Extract_data_from_MongoDB_using_Python.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pymongo\n",
    "from pymongo import MongoClient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Establish connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "connection = MongoClient('localhost', 27017)\n",
    "db = connection.EmployeeDB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract database and collection details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = db.employees\n",
    "EmployeeList = data.find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print each individual record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in EmployeeList:\n",
    "    print(\"Name: \" + item[\"name\"] + \" \" + \"Dept: \" + item[\"dept\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a dataframe to give structure to the NoSQL data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5cb251e12c6fe759b793a293</td>\n",
       "      <td>IT</td>\n",
       "      <td>Nitin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5cb251fe2c6fe759b793a294</td>\n",
       "      <td>HR</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5cb2520e2c6fe759b793a295</td>\n",
       "      <td>Admin</td>\n",
       "      <td>Ronny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                        _id   dept   name\n",
       "0  5cb251e12c6fe759b793a293     IT  Nitin\n",
       "1  5cb251fe2c6fe759b793a294     HR   John\n",
       "2  5cb2520e2c6fe759b793a295  Admin  Ronny"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(list(data.find()))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "cell_type": "code",
   "execution_count": null,
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