Projet_GDELT_Mongo_Requests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import des fonctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo\n",
    "from pymongo import MongoClient\n",
    "import pprint\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "client = MongoClient()\n",
    "collection = client.mention.data_mention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defintion de la classe de requête"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette classe permet deux choses : Faire les requêtes en direct sur la database principale et visualiser le résultat ou de lancer une requête et sauvegarder son output sous forme d'une nouvelle table.\n",
    "\n",
    "Description des sous fonctions : \n",
    "\n",
    "#### adding:\n",
    "\n",
    "    Input:\n",
    "    dico : un dictionnaire ajouté en première place du pipeline\n",
    "    \n",
    "    Fonction:\n",
    "    Permet d'ajouter un paramètre en première position du pipeline d'aggregation, par exemple, un matching.\n",
    "    ex: adding({'$match':{'Date':'20180102'}})\n",
    "    \n",
    "#### requete1:\n",
    "\n",
    "    Input\n",
    "    out : (default : False). Permet de sauvegarder la table de la requete si True.\n",
    "    \n",
    "    Fonction\n",
    "    Affiche les événements (valeurs de la table events) qui y font référence (dans les derniers 6 mois).\n",
    "    \n",
    "#### requete2:\n",
    "\n",
    "    Input\n",
    "    out : (default : False). Permet de sauvegarder la table de la requete si True.\n",
    "    Acteur : Nom d'un acteur pour filtrer la base de donnée\n",
    "    Date : Date à partir de laquelle on filtre les 6 mois\n",
    "    Plot : (default : False). Affiche le graph des évènement\n",
    "    Filt : (default : 'W'). Paramètre d'aggregation des dates de pandas. Par défaut 'W' = week\n",
    "    \n",
    "    Fonction\n",
    "    Affiche le nombre d’articles/évènements qu’il y a eu pour chaque triplet (jour, pays de l’évènement, langue de l’article).\n",
    "    \n",
    "    \n",
    "#### requete3:\n",
    "\n",
    "    Input\n",
    "    out : (default : False). Permet de sauvegarder la table de la requete si True.\n",
    "    \n",
    "    Fonction\n",
    "    Trouve les sujets (acteurs) qui ont eu le plus d’articles positifs/negatifs pour chaque triplet (mois, pays, langue de l’article).\n",
    "    \n",
    "    \n",
    "#### requete4:\n",
    "\n",
    "    Input\n",
    "    out : (default : False). Permet de sauvegarder la table de la requete si True.\n",
    "    arg : ('Month', 'Date', 'Year') Permet de grouper les données suivant une échelle de temps\n",
    "    top : La limite des sujets polémiques filtrés. Nombres d'entrées de la table final\n",
    "    \n",
    "    Fonction\n",
    "    Trouve quels sont les acteurs/pays/organisations qui divisent le plus. Pour ce faire notre approche se base sur un recenssement des pays qui ont un ton positif ou negatif et regarde quelles sont les sujets qui ont le plus de partisants de chaque coté de l'opinion\n",
    "    \n",
    "    \n",
    "#### requete5:\n",
    "\n",
    "    Input\n",
    "    pays1 : Premier pays pour la comparaison\n",
    "    pays2 : La limite des sujets polémiques filtrés. Nombres d'entrées de la table final\n",
    "    \n",
    "    Fonction\n",
    "    Compare l'évolution des relations entre deux pays suivant 5 thème prédéfinis\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Request:\n",
    "    \n",
    "    def __init__(self,db):\n",
    "        self.data = db\n",
    "        self.cons = []\n",
    "        self.pipe = []\n",
    "        \n",
    "    def adding(self,dico):\n",
    "        self.cons.append(dico)\n",
    "        \n",
    "    def pop(self):\n",
    "        self.cons.pop(-1)\n",
    "        \n",
    "    def requete1(self,out=False):\n",
    "        \n",
    "        #Première projection : Pour optimiser la mémoire on ne garde que les colonnes qui nous intéressent\n",
    "        project = {'Language' : 1,'Mention': 1, 'EventID': 1, 'Date':1, 'Actor1Country':1,'Actor2Country':1}\n",
    "\n",
    "        #On merge les features Actor1Country et Actor2Country pour préparer le unwind\n",
    "        project2 = {'Language' : 1,\n",
    "                   'EventID': 1,\n",
    "                   'Date':1,\n",
    "                   'Mention.Language':1,\n",
    "                   'Actor':['$Actor1Country','$Actor2Country']}\n",
    "\n",
    "        #On groupby Langue/Date/Actor/Even. On compte chaque article associé\n",
    "        group1 = {'_id': {'langue':'$Mention.Language',\n",
    "                         'date':'$Date',\n",
    "                         'actor':'$Actor',\n",
    "                         'even' : '$EventID'\n",
    "                        },\n",
    "                 'num_article': {'$sum':1}\n",
    "                 }\n",
    "        #On prend ensuite la moyenne pour un triplet Date/Actor/Langue\n",
    "        group2 = {'_id' : {'date': '$_id.date',\n",
    "                          'actor': '$_id.actor',\n",
    "                          'langue': '$_id.langue'\n",
    "                         },\n",
    "                 'avg' : {'$avg':'$num_article'},\n",
    "                 }\n",
    "        #\n",
    "        match1 = {'_id.actor': { \"$exists\": True, \"$ne\": '' }}\n",
    "\n",
    "        #On \"sort\" chaque champ qui nous intéresse de _id pour ensuite passage en DataFrame et visualisation\n",
    "        project3 = {'Date':'$_id.date',\n",
    "                    'Actor':'$_id.actor',\n",
    "                    'Language':'$_id.langue',\n",
    "                    'Mean':'$avg',\n",
    "                    }\n",
    "\n",
    "\n",
    "\n",
    "        #Requete final\n",
    "        self.pipe = self.cons+[ {'$project': project},\n",
    "                                {'$unwind': '$Mention'},\n",
    "                                {'$project': project2},\n",
    "                                {'$unwind': '$Actor'},\n",
    "                                {'$group' : group1},\n",
    "                                {'$match': match1},\n",
    "                                {'$group': group2},\n",
    "                                {'$project': project3}\n",
    "                               ]\n",
    "        \n",
    "        if out:\n",
    "            self.pipe.append({'$out':'req1'})\n",
    "            \n",
    "        self.req = self.data.aggregate(self.pipe , allowDiskUse= True )\n",
    "        \n",
    "        if not out:\n",
    "            self.df = pd.DataFrame(list(self.req)).drop(columns=['_id'])\n",
    "        \n",
    "    def requete2(self,Acteur, Date, plot = False,Filt = 'W',out=False):\n",
    "\n",
    "        # On ne garde que les documents dans lesquels l'acteur recherché est présent (dans actor1 ou actor2)\n",
    "        match1 = {'$or':[{'Actor1Name': str(Acteur).upper()}, \n",
    "                         {'Actor2Name': str(Acteur).upper()}]}\n",
    "\n",
    "        # On réduit les colonnes à celles dont ont aura besoin uniquement pour accélerer la requête\n",
    "        project1 = {'EventID': 1,\n",
    "                    'Date':{'$toDate': '$Date'},\n",
    "                    'DiffDays':{'$divide':[{'$subtract':[{'$toDate': Date}, {'$toDate': '$Date'}]},1000 * 60 * 60 * 24]},\n",
    "                    'Language':1,\n",
    "                    'Actor':['$Actor1Name', '$Actor2Name']}\n",
    "\n",
    "        # Après avoir transformé actor1 et actor2 en un seul champ Actor, on récupère les interlocuteurs et les events datant de moins de 6mois \n",
    "        match2 = {'$and':[{'Actor': {'$ne': str(Acteur).upper()}}, \n",
    "                          {'DiffDays': {'$lt':6*30}}]\n",
    "                 }\n",
    "        # On regroupe les données par eventID\n",
    "        group1 = {'_id':{'Stakeholder':'$Actor','Event':'$EventID', 'Date':'$Date', 'Anciennete':'$DiffDays'}}\n",
    "\n",
    "        # On stack l'id pour récupérer les champs en vue d'intégration dans un dataframe (pour visualisation)\n",
    "        project2 = {'Stakeholder':'$_id.Stakeholder',\n",
    "                    'Event':'$_id.Event',\n",
    "                    'Date':'$_id.Date', \n",
    "                    'Anciennete':'$_id.DiffDays'}\n",
    "\n",
    "        # Exécution de la requête\n",
    "        self.pipe = self.cons+ [{'$match': match1},\n",
    "                                     {'$project': project1},\n",
    "                                     {'$unwind': '$Actor'},\n",
    "                                     {'$match': match2},\n",
    "                                     {'$group': group1},\n",
    "                                     {'$project': project2},\n",
    "                                     ]\n",
    "        self.req = self.data.aggregate(self.pipe, allowDiskUse= True)\n",
    "        \n",
    "        # Result in a dataframe\n",
    "        self.df = pd.DataFrame(list(self.req)).drop(columns=['_id'])\n",
    "        \n",
    "        if plot==True:\n",
    "            reshap = self.df.groupby('Date').count()['Event'].resample(Filt).sum()\n",
    "            plt.figure(figsize=(15,8))\n",
    "            plt.title(f'number of events for {Acteur} by {Filt}', size= 20)\n",
    "            sns.barplot(reshap.index,reshap.values),\n",
    "            plt.xticks(rotation=90)\n",
    "            plt.show()\n",
    "            \n",
    "    def requete3(self,out=False):\n",
    "        project1 = {'Month' : 1,\n",
    "                    'Mention': 1, \n",
    "                    'Actor1Name':1, \n",
    "                    'Actor2Name':1, \n",
    "                    'Actor1Country':1, \n",
    "                    'Actor2Country':1}\n",
    "\n",
    "        project2 = {'Month' : 1,\n",
    "                    'Language':'$Mention.Language', \n",
    "                    'INT_Tone': {\n",
    "                                   '$convert':\n",
    "                                      {\n",
    "                                         'input': '$Mention.Tone',\n",
    "                                         'to': 'double',\n",
    "                                         'onError': 0, \n",
    "                                      }\n",
    "                                } , \n",
    "                    'Actor1':{'Country':'$Actor1Country','Name':'$Actor1Name'}, \n",
    "                    'Actor2':{'Country':'$Actor2Country','Name':'$Actor2Name'}}\n",
    "\n",
    "        project3 = {'Month' : 1,\n",
    "                    'Language': 1,\n",
    "                    'INT_Tone':1, \n",
    "                    'Actors' : ['$Actor1','$Actor2']}\n",
    "\n",
    "        project4 = {'Name' : '$Actors.Name',\n",
    "                   'Country': '$Actors.Country',\n",
    "                   'Language' : 1,\n",
    "                   'Month': 1,\n",
    "                   'Pos': {'$cond': [{'$gt': [\"$INT_Tone\", 10]}, 1, 0]},\n",
    "                   'Neg': {'$cond': [{'$lt': [\"$INT_Tone\", -10]}, 1, 0]},           \n",
    "                   }\n",
    "\n",
    "        group1 = {'_id': {'Month':'$Month','Country':'$Country','Language':'$Language', 'Actor':'$Name'},\n",
    "                 'Count_tone_pos': {'$sum': '$Pos'}, \n",
    "                 'Count_tone_neg': {'$sum': '$Neg'}\n",
    "                 }\n",
    "\n",
    "        group2 = {\n",
    "                     \"_id\": {'Month':'$_id.Month','Country':'$_id.Country','Language':'$_id.Language'},\n",
    "                     \"max_pos\": { \"$max\": \"$Count_tone_pos\" },\n",
    "                     \"max_neg\": { \"$max\": \"$Count_tone_neg\"},\n",
    "                     \"doc\": { \"$push\": {\n",
    "                           \"_id\": \"$_id.Actor\",\n",
    "                           \"name\": \"$id_.Actor\",\n",
    "                           \"quantity_pos\": \"$Count_tone_pos\",\n",
    "                           \"quantity_neg\": \"$Count_tone_neg\"\n",
    "                           }\n",
    "                      }}\n",
    "        project5 =  {\n",
    "                        \"_id\":1,\n",
    "                        \"Most_Liked_Actor\": {\n",
    "                           \"$setDifference\": [\n",
    "                              { \"$map\": {\n",
    "                                  \"input\": \"$doc\",\n",
    "                                  \"as\": \"doc\",\n",
    "                                  \"in\": {\n",
    "                                      \"$cond\": [\n",
    "                                              { \"$and\": [{\"$eq\": [ \"$max_pos\", \"$$doc.quantity_pos\" ]},\n",
    "                                                         {\"$gt\": [\"$$doc.quantity_pos\", 0 ]}] },\n",
    "                                          \"$$doc._id\",\n",
    "                                          False\n",
    "                                      ]\n",
    "                                  }\n",
    "                              }},\n",
    "                              [False]\n",
    "                           ]\n",
    "                       },\n",
    "                           \"Most_Disliked_Actor\": {\n",
    "                               \"$setDifference\": [\n",
    "                                  { \"$map\": {\n",
    "                                      \"input\": \"$doc\",\n",
    "                                      \"as\": \"doc\",\n",
    "                                      \"in\": {\n",
    "                                          \"$cond\": [\n",
    "                                              { \"$and\": [{\"$eq\": [ \"$max_neg\", \"$$doc.quantity_neg\" ]},\n",
    "                                                         {\"$gt\": [ \"$$doc.quantity_neg\", 0 ]}] },\n",
    "                                              \"$$doc._id\",\n",
    "                                              False\n",
    "                                          ]\n",
    "                                      }\n",
    "                                  }},\n",
    "                                  [False]\n",
    "                               ]\n",
    "                           }\n",
    "                   }\n",
    "        project6 = {'Month':'$_id.Month',\n",
    "                    'Country':'$_id.Country',\n",
    "                    'Language':'$_id.Language',\n",
    "                    'Most_Liked_Actor':1,\n",
    "                    'Most_Disliked_Actor':1\n",
    "                    }\n",
    "\n",
    "\n",
    "        self.pipe = self.cons + [{'$match': {'Mention.Tone': { \"$exists\": True,\n",
    "                                                              \"$ne\": ''}}},\n",
    "                                    {'$project': project1},\n",
    "                                    {'$unwind': '$Mention'},\n",
    "                                    {'$project': project2},\n",
    "                                    {'$project': project3},\n",
    "                                    {'$unwind': '$Actors'},\n",
    "                                    {'$match': {'Actors.Country': { \"$exists\": True, \"$ne\": '' }}},\n",
    "                                    {'$match': {'Actors.Name': { \"$exists\": True, \"$ne\": '' }}},\n",
    "                                    {'$project': project4},\n",
    "                                    {'$match': {'$and' : [{'Name': { \"$exists\": True, \"$ne\": '' }}, {'$or':[{'Neg': 1},{'Pos':1}]}]}}, \n",
    "                                    {'$group': group1},\n",
    "                                    {'$group':group2},\n",
    "                                    {'$project': project5},\n",
    "                                    {'$project': project6}\n",
    "                                     ]\n",
    "        if  out:\n",
    "            self.pipe.append({'$out':'req3'})\n",
    "            \n",
    "        self.req = self.data.aggregate(self.pipe, allowDiskUse= True)\n",
    "        \n",
    "        if not out:\n",
    "            df = pd.DataFrame(list(self.req)).drop(columns=['_id'])\n",
    "            self.df = df\n",
    "    \n",
    "    def requete4(self, arg, top,out=False):\n",
    "\n",
    "        #On projette sur les features utiles\n",
    "        project1 = {f'{arg}' : f'${arg}',\n",
    "                    'Mention': 1, \n",
    "                    'Actor1Name':1, \n",
    "                    'Actor2Name':1, \n",
    "                    'Actor1Country':1, \n",
    "                    'Actor2Country':1}\n",
    "\n",
    "        #Cette phase de projection permet de convertir le ton en chiffre. Certaines valeurs\n",
    "        #présentent un format inadapté. En cas d'erreur, on renvoit 0\n",
    "        project2 = {f'{arg}' : 1,\n",
    "                    'Language':'$Mention.Language', \n",
    "                    'INT_Tone': {\n",
    "                                   '$convert':\n",
    "                                      {\n",
    "                                         'input': '$Mention.Tone',\n",
    "                                         'to': 'double',\n",
    "                                         'onError': 0, \n",
    "                                      }\n",
    "                                } , \n",
    "                    'Actor1':{'Country':'$Actor1Country','Name':'$Actor1Name'}, \n",
    "                    'Actor2':{'Country':'$Actor2Country','Name':'$Actor2Name'}}\n",
    "\n",
    "        #On creer une liste sur les actors pour pouvoir faire une opération d'unwind ensuite\n",
    "        project3 = {f'{arg}' : 1,\n",
    "                    'INT_Tone':1,\n",
    "                    'Language':1,\n",
    "                    'Actors' : ['$Actor1','$Actor2']}\n",
    "\n",
    "        #On regarde si la l'article a une conotation positive ou négative\n",
    "        project4 = {'Name' : '$Actors.Name',\n",
    "                   'Language' : 1,\n",
    "                   f'{arg}': 1,\n",
    "                   'Pos': {'$cond': [{'$gt': [\"$INT_Tone\", 10]}, 1, 0]},\n",
    "                   'Neg': {'$cond': [{'$lt': [\"$INT_Tone\", -10]}, 1, 0]},           \n",
    "                   }\n",
    "        \n",
    "        #On calcul un ratio entre tons positifs et négatifs, cela nous permet de savoir\n",
    "        #Pour un pays donné si le pays est perçu de manière positive ou négatif\n",
    "        project5 = {'Ratio_pos' : {'$divide': [{'$add': [ \"$Count_tone_pos\", 1 ]},\n",
    "                                               {'$add': [ \"$Count_tone_neg\", 1 ]}]},\n",
    "                    'Count_tot': 1       \n",
    "                   }\n",
    "        \n",
    "        #On creer deux nouvelles colonnes qui vont nous permettre de compter le nombre\n",
    "        #de pays à voir un sujet positif ou negatif\n",
    "        project6 = {'Pos': {'$cond': [{'$gt': [\"$Ratio_pos\", 1.001]}, 1, 0]},\n",
    "                    'Neg': {'$cond': [{'$lt': [\"$Ratio_pos\", 0.999]}, 1, 0]}, \n",
    "                    'Count_tot': 1,\n",
    "                    'Ratio_pos':1\n",
    "                   }\n",
    "        \n",
    "        #Cette projection sert à creer deux listes de pays suivant qu'ils perçoivent un sujet\n",
    "        #positivement ou negativement\n",
    "        project7 = {'Majority' : {'$max': [ \"$Count_langue_pos\", \"$Count_langue_neg\"]},\n",
    "                    'Minority' : {'$min': [ \"$Count_langue_pos\", \"$Count_langue_neg\"]},\n",
    "                    'Count_tot_langue': {'$add': [ \"$Count_langue_neg\", \"$Count_langue_pos\" ]},\n",
    "                    'Count_langue_pos':1,\n",
    "                    'Count_langue_neg':1,\n",
    "                    \"Most_Positif_Topic\": {\n",
    "                           \"$setDifference\": [\n",
    "                              { \"$map\": {\n",
    "                                  \"input\": \"$doc\",\n",
    "                                  \"as\": \"doc\",\n",
    "                                  \"in\": {\n",
    "                                      \"$cond\": [\n",
    "                                              { \"$and\": [{\"$eq\": [ 1, \"$$doc.quantity_pos\" ]},\n",
    "                                                         {\"$gt\": [\"$$doc.quantity_pos\", 0 ]}] },\n",
    "                                          \"$$doc._id\",\n",
    "                                          False\n",
    "                                      ]\n",
    "                                  }\n",
    "                              }},\n",
    "                              [False]\n",
    "                           ]\n",
    "                       },\n",
    "                       \"Most_Negatif_Topic\": {\n",
    "                           \"$setDifference\": [\n",
    "                              { \"$map\": {\n",
    "                                  \"input\": \"$doc\",\n",
    "                                  \"as\": \"doc\",\n",
    "                                  \"in\": {\n",
    "                                      \"$cond\": [\n",
    "                                          { \"$and\": [{\"$eq\": [ 1, \"$$doc.quantity_neg\" ]},\n",
    "                                                     {\"$gt\": [ \"$$doc.quantity_neg\", 0 ]}] },\n",
    "                                          \"$$doc._id\",\n",
    "                                          False\n",
    "                                      ]\n",
    "                                  }\n",
    "                              }},\n",
    "                              [False]\n",
    "                           ]\n",
    "                       }\n",
    "                   }\n",
    "\n",
    "        #Le premier group by pour voir combien d'articles par langue parlent d'un article\n",
    "        #en bien ou en mal\n",
    "        group1 = {'_id': {f'{arg}':f'${arg}','Language':'$Language', 'Actor':'$Name'},\n",
    "                 'Count_tone_pos': {'$sum': '$Pos'}, \n",
    "                 'Count_tone_neg': {'$sum': '$Neg'},\n",
    "                  'Count_tot': {'$sum':1}\n",
    "                 }\n",
    "\n",
    "        #On groupe par langue pour avoir le ton moyen par langue, ce qui est notre critère de discrimination\n",
    "        group3 = {'_id': {f'{arg}':f'$_id.{arg}','Actor':'$_id.Actor'},\n",
    "                 'Count_langue_pos': {'$sum': '$Pos'}, \n",
    "                 'Count_langue_neg': {'$sum': '$Neg'},\n",
    "                 'doc': { \"$push\": {\n",
    "                       \"_id\": \"$_id.Language\",\n",
    "                       \"quantity_pos\": \"$Pos\",\n",
    "                       \"quantity_neg\": \"$Neg\"\n",
    "                       }\n",
    "                 }\n",
    "                 }\n",
    "\n",
    "        #On selectionne \n",
    "        project8 = {f'{arg}': f'$_id.{arg}',\n",
    "                    'Actor':\"$_id.Actor\",\n",
    "                    'Count_langue_neg':1,\n",
    "                    'Count_langue_pos':1,\n",
    "                    'Most_Negatif_Topic':1,\n",
    "                    'Most_Positif_Topic':1\n",
    "                   }\n",
    "        self.pipe = self.cons + [{'$match': {'Mention.Tone': { \"$exists\": True, \"$ne\": '' }}},\n",
    "                                    {'$project': project1},\n",
    "                                    {'$unwind': '$Mention'},\n",
    "                                    {'$project': project2},\n",
    "                                    {'$project': project3},\n",
    "                                    {'$unwind': '$Actors'},\n",
    "                                    {'$project': project4},\n",
    "                                    {'$match': {'$and' : [{'Name': { \"$exists\": True, \"$ne\": '' }}, {'$or':[{'Neg': 1},{'Pos':1}]}]}}, #On filtre les données vides\n",
    "                                    {'$group': group1},\n",
    "                                    {'$project': project5},\n",
    "                                    {'$project': project6},\n",
    "                                    {'$group': group3},\n",
    "                                    {'$project': project7},\n",
    "                                    {'$match': {'Count_langue_neg': { \"$exists\": True, \"$ne\": 0 },\n",
    "                                                'Count_langue_pos': { \"$exists\": True, \"$ne\": 0 }}},\n",
    "                                    {'$sort': {'Minority':-1,'Count_tot_langue':-1}},\n",
    "                                    {'$project': project8},\n",
    "                                    {'$limit':top} \n",
    "                                    ]\n",
    "        if out:\n",
    "            self.pipe.append({'$out':'req4'})\n",
    "            \n",
    "        self.req = collection.aggregate(self.pipe , allowDiskUse= True)\n",
    "        \n",
    "        if not out:\n",
    "            self.df = pd.DataFrame(list(self.req)).drop(columns=['_id'])\n",
    "            \n",
    "    def requete5(self,pays1, pays2):\n",
    "\n",
    "        # Matching only on countries given in argument\n",
    "        match1 = {'$and':[{'LocationsList': {'$in':[str(pays1).title()]}}, {'LocationsList': {'$in':[str(pays1).title()]}}]}\n",
    "\n",
    "\n",
    "\n",
    "        # Keeping only date and transforming tone to double\n",
    "\n",
    "        project1 = {'Date':1,\n",
    "                    'INT_Tone': {'$toDouble': '$GkgTone'},\n",
    "                    }\n",
    "\n",
    "        # Grouping by date and computing average tone\n",
    "        group1 = {'_id':{'Date':\"$Date\"},\n",
    "                 'Average_Tone': {'$avg':'$INT_Tone'},\n",
    "                 }\n",
    "\n",
    "\n",
    "\n",
    "        # Getting the _id attributes in order to put in dataframe (for visualization afterward)\n",
    "\n",
    "        project2 = {'Average_Tone':1,\n",
    "                    'Date': '$_id.Date',\n",
    "                    }\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        # Computing request\n",
    "        self.pipe = [{'$match': match1},\n",
    "                    {'$project': project1},\n",
    "                    {'$group': group1},\n",
    "                    {'$project': project2}\n",
    "                    ]\n",
    "\n",
    "        self.req = self.data.aggregate(self.pipe)\n",
    "\n",
    "\n",
    "\n",
    "        #Putting result in dataframe\n",
    "        df = pd.DataFrame(list(self.req)).drop(columns=['_id'])\n",
    "        df = df.sort_values(by=['Date'])\n",
    "\n",
    "        # Date to datetime format\n",
    "        df['Date'] = pd.to_datetime(df['Date'])\n",
    "        df = df.set_index('Date')\n",
    "\n",
    "        # Computing moving mean of tone to see evolution trend\n",
    "        df[\"Moving_Mean\"] = df['Average_Tone'].rolling(window=30).mean()\n",
    "\n",
    "        self.df = df\n",
    "\n",
    "        # Plot\n",
    "\n",
    "        fig = plt.figure(figsize=(10,5))\n",
    "        plt.title(\"Time Evolution of relation between {} and {}\".format(str(pays1).title(), str(pays2).title()))\n",
    "        plt.plot(df['Average_Tone'], label='Average Tone')\n",
    "        plt.plot(df['Moving_Mean'], label='Moving Mean')\n",
    "        plt.legend()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Première approche des données\n",
    "\n",
    "On charge un client mongo, on vérifie que les tables de bases sont disponibles\n",
    "La base de donnée est constitué initialement de 2 tables :\n",
    "La table even, et la table gkg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure des données Mention "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': ObjectId('5c487727a96a914e9ccbfafa'),\n",
       " 'EventID': '719028195',\n",
       " 'Date': '20180101',\n",
       " 'Year': '2018',\n",
       " 'Month': '01',\n",
       " 'Actor1Name': 'BERLIN',\n",
       " 'Actor1Country': 'DEU',\n",
       " 'Actor2Name': 'LONDON',\n",
       " 'Actor2Country': 'GBR',\n",
       " 'Contains_2actors': 1,\n",
       " 'Mention': [{'MentionDate': '20180101001500',\n",
       "   'MentionYear': '2018',\n",
       "   'MentionMonth': '01',\n",
       "   'Tone': '5.83333333333334',\n",
       "   'Language': 'eng'},\n",
       "  {'MentionDate': '20180101021500',\n",
       "   'MentionYear': '2018',\n",
       "   'MentionMonth': '01',\n",
       "   'Tone': '6.48148148148148',\n",
       "   'Language': 'eng'},\n",
       "  {'MentionDate': '20180101024500',\n",
       "   'MentionYear': '2018',\n",
       "   'MentionMonth': '01',\n",
       "   'Tone': '6.44171779141105',\n",
       "   'Language': 'eng'},\n",
       "  {'MentionDate': '20180101050000',\n",
       "   'MentionYear': '2018',\n",
       "   'MentionMonth': '01',\n",
       "   'Tone': '6.19718309859155',\n",
       "   'Language': 'eng'},\n",
       "  {'MentionDate': '20180101063000',\n",
       "   'MentionYear': '2018',\n",
       "   'MentionMonth': '01',\n",
       "   'Tone': '6.34441087613293',\n",
       "   'Language': 'eng'}]}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = MongoClient()\n",
    "collection = client.mention.data_mention\n",
    "\n",
    "collection.find_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure des données  GKG "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': ObjectId('5c489873a96a914e9c5507f3'),\n",
       " 'ArticleID': '20180810001500-1340',\n",
       " 'Date': '20180810',\n",
       " 'LocationsList': ['Jersey'],\n",
       " 'GkgTone': '-1.45243282498184',\n",
       " 'Category': ['diplomacy', 'other', 'society', 'security_violence']}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection = client.gkg.data_gkg\n",
    "\n",
    "collection.find_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# On relance un preprocessing pour générer des tables sur les requêtes longues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le paramètre out=True permet de sauvegarder automatiquement la table associé à une requête donné.\n",
    "\n",
    "Cela permet de charger la base une fois pour toute et de faire les requêtes de manière efficace derriere.\n",
    "Pour les requètes 2 et 5, nous avons estimé que le temps de processing était suffisament faible pour ne pas à sauvegarder de table personnalisées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MongoClient()\n",
    "collection = client.mention.data_mention\n",
    "\n",
    "\n",
    "#Génere une nouvelle table pour la requete 1\n",
    "req1 = Request()\n",
    "req1.requete1('Date', 10000,out=True)\n",
    "\n",
    "#Génere une nouvelle table pour la requete 3\n",
    "req3 = Request()\n",
    "req3.requete3(out=True)\n",
    "\n",
    "#Génere une nouvelle table pour la requete 1\n",
    "req4 = Request()\n",
    "req4.requete4('Date', 10000,out=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Structure des données req1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Structure des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': {'date': '20180101', 'actor': 'AFG', 'langue': 'fra'},\n",
       " 'Date': '20180101',\n",
       " 'Actor': 'AFG',\n",
       " 'Language': 'fra',\n",
       " 'Mean': 1.8823529411764706}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection = client.mention.req1\n",
    "collection.find_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Table pandas globale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list(collection.find())).drop(columns=['_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Il y a 888181 lignes dans le tableau global de la requete 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actor</th>\n",
       "      <th>Date</th>\n",
       "      <th>Language</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>20180101</td>\n",
       "      <td>fra</td>\n",
       "      <td>1.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>20180101</td>\n",
       "      <td>ara</td>\n",
       "      <td>1.260870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>20180101</td>\n",
       "      <td>mkd</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>20180101</td>\n",
       "      <td>fas</td>\n",
       "      <td>1.378641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFR</td>\n",
       "      <td>20180101</td>\n",
       "      <td>ara</td>\n",
       "      <td>1.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Actor      Date Language      Mean\n",
       "0   AFG  20180101      fra  1.882353\n",
       "1   AFG  20180101      ara  1.260870\n",
       "2   ALB  20180101      mkd  1.000000\n",
       "3   AFG  20180101      fas  1.378641\n",
       "4   AFR  20180101      ara  1.142857"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Il y a {len(df)} lignes dans le tableau global de la requete 1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtrage par pays\n",
      "\n",
      "    Actor      Date Language      Mean\n",
      "102   FRA  20180101      eng  2.388496\n",
      "143   FRA  20180101      ben  2.666667\n",
      "144   FRA  20180101      hun  1.000000\n",
      "230   FRA  20180101      dan  5.454545\n",
      "231   FRA  20180101      est  1.000000\n",
      "\n",
      "\n",
      "Filtrage par date\n",
      "\n",
      "       Actor      Date Language      Mean\n",
      "470955   AFG  20180710      bos  1.000000\n",
      "470956   AFG  20180710      hun  1.750000\n",
      "470984   AFG  20180710      bul  2.615385\n",
      "470985   AFG  20180710      ces  1.000000\n",
      "470986   AFG  20180710      deu  4.419355\n",
      "\n",
      "\n",
      "Filtrage par langue\n",
      "\n",
      "    Actor      Date Language      Mean\n",
      "43    AFG  20180101      ukr  1.000000\n",
      "133   CZE  20180101      ukr  1.636364\n",
      "161   CAN  20180101      ukr  1.416667\n",
      "280   ARM  20180101      ukr  1.000000\n",
      "321   AUS  20180101      ukr  1.000000\n"
     ]
    }
   ],
   "source": [
    "### Possibilité de filtrer par Date, Actor ou Language\n",
    "##Par Actor\n",
    "print('Filtrage par pays\\n')\n",
    "print(df[df.Actor == 'FRA'].head())\n",
    "print('\\n')\n",
    "##Par Date\n",
    "print('Filtrage par date\\n')\n",
    "print(df[df.Date == '20180710'].head())\n",
    "print('\\n')\n",
    "##Par Langue\n",
    "print('Filtrage par langue\\n')\n",
    "print(df[df.Language == 'ukr'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure des données req3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Structure globale "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': {'Month': '10', 'Country': 'LUX', 'Language': 'ita'},\n",
       " 'Most_Liked_Actor': ['LUXEMBOURG'],\n",
       " 'Most_Disliked_Actor': ['LUXEMBOURG'],\n",
       " 'Month': '10',\n",
       " 'Country': 'LUX',\n",
       " 'Language': 'ita'}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection = client.mention.req3\n",
    "collection.find_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Table pandas globale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list(collection.find())).drop(columns=['_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creation d'une table pour la requete 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Il y a 28638 lignes dans le tableau global de la requete 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Month</th>\n",
       "      <th>Most_Disliked_Actor</th>\n",
       "      <th>Most_Liked_Actor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUX</td>\n",
       "      <td>ita</td>\n",
       "      <td>10</td>\n",
       "      <td>[LUXEMBOURG]</td>\n",
       "      <td>[LUXEMBOURG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRA</td>\n",
       "      <td>hrv</td>\n",
       "      <td>01</td>\n",
       "      <td>[BRAZIL]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UKR</td>\n",
       "      <td>lav</td>\n",
       "      <td>01</td>\n",
       "      <td>[UKRAINE]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WST</td>\n",
       "      <td>fra</td>\n",
       "      <td>04</td>\n",
       "      <td>[NATO]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SEA</td>\n",
       "      <td>ind</td>\n",
       "      <td>08</td>\n",
       "      <td>[SOUTHEAST ASIA]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Language Month Most_Disliked_Actor Most_Liked_Actor\n",
       "0     LUX      ita    10        [LUXEMBOURG]     [LUXEMBOURG]\n",
       "1     BRA      hrv    01            [BRAZIL]               []\n",
       "2     UKR      lav    01           [UKRAINE]               []\n",
       "3     WST      fra    04              [NATO]               []\n",
       "4     SEA      ind    08    [SOUTHEAST ASIA]               []"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Il y a {len(df)} lignes dans le tableau global de la requete 1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtrage par pays\n",
      "\n",
      "     Country Language Month    Most_Disliked_Actor Most_Liked_Actor\n",
      "4        SEA      ind    08       [SOUTHEAST ASIA]               []\n",
      "450      SEA      deu    04       [SOUTHEAST ASIA]               []\n",
      "606      SEA      deu    02       [SOUTHEAST ASIA]               []\n",
      "990      SEA      fra    11           [POLYNESIAN]               []\n",
      "1231     SEA      ind    02  [HAMBALI, ZULKARNAEN]     [ZULKARNAEN]\n",
      "\n",
      "\n",
      "Filtrage par mois\n",
      "\n",
      "   Country Language Month Most_Disliked_Actor Most_Liked_Actor\n",
      "3      WST      fra    04              [NATO]               []\n",
      "5      IRL      pol    04           [IRELAND]               []\n",
      "15     TWN      ben    04            [TAIWAN]               []\n",
      "66     LVA      axe    04                  []         [LATVIA]\n",
      "95     ECU      ben    04           [ECUADOR]               []\n",
      "\n",
      "\n",
      "Filtrage par Langue\n",
      "\n",
      "    Country Language Month Most_Disliked_Actor Most_Liked_Actor\n",
      "9       SWE      ukr    09            [SWEDEN]               []\n",
      "62      NOR      ukr    10              [OSLO]               []\n",
      "80      IDN      ukr    10         [INDONESIA]               []\n",
      "84      THA      ukr    01          [THAILAND]               []\n",
      "110     PAK      ukr    03          [PAKISTAN]               []\n"
     ]
    }
   ],
   "source": [
    "### Possibilité de filtrer par Date, Actor ou Language\n",
    "##Par Actor\n",
    "print('Filtrage par pays\\n')\n",
    "print(df[df.Country == 'SEA'].head())\n",
    "print('\\n')\n",
    "##Par Date\n",
    "print('Filtrage par mois\\n')\n",
    "print(df[df.Month == '04'].head())\n",
    "print('\\n')\n",
    "##Par Langue\n",
    "print('Filtrage par Langue\\n')\n",
    "print(df[df.Language == 'ukr'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requete 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La requete 4 peut être accessible via la table prégénéré pour plus d'efficacité. Nous voulons néanmoins montrer que les requêtes sont également accessible depuis la table 'even.mention' général en y ajoutant des contraintes via la sous-fonction 'adding' pour filtrer les données en amont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MongoClient()\n",
    "collection = client.mention.data_mention\n",
    "req4 = Request(collection)\n",
    "#Avec adding on creer un filtre pour le mois de novembre\n",
    "req4.adding({\"$match\":{\"Month\":\"11\"}})\n",
    "req4.requete4('Month', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actor</th>\n",
       "      <th>Count_langue_neg</th>\n",
       "      <th>Count_langue_pos</th>\n",
       "      <th>Month</th>\n",
       "      <th>Most_Negatif_Topic</th>\n",
       "      <th>Most_Positif_Topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACTOR</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>[sqi, ron, lav, slv, ita, msa, kor, rus, swe, ...</td>\n",
       "      <td>[hun, tam, por, tur, hin, urd, srp, deu, mkd, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JAPAN</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>[ukr, por, tel, spa, ind, eng, rus, fra, mal, ...</td>\n",
       "      <td>[tam, msa, bos, ita, sqi, axe, kor, tur, srp, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UNITED KINGDOM</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>[guj, dan, nld, fra, ron, heb, kan, rus, swe, ...</td>\n",
       "      <td>[nor, hye, srp, bos, pol, lit, sqi, deu, urd]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRINCE</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>[eng, nep, tel, slv, spa, ell, ind, hun, kor]</td>\n",
       "      <td>[nor, ita, ara, deu, mkd, lit, fin, ron, cat, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WRITER</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>[deu, hin, axe, rus, msa, kan, pol, hrv, swe, ...</td>\n",
       "      <td>[lit, hye, mon, srp, bos, ukr, kor, ara]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PRESIDENT</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>[kat, cat, nep, slv, dan, guj, kaz, mar, ita, ...</td>\n",
       "      <td>[mon, por, srp, axe, ben, hun, hye]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ITALY</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>[por, ron, mkd, heb, rus, hrv, urd, slv, fra, ...</td>\n",
       "      <td>[hye, guj, mar, kor, hin, dan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PRIME MINISTER</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>[dan, ell, mal, mkd, nep, deu, urd, slk, tur, ...</td>\n",
       "      <td>[hun, hye, lit, ben, sqi, mon]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AMBASSADOR</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>[lit, ukr, ell, rus, eng, fra]</td>\n",
       "      <td>[dan, cat, ron, mkd, ara, tur, srp, ben, kor, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>INDUSTRY</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>[ell, fin, pol, bul, rus, ara, kor, hin, heb, ...</td>\n",
       "      <td>[ita, eng, ind, fra, deu, srp]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actor  Count_langue_neg  Count_langue_pos Month  \\\n",
       "0           ACTOR                21                10    11   \n",
       "1           JAPAN                10                10    11   \n",
       "2  UNITED KINGDOM                28                 9    11   \n",
       "3          PRINCE                 9                18    11   \n",
       "4          WRITER                13                 8    11   \n",
       "5       PRESIDENT                43                 7    11   \n",
       "6           ITALY                31                 6    11   \n",
       "7  PRIME MINISTER                29                 6    11   \n",
       "8      AMBASSADOR                 6                15    11   \n",
       "9        INDUSTRY                13                 6    11   \n",
       "\n",
       "                                  Most_Negatif_Topic  \\\n",
       "0  [sqi, ron, lav, slv, ita, msa, kor, rus, swe, ...   \n",
       "1  [ukr, por, tel, spa, ind, eng, rus, fra, mal, ...   \n",
       "2  [guj, dan, nld, fra, ron, heb, kan, rus, swe, ...   \n",
       "3      [eng, nep, tel, slv, spa, ell, ind, hun, kor]   \n",
       "4  [deu, hin, axe, rus, msa, kan, pol, hrv, swe, ...   \n",
       "5  [kat, cat, nep, slv, dan, guj, kaz, mar, ita, ...   \n",
       "6  [por, ron, mkd, heb, rus, hrv, urd, slv, fra, ...   \n",
       "7  [dan, ell, mal, mkd, nep, deu, urd, slk, tur, ...   \n",
       "8                     [lit, ukr, ell, rus, eng, fra]   \n",
       "9  [ell, fin, pol, bul, rus, ara, kor, hin, heb, ...   \n",
       "\n",
       "                                  Most_Positif_Topic  \n",
       "0  [hun, tam, por, tur, hin, urd, srp, deu, mkd, ...  \n",
       "1  [tam, msa, bos, ita, sqi, axe, kor, tur, srp, ...  \n",
       "2      [nor, hye, srp, bos, pol, lit, sqi, deu, urd]  \n",
       "3  [nor, ita, ara, deu, mkd, lit, fin, ron, cat, ...  \n",
       "4           [lit, hye, mon, srp, bos, ukr, kor, ara]  \n",
       "5                [mon, por, srp, axe, ben, hun, hye]  \n",
       "6                     [hye, guj, mar, kor, hin, dan]  \n",
       "7                     [hun, hye, lit, ben, sqi, mon]  \n",
       "8  [dan, cat, ron, mkd, ara, tur, srp, ben, kor, ...  \n",
       "9                     [ita, eng, ind, fra, deu, srp]  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#On accède au dataframe via l'attribut df\n",
    "req4.df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avec notre analyse, on retrouve effectivement certains sujet au coeurs des débats du mois de Novembre :\n",
    "UK/Prime Minister et son prexit, Italie avec sa politique européene. \n",
    "\n",
    "Certaines thématiques semblement également logiques, comme 'INDUSTRY' ou 'PRESIDENT'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requete 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La requete 2 a un temps d'execution raisonnable, on peut procéder à la requete directement en utilisant la class Request sans passer par la sauvegarde 'out'\n",
    "\n",
    "Nous allons montrer deux exemple : Argentina et Google"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "client = MongoClient()\n",
    "collection = client.mention.data_mention\n",
    "\n",
    "req = Request(collection)\n",
    "req.requete2('Argentina','2018-07-01',plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Event</th>\n",
       "      <th>Stakeholder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>807253059</td>\n",
       "      <td>UNITED STATES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>807185970</td>\n",
       "      <td>GERMANY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>807167789</td>\n",
       "      <td>WASHINGTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>807148167</td>\n",
       "      <td>NEW DELHI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>807147551</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Event    Stakeholder\n",
       "0 2018-12-03  807253059  UNITED STATES\n",
       "1 2018-12-03  807185970        GERMANY\n",
       "2 2018-12-03  807167789     WASHINGTON\n",
       "3 2018-12-03  807148167      NEW DELHI\n",
       "4 2018-12-03  807147551               "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##On peut acceder a la dataframe également via l'attribut df\n",
    "req.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "req = Request(client.mention.data_mention)\n",
    "req.requete2('GOOGLE','2018-08-01',plot=True,Filt='D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Requete 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "client = MongoClient()\n",
    "collection = client.gkg.data_gkg\n",
    "req5 = Request(collection)\n",
    "req5.requete5('France', 'Germany')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "client = MongoClient()\n",
    "collection = client.gkg.data_gkg\n",
    "req5 = Request(collection)\n",
    "req5.requete5('Germany', 'China')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average_Tone</th>\n",
       "      <th>Moving_Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>-1.010719</td>\n",
       "      <td>-1.019003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>-1.109712</td>\n",
       "      <td>-1.018110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>-1.112416</td>\n",
       "      <td>-1.009350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>-0.980915</td>\n",
       "      <td>-0.991360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-03</th>\n",
       "      <td>-1.120084</td>\n",
       "      <td>-0.987275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Average_Tone  Moving_Mean\n",
       "Date                                 \n",
       "2018-01-30     -1.010719    -1.019003\n",
       "2018-01-31     -1.109712    -1.018110\n",
       "2018-02-01     -1.112416    -1.009350\n",
       "2018-02-02     -0.980915    -0.991360\n",
       "2018-02-03     -1.120084    -0.987275"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comme d'habitude, la dataframe est stocké avec les arguments dans l'attribut df\n",
    "req5.df.dropna().head()"
   ]
  }
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