Plot 2-D Histogram in Python using Matplotlib

2D Histogram is used to analyze the relationship among two data variables which has wide range of values. A 2D histogram is very similar like 1D histogram. The class intervals of the data set are plotted on both x and y axis. Unlike 1D histogram, it drawn by including the total number of combinations of the values which occur in intervals of x and y, and marking the densities. It is useful when there is a large amount of data in a discrete distribution, and simplifies it by visualizing the points where the frequencies if variables are dense.

Creating a 2D Histogram

Matplotlib library provides an inbuilt function matplotlib.pyplot.hist2d() which is used to create 2D histogram.Below is the syntax of the function:

matplotlib.pyplot.hist2d(x, y, bins=(nx, ny), range=None, density=False, weights=None, cmin=None, cmax=None, cmap=value)

Here (x, y) specify the coordinates of the data variables, the length of the X data and Y variables should be same.The number of bins can be specified by the attribute bins=(nx, ny) where nx and ny is the number of bins to be used in the horizontal and vertical directions respectively.cmap=value is used to set the color scale.The range=None is an optional parameter used to set rectangular area in which data values are counted for plot.density=value is optional parameter accepting boolean values used to normalize histogram.
The code below code creates a simple 2D histogram using matplotlib.pyplot.hist2d() function having some random values of x and y:

# Import libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
  
# Creating dataset 
n = 100
x = np.random.standard_normal(n) 
y = 3.0 * x  
  
fig = plt.subplots(figsize =(10, 7)) 
# Creating plot 
plot.hist2d(x, y) 
plot.title("Simple 2D Histogram") 
  
# show plot 
plot.show() 

Output:

Customizing 2D Histogram

The matplotlib.pyplot.hist2d() function has a wide range of methods which we can use to customize and create the plot for better view and understanding.

# Import libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
  
# Creating dataset 
x = np.random.normal(size = 500000) 
y = x * 3 + 4 * np.random.normal(size = 500000) 
  
fig = plt.subplots(figsize =(10, 7)) 
# Creating plot 
plot.hist2d(x, y) 
plot.title("Simple 2D Histogram") 
  
# show plot 
plot.show() 

Output:

Some of the customization of the above graph are listed below:

Changing the bin scale:-

# Import libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
  
# Creating dataset 
x = np.random.normal(size = 500000) 
y = x * 3 + 4 * np.random.normal(size = 500000) 
  
# Creating bins 
x_min = np.min(x) 
x_max = np.max(x) 
  
y_min = np.min(y) 
y_max = np.max(y) 
  
x_bins = np.linspace(x_min, x_max, 50) 
y_bins = np.linspace(y_min, y_max, 20) 
  
fig, ax = plt.subplots(figsize =(10, 7)) 
# Creating plot 
plt.hist2d(x, y, bins =[x_bins, y_bins]) 
plt.title("Changing the bin scale") 
  
ax.set_xlabel('X-axis')  
ax.set_ylabel('X-axis')  
  
# show plot 
plt.tight_layout()  
plot.show() 

Output:

Changing the color scale and adding color bar:-

# Import libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
  
# Creating dataset 
x = np.random.normal(size = 500000) 
y = x * 3 + 4 * np.random.normal(size = 500000) 
  
# Creating bins 
x_min = np.min(x) 
x_max = np.max(x) 
  
y_min = np.min(y) 
y_max = np.max(y) 
  
x_bins = np.linspace(x_min, x_max, 50) 
y_bins = np.linspace(y_min, y_max, 20) 
  
fig, ax = plt.subplots(figsize =(10, 7)) 
# Creating plot 
plt.hist2d(x, y, bins =[x_bins, y_bins], cmap = plt.cm.nipy_spectral) 
plt.title("Changing the color scale and adding color bar") 
  
# Adding color bar 
plt.colorbar() 
  
ax.set_xlabel('X-axis')  
ax.set_ylabel('X-axis')  
  
# show plot 
plt.tight_layout()  
plot.show() 

Output:

Filtering data:-

# Import libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
  
# Creating dataset 
x = np.random.normal(size = 500000) 
y = x * 3 + 4 * np.random.normal(size = 500000) 
  
# Creating bins 
x_min = np.min(x) 
x_max = np.max(x) 
  
y_min = np.min(y) 
y_max = np.max(y) 
  
x_bins = np.linspace(x_min, x_max, 50) 
y_bins = np.linspace(y_min, y_max, 20) 
  
# Creating data filter 
data = np.c_[x, y] 
  
for i in range(10000): 
    x_idx = random.randint(0, 500000) 
    data[x_idx, 0] = -9999
  
data = data[data[:, 0]!=-9999] 
  
fig, ax = plt.subplots(figsize =(10, 7)) 
# Creating plot 
plt.hist2d(data[:, 0], data[:, 1], bins =[x_bins, y_bins]) 
plt.title("Filtering data") 
  
ax.set_xlabel('X-axis')  
ax.set_ylabel('X-axis')  
  
# show plot 
plt.tight_layout()  
plot.show() 

Output:

Using matplotlib hexbin function:-

# Import libraries 
import numpy as np 
import matplotlib.pyplot as plt 
import random 
  
# Creating dataset 
x = np.random.normal(size = 500000) 
y = x * 3 + 4 * np.random.normal(size = 500000) 
  
fig, ax = plt.subplots(figsize =(10, 7)) 
# Creating plot 
plt.title("Using matplotlib hexbin function") 
plt.hexbin(x, y, bins = 50) 
  
ax.set_xlabel('X-axis')  
ax.set_ylabel('Y-axis')  
  
# show plot 
plt.tight_layout()  
plot.show() 

Output:

My Personal Notes

Creating a 2D Histogram

Customizing 2D Histogram

Some of the customization of the above graph are listed below:

Changing the bin scale:-

Changing the color scale and adding color bar:-

Filtering data:-

Using matplotlib hexbin function:-

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