How to create hexbin plot with bin sizes and colors?

A Python library for producing static, animated, and interactive visualizations is called Matplotlib. It offers an easy-to-use interface for creating many plots, charts, and graph styles. Users may produce excellent visual representations of data. It gets insights and explains discoveries.  

Matplotlib supports hexbin plots as a kind of data visualization. It is useful when dealing with huge datasets. It is when the distribution of data points makes interpreting individual points challenging. This plot aggregates data points into hexagonal cells that present each data point.  

Each hexagon in the plot is colored and sized to show the number of data points within that bin. This technique allows you to see the density or frequency of data points in a 2D space. Hexbin plots capture both the spatial distribution of data. It provides a smooth representation of density by employing hexagonal bins.  

How to create hexbin plots with bin colors and sizes? 

To create a hexbin plot with bin sizes and colors, you can use the hexbin function from the library.  

The syntax for the hexbin function:  

plt.hexbin(x, y, gridsize=None, cmap=None, mincnt=None)  

• x: The x-coordinate values of the data points.  
• y: The y-coordinate values of the data points.  
• gridsize: The number of hexagons in the x and y directions. By default, it is determined based on the data.  
• cmap: The colormap used for coloring the hexagons. By default, it uses the default colormap.  
• mincnt: The minimum number of points required to color a hexagon. By default, all hexagons are colored.  

Steps: 

• Start by importing the necessary libraries, including matplotlib.pyplot and numpy.  
• Next, either generate random data or load your dataset.  
• Once you have your data, pass the x and y coordinates to the hexbin function. To customize the plot, you should do it with gridsize, cmap, gridcolor, and mincnt.  
• The gridsize parameter determines the number of hexagons in the x and y directions. It is when the cmap parameter controls the colormap for coloring the hexagons.  
• You can adjust the hexagon size, color saturation, and color gradient. It sets the extent, alpha, and vmin/vmax parameters.  
• Adding a colorbar using plt.colorbar helps in interpreting the color scale. Remember to label the x-axis and y-axis and provide a title for the plot.  
• Finally, display the plot using plt.show() or save it as an image file using plt.savefig. 

Preview of hexbin plot with bin sizes and colors

Code

A hexbin plot of df["x"] and df["y"] is produced by the first plot made with plt.hexbin. The boolean mask that results from setting the C parameter to df["z"]=="B" is used to colour the hexbins. Hexbins with df["z"]=="B" have a distinct colour from those with df["z"]=="A." The x and y number of hexagons in the grid are determined by the gridsize parameter, and the colormap is determined by the cmap parameter.

Follow the steps carefully to get the output easily.

1. Install Visual Studio Code in your computer.

2. Install the required library by using the following commands:

pip install matplotlib

pip install pandas

3. If your system is not reflecting the installation, try running the above command by opening windows powershell as administrator.

4. Open the folder in the code editor, copy and paste the above kandi code snippet in the python file.

5. Run the code using the run command.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "How to create hexbin plot with bin sizes and colors" in kandi. You can try any such use case!

Dependent Libraries

If you do not have pandas and matplotlib that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the pages in kandi.

You can search for any dependent library on kandi like matplotlib.

Environment tested

1. This code had been tested using python version 3.8.0
2. pandas version 1.5.3 has been used.
3. matplotlib version 3.7.1 has been used.

Support

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2. For further learning resources, visit the Open Weaver Community learning page.

FAQ 

1. What is a hexbin plot, and how does it differ from a typical binning plot?  

A hexbin plot represents data density using hexagonal bins. It provides accurate visualization compared to typical binning plots that use rectangular bins. Hexbin plots are particularly useful for large datasets and capturing complex spatial patterns.  

2. How can I create a Hexbin Scatterplot to visualize my data?  

To create a Hexbin Scatterplot in Matplotlib, use the `plt.hexbin()` function. You should pass the x and y coordinates of your data points. To customize the plot, You should include `gridsize` and `cmap`.  

3. What is the purpose of using polygons instead of circles in a hexbin plot?  

Using polygons, hexagons, rather than circles in a hexbin plot is intended. It provides economical use of space and a more uniform patterning of the plotting area. Hexagons provide better packing efficiency compared to circles. It allows a more accurate representation of data density. It avoids grid-like artifacts that can occur with rectangular bins.  

4. How do I adjust the size of the hexagons in my graphic area?  

To adjust the size of the hexagons in your graphic area in a hexbin plot, you can use the gridsize parameter. By increasing the gridsize, you can create smaller hexagons. It results in a denser plot with smaller individual hexagons. Conversely, reducing the gridsize will create larger hexagons and a sparser plot.  

5. How can color saturation highlight certain features within the hexagonal bins?  

The hexagon transparency can be adjusted using the alpha parameter. We can call it using the plt.hexbin() function. The alpha parameter accepts a value between 0 and 1. 0 represents transparent (invisible) hexagons, and 1 represents opaque (solid) hexagons.