JONAS'S GARDEN

Jupyter notebook crashcourse

3 min read

1. What Jupyter Notebook Actually Is

Think of it as an interactive coding document. A notebook contains cells that can include:

  • Code (Python usually)
  • Markdown (formatted text for explanations)
  • Outputs (prints, charts, tables, etc.)

Typical workflow:

Write code → run cell → see result immediately

That’s why it’s great for:

  • AI / ML experiments
  • Algorithm demonstrations
  • Data analysis
  • Teaching / documentation

2. Installing Jupyter

If you use Python with venv

pip install notebook

Run it:

jupyter notebook

Then your browser opens.

pip install jupyterlab

Run:

jupyter lab

JupyterLab is basically the upgraded UI.

3. Creating a Notebook

Create a new notebook:

New → Python 3

It creates a file:

example.ipynb

This is just a JSON file storing code + outputs.

4. Cells (Most Important Concept)

There are two main types.

Code Cell

Runs Python code.

Example:

x = 5
y = 10
x + y

Output:

15

Markdown Cell

For notes and documentation.

Example:

# Title
## Subtitle
**bold**
*italic*

- list item
- list item

5. Essential Shortcuts

These make you fast.

Run cell:

Shift + Enter

Run and create new cell:

Alt + Enter

Command mode:

Esc

Edit mode:

Enter

Create cells:

A → new cell above
B → new cell below

Delete cell:

DD

Change cell type:

M → markdown
Y → code

6. Running Cells

Cells run independently.

Example:

Cell 1:

x = 10

Cell 2:

print(x)

Output:

10

But if you restart kernel and run cell 2 first → error.

This is the biggest beginner mistake.

7. Kernel (Important Concept)

Kernel = the Python runtime executing code.

Controls:

Restart Kernel
Restart & Run All
Interrupt

Use restart when:

  • variables get messy

  • notebook acts weird

8. Displaying Plots

Jupyter shines with visualizations.

Example:

import matplotlib.pyplot as plt
 
x = [1,2,3,4]
y = [1,4,9,16]
 
plt.plot(x,y)
plt.show()

Plot appears inside notebook.

9. Nice Features

Show last value automatically

2 + 2

Output:

4

No print() needed.

Rich outputs

Tables:

import pandas as pd
 
df = pd.DataFrame({
    "name": ["Jonas", "Alex"],
    "score": [90, 85]
})
 
df

Shows a nice formatted table.

10. Typical Folder Structure


notebooks/
    bfs_demo.ipynb
    dfs_demo.ipynb
    astar_visualization.ipynb

src/
    bfs.py
    dfs.py
    astar.py

Use notebooks for:

  • explanations

  • experiments

  • visualization

11. Exporting Notebooks

You can export as:

File → Export As

Options:

  • HTML

  • PDF

  • Python script

Example:

bfs.ipynb → bfs.py

12. Magic Commands (Super Useful)

Special commands starting with %.

Example:

measure runtime

%timeit sum(range(1000))

run external script

%run script.py

list variables

%who

Graph View

Backlinks