# Hexagonal Binning Using Matplotlib And Numpy

## What is Hexagonal Binning?

• The XY plane of the graph is made as a tightly packed grid of hexagons.

• The number of data points (x,y), falling within each hexagon is counted

• The hexagons are painted with a color range in proportion to the count of data points inside them

• In some schemes, the empty bin is marked with a distinct color like white.

## Why Hexagonal Binning required:

• We know that a scatter plot is drawn by marking x,y positions using a marker on a 2D plane.

• The scatter plot is used to find distribution, range, outliers and clusters in a dataset.

• However, finding range or identifying clusters becomes difficult to impossible when the data points remain very close to each other and data is scattered all around the scatter plot.

• To easily identify ranges, patterns and clusters in the scatter plot of a large sized data, Hexagonal binning is used.

## Hexagonal binning using Python Matplotlib:

• The function hexbin() in Matplotlib.pyplot() is used for plotting data with Hexagonal binning.

## Example:

 import matplotlib.pyplot as plot import numpy as np   # Set the random seed for data generation using numpy np.random.seed(1)   # Create random X data using numpy random module xData = np.random.random_integers(1, 10, 100)   # Create random Y data using numpy random module #yData = np.random.random_integers(1, 50, 500) yData = np.arange(0, 100, 1)   # Plot the hexbin using the data genererated by numpy plot.hexbin(xData, yData, gridsize=50)   # Provide the title for the plot plot.title('Hexagonal binning using Python Matplotlib')   # Give x axis label for the spike raster plot plot.xlabel('XData')   # Give y axis label for the spike raster plot plot.ylabel('YData')   # Display the plot plot.show()

Output: 