pypinfo: View PyPI download statistics with ease.
pypinfo is a simple CLI to access PyPI download statistics via Google's BigQuery.
Table of contents
Usage
Click to unfold usage
```console $ pypinfo Usage: pypinfo [OPTIONS] [PROJECT] [FIELDS]... COMMAND [ARGS]... Valid fields are: project | version | file | pyversion | percent3 | percent2 | impl | impl-version | openssl | date | month | year | country | installer | installer-version | setuptools-version | system | system-release | distro | distro-version | cpu | libc | libc-version Options: -a, --auth TEXT Path to Google credentials JSON file. --run / --test --test simply prints the query. -j, --json Print data as JSON, with keys `rows` and `query`. -i, --indent INTEGER JSON indentation level. -t, --timeout INTEGER Milliseconds. Default: 120000 (2 minutes) -l, --limit INTEGER Maximum number of query results. Default: 10 -d, --days INTEGER Number of days in the past to include. Default: 30 -sd, --start-date TEXT Must be negative or YYYY-MM[-DD]. Default: -31 -ed, --end-date TEXT Must be negative or YYYY-MM[-DD]. Default: -1 -m, --month TEXT Shortcut for -sd & -ed for a single YYYY-MM month. -w, --where TEXT WHERE conditional. Default: file.project = "project" -o, --order TEXT Field to order by. Default: download_count --all Show downloads by all installers, not only pip. -pc, --percent Print percentages. -md, --markdown Output as Markdown. -v, --verbose Print debug messages to stderr. --version Show the version and exit. -h, --help Show this message and exit. ``` pypinfo accepts 0 or more options, followed by exactly 1 project, followed by 0 or more fields. By default only the last 30 days are queried. Let's take a look at some examples! _Tip_: If queries are resulting in NoneType errors, increase timeout. ### Downloads for a project ```console $ pypinfo requests Served from cache: False Data processed: 2.83 GiB Data billed: 2.83 GiB Estimated cost: $0.02 | download_count | | -------------- | | 116,353,535 | ``` ### All downloads ```console $ pypinfo "" Served from cache: False Data processed: 116.15 GiB Data billed: 116.15 GiB Estimated cost: $0.57 | download_count | | -------------- | | 8,642,447,168 | ``` ### Downloads for a project by Python version ```console $ pypinfo django pyversion Served from cache: False Data processed: 967.33 MiB Data billed: 968.00 MiB Estimated cost: $0.01 | python_version | download_count | | -------------- | -------------- | | 3.8 | 1,735,967 | | 3.6 | 1,654,871 | | 3.7 | 1,326,423 | | 2.7 | 876,621 | | 3.9 | 524,570 | | 3.5 | 258,609 | | 3.4 | 12,769 | | 3.10 | 3,050 | | 3.3 | 225 | | 2.6 | 158 | | Total | 6,393,263 | ``` ### All downloads by country code ```console $ pypinfo "" country Served from cache: False Data processed: 150.40 GiB Data billed: 150.40 GiB Estimated cost: $0.74 | country | download_count | | ------- | -------------- | | US | 6,614,473,568 | | IE | 336,037,059 | | IN | 192,914,402 | | DE | 186,968,946 | | NL | 182,691,755 | | None | 141,753,357 | | BE | 111,234,463 | | GB | 109,539,219 | | SG | 106,375,274 | | FR | 86,036,896 | | Total | 8,068,024,939 | ``` ### Downloads for a project by system and distribution ```console $ pypinfo cryptography system distro Served from cache: False Data processed: 2.52 GiB Data billed: 2.52 GiB Estimated cost: $0.02 | system_name | distro_name | download_count | | ----------- | ------------------------------- | -------------- | | Linux | Ubuntu | 19,524,538 | | Linux | Debian GNU/Linux | 11,662,104 | | Linux | Alpine Linux | 3,105,553 | | Linux | Amazon Linux AMI | 2,427,975 | | Linux | Amazon Linux | 2,374,869 | | Linux | CentOS Linux | 1,955,181 | | Windows | None | 1,522,069 | | Linux | CentOS | 568,370 | | Darwin | macOS | 489,859 | | Linux | Red Hat Enterprise Linux Server | 296,858 | | Total | | 43,927,376 | ``` ### Most popular projects in the past year ```console $ pypinfo --days 365 "" project Served from cache: False Data processed: 1.69 TiB Data billed: 1.69 TiB Estimated cost: $8.45 | project | download_count | | --------------- | -------------- | | urllib3 | 1,382,528,406 | | six | 1,172,798,441 | | botocore | 1,053,169,690 | | requests | 995,387,353 | | setuptools | 992,794,567 | | certifi | 948,518,394 | | python-dateutil | 934,709,454 | | idna | 929,781,443 | | s3transfer | 877,565,186 | | chardet | 854,744,674 | | Total | 10,141,997,608 | ``` ### Downloads between two YYYY-MM-DD dates ```console $ pypinfo --start-date 2018-04-01 --end-date 2018-04-30 setuptools Served from cache: False Data processed: 571.37 MiB Data billed: 572.00 MiB Estimated cost: $0.01 | download_count | | -------------- | | 8,972,826 | ``` ### Downloads between two YYYY-MM dates - A yyyy-mm ``--start-date`` defaults to the first day of the month - A yyyy-mm ``--end-date`` defaults to the last day of the month ```console $ pypinfo --start-date 2018-04 --end-date 2018-04 setuptools Served from cache: False Data processed: 571.37 MiB Data billed: 572.00 MiB Estimated cost: $0.01 | download_count | | -------------- | | 8,972,826 | ``` ### Downloads for a single YYYY-MM month ```console $ pypinfo --month 2018-04 setuptools Served from cache: False Data processed: 571.37 MiB Data billed: 572.00 MiB Estimated cost: $0.01 | download_count | | -------------- | | 8,972,826 | ``` ### Percentage of Python 3 downloads of the top 100 projects in the past year Let's use ``--test`` to only see the query instead of sending it. ```console $ pypinfo --test --days 365 --limit 100 "" project percent3 SELECT file.project as project, ROUND(100 * SUM(CASE WHEN REGEXP_EXTRACT(details.python, r"^([^\.]+)") = "3" THEN 1 ELSE 0 END) / COUNT(*), 1) as percent_3, COUNT(*) as download_count, FROM `bigquery-public-data.pypi.file_downloads` WHERE timestamp BETWEEN TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL -366 DAY) AND TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL -1 DAY) AND details.installer.name = "pip" GROUP BY project ORDER BY download_count DESC LIMIT 100 ``` ### Downloads for a given version pypinfo supports [PEP 440 version matching](https://peps.python.org/pep-0440/#version-matching). We can use it to query stats on a given major version. ```console $ pypinfo -pc 'pip==21.*' pyversion version Served from cache: False Data processed: 34.45 MiB Data billed: 35.00 MiB Estimated cost: $0.01 | python_version | version | percent | download_count | | -------------- | ------- | ------- | -------------- | | 3.6 | 21.3.1 | 78.74% | 10,430 | | 3.8 | 21.3.1 | 7.81% | 1,034 | | 3.7 | 21.2.1 | 3.59% | 476 | | 3.7 | 21.3.1 | 2.60% | 345 | | 3.7 | 21.0.1 | 2.25% | 298 | | 3.8 | 21.0.1 | 1.58% | 209 | | 3.8 | 21.2.1 | 1.42% | 188 | | 3.7 | 21.1.2 | 0.81% | 107 | | 3.9 | 21.3.1 | 0.69% | 92 | | 3.8 | 21.1.1 | 0.51% | 67 | | Total | | | 13,246 | ``` We can also use it to query stats on an exact version: ```console $ pypinfo -pc 'numpy==1.23rc3' pyversion version Served from cache: False Data processed: 34.01 MiB Data billed: 35.00 MiB Estimated cost: $0.01 | python_version | version | percent | download_count | | -------------- | --------- | ------- | -------------- | | 3.9 | 1.23.0rc3 | 63.33% | 38 | | 3.8 | 1.23.0rc3 | 28.33% | 17 | | 3.10 | 1.23.0rc3 | 8.33% | 5 | | Total | | | 60 | ``` Check how many downloads came from continuous integration servers: ```console ❯ pypinfo --percent --days 5 pillow ci Served from cache: False Data processed: 384.22 MiB Data billed: 385.00 MiB Estimated cost: $0.01 | ci | percent | download_count | | ----- | ------- | -------------- | | None | 79.37% | 11,963,127 | | True | 20.63% | 3,109,931 | | Total | | 15,073,058 | ```Installation
Click to unfold installation
pypinfo is distributed on **PyPI** as a universal wheel and is available on Linux, macOS and Windows. This is relatively painless, I swear. ### Create project 1. Go to https://bigquery.cloud.google.com. 2. Sign up if you haven't already. The first TB of queried data each month is free. Each additional TB is $5. 3. Sign in on your account if you are not already; 4. Go to https://console.developers.google.com/cloud-resource-manager and click CREATE PROJECT if you don't already have one:  5. This takes you to [https://console.developers.google.com/projectcreate](https://console.developers.google.com/projectcreate). Fill out the form and click CREATE. Any name is fine, but I recommend you choose something to do with PyPI like pypinfo. This way you know what the project is designated for:  6. A while after creation, at the left-top corner, select the project name of your choice on dropdown component AND at the left-top corner "Navigation Menu", select option "Cloud Overview > Dashboard":  ### Enable BigQuery API 7. Click on top-left button "Navigation Menu" and click on option "API and services > Library":  8. Perform a search with keywords "big query api" on available text field:  9. Enable Big Query API by button "Enable" press:  10. After enabling, click CREATE CREDENTIALS:  **Note**: You will be requested to go back to Big Query panel. In this case, click on top-left button "Navigation Menu", option "API and services > Enabled APIs and services" and on consequent page, on item "Big Query API":  11. On the page after clicking the "CREATE CREDENTIALS" button, choose "BigQuery API", "Application Data" and "No, I'm not using them":  12. Fill account details and press button "Create and Continue":  13. Select role "BigQuery User" (option path "BigQuery > Big Query User"), press button "Done":  14. On Big Query API panel (See **Note** on item *10*), click on tab "CREDENTIALS". On section "Service accounts", click on created credentials on items 11, 12 and 13.  15. On page from credential click, click on tab "KEYS". On dropdown menu "ADD KEY", click on option "Create new key":  16. On appearing box, click on option "JSON" and press button "CREATE": This will start the download of credentials on a JSON file with name pattern `{name}-{credentials_hash}.json`:  ### Installation and authentication 17. Run `python -m pip install pypinfo` in the terminal. 18. `pypinfo --auth path/to/your_credentials.json`, or set an environment variable `GOOGLE_APPLICATION_CREDENTIALS` that points to the file.Credits
- Donald Stufft for maintaining PyPI all these years;
- Google for donating BigQuery capacity to PyPI;
- Paul Kehrer for his awesome blog post.