Plot 多个直方图作为网格

Question

I am trying to plot multiple histograms on the same window using a list of tuples. I have managed to get it to sketch only 1 tuple at a time and I just can't seem to get it to work with all of them.

import numpy as np
import matplotlib.pyplot as plt

a = [(1, 2, 0, 0, 0, 3, 3, 1, 2, 2), (0, 2, 3, 3, 0, 1, 1, 1, 2, 2), (1, 2, 0, 3, 0, 1, 2, 1, 2, 2),(2, 0, 0, 3, 3, 1, 2, 1, 2, 2),(3,1,2,3,0,0,1,2,3,1)] #my list of tuples

q1,q2,q3,q4,q5,q6,q7,q8,q9,q10 = zip(*a) #split into [(1,0,1,2,3) ,(2,2,2,0,1),..etc] where q1=(1,0,1,2,3)

labels, counts = np.unique(q1,return_counts=True) #labels = 0,1,2,3 and counts the occurence of 0,1,2,3

ticks = range(len(counts))
plt.bar(ticks,counts, align='center')
plt.xticks(ticks, labels)
plt.show()

As you can see from the above code, I can plot one tuple at a time say q1,q2 etc but how do I generalise it so that it plots all of them.

I've tried to mimic this python plot multiple histograms , which is exactly what I want however I had no luck.

Thank you for your time:)

Answer 1

You need to define a grid of axes with plt.subplots taking into account the amount of tuples in the list, and how many you want per row. Then iterate over the returned axes, and plot the histograms in the corresponding axis. You could use Axes.hist , but I've always preferred to use ax.bar , from the result of np.unique , which also can return the counts of unique values:

from matplotlib import pyplot as plt
import numpy as np

l = list(zip(*a))
n_cols = 2
fig, axes = plt.subplots(nrows=int(np.ceil(len(l)/n_cols)), 
                         ncols=n_cols, 
                         figsize=(15,15))

for i, (t, ax) in enumerate(zip(l, axes.flatten())):
    labels, counts = np.unique(t, return_counts=True)
    ax.bar(labels, counts, align='center', color='blue', alpha=.3)
    ax.title.set_text(f'Tuple {i}')

plt.tight_layout()  
plt.show()

You can customise the above to whatever amount of rows/cols you prefer, for 3 rows for instance:

l = list(zip(*a))
n_cols = 3
fig, axes = plt.subplots(nrows=int(np.ceil(len(l)/n_cols)), 
                         ncols=n_cols, 
                         figsize=(15,15))

for i, (t, ax) in enumerate(zip(l, axes.flatten())):
    labels, counts = np.unique(t, return_counts=True)
    ax.bar(labels, counts, align='center', color='blue', alpha=.3)
    ax.title.set_text(f'Tuple {i}')

plt.tight_layout()  
plt.show()