sssilva1980
52e38435fa
Add a comprehensive README that explains how to prepare a Python virtual environment, install packages, and register an ipykernel for running the lab notebooks in VS Code. Include two starter notebooks: TASK0_Datasaurus_Starter.ipynb (datasaurus warm-up with data loading, summary stats, optional SweetViz report, and faceted scatter plotting) and VisInt_Lab_01_Task_0.ipynb (lab header/metadata). Add .gitignore to exclude a local /.venv directory.
162 lines
3.5 KiB
Markdown
162 lines
3.5 KiB
Markdown
# VI_Lab_01_EDA
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pip install ipykernel
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***
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Below is a clean, ready‑to‑ship **README.md** you can drop directly into your ZIP bundle.
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It explains **exactly how students should prepare their environment in VS Code**, including:
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* Installing Python
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* Creating a **virtual environment**
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* Installing required packages
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* Setting up the **Jupyter kernel** to use that venv
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* Opening and running the notebooks in VS Code
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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).
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If you want, I can also generate a **requirements.txt**, **environment.yml**, or a **bootstrap script**.
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***
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# 📘 README — Preparing Your Environment for Jupyter in VS Code
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## (Virtual Environment + Kernel Setup)
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This guide explains exactly how to prepare your system to run the EDA lab notebooks in **VS Code** using a clean Python **virtual environment**.
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The steps work on **Windows, macOS, and Linux**.
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***
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# 1. Install the Required Tools
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### 1.1 Install Python (3.9+ recommended)
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Download from the official Python site (*python.org*) or using Microsoft Store.
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Make sure to check:
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* **Windows** → Add Python to PATH if installed from official site
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* **macOS/Linux** → Python is usually included, but upgrade if needed
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### 1.2 Install VS Code
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Install from the official VS Code site.
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### 1.3 Install VS Code Extensions
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Open VS Code → **Extensions Panel** → install:
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* **Python**
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* **Jupyter**
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These two extensions enable:
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* Notebook execution
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* Kernel selection
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* Virtual environment detection
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* Interactive cells
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***
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# 2. Create a Virtual Environment
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Choose a folder where you will store your lab materials.
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Open a terminal *inside that folder*:
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### **Windows (PowerShell)**
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```powershell
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python -m venv venv
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.\venv\Scripts\activate
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```
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### **macOS / Linux**
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```bash
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python3 -m venv venv
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source venv/bin/activate
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```
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You should now see `(venv)` at the start of your terminal prompt.
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***
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# 3. Install Required Python Packages
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Inside the active virtual environment, run:
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```bash
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pip install numpy pandas matplotlib sweetviz dtale jupyter
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```
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If you are using the Task 0 datasets, also install:
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```bash
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pip install seaborn
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```
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> 💡 **Tip:**
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> If you have a `requirements.txt` in the bundle, run:
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>
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> ```bash
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> pip install -r requirements.txt
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> ```
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***
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# 4. Register the Virtual Environment as a Jupyter Kernel
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VS Code can automatically detect your venv, but we ensure explicit registration:
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```bash
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python -m ipykernel install --user --name eda-env --display-name "EDA Lab Environment"
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```
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You will now see **EDA Lab Environment** as a selectable kernel inside VS Code notebooks.
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***
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# 5. ✅ Open the Lab in VS Code
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1. Launch **VS Code**
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2. Use **File → Open Folder** and choose the folder containing the lab files
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3. Open any `.ipynb` file (e.g., `EDA_Lab_Starter.ipynb`)
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4. At the top‑right corner of the notebook, click the **kernel selector**
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5. Choose:
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**EDA Lab Environment (Python venv)**
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This ensures the notebook runs using the correct interpreter.
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***
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# 6. 🔍 (Optional) Verify Your Setup
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In a notebook cell, run:
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```python
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import sys
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sys.executable
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```
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It should show the Python path inside your `venv`, e.g.:
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* Windows: `…/venv/Scripts/python.exe`
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* macOS/Linux: `…/venv/bin/python`
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Then check that the packages are available:
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```python
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import pandas, sweetviz, dtale
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print("Environment OK")
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```
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