在模块statmodels.stats.stattools中有一个函数medcouple（），它是调整后箱线图中使用的偏度的度量。
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使用此变量，您可以计算定义离群值的区间。

下面是一个可能的解决方案，遵循Christophe's answer并解决@banderlog013的评论。
1.查找识别异常值和绘制箱线图所需的参数：

import statsmodels.api as sm
import datetime
import seaborn as sns
def adjusted_boxplot_params(data:pd.Series):
    ''' 
    Returns:
    @outlier_range_lower: lower bound of normal expected values. Everything below are outlier candidates.
    @outlier_range_upper: upper bound of normal expected values. Above are outlier candidates.
    @whis: (lowWhis,highWhis) whis parameter for the boxplot method, in a form (low_percentage, high_percentage), as expected by pyplot.boxplot().
    '''

    q1 = data.quantile(0.25)
    q3 = data.quantile(0.75)

    # Calculate the interquartile range
    iqr = q3 - q1
    #calculate medcouple
    y = np.asarray(data, dtype=np.double) 
    MC = sm.stats.stattools.medcouple(y) 

    # Define the outlier range 
    if (MC>0):
        outlier_range_lower = q1 - 1.5 * np.exp(-4*MC) * iqr
        outlier_range_upper = q3 + 1.5 * np.exp(3*MC)  * iqr
    else:
        outlier_range_lower = q1 - 1.5 * np.exp(-3*MC) * iqr
        outlier_range_upper = q3 + 1.5 * np.exp(4*MC)  * iqr

    whis = np.interp([outlier_range_lower, outlier_range_upper], np.sort(data), np.linspace(0, 1, data.size)) * 100 #Ref: https://stackoverflow.com/a/65390045/7745170

    return outlier_range_lower, outlier_range_upper, whis

1.从dataframe列中选择离群值，并根据需要绘制调整后的箱线图：

def select_outliers_adjusted_boxplot(df, col_name:str,show_plot=False): 
    ''' selects rows in the dataframe df which coll_name falls out of bounds [min,max]
    @df: dataframe 
    @col_name: column to analyze 
    Returns:
    @otuliers: df with selected rows from input df.
    '''

    outlier_range_lower, outlier_range_upper, whis = adjusted_boxplot_params(df[col_name])
    # Select the rows where the duration is outside the outlier range
    outliers = df[(df[col_name] < outlier_range_lower) |
                  (df[col_name] > outlier_range_upper)]

    if show_plot==True:
        plot = sns.boxplot(x=df[col_name,whis=whis) nacrtaj_boxplot_durations(df[col_name], plot_title,whis)
        plt.show(plot2)

    # Return the DataFrame containing the outliers
    return outliers