https://github.com/Coko7/vegapull-records
Cards dataset for One Piece TCG
https://github.com/Coko7/vegapull-records
data one-piece one-piece-card-game one-piece-tcg tcg
Last synced: about 1 year ago
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Cards dataset for One Piece TCG
- Host: GitHub
- URL: https://github.com/Coko7/vegapull-records
- Owner: Coko7
- License: gpl-3.0
- Created: 2024-08-28T07:04:42.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-28T07:16:52.000Z (almost 2 years ago)
- Last Synced: 2024-08-28T08:28:59.287Z (almost 2 years ago)
- Topics: data, one-piece, one-piece-card-game, one-piece-tcg, tcg
- Homepage:
- Size: 540 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 👒 vegapull-records
Collection of datasets for One Piece TCG.
The data in this repository has been obtained using the [vegapull scraper CLI](https://github.com/Coko7/vegapull).
## 🗃️ File structure
Datasets are categorized by language and contain JSON data for all TCG cards:
- `packs.json`: contains the list of packs
- `cards_{PACK-ID}.json`: contains the list of cards for pack `PACK-ID`
Example:
```
data/
├── english/
│ ├── cards_569001.json
│ ├── cards_569002.json
│ ├── ...
│ ├── cards_569901.json
│ └── packs.json
└── japanese/
└── cards.json
```
**NOTE:** The current data for the japanese version was fetched using an older version of the CLI where all of the data
was put in a single JSON file (`cards.json`).
## 🖼️ Where can I download images?
Storing images directly on GitHub is not the best approach.
Instead, these images are zipped into an archive and can be downloaded along with the latest release.
## ⌛ How old is the data?
- 🇬🇧 English: April 27, 2025
- 🇯🇵 Japan: August 31, 2024
## ⚠️ Disclaimer
I had some trouble when fetching data for the japanese version:
- Wrong formatting/missing data for `colors` or `counter` values on some cards
- Duplicate cards with same data and art
These issues are with the original data itself and I did not find a clean/reliable way to handle them.
I tried to patch them by writing some custom code when fetching data for the `jp` version.
Fortunately, the `english` version does not seem to suffer from these problems.
So, keep this in mind when picking a dataset. I would recommend working with the `english` dataset.