{
  "id": 396919,
  "title": "\"Your notebook tried to allocate more memory than is available. It has restarted.\"",
  "url": "/competitions/predict-student-performance-from-game-play/discussion/396919",
  "author_name": "",
  "post_date": "2023-03-23T10:40:00.587935300Z",
  "votes": 2,
  "comment_count": 4,
  "views": 0,
  "content": "<p>Hello. I get above message when trying to load train data. I am new to kaggle. In previous days I was able to load data. Any ideas? thanks</p>",
  "messages": [
    {
      "id": "2193491",
      "postDate": "03/23/2023 10:40:00",
      "content": "<p>Hello. I get above message when trying to load train data. I am new to kaggle. In previous days I was able to load data. Any ideas? thanks</p>",
      "rawMarkdown": "Hello. I get above message when trying to load train data. I am new to kaggle. In previous days I was able to load data. Any ideas? thanks",
      "votes": null
    },
    {
      "id": "2193606",
      "postDate": "03/23/2023 11:36:16",
      "content": "<p>Hi, Maybe you want to load whole Training Set at once.<br>\nI use the following code to explore a part of Training Set:</p>\n<pre><code>train_df = pd.DataFrame()\n chunk  pd.read_csv(WORK_PATH + ,chunksize=) : \n    train_df = pd.concat([train_df, chunk.loc[: , [,,,]]])\n</code></pre>\n<p>and Learning from some discussion of this fun competitions, you can also define <code>dtype</code> in <code>read_csv</code> to load the Training Set with low memory.</p>\n<p><em>Have a good day</em></p>",
      "rawMarkdown": "Hi, Maybe you want to load whole Training Set at once.\nI use the following code to explore a part of Training Set:\n```python\ntrain_df = pd.DataFrame()\nfor chunk in pd.read_csv(WORK_PATH + 'train.csv',chunksize=100000) : \n    train_df = pd.concat([train_df, chunk.loc[: , ['session_id','index','level','level_group']]])\n```\nand Learning from some discussion of this fun competitions, you can also define `dtype` in `read_csv` to load the Training Set with low memory.\n\n*Have a good day*",
      "votes": null
    },
    {
      "id": "2193648",
      "postDate": "03/23/2023 11:56:55",
      "content": "<p>Thanks! it is helpful</p>",
      "rawMarkdown": "Thanks! it is helpful",
      "votes": null
    },
    {
      "id": "2193812",
      "postDate": "03/23/2023 14:13:04",
      "content": "<p>Here you can see how to download the entire Training Set quickly in 1028 MB <a href=\"https://www.kaggle.com/code/vadimkamaev/reading-data-1028-mb\" target=\"_blank\">https://www.kaggle.com/code/vadimkamaev/reading-data-1028-mb</a></p>",
      "rawMarkdown": "Here you can see how to download the entire Training Set quickly in 1028 MB https://www.kaggle.com/code/vadimkamaev/reading-data-1028-mb",
      "votes": null
    },
    {
      "id": "2194669",
      "postDate": "03/24/2023 04:57:27",
      "content": "<p>Thank you! It worked and I also understood well dtype conversion well with this example.</p>",
      "rawMarkdown": "Thank you! It worked and I also understood well dtype conversion well with this example.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2193606,
      "author_name": "huhaibo2000",
      "author_url": "",
      "post_date": "03/23/2023 11:36:16",
      "content": "<p>Hi, Maybe you want to load whole Training Set at once.<br>\nI use the following code to explore a part of Training Set:</p>\n<pre><code>train_df = pd.DataFrame()\n chunk  pd.read_csv(WORK_PATH + ,chunksize=) : \n    train_df = pd.concat([train_df, chunk.loc[: , [,,,]]])\n</code></pre>\n<p>and Learning from some discussion of this fun competitions, you can also define <code>dtype</code> in <code>read_csv</code> to load the Training Set with low memory.</p>\n<p><em>Have a good day</em></p>",
      "votes": null,
      "replies": [
        {
          "id": 2193648,
          "author_name": "davitkhantadze",
          "author_url": "",
          "post_date": "03/23/2023 11:56:55",
          "content": "<p>Thanks! it is helpful</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 2193812,
      "author_name": "vadimkamaev",
      "author_url": "",
      "post_date": "03/23/2023 14:13:04",
      "content": "<p>Here you can see how to download the entire Training Set quickly in 1028 MB <a href=\"https://www.kaggle.com/code/vadimkamaev/reading-data-1028-mb\" target=\"_blank\">https://www.kaggle.com/code/vadimkamaev/reading-data-1028-mb</a></p>",
      "votes": null,
      "replies": [
        {
          "id": 2194669,
          "author_name": "davitkhantadze",
          "author_url": "",
          "post_date": "03/24/2023 04:57:27",
          "content": "<p>Thank you! It worked and I also understood well dtype conversion well with this example.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2193491": "Hello. I get above message when trying to load train data. I am new to kaggle. In previous days I was able to load data. Any ideas? thanks",
    "2193606": "Hi, Maybe you want to load whole Training Set at once.\nI use the following code to explore a part of Training Set:\n```python\ntrain_df = pd.DataFrame()\nfor chunk in pd.read_csv(WORK_PATH + 'train.csv',chunksize=100000) : \n    train_df = pd.concat([train_df, chunk.loc[: , ['session_id','index','level','level_group']]])\n```\nand Learning from some discussion of this fun competitions, you can also define `dtype` in `read_csv` to load the Training Set with low memory.\n\n*Have a good day*",
    "2193648": "Thanks! it is helpful",
    "2193812": "Here you can see how to download the entire Training Set quickly in 1028 MB https://www.kaggle.com/code/vadimkamaev/reading-data-1028-mb",
    "2194669": "Thank you! It worked and I also understood well dtype conversion well with this example."
  },
  "source": "meta"
}