Matplotlib练习题

Matplotlib 是一个 Python 的 2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。通过 Matplotlib，开发者可以仅需要几行代码，便可以生成绘图，直方图，功率谱，条形图，错误图，散点图等。

# 11.1 Plotting a function  
import matplotlib  
import matplotlib.pyplot as plt  
import numpy as np  
import math  
  
# Data for plotting  
x = np.arange(0.0, 2.0, 0.01)  
y = (np.sin(x-2)**2)*(math.e**(-x**2))  
  
# Note that using plt.subplots below is equivalent to using  
# fig = plt.figure() and then ax = fig.add_subplot(111)  
fig, ax = plt.subplots()  
ax.plot(x, y)  
  
ax.set(xlabel='x', ylabel='f(x)',  
       title='f(x) = [(sin(x-2))^2]*e^(-x^2)')  
ax.grid()  
  
plt.show()

输出显示为：

import numpy as np  
import matplotlib.pyplot as plt  
n = 20  
m = 10  
X = np.empty([n, m])  
for i in range(n):  
    row = np.random.normal(10, 1, m)  
    X[i] = row  
  
b = np.random.normal(5, 1, m)  
z = np.random.normal(0, 1, n)  
  
y = np.matmul(X, b) + z  
print(y)  
  
b_ = np.linalg.lstsq(X, y, rcond=None)[0]  
print(b)  
print(b_)  
  
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])  
  
plt.plot(x, b, 'b', label='true data', markersize=10)  
plt.plot(x, b_, 'r', label='estimated data', markersize=10)  
plt.legend()  
plt.show()

输出显示：

import numpy as np  
from scipy import stats  
import matplotlib.mlab as mlab  
import matplotlib.pyplot as plt  
  
z = 100+10*np.random.randn(10000)  
n, bins, patches = plt.hist(z, 50, normed=1, facecolor='green', alpha=0.75)  
  
plt.ylabel('Probability')  
plt.title(r'histogram')  
plt.axis([60, 140, 0, 0.05])  
plt.grid(True)  
  
kernel = stats.gaussian_kde(z)  
  
xs = np.linspace(60, 140)  
plt.plot(xs, kernel(xs))  
  
plt.show()

输出显示：

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