pandas.Series.median#

Series.median(*, axis=0, skipna=True, numeric_only=False, **kwargs)[source]#

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.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, 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