Skip to main content

Python interface to PyPI Stats API https://pypistats.org/api

Project description

pypistats

PyPI version Supported Python versions PyPI downloads Azure Pipelines status GitHub Actions status codecov GitHub DOI Code style: Black

Python 3.6+ interface to PyPI Stats API to get aggregate download statistics on Python packages on the Python Package Index without having to execute queries directly against Google BigQuery.

Data is available for the last 180 days. (For longer time periods, pypinfo can help, you'll need an API key and get free quota.)

Installation

From PyPI

pip install --upgrade pypistats

From source

git clone https://github.com/hugovk/pypistats
cd pypistats
pip install .

Example command-line use

Run pypistats with a subcommand (corresponding to PyPI Stats endpoints), then options for that subcommand.

Top-level help:

$ pypistats --help
usage: pypistats [-h] [-V] {recent,overall,python_major,python_minor,system} ...

positional arguments:
  {recent,overall,python_major,python_minor,system}

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit

Help for a subcommand:

$ pypistats recent --help
usage: pypistats recent [-h] [-p {day,week,month}] [-f {html,json,markdown,rst,tsv}] [-j] [-v] package

Retrieve the aggregate download quantities for the last day/week/month

positional arguments:
  package

optional arguments:
  -h, --help            show this help message and exit
  -p {day,week,month}, --period {day,week,month}
  -f {html,json,markdown,rst,tsv}, --format {html,json,markdown,rst,tsv}
                        The format of output (default: markdown)
  -j, --json            Shortcut for "-f json" (default: False)
  -v, --verbose         Print debug messages to stderr (default: False)

Get recent downloads:

$ pypistats recent pillow
| last_day  | last_month | last_week |
| --------: | ---------: | --------: |
| 1,083,451 | 30,750,398 | 7,088,038 |

Help for another subcommand:

$ pypistats python_minor --help
usage: pypistats python_minor [-h] [-V VERSION]
                              [-f {html,json,markdown,rst,tsv}] [-j]
                              [-sd yyyy-mm[-dd]|name] [-ed yyyy-mm[-dd]|name]
                              [-m yyyy-mm|name] [-l] [-t] [-d] [--monthly]
                              [-v]
                              package

Retrieve the aggregate daily download time series by Python minor version
number

positional arguments:
  package

optional arguments:
  -h, --help            show this help message and exit
  -V VERSION, --version VERSION
                        eg. 2.7 or 3.6 (default: None)
  -f {html,json,markdown,rst,tsv}, --format {html,json,markdown,rst,tsv}
                        The format of output (default: markdown)
  -j, --json            Shortcut for "-f json" (default: False)
  -sd yyyy-mm[-dd]|name, --start-date yyyy-mm[-dd]|name
                        Start date (default: None)
  -ed yyyy-mm[-dd]|name, --end-date yyyy-mm[-dd]|name
                        End date (default: None)
  -m yyyy-mm|name, --month yyyy-mm|name
                        Shortcut for -sd & -ed for a single month (default:
                        None)
  -l, --last-month      Shortcut for -sd & -ed for last month (default: False)
  -t, --this-month      Shortcut for -sd for this month (default: False)
  -d, --daily           Show daily downloads (default: False)
  --monthly             Show monthly downloads (default: False)
  -v, --verbose         Print debug messages to stderr (default: False)

Get version downloads:

$ pypistats python_minor pillow --last-month
| category | percent | downloads  |
| -------- | ------: | ---------: |
| 3.7      |  35.93% | 11,002,680 |
| 3.6      |  33.00% | 10,107,822 |
| 3.8      |  15.04% |  4,605,236 |
| 3.9      |   5.03% |  1,540,571 |
| 3.5      |   4.73% |  1,449,591 |
| null     |   3.39% |  1,037,124 |
| 2.7      |   2.84% |    870,677 |
| 3.4      |   0.03% |     10,055 |
| 3.10     |   0.01% |      2,863 |
| 2.6      |   0.00% |         58 |
| 3.3      |   0.00% |         44 |
| 3.2      |   0.00% |         39 |
| Total    |         | 30,626,760 |

Date range: 2021-04-01 - 2021-04-30

The table is Markdown, ready for pasting in GitHub issues and PRs:

category percent downloads
3.7 35.93% 11,002,680
3.6 33.00% 10,107,822
3.8 15.04% 4,605,236
3.9 5.03% 1,540,571
3.5 4.73% 1,449,591
null 3.39% 1,037,124
2.7 2.84% 870,677
3.4 0.03% 10,055
3.10 0.01% 2,863
2.6 0.00% 58
3.3 0.00% 44
3.2 0.00% 39
Total 30,626,760

These are equivalent (in May 2019):

pypistats python_major pip --last-month
pypistats python_major pip --month april
pypistats python_major pip --month apr
pypistats python_major pip --month 2019-04

And:

pypistats python_major pip --start-date december --end-date january
pypistats python_major pip --start-date dec      --end-date jan
pypistats python_major pip --start-date 2018-12  --end-date 2019-01

Example programmatic use

Return values are from the JSON responses documented in the API: https://pypistats.org/api/

import pypistats
from pprint import pprint

# Call the API
print(pypistats.recent("pillow"))
print(pypistats.recent("pillow", "day", format="markdown"))
print(pypistats.recent("pillow", "week", format="rst"))
print(pypistats.recent("pillow", "month", format="html"))
pprint(pypistats.recent("pillow", "week", format="json"))
print(pypistats.recent("pillow", "day"))

print(pypistats.overall("pillow"))
print(pypistats.overall("pillow", mirrors=True, format="markdown"))
print(pypistats.overall("pillow", mirrors=False, format="rst"))
print(pypistats.overall("pillow", mirrors=True, format="html"))
pprint(pypistats.overall("pillow", mirrors=False, format="json"))

print(pypistats.python_major("pillow"))
print(pypistats.python_major("pillow", version=2, format="markdown"))
print(pypistats.python_major("pillow", version=3, format="rst"))
print(pypistats.python_major("pillow", version="2", format="html"))
pprint(pypistats.python_major("pillow", version="3", format="json"))

print(pypistats.python_minor("pillow"))
print(pypistats.python_minor("pillow", version=2.7, format="markdown"))
print(pypistats.python_minor("pillow", version="2.7", format="rst"))
print(pypistats.python_minor("pillow", version=3.7, format="html"))
pprint(pypistats.python_minor("pillow", version="3.7", format="json"))

print(pypistats.system("pillow"))
print(pypistats.system("pillow", os="darwin", format="markdown"))
print(pypistats.system("pillow", os="linux", format="rst"))
print(pypistats.system("pillow", os="darwin", format="html"))
pprint(pypistats.system("pillow", os="linux", format="json"))

NumPy and pandas

To use with either NumPy or pandas, make sure they are first installed, or:

pip install --upgrade "pypistats[numpy]"
pip install --upgrade "pypistats[pandas]"
pip install --upgrade "pypistats[numpy,pandas]"

Return data in a NumPy array for further processing:

import pypistats
numpy_array = pypistats.overall("pyvista", total=True, format="numpy")
print(type(numpy_array))
# <class 'numpy.ndarray'>
print(numpy_array)
# [['with_mirrors' '2019-09-20' '2.23%' 1204]
#  ['without_mirrors' '2019-09-20' '2.08%' 1122]
#  ['with_mirrors' '2019-09-19' '0.92%' 496]
#  ...
#  ['with_mirrors' '2019-10-26' '0.02%' 13]
#  ['without_mirrors' '2019-10-26' '0.02%' 12]
#  ['Total' None None 54041]]

Or in a pandas DataFrame:

import pypistats
pandas_dataframe = pypistats.overall("pyvista", total=True, format="pandas")
print(type(pandas_dataframe))
# <class 'pandas.core.frame.DataFrame'>
print(pandas_dataframe)
#             category        date percent  downloads
# 0       with_mirrors  2019-09-20   2.23%       1204
# 1    without_mirrors  2019-09-20   2.08%       1122
# 2       with_mirrors  2019-09-19   0.92%        496
# 3       with_mirrors  2019-08-22   0.90%        489
# 4    without_mirrors  2019-09-19   0.86%        466
# ..               ...         ...     ...        ...
# 354  without_mirrors  2019-11-03   0.03%         15
# 355  without_mirrors  2019-11-16   0.03%         15
# 356     with_mirrors  2019-10-26   0.02%         13
# 357  without_mirrors  2019-10-26   0.02%         12
# 358            Total        None    None      54041
#
# [359 rows x 4 columns]

For example, create charts with pandas:

# Show overall downloads over time, excluding mirrors
import pypistats
data = pypistats.overall("pillow", total=True, format="pandas")
data = data.groupby("category").get_group("without_mirrors").sort_values("date")

chart = data.plot(x="date", y="downloads", figsize=(10, 2))
chart.figure.show()
chart.figure.savefig("overall.png")  # alternatively

overall.png

# Show Python 3 downloads over time
import pypistats
data = pypistats.python_major("pillow", total=True, format="pandas")
data = data.groupby("category").get_group(3).sort_values("date")

chart = data.plot(x="date", y="downloads", figsize=(10, 2))
chart.figure.show()
chart.figure.savefig("python3.png")  # alternatively

python3.png

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pypistats-0.13.0.tar.gz (123.4 kB view details)

Uploaded Source

Built Distribution

pypistats-0.13.0-py3-none-any.whl (11.5 kB view details)

Uploaded Python 3

File details

Details for the file pypistats-0.13.0.tar.gz.

File metadata

  • Download URL: pypistats-0.13.0.tar.gz
  • Upload date:
  • Size: 123.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.7.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.6

File hashes

Hashes for pypistats-0.13.0.tar.gz
Algorithm Hash digest
SHA256 8f8924f52b7e8a7263db5380e38f373a88f33b41aac74c6a5e08189567f41069
MD5 3ed788b0dd6094ed537f6e912241a215
BLAKE2b-256 88b2c1faa271fd497b807e7fa35cf43654102e82e5e1619453cf7d294f09fa1c

See more details on using hashes here.

Provenance

File details

Details for the file pypistats-0.13.0-py3-none-any.whl.

File metadata

  • Download URL: pypistats-0.13.0-py3-none-any.whl
  • Upload date:
  • Size: 11.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.7.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.6

File hashes

Hashes for pypistats-0.13.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ae55fcf4750d5daf611816231dd801f16aa2e87b4a9e2c2c32617ba2e91e0c19
MD5 7ac3b4f3f97a54ac8cd082eaecf8b4f2
BLAKE2b-256 a6b3b8dd7a2c6e7e0bacd1a073c59f17b42ca04b45d05147d29ff2bf94d5a647

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page