matplotlib绘制散点图之基本配置——万能模板案例（二）

### 在二维坐标系上，位置表示(x,y)二维数据
x = df.sepal_length  # x 表示花瓣长
y = df.sepal_width   # y 表示花瓣宽
s = (df.petal_length * df.petal_width)*np.pi   # s(size) 表示花萼面积
c = (df.petal_length * df.petal_width)*np.pi  
plt.scatter(x,y,s=s*5, c=c,cmap=plt.cm.RdYlBu_r)
plt.xlabel('sepal_length')
plt.ylabel('sepal_width')

# 在二维坐标系上，位置表示(x,y)二维数据
x = df.sepal_length  # x 表示花瓣长
y = df.sepal_width   # y 表示花瓣宽
s = (df.petal_length * df.petal_width)*np.pi   # s(size) 表示花萼面积
#print(df.species)
#colormap = {"setosa":"#FF0000", "versicolor":"green", "virginica":"b"}  # 定义一个字典将species字符串映射到颜色字符串上
colormap = {"setosa":1, "versicolor":5, "virginica":6}  # 定义一个字典将species字符串映射到颜色字符串上
c = df.species.map(colormap)
#print(c)
plt.scatter(x,y,s=s*5, c=c,cmap=plt.cm.coolwarm, alpha=0.7, edgecolors='face')
plt.xlabel('sepal_length')
plt.ylabel('sepal_width')

plt.scatter(df['burglary'], df['larceny_theft'],
            s=df['population']*2e-5, 
            c=df['motor_vehicle_theft'], cmap=plt.cm.coolwarm, 
            edgecolors='b', 
            alpha=0.75)
for idx,statename in df['state'].items():
    plt.text(x=df['burglary'][idx],y=df['larceny_theft'][idx]-df['population'][idx]*2e-5*0.5,s=statename,fontsize=6,ha='center',va='top')

df.plot.scatter(x='burglary',y='larceny_theft',c='motor_vehicle_theft',cmap=plt.cm.coolwarm,s=df['population']*2e-5)
for i in df.index:
    if i in top5_motor_theft_index:  # 偷车贼最多的5个州
        plt.text(df.loc[i,'burglary']+10, df.loc[i,'larceny_theft']-10, df.loc[i,'state'], color='red')  # 一个文本框