From 4f876a7095a65585f585ccd533e3026222844574 Mon Sep 17 00:00:00 2001 From: "tristan.kirscher" Date: Wed, 1 May 2024 09:56:19 +0200 Subject: [PATCH] [~] update README.md --- README.md | 30 +++++++++++++++++------------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 17463ca..37a6599 100644 --- a/README.md +++ b/README.md @@ -8,11 +8,15 @@ ResultAthle is a project aimed at making statistical tools more accessible at th ## Table of Contents -- [Installation](#installation) -- [Usage](#usage) -- [Features under development](#features-under-development) -- [Contributing](#contributing) -- [License](#license) +- [ResultAthle](#resultathle) + - [Table of Contents](#table-of-contents) + - [Installation](#installation) + - [Usage](#usage) + - [Python package](#python-package) + - [HTML dashboard with Quarto](#html-dashboard-with-quarto) + - [Features Under Development](#features-under-development) + - [Contributing](#contributing) + - [License](#license) ## Installation @@ -24,18 +28,18 @@ pip install -r requirements.txt ## Usage -To retrieve the '.csv' file containing the list of results from a running competition on the bases.athle results site, you can use the 'scraping.ipynb' notebook or simply run the following command: +### Python package -```sh -python scraping.py "url" nb_pages +To retrieve the '.csv' file containing the list of results from a running competition on the bases.athle results site, you can use the 'scraping.py' module: + +```python +from utils.scraping import get_results +header, data = get_results(url, nb_pages) ``` Where 'url' is the [bases.athle](https://bases.athle.fr/) URL of the competition to scrape and 'nb_pages' is the number of result pages you want to scrape. -Example : -![cli_output_example](/src/cli_output_example.png) - -## Get HTML file with Quarto +### HTML dashboard with Quarto Quarto is to be downloaded here: [Quarto URL](https://quarto.org/docs/get-started/). @@ -59,7 +63,7 @@ We are continuously working to improve ResultAthle and add new features. Here ar - **Advanced Scraping Functions:** We are in the process of enhancing our web scraping capabilities to provide a more robust and sophisticated data extraction process. This will allow us to gather more detailed and comprehensive data from athletics competitions. -- **Visualization:** We are working on new visualization features that will allow users to better understand and interpret the data. This includes various types of charts and graphs. +- **Visualization:** We are working on new visualization features that will allow users to better understand and interpret the running performance. This includes various types of charts and graphs. - **Performance Analysis:** We are developing new features for analyzing athletic performance. This will include statistical analysis and machine learning algorithms to identify patterns and trends in the data.