumPy APIs are supported. Check the engine documentation for limitations.

Added in version 3.0.0.

**kwargs

Additional keyword arguments to pass as keywords arguments to arg.

Added in version 3.0.0.

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 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

>>> 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

For categorical data, the function is only applied to the categories:

>>> s = pd.Series(list("cabaa"))
>>> s.map(print)
c
a
b
a
a
0    None
1    None
2    None
3    None
4    None
dtype: object

>>> s_cat = s.astype("category")
>>> s_cat.map(print)  # function called once per unique category
a
b
c
0    None
1    None
2    None
3    None
4    None
dtype: object