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Create custom layouts of subplots in Matplotlib using the subplot2grid function.

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by Dejaswarooba dot icon Updated: Jul 7, 2023

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Matplotlib is a well-known Python visualization library. It offers various functions and comprehensive documentation for building various plot categories. It also provides a high level of customization. Users can change their visualizations' colors, fonts, axes, and labels. One of Matplotlib's key advantages is its ability to create interactive visualizations.  

 

Matplotlib's widget package includes several interactive widgets. It allows users to include interactivity and communication in their plots. Whether working with plots or creating individual plots, the Pyplot module enhances your visualizations. With Matplotlib, you can create line plots, scatter plots, box plots, and more.  

 

Using NumPy integration, you can plot data from arrays and work with various datasets. If you're creating a `single figure` with subplots, Matplotlib provides the necessary tools. It creates common layouts, or you can build your custom layouts.  

 

From basic to advanced features, Matplotlib offers versatile tools for data visualization. With Matplotlib, you have the flexibility to create different types of plots. You can arrange them in custom layouts using the `subplot2grid` function. You can customize the appearance of your visualizations to convey your data's insights.  

 

subplot2grid function: 

Matplotlib creates custom layouts of subplots using the subplot2grid function. With this function, you can define a grid of cells and specify the location. You can specify the size of each subplot within the grid. This allows arranging many plots in a single figure with different configurations. Using this function, you can create complex arrangements within a single figure.  

 

The subplot2grid function provides flexibility in designing different layouts. It includes column subplots, bottom subplots, and adjacent subplots. It allows you to create subplots that span many rows or columns. You can create subplots in separate cells or a subplot that occupies the entire row. It enables creating `inset axes` or subplots with a `shared y-axis.`  

 

Syntax of subplot2grid: 

The subplot2grid function takes grid, loc, and optional arguments: rowspan and colspan. Here's a breakdown of these arguments:  

  • grid: It specifies the grid layout of the subplots. It takes a tuple (nrows, ncols) that defines the grid's number of rows and columns. For example, grid = (3, 3) represents a 3x3 grid.  
  • loc: It determines the starting cell position of the subplot within the grid. It takes a tuple(row, col) that specifies the row and column indices where the subplot will be placed.  
  • rowspan: It specifies the number of rows the subplot will span within the grid. It is set to 1 by default, meaning the subplot occupies a single row.  
  • colspan: It will specify the number of columns the subplot will span within the grid. It is set to 1 by default, indicating that the subplot occupies a single column. 


By manipulating the arguments, you can create subplots. They can span many rows or columns, allowing for various layout configurations.  

 

Using subplot2grid (grid, (0,0),rowspan=2), you can create a subplot that starts at the top-left cell (0, 0) and spans two rows. This is useful when you want to create a larger subplot that covers many cells.  

 

You can create a subplot in the bottom row using subplot2grid (grid, (2, 0), colspan=3). It spans the entire width of the grid. This is convenient when you want to create a subplot. It occupies the entire row, such as a summary or aggregated plot.  

 

Combining subplot2grid allows you to create sophisticated and appealing layouts for your subplots. This flexibility allows designing complex figures that convey insights from your data. 


Preview of the output obtained when subplot2grid function is used.

Code

A 3x2 grid of subplots are created using subplot2grid and set the face color of each subplot is set to dark blue-gray color using the facecolor parameter.

Adding charts to a matplotlib subgrid

PythondotLines of Code : 11dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
f, ax = plt.subplots(2, 2 , gridspec_kw={"height_ratios": [2, 1]})
for cAx in ax.flatten():
    cAx.set_facecolor('#131722')
f.savefig("test.png", facecolor='#131722')

fig = plt.figure(facecolor='#131722',dpi=155, figsize=(8, 6))
ax1 = plt.subplot2grid((3,2), (0,0), facecolor='#131722', rowspan=2)
ax2 = plt.subplot2grid((3,2), (0,1), facecolor='#131722', rowspan=2)
ax3 = plt.subplot2grid((3,2), (2,0), facecolor='#131722')
ax4 = plt.subplot2grid((3,2), (2,1), facecolor='#131722')

Follow the steps carefully to get the output easily.

  • Install Visual Studio Code in your computer.
  • Install the required library by using the following command - pip install matplotlib
  • If your system is not reflecting the installation, try running the above command by opening windows powershell as administrator.
  • Open the folder in the code editor, copy and paste the above kandi code snippet in the python file.
  • Add the line import matplotlib.pyplot as plt in the beginning.
  • 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 "subplots in Matplotlib using the subplot2grid function" in kandi. You can try any such use case!

Dependent Libraries

matplotlibby matplotlib

Python doticonstar image 17559 doticonVersion:v3.7.1doticon
no licences License: No License (null)

matplotlib: plotting with Python

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            matplotlibby matplotlib

            Python doticon star image 17559 doticonVersion:v3.7.1doticonno licences License: No License

            matplotlib: plotting with Python
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have 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 page 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. matplotlib version 3.7.1 has been used.

                      Support

                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page.

                      FAQ 

                      1. What is Matplotlib, and how does it relate to Pyplot?  

                      Matplotlib is a Python library. It helps in creating static, animated, and interactive visualizations. Pyplot is a module within Matplotlib. It provides a simplified interface for creating plots and charts.  

                       

                      2. How can I create a column subplot with many plots in Matplotlib? 

                      You can use the subplots() function to create a column subplot layout. Then use the returned Axes objects to plot your plots using the plot() function.  

                       

                      3. How do I share an axis between different plots in pyplot?  

                      You can use the sharex or sharey parameter to share an axis between plots when creating the subplots.  

                       

                      4. Can a figure containing subplots with the enclosing figure object be drawn?  

                      Yes. It draws a figure containing subplots using the enclosing figure object. You can achieve this by specifying the number of rows and columns for the subplot's layout. Then accessing each subplot through the returned axes object.  

                       

                      5. Does having many subplots on one figure affect performance if the number of data points is large?  

                      Having many subplots on one figure can affect performance if the number of data points is large. Each subplot requires resources to render and display the data. The more subplots you have, the more resources are required. If you have many data points and subplots, it leads to increased memory usage. It also leads to longer rendering times. This can impact the responsiveness or the time it takes to generate and display the plots. 

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