使用较新的numpy.polynomial软件包的另一种方法可能更容易阅读：

import matplotlib.pyplot as plt
from numpy.polynomial import Polynomial

xdata = np.array([0,1,2,3,4,5,6,7,8,9])
ydata = np.array([0,1,2,2.5,3.5,4,5,5.5,6,7])

poly_fit = Polynomial.fit(xdata, ydata, 1)

plt.plot(xdata, ydata, 'o', *poly_fit.linspace())

如果使用matplotlib，则可以使用polyfit生成趋势线：import matplotlib.pyplot as plt import numpy as np
`

# create sample data
xdata = np.array([0,1,2,3,4,5,6,7,8,9])
ydata = np.array([0,1,2,2.5,3.5,4,5,5.5,6,7])

# calculate polynomial
z = np.polyfit(xdata, ydata, 1)
f = np.poly1d(z)

# calculate new x's and y's
x_new = np.linspace(xdata[0], xdata[-1], 50)
y_new = f(x_new)

plt.plot(xdata, ydata, 'o', x_new, y_new)
plt.xlim([xdata[0]-1, xdata[-1] + 1])
plt.show()

`