Devoir_NEO4J_2022_SUJET.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b996a0de-37da-4429-9833-819413e5e8a6",
   "metadata": {},
   "source": [
    "# DEVOIR NEO4J"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84250a7-2a92-4652-bd2a-d81cbac2006a",
   "metadata": {},
   "source": [
    "Vous disposez d'une application qui gère les notes associées aux films. Le modéle retenu est graphe avec la modélisation suivante : "
   ]
  },
  {
   "attachments": {
    "f26dd7ab-8f73-4e60-ab65-f6dd51e7f20a.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAB9CAYAAABgfe+GAAAKrWlDQ1BJQ0MgUHJvZmlsZQAASImVlwdQk9kWgO//p4eElhDphN6ktwBSQg+9N1EJSYBQQggEFbuyuAIriooIKgsiRRRcCyBrxYJtUVDAgi7IIqKuiwUbKu8HhuDum/fevDNz5nw5Ofecc+/cO3N+AMhUtlCYBssCkC7IFoV6u9GjY2LpuKeABCBAABSgz+ZkCZnBwf4AkTn7d3nfh8QicsdkOte///9fRY7Ly+IAAAUjnMDN4qQjfALRVxyhKBsAVA3i116eLZzmqwhTRUiDCA9Mc9Isj09zwgyj0TMx4aHuCCsCgCex2aIkAEg6iJ+ew0lC8pA8EDYXcPkChJHfwDk9PYOLMFIXGCAxQoSn8zMSvsuT9LecCZKcbHaShGf3MiN4D36WMI298v88jv8t6WniuRp6iJKSRT6hiKUhZ3YvNcNPwoKEwKA55nNn4mc4WewTMcecLPfYOeayPfwka9MC/ec4ke/FkuTJZoXPMS/LM2yORRmhklqJInfmHLNF83XFqRESfzKPJcmfmxweNcc5/MjAOc5KDfObj3GX+EXiUEn/PIG323xdL8ne07O+2y+fJVmbnRzuI9k7e75/noA5nzMrWtIbl+fhOR8TIYkXZrtJagnTgiXxvDRviT8rJ0yyNhu5kPNrgyVnmML2DZ5jEAx8gD+gA0tgAxjADdiCEIB0lc1bMX1HgXuGcKWIn5ScTWcir4xHZwk4pgvpluaWlgBMv9nZK/H23sxbhGj4eV/GbQDsGhCom/ex4wFoQ05JQXvep3sQAJk/ADjD4YhFObO+6ecEMIAIZAAVKAF1oA0MgAnSoy1wBK7AE/iCIBAOYsBSwAHJIB2IwHKwGmwA+aAQbAO7QDmoBAdAPTgCjoFWcBpcAFfADXAb9IKHYBCMgBdgHLwHkxAE4SAyRIGUIA1IFzKGLCEG5Ax5Qv5QKBQDxUNJkAASQ6uhTVAhVAKVQ1VQA/QLdAq6AF2DuqH70BA0Br2BPsMomARTYTVYDzaDGTAT9oPD4SVwEpwJ58J58Fa4DK6GD8Mt8AX4BtwLD8Iv4AkUQEmhaChNlAmKgXJHBaFiUYkoEWotqgBViqpGNaHaUZ2oO6hB1EvUJzQWTUHT0SZoR7QPOgLNQWei16KL0OXoenQL+hL6DnoIPY7+hiFjVDHGGAcMCxONScIsx+RjSjG1mJOYy5hezAjmPRaLpWH1sXZYH2wMNgW7CluE3Ydtxp7HdmOHsRM4HE4JZ4xzwgXh2LhsXD5uD+4w7hyuBzeC+4iXwmvgLfFe+Fi8AL8RX4o/hD+L78GP4icJsgRdggMhiMAlrCQUE2oI7YRbhBHCJFGOqE90IoYTU4gbiGXEJuJl4gDxrZSUlJaUvVSIFF9qvVSZ1FGpq1JDUp9I8iQjkjspjiQmbSXVkc6T7pPekslkPbIrOZacTd5KbiBfJD8mf5SmSJtKs6S50uukK6RbpHukX8kQZHRlmDJLZXJlSmWOy9ySeSlLkNWTdZdly66VrZA9JdsvOyFHkbOQC5JLlyuSOyR3Te6ZPE5eT95TniufJ39A/qL8MAVF0aa4UziUTZQaymXKCBVL1aeyqCnUQuoRahd1XEFewVohUmGFQoXCGYVBGoqmR2PR0mjFtGO0PtrnBWoLmAt4C7YsaFrQs+CDooqiqyJPsUCxWbFX8bMSXclTKVVpu1Kr0iNltLKRcojycuX9ypeVX6pQVRxVOCoFKsdUHqjCqkaqoaqrVA+o3lSdUFNX81YTqu1Ru6j2Up2m7qqeor5T/az6mAZFw1mDr7FT45zGc7oCnUlPo5fRL9HHNVU1fTTFmlWaXZqTWvpaEVobtZq1HmkTtRnaido7tTu0x3U0dAJ0Vus06jzQJegydJN1d+t26n7Q09eL0tus16r3TF9Rn6Wfq9+oP2BANnAxyDSoNrhriDVkGKYa7jO8bQQb2RglG1UY3TKGjW2N+cb7jLsXYhbaLxQsrF7Yb0IyYZrkmDSaDJnSTP1NN5q2mr4y0zGLNdtu1mn2zdzGPM28xvyhhbyFr8VGi3aLN5ZGlhzLCsu7VmQrL6t1Vm1Wr62NrXnW+63v2VBsAmw223TYfLW1sxXZNtmO2enYxdvttetnUBnBjCLGVXuMvZv9OvvT9p8cbB2yHY45/OVo4pjqeMjx2SL9RbxFNYuGnbSc2E5VToPOdOd455+dB100Xdgu1S5PXLVdua61rqNMQ2YK8zDzlZu5m8jtpNsHdwf3Ne7nPVAe3h4FHl2e8p4RnuWej720vJK8Gr3GvW28V3mf98H4+Pls9+lnqbE4rAbWuK+d7xrfS34kvzC/cr8n/kb+Iv/2ADjAN2BHwECgbqAgsDUIBLGCdgQ9CtYPzgz+NQQbEhxSEfI01CJ0dWhnGCVsWdihsPfhbuHF4Q8jDCLEER2RMpFxkQ2RH6I8okqiBqPNotdE34hRjuHHtMXiYiNja2MnFnsu3rV4JM4mLj+ub4n+khVLri1VXpq29MwymWXsZcfjMfFR8Yfiv7CD2NXsiQRWwt6EcY47ZzfnBdeVu5M7xnPilfBGE50SSxKfJTkl7UgaS3ZJLk1+yXfnl/Nfp/ikVKZ8SA1KrUudSotKa07Hp8ennxLIC1IFlzLUM1ZkdAuNhfnCwUyHzF2Z4yI/UW0WlLUkqy2bigxHN8UG4h/EQznOORU5H5dHLj++Qm6FYMXNlUYrt6wczfXKPbgKvYqzqmO15uoNq4fWMNdUrYXWJqztWKe9Lm/dyHrv9fUbiBtSN/y20XxjycZ3m6I2teep5a3PG/7B+4fGfOl8UX7/ZsfNlT+if+T/2LXFasueLd8KuAXXC80LSwu/FHGKrv9k8VPZT1NbE7d2FdsW79+G3SbY1rfdZXt9iVxJbsnwjoAdLTvpOwt2vtu1bNe1UuvSyt3E3eLdg2X+ZW17dPZs2/OlPLm8t8Ktonmv6t4tez/s4+7r2e+6v6lSrbKw8vPP/J/vVXlXtVTrVZcewB7IOfC0JrKm8yDjYEOtcm1h7dc6Qd1gfWj9pQa7hoZDqoeKG+FGcePY4bjDt494HGlrMmmqaqY1Fx4FR8VHn/8S/0vfMb9jHccZx5tO6J7Ye5JysqAFalnZMt6a3DrYFtPWfcr3VEe7Y/vJX01/rTutebrijMKZ4rPEs3lnp87lnps4Lzz/8kLSheGOZR0PL0ZfvHsp5FLXZb/LV694XbnYyew8d9Xp6ulrDtdOXWdcb71he6Plps3Nk7/Z/Hayy7ar5Zbdrbbb9rfbuxd1n+1x6blwx+POlbusuzd6A3u7+yL67vXH9Q/e4957dj/t/usHOQ8mH64fwAwUPJJ9VPpY9XH174a/Nw/aDp4Z8hi6+STsycNhzvCLP7L++DKS95T8tHRUY7ThmeWz02NeY7efL34+8kL4YvJl/p9yf+59ZfDqxF+uf90cjx4feS16PfWm6K3S27p31u86JoInHr9Pfz/5oeCj0sf6T4xPnZ+jPo9OLv+C+1L21fBr+ze/bwNT6VNTQraIPTMKoBCFExMBeFMHADkGAAoyVxAXz87UMwLNfgfMEPhPPDt3z4gtAA2uYHrIAQGI3Tc90iJWBtFgRMNdAWxlJdG5+XdmVp8WJgkZsycAWNfXP7ZGE/xDZuf47/r+pwXTWa3BP+2/ALIEBS6mlMocAAAAVmVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAADkoYABwAAABIAAABEoAIABAAAAAEAAALToAMABAAAAAEAAAB9AAAAAEFTQ0lJAAAAU2NyZWVuc2hvdCcq46wAAAHWaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA2LjAuMCI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjEyNTwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj43MjM8L2V4aWY6UGl4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpVc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KXJgWrAAAQABJREFUeAHtnQecU1X2x39JpvdemMIMvasIiAWwd13X3rtb/Ou66upa1l5wrevaVtfeV0WxYkFFBAVEQHodZmB67zXlf87NvJdkCsxkwiSTnMMnvH7fvd8kk/POPcXQZrbZICIEhIAQEAJCQAgIASEgBIRAvwkY+32FXCAEhIAQEAJCQAgIASEgBISAIiDKtHwQhIAQEAJCQAgIASEgBISAmwREmXYTnFwmBISAEBACQkAICAEhIAREmZbPgBAQAkJACAgBISAEhIAQcJOAKNNugpPLhIAQEAJCQAgIASEgBISAKNPyGRACQkAICAEhIASEgBAQAm4SEGXaTXBymRAQAkJACAgBISAEhIAQEGVaPgNCQAgIASEgBISAEBACQsBNAqJMuwlOLhMCQkAICAEhIASEgBAQAqJMy2dACAgBISAEhIAQEAJCQAi4SUCUaTfByWVCQAgIASEgBISAEBACQkCUafkMCAEhIASEgBAQAkJACAgBNwmIMu0mOLlMCAgBISAEhIAQEAJCQAiIMi2fASEgBISAEBACQkAICAEh4CYBUabdBCeXCQEhIASEgBAQAkJACAgBUablMyAEhIAQEAJCQAgIASEgBNwkIMq0m+DkMiEgBISAEBACQkAICAEhIMq0fAaEgBAQAkJACAgBISAEhICbBESZdhOcXCYEhIAQEAJCQAgIASEgBESZls+AEBACQkAICAEhIASEgBBwk4Ao026Ck8uEgBAQAkJACAgBISAEhIAo0/IZEAJCQAgIASEgBISAEBACbhIIcvM6uUwI9JtAXXMHCipaUFzdisr6dvB2S7sVFqsNRnqsCws2ISY8CInRIUiPD0V2cgSSY0L6fR+5QAgIASJgMcNWXwk01cLW1qy2YTAAQaEwhEcBUQkwRMUJKiEgBISAEBggAVGmBwhQLu+dQF5ZE1Zsq8XqnXXYsKsBxTWtvZ/cyxFWrCdkRWP/nBhMHxWHSdkxvZwpu4VA4BKwscKctwbWgg2wFW2BrWwnbNUlewcSEgZDcjaM6aNgyBwHQ+4UGIdP2vt1coYQEAJCQAjoBAxtZptN35IVITBAAjtKm/D1mgp8t64SO0iZ9rSkxoXiyElJOHb/ZOyXE+vp5qU9ITBkCNjKC2Bd+z2sG36EdedvHuu3ISIGxvGHwDhpDoz7HQU1beSx1qUhISAEhID/ERBl2v/eU6+MiJXnecuK8fOWmh7vb6Dp5dCQIIQEByE4KAgmkxEm8u0wGmnaGfyywUrPdVZy+TBbLDCbLWjvMKO93UxuINYe25xIFuvTZ6bj9wel93hcdgoBfyRg/fVLWJZ/AuvWFb0Pz2SCgazOCCaXDvq+wWiirxmHyJDthL5PNnIBgbkDaG+DrX0PM0ZhkTBNPwmmmb+DIWNs7/eTI0JACAiBACYgynQAv/meGPrXv1Xg9e93Y2NhQ7fmwkJDEBEeivCwEISFBHc73tcdrFS3tLajubUNzS1t3S4bFh+Giw7PxDmHZnQ7JjuEgL8QsPz4P1h+eAe2ysJuQzKERcAQGUuvaID8oQ0hod3O6XUHPcTaWpro1QBbE70aa8m/2tLtdOOUI2A6/AIYR+zf7ZjsEAJCQAgEMgFRpgP53R/A2NcV1OO5r/KxbKurJTqYLM/RkeGIjghDUBBZwzwsVrKqNTa3oqGpBa1tZFlzktHpkfjzcTk4nNxARISAvxCwrFwAy9cvgt06nMUQGg5DXBIMsYngdU+KraEG1roq2GopgLGLJ6DpwONhOv4Pytfak/eUtoSAEOhOwNpej7aaLeio24GOxt0wN5fB0kbfz45G2Kz230CDMQSmkGgYQxMQFJGK4OhshMSORGj8eJqZohkqkX1OwGvKdFVVFWJjY0nhkhjIff4ue/gGT32xE698t8ulVbZCx0ZHIIqU6MEStlbXNTSjqcV1mvrU6Wm44dSRKjPIYPVF7iMEPE3AVp4P8yf/hnX9YpemDdHxMCamgpf7XMgdxFpdDmtVKdDhOisUdPI1MB196T7vgtxACAQWARuain5AS8lPaCn7Be21Wwc0/NDESQhPnYGI9EMQnnbwgNqSi3sn4DVl+rprr8b1N96EnJxcbN++Ddu3bcPxJ5zYe0/liNcJbClqxIMfbgNbpTVhH+j42KhBVaK1e2tLVqpr6huVK4i2L40CFW89YzRmjU/UdslSCAwZApal82Ce9zD5NzvcLQzRcTAmZ5Arh3cy2lgrS2CtKLL7WneSNI6ahqAzb4YhbcSQYSsdFQK+SKCl9Gc07PwcTbu+gtVMqSz7IByLxGLrMnvU26WmsAREZR+PqNyTEZa0X2+nyX43CHjNLOz83peVlmLtb2tEmXbjDRysS75YVYa73t2ickJr92QlOoFe3hb2yQ6nPxL1jc2oqmlQgYyltW247qX1uPbEXFx2ZLa3uyj3FwJ9JmB+9z5Yln3sOJ/8n02p2cqlw7Fz8NeMSekwJqTAWrYbrFizWLevRPtjFyL43DthJPcPESEgBPpHoH77B6jf+i65cmzq8cKw0AiEhUYilFy5QoLDKICfXDqCgimA39mN0kZhDhS4b+lAh7mdAvdb0dZOrpCtTWqpNWxprUbd1rfVKyzlQMSOOQ9Rw0/QDstyAAS8pkxznw0qi0Pfn6oGME65dAAEXiaXjqfJtUOTUAomTIqPoS+4+0GFWlueXMZERSAiLBQVNfV6oCK7pJTUtOE2slKLCAFfJmCrLYf5jdth3bFa76YxIZVyQOf4Tno6+gHn/rCLibWYclm3tZD7Rzs63vgHgqqKYDr2Cr3vsiIEhEDvBOp3fIjaDS+io8E1FsJIWa6iI+MRSQHFkRGxXZTm3tozUIYszpIVRFmzKH6CrtPETFl7mlrq0Ui56BsaHTFOreW/gl+1G19B/MQrEZl9rHaJLN0g4FVl2rm/2nSF8z5Z9z6Bf3+eh1cpW4cmHFyYkuj4omr7fWXJQY/pyfGorm1Urh/crw9+LkZDixlzLxzvK92UfggBFwK2Egouevkm2CocsQjGzJEwxqe4nOcrG4Yo+pEfPQXWwjxYaytUt8xfPEeZQGoQdPrffKWb0g8h4HMEWstXouq3fytF1rlzEeHRFHeUhJgYqkzaaWh0Pu7uehBZsWOjE9XLSm5jdQ1VqKPKqK1cFZWkrXoDSn+8HpEZhyNh/+sQEjfG3VsF9HVe85n+yzVXY8OG9TCSzw9PT/ArNLQznRPte+SxJ7Dffvs2BVMLpVprbW1FC6Vb42V7ewc4G0VICKVyo4C6uLhYtR2on5B/fZaH1xc5FGlfcevo6/tR39iCiuo6/fSj90vGwxdN0LdlRQj4AgFb8TZ0vPBXypxRZu8Ou3VkjYYhgtLcDQGxlhcq1w+tq6ZDz0DQWbdqm7IUAkKgk0DVmieUNdoZSBRZoONjU8kKPbixEA1kqa6pLaVZ3Ebn7iDxgBsQN0FmmFyg9GHDa8p0TU011QtopxICrgUYNUf6xMQkpdT2YQx9PoULgRSXlKlXaVmFKgyyt4tjoqOQnJSAJHqlpnCffMu1YW/9d/f4C98U4D+U+k4TduvgbB1DTTjTR2kF5c3tlFMo08c954zVNmUpBLxKwFZViI5nr4atqlj1wxAeSeW8x8JAxVaGkliry2AtytO7bJpzHoJ+f6O+LStCIJAJtNdtR8Xyu9Fa4XDhYl/opIRhiIqM8yoatlRXVhejwylbTwRZqVMOuhum8GSv9m0o3dxryrQGaf26tVizejXq6+uQlT0cRxx5FKKiPBvUll9QiMLiUpSW2qcjg4ODkZmRitiYGISHhzle5G/b2tamlPxWUvQbGhrJx6gJdXUNqKisVl2Oi4tBanISUlISkZSYoCr5aWPxl+X85SW4931HOp6hqkhr70cTzTyUVjh8xS4/KhvXnJCrHZalEPAOAfrxav/3VbDt3qjub6BiK6accQBNyw5FsdVUwFK4Xe960ElXw3TM5fq2rAiBQCTQtPsblP90K2XooPiCTmElml++ImzErKAH+2ptdow6FhSZjpRDHkJ4yjRf6aZP98NryjQX37j7zn9gHSnThx02WynQeXnbsX7detx7/wM4cNr0AYMrr6jCr6vWoam5hdw1gjEsPQVZmenKwtxfH22LxYrKqmqUl1ehrLwStXX1SpFOTIxX7bGCzYr2UJc1O+tw+TNr9GEkxEUhPsazDzd644O4woVeyiodFup7zx2Hk6elDmIP5FZCwJVAx6u3wLpmodrJRVdMI8gFiSL1h7J0tVAHXzIXxgOOGcpDkr4LAbcJcOaMyl8e0K/n4MC0lOGUfco3f1M5SLG0ooBm7R0F0VIPe1QyfujvYO8rXlOm33jtVWzatBH3PTCXlFKT3sMN69fhjttvw+tvvo2oaPd8BtkXes1vG1FE1ujIyAhMmjBGKdH6TTywwv7V5RWVpFhXoaS0nHyu2yhtTbCyWKeQO0h6arKyeHvgVoPWRIfFhvMeX4m8MntgAmfHSE4Y+g8IGsDa+iZU1drLngebDHjnhmkYkTr0XFe08chy6BKwfPMyzJ8/ax8AZcgwjZwELgnuD6JS55EftRKayg7525tSLdEf3lgZQ78I1G58CVWrH9eviYlKIL2AMuEYjPo+X1wxU2q9EioY1dTsqCeRMvN+RI/8vS9212f6ZLrjzrvv9kZvnn3m37jp5lsQG+fqL5SSkortO7YjmhTpzMysfndt67ad+Hn5Ksqt2I4pk8ZhxrT9yJ3DPaV8TzfnB4AYapet3WNG51Jf0xEREU7pZ5qQt3MXtmzNQ2FRKerJVYRzarM7Cae88WV5eP52LN1sd2fhAMy0ZNf3xpf73pe+8ZjYb769g6q60XtSUNEi1um+gJNzPErAumMVzG/eqbdpyh7jtUIseic8uMKZPmyU5xat9FBOeW9tZOkyTTvRg3eQpoSAbxPgdHNVqx/TO5kQl0oWaVak7UVW9AM+uGKkh3vO/sE+1JyrmqWp8DsER2VReXKJN+rtLfNaarya6hokJPZcnS6Jgg+rq+1KXW8d72n/r6vXY2f+bkwkS/SYUTkuFu+ezvfkPg5U5NeokcNVs1VVNcodpIxcTXbk2dNdJSbEK8s1BzImkXuIL8mPm6rw/k/2ICjulz9ZpJ0587haaVahgxTq5dtq8NbiQlwwO9P5FFkXAvuUgHn+E3r7xhSqaEipsPxNTBkjYGkmQwIp1dbNy2BZ9BZMh1/gb8OU8QiBbgTqt88jRfpRfb+v+UfrHdvLSnpqLgxkAKyts8ealf98K4yhMSqF3l4uDcjDXjOVjh03Dj8tXdINOqfIW/bzTxg7lgJx+ijsPL9sxWrs2l2M2YfNwPixIwdVke6pm+xLPWH8aBwxeyZOO+VYHDLzQMSTT3VhYQkWLV6G+Z9+g6XLfiUrfAEFOjb11MSg7nt2Qb5+Pw445DLh/ihsGUiKc8xUPL1gJyrq2/1xqDImHyRg+eYVCjjcpHrGqe+MVNnQL4WLuwzL0Ydm/vQp2GpK9W1ZEQL+SKCl5CfK2uGYdUpMSPepQMP+Mk9LHo64GEdGj/IlN6G91pGcoL/t+fP5XnPzyB0xAndRAGJ4WBhS09KU8rtt6xY88s+HkELbvz/9jD5xt9J8PSvSHBQ4hxRpTmHna6IqGkVHIi0tWVmuc3OzEU3+yC2Utq1gVxG2bMvDTso4Ul/fSPm2rYqJsx/5vh7PO0uK8Mkv9h+6cHKF8FertMaRc4nzQ1tbu1mVR2/rsGLW+J5nSbRrZCkEBkrAVleOjpeooInNqppSuaQpp7S/CgdVggKZbC1kLOAxtzXBOGmOvw5XxhXgBCwtFShZ9GdYO+zGsfjYFKQk9d9V1dcwcuo+dvfgEuU2awfaqtYjZvSZ1E3fd1kZTJZeC0DkQW7buhXPP/cMVq9ZjdaWFqSm2pXoc847v885ppf8tJKybNQoi3RCvO9W5tvTm1pbWw92Bykrr1DZQvjc2Nho8rFKRkoypeCjBwSTqf+TCKycx+wlEwf7cx9/38+6dXZYSgIp80M7o8CeWGvHzPTQsqu4gvzZCQDJvJunIzfFPwLAtDHK0rcImD98BJbF/1OdMiamkeU2ANIzkjJt3kK5danyGkvIjW/AkCWVSBUM+c+vCJT+cK3yLeZBcRnwrGGj/WZ8/DtZULhJr5oYM/ocJM9wWOD9ZqADGIhXlWnnfndQwGAwVR50lu++XYgjjzraeZfLOmfs2FVYTBbpg5Ty6XJwiG5wysDKSs3fmlLwkaLNlm32sWZfa371NQXf8l/WKN/ggw+a2qsy/s6PRXjkY3tu2KgImiVI8q+gwz19DKrrqOQ4vVjOPHgYbjvDf/747WnccmzwCbCLQ/s9J9tvTK5GQWOngv7gDX5HvHBHa0URrKX2uBHTtBMQdOF9XuiF3FII7DsCdVveROXKueoGXL47J3MCeOlPwuXH8ztz4vO4Umc9gajsY/1piAMaS//NnQO6Xe8Xd1Wk+cx1a9f2ekFNbR225xVg2tTJfqNI82BZceaCMJMnjcXRRxyKU086GtMPnEIp/sJVIOPC75fi08+/BSvK7BrCriK9SWlZJbjS46Ifl6GNitD0JB/8XKzvjiVXlECSOKroqEVXM4eaRkduzUDiIGPd9wQsSz/Qb2JMSg8YRZoHbUyi4hSdioVl5QLK7mFXrHUgsiIEhjABc1MxqlY5Ag5Tk7L9TpHmt4crNqYmZ+vvFAdZ2qxmfTvQV3xGme7PG8FTDit/XafcH4al+3fhDS5fzoVmDjxgMk48/nAcf8xsCmwcpXyr167dhM+//B5ffrMYq3/boMqkm832D3d1TS1Zpe3KYU1NHb5d9BOamuz5ozXWnMFjZ7l9XwRVfwwL9a8naW2cvS35wSUmivw6O+WTlRIgpbGQpWcJWH6erzdoIBePgBKyxBsTHH+nLcs+Dqjhy2D9m0D12qeVLzGPkoP1oqN8K1OXJ+mzH3hUpN2d1txYhBoau4idwJBM2cAZMOrqG3DsUYcF3PsYFRVJ1SIjMXKEUwo+9rcmK7SWgi8hIQ7BQa5vbTNVgWSFetYh0xHf6Vv+5apynV+0k1Kp7wyAFS5MU9dgf6BYQDwuOXzoB4wEwNs2pIZoXfUVJWqtVX02xCXBEOy/QYe9vTGsTFs7C7lYV34BnHJtb6fKfiEwZAi0VqxCQ5794dBEGWySEzOGTN/d7WhyYibV06hTl9ds+C+iR51JOagz3W3Ob64bcpZprjy4fuNW5OZkqaIpfvNOuDkQlYJv3CgcMceegu/Qgw9EQnwc2A2mqzC7RT8up4qNFWg3W7FwrT1/JFto2V86EIVTAHIxF5atxY3YXGT3oQ5EFjLmfUPA0lkynFs3xjnSTO2bu/loq+QfruXTtlHeWuumpT7aUemWEOg7AS7OokliwjCKTXI1YmnH/GnJJdG5CI0mdZte1VYDejnklGkOOOS0ZhNIgRRxJRAUZEJ6WgomTxxDaWx69v9ldkt/XokPF28Flw9niYoIPEuZM7lIp/EvIdcXESHgMQIdVLRk3SJ7c6xQRgdOgG9XhsZYR/pJ6/rFXQ/LthAYUgTaqtbq2TuCabbJWcEcUgNxo7OJ8el6vFHd1ndgbnbMcrvRnF9c4nVlmtPjffH5Z/h4/odYsWI5lXt2OLRfd/0N3SDvorzMnNGCy3OL9EyAUwXuTRauKtJPYX/pQJbIcMf4l23dO7tAZiVj7x8B6+bllGPZ/tBqjPZfX8q+UNEs03yuddPPfblEzhECPkugftv7et/YlziQhC3wzmOu3+5gEUgcnMfqVWX6gfvuwXV/+T8s/mERVq9ahWef/jfOPfsMbN2yxbmP+noT+f1WUzBddhZFh4v0SqC8vLt1NSoyQgUy7jd5vKrKWGuL0a8PD3Blmv3LNR/zVXl1aCMXGBEh4AkC1u2/6s0YouyBO/qOQFshdzKNga26GLby/EAjIOP1EwLWjkbU59mDitlNMj428Ny34pweIBo6WfjJ2+vWMLzm4LPwm69RWlKCjz7+DKGhDsvgtwu/wT1334G33nmv24B25u9W+ZIzMgIsGr4biT3vqK2vV+4eXMSG/adVQKJTefC6ZjPyyuxBd6GULcRolEpGnMmko3NWZMOuBkwdEeCKz54/YnK0jwSs+Y70noZIxwNsHy/3u9OYga3RHs9hzV8HU0qO341RBuT/BBoLFuiVTGOjKajY4FW7pFeAh5BrC1dHbKTgak4P2Fz8IyKGzfJKX3zhpl77BKz8ZQXOv+hiF0WagRx19DGIiY6hSoDdfXAKKK9yxrA0BJlMvsDOZ/vAGTs4EHE8+ZWnpiaBy2c7yzYKtNOElWkRwJkDByKKCIGBE7DBtnuTakaV1vazIg7u8DGER+mX2Qo36+uyIgSGEoGm3Qv17kZHJejrgbYS4zT2pt3fBtrwXcbrNWW6tLQUaWk9W5hTaX9JscOnl3vMxUlaWttUbmmXEchGvwlouaX5Qs5mIUIcQhwcnPkIGyHgLgFb6U69jDbCItxtxq+uM4Q7CkPZSvL8amwymMAgYO1oIivsEjVYDjyMcHpADAwCjlFGR8XBQP9Ymot+cBwIwDWvKdNcNtvYy9QIV6Wz0HFnaWltVZuREY4iG87HZb3vBIqq7Sz5iuBgsfIrDk55uYuqHHz6TlXODDQCGzdtA/8d601slYX6IUOoBEwrGGyd70wfZqvcrfORFSEwVAi0lFFQcadERgS26xa7t2gMzC3laKu2z8RpfAJp6TDHeWHUl192MSnU9FxDL2dpJcX5lN+d5rxLL5sdSYF0IgMjUF7XpjcgLjN2FEEmCo6ijyEnXnDmo4Mawiv33HUHqqocQaljxozFMccdh7Fjx+mjqic/+ztuvwUXXHQJZsw4CM8+8xQ2bdzY7bt5wNSpZMUPwbKff3Y5FhcXh3vvfxBvvfk6li9bptqNjIzE8OE5OOOss5Cc7H/R7hs3bwe/xo4ZgfFjR1EJYdcHU1ttmc4XZMESsRMwhITC1mKGrabU/oXr8vdfOAkBXybAhVo0iQwPbGWaOURERKOx2R4HwWxCE8ZreAJq6TVl+p777kd7WzvIq9AFOJcKZ0lMTHLZ39JiVwDFMu2Cxa2N2iZHDmoTKZGelP9dMxrPfVuGRZvqXZqdkhWBe8/IwgXPbUNTW+/WPJeLBnnDRFHZZosVznwGuQv75Hbr1q7Ftdf9FTk5ubDarPhtzRpcf921mP/J50ox5pt+teALtLW14b133lbK9JlnnYOW5mZ6iG3G5ZdejLfefU8pz6wgv/D8fzBh4kSceOLJqr/8HTZ1xjHk5+dj/IQJ6lgzXb9m9SpcctEFeObZ55E7YsQ+GZ+3G92yNQ/8Gj0yR8UphHTGIdgaqvWuGYLshYH0Hf1YMUQnIuzcu2BrqETru/fRla5/M41UkSz09JthrSpC24f/7EfLPZxqCkb45Y+h9f37YavtHrfSwxX93+XkO25rrKHc277hc1pcXIylPy5GSUmx+q4ce/wJCAtznVHYvWsXVZCNR1R0NIqLqJxyTTUZ2oPoQcr+4u9BeHg4uSP2LbtDe3u7miQPpgdUkaFBoK1yvd7R8DCH25K+M8BWwsMccRBtVQ42AYYBXlOmd+/eTVZA+49CV8s0++Ckp7umv2Ofac4t3fXcQHvDPDHe5jaL3kxvrjb6Cf1cUTMNPVzDxid++bIYSJkm/yJS9h18fLm/fe0bf8/4+zQ8J0ddkps7Ah9+8D42bdqI/fbbX+379JOPcd8Dc3HdtVer4N+UFLslubHRHozJirj+3aP24uMT9PZc+tHlGCvWrGz/57ln8M9HHnM51d82tu3IB79G5mZj/HiyVLfa2alxDjRomr48hqh4GNNHwlqy3QWdaexBpF/TZ9YTXzBqp+OXTylIxanvLnfzwAaVXdaFGfmAMv3T0iV45J9zcdrpZ2A0zdzw9ttvvYHnX3wFsbH2zD4VFeW4+MLzcOxxx+PW2+/Ar7+uxM8/LaUiYmY6fykmTpqkHiozMrJw8qmnqgdWfvjUhP823n3v/dj/gKnYuGGDul9DQ4NyFUpLT8dNN9/i8sDJGa84u1VZWRnNIo3F2eecpx/nB+R1635T92NFnpX3g2YerCv/i77/Ts0KRUZF4euvvtTP05R+/j5PnjJF9cNEMypqP7nf8DqPl/MIF9CDMT8ccLatYRkZ5BLo/8HqrRSXFbaXVLFttZvVWxpED8j8CnQJC3V8xttqe05rHAiMvKZMP/fM0/RHxK600O+vLoW7d6G2thZLl/3i+PGmo61kNROrtI5pQCta5UNuxBO/v+50JshkwH5krY6NCEJpXTs2FbW42NtiI0wYnx6ORrJiby1tofLn9g/JsPgQtHVYUd9iwei0MORXtqHZQ5ZuTddvN1vwA5VdH2oyZxYpVb1IS2sLmpqa1Hdu/fr1aCaLM7t7sPAPO/9gsrJ95FFH44vPPsWll1/h0pKuSHfu3Zm3A0vIiqftZ2Vg5MhR6mjXzxS7jcx7/32X9rSNjZu3oaLCYcHV9g/l5Y6du8CvQ0tKodtc+UFtgGKhVHJBY2ei3VmZJsXUNGoa+JjBKe8r38pAFiNjSo76klspp7OtpUH1wEhlj20dbWTpdrj+GCjFlSE0AtaaElhLd5Bu7pi9AikMxlR6mCJXFVbkbW32tJqqMTf+44dW/U++ud2NFjx7SUtLC/4590F62HsU48ZPUI2fcOJJ+OtfrsH8j+bhkksvV/s+mT8fF1x4sSowxrM4p5z6O/Xig0cfMRv33vcgUlJT1bmrSNHOysrCq6+/pbb5P/av5+/LbvqNu+nG6/HA3IeUYs3Hvvn6K9zw12vx/ocfK8X2pf++gKVLfsR1N9yIYcOG4deVK1VNhrkPPUxK+2QqcLZM7Tv0sMPAbpFc/IwfBp58+ln1PfycvsNHH3MMpk2bQZmKQlQxtDY6r9Hcgf/R7NMZZ52tlOmrrrgUMw6aqfrFBdP4dcyxxyE3N1f1cb/9D6Bquu2qz+wW9reb/94nq/vNf7sBHR0deOLJp3h4Svih/t2338J33y5UbfLDxxVX/ZFmoR2VMbVzvbVc/ssaepiIxuRJ48AzlV2F/YKt7fbvUWiI66xF13MDZZs/0yHEor29FR31OwNl2N3G6T1l+vn/unSG/yC88J9nUVtTg3voj5L2I62dxP6IWh5gbZ8s3SPQVdlxrxX3r4oKM+Hhc7NVOfPimnZMyEjCjrI23Du/UDV68Kho/N8xqdhW2oo4Urajw424/f3dKKvrwHkzE0n57sD0EVEYFh+Mv7yR7zFlWhsRf/aSk3U1SNs9pJf33X0Xgsn9gH/QyiiTzs233Kamo3lQn34yH0ccdZRy6Zg1+3BwMaVLLru823fQGUB+/k5ltdL2sbVNU6a1fdoylKbK2zu8rzRp/RmspQ2edWeybF2OkKMuA5a8B3QqoabsSWRpoIekMno/nJRp0wjybZ9zPrl+0HeK3nNjcjbaf3wXlm2/KOXbmD4KbR8/rqMIPvQscusog/XXBQg99Xq0vnefcvMwxKUh9Pg/kCJOFmS6p/HIS9C+8GVYdm3Qr+3vik05NtivWvjdUjTF5KnPGhe/UDNblPeeZ8wMnUvOg8/fST5uX7oet59H56jznNvg67R2DJgwfnSPXV23bq1SgjVFWjvpsSeeJKuz3eDDS1ZQX3zlNezcmacKjbHSqQkroezuoQlvs7XXWbj/LK+/+gpZmc/VFWnex23xd6qcrNA8jvf+9y7+98GH4FgEluNPOFGN/al//wv/eeElWKh9nvVh5V6T5597Vj0IX3vd9UopZutyYlISzqJ7afLqyy+RojwCl152hVLu+e/BA3P/SX6vrrFInLo2MzMLjz7+L+1Scu96Dnff+Q88TS5be5JdBQWooNS2/MBRWLhbtcPnv0tKPFc5vv/Bh+i3vANvvv4a/nHr3/HcCy/uqblBP7Z9RwH4NWniGIwbM9Ll/pxPWRPO5CFiJxAcFKqUaZulXZUWD4rwvxiZvb3Xrt/2vZ29j47zF/fhh+biEHrKfu3Nt/UfeefbsYsHu3qIDJxAaJDjiZv/mHZ9cBn4Hfbcwv7ZEUpJZv9puj3iI4Mw9+wspMQEq+qD1x6bhvs/LsRGslazXDEnBX8+MhV3f0SKAcmpU+Pxwvfl+G5DncPCpY4M7D9mwRIeYsKEcT3/8A7sDt67eu4/H6Hp6zGqA+z3eclF5ysrNFvLvvv2W8QnxOOT+R+p4zW1NVj5yy+YPmNGrx1mC/Y5557f63HnAzxdzNa1nkRxHtfTEd/f98FHC3rsZFZmOn1+RiH8m/WwbO08pfOz1eMFfdyprMYUtGcacQC1a585YRcP89YVLi0YKMMAK9IdP38I8+af1DHT6BkImXUeWou3qfPD9j9auY2wzzIHR5qyxqOV3Tu6SMjhF8C8ZRnMq7+2t5MzBcGkUFvevJ2UayfrdZfr9rhJfvuaDM/JRmtsOmz0ObQSI6uVnILouH3J21b1AOi830KuWB1WCmDs6Xzap7Vlo7Y4RoCXiYm9l3IvKizs8fPJLg78Yvlx8Q/0/RlNBbASVC0EdotyVaYt+rl8PivTFeUVePGF5+0uFKRYs8J6+plnYQPNDJ1+xll8mlLWqyor1bVnnX0u2C2E3TumHnigrkirE+k//s7dd8/dSkk1k3LfVVmvratV1nA+vydlnl0/Pvv0E7z48qvKZaODLM4sXdvhffzw0HX/ZZdfiZNOOBalpSWU1jadT+tR2Jp/0imnoo5mmBd88Tmu+sOf1Hnr1v6Gw2bNAqe+ZWGl/14q0Mb98EWf8fUbtmLLlh3KSj2CXLdYLC2Vasn/BZlC9PWBrjS1WrG7oh2pccGIj3Zygxpow4N0fbBTHISltQKiTA8SeO02jeQvxk/a69etw1333Kumr7RjXZesTLe3d6g/oIOt/HXty1Dfjg53PENxCsLBzuixvbwVweTm8feTh+HHLQ3YUNiCP71inx6aPS4GreTGEU3W64NG2gMb2BJ9ygHxiAi1PwSwxfpbUqQ9LfyDzRIV5uDj6Xt4oz3tIUG7d1Z2NhJoapWtYL/9tgbTpk9X1int+Buvvaqs1UqZ7kEJ7Nqedh0vux7jAMZXX3kJvzvtdOfT/HI9Y1iqsn7GxkSr8ZmdfAlJO/HImFmJDhpzkFKmDeHRMGWORwdZqlnB1sSYMVa5cWiKNO+3bFsB2/STSGmeoBRsa8UumHL3h3nd9zBlTyT3jlJ7dg0KQNTEEBGr3ETMG5fAREq0s5joHpaC9c67+r7uxGI0TfUbvFyKma2/tfQA2VXYUsy/UZMmT8FHH35ACmgI2LLLVtVfVqxAZWWFcnmwK/WuyicroxGREcrizeus3Bo7FXN2twqj3zMWDmC8/bZblKWZAx+vu/5GVFdXqSDHrv1hn2X2562rq1Pnsz81+06zws/9z8rKxl/pepauyvTWLVvw6MMPKTeQOAqgVOeQrzfLdddcjSBqm9thBfrmv9/a7Xo+j++flpqmZrZ6U6bZGv0NPQy8TQHLlfSQ8Lfr/4oryZWDf7P5YeBhckVhH/CZ5N89ecp+5FrjmB3he3SVTZQtR5uN4FmGnmYp9rif7qvNcGgzGM4zHF2v7Xr/DnL5W7VmA7Zu34mJ48cgts3xu8OW/4HKxl2tuPP1YmwtbEVkuAkNzRZMGx2B289Lx+iMoWP5dmZhdWI0UD5D6fqBfxrcHO3iHxbhiccfBfumvfrGW3sNbojojKpuampGVJTD4d3N2wf0ZQlRjh9MtvJ4Uplmf+zI0O5P1to++tuE0toOXP9WPo6bHIezD0rE8BNDsaagCY8uKEEiWanDQ4w4mZRnZ1lX2IzwYLsyvavKntnF+fhA1/kHUcttnhjt4DPQdn3levpNcZH4uHgUFRXik4/nk08ouQ44yXGUxeDll/4LTpfH0+49yVcLFmDzpk32Q3ROBGUwuIl+hFm0Y/zDun79OvUdP+nkU+zn+uH/aanJmDhhNOLj7IFq2hANVNBAExspYD2T1M7o29Ky41cEz/y9yoDByrDyYaZyvs7Clmlbs+NHXztma6J9lMaKxULWbNPo6XZlmtrh7a5iiLSPh5V3ekzSD6v80D08ZOkn7G3FyR9bu8feLtmXxydNnqxcmzTlWLsXu2NwsCH7OOfvzMeVf/ijUjJZUeXgva++XKDcLFhZZnG25PI5HMR7apcUr3we+yNz2kl2t+DAwf++9ArvJsXzOqXQDhuWgWU/2WcU1IHO/zgAki31ycnJqh+snLKl20J/VNlafN89d4Gt7CNGjlSWZc3thNNi3nLz33DLbf9wccXiPrKiesdd9+jj4n2sbPOSgxG7SkNjA2I6AzK7HuPthd98pazWeTt2qLFwO2tWrwan1Dz6mGMxavRoLPz6a/yXMgLtoLiLiygV50WXXNpTU2pfeUUVKiq9H1PR2NgM9qfOCdkI7ZdJc9vptfN7ObB6RzOufKIAFxyZgP/8JRtJMUGorDfj0Q/KcOW/CvDhHSOQSPuGghgNjs+K1WKfUR4K/fZkH732Tt12y80qLRf/8PJLE83q/MFHH2u71JIt0yzs6iHKtELh9n9p8XaW3AA/eTuX0na70c4L2Qf64NFR+GyNq6Vn+ohIVDealRsHBxGyZfqlH8rVVcPiQvDQOdk4YUocWFHmbBp3fkDZXjrbZIs0ByNW0fUsWjBi52GPLMys5XdKWpyDj7ZvKC/ve+BBZJD/o7PcdsedVP0yBH+++hpM2W8/50PKmvbUM88pK3M4TTs//Zyrj+RFF1+KKrKeKelUqrQfbudj0VHRyB4+XAU3utzATzZSyK9+4oSxSExwKM3OQ3MJCKSAP08IB/9Zdq2DiRRcZVleY3e/cG7bVl8FQ0ySvThKp/WRzKLKAmzbYH/fLNtX2pXyuFQYM8ehY2n3AFFbXYVqtn3x27DVO6a3jWQNt1UUON+yX+u2TvcCA5ci9oFsCJwD/fwLL8Jf/u9q/Pn/rlGZK5YsXow8UvZmHnwInnryCZx08sl6sCEPNp0CbjlDDfsss8LI0lWZdt5WJ3T+d+HFl+ChuQ9QcOB0PWDxtzWrsXz5MuUecfAhh+Lxxx6hGdu1yiquXfvaKy/jxJNOVgowK/DsD81+zSwcPMzZPNaSKwUr09ynILKccvDgLTfdSG4lZ5KLxWytKbVkJZyt0Zypo6tw+3y9s7ClvpmMWZkZmc67XdY/nPeByik/j7IFcR84uJBdPViZ5ocPfjjhhxJ+seL/x6suV77j/HDSk8w+bIZy/2HXHfsMALsBObn/0LqrW4/TtjqPz7e7+rAbUY8uRKoNu0tQWVklKqtcf7uc++WcSZZs3s6H+r3+2LxynDA9Fjecnqpfywr1g5dl4Nhbt+GzFXW45GhHcGZBOQXqkyU7JzUEYzLDyNBhv2xjQSuyU0JQT1bttTtbSAE3kXU7kj4noGB9G13Tgv1HRqC0pgOVdWZMyglHa7sVq3e0oIWWB46iRACRDmVY70x/VpyNLp2JJfpzuT+c6/ptGcQRvTdvvv1u9EF2Fv6i9CSsQIdRxgH+oCcnOz5gPZ0r+/ZMICsxXD+ho8P+Q6DvGODKos31uPJwsshMjceSrQ3qyzpzVBRmjozGgrV2C9rY9DBceEgybvnfLlQ0dKCOCjiwAt3MX3CyULM19MwZiZj3SxVCyL/7SvKZZg+MX/ObBti73i/nDB6aZCU5+Gj7hvKSo/+7ClvFWDIye/5h5KltTaZOPVBbVUv+4dbS7LkcoI09Het67lDfnn0YW2x7F0OiQ0mxtXku3sOyZTmCj7pU3diyc223Dlh2b0AwRdYHH3wGOn76QB0Pnnkap5Mg14x1atuulK9HyGwKUizN0zN9ODdma2+BJW+NUrrbF71BT97tCBp3MIImH4HWDx50PrXv62zF7QyeRJKDT98b2DdnXn7FVSq7DftGsw/ziFGj8K8nn1aKWyEpfex+4SysuD737NPgYLtkskBzPILmX83nsTK6etUqnHv2GUrJZsWaj99zHynR02fgiiv/gD9ceRlZascoH2hW8tgHm8/j3NZ33X0v7vjHbZhz+BFKcWe3Es7b/tDDj6puKGWZznWWhIRE5afM+zhAkS3L/6YHAY6BYD/l77/7VrXP/WA3jZjYGHKbMKrxqlzZtJ+XnNmH2+eZOrZqc1YLfrB47JGHKfvGVb36N2/evEnNZr348mtK4ed+sMJ86cUX4Ia/3aSs0ZmU4WTixEl8SP3t4ZSdzc29/11n45qJFbXO4E114T78r7CwpMfWlQsXx9GUkLLdaSgn1bzHc/uyM7+sHWvIMv2P8+z+487XsJI8/66RlIXHsffJ+eX4YEkN9suNwBZyCclNC8VTV2chNNiAm14sxMETIvHzxiZkJofg161NOG5aLB64dJhSoC97vAD3XzIMt7xchDMPs7tLXvPsbsRHmZTr5M0vFuGJP2Zi1qQoxw37vebEwslK3e9mhvAFrt/GQRzIO2+9iRtvulndkdP6xMTE6IEJ/3zoQeW3pVmptW5lZqYhf1eRKoyg7ZNl/wmMTHNEbrd7WJn+fHWN8ne+4JAkXE5KcAeltOM0eF+SIv3uskrV2R+ooMvEjAg8d1kuGlstyi1kRV4jvllfq6zO91FWj78en65cQNrNVhWI+MzCsv4PtB9XsD++Js58tH2yFAL9JWBIzXFcMsB0co6GSFEq3IxgchtRirGTy4R+Dh1r//I5BB9+EcIvfUTtttaVo33Bc6QQOyzkKjvIcX+k7Byv6Jd2XelY8j8Ezzkf4RfPBbuq2KpL0L74HaWYdz23L9u21mb9NGOa/YFO3+HlFbbcdrXecpd6yo/O/sNvvfOe3uPX3nhbX+cVdpViNwyllNLsAC/5xfnZWdi9kc/J27GdfKujlMLMKfo01wFWuF9+9XWV/q6iokJl/+B92nG2mMdTMKSzHDhtGrmk7Fa7TjrlFNXmuHHjVVDjls2bqSCVvQ+s6E+mh2Vug3Nq30Rp7Fj51vrImXw40LKV+qNcT0gpZ9cTttr3xEfrw0dklT711NN0RZr388N6VvZwlf2EC0fd9veb1dhTKYUgx2twmsQDujysa+15Y+n8O8D3z8nOUPqGVnm5vsIxa8kWcXelgJRpVppHDQvVm8graUMFWY41iab0sBOyw7BkQyM+/rkWn9w1SgUnsrX58sfz8ca3VbjyeJqBItlW1KYU8OAgA5bS+X96ahf5XdsVdQu5Xs5bUotvHxqDlLggXPDPnfj9IXG46gT7td/91oCbXyrCoofHKOVcu39/lux+pIkhyMFI2xcIS0ObuRdT8D4e/aknn4BPPrO7d3AQ4njK78k+VSynnXIiPqLqbF2V6arqGnz/wzIcMXvmHqOz93HX/aL5mbf8SIor+UvTH8rhw5I9PiYT/aVIpimrEJoX4zzSPblmcFYRzifNOaM56LCrcAo97mNP13Y9d6DbJRU1lHvZrmjMu2k6clMdDxwDbVuuD1wC7Q+dDRtZflmCJpElm4KoBlMMIfZZFrYyD0goMNFAEfsDzTFtraQ81iX5qitBZ9wM06yzB9QtuXjwCbDSz8HLytpOvx+8ZJeQCHIJ44eMrr/bHHvBwgazckqZt+znn5QFOycnB+zS4mzRH/zRuN7xo0++olkFK0aNHI7xY0d2c1Fr3PU1yn68Xl2UTDNPifHprg30cevLlfW49ZUi/PrUeN3o/ti8Mny7pkG1UN9kwSgKQHz1xhzc/WYJSqs7cPYczVubsmOub8T6/Ba8d/sInHTHdlx2bCLOnGU/3kiZQWZetxlfPTAaZprS5eOv/i1HBTaW15px5N+3Kqs1K+ua3E59mXt5Bg6fYo+p0Pb3dVlaXoDaertbWObx7yI0sftsaF/bGqrnec0yvSdgvan3iZS+iwOdCnYXizK9J4B7OVZTW4cx5He1vqiVrBFU8Yys08HBnv0oWOhLzIGGe5I2UpTL67sr0do1bLUeLGlttaeJSogKEUV6sKAHwH2MwyfD0qlM25oaKB2dPahvsIY+YCVa6yhZwF0KuWj7+7m0NdsVK77MOHxiP6+W032BQCMFIa5Zs0q3ZNut7xbMmXNEj+5frERr0ltQpnbc28sxo3MxdvRIZWTqqS9BYYn6bjPN1LgrI9ND1axtQXmbctngdm48I1W9eP1fH5VjTZ59FqecfkfzStvw1ned/iV8Agn7SWvirBhr+5zdUDQLeAVlxmKZ/1OdrsTz9oTh4eTO4+RXwjv7IWanGTJTmN3i3Y/L/eJUz2pQ7iLpQXvu+nSrNZ2VlY48qi42dX/5Q6wx2duysbEJHBVdVk6R0bRsp8pUqaFhWA+7layZFMlYDyvTe+uTLx1vofFzcArL9FE9B5P5Un+lL0OHgGHUAcDyj1WHbY21g65M+xopW6Ndmea0fgZKyScy9AhwwKa/prrk9Hd7kqDIYfrhdieXKX1nH1dYmebUd68trMbdF7pat9kQ9cPaBsSSTzPLcFKaOUBwLgUmalJU2YGqBodLiLa/t2UY+VazZJNPNct9l6QjM8mhjLNryORc92OFOjQWNPMWFOk6HnXDAPhvcOccnYByJDHnvuRXNVU9LC0pUSVR2X+an3R7k+ysYcqSunnrjt5OCfj9bW1U/pUCKVauWocvvlyEL79ZjLXrN6tIZq4CduxRs3DpKdN0Tk0BXgzHefyHjHNMpemAZEUIuEnAqNLK2S+2NdgDcN1sashfxg8T5JyrxmGgQEYRITDUCLCiaAyyK53tHe4HFXM85e3npuMT8oVmK3QtuXWwsBvG38l/uajKYfVm/+ZvVjXgp42N6hxOn3fNs7uwi7J79FfYgn3s1Bg8/F4ZxStxcSTg/R9r8BBtR4a5rw62UcAzS0hMbn+75Dfne80yzZWeOD2elr1j7Zo1lBh/ntrmaOLehAsi5AzPxMZN25E5LE3S5BEodtWoqKwifzR6keW5rp6mkykCOoFSduXkZCKVsp/wurO1fyLNvA1PJpeZCkqPQ5ZZboP9pwNRmpodfxRnTwjMKapAfN8HY8xckMQ4ciqsO1aBg++4LLchfCBR84PR631zD1udY5raNHHWvrmJtCoE9jGBkLixaK1cQ0a9NvKvpqwp5C/ujkwbE4E3b87FXW8U4+WvKhFFRVs4Vd3Z5Pv8zDVZeOZTuw8yp8G772LOxlFMs8pWSmVrVArxSTPccxm744J0VShm1o1bwBbrUcPCcM9F6W67ebSq4Gr7zG5I3J4t++5wGirXeC0AcSCAOshNYcHXPyCKFPIjDw88CwdX6qumYMwyUpxZga6uqVUPITHRUVQoIBGpKUlITkrcq3L83Jf5+O/CAvVWJMRGIZ5egSaNpEiXVdothkdNTsIjl8jUc6B9Bvb1eC2L/wfzh4+o2xiTyLKVnrOvb+l77ZMblXnjL5QFhCxwpHyEzl1EZqzAjPr3vTdHetQfApUr56Juy5vqksz0UaSHDNw1kMuJl1CQIbt0cEaO3qSKrNLx0UF6junezuvLfs4K0kbKeYxTIGJfrut6Tg1lCiqjiqosiVNvQtz4S9V6oP3n3iOVByhxWpyu4mw5nUKlRnsTjhhmn+llK9Yo/+kRudm9neo3+2tr6+1+zxWU57KyRuUx5bzbrDzn5mYiLSVZlZrtz4BPPDBFV6brm1oCUpluaHRkOTjxwNT+4JNzhUCfCBgPPA7oVKatNeUwpg2nrB69/2D2qdEhdhKPWynS1G/TtBNFkR5i759010EgLPkAXZlubmnwiDLNLhZakKDjTt3XPFkRkXNUhwYPfDaaGWjCbAJVvKZMP04J4C2dlXI0Vw/9TaAZg7fefU/f7GklMyMdqamFyhd42LBUVdClp/OG6j4um263PFdS0GA12qiSFacQSk7iqmujyXUjCbGx7qWx0ZgMT47AEZOS8P36SuXmUU8lU2OiIrTDfr9sJd/y5tY2Nc6cFDsLvx+0DHDQCRjIcmWacQosKz7lah6wVpWCLdSBJDYasybGg07RVmUpBIYcgfDUGXqfG5vrkIIsfTsQV5qIAYsxNBZhSb0bQf2djdeU6dfefLtXtt2U617OnHbAZHy1cDGWLF2J2bNmUHnk4F7O9P3dHDTI/s7lZHlm142m5hbl4xwfF0OW5yzlusFli7Wk/Z4a0dmHDlPKNLdX2xBYynRtvaPy1jnEQUQI7CsCpkPPsCvTdAPOtRxIyrStpkL5izNb4+jpMI4IXOvVvvp8SbuDR8AUloDwlOloKf+FyrW3gn2Gw0IDxwjlTLqhqZaCGO3pbSOHzXY+FHDrXlOmOen7O2+/hV278nHmWeeoCksffTQPFZTUffu2bXj3/XkuAXM9vTPh4WGYdch0/PjTSixavAxzZh1EzvmOdC89XeMr+8xkoWKLs6Y8c9AgS1RkBFnck0l5TkQKWZ89nf+56/gPGh2PQ8clYOnmapUlhRXMuJjIrqf53TYXaGnqLNIyLCEM5xzqSDvkd4OVAXmdgGH4JBinHAnr2u9UFUJrRRGMyYHxmeOxamKac562KkshMGQJRGYdpZRpHkB9Q3XgKtM0dk2YSSCL1wIQ7737TkRERGDGQTPx5huvo6mpEbNnH45Ro0dTQZZE7H/A1D6/L6yILl6yQlmm58w+yCddPlTQIAUKlpWT5Zks0NXV9qDBkJBgUprtQYMcOBgR4X6uxz4D63Liqrw6XPms3Yed/dazhyVRRauB+1J1uY1PbRaWVpHrjD390D/OGoPTDwqsaXefejMCpDO2gvVof+JS+2g5H+vY/YHg3jMX+QMWVqStpfbgJOO4mQj+09P+MCwZQ4ATsLRUIP/DwxWFIKoOOio38NwbuGjN9vzfFANTaDxyzlwS0J8Kr1mmV6/6Fe998BGCyZLc2NiIX1Ysxx//fLVbbwanyzvy8EPwIynUixYvx8EzDhiwP7FbHelyUW0dBQ12pqurqKxWQYN8CivPkyaMUcGD8XHupbfpcqsBbU4dEYvTSJmcv7xEZQWpqmlAatLAI5QH1Kl9eHF1XaOuSB9AYxdFeh/ClqZ1AmydNh1C7h4/zQNsVqqMuAumrNH6cX9bsbW16Io0j8107JX+NkQZT4ASMIUnI2r4CWgsWACu/ldbX0kzuoGVVlUrH84fgegRpwXoJ8ExbK8p0xbys2FFmoXLjbKVeiASSRbdI+YcjOUr1+Cb75Zg9MgcTKBAveCgwRuiI2jQXmmQgwZZOFBw1IjhSolOogBCk8k4kKHuk2uvOSEX36+rRF1zBzhdXLifBiNyTu0aUqY1uZbGLSIEBouA6cQ/w/rbQtia6mCrrYSVyosb41MG6/aDeh9r8U79fqbZ55KvNFniRYSAnxCIGXWmUqZ5ODW1ZQGlTNvIGMAp8TRhFoEug6dpDgLp0NAQzD50BgqLSvHbuk2qCuCUyePAVRP3hbSTm4DmtsG+z02UXo4lIjwc6ekpKmiQXTfYlcPXJSEqGDf+biTufGez6mpFdT35nwerl6/3va/94we4imp75DFfc9XRw7F/rvdnBvrafzlv6BMwRFFmj9/9Fea371GDsRbtVEVcDGEDMyb4Ghlr2W7YGu3fNUNSFoJO/YuvdVH6IwQGRCA8bSbCUw9CS9lymulsCSjrdFVNqSpYwwCjc09FcEzOgFj6w8Ve85k+cs5hmD7DnmKmqqoKNVSEhP2lWdhv96GHHx0QXwsF+HGVxG078km5DVMKdXZ2hgrwc7dhDhqsJHcNdt0oI+W5rs4eNMhBglwkhYMGWXmOihq6AXwPfLAV85aVKEQ8roxUsqRz7VM/kJKKGnDgIcuM0XH4zx8Dz8/ND95GvxiC+Z17YVn+iRqLITwSppGT6A+ff3zP2OJu2b1Nf5+C//gUjOMP1rdlRQj4C4Hm4sUo+f7PajjBQSEYmTOZ1v07h3yHuR15Bev06tVZJ36IkPix/vKWuj0OrynTXy74Qizxll4AAA2CSURBVL0ZXdPgadsnneyZXKSccq5gVxEKdhcp5Zd9lLOy0jE8KwNsyd6T1FNgY31DI5XnblRBg1VVNfrpSYnxSnHmoimJCfH6fn9Yuezp1fgtv14NJTwsBMNSEob8sMrJIq0VaGEr/Gt/mYoMyuIhIgS8QoD8LNsfvxS2oi3q9oYYemgdPvR/kGxN9bDkbdCRBp10NUzHXK5vy4oQ8DcCpT9ci6ZCytJDkhifhuTETH8bost4ist2kl5UpfbFjjkPSdP/4XI8UDe8pkx/u/AbHHX0MYPKvZ6UYqVYk3Ld2tam3C84vZ6Wn5oVefZzZgWcXTichUt1p6YmKQWa/Z79OdtFYVULrnhmDSrq7T7fEeGhSE8eug8MlTX1qKMc2po8+4cpmDlm6I5HG4cshzYBW/E2tD9JQXlt9nznhvhkmDJHDdlB2ZobYcnfRIVpzGoMphknI+j8u4fseKTjQqAvBNprtmD3F6frp2ZnjKXZ8Gh9259WOA1gcVmeGpIxJAbZp34BzuQhQvMRbWbSIL0gp558Aj75bIG68+uvvYKRI0fh0MNmDVpPqsithH2cW6gCXmtrK1po+l8FDHbiYFeTOCqYwlZnrjo4FPyePQmPLdN/ev43tHVYVbMRYaFUdZGKxgyxMsjs+82VHTW5//xxOHGqlA3XeMjSuwSsm5ai4/nr9E4MVYXa1tQAyy6KtzDbFWnjuIMpDd5T+rhkRQj4M4Gadc+ieu0zaoihIeHIyZqw1zoZQ41HR0c78gs36r7SyQfdAwk8dLyLPhGAWFdXR8qsPXjP0bV9u8ZKsr+5Z3iS2H45MfjX5ZPwl5fWocNsU2W3i8uqkZoYu88LyXhiHDzLUFZZR4VZWvXm7qB80qJI6zhkxQcIGMcfiuBL5qLjtVtVb7haoIUsu6asMVQucGj4UNvqKe3nLvKRpgh/FuPIqQi+/BG1Lv8JgUAgED/5ajSXLkNr+a8qGLGkPB/DUv0rU1RpRb6uSHNaQFGkXT/ZvvHX2jvGcVcSstWNAFdHfPaqKYiNsD9zcZGTQlKomyh1ni9Laxv1k4qyOCvS9547Dr+Xwiy+/LYFbN+MBxyD4Evn6uO31dfAsoMCfFoc5e71gz62Yq0ohqWA/L41RXrMDAT/4V9AiMQj+NhbJd3ZxwSSZ9wJgylU3YV9iiuri/fxHQev+dLyAvrdt8dRBUVliJ90D+i9pkxbzBZs3bJFvapralBaUoJtW7eq1/ZtW3voquzyBoEDR8bhhT/vj1Fp9gwlVkovV1pZCy7s4ovC5dCLyqrQ3mGfbo4KC8K/r5iMk6eJa4cvvl/SJzsB4/6kULNbRKevpa21GZbta2GtKvNNROTOwRk7rKUFev/UQ8HVNNUd6l9p/vQByooQ2AOBkNhRSDn4fv0MVqY5//RQl4qqIkr7V6EPI2Xm/eQn7b9F3fSB9nPFaz7TZ51xGhkzbHp6Fa3fWjaPDz/+VNslSx8g0NRqwT3vbcHCtY4vVQilzkuIi0IkBXF6W9gaXU2pCrkoiyZThsfgzrPHYkSq/LhrTGTp2wRspXnoePtu2HZt1DvKmT6MadkwhIbr+7y5YiVXFKVEUzlhTUxHXYKgU67VNmUpBAKWQO2GF1G15gl9/KnJ2YiPTdG3h9IKPxA4W9hTDn5Aqh328gZ6TZnupT+y28cJvLZoN578zB7Nq3U1krJ9xMVEISx08IvTdNAMRy1laalvdPW5P39WJv5GRWhEhMCQI0Bub+b351LZ8Q9dum5MyYQxOcNrvtQcZGitKIStodbRL7KkB591C4xTj3PskzUhEOAEqlY/jtqNL+kUOF0ep80bSlJeWYjq2lK9y4lTb0Lc+Ev1bVlxJSDKtCsP2eoDgU2FDXjy851Ysc2Rd5svY6U6JiqC0gLZ/cb60JTbp7AlmrN0NHRWndQaGknuKNedNAKHjR/6ubG1MckyMAlYV30F88dPwuZUthemIBiT0mFMoB/moMGJH+fc0daqUuqHPbes9m4Y9z8aQaddD0OcuFBpTGQpBDQCVaseQe2mV7VNZZ1mK7WvC3sHlJRzLulqvauJB9yAuAlX6Nuy0p2AKNPdmciePhKYv6IULy4sQHG1a0Aiu39ERYQhkl687ikxkxWagwobKQCSlWlnCQ8x4Yqjs3H5kb7/x8q537IuBPZIoK0F5gXPwbLo7W6nGeJTYIxLgiEqttuxAe+gjCJWqmTI1Qxtza7xEYaU4Qg6/o9kjT52wLeRBoSAPxOoXvs0atY9pw8xMiIGacnDKSPWvjc46Tftx0praxNKKwro99WRTjZp2m2IHXtBP1oJzFNFmQ7M992jo35rcSHeWVLUTanmmwST9YyrKLILSGhIcL+Ua3bh4AwirDi3UiEdXu8qkaEmnHtYBi6ck0lZRwbfzaRrf2RbCOwLAraSHbB89zosv3zevfngEBiiKQd8ZCwMkTH0pQvpfk4f9thaGsFWaFtDHWyNTq4cndca4lJgOvxCep3fh9bkFCEgBJhA/bZ3UbHiPh2G0WhCSlIWuUYm6ft8YaW6phTlVYV6VwzGEKQc+hCissWFS4eyhxVRpvcARw71j8AXq8rw6S9lWN7F/aNrK8FBJlVB0mgy2ovAGOgMKh3E00sWyhZisVgpt7WlW3CqczsTsqJx8oGpKt1daLDXktI4d0nWhcA+J2Ar3Um+1PNgWf4JVU50WI9cbszKdVgEDJyeji1g9EBroB9w+o9Ooy8afcdsFgsVWGmHraON2mmhNHzUVmd6O5e2aMOQPQGmmafBdMjpXQ/JthAQAn0g0EI5qCuW34WORoeyGkUPv0kJGWRo8m6AfHNLPSqqiil4v1EfSWjiJHBRltD4cfo+WdkzAVGm98xHjrpBYHdlC75fX4klm6qxckd3C5cbTapLJmRG41DyhT58YhLGZ0a524xcJwSGPgFyw7CQT7X1t+9g3fBjr4qwuwM1JGbAOHkO2C/amDPF3WbkOiEgBDoJWDsaULlyLhryPnZhEheTTFmxUqnK8uBmxWKXjmpK3Vff6PCN5o7FjrsYSQf+3aWPsrF3AqJM752RnDEAAs1tFqzJr8PG3Q3YWtyE/Ipm5Q7C+3uTkCAj0uPDMDw5HKPTI8FWaE5zlxjt3vR1b/eR/ULALwiQddm6dQWseathy18PaxEVUaEfyv4IK8+GrPEw5k6BcdSBMGSM7c/lcq4QEAJ9JNBYsADVa54kK/VulytiohMQG51EsUbkqrUPpbGpljJgVYKXzhKaMAEJ+/8VEemHOu+W9T4SEGW6j6DkNM8SaGw1o77ZjJZ2C7l22KhysgFh5K4RHR6sV1z07B2lNSEQOARs5P9o4wpsdRXkB00/muwSQtZsGMinKohcP8KjyM86QWXiMJD/JsLsRZkCh5CMVAh4k4CNAhOfR83GF2Ezu6Z1ZQt1dGQ82A0kPMwzM7DNLQ1KeW5orCEXSkctBiZgCk9C/MSrKMjwQm8CGfL3FmV6yL+FMgAhIASEgBAQAkJgqBGwttejdvMbKkjR0urqbsFjMVEqTFaow+lhNzQknAL4wygTCMVEqPiH7qO1Wi1KWW5vb6WA/Rbyg25SvtC8v6sER2UhZsy5iBt3ET1kU0yFyIAIiDI9IHxysRAQAkJACAgBISAEBkagIW8+GnZ+hpbSn/faEGcEMdHLwDNNJFYK3rdaKZ0lBRfvTSIzj0RU7imUpUNSW+6NVX+OizLdH1pyrhAQAkJACAgBISAE9hEBc1MJmot/QHPJT2gp+wVsvR6ImMKSEJ42Q/lCR2TMgSk0fiDNybW9EBBluhcwslsICAEhIASEgBAQAt4k0F67FW01m9FRt0MFLZqbymBpq4G1g/LCW9n/2UBeGiEwBkfBFJaIoIg0BEdnISR2lEptFxyT683uB8y9RZkOmLdaBioEhIAQEAJCQAgIASHgaQJS7cLTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAhIMp0wLzVMlAhIASEgBAQAkJACAgBTxMQZdrTRKU9ISAEhIAQEAJCQAgIgYAh8P/RRoG24EuYdAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "fc3cb67c-40d2-4971-a46c-0b89379701f3",
   "metadata": {},
   "source": [
    "![image.png](attachment:f26dd7ab-8f73-4e60-ab65-f6dd51e7f20a.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938d7d1a-4156-40de-8690-f7e9a2da6901",
   "metadata": {},
   "source": [
    "Il y a trois types de nœuds :\n",
    "* User : décrit un utilisateur et contient les attributs suivants :\n",
    "    * o id : int, sex : string, age : int, occupation : string, zip code : int\n",
    "* Movie : représente un film et contient les attributs suivants :\n",
    "    * o id : int, title : string, date : int\n",
    "* Genre : fait référence au genre d’un film et contient les attributs suivants :\n",
    "    * o id : int, name : string\n",
    "\n",
    "Le jeu de données exploite également trois types de relations :\n",
    "* RATED : spécifie la note qu’a laissée un utilisateur à propos d’un film ; contient les attributs suivants :\n",
    "    * o note : int, timestamp : int\n",
    "* FRIEND_OF: représente un lien d’amitié entre deux utilisateurs.\n",
    "* CATEGORIZED_AS : permet d’assigner un genre à un film.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b5618e-e0cc-4e1c-bec3-16d89b858ae2",
   "metadata": {},
   "source": [
    "## Pré-requis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f5fac9-bcde-4727-bd1d-e624c98a2bb1",
   "metadata": {},
   "source": [
    "Démarrer Neo4J"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1d4258f8-dc38-441f-940e-ff76a923d365",
   "metadata": {},
   "source": [
    "docker start Neo4j"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d9df1b-def6-4283-baec-fbea1b1e03b7",
   "metadata": {},
   "source": [
    "Connectez vous à Neo4J : \n",
    "\n",
    "http://127.0.0.1:7474/browser/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddeb32ae-063a-4a29-b674-1cdf5d85658b",
   "metadata": {},
   "source": [
    "Supprimer l'ensemble des noeuds et des relations contenu dans votre base de données Neo4J :"
   ]
  },
  {
   "cell_type": "raw",
   "id": "72c731aa-af5d-4bf4-aa30-828e8911a82e",
   "metadata": {},
   "source": [
    "match(n) detach delete n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8436d8b-71cd-4963-9a3c-8c7053e65b64",
   "metadata": {},
   "source": [
    "Lancer les commandes suivantes pour charger le jeu de données dans Neo4J :"
   ]
  },
  {
   "cell_type": "raw",
   "id": "273df821-f699-4839-8fb7-156399affe14",
   "metadata": {},
   "source": [
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/users.csv' AS users\n",
    "CREATE (u:User {id : toInteger(users.id), sex : users.sex, age : toInteger(users.age),\n",
    "occupation : users.occupation, zip_code : users.zip_code});"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f86e5c12-55cb-4b5b-9104-370c1455da8f",
   "metadata": {},
   "source": [
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/users.csv' AS users\n",
    "CREATE (u:User {id : toInteger(users.id), sex : users.sex, age : toInteger(users.age),\n",
    "occupation : users.occupation, zip_code : users.zip_code});"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7b5f349c-6bd0-4d4e-bfe7-51cfd0bee3a0",
   "metadata": {},
   "source": [
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/movies.csv' AS movies\n",
    "CREATE (m:Movie {id : toInteger(movies.id), title : movies.title, date :\n",
    "toInteger(movies.date)});"
   ]
  },
  {
   "cell_type": "raw",
   "id": "25bcc99b-20e3-4652-85c0-58e416ed87b5",
   "metadata": {},
   "source": [
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/genres.csv' AS genres\n",
    "CREATE (u:Genre {id : toInteger(genres.id), name : genres.name});"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5b654a48-40de-4763-ad11-ce58ad15762b",
   "metadata": {},
   "source": [
    "\n",
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/mov_genre.csv' AS mov_genre\n",
    "MERGE (m : Movie { id : toInteger(mov_genre.mov_id) })\n",
    "MERGE (g : Genre { id : toInteger(mov_genre.genre) })\n",
    "CREATE (m)-[:CATEGORIZED_AS]->(g);"
   ]
  },
  {
   "cell_type": "raw",
   "id": "50464119-c17c-45e6-aaa4-4661010db9a2",
   "metadata": {},
   "source": [
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/ratings.csv' AS ratings\n",
    "MERGE (u : User { id : toInteger(ratings.user_id) })\n",
    "MERGE (m : Movie { id : toInteger(ratings.mov_id) })\n",
    "CREATE (u)-[:RATED { note : toInteger(ratings.rating), timestamp :\n",
    "toInteger(ratings.timestamp) } ]->(m);"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f40a12fb-48f0-4f00-9c22-3a42858af03a",
   "metadata": {},
   "source": [
    "LOAD CSV WITH HEADERS FROM\n",
    "'http://camillepradel.fr/teaching/nosql/movielens_export/friends.csv' AS friends\n",
    "MERGE (u1:User { id : toInteger(friends.user1_id) })\n",
    "MERGE (u2:User { id : toInteger(friends.user2_id) })\n",
    "CREATE (u1)-[:FRIEND_OF]->(u2);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded41550-f684-4e42-b7e5-90b91cd1e565",
   "metadata": {},
   "source": [
    "La requête Cypher suivante permet d’extraire un sous-graphe du jeu de données (l’ensemble des éléments liés à l’utilisateur qui a pour id la valeur 1. Exécutez-là dans le Browser Neo4j"
   ]
  },
  {
   "cell_type": "raw",
   "id": "208385d8-0b33-4808-b0c7-43c1e822fca5",
   "metadata": {},
   "source": [
    "MATCH (u1:User {id:1})-[:RATED]->(m:Movie)-[:CATEGORIZED_AS]->(g:Genre),\n",
    "(u1)-[:FRIEND_OF]->(un:User)\n",
    "RETURN *\n",
    "LIMIT 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d05c09-4e56-4875-b917-5de7ae04113a",
   "metadata": {},
   "source": [
    "# Questions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2ca502-3e56-40c9-b14e-20bafe404b7e",
   "metadata": {},
   "source": [
    "(1 pt) Q1 - Combien y a-t-il d’utilisateurs dans la base de données ? \n",
    "\n",
    "https://neo4j.com/docs/cypher-manual/current/functions/aggregating/#_counting_with_and_without_duplicates"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fe15da14-ea1e-4b8a-8f23-ebe9a7e50c40",
   "metadata": {},
   "source": [
    "MATCH(u:User) return  count(DISTINCT u.id), count(u.id);\n",
    "#The database has 200 users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b711e4-9d3c-40a3-8aa9-dc59f710c757",
   "metadata": {},
   "source": [
    "(1 pt) Q2 - Quel est l’âge, le sexe et l'occupation de l’utilisateur dont l'identifiant id a pour valeur 1 ?  "
   ]
  },
  {
   "cell_type": "raw",
   "id": "3eb3a566-ec8e-4879-a817-efe06e96fdfe",
   "metadata": {},
   "source": [
    "Match(u:User) WHERE u.id=1 return u.age, u.sex, u.occupation;\n",
    "#Age: 24, sex: M, occupation: technician"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93377a96-a34f-448a-b454-4351ef285af7",
   "metadata": {},
   "source": [
    "(1 pt) Q3 - Quelles sont les différentes valeurs d' occupations associée à un utilisateur ? "
   ]
  },
  {
   "cell_type": "raw",
   "id": "3e319819-a8f0-48ea-a9c4-e3085c26acc1",
   "metadata": {},
   "source": [
    "#All different possible occupations which appear in the database\n",
    "Match(u:User) return u.occupation;\n",
    "#technician, other, writer, executive, admistrator, student, lawyer, educator, entertainment, programmer, etc.\n",
    "#Number of different occupation ==> 21\n",
    "Match(u:User) return count(DISTINCT u.occupation);\n",
    "Match(u:User) return collect(u.occupation);\n",
    "#[\"technician\", \"other\", \"writer\", \"technician\", \"other\", \"executive\", \"administrator\", \"administrator\", \"student\", \"lawyer\", \"other\", \"other\", \"educator\", \"scientist\", \"educator\", \"entertainment\", \"programmer\", \"other\", \"librarian\", \"homemaker\", \"writer\", \"writer\", \"artist\", \"artist\", \"engineer\", \"engineer\", \"librarian\", \"writer\", \"programmer\", \"student\", \"artist\", \"student\", \"student\", \"administrator\", \"homemaker\", \"student\", \"student\", \"other\", \"entertainment\", \"scientist\", \"engineer\", \"administrator\", \"librarian\", \"technician\", \"programmer\", \"marketing\", \"marketing\", \"administrator\", \"student\", \"writer\", \"educator\", \"student\", \"programmer\", \"executive\", \"programmer\", \"librarian\", \"none\", \"programmer\", \"educator\", \"healthcare\", \"engineer\", \"administrator\", \"marketing\", \"educator\", \"educator\", \"student\", \"student\", \"student\", \"engineer\", \"engineer\", \"scientist\", \"administrator\", \"student\", \"scientist\", \"entertainment\", \"student\", \"technician\", \"administrator\", \"administrator\", \"administrator\", \"student\", \"programmer\", \"other\", \"executive\", \"educator\", \"administrator\", \"administrator\", \"librarian\", \"administrator\", \"educator\", \"marketing\", \"entertainment\", \"executive\", \"student\", \"administrator\", \"artist\", \"artist\", \"executive\", \"student\", \"executive\", \"student\", \"programmer\", \"student\", \"student\", \"engineer\", \"retired\", \"scientist\", \"educator\", \"other\", \"student\", \"engineer\", \"salesman\", \"executive\", \"programmer\", \"engineer\", \"healthcare\", \"student\", \"administrator\", \"programmer\", \"other\", \"librarian\", \"writer\", \"artist\", \"student\", \"lawyer\", \"lawyer\", \"none\", \"marketing\", \"marketing\", \"none\", \"administrator\", \"other\", \"engineer\", \"programmer\", \"student\", \"other\", \"educator\", \"doctor\", \"student\", \"student\", \"programmer\", \"other\", \"technician\", \"programmer\", \"entertainment\", \"artist\", \"librarian\", \"engineer\", \"marketing\", \"artist\", \"administrator\", \"educator\", \"student\", \"student\", \"other\", \"educator\", \"engineer\", \"educator\", \"student\", \"programmer\", \"lawyer\", \"artist\", \"administrator\", \"healthcare\", \"other\", \"educator\", \"other\", \"other\", \"other\", \"healthcare\", \"educator\", \"marketing\", \"other\", \"administrator\", \"scientist\", \"scientist\", \"programmer\", \"other\", \"entertainment\", \"administrator\", \"executive\", \"programmer\", \"scientist\", \"librarian\", \"librarian\", \"executive\", \"educator\", \"student\", \"artist\", \"administrator\", \"administrator\", \"educator\", \"student\", \"administrator\", \"scientist\", \"writer\", \"technician\", \"student\", \"writer\", \"programmer\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9124a9-0c39-4022-abf9-09144fad5e16",
   "metadata": {},
   "source": [
    "(1 pt) Q4 - Parmi les utilisateurs(User) quelles sont les 5 occupations les plus populaires ? (1 pt)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "87dfa993-ec8f-4764-87f6-8eeed5adeedc",
   "metadata": {},
   "source": [
    "Match(u:User) return u.occupation, count(*) as occurences ORDER BY occurences DESC\n",
    "#Pick the first five: student, administrator, other, educator, programmer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20b7d16b-c4af-4665-887a-f4d87222180f",
   "metadata": {},
   "source": [
    "(1 pt) Q5 - Compter le nombre d'arc par type ?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "361a0d29-0893-4a56-a7a8-875ea737f5a0",
   "metadata": {},
   "source": [
    "Match ()-[r:CATEGORIZED_AS]-() return count(r)  #1668\n",
    "Match ()-[r:FRIEND_OF]-() return count(r) #1600\n",
    "Match ()-[r:RATED]-() return count(r) #45240"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f04db3-0b7b-4d7f-a5f5-c595afaeac57",
   "metadata": {},
   "source": [
    "(1 pt) Q6 - Lister les films notés au dessus de 4 par l'utilisateur dont l'id égale 1  ? "
   ]
  },
  {
   "cell_type": "raw",
   "id": "825c14eb-cb3c-45e9-84ed-147805df9cea",
   "metadata": {},
   "source": [
    "MATCH (u:User)-[r:RATED]->(mv:Movie) WHERE u.id=1 and r.note>=4 return mv.title "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2340ca73-a6f1-4859-830c-05eebcf4c8f5",
   "metadata": {},
   "source": [
    "(2 pts) Q7 - Quelle est la note moyenne du film \"Braveheart (1995)\" ? "
   ]
  },
  {
   "cell_type": "raw",
   "id": "3062668f-c1df-4790-a5a7-8314ef34e293",
   "metadata": {},
   "source": [
    "MATCH (mv:Movie)-[r:RATED]-() WHERE mv.title = 'Braveheart (1995)'\n",
    "RETURN avg(r.note);\n",
    "#4.26923076923077"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afe8a12-263e-4580-8af0-8f3710ba8b65",
   "metadata": {
    "tags": []
   },
   "source": [
    "(3 pts) Q8 - Quels sont les titres des films dont la note moyenne dépasse 4 ? \n",
    " \n",
    "Indice : utiliser la clause \" with \"\n",
    "https://neo4j.com/docs/cypher-manual/current/clauses/with/"
   ]
  },
  {
   "cell_type": "raw",
   "id": "bb17fd95-2878-4e1e-894a-a337568c8146",
   "metadata": {},
   "source": [
    "MATCH (mv:Movie)-[r:RATED]-() WITH mv, avg(r.note) AS aver WHERE aver >=4\n",
    "RETURN mv.title;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46af7bfb-d4be-4acc-86a7-e50414adde62",
   "metadata": {},
   "source": [
    "(2 pts) Q9 - Quel film est le mieux noté parmi le genre Crime ?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "225688e7-0fcc-4016-8806-6fdb6888798c",
   "metadata": {},
   "source": [
    "Match ()-[r:RATED]->(mv:Movie)-[:CATEGORIZED_AS]->(g:Genre) WHERE g.name = 'Crime' WITH mv,g, avg(r.note) AS avgnote  return avgnote, mv.title order by avgnote DESC;\n",
    "#\"Incognito (1997)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd02f65-a4f1-4b02-9194-56ceabeb0c06",
   "metadata": {},
   "source": [
    "(1 pts) Q10 - Créer un utilisateur vous répresentant ?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "35fb4472-b6a0-426f-8994-c1a7b4bef611",
   "metadata": {},
   "source": [
    "create(u:User { id: 201, sex: 'F', age:33, occupation: 'scientist', zip_code: '63400'}) return u;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35bded98-5d12-44c1-8edf-46e5aa7fa6f4",
   "metadata": {
    "tags": []
   },
   "source": [
    "(1 pts) Q11 - Créer une relation FRIEND_OF entre vous et les utilisateurs 1, 132  et 724  :"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3dbca782-22c9-4947-8b48-e28876ef12b0",
   "metadata": {},
   "source": [
    "Match(u1:User),(u2:User) WHERE u1.id=1 AND u2.id=201 CREATE (u1)-[r:FRIEND_OF { id: u1.id + '<->' + u2.id}]->(u2) Return u1;\n",
    "Match(u1:User),(u2:User) WHERE u1.id=132 AND u2.id=201 CREATE (u1)-[r:FRIEND_OF { id: u1.id + '<->' + u2.id}]->(u2) Return u1;\n",
    "Match(u1:User),(u2:User) WHERE u1.id=132 AND u2.id=724 CREATE (u1)-[r:FRIEND_OF { id: u1.id + '<->' + u2.id}]->(u2) Return u1; #No changes, u.id=724 does not exist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d851ae5-7c35-464e-859a-7210f7cf11a5",
   "metadata": {},
   "source": [
    "(1 pt) Q12 - Selectionner tous les amis de l'utilisateur dont l'id est 1 : "
   ]
  },
  {
   "cell_type": "raw",
   "id": "fe41c84d-7b34-4a96-bc5e-e9bdc4832ab3",
   "metadata": {},
   "source": [
    " Match(u:User)-[r:FRIEND_OF]-(u2:User) WHERE u.id=1 return u2.id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18931ca3-2ed8-445a-9165-6daadb09c1c8",
   "metadata": {},
   "source": [
    "(2 pts) Q13 - Lister les films commun notés entre l'utilisateur id=1 et ses amis ? "
   ]
  },
  {
   "cell_type": "raw",
   "id": "dc9bad84-8afc-4c00-888e-08de4b2681d4",
   "metadata": {},
   "source": [
    "Match (mv1:Movie)<-[r1:RATED]-(u1:User {id: 1})-[f:FRIEND_OF]->(u2:User)-[r2:RATED]->(mv2:Movie) WITH mv2, mv1.id as mvs WHERE mv2.id in mvs return DISTINCT mv2.title"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107143bb-e862-40dd-8658-d65f81268431",
   "metadata": {},
   "source": [
    "(3pts) Q14 - Recommander aux amis de l'utilisateur id=1 des films qu'ils n'ont pas noté et qui ont été appréciés par l'utilisateur id=1  ?\n",
    "On définira les films appréciés tous les films notés au dessus de 4.\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "163feef3-5be4-4789-8e05-cf10687f0057",
   "metadata": {},
   "source": [
    "Match (mv1:Movie)<-[r1:RATED]-(u1:User {id: 1})-[f:FRIEND_OF]->(u2:User)-[r2:RATED]->(mv2:Movie) WITH mv2,r1,mv1,mv1.id as mvs WHERE r1.note >= 4 AND NOT mv2.id IN mvs return mv1.id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24aa5c05-78a9-45af-9f89-ea5da937d989",
   "metadata": {},
   "source": [
    "(2 pts) Q15 -D'aprés vous, les arcs FRIEND_OF sont ils bien implémentés, expliquez pourquoi ? "
   ]
  },
  {
   "cell_type": "raw",
   "id": "84468ca4-1782-413b-a48c-9793e6dd200a",
   "metadata": {},
   "source": [
    "The arcs FRIEND_OF are uni-directional, if u1 is a friend of u2, both, u1-friend->u2 and u2-friend->u1 is implemented. \"FRIEND_OF\" is always a bidirectional property. However, that way, a relationship chain can be followed in any possible direction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b799a7e3-701b-494c-83cd-3ddd89aedb19",
   "metadata": {},
   "source": [
    "(3 pts) Q16 - En prenant en compte la remarque précédente, identifier les amis et les amis des amis de l’utilisateur dont l'identifiant est 1 ?\n",
    "Expliquer votre raisonnement "
   ]
  },
  {
   "cell_type": "raw",
   "id": "6475aaa4-cdc6-4f85-a6f2-8c3570f904b1",
   "metadata": {},
   "source": [
    "Match (u1:User {id: 1})<-[f1:FRIEND_OF]->(u2:User)<-[f2:FRIEND_OF]->(u3:User)  Return u2.id, u3.id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b06895-8ead-4749-a7e5-1711e7ff85a2",
   "metadata": {},
   "source": [
    "(4 pts) Q17 - Proposer pour la question Q13, une solution pour résoudre l'anomalie et appliquer celle-ci au graphe entier ?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "96220e07-7571-498f-9ca0-3841b8c84b40",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a35291da-d691-4479-8568-c67d70242458",
   "metadata": {},
   "source": [
    "(4 pts) Q18 - Suite à vos conseils en Q12, les amis de l'utilisateurs 1 ont regardé et ont donné la note de 4 à tous films appréciés par l'utilisateur id:1. Modifier le graphe, afin d'intégrer les notes des amis de l'utilisateur 1 :\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "82b2309e-e1b1-467d-8a3c-dd929f24e6bc",
   "metadata": {},
   "source": [
    "Match (mv1:Movie)<-[r1:RATED]-(u1:User {id: 1})-[f:FRIEND_OF]->(u2:User)-[r2:RATED]->(mv2:Movie) WITH mv2,r1,mv1,mv1.id as mvs WHERE r1.note >= 4 AND NOT mv2.id IN mvs CREATE (u2)-[r3:RATED {note: 4}]->(mv2) return r3.note  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d3b2b0-3720-4890-b2c8-69f4f8961247",
   "metadata": {},
   "source": [
    "(6 pts) Q19 - A partir des requêtes précédentes, la base de données orientée graphe est elle mieux adaptée qu'une base de données orientée clé-valeur ou document ?\n",
    "justifiez votre réponse ?"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0aeb8b3a-cd35-42e9-b122-4830d60400c6",
   "metadata": {},
   "source": [
    "Key-value based databases contain unique assignments of a key to a value. It is not flexible to display relations and decisions have to be taken. For instance, one can define a movie m as key and as value all the users who rated the movie m. But then, the relations between users cannot be displayed. In document-oriented databases, all properties can relations can be entered, but it is difficult to follow relations. In this example, each user and movie can be a document and all relations can be entered as properties. In principle, the same operations can be done as in a graph-based database but it is much more cumbersome to implement and to call properties/follow relations."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}