Pipenv is a Python packaging tool that does one thing reasonably well — application dependency management. However, it is also plagued by issues, limitations and a break-neck development process. In the past, Pipenv’s promotional material was highly misleading as to its purpose and backers. In this post, I will explore the problems with Pipenv. Was it really recommended by Python.org? Can everyone — or at least, the vast majority of people — benefit from it? (This post has been updated in February 2020 and May 2020 to reflect the current state of Pipenv.)

“Officially recommended tool”, or how we got here¶ “Pipenv — the officially recommended Python packaging tool from Python.org, free (as in freedom).” Pipenv’s README used to have a version of the above line in their README for many months: it was added on 2017-08-31 and eventually disappeared on 2018-05-19. For a short while (2018-05-16), it was clarified (managing application dependencies, and PyPA instead of Python.org), and for about 15 minutes, the tagline called Pipenv the world’s worst or something along these lines (this coming from the maintainer). The README tagline claimed that Pipenv is the be-all, end-all of Python packaging. The problem is: it isn’t that. There are some use cases that benefit from Pipenv, but for many others, trying to use that tool will only lead to frustration. We will explore this issue later. Another issue with this tagline was the Python.org and official parts. The thing that made it “official” was a short tutorial on packaging.python.org, which is the PyPA’s packaging user guide. Also of note is the Python.org domain used. It makes it sound as if Pipenv was endorsed by the Python core team. PyPA (Python Packaging Authority) is a separate organization — they are responsible for the packaging parts (including pypi.org, setuptools, pip, wheel, virtualenv, etc.) of Python. This made the endorsement misleading. Of course, PyPA is a valued part of the Python world; an endorsement by the core team — say, inclusion in official Python distributions — is something far more important. This tagline has led to many discussions and flamewars, perhaps with this Reddit thread from May being the most heated and most important. The change was the direct result of this Reddit thread. I recommend reading this thread in full.

What pipenv does¶ We’ve already learned that Pipenv is used to manage application dependencies. Let’s learn what that term really means. Application dependencies¶ Here is an example use case for Pipenv: I’m working on a website based on Django. I create ~/git/website and run pipenv install Django in that directory. Pipenv: automatically creates a virtualenv somewhere in my home directory

writes a Pipfile, which lists Django as my dependency

installs Django using pip

proceeds to write Pipfile.lock , which stores the exact version and source file hash of each package installed (including pytz , Django’s dependency). The last part of the process was the most time consuming. At one point, while locking the dependency versions, Pipenv hangs for 46 seconds. This is one of Pipenv’s notable issues: it’s slow. Of course, this isn’t the only one, but it defintely doesn’t help. Losing 46 seconds isn’t much, but when we get to the longer waits in the timing test section later, we’ll see something that could easily discourage users from using a package. Running scripts (badly)¶ But let’s continue with our workflow. pipenv run django-admin startproject foobanizer is what I must use now, which is rather unwieldy to type, and requires running pipenv even for the smallest things. (The manage.py script has /usr/bin/env python in its shebang.) I can run pipenv shell to get a new shell which runs the activate script by default, giving you the worst of both worlds when it comes to virtualenv activation: the unwieldiness of a new shell, and the activate script, which the proponents of the shell spawning dislike. Using pipenv shell means spawning a new subshell, executing the shell startup scripts (eg. .bashrc ), and requiring you to exit with exit or ^D. If you type deactivate , you are working with an extra shell, but now outside of the virtualenv. Or you can use the --fancy mode that manipulates $PATH before launching the subshell, but it requires a specific shell configuration, in which $PATH is not overridden in non-login shells — and also often changing the config of your terminal emulator to run a login shell, as many of the Linux terminals don’t do it. Now, why does all this happen? Because a command cannot manipulate the environment of the shell it spawns. This means that Pipenv must pretend what it does is a reasonable thing instead of a workaround. This can be solved with manual activation using source $(pipenv --venv)/bin/activate (can be made into a neat alias), or shell wrappers (similar to what virtualenvwrapper does). Finishing it all up¶ Anyway, I want a blog on my site. I want to write them in Markdown syntax, so I run pipenv install Markdown , and a few long seconds later, it’s added to both Pipfiles. Another thing I can do is pipenv install --dev ipython and get a handy shell for tinkering, but it will be marked as a development dependency — so, not installed in production. That last part is an important advantage of using Pipenv. When I’m done working on my website, I commit both Pipfiles to my git repository, and push it to the remote server. Then I can clone it to, say, /srv/website . Now I can just pipenv install to get all the production packages installed (but not the development ones — Django, pytz, Markdown will be installed, but IPython and all its million dependencies won’t). There’s just one caveat: by default, the virtualenv will still be created in the current user’s home directory. This is a problem in this case, since it needs to be accessible by nginx and uWSGI, which do not have access to my (or root’s) home directory, and don’t have a home directory of their own. This can be solved with export PIPENV_VENV_IN_PROJECT=1 . But note that I will now need to export this environment variable every time I work with the app in /srv via Pipenv. The tool supports loading .env files, but only when running pipenv shell and pipenv run . You can’t use it to configure Pipenv. And to run my app with nginx/uWSGI, I will need to know the exact virtualenv path anyway, since I can’t use pipenv run as part of uWSGI configuration.

What pipenv doesn’t do¶ The workflow I mentioned above looks pretty reasonable, right? There are some deficiencies, but other than that, it seems to work well. The main issue with Pipenv is: it works with one workflow, and one workflow only. Try to do anything else, and you end up facing multiple obstacles. Setup.py, source distributions, and wheels¶ Pipenv only concerns itself with managing dependencies. It isn’t a packaging tool. If you want your thing up on PyPI, Pipenv won’t help you with anything. You still need to write a setup.py with install_requires , because the Pipfile format only specifies the dependencies and runtime requirements (Python version), there is no place in it for the package name, and Pipenv does not mandate/expect you to install your project. It can come in handy to manage the development environment (as a requirements.txt replacement, or something used to write said file), but if your project has a setup.py , you still need to manually manage install_requires . Pipenv can’t create wheels on its own either. And pip freeze is going to be a lot faster than Pipenv ever will be. Working outside of the project root¶ Another issue with Pipenv is the use of the working directory to select the virtual environment. Let’s say I’m a library author. A user of my foobar library has just reported a bug and attached a repro.py file that lets me reproduce the issue. I download that file to ~/Downloads on my filesystem. With plain old virtualenv, I can easily confirm the reproduction in a spare shell with: $ ~/virtualenvs/foobar/bin/python ~/Downloads/repro.py And then I can launch my fancy IDE to fix the bug. I don’t have to cd into the project. But with Pipenv, I can’t really do that. If I put the virtualenv in .venv with the command line option, I can type ~/git/foobar/.venv/bin/python ~/Downloads/repro.py . If I use the centralized directory + hashes thing, Tab completion becomes mandatory, if I haven’t memorized the hash. $ cd ~/git/foobar $ pipenv run python ~/Downloads/repro.py What if I had two .py files, or repro.py otherwise depended on being in the current working directory? $ cd ~/git/foobar $ pipenv shell ( foobar-Mwd1l2m9 ) $ cd ~/Downloads ( foobar-Mwd1l2m9 ) $ python repro.py ( foobar-Mwd1l2m9 ) $ exit # (not deactivate!) This is becoming ugly fairly quickly. Also, with virtualenvwrapper, I can do this: $ cd ~/Downloads $ workon foobar ( foobar ) $ python repro.py ( foobar ) $ deactivate And let’s not forget that Pipenv doesn’t help me to write a setup.py , distribute code, or manage releases. It just manages dependencies. And it does it pretty badly. Nikola¶ I’m a co-maintainer of a static site generator, Nikola. As part of this, I have the following places where I need to run Nikola: ~/git/nikola

~/git/nikola-site

~/git/nikola-plugins

~/git/nikola-themes

~/website (this blog)

/Volumes/RAMDisk/n (demo site, used for testing and created when needed, on a RAM disk) That list is long. End users of Nikola probably don’t have a list that long, but they might just have more than one Nikola site. For me, and for the aforementioned users, Pipenv does not work. To use Pipenv, all those repositories would need to live in one directory. I would also need to have a separate Pipenv environment for nikola-users , because that needs Django. Moreover, the Pipfile would have to be symlinked from ~/git/nikola if we were to make use of those in the project. So, I would have a ~/nikola directory just to make Pipenv happy, do testing/bug reproduction on a SSD (and wear it out faster), and so on… Well, I could also use the virtualenv directly. But in that case, Pipenv loses its usefulness, and makes my workflow more complicated. I can’t use virtualenvwrapper , because I would need to hack a fuzzy matching system onto it, or memorize the random string appended to my virtualenv name. All because Pipenv relies on the current directory too much. Nikola end users who want to use Pipenv will also have a specific directory structure forced on them. What if the site serves as docs for a project, and lives inside another project’s repo? Two virtualenvs, 100 megabytes wasted. Or worse, Nikola ends up in the other project’s Pipfile, which is technically good for our download stats, but not really good for the other project’s contributors.

The part where I try to measure times¶ Pipenv is famous for being slow. But how slow is it really? I put it to the test. I used two test environments: Remote: a DigitalOcean VPS, the cheapest option (1 vCPU), Python 3.6/Fedora 28, in Frankfurt

Local: my 2015 13” MacBook Pro (base model), Python 3.7, on a rather slow Internet connection (10 Mbps on a good day, and the test was not performed on one of them) Both were runninng Pipenv 2018.7.1, installed from pip. And with the following cache setups: Removed: ~/.cache/pipenv removed

Partial: rm -rf ~/.cache/pipenv/depcache-py*.json ~/.cache/pipenv/hash-cache/

Kept: no changes done from previous run Well, turns out Pipenv likes doing strange things with caching and locking. A look at the Activity Monitor hinted that there is network activity going on when Pipenv displays its Locking [packages] dependencies... line and hangs. Now, the docs don’t tell you that. The most atrocious example was a local Nikola install that was done in two runs: the first pipenv install Nikola run was interrupted right after it was done installing packages, so the cache had all the necessary wheels in it. The install took 10 minutes and 7 seconds, 9:50 of which were taken by locking dependencies and installing the locked dependencies — so, roughly nine and a half minutes were spent staring at a static screen, with the tool doing something in the background — and Pipenv doesn’t tell you what happens in this phase. (Updated 2018-07-22: In the pipenv measurements: the first entry is the total time of pipenv executon. The second is the long wait for pipenv to do its “main” job: locking dependencies and installing them. The timing starts when pipenv starts locking dependencies and ends when the prompt appears. The third item is pipenv’s reported installation time. So, pipenv install ⊇ locking/installing ⊇ Pipfile.lock install.) Task Action Measurement method Environment Cache Times in seconds Attempt 1 Attempt 2 Attempt 3 Average 1 virtualenv time Remote (not applicable) 3.911 4.052 3.914 3.959 2 pip install Nikola time Remote Removed 11.562 11.943 11.773 11.759 3 pip install Nikola time Remote Kept 7.404 7.681 7.569 7.551 4 pipenv install Nikola time Remote Removed 67.536 62.973 71.305 67.271 ├─ locking/installing from lockfile stopwatch 42.6 40.5 39.6 40.9 └─ Pipfile.lock install pipenv 14 14 13 13.667 5 adding Django to an environment time Remote Kept (only Nikola in cache) 39.576 — — 39.576 ├─ locking/installing from lockfile stopwatch 32 — — 32 └─ Pipfile.lock install pipenv 14 — — 14 6 adding Django to another environment time Remote Kept (both in cache) 37.978 — — 37.978 ├─ locking/installing from lockfile stopwatch 30.2 — — 30.2 └─ Pipfile.lock install pipenv 14 — — 14 7 pipenv install Django time Remote Removed 20.612 20.666 20.665 20.648 ├─ locking/installing from lockfile stopwatch 6.6 6.4 6 6.333 └─ Pipfile.lock install pipenv 1 1 1 1 8 pipenv install Django (new env) time Remote Kept 17.615 — — 17.615 ├─ locking/installing from lockfile stopwatch 3.5 — — 3.5 └─ Pipfile.lock install pipenv 1 — — 1 9 pipenv install Nikola time Remote Partial 61.507 — — 61.507 ├─ locking/installing from lockfile stopwatch 38.40 — — 38.40 └─ Pipfile.lock install pipenv 14 — — 14 10 pipenv install Django time Local Removed 73.933 — — 73.933 ├─ locking/installing from lockfile stopwatch 46 — — 46 └─ Pipfile.lock install pipenv 0 — — 0 11 virtualenv time Local (not applicable) 5.864 — — 5.864 12 pip install Nikola (cached) time Local Kept 10.951 — — 10.951 13 pipenv install Nikola time Local Partial, after interruption 607.647 (10m 7s) 607.647 ├─ locking/installing from lockfile stopwatch 590.85 (9m 50s) 590.85 └─ Pipfile.lock install pipenv 6 6 14 pipenv install time Local Kept 31.399 (L/I: 10.51 s) 31.399

Pip is here to stay!¶ But in all the talk about new tools, we’re forgetting about the old ones, and they do their job well — so well in fact, that the new tools still need them under the covers. Pip is fast. It does its job well enough. It lacks support for splitting packages between production and development (as Pipenv and Poetry do). This means that pip freeze and pip install are instant, at the cost of (a) needing two separate environments, or (b) installing development dependencies in production (which should only be a waste of HDD space and nothing more in a well-architected system). But at the same time, pip-tools can help keep the environments separate, as long as you take some time to write separate requirements.in files. The virtualenv management features can be provided by virtualenvwrapper. That tool’s main advantage is the shell script implementation, which means that workon foo activates the foo virtualenv without spawning a new subshell (an issue with Pipenv and Poetry, that I already covered when describing Pipenv’s operation in the Running scripts (badly) chapter.) An argument often raised by Pipenv proponents is that one does not need to concern itself with creating the virtualenv, and doesn’t need to care where it is. Unfortuntately, many tools require this knowledge from their user, or force a specific location, or require it to be different to the home directory. And for a reasonable project template with release automation — well, I have my own entry in that category, called (rather unoriginally) the Python Project Template (PyPT). Yes, setup.py files are not ideal, since they use .py code and a function execution, making access to meta information hard ( ./setup.py egg_info creates tool-accessible text files). Their main advantage is that they are the only format that is widely supported — pip is the de-facto default Python package manager (which is pre-installed on Windows and Mac), and other tools would require installation/bootstrapping first.

The break-neck pace of Pipenv¶ A good packaging tool is stable. In other words, it doesn’t change often, and it strives to support existing environments. It wouldn’t be fun to re-download everything on your system, because someone decided that /usr is now called /stuff , and all the files in /usr would become forgotten and not removed. Well, this is what Pipenv did: Date/Time (UTC) Event 2017-01-31 22:01 v3.2.14 released. pipenv --three creates ./.venv (eg. ~/git/foo/.venv ). Last version with the original behavior of pipenv. 2017-02-01 05:36 v3.3.0 released. pipenv --three creates ~/.local/share/virtualenvs/foo (to be precise, $WORKON_HOME/foo ). 2017-02-01 06:10 Issue #178 is reported regarding the behavior change. 2017-02-01 06:18 Kenneth Reitz responds: “no plans for making it configurable.” and closes the issue. 2017-02-02 03:05 Kenneth Reitz responds: “added PIPENV_VENV_IN_PROJECT mode for classic operation. Not released yet.” 2017-02-02 04:29 v3.3.3 released. The default is still uses a “remote” location, but .venv can now be used. 2017-03-02 13:48 v3.5.0 released. The new default path is $WORKON_HOME/foo-HASH , eg. ~/.local/share/virtualenvs/foo-7pl2iuUI . Over the course of a month, the location of the virtualenv changed twice. If the user didn’t read the changelog and didn’t manually intervene (also of note, the option name was mentioned in the issue and in v3.3.4’s changelog), they would have a stale .venv directory, since the new scheme was adopted for them. And then, after switching to v3.5.0, they would have a stale virtualenv hidden somewhere in their home directory, because pipenv decided to add hashes. Also, this is not configurable. One cannot disable the hashes in paths, even though users wanted to. It would also help people who want to mix Pipenv and virtualenvwrapper. Pipenv is a very opinionated tool, and if the dev team changes their mind, the old way is not supported. Pipenv moves fast and doesn’t care if anything breaks. As an example, between 2018-03-13 13:21 and 2018-03-14 13:44 (a little over 24 hours), Pipenv had 10 releases, ranging from v11.6.2 to v11.7.3. The changelog is rather unhelpful when it comes to informing users what happened in each of the releases. Extra reading: Kenneth Reitz, A Letter to /r/python (with some notes about bipolar disorder) (replaced with Wayback Machine link on 2020-02-07)

Reddit comment threads for the letter: first and second