VI_Lab_01_EDA

pip install ipykernel

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Below is a clean, ready‑to‑ship README.md you can drop directly into your ZIP bundle.
It explains exactly how students should prepare their environment in VS Code, including:

• Installing Python
• Creating a virtual environment
• Installing required packages
• Setting up the Jupyter kernel to use that venv
• Opening and running the notebooks in VS Code

It uses current and correct instructions based on official VS Code documentation (Python + Jupyter extensions and venv usage) (installation workflow and environment activation practices align with Python & VS Code official practices, which are stable across versions).

If you want, I can also generate a requirements.txt, environment.yml, or a bootstrap script.

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📘 README — Preparing Your Environment for Jupyter in VS Code

(Virtual Environment + Kernel Setup)

This guide explains exactly how to prepare your system to run the EDA lab notebooks in VS Code using a clean Python virtual environment.

The steps work on Windows, macOS, and Linux.

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1. Install the Required Tools

1.1 Install Python (3.9+ recommended)

Download from the official Python site (python.org) or using Microsoft Store.

Make sure to check:

• Windows → Add Python to PATH if installed from official site
• macOS/Linux → Python is usually included, but upgrade if needed

1.2 Install VS Code

Install from the official VS Code site.

1.3 Install VS Code Extensions

Open VS Code → Extensions Panel → install:

• Python
• Jupyter

These two extensions enable:

• Notebook execution
• Kernel selection
• Virtual environment detection
• Interactive cells

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2. Create a Virtual Environment

Choose a folder where you will store your lab materials.
Open a terminal inside that folder:

Windows (PowerShell)

python -m venv venv
.\venv\Scripts\activate

macOS / Linux

python3 -m venv venv
source venv/bin/activate

You should now see (venv) at the start of your terminal prompt.

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3. Install Required Python Packages

Inside the active virtual environment, run:

pip install numpy pandas matplotlib sweetviz dtale jupyter

If you are using the Task 0 datasets, also install:

pip install seaborn

💡 Tip:
If you have a requirements.txt in the bundle, run:

pip install -r requirements.txt

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4. Register the Virtual Environment as a Jupyter Kernel

VS Code can automatically detect your venv, but we ensure explicit registration:

python -m ipykernel install --user --name eda-env --display-name "EDA Lab Environment"

You will now see EDA Lab Environment as a selectable kernel inside VS Code notebooks.

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5. ✅ Open the Lab in VS Code

1. Launch VS Code
2. Use File → Open Folder and choose the folder containing the lab files
3. Open any .ipynb file (e.g., EDA_Lab_Starter.ipynb)
4. At the top‑right corner of the notebook, click the kernel selector
5. Choose:
   EDA Lab Environment (Python venv)

This ensures the notebook runs using the correct interpreter.

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6. 🔍 (Optional) Verify Your Setup

In a notebook cell, run:

import sys
sys.executable

It should show the Python path inside your venv, e.g.:

• Windows: …/venv/Scripts/python.exe
• macOS/Linux: …/venv/bin/python

Then check that the packages are available:

import pandas, sweetviz, dtale
print("Environment OK")