maxframe.dataframe.DataFrame.map#

DataFrame.map(func, na_action=None, dtypes=None, dtype=None, skip_infer=False, **kwargs)#

Apply a function to a Dataframe elementwise.

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.

  • dtypes (Series, default None) – Specify dtypes of returned DataFrames.

  • dtype (np.dtype, default None) – Specify dtypes of all columns of returned DataFrames, only effective when dtypes is not specified.

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

  • **kwargs – Additional keyword arguments to pass as keywords arguments to func.

Returns:

Transformed DataFrame.

Return type:

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

>>> import maxframe.dataframe as md
>>> df = md.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df.execute()
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x))).execute()
   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] = md.NA
>>> df_copy.map(lambda x: len(str(x)), na_action='ignore').execute()
     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).execute()
     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).execute()
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

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

>>> (df ** 2).execute()
           0          1
0   1.000000   4.494400
1  11.262736  20.857489