I am trying to visualize a random array with the square shape of (10, 10) in Python using matplotlib (3.5 version). I am also including the xaxis and yaxis ticks, but the ticks for 10 show empty data. Does anyone know how to sort it out?

Here's my code:

import numpy as np

from matplotlib import pyplot as plt
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'medium',
          'figure.figsize': (15, 5),
         'axes.labelsize': 'x-large',
         'axes.titlesize':'x-large',
         'xtick.labelsize':'x-large',
         'ytick.labelsize':'x-large'}
pylab.rcParams.update(params)

arr = np.random.rand(10, 10)
plt.imshow(arr)
plt.ylim(0, 10)
plt.xlim(0, 10)

plt.xticks(np.arange(0.0, 11.0, 1.0))
plt.yticks(np.arange(0.0, 11.0, 1.0))

plt.show()

This is the produced image:

CodePudding user response：

As other users pointed, Python arrays start indexing in '0'. You could trick the ticks to show the values you want:

1. create data to plot

   import numpy as np
   
   from matplotlib import pyplot as plt
   import matplotlib.pylab as pylab
   params = {'legend.fontsize': 'medium',
             'figure.figsize': (15, 5),
            'axes.labelsize': 'x-large',
            'axes.titlesize':'x-large',
            'xtick.labelsize':'x-large',
            'ytick.labelsize':'x-large'}
   pylab.rcParams.update(params)
   
   arr = np.random.rand(10, 10)
   
   
   

2. Then you can use plt.xticks(tickPosition,tickValues) and same with yticks. Note that every number point to the center of every value in your image so also you have to change your lim positions:

    plt.figure()
    plt.imshow(arr)
    plt.ylim(-0.5, 9.5) #to show no blank spaces
    plt.xlim(-0.5, 9.5) #to show no blank spaces
    
    plt.xticks(np.arange(0, 10),np.arange(1, 11)) #trick the ticks
    plt.yticks(np.arange(0, 10),np.arange(1, 11))

This will give you the next figure

3. Also you can set values at start (left-down corner) just tricking a little more:

    plt.xticks(np.arange(-0.5, 9.5),np.arange(1, 11))
    plt.yticks(np.arange(-0.5, 9.5),np.arange(1, 11))

this will give you this result: