Using Matplolib with Jupyter Notebook

Using Matplotlib with Jupyter Notebook: A Complete Guide

Matplotlib is a widely used Python library for creating static, animated, and interactive plots. When paired with Jupyter Notebook, it becomes an incredibly powerful tool for exploratory data analysis and visualization. This guide will walk you through how to effectively use Matplotlib within Jupyter environments, from basic setup to interactive plotting.

Installing Matplotlib

If you haven’t already installed Matplotlib, you can easily do so using pip or conda, depending on your environment.

# Using pip
pip install matplotlib

# Using conda (Anaconda users)
conda install matplotlib

Starting Jupyter Notebook

To open Jupyter Notebook, type the following command in your terminal or command prompt:

jupyter notebook

This will launch a new notebook interface in your default web browser.

Importing Matplotlib in the Notebook

Once inside your notebook, you need to import Matplotlib's pyplot module, which offers simple commands to make various plots.

import matplotlib.pyplot as plt

Rendering Plots Inline

To display plots directly inside the notebook, use the magic command %matplotlib inline. This is generally placed at the top of your notebook:

%matplotlib inline

If you want interactive plots (zoom, pan, etc.), you can use:

%matplotlib notebook

Creating a Simple Line Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 12, 8, 6, 4]

plt.plot(x, y)
plt.title('Simple Line Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

Other Plot Types in Jupyter Notebook

Matplotlib supports a variety of charts. Below are a few commonly used plot types:

Bar Plot

categories = ['A', 'B', 'C']
values = [5, 7, 3]

plt.bar(categories, values)
plt.title('Bar Plot Example')
plt.show()

Scatter Plot

x = [1, 2, 3, 4, 5]
y = [2, 5, 4, 7, 6]

plt.scatter(x, y)
plt.title('Scatter Plot Example')
plt.show()

Histogram

data = [1,2,2,3,3,3,4,4,4,4,5,5,5]

plt.hist(data, bins=5)
plt.title('Histogram Example')
plt.show()

Saving Plots in Jupyter Notebook

Matplotlib also allows you to save your plots as image files using savefig():

plt.plot([1, 2, 3], [4, 5, 6])
plt.title('Saving Plot Example')
plt.savefig('output_plot.png')

This will save the plot as a PNG image in the current working directory.

Switching Back to Static Mode

If you’ve enabled interactive mode with %matplotlib notebook and want to switch back to static inline plots, simply run:

%matplotlib inline

Conclusion

Combining Matplotlib with Jupyter Notebook provides a flexible and interactive environment for visualizing data. Whether you're analyzing datasets, building reports, or teaching, this integration enhances your workflow by making your plots more dynamic, readable, and shareable.