How to Install Jupyter Notebook: Step-by-Step Guide for Windows (2026)

Jupyter Notebook is an open-source web-based platform that allows you to write and run code in an interactive environment.

*docs.jupyter.org

It is widely used for:

• Python programming
• Data analysis
• Machine learning
• AI experimentation
• Research and education

Unlike traditional coding tools, Jupyter Notebook lets you combine:

• Code execution
• Visual outputs (charts, graphs)
• Text explanations

—all in a single interface.

In today’s AI-driven world, tools like Jupyter Notebook are not optional—they are essential.

Key reasons professionals use Jupyter:

• Supports major programming languages (Python, R, Julia)
• Works seamlessly with AI libraries like TensorFlow, PyTorch
• Ideal for rapid prototyping and experimentation
• Used in industries like finance, healthcare, marketing, and tech

Real-world example:

A data scientist can:

• Import a dataset
• Clean and transform data
• Visualise insights
• Train machine learning models

—all within a single notebook.

There are two primary methods:

This is the most beginner-friendly method and highly recommended if you’re new to Python or data science.

Step 1: Download Anaconda

• Visit the official Anaconda website
• Download the latest version for Windows (Python 3.x)
• Choose the 64-bit installer

Step 2: Install Anaconda

Follow these steps carefully:

• Open the downloaded installer
• Click Next → Agree
• Select Just Me (recommended)
• Choose installation location
• Optionally enable Add Anaconda to PATH

Step 3: Complete Installation

Once installation finishes:

• Open Anaconda Navigator
• Locate Jupyter Notebook
• Click Launch

Step 4: Launch via Command Prompt (Optional)

You can also launch Jupyter Notebook manually:

jupyter notebook

Here’s how Jupyter Notebook looks post installation:

After launching:

• A browser window opens
• You see a file directory
• You can create a new notebook
• Code runs inside cells

If you already have Python installed, this method is faster and more flexible.

Step 1: Install Python

• Download Python 3.8 or above
• Ensure Add Python to PATH is checked

Step 2: Open Command Prompt

Press:

Windows + R → cmd → Enter

Step 3: Install Jupyter Notebook

Run:

pip install notebook

Step 4: Launch Jupyter Notebook

jupyter notebook

Modern best practices recommend using virtual environments.

Why?

Here’s why:

• Avoid dependency conflicts
• Manage multiple projects easily
• Keep environments isolated

Create a virtual environment:

Here’s how:

python -m venv myenv

Activate it:

myenv\Scripts\activate

Then install Jupyter:

pip install notebook

Many learners immediately want to move into AI after installation.

Here’s how to install TensorFlow.

Step 1: Install TensorFlow

pip install tensorflow

Step 2: Verify Installation

Open Jupyter Notebook and run:

import tensorflow as tf

print(tf.__version__)

Step 3: Add TensorFlow Kernel (Optional Advanced Step)

If using multiple environments:

pip install ipykernel

python -m ipykernel install –user –name=myenv

This allows you to switch environments inside Jupyter.

Many learners install tools but don’t know how to use them effectively.

A smart next step is to explore a free introductory course in Python or Data Science. You can check out this one on Python for Data Analysis by Jaro Education.

This helps you:

• Practice coding inside Jupyter Notebook
• Understand real-world workflows
• Build foundational skills before advanced topics

Even in 2026, installation errors are extremely common.

1. Python Not Recognised

Cause: Python not added to PATH

Fix:
Reinstall Python and enable “Add to PATH”

2. pip Not Working

python -m ensurepip –upgrade

3. Jupyter Notebook Not Opening

python -m notebook

Or upgrade:

pip install –upgrade notebook

4. Permission Errors

Run Command Prompt as Administrator.

5. Port Already in Use

Use a different port:

jupyter notebook –port=8889

Understanding installation is just the beginning.

Where Jupyter is used:

• Data analysis (Pandas, NumPy)
• Data visualisation (Matplotlib, Seaborn)
• Machine learning (Scikit-learn, TensorFlow)
• Business analytics
• Academic research

Example Scenario:

A business analyst uses Jupyter Notebook to:

• Analyse sales data
• Create dashboards
• Predict future demand

These will help you transition from setup to skill-building.

Here’s a detailed comparison of pip vs Anaconda:

Installing Jupyter Notebook is only the first step.

Recommended Learning Path:

1. Learn Python fundamentals
2. Practice using Jupyter Notebook
3. Work with datasets
4. Learn Pandas and NumPy
5. Explore machine learning
6. Build projects

Mastering Jupyter Notebook opens doors to several career paths.

Roles you can pursue:

• Data Analyst
• Machine Learning Engineer
• AI Engineer
• Business Analyst
• Research Analyst

For working professionals:

Jupyter helps in:

• Automating reports
• Analysing large datasets
• Making data-driven decisions

Here are some pro tips for you if you’re a beginner:

• Always use virtual environments
• Keep Python updated
• Practice daily in Jupyter
• Work on real datasets
• Don’t rely only on theory

Learning how to install Jupyter Notebook is your gateway into data science and AI.

To summarise:

• Use Anaconda for simplicity
• Use pip for flexibility
• Install TensorFlow for machine learning
• Focus on learning, not just setup

Once your environment is ready, the real journey begins—building projects and solving real problems.