Python plt.colorbar（）的作用是什么，colorbar上的数字表示什么？

Question

Can someone explain to me what and how colorbar is used? I dont get the significance of colorbar and the number is it shows? What does the number on colorbar means? Please explain to me... Data is from datacamp: Two columns with percentage of students taking biology and business from year 1970 to 2010. Below code makes a 2dhist but I dont understand what colorbar labels signifies? is it percentage??

plt.hist2d(data['Biology'], data['Business'], bins=(5,5))
plt.colorbar()
plt.show()

Answer 1

You have not uploaded your data so I am going to post a toy example and explain some things.

from matplotlib.colors import LogNorm
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)

# normal distribution center at x=0 and y=5
x = np.random.randn(100000)
y = np.random.randn(100000) + 5

plt.hist2d(x, y, bins=40, norm=LogNorm())
plt.xlabel('x')
plt.ylabel('y')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts')
plt.show()

Here, I have created 2 variables. The x has mean=0 and the y has mean=5 . When you plot the 2D hist, you see a 2D histogram. Think about it like looking at a histogram from the "top".

print(x.mean())
print(y.mean())
#0.0015767005081253399
#5.005093241323296

Now, you can see that the colors at the center of this 2D histogram are yellowish and correspond to the highest values of the colorbar.

This is reasonable since the histogram of x should have a peak at 0 and the histogram of y should have a peak at 5 .

You plot their positions on the xy plane and as you can see they are so dense and overlap with each other. You want to view the distribution better by count of boxes in the plane, so you try a 2D diagram.

This is exactly what the colors and the colorbar's values mean. It's the height/frequency/counts of each bin if you had a 1D classical histogram.

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EDIT 1: By using fewer bins it's like zooming in the initial plot.

from matplotlib.colors import LogNorm
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)

# normal distribution center at x=0 and y=5
x = np.random.randn(100000)
y = np.random.randn(100000) + 5

plt.hist2d(x, y, bins=(5,5), norm=LogNorm())
plt.xlabel('x')
plt.ylabel('y')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts')
plt.show()