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How to create interactive plots in Matplotlib using tools such as zooming and panning?

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by sneha@openweaver.com dot icon Updated: Jul 20, 2023

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Matplotlib is a powerful data visualization library in Python. It enables users to create various static visualization. Also, allow users to create various animated and interactive visualizations. The primary purpose is to help users create visual representations of their data. It is done in an easy and customizable manner.  

 

It offers a complete set of tools for creating plots. It provides charts, histograms, scatterplots, bar plots, and other visualizations. Matplotlib is flexible. This allows users to fine-tune every aspect of their plots. It includes colors, labels, annotations, fonts, and styles.  

 

Matplotlib can be used for a variety of tasks, such as:  

  • Exploratory Data Analysis (EDA)  
  • Presentation and Reporting  
  • Publication-Quality Plots  
  • Comparing Data  
  • Trend Analysis  
  • Customization and Interactivity  


Matplotlib offers a variety of interfaces. This makes it versatile and adaptable to different use cases. The most used interface is the pyplot module. This module provides a MATLAB-like interface for creating and manipulating plots. Matplotlib is a powerful and flexible data visualization library. This allows users to create high-quality plots and charts. This helps to explore, analyze, and communicate data. Histograms, scatter plots, and bar charts are the plot types used in this library.  

 

Let's discuss each of these in more detail:  

  • Histograms: This shows the distribution of a single variable or a set of continuous data.  
  • Scatter Plots: Scatter plots visualize the relationship between two variables.  
  • Bar Charts: Bar charts, also known as bar graphs. These are used to compare categorical data or different categories of a variable.  


When creating a plot with Matplotlib, some parameters can customize the plot's appearance and behavior.  

 

Here are some of the important parameters that can be modified:  

  • Plot Type - The choice of plot type depends on the data and the visualization needs.  
  • X-axis and Y-axis Limits - You can adjust the x-axis and y-axis limits. It is used to control the range of values displayed on the plot.  
  • Title and Labels - The title () function sets the plot's title. You can specify the title as a string.  
  • Legend - The legend () function is used to display a legend in the plot.  
  • Grid - The grid () function allows you to display grid lines on the plot.  
  • Line and Marker Styles - Used to adjust the properties like line style, line width, marker, and marker size.  
  • Color - Using the color parameter, you can specify the color of lines, markers, and other plot elements.  
  • Figure Size - The size, which includes the entire plot area, can be adjusted using the figure () function.  
  • Subplots - Matplotlib allows you to create many plots within the same figure. This can be done with the help of subplots.  

 

In conclusion, using Matplotlib in research and academic writing plays a major role. It is due to its immense power as a data analysis tool. Matplotlib is an open-source library. This is used for creating static, animated, and interactive visualizations in Python. It has versatility, flexibility, and extensive functionality. This makes it an indispensable asset for researchers across various disciplines. 


Here is an example of creating interactive plots in Matplotlib using tools like zooming and panning.



Fig1: Preview of Code.



Fig2: Preview of the Output.

Code


In this solution, we're creating interactive plots in Matplotlib using tools such as zooming and panning.

How to zoom inside a plot

PythondotLines of Code : 30dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes

# let's plot something similar to your stuff
t = np.linspace(-5, 5, 2001)
y = np.exp(-20*t**2)
fig, ax = plt.subplots()
for i in range(14):
    start = 900-10*i
    ax.plot(t[1000:1500], -5*y[start:start+500]/(1+i*0.3)+i, 'k')
ax.set_ylim((15, -10)) ; ax.set_yticks(range(14))

# now, create an inset axis, in the upper right corner, with
# a zoom factor of two
zax = zoomed_inset_axes(ax, 2, loc=1)

# plot again (PLOT AGAIN) the same stuff as before in the new axes
for i in range(14):
    start = 900-10*i
    zax.plot(t[1000:1500], -5*y[start:start+500]/(1+i*0.3)+i, 'k')

# and eventually specify the x, y limits for the zoomed plot,
# as well as the tick positions
zax.set_xlim((0.2, 0.7)) ; zax.set_xticks((0.2, 0.3, 0.4, 0.5, 0.6, 0.7))
zax.set_ylim((1, -6)) ; zax.set_yticks([1]+[-i*0.5 for i in range(12)]) ;

# that's all folks
plt.show()

Instructions

Follow the steps carefully to get the output easily.

  1. Install Idle Python on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Numpy - pip install numpy
  4. Install matplotlib - pip install matplotlib
  5. Copy the snippet using the 'copy' button and paste it into that file.
  6. Run the file using run button.


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


I found this code snippet by searching for "Zoom on interactive plot" 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
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
                                    Security
                                      License
                                        Reuse

                                          You can also search for any dependent libraries on kandi like "matplotlib / numpy"

                                          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 Python3.9.6.
                                          2. The solution is tested on numpy 1.21.5 version.


                                          Using this solution, we are able to create interactive plots in Matplotlib using tools such as zooming and panning.


                                          This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to create interactive plots in Matplotlib using tools such as zooming and panning.

                                          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 are the benefits of matplotlib plots in a notebook environment?  

                                          Matplotlib is a used plotting library in Python. It provides various tools for creating high-quality visualizations.  

                                           

                                          There are several benefits:  

                                          • Interactive Exploration  
                                          • Seamless Integration  
                                          • Rapid Prototyping  
                                          • Data Exploration and Analysis  
                                          • Reproducibility  
                                          • Integration with Data Science Ecosystem  

                                           

                                          2. How to find information from the Matplotlib documentation about interactive plots?  

                                          You should refer to the official documentation to find information about interactive plots. Here's a guide to accessing the documentation and exploring the interactive plotting features:  

                                          • Open your web browser and visit the Matplotlib website.  
                                          • On the homepage, navigate to the "Documentation" section. You can find it in the top navigation bar.  
                                          • Click on the "Documentation" link to access the Matplotlib documentation.  
                                          • You will see a sidebar on the left side of the documentation page with various sections and topics. Look for the "Interactive plots" section or similar keywords related to interactivity.  
                                          • Click on the relevant section or topic related to interactive plots. This will open the corresponding documentation page.  
                                          • On the documentation page, you can find comprehensive information about interactive plotting. It includes different methods, tools, and features for creating interactive visualizations.  

                                           

                                          3. What plotting library should I use to create an interactive plot?  

                                          If you're looking to create interactive plots, one of the popular and used libraries is Plotly. Plotly is a powerful visualization library. It allows you to create interactive and customizable plots. It supports various plot types, including line, scatter, bar, and 3D plots.  

                                           

                                          4. How does Python Data Science help with creating interactive plots?  

                                          Python Data Science provides several powerful libraries. It can help with creating interactive plots. Some of the popular libraries for interactive data visualization in Python are:  

                                          • Matplotlib  
                                          • Plotly  
                                          • Bokeh  
                                          • Seaborn  


                                          5. What is the best way to add interactive functionality to my matplotlib plot?  

                                          To add interactive functionality, you can leverage various libraries and techniques. Here are a few popular options:  

                                          • Mpld3  
                                          • Bokeh  
                                          • Plotly  
                                          • IPywidgets  
                                          • Interactive backends 

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