一、基础图表函数概述
| 函数 | 说明 |
| plt.plot(x,y,fmt,…) | 绘制一个坐标图 |
| plt.boxplot(data,notch,position) | 绘制一个箱形图 |
| plt.bar(left,height,width,bottom) | 绘制一个条形图 |
| plt.barh(width,bottom,left,height) | 绘制一个横向条形图 |
| plt.polar(theta, r) | 绘制极坐标图 |
| plt.pie(data, explode) | 绘制饼图 |
| plt.psd(x,NFFT=256,pad_to,Fs) | 绘制功率谱密度图 |
| plt.specgram(x,NFFT=256,pad_to,F) | 绘制谱图 |
| plt.cohere(x,y,NFFT=256,Fs) | 绘制X‐Y的相关性函数 |
| plt.scatter(x,y) | 绘制散点图，其中，x和y长度相同 |
| plt.step(x,y,where) | 绘制步阶图 |
| plt.hist(x,bins,normed) | 绘制直方图 |
| plt.contour(X,Y,Z,N) | 绘制等值图 |
| plt.vlines() | 绘制垂直图 |
| plt.stem(x,y,linefmt,markerfmt) | 绘制柴火图 |
| plt.plot_date() | 绘制数据日期 |
使用Spyder - Python开发环境
二、饼图的绘制
import matplotlib.pyplot as plt labels = 'Frogs', 'Hogs' ,'Dogs' ,'Logs' sizes = [15, 30, 45, 10] explode = (0, 0.1, 0, 0) plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False,startangle=90) plt.show()
import matplotlib.pyplot as plt labels = 'Frogs', 'Hogs' ,'Dogs' ,'Logs' sizes = [15, 30, 45, 10] explode = (0, 0.1, 0, 0) plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False,startangle=90) plt.axis('equal') plt.show()
三、直方图的绘制
import numpy as np import matplotlib.pyplot as plt np.random.seed(0) mu, sigma = 100, 20 # 均值和标准差 a = np.random.normal(mu, sigma, size=100) plt.hist(a, 20, normed=1, histtype='stepfilled', facecolor='b', alpha=0.75) # 第二个参数bin：直方图的个数 plt.title('Histogram') plt.show()
四、极坐标的绘制
import numpy as np import matplotlib.pyplot as plt N = 20 theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False) radii = 10 * np.random.rand(N) width = np.pi / 4 * np.random.rand(N) ax = plt.subplot(111, projection='polar') bars = ax.bar(theta, radii, width = width, bottom = 0.0) for r, bar in zip(radii, bars): bar.set_facecolor(plt.cm.viridis(r / 10.)) bar.set_alpha(0.5) plt.show()
五、散点图的绘制
import numpy as np import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot(10 * np.random.randn(100), 10 * np.random.randn(100), 'o') ax.set_title('Simple Scatter') plt.show()
