散点图：是指在回归分析中，数据点在直角坐标系平面上的分布图，散点图表示因变量随自变量而变化的大致趋势，据此可以选择合适的函数对数据点进行拟合。

1.画点：scatter

import matplotlib.pyplot as plt import numpy as np #画点 plt.scatter(np.arange(5),np.arange(5)) plt.show()

2.散点图

x=np.random.normal(0,1,500) y=np.random.normal(0,1,500) plt.scatter(x,y,s=50,c='r',alpha=0.5) plt.xlim((-2,2)) plt.ylim((-2,2)) #去掉X/Y轴默认坐标 plt.xticks(()) plt.yticks(()) plt.show()

3.改变散点形状和颜色：这个案例来自matplotlib官网

import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) N = 100 r0 = 0.6 x = 0.9 * np.random.rand(N) y = 0.9 * np.random.rand(N) area = (20 * np.random.rand(N))**2 # 0 to 10 point radii c = np.sqrt(area) r = np.sqrt(x * x + y * y) area1 = np.ma.masked_where(r < r0, area) area2 = np.ma.masked_where(r >= r0, area) plt.scatter(x, y, s=area1, marker='^', c=c) plt.scatter(x, y, s=area2, marker='o', c=c) plt.show()

# Show the boundary between the regions: theta = np.arange(0, np.pi / 2, 0.01) plt.plot(r0 * np.cos(theta), r0 * np.sin(theta)) plt.show()