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# pandas.Series.agg

Series.agg(func=None, axis=0, *args, **kwargs)

  Aggregate using one or more operations over the specified axis.

  This method applies one or more aggregation functions to the Series and returns a scalar, Series, or DataFrame depending on the function(s) provided.

  Parameters:
    func function, str, list or dict

      Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Series.apply.

      Accepted combinations are:

      - function

      - string function name

      - list of functions and/or function names, e.g. `[np.sum, 'mean']`

      - dict of axis labels -> functions, function names or list 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:
    scalar, Series or DataFrame

      The return can be:

      - scalar : when Series.agg is called with single function

      - Series : when DataFrame.agg is called with a single function

      - DataFrame : when DataFrame.agg is called with several functions

  See also

  `Series.apply`

    Invoke function on a Series.

  `Series.transform`

    Transform function producing a Series with like indexes.

  Notes

  The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., `numpy.mean(arr_2d)` as opposed to `numpy.mean(arr_2d, axis=0)`.

  agg is an alias for aggregate. Use the alias.

  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.

  A passed user-defined-function will be passed a Series for evaluation.

  If `func` defines an index relabeling, `axis` must be `0` or `index`.

  Examples

      >>> s = pd.Series([1, 2, 3, 4])
      >>> s
      0    1
      1    2
      2    3
      3    4
      dtype: int64

      >>> s.agg("min")
      1

      >>> s.agg(["min", "max"])
      min   1
      max   4
      dtype: int64