There is a war going on. A war between those that say Python requirements should be explicit and those that say requirements should be implicit. Before I continue, I’m going to be talking about requirements.txt, not The difference between explicit and implicit requirements comes down to whether the line says Django==1.9.7 or Django, respectively. Going deeper, you could also say that adding dependencies of dependencies is explicit, and you could loosely pin like Django<1.10.

The advantage of explicit requirements is you get a repeatable environment. Especially if you’re also specifying dependencies of dependencies. The advantages of implicit requirements are readability and automatic security upgrades.

Here at TabbedOut, we’ve developed a technique that works very well I’d like to share: Use pip-tools to manage your requirements. You get the best of both worlds, at the expense of some extra boilerplate. Here’s how we do it:

  1. Be in a virtualenv
  2. Use our Makefile boilerplate (see below)
  3. pip install pip-tools
  4. Write a “sloppy” requirements.txt using implicit requirements, but name it
  5. Run make requirements.txt
  6. Check all this into your codebase


  • is easy to maintain
  • requirements.txt has pinned versions so your virtualenv matches your collaborators and production
  • You automatically get patches and security fixes when you run make requirements.txt, and there are no surprises because it goes through your code review process


  • Try to loosely pin requirements in your Though it doesn’t matter that much because you’ll catch it when you see a major version change in requirements.txt.
  • Specifying an exact version in is an anti-pattern, and you should document why. Often it’s because there’s a bug or backwards-incompatible change.

Makefile boilerplate

Here’s what a Makefile might contain:

help: ## Shows this help
	@echo "$$(grep -h '#\{2\}' $(MAKEFILE_LIST) | sed 's/: #\{2\} /	/' | column -t -s '	')"

install: ## Install requirements
	@[ -n "${VIRTUAL_ENV}" ] || (echo "ERROR: This should be run from a virtualenv" && exit 1)
	pip install -r requirements.txt

.PHONY: requirements.txt
requirements.txt: ## Regenerate requirements.txt
	pip-compile --upgrade --output-file $@
  • help: This is just a fast way of making your Makefile self-documenting.
  • install: Nowadays, you need Python and non-Python requirements. Putting it all in one make target makes it easier for developers to jump into a project.
  • PHONY: When you run make requirements.txt, you want it to run every time. Not just when changes. That’s because new versions may have been uploaded to PyPI. I always group my PHONY with my target. Even though it adds more lines, your Makefile will be more maintainable because you’re not trying to keep a list off the screen up to date.
  • requirements.txt: Why make requirements.txt over make requirements? Because best practice dictates that if the output of a make target is a file, that file should also be the name of the target. That way, you can use the automatic variable $@ and it’s explicit, even at the cost of needing the PHONY.
  • –upgrade: Without this, pip-tools doesn’t actually upgrade your dependencies.
  • –output-file $@: pip-tools does this by default, but explicit is better than implicit. I would prefer to do pip-compile --upgrade > $@ but pip-tools 1.6 does a poor job of dealing with stdout (see below).


  • When you change, you do have to remember to run make requirements, but you could automate that with a git-hook or CI process. In practice, we’ve found that running make requirements.txt is fine.
  • pip-tools==1.6 does not work with the latest pip (8.1.2). See #358
  • pip-tools==1.6 has a poor understanding of how stdin and stdout are supposed to work. Hopefully this gets fixed soon but is only a minor annoyance. #362 #360 #353 #104
  • The compilation step can depend on your platform. I’ve only noticed this with ipython, which needs packages for interacting with the terminal like gnureadline. It hasn’t been trouble for us, but it could be for you. A workaround is to run the process in a Docker container.

Sample Scenarios

If you need more convincing, here are some problems this approach solves for us:

I thought I was getting xyzpackage version 3, why is version 2 getting installed? Pip tools flattens all your requirements, and annotates which package specified what. So in requirements.txt, you’ll see xyzpackage==2.4    # via scumbagpackage and see that scumbagpackage was responsible.

What packages am I actually using? In a large project, your requirements.txt will balloon as you run into bugs and start pinning dependencies of dependencies. Then one day, you’ll realize you don’t know what packages you’re actually using. With a much simpler, there’s less to sort through and fully pinned packages stick out like sore thumbs.

It works for me Sometimes a project will work only for you. You check your installed versions against requirements.txt and they match. But what you didn’t realize is a dependency of a dependency broke something. Since pip-tools freezes everything, you’ll have the same version of every package installed. And if something does break, you’ll have history to trace down what changed.

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