目录
- 一、基础图表函数概述
- 二、饼图的绘制
- 三、直方图的绘制
- 四、极坐标的绘制
- 五、散点图的绘制
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
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201207233500904.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2MjA3MDI0,size_16,color_FFFFFF,t_70)
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()
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201207233051567.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2MjA3MDI0,size_16,color_FFFFFF,t_70)
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()
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201207233212910.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2MjA3MDI0,size_16,color_FFFFFF,t_70)
三、直方图的绘制
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
mu, sigma = 100, 20
a = np.random.normal(mu, sigma, size=100)
plt.hist(a, 20, normed=1, histtype='stepfilled', facecolor='b', alpha=0.75)
plt.title('Histogram')
plt.show()
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201207233309238.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2MjA3MDI0,size_16,color_FFFFFF,t_70)
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()
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201207233329817.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2MjA3MDI0,size_16,color_FFFFFF,t_70)
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()
![在这里插入图片描述](https://img-blog.csdnimg.cn/2020120723334669.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ2MjA3MDI0,size_16,color_FFFFFF,t_70)
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