{
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      "cell_type": "code",
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      "metadata": {
        "_cell_guid": "cc23329a-ea6f-2b8d-de81-7cb804d6f5e1"
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      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "67db15de-8d88-4498-cbac-375cf735a039"
      },
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      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#train_date_part = pd.read_csv('../input/test_numeric.csv', nrows=10000)\n",
        "#print(train_date_part.shape)\n",
        "#print(1.0 * train_date_part.count().sum() / train_date_part.size)\n",
        "#print(train_date_part[:2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "85712475-406c-71ad-d68a-4973260e5fad"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "#train_date_part = pd.read_csv('../input/test_numeric.csv', nrows=10000)\n",
        "#print(train_date_part.shape)\n",
        "#print(1.0 * train_date_part.count().sum() / train_date_part.size)\n",
        "#print(train_date_part[:2])"
      ]
    }
  ],
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
      "file_extension": ".py",
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