擅长:python、mysql、java
<p>我想出了解决这个问题的另一个办法。分享这个是因为它可能有帮助。在</p>
<pre><code>fig=plt.figure()
ax = fig.add_subplot(111)
z=np.arange(len(x)) + 1
print z
print y
rank = [np.log10(i) for i in z]
freq = [np.log10(i) for i in y]
m, b, r_value, p_value, std_err = stats.linregress(rank, freq)
print "slope: ", m
print "r-squared: ", r_value**2
print "intercept:", b
plt.plot(rank, freq, 'o',color = 'r')
abline_values = [m * i + b for i in rank]
plt.plot(rank, abline_values)
</code></pre>
<p>这基本上也达到了目标。它使用stats模块。在</p>