{
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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      "pygments_lexer": "ipython3",
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  "cells": [
    {
      "metadata": {
        "_cell_guid": "6351d315-92a2-7318-faa2-3201e7b5ae03",
        "_active": true,
        "collapsed": false
      },
      "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\nimport numpy as np # linear algebra\nimport 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\nfrom subprocess import check_output\n#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\nprint(check_output([\"ps\", \"-aux\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.",
      "execution_count": 4,
      "cell_type": "code",
      "outputs": [],
      "execution_state": "idle"
    },
    {
      "metadata": {
        "_cell_guid": "d4942e05-7bfc-1350-6239-2e35ce81886e",
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      "source": null,
      "execution_count": null,
      "cell_type": "code",
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