在进行数据的可视化分析时，可能我们分析的两个指标，他们的取值区间相差很大，如果采用一个y轴，就不利于我们观察另一个指标。所以，记录一下我在项目的处理过程中采用的方法：

首先观察一下，我用到的数据源

# 首先进行数据清洗
# 获取国家名称
country = match_data['Home Team Name'].unique().tolist()
# 创建两个列表，用于存储主客国家的进球数
Home_goals,Away_goals =[],[] 
for i in country:
    h_goals = match_data.loc[match_data['Home Team Name'] == i ,'Home Team Goals'].sum()
    Home_goals.append(h_goals)
    a_goals = match_data.loc[match_data['Away Team Name'] == i ,'Away Team Goals'].sum()
    Away_goals.append(a_goals)
goals_data =pd.DataFrame({'Country':country,'Home_goals':Home_goals,'Away_goals':Away_goals}) 
goals_data['total_goals'] = goals_data['Home_goals']+goals_data['Away_goals']
goals_data['Home_rate'] = goals_data['Home_goals']/goals_data['total_goals']
# 按照总进球数进行降序排列，取前10个国家进行分析
top_goals_data= goals_data.sort_values(by = 'total_goals',ascending = False)[:10]
print(top_goals_data)

 画图

fig,ax1= plt.subplots(figsize = (15,5))
ax2 =  ax1.twinx()  # 创建具有双y轴的子图
top_goals_data.plot(x= 'Country' ,y= ['Home_goals','Away_goals','total_goals'],kind = 'bar',ax=ax1)
ax1.set_xlabel("Country")
ax1.set_ylabel("Goals")

# 绘制第二个图形
ax2.plot(top_goals_data.Country,top_goals_data.Home_rate,marker ='o')
ax2.set_ylabel("Home_rate")
# 图形上面显示数值标签
for i,v in zip(top_goals_data.Country,top_goals_data.Home_rate):
    plt.text(i,v,round(v,4))

plt.show()

效果呈现如下：

完工~