pandas.Series.transform#

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.

See also

Series.agg

Only perform aggregating type operations.

Series.apply

Invoke function on a Series.

Notes

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