pandas.Series.mean — pandas 2.2.3 documentation (original) (raw)

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.

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. 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