mongoDB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "92a06bf5-8e9c-4ab5-9cb5-d97ef9a46e14",
   "metadata": {},
   "source": [
    "<img src=\"rades-iset.jpg\" style=\"width: 500px; height:150px; display: block; margin: auto;\">\n",
    "<img src=\"kuzt9r42or1fxvlq2-Meta_Generic.png\" style=\"display: block; margin: auto;\">\n",
    "<center><h1>Elaborated By: Aziz Zina</h1></center><br>\n",
    "<img src=\"HTP_50-modified.png\" style=\"max-width: 300px; height: auto; display: block; margin: auto;\">\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a730aa19-c47b-4cf4-80d7-7ec4403bd3d4",
   "metadata": {},
   "source": [
    "# What is mongoDB\n",
    "<hr><br>\n",
    "<i><b>MongoDB</b> is a NoSQL database known for its flexibility, scalability, and JSON-like document storage. It's widely used for handling large amounts of diverse data in modern applications.</i>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18638a3e-8e7f-4c5e-a623-0aa27b61c5bb",
   "metadata": {},
   "source": [
    "# Install \"pymongo\"\n",
    "<hr><br>\n",
    "<i><b>pymongo</b> is a Python library for connecting and interacting with MongoDB databases, enabling operations like inserting, querying, and updating documents.</i><br><br>\n",
    "First of all, we need to install it using \"<i>pip install pymongp</i>\"\n",
    "<br>\n",
    "And now we import the library and check the version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f18fb2e4-6a99-4b81-bd3d-589dd10fe248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.6.1\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "print(pymongo.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eee19a2-47c6-40dd-b584-526f50402989",
   "metadata": {},
   "source": [
    "# Connect to MongoDB\n",
    "<hr><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffd73741-4244-4816-93f5-7a6727b90a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymongo import MongoClient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4775d577-781c-473b-b25d-99ea6da4813d",
   "metadata": {},
   "source": [
    "You can either connect to an online cluster with Atlas:\n",
    "But first you need to install \"srv\" library:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b02d48-2900-48af-9ea7-ad40d2b52ac5",
   "metadata": {},
   "source": [
    "pip install \"pymongo[srv]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "990ff99e-488d-450d-b16e-694a83b61301",
   "metadata": {},
   "outputs": [],
   "source": [
    "connection_string = \"mongodb+srv://admin:Password123@cluster0.5zd4fhu.mongodb.net/?retryWrites=true&w=majority\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81033d1-a8c0-4ca0-811d-fb230c0f7324",
   "metadata": {},
   "source": [
    "Or you can connect to localhoast:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87939060-9dfe-4cb9-a3cd-adec22a7bdaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "connection_string = \"mongodb://127.0.0.1:27017\"\n",
    "client = MongoClient(connection_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642ee3c2-caf3-4f05-b592-df59a5709a19",
   "metadata": {},
   "source": [
    "Choose the database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bde8a0a-b49c-4fe7-a4fd-287366c9e40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "db = client['test_notebook']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a85d7d-e76d-4d96-8f65-2b50aa1fa890",
   "metadata": {},
   "source": [
    "Choose the collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b6b85f4-496a-4814-b364-78f30e11fcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = db['user']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5b46a4-1266-4ed2-a7d4-d6cfe1e2863f",
   "metadata": {},
   "source": [
    "# Insert data into MongoDB\n",
    "<hr><br>\n",
    "Create a user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dcd10f57-7367-4542-928b-4f61c5fa1267",
   "metadata": {},
   "outputs": [],
   "source": [
    "user = { \"name\": \"Aziz\", \"age\": 21, \"phone\": \"93505147\" }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892a5c74-01bc-4a43-8e2f-e980bfa3054f",
   "metadata": {},
   "source": [
    "Insert the user into the MongoDB using \"<i>insert_one</i>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5fb6ca5d-dfe8-4b6a-b7bf-97516b9ce90a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted document with ID: 6576e7bb085308e7dd98da09\n"
     ]
    }
   ],
   "source": [
    "result = collection.insert_one(user)\n",
    "print(f\"Inserted document with ID: {result.inserted_id}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245bf55c-c24e-42fc-8278-16a38e751293",
   "metadata": {},
   "source": [
    "We can also insert many users, we create the user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca9fa074-cccd-4e0b-9e0b-d7080fe6d654",
   "metadata": {},
   "outputs": [],
   "source": [
    "users = [\n",
    "    {'name': 'Oussema', 'age': 25, 'phone': '98417306'},\n",
    "    {'name': 'Lamjed', 'age': 30, 'phone': '24696124'},\n",
    "    {'name': 'Saloua', 'age': 18, 'phone': '54897542'},\n",
    "    {'name': 'Firas', 'age': 22, 'phone': '24303822'},\n",
    "    {'name': 'Aymen', 'age': 16, 'phone': '95748120'}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd5bd2b-3b3c-4d08-96cf-ddac959097b0",
   "metadata": {},
   "source": [
    "Now we insert the users all at once using \"<i>insert_many</i>\" and check their id's:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52f4ba4e-924f-4e68-80f4-bc25a7d01e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inserted document IDs: [ObjectId('6576e7bb085308e7dd98da0a'), ObjectId('6576e7bb085308e7dd98da0b'), ObjectId('6576e7bb085308e7dd98da0c'), ObjectId('6576e7bb085308e7dd98da0d'), ObjectId('6576e7bb085308e7dd98da0e')]\n"
     ]
    }
   ],
   "source": [
    "result = collection.insert_many(users)\n",
    "print(f\"Inserted document IDs: {result.inserted_ids}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c89675-932a-45c8-8313-4bf66c245375",
   "metadata": {},
   "source": [
    "# Query data from MongoDB\n",
    "<hr><br>\n",
    "\n",
    "Choose the query (For this example we will select all the users that have an age <b>greater than 20</b>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e76b7cc5-4762-4812-88de-ef1720bcb9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = {'age': {'$gt': 20}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa18b97-3cba-4abc-92f4-0d2ef9095334",
   "metadata": {},
   "source": [
    "Now we check the results, using the \"<i>find</i>\" method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b3261fc5-e073-4706-91ff-26083db663e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'_id': ObjectId('6576e7bb085308e7dd98da09'), 'name': 'Aziz', 'age': 21, 'phone': '93505147'}\n",
      "{'_id': ObjectId('6576e7bb085308e7dd98da0a'), 'name': 'Oussema', 'age': 25, 'phone': '98417306'}\n",
      "{'_id': ObjectId('6576e7bb085308e7dd98da0b'), 'name': 'Lamjed', 'age': 30, 'phone': '24696124'}\n",
      "{'_id': ObjectId('6576e7bb085308e7dd98da0d'), 'name': 'Firas', 'age': 22, 'phone': '24303822'}\n"
     ]
    }
   ],
   "source": [
    "result = collection.find(query)\n",
    "for document in result:\n",
    "    print(document)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee70a62-a55e-46a2-9cf3-07222cd2f6c2",
   "metadata": {},
   "source": [
    "We can display the data in a different way, a more clean way and we are to do that using \"<i>pandas</i>\" library:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d54e777-1712-48b6-af91-c13e9fa7c7e2",
   "metadata": {},
   "source": [
    "First we import the library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b246ad00-08a0-4116-ac51-b042767d18d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e068167a-f5b5-4080-8c40-9dc29fe2a558",
   "metadata": {},
   "source": [
    "And we set our array of users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01c37a53-3608-4a0e-ae5c-43a402ae6c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>name</th>\n",
       "      <th>age</th>\n",
       "      <th>phone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6576e7bb085308e7dd98da09</td>\n",
       "      <td>Aziz</td>\n",
       "      <td>21</td>\n",
       "      <td>93505147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6576e7bb085308e7dd98da0a</td>\n",
       "      <td>Oussema</td>\n",
       "      <td>25</td>\n",
       "      <td>98417306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6576e7bb085308e7dd98da0b</td>\n",
       "      <td>Lamjed</td>\n",
       "      <td>30</td>\n",
       "      <td>24696124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6576e7bb085308e7dd98da0c</td>\n",
       "      <td>Saloua</td>\n",
       "      <td>18</td>\n",
       "      <td>54897542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6576e7bb085308e7dd98da0d</td>\n",
       "      <td>Firas</td>\n",
       "      <td>22</td>\n",
       "      <td>24303822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6576e7bb085308e7dd98da0e</td>\n",
       "      <td>Aymen</td>\n",
       "      <td>16</td>\n",
       "      <td>95748120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id     name  age     phone\n",
       "0  6576e7bb085308e7dd98da09     Aziz   21  93505147\n",
       "1  6576e7bb085308e7dd98da0a  Oussema   25  98417306\n",
       "2  6576e7bb085308e7dd98da0b   Lamjed   30  24696124\n",
       "3  6576e7bb085308e7dd98da0c   Saloua   18  54897542\n",
       "4  6576e7bb085308e7dd98da0d    Firas   22  24303822\n",
       "5  6576e7bb085308e7dd98da0e    Aymen   16  95748120"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = collection.find()\n",
    "entries = list(users)\n",
    "df = pd.DataFrame(entries)\n",
    "entries[:10]\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555fcb25-5fed-4283-8e7e-57de0e2973af",
   "metadata": {},
   "source": [
    "# Update data in MongoDB\n",
    "<hr><br>\n",
    "First we need to search for the user we want to alter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3a0a170c-d8ec-4373-b3d0-9fad37372cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = { \"name\": \"Oussema\" }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c80a6f-3731-46f9-9c28-3223c4adc3bf",
   "metadata": {},
   "source": [
    "Change his old data with his new data, we will use \"<i>$set</i>\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d4799822-9c68-4013-a904-0013410425aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_values = { \"$set\": { \"age\": 31 } }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31047797-11d6-4771-a9d2-c18f540bc2ab",
   "metadata": {},
   "source": [
    "Update the user using \"<i>update_one</i>\" and display results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e7382f7e-5fab-4e63-8129-1945a013ec67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched 1 document and modified 1 document\n"
     ]
    }
   ],
   "source": [
    "result = collection.update_one(query, new_values)\n",
    "print(f\"Matched {result.matched_count} document and modified {result.modified_count} document\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69b387e-41c6-454d-9b57-c2f24175e8d2",
   "metadata": {},
   "source": [
    "We can also to modify multiple users all at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec538e42-0216-4ddb-b7f0-43d3b1e6ccec",
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_criteria = {'age': {'$gt': 20}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5fc96f7-26ea-40ea-bc25-c67e8860a09a",
   "metadata": {},
   "source": [
    "Apply the new values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "556ea8ba-7859-4107-b9a8-a52915693cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "update_operation = {'$set': {'status': 'adult'}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006f2f9e-6de4-462a-b38f-0f727e7f8d59",
   "metadata": {},
   "source": [
    "Update them using \"<i>update_many</i>\" and display results: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a091bf7c-2ad5-461d-a948-2b7eaf5aa1ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched 4 documents and modified 4 documents.\n"
     ]
    }
   ],
   "source": [
    "result = collection.update_many(filter_criteria, update_operation)\n",
    "print(f\"Matched {result.matched_count} documents and modified {result.modified_count} documents.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0476b67-3431-4bf4-9fee-e24c3762b12c",
   "metadata": {},
   "source": [
    "We are going to do the same thing, but this time we will set the status \"minor\" to the rest of the users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "020cabae-063d-452c-877d-8e230afd64d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_criteria = {'age': {'$lte': 20}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6445cedd-0272-49dc-80ea-8e3895209b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "update_operation = {'$set': {'status': 'minor'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0f9ad94-6cdf-4500-b06c-70d01ff5f9a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched 2 documents and modified 2 documents.\n"
     ]
    }
   ],
   "source": [
    "result = collection.update_many(filter_criteria, update_operation)\n",
    "print(f\"Matched {result.matched_count} documents and modified {result.modified_count} documents.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19719c86-472f-484c-bdd1-610e989edfb2",
   "metadata": {},
   "source": [
    "We can check our data either it's modified or not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4bcebef-1ff1-48be-a052-291560769ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>name</th>\n",
       "      <th>age</th>\n",
       "      <th>phone</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6576e7bb085308e7dd98da09</td>\n",
       "      <td>Aziz</td>\n",
       "      <td>21</td>\n",
       "      <td>93505147</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6576e7bb085308e7dd98da0a</td>\n",
       "      <td>Oussema</td>\n",
       "      <td>31</td>\n",
       "      <td>98417306</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6576e7bb085308e7dd98da0b</td>\n",
       "      <td>Lamjed</td>\n",
       "      <td>30</td>\n",
       "      <td>24696124</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6576e7bb085308e7dd98da0c</td>\n",
       "      <td>Saloua</td>\n",
       "      <td>18</td>\n",
       "      <td>54897542</td>\n",
       "      <td>minor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6576e7bb085308e7dd98da0d</td>\n",
       "      <td>Firas</td>\n",
       "      <td>22</td>\n",
       "      <td>24303822</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6576e7bb085308e7dd98da0e</td>\n",
       "      <td>Aymen</td>\n",
       "      <td>16</td>\n",
       "      <td>95748120</td>\n",
       "      <td>minor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id     name  age     phone status\n",
       "0  6576e7bb085308e7dd98da09     Aziz   21  93505147  adult\n",
       "1  6576e7bb085308e7dd98da0a  Oussema   31  98417306  adult\n",
       "2  6576e7bb085308e7dd98da0b   Lamjed   30  24696124  adult\n",
       "3  6576e7bb085308e7dd98da0c   Saloua   18  54897542  minor\n",
       "4  6576e7bb085308e7dd98da0d    Firas   22  24303822  adult\n",
       "5  6576e7bb085308e7dd98da0e    Aymen   16  95748120  minor"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = collection.find()\n",
    "entries = list(users)\n",
    "df = pd.DataFrame(entries)\n",
    "entries[:10]\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f484bb-b0ef-4d75-a77f-37cc605c9081",
   "metadata": {},
   "source": [
    "# Delete data from MongoDB:\n",
    "<hr><br>\n",
    "\n",
    "We can delete one user, first we need to find him:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b86e3a46-b62d-4447-8f2b-46c52fc9418d",
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_criteria = {'name': 'Lamjed'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5aea20-eeaf-424c-91af-785ae98405ea",
   "metadata": {},
   "source": [
    "And just delete him from the database using \"<i>update_one</i>\", then display results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13c95bf8-943f-4870-9c4a-5071a035558a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted 1 document.\n"
     ]
    }
   ],
   "source": [
    "result = collection.delete_one(filter_criteria)\n",
    "print(f\"Deleted {result.deleted_count} document.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f0d914-e986-45c7-b055-ac0f91e47896",
   "metadata": {},
   "source": [
    "We can also delete multiple users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "536b8bec-80f5-4235-a8a3-95d381ee9da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "filter_criteria = {'age': {'$gt': 25}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6625647-0a71-455e-8b18-5986d64a6ea6",
   "metadata": {},
   "source": [
    "And now we delete them using \"<i>update_many</i>\" and display the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2dd6c002-16cc-4767-9fda-e040b5ce800d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted 1 documents.\n"
     ]
    }
   ],
   "source": [
    "result = collection.delete_many(filter_criteria)\n",
    "print(f\"Deleted {result.deleted_count} documents.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbcdf64-cdef-4172-89af-ea749be05001",
   "metadata": {},
   "source": [
    "# Indexes\n",
    "<hr><br>\n",
    "<i><b>Indexes</b> in MongoDB can significantly <u>improve query performance</u> by allowing the database to quickly locate and retrieve specific documents. MongoDB supports various types of indexes, such as single field, compound, and text indexes.<br>\r\n",
    "MongoDB automatically creates a default index on the \"_id\" field for every collection.</i> <br><br>\n",
    "This is how to create an index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6e59a63-9f69-4bbf-837a-18ec3dfafe9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'name_1'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection.create_index([(\"name\", pymongo.ASCENDING)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40bf4a0-aa26-4ba6-8830-6b65da60bff7",
   "metadata": {},
   "source": [
    "We can also create a compound index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "799505ad-cc71-40c9-af2c-54c7ee2d2b9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'name_1_tel_-1'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection.create_index([(\"name\", pymongo.ASCENDING),(\"tel\", pymongo.DESCENDING)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0354b7e6-6fd2-48a7-b402-20e199acc862",
   "metadata": {},
   "source": [
    "We can use the \"explain\" method to get information about how MongoDB executes a query, including whether it uses an index.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2b43f6aa-16b2-4f3b-839a-5354763730f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'executionSuccess': True, 'nReturned': 1, 'executionTimeMillis': 45, 'totalKeysExamined': 1, 'totalDocsExamined': 1, 'executionStages': {'stage': 'nlj', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 33, 'opens': 1, 'closes': 2, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'totalDocsExamined': 1, 'totalKeysExamined': 1, 'collectionScans': 0, 'collectionSeeks': 1, 'indexScans': 0, 'indexSeeks': 1, 'indexesUsed': ['name_1'], 'innerOpens': 1, 'innerCloses': 1, 'outerProjects': [], 'outerCorrelated': [4, 7, 8, 9, 10], 'outerStage': {'stage': 'cfilter', 'planNodeId': 1, 'nReturned': 1, 'executionTimeMillisEstimate': 33, 'opens': 1, 'closes': 2, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'numTested': 1, 'filter': '(exists(s5) && exists(s6)) ', 'inputStage': {'stage': 'ixseek', 'planNodeId': 1, 'nReturned': 1, 'executionTimeMillisEstimate': 33, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'indexName': 'name_1', 'keysExamined': 1, 'seeks': 1, 'numReads': 2, 'indexKeySlot': 9, 'recordIdSlot': 4, 'snapshotIdSlot': 7, 'indexIdentSlot': 8, 'outputSlots': [], 'indexKeysToInclude': '00000000000000000000000000000000', 'seekKeyLow': 's5 ', 'seekKeyHigh': 's6 '}}, 'innerStage': {'stage': 'limit', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 0, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'limit': 1, 'inputStage': {'stage': 'seek', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 0, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 0, 'numReads': 1, 'recordSlot': 11, 'recordIdSlot': 12, 'seekKeySlot': 4, 'snapshotIdSlot': 7, 'indexIdentSlot': 8, 'indexKeySlot': 9, 'indexKeyPatternSlot': 10, 'fields': [], 'outputSlots': []}}}, 'allPlansExecution': [{'nReturned': 1, 'executionTimeMillisEstimate': 33, 'totalKeysExamined': 1, 'totalDocsExamined': 1, 'score': 2.5002, 'executionStages': {'stage': 'nlj', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 33, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'totalDocsExamined': 1, 'totalKeysExamined': 1, 'collectionScans': 0, 'collectionSeeks': 1, 'indexScans': 0, 'indexSeeks': 1, 'indexesUsed': ['name_1'], 'innerOpens': 1, 'innerCloses': 1, 'outerProjects': [], 'outerCorrelated': [4, 7, 8, 9, 10], 'outerStage': {'stage': 'cfilter', 'planNodeId': 1, 'nReturned': 1, 'executionTimeMillisEstimate': 33, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'numTested': 1, 'filter': '(exists(s5) && exists(s6)) ', 'inputStage': {'stage': 'ixseek', 'planNodeId': 1, 'nReturned': 1, 'executionTimeMillisEstimate': 33, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'indexName': 'name_1', 'keysExamined': 1, 'seeks': 1, 'numReads': 2, 'indexKeySlot': 9, 'recordIdSlot': 4, 'snapshotIdSlot': 7, 'indexIdentSlot': 8, 'outputSlots': [], 'indexKeysToInclude': '00000000000000000000000000000000', 'seekKeyLow': 's5 ', 'seekKeyHigh': 's6 '}}, 'innerStage': {'stage': 'limit', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 0, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 1, 'limit': 1, 'inputStage': {'stage': 'seek', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 0, 'opens': 1, 'closes': 1, 'saveState': 2, 'restoreState': 2, 'isEOF': 0, 'numReads': 1, 'recordSlot': 11, 'recordIdSlot': 12, 'seekKeySlot': 4, 'snapshotIdSlot': 7, 'indexIdentSlot': 8, 'indexKeySlot': 9, 'indexKeyPatternSlot': 10, 'fields': [], 'outputSlots': []}}}}, {'nReturned': 1, 'executionTimeMillisEstimate': 12, 'totalKeysExamined': 1, 'totalDocsExamined': 1, 'score': 2.5002, 'executionStages': {'stage': 'nlj', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 12, 'opens': 1, 'closes': 2, 'saveState': 1, 'restoreState': 1, 'isEOF': 1, 'totalDocsExamined': 1, 'totalKeysExamined': 1, 'collectionScans': 0, 'collectionSeeks': 1, 'indexScans': 0, 'indexSeeks': 1, 'indexesUsed': ['name_1_tel_-1'], 'innerOpens': 1, 'innerCloses': 1, 'outerProjects': [], 'outerCorrelated': [4, 7, 8, 9, 10], 'outerStage': {'stage': 'cfilter', 'planNodeId': 1, 'nReturned': 1, 'executionTimeMillisEstimate': 12, 'opens': 1, 'closes': 2, 'saveState': 1, 'restoreState': 1, 'isEOF': 1, 'numTested': 1, 'filter': '(exists(s5) && exists(s6)) ', 'inputStage': {'stage': 'ixseek', 'planNodeId': 1, 'nReturned': 1, 'executionTimeMillisEstimate': 12, 'opens': 1, 'closes': 1, 'saveState': 1, 'restoreState': 1, 'isEOF': 1, 'indexName': 'name_1_tel_-1', 'keysExamined': 1, 'seeks': 1, 'numReads': 2, 'indexKeySlot': 9, 'recordIdSlot': 4, 'snapshotIdSlot': 7, 'indexIdentSlot': 8, 'outputSlots': [], 'indexKeysToInclude': '00000000000000000000000000000000', 'seekKeyLow': 's5 ', 'seekKeyHigh': 's6 '}}, 'innerStage': {'stage': 'limit', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 0, 'opens': 1, 'closes': 1, 'saveState': 1, 'restoreState': 1, 'isEOF': 1, 'limit': 1, 'inputStage': {'stage': 'seek', 'planNodeId': 2, 'nReturned': 1, 'executionTimeMillisEstimate': 0, 'opens': 1, 'closes': 1, 'saveState': 1, 'restoreState': 1, 'isEOF': 0, 'numReads': 1, 'recordSlot': 11, 'recordIdSlot': 12, 'seekKeySlot': 4, 'snapshotIdSlot': 7, 'indexIdentSlot': 8, 'indexKeySlot': 9, 'indexKeyPatternSlot': 10, 'fields': [], 'outputSlots': []}}}}]}\n"
     ]
    }
   ],
   "source": [
    "query = collection.find({\"name\": \"Firas\"}).explain()['executionStats']\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13cc338-9197-44d3-b75b-fc59c1ee44cb",
   "metadata": {},
   "source": [
    "We will create a graph to see the size of the indexes in our collection:<br>\n",
    "First we need to import the \"<i>Matplotlib</i>\" library from python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f149d37-2fc5-4e8d-9dce-d267ffdc0149",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b64f27d-331a-4f96-b350-1428a9c35f29",
   "metadata": {},
   "source": [
    "We store the indexes inside an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "91f192ee-8691-49ab-8ce0-db24b7d1a094",
   "metadata": {},
   "outputs": [],
   "source": [
    "indexes = list(collection.list_indexes())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4164a947-2e81-4ca2-a459-cbd794605930",
   "metadata": {},
   "source": [
    "Now we extract data for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "593df68b-a270-4cea-8873-4deb9279a57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_names = [index[\"name\"] for index in indexes]\n",
    "index_key_sizes = [sum(field_size for field_size in index[\"key\"].values()) for index in indexes]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b58fe2-c419-4946-8eba-6cb8db746b1d",
   "metadata": {},
   "source": [
    "And finally we create a bar chart using the \"<i>matplotlib</i>\" library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "16f53813-2aee-4dab-87ba-58cb3df8bdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RTExMrv1sX8s/H8ef3wcX2671+xLejUt1KFBWrFiR41/sF+dS5HSq/aKpU6fqrbfe0qRJk1S3bt1sy8PDw9WkSRO99dZbOQawP3+U/Pfff3dZVqxYMVWqVCnHj0z/2T333KP169dr3bp1zrb09HS9/fbbioqKyrNLD7ZiY2MVFhamKVOmuNzQcNq0aTp27JhL386dO2vfvn165513sm3n9OnTLpdknnnmGaWkpGj69OkaN26coqKilJCQcNnX56+CgoK0ePFiVapUSV27dtXy5cudtWRmZmrkyJHZ1jl//ny22rt27SqHw6F+/fpp165d+sc//uGy/OjRo9m2c/EXsrs1X62LQeOvx2DLnXGck7i4OLVq1Urvvvuu5s+fn215RkaG86aspUuXVkxMjKZPn+5S708//aSlS5fqnnvucat22zHWvn17+fj4aMSIEdkup/75/4eiRYtavY6xsbEKDw/XlClTXH7en3/+ubZu3ao2bdq4dRy4vnHGCQVKnz59dOrUKXXo0EFVqlRRRkaG1q5dq9mzZysqKsrlcs6fHTlyRI8//riqVasmf39/ffDBBy7LO3TooKJFi2rSpElq0KCBatSood69e+uWW25Ramqq1q1bp99++02bNm2SdOHyT5MmTVS7dm2FhITo+++/17x58y77tSeDBw/Wf/7zH7Vu3Vp9+/ZVSEiIpk+fruTkZP3f//3fNb+5Y+HChfXiiy/qkUceUdOmTdWlSxclJydr6tSp2eY4/fOf/9ScOXP06KOPasWKFapfv74yMzO1bds2zZkzR0uWLFFsbKy+/PJLTZ48WYmJibrjjjskXQitTZo00QsvvKCxY8e6VWNYWJiWLVum+vXrq3379lq+fLkaN26sRx55RElJSdq4caNatmypwoULa/v27Zo7d64mTpyoTp06uWyjVatWmjt3rkqUKJHtF+GIESO0evVqtWnTRuXLl9ehQ4c0efJklStXzmUi/7VQu3ZtSRfOjsbHx8vX11d///vf3dqG7TjOzYwZM9SyZUt17NhRbdu2VbNmzVS0aFFt375ds2bN0oEDB5z3cnr55ZfVunVrxcXFqVevXjp9+rRef/11BQcHu333c9sxVqlSJT333HMaOXKkGjZsqI4dO8rf31/fffedypQpo6SkJOdr+eabb+rFF19UpUqVFB4enu2MknThfTBmzBj17NlTjRs3VteuXZWamqqJEycqKipK/fv3d+s4cJ3z4Cf6ALd9/vnn5sEHHzRVqlQxxYoVM35+fqZSpUqmT58+JjU11aXvnz8mfvGj3Lk9/vwR6Z07d5ru3bubUqVKmcKFC5uyZcuae++918ybN8/Z58UXXzR169Y1JUqUMIGBgaZKlSrmpZdeyvG2CH+1c+dO06lTJ1OiRAkTEBBg6tataz777LNs/ZQHtyOYO3euS7+Lr8PUqVNd2idPnmwqVKhg/P39TWxsrFm9enW2j4obc+Fj22PGjDHVq1c3/v7+pmTJkqZ27dpm+PDh5vjx4yYtLc2UL1/e3HHHHebcuXMu6/bv39/4+PiYdevWuXUsF23dutWEhoaakJAQ89NPPxljLtw2oXbt2iYwMNAUL17c1KhRwwwaNMjs378/2/pz5swxkszDDz+cbdny5ctNu3btTJkyZYyfn58pU6aM6dq1q/n1118vWasxud+O4LvvvnPpl9O9g3Jy/vx506dPHxMWFmYcDofz1gQXf3Yvv/xytnUkmcTERJc2m3F8KadOnTKvvPKKqVOnjvO9duutt5o+ffqYHTt2uPT94osvTP369U1gYKAJCgoybdu2NVu2bHHpY3M7AmMuP8b+7L333jO1atVy9mvcuLFZtmyZc/nBgwdNmzZtTPHixV1ur5Hbz2L27NnO7YWEhJhu3bqZ3377zaVPQkKCKVq0aLbXKzExMcfbSOD64zDmKmcqAkABsGDBArVv316rV6++5pOeAVw/CE4Abgj33nuvtm7dqh07dlz1BGwANy7mOAG4rs2aNUv//e9/tXDhQk2cOJHQBOCqcMYJwHXN4XCoWLFi6tKli6ZMmeJy7y4AcBf/gwC4rvG3IYC8xH2cAAAALBGcAAAALN1wl+qysrK0f/9+FS9enEmiAABAxhidOHFCZcqUueyNiG+44LR//36v+aJIAADgPfbu3aty5cpdss8NF5yKFy8u6cKLExQU5OFqAACAp6WlpSkyMtKZES7lhgtOFy/PBQUFEZwAAICTzRQeJocDAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABY8mhwWr16tdq2basyZcrI4XBo/vz5l11n5cqVuuOOO+Tv769KlSpp2rRp+V4nAACA5OHglJ6erujoaE2aNMmqf3Jystq0aaO7775bGzdu1FNPPaWHHnpIS5YsyedKAQAApEKe3Hnr1q3VunVr6/5TpkxRhQoV9Oqrr0qSqlatqq+++krjx49XfHx8fpUJAAAgqYDNcVq3bp2aN2/u0hYfH69169blus7Zs2eVlpbm8gAAALgSHj3j5K6DBw8qIiLCpS0iIkJpaWk6ffq0AgMDs62TlJSk4cOHX6sSJUmO4Y5ruj8UHCbReLoExidy5Q3jE/B2BeqM05UYMmSIjh8/7nzs3bvX0yUBAIACqkCdcSpVqpRSU1Nd2lJTUxUUFJTj2SZJ8vf3l7+//7UoDwAAXOcK1BmnuLg4LV++3KVt2bJliouL81BFAADgRuLR4HTy5Elt3LhRGzdulHThdgMbN25USkqKpAuX2bp37+7s/+ijj2rXrl0aNGiQtm3bpsmTJ2vOnDnq37+/J8oHAAA3GI8Gp++//161atVSrVq1JEkDBgxQrVq1NHToUEnSgQMHnCFKkipUqKCFCxdq2bJlio6O1quvvqp3332XWxEAAIBrwqNznJo0aSJjcv8UR053BW/SpIl+/PHHfKwKAAAgZwVqjhMAAIAnEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAsEZwAAAAseTw4TZo0SVFRUQoICFC9evW0fv36S/afMGGCbrvtNgUGBioyMlL9+/fXmTNnrlG1AADgRubR4DR79mwNGDBAiYmJ+uGHHxQdHa34+HgdOnQox/4ffvihBg8erMTERG3dulX//ve/NXv2bD377LPXuHIAAHAj8mhwGjdunHr37q2ePXuqWrVqmjJliooUKaL33nsvx/5r165V/fr19cADDygqKkotW7ZU165dL3uWCgAAIC94LDhlZGRow4YNat68+f+K8fFR8+bNtW7duhzXueuuu7RhwwZnUNq1a5cWLVqke+65J9f9nD17VmlpaS4PAACAK1HIUzs+cuSIMjMzFRER4dIeERGhbdu25bjOAw88oCNHjqhBgwYyxuj8+fN69NFHL3mpLikpScOHD8/T2gEAwI3J45PD3bFy5UqNGjVKkydP1g8//KCPPvpICxcu1MiRI3NdZ8iQITp+/LjzsXfv3mtYMQAAuJ547IxTaGiofH19lZqa6tKempqqUqVK5bjOCy+8oH/+85966KGHJEk1atRQenq6Hn74YT333HPy8cmeA/39/eXv75/3BwAAAG44Hjvj5Ofnp9q1a2v58uXOtqysLC1fvlxxcXE5rnPq1Kls4cjX11eSZIzJv2IBAADkwTNOkjRgwAAlJCQoNjZWdevW1YQJE5Senq6ePXtKkrp3766yZcsqKSlJktS2bVuNGzdOtWrVUr169bRjxw698MILatu2rTNAAQAA5BePBqcuXbro8OHDGjp0qA4ePKiYmBgtXrzYOWE8JSXF5QzT888/L4fDoeeff1779u1TWFiY2rZtq5deeslThwAAAG4gDnODXeNKS0tTcHCwjh8/rqCgoHzZh2O4I1+2i4LPJHr+7cb4RG68YXwCnuBONihQn6oDAADwJIITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACAJYITAACApUJXslJKSor27NmjU6dOKSwsTNWrV5e/v39e1wYAAOBVrIPT7t279eabb2rWrFn67bffZIxxLvPz81PDhg318MMP6/7775ePDyeyAADA9ccq4fTt21fR0dFKTk7Wiy++qC1btuj48ePKyMjQwYMHtWjRIjVo0EBDhw5VzZo19d133+V33QAAANec1RmnokWLateuXbrpppuyLQsPD1fTpk3VtGlTJSYmavHixdq7d6/q1KmT58UCAAB4klVwSkpKst5gq1atrrgYAAAAb+b2ZKTTp0/r1KlTzud79uzRhAkTtGTJkjwtDAAAwNu4HZzatWunGTNmSJKOHTumevXq6dVXX1X79u315ptv5nmBAAAA3sLt4PTDDz+oYcOGkqR58+YpIiJCe/bs0YwZM/Taa6/leYEAAADewu3gdOrUKRUvXlyStHTpUnXs2FE+Pj668847tWfPnjwvEAAAwFu4HZwqVaqk+fPna+/evVqyZIlatmwpSTp06JCCgoLyvEAAAABv4XZwGjp0qAYOHKioqCjVrVtXcXFxki6cfapVq1aeFwgAAOAt3P7KlU6dOqlBgwY6cOCAoqOjne3NmjVThw4d8rQ4AAAAb3JF341SqlQpFS9eXMuWLdPp06clSXXq1FGVKlXytDgAAABv4nZw+v3339WsWTNVrlxZ99xzjw4cOCBJ6tWrl55++uk8LxAAAMBbuB2c+vfvr8KFCyslJUVFihRxtnfp0kWLFy/O0+IAAAC8idtznJYuXaolS5aoXLlyLu233nortyMAAADXNbfPOKWnp7ucabro6NGj8vf3z5OiAAAAvJHbwalhw4bOr1yRJIfDoaysLI0dO1Z33313nhYHAADgTdy+VDd27Fg1a9ZM33//vTIyMjRo0CD9/PPPOnr0qL7++uv8qBEAAMAruH3G6fbbb9evv/6qBg0aqF27dkpPT1fHjh31448/qmLFivlRIwAAgFdw+4xTSkqKIiMj9dxzz+W47Oabb86TwgAAALyN22ecKlSooMOHD2dr//3331WhQoU8KQoAAMAbuR2cjDFyOBzZ2k+ePKmAgIA8KQoAAMAbWV+qGzBggKQLn6J74YUXXG5JkJmZqW+//VYxMTF5XiAAAIC3sA5OP/74o6QLZ5w2b94sPz8/5zI/Pz9FR0dr4MCBeV8hAACAl7AOTitWrJAk9ezZUxMnTlRQUFC+FQUAAOCN3J7j1KhRIxUuXDg/agEAAPBqbgenwYMHKyIiQr169dLatWvzoyYAAACv5HZw2rdvn6ZPn64jR46oSZMmqlKlisaMGaODBw/mR30AAABew+3gVKhQIXXo0EELFizQ3r171bt3b82cOVM333yz7rvvPi1YsEBZWVn5USsAAIBHuR2c/iwiIkINGjRQXFycfHx8tHnzZiUkJKhixYpauXJlHpUIAADgHa4oOKWmpuqVV15R9erV1aRJE6Wlpemzzz5TcnKy9u3bp86dOyshISGvawUAAPAot4NT27ZtFRkZqWnTpql3797at2+f/vOf/6h58+aSpKJFi+rpp5/W3r1787xYAAAAT3L7S37Dw8O1atUqxcXF5donLCxMycnJV1UYAACAt3E7OP373/++bB+Hw6Hy5ctfUUEAAADe6ormOC1fvlz33nuvKlasqIoVK+ree+/VF198kde1AQAAeBW3g9PkyZPVqlUrFS9eXP369VO/fv0UFBSke+65R5MmTXK7gEmTJikqKkoBAQGqV6+e1q9ff8n+x44d0xNPPKHSpUvL399flStX1qJFi9zeLwAAgLvcvlQ3atQojR8/Xk8++aSzrW/fvqpfv75GjRqlJ554wnpbs2fP1oABAzRlyhTVq1dPEyZMUHx8vH755ReFh4dn65+RkaEWLVooPDxc8+bNU9myZbVnzx6VKFHC3cMAAABwm9tnnI4dO6ZWrVpla2/ZsqWOHz/u1rbGjRun3r17q2fPnqpWrZqmTJmiIkWK6L333sux/3vvvaejR49q/vz5ql+/vqKiotS4cWNFR0e7exgAAABuczs43Xffffr444+ztS9YsED33nuv9XYyMjK0YcMG520MJMnHx0fNmzfXunXrclznk08+UVxcnJ544glFRETo9ttv16hRo5SZmZnrfs6ePau0tDSXBwAAwJWwulT32muvOf9drVo1vfTSS1q5cqXzlgTffPONvv76az399NPWOz5y5IgyMzMVERHh0h4REaFt27bluM6uXbv05Zdfqlu3blq0aJF27Nihxx9/XOfOnVNiYmKO6yQlJWn48OHWdQEAAOTGYYwxl+tUoUIFu405HNq1a5dV3/3796ts2bJau3atyz2hBg0apFWrVunbb7/Ntk7lypV15swZJScny9fXV9KFy30vv/yyDhw4kON+zp49q7Nnzzqfp6WlKTIyUsePH1dQUJBVre5yDHfky3ZR8JnEy77d8h3jE7nxhvEJeEJaWpqCg4OtsoHVGaf8uJllaGiofH19lZqa6tKempqqUqVK5bhO6dKlVbhwYWdokqSqVavq4MGDysjIkJ+fX7Z1/P395e/vn7fFAwCAG9JVfcnv1fDz81Pt2rW1fPlyZ1tWVpaWL1+e613J69evrx07digrK8vZ9uuvv6p06dI5hiYAAIC8ZBWcRo8erVOnTllt8Ntvv9XChQut+g4YMEDvvPOOpk+frq1bt+qxxx5Tenq6evbsKUnq3r27hgwZ4uz/2GOP6ejRo+rXr59+/fVXLVy40O1bIAAAAFwpq0t1W7ZsUfny5fW3v/1Nbdu2VWxsrMLCwiRJ58+f15YtW/TVV1/pgw8+0P79+zVjxgyrnXfp0kWHDx/W0KFDdfDgQcXExGjx4sXOCeMpKSny8flftouMjNSSJUvUv39/1axZU2XLllW/fv30zDPPuHvcAAAAbrOaHC5JmzZt0htvvKF58+YpLS1Nvr6+8vf3d56JqlWrlh566CH16NFDAQEB+Vr01XBnAtiVYvItcuMNk28Zn8iNN4xPwBPyfHK4JEVHR+udd97RW2+9pf/+97/as2ePTp8+rdDQUMXExCg0NPSqCwcAAPBmbn/lio+Pj2JiYhQTE5MP5QAAAHgvj32qDgAAoKAhOAEAAFgiOAEAAFgiOAEAAFi66uCUlpam+fPna+vWrXlRDwAAgNdyOzh17txZb7zxhiTp9OnTio2NVefOnVWzZk393//9X54XCAAA4C3cDk6rV69Ww4YNJUkff/yxjDE6duyYXnvtNb344ot5XiAAAIC3cDs4HT9+XCEhIZKkxYsX6/7771eRIkXUpk0bbd++Pc8LBAAA8BZuB6fIyEitW7dO6enpWrx4sVq2bClJ+uOPP7z6q1YAAACultt3Dn/qqafUrVs3FStWTDfffLOaNGki6cIlvBo1auR1fQAAAF7D7eD0+OOPq27dutq7d69atGghH58LJ61uueUW5jgBAIDrmtvBSZJiY2NVs2ZNJScnq2LFiipUqJDatGmT17UBAAB4FbfnOJ06dUq9evVSkSJFVL16daWkpEiS+vTpo9GjR+d5gQAAAN7C7eA0ZMgQbdq0SStXrnSZDN68eXPNnj07T4sDAADwJm5fqps/f75mz56tO++8Uw6Hw9levXp17dy5M0+LAwAA8CZun3E6fPiwwsPDs7Wnp6e7BCkAAIDrjdvBKTY2VgsXLnQ+vxiW3n33XcXFxeVdZQAAAF7G7Ut1o0aNUuvWrbVlyxadP39eEydO1JYtW7R27VqtWrUqP2oEAADwCm6fcWrQoIE2btyo8+fPq0aNGlq6dKnCw8O1bt061a5dOz9qBAAA8ApXdB+nihUr6p133snrWgAAALya22ecmjZtquHDh2dr/+OPP9S0adM8KQoAAMAbuX3GaeXKldq8ebN+/PFHzZw5U0WLFpUkZWRkMMcJAABc19w+4yRJX3zxhQ4ePKg777xTu3fvzuOSAAAAvNMVBafSpUtr1apVqlGjhurUqaOVK1fmcVkAAADex+3gdPG+Tf7+/vrwww/Vr18/tWrVSpMnT87z4gAAALyJ23OcjDEuz59//nlVrVpVCQkJeVYUAACAN3I7OCUnJys0NNSl7f7779dtt92mDRs25FlhAAAA3sbt4FS+fPkc22+//XbdfvvtV10QAACAt7IKTh07dtS0adMUFBSkjh07XrLvRx99lCeFAQAAeBur4BQcHOycFB4cHJyvBQEAAHgrq+A0derUHP8NAABwI7mi76r7s1WrVik9PV1xcXEqWbJkXtQEAADglayD05gxY3Ty5EmNHDlS0oXbErRu3VpLly6VJIWHh2v58uWqXr16/lQKAADgYdY3wJw9e7bLp+bmzZun1atXa82aNTpy5IhiY2Nz/PJfAACA64V1cEpOTlbNmjWdzxctWqROnTqpfv36CgkJ0fPPP69169blS5EAAADewDo4nT9/Xv7+/s7n69at01133eV8XqZMGR05ciRvqwMAAPAi1sGpYsWKWr16tSQpJSVFv/76qxo1auRc/ttvv+mmm27K+woBAAC8hPXk8CeeeEJPPvmk1qxZo2+++UZxcXGqVq2ac/mXX36pWrVq5UuRAAAA3sA6OPXu3Vu+vr769NNP1ahRIyUmJros379/vx588ME8LxAAAMBbOIwxxtNFXEtpaWkKDg7W8ePHFRQUlC/7cAx35Mt2UfCZRM+/3RifyI03jE/AE9zJBtZznAAAAG50BCcAAABLBCcAAABLBCcAAABLbgenqVOn6tSpU/lRCwAAgFdzOzgNHjxYpUqVUq9evbR27dr8qAkAAMAruR2c9u3bp+nTp+vIkSNq0qSJqlSpojFjxujgwYP5UR8AAIDXcDs4FSpUSB06dNCCBQu0d+9e9e7dWzNnztTNN9+s++67TwsWLFBWVlZ+1AoAAOBRVzU5PCIiQg0aNFBcXJx8fHy0efNmJSQkqGLFilq5cmUelQgAAOAdrig4paam6pVXXlH16tXVpEkTpaWl6bPPPlNycrL27dunzp07KyEhIa9rBQAA8Ci3g1Pbtm0VGRmpadOmqXfv3tq3b5/+85//qHnz5pKkokWL6umnn9bevXvzvFgAAABPsv6S34vCw8O1atUqxcXF5donLCxMycnJV1UYAACAt3E7OP373/92/vvMmTMKCAjI1sfhcKh8+fJXVxkAAICXcftSXVZWlkaOHKmyZcuqWLFi2rVrlyTphRdecAlVAAAA1xu3g9OLL76oadOmaezYsfLz83O233777Xr33XfztDgAAABv4nZwmjFjht5++21169ZNvr6+zvbo6Ght27YtT4sDAADwJld05/BKlSpla8/KytK5c+fypCgAAABv5HZwqlatmtasWZOtfd68eYqJicmLmgAAALyS25+qGzp0qBISErRv3z5lZWXpo48+0i+//KIZM2bos88+y48aAQAAvILbZ5zatWunTz/9VF988YWKFi2qoUOHauvWrfr0008veW8nAACAgs46OI0fP97574YNG2rZsmU6dOiQTp06pa+++kpxcXGKj4/PlyIBAAC8gXVwevbZZzVjxowcl6Wnp6tVq1b6/fff86wwAAAAb2MdnN5//3098sgj+uSTT1zaT548qfj4eB0+fFgrVqzI8wIBAAC8hfXk8E6dOunYsWPq2rWrFi5cqCZNmig9PV2tW7dWamqqVq1apdKlS+dnrQAAAB7l1qfqHnroIR09elTt2rXTggULNHToUO3fv1+rVq1SmTJl8qtGAAAAr+D27QgGDRqko0ePqlmzZoqKitLKlStVrly5/KgNAADAq1gHp44dO7o8L1y4sEJDQ9WvXz+X9o8++ihvKgMAAPAy1sEpODjY5XnXrl3zvBgAAABvZh2cpk6dmp91AAAAeD237xwOAABwoyI4AQAAWCI4AQAAWCI4AQAAWCI4AQAAWLL6VN1fv5/uUu67774rLgYAAMCbWQWn9u3bW23M4XAoMzPzauoBAADwWlbBKSsrK7/rAAAA8HrMcQIAALDk9pf8SlJ6erpWrVqllJQUZWRkuCzr27dvnhQGAADgbdwOTj/++KPuuecenTp1Sunp6QoJCdGRI0dUpEgRhYeHE5wAAMB1y+1Ldf3791fbtm31xx9/KDAwUN9884327Nmj2rVr65VXXsmPGgEAALyC28Fp48aNevrpp+Xj4yNfX1+dPXtWkZGRGjt2rJ599tn8qBEAAMAruB2cChcuLB+fC6uFh4crJSVFkhQcHKy9e/fmbXUAAABexO3gVKtWLX333XeSpMaNG2vo0KGaOXOmnnrqKd1+++1XVMSkSZMUFRWlgIAA1atXT+vXr7dab9asWXI4HNb3mQIAALgabgenUaNGqXTp0pKkl156SSVLltRjjz2mw4cP66233nK7gNmzZ2vAgAFKTEzUDz/8oOjoaMXHx+vQoUOXXG/37t0aOHCgGjZs6PY+AQAAroTDGGM8WUC9evVUp04dvfHGG5Iu3GwzMjJSffr00eDBg3NcJzMzU40aNdKDDz6oNWvW6NixY5o/f77V/tLS0hQcHKzjx48rKCgorw7DhWO4I1+2i4LPJHr07SaJ8YncecP4BDzBnWzg9hmnpk2b6tixYznutGnTpm5tKyMjQxs2bFDz5s3/V5CPj5o3b65169blut6IESMUHh6uXr16XXYfZ8+eVVpamssDAADgSrgdnFauXJntppeSdObMGa1Zs8atbR05ckSZmZmKiIhwaY+IiNDBgwdzXOerr77Sv//9b73zzjtW+0hKSlJwcLDzERkZ6VaNAAAAF1nfAPO///2v899btmxxCTaZmZlavHixypYtm7fV/cWJEyf0z3/+U++8845CQ0Ot1hkyZIgGDBjgfJ6WlkZ4AgAAV8Q6OMXExMjhcMjhcOR4SS4wMFCvv/66WzsPDQ2Vr6+vUlNTXdpTU1NVqlSpbP137typ3bt3q23bts62i19AXKhQIf3yyy+qWLGiyzr+/v7y9/d3qy4AAICcWAen5ORkGWN0yy23aP369QoLC3Mu8/PzU3h4uHx9fd3auZ+fn2rXrq3ly5c7bymQlZWl5cuX68knn8zWv0qVKtq8ebNL2/PPP68TJ05o4sSJnEkCAAD5yjo4lS9fXtL/zvDklQEDBighIUGxsbGqW7euJkyYoPT0dPXs2VOS1L17d5UtW1ZJSUkKCAjIdq+oEiVKSNIV30MKAADAlttf8itduGQ2YcIEbd26VZJUrVo19evXL9tlMhtdunTR4cOHNXToUB08eFAxMTFavHixc8J4SkqK807lAAAAnuT2fZyWLFmi++67TzExMapfv74k6euvv9amTZv06aefqkWLFvlSaF7hPk7wJG+4Tw7jE7nxhvEJeII72cDtM06DBw9W//79NXr06GztzzzzjNcHJwAAgCvl9jWwrVu35njjyQcffFBbtmzJk6IAAAC8kdvBKSwsTBs3bszWvnHjRoWHh+dFTQAAAF7J+lLdiBEjNHDgQPXu3VsPP/ywdu3apbvuukvShTlOY8aMcbnRJAAAwPXGenK4r6+vDhw4oLCwME2YMEGvvvqq9u/fL0kqU6aM/vWvf6lv375yOLx74imTw+FJ3jD5lvGJ3HjD+AQ8IV8mh1/MVw6HQ/3791f//v114sQJSVLx4sWvolwAAICCwa1P1f31bBKBCQAA3EjcCk6VK1e+7KW4o0ePXlVBAAAA3sqt4DR8+HAFBwfnVy0AAABeza3g9Pe//51bDgAAgBuW9X2cvP3TcgAAAPnNOji5+ZV2AAAA1x3rS3VZWVn5WQcAAIDXc/srVwAAAG5UBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLBCcAAABLXhGcJk2apKioKAUEBKhevXpav359rn3feecdNWzYUCVLllTJkiXVvHnzS/YHAADIKx4PTrNnz9aAAQOUmJioH374QdHR0YqPj9ehQ4dy7L9y5Up17dpVK1as0Lp16xQZGamWLVtq375917hyAABwo3EYY4wnC6hXr57q1KmjN954Q5KUlZWlyMhI9enTR4MHD77s+pmZmSpZsqTeeOMNde/e/bL909LSFBwcrOPHjysoKOiq68+JY7gjX7aLgs8kevTtJonxidx5w/gEPMGdbODRM04ZGRnasGGDmjdv7mzz8fFR8+bNtW7dOqttnDp1SufOnVNISEh+lQkAACBJKuTJnR85ckSZmZmKiIhwaY+IiNC2bdustvHMM8+oTJkyLuHrz86ePauzZ886n6elpV15wQAA4Ibm8TlOV2P06NGaNWuWPv74YwUEBOTYJykpScHBwc5HZGTkNa4SAABcLzwanEJDQ+Xr66vU1FSX9tTUVJUqVeqS677yyisaPXq0li5dqpo1a+bab8iQITp+/LjzsXfv3jypHQAA3Hg8Gpz8/PxUu3ZtLV++3NmWlZWl5cuXKy4uLtf1xo4dq5EjR2rx4sWKjY295D78/f0VFBTk8gAAALgSHp3jJEkDBgxQQkKCYmNjVbduXU2YMEHp6enq2bOnJKl79+4qW7askpKSJEljxozR0KFD9eGHHyoqKkoHDx6UJBUrVkzFihXz2HEAAIDrn8eDU5cuXXT48GENHTpUBw8eVExMjBYvXuycMJ6SkiIfn/+dGHvzzTeVkZGhTp06uWwnMTFRw4YNu5alAwCAG4zH7+N0rXEfJ3iSN9wnh/GJ3HjD+AQ8ocDcxwkAAKAgITgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABYIjgBAABY8orgNGnSJEVFRSkgIED16tXT+vXrL9l/7ty5qlKligICAlSjRg0tWrToGlUKAABuZB4PTrNnz9aAAQOUmJioH374QdHR0YqPj9ehQ4dy7L927Vp17dpVvXr10o8//qj27durffv2+umnn65x5QAA4EbjMMYYTxZQr1491alTR2+88YYkKSsrS5GRkerTp48GDx6crX+XLl2Unp6uzz77zNl25513KiYmRlOmTLns/tLS0hQcHKzjx48rKCgo7w7kTxzDHfmyXRR8JtGjbzdJjE/kzhvGJ+AJ7mQDj55xysjI0IYNG9S8eXNnm4+Pj5o3b65169bluM66detc+ktSfHx8rv0BAADySiFP7vzIkSPKzMxURESES3tERIS2bduW4zoHDx7Msf/Bgwdz7H/27FmdPXvW+fz48eOSLqTLfHMm/zaNgi1fx50txidy4RXjE/CAi2Pf5iKcR4PTtZCUlKThw4dna4+MjPRANbjRBY8O9nQJQK4Yn7jRnThxQsHBl34feDQ4hYaGytfXV6mpqS7tqampKlWqVI7rlCpVyq3+Q4YM0YABA5zPs7KydPToUd10001yOJjrkZ/S0tIUGRmpvXv35tt8MuBqMEbhzRif144xRidOnFCZMmUu29ejwcnPz0+1a9fW8uXL1b59e0kXgs3y5cv15JNP5rhOXFycli9frqeeesrZtmzZMsXFxeXY39/fX/7+/i5tJUqUyIvyYSkoKIg3PbwaYxTejPF5bVzuTNNFHr9UN2DAACUkJCg2NlZ169bVhAkTlJ6erp49e0qSunfvrrJlyyopKUmS1K9fPzVu3Fivvvqq2rRpo1mzZun777/X22+/7cnDAAAANwCPB6cuXbro8OHDGjp0qA4ePKiYmBgtXrzYOQE8JSVFPj7/+/DfXXfdpQ8//FDPP/+8nn32Wd16662aP3++br/9dk8dAgAAuEF4/D5OuH6dPXtWSUlJGjJkSLbLpYA3YIzCmzE+vRPBCQAAwJLHv3IFAACgoCA4AQAAWCI4Ic80adLE5TYROYmKitKECROuST0AADvDhg1TTEyMp8soEAhOyDMfffSRRo4c6ekyAI84c+aMevTooRo1aqhQoULOe9MB7rjaceSpALR69Wq1bdtWZcqUkcPh0Pz58695DdcKwQl5JiQkRMWLF/d0GYBHZGZmKjAwUH379s32ReSArYI6jtLT0xUdHa1JkyZ5upR8R3BCnvnrpbpDhw6pbdu2CgwMVIUKFTRz5kzPFYcCpUmTJurbt68GDRqkkJAQlSpVSsOGDXMuHzdunGrUqKGiRYsqMjJSjz/+uE6ePOlcPm3aNJUoUUKfffaZbrvtNhUpUkSdOnXSqVOnNH36dEVFRalkyZLq27evMjMzneudPXtWAwcOVNmyZVW0aFHVq1dPK1eutKq5aNGievPNN9W7d+9cvwIK19aNNo6mTZum4cOHa9OmTXI4HHI4HJo2bZok6dixY3rooYcUFhamoKAgNW3aVJs2bXJr+5fSunVrvfjii+rQoUOebdNbefwGmLh+9ejRQ/v379eKFStUuHBh9e3bV4cOHfJ0WSggpk+frgEDBujbb7/VunXr1KNHD9WvX18tWrSQj4+PXnvtNVWoUEG7du3S448/rkGDBmny5MnO9U+dOqXXXntNs2bN0okTJ9SxY0d16NBBJUqU0KJFi7Rr1y7df//9ql+/vrp06SJJevLJJ7VlyxbNmjVLZcqU0ccff6xWrVpp8+bNuvXWWz31UuAq3EjjqEuXLvrpp5+0ePFiffHFF5L+9zUif/vb3xQYGKjPP/9cwcHBeuutt9SsWTP9+uuvCgkJybearksGyCONGzc2/fr1M8YY88svvxhJZv369c7lW7duNZLM+PHjPVMgCozGjRubBg0auLTVqVPHPPPMMzn2nzt3rrnpppucz6dOnWokmR07djjbHnnkEVOkSBFz4sQJZ1t8fLx55JFHjDHG7Nmzx/j6+pp9+/a5bLtZs2ZmyJAhbtWfkJBg2rVr59Y6yHs34jhKTEw00dHRLm1r1qwxQUFB5syZMy7tFStWNG+99Vau610pSebjjz/Ok215I844IV9s3bpVhQoVUu3atZ1tVapU4QuWYa1mzZouz0uXLu08Y/nFF18oKSlJ27ZtU1pams6fP68zZ87o1KlTKlKkiCSpSJEiqlixonP9iIgIRUVFqVixYi5tF7e5efNmZWZmqnLlyi77PXv2rG666aZ8OUbkP8aRtGnTJp08eTLb/k+fPq2dO3e6ta01a9aodevWzudvvfWWunXrlid1FhQEJwBeqXDhwi7PHQ6HsrKytHv3bt1777167LHH9NJLLykkJERfffWVevXqpYyMDOcvvJzWz22bknTy5En5+vpqw4YN8vX1den351+SKFgYRxdqKl26dI7zrNz9YzY2NlYbN250Pr/4vbI3EoIT8kWVKlV0/vx5bdiwQXXq1JEk/fLLLzp27JhnC0OBt2HDBmVlZenVV191fgH4nDlzrnq7tWrVUmZmpg4dOqSGDRte9fbg3a7XceTn5+cyUV2S7rjjDh08eFCFChVSVFTUVW0/MDBQlSpVuqptFHQEJ+SL2267Ta1atdIjjzyiN998U4UKFdJTTz2lwMBAT5eGAq5SpUo6d+6cXn/9dbVt21Zff/21pkyZctXbrVy5srp166bu3bvr1VdfVa1atXT48GEtX75cNWvWVJs2bS67jS1btigjI0NHjx7ViRMnnH+Zc2NB73O9jqOoqCglJydr48aNKleunIoXL67mzZsrLi5O7du319ixY1W5cmXt379fCxcuVIcOHRQbG3uVR33hrNaOHTuczy/WEBISoptvvvmqt+9NuB0B8s3UqVNVpkwZNW7cWB07dtTDDz+s8PBwT5eFAi46Olrjxo3TmDFjdPvtt2vmzJlKSkrKk21PnTpV3bt319NPP63bbrtN7du313fffWf9H/8999yjWrVq6dNPP9XKlStVq1Yt1apVK09qQ966XsfR/fffr1atWunuu+9WWFiY/vOf/8jhcGjRokVq1KiRevbsqcqVK+vvf/+79uzZk2eX2r7//nuXOgcMGKBatWpp6NChebJ9b+IwxhhPFwEAAFAQcMYJAADAEsEJ1lJSUlSsWLFcHykpKdbbmjlzZq7bqV69ej4eBXBlWrduneuYHTVqlKfLQwFxteOoevXqua6fV9/OcKn/59esWZMn+yjIuFQHa+fPn9fu3btzXR4VFaVChew+b3DixAmlpqbmuKxw4cIqX778lZQI5Jt9+/bp9OnTOS4LCQnh7suwcrXjaM+ePTp37lyOyyIiIvLk+0L/PMn7r8qWLXvDf8iH4AQAAGCJS3UAAACWCE4AAACWCE4AAACWCE4AAACWCE4AvI7D4dD8+fM9XQYAZENwApBnevToofbt23u6jMvavXu3HA6HwsPDdeLECZdlMTExGjZsmGcKA+D1CE4AblgnTpzQK6+84ukyABQgBCcA+aZJkybq27evBg0apJCQEJUqVSrb2Zzt27erUaNGCggIULVq1bRs2bJs29m7d686d+6sEiVKKCQkRO3atXPejHXbtm0qUqSIPvzwQ2f/OXPmKDAwUFu2bLlkfX369NG4ceN06NChXPu8//77io2NVfHixVWqVCk98MADLv1Xrlwph8OhJUuWqFatWgoMDFTTpk116NAhff7556pataqCgoL0wAMP6NSpU871srKylJSUpAoVKigwMFDR0dGaN2+ec/kff/yhbt26KSwsTIGBgbr11ls1derUSx4PgPxHcAKQr6ZPn66iRYvq22+/1dixYzVixAhnOMrKylLHjh3l5+enb7/9VlOmTNEzzzzjsv65c+cUHx+v4sWLa82aNfr6669VrFgxtWrVShkZGapSpYpeeeUVPf7440pJSdFvv/2mRx99VGPGjFG1atUuWVvXrl1VqVIljRgxItc+586d08iRI7Vp0ybNnz9fu3fvVo8ePbL1GzZsmN544w2tXbvWGfQmTJigDz/8UAsXLtTSpUv1+uuvO/snJSVpxowZmjJlin7++Wf1799f//jHP7Rq1SpJ0gsvvKAtW7bo888/19atW/Xmm28qNDTU9mUHkF8MAOSRhIQE065dO+fzxo0bmwYNGrj0qVOnjnnmmWeMMcYsWbLEFCpUyOzbt8+5/PPPPzeSzMcff2yMMeb99983t912m8nKynL2OXv2rAkMDDRLlixxtrVp08Y0bNjQNGvWzLRs2dKl/18lJycbSebHH380ixcvNoULFzY7duwwxhgTHR1tEhMTc133u+++M5LMiRMnjDHGrFixwkgyX3zxhbNPUlKSkWR27tzpbHvkkUdMfHy8McaYM2fOmCJFipi1a9e6bLtXr16ma9euxhhj2rZta3r27JlrHQA8w+6LxQDgCtWsWdPleenSpZ2XurZu3arIyEiVKVPGuTwuLs6l/6ZNm7Rjx45s38F15swZ7dy50/n8vffeU+XKleXj46Off/5ZDofDqr74+Hg1aNBAL7zwgsvlvos2bNigYcOGadOmTfrjjz+UlZUl6cKXXv/5jNafjzMiIkJFihTRLbfc4tK2fv16SRe+C+zUqVNq0aKFy74yMjJUq1YtSdJjjz2m+++/Xz/88INatmyp9u3b66677rI6JgD5h+AEIF8VLlzY5bnD4XCGDxsnT55U7dq1c/zm97CwMOe/N23apPT0dPn4+OjAgQMqXbq09T5Gjx6tuLg4/etf/3JpT09PV3x8vOLj4zVz5kyFhYUpJSVF8fHxysjIcOn75+N0OByXPO6TJ09KkhYuXKiyZcu69PP395cktW7dWnv27NGiRYu0bNkyNWvWTE888QST2QEPIzgB8JiqVatq7969LkHnm2++celzxx13aPbs2QoPD1dQUFCO2zl69Kh69Oih5557TgcOHFC3bt30ww8/WH+Le926ddWxY0cNHjzYpX3btm36/fffNXr0aEVGRkqSvv/+e3cPM5tq1arJ399fKSkpaty4ca79wsLClJCQoISEBDVs2FD/+te/CE6AhzE5HIDHNG/eXJUrV1ZCQoI2bdqkNWvW6LnnnnPp061bN4WGhqpdu3Zas2aNkpOTtXLlSvXt21e//fabJOnRRx9VZGSknn/+eY0bN06ZmZkaOHCgW7W89NJL+vLLL/XLL784226++Wb5+fnp9ddf165du/TJJ59o5MiRV33cxYsX18CBA9W/f39Nnz5dO3fu1A8//KDXX39d06dPlyQNHTpUCxYs0I4dO/Tzzz/rs88+U9WqVa963wCuDsEJgMf4+Pjo448/1unTp1W3bl099NBDeumll1z6FClSRKtXr9bNN9+sjh07qmrVqurVq5fOnDmjoKAgzZgxQ4sWLdL777+vQoUKqWjRovrggw/0zjvv6PPPP7eupXLlynrwwQd15swZZ1tYWJimTZumuXPnqlq1aho9enSenfEZOXKkXnjhBSUlJalq1apq1aqVFi5cqAoVKkiS/Pz8NGTIENWsWVONGjWSr6+vZs2alSf7BnDlHMYY4+kiAAAACgLOOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFgiOAEAAFj6fwrtxyGn9+lOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.bar(index_names, index_key_sizes, color='green')\n",
    "plt.xlabel('Index Names')\n",
    "plt.ylabel('Total Key Sizes (bytes)')\n",
    "plt.title('Sizes of Index Keys in the Collection')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3b7b2c-6709-4e53-b524-57ce95b6b84f",
   "metadata": {},
   "source": [
    "We can drop indexes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7d636dbb-2162-4076-8968-31f576ff2e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_drop = \"name_1_tel_-1\"\n",
    "collection.drop_index(index_to_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0bb8ec7-94e4-4b4c-94a8-4486abcce0b1",
   "metadata": {},
   "source": [
    "# Views\n",
    "<hr><br>\n",
    "<i><b>Views</b> allow you to <u>create a virtual collection</u> that is based on the result of an aggregation pipeline. This can be useful for simplifying complex queries or creating a denormalized view of your data.</i> <br>\n",
    "<br>This is how to create a vue:\n",
    "<br>\n",
    "<li>We will check if the user is an Adult</li>\n",
    "<li>We will exclude the \"_id\" field and project the others</li>\n",
    "<li>We will sort the users by name field in an ascending order</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5d408d0f-7e60-4dd3-9f3a-6975911b6841",
   "metadata": {},
   "outputs": [],
   "source": [
    "view_pipeline = [\n",
    "    {\"$match\": {\"status\": \"adult\"}},\n",
    "    {\"$project\": {\"name\": 1, \"age\": 1, \"phone\": 1, \"_id\": 0}},\n",
    "    {\"$sort\": {\"name\": pymongo.ASCENDING}}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb6f8f1-312f-49c2-b1eb-d4fec285529c",
   "metadata": {},
   "source": [
    "Give it a name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89abb83c-5bb8-4a0f-a76d-645fa4c6addd",
   "metadata": {},
   "outputs": [],
   "source": [
    "view_name = \"user_view\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1ca9d2ca-ad34-42ae-9447-0f48655d575a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Collection(Database(MongoClient(host=['127.0.0.1:27017'], document_class=dict, tz_aware=False, connect=True), 'test_notebook'), 'user_view')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "options = {\"viewOn\": \"user\", \"pipeline\": view_pipeline}\n",
    "db.create_collection(view_name, **options)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0ddd03-b152-4b29-bb90-c0b21aadc692",
   "metadata": {},
   "source": [
    "Now we will check the data inside the \"View\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ef2cc15-1354-464a-9d67-26c23bca1004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'Aziz', 'age': 21, 'phone': '93505147'}\n",
      "{'name': 'Firas', 'age': 22, 'phone': '24303822'}\n"
     ]
    }
   ],
   "source": [
    "view = db[\"user_view\"]\n",
    "result = view.find()\n",
    "for doc in result:\n",
    "    print(doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf99c3a-04ec-407c-a518-0f1e82231c62",
   "metadata": {},
   "source": [
    "We can Drop the view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d0ed3a86-204a-4aeb-bfc9-f9e86e792f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "view_name_to_drop = view_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f9c3d634-dd42-4c1a-bed3-49e8fcc436c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The view 'user_view' was dropped successfully.\n"
     ]
    }
   ],
   "source": [
    "db[view_name_to_drop].drop()\n",
    "print(f\"The view '{view_name_to_drop}' was dropped successfully.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affeb058-66a5-4d7f-899c-4cb877ade3a1",
   "metadata": {},
   "source": [
    "# Aggregations\n",
    "<hr><br>\n",
    "<i><b>Aggregations</b> in MongoDB allow you to perform <u>data transformations and computations</u> on your data. Aggregation pipelines consist of stages, each processing the documents in sequence.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62a3ddf-8636-4666-81e1-8918a4e5d841",
   "metadata": {},
   "source": [
    "We create an aggregation pipeline:<br>\n",
    "We want a pipeline that computes the overall average age from all documents, stored in \"average_field.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93f1e37e-5e95-40e5-a3ce-4efd67834937",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = [\n",
    "    {\"$group\": {\"_id\": \"$status\", \"average_age\": {\"$avg\": \"$age\"}}},\n",
    "    {\"$sort\": {\"_id\": 1}}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecfe867-64d8-4cca-8eee-dbee223eb13b",
   "metadata": {},
   "source": [
    "We need to execute this aggregation pipeline on a specific collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "09a13758-51f3-4c0b-83cf-5fe2c96583bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = collection.aggregate(pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28260e7c-e81e-448a-afab-68e6f4c8726f",
   "metadata": {},
   "source": [
    "Display the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bc05b990-1a0c-4458-96d2-fbe9fecb8460",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'_id': 'adult', 'average_age': 21.5}\n",
      "{'_id': 'minor', 'average_age': 17.0}\n"
     ]
    }
   ],
   "source": [
    "for doc in result:\n",
    "    print(doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d07d8d3-ff65-4ebd-855c-5b583f01d838",
   "metadata": {},
   "source": [
    "We can display a line chart so we can be able to view the pipeline:<br>\n",
    "First we execute the pipeline and store it inside of a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d78cf8e7-b7e5-4398-b7d7-11678d5fb0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_graph = list(collection.aggregate(pipeline))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b05303a-2ae4-41fa-b850-7b230a5c42b8",
   "metadata": {},
   "source": [
    "Extract data for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3c72e96c-2701-44ec-bf73-6c8ce0037de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "statuses = [entry[\"_id\"] for entry in result_graph]\n",
    "average_ages = [entry[\"average_age\"] for entry in result_graph]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51386ad9-d0da-497e-890c-3742917d8645",
   "metadata": {},
   "source": [
    "And finally create the line chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "43a92734-3b56-48c3-8d62-096b87bc8c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(statuses, average_ages, marker='x', linestyle='-')\n",
    "plt.xlabel('Status')\n",
    "plt.ylabel('Average Age')\n",
    "plt.title('Average Age for Each Status')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
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