Structuring Your First Multi-File Python Project

Organize a Python sales analysis project with separate folders for code, data, and output — the same layout professionals use every day.

Writing all your Python in a single file is fine for small experiments, but real projects grow quickly and need a structure that keeps things findable. Separating your source code, raw data files, and generated outputs into dedicated folders means you always know where to look — and so does anyone else who picks up your project. In this page, you’ll set up a simple but professional layout for a sales analysis project inside your existing workspace.

Organize your workspace

You already have a python-for-ai workspace from earlier in the course. Open it in VS Code and add a dedicated project folder with this layout:

python-for-ai/
├── hello.py                 # Your existing practice file
└── sales-analysis/          # New project folder
    ├── data/                # CSV files and raw input data
    └── output/              # Generated reports and results

Create folders in VS Code by right-clicking in the Explorer panel and selecting New Folder, or by clicking the new-folder icon at the top of the Explorer pane.

Create the data file

Inside sales-analysis/data/, create a file called sales.csv with the following content:

date,product,quantity,price
2024-01-01,Laptop,2,999.99
2024-01-01,Mouse,5,29.99
2024-01-02,Keyboard,3,79.99
2024-01-02,Monitor,1,299.99
2024-01-03,Laptop,1,999.99
2024-01-03,Mouse,10,29.99
2024-01-04,Keyboard,2,79.99
2024-01-05,Monitor,2,299.99

Copy and paste this data directly into a new file. Make sure the file extension is .csv, not .txt — VS Code may try to add .txt if you’re not careful.

Understanding file paths in your project

When your script runs, Python looks for files relative to the folder it’s running from. If you keep your script in the sales-analysis/ folder, these paths will work:

# Paths are relative to where your script lives
"data/sales.csv"        # The CSV file in the data subfolder
"output/report.json"    # Where to write results

# Your layout makes this straightforward:
# python-for-ai/
#   └── sales-analysis/
#       ├── analyzer.py     ← script runs from here
#       ├── data/sales.csv  ← data is here
#       └── output/         ← results go here

Create your first script

In the sales-analysis/ folder (not inside a subfolder), create analyzer.py:

import os

# Confirm you're running from the right place
print("Current directory:", os.getcwd())

# Verify the data file is where you expect it
data_path = "data/sales.csv"
if os.path.exists(data_path):
    print(f"✅ Found {data_path}")
else:
    print(f"❌ Cannot find {data_path}")
    print("Make sure you're running from the sales-analysis folder!")

Running your code

VS Code gives you two convenient ways to run Python code:

Option 1: The Play button

1. Open analyzer.py in VS Code
2. Click the ▶️ button in the top-right corner
3. Output appears in the terminal below

Option 2: Interactive mode (recommended for learning)

1. Open analyzer.py in VS Code
2. Place your cursor on any line (or select a block)
3. Press Shift + Enter
4. The code runs in an interactive window and you see results immediately

Interactive mode lets you run code line by line and inspect variables as you go. It’s excellent for understanding what each step does and debugging as you write.

Keeping analyzer.py at the top of your project folder — alongside data/ and output/ — means file paths stay simple and consistent whether you use the Play button or interactive mode.

Best practices

1. Keep data separate — Never mix your Python scripts and your data files in the same folder
2. Use clear names — data/ for inputs, output/ for results makes the project self-documenting
3. Put scripts at the project root — Your main script should sit at the same level as data/ and output/
4. Test incrementally — Use Shift + Enter to run and verify each section as you write it

Python paths

Learn how Python finds your files and imports