## 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: