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# pandas.Series.sum

Series.sum(*, axis=None, skipna=True, numeric_only=False, min_count=0, **kwargs)

  Return the sum of the values over the requested axis.

  This is equivalent to the method `numpy.sum`.

  Parameters:
    axis{index (0)}

      Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

      Warning

      The behavior of DataFrame.sum with `axis=None` is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

      Added in version 2.0.0.

    skipna bool, default True

      Exclude NA/null values when computing the result.

    numeric_only bool, default False

      Include only float, int, boolean columns. Not implemented for Series.

    min_count int, default 0

      The required number of valid values to perform the operation. If fewer than `min_count` non-NA values are present the result will be NA.

    **kwargs

      Additional keyword arguments to be passed to the function.

  Returns:
    scalar or Series (if level specified)

      Sum of the values for the requested axis.

  See also

  `numpy.sum`

    Equivalent numpy function for computing sum.

  `Series.mean`

    Mean of the values.

  `Series.median`

    Median of the values.

  `Series.std`

    Standard deviation of the values.

  `Series.var`

    Variance of the values.

  `Series.min`

    Minimum value.

  `Series.max`

    Maximum value.

  Examples

      >>> idx = pd.MultiIndex.from_arrays(
      ...     [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]],
      ...     names=["blooded", "animal"],
      ... )
      >>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx)
      >>> s
      blooded  animal
      warm     dog       4
               falcon    2
      cold     fish      0
               spider    8
      Name: legs, dtype: int64

      >>> s.sum()
      14

  By default, the sum of an empty or all-NA Series is `0`.

      >>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
      0.0

  This can be controlled with the `min_count` parameter. For example, if you’d like the sum of an empty series to be NaN, pass `min_count=1`.

      >>> pd.Series([], dtype="float64").sum(min_count=1)
      nan

  Thanks to the `skipna` parameter, `min_count` handles all-NA and empty series identically.

      >>> pd.Series([np.nan]).sum()
      0.0

      >>> pd.Series([np.nan]).sum(min_count=1)
      nan