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# pandas.Series.median

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

  Return the median of the values over the requested axis.

  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 to be passed to the function.

  Returns:
    scalar or Series (if level specified)

      Median 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.median()
      2.0

  With a DataFrame

      >>> df = pd.DataFrame({"a": [1, 2], "b": [2, 3]}, index=["tiger", "zebra"])
      >>> df
             a   b
      tiger  1   2
      zebra  2   3
      >>> df.median()
      a   1.5
      b   2.5
      dtype: float64

  Using axis=1

      >>> df.median(axis=1)
      tiger   1.5
      zebra   2.5
      dtype: float64

  In this case, numeric_only should be set to True to avoid getting an error.

      >>> df = pd.DataFrame({"a": [1, 2], "b": ["T", "Z"]}, index=["tiger", "zebra"])
      >>> df.median(numeric_only=True)
      a   1.5
      dtype: float64