python_matplotlib_subplot子图绘制区域的设定示例（matplotlib设置图例位置）

python_matplotlib_subplot子图绘制区域的设定示例（matplotlib设置图例位置）

文章目录

​​code​​​​布局理解​​​​result​​

code

# -*- coding: utf-8 -*-"""Created on Thu Nov 03 16:57:26 2016@author: gsdx"""import matplotlib.pyplot as pltimport numpy as npdef f(t): return np.exp(-t) * np.cos(2 * np.pi * t)t1 = np.arange(0, 5, 0.1)t2 = np.arange(0, 5, 0.02)def bar_plot(): plt.subplot(321) k = 10 x = np.arange(k) y = np.random.rand(k) plt.bar(x, y) # 画出 x 和 y 的柱状图 # 增加数值 for x, y in zip(x, y): plt.text(x, y , '%.2f' % y, ha='center', va='bottom')def scatter_plot(): plt.subplot(322) # plt.plot(t2, np.cos(2 * np.pi * t2), 'r--') k = 500 x = np.random.rand(k) y = np.random.rand(k) size = np.random.rand(k) * 50 # 生成每个点的大小 colour = np.arctan2(y, x) # 生成每个点的颜色大小 plt.scatter(x, y, s=size, c=colour) plt.colorbar() # 添加颜色栏# plt.figure(12)def pie_plot(): plt.subplot(323) # plt.plot(t1, f(t1), 'bo', t2, f(t2), 'r--') labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'] explode = (0, 0.1, 0, 0) #only "explode" the 2nd slice (i.e. 'Hogs') plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90) # Set aspect ratio to be equal so that pie is drawn as a circle. plt.axis('equal') # plt.pause(5)def line_plot(): plt.subplot(324) plt.plot([1, 2, 3, 4], [1, 4, 9, 16])def histgram_plot(): plt.subplot(313) mu,sigma=-1,1 array_x=mu+sigma*np.random.randn(10000) # array_x=np.random.normal(-1,1,10000) plt.hist(array_x,bins=100,density=1) # print(bins) # set the title and x,y lables of the chart: plt.title('Histogram of normal attributions') plt.xlabel('x') plt.ylabel('y') plt.text(0,0, r'$\mu=-1,\ \sigma=1$') """ Convenience method to get or set axis properties. """ #if you not specify the axis ,then the chart axes will auto adjust to display properly! # plt.axis([-10,10,0,1]) ''' set if you want to display the grid: ''' plt.grid(True)bar_plot()scatter_plot()pie_plot()line_plot()histgram_plot()plt.show()

布局理解

result

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