logarithmic scale fitting python
fig=plt.figure()
ax = fig.add_subplot(111)
z=np.arange(1, len(x)+1) #start at 1, to avoid error from log(0)
logA = np.log(z) #no need for list comprehension since all z values >= 1
logB = np.log(y)
m, c = np.polyfit(logA, logB, 1, w=np.sqrt(y)) # fit log(y) = m*log(x) + c
y_fit = np.exp(m*logA + c) # calculate the fitted values of y
plt.plot(z, y, color = 'r')
plt.plot(z, y_fit, ':')
ax.set_yscale('symlog')
ax.set_xscale('symlog')
#slope, intercept = np.polyfit(logA, logB, 1)
plt.xlabel("Pre_referer")
plt.ylabel("Popularity")
ax.set_title('Pre Referral URL Popularity distribution')
plt.show()