Matplotlib - Jupyter Notebook

Jupyter is a loose acronym meaning Julia, Python, and R. These programming languages were the first target languages of the Jupyter application, but nowadays, the notebook technology also supports many other languages.

In 2001, Fernando Pérez started developing Ipython. IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python.

Matplotlib in Jupyter Notebook provides an interactive environment for creating visualizations right alongside our code. Let's go through the steps to start using Matplotlib in a Jupyter Notebook.

Matplotlib library in Jupyter Notebook provides a convenient way to visualize data interactively by allowing for an exploratory and explanatory workflow when working on data analysis, machine learning or any other Python-based project.

Consider the following features provided by IPython −

Interactive shells (terminal and Qt-based).
A browser-based notebook with support for code, text, mathematical expressions, inline plots and other media.
Support for interactive data visualization and use of GUI toolkits.
Flexible, embeddable interpreters to load into one's own projects.

In 2014, Fernando Pérez announced a spin-off project from IPython called Project Jupyter. IPython will continue to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython will move under the Jupyter name. Jupyter added support for Julia, R, Haskell and Ruby.

Starting Jupyter Notebook

The below are the steps to be done by one by one to work in the Jupyter Notebook.

Launch Jupyter Notebook

Open Anaconda Navigator.

Launch Jupyter Notebook from the Navigator or in the terminal/Anaconda Prompt type jupyter notebook and hit Enter.

Create or Open a Notebook

Once Jupyter Notebook opens in our web browser then navigate to the directory where we want to work.

After click on "New" and choose a Python notebook which is often referred to as an "Untitled" notebook.

Import Matplotlib

In a Jupyter Notebook cell import Matplotlib library by using the lines of code.

import matplotlib.pyplot as plt
%matplotlib inline

%matplotlib inline is a magic command that tells Jupyter Notebook to display Matplotlib plots inline within the notebook.

Create Plots

We can now use Matplotlib functions to create our plots. For example let’s create a line plot by using the numpy data.

Example

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

Interact with Plots

Once the plot is generated then it will be displayed directly in the notebook below the cell. We can interact with the plot i.e. panning, zooming can be done if we used %matplotlib notebook instead of %matplotlib inline at the import stage.

Multiple Plots

We can create multiple plots by creating new cells and running more Matplotlib commands.

Markdown Cells

We can add explanatory text in Markdown cells above or between code cells to describe our plots or analysis.

Saving Plots

We can use plt.savefig('filename.png') to save a plot as an image file within our Jupyter environment.

Closing Jupyter Notebook

Once we have finished working in the notebook we can shut it down from the Jupyter Notebook interface or close the terminal/Anaconda Prompt where Jupyter Notebook was launched.

Hide Matplotlib descriptions in Jupyter notebook

To hide matplotlib descriptions of an instance while calling plot() method, we can take the following steps

Open Ipython instance.
import numpy as np
from matplotlib, import pyplot as plt
Create points for x, i.e., np.linspace(1, 10, 1000)
Now, plot the line using plot() method.
To hide the instance, use plt.plot(x); i.e., (with semi-colon)
Or, use _ = plt.plot(x)

Example

In this example we are hiding the description code.

import numpy as np
from matplotlib import pyplot as plt
x = np.linspace(1, 10, 1000)
plt.plot(x)
plt.show()

Output

[<matplotlib.lines.Line2D at 0x1f6d31d9130>]

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