{
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    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "5fc65c53-42dc-cb63-1093-780c27729c6b"
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      "source": [
        "Hello this is an introduction  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f83b3d63-8ef9-0b7d-5b90-422ebde975b2"
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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": "12a3755c-24cd-cf1f-4977-cc7dd61bec96"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "from collections import OrderedDict\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "with open('../input/documents_meta.csv', 'r') as f:\n",
        "    r = csv.reader(f)\n",
        "    dict2 = {row[0]: row[1:] for row in r}\n",
        "\n",
        "with open('../input/promoted_content.csv', 'r') as f:\n",
        "    r = csv.reader(f)\n",
        "    dict1 = OrderedDict((row[0], row[1:]) for row in r)\n",
        "\n",
        "result = OrderedDict()\n",
        "for d in (dict1, dict2):\n",
        "    for key, value in d.items():\n",
        "        result.setdefault(key, []).extend(value)\n",
        "\n",
        "with open('ab_combined.csv', 'w') as f:\n",
        "    w = csv.writer(f)\n",
        "    for key, value in result.items():\n",
        "        w.writerow([key] + value)\n",
        "        \n",
        "data=pd.read_csv('ab_combined.csv',low_memory=False)\n",
        "data"
      ]
    }
  ],
  "metadata": {
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      "display_name": "Python 3",
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      "codemirror_mode": {
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