transform(self, func: 'AggFuncType', axis: 'Axis' = 0, *args, **kwargs) -> 'DataFrame | Series'

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Parameters

func : function, 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'}

Parameter needed for compatibility with DataFrame.

*args :

Positional arguments to pass to func .

**kwargs :

Keyword arguments to pass to func .

Raises

ValueError : If the returned Series has a different length than self.

Returns

Series

A Series that must have the same length as self.

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

See Also

Series.agg

Only perform aggregating type operations.

Series.apply

Invoke function on a Series.

Examples

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> 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:

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series(range(3))
... s
0    0
1    1
2    2
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> 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:

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> 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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.series.Series.apply pandas.core.series.Series.aggregate

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