pandas.DataFrame.transform

DataFrame.transform(func, axis=0, *args, **kwargs) [source]

Call func on self producing a DataFrame with transformed values and that has the same axis length as self.

New in version 0.20.0.

Parameters:	func : function, str, list or dict Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. Accepted combinations are: function string function name list of functions and/or function names, e.g. [np.exp. 'sqrt'] dict of axis labels -> functions, function names or list of such. axis : {0 or ‘index’, 1 or ‘columns’}, default 0 If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row. *args Positional arguments to pass to func. **kwargs Keyword arguments to pass to func.
Returns:	DataFrame A DataFrame that must have the same length as self.
Raises:	ValueError : If the returned DataFrame has a different length than self.

See also

DataFrame.agg

Only perform aggregating type operations.

DataFrame.apply

Invoke function on a DataFrame.

Examples

>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})
>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056