pandas 1.4.2

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

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 DataFrame or when passed to DataFrame.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', 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 .

Raises

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

Returns

DataFrame

A DataFrame that must have the same length as self.

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

See Also

DataFrame.agg

Only perform aggregating type operations.

DataFrame.apply

Invoke function on a DataFrame.

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 DataFrame must have the same length as the input DataFrame, 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.frame.DataFrame.apply pandas.core.frame.DataFrame.aggregate

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File: /pandas/core/frame.py#8667
type: <class 'function'>
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