python数据可视化--matplotlib用户手册入门：pyplot画图

import matplotlib.ptplot as plt
import numpy as np
#x = np.arange(10)

x = np.linspace(0,2,100)
plt.plot(x,x,label = ‘liner‘)
plt.plot(x,x**2,label = ‘quadratic‘)
plt.plot(x,x**3,label = ‘cubic‘)
plt.xlabel(‘x label‘)
plt.ylabel(‘y label‘)
plt.title(‘Simple Plot‘)
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0,10,0.2)
y = np.cos(x)
fig =plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y)
plt.show()


import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0,10,0.2)
y = np.cos(x)
fig =plt.figure()
ax2 = fig.add_subplot(2,2,2)
ax2.plot(x,y)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0,5,0.2)
plt.plot(t,t,‘r_‘,t,t**2,‘bs‘,t,t**3,‘g^‘)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
data = {
    ‘a‘:np.arange(50),
    ‘c‘:np.random.randint(0,50,50),
    ‘d‘:np.random.randn(50)
}
data[‘b‘] = data[‘a‘] + 10*np.random.randn(50)
data[‘d‘] = np.abs(data[‘d‘])*100

# x坐标 数组a,y坐标 数组b,颜色c 数组c 大小s数组d
plt.scatter(‘a‘,‘b‘,c = ‘c‘,s = ‘d‘,data= data)
plt.xlabel(‘entry a‘)
plt.ylabel(‘entry b‘)
plt.show()

import matplotlib.pyplot as plt
import numpy as np
names = [1,2,3]
values = [1,10,100]
# 设置画布大小
plt.figure(1,figsize = (9,3))
# 画出三幅图，分别设置
plt.subplot(1,3,1)
plt.bar(names,values)
plt.subplot(1,3,2)
plt.scatter(names,values)
plt.subplot(1,3,3)
plt.plot(names,values)
plt.suptitle(‘Categorical Plotting‘)
plt.show()

import matplotlib .pyplot as plt
plt.figure(1)
plt.subplot(2,1,1)
plt.plot([1,2,3])
plt.title(‘Easy as 1,2,3‘)
plt.subplot(2,1,2)
plt.plot([4,5,6])
plt.show()

plt.figure(2)
plt.plot([4,5,6])
plt.show()

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(19680801)
data = np.random.randn(2,100)

fig,axs = plt.subplots(2,2,figsize = (5,5))
axs[0,0].hist(data[0])
axs[1,0].scatter(data[0],data[1])
axs[0,1].plot(data[0],data[1])
axs[1,1].hist2d(data[0],data[1])

plt.show()

import matplotlib.pyplot as plt 
import numpy as np
mu ,sigma = 100,15
x = mu + sigma *np.random.randn(100000)

n,bins, patches = plt.hist(x,50,normed = True ,facecolor = ‘g‘,alpha = 0.75)

plt.xlabel(‘Smarts‘)
plt.ylabel(‘Probability‘)
plt.title(‘Histogram of IQ‘)
# 支持latex/
plt.text(60,0.025,r‘$\mu =100,\ \sigma = 15$‘)
plt.axis([40,160,0,0.03])
plt.grid(True)
plt.show()