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Classe « DataFrame »

Méthode pandas.DataFrame.map

Signature de la méthode map

def map(self, func: 'PythonFuncType', na_action: 'str | None' = None, **kwargs) -> 'DataFrame' 

Description

help(DataFrame.map)

Apply a function to a Dataframe elementwise.

.. versionadded:: 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
----------
func : callable
    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

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