Matplotlib基础绘图函数示例
pyplot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) |
绘制一个箱体图 |
| 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.pas(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 |
pyplot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()
pyplot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()
pyplot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()
pyplot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()
