I have a Pandas DataFrame that has the following values in a Series
x = [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]I was instructed to plot two histograms in a Jupyter notebook with Python 3.6.
x.plot.hist(bins=8)
plt.show()I chose 8 bins because that looked best to me. I have also been instructed to plot another histogram with the log of x.
x.plot.hist(bins=8)
plt.xscale('log')
plt.show()This histogram looks TERRIBLE. Am I not doing something right? I've tried fiddling around with the plot, but everything I've tried just seems to make the histogram look even worse. Example:
x.plot(kind='hist', logx=True)I was not given any instructions other than plot the log of X as a histogram.
For the record, I have imported pandas, numpy, and matplotlib and specified that the plot should be inline.
23 Answers
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 pd
import numpy as np
import matplotlib.pyplot as plt
x = [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 scale
plt.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 np
import matplotlib.pyplot as plt
def 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).
