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Working With Images In Matplotlib

9. Create Multiple Subplots in Matplotlib

The image module in matplotlib library is used for working with images in Python. The image module also includes two useful methods which are imread which is used to read images and imshow which is used to display the image.

Python3 
# importing required libraries 
import matplotlib.pyplot as plt 
import matplotlib.image as img 
# reading the image 
testImage = img.imread('g4g.png') 
# displaying the image 
plt.imshow(testImage)

Output:

Refer to the below articles to get detailed information while working with Images:



Customizing Styles in Matplotlib

Here, we’ll delve into the fundamentals of Matplotlib, exploring its various classes and functionalities to help you unleash the full potential of your data visualization projects. From basic plotting techniques to advanced customization options, this guide will equip you with the knowledge needed to create stunning visualizations with Matplotlib. So, let’s dive in and discover how to effectively utilize Matplotlib for your data visualization needs.

Table of Content

  • Getting Started with Matplotlib
  • Exploring Different Plot Styles with Matplotlib
  • Matplotlib Figure Class
  • Python Pyplot Class
  • Matplotlib Axes Class
  • Set Colors in Matplotlib
  • Add Text, Font and Grid lines in Matplotlib
  • Custom Legends with Matplotlib
  • Matplotlib Ticks and Tick Labels
  • Style Plots using Matplotlib
  • Create Multiple Subplots in Matplotlib
  • Working With Images In Matplotlib

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Getting Started with Matplotlib

Matplotlib is easy to use and an amazing visualizing library in Python. It is built on NumPy arrays and designed to work with the broader SciPy stack and consists of several plots like line, bar, scatter, histogram, etc. Before we start learning about Matplotlib we first have to set up the environment and will also see how to use Matplotlib with Jupyter Notebook...

Exploring Different Plot Styles with Matplotlib

Matplotlib’s versatile styling capabilities empower you to craft visualizations that captivate and inform your audience. Join us as we embark on a journey to unlock the full potential of Matplotlib’s plot styles and elevate your data visualization endeavors to new heights....

1. Matplotlib Figure Class

Figure class is the top-level container that contains one or more axes. It is the overall window or page on which everything is drawn....

2. Python Pyplot Class

Pyplot is a Matplotlib module that provides a MATLAB-like interface. Pyplot provides functions that interact with the figure i.e. creates a figure, decorates the plot with labels, and creates a plotting area in a figure....

3. Matplotlib Axes Class

Axes class is the most basic and flexible unit for creating sub-plots. A given figure may contain many axes, but a given axes can only be present in one figure. The axes() function creates the axes object. Let’s see the below example....

4. Set Colors in Matplotlib

Color plays a vital role in data visualization, conveying information, highlighting patterns, and making plots visually appealing. Matplotlib, a powerful plotting library in Python, offers extensive options for customizing colors in plots....

5. Add Text, Font and Grid lines in Matplotlib

Adding text annotations and grid lines in Matplotlib enhances the readability and clarity of plots. Here’s how you can incorporate text annotations and grid lines into your Matplotlib plots....

6. Custom Legends with Matplotlib

A legend is an area describing the elements of the graph. In simple terms, it reflects the data displayed in the graph’s Y-axis. It generally appears as the box containing a small sample of each color on the graph and a small description of what this data means....

7. Matplotlib Ticks and Tick Labels

You might have seen that Matplotlib automatically sets the values and the markers(points) of the x and y axis, however, it is possible to set the limit and markers manually. set_xlim() and set_ylim() functions are used to set the limits of the x-axis and y-axis respectively. Similarly, set_xticklabels() and set_yticklabels() functions are used to set tick labels....

8. Style Plots using Matplotlib

Matplotlib styles allow you to change the overall appearance of your plots, including colors, fonts, gridlines, and more. By applying different styles, you can tailor your visualizations to match your preferences or the requirements of your audience. Matplotlib provides a variety of built-in styles to choose from, each offering a unique look and feel....

9. Create Multiple Subplots in Matplotlib

Till now you must have got a basic idea about Matplotlib and plotting some simple plots, now what if you want to plot multiple plots in the same figure. This can be done using multiple ways. One way was discussed above using the add_axes() method of the figure class. Let’s see various ways multiple plots can be added with the help of examples....

10. Working With Images In Matplotlib

The image module in matplotlib library is used for working with images in Python. The image module also includes two useful methods which are imread which is used to read images and imshow which is used to display the image....

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9. Create Multiple Subplots in Matplotlib

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