1_Data_Loading_Loan_Data_from_Kaggle.ipynb

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    "colab": {
      "name": "DFC605 - 1. Data Loading : Loan Data from Kaggle.ipynb",
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      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "197w6kz69TxoTVZAMUV5Wmm9_jaf3HHoa",
      "authorship_tag": "ABX9TyPknd17LojHvhU4afa6ROMN",
      "include_colab_link": true
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    "kernelspec": {
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mlvlab/DFE604-2020F/blob/master/1_Data_Loading_Loan_Data_from_Kaggle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6tTHX3m5uqs",
        "colab_type": "text"
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      "source": [
        "Goals \n",
        "-----\n",
        "* Find a dataset on Kaggle, Internet, or prepare your own dataset\n",
        "* Store your csv file on Google Drive\n",
        "* Load your data on Colab\n",
        "* Calculate basic statistics of your data\n",
        "* Remove unnecessary columns or rows (preprocessing)\n",
        "\n",
        "References\n",
        "-----\n",
        "* [Load Data from Kaggle](https://www.kaggle.com/zhijinzhai/loandata)\n",
        "* [csv reader](https://docs.python.org/3/library/csv.html)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYms_SWM7h15",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c5c29c00-884e-4749-8ac2-d7e9e350fbad"
      },
      "source": [
        "cd '/content/drive/My Drive/Colab Notebooks/DFC605-2020S/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/DFC605-2020S\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfYH6xOvRwJs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53705aa8-2c93-4c31-e444-262cb62d2224"
      },
      "source": [
        "pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/Colab Notebooks/DFC605-2020S'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-TI9NMp7kha",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fcf2476f-4db3-4fbd-864a-3a23aec69a91"
      },
      "source": [
        "ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'DFC605 - 1. Data Loading : Loan Data from Kaggle.ipynb'\n",
            "'Loan payments data.csv'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7GXEb0b7k-Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-poHy1r8giW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "389bb7d8-4064-41d2-bcf6-6673d5b47481"
      },
      "source": [
        "with open('Loan payments data.csv', newline='') as csvfile:\n",
        "  csvreader = csv.reader(csvfile, delimiter=',')\n",
        "  for row in csvreader:\n",
        "    print(','.join(row))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loan_ID,loan_status,Principal,terms,effective_date,due_date,paid_off_time,past_due_days,age,education,Gender\n",
            "xqd20166231,PAIDOFF,1000,30,9/8/2016,10/7/2016,9/14/2016 19:31,,45,High School or Below,male\n",
            "xqd20168902,PAIDOFF,1000,30,9/8/2016,10/7/2016,10/7/2016 9:00,,50,Bechalor,female\n",
            "xqd20160003,PAIDOFF,1000,30,9/8/2016,10/7/2016,9/25/2016 16:58,,33,Bechalor,female\n",
            "xqd20160004,PAIDOFF,1000,15,9/8/2016,9/22/2016,9/22/2016 20:00,,27,college,male\n",
            "xqd20160005,PAIDOFF,1000,30,9/9/2016,10/8/2016,9/23/2016 21:36,,28,college,female\n",
            "xqd20160706,PAIDOFF,300,7,9/9/2016,9/15/2016,9/9/2016 13:45,,35,Master or Above,male\n",
            "xqd20160007,PAIDOFF,1000,30,9/9/2016,10/8/2016,10/7/2016 23:07,,29,college,male\n",
            "xqd20160008,PAIDOFF,1000,30,9/9/2016,10/8/2016,10/5/2016 20:33,,36,college,male\n",
            "xqd20160909,PAIDOFF,1000,30,9/9/2016,10/8/2016,10/8/2016 16:00,,28,college,male\n",
            "xqd20160010,PAIDOFF,800,15,9/10/2016,9/24/2016,9/24/2016 13:00,,26,college,male\n",
            "xqd20160011,PAIDOFF,300,7,9/10/2016,9/16/2016,9/11/2016 19:11,,29,college,male\n",
            "xqd20160012,PAIDOFF,1000,15,9/10/2016,10/9/2016,10/9/2016 16:00,,39,High School or Below,male\n",
            "xqd20160013,PAIDOFF,1000,30,9/10/2016,10/9/2016,10/7/2016 23:32,,26,college,male\n",
            "xqd20160014,PAIDOFF,900,7,9/10/2016,9/16/2016,9/13/2016 21:57,,26,college,female\n",
            "xqd20160015,PAIDOFF,1000,7,9/10/2016,9/16/2016,9/15/2016 14:27,,27,High School or Below,male\n",
            "xqd20160016,PAIDOFF,800,15,9/10/2016,9/24/2016,9/24/2016 16:00,,26,college,male\n",
            "xqd20160017,PAIDOFF,1000,30,9/10/2016,10/9/2016,9/27/2016 14:21,,40,High School or Below,male\n",
            "xqd20160018,PAIDOFF,1000,15,9/10/2016,9/24/2016,9/23/2016 18:49,,32,High School or Below,male\n",
            "xqd20160019,PAIDOFF,1000,30,9/10/2016,10/9/2016,10/5/2016 22:05,,32,High School or Below,male\n",
            "xqd20160020,PAIDOFF,800,30,9/10/2016,10/9/2016,9/23/2016 7:42,,26,college,male\n",
            "xqd20160021,PAIDOFF,1000,30,9/10/2016,10/9/2016,10/9/2016 9:00,,26,college,male\n",
            "xqd20160022,PAIDOFF,1000,30,9/10/2016,10/9/2016,10/8/2016 17:09,,43,High School or Below,female\n",
            "xqd20160023,PAIDOFF,1000,30,9/10/2016,10/9/2016,10/9/2016 23:00,,25,High School or Below,male\n",
            "xqd20160024,PAIDOFF,1000,15,9/10/2016,9/24/2016,9/24/2016 13:00,,26,college,male\n",
            "xqd20160025,PAIDOFF,1000,30,9/10/2016,10/9/2016,10/3/2016 12:50,,26,college,male\n",
            "xqd20160026,PAIDOFF,1000,30,9/10/2016,10/9/2016,9/29/2016 12:18,,29,High School or Below,male\n",
            "xqd20160027,PAIDOFF,800,15,9/10/2016,9/24/2016,9/21/2016 20:16,,39,Bechalor,male\n",
            "xqd20170088,PAIDOFF,1000,15,9/10/2016,9/24/2016,9/23/2016 8:21,,34,Bechalor,male\n",
            "xqd20160029,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/22/2016 19:17,,31,college,male\n",
            "xqd20160030,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 17:33,,33,college,male\n",
            "xqd88160031,PAIDOFF,800,15,9/11/2016,9/25/2016,9/24/2016 14:41,,33,High School or Below,male\n",
            "xqd20160032,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/7/2016 21:48,,37,college,male\n",
            "xqd20160033,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 17:44,,27,college,male\n",
            "xqd22169034,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 7:24,,37,college,male\n",
            "xqd20160035,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 21:49,,33,college,male\n",
            "xqd20160036,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 9:00,,29,Bechalor,male\n",
            "xqd20160037,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 16:00,,27,High School or Below,male\n",
            "xqd20160038,PAIDOFF,700,15,9/11/2016,9/25/2016,9/25/2016 13:00,,33,High School or Below,male\n",
            "xqd20160039,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 9:00,,24,college,male\n",
            "xqd20160040,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 11:33,,21,Bechalor,male\n",
            "xqd20160041,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 9:00,,32,college,female\n",
            "xqd20160042,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 14:36,,30,college,male\n",
            "xqd20160043,PAIDOFF,1000,7,9/11/2016,9/24/2016,9/24/2016 9:00,,31,Bechalor,male\n",
            "xqd20160044,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/20/2016 15:00,,30,college,male\n",
            "xqd20160045,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/21/2016 22:29,,24,Bechalor,female\n",
            "xqd20160046,PAIDOFF,800,7,9/11/2016,9/17/2016,9/12/2016 22:17,,35,High School or Below,male\n",
            "xqd20160047,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 14:14,,22,High School or Below,male\n",
            "xqd20160048,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 8:53,,32,college,male\n",
            "xqd20160049,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,32,Bechalor,male\n",
            "xqd20160050,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 19:21,,50,High School or Below,male\n",
            "xqd20160051,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 13:00,,27,college,female\n",
            "xqd20160052,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,35,Bechalor,female\n",
            "xqd20160053,PAIDOFF,800,15,9/11/2016,9/25/2016,9/13/2016 4:34,,35,Bechalor,female\n",
            "xqd20160054,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,34,High School or Below,male\n",
            "xqd20160055,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 16:00,,21,High School or Below,male\n",
            "xqd20160056,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 16:00,,25,college,male\n",
            "xqd20160057,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,27,High School or Below,male\n",
            "xqd20160058,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/7/2016 2:33,,26,Bechalor,male\n",
            "xqd20160059,PAIDOFF,800,15,9/11/2016,9/25/2016,9/24/2016 11:40,,44,High School or Below,female\n",
            "xqd20160060,PAIDOFF,800,15,9/11/2016,9/25/2016,9/22/2016 6:38,,39,Master or Above,male\n",
            "xqd20160061,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/30/2016 21:12,,34,Bechalor,male\n",
            "xqd20160062,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/24/2016 13:42,,37,college,male\n",
            "xqd20160063,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 7:25,,34,High School or Below,male\n",
            "xqd20160064,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/12/2016 11:40,,45,college,male\n",
            "xqd20160065,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 14:38,,24,High School or Below,male\n",
            "xqd20160066,PAIDOFF,900,15,9/11/2016,9/25/2016,9/25/2016 23:00,,28,college,male\n",
            "xqd20160067,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 12:04,,28,Bechalor,male\n",
            "xqd20160068,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,37,High School or Below,male\n",
            "xqd20160069,PAIDOFF,300,7,9/11/2016,9/17/2016,9/14/2016 22:05,,35,college,male\n",
            "xqd20160070,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/24/2016 13:27,,43,Bechalor,male\n",
            "xqd20160071,PAIDOFF,800,15,9/11/2016,9/25/2016,9/22/2016 21:18,,29,college,male\n",
            "xqd20160072,PAIDOFF,800,15,9/11/2016,9/25/2016,9/24/2016 22:53,,29,High School or Below,male\n",
            "xqd20160073,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 16:00,,33,Bechalor,female\n",
            "xqd20160074,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 9:00,,34,college,male\n",
            "xqd20160075,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 9:00,,25,college,male\n",
            "xqd20160076,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 13:12,,30,High School or Below,male\n",
            "xqd20160077,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 13:49,,31,Bechalor,male\n",
            "xqd20160078,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,35,college,male\n",
            "xqd20160079,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/30/2016 14:29,,37,college,female\n",
            "xqd20160080,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,44,High School or Below,female\n",
            "xqd20160081,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/21/2016 16:18,,28,High School or Below,male\n",
            "xqd20160082,PAIDOFF,1000,7,9/11/2016,9/17/2016,9/13/2016 14:53,,25,college,male\n",
            "xqd20160083,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 9:00,,29,college,male\n",
            "xqd20160084,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 13:00,,33,college,male\n",
            "xqd20160085,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 13:00,,37,High School or Below,female\n",
            "xqd20160086,PAIDOFF,1000,30,9/11/2016,11/9/2016,11/9/2016 9:00,,33,college,male\n",
            "xqd20160087,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:00,,24,High School or Below,female\n",
            "xqd20160088,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/17/2016 13:01,,27,college,female\n",
            "xqd20160089,PAIDOFF,800,15,9/11/2016,9/25/2016,9/21/2016 9:35,,43,Bechalor,male\n",
            "xqd90160090,PAIDOFF,800,15,9/11/2016,9/25/2016,9/24/2016 20:33,,46,High School or Below,female\n",
            "xqd91160291,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 9:00,,34,college,female\n",
            "xqd90160092,PAIDOFF,1000,7,9/11/2016,9/17/2016,9/17/2016 9:00,,32,Bechalor,female\n",
            "xqd90163093,PAIDOFF,800,15,9/11/2016,9/25/2016,9/24/2016 0:12,,38,High School or Below,male\n",
            "xqd20160094,PAIDOFF,800,15,9/11/2016,9/25/2016,9/21/2016 12:43,,27,High School or Below,male\n",
            "xqd20167095,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 13:00,,33,High School or Below,male\n",
            "xqd20160096,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 20:49,,36,college,male\n",
            "xqd20160097,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/20/2016 5:38,,26,High School or Below,male\n",
            "xqd20160098,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:01,,34,college,male\n",
            "xqd20160099,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:01,,22,High School or Below,male\n",
            "xqd20160100,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 16:55,,31,Bechalor,female\n",
            "xqd20160101,PAIDOFF,1000,7,9/11/2016,9/17/2016,9/17/2016 9:00,,29,High School or Below,male\n",
            "xqd20160102,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 9:00,,38,college,male\n",
            "xqd20160103,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 16:00,,30,college,male\n",
            "xqd20160104,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 23:48,,45,High School or Below,male\n",
            "xqd20160105,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/22/2016 13:15,,35,college,male\n",
            "xqd20160106,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/23/2016 13:31,,30,college,male\n",
            "xqd20160107,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 17:19,,31,High School or Below,male\n",
            "xqd20160108,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:01,,31,High School or Below,male\n",
            "xqd20160109,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 13:05,,28,college,male\n",
            "xqd20160110,PAIDOFF,1000,7,9/11/2016,9/24/2016,9/24/2016 13:00,,29,college,male\n",
            "xqd20160111,PAIDOFF,800,15,9/11/2016,9/25/2016,9/20/2016 20:47,,29,college,male\n",
            "xqd20160112,PAIDOFF,1000,30,9/11/2016,11/9/2016,11/9/2016 9:00,,27,college,female\n",
            "xqd20160113,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 9:01,,27,college,male\n",
            "xqd20160114,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 13:01,,33,college,male\n",
            "xqd20160115,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 21:39,,28,college,male\n",
            "xqd20160116,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 23:00,,25,High School or Below,male\n",
            "xqd20160117,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/7/2016 14:23,,40,college,male\n",
            "xqd20160118,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/6/2016 15:25,,23,High School or Below,male\n",
            "xqd20160119,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/8/2016 6:56,,35,Bechalor,male\n",
            "xqd20160120,PAIDOFF,800,15,9/11/2016,9/25/2016,9/16/2016 11:58,,24,college,male\n",
            "xqd20160121,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 16:01,,34,college,male\n",
            "xqd20160122,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/27/2016 7:02,,22,High School or Below,male\n",
            "xqd20160123,PAIDOFF,1000,15,9/11/2016,10/25/2016,10/25/2016 9:00,,20,college,male\n",
            "xqd20160124,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/24/2016 11:02,,23,college,male\n",
            "xqd20160125,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/29/2016 18:57,,33,college,male\n",
            "xqd20160126,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 13:01,,26,college,male\n",
            "xqd20160127,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 9:00,,28,High School or Below,male\n",
            "xqd20160128,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 12:24,,43,High School or Below,male\n",
            "xqd78160129,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/25/2016 13:00,,34,Bechalor,male\n",
            "xqd20160130,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/26/2016 4:41,,38,Bechalor,male\n",
            "xqd20160131,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/22/2016 15:44,,26,High School or Below,male\n",
            "xqd20160132,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 16:00,,43,High School or Below,male\n",
            "xqd20160133,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/10/2016 16:13,,26,High School or Below,male\n",
            "xqd20160134,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/30/2016 7:12,,33,college,female\n",
            "xqd20160135,PAIDOFF,800,15,9/11/2016,9/25/2016,9/23/2016 11:26,,24,college,male\n",
            "xqd20160136,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/12/2016 10:26,,30,High School or Below,male\n",
            "xqd20160137,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 13:00,,32,High School or Below,female\n",
            "xqd20160138,PAIDOFF,1000,15,9/11/2016,10/25/2016,10/25/2016 9:00,,22,college,male\n",
            "xqd20160139,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/22/2016 21:45,,47,High School or Below,male\n",
            "xqd56160140,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/9/2016 20:28,,20,High School or Below,male\n",
            "xqd20160141,PAIDOFF,1000,30,9/11/2016,10/10/2016,10/1/2016 16:48,,28,High School or Below,male\n",
            "xqd20160142,PAIDOFF,800,15,9/11/2016,9/25/2016,9/25/2016 9:01,,35,college,male\n",
            "xqd20160143,PAIDOFF,1000,7,9/11/2016,9/17/2016,9/15/2016 20:36,,27,High School or Below,male\n",
            "xqd20160144,PAIDOFF,800,15,9/11/2016,9/25/2016,9/21/2016 15:33,,33,college,female\n",
            "xqd20160145,PAIDOFF,1000,30,9/11/2016,10/10/2016,9/29/2016 13:36,,30,High School or Below,male\n",
            "xqd20160146,PAIDOFF,1000,15,9/11/2016,9/25/2016,9/22/2016 20:51,,31,college,male\n",
            "xqd20160147,PAIDOFF,1000,30,9/11/2016,11/9/2016,11/9/2016 23:00,,26,college,female\n",
            "xqd20160148,PAIDOFF,300,7,9/12/2016,9/18/2016,9/18/2016 9:00,,37,Master or Above,male\n",
            "xqd20160149,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 9:00,,26,Bechalor,male\n",
            "xqd20160150,PAIDOFF,800,15,9/12/2016,9/26/2016,9/24/2016 10:14,,35,Bechalor,male\n",
            "xqd20160151,PAIDOFF,1000,15,9/12/2016,10/26/2016,10/26/2016 9:00,,29,college,male\n",
            "xqd34160152,PAIDOFF,800,15,9/12/2016,9/26/2016,9/23/2016 20:30,,23,college,male\n",
            "xqd20160153,PAIDOFF,500,15,9/12/2016,9/26/2016,9/13/2016 20:17,,23,college,female\n",
            "xqd20160154,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 9:00,,30,college,male\n",
            "xqd20160155,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/10/2016 7:01,,34,college,male\n",
            "xqd20160156,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 13:00,,36,High School or Below,female\n",
            "xqd20160157,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 9:00,,26,Bechalor,male\n",
            "xqd20160158,PAIDOFF,800,15,9/12/2016,9/26/2016,9/24/2016 14:55,,29,High School or Below,male\n",
            "xqd12160159,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 9:00,,28,college,female\n",
            "xqd20160160,PAIDOFF,1000,30,9/12/2016,10/11/2016,9/25/2016 20:56,,27,High School or Below,male\n",
            "xqd20160161,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/22/2016 10:49,,24,High School or Below,male\n",
            "xqd20160162,PAIDOFF,800,15,9/12/2016,9/26/2016,9/25/2016 22:09,,31,Bechalor,male\n",
            "xqd20160163,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 9:00,,28,High School or Below,male\n",
            "xqd28160164,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 9:00,,27,college,female\n",
            "xqd20160165,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 19:33,,25,High School or Below,male\n",
            "xqd20160166,PAIDOFF,1000,30,9/12/2016,11/10/2016,11/10/2016 16:00,,24,High School or Below,male\n",
            "xqd20160167,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 16:00,,28,college,male\n",
            "xqd20160168,PAIDOFF,800,30,9/12/2016,10/11/2016,10/11/2016 16:00,,28,college,male\n",
            "xqd20160169,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 13:00,,35,High School or Below,male\n",
            "xqd27160170,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 13:00,,38,college,male\n",
            "xqd20160171,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 16:00,,38,High School or Below,male\n",
            "xqd20160172,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 16:00,,29,college,male\n",
            "xqd20160173,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 13:00,,35,High School or Below,male\n",
            "xqd20160174,PAIDOFF,1000,30,9/12/2016,10/11/2016,9/17/2016 7:39,,24,college,male\n",
            "xqd20160175,PAIDOFF,800,15,9/12/2016,9/26/2016,9/22/2016 10:30,,39,High School or Below,male\n",
            "xqd20160176,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 13:00,,25,college,male\n",
            "xqd20160177,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 16:00,,38,High School or Below,male\n",
            "xqd20160178,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/10/2016 20:41,,30,college,male\n",
            "xqd20160179,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 9:00,,21,High School or Below,male\n",
            "xqd20160180,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/10/2016 8:04,,46,college,male\n",
            "xqd20160181,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/24/2016 11:00,,31,High School or Below,female\n",
            "xqd20160182,PAIDOFF,300,7,9/12/2016,9/18/2016,9/17/2016 9:25,,29,High School or Below,male\n",
            "xqd20160183,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/7/2016 11:53,,35,High School or Below,male\n",
            "xqd20160184,PAIDOFF,800,15,9/12/2016,9/26/2016,9/25/2016 8:39,,30,High School or Below,male\n",
            "xqd20160185,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 9:00,,27,High School or Below,male\n",
            "xqd20160186,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/10/2016 20:28,,31,High School or Below,female\n",
            "xqd20160187,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/1/2016 10:18,,33,Bechalor,male\n",
            "xqd20160188,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 16:00,,34,High School or Below,male\n",
            "xqd20160189,PAIDOFF,800,15,9/12/2016,9/26/2016,9/19/2016 21:07,,28,college,male\n",
            "xqd20160190,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 9:00,,42,college,male\n",
            "xqd20160191,PAIDOFF,1000,30,9/12/2016,10/11/2016,9/30/2016 14:38,,32,college,male\n",
            "xqd20160192,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 13:00,,30,High School or Below,male\n",
            "xqd20160193,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/14/2016 20:31,,25,High School or Below,female\n",
            "xqd20160194,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 9:00,,27,High School or Below,female\n",
            "xqd20160195,PAIDOFF,800,15,9/12/2016,9/26/2016,9/24/2016 16:15,,21,college,male\n",
            "xqd20160196,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/10/2016 15:49,,24,college,male\n",
            "xqd20160197,PAIDOFF,1000,30,9/12/2016,11/10/2016,11/10/2016 13:00,,29,college,male\n",
            "xqd20160198,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/23/2016 10:32,,40,college,male\n",
            "xqd20160199,PAIDOFF,1000,30,9/12/2016,10/11/2016,9/30/2016 14:03,,29,High School or Below,male\n",
            "xqd20160200,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/9/2016 14:17,,29,college,male\n",
            "xqd20160201,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/20/2016 8:26,,30,college,male\n",
            "xqd20160202,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 23:00,,26,High School or Below,female\n",
            "xqd20160203,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/24/2016 20:47,,36,High School or Below,male\n",
            "xqd20160204,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 16:00,,27,college,male\n",
            "xqd20160205,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 9:01,,20,college,male\n",
            "xqd20160206,PAIDOFF,1000,7,9/12/2016,9/18/2016,9/16/2016 14:52,,26,Bechalor,female\n",
            "xqd20160207,PAIDOFF,1000,30,9/12/2016,11/10/2016,11/10/2016 13:00,,26,college,male\n",
            "xqd20160208,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/9/2016 10:00,,27,college,male\n",
            "xqd20160209,PAIDOFF,300,7,9/12/2016,9/18/2016,9/12/2016 14:40,,23,High School or Below,male\n",
            "xqd20160210,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 16:00,,39,High School or Below,male\n",
            "xqd20160211,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/23/2016 21:58,,27,High School or Below,male\n",
            "xqd20160212,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/8/2016 18:48,,30,High School or Below,male\n",
            "xqd20160213,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/10/2016 16:41,,33,High School or Below,female\n",
            "xqd20160214,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 13:01,,27,High School or Below,male\n",
            "xqd20160215,PAIDOFF,1000,30,9/12/2016,10/11/2016,9/16/2016 2:34,,35,High School or Below,male\n",
            "xqd20160216,PAIDOFF,1000,30,9/12/2016,11/10/2016,11/10/2016 16:00,,29,college,female\n",
            "xqd20160217,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/21/2016 8:11,,50,High School or Below,male\n",
            "xqd20160218,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 9:00,,31,High School or Below,female\n",
            "xqd20160219,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 13:00,,31,High School or Below,male\n",
            "xqd20160220,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 13:01,,29,High School or Below,male\n",
            "xqd20160221,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 23:00,,35,college,male\n",
            "xqd20160222,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 9:01,,39,college,male\n",
            "xqd20160223,PAIDOFF,1000,30,9/12/2016,11/10/2016,11/10/2016 13:00,,29,college,male\n",
            "xqd20160224,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 23:00,,30,High School or Below,male\n",
            "xqd20160225,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/9/2016 10:00,,33,Bechalor,male\n",
            "xqd20160226,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 13:01,,26,High School or Below,male\n",
            "xqd20160227,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/23/2016 14:01,,25,High School or Below,male\n",
            "xqd20160228,PAIDOFF,800,15,9/12/2016,9/26/2016,9/25/2016 13:29,,37,Bechalor,male\n",
            "xqd20160229,PAIDOFF,800,15,9/12/2016,9/26/2016,9/25/2016 14:50,,26,High School or Below,male\n",
            "xqd20160230,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 9:00,,26,college,male\n",
            "xqd20160231,PAIDOFF,1000,15,9/12/2016,10/26/2016,10/26/2016 9:00,,27,college,male\n",
            "xqd20160232,PAIDOFF,1000,7,9/12/2016,9/25/2016,9/25/2016 9:01,,34,college,female\n",
            "xqd20160233,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/8/2016 15:35,,37,college,male\n",
            "xqd20160234,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/11/2016 16:01,,36,High School or Below,male\n",
            "xqd20160235,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 19:35,,33,High School or Below,male\n",
            "xqd20160236,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/9/2016 21:28,,30,High School or Below,male\n",
            "xqd20160237,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/7/2016 16:45,,30,college,male\n",
            "xqd20160238,PAIDOFF,800,15,9/12/2016,9/26/2016,9/24/2016 12:13,,36,High School or Below,male\n",
            "xqd20160239,PAIDOFF,1000,15,9/12/2016,10/11/2016,10/11/2016 9:01,,29,college,male\n",
            "xqd20160240,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/14/2016 23:02,,36,High School or Below,male\n",
            "xqd20160241,PAIDOFF,1000,30,9/12/2016,10/11/2016,10/8/2016 11:03,,32,High School or Below,male\n",
            "xqd20160242,PAIDOFF,1000,15,9/12/2016,9/26/2016,9/26/2016 9:00,,29,High School or Below,female\n",
            "xqd20160243,PAIDOFF,800,15,9/12/2016,9/26/2016,9/26/2016 23:00,,36,Bechalor,male\n",
            "xqd20160244,PAIDOFF,800,15,9/12/2016,9/26/2016,9/25/2016 19:31,,30,High School or Below,female\n",
            "xqd20160245,PAIDOFF,1000,7,9/13/2016,9/19/2016,9/14/2016 19:48,,31,college,male\n",
            "xqd20160246,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/12/2016 23:00,,19,High School or Below,female\n",
            "xqd20160247,PAIDOFF,800,15,9/13/2016,9/27/2016,9/25/2016 12:48,,26,college,male\n",
            "xqd20160248,PAIDOFF,800,15,9/13/2016,9/27/2016,9/26/2016 21:18,,34,High School or Below,male\n",
            "xqd20160249,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/7/2016 10:22,,35,High School or Below,male\n",
            "xqd20160250,PAIDOFF,1000,15,9/13/2016,9/27/2016,9/26/2016 6:17,,35,Bechalor,female\n",
            "xqd20160251,PAIDOFF,800,15,9/13/2016,9/27/2016,9/22/2016 16:57,,38,college,male\n",
            "xqd20160252,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/9/2016 21:57,,29,college,male\n",
            "xqd20160253,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/4/2016 12:59,,28,High School or Below,male\n",
            "xqd20160254,PAIDOFF,500,7,9/13/2016,9/19/2016,9/17/2016 20:51,,22,High School or Below,male\n",
            "xqd20160255,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/12/2016 23:00,,32,college,male\n",
            "xqd20160256,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/8/2016 15:51,,31,college,male\n",
            "xqd20160257,PAIDOFF,800,15,9/13/2016,9/27/2016,9/27/2016 9:00,,28,college,male\n",
            "xqd20160258,PAIDOFF,1000,15,9/13/2016,9/27/2016,9/27/2016 9:00,,37,college,female\n",
            "xqd20160259,PAIDOFF,1000,7,9/13/2016,9/19/2016,9/16/2016 15:57,,25,college,male\n",
            "xqd20160260,PAIDOFF,1000,30,9/13/2016,10/12/2016,10/12/2016 9:00,,19,High School or Below,male\n",
            "xqd20160261,PAIDOFF,800,15,9/13/2016,9/27/2016,9/26/2016 7:48,,51,college,male\n",
            "xqd20160262,PAIDOFF,1000,15,9/13/2016,9/27/2016,9/21/2016 16:53,,29,High School or Below,male\n",
            "xqd20160263,PAIDOFF,800,30,9/13/2016,10/12/2016,10/11/2016 0:29,,23,college,female\n",
            "xqd20160264,PAIDOFF,1000,15,9/13/2016,9/27/2016,9/25/2016 10:37,,30,High School or Below,male\n",
            "xqd20160265,PAIDOFF,800,15,9/13/2016,9/27/2016,9/27/2016 13:00,,23,college,male\n",
            "xqd20160266,PAIDOFF,1000,15,9/13/2016,9/27/2016,9/26/2016 15:10,,34,Bechalor,female\n",
            "xqd20160267,PAIDOFF,800,15,9/13/2016,9/27/2016,9/24/2016 12:46,,31,Bechalor,female\n",
            "xqd20160268,PAIDOFF,1000,15,9/14/2016,9/28/2016,9/28/2016 9:00,,24,High School or Below,male\n",
            "xqd20160269,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 9:00,,42,High School or Below,male\n",
            "xqd20160270,PAIDOFF,800,30,9/14/2016,10/13/2016,10/6/2016 12:09,,40,college,female\n",
            "xqd20160271,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/14/2016 11:03,,29,High School or Below,male\n",
            "xqd20160272,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/8/2016 17:12,,32,college,female\n",
            "xqd20160273,PAIDOFF,1000,30,9/14/2016,11/12/2016,11/12/2016 9:00,,28,college,male\n",
            "xqd20160274,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 9:00,,35,High School or Below,male\n",
            "xqd20160275,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 13:00,,30,Bechalor,male\n",
            "xqd20160276,PAIDOFF,800,15,9/14/2016,9/28/2016,9/27/2016 15:52,,44,college,male\n",
            "xqd20160277,PAIDOFF,800,15,9/14/2016,9/28/2016,9/28/2016 13:00,,37,High School or Below,male\n",
            "xqd20160278,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 9:00,,31,college,male\n",
            "xqd20160279,PAIDOFF,800,15,9/14/2016,9/28/2016,9/15/2016 0:43,,36,college,male\n",
            "xqd20160280,PAIDOFF,800,30,9/14/2016,10/13/2016,10/10/2016 10:25,,31,college,male\n",
            "xqd20160281,PAIDOFF,800,15,9/14/2016,9/28/2016,9/27/2016 20:41,,42,High School or Below,male\n",
            "xqd20160282,PAIDOFF,1000,15,9/14/2016,9/28/2016,9/28/2016 9:00,,28,Bechalor,male\n",
            "xqd20160283,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/6/2016 6:51,,30,college,male\n",
            "xqd20160284,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/12/2016 6:25,,30,High School or Below,male\n",
            "xqd20160285,PAIDOFF,1000,15,9/14/2016,9/28/2016,9/27/2016 22:50,,24,Bechalor,male\n",
            "xqd20160286,PAIDOFF,1000,30,9/14/2016,11/12/2016,11/12/2016 9:00,,34,Bechalor,male\n",
            "xqd20160287,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/12/2016 12:30,,29,college,male\n",
            "xqd20160288,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/12/2016 3:49,,38,High School or Below,female\n",
            "xqd20160289,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 13:00,,34,Bechalor,male\n",
            "xqd20160290,PAIDOFF,800,15,9/14/2016,9/28/2016,9/27/2016 7:48,,28,High School or Below,male\n",
            "xqd20160291,PAIDOFF,1000,15,9/14/2016,9/28/2016,9/22/2016 9:28,,30,college,female\n",
            "xqd20160292,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/11/2016 16:33,,41,High School or Below,male\n",
            "xqd20160293,PAIDOFF,1000,30,9/14/2016,10/13/2016,9/18/2016 16:56,,29,college,male\n",
            "xqd20160294,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 9:00,,37,High School or Below,male\n",
            "xqd20160295,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 13:00,,36,Bechalor,male\n",
            "xqd20160296,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 13:00,,30,college,female\n",
            "xqd20160297,PAIDOFF,800,15,9/14/2016,9/28/2016,9/21/2016 4:42,,27,college,male\n",
            "xqd20160298,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 9:00,,29,High School or Below,male\n",
            "xqd20160299,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 9:00,,40,High School or Below,male\n",
            "xqd20160300,PAIDOFF,1000,30,9/14/2016,10/13/2016,10/13/2016 11:00,,28,college,male\n",
            "xqd20160301,COLLECTION,1000,15,9/9/2016,9/23/2016,,76,29,college,male\n",
            "xqd20160302,COLLECTION,1000,30,9/9/2016,10/8/2016,,61,37,High School or Below,male\n",
            "xqd20160303,COLLECTION,1000,30,9/9/2016,10/8/2016,,61,33,High School or Below,male\n",
            "xqd20160304,COLLECTION,800,15,9/9/2016,9/23/2016,,76,27,college,male\n",
            "xqd20160305,COLLECTION,800,15,9/9/2016,9/23/2016,,76,24,Bechalor,male\n",
            "xqd20160306,COLLECTION,1000,15,9/10/2016,9/24/2016,,75,31,High School or Below,female\n",
            "xqd20160307,COLLECTION,800,15,9/10/2016,10/9/2016,,60,28,college,male\n",
            "xqd20160308,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,40,High School or Below,male\n",
            "xqd20160309,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,33,college,male\n",
            "xqd20160310,COLLECTION,800,15,9/10/2016,9/24/2016,,75,41,college,male\n",
            "xqd20160311,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,30,college,male\n",
            "xqd20160312,COLLECTION,800,15,9/10/2016,9/24/2016,,75,26,High School or Below,female\n",
            "xqd20160313,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,27,High School or Below,male\n",
            "xqd20160314,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,20,High School or Below,male\n",
            "xqd20160315,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,24,college,male\n",
            "xqd20160316,COLLECTION,1000,15,9/10/2016,10/9/2016,,60,26,High School or Below,male\n",
            "xqd20160317,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,30,High School or Below,male\n",
            "xqd20160318,COLLECTION,1000,15,9/10/2016,9/24/2016,,75,29,High School or Below,male\n",
            "xqd20160319,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,22,Bechalor,male\n",
            "xqd20160320,COLLECTION,1000,15,9/10/2016,9/24/2016,,75,24,Bechalor,male\n",
            "xqd20160321,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,25,college,male\n",
            "xqd20160322,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,28,High School or Below,male\n",
            "xqd20160323,COLLECTION,1000,30,9/10/2016,10/9/2016,,60,37,college,male\n",
            "xqd20160324,COLLECTION,800,15,9/10/2016,9/24/2016,,75,32,college,male\n",
            "xqd20160325,COLLECTION,1000,15,9/10/2016,9/24/2016,,75,34,college,male\n",
            "xqd20160326,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,28,Bechalor,male\n",
            "xqd20160327,COLLECTION,800,15,9/11/2016,9/25/2016,,74,35,Bechalor,male\n",
            "xqd20160328,COLLECTION,1000,30,9/11/2016,11/9/2016,,29,27,college,male\n",
            "xqd20160329,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,24,High School or Below,female\n",
            "xqd20160330,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,44,Bechalor,male\n",
            "xqd20160331,COLLECTION,1000,15,9/11/2016,10/25/2016,,44,31,college,male\n",
            "xqd20160332,COLLECTION,800,15,9/11/2016,9/25/2016,,74,27,college,male\n",
            "xqd20160333,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,21,High School or Below,male\n",
            "xqd20160334,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,30,High School or Below,female\n",
            "xqd20160335,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,38,college,female\n",
            "xqd20160336,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,34,High School or Below,male\n",
            "xqd20160337,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,31,college,male\n",
            "xqd20160338,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,23,High School or Below,male\n",
            "xqd20160339,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,27,college,female\n",
            "xqd20160340,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,39,High School or Below,male\n",
            "xqd20160341,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,30,High School or Below,female\n",
            "xqd20160342,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,25,college,male\n",
            "xqd20160343,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,50,Master or Above,male\n",
            "xqd20160344,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,23,High School or Below,male\n",
            "xqd20160345,COLLECTION,800,15,9/11/2016,9/25/2016,,74,38,Bechalor,male\n",
            "xqd20160346,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,27,High School or Below,male\n",
            "xqd20160347,COLLECTION,1000,30,9/11/2016,11/9/2016,,29,31,High School or Below,male\n",
            "xqd20160348,COLLECTION,800,15,9/11/2016,9/25/2016,,74,40,college,male\n",
            "xqd20160349,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,32,High School or Below,male\n",
            "xqd20160350,COLLECTION,800,15,9/11/2016,9/25/2016,,74,29,college,male\n",
            "xqd20160351,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,26,High School or Below,male\n",
            "xqd20160352,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,25,college,male\n",
            "xqd20160353,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,35,High School or Below,male\n",
            "xqd20160354,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,41,High School or Below,male\n",
            "xqd20160355,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,37,High School or Below,male\n",
            "xqd20160356,COLLECTION,1000,15,9/11/2016,10/10/2016,,59,34,college,male\n",
            "xqd20160357,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,45,High School or Below,male\n",
            "xqd20160358,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,26,Bechalor,male\n",
            "xqd20160359,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,32,college,male\n",
            "xqd20160360,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,28,High School or Below,male\n",
            "xqd20160361,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,34,college,male\n",
            "xqd20160362,COLLECTION,800,15,9/11/2016,9/25/2016,,74,29,High School or Below,male\n",
            "xqd20160363,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,26,High School or Below,male\n",
            "xqd20160364,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,26,college,male\n",
            "xqd20160365,COLLECTION,800,15,9/11/2016,9/25/2016,,74,22,college,male\n",
            "xqd20160366,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,27,High School or Below,female\n",
            "xqd20160367,COLLECTION,800,30,9/11/2016,10/10/2016,,59,33,High School or Below,male\n",
            "xqd20160368,COLLECTION,800,15,9/11/2016,9/25/2016,,74,28,Bechalor,male\n",
            "xqd20160369,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,24,college,male\n",
            "xqd20160370,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,37,High School or Below,male\n",
            "xqd20160371,COLLECTION,800,15,9/11/2016,9/25/2016,,74,36,High School or Below,male\n",
            "xqd20160372,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,18,college,male\n",
            "xqd20160373,COLLECTION,800,15,9/11/2016,9/25/2016,,74,25,High School or Below,male\n",
            "xqd20160374,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,40,High School or Below,male\n",
            "xqd20182575,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,29,college,male\n",
            "xqd20160376,COLLECTION,800,15,9/11/2016,9/25/2016,,74,26,High School or Below,female\n",
            "xqd20151038,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,30,college,male\n",
            "xqd20160378,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,33,college,male\n",
            "xqd20197340,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,30,college,male\n",
            "xqd20160380,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,32,college,male\n",
            "xqd20160381,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,25,High School or Below,male\n",
            "xqd20160382,COLLECTION,800,15,9/11/2016,9/25/2016,,74,35,High School or Below,male\n",
            "xqd20175721,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,30,Bechalor,male\n",
            "xqd20160384,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,26,High School or Below,male\n",
            "xqd20160385,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,29,High School or Below,male\n",
            "xqd20160386,COLLECTION,1000,30,9/11/2016,11/9/2016,,29,26,High School or Below,male\n",
            "xqd20160387,COLLECTION,800,15,9/11/2016,9/25/2016,,74,46,High School or Below,male\n",
            "xqd20160388,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,36,High School or Below,male\n",
            "xqd20160389,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,38,Bechalor,male\n",
            "xqd20160390,COLLECTION,1000,15,9/11/2016,10/25/2016,,44,32,High School or Below,male\n",
            "xqd20160391,COLLECTION,1000,15,9/11/2016,9/25/2016,,74,30,college,male\n",
            "xqd20125284,COLLECTION,800,15,9/11/2016,9/25/2016,,74,35,High School or Below,male\n",
            "xqd20160393,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,29,college,female\n",
            "xqd20160394,COLLECTION,1000,30,9/11/2016,11/9/2016,,29,26,college,male\n",
            "xqd20160395,COLLECTION,800,15,9/11/2016,9/25/2016,,74,32,High School or Below,male\n",
            "xqd20160396,COLLECTION,1000,30,9/11/2016,10/10/2016,,59,25,High School or Below,male\n",
            "xqd20160397,COLLECTION,1000,30,9/12/2016,10/11/2016,,58,33,High School or Below,male\n",
            "xqd20160398,COLLECTION,800,15,9/12/2016,9/26/2016,,73,39,college,male\n",
            "xqd20160399,COLLECTION,1000,30,9/12/2016,11/10/2016,,28,28,college,male\n",
            "xqd20160400,COLLECTION,1000,30,9/12/2016,10/11/2016,,58,26,college,male\n",
            "xqd20160401,COLLECTION_PAIDOFF,1000,30,9/9/2016,10/8/2016,10/10/2016 11:45,2,26,college,male\n",
            "xqd20160402,COLLECTION_PAIDOFF,1000,15,9/9/2016,9/23/2016,9/27/2016 17:00,4,28,college,male\n",
            "xqd20320403,COLLECTION_PAIDOFF,1000,30,9/9/2016,11/7/2016,11/20/2016 14:10,13,39,college,male\n",
            "xqd20160404,COLLECTION_PAIDOFF,1000,15,9/9/2016,9/23/2016,9/28/2016 15:38,5,29,Bechalor,male\n",
            "xqd20190405,COLLECTION_PAIDOFF,800,15,9/9/2016,9/23/2016,9/26/2016 17:22,3,33,High School or Below,male\n",
            "xqd20160406,COLLECTION_PAIDOFF,1000,30,9/10/2016,10/9/2016,10/21/2016 14:00,12,27,college,male\n",
            "xqd20160407,COLLECTION_PAIDOFF,800,15,9/10/2016,9/24/2016,9/26/2016 11:03,2,34,college,male\n",
            "xqd20160408,COLLECTION_PAIDOFF,1000,30,9/10/2016,10/9/2016,11/5/2016 15:39,27,26,High School or Below,male\n",
            "xqd20110409,COLLECTION_PAIDOFF,1000,30,9/10/2016,10/9/2016,11/22/2016 15:53,44,28,High School or Below,male\n",
            "xqd20160410,COLLECTION_PAIDOFF,1000,15,9/10/2016,9/24/2016,9/29/2016 10:30,5,32,Bechalor,male\n",
            "xqd20160411,COLLECTION_PAIDOFF,800,15,9/10/2016,10/9/2016,10/10/2016 15:18,1,27,college,female\n",
            "xqd20160412,COLLECTION_PAIDOFF,1000,30,9/10/2016,10/9/2016,11/5/2016 10:49,27,21,college,male\n",
            "xqd20160413,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,9/27/2016 17:10,2,39,college,male\n",
            "xqd20169083,COLLECTION_PAIDOFF,1000,15,9/11/2016,9/25/2016,9/26/2016 11:35,1,38,college,male\n",
            "xqd20160415,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/12/2016 9:59,2,36,High School or Below,female\n",
            "xqd20160416,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,9/27/2016 17:14,2,33,college,male\n",
            "xqd20160417,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 12:45,1,21,college,female\n",
            "xqd20160418,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,9/28/2016 11:38,3,25,High School or Below,male\n",
            "xqd20160419,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,10/7/2016 13:21,12,29,college,male\n",
            "xqd20160420,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,11/4/2016 15:37,25,33,High School or Below,male\n",
            "xqd20160421,COLLECTION_PAIDOFF,1000,15,9/11/2016,9/25/2016,9/28/2016 17:39,3,47,High School or Below,female\n",
            "xqd20160422,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/12/2016 9:52,2,33,college,male\n",
            "xqd20160423,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,9/29/2016 15:12,4,23,High School or Below,male\n",
            "xqd20160424,COLLECTION_PAIDOFF,1000,15,9/11/2016,10/10/2016,10/12/2016 11:17,2,24,college,male\n",
            "xqd20880425,COLLECTION_PAIDOFF,1000,30,9/11/2016,11/9/2016,11/10/2016 22:58,1,27,High School or Below,male\n",
            "xqd20160426,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,11/3/2016 15:23,24,32,Bechalor,male\n",
            "xqd20160427,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 16:44,1,33,college,male\n",
            "xqd20160428,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 11:02,2,27,college,female\n",
            "xqd20160429,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/12/2016 13:17,2,35,High School or Below,male\n",
            "xqd20160430,COLLECTION_PAIDOFF,500,15,9/11/2016,10/10/2016,10/11/2016 17:22,1,37,Bechalor,male\n",
            "xqd20160431,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,9/28/2016 14:02,3,28,Bechalor,male\n",
            "xqd20160432,COLLECTION_PAIDOFF,1000,15,9/11/2016,9/25/2016,9/29/2016 13:42,4,33,college,male\n",
            "xqd20160433,COLLECTION_PAIDOFF,800,7,9/11/2016,9/17/2016,9/19/2016 15:00,2,34,Bechalor,female\n",
            "xqd20160434,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/12/2016 14:32,2,29,college,male\n",
            "xqd20160435,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 11:33,1,34,Bechalor,male\n",
            "xqd20160436,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 16:27,1,29,Bechalor,male\n",
            "xqd20790437,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,11/15/2016 15:27,36,24,High School or Below,male\n",
            "xqd20160438,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 16:13,1,34,High School or Below,male\n",
            "xqd20160439,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/17/2016 10:06,7,25,college,female\n",
            "xqd20160440,COLLECTION_PAIDOFF,1000,30,9/11/2016,11/9/2016,11/14/2016 13:15,5,24,college,male\n",
            "xqd20160441,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/24/2016 16:20,14,30,college,male\n",
            "xqd20160442,COLLECTION_PAIDOFF,1000,15,9/11/2016,9/25/2016,9/27/2016 16:35,2,28,college,male\n",
            "xqd20160443,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 11:48,1,24,High School or Below,male\n",
            "xqd20160444,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,11/7/2016 19:21,28,26,college,female\n",
            "xqd20160445,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/12/2016 16:22,2,24,High School or Below,male\n",
            "xqd20160446,COLLECTION_PAIDOFF,1000,15,9/11/2016,9/25/2016,9/27/2016 17:24,2,29,college,male\n",
            "xqd20420447,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,11/4/2016 11:07,25,31,college,male\n",
            "xqd20160448,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,11/2/2016 9:39,23,26,college,male\n",
            "xqd20160449,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/13/2016 18:18,3,25,High School or Below,male\n",
            "xqd20160450,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/11/2016 11:29,1,29,college,male\n",
            "xqd20160451,COLLECTION_PAIDOFF,1000,30,9/11/2016,10/10/2016,10/13/2016 16:27,3,38,college,male\n",
            "xqd20160452,COLLECTION_PAIDOFF,800,15,9/11/2016,9/25/2016,9/29/2016 11:19,4,41,college,male\n",
            "xqd20390453,COLLECTION_PAIDOFF,1000,15,9/11/2016,9/25/2016,9/28/2016 11:17,3,26,High School or Below,male\n",
            "xqd20160454,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/14/2016 11:04,3,26,High School or Below,male\n",
            "xqd20160455,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/17/2016 17:40,6,35,High School or Below,male\n",
            "xqd20160456,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,9/28/2016 9:42,2,37,college,male\n",
            "xqd20160457,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,11/18/2016 15:52,38,25,college,male\n",
            "xqd20160458,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/30/2016 14:19,19,24,college,male\n",
            "xqd20160459,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/13/2016 15:10,2,34,college,male\n",
            "xqd20160460,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,9/28/2016 13:36,2,33,college,male\n",
            "xqd20490461,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,9/28/2016 15:34,2,38,Bechalor,male\n",
            "xqd20160462,COLLECTION_PAIDOFF,1000,30,9/12/2016,11/10/2016,11/17/2016 11:55,7,38,High School or Below,male\n",
            "xqd20160463,COLLECTION_PAIDOFF,1000,30,9/12/2016,11/10/2016,11/15/2016 18:51,5,26,college,male\n",
            "xqd20870464,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,9/30/2016 10:23,4,37,Bechalor,male\n",
            "xqd20160465,COLLECTION_PAIDOFF,1000,30,9/12/2016,11/10/2016,11/11/2016 17:17,1,42,High School or Below,female\n",
            "xqd20169466,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/12/2016 12:54,1,49,High School or Below,female\n",
            "xqd20160467,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/15/2016 9:48,4,26,High School or Below,male\n",
            "xqd20160468,COLLECTION_PAIDOFF,1000,15,9/12/2016,10/26/2016,10/27/2016 11:14,1,41,High School or Below,male\n",
            "xqd20160469,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/15/2016 14:14,4,38,High School or Below,male\n",
            "xqd25660470,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,12/2/2016 9:45,52,26,High School or Below,male\n",
            "xqd20160471,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,9/28/2016 15:02,2,32,High School or Below,male\n",
            "xqd20160472,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,11/4/2016 14:46,24,27,Bechalor,male\n",
            "xqd20160473,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,11/16/2016 12:12,51,33,college,male\n",
            "xqd20160474,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/14/2016 19:02,3,30,High School or Below,male\n",
            "xqd20160475,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,9/28/2016 11:34,2,26,High School or Below,female\n",
            "xqd20160476,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,11/9/2016 18:12,29,35,college,female\n",
            "xqd20160477,COLLECTION_PAIDOFF,800,15,9/12/2016,10/26/2016,10/31/2016 13:07,5,46,college,female\n",
            "xqd20160478,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/20/2016 17:38,9,27,college,male\n",
            "xqd20160479,COLLECTION_PAIDOFF,1000,15,9/12/2016,10/11/2016,11/7/2016 8:55,27,22,High School or Below,male\n",
            "xqd20160480,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/12/2016 18:26,1,27,Bechalor,male\n",
            "xqd20160481,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,10/25/2016 13:44,29,30,Bechalor,male\n",
            "xqd20160482,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,9/29/2016 15:07,3,27,High School or Below,male\n",
            "xqd20160483,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,9/27/2016 11:40,1,47,college,male\n",
            "xqd20160484,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/18/2016 19:08,7,30,college,male\n",
            "xqd20160485,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/15/2016 9:23,4,26,college,male\n",
            "xqd20160486,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/14/2016 10:07,3,38,High School or Below,male\n",
            "xqd20160487,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,11/21/2016 11:36,56,46,High School or Below,male\n",
            "xqd20160488,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/13/2016 12:02,2,35,Bechalor,male\n",
            "xqd20160489,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,10/9/2016 19:30,13,45,college,male\n",
            "xqd20160490,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/12/2016 18:04,1,36,college,male\n",
            "xqd20160491,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/17/2016 10:53,6,38,High School or Below,male\n",
            "xqd20160492,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,11/9/2016 13:41,29,27,college,male\n",
            "xqd20160493,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/25/2016 17:44,14,27,Bechalor,male\n",
            "xqd20160494,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,9/29/2016 12:45,3,29,college,male\n",
            "xqd20160495,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/13/2016 14:45,2,30,High School or Below,male\n",
            "xqd20160496,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/14/2016 19:08,3,28,High School or Below,male\n",
            "xqd20160497,COLLECTION_PAIDOFF,1000,15,9/12/2016,9/26/2016,10/10/2016 20:02,14,26,High School or Below,male\n",
            "xqd20160498,COLLECTION_PAIDOFF,800,15,9/12/2016,9/26/2016,9/29/2016 11:49,3,30,college,male\n",
            "xqd20160499,COLLECTION_PAIDOFF,1000,30,9/12/2016,11/10/2016,11/11/2016 22:40,1,38,college,female\n",
            "xqd20160500,COLLECTION_PAIDOFF,1000,30,9/12/2016,10/11/2016,10/19/2016 11:58,8,28,High School or Below,male\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xn-O_Ax_Seqd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8bb194b7-eb94-4e2f-9fd9-54987a524994"
      },
      "source": [
        "row"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['xqd20160500',\n",
              " 'COLLECTION_PAIDOFF',\n",
              " '1000',\n",
              " '30',\n",
              " '9/12/2016',\n",
              " '10/11/2016',\n",
              " '10/19/2016 11:58',\n",
              " '8',\n",
              " '28',\n",
              " 'High School or Below',\n",
              " 'male']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gM--yBIY9HT2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d642cc59-74f8-4aed-c675-f67e6610177e"
      },
      "source": [
        "type(row)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6VtpoMs9Q0j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rows = []\n",
        "with open('Loan payments data.csv', newline='') as csvfile:\n",
        "  csvreader = csv.reader(csvfile, delimiter=',')\n",
        "  for row in csvreader:\n",
        "    rows.append(row)\n",
        "\n",
        "csvheader = rows[0]\n",
        "csvdata = rows[1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nl47qQfeS6Pn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "icol = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NB76uOfsTFIF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5df493f0-0171-41c4-878b-20c7f7399a33"
      },
      "source": [
        "set([sample[icol] for sample in csvdata])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'female', 'male'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7nIA2USUOVb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "column = [sample[icol] for sample in csvdata]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WC0Cw5YzTgHm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import Counter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_GOvILMUJ9f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "c = Counter(column)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-yf_7IyURvY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels = list(c.keys())\n",
        "counts = list(c.values())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XRCifc2UUOw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "63aa1286-3e4a-4354-e4eb-1234142e126b"
      },
      "source": [
        "counts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[423, 77]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fUVihAkUaCH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b125026-b931-4fed-a8ed-8dd756400c3b"
      },
      "source": [
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['male', 'female']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgpEzTCOUkOo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "6b29ffa3-c335-4004-fc4a-1257325bc160"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.bar(labels,counts)\n",
        "plt.title('Gender')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0MF3Mk-VC_9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "31c1f2a2-655c-40dc-d373-c8e9ff0252d0"
      },
      "source": [
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['male', 'female']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNxROjTMV_1r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "values = [1,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SoZLA7EvWDtb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_and_vals = zip(labels, values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64fAT420VOh7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mapping={}\n",
        "for k, v in zip(labels, values):\n",
        "  mapping[k] = v"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLqvc3ciWVGK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5de7c16-83fd-4aad-8e04-7267d8009418"
      },
      "source": [
        "mapping"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'female': -1, 'male': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztVWrdTOWWfu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_column =[] \n",
        "for v in column:\n",
        "  num_column.append(mapping[v])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfkEldqjWaXn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "3b233968-cd7a-405f-8d2d-abdcfb2454ac"
      },
      "source": [
        "num_column[:10], column[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([1, -1, -1, 1, -1, 1, 1, 1, 1, 1],\n",
              " ['male',\n",
              "  'female',\n",
              "  'female',\n",
              "  'male',\n",
              "  'female',\n",
              "  'male',\n",
              "  'male',\n",
              "  'male',\n",
              "  'male',\n",
              "  'male'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjktZAQMWlfN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hPycH56XWVQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('Loan payments data.csv', delimiter = ',')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QdL1quRXhUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "100c83f4-9f39-4372-a02d-d8bf43238aae"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>effective_date</th>\n",
              "      <th>due_date</th>\n",
              "      <th>paid_off_time</th>\n",
              "      <th>past_due_days</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>xqd20166231</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/14/2016 19:31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>xqd20168902</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>10/7/2016 9:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xqd20160003</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/25/2016 16:58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xqd20160004</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>9/22/2016</td>\n",
              "      <td>9/22/2016 20:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>xqd20160005</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>9/23/2016 21:36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>xqd20160496</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/11/2016</td>\n",
              "      <td>10/14/2016 19:08</td>\n",
              "      <td>3.0</td>\n",
              "      <td>28</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>xqd20160497</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>9/26/2016</td>\n",
              "      <td>10/10/2016 20:02</td>\n",
              "      <td>14.0</td>\n",
              "      <td>26</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>xqd20160498</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>9/26/2016</td>\n",
              "      <td>9/29/2016 11:49</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>xqd20160499</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>11/10/2016</td>\n",
              "      <td>11/11/2016 22:40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>xqd20160500</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/11/2016</td>\n",
              "      <td>10/19/2016 11:58</td>\n",
              "      <td>8.0</td>\n",
              "      <td>28</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Loan_ID         loan_status  ...             education  Gender\n",
              "0    xqd20166231             PAIDOFF  ...  High School or Below    male\n",
              "1    xqd20168902             PAIDOFF  ...              Bechalor  female\n",
              "2    xqd20160003             PAIDOFF  ...              Bechalor  female\n",
              "3    xqd20160004             PAIDOFF  ...               college    male\n",
              "4    xqd20160005             PAIDOFF  ...               college  female\n",
              "..           ...                 ...  ...                   ...     ...\n",
              "495  xqd20160496  COLLECTION_PAIDOFF  ...  High School or Below    male\n",
              "496  xqd20160497  COLLECTION_PAIDOFF  ...  High School or Below    male\n",
              "497  xqd20160498  COLLECTION_PAIDOFF  ...               college    male\n",
              "498  xqd20160499  COLLECTION_PAIDOFF  ...               college  female\n",
              "499  xqd20160500  COLLECTION_PAIDOFF  ...  High School or Below    male\n",
              "\n",
              "[500 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zq9JErmKXj0s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "2c9580a8-794f-4c01-f9c2-6a602cd0c631"
      },
      "source": [
        "df['Gender']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        male\n",
              "1      female\n",
              "2      female\n",
              "3        male\n",
              "4      female\n",
              "        ...  \n",
              "495      male\n",
              "496      male\n",
              "497      male\n",
              "498    female\n",
              "499      male\n",
              "Name: Gender, Length: 500, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMYK598ZXqVj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_female = df[df['Gender']=='female']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EU8m5W6X0hJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "d786be5c-b3ca-4408-e6c2-59f22d5e9382"
      },
      "source": [
        "df_female"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
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              "      <th>2</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>xqd20160005</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>9/23/2016 21:36</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>xqd20160014</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>900</td>\n",
              "      <td>7</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>9/16/2016</td>\n",
              "      <td>9/13/2016 21:57</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>xqd20160022</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>10/9/2016</td>\n",
              "      <td>10/8/2016 17:09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>xqd20169466</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/11/2016</td>\n",
              "      <td>10/12/2016 12:54</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>xqd20160475</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>9/26/2016</td>\n",
              "      <td>9/28/2016 11:34</td>\n",
              "      <td>2.0</td>\n",
              "      <td>26</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>xqd20160476</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/11/2016</td>\n",
              "      <td>11/9/2016 18:12</td>\n",
              "      <td>29.0</td>\n",
              "      <td>35</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>xqd20160477</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/26/2016</td>\n",
              "      <td>10/31/2016 13:07</td>\n",
              "      <td>5.0</td>\n",
              "      <td>46</td>\n",
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              "      <td>female</td>\n",
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              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>xqd20160499</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>11/10/2016</td>\n",
              "      <td>11/11/2016 22:40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Loan_ID         loan_status  ...             education  Gender\n",
              "1    xqd20168902             PAIDOFF  ...              Bechalor  female\n",
              "2    xqd20160003             PAIDOFF  ...              Bechalor  female\n",
              "4    xqd20160005             PAIDOFF  ...               college  female\n",
              "13   xqd20160014             PAIDOFF  ...               college  female\n",
              "21   xqd20160022             PAIDOFF  ...  High School or Below  female\n",
              "..           ...                 ...  ...                   ...     ...\n",
              "465  xqd20169466  COLLECTION_PAIDOFF  ...  High School or Below  female\n",
              "474  xqd20160475  COLLECTION_PAIDOFF  ...  High School or Below  female\n",
              "475  xqd20160476  COLLECTION_PAIDOFF  ...               college  female\n",
              "476  xqd20160477  COLLECTION_PAIDOFF  ...               college  female\n",
              "498  xqd20160499  COLLECTION_PAIDOFF  ...               college  female\n",
              "\n",
              "[77 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txwR3oOHX7k1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_copied = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SioqoobJYJ5T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_copied.replace('male', 1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2S_mUduqYW95",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_copied.replace('female', -1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8ydhGDUYYFE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "4b78a562-f059-421d-a2d6-3dd666ec620f"
      },
      "source": [
        "df_copied"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>9/14/2016 19:31</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>10/7/2016</td>\n",
              "      <td>10/7/2016 9:00</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>-1</td>\n",
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              "    <tr>\n",
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              "      <td>xqd20160003</td>\n",
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              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/25/2016 16:58</td>\n",
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              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xqd20160004</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>9/22/2016</td>\n",
              "      <td>9/22/2016 20:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>9/23/2016 21:36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>xqd20160496</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/11/2016</td>\n",
              "      <td>10/14/2016 19:08</td>\n",
              "      <td>3.0</td>\n",
              "      <td>28</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>xqd20160497</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>9/26/2016</td>\n",
              "      <td>10/10/2016 20:02</td>\n",
              "      <td>14.0</td>\n",
              "      <td>26</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>xqd20160498</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>9/26/2016</td>\n",
              "      <td>9/29/2016 11:49</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30</td>\n",
              "      <td>college</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>xqd20160499</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>11/10/2016</td>\n",
              "      <td>11/11/2016 22:40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38</td>\n",
              "      <td>college</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>xqd20160500</td>\n",
              "      <td>COLLECTION_PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/12/2016</td>\n",
              "      <td>10/11/2016</td>\n",
              "      <td>10/19/2016 11:58</td>\n",
              "      <td>8.0</td>\n",
              "      <td>28</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Loan_ID         loan_status  ...             education  Gender\n",
              "0    xqd20166231             PAIDOFF  ...  High School or Below       1\n",
              "1    xqd20168902             PAIDOFF  ...              Bechalor      -1\n",
              "2    xqd20160003             PAIDOFF  ...              Bechalor      -1\n",
              "3    xqd20160004             PAIDOFF  ...               college       1\n",
              "4    xqd20160005             PAIDOFF  ...               college      -1\n",
              "..           ...                 ...  ...                   ...     ...\n",
              "495  xqd20160496  COLLECTION_PAIDOFF  ...  High School or Below       1\n",
              "496  xqd20160497  COLLECTION_PAIDOFF  ...  High School or Below       1\n",
              "497  xqd20160498  COLLECTION_PAIDOFF  ...               college       1\n",
              "498  xqd20160499  COLLECTION_PAIDOFF  ...               college      -1\n",
              "499  xqd20160500  COLLECTION_PAIDOFF  ...  High School or Below       1\n",
              "\n",
              "[500 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJX9w-ExYftt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_copied.to_csv('Loan_payments_data_pandas_test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0sOwV_dYyHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}