Skip to main content
`Ctrl`+`K`

# pandas.Series.mean

Series.mean(*, axis=0, skipna=True, numeric_only=False, **kwargs)

  Return the mean of the values over the requested axis.

  This method computes the arithmetic mean of the Series values, optionally skipping missing values.

  Parameters:
    axis{index (0)}

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

      For DataFrames, specifying `axis=None` will apply the aggregation across both axes.

      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.

    **kwargs

      Additional keyword arguments have no effect but might be accepted for compatibility with NumPy. See NumPy compatibility kwargs for more.

  Returns:
    scalar

      Mean of the values for the requested axis.

  See also

  `numpy.median`

    Equivalent numpy function for computing median.

  `Series.sum`

    Sum 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

      >>> s = pd.Series([1, 2, 3])
      >>> s.mean()
      2.0