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# pandas.Series.transform

Series.transform(func, axis=0, *args, **kwargs)

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

  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’}

      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:
    ValueError If 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