By Mark 
hexbin is a feature of Matplotlib that is used to aggregate and visualize 2D data points into a hexagonal-shaped grid. It is often useful when you have a lot of data and there is a lot of overlap in a scatterplot.
By representing the density of data points contained in each hexagonal cell with a color, hexbin makes it easy to see the distribution of your data at a glance.
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It is commonly used as plt.hexbin(x, y), where x, y are the data corresponding to the x- and y-axis, respectively. The function calculates the density and applies a color map to display the result.
Basic code usage
plt.hexbin(x, y, gridsize=30, cmap='Blues')Key parameters
| Parameters | Description. |
|---|---|
x, y | 2D Data (Enter x, y coordinates like a scatter plot) |
gridsize | Number of hexagons (defaults to 100, smaller values increase the size) |
cmap | Color map (e.g. ‘Blues’, ‘inferno’) |
extent | (xmin, xmax, ymin, ymax) range specification |
mincnt | Show only minimum count or more |
reduce_C_function | Data aggregation method (default: np.sum) |
Hexbin Code
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
x = np.random.randn(10000)
y = np.random.randn(10000)
plt.figure(figsize=(10, 6))
hb = plt.hexbin(x, y, gridsize=50, cmap='Blues')
plt.colorbar(hb, label='Count')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Hexbin Plot Example')
plt.grid(True) # Add grid for better visualization
plt.show()
hexbin vs scatter
| Characteristics | scatter | hexbin |
|---|---|---|
| Individual Data Points | O | X (grouped into hexagons) |
| Density representation | Low | High (represented by color) |
| Resolve data overlap | X | O |
| Big data visualization | Inadequate | Appropriate |
I didn’t even know about hexbin plots until I started organizing this post, but after looking into them, I realized they can be cleaner and more effective than scatter plots, especially when working with large datasets.
This will be the last time I use this type of plot. Moving forward, I plan to go beyond simply drawing charts and focus on creating visualizations that support real analysis. Data visualization is more than just aesthetics—it’s a powerful analytical tool that helps make complex data more intuitive.
However, I won’t be using visualizations the way they are typically applied in the stock market—where chart patterns dictate decision-making.
Instead, my focus will be on numerical representation and analytical insights. The goal is to interpret data objectively and logically, emphasizing the discovery of meaningful patterns rather than relying on visual shapes alone.