{
  "id": 215345,
  "title": "Is it possible to work with the train data without using the pre-written code from the GitHub Link?",
  "url": "/competitions/indoor-location-navigation/discussion/215345",
  "author_name": "",
  "post_date": "2021-01-29T13:58:50.423754200Z",
  "votes": 11,
  "comment_count": 4,
  "views": 0,
  "content": "<p>Hello there,</p>\n<p>this competition seems very interesting and exciting,  the data sets look accordingly.</p>\n<p>In the given GitHub Link <a href=\"https://github.com/location-competition/indoor-location-competition-20\" target=\"_blank\">https://github.com/location-competition/indoor-location-competition-20</a><br>\nthere are a lot of scripts that seem to be helpful.</p>\n<p>My question is:  Is it recommended to use these functions to work with the train data or can you also simply read the data in like in every other competition ?  Or is this too tricky and cumbersome in this case ?</p>\n<p>Thanks a lot for your answers !</p>",
  "messages": [
    {
      "id": "1176091",
      "postDate": "01/29/2021 13:58:50",
      "content": "<p>Hello there,</p>\n<p>this competition seems very interesting and exciting,  the data sets look accordingly.</p>\n<p>In the given GitHub Link <a href=\"https://github.com/location-competition/indoor-location-competition-20\" target=\"_blank\">https://github.com/location-competition/indoor-location-competition-20</a><br>\nthere are a lot of scripts that seem to be helpful.</p>\n<p>My question is:  Is it recommended to use these functions to work with the train data or can you also simply read the data in like in every other competition ?  Or is this too tricky and cumbersome in this case ?</p>\n<p>Thanks a lot for your answers !</p>",
      "rawMarkdown": "Hello there,\n\nthis competition seems very interesting and exciting,  the data sets look accordingly.\n\nIn the given GitHub Link https://github.com/location-competition/indoor-location-competition-20\nthere are a lot of scripts that seem to be helpful.\n\nMy question is:  Is it recommended to use these functions to work with the train data or can you also simply read the data in like in every other competition ?  Or is this too tricky and cumbersome in this case ?\n\nThanks a lot for your answers !",
      "votes": null
    },
    {
      "id": "1176365",
      "postDate": "01/29/2021 15:39:59",
      "content": "<p>I posted a topic on how to easily parse the trace files with pandas. Here is the <a href=\"https://www.kaggle.com/c/indoor-location-navigation/discussion/215381\" target=\"_blank\">link</a>.</p>",
      "rawMarkdown": "I posted a topic on how to easily parse the trace files with pandas. Here is the [link](https://www.kaggle.com/c/indoor-location-navigation/discussion/215381).",
      "votes": null
    },
    {
      "id": "1176390",
      "postDate": "01/29/2021 15:52:46",
      "content": "<p>Thank you, that does help a lot !</p>",
      "rawMarkdown": "Thank you, that does help a lot !",
      "votes": null
    },
    {
      "id": "1178596",
      "postDate": "01/31/2021 02:17:26",
      "content": "<p>Hi! I post a notebook to run the scripts. Here is the <a href=\"https://www.kaggle.com/nayuts/get-understand-dataset-by-run-processing-scripts\" target=\"_blank\">link</a>. </p>\n<p>They outputs visualization results if we specify the data path.</p>\n<p>If you want to see the sample results of the scripts, understand codes or run your forked and customized code on your repository, please refer.</p>",
      "rawMarkdown": "Hi! I post a notebook to run the scripts. Here is the [link](https://www.kaggle.com/nayuts/get-understand-dataset-by-run-processing-scripts). \n\nThey outputs visualization results if we specify the data path.\n\nIf you want to see the sample results of the scripts, understand codes or run your forked and customized code on your repository, please refer.",
      "votes": null
    },
    {
      "id": "1179894",
      "postDate": "02/01/2021 00:16:25",
      "content": "<p>I made <a href=\"https://www.kaggle.com/satokiogiso/read-data-as-pandas-dataframe\" target=\"_blank\">a kernel that reads train log files as pandas dataframe</a>. </p>",
      "rawMarkdown": "I made [a kernel that reads train log files as pandas dataframe](https://www.kaggle.com/satokiogiso/read-data-as-pandas-dataframe).",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1176365,
      "author_name": "tolgadincer",
      "author_url": "",
      "post_date": "01/29/2021 15:39:59",
      "content": "<p>I posted a topic on how to easily parse the trace files with pandas. Here is the <a href=\"https://www.kaggle.com/c/indoor-location-navigation/discussion/215381\" target=\"_blank\">link</a>.</p>",
      "votes": null,
      "replies": [
        {
          "id": 1176390,
          "author_name": "jonas0",
          "author_url": "",
          "post_date": "01/29/2021 15:52:46",
          "content": "<p>Thank you, that does help a lot !</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1178596,
      "author_name": "nayuts",
      "author_url": "",
      "post_date": "01/31/2021 02:17:26",
      "content": "<p>Hi! I post a notebook to run the scripts. Here is the <a href=\"https://www.kaggle.com/nayuts/get-understand-dataset-by-run-processing-scripts\" target=\"_blank\">link</a>. </p>\n<p>They outputs visualization results if we specify the data path.</p>\n<p>If you want to see the sample results of the scripts, understand codes or run your forked and customized code on your repository, please refer.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1179894,
      "author_name": "satokiogiso",
      "author_url": "",
      "post_date": "02/01/2021 00:16:25",
      "content": "<p>I made <a href=\"https://www.kaggle.com/satokiogiso/read-data-as-pandas-dataframe\" target=\"_blank\">a kernel that reads train log files as pandas dataframe</a>. </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1176091": "Hello there,\n\nthis competition seems very interesting and exciting,  the data sets look accordingly.\n\nIn the given GitHub Link https://github.com/location-competition/indoor-location-competition-20\nthere are a lot of scripts that seem to be helpful.\n\nMy question is:  Is it recommended to use these functions to work with the train data or can you also simply read the data in like in every other competition ?  Or is this too tricky and cumbersome in this case ?\n\nThanks a lot for your answers !",
    "1176365": "I posted a topic on how to easily parse the trace files with pandas. Here is the [link](https://www.kaggle.com/c/indoor-location-navigation/discussion/215381).",
    "1176390": "Thank you, that does help a lot !",
    "1178596": "Hi! I post a notebook to run the scripts. Here is the [link](https://www.kaggle.com/nayuts/get-understand-dataset-by-run-processing-scripts). \n\nThey outputs visualization results if we specify the data path.\n\nIf you want to see the sample results of the scripts, understand codes or run your forked and customized code on your repository, please refer.",
    "1179894": "I made [a kernel that reads train log files as pandas dataframe](https://www.kaggle.com/satokiogiso/read-data-as-pandas-dataframe)."
  },
  "source": "meta"
}