pcs-scraper


Namepcs-scraper JSON
Version 0.2.0 PyPI version JSON
download
home_pagehttps://github.com/lucaskoensgen/pcs_scraper
SummaryA python-based api to access procyclingstats data
upload_time2023-03-21 19:34:08
maintainer
docs_urlNone
authorLucas Koensgen
requires_python>=3.9
licenseGPLv3
keywords cycling web-scraping statistics procyclingstats peloton
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # pcs_scraper
**v0.1.0**
A python package to query, organize and prepare pandas dataframes from procyclingstats.com data to facilitate further analysis

## Project Description

This project was undertaken as a side project while working as a data/race analyst with professional cycling teams. While commonplace for many other major sports, I couldn't find any available packages that provided access to professional cycling datasets. There were, however, already fantastic websites devoted to cataloguing this data and presenting it for free. The most user-friendly website I found was procyclingstats.com (PCS) but they didn't have a publicly available api, so I decided to make this package to interact with their posted data programmatically. 

_pcs_scraper_ lets users interact with PCS through three fundamental and distinct classes:
1. Riders
2. Teams
3. Races

In the next versions of this project I would like to link the statistical data from PCS with rider Strava data.

## Installation

###### Via pip:
pip install pcs-scraper

###### Via conda:
Coming soon

###### Via source code:
Fork/clone this repo and create a conda environment to develop in using:
```
# create environment using existing environment file
conda env create -f environment.yml

# add pcs_scraper to path for environment
cd .../anaconda3/envs/pcs_env/lib/python3.9/site-packages
nano packages.pth 
# then in nano type **full path** to main pcs_scraper directory (ie. .../Users/name/Desktop/pcs_scraper)
# press control+O to save file, press control+X to exit nano
```

## Usage
###### Basic
```
# for specific rider

# import 
import pcs_scraper as pcs

# request rider object for tadej pogacar
pogacar = pcs.Rider(name = 'tadej-pogacar')

# get pogacar's entire race history 
pogacar_race_hx = pogacar.get_race_history()
```
```
# for specific race

# import
import pcs_scraper as pcs

# request race object for tour de france
tdf = pcs.Race(name = 'tour-de-france', year = 2021)

	# if unsure about spelling of race name according to PCS you can search using:
	# race_options = pcs.race_options_by_year(2021)
	# can refine output using race circuit or classification when requesting
	# race_options = pcs.race_options_by_year(2021, classification = '2.UWT', circuit = 'UCI World Tour')

# request the GC results
tdf_final_gc = tdf.get_results()
```
```
# for specific team

# import
import pcs_scraper as pcs

# request team object for Ineos 
ineos = pcs.Team(name = 'ineos-grenadiers', year = 2021)

	# if unsure about spelling of team name according to PCS you can search using:
	# team_options_2021 = pcs.teams_by_year(year = 2021, gender = 'M')

# get the riders from the team
ineos_2021_riders = ineos.get_riders()
```

###### Practical Examples
Coming soon

## Documentation
Coming soon



            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/lucaskoensgen/pcs_scraper",
    "name": "pcs-scraper",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": "",
    "keywords": "cycling,web-scraping,statistics,procyclingstats,peloton",
    "author": "Lucas Koensgen",
    "author_email": "l.koensgen@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/2a/e5/a82e5deb44162702be2c22a572078afd1aeae2973396828208d03c00892a/pcs-scraper-0.2.0.tar.gz",
    "platform": null,
    "description": "# pcs_scraper\n**v0.1.0**\nA python package to query, organize and prepare pandas dataframes from procyclingstats.com data to facilitate further analysis\n\n## Project Description\n\nThis project was undertaken as a side project while working as a data/race analyst with professional cycling teams. While commonplace for many other major sports, I couldn't find any available packages that provided access to professional cycling datasets. There were, however, already fantastic websites devoted to cataloguing this data and presenting it for free. The most user-friendly website I found was procyclingstats.com (PCS) but they didn't have a publicly available api, so I decided to make this package to interact with their posted data programmatically. \n\n_pcs_scraper_ lets users interact with PCS through three fundamental and distinct classes:\n1. Riders\n2. Teams\n3. Races\n\nIn the next versions of this project I would like to link the statistical data from PCS with rider Strava data.\n\n## Installation\n\n###### Via pip:\npip install pcs-scraper\n\n###### Via conda:\nComing soon\n\n###### Via source code:\nFork/clone this repo and create a conda environment to develop in using:\n```\n# create environment using existing environment file\nconda env create -f environment.yml\n\n# add pcs_scraper to path for environment\ncd .../anaconda3/envs/pcs_env/lib/python3.9/site-packages\nnano packages.pth \n# then in nano type **full path** to main pcs_scraper directory (ie. .../Users/name/Desktop/pcs_scraper)\n# press control+O to save file, press control+X to exit nano\n```\n\n## Usage\n###### Basic\n```\n# for specific rider\n\n# import \nimport pcs_scraper as pcs\n\n# request rider object for tadej pogacar\npogacar = pcs.Rider(name = 'tadej-pogacar')\n\n# get pogacar's entire race history \npogacar_race_hx = pogacar.get_race_history()\n```\n```\n# for specific race\n\n# import\nimport pcs_scraper as pcs\n\n# request race object for tour de france\ntdf = pcs.Race(name = 'tour-de-france', year = 2021)\n\n\t# if unsure about spelling of race name according to PCS you can search using:\n\t# race_options = pcs.race_options_by_year(2021)\n\t# can refine output using race circuit or classification when requesting\n\t# race_options = pcs.race_options_by_year(2021, classification = '2.UWT', circuit = 'UCI World Tour')\n\n# request the GC results\ntdf_final_gc = tdf.get_results()\n```\n```\n# for specific team\n\n# import\nimport pcs_scraper as pcs\n\n# request team object for Ineos \nineos = pcs.Team(name = 'ineos-grenadiers', year = 2021)\n\n\t# if unsure about spelling of team name according to PCS you can search using:\n\t# team_options_2021 = pcs.teams_by_year(year = 2021, gender = 'M')\n\n# get the riders from the team\nineos_2021_riders = ineos.get_riders()\n```\n\n###### Practical Examples\nComing soon\n\n## Documentation\nComing soon\n\n\n",
    "bugtrack_url": null,
    "license": "GPLv3",
    "summary": "A python-based api to access procyclingstats data",
    "version": "0.2.0",
    "split_keywords": [
        "cycling",
        "web-scraping",
        "statistics",
        "procyclingstats",
        "peloton"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "e91fea59c1c5838cd1711a306220a433f7674382aa232d93887c1dabdacefec9",
                "md5": "da59eb6e46d2ba960b653cedd18ceaf1",
                "sha256": "7c0a57636657dd51541dfbca5f027fae2dbe7470c848ea5b7eea35a2963f9ae5"
            },
            "downloads": -1,
            "filename": "pcs_scraper-0.2.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "da59eb6e46d2ba960b653cedd18ceaf1",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.9",
            "size": 36916,
            "upload_time": "2023-03-21T19:34:06",
            "upload_time_iso_8601": "2023-03-21T19:34:06.433882Z",
            "url": "https://files.pythonhosted.org/packages/e9/1f/ea59c1c5838cd1711a306220a433f7674382aa232d93887c1dabdacefec9/pcs_scraper-0.2.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "2ae5a82e5deb44162702be2c22a572078afd1aeae2973396828208d03c00892a",
                "md5": "ddace77554553ad9753342aabb73355a",
                "sha256": "b160c2e89a2b07394fb5381b669e199a125fd682159fa3128615a6d98f736fd4"
            },
            "downloads": -1,
            "filename": "pcs-scraper-0.2.0.tar.gz",
            "has_sig": false,
            "md5_digest": "ddace77554553ad9753342aabb73355a",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9",
            "size": 35106,
            "upload_time": "2023-03-21T19:34:08",
            "upload_time_iso_8601": "2023-03-21T19:34:08.138598Z",
            "url": "https://files.pythonhosted.org/packages/2a/e5/a82e5deb44162702be2c22a572078afd1aeae2973396828208d03c00892a/pcs-scraper-0.2.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-03-21 19:34:08",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "github_user": "lucaskoensgen",
    "github_project": "pcs_scraper",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "requirements": [],
    "lcname": "pcs-scraper"
}
        
Elapsed time: 0.05419s