Matplotlib – Jupyter Notebook

Introduction to Matplotlib in Jupyter Notebook

Table of Contents

Jupyter is a loose acronym that stands for Julia, Python, and R. These were the first programming languages supported by the Jupyter application. However, today Jupyter Notebook supports a wide range of languages beyond these three.

In 2001, Fernando Pérez began developing IPython, a command shell designed for interactive computing. Originally tailored for Python, IPython evolved to support multiple languages and introduced many features that became the backbone of Jupyter Notebooks.

Matplotlib in Jupyter Notebook provides a powerful, interactive environment to create visualizations right beside your code, enabling seamless data exploration and explanation in analytics or machine learning workflows.

Key Features of IPython

Interactive shells (both terminal and Qt-based)
A browser-based notebook supporting code, text, math expressions, plots, and media
Support for interactive data visualization with GUI toolkits
Embeddable interpreters for custom projects

In 2014, Fernando Pérez announced the separation of the language-agnostic parts of IPython into a new project called Project Jupyter. IPython continues as the Python kernel for Jupyter, while Jupyter expanded support to other languages like Julia, R, Haskell, and Ruby.

Starting Jupyter Notebook

Open Anaconda Navigator

Launch Jupyter Notebook from Navigator or by typing jupyter notebook in Anaconda Prompt/Terminal

Once opened in your browser, navigate to your desired working directory
Click on “New” and select Python 3 Notebook (appears as Untitled)

Importing Matplotlib

In a Jupyter Notebook cell, import the necessary Matplotlib library:

    import matplotlib.pyplot as plt

    %matplotlib inline

The magic command %matplotlib inline ensures that plots appear directly below the code cells within the notebook.

Creating a Line Plot

Let’s use NumPy and Matplotlib to create a simple sine wave plot:

    import numpy as np

    import matplotlib.pyplot as plt

    # Generating sample data

    x = np.linspace(0, 20, 200)

    y = np.sin(x)

    # Plotting the data

    plt.figure(figsize=(8, 4))

    plt.plot(x, y, label=’sin(x)’)

    plt.title(‘Sine Wave’)

    plt.xlabel(‘x’)

    plt.ylabel(‘sin(x)’)

    plt.legend()

    plt.grid(True)

    plt.show()

Output:

Interactive Plots

By default, %matplotlib inline generates static plots. To allow panning, zooming, and interactivity, replace it with:

    %matplotlib notebook
  

Working with Multiple Plots and Markdown

Create new plots in separate notebook cells.
Use Markdown cells to describe your code and plots for better readability.

Saving Your Plots

To save a plot as an image file (e.g., PNG):

    plt.savefig(‘plot.png’)
  

Closing Jupyter Notebook

After completing your work, you can shut down the notebook from the browser interface or close the terminal/Anaconda prompt that launched it.

Hiding Matplotlib Descriptions

To avoid seeing object descriptions like [<matplotlib.lines.Line2D at ...>], use either of the following:

    plt.plot(x);

    # or

    _ = plt.plot(x)

Example

    import numpy as np

    from matplotlib import pyplot as plt

    x = np.linspace(1, 10, 1000)

    plt.plot(x)

    plt.show()