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Animating Insights: A Guide to Interactive Visualizations with Matplotlib

Introduction to Matplotlib Animation

Data visualization is an important aspect of data analysis. While tables and statistics provide numerical results, visualizations aid in understanding patterns and trends within the data.

Matplotlib is a popular Python library for 2D-plotting, which offers a range of visualization tools. One such tool is its animation function, which allows for the creation of interactive visualizations.

In this article, we will explore the animation function of Matplotlib in detail, including its installation process, its various functionalities, and how it can be used to create interactive visualizations with user input.

Explanation of Matplotlib Library

Before diving into the details of the animation function, let’s delve into Matplotlib briefly. Matplotlib is a 2D plotting library used for creating professional-quality figures.

Its primary object hierarchy consists of a figure class followed by axes, artists, and canvas. A figure is the window that shows plotted data.

Axes are the number lines and ticks surrounding the axes, while artists are individual objects such as text, lines, scatter plots, and images that are added to the axes. Canvas is a layer that contains the figure and the axes.to Animation Function

The animation keyword in Matplotlib allows for the creation of animated visualizations that convey changes in the data over time.

Animations are generated by sequentially updating the axes artists (line, plot, image, text, scatter, etc.) in a loop. The function for creating animation is included in the matplotlib.animation module, and it requires the creation of a figure and axes.

Use of Animation with User Input

One of the key benefits of the Matplotlib animation function is its ability to create interactive visualizations. The function incorporates user input, such as mouse clicks and key presses, to dynamically update the visualization.

By using animations and user input, data analysts can create more meaningful and engaging visualizations. As a result, end-users can interact with the visualizations, leading to better insights and understanding of the data.

Installing Matplotlib in Python

Matplotlib is a third-party package, and before installation, NumPy must be installed because it provides array objects for Matplotlib to plot. NumPy is a Python package that is required for scientific computing and technical applications.

Most Python software packages can be installed using the pip command, which is a package manager that simplifies the installation process. The following steps provide a guide on how to install Matplotlib in Python:


Open a terminal window. 2.

Enter the following command to install NumPy:

pip install numpy

3. Once NumPy is installed, enter the following command to install Matplotlib:

pip install matplotlib


Finally, you can import Matplotlib using the following command:

import matplotlib.pyplot as plt

The above command imports the pyplot module of Matplotlib.

Dependence on NumPy

NumPy is a critical package for scientific computing in Python. Without NumPy, Matplotlib cannot plot data objects.

NumPy provides a numerical array by allowing mathematical operations on arrays. It facilitates the foundation of multiple scientific libraries and applications in Python.

These libraries manipulate data using NumPy arrays to build visualization libraries, machine learning, and deep learning, among other scientific packages.

Installation process

Installing Matplotlib is relatively easy, with the pip command executing the installation. However, you should be mindful of the version, as the library has evolved over several versions with varying functionalities.

The official documentation provides information on how to migrate to the latest version of Matplotlib, as well as an installation guide.


In conclusion, Matplotlib is an essential tool for data visualization, providing a vast range of visualization tools for data analysts. One of the most remarkable functionalities is the animation function that facilitates the creation of interactive visualizations.

With the correct installation of Matplotlib and its dependence on NumPy, analysts can create meaningful visualizations using the function while incorporating user input. The above guide is an excellent starting point in developing simple and interactive visualizations with Matplotlib in Python.

Creating Animation in Matplotlib

In the previous section, we discussed the concept and significance of animations in Matplotlib visualizations. In this section, we will explore the mechanics of creating animations using the FuncAnimation class in Matplotlib.to FuncAnimation Class

FuncAnimation is a class in Matplotlib’s animation module that renders a sequence of frames to create an animation.

It provides a simple interface to generate and customize animations by updating a function that defines the plot with each new frame. This class can be used to generate a sequence of frames that can be played or saved to create an animation.

Creating Figure and Axes Instance

To create an animation in Matplotlib, we first need to create a figure and axes instance. A Figure object represents a single figure window, which can contain one or more subplots.

The Axes object represents a single plot with an X and Y axis. We can create these instances using the following code:


import matplotlib.pyplot as plt

fig, ax = plt.subplots()


The ‘plt.subplots()’ function initializes a new figure and returns a tuple containing the created Figure object and a single Axes object.

Defining Animation Function and Using Frames Argument

Once we have created the figure and axes object, we can define the animation function. The animation function is called at each iteration of the animation loop and is responsible for updating the plot in each frame.

It accepts a single argument i, which represents the current frame number. The frames parameter of FuncAnimation specifies the number of frames in the animation.

Lets consider a simple example to plot a moving circle using the animation function. The following code creates a circle object in each frame and moves it across the plot.


import numpy as np

import matplotlib.pyplot as plt

from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots()

def animate(i):

x = np.cos(i)

y = np.sin(i)


ax.set_xlim(-2, 2)

ax.set_ylim(-2, 2)

ax.add_artist(plt.Circle((x, y), 0.1))

anim = FuncAnimation(fig, animate, frames=100, interval=50)



In the above code, we define the ‘animate’ function, which accepts a single argument ‘i’, corresponding to the current frame number. The ‘cos’ and ‘sin’ functions are used to determine the position of the circle at each frame.

We also set the x and y limits of the plot and add the circle to the axes object at each frame. The parameters of the FuncAnimation function are the figure object, the animation function, the frames argument, and the interval argument (time duration between frames).

Saving Animation as a Movie

After creating an animation, it can be saved as a movie or GIF file. There are several file formats supported by Matplotlib to save animations, including MP4, AVI, GIF, and HTML.

Once we have created and defined the animation function, we can write the following command to save the animation as a movie. “`

anim.save(‘animation.mp4′, writer=’ffmpeg’)


The above code saves the animation in MP4 format using the ffmpeg video codec.

We can specify other writers to save animations in different formats. The file format is inferred from the file extension specified in the filename argument.


In this article, we have discussed the creation of animations using the FuncAnimation class in Matplotlib. We understood the importance of figure and axes instances, defining the animation function and using frames argument, and saving the animation as a movie.

Animations help in building more meaningful and interactive visualizations. All the discussed functionalities of Matplotlib are useful to create visualizations that capture insights and help us better understand the data.

Matplotlib is a powerful library that can be used to create professional-quality 2D plots in Python. One of its best functionalities includes the creation of animations.

The article explored the mechanics of creating animations in Matplotlib, including the use of the FuncAnimation class to create figure and axes instances, defining the animation function and using frames argument, and saving animations as movies or GIFs. Animations can help data analysts and scientists in creating a clearer understanding of data patterns and trends. Thus, mastering the creation of animations is a valuable tool in visualizing and presenting data.

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