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GitHub city ranking creator

Project description

Build Status GitHub licenseCoverage Status Known Vulnerabilities Dependency Status

What is this?

This is a small library which gets all GitHub users given a city. Original idea is Top-GitHub-Users-Data by [@JJ](https://github.com/JJ), an adaptation of top-github-users from [@paulmillr](https://github.com/paulmillr/).

What I can do with this?

This is an amazing Python library to study the GitHub community in a location. You can get all the GitHub users from a given location and obtain some data. For instance, you can generate one ranking like this ranking with the users from Spain (and its provinces).

What I need to run this?

You will need to install Python 3. Python 2 is not supported.

In addition, you will need to get an ID and Secret for a GitHub application, after registering your own application here!.

Dependencies

There is a requirements.txt file included in this repo. Install all dependences with pip install -r requirements.txt.

How to install

There are two options to install this library and its dependencies.

Install from the source code

You need to clone (or download) this repository. Then, go to src folder and run:

python setup.py install

Install from pip

This library is available to be installed using pip.

pip install githubcity

Getting started

You can see one example about how to use this library here.

Basic example

idGH = os.environ.get('GH_ID')
secretGH = os.environ.get('GH_SECRET')
configuration = {
   "excludedLocations": [],
   "excludedUsers": [],
   "intervals": [
       [
           "2008-01-01",
           "2015-12-30"
       ]
   ],
   "last_date": "2015-12-30",
   "locations": [
       "Ceuta"
       ],
   "name": "Ceuta"
       }
ciudad = GitHubCity(idGH, secretGH, configuration)
ciudad.calculateBestIntervals()
ciudad.addFilter("repos", ">1")
ciudad.addFilter("followers", ">1")
ciudad.getCityUsers()

Excluding users

You can generate a JSON file like this (each element is an use

Project details


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githubcity-1.0.4.tar.gz (10.9 kB view hashes)

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