{
  "id": 23438,
  "title": "train_categorical.csv is all NAN ?",
  "url": "/competitions/bosch-production-line-performance/discussion/23438",
  "author_name": "",
  "post_date": "2016-09-07T10:27:33.480Z",
  "votes": null,
  "comment_count": 2,
  "views": 523,
  "content": "<p>Hello every one,</p>\n\n<p>I'm reading train_categorical like so: </p>\n\n<p>train_categorical = pd.read_csv('Data/train_categorical.csv',chunksize=10000, low_memory=False)</p>\n\n<p>But It seems that all data are NAN! </p>\n\n<p>Am I reading data in the wrong way ?</p>\n\n<p>Thank you.</p>",
  "messages": [
    {
      "id": "134575",
      "postDate": "09/07/2016 10:27:33",
      "content": "<p>Hello every one,</p>\n\n<p>I'm reading train_categorical like so: </p>\n\n<p>train_categorical = pd.read_csv('Data/train_categorical.csv',chunksize=10000, low_memory=False)</p>\n\n<p>But It seems that all data are NAN! </p>\n\n<p>Am I reading data in the wrong way ?</p>\n\n<p>Thank you.</p>",
      "rawMarkdown": "Hello every one,\r\n\r\nI'm reading train_categorical like so: \r\n\r\n train_categorical = pd.read_csv('Data/train_categorical.csv',chunksize=10000, low_memory=False)\r\n\r\nBut It seems that all data are NAN! \r\n\r\nAm I reading data in the wrong way ?\r\n\r\nThank you.",
      "votes": null
    },
    {
      "id": "134632",
      "postDate": "09/07/2016 16:52:49",
      "content": "<p>Nope. The data is just very sparse. If you for instance write something along the lines of the below piece of code then you can view the non missing data observations in the different columns and rows.</p>\n\n<pre><code>for col in train_categorical.columns[1:]:\n\n       non_empty_rows = train_categorical[col][categorical[col].notnull()]\n       if not non_empty_rows.empty:\n            print col\n            print non_empty_rows \n            print\n</code></pre>",
      "rawMarkdown": "Nope. The data is just very sparse. If you for instance write something along the lines of the below piece of code then you can view the non missing data observations in the different columns and rows.\r\n\r\n    for col in train_categorical.columns[1:]:\r\n    \r\n           non_empty_rows = train_categorical[col][categorical[col].notnull()]\r\n           if not non_empty_rows.empty:\r\n                print col\r\n                print non_empty_rows \r\n                print",
      "votes": null
    },
    {
      "id": "134757",
      "postDate": "09/08/2016 09:36:20",
      "content": "<p>Thank you zjsc for your answer</p>",
      "rawMarkdown": "Thank you zjsc for your answer",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 134632,
      "author_name": "zjscnovo",
      "author_url": "",
      "post_date": "09/07/2016 16:52:49",
      "content": "<p>Nope. The data is just very sparse. If you for instance write something along the lines of the below piece of code then you can view the non missing data observations in the different columns and rows.</p>\n\n<pre><code>for col in train_categorical.columns[1:]:\n\n       non_empty_rows = train_categorical[col][categorical[col].notnull()]\n       if not non_empty_rows.empty:\n            print col\n            print non_empty_rows \n            print\n</code></pre>",
      "votes": null,
      "replies": []
    },
    {
      "id": 134757,
      "author_name": "younasm",
      "author_url": "",
      "post_date": "09/08/2016 09:36:20",
      "content": "<p>Thank you zjsc for your answer</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "134575": "Hello every one,\r\n\r\nI'm reading train_categorical like so: \r\n\r\n train_categorical = pd.read_csv('Data/train_categorical.csv',chunksize=10000, low_memory=False)\r\n\r\nBut It seems that all data are NAN! \r\n\r\nAm I reading data in the wrong way ?\r\n\r\nThank you.",
    "134632": "Nope. The data is just very sparse. If you for instance write something along the lines of the below piece of code then you can view the non missing data observations in the different columns and rows.\r\n\r\n    for col in train_categorical.columns[1:]:\r\n    \r\n           non_empty_rows = train_categorical[col][categorical[col].notnull()]\r\n           if not non_empty_rows.empty:\r\n                print col\r\n                print non_empty_rows \r\n                print",
    "134757": "Thank you zjsc for your answer"
  },
  "source": "meta"
}