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Calculate detailed download stats and generate HTML and badges for PyPI packages

Project description


GitHub Forks GitHub Open Issues Code Health travis-ci for master branch coverage report for master branch sphinx documentation for latest release Project Status: Active - The project has reached a stable, usable state and is being actively developed.


This package retrieves download statistics from Google BigQuery for one or more PyPI packages, caches them locally, and then generates download count badges as well as an HTML page of raw data and graphs (generated by bokeh ). It’s intended to be run on a schedule (i.e. daily) and have the results uploaded somewhere.

It would certainly be nice to make this into a real service (and some extension points for that have been included), but at the moment I have neither the time to dedicate to that, the money to cover some sort of hosting and bandwidth, nor the desire to handle how to architect this for over 85,000 projects as opposed to my few.

Hopefully stats like these will eventually end up in the official PyPI; see warehouse #699, #188 and #787 for reference on that work. For the time being, I want to (a) give myself a way to get simple download stats and badges like the old PyPI legacy (downloads per day, week and month) as well as (b) enable some higher-granularity analysis.

Note this package is very young; I wrote it as an evening/weekend project, hoping to only take a few days on it. Though writing this makes me want to bathe immediately, it has no tests. If people start using it, I’ll change that.

For a live example of exactly how the output looks, you can see the download stats page for my awslimitchecker project, generated by a cronjob on my desktop, at:


Sometime in February 2016, download stats stopped working on As I later learned, what we currently (August 2016) know as pypi is really the pypi-legacy codebase, and is far from a stable hands-off service. The small team of interpid souls who keep it running have their hands full simply keeping it online, while also working on its replacement, warehouse (which as of August 2016 is available online at While the actual web UI hasn’t been switched over to the warehouse code yet (it’s still under development), the current Warehouse service does provide full access to pypi. It’s completely understandable that, given all this and the “life support” status of the legacy pypi codebase, download stats in a legacy codebase are their last concern.

However, current download statistics (actually the raw log information) since January 22, 2016 are available in a Google BigQuery public dataset and being updated in near-real-time. There may be download statistics functionality


  • Python 2.7+ (currently tested with 2.7, 3.2, 3.3, 3.4)

  • Python VirtualEnv and pip (recommended installation method; your OS/distribution should have packages for these)

pypi-download-stats relies on bokeh to generate pretty SVG charts that work offline, and google-api-python-client for querying BigQuery. Each of those have additional dependencies.


It’s recommended that you install into a virtual environment (virtualenv / venv). See the virtualenv usage documentation for information on how to create a venv.

This isn’t on pypi yet, ironically. Until it is:

$ pip install git+


You’ll need Google Cloud credentials for a project that has the BigQuery API enabled. The recommended method is to generate system account credentials; download the JSON file for the credentials and export the path to it as the GOOGLE_APPLICATION_CREDENTIALS environment variable. The system account will need to be added as a Project Member.


Run with -h for command-line help:

usage: pypi-download-stats [-h] [-V] [-v] [-Q | -G] [-o OUT_DIR]
                           [-p PROJECT_ID] [-c CACHE_DIR] [-B BACKFILL_DAYS]
                           [-P PROJECT | -U USER]

pypi-download-stats - Calculate detailed download stats and generate HTML and
badges for PyPI packages - <>

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -v, --verbose         verbose output. specify twice for debug-level output.
  -Q, --no-query        do not query; just generate output from cached data
  -G, --no-generate     do not generate output; just query data and cache
  -o OUT_DIR, --out-dir OUT_DIR
                        output directory (default: ./pypi-stats
  -p PROJECT_ID, --project-id PROJECT_ID
                        ProjectID for your Google Cloud user, if not using
                        service account credentials JSON file
  -c CACHE_DIR, --cache-dir CACHE_DIR
                        stats cache directory (default: ./pypi-stats-cache)
  -B BACKFILL_DAYS, --backfill-num-days BACKFILL_DAYS
                        number of days of historical data to backfill, if
                        missing (defaut: 7). Note this may incur BigQuery
                        charges. Set to -1 to backfill all available history.
  -P PROJECT, --project PROJECT
                        project name to query/generate stats for (can be
                        specified more than once; this will reduce query cost
                        for multiple projects)
  -U USER, --user USER  Run for all PyPI projects owned by the specifieduser.

To run queries and generate reports for PyPI projects “foo” and “bar”, using a Google Cloud credentials JSON file at foo.json:

$ pypi-download-stats -P foo -P bar

To run queries but not generate reports for all PyPI projects owned by user “myname”:

$ pypi-download-stats -G -U myname

To generate reports against cached query data for the project “foo”:

$ pypi-download-stats -Q -P foo

To run nightly and upload results to a website-hosting S3 bucket, I use the following script via cron (note the paths are specific to my purpose; also note the two commands, as s3cmd does not seem to set the MIME type for the SVG images correctly):

#!/bin/bash -x

export GOOGLE_APPLICATION_CREDENTIALS=/home/jantman/.ssh/pypi-bigquery.json
cd /home/jantman/GIT/pypi-download-stats
bin/pypi-download-stats -vv -U jantman

# sync html files
~/venvs/foo/bin/s3cmd -r --delete-removed --stats --exclude='*.svg' sync pypi-stats s3://jantman-personal-public/
# sync SVG and set mime-type, since s3cmd gets it wrong
~/venvs/foo/bin/s3cmd -r --delete-removed --stats --exclude='*.html' --mime-type='image/svg+xml' sync pypi-stats s3://jantman-personal-public/


At this point… I have no idea. Some of the download tables are 3+ GB per day. I imagine that backfilling historical data from the beginning of what’s currently there (20160122) might incur quite a bit of data cost.

Bugs and Feature Requests

Bug reports and feature requests are happily accepted via the GitHub Issue Tracker. Pull requests are welcome. Issues that don’t have an accompanying pull request will be worked on as my time and priority allows.


To install for development:

  1. Fork the pypi-download-stats repository on GitHub

  2. Create a new branch off of master in your fork.

$ virtualenv pypi-download-stats
$ cd pypi-download-stats && source bin/activate
$ pip install -e
$ cd src/pypi-download-stats

The git clone you’re now in will probably be checked out to a specific commit, so you may want to git checkout BRANCHNAME.


  • pep8 compliant with some exceptions (see pytest.ini)


There isn’t any right now. I’m bad. If people actually start using this, I’ll refactor and add tests, but for now this started as a one-night project.

Release Checklist

  1. Open an issue for the release; cut a branch off master for that issue.

  2. Confirm that there are CHANGES.rst entries for all major changes.

  3. Ensure that Travis tests passing in all environments.

  4. Ensure that test coverage is no less than the last release (ideally, 100%).

  5. Increment the version number in pypi-download-stats/ and add version and release date to CHANGES.rst, then push to GitHub.

  6. Confirm that README.rst renders correctly on GitHub.

  7. Upload package to testpypi:

  8. Create a pull request for the release to be merged into master. Upon successful Travis build, merge it.

  9. Tag the release in Git, push tag to GitHub:

    • tag the release. for now the message is quite simple: git tag -a X.Y.Z -m 'X.Y.Z released YYYY-MM-DD'

    • push the tag to GitHub: git push origin X.Y.Z

  1. Upload package to live pypi:

    • twine upload dist/*

  1. make sure any GH issues fixed in the release were closed.

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