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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",
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      "metadata": {
        "_cell_guid": "fbfa41b8-7cc4-3f54-be0c-f2871bca9254"
      },
      "outputs": [],
      "source": [
        "reader = pd.read_csv('../input/page_views_sample.csv',iterator=True)\n",
        "page_df = reader.get_chunk(50)\n",
        "page_df.head()"
      ]
    },
    {
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      "metadata": {
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      "source": [
        "reader = pd.read_csv('../input/clicks_train.csv',iterator=True)\n",
        "click_df = reader.get_chunk(50)\n",
        "click_df.head()"
      ]
    },
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