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https://github.com/living-with-machines/gh_orgstats
GitHub stats for Github Organizations
https://github.com/living-with-machines/gh_orgstats
github github-api
Last synced: about 2 months ago
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GitHub stats for Github Organizations
- Host: GitHub
- URL: https://github.com/living-with-machines/gh_orgstats
- Owner: Living-with-machines
- License: apache-2.0
- Created: 2020-12-01T14:32:39.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-16T14:23:42.000Z (4 months ago)
- Last Synced: 2024-10-18T09:26:00.677Z (4 months ago)
- Topics: github, github-api
- Language: Jupyter Notebook
- Homepage: https://living-with-machines.github.io/gh_orgstats/
- Size: 1.1 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Github OrgStats
`gh_orgstats` is intended to provide some easy ways of getting stats for a GitHub org. `gh_orgstats` does this by wrapping some functions around [PyGithub](https://github.com/PyGithub/PyGithub). This code is mainly intended to help generate reports as part of a GitHub actions pipeline to update stats for a funder. For an example of this being used for generating a weekly report see the [Living with Machines Github stats report](https://github.com/Living-with-machines/github_stats_report)
To use `PyGithub` we need to authenticate with GitHub this is done via a token. This token is used to authenticate access and requires at least scope for public repos. See https://github.com/settings/tokens to register a token.
```
from dotenv import load_dotenv
import os
```
In this case we use `dotenv` to load the token from a `.env` files.
```
load_dotenv()
GH_TOKEN = os.getenv("GH_TOKEN")
```
Currently all functionality is contained within the `stats` module.
```
from gh_orgstats.stats import *
```
The `OrgStats` class is used to get stats for a GitHub organization. To create an instance of this class we need to pass a GitHub token to authenticate and the name of the Organization you want stats for.
```
test_org = OrgStats(GH_TOKEN, "ghorgstatstestorg")
test_org
```
OrgStats: ghorgstatstestorg
### Organization repositories
As a start we can grab the repositories for an organization via the `repos` attribute of our OrgStats instance
```
test_org.repos
```
[Repository(full_name="ghorgstatstestorg/repo1"),
Repository(full_name="ghorgstatstestorg/repo2"),
Repository(full_name="ghorgstatstestorg/private_repo_1")]
We can also get a sense of what is in the repository by looking at the file extensions for each repository.
### Repository file types
```
test_org.get_org_file_ext_frequency()
```
{'repo1': {'.md': 1},
'repo2': {'.md': 1, '.py': 1},
'private_repo_1': {'.md': 1}}
#### Filtering by publication status
We can also filter this by publication status
```
test_org.get_org_file_ext_frequency(pub_status='public')
```
{'repo1': {'.md': 1}, 'repo2': {'.md': 1, '.py': 1}}
### Snapshot stats
Snapshot stats are captured based on the current view and aren't updated. These include forks and clones
```
test_org.snapshot_stats.to_dict()
```
{'stars': {'repo1': 1, 'repo2': 0}, 'forks': {'repo1': 0, 'repo2': 0}}
### Traffic stats
We can also get a longer view by using traffic stats for views and clones
```
test_org.get_org_views_traffic(save_dir='readme_dir')
```
`get_org_views_traffic` will grab data via the GitHub api and update a CSV for each repository under the organization (by default only public) with views counts. This is largely intended to be used to semi-regularly update these stats by running this code as part of a GitHub Action or cron job.
If you want to load a DataFrame of traffic you can pass `load=True`
```
test_org.get_org_views_traffic(save_dir='readme_dir', load=True).to_dict()
```
{('repo1', 'total_views'): {Timestamp('2020-11-30 00:00:00'): 2,
Timestamp('2020-12-01 00:00:00'): 1},
('repo1', 'unique_views'): {Timestamp('2020-11-30 00:00:00'): 1,
Timestamp('2020-12-01 00:00:00'): 1},
('repo2', 'total_views'): {Timestamp('2020-11-30 00:00:00'): 8.0,
Timestamp('2020-12-01 00:00:00'): nan},
('repo2', 'unique_views'): {Timestamp('2020-11-30 00:00:00'): 1.0,
Timestamp('2020-12-01 00:00:00'): nan}}
Similarly the same can be done for clones