REQUIREMENTS.TXT
A beginner's guide to managing Python dependencies with requirements.txt
WHAT IS REQUIREMENTS.TXT?
Think of requirements.txt like a shopping list for your Python project. It's a simple text file that lists every library your project needs, along with the exact versions that work together.
When you share your code with a colleague or deploy it to a server, this file lets them recreate your exact setup with a single command - no guesswork, no version conflicts, no "it works on my machine" problems.
WITHOUT REQUIREMENTS.TXT
Sharing your web scraping project:
- ❌ You: "Install BeautifulSoup, requests, pandas... uh, maybe lxml?"
- ❌ Teammate: "Which versions? I have pandas 2.0, yours is 1.5"
- ❌ Teammate installs latest versions, code breaks
- ❌ Hours wasted debugging version mismatches
WITH REQUIREMENTS.TXT
Sharing the same project:
-
✓
You: "Just run
pip install -r requirements.txt" -
✓
File lists exact versions:
pandas==1.5.3 - ✓ Teammate gets identical setup in 30 seconds
- ✓ Code works perfectly on first try
WHEN TO USE REQUIREMENTS.TXT
Your newsroom team needs to run your election data analysis script
Academic paper reviewers need to reproduce your methodology
Moving your web scraper from laptop to cloud server
Document exact dependencies so you can recreate setup years later
New hire gets your project running on day one without IT support
Automated testing systems need consistent library versions
WHEN YOU ABSOLUTELY NEED ONE
Without it, nobody can run your code. Period.
Team coordination fails without dependency synchronization
Servers, cloud platforms, Docker containers all require it
Reproducibility requires exact dependency documentation
WHAT CAN GO WRONG WITHOUT REQUIREMENTS.TXT?
Teammate installs latest pandas (2.1), you built on pandas (1.5). Different results, broken code, lost trust.
You return to an old project. Libraries have updated. Nothing works. You can't remember what versions you used. Project is dead.
Published your findings, but peer reviewers can't recreate your analysis. Your credibility is questioned.
Your scraper works locally but crashes on the server. Different library versions. Hours of debugging during deadline.
New team member spends 4 hours guessing which libraries to install. You become unpaid tech support.
Different pandas versions handle missing data differently. Results change subtly. You don't notice until it's too late.
QUICK START GUIDE
source myproject-env/bin/activate
pip install requests
pip install beautifulsoup4
pip install pandas
pip freeze > requirements.txt
# First activate your environment, then:
pip install -r requirements.txt
WITHOUT REQUIREMENTS.TXT
Giving someone your grandmother's cookie recipe but saying "just use flour, sugar, eggs, and chocolate." They use different brands, different amounts, different chocolate types. The cookies taste completely different. They blame your recipe.
WITH REQUIREMENTS.TXT
Giving them the recipe with EXACT specifications: "2 cups King Arthur flour, 1 cup Domino sugar, 3 Grade A large eggs, 1 cup Ghirardelli semi-sweet chocolate chips." They make identical cookies every time.
THE GOLDEN RULE
EVERY PROJECT NEEDS REQUIREMENTS.TXT. NO EXCEPTIONS.
Create it BEFORE you share code, BEFORE you deploy, BEFORE you publish research. It takes 10 seconds with pip freeze > requirements.txt but saves hours of debugging and protects your professional reputation. Think of it as citing your sources - it's basic scholarly practice.