VI_Lab_01_EDA/deploy

Lab 02 — Environment Setup

This document explains how to set up your Python environment and install all required packages before the lab session.

---

Requirements

• Python 3.10 or higher (3.11 recommended or live wildly and go for the latest one. I have not tested it...)
• pip (comes bundled with Python)
• A code editor with Jupyter notebook support — VS Code with the Jupyter extension is recommended

---

Step 1: Create a virtual environment

It is strongly recommended to work inside a virtual environment to avoid conflicts with other Python projects on your machine.

Open a terminal in the folder where you will work and run:

# Create the environment (only needed once)
python -m venv .venv

Then activate it:

# On Windows
.venv\Scripts\activate

# On macOS / Linux
source .venv/bin/activate

You should see (.venv) appear at the start of your terminal prompt. You need to activate the environment every time you open a new terminal.

---

Step 2: Install required packages

With the environment active, run the following commands:

# Core data libraries
pip install "numpy<2.0"
pip install pandas matplotlib seaborn

# Automated EDA and profiling
pip install sweetviz

# Interactive dataframe explorer
pip install dtale

# Jupyter notebook support
pip install notebook ipykernel

Why numpy<2.0? Several packages (including dtale and sweetviz) are not yet fully compatible with NumPy 2.x. Pinning to a 1.x version avoids runtime errors that can be difficult to diagnose.

Alternatively, you can install everything in a single command:

pip install "numpy<2.0" pandas matplotlib seaborn sweetviz dtale notebook ipykernel

---

Step 3: Verify the installation

Run the following in a terminal (with the environment active) to confirm everything is working:

python -c "
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sweetviz as sv
import dtale
import numpy as np
print('numpy   :', np.__version__)
print('pandas  :', pd.__version__)
print('seaborn :', sns.__version__)
print('sweetviz: OK')
print('dtale   : OK')
print('All packages installed successfully.')
"

---

Step 4: D-Tale in VS Code (Windows)

D-Tale opens in a browser tab via a local server. On Windows, VS Code may not automatically forward the port if D-Tale binds to a network adapter other than the loopback address. All lab notebooks already include the correct launch code:

d = dtale.show(df, host='127.0.0.1', subprocess=False, open_browser=False)
print('Open D-Tale at:', d._url)

If the URL does not open automatically, copy it from the output and paste it into your browser. If the page does not load, check the Ports panel at the bottom of VS Code and confirm port 40000 is being forwarded.

---

Files for this lab

File	Description
lab01_task1_datasets.ipynb	Task 1 — Datasaurus Dozen: why visualisation is essential
lab01_task2_telemetry.ipynb	Task 2 — Guided EDA and cleaning of game telemetry data
lab01_task3_git_activity.ipynb	Task 3 — Independent EDA and cleaning of Git classroom activity data
datasaurus.csv	Dataset for Task 1
dataset_A_indie_game_telemetry.csv	Dataset for Task 2
dataset_D_git_classroom_activity.csv	Dataset for Task 3

---

Troubleshooting

ModuleNotFoundError when running a notebook
The notebook is using a different Python kernel, not the one from your virtual environment. In VS Code, click the kernel name in the top right of the notebook and select Python (lab02).

NumPy version conflict errors
Make sure you installed numpy<2.0 as described in Step 2. If you already have a newer version, downgrade with:

pip install "numpy<2.0" --force-reinstall