Crafting Unique Data Visualizations with Matplotlib in Python

Table of Contents

Introduction to Matplotlib and Data Visualization

Overview

This guide provides an introduction to Matplotlib, a powerful plotting library used for creating static, interactive, and animated visualizations in Python. By following along, you’ll learn how to set up your environment and create basic plots.

Prerequisites

Ensure Python is installed on your system. You can download it from python.org.
Install Matplotlib. You can use pip for this:
```
pip install matplotlib
```

Setting Up Your Environment

Create a new Python file, e.g., plotting_tutorial.py. Import the necessary libraries:

import matplotlib.pyplot as plt

Basic Example: Plotting a Simple Line Graph

We’ll start with a basic example: plotting a simple line graph.

Define your data:

# Sample data
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

Plot the data:
```
# Plotting the data
plt.plot(x, y)
```

Add title, labels, and grid:

plt.title('Basic Line Plot')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.grid(True)

Display the plot:
```
# Display the plot
plt.show()
```

Complete Script

Here’s the complete script for the basic example:

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

# Plotting the data
plt.plot(x, y)

# Adding title, labels, and grid
plt.title('Basic Line Plot')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.grid(True)

# Display the plot
plt.show()

Conclusion

This introductory section covered the basics of setting up Matplotlib and plotting a simple line graph in Python. You learned how to define data, plot it, add titles and labels, and display the graph. In future sections, you will explore more advanced features and customization options in Matplotlib.

Setting Up Your Python Environment for Data Visualization with Matplotlib

Step 1: Create a Virtual Environment

First, create a virtual environment to manage dependencies and avoid conflicts with other projects. Open your terminal or command prompt and navigate to the directory where your project will reside.

# Navigate to your project directory (substitute 'your_project_directory' with actual path)
cd your_project_directory

# Create a virtual environment named 'venv'
python -m venv venv

Step 2: Activate the Virtual Environment

Activate the virtual environment. The command varies based on your operating system:

Windows
```
venv\Scripts\activate
```
macOS and Linux
```
source venv/bin/activate
```

Step 3: Install Matplotlib and Dependencies

With the virtual environment activated, install Matplotlib along with any additional dependencies needed for your project.

# Ensure you have the latest version of pip
pip install --upgrade pip

# Install Matplotlib
pip install matplotlib

# Example: Install other dependencies if necessary
pip install numpy pandas

Step 4: Verify Installation

Create a simple Python script to verify that Matplotlib is correctly installed and that you can generate a basic plot.

Create a file named test_plot.py.

Add the following code to test_plot.py:

import matplotlib.pyplot as plt
import numpy as np

# Generate some data to plot
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create the plot
plt.plot(x, y, label="Sine Wave")

# Add labels and title
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Test Plot')

# Add legend
plt.legend()

# Save the plot as a PNG file
plt.savefig('test_plot.png')

# Display the plot
plt.show()

Run the script:
```
python test_plot.py
```

Step 5: Confirm Functionality

Review the generated plot to ensure that everything is functioning correctly. A window should display the plot, and a file named test_plot.png should be generated in your project directory.

Conclusion

Your environment is now set up and ready for creating custom data visualizations using Matplotlib in Python.

Basic Plotting Techniques

In this unit, we will cover the essential plotting functions provided by Matplotlib. We will use various examples to demonstrate how to create basic plots such as line plots, scatter plots, bar plots, and histograms.

1. Line Plot

Line plots are useful for visualizing data trends over intervals. Here’s how you can create a simple line plot:

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create line plot
plt.figure(figsize=(8, 6))
plt.plot(x, y, marker='o', linestyle='-', color='b', label='Prime Numbers')

# Add titles and labels
plt.title('Line Plot Example')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()

# Display the plot
plt.show()

2. Scatter Plot

Scatter plots are useful for visualizing the relationship between two variables. Here’s how to create a scatter plot:

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

# Create scatter plot
plt.figure(figsize=(8, 6))
plt.scatter(x, y, color='r', label='Squared Numbers')

# Add titles and labels
plt.title('Scatter Plot Example')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()

# Display the plot
plt.show()

3. Bar Plot

Bar plots are great for comparing quantities of different groups. Here’s how to create a bar plot:

import matplotlib.pyplot as plt

# Data
categories = ['Category A', 'Category B', 'Category C']
values = [20, 35, 30]

# Create bar plot
plt.figure(figsize=(8, 6))
plt.bar(categories, values, color=['red', 'green', 'blue'])

# Add titles and labels
plt.title('Bar Plot Example')
plt.xlabel('Categories')
plt.ylabel('Values')

# Display the plot
plt.show()

4. Histogram

Histograms are used to visualize the distribution of data. Here’s how to create a histogram:

import matplotlib.pyplot as plt
import numpy as np

# Data
data = np.random.randn(1000)

# Create histogram
plt.figure(figsize=(8, 6))
plt.hist(data, bins=30, color='purple', edgecolor='black')

# Add titles and labels
plt.title('Histogram Example')
plt.xlabel('Bins')
plt.ylabel('Frequency')

# Display the plot
plt.show()

Conclusion

These basic plotting techniques provide the foundation for more complex data visualizations. By understanding how to create line plots, scatter plots, bar plots, and histograms, you can effectively visualize various types of data. Matplotlib offers extensive customization options for each plot type, allowing you to create informative and visually appealing charts.

Customizing Plot Aesthetics

In this part, we’ll dive into customizing the aesthetics of your plots using Matplotlib. We’ll cover a variety of customization techniques including colors, markers, line styles, grid lines, and annotations to make your plots visually appealing and informative.

Import Libraries and Data

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

Customizing Line Styles and Colors

plt.figure(figsize=(10, 6))

# Custom line styles and colors
plt.plot(x, y1, label='Sine Wave', color='blue', linestyle='--', linewidth=2, marker='o', markersize=5)
plt.plot(x, y2, label='Cosine Wave', color='green', linestyle='-', linewidth=2, marker='x', markersize=5)

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Custom Line Styles and Colors')
plt.legend()
plt.show()

Adding Grid Lines and Ticks

plt.figure(figsize=(10, 6))

plt.plot(x, y1, label='Sine Wave', color='blue', linestyle='--')
plt.plot(x, y2, label='Cosine Wave', color='green', linestyle='-')

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Grid Lines and Custom Ticks')

# Adding grid lines
plt.grid(True)

# Customizing ticks
plt.xticks(np.arange(0, 11, step=1))
plt.yticks(np.arange(-1, 1.5, step=0.5))

plt.legend()
plt.show()

Customizing Annotations

plt.figure(figsize=(10, 6))

plt.plot(x, y1, label='Sine Wave', color='blue', linestyle='--')
plt.plot(x, y2, label='Cosine Wave', color='green', linestyle='-')

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Annotations')

# Adding annotations
plt.annotate('Local Max', xy=(1.57, 1), xytext=(3, 1.5),
             arrowprops=dict(facecolor='black', shrink=0.05))

plt.annotate('Local Min', xy=(4.71, -1), xytext=(6, -1.5),
             arrowprops=dict(facecolor='red', shrink=0.05))

plt.legend()
plt.show()

Customizing Plot Background and Spines

fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(x, y1, label='Sine Wave', color='blue', linestyle='--')
ax.plot(x, y2, label='Cosine Wave', color='green', linestyle='-')

ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_title('Background and Spines')

# Changing background color
ax.set_facecolor('#f2f2f2')

# Customizing spines
ax.spines['top'].set_color('none')   # Removing top spine
ax.spines['right'].set_color('none') # Removing right spine
ax.spines['left'].set_color('red')
ax.spines['bottom'].set_color('blue')

ax.yaxis.tick_left()
ax.xaxis.tick_bottom()

ax.legend()
plt.show()

Complete Example with All Customizations

fig, ax = plt.subplots(figsize=(10, 6))

ax.plot(x, y1, label='Sine Wave', color='blue', linestyle='--', linewidth=2, marker='o', markersize=5)
ax.plot(x, y2, label='Cosine Wave', color='green', linestyle='-', linewidth=2, marker='x', markersize=5)

ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_title('Complete Customization Example')

ax.grid(True)
ax.set_facecolor('#f2f2f2')
ax.set_xticks(np.arange(0, 11, 1))
ax.set_yticks(np.arange(-1, 1.5, 0.5))

ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('red')
ax.spines['bottom'].set_color('blue')

ax.annotate('Local Max', xy=(1.57, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05))
ax.annotate('Local Min', xy=(4.71, -1), xytext=(6, -1.5), arrowprops=dict(facecolor='red', shrink=0.05))

ax.legend()
plt.show()

This code demonstrates manipulating various aspects of Matplotlib plots to create visually polished and informative charts.

Working with Multiple Plots and Subplots

Overview

Subplots are a convenient way to display multiple plots in a single figure. Matplotlib offers several ways to create subplots to accommodate various plot arrangements.

Grid of Subplots using `plt.subplot`

To create a grid of subplots, use the plt.subplot function, which takes three arguments: the number of rows, the number of columns, and the index of the subplot.

import matplotlib.pyplot as plt

# Data for plotting
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 2, 3, 4, 5]

# Create a figure
fig = plt.figure()

# First subplot
ax1 = fig.add_subplot(2, 1, 1)
ax1.plot(x, y1)
ax1.set_title('First Subplot')

# Second subplot
ax2 = fig.add_subplot(2, 1, 2)
ax2.plot(x, y2)
ax2.set_title('Second Subplot')

# Adjust layout
plt.tight_layout()
plt.show()

Using `plt.subplots` for an Array of Subplots

plt.subplots is another useful function for creating an array of subplots. This function returns a figure and an array of Axes objects.

import matplotlib.pyplot as plt

# Data for plotting
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 2, 3, 4, 5]
y3 = [25, 16, 9, 4, 1]
y4 = [5, 4, 3, 2, 1]

# Create a figure and subplots array
fig, axs = plt.subplots(2, 2)  # 2x2 grid

# Plot on each subplot
axs[0, 0].plot(x, y1)
axs[0, 0].set_title('Subplot 1')

axs[0, 1].plot(x, y2)
axs[0, 1].set_title('Subplot 2')
axs[0, 1].set_yscale('log')    # Example of customizing a subplot

axs[1, 0].plot(x, y3)
axs[1, 0].set_title('Subplot 3')
axs[1, 0].annotate('Point (3,9)', xy=(3,9), xytext=(3,20),
             arrowprops=dict(facecolor='black', shrink=0.05))

axs[1, 1].plot(x, y4)
axs[1, 1].set_title('Subplot 4')

# Adjust layout
plt.tight_layout()
plt.show()

Sharing X or Y Axis

In cases where you need subplots to share an axis (for instance, the X-axis), plt.subplots allows you to specify this with the sharex or sharey parameters.

import matplotlib.pyplot as plt

# Data for plotting
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [25, 16, 9, 4, 1]

# Create subplots with shared X-axis
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)

# Plot on each subplot
ax1.plot(x, y1)
ax1.set_title('Subplot 1')

ax2.plot(x, y2)
ax2.set_title('Subplot 2')

# Adjust layout
plt.tight_layout()
plt.show()

Combining Different Types of Plots

You can combine different types of plots in subplots to enhance the visualization aspect.

import matplotlib.pyplot as plt
import numpy as np

# Data for plotting
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create a figure with subplots
fig, axs = plt.subplots(2, 1)  # 2x1 grid

# Line plot
axs[0].plot(x, y1, color='r')
axs[0].set_title('Sine Wave')

# Scatter plot
axs[1].scatter(x, y2, color='b')
axs[1].set_title('Cosine Wave')

# Adjust layout
plt.tight_layout()
plt.show()

This code covers how to work with multiple plots and subplots in Matplotlib. Use these techniques to create complex, informative visualizations.

Advanced Customization Techniques for Matplotlib Visualizations

This section focuses on advanced customization techniques to create highly detailed and customized visualizations using Matplotlib in Python. You will learn how to enhance your plots by customizing legends, annotations, adding custom ticks, and more.

Customizing Legends

import matplotlib.pyplot as plt
import numpy as np

# Sample Data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create the plot
fig, ax = plt.subplots()
line1, = ax.plot(x, y1, label='Sine Wave', linestyle='-', linewidth=2, color='blue')
line2, = ax.plot(x, y2, label='Cosine Wave', linestyle='--', linewidth=2, color='red')

# Customizing the legend
legend = ax.legend(loc='upper right', fontsize='medium', title='Trigonometric Functions', title_fontsize='large', shadow=True)

# Add a frame to the legend
legend.get_frame().set_edgecolor('black')
legend.get_frame().set_linewidth(1.5)

plt.show()

Adding Annotations

# Sample Data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create the plot
fig, ax = plt.subplots()
ax.plot(x, y, label='Sine Wave', color='green')

# Adding annotation
ax.annotate('Local Max', xy=(np.pi/2, 1), xytext=(np.pi, 1.5),
             arrowprops=dict(facecolor='black', shrink=0.05))
ax.annotate('Local Min', xy=(3*np.pi/2, -1), xytext=(2*np.pi, -1.5),
             arrowprops=dict(facecolor='red', shrink=0.05))

plt.show()

Customizing Ticks

# Sample Data
x = np.linspace(0, 10, 100)
y = x ** 2

# Create the plot
fig, ax = plt.subplots()
ax.plot(x, y, label='Quadratic')

# Customizing ticks
ax.set_xticks([0, 2, 4, 6, 8, 10])
ax.set_xticklabels(['Start', 'Two', 'Four', 'Six', 'Eight', 'End'], rotation=45, fontsize='small')
ax.set_yticks([0, 20, 40, 60, 80, 100])
ax.set_yticklabels(['Zero', 'Twenty', 'Forty', 'Sixty', 'Eighty', 'Hundred'], fontsize='small')

plt.show()

Customizing Grids

# Sample Data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create the plot
fig, ax = plt.subplots()
ax.plot(x, y, label='Sine Wave')

# Customizing grid
ax.grid(True, which='both', linestyle='--', linewidth=0.5, color='b')

# Specifying background for the minor grid
ax.minorticks_on()
ax.grid(which='minor', linestyle=':', linewidth='0.5', color='gray')
ax.set_facecolor('#f0f0f0')

plt.show()

Using Custom Colors and Colormaps

from matplotlib import cm

# Sample Data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create the plot
fig, ax = plt.subplots()
colors = cm.viridis(np.linspace(0, 1, 10))

# Using custom colors
for i in range(10):
    ax.plot(x, y1+i, label=f'Sine {i}', color=colors[i])

# Customizing the colormap
sm = plt.cm.ScalarMappable(cmap='viridis')
plt.colorbar(sm, ax=ax, label='Color Scale - Viridis')

plt.show()

These implementations will enhance your visualizations by fine-tuning various elements such as legends, annotations, ticks, grids, and colors. You can apply these strategies to create detailed and highly customized plots using Matplotlib.

Creating Interactive Visualizations with Matplotlib and ipywidgets

To create interactive visualizations in Python using Matplotlib, you can leverage the ipywidgets library to add interactive controls like sliders, checkboxes, and dropdowns. Below is a step-by-step implementation of creating an interactive plot using Matplotlib and ipywidgets.

Step-by-Step Implementation

Step 1: Install Required Libraries

Ensure you have ipympl and ipywidgets installed. If you haven’t installed them yet, you can do it using:

pip install ipympl ipywidgets

Step 2: Import Libraries

First, import the necessary libraries:

import matplotlib.pyplot as plt
import numpy as np
import ipywidgets as widgets
from ipywidgets import interact

Step 3: Enable Interactive Mode for Jupyter

To enable interactive mode within a Jupyter Notebook:

%matplotlib widget

Step 4: Define the Interactive Function

Define a function that will update the plot based on the input parameters. Here is an example function that plots a sine wave with adjustable frequency and amplitude:

def interactive_plot(freq, amplitude):
    x = np.linspace(0, 2 * np.pi, 1000)
    y = amplitude * np.sin(freq * x)
    
    plt.figure(figsize=(10, 6))
    plt.plot(x, y, label=f'Sine wave: {amplitude}*sin({freq}*x)')
    plt.xlabel('x')
    plt.ylabel('y')
    plt.title('Interactive Sine Wave')
    plt.legend()
    plt.grid(True)
    plt.show()

Step 5: Create Widgets

Use ipywidgets to create sliders for the frequency and amplitude:

freq_slider = widgets.FloatSlider(value=1.0, min=0.1, max=10.0, step=0.1, description='Frequency:')
amp_slider = widgets.FloatSlider(value=1.0, min=0.1, max=10.0, step=0.1, description='Amplitude:')

Step 6: Display the Interactive Plot

Use the interact function to link the sliders to your plotting function:

interact(interactive_plot, freq=freq_slider, amplitude=amp_slider);

Complete Code

Here is the complete implementation in one place:

import matplotlib.pyplot as plt
import numpy as np
import ipywidgets as widgets
from ipywidgets import interact

# Enable interactive mode for Jupyter
%matplotlib widget

# Define the interactive plotting function
def interactive_plot(freq, amplitude):
    x = np.linspace(0, 2 * np.pi, 1000)
    y = amplitude * np.sin(freq * x)
    
    plt.figure(figsize=(10, 6))
    plt.plot(x, y, label=f'Sine wave: {amplitude}*sin({freq}*x)')
    plt.xlabel('x')
    plt.ylabel('y')
    plt.title('Interactive Sine Wave')
    plt.legend()
    plt.grid(True)
    plt.show()

# Create sliders
freq_slider = widgets.FloatSlider(value=1.0, min=0.1, max=10.0, step=0.1, description='Frequency:')
amp_slider = widgets.FloatSlider(value=1.0, min=0.1, max=10.0, step=0.1, description='Amplitude:')

# Display the interactive plot
interact(interactive_plot, freq=freq_slider, amplitude=amp_slider);

Now, you have a practical implementation to create interactive visualizations using Matplotlib and ipywidgets that you can directly use and modify within your Python environment.

Animating Plots and Creating GIFs

Animating plots in Matplotlib can be accomplished using the FuncAnimation class from the matplotlib.animation module. Once the animation is created, it can be saved as a GIF using PillowWriter. Below is a step-by-step practical implementation for animating a simple sine wave plot and saving it as a GIF.

Implementation

1. Import Required Libraries

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

2. Create the Initial Sine Wave Plot

# Create a figure and axis
fig, ax = plt.subplots()

# Set axis limits
ax.set_xlim(0, 2 * np.pi)
ax.set_ylim(-1, 1)

# Plot an empty line
line, = ax.plot([], [], lw=2)

# Initial empty frame
def init():
    line.set_data([], [])
    return line,

3. Define the Update Function

The update function will be called for each frame of the animation.

def update(frame):
    x = np.linspace(0, 2 * np.pi, 100)
    y = np.sin(x + 0.1 * frame)  # Update sine wave based on frame
    line.set_data(x, y)
    return line,

4. Create the Animation

# Create animation object
ani = animation.FuncAnimation(fig, update, frames=100, init_func=init, blit=True)

5. Saving the Animation as a GIF

# Save animation as GIF
ani.save('sine_wave.gif', writer='pillow', fps=30)

Complete Code

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

# Create a figure and axis
fig, ax = plt.subplots()

# Set axis limits
ax.set_xlim(0, 2 * np.pi)
ax.set_ylim(-1, 1)

# Plot an empty line
line, = ax.plot([], [], lw=2)

# Initial empty frame
def init():
    line.set_data([], [])
    return line,

# Update function for each frame
def update(frame):
    x = np.linspace(0, 2 * np.pi, 100)
    y = np.sin(x + 0.1 * frame)  # Update sine wave based on frame
    line.set_data(x, y)
    return line,

# Create animation object
ani = animation.FuncAnimation(fig, update, frames=100, init_func=init, blit=True)

# Save animation as GIF
ani.save('sine_wave.gif', writer='pillow', fps=30)

# Show plot
plt.show()

Now, you’ve successfully created an animated sine wave plot and saved it as a GIF. You can inspect the saved file sine_wave.gif to see your animation in action.

Final Thoughts

This guide has provided a comprehensive introduction to data visualization using Matplotlib in Python.

From setting up your environment and creating basic plots to advanced customization techniques and interactive visualizations, you now have the tools and knowledge to craft unique and insightful data visualizations.

By mastering these techniques, you can effectively communicate complex data insights and trends through visually appealing and informative charts.

Whether you’re a beginner or an experienced data analyst, the skills covered in this guide will enhance your ability to present data in a clear and impactful way.

Keep experimenting with different styles and customizations to make your visualizations stand out and convey your data stories compellingly. Happy plotting!

Mastering Data Analytics with Matplotlib in Python

« Older Entries

Crafting Unique Data Visualizations with Matplotlib in Python

Introduction to Matplotlib and Data Visualization

Overview

Prerequisites

Setting Up Your Environment

Basic Example: Plotting a Simple Line Graph

Complete Script

Conclusion

Setting Up Your Python Environment for Data Visualization with Matplotlib

Step 1: Create a Virtual Environment

Step 2: Activate the Virtual Environment

Step 3: Install Matplotlib and Dependencies

Step 4: Verify Installation

Step 5: Confirm Functionality

Conclusion

Basic Plotting Techniques

1. Line Plot

2. Scatter Plot

3. Bar Plot

4. Histogram

Conclusion

Customizing Plot Aesthetics

Import Libraries and Data

Customizing Line Styles and Colors

Adding Grid Lines and Ticks

Customizing Annotations

Customizing Plot Background and Spines

Complete Example with All Customizations

Working with Multiple Plots and Subplots

Overview

Grid of Subplots using plt.subplot

Using plt.subplots for an Array of Subplots

Sharing X or Y Axis

Combining Different Types of Plots

Advanced Customization Techniques for Matplotlib Visualizations

Customizing Legends

Adding Annotations

Customizing Ticks

Customizing Grids

Using Custom Colors and Colormaps

Creating Interactive Visualizations with Matplotlib and ipywidgets

Step-by-Step Implementation

Step 1: Install Required Libraries

Step 2: Import Libraries

Step 3: Enable Interactive Mode for Jupyter

Step 4: Define the Interactive Function

Step 5: Create Widgets

Step 6: Display the Interactive Plot

Complete Code

Animating Plots and Creating GIFs

Implementation

1. Import Required Libraries

2. Create the Initial Sine Wave Plot

3. Define the Update Function

4. Create the Animation

5. Saving the Animation as a GIF

Complete Code

Final Thoughts

Related Posts

Grid of Subplots using `plt.subplot`

Using `plt.subplots` for an Array of Subplots