pandas.DataFrame.map#

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

Apply a function to a Dataframe elementwise.

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

This method applies a function that accepts and returns a scalarto 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 tofunc.

Returns:

DataFrame

Transformed DataFrame.

See also

DataFrame.apply

Apply a function along input axis of DataFrame.

DataFrame.replace

Replace values given into_replace withvalue.

Series.map

Apply a function elementwise on a Series.

Examples

>>>df=pd.DataFrame([[1,2.12],[3.356,4.567]])>>>df       0      10  1.000  2.1201  3.356  4.567

>>>df.map(lambdax:len(str(x)))   0  10  3  41  5  5

Like Series.map, NA values can be ignored:

>>>df_copy=df.copy()>>>df_copy.iloc[0,0]=pd.NA>>>df_copy.map(lambdax:len(str(x)),na_action='ignore')     0  10  NaN  41  5.0  5

It is also possible to usemap with functions that are notlambda functions:

>>>df.map(round,ndigits=1)     0    10  1.0  2.11  3.4  4.6

Note that a vectorized version offunc often exists, which willbe much faster. You could square each number elementwise.

>>>df.map(lambdax:x**2)           0          10   1.000000   4.4944001  11.262736  20.857489

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

>>>df**2           0          10   1.000000   4.4944001  11.262736  20.857489