Jupyter Notebook Interface

What is Jupyter Notebook?

Jupyter Notebook is an interactive environment where you can mix:

Code (Python)
Text/notes (Markdown)
Outputs (tables, charts, images)

It’s widely used for:

Exploratory data analysis (EDA)
Learning and experimentation
Quick visualization
Sharing analysis as a single document

Launching Jupyter Notebook

With Anaconda

Open Anaconda Navigator
Choose your environment (optional)
Click Launch on Jupyter Notebook

From terminal

Activate your environment first, then run:

command

jupyter notebook

A browser tab opens with the Notebook dashboard.

The Notebook dashboard (files home)

The first screen usually shows:

A list of files/folders
Buttons to create new notebooks

Recommended structure for your data analytics projects:

A folder per project (e.g., titanic-eda/)
A data/ folder for CSV files
A notebooks/ folder for .ipynb notebooks
A reports/ folder for exported HTML/PDF (optional)

Creating a new notebook

From the dashboard:

Click New → Python 3 (ipykernel) (or similar)

A new .ipynb opens in a new tab.

Understanding cells

Cells are the core concept of notebooks.

Code cell

Runs Python code
Produces outputs

Example:

example

import math
math.sqrt(81)

Markdown cell

Used for notes and documentation
Supports headings, lists, links, tables, etc.

Example (Markdown cell):

markdown

# Titanic EDA
This notebook explores passenger survival factors.

Running cells

Run current cell: Shift + Enter
Run and insert new cell below: Alt + Enter
Run without moving: Ctrl + Enter

When a cell runs:

In [1]: indicates the execution order
Outputs appear below the cell

Kernels (the “engine” behind the notebook)

A kernel is the process that runs your code.

Key idea:

If you restart the kernel, you lose variables in memory.
You must run cells again to recreate them.

Common kernel actions:

Restart: clears memory and restarts Python
Interrupt: stops long-running code

In the menu:

Kernel → Interrupt
Kernel → Restart

Saving notebooks

A notebook is stored as a .ipynb file (JSON-based format).

Save: Ctrl + S
Rename: click the title at the top

Visualize it

Each time you run a cell, the kernel executes it and hands the result back to the notebook document.

diagram Notebook cell execution flow mermaid

  flowchart LR
  A["Write code in a cell"] --> B["Press Shift+Enter"]
  B --> C["Kernel executes code"]
  C --> D["Kernel returns result"]
  D --> E["Output shown below the cell"]
  D --> F["In [n] execution count updates"]

What happens between pressing Shift+Enter and seeing output

A recommended data analytics notebook workflow

Import libraries (NumPy, Pandas, Matplotlib)
Load data (CSV/Excel/SQL)
Inspect data (head(), info(), describe())
Clean data (missing values, duplicates, types)
Analyze (groupby, aggregates)
Visualize (plots)
Summarize findings (Markdown cells)

Helpful settings

Auto-reload imports (optional)

In a notebook, you can auto-reload local modules:

autoreload

%load_ext autoreload
%autoreload 2

Continue to: Jupyter Shortcuts & Magic Commands to speed up your workflow and learn % / %% notebook magics.