在一个图中并排绘制多个图形

Question

I have made multiple histograms using the following generalised snippet of code (mdified sligtly for each one):

import matplotlib.pyplot as plt

plt.hist(df['count'], color = 'blue', edgecolor = 'black', bins = int(12/1))
plt.title('Histogram')
plt.xlabel('No. of individuals')
plt.ylabel('No. of images')

As it stands, I'm having to run this code as 8 different commands, to produce 8 seperate histograms. How therefore can I plot all 8 graphs as a single figure, with the individual plots set side-by-side?

Is this possible?

Thanks, R

Answer 1

So in order to address the issue, I will show you the way to solve for 3 different histograms on the same plot, the same logic can be applied to any k number of histograms.

import numpy as np
import matplotlib.pyplot as plt

n = 5000
mean_mu1 = 60
sd_sigma1 = 15
data1 = np.random.normal(mean_mu1, sd_sigma1, n)
mean_mu2 = 80
sd_sigma2 = 15
data2 = np.random.normal(mean_mu2, sd_sigma2, n)
mean_mu3 = 100
sd_sigma3 = 15
data3 = np.random.normal(mean_mu3, sd_sigma3, n)

plt.figure(figsize=(8,6))
plt.hist(data1, bins=100, alpha=0.5, label="data1")
plt.hist(data2, bins=100, alpha=0.5, label="data2")
plt.hist(data3, bins=100, alpha=0.5, label="data3")
plt.xlabel("Data", size=14)
plt.ylabel("Count", size=14)
plt.title("Multiple Histograms with Matplotlib")
plt.legend(loc='upper right')
plt.savefig("overlapping_histograms_with_matplotlib_Python_2.png")

This will generate the following:

Source: https://datavizpyr.com/overlapping-histograms-with-matplotlib-in-python/

Note

This kind of plot will be very difficult to read when you deal with 8 different histograms.

Edit

Based on the comment, you wish to plot k different histograms in 1 big figure.

I'll show the logic to do so with 4 different histograms:

import numpy as np
import matplotlib.pyplot as plt

n = 5000
mean_mu1 = 60
sd_sigma1 = 15
data1 = np.random.normal(mean_mu1, sd_sigma1, n)
mean_mu2 = 80
sd_sigma2 = 15
data2 = np.random.normal(mean_mu2, sd_sigma2, n)
mean_mu3 = 100
sd_sigma3 = 15
data3 = np.random.normal(mean_mu3, sd_sigma3, n)
mean_mu4 = 120
sd_sigma4 = 15
data4 = np.random.normal(mean_mu4, sd_sigma4, n)
data = [data1, data2, data3, data4]
f, a = plt.subplots(2,2)
a = a.ravel()
for idx, ax in enumerate(a):
    ax.hist(data[idx])

plt.show()

This will output the following: