pandas.Series.skew — pandas 3.0.0.dev0+2103.g41968a550a documentation (original) (raw)
Series.skew(*, axis=0, skipna=True, numeric_only=False, **kwargs)[source]#
Return unbiased skew over requested axis.
Normalized by N-1.
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 scalar
Value containing the calculation referenced in the description.
See also
Return unbiased skew over requested axis.
Return unbiased variance over requested axis.
Return unbiased standard deviation over requested axis.
Examples
s = pd.Series([1, 2, 3]) s.skew() 0.0
With a DataFrame
df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]}, ... index=['tiger', 'zebra', 'cow']) df a b c tiger 1 2 1 zebra 2 3 3 cow 3 4 5 df.skew() a 0.0 b 0.0 c 0.0 dtype: float64
Using axis=1
df.skew(axis=1) tiger 1.732051 zebra -1.732051 cow 0.000000 dtype: float64
In this case, numeric_only should be set to True to avoid getting an error.
df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']}, ... index=['tiger', 'zebra', 'cow']) df.skew(numeric_only=True) a 0.0 dtype: float64