from pyspark.sql import functions as f

class Outlier():

def __init__(self, df):
    self.df = df

def _calculate_bounds(self):
    bounds = {
        c: dict(
            zip(["q1", "q3"], self.df.approxQuantile(c, [0.25, 0.75], 0))
        )
        for c, d in zip(self.df.columns, self.df.dtypes) if d[1] in ["bigint", "double"]
    }

    for c in bounds:
        iqr = bounds[c]['q3'] - bounds[c]['q1']
        bounds[c]['min'] = bounds[c]['q1'] - (iqr * 1.5)
        bounds[c]['max'] = bounds[c]['q3'] + (iqr * 1.5)

    return bounds

def _flag_outliers_df(self):
    bounds = self._calculate_bounds()

    outliers_col = [
        f.when(
            ~f.col(c).between(bounds[c]['min'], bounds[c]['max']),
            f.col(c)
        ).alias(c + '_outlier')
        for c in bounds]

    return self.df.select(*outliers_col)

def show_outliers(self):

    outlier_df = self._flag_outliers_df()

    for outlier in outlier_df.columns:
        outlier_df.select(outlier).filter(f.col(outlier).isNotNull()).show()

然后我使用：outlier（df）.show_outliers（）查看异常值，如下图所示：一列的相应异常值第二列的相应异常值
如何计算每列的异常值数？换句话说，如何计算pres\u输出和temp\u输出中的值数？