3,4,5])y=np.array([2,4,1,3,5])labels=['A','B','C','D','E']# 创建散点图并添加点标签plt.figure(figsize=(8,6))plt.scatter(x,y)fori,labelinenumerate(labels):plt.annotate(label,(x[i],y[i]),xytext=(5,5),textcoords='offset points')plt.title('Scatter Plot with Point ...
'Point D','Point E']# 创建图形和坐标轴fig,ax=plt.subplots()# 绘制散点图scatter=ax.scatter(x,y)# 为每个点添加带箭头的标签fori,labelinenumerate(labels):ax.annotate(label,(x[i],y[i]),xytext=(5,5),textcoords
Scatter()所绘制的散列图可以指定每个 点的颜色和大小。 下面的程序演示了 scatter()的用法,效果如图4-27所示。 plt.figure(figsize=(8,4)) x = np.random.random(100) y = np.random.random(100) plt.scatter(x, y, s=x*1000, c=y, marker=(5, 1), alpha=0.8, lw=2, facecolors="none...
# Define the axesax_main = fig.add_subplot(grid[:-1,:-1])ax_right = fig.add_subplot(grid[:-1, -1], xticklabels=[], yticklabels=[])ax_bottom = fig.add_subplot(grid[-1,0:-1], xticklabels=[], yticklabels=[]) # Scatterplot on main axax_main.scatter('displ','hwy', s...
23,17,20,21,20,22,15,11,15,5,13,17,10,11,13,12,13,6] # 设置图形大小 plt.figure(figsize=(20, 8), dpi=80) # 绘制散点 plt.scatter(x_3, y_3, label="March") plt.scatter(x_10, y_10, label="October") x = list(x_3) + list(x_10) xtick_labels = ["March {}th"....
1. 散点图(Scatter plot) 2. 带边界的气泡图(Bubble plot with Encircling) 3. 带线性回归最佳拟合线的散点图 (Scatter plot with linear regression line of best fit) 4. 抖动图 (Jittering with stripplot) 5. 计数图 (Counts Plot) 6. 边缘直方图 (Marginal Histogram) 7. 边缘箱形图 (Marginal Box...
pyplot as pltimport numpy as npx = np.random.rand(100)y = np.random.rand(100)plt.scatter(x...
这个示例演示了如何绘制一个简单的散点图。列表x和y包含水平和垂直坐标数据,而colors列表则定义了用于每个数据点的颜色。使用plt.scatter()函数确定样式参数(如点的大小和形状),以及通过alpha参数调整点的透明度。 绘制饼图 import matplotlib.pyplot as plt #导入Matplotlib模块labels = ['A', 'B', 'C', 'D'...
import matplotlib.pyplot as plt import numpy as np # 创建数据 x = np.random.rand(50) y = np.random.rand(50) colors = np.random.rand(50) sizes = 100 * np.random.rand(50) # 绘制散点图 plt.scatter(x, y, c=colors, s=sizes, alpha=0.6) # 添加标题和坐标轴标签 plt.title('Scatt...
散点图scatter # 生成1024个呈标准正态分布的二维数据组 (平均数是0,方差为1) 作为一个数据集 n = 1024 x = np.random.normal(0,1,n) y = np.random.normal(0,1,n) t = np.arctan2(y,x) # 颜色 plt.scatter(x,y,s=75,c=t,alpha=0.5) # size=75,透明的alpha=50% ...