pandas.Series.transform — pandas 3.0.0.dev0+2099.g3832e85779 documentation (original) (raw)

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

Call func on self producing a Series with the same axis shape as self.

Parameters:

funcfunction, str, list-like or dict-like

Function to use for transforming the data. If a function, must either work when passed a Series or when passed to Series.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.

Accepted combinations are:

• function
• string function name
• list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
• dict-like of axis labels -> functions, function names or list-like of such.

axis{0 or ‘index’}

Unused. Parameter needed for compatibility with DataFrame.

*args

Positional arguments to pass to func.

**kwargs

Keyword arguments to pass to func.

Returns:

Series

A Series that must have the same length as self.

Raises:

ValueErrorIf the returned Series has a different length than self.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methodsfor more details.

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 Series must have the same length as the input Series, 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

You can call transform on a GroupBy object:

df = pd.DataFrame({ ... "Date": [ ... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05", ... "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"], ... "Data": [5, 8, 6, 1, 50, 100, 60, 120], ... }) df Date Data 0 2015-05-08 5 1 2015-05-07 8 2 2015-05-06 6 3 2015-05-05 1 4 2015-05-08 50 5 2015-05-07 100 6 2015-05-06 60 7 2015-05-05 120 df.groupby('Date')['Data'].transform('sum') 0 55 1 108 2 66 3 121 4 55 5 108 6 66 7 121 Name: Data, dtype: int64

df = pd.DataFrame({ ... "c": [1, 1, 1, 2, 2, 2, 2], ... "type": ["m", "n", "o", "m", "m", "n", "n"] ... }) df c type 0 1 m 1 1 n 2 1 o 3 2 m 4 2 m 5 2 n 6 2 n df['size'] = df.groupby('c')['type'].transform(len) df c type size 0 1 m 3 1 1 n 3 2 1 o 3 3 2 m 4 4 2 m 4 5 2 n 4 6 2 n 4