plotting a histogram on a Log scale with Matplotlib

Specifying bins=8 in the hist call means that the range between the minimum and maximum value is divided equally into 8 bins. What is equal on a linear scale is distorted on a log scale.

What you could do is specify the bins of the histogram such that they are unequal in width in a way that would make them look equal on a logarithmic scale.

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltx = [2, 1, 76, 140, 286, 267, 60, 271, 5, 13, 9, 76, 77, 6, 2, 27, 22, 1, 12, 7,      19, 81, 11, 173, 13, 7, 16, 19, 23, 197, 167, 1]x = pd.Series(x)# histogram on linear scaleplt.subplot(211)hist, bins, _ = plt.hist(x, bins=8)# histogram on log scale. # Use non-equal bin sizes, such that they look equal on log scale.logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))plt.subplot(212)plt.hist(x, bins=logbins)plt.xscale('log')plt.show()

Here is one more solution without using a subplot or plotting two things in the same image.

import numpy as npimport matplotlib.pyplot as pltdef plot_loghist(x, bins):  hist, bins = np.histogram(x, bins=bins)  logbins = np.logspace(np.log10(bins[0]),np.log10(bins[-1]),len(bins))  plt.hist(x, bins=logbins)  plt.xscale('log')plot_loghist(np.random.rand(200), 10)

plot another histogram with the log of x.

is not the same as plotting x on the logarithmic scale. Plotting the logarithm of x would be

np.log(x).plot.hist(bins=8)plt.show()

The difference is that the values of x themselves were transformed: we are looking at their logarithm.

This is different from plotting on the logarithmic scale, where we keep x the same but change the way the horizontal axis is marked up (which squeezes the bars to the right, and stretches those to the left).