# pandas.DataFrame.transform

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

  Call `func` on self producing a DataFrame with transformed values.

  Produced DataFrame will have same axis length 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 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.

  Returns
    DataFrame

      A DataFrame that must have the same length as self.

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

  See also

  `DataFrame.agg`

    Only perform aggregating type operations.

  `DataFrame.apply`

    Invoke function on a DataFrame.

  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 DataFrame must have the same length as the input DataFrame, 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