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How to create grid plot with shared axes and legends?

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by kanika dot icon Updated: Jul 12, 2023

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A grid plot is also known as a grid chart or grid display. It is a visual data representation that organizes information in a grid-like structure. It analyzes and distributes data that has two or more variables or dimensions. A grid plot consists of a series of cells or small squares arranged in rows and columns. Each cell represents a unique combination of values from the variables being analyzed.  

 

The cells are filled with color, line style, shading, or patterns. It helps indicate the magnitude or intensity of the data. Grid plots are useful for visualizing patterns, relationships, and distributions within complex datasets. It compares and contrasts data across different combinations of variables. It identifies trends or anomalies. By organizing data in a grid format, grid alignment provides a systematic way. It helps explore multidimensional data and gain insights.  

 

Grid plots can visualize various data types. It includes numeric data, categorical data, and Mixed Data. Numeric values can be represented using Heatmaps, Contour plots, and Surface plots. Categorical Data represents Contingency tables, Stacked charts, Clustered column charts, and Mixed data. It can be represented by using Combination plots, small multiples.  

 

Grid plot data can create various plots, including bar, grid lines, scatter, and Box Plots. The choice of subplots depends on the data characteristics and the analysis objectives. Effective decision-making involves a combination of Identify and Analyze Trends. It is based on Detect Outliers and Anomalies and Compare Patterns and Relationships. It enhances identifying outliers and patterns, leading to better insights and decision-making. Interpreting grid plot data involves analyzing the patterns, relationships, and variations. It is done within the plot to make informed decisions.  

 

Here are some steps to help you interpret grid plot data:  

  • Understand the Variables  
  • Analyze Patterns  
  • Identify Relationships  
  • Spot Outliers or Anomalies  
  • Consider Context  


Using grid plot data not only enhances the quality of decision-making. But it also contributes to the development of data analysis skills. Regular engagement sharpens your ability to identify patterns, interpret visualizations, and extract insights. It strengthens critical thinking, hypothesis generation, and the analytical mindset for data analysis. In conclusion, leveraging grid plot data empowers you. It makes well-informed decisions, uncovers hidden insights, and improves data analysis skills. It drives success in various domains and disciplines. 


Here is an example of creating grid plot with shared axes.



Fig1: Preview of the Code and output.

Code


In this solution, we are creating grid plot with shared axes and legends.

Plotting grids across the subplots Python matplotlib

PythondotLines of Code : 20dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import matplotlib.pyplot as plt

d = [1,2,3,4,5,6,7,8,9]
f = [0,1,0,0,1,0,1,1,0]

fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True)
ax3 = fig.add_subplot(111, zorder=-1)
for _, spine in ax3.spines.items():
    spine.set_visible(False)
ax3.tick_params(labelleft=False, labelbottom=False, left=False, right=False )
ax3.get_shared_x_axes().join(ax3,ax1)
ax3.grid(axis="x")


line1 = ax1.plot(d, marker='.', color='b', label="1 row")
line1 = ax2.plot(f, marker='.', color='b', label="1 row")
ax1.grid()
ax2.grid()
plt.show()

Instructions

Follow the steps carefully to get the output easily.

  1. Install Jupyter Notebook on your computer.
  2. Open terminal and install the required libraries with following commands.
  3. Install matplotlib - pip install matplotlib.
  4. Copy the code using the "Copy" button above and paste it into your IDE's Python file.
  5. Run the file.


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 "Create grid plot with shared axes and legends" 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 or numpy 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 respective page in kandi.


                      You can search for any dependent library on kandi like matplotlib

                      Environment Tested


                      I tested this solution in the following versions. Be mindful of changes when working with other versions.

                      1. The solution is created in Python 3.9.6
                      2. The solution is tested on matplotlib version 3.5.0


                      Using this solution, we are able to create grid plots with legends and shared axes.

                      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. How do I draw grid lines in a grid plot in Python? 

                      You can draw grid lines in a grid plot, depending on the specific visualization library you are using. Here are examples using two popular libraries, Matplotlib and Seaborn:  

                      • Using Matplotlib: To draw grid lines in a Matplotlib plot, you can use the grid() function. It can be applied to an existing plot or a subplot within a grid plot.  
                      • Using Seaborn: Seaborn is a higher-level library built on top of Matplotlib. You can use the sns.set_style() function with the grid() option to draw grid lines in a Seaborn plot.  


                      2. What are the advantages of using histogram plots over other plots?  

                      Histogram plots offer several advantages, making them useful for data analysis tasks. Here are some advantages of using histogram plots:  

                      • Distribution Visualization  
                      • Bin Selection  
                      • Data Summarization  
                      • Easy Comparison  
                      • Identifying Outliers  
                      • Data Preprocessing  

                       

                      3. How can I ensure proper grid alignment when creating a plot in Python?  

                      To ensure proper grid alignment when creating a plot in Python, you can follow these steps:  

                      • Determine the desired number of rows and columns for your grid layout. This will depend on the number of subplots you want to create and the desired arrangement.  
                      • Use the plotting library to create subplots within the specified grid layout.  
                      • When creating each subplot, specify the indices to position them within the grid. The indices start from 1 and increment as you move across rows and columns.  
                      • Fine-tune the spacing between subplots to ensure proper alignment.  

                       

                      4. What is the NumPy library, and how can it be used to create a grid plot?  

                      The NumPy library is a fundamental Python package for scientific computing. It provides powerful tools and functions. It helps to work with multidimensional arrays, linear algebra, and random number generation. NumPy serves as the foundation for many other scientific computing libraries in Python.  

                       

                      While NumPy does not have built-in functions, you can use it with libraries to create them. NumPy's ability helps handle numerical operations and array manipulations. This makes it a valuable tool for data preprocessing and manipulation. It helps in computation in preparation for creating grid plots using visualization libraries. It allows for advanced data processing and analysis. It helps generate grid plot data and perform data computations before plotting.  

                       

                      5. Is there an easy way to create a basic plot with Python libraries?  

                      Yes, several Python libraries provide an easy way to create basic plots. Two popular libraries for creating basic plots in Python are Matplotlib and Seaborn.  

                      Matplotlib:  

                      Matplotlib is a used plotting library. It provides a flexible and comprehensive set of functions. It helps in creating various types of plots.  

                      Seaborn:  

                      Seaborn is built on top of Matplotlib. It provides a higher-level interface for creating pleasing statistical visualizations. Both libraries offer various customization options. It allows you to create more complex and appealing plots.  

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