# VI_Lab_01_EDA

## Datasaurus dozen
https://cran.r-project.org/web/packages/datasauRus/index.html






pip install ipykernel

***

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**.

***

# 📘 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**.

***

# 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

***

# 2. Create a Virtual Environment

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

### **Windows (PowerShell)**

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

### **macOS / Linux**

```bash
python3 -m venv venv
source venv/bin/activate
```

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

***

# 3. Install Required Python Packages

Inside the active virtual environment, run:

```bash
pip install numpy pandas matplotlib sweetviz dtale jupyter
```

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

```bash
pip install seaborn
```

> 💡 **Tip:**  
> If you have a `requirements.txt` in the bundle, run:
>
> ```bash
> pip install -r requirements.txt
> ```

***

# 4. Register the Virtual Environment as a Jupyter Kernel

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

```bash
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.

***

# 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.

***

# 6. 🔍 (Optional) Verify Your Setup

In a notebook cell, run:

```python
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:

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

