方法一：Datafram.skew(axis=0)

例程：

data_frame2= pd.DataFrame({'A':[1,2,3,4],'b':[1,0,3,1]})
data_frame2.skew(axis = 0)
Out[62]:
A    0.000000
b    1.129338
dtype: float64

help(data_frame2.skew)

out:


Help on method skew in module pandas.core.frame:

skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) method of pandas.core.frame.DataFrame instance
    Return unbiased skew over requested axis
    Normalized by N-1
    
    Parameters
    ----------
    axis : {index (0), columns (1)}
    skipna : boolean, default True
        Exclude NA/null values. If an entire row/column is NA, the result
        will be NA
    level : int or level name, default None
        If the axis is a MultiIndex (hierarchical), count along a
        particular level, collapsing into a Series
    numeric_only : boolean, default None
        Include only float, int, boolean columns. If None, will attempt to use
        everything, then use only numeric data. Not implemented for Series.
    
    Returns
    -------
    skew : Series or DataFrame (if level specified)
 

方法2：scipy.stats.skew

numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index

# Check the skew of all numerical features
skewed_feats = all_data[numeric_feats].apply(lambda x:skew(x.dropna())).sort_values(ascending=False)
print("\nSkew in numerical features: \n")
skewness = pd.DataFrame({'Skew' :skewed_feats})
print(skewness.shape)
skewness.head()

out:

Skew in numerical features: 

(220, 1)
Out[145]:
Skew
Condition2_RRAn	53.981
RoofMatl_Membran	53.981
Exterior2nd_Other	53.981
Condition2_RRAe	53.981
MiscFeature_TenC	53.981



help(skew)
out:

Help on function skew in module scipy.stats.stats:

skew(a, axis=0, bias=True, nan_policy='propagate')
    Computes the skewness of a data set.
    
    For normally distributed data, the skewness should be about 0. A skewness
    value > 0 means that there is more weight in the left tail of the
    distribution. The function `skewtest` can be used to determine if the
    skewness value is close enough to 0, statistically speaking.
    
    Parameters
    ----------
    a : ndarray
        data
    axis : int or None, optional
        Axis along which skewness is calculated. Default is 0.
        If None, compute over the whole array `a`.
    bias : bool, optional
        If False, then the calculations are corrected for statistical bias.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan. 'propagate' returns nan,
        'raise' throws an error, 'omit' performs the calculations ignoring nan
        values. Default is 'propagate'.
    
    Returns
    -------
    skewness : ndarray
        The skewness of values along an axis, returning 0 where all values are
        equal.
    
    References
    ----------
    
    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
       Probability and Statistics Tables and Formulae. Chapman & Hall: New
       York. 2000.
       Section 2.2.24.1
 

參考閱讀見如下：

scipy.stats.skew

scipy.stats.skew(a, axis=0, bias=True, nan_policy='propagate')[source]

Compute the skewness of a data set.

For normally distributed data, the skewness should be about 0. For unimodal continuous distributions, a skewness value > 0 means that there is more weight in the right tail of the distribution. The function 

References

Examples

>>>

>>> from scipy.stats import skew
>>> skew([1, 2, 3, 4, 5])
0.0
>>> skew([2, 8, 0, 4, 1, 9, 9, 0])
0.2650554122698573