{
  "id": 350459,
  "title": "Columnar Version of Competition Dataset",
  "url": "/competitions/open-problems-multimodal/discussion/350459",
  "author_name": "",
  "post_date": "2022-09-05T20:27:06.126694400Z",
  "votes": 5,
  "comment_count": 1,
  "views": 0,
  "content": "<p>I've created a columnar version of the competition dataset. This enables easy and fast inspection of feature columns (loading speeds are orders of magnitude faster compared to loading columns from the original dataset).</p>\n<p>It can be found here:<br>\n<a href=\"https://www.kaggle.com/datasets/leohash/mmscel-data-transposed\" target=\"_blank\">https://www.kaggle.com/datasets/leohash/mmscel-data-transposed</a></p>\n<p>A notebook with an example how to load data from the dataset into a pd.DataFrame can be found here:<br>\n<a href=\"https://www.kaggle.com/code/leohash/example-of-loading-transposed-tables\" target=\"_blank\">https://www.kaggle.com/code/leohash/example-of-loading-transposed-tables</a></p>\n<p>I hope that this is useful for people who want to dig deeper into features of features ;)</p>",
  "messages": [
    {
      "id": "1927634",
      "postDate": "09/05/2022 20:27:06",
      "content": "<p>I've created a columnar version of the competition dataset. This enables easy and fast inspection of feature columns (loading speeds are orders of magnitude faster compared to loading columns from the original dataset).</p>\n<p>It can be found here:<br>\n<a href=\"https://www.kaggle.com/datasets/leohash/mmscel-data-transposed\" target=\"_blank\">https://www.kaggle.com/datasets/leohash/mmscel-data-transposed</a></p>\n<p>A notebook with an example how to load data from the dataset into a pd.DataFrame can be found here:<br>\n<a href=\"https://www.kaggle.com/code/leohash/example-of-loading-transposed-tables\" target=\"_blank\">https://www.kaggle.com/code/leohash/example-of-loading-transposed-tables</a></p>\n<p>I hope that this is useful for people who want to dig deeper into features of features ;)</p>",
      "rawMarkdown": "I've created a columnar version of the competition dataset. This enables easy and fast inspection of feature columns (loading speeds are orders of magnitude faster compared to loading columns from the original dataset).\n\nIt can be found here:\nhttps://www.kaggle.com/datasets/leohash/mmscel-data-transposed\n\nA notebook with an example how to load data from the dataset into a pd.DataFrame can be found here:\nhttps://www.kaggle.com/code/leohash/example-of-loading-transposed-tables\n\nI hope that this is useful for people who want to dig deeper into features of features ;)",
      "votes": null
    },
    {
      "id": "1927879",
      "postDate": "09/06/2022 03:42:08",
      "content": "<p>Very good work! The notebook is great and is a good learning resource for beginners like me, thanks for this share!</p>",
      "rawMarkdown": "Very good work! The notebook is great and is a good learning resource for beginners like me, thanks for this share!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1927879,
      "author_name": "ravi20076",
      "author_url": "",
      "post_date": "09/06/2022 03:42:08",
      "content": "<p>Very good work! The notebook is great and is a good learning resource for beginners like me, thanks for this share!</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1927634": "I've created a columnar version of the competition dataset. This enables easy and fast inspection of feature columns (loading speeds are orders of magnitude faster compared to loading columns from the original dataset).\n\nIt can be found here:\nhttps://www.kaggle.com/datasets/leohash/mmscel-data-transposed\n\nA notebook with an example how to load data from the dataset into a pd.DataFrame can be found here:\nhttps://www.kaggle.com/code/leohash/example-of-loading-transposed-tables\n\nI hope that this is useful for people who want to dig deeper into features of features ;)",
    "1927879": "Very good work! The notebook is great and is a good learning resource for beginners like me, thanks for this share!"
  },
  "source": "meta"
}