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另一篇部落格：https://blog.csdn.net/weixin_40924580/article/details/82809484 有關於使用matplotlib檢視titanic資料特徵的方法
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資料下載——儲存——匯入

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')

# names = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model_year','origin','car_name']
# df =pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data',sep='\s+',names=names)
# df.to_csv("H:/seaborn2.csv")

df = pd.read_csv('H:/seaborn2.csv')
df['maker'] = df.car_name.map(lambda x:x.split()[0])        #以空格分割，取第一個
df.origin = df.origin.map({1:'Americ',2:'Europe',3:'Asia'})
df.applymap(lambda x:np.nan if x=='?' else x).dropna()
 

# sns.factorplot(data=df,x="model_year",y="mpg",col="origin")         #！1 按照origin維度取值不同畫圖

# sns.factorplot(data=df,x="model_year",y="mpg",col="origin",kind = 'bar')         #！2 柱狀圖

# sns.factorplot("model_year",data=df,col="origin",kind = 'count')    #！3 對某一列根據origin計數

# g = sns.FacetGrid(df,col='origin')                                #！4 並檢視分佈
# g.map(sns.distplot,'mpg')
 

# g = sns.FacetGrid(df, col="origin")                               #！5散點圖
# g.map(plt.scatter, "horsepower", "mpg")

# g = sns.FacetGrid(df, col="origin")                                 #！6 畫圖同時包含迴歸
# g.map(sns.regplot, "displacement", "mpg")
# plt.xlim(0, 250)
# plt.ylim(0, 60)

# df['tons'] = (df.weight/2000).astype(int)                       #! 7密度繪圖
# g = sns.FacetGrid(df, col="origin", row="tons")
# g.map(sns.kdeplot, "displacement", "mpg")
# plt.xlim(0, 250)
# plt.ylim(0, 60)
 

# sns.jointplot("mpg", "weight", data=df, kind='kde')                     #！8聯合繪圖

# sns.jointplot("mpg", "weight", data=df, kind='reg')                     #！9聯合繪圖加回歸

# g = sns.JointGrid(x="weight", y="mpg", data=df)                         #!10多項式聯合迴歸，"order"
# g.plot_joint(sns.regplot, order=2)
# g.plot_marginals(sns.distplot)