How to hist() plot each data array row of a 2d NumPy array with Matplotlib?

Question

I have a 3x10 2d ndarray that I would like to do a matplotlib hist plot. I want a hist plot of each array row in one subplot. I tried supplying the ndarray directly but discovered matplotlib would provide hist plots of each column of the ndarray, which is not what I want. How can I achieve my objective? Presently, I have to explicitly declare the hist() commands for each row and I would prefer to avoid this approach.

import numpy as np
import matplotlib.pyplot as plt

d = np.array([[1, 2, 2, 2, 3, 1, 3, 1,   2,  4, 5],
              [4, 4, 5, 5, 3, 6, 6,  7,   6,  5, 7],
              [5, 6, 7, 7, 8, 8, 9, 10, 11, 12, 10]] )

print( '\nd', d )
             
fig, ax = plt.subplots(4, 1)
dcount, dbins, dignored = ax[0].hist( d, bins=[2, 4, 6, 8, 10, 12], histtype='bar', label='d' )
d0count, d0bins, d0ignored = ax[1].hist( d[0,:], bins=[2, 4, 6, 8, 10, 12], histtype='bar', label='d0', alpha=0.2 )
d1count, d1bins, d1ignored = ax[2].hist( d[1,:], bins=[2, 4, 6, 8, 10, 12], histtype='bar', label='d1', alpha=0.2 )
d2count, d2bins, d2ignored = ax[3].hist( d[2,:], bins=[2, 4, 6, 8, 10, 12], histtype='bar', label='d2', alpha=0.2 )
ax[0].legend()
ax[1].legend()
ax[2].legend()
ax[3].legend()
print( '\ndcount', dcount )
print( '\ndbins', dbins )
print( '\ndignored', dignored )
print( '\nd0count', d0count )
print( '\nd0bins', d0bins )
print( '\nd0ignored', d0ignored )
print( '\nd1count', d0count )
print( '\nd1bins', d0bins )
print( '\nd1ignored', d0ignored )
print( '\nd2count', d0count )
print( '\nd2bins', d0bins )
print( '\nd2ignored', d0ignored )
plt.show()

Answer 1

# import needed packages
import numpy as np
import matplotlib.pyplot as plt

---

Create data to plot

Using list comprehension and numpy.random.normal:

gaussian0=[np.random.normal(loc=0, scale=1.5) for _ in range(100)]
gaussian1=[np.random.normal(loc=2, scale=0.5) for _ in range(100)]

gaussians = [gaussian0, gaussian1]

---

Plot with one hist call only

for gaussian in gaussians:
    plt.hist(gaussian,alpha=0.5)
plt.show()

Resulting in:

Answer 2

I found a simpler way. Transpose d. That is, replace

dcount, dbins, dignored = ax[0].hist( d, bins=[2, 4, 6, 8, 10, 12], histtype='bar', label='d' )

with

dcount, dbins, dignored = ax[0].hist( d.T, bins=[2, 4, 6, 8, 10, 12], histtype='bar', label=['d0', 'd1','d2'], alpha=0.5 )

I was hoping for matplotlib's hist() command would have some command to do it but did not find it. Transposing the numpy array worked. I wonder if this is the usual way matplotlib user to do so?