[PYTHON] The vertical and horizontal axes of the matplotlib histogram are unpleasant, so make it feel good

I often draw histograms with Python's matplotlib, but sometimes I don't like the vertical and horizontal axes, so this is a memo for fine-tuning it.

vertical and horizontal axes of hist

As an example, I will show you the following graph.

%matplotlib inline
import matplotlib.pyplot as plt
from scipy import stats

norm_rvs = stats.norm.rvs(loc=50, scale=30, size=100, random_state=0)

plt.hist(norm_rvs, bins=10, alpha=0.5, ec='navy')
plt.show()

Look at this

――Um, it feels bad because the breaks in the histogram bars are halfway! ――Um, it feels bad if the scale on the vertical axis is not an integer!

That's why.

I want to make the scale on the vertical axis an integer

You can get information about the bar breaks and heights in the histogram by doing the following:

Y, X, _ = plt.hist(norm_rvs, bins=10, alpha=0.5, ec='navy')
print(X)
print(Y)
plt.show()

[-26.58969448 -12.12146116   2.34677216  16.81500548  31.2832388
  45.75147212  60.21970544  74.68793876  89.15617208 103.6244054
 118.09263872]
[ 1.  5.  7. 13. 17. 18. 16. 11.  7.  5.]

Let's use that information to make the vertical axis an integer.

import numpy as np

Y, X, _ = plt.hist(norm_rvs, bins=10, alpha=0.5, ec='navy')
y_max = int(max(Y)) + 1
plt.yticks(np.arange(0, y_max, 2)) #It is hard to see even if it is in 1 increments, so make it in 2 increments.
plt.show()

I want to make the bar breaks look good

Specify the range on the horizontal axis and adjust the number of bins nicely.

Y, X, _ = plt.hist(norm_rvs, bins=13, alpha=0.5, ec='navy', range=(-10, 120))
print(X)
print(Y)
y_max = int(max(Y)) + 1
plt.yticks(np.arange(0, y_max, 2))
plt.show()

[-10.   0.  10.  20.  30.  40.  50.  60.  70.  80.  90. 100. 110. 120.]
[ 3.  5.  6. 10. 11.  9. 15. 13.  9.  6.  5.  5.  2.]

I want to make multiple histograms look good

Now, you may want to compare multiple histograms side by side.

norm_rvs2 = stats.norm.rvs(loc=75, scale=55, size=100, random_state=0)

plt.hist(norm_rvs, bins=10, alpha=0.5, ec='navy')
plt.hist(norm_rvs2, bins=10, alpha=0.5, ec='red')
plt.show()

It feels bad like this! It tends to be. Let's make this feel good as well.

bins = 20
range=(-50, 200)

Y1, X1, _ = plt.hist(norm_rvs, bins=bins, alpha=0.5, ec='navy', range=range)
Y2, X2, _ = plt.hist(norm_rvs2, bins=bins, alpha=0.5, ec='red', range=range)
y_max = int(max(max(Y1), max(Y2))) + 1
plt.yticks(np.arange(0, y_max, 2))
plt.show()

Personally, I prefer to arrange them vertically as follows.

bins = 20
range=(-50, 200)

fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8,8))
Y1, X1, _ = axes[0].hist(norm_rvs, bins=bins, alpha=0.5, ec='navy', range=range)
Y2, X2, _ = axes[1].hist(norm_rvs2, bins=bins, alpha=0.5, ec='red', range=range)
y_max = int(max(max(Y1), max(Y2))) + 1
axes[0].set_ylim([0, y_max])
axes[1].set_ylim([0, y_max])
axes[0].set_yticks(np.arange(0, y_max, 2))
axes[1].set_yticks(np.arange(0, y_max, 2))
plt.show()

That's all from the scene!