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