Vibe Coding in Python:
Build Apps & Scripts with AI

Python is the undisputed king of AI vibe coding. Every major AI model — ChatGPT, Claude, Gemini, DeepSeek — was trained on enormous amounts of Python. The result: AI-generated Python is clean, readable, and almost always runs first-try. Its plain-English syntax means you can read and understand the code even if you've never written a line before.

What makes Python ideal for vibe coders: a massive ecosystem of libraries for every task imaginable (web, data, automation, AI, scraping), a simple environment setup, and the fact that scripts can be as short as five lines and still do something genuinely useful.

What You Can Build with Vibe Coding in Python

Setting Up Your Python Vibe Coding Environment

1. Install Python 3.11+

   Download from python.org. On Windows, tick "Add Python to PATH" during install. On Mac, Homebrew is easiest: brew install python. Verify with python --version in your terminal.

2. Install VS Code + Python extension

   Download VS Code, then install the Python extension by Microsoft from the Extensions panel. This gives you syntax highlighting, linting, debugging, and one-click run buttons.

3. Install GitHub Copilot (in-editor AI)

   Install the GitHub Copilot extension in VS Code. Sign in with GitHub (free 30-day trial, then $10/month). Copilot auto-completes entire functions as you type and explains code on demand — it's the fastest in-editor Python pair programmer available.

4. Create a virtual environment for each project

   In your project folder: python -m venv venv, then activate it (source venv/bin/activate on Mac/Linux, venv\Scripts\activate on Windows). Always work inside a virtual environment — it keeps your project dependencies isolated and reproducible.

5. Set up a ChatGPT or Claude tab alongside VS Code

   Keep ChatGPT or Claude open in a browser tab. Use it to generate full scripts, debug errors, and explain unfamiliar library usage. Paste errors directly into chat to get instant fixes.

⚠️ Affiliate disclosure: Some links on this page may be affiliate links. We may earn a small commission at no cost to you. We only recommend tools we actively use and evaluate.

Copy-Paste Python Vibe Coding Prompts

These prompts are optimised for ChatGPT (GPT-5.4), Claude Opus 4.6, and DeepSeek-V3. Use them on the web or via Copilot in VS Code.

Automation Script — CSV Processor

Write a Python script that processes a CSV file of customer records.

Requirements:
- Input: customers.csv with columns: name, email, signup_date, plan (free/pro/enterprise), monthly_revenue
- Output: summary_report.csv and a printed summary to the console
- Tasks the script must perform:
  1. Clean data: strip whitespace from all string fields, normalise email to lowercase
  2. Parse signup_date as a date
  3. Calculate days_since_signup for each customer
  4. Group by plan and calculate: count, total_revenue, average_revenue per group
  5. Write cleaned + enriched data to summary_report.csv
  6. Print a formatted table to console showing plan breakdown

Use Python standard library + pandas only.
Include error handling for missing file and malformed dates.
Add docstrings and type hints throughout.
Output: single file report_processor.py + requirements.txt

FastAPI REST API

Build a complete FastAPI REST API for a task management app.

Requirements:
- Python 3.11 + FastAPI + SQLModel (SQLite, auto-created on startup)
- Task model: id, title, description, status (todo/in_progress/done), priority (low/medium/high), created_at, due_date (optional)
- Endpoints:
  GET /tasks — list all, supports ?status=todo and ?priority=high filters
  GET /tasks/{id} — get one or 404
  POST /tasks — create
  PATCH /tasks/{id} — update fields (partial update)
  DELETE /tasks/{id} — delete or 404
  GET /tasks/stats — return count by status and by priority
- Pydantic schemas: TaskCreate, TaskUpdate, TaskResponse
- CORS enabled for localhost
- Include startup event to create tables

Output all files with paths. Include requirements.txt.
Add docstrings. Use clear type hints throughout.

Streamlit Data Dashboard

Create a Streamlit dashboard for analysing monthly sales data.

Requirements:
- Input: upload a CSV with columns: date, product, category, units_sold, revenue, region
- Sidebar filters: date range (date_input), category (multiselect), region (multiselect)
- Dashboard sections:
  1. KPI row: total revenue, total units, avg order value, top product (st.metric cards)
  2. Revenue over time — line chart (Plotly, grouped by category)
  3. Revenue by region — horizontal bar chart
  4. Top 10 products — sortable table with revenue + units columns
  5. Category breakdown — pie chart
- Filters must affect all charts simultaneously
- Handle empty filter results gracefully (show "No data" message)

Use: streamlit, pandas, plotly.express
Output: app.py + requirements.txt
Make the layout clean and professional (use st.columns, st.container).

Web Scraper

Write a Python web scraper for product listings.

Requirements:
- Target: scrape product name, price, rating, and review count from a product listing page
- Use requests + BeautifulSoup4
- Add a 1–2 second random delay between requests (to be polite)
- Handle HTTP errors (404, 429, 500) with informative messages
- Save results to products.csv (append mode — don't overwrite on re-run)
- Accept the URL as a command-line argument: python scraper.py "https://example.com/products"
- Log progress to console: "Scraped X products from [url]"

Include: User-Agent header rotation (provide 3 common UA strings)
Output: scraper.py + requirements.txt
Add comments explaining each step.

Telegram Bot

Build a Python Telegram bot for a small business.

Requirements:
- Use python-telegram-bot v20 (async)
- Bot token from environment variable TELEGRAM_BOT_TOKEN
- Commands:
  /start — welcome message + list of commands
  /hours — reply with opening hours (hardcoded, easy to edit)
  /contact — reply with contact info + email
  /price — reply with a formatted price list
  /quote — accept text after command (e.g. /quote John, web design) and save to quotes.json with timestamp and username
- Admin feature: /quotes command (only works for your Telegram user_id, hardcoded) — reply with all stored quotes in readable format
- Error handling: unknown commands get a friendly "I don't know that command" reply

Output: bot.py + requirements.txt + .env.example
Add setup instructions as comments at the top of bot.py.

The Python Vibe Coding Workflow

A repeatable process for building Python projects with AI:

1. Write a detailed requirements prompt

   Describe what the script should do, what inputs it takes, what outputs it produces, and what libraries to use. Be specific about data formats and edge cases. The more detail you give, the less back-and-forth you'll need.

2. Generate and save the output

   Paste the AI output into your project folder as the named file. Create a requirements.txt if one wasn't generated, listing every import that isn't a standard library module. Run pip install -r requirements.txt in your virtual environment.

3. Run it and capture the error

   Run python filename.py. If it errors, copy the full traceback immediately and paste it into your AI chat with "fix this". If it runs but produces wrong output, describe the expected vs actual behaviour.

4. Iterate in small steps

   Once the core script works, add features one at a time. Ask AI to "add X to this existing script" — paste the current working code each time so AI has the full context. Avoid asking for too many changes at once.

5. Ask for tests and documentation

   Once it works, prompt: "Write pytest unit tests for this script" and "add clear docstrings to every function." This makes your code maintainable even if you don't fully understand every line.

Python-Specific Pitfalls When Vibe Coding

• Virtual environment not activated: The most common cause of "module not found" errors. Always activate your venv before running Python or installing packages. In VS Code, click the Python interpreter button in the bottom bar and select your venv.
• Version mismatches: AI may generate code using Python 3.10+ syntax (match statements, newer type hints) that fails on Python 3.8. Specify your Python version in prompts if you're on an older installation.
• Library version conflicts: AI might suggest a library API that changed in a newer version. If you get an AttributeError on a library call, paste the error with your installed version (pip show libraryname) and ask AI to fix it.
• Async/sync mixing: FastAPI and modern Python use async functions. AI-generated async code occasionally mixes sync blocking calls (like requests) inside async functions. If you see event loop errors, ask AI to review and fix any blocking calls.
• Hardcoded credentials: AI sometimes puts API keys directly in code. Always replace these with environment variables. Ask AI: "refactor this to load credentials from environment variables using python-dotenv."

Which AI Is Best for Python?

• GitHub Copilot (VS Code): Best for in-editor flow. Autocompletes functions, generates entire classes from docstrings, and explains code inline. The default choice if you're working in VS Code daily.
• Claude Opus 4.6: Best for large, complex Python projects. Produces the most readable, well-structured Python with excellent comments. Handles long files and multi-file context better than GPT.
• GPT-5.4 (ChatGPT): Excellent all-rounder. Strong on web frameworks, data libraries, and integrations. The go-to for quick generation of boilerplate, configuration, and one-off scripts.
• DeepSeek-V3 (free): Outstanding Python quality at zero cost. For anyone on a budget, DeepSeek is a genuine replacement for paid models on most Python tasks.
• Gemini 2.0 Pro: Strong for tasks that involve Google services (Sheets, Drive, BigQuery) — its integration with Google's ecosystem gives it an edge for workspace automation.

Frequently Asked Questions

Related Guides

• What Is Vibe Coding? The Complete 2026 Guide
• Vibe Coding in Java
• Vibe Coding in Flutter
• Free AI Coding with DeepSeek
• Full AI Model Comparison — GPT vs Claude vs Gemini vs DeepSeek