creativecommons / creativecommons/quantifying
[Feature] Post-GSoC '24: Solidify Processing Scripts for Quarterly Analysis
- 主要言語
- Python
- スター
- 48
- フォーク
- 74
- PR マージ指標
- 30日以内にマージされた PR はありません
説明
## Context
Automating Quantifying the Commons was a project endeavor for the Google Summer of Code 2024 program, in which a baseline automation software for data gathering, processing, and analysis was successfully developed. However given the time and resource constraints that we had to consider, there are still addressable endeavors to improve this codebase over the upcoming quarters and years. This is the first (1) of five (5) issues raised specifically for post-GSoC contributions.
## Problem
Due to only having one quarter’s worth of data, current processing scripts (`2-process`) are not fully optimized for long-term data analysis, which makes it difficult to accurately assess trends and patterns over quarterly periods.
## Description
This feature involves refining the processing scripts to handle data collected over a larger period, enabling more robust quarterly analysis. The focus will be on adding code that can effectively compare details of each data source by quarter (ex. `2024Q3` data is compared to all previous quarters’ data) and adding them into separate datasets for report generation.
**NOTE**: since contributing to this specific issue is limited by access to API data fetching and the fact that the solution is long-term, this issue is being set as a discussion for all open-source developers to be able to pitch their ideas for final implementation by the developer(s) who work on the codebase.
## Implementation
- [x] I would be interested in implementing this feature.
コントリビューションガイド
調査の方向性
まず `2-process` 配下の処理スクリプトを読み、API から取得したデータが四半期をまたいでどのように収集され、表現されているかを追跡します。意図する結果は、レポート生成のために、2024Q3 など各データソースをそれ以前のすべての四半期と比較する個別のデータセットです。実装の詳細については引き続き議論の余地があります。
索引モデルが issue の本文から書いたものです。
評価
- 技術スタック
- python
- 領域
- data-engineering
- issue の種類
- 機能追加
- 難易度
- 5/5
- 見積もり時間
- 1週間以上
- 活発さ
- 停滞
- 明瞭さ
- 説明が足りない
- 初心者へのやさしさ
- 25/100