pandas.Series.mean#

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

Return the mean 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.

New 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. Not implemented for Series.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

scalar or scalar

Examples

>>> s = pd.Series([1, 2, 3])
>>> s.mean()
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.mean()
a   1.5
b   2.5
dtype: float64

Using axis=1

>>> df.mean(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.mean(numeric_only=True)
a   1.5
dtype: float64