pandas.DataFrame.skew — pandas 3.0.0.dev0+2106.gb2b2d04e41 documentation (original) (raw)

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

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters:

axis{index (0), columns (1)}

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:

Series or scalar

Unbiased skew over requested axis.

See also

Dataframe.kurt

Returns unbiased kurtosis 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