10 Jupyter Notebooks#

Goal#

Learn how to use Jupyter Notebooks for interactive coding, data exploration, and scientific communication. Notebooks combine code, text, visualizations, and equations in a single document.

Prerequisites#

1. Introduction#

A Jupyter Notebook is an interactive environment where you run Python code in “cells” and see results immediately. Unlike regular Python scripts, notebooks let you:

Write code in small chunks and run them independently
Visualize plots and data directly in the notebook
Mix code with formatted text (markdown), equations, and images
Tell a story with your data analysis
Share your work easily (notebooks are just JSON files viewable on GitHub)

Jupyter is widely used in scientific research, data science, and education.

2. Installation and Setup#

2.1 Install Jupyter and IPython#

Jupyter usually comes with Anaconda/Miniconda. If you need to install it:

pip install jupyter jupyterlab

2.2 Launch Jupyter#

From your terminal, navigate to your project directory and run:

jupyter notebook

or for the newer interface:

jupyter lab

Your browser will open to localhost:8888 showing your file browser. If you have VS Code, you can also run notebooks directly in the editor (see X Code Editors and IDEs).

3. Notebook Basics#

Creating a New Notebook#

Click New button and select Python 3
A new notebook will open with an empty code cell

Cell Types#

Code Cells - Contains Python code:

x = 5
y = 3
print(x + y)

Run with Shift+Enter or the ▶ button.

Markdown Cells - Contains formatted text:

# Section Title
This is **bold** text. Here's a [link](https://example.com).

Writing and Organizing Cells#

Add a cell: Click the + button in the toolbar
Change cell type: Use the dropdown (Code/Markdown)
Run a cell: Press Shift+Enter (runs and moves to next) or Ctrl+Enter (stays in cell)
Delete a cell: Press D twice

Output and Variables#

Variables persist across cells:

Cell 1:

name = "Alice"
age = 25

Cell 2:

print(f"{name} is {age} years old")  # Works! Prints "Alice is 25 years old"

4. Markdown in Notebooks#

Use Markdown cells to document your analysis:

# Main Title

## Subsection

This is regular text with **bold**, *italic*, and `code`.

### Lists
- Item 1
- Item 2

### Equations (LaTeX)
Inline equation: $E = mc^2$

Block equation:
$$
\int_0^{\infty} e^{-x^2} dx = \frac{\sqrt{\pi}}{2}
$$

5. Displaying Plots and Data#

Inline Plotting#

import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.show()

The plot appears directly in the notebook below the cell.

DataFrames#

import pandas as pd

data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)
print(df)  # Displays as a formatted table

6. Magic Commands#

Jupyter has special commands (starting with %) for convenience:

# Show execution time
%timeit sum(range(100))

# Display plots inline (usually automatic)
%matplotlib inline

# Run a Python script
%run my_script.py

# List all variables
%whos

7. Keyboard Shortcuts#

Shortcut	Action
Shift+Enter	Run cell and move to next
Ctrl+Enter	Run cell and stay
A	Insert cell above
B	Insert cell below
D, D	Delete cell
M	Change to Markdown
Y	Change to Code
Ctrl+/	Toggle comment

8. Best Practices#

Organize Your Notebook#

Start with a markdown cell describing the purpose
Import all libraries at the top
Use markdown cells to separate sections
Add descriptive comments to complex code

Example Structure#

[Markdown] Title and Overview
[Code] Import libraries
[Markdown] Data Loading Section
[Code] Load data
[Markdown] Data Exploration
[Code] Visualize data
[Markdown] Analysis
[Code] Perform calculations

Checkpoint: Restart and Run All#

Occasionally click Kernel → Restart & Run All to ensure your notebook works from scratch (not relying on out-of-order execution).

10. Troubleshooting#

Kernel seems stuck? Click Kernel → Interrupt or restart with Kernel → Restart
Variables not showing? You might be in the wrong kernel (check top right)
Code not running? Make sure you’re in a Code cell, not Markdown
Import errors? Make sure the package is installed in your active environment

Next Steps#

Now that you can run Python interactively, let’s learn about powerful data and visualization libraries: 11 NumPy and Pandas.