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.

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