Week 1 - Python Foundations

Overview

Python Setup

Data Types and Variables

Control Flow: Logic and Loops

Functions and Modules

Type Hints for Clearer Code

Command-Line Interface Habits

🐛 Errors and Debugging

📝 Logging in Python

File Operations

Azure Setup and Account Access

🛠️ Practice

🎒 Assignment

⚠️ Gotchas & Pitfalls

🗓️ Lesson Plan

🎒 Week 1 Assignment: The Data Cleaning Pipeline

In this assignment, you will build a robust command-line tool to clean a "messy" dataset. This mimics a very common real-world task for data engineers: taking raw, inconsistent data and transforming it into a clean, usable format.

Task 1 – The Cleaner Pipeline

week_1__messy_users.csv

You have been given a file data/messy_users.csv. It contains user data, but it is full of errors: whitespace issues, inconsistent capitalization, missing fields, and badly formatted numbers.

Instead of one big script, you will build a modular pipeline:

1. src/utils.py: Create functions for cleaning individual fields (e.g., clean_salary, clean_name).
2. src/cleaner.py: The main entry point that uses functions from utils.py to process the entire file.

Cleaning Rules

1. Name: Remove any leading/trailing whitespace.
2. Email: Convert to lowercase.
3. Department: If missing, set to "Unknown".
4. Salary:

• Remove any extra characters like " or , (e.g. "68,000" should become integer 68000).
• Convert to an integer.

1. Validation:

• If the name is empty (after cleaning), skip the row and log a warning.
• If the email is empty, skip the row and log a warning.

Technical Requirements

• Modularity: Your utils.py should only contains functions. Your cleaner.py must import these functions.
• Main Guard: Use if __name__ == "__main__": in cleaner.py.
• Path Handling: Avoid hardcoding paths like data/file.csv. Use the pathlib module to build paths that work on all OSs.
• CLI Arguments: The script must accept input and output file paths as command-line arguments.
• Logging: Use the logging module (not print) to report progress. Add INFO logs for status and WARNING logs for skips.
• Type Hinting: All functions must have type hints.
• Error Handling: Use try/except blocks to handle file not found errors gracefully.

Task 2 – AI Debugging Report

We want you to practice using AI as a tool for debugging, not just generating code.

1. Introduce a bug into your code intentionally (or use a real one you encountered).
2. Ask an LLM (ChatGPT, Claude, etc.) to help you fix it.
3. Create a file AI_DEBUG.md and document:

• The Error: What went wrong? (Paste the traceback).
• The Prompt: What did you ask the AI?
• The Solution: What did the AI suggest? Did it work?
• Reflection: Did you understand why it was broken?

Task 3 – Azure Setup

Data engineering often happens in the cloud. We need to verify you are ready for the upcoming cloud modules.

1. Log into portal.azure.com.
2. Take a screenshot of the portal dashboard showing your account logged in.
3. Save the image as assets/azure_proof.png.

Submission

1. Ensure your project structure looks like this:

    week1-assignment/
    ├── src/
    │   ├── cleaner.py
    │   └── utils.py
    ├── data/
    │   └── messy_users.csv
    ├── output/
    │   └── (clean_users.json will be generated here)
    ├── assets/
    │   └── azure_proof.png
    ├── AI_DEBUG.md
    └── README.md

1. Create a git branch week1/your-name.
2. Commit your changes.
3. Push to the repository and open a Pull Request.

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