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Django file download count
Django file download count
Django file download count
Django file download count

Analyzing PyPI package downloads¶

This section covers how to use the public PyPI download statistics dataset to learn more about downloads of a package (or packages) hosted on PyPI. For example, you can use it to discover the distribution of Python versions used to download a package.

Background¶

PyPI does not display download statistics for a number of reasons: 1

Inefficient to make work with a Content Distribution Network (CDN): Download statistics change constantly. Including them in project pages, which are heavily cached, would require invalidating the cache more often, and reduce the overall effectiveness of the cache.
Highly inaccurate: A number of things prevent the download counts from being accurate, some of which include:
- ’s download cache (lowers download counts)
- Internal or unofficial mirrors (can both raise or lower download counts)
- Packages not hosted on PyPI (for comparisons sake)
- Unofficial scripts or attempts at download count inflation (raises download counts)
- Known historical data quality issues (lowers download counts)
Not particularly useful: Just because a project has been downloaded a lot doesn’t mean it’s good; Similarly just because a project hasn’t been downloaded a lot doesn’t mean it’s bad!

In short, because it’s value is low for various reasons, and the tradeoffs required to make it work are high, it has been not an effective use of limited resources.

Public dataset¶

As an alternative, the Linehaul project streams download logs from PyPI to Google BigQuery2, where they are stored as a public dataset.

Data schema¶

Linehaul writes an entry in a table for each download. The table contains information about what file was downloaded and how it was downloaded. Some useful columns from the table schema include:

Column	Description	Examples
timestamp	Date and time
file.project	Project name	,
file.version	Package version	,
details.installer.name	Installer	pip, bandersnatch
details.python	Python version	,

Useful queries¶

Run queries in the BigQuery web UI by clicking the “Compose query” button.

Note that the rows are stored in a partitioned, which helps limit the cost of queries. These example queries analyze downloads from recent history by filtering on the column.

Counting package downloads¶

The following query counts the total number of downloads for the project “pytest”.

#standardSQL SELECT COUNT(*) AS num_downloads FROM `the-psf.pypi.file_downloads` WHERE file.project = 'pytest' -- Only query the last 30 days of history AND DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AND CURRENT_DATE())

To only count downloads from pip, filter on the column.

#standardSQL SELECT COUNT(*) AS num_downloads FROM `the-psf.pypi.file_downloads` WHERE file.project = 'pytest' AND details.installer.name = 'pip' -- Only query the last 30 days of history AND DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AND CURRENT_DATE())

Package downloads over time¶

To group by monthly downloads, use the function. Also filtering by this column reduces corresponding costs.

#standardSQL SELECT COUNT(*) AS num_downloads, DATE_TRUNC(DATE(timestamp), MONTH) AS `month` FROM `the-psf.pypi.file_downloads` WHERE file.project = 'pytest' -- Only query the last 6 months of history AND DATE(timestamp) BETWEEN DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 6 MONTH), MONTH) AND CURRENT_DATE() GROUP BY `month` ORDER BY `month` DESC

num_downloads	month
1956741	2018-01-01
2344692	2017-12-01
1730398	2017-11-01
2047310	2017-10-01
1744443	2017-09-01
1916952	2017-08-01

Python versions over time¶

Extract the Python version from the column. Warning: This query processes over 500 GB of data.

#standardSQL SELECT REGEXP_EXTRACT(details.python, r"[0-9]+\.[0-9]+") AS python_version, COUNT(*) AS num_downloads, FROM `the-psf.pypi.file_downloads` WHERE -- Only query the last 6 months of history DATE(timestamp) BETWEEN DATE_TRUNC(DATE_SUB(CURRENT_DATE(), INTERVAL 6 MONTH), MONTH) AND CURRENT_DATE() GROUP BY `python_version` ORDER BY `num_downloads` DESC

python	num_downloads
3.7	12990683561
3.6	9035598511
2.7	8467785320
3.8	4581627740
3.5	2412533601
null	1641456718

Caveats¶

In addition to the caveats listed in the background above, Linehaul suffered from a bug which caused it to significantly under-report download statistics prior to July 26, 2018. Downloads before this date are proportionally accurate (e.g. the percentage of Python 2 vs. Python 3 downloads) but total numbers are lower than actual by an order of magnitude.

Источник: [https://torrent-igruha.org/3551-portal.html]

Django file download count