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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",
        "from time import time\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": "dd9efb82-51ed-2db0-c9e6-892fbe4e2ee1"
      },
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      "source": [
        "time0=time()\n",
        "data=pd.read_csv(\"../input/train_categorical.csv\", usecols=['L0_S1_F25'], low_memory=False) ##nrows = 10000, \n",
        "print(\"total seconds used: \" + str(time()-time0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "fb0ca490-0aa9-bd3a-174a-c5e516a441df"
      },
      "outputs": [],
      "source": ""
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