pandas.DataFrame.map#

DataFrame.map(func, na_action=None, **kwargs)[source]#

Apply a function to a Dataframe elementwise.

New in version 2.1.0: DataFrame.applymap was deprecated and renamed to DataFrame.map.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters:
funccallable

Python function, returns a single value from a single value.

na_action{None, ‘ignore’}, default None

If ‘ignore’, propagate NaN values, without passing them to func.

**kwargs

Additional keyword arguments to pass as keywords arguments to func.

Returns:
DataFrame

Transformed DataFrame.

See also

DataFrame.apply

Apply a function along input axis of DataFrame.

DataFrame.replace

Replace values given in to_replace with value.

Series.map

Apply a function elementwise on a Series.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x)))
   0  1
0  3  4
1  5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.map(lambda x: len(str(x)), na_action='ignore')
     0  1
0  NaN  4
1  5.0  5

It is also possible to use map with functions that are not lambda functions:

>>> df.map(round, ndigits=1)
     0    1
0  1.0  2.1
1  3.4  4.6

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.map(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid map in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489