数据质量分析—绘制箱型图

箱型图如下所示：

计算过程：

（1）计算上四分位数（Q3），中位数，下四分位数（Q1） 
（2）计算上四分位数和下四分位数之间的差值，即四分位数差（IQR，interquartile range）Q3-Q1 
（3）绘制箱线图的上下范围，上限为上四分位数，下限为下四分位数。在箱子内部中位数的位置绘制横线。 
（4）大于上四分位数1.5倍四分位数差的值，或者小于下四分位数1.5倍四分位数差的值，划为异常值（outliers）。 
（5）异常值之外，最靠近上边缘和下边缘的两个值处，画横线，作为箱线图的触须。 
（6）极端异常值，即超出四分位数差3倍距离的异常值，用实心点表示；较为温和的异常值，即处于1.5倍-3倍四分位数差之间的异常值，用空心点表示。 
（7）为箱线图添加名称，数轴等

对于异常值处理可取消异常值，或当作缺失值来处理。

如下是代码，  其中 whis 参数对异常值进行限定。其含义是 大于 上四分位点的whis倍、小于下四分位点whis倍的数值则显示为异常点。

"""
数据质量分析
用箱型图进行异常值检测
"""
import pandas as pd
import matplotlib.pyplot as plt


def box_plot():
    """
    绘制箱型图
    :return:
    """
    feature = pd.read_csv('./dataset/feature')
    data = feature['licheng']

    # plt.boxplot(data, sym="o", whis=1.5)
    # plt.boxplot(data, sym="o", whis=0.01)
    plt.boxplot(data, sym="o", whis=1)
    plt.show()


if __name__ == '__main__':
    box_plot()

licheng,youxi,bingbang
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