绘制散点图

• 不同条件（维度）之间的内在关联关系
• 观察数据的离散聚合程度

01.基本操作，导入数据后绘制散点图

import matplotlib.pyplot as plt
import matplotlib
#数据
y_3=[11,13,15,13,20,21,18,19,16,13,14,13,15,13,15,17,18,19,15,13,13,15,15,13,23,21,25,26,23,22,21]
y_10=[13,15,17,18,19,15,13,13,15,15,13,23,21,25,26,30,11,13,15,13,20,21,18,19,16,13,14,13,16,15,18]

x = range(1,32)
#设置图片大小
plt.figure(figsize=(20,8),dpi = 80)

plt.scatter(x,y_3)
plt.scatter(x,y_10)

#展示
plt.show()

02.对x轴刻度进行优化，显示中文字体

import matplotlib.pyplot as plt
import matplotlib
#设置中文字体
matplotlib.rc("font",family = 'MicroSoft YaHei',weight = 'bold' )
#数据
y_3=[11,13,15,13,20,21,18,19,16,13,14,13,15,13,15,17,18,19,15,13,13,15,15,13,23,21,25,26,23,22,21]
y_10=[13,15,17,18,19,15,13,13,15,15,13,23,21,25,26,30,11,13,15,13,20,21,18,19,16,13,14,13,16,15,18]

x_3 = range(1,32)
x_10 = range(51,82)
#设置图片大小
plt.figure(figsize=(20,8),dpi = 80)

plt.scatter(x_3,y_3)
plt.scatter(x_10,y_10)

#调整x轴的刻度
_x = list(x_3)+list(x_10)
_xtick_labels = ["3月{}日".format(i) for i in x_3]
_xtick_labels += ["10月{}日".format(i-50) for i in x_10]
plt.xticks(_x[::3],_xtick_labels[::3],rotation=45)
#展示
plt.show()

03.添加图例和说明

import matplotlib.pyplot as plt
import matplotlib
#设置中文字体
matplotlib.rc("font",family = 'MicroSoft YaHei',weight = 'bold' )
#数据
y_3=[11,13,15,13,20,21,18,19,16,13,14,13,15,13,15,17,18,19,15,13,13,15,15,13,23,21,25,26,23,22,21]
y_10=[13,15,17,18,19,15,13,13,15,15,13,23,21,25,26,30,11,13,15,13,20,21,18,19,16,13,14,13,16,15,18]

x_3 = range(1,32)
x_10 = range(51,82)
#设置图片大小
plt.figure(figsize=(20,8),dpi = 80)

plt.scatter(x_3,y_3,label = "3月份")
plt.scatter(x_10,y_10,label = "10月份")

#调整x轴的刻度
_x = list(x_3)+list(x_10)
_xtick_labels = ["3月{}日".format(i) for i in x_3]
_xtick_labels += ["10月{}日".format(i-50) for i in x_10]
plt.xticks(_x[::3],_xtick_labels[::3],rotation=45)

#添加图例
plt.legend(loc = 1 )
#添加描述信息
plt.xlabel("月份")
plt.ylabel("温度℃")
plt.title("天气图表")
#展示
plt.show()