一、画箱型图，直观观察数据

import pandas as pd
import matplotlib.pyplot as plt
# 一行，八列
fig,axes = plt.subplots(1,8) 
# box表示箱体，ax表示绘制子图，subplots判断图片中是否有子图，sym参数表示异常值标记的方式
df.plot(kind='box', ax=axes, subplots=True, title='Different boxplots', sym='r+')
# 调整子图之间的间距
fig.subplots_adjust(wspace=6,hspace=6)

二、画出散点图，直观观察极端值

df1 = df.copy()
## 得到df1的多少个列下标编号
df1["index"] = df1.index 
# 画出每个特性的散点图，直观感受是否有离群点
# 此时会得到x坐标标签为index，y坐标标签同title为Length等等，kind表示方式为点，s代表点的大小
features = ["Length","Diameter","Height","Whole weight","Shucked weight","Viscera weight","Shell weight","Rings"]
for i in features:
    s = df1.plot(title=i,
            kind='scatter',   
            x="index",
            y=i,
            s=8,
            figsize=(8, 6))

总结：DataFrame.plot()函数，会对DataFrame的内容进行针对性的绘制可视化图。

三、数据处理

为方便数据处理，决定将所有特征值在 [ 0,中位数 * 3.5 ] 之外的数字去除 （玄学调参，不讲道理）

# 如果df[条件]，条件满足，则保留在df里
features = ["Length","Diameter","Height","Whole weight","Shucked weight","Viscera weight","Shell weight","Rings"]
for i in features:
    df = df[df[i] > 0]
    df = df[df[i] < df[i].median() * 3.5]