amlo_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "from keras.preprocessing import text\r\n",
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "from datetime import date, datetime\r\n",
    "import csv\r\n",
    "import re\r\n",
    "import string\r\n",
    "import pickle\r\n",
    "import nltk\r\n",
    "from gensim.models.fasttext import FastText\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "from time import sleep\r\n",
    "from wordcloud import WordCloud\r\n",
    "from sklearn.manifold import TSNE\r\n",
    "import matplotlib.cm as cm\r\n",
    "from mpl_toolkits.mplot3d import Axes3D\r\n",
    "from matplotlib.axes._axes import _log as matplotlib_axes_logger\r\n",
    "import time\r\n",
    "matplotlib_axes_logger.setLevel('ERROR')\r\n",
    "\r\n",
    "pd.set_option('display.max_rows', None)\r\n",
    "import warnings\r\n",
    "import matplotlib.cbook\r\n",
    "warnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    " nltk.download('stopwords')\r\n",
    " nltk.download('punkt')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\ramse\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\ramse\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "df = pd.read_csv('C:/Users/ramse/Downloads/amlo_speechs.csv')\r\n",
    "columns = ['id_speech', 'date','title','url','content']\r\n",
    "df = df[columns]\r\n",
    "df['content'] = df['title'] + ' ' +  df['content']\r\n",
    "df = df[df['content'].notna()]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "aditional_stop_words = ['fotogalería','fotografía', 'foto','conocer', 'diciendo', 'varias', 'grandes', 'pasado', 'trata', 'hicieron', 'haremos', 'complementado', 'complementando', 'ejemplo','méxico', 'aplicara', 'requiere', 'requieren', 'poner', 'ponemos', 'seguimiento', 'establecer', 'establecimos', 'pedazo', 'reeditar', 'significa', 'conducida', 'afirmó','llamó','detalló','destacó','presentó','inició','adelantó','terminó','planteó','sucedió','empezó','entregó','logró','reiteró','acabó','decidió','salió','compró','resaltó','prosiguió','respondió','aclaró','enfatizó','aprobó','creó','comprometió','recibió','remarcó','publicó','encabezó','convocó','puntualizó','aumentó','denunció','agradeció','creció','tocó','ganó','abandonó','ayudó','estableció','alertó','comenzó','contestó','firmó','visitó','consideró','envió','nació','cambió','invitó','costó','cayó','reconoció','declaró','reunió','ocurrió','calificó','ofreció','reafirmó','construyó','permitió','participó','lamentó','mandó','precisó','bajó','cumplió','convirtió','utilizó','concedió','apuntó','recomendó','aseveró','avanzó','refirió','registró','advirtió','concluyó','solicitó','entró','significó','desató','canceló','perdió','volvió','vendió','acordó','aplicó','resolvió','celebró','votó','negó','afectó','escribió','refrendó','aceptó','surgió','abundó','cuestionó','instruyó','reformó','pagó','trató','ejemplificó','resultó','opinó','siguió','generó','rememoró','actuó','subió','encontró','apoyó','demostró','describió','autorizó','alcanzó','sacó','debió','cometió','desapareció','abrió','funcionó','asistió','fundó','incrementó','causó','pensó','ordenó','murió','continuó','robó','echó','emitió','gustó','enfrentó','criticó','otorgó','argumentó','atendió','cerró','confió','padeció','nombró','disminuyó','trabajó','observó','rechazó','adquirió','modificó','lanzó','originó','inauguró','falleció','dedicó','recalcó','reportó','garantizó','olvidó','gobernó','mostró','regresó','faltó','sugirió','acudió','formó','sirvió','resistió','acompañó','definió','vivió','conoció','pronunció','amplió','estimó','ejerció','quitó','dirigió','citó','finalizó','destinó','luchó','contrató','exhortó','recuperó','escuchó','leyó','platicó','prometió','atrevió','elevó','usó','determinó','transmitió','eligió','metió','duró','pegó','triunfó','pronosticó','contó','acotó','confirmó','felicitó','manejó','integró','privatizó','recorrió','apareció','buscó','trasladó','tardó','heredó','constituyó','portó','instaló','detectó','consistió','sufrió','aportó','desarrolló','comunicó','impulsó','pintó','exigió','difundió','profundizó','aprovechó','incluyó','contribuyó','externó','pareció','estudió','enteró','evitó','rebasó','suscribió','engañó','elaboró','benefició','levantó','separó','importó','entendió','optó','llovió','falló','paró','promovió','provocó','inventó','apostó','agravó','comparó','identificó','acusó','dominó','endeudó','vacunó','imperó','implementó','chenalhó','abordó','condenó','organizó','aguantó','implicó','encargó','enumeró','coincidió','representó','traicionó','sonó','consiguió','extendió','demandó','rompió','equivocó','cobró','rindió','existió','compartió','consultó','arrancó','mintió','comprobó','asumió','precipitó','eliminó','violó','protegió','liberó','fortaleció','respaldó','fijó','descartó','viajó','saqueó','suspendió','rescató','renunció','intentó','defendió','deseó','proporcionó','colocó','ingresó','proclamó','supervisó','urgió','asignó','prevaleció','calculó','montó','predominó','previó','cubrió','correspondió','acuñó','constató','diseñó','empobreció','resumió','fracasó','repitió','respetó','anticipó','escogió','saludó','invadió','donó','dolió','ocupó','acercó','reveló','molestó','sometió','sumó','dañó','desempeñó','financió','descubrió','derivó','ocasionó','caracterizó','despertó','revisó','alentó','desplomó','entrevistó','confesó','estancó','salvó','preocupó','sintió','negoció','sorprendió','retomó','evaluó','dictó','obligó','expulsó','vivó','estabilizó','pudrió','aplaudió','operó','regaló','arribó','desatendió','giró','contempló','incorporó','fungió','retiró','ratificó','brindó','impidió','coció','venció','prestó','atestiguó','filtró','concentró','restauró','disparó','encomendó','mejoró','devolvió','superó','corrompió','notificó','grabó','deslindó','nacionalizó','exoneró','abusó','pactó','analizó','distribuyó','probó','especificó','ascendió','permaneció','dividió','concretó','esclavizó','relató','marcó','corrió','consumó','despidió','prohibió','cuidó','destruyó','transformó','enseñó','desmintió','corrigió','emprendió','agarró','convenció','caminó','llenó','investigó','avaló','fabricó','condonó','procedió','interesó','polarizó','designó','intensificó','toleró','asesinó','cortó','transfirió','empleó','inundó','declinó','repartió','lavó','jugó','desplegó','interrumpió','unió','soltó','licitó','desechó','expropió','requirió','arraigó','golpeó','anotó','postuló','desvió','valió','abolió','motivó','auguró','amenazó','alió','simuló','planeó','incumplió','pompó','movió','influyó','circuló','perjudicó','conformó','conversó','transcurrió','promedió','adoptó','promulgó','protestó','admitió','traficó','depositó','arregló','reclamó','empeoró','conmemoró','edificó','castigó','escapó','coordinó','preparó','descuidó','ocultó','reforzó','politizó','impactó','recurrió','desmanteló','enriqueció','reflexionó','frenó','guardó','fugó','depreció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'todo', 'voy', 'luego', 'ustedes', 'mucha', 'muchas', 'parte', 'buenos', 'años', 'puede', 'pueden', 'puedes', 'manifestó', 'medio', 'hace', 'tres', 'manera', 'expresó', 'explicó', 'cosas', 'cosa', 'acuerdo', 'comentó', 'ayer', 'momento', 'ir', 'poder', 'siempre', 'ademas', 'además', 'nueva', 'nuevas', 'nuevos', 'tiempo', 'hizo', 'cada', 'cuatro','haciendo', 'todavia', 'todavía', 'haber', 'cada', 'cabo', 'arriba', 'quieren', 'mañana', 'expuso', 'asunto', 'despues', 'después', 'uploads', 'https', 'content', 'boletin', 'aseguró', 'boletín', 'hecho', 'indicó', 'informo', 'informó', 'org', 'wp', 'audio', 'the', 'and', 'dar', 'seguir', 'sido', 'gran', 'ello', 'mayor', 'veces', 'éramos', 'ésa','ésas','ése','ésos','ésta','éstas','éste','éstos','última','últimas','último','últimos', 'subrayó', 'nadie', 'ni', 'ningunas', 'ninguna', 'ninguno', 'ningunos', 'ningún', 'mejor', 'mencionó', 'habló','misma','toca','quiénes','que','quedó','queremos','quien','quienes','quiere','quiza','quizas','quizá','quizás','quién','quiénes','qué', 'quiero', 'lugar', 'creo', 'menos', 'claro','cinco','debe','deben','vez','enero', 'febrero', 'marzo','abril','mayo', 'junio', 'julio','agosto', 'septiembre','octubre','noviembre','diciembre', 'aquel','aquella','aquellas','aquello','aquellos','aqui','aquél','aquélla','aquéllas','aquéllos','aquí', 'hacia','da','dado','dan','dar','de', 'buen', 'pregunta']\r\n",
    "stop_words = nltk.corpus.stopwords.words('spanish')\r\n",
    "stop_words.extend(aditional_stop_words)\r\n",
    "stop_words = set(stop_words)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "ner_cambios_dict = {'organización naciones unidas':'onu','naciones unidas': 'onu','comisión federal de electricidad':'cfe','instituto mexicano del seguro social': 'imss',\r\n",
    "                  'petróleos mexicanos': 'pemex','petroleos mexicanos': 'pemex','petróleos de méxico': 'pemex',\r\n",
    "                  'procuraduría federal del consumidor':'profeco','servicio de administración tributaria':'sat','organización mundial de la salud': 'oms','secretaría de educación pública':'sep',\r\n",
    "                  'instituto de salud para el bienestar':'insabi','instituto nacional de estadística y geografía':'inegi','consejo nacional de ciencia y tecnología':'conacyt',\r\n",
    "                  'baja california': 'bajacalifornia','ciudad de méxico':'cdmx','distrito federal': 'cdmx','estado de méxico': 'edomex','nuevo león': 'nuevoleón','quintana roo':'quintanaroo','san luis potosí': 'sanluispotosí',\r\n",
    "                    'coronavirus': 'covid','sars cov': 'covid','sars covid': 'covid','sars': 'covid',\r\n",
    "                    'vacunación':'vacuna','vacunados':'vacuna','vacunaremos':'vacuna','vacunamos':'vacuna','vacunándonos':'vacuna','vacunadores':'vacuna','vacunarlos':'vacuna','vacunarlos':'vacuna','vacunen':'vacuna','vacunará':'vacuna','vacuno':'vacuna','vacunar':'vacuna','vacuné':'vacuna','vacunas':'vacuna','vacunan':'vacuna', 'vacunal' :'vacuna','vacunado':'vacuna', 'vacunarse':'vacuna','vacunadas':'vacuna','vacunara':'vacuna','vacunando':'vacuna','vacunales':'vacuna','vacunador':'vacuna','vacunaron':'vacuna','vacune' :'vacuna','vacunemos':'vacuna', 'vacunada':'vacuna',\r\n",
    "                        'carlos salinas de gortari': 'salinas',\r\n",
    "                        'carlos de gortari': 'salinas',\r\n",
    "                        'carlos salinas': 'salinas',\r\n",
    "                        'donald john trump':'trump',\r\n",
    "                        'donald trump':'trump',\r\n",
    "                        'barack hussein obama': 'obama',\r\n",
    "                        'barack obama': 'obama',\r\n",
    "                        'ernesto zedillo ponce de león':'zedillo',\r\n",
    "                        'ernesto zedillo':'zedillo',\r\n",
    "                  'luis videgaray caso':'videgaray',\r\n",
    "                  'luis videgaray':'videgaray',\r\n",
    "                  'emilio lozoya austin':'lozoya',\r\n",
    "                  'emilio lozoya':'lozoya',\r\n",
    "                  'enrique peña nieto': 'peñanieto',\r\n",
    "                  'enrique peña': 'peñanieto',\r\n",
    "                  'peña nieto': 'peñanieto',\r\n",
    "                  'peña': 'peñanieto',\r\n",
    "                  'ricardo anaya cortés': 'anaya',\r\n",
    "                  'ricardo anaya': 'anaya',\r\n",
    "                  'josé antonio meade kuribreña':'meade',\r\n",
    "                  'josé antonio meade':'meade',\r\n",
    "                  'antonio meade':'meade',\r\n",
    "                  'vicente fox quesada':'fox',\r\n",
    "                  'vicente fox': 'fox',\r\n",
    "                  'felipe de jesús calderón hinojosa​​': 'calderon',\r\n",
    "                  'felipe calderón hinojosa​​': 'calderon',\r\n",
    "                  'felipe calderón': 'calderon',\r\n",
    "                  'margarita ester zavala gómez':'margarita',\r\n",
    "                  'margarita zavala gómez':'margarita',\r\n",
    "                  'margarita zavala':'margarita',\r\n",
    "                  'claudia sheinbaum pardo':'sheinbaum',\r\n",
    "                  'claudia sheinbaum':'sheinbaum',\r\n",
    "                  'marcelo luis ebrard casaubón':'ebrard',\r\n",
    "                  'marcelo luis ebrard':'ebrard',\r\n",
    "                  'luis ebrard':'ebrard',                \r\n",
    "                  'marcelo ebrard':'ebrard',\r\n",
    "                  'ricardo monreal ávila':'monreal',\r\n",
    "                  'ricardo monreal':'monreal',\r\n",
    "                  'tatiana clouthier carrillo': 'tclouthier',\r\n",
    "                  'tatiana clouthier': 'tclouthier',                  \r\n",
    "                  'manuel jesús clouthier carrillo': 'mclouthier',                  \r\n",
    "                  'manuel clouthier': 'mclouthier',\r\n",
    "                  'clouthier carrillo': 'clouthier',\r\n",
    "                  'miguel ángel osorio chong': 'chong',\r\n",
    "                  'osorio chong': 'chong',\r\n",
    "                  'miguel ángel yunes linares': 'yunes',\r\n",
    "                  'elba esther gordillo':'gordillo',\r\n",
    "                  'esther gordillo':'gordillo',\r\n",
    "                  'beatriz gutiérrez mueller': 'mueller',\r\n",
    "                  'beatriz mueller': 'mueller',\r\n",
    "                  'partido revolucionario institucional':'pri',\r\n",
    "                  'partido acción nacional': 'pan',                  \r\n",
    "                  'partido de la revolución democrática': 'prd',\r\n",
    "                  'instituto nacional electoral': 'ine',\r\n",
    "                  'instituto nacional de migración':'inm',\r\n",
    "                  'instituto mexicano del seguro social':'imss',\r\n",
    "                  'comisión nacional de los derechos humanos':'cndh',\r\n",
    "                  'comisión de los derechos humanos':'cndh',\r\n",
    "                  'comisión de derechos humanos':'cndh',\r\n",
    "                  'comisión derechos humanos':'cndh',\r\n",
    "                  'derechos humanos':'cndh',                \r\n",
    "                  'corruptos':'corrupción',\r\n",
    "                  'corrupto':'corrupción',\r\n",
    "                  'migrantes': 'migración',\r\n",
    "                  'migrante': 'migración',\r\n",
    "                  'migratorias': 'migración',\r\n",
    "                  'migratoria': 'migración',\r\n",
    "                  'migratorios':'migración', \r\n",
    "                  'migratorio':'migración',                  \r\n",
    "                  'hospitalizados': 'enfermos',\r\n",
    "                  'hospitales':'hospital',\r\n",
    "                  'hospitalarios': 'hospital',\r\n",
    "                  'hospitalario': 'hospital',\r\n",
    "                  'hospitalarias': 'hospital',                          \r\n",
    "                  'hospitalaria': 'hospital',\r\n",
    "                  'económicas':'economía',\r\n",
    "                  'económica':'economía',\r\n",
    "                  'económicos': 'economía',\r\n",
    "                  'económico': 'economía',\r\n",
    "                  'economías': 'economía',\r\n",
    "                  'económicamente': 'economía',                      \r\n",
    "                  'remesadoras': 'remesas',\r\n",
    "                  'remesadora': 'remesas',\r\n",
    "                  'remesa': 'remesas',\r\n",
    "                  'estudiantes':'estudiante',                  \r\n",
    "                  'alumnos':'estudiante',\r\n",
    "                  'alumno':'estudiante',\r\n",
    "                  'reforma educativa':'reformaeducativa',\r\n",
    "                  'educativos':'educación',\r\n",
    "                  'educativo':'educación',\r\n",
    "                  'educar':'educación',\r\n",
    "                  'escuelas':'escuela',\r\n",
    "                  'universidades':'universidad', \r\n",
    "                  'becas':'beca',\r\n",
    "                  'canasta básica':'canastabásica',\r\n",
    "                  'alimentaria':'alimentación', \r\n",
    "                   'alimentar':'alimentación',\r\n",
    "                  'alimentos':'alimentación',\r\n",
    "                  'alimento':'alimentación',\r\n",
    "                  'alimentarios':'alimentación', \r\n",
    "                  'alimentario': 'alimentación',\r\n",
    "                  'alimentando':'alimentación',\r\n",
    "                  'alimentados': 'alimentación',\r\n",
    "                  'alimentarse': 'alimentación',\r\n",
    "                  'alimentaban': 'alimentación',\r\n",
    "                  'alimentarias': 'alimentación',\r\n",
    "                  'alimentarnos': 'alimentación',\r\n",
    "                  'alimenticias': 'alimentación',\r\n",
    "                  'alimenticio': 'alimentación',                                   \r\n",
    "                  'privatizando': 'privatización',\r\n",
    "                  'alimentado': 'alimentación',\r\n",
    "                  'alimentan': 'alimentación',\r\n",
    "                  'alimenta': 'alimentación',                  \r\n",
    "                  'impuestos': 'impuesto',\r\n",
    "                   'ahorraremos':'ahorro',\r\n",
    "                   'ahorramos':'ahorro',\r\n",
    "                   'ahorré':'ahorro',\r\n",
    "                   'ahorrar':'ahorro',\r\n",
    "                   'gastamos':'gasto',\r\n",
    "                   'gastaremos':'gasto',\r\n",
    "                   'gastó':'gasto',\r\n",
    "                   'sueldos':'salarios',\r\n",
    "                   'sueldo':'salarios',\r\n",
    "                   'invirtió':'inversión',\r\n",
    "                   'invertir':'inversión',\r\n",
    "                   'invertiremos':'inversión',\r\n",
    "                   'invertimos':'inversión',\r\n",
    "                   'reforma energética':'energética',\r\n",
    "                   'energetica':'energética',\r\n",
    "                   'energetica':'energética',\r\n",
    "                   'petróleos':'petróleo',\r\n",
    "                   'electricidad':'energética',\r\n",
    "                   'energía eléctrica':'energética',\r\n",
    "                   'eléctricos':'energética',\r\n",
    "                   'eléctricas':'energética',\r\n",
    "                   'eléctrica':'energética',\r\n",
    "                   'eléctrico':'energética',\r\n",
    "                   'energía':'energética',\r\n",
    "                   'gasolineras':'gasolina',\r\n",
    "                   'gasolinera':'gasolina',\r\n",
    "                   'gasolinazo':'gasolina',\r\n",
    "                   'combustibles':'combustible',\r\n",
    "                   'enfermos':'enfermedad',\r\n",
    "                   'enfermedades':'enfermedad',\r\n",
    "                   'pacientes':'enfermedad',\r\n",
    "                   'paciente':'enfermedad',                   \r\n",
    "                   'contagios':'contagio',\r\n",
    "                   'contagiados':'contagio',\r\n",
    "                   'contagiado':'contagio',\r\n",
    "                   'contagiar':'contagio',\r\n",
    "                   'infecciónes':'infección',\r\n",
    "                   'infectar':'infección',\r\n",
    "                   'medicinas':'medicina',\r\n",
    "                   'medicamentos':'medicina',\r\n",
    "                   'médicos':'médico',               \r\n",
    "                   'doctores':'médico',\r\n",
    "                   'clínicas':'hospital',\r\n",
    "                   'clínica':'hospital',\r\n",
    "                   'drogas':'narcotráfico',\r\n",
    "                   'droga':'narcotráfico',\r\n",
    "                   'narco':'narcotráfico',\r\n",
    "                   'cártel':'narcotráfico', \r\n",
    "                   'cartel':'narcotráfico',\r\n",
    "                   'narcotráficotráfico':'narcotráfico',\r\n",
    "                   'pueblos indígenas': 'indígenas',\r\n",
    "                   'indígena': 'indígenas',\r\n",
    "                   'avión presidencial': 'avión',\r\n",
    "                   'tren maya': 'tren',\r\n",
    "                   'homicidios': 'homicidio',\r\n",
    "                   'asesinatos': 'homicidio',\r\n",
    "                   'asesinato': 'homicidio',                   \r\n",
    "                   'extorsiónadores':'extorsión',\r\n",
    "                   'extorsionados':'extorsión',\r\n",
    "                   'extorsionado':'extorsión',\r\n",
    "                   'extorsionadas':'extorsión',\r\n",
    "                   'extorsionada':'extorsión',\r\n",
    "                   'extorsionar':'extorsión',\r\n",
    "                   'extorsionando':'extorsión',\r\n",
    "                   'extorsiones':'extorsión',\r\n",
    "                   'extorsiona':'extorsión',                  \r\n",
    "                   'sobornos':'soborno',                        \r\n",
    "                   'mordidas':'soborno',\r\n",
    "                   'mordida':'soborno',\r\n",
    "                   'cohecho':'soborno',\r\n",
    "                   'delincuenciales': 'delincuencia',\r\n",
    "                   'delincuencial':'delincuencia',\r\n",
    "                   'delincuentes': 'delincuencia',\r\n",
    "                   'delincuente': 'delincuencia',\r\n",
    "                   'desapariciones':'desaparición',\r\n",
    "                   'desaparecidos':'desaparición',\r\n",
    "                   'secuestradores':'secuestro',\r\n",
    "                   'secuestrados':'secuestro',\r\n",
    "                   'secuestros': 'secuestro',\r\n",
    "                   'trata de personas': 'trata',\r\n",
    "                   'huachicoleros':'huachicol',\r\n",
    "                   'huachicolero':'huachicol',\r\n",
    "                   'huachicoleo': 'huachicol',\r\n",
    "                   'guachicoleros': 'huachicol',\r\n",
    "                   'guachicolero': 'huachicol',\r\n",
    "                   'guachicoleo': 'huachicol',\r\n",
    "                   'guachicol': 'huachicol',\r\n",
    "                   'cuarta transformación': '4t',\r\n",
    "                   'crecimientos':'crecimiento',\r\n",
    "                   'basificaciones':'basificación', \r\n",
    "                   'basificarlos':'basificación',\r\n",
    "                   'basificados':'basificación',\r\n",
    "                   'basificar': 'basificación',\r\n",
    "                   'estanca': 'estancamiento',\r\n",
    "                   'deportando': 'deportación',\r\n",
    "                   'deportaciones': 'deportación',\r\n",
    "                   'deportados': 'deportación'\r\n",
    "                  }"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "def clean_text_step_1(text):\r\n",
    "    text = text.lower()\r\n",
    "    text = re.sub('\\[.*?¿\\]\\%', ' ', text)\r\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), ' ', text)\r\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\r\n",
    "    return text\r\n",
    "\r\n",
    "def clean_text_step_2(text):\r\n",
    "    text = re.sub('[‘’“”…«»]', '', text)\r\n",
    "    text = re.sub('\\n', ' ', text)\r\n",
    "    return text\r\n",
    "     \r\n",
    "step_1 = lambda x: clean_text_step_1(x)\r\n",
    "df.content = pd.DataFrame(df.content.apply(step_1))\r\n",
    "step_2 = lambda x: clean_text_step_2(x)\r\n",
    "df.content = pd.DataFrame(df.content.apply(step_2))\r\n",
    "\r\n",
    "def normalize_corpus(d):\r\n",
    "    for k in ner_cambios_dict:\r\n",
    "        d = d.replace(' ' + k + ' ' , ' ' + ner_cambios_dict[k] + ' ')\r\n",
    "    \r\n",
    "    tks = nltk.word_tokenize(d)\r\n",
    "    tks = [re.sub(r\"[–¿¡—·]\", '', t) for t in tks]\r\n",
    "\r\n",
    "    f_tks = [t for t in tks if t not in stop_words]\r\n",
    "    return ' '.join(f_tks)\r\n",
    "\r\n",
    "def preprocessing_text(corpus):\r\n",
    "    n_c = np.vectorize(normalize_corpus)\r\n",
    "    return n_c(corpus)\r\n",
    "\r\n",
    "step_3 = lambda x: preprocessing_text(x)\r\n",
    "df.content = pd.DataFrame(df.content.apply(step_3))\r\n",
    "\r\n",
    "df = df[[\"date\", \"content\"]]\r\n",
    "df['year'] = pd.DatetimeIndex(df['date']).year\r\n",
    "df['month'] = pd.DatetimeIndex(df['date']).month\r\n",
    "\r\n",
    "df_cleaned = df[[\"date\", \"year\", \"month\", \"content\"]]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "years = list(range(2011, 2022))\r\n",
    "months = list(range(1, 13))\r\n",
    "speechs_per_year = []\r\n",
    "words_per_speech = []\r\n",
    "total_words = []\r\n",
    "unique_words = []\r\n",
    "date = []\r\n",
    "ny = []\r\n",
    "nm = []\r\n",
    "\r\n",
    "for iny, y in enumerate(years):\r\n",
    "    for inm, m in enumerate(months):\r\n",
    "        values = df_cleaned.loc[(df_cleaned['year'] == y) & (df_cleaned['month'] == m) ].content.values\r\n",
    "        speechs = len(values) + 1\r\n",
    "        all_words = ' '.join(values)\r\n",
    "        words = len(all_words.split())\r\n",
    "        uniques = len(set(all_words.split()))\r\n",
    "        wps = words / speechs\r\n",
    "        speechs_per_year.append(speechs)\r\n",
    "        words_per_speech.append(wps)\r\n",
    "        total_words.append(words)\r\n",
    "        unique_words.append(uniques)\r\n",
    "        date.append(str(y) + '-' + str(m))\r\n",
    "        ny.append(y)\r\n",
    "        nm.append(m)\r\n",
    "\r\n",
    "amlo_words = pd.DataFrame()\r\n",
    "amlo_words['year'] = ny\r\n",
    "amlo_words['month'] = nm\r\n",
    "amlo_words['date'] = date\r\n",
    "amlo_words['total_words'] = total_words\r\n",
    "amlo_words['unique_words'] = unique_words\r\n",
    "amlo_words['speechs_per_month'] = speechs_per_year\r\n",
    "amlo_words['words_per_speech'] = words_per_speech\r\n",
    "\r\n",
    "amlo_words.head(200)\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>date</th>\n",
       "      <th>total_words</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>2011-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>1904</td>\n",
       "      <td>907</td>\n",
       "      <td>17</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-1</td>\n",
       "      <td>9036</td>\n",
       "      <td>3007</td>\n",
       "      <td>62</td>\n",
       "      <td>145.741935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>2012-2</td>\n",
       "      <td>6388</td>\n",
       "      <td>2598</td>\n",
       "      <td>38</td>\n",
       "      <td>168.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-3</td>\n",
       "      <td>6835</td>\n",
       "      <td>2652</td>\n",
       "      <td>48</td>\n",
       "      <td>142.395833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-4</td>\n",
       "      <td>21137</td>\n",
       "      <td>5392</td>\n",
       "      <td>123</td>\n",
       "      <td>171.845528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-5</td>\n",
       "      <td>18483</td>\n",
       "      <td>4959</td>\n",
       "      <td>164</td>\n",
       "      <td>112.701220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>2012-6</td>\n",
       "      <td>24862</td>\n",
       "      <td>5522</td>\n",
       "      <td>142</td>\n",
       "      <td>175.084507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>2012-7</td>\n",
       "      <td>10990</td>\n",
       "      <td>3308</td>\n",
       "      <td>55</td>\n",
       "      <td>199.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>2012-8</td>\n",
       "      <td>10380</td>\n",
       "      <td>3073</td>\n",
       "      <td>41</td>\n",
       "      <td>253.170732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>2012-9</td>\n",
       "      <td>5525</td>\n",
       "      <td>2669</td>\n",
       "      <td>23</td>\n",
       "      <td>240.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>2012-10</td>\n",
       "      <td>18320</td>\n",
       "      <td>4036</td>\n",
       "      <td>89</td>\n",
       "      <td>205.842697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-11</td>\n",
       "      <td>12217</td>\n",
       "      <td>3444</td>\n",
       "      <td>62</td>\n",
       "      <td>197.048387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>945</td>\n",
       "      <td>659</td>\n",
       "      <td>5</td>\n",
       "      <td>189.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-1</td>\n",
       "      <td>17070</td>\n",
       "      <td>4036</td>\n",
       "      <td>95</td>\n",
       "      <td>179.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-2</td>\n",
       "      <td>20234</td>\n",
       "      <td>4075</td>\n",
       "      <td>136</td>\n",
       "      <td>148.779412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-3</td>\n",
       "      <td>20196</td>\n",
       "      <td>3817</td>\n",
       "      <td>93</td>\n",
       "      <td>217.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>2013-4</td>\n",
       "      <td>11960</td>\n",
       "      <td>2721</td>\n",
       "      <td>76</td>\n",
       "      <td>157.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>2013-5</td>\n",
       "      <td>10638</td>\n",
       "      <td>2429</td>\n",
       "      <td>64</td>\n",
       "      <td>166.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>2013-6</td>\n",
       "      <td>14433</td>\n",
       "      <td>3471</td>\n",
       "      <td>53</td>\n",
       "      <td>272.320755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>2013-7</td>\n",
       "      <td>5215</td>\n",
       "      <td>1792</td>\n",
       "      <td>26</td>\n",
       "      <td>200.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>2013-8</td>\n",
       "      <td>15891</td>\n",
       "      <td>3749</td>\n",
       "      <td>59</td>\n",
       "      <td>269.338983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>2013-9</td>\n",
       "      <td>12448</td>\n",
       "      <td>3361</td>\n",
       "      <td>50</td>\n",
       "      <td>248.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-10</td>\n",
       "      <td>9563</td>\n",
       "      <td>2927</td>\n",
       "      <td>40</td>\n",
       "      <td>239.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>2013-11</td>\n",
       "      <td>5112</td>\n",
       "      <td>1958</td>\n",
       "      <td>22</td>\n",
       "      <td>232.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>2013-12</td>\n",
       "      <td>3101</td>\n",
       "      <td>1325</td>\n",
       "      <td>35</td>\n",
       "      <td>88.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-1</td>\n",
       "      <td>7798</td>\n",
       "      <td>2525</td>\n",
       "      <td>27</td>\n",
       "      <td>288.814815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-2</td>\n",
       "      <td>8326</td>\n",
       "      <td>2728</td>\n",
       "      <td>28</td>\n",
       "      <td>297.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-3</td>\n",
       "      <td>13342</td>\n",
       "      <td>3568</td>\n",
       "      <td>36</td>\n",
       "      <td>370.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-4</td>\n",
       "      <td>8442</td>\n",
       "      <td>2579</td>\n",
       "      <td>23</td>\n",
       "      <td>367.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-5</td>\n",
       "      <td>11380</td>\n",
       "      <td>3127</td>\n",
       "      <td>30</td>\n",
       "      <td>379.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-6</td>\n",
       "      <td>12454</td>\n",
       "      <td>3201</td>\n",
       "      <td>27</td>\n",
       "      <td>461.259259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>2014-7</td>\n",
       "      <td>11460</td>\n",
       "      <td>3294</td>\n",
       "      <td>28</td>\n",
       "      <td>409.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>2014-8</td>\n",
       "      <td>8168</td>\n",
       "      <td>2590</td>\n",
       "      <td>22</td>\n",
       "      <td>371.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2014</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-9</td>\n",
       "      <td>12320</td>\n",
       "      <td>3375</td>\n",
       "      <td>30</td>\n",
       "      <td>410.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>2014-10</td>\n",
       "      <td>16163</td>\n",
       "      <td>4455</td>\n",
       "      <td>41</td>\n",
       "      <td>394.219512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>14584</td>\n",
       "      <td>3894</td>\n",
       "      <td>33</td>\n",
       "      <td>441.939394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>7949</td>\n",
       "      <td>2476</td>\n",
       "      <td>21</td>\n",
       "      <td>378.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-1</td>\n",
       "      <td>13771</td>\n",
       "      <td>3456</td>\n",
       "      <td>25</td>\n",
       "      <td>550.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-2</td>\n",
       "      <td>12205</td>\n",
       "      <td>3364</td>\n",
       "      <td>28</td>\n",
       "      <td>435.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-3</td>\n",
       "      <td>11649</td>\n",
       "      <td>3193</td>\n",
       "      <td>26</td>\n",
       "      <td>448.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-4</td>\n",
       "      <td>18419</td>\n",
       "      <td>4642</td>\n",
       "      <td>33</td>\n",
       "      <td>558.151515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-5</td>\n",
       "      <td>24118</td>\n",
       "      <td>4931</td>\n",
       "      <td>40</td>\n",
       "      <td>602.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-6</td>\n",
       "      <td>5820</td>\n",
       "      <td>1927</td>\n",
       "      <td>18</td>\n",
       "      <td>323.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-7</td>\n",
       "      <td>10579</td>\n",
       "      <td>3044</td>\n",
       "      <td>27</td>\n",
       "      <td>391.814815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-8</td>\n",
       "      <td>8203</td>\n",
       "      <td>2637</td>\n",
       "      <td>19</td>\n",
       "      <td>431.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-9</td>\n",
       "      <td>11684</td>\n",
       "      <td>3361</td>\n",
       "      <td>29</td>\n",
       "      <td>402.896552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>11936</td>\n",
       "      <td>3473</td>\n",
       "      <td>32</td>\n",
       "      <td>373.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>2015-11</td>\n",
       "      <td>14593</td>\n",
       "      <td>3764</td>\n",
       "      <td>40</td>\n",
       "      <td>364.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-12</td>\n",
       "      <td>9727</td>\n",
       "      <td>2864</td>\n",
       "      <td>22</td>\n",
       "      <td>442.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-1</td>\n",
       "      <td>15247</td>\n",
       "      <td>3622</td>\n",
       "      <td>33</td>\n",
       "      <td>462.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-2</td>\n",
       "      <td>17585</td>\n",
       "      <td>4056</td>\n",
       "      <td>38</td>\n",
       "      <td>462.763158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-3</td>\n",
       "      <td>15218</td>\n",
       "      <td>3890</td>\n",
       "      <td>38</td>\n",
       "      <td>400.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-4</td>\n",
       "      <td>21398</td>\n",
       "      <td>4696</td>\n",
       "      <td>45</td>\n",
       "      <td>475.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-5</td>\n",
       "      <td>23612</td>\n",
       "      <td>5047</td>\n",
       "      <td>50</td>\n",
       "      <td>472.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-6</td>\n",
       "      <td>4660</td>\n",
       "      <td>1876</td>\n",
       "      <td>14</td>\n",
       "      <td>332.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-7</td>\n",
       "      <td>1668</td>\n",
       "      <td>845</td>\n",
       "      <td>6</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-8</td>\n",
       "      <td>10486</td>\n",
       "      <td>3254</td>\n",
       "      <td>23</td>\n",
       "      <td>455.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>2016-9</td>\n",
       "      <td>10708</td>\n",
       "      <td>3245</td>\n",
       "      <td>31</td>\n",
       "      <td>345.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>9487</td>\n",
       "      <td>2906</td>\n",
       "      <td>29</td>\n",
       "      <td>327.137931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>11543</td>\n",
       "      <td>3448</td>\n",
       "      <td>25</td>\n",
       "      <td>461.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>2982</td>\n",
       "      <td>1282</td>\n",
       "      <td>11</td>\n",
       "      <td>271.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-1</td>\n",
       "      <td>10385</td>\n",
       "      <td>2976</td>\n",
       "      <td>33</td>\n",
       "      <td>314.696970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-2</td>\n",
       "      <td>17272</td>\n",
       "      <td>4551</td>\n",
       "      <td>42</td>\n",
       "      <td>411.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-3</td>\n",
       "      <td>19455</td>\n",
       "      <td>4693</td>\n",
       "      <td>43</td>\n",
       "      <td>452.441860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-4</td>\n",
       "      <td>9579</td>\n",
       "      <td>2980</td>\n",
       "      <td>28</td>\n",
       "      <td>342.107143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-5</td>\n",
       "      <td>13559</td>\n",
       "      <td>3699</td>\n",
       "      <td>45</td>\n",
       "      <td>301.311111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>2017-6</td>\n",
       "      <td>8590</td>\n",
       "      <td>2316</td>\n",
       "      <td>25</td>\n",
       "      <td>343.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>2017-7</td>\n",
       "      <td>3559</td>\n",
       "      <td>1457</td>\n",
       "      <td>14</td>\n",
       "      <td>254.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2017-8</td>\n",
       "      <td>9768</td>\n",
       "      <td>2927</td>\n",
       "      <td>35</td>\n",
       "      <td>279.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>2017-9</td>\n",
       "      <td>5644</td>\n",
       "      <td>2271</td>\n",
       "      <td>20</td>\n",
       "      <td>282.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>2017-10</td>\n",
       "      <td>8830</td>\n",
       "      <td>3036</td>\n",
       "      <td>28</td>\n",
       "      <td>315.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>2017-11</td>\n",
       "      <td>12549</td>\n",
       "      <td>4116</td>\n",
       "      <td>37</td>\n",
       "      <td>339.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>2017-12</td>\n",
       "      <td>12590</td>\n",
       "      <td>3987</td>\n",
       "      <td>41</td>\n",
       "      <td>307.073171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-1</td>\n",
       "      <td>16962</td>\n",
       "      <td>4829</td>\n",
       "      <td>46</td>\n",
       "      <td>368.739130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-2</td>\n",
       "      <td>7372</td>\n",
       "      <td>2727</td>\n",
       "      <td>30</td>\n",
       "      <td>245.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-3</td>\n",
       "      <td>1214</td>\n",
       "      <td>573</td>\n",
       "      <td>23</td>\n",
       "      <td>52.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-4</td>\n",
       "      <td>20283</td>\n",
       "      <td>5094</td>\n",
       "      <td>52</td>\n",
       "      <td>390.057692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-5</td>\n",
       "      <td>21417</td>\n",
       "      <td>4940</td>\n",
       "      <td>49</td>\n",
       "      <td>437.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>2018-6</td>\n",
       "      <td>18151</td>\n",
       "      <td>4530</td>\n",
       "      <td>38</td>\n",
       "      <td>477.657895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-7</td>\n",
       "      <td>7603</td>\n",
       "      <td>2504</td>\n",
       "      <td>38</td>\n",
       "      <td>200.078947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>2018-8</td>\n",
       "      <td>13628</td>\n",
       "      <td>4167</td>\n",
       "      <td>47</td>\n",
       "      <td>289.957447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-9</td>\n",
       "      <td>21729</td>\n",
       "      <td>5223</td>\n",
       "      <td>56</td>\n",
       "      <td>388.017857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>27876</td>\n",
       "      <td>5912</td>\n",
       "      <td>83</td>\n",
       "      <td>335.855422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>12039</td>\n",
       "      <td>3921</td>\n",
       "      <td>42</td>\n",
       "      <td>286.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>50021</td>\n",
       "      <td>9234</td>\n",
       "      <td>86</td>\n",
       "      <td>581.639535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-1</td>\n",
       "      <td>91835</td>\n",
       "      <td>12409</td>\n",
       "      <td>111</td>\n",
       "      <td>827.342342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-2</td>\n",
       "      <td>84509</td>\n",
       "      <td>12594</td>\n",
       "      <td>105</td>\n",
       "      <td>804.847619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-3</td>\n",
       "      <td>82075</td>\n",
       "      <td>12642</td>\n",
       "      <td>100</td>\n",
       "      <td>820.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-4</td>\n",
       "      <td>74577</td>\n",
       "      <td>12075</td>\n",
       "      <td>92</td>\n",
       "      <td>810.619565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-5</td>\n",
       "      <td>95895</td>\n",
       "      <td>14044</td>\n",
       "      <td>108</td>\n",
       "      <td>887.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-6</td>\n",
       "      <td>92218</td>\n",
       "      <td>13489</td>\n",
       "      <td>113</td>\n",
       "      <td>816.088496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-7</td>\n",
       "      <td>124435</td>\n",
       "      <td>15927</td>\n",
       "      <td>116</td>\n",
       "      <td>1072.715517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>2019-8</td>\n",
       "      <td>121277</td>\n",
       "      <td>15059</td>\n",
       "      <td>127</td>\n",
       "      <td>954.937008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>2019-9</td>\n",
       "      <td>109467</td>\n",
       "      <td>14794</td>\n",
       "      <td>120</td>\n",
       "      <td>912.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>120747</td>\n",
       "      <td>15879</td>\n",
       "      <td>117</td>\n",
       "      <td>1032.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>2019-11</td>\n",
       "      <td>89392</td>\n",
       "      <td>13900</td>\n",
       "      <td>103</td>\n",
       "      <td>867.883495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>83945</td>\n",
       "      <td>13511</td>\n",
       "      <td>98</td>\n",
       "      <td>856.581633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-1</td>\n",
       "      <td>106103</td>\n",
       "      <td>14740</td>\n",
       "      <td>113</td>\n",
       "      <td>938.964602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-2</td>\n",
       "      <td>98684</td>\n",
       "      <td>15138</td>\n",
       "      <td>119</td>\n",
       "      <td>829.277311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-3</td>\n",
       "      <td>110955</td>\n",
       "      <td>16000</td>\n",
       "      <td>101</td>\n",
       "      <td>1098.564356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-4</td>\n",
       "      <td>87444</td>\n",
       "      <td>13347</td>\n",
       "      <td>69</td>\n",
       "      <td>1267.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-5</td>\n",
       "      <td>84360</td>\n",
       "      <td>13485</td>\n",
       "      <td>59</td>\n",
       "      <td>1429.830508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>2020-6</td>\n",
       "      <td>94758</td>\n",
       "      <td>13961</td>\n",
       "      <td>93</td>\n",
       "      <td>1018.903226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>2020-7</td>\n",
       "      <td>87277</td>\n",
       "      <td>13616</td>\n",
       "      <td>82</td>\n",
       "      <td>1064.353659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>2020-8</td>\n",
       "      <td>89177</td>\n",
       "      <td>13527</td>\n",
       "      <td>88</td>\n",
       "      <td>1013.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>2020-9</td>\n",
       "      <td>104148</td>\n",
       "      <td>15938</td>\n",
       "      <td>91</td>\n",
       "      <td>1144.483516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>105452</td>\n",
       "      <td>15806</td>\n",
       "      <td>110</td>\n",
       "      <td>958.654545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>104062</td>\n",
       "      <td>15147</td>\n",
       "      <td>87</td>\n",
       "      <td>1196.114943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>92548</td>\n",
       "      <td>14325</td>\n",
       "      <td>107</td>\n",
       "      <td>864.934579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-1</td>\n",
       "      <td>78350</td>\n",
       "      <td>12543</td>\n",
       "      <td>77</td>\n",
       "      <td>1017.532468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-2</td>\n",
       "      <td>76011</td>\n",
       "      <td>12956</td>\n",
       "      <td>85</td>\n",
       "      <td>894.247059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-3</td>\n",
       "      <td>118440</td>\n",
       "      <td>16464</td>\n",
       "      <td>119</td>\n",
       "      <td>995.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>2021-4</td>\n",
       "      <td>85854</td>\n",
       "      <td>13827</td>\n",
       "      <td>54</td>\n",
       "      <td>1589.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-5</td>\n",
       "      <td>77224</td>\n",
       "      <td>13344</td>\n",
       "      <td>67</td>\n",
       "      <td>1152.597015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-6</td>\n",
       "      <td>87328</td>\n",
       "      <td>14342</td>\n",
       "      <td>85</td>\n",
       "      <td>1027.388235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "      <td>2021-7</td>\n",
       "      <td>85220</td>\n",
       "      <td>14294</td>\n",
       "      <td>77</td>\n",
       "      <td>1106.753247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2021</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-8</td>\n",
       "      <td>101709</td>\n",
       "      <td>15656</td>\n",
       "      <td>90</td>\n",
       "      <td>1130.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2021</td>\n",
       "      <td>9</td>\n",
       "      <td>2021-9</td>\n",
       "      <td>43931</td>\n",
       "      <td>10314</td>\n",
       "      <td>46</td>\n",
       "      <td>955.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>2021-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2021</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>2021-12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month     date  total_words  unique_words  speechs_per_month  \\\n",
       "0    2011      1   2011-1            0             0                  1   \n",
       "1    2011      2   2011-2            0             0                  1   \n",
       "2    2011      3   2011-3            0             0                  1   \n",
       "3    2011      4   2011-4            0             0                  1   \n",
       "4    2011      5   2011-5            0             0                  1   \n",
       "5    2011      6   2011-6            0             0                  1   \n",
       "6    2011      7   2011-7            0             0                  1   \n",
       "7    2011      8   2011-8            0             0                  1   \n",
       "8    2011      9   2011-9            0             0                  1   \n",
       "9    2011     10  2011-10            0             0                  1   \n",
       "10   2011     11  2011-11            0             0                  1   \n",
       "11   2011     12  2011-12         1904           907                 17   \n",
       "12   2012      1   2012-1         9036          3007                 62   \n",
       "13   2012      2   2012-2         6388          2598                 38   \n",
       "14   2012      3   2012-3         6835          2652                 48   \n",
       "15   2012      4   2012-4        21137          5392                123   \n",
       "16   2012      5   2012-5        18483          4959                164   \n",
       "17   2012      6   2012-6        24862          5522                142   \n",
       "18   2012      7   2012-7        10990          3308                 55   \n",
       "19   2012      8   2012-8        10380          3073                 41   \n",
       "20   2012      9   2012-9         5525          2669                 23   \n",
       "21   2012     10  2012-10        18320          4036                 89   \n",
       "22   2012     11  2012-11        12217          3444                 62   \n",
       "23   2012     12  2012-12          945           659                  5   \n",
       "24   2013      1   2013-1        17070          4036                 95   \n",
       "25   2013      2   2013-2        20234          4075                136   \n",
       "26   2013      3   2013-3        20196          3817                 93   \n",
       "27   2013      4   2013-4        11960          2721                 76   \n",
       "28   2013      5   2013-5        10638          2429                 64   \n",
       "29   2013      6   2013-6        14433          3471                 53   \n",
       "30   2013      7   2013-7         5215          1792                 26   \n",
       "31   2013      8   2013-8        15891          3749                 59   \n",
       "32   2013      9   2013-9        12448          3361                 50   \n",
       "33   2013     10  2013-10         9563          2927                 40   \n",
       "34   2013     11  2013-11         5112          1958                 22   \n",
       "35   2013     12  2013-12         3101          1325                 35   \n",
       "36   2014      1   2014-1         7798          2525                 27   \n",
       "37   2014      2   2014-2         8326          2728                 28   \n",
       "38   2014      3   2014-3        13342          3568                 36   \n",
       "39   2014      4   2014-4         8442          2579                 23   \n",
       "40   2014      5   2014-5        11380          3127                 30   \n",
       "41   2014      6   2014-6        12454          3201                 27   \n",
       "42   2014      7   2014-7        11460          3294                 28   \n",
       "43   2014      8   2014-8         8168          2590                 22   \n",
       "44   2014      9   2014-9        12320          3375                 30   \n",
       "45   2014     10  2014-10        16163          4455                 41   \n",
       "46   2014     11  2014-11        14584          3894                 33   \n",
       "47   2014     12  2014-12         7949          2476                 21   \n",
       "48   2015      1   2015-1        13771          3456                 25   \n",
       "49   2015      2   2015-2        12205          3364                 28   \n",
       "50   2015      3   2015-3        11649          3193                 26   \n",
       "51   2015      4   2015-4        18419          4642                 33   \n",
       "52   2015      5   2015-5        24118          4931                 40   \n",
       "53   2015      6   2015-6         5820          1927                 18   \n",
       "54   2015      7   2015-7        10579          3044                 27   \n",
       "55   2015      8   2015-8         8203          2637                 19   \n",
       "56   2015      9   2015-9        11684          3361                 29   \n",
       "57   2015     10  2015-10        11936          3473                 32   \n",
       "58   2015     11  2015-11        14593          3764                 40   \n",
       "59   2015     12  2015-12         9727          2864                 22   \n",
       "60   2016      1   2016-1        15247          3622                 33   \n",
       "61   2016      2   2016-2        17585          4056                 38   \n",
       "62   2016      3   2016-3        15218          3890                 38   \n",
       "63   2016      4   2016-4        21398          4696                 45   \n",
       "64   2016      5   2016-5        23612          5047                 50   \n",
       "65   2016      6   2016-6         4660          1876                 14   \n",
       "66   2016      7   2016-7         1668           845                  6   \n",
       "67   2016      8   2016-8        10486          3254                 23   \n",
       "68   2016      9   2016-9        10708          3245                 31   \n",
       "69   2016     10  2016-10         9487          2906                 29   \n",
       "70   2016     11  2016-11        11543          3448                 25   \n",
       "71   2016     12  2016-12         2982          1282                 11   \n",
       "72   2017      1   2017-1        10385          2976                 33   \n",
       "73   2017      2   2017-2        17272          4551                 42   \n",
       "74   2017      3   2017-3        19455          4693                 43   \n",
       "75   2017      4   2017-4         9579          2980                 28   \n",
       "76   2017      5   2017-5        13559          3699                 45   \n",
       "77   2017      6   2017-6         8590          2316                 25   \n",
       "78   2017      7   2017-7         3559          1457                 14   \n",
       "79   2017      8   2017-8         9768          2927                 35   \n",
       "80   2017      9   2017-9         5644          2271                 20   \n",
       "81   2017     10  2017-10         8830          3036                 28   \n",
       "82   2017     11  2017-11        12549          4116                 37   \n",
       "83   2017     12  2017-12        12590          3987                 41   \n",
       "84   2018      1   2018-1        16962          4829                 46   \n",
       "85   2018      2   2018-2         7372          2727                 30   \n",
       "86   2018      3   2018-3         1214           573                 23   \n",
       "87   2018      4   2018-4        20283          5094                 52   \n",
       "88   2018      5   2018-5        21417          4940                 49   \n",
       "89   2018      6   2018-6        18151          4530                 38   \n",
       "90   2018      7   2018-7         7603          2504                 38   \n",
       "91   2018      8   2018-8        13628          4167                 47   \n",
       "92   2018      9   2018-9        21729          5223                 56   \n",
       "93   2018     10  2018-10        27876          5912                 83   \n",
       "94   2018     11  2018-11        12039          3921                 42   \n",
       "95   2018     12  2018-12        50021          9234                 86   \n",
       "96   2019      1   2019-1        91835         12409                111   \n",
       "97   2019      2   2019-2        84509         12594                105   \n",
       "98   2019      3   2019-3        82075         12642                100   \n",
       "99   2019      4   2019-4        74577         12075                 92   \n",
       "100  2019      5   2019-5        95895         14044                108   \n",
       "101  2019      6   2019-6        92218         13489                113   \n",
       "102  2019      7   2019-7       124435         15927                116   \n",
       "103  2019      8   2019-8       121277         15059                127   \n",
       "104  2019      9   2019-9       109467         14794                120   \n",
       "105  2019     10  2019-10       120747         15879                117   \n",
       "106  2019     11  2019-11        89392         13900                103   \n",
       "107  2019     12  2019-12        83945         13511                 98   \n",
       "108  2020      1   2020-1       106103         14740                113   \n",
       "109  2020      2   2020-2        98684         15138                119   \n",
       "110  2020      3   2020-3       110955         16000                101   \n",
       "111  2020      4   2020-4        87444         13347                 69   \n",
       "112  2020      5   2020-5        84360         13485                 59   \n",
       "113  2020      6   2020-6        94758         13961                 93   \n",
       "114  2020      7   2020-7        87277         13616                 82   \n",
       "115  2020      8   2020-8        89177         13527                 88   \n",
       "116  2020      9   2020-9       104148         15938                 91   \n",
       "117  2020     10  2020-10       105452         15806                110   \n",
       "118  2020     11  2020-11       104062         15147                 87   \n",
       "119  2020     12  2020-12        92548         14325                107   \n",
       "120  2021      1   2021-1        78350         12543                 77   \n",
       "121  2021      2   2021-2        76011         12956                 85   \n",
       "122  2021      3   2021-3       118440         16464                119   \n",
       "123  2021      4   2021-4        85854         13827                 54   \n",
       "124  2021      5   2021-5        77224         13344                 67   \n",
       "125  2021      6   2021-6        87328         14342                 85   \n",
       "126  2021      7   2021-7        85220         14294                 77   \n",
       "127  2021      8   2021-8       101709         15656                 90   \n",
       "128  2021      9   2021-9        43931         10314                 46   \n",
       "129  2021     10  2021-10            0             0                  1   \n",
       "130  2021     11  2021-11            0             0                  1   \n",
       "131  2021     12  2021-12            0             0                  1   \n",
       "\n",
       "     words_per_speech  \n",
       "0            0.000000  \n",
       "1            0.000000  \n",
       "2            0.000000  \n",
       "3            0.000000  \n",
       "4            0.000000  \n",
       "5            0.000000  \n",
       "6            0.000000  \n",
       "7            0.000000  \n",
       "8            0.000000  \n",
       "9            0.000000  \n",
       "10           0.000000  \n",
       "11         112.000000  \n",
       "12         145.741935  \n",
       "13         168.105263  \n",
       "14         142.395833  \n",
       "15         171.845528  \n",
       "16         112.701220  \n",
       "17         175.084507  \n",
       "18         199.818182  \n",
       "19         253.170732  \n",
       "20         240.217391  \n",
       "21         205.842697  \n",
       "22         197.048387  \n",
       "23         189.000000  \n",
       "24         179.684211  \n",
       "25         148.779412  \n",
       "26         217.161290  \n",
       "27         157.368421  \n",
       "28         166.218750  \n",
       "29         272.320755  \n",
       "30         200.576923  \n",
       "31         269.338983  \n",
       "32         248.960000  \n",
       "33         239.075000  \n",
       "34         232.363636  \n",
       "35          88.600000  \n",
       "36         288.814815  \n",
       "37         297.357143  \n",
       "38         370.611111  \n",
       "39         367.043478  \n",
       "40         379.333333  \n",
       "41         461.259259  \n",
       "42         409.285714  \n",
       "43         371.272727  \n",
       "44         410.666667  \n",
       "45         394.219512  \n",
       "46         441.939394  \n",
       "47         378.523810  \n",
       "48         550.840000  \n",
       "49         435.892857  \n",
       "50         448.038462  \n",
       "51         558.151515  \n",
       "52         602.950000  \n",
       "53         323.333333  \n",
       "54         391.814815  \n",
       "55         431.736842  \n",
       "56         402.896552  \n",
       "57         373.000000  \n",
       "58         364.825000  \n",
       "59         442.136364  \n",
       "60         462.030303  \n",
       "61         462.763158  \n",
       "62         400.473684  \n",
       "63         475.511111  \n",
       "64         472.240000  \n",
       "65         332.857143  \n",
       "66         278.000000  \n",
       "67         455.913043  \n",
       "68         345.419355  \n",
       "69         327.137931  \n",
       "70         461.720000  \n",
       "71         271.090909  \n",
       "72         314.696970  \n",
       "73         411.238095  \n",
       "74         452.441860  \n",
       "75         342.107143  \n",
       "76         301.311111  \n",
       "77         343.600000  \n",
       "78         254.214286  \n",
       "79         279.085714  \n",
       "80         282.200000  \n",
       "81         315.357143  \n",
       "82         339.162162  \n",
       "83         307.073171  \n",
       "84         368.739130  \n",
       "85         245.733333  \n",
       "86          52.782609  \n",
       "87         390.057692  \n",
       "88         437.081633  \n",
       "89         477.657895  \n",
       "90         200.078947  \n",
       "91         289.957447  \n",
       "92         388.017857  \n",
       "93         335.855422  \n",
       "94         286.642857  \n",
       "95         581.639535  \n",
       "96         827.342342  \n",
       "97         804.847619  \n",
       "98         820.750000  \n",
       "99         810.619565  \n",
       "100        887.916667  \n",
       "101        816.088496  \n",
       "102       1072.715517  \n",
       "103        954.937008  \n",
       "104        912.225000  \n",
       "105       1032.025641  \n",
       "106        867.883495  \n",
       "107        856.581633  \n",
       "108        938.964602  \n",
       "109        829.277311  \n",
       "110       1098.564356  \n",
       "111       1267.304348  \n",
       "112       1429.830508  \n",
       "113       1018.903226  \n",
       "114       1064.353659  \n",
       "115       1013.375000  \n",
       "116       1144.483516  \n",
       "117        958.654545  \n",
       "118       1196.114943  \n",
       "119        864.934579  \n",
       "120       1017.532468  \n",
       "121        894.247059  \n",
       "122        995.294118  \n",
       "123       1589.888889  \n",
       "124       1152.597015  \n",
       "125       1027.388235  \n",
       "126       1106.753247  \n",
       "127       1130.100000  \n",
       "128        955.021739  \n",
       "129          0.000000  \n",
       "130          0.000000  \n",
       "131          0.000000  "
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "fig, ax = plt.subplots(figsize=(25, 6))\r\n",
    "fig.suptitle('Total words by month (2011 - 2021)')\r\n",
    "ax.plot(amlo_words.date, amlo_words.total_words, color='gray', linewidth=2, marker=\".\")\r\n",
    "ax.set_ylabel('Count of words')\r\n",
    "ax.set_xlabel('Dates')\r\n",
    "plt.axvspan(0, 23, alpha=0.06, facecolor='g', label='Período de Vicente Fox')\r\n",
    "plt.axvspan(23, 95, alpha=0.06, facecolor='b', label='Período Peña nieto' )\r\n",
    "plt.axvspan(95, 132, alpha=0.06, facecolor='r', label='Período Amlo')\r\n",
    "ax.legend(loc='upper center')\r\n",
    "plt.xticks(rotation=90,fontsize=8)\r\n",
    "plt.plot()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "df_cleaned_grouped = df_cleaned.groupby(['year','month'])['content'].apply(' '.join).reset_index()\r\n",
    "df_cleaned_grouped[\"date\"] = df_cleaned_grouped['year'].astype(str) + '-' + df_cleaned_grouped['month'].astype(str) + '-' + '01'\r\n",
    "df_cleaned_grouped['date'] = df_cleaned_grouped['date'].astype('datetime64[ns]')\r\n",
    "df_cleaned_grouped = df_cleaned_grouped.sort_values([\"date\"], ascending = (True))\r\n",
    "df_cleaned_grouped.head(5)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>content</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>solicita amlo ife trife vigilen manejo recurso...</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>convoca amlo luchar desterrar actual régimen c...</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>reunion puebla puebla reunion puebla puebla in...</td>\n",
       "      <td>2012-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>asegura única posibilidad atras tlajomulco jal...</td>\n",
       "      <td>2012-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>fotos amlo foro gobierno seguridad pública jus...</td>\n",
       "      <td>2012-04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month                                            content       date\n",
       "0  2011     12  solicita amlo ife trife vigilen manejo recurso... 2011-12-01\n",
       "1  2012      1  convoca amlo luchar desterrar actual régimen c... 2012-01-01\n",
       "2  2012      2  reunion puebla puebla reunion puebla puebla in... 2012-02-01\n",
       "3  2012      3  asegura única posibilidad atras tlajomulco jal... 2012-03-01\n",
       "4  2012      4  fotos amlo foro gobierno seguridad pública jus... 2012-04-01"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "years_range = [list(range(2011, 2013)), list(range(2013, 2019)), list(range(2019, 2022))]\r\n",
    "conf = [(4, 6), (12, 6), (6, 6)]\r\n",
    "sizes = [[20,14], [20,45], [20,22]]\r\n",
    "months = list(range(1, 13))\r\n",
    "\r\n",
    "wc = WordCloud(stopwords=stop_words, collocations=False, width= 500, height= 500, background_color=\"white\", colormap=\"Paired\",\r\n",
    "               max_font_size=100, min_font_size=10, max_words=2000, contour_width=2, margin = 2, contour_color='black')\r\n",
    "\r\n",
    "for iry, years in enumerate(years_range):\r\n",
    "    plt.rcParams['figure.figsize'] = sizes[iry]\r\n",
    "    for iny, y in enumerate(years):\r\n",
    "        for inm, m in enumerate(months):\r\n",
    "            text = df_cleaned_grouped.loc[(df_cleaned_grouped['year'] == y) & (df_cleaned_grouped['month'] == m)].content.values\r\n",
    "            if len(text) > 0:\r\n",
    "                wc.generate(text[0])\r\n",
    "            else:\r\n",
    "                wc.generate('sindatos vacio nodata empty notfound nohay')\r\n",
    "            plt.subplot(conf[iry][0], conf[iry][1], ((inm + (iny * 12)) + 1))\r\n",
    "            plt.imshow(wc, interpolation=\"bilinear\")\r\n",
    "            plt.axis(\"off\")\r\n",
    "            plt.title(str(years[iny]) + ' ' + str(months[inm]))\r\n",
    "    plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "dict_subjects = {\r\n",
    "                 'Economy': ['gasto', 'remesas', 'ahorro', 'economía', 'finanzas', 'inflación', 'salarios', 'inversión', 'impuesto', 'austeridad'],\r\n",
    "                 'Political institutions':['pan', 'prd', 'pri', 'morena', 'pt'],\r\n",
    "                 'General institutions':['onu', 'inm', 'ine', 'oms', 'imss', 'cfe', 'cndh', 'inegi', 'sep', 'conacyt', 'insabi', 'sat', 'profeco'],\r\n",
    "                 'Education': ['maestros','maestras', 'estudiante', 'educación', 'escuela', 'universidad', 'beca', 'reformaeducativa', 'libros', 'escuela', 'primaria', 'profesores'],\r\n",
    "                 'Energetic': ['energética', 'aceite', 'gas', 'pemex', 'petróleo', 'gasolina', 'combustible'],\r\n",
    "                 'Migration':['migración', 'extranjeros','frontera', 'centroamericanos','deportaciones', 'centroamérica'],\r\n",
    "                 'alimentation':['hambre', 'alimentación', 'canastabásica', 'comida', 'alimentos', 'desnutrición', 'nutrición', 'comida', 'pobreza'],\r\n",
    "                 'Southern federative entities':['campeche','chiapas', 'quintanaroo', 'tabasco', 'veracruz','yucatán'],\r\n",
    "                 'Central federative entities':['cdmx','edomex', 'guerrero', 'hidalgo', 'oaxaca', 'puebla','querétaro', 'tlaxcala'],\r\n",
    "                 'North federal entities':['aguascalientes','bajacalifornia','bajacaliforniasur', 'chihuahua','coahuila','colima','durango', 'guanajuato', 'guerrero', 'jalisco', 'michoacán', 'morelos', 'nayarit', 'nuevoleón', 'sanluispotosí', 'sinaloa', 'sonora', 'tamaulipas','zacatecas'],\r\n",
    "                 'Health': ['bienestar', 'salud', 'médico', 'camas', 'vacuna', 'enfermedad', 'contagio', 'infección', 'pandemia','epidemia', 'covid', 'hospital', 'dosis', 'doctores', 'enfermeras', 'paramédicos', 'enfermeros', 'hospitalización'] ,\r\n",
    "                 'Corruption':['ayotzinapa', 'justicia', 'corrupción', 'extorsión', 'soborno', 'desaparición', 'secuestro', 'narcotráfico', 'homicidio', 'anticorrupción'],\r\n",
    "                 'Political rivals': ['zedillo', 'salinas', 'peñanieto', 'anaya', 'meade', 'fox', 'calderon', 'margarita', 'videgaray', 'lozoya', 'chong', 'yunes'],\r\n",
    "                 'Political allies': ['gordillo', 'buylla', 'tclouthier', 'mclouthier', 'clouthier', 'aliados', 'aliado', 'ebrard', 'sheinbaum', 'monreal', 'gatell', 'alcocer', 'mueller'],\r\n",
    "                 'International people':['trump', 'obama', 'biden'],\r\n",
    "                 'Other problems': ['4t', 'indígenas', 'avión', 'tren', 'huachicol', 'méxico', 'pueblo'],\r\n",
    "                 'Countries': ['venezuela', 'chile', 'colombia', 'bolivia', 'argentina', 'cuba', 'honduras', 'nicaragua', 'canada', 'panama', 'españa', 'francia', 'alemania', 'china', 'rusia']\r\n",
    "                 }"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "from collections import Counter\r\n",
    "years_range = list(range(2011, 2022))\r\n",
    "for k in dict_subjects:\r\n",
    "    years = []\r\n",
    "    words = []\r\n",
    "    freqs = []   \r\n",
    "    for i, year in enumerate(years_range):        \r\n",
    "        text = df_cleaned_grouped.loc[(df_cleaned_grouped['year'] == year)].content.values\r\n",
    "        split_it = ' '.join(text).split()\r\n",
    "        c = Counter(split_it)\r\n",
    "        freq_terms = {}\r\n",
    "        for word in dict_subjects[k]:\r\n",
    "            words.append(word)\r\n",
    "            years.append(year)\r\n",
    "            freqs.append(c[word])\r\n",
    "    df_plot = pd.DataFrame()\r\n",
    "    df_plot['year'] = years\r\n",
    "    df_plot['word'] = words\r\n",
    "    df_plot['frequency'] = freqs\r\n",
    "    plt.rcParams[\"figure.figsize\"] = [10, 6]\r\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\r\n",
    "    agg_freq = df_plot.groupby(['word', 'year'])['frequency'].sum().unstack().fillna(0)\r\n",
    "    agg_freq.plot( kind='bar', stacked=True, mark_right = True)\r\n",
    "\r\n",
    "    plt.title(\"Frequencies of words related to '\" + k + \"' grouped by year and word\")\r\n",
    "    plt.xticks(rotation=45, ha='center')\r\n",
    "    plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "dates = pd.period_range(('2011, 1, 1'), ('2021, 12, 31'), freq='M')\r\n",
    "dates = dates.strftime('%Y-%m')\r\n",
    "\r\n",
    "gap_dates = []\r\n",
    "count = 0\r\n",
    "for k in dict_subjects:\r\n",
    "    count = 0\r\n",
    "    for d in dates:\r\n",
    "        words = (' '.join(df_cleaned_grouped.loc[(df_cleaned_grouped['date'] == d)].content.values)).split()\r\n",
    "        for w in words:\r\n",
    "            if w in dict_subjects[k]:\r\n",
    "                count += 1\r\n",
    "                break\r\n",
    "    gap_dates.append(count)\r\n",
    "        \r\n",
    "max_total_date = max(gap_dates)\r\n",
    "\r\n",
    "for i, td in enumerate(gap_dates):\r\n",
    "    gap_dates[i] = max_total_date - td\r\n",
    "\r\n",
    "\r\n",
    "fig = plt.figure(figsize=(24, 4))\r\n",
    "\r\n",
    "for i, k in enumerate(dict_subjects):\r\n",
    "        fig.suptitle(k)\r\n",
    "        cfd = nltk.ConditionalFreqDist(\r\n",
    "            (word,y)\r\n",
    "            for y in dates\r\n",
    "            for w in (' '.join(df_cleaned_grouped.loc[(df_cleaned_grouped['date'] == y)].content.values)).split()            \r\n",
    "            for word in dict_subjects[k]            \r\n",
    "            if w == word)\r\n",
    "\r\n",
    "        plt.figure(figsize=(22, 5))        \r\n",
    "        plt.axvspan(1, 84 - gap_dates[i], alpha=0.06, facecolor='b', label='Peña nieto')\r\n",
    "        plt.axvspan(84 - gap_dates[i] , 122, alpha=0.06, facecolor='r', label='AMLO')\r\n",
    "        plt.title(\"Frequency distribution of words related to '\" + k + \"'\")\r\n",
    "        plt.legend(loc='upper right')        \r\n",
    "        plt.xticks(rotation=90,fontsize=8.5)\r\n",
    "        fig.autofmt_xdate()\r\n",
    "        cfd.plot()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Word Embeddings "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "tokenized_corpus = [nltk.word_tokenize(speechs) for speechs in df_cleaned_grouped['content']]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "embedding_size = 100\r\n",
    "window_size = 60\r\n",
    "min_word = 10\r\n",
    "down_sampling = 1e-3\r\n",
    "\r\n",
    "ft_model = FastText(tokenized_corpus, vector_size = embedding_size, window=window_size, min_count=min_word, sample=down_sampling, workers=8, sg=1, epochs=60)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Visualizing Word Embeddings using t-SNE and PCA"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "source": [
    "number_similar_words = 10\r\n",
    "topics = ['xenofobia']\r\n",
    "\r\n",
    "similar_clusters = {word: [item[0] for item in ft_model.wv.most_similar([word], topn=number_similar_words)]\r\n",
    "                  for word in topics}\r\n",
    "\r\n",
    "all_words = []\r\n",
    "for k in topics:\r\n",
    "    all_words.extend([k] + similar_clusters[k])\r\n",
    "\r\n",
    "for k,v in similar_clusters.items():\r\n",
    "    print(k+\":\"+str(v))\r\n",
    "\r\n",
    "len(all_words) "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "xenofobia:['racial', 'gueto', 'fraternidad', 'fobia', 'phoenix', 'muro', 'trump', 'miserias', 'estadounidenses', 'americana']\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "metadata": {},
     "execution_count": 85
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "source": [
    "import matplotlib.patheffects as PathEffects\r\n",
    "import imageio\r\n",
    "import matplotlib.patches as mpatches\r\n",
    "from IPython import display\r\n",
    "from matplotlib.ticker import NullFormatter\r\n",
    "from sklearn.decomposition import PCA\r\n",
    "import psutil\r\n",
    "import os\r\n",
    "import shutil\r\n",
    "import ray\r\n",
    "ray.init(num_cpus= psutil.cpu_count(logical=False), ignore_reinit_error=True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'node_ip_address': '192.168.15.2',\n",
       " 'raylet_ip_address': '192.168.15.2',\n",
       " 'redis_address': '192.168.15.2:6379',\n",
       " 'object_store_address': 'tcp://127.0.0.1:50523',\n",
       " 'raylet_socket_name': 'tcp://127.0.0.1:56511',\n",
       " 'webui_url': None,\n",
       " 'session_dir': 'C:\\\\Users\\\\ramse\\\\AppData\\\\Local\\\\Temp\\\\ray\\\\session_2021-09-17_21-37-51_408457_32248',\n",
       " 'metrics_export_port': 60369,\n",
       " 'node_id': '60af2a61d24c6e16526fdffd7e833f53a2dc90534eb51b0a6fb46e46'}"
      ]
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "source": [
    "ray.shutdown()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "@ray.remote\r\n",
    "def f(p, w):\r\n",
    "    tsne = TSNE(perplexity=p, n_components=2, init='pca', random_state=12)\r\n",
    "    return tsne.fit_transform(w)\r\n",
    "\r\n",
    "class plot_tsne(object):\r\n",
    "\r\n",
    "    def __init__(self, word_vectors, words, topics, title, nnw, perplexities):\r\n",
    "        self.id = time.time_ns()\r\n",
    "        self.word_vectors = word_vectors\r\n",
    "        self.words = words\r\n",
    "        self.topics = topics\r\n",
    "        self.title = title\r\n",
    "        self.filename =  f'plot-tsne-{self.id}'\r\n",
    "        self.nnw = nnw + 1\r\n",
    "        self.perplexities = perplexities\r\n",
    "        self.tsnes = {}\r\n",
    "        self.pca = None\r\n",
    "        self.images = []\r\n",
    "    \r\n",
    "\r\n",
    "    def __reduce_dimensionality_tsne(self):        \r\n",
    "        result_ids = []    \r\n",
    "        for p in self.perplexities:\r\n",
    "            result_ids.append(f.remote(p, self.word_vectors))\r\n",
    "        results = ray.get(result_ids)\r\n",
    "        for i, p in enumerate(self.perplexities):\r\n",
    "            self.tsnes[p] = results[i]   \r\n",
    "\r\n",
    "    def __reduce_dimensionality_pca(self):\r\n",
    "        pca = PCA(n_components=3)\r\n",
    "        self.pca = pca.fit_transform(self.word_vectors)        \r\n",
    "\r\n",
    "\r\n",
    "    def __create_image(self, plt, pth):\r\n",
    "        if self.filename:\r\n",
    "            image = 'tmp//' + self.filename + str(pth) + '.png'\r\n",
    "            plt.savefig(image, format='png', dpi=80, bbox_inches='tight')\r\n",
    "            self.images.append(image)\r\n",
    "\r\n",
    "    def __create_gif(self, d, v):\r\n",
    "        images = [imageio.imread(img) for img in self.images]\r\n",
    "        path = d + '//'\r\n",
    "        imageio.mimsave(path + self.filename + '.gif', images, duration = v)\r\n",
    "        folder = 'tmp/'\r\n",
    "        for flnm in os.listdir(folder):\r\n",
    "            f_path = os.path.join(folder, flnm)\r\n",
    "            try:\r\n",
    "                if os.path.isfile(f_path) or os.path.islink(f_path):\r\n",
    "                    os.unlink(f_path)\r\n",
    "                elif os.path.isdir(f_path):\r\n",
    "                    shutil.rmtree(f_path)\r\n",
    "            except Exception as e:\r\n",
    "                print('Failed deleting file %s: %s' % (f_path, e))\r\n",
    "\r\n",
    "\r\n",
    "    def plot_nearests_words_2d(self, gif=False,  velocity = 0.5, annotations = False):\r\n",
    "        self.__reduce_dimensionality_tsne()\r\n",
    "        colors = cm.rainbow(np.linspace(0, 1, len(self.topics)))\r\n",
    "        topic_color = dict(zip(self.topics, colors))\r\n",
    "\r\n",
    "        for p in self.tsnes:\r\n",
    "            fig = plt.figure(figsize=(25, 15))\r\n",
    "            tsne_components = self.tsnes[p]\r\n",
    "            for v in range(0, len(self.words), self.nnw):\r\n",
    "\r\n",
    "                words = self.words[v:v + self.nnw]\r\n",
    "                label = words[0]\r\n",
    "                color = topic_color[label]\r\n",
    "                tsnec = tsne_components[v:v+self.nnw:]\r\n",
    "                alpha = 1\r\n",
    "                n_size = 15\r\n",
    "\r\n",
    "                plt.scatter(x = tsnec[0:1,0], y= tsnec[0:1,1], c=color, s = 12, alpha = alpha - 0.2, label = label)\r\n",
    "                plt.scatter(x = tsnec[1:,0], y= tsnec[1:,1], c=color, s = 12, alpha = alpha - 0.5)\r\n",
    "\r\n",
    "                if annotations:\r\n",
    "                    for i, w in enumerate(words):\r\n",
    "                        x = tsnec[i, 0]\r\n",
    "                        y = tsnec[i, 1]\r\n",
    "                        plt.annotate(w, alpha=alpha, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', size=n_size)\r\n",
    "                        alpha = 0.5\r\n",
    "                        n_size= 8\r\n",
    "\r\n",
    "            plt.legend(loc=4)\r\n",
    "            plt.title(self.title + ' Perplexity: ' + str(p))\r\n",
    "            plt.grid(True)\r\n",
    "            plt.xlim(-55, 55)\r\n",
    "            plt.ylim(-55, 55)\r\n",
    "            self.__create_image(plt, p)\r\n",
    "            plt.show()\r\n",
    "        if gif:\r\n",
    "            self.__create_gif('2d', velocity)\r\n",
    "\r\n",
    "\r\n",
    "    def plot_nearests_words_3d(self, gif=False, velocity = 0.5, annotations = False):\r\n",
    "        self.__reduce_dimensionality_pca()\r\n",
    "        colors = cm.rainbow(np.linspace(0, 1, len(self.topics)))\r\n",
    "        topic_color = dict(zip(self.topics, colors))\r\n",
    "\r\n",
    "\r\n",
    "        fig = plt.figure(figsize=(20, 10))\r\n",
    "        ax3d = Axes3D(fig)\r\n",
    "        tsne_components = self.pca\r\n",
    "        for v in range(0, len(self.words), self.nnw):\r\n",
    "\r\n",
    "            words = self.words[v:v + self.nnw]\r\n",
    "            label = words[0]\r\n",
    "            color = topic_color[label]\r\n",
    "            tsnec = tsne_components[v:v+self.nnw:]\r\n",
    "            alpha = 1\r\n",
    "            n_size = 15\r\n",
    "\r\n",
    "            ax3d.scatter(tsnec[0:1,0], tsnec[0:1,1], tsnec[0:1,2], s = 10,  c=color, alpha = alpha - 0.2, label = label)\r\n",
    "            ax3d.scatter(tsnec[1:,0], tsnec[1:,1], tsnec[1:,2], s = 10, c=color, alpha = alpha - 0.5)\r\n",
    "\r\n",
    "            if annotations:\r\n",
    "                for i, w in enumerate(words):\r\n",
    "                    x = tsnec[i, 0]\r\n",
    "                    y = tsnec[i, 1]\r\n",
    "                    z = tsnec[i, 2]\r\n",
    "                    ax3d.text(x,y,z,w, size=n_size)\r\n",
    "                    alpha = 0.5\r\n",
    "                    n_size= 8\r\n",
    "\r\n",
    "        ax3d.legend(loc=4)\r\n",
    "        ax3d.set_xlabel(\"PC-1\", fontsize=12)\r\n",
    "        ax3d.set_ylabel(\"PC-2\", fontsize=12)\r\n",
    "        ax3d.set_zlabel(\"PC-3\", fontsize=12)\r\n",
    "        ax3d.set_title(self.title , fontsize=12)\r\n",
    "        ax3d.set_facecolor('white')\r\n",
    "        ax3d.grid(True)\r\n",
    "        ax3d.w_xaxis.set_pane_color((0.0, 0.0, 0.0, 0.0))\r\n",
    "        ax3d.w_yaxis.set_pane_color((0.0, 0.0, 0.0, 0.0))\r\n",
    "        ax3d.w_zaxis.set_pane_color((0.0, 0.0, 0.0, 0.0))\r\n",
    "        for angle in range(0, 360, 2):\r\n",
    "            ax3d.view_init(30, angle)\r\n",
    "            pth = '-3-' + str(angle)\r\n",
    "            self.__create_image(plt, pth)\r\n",
    "        if gif:\r\n",
    "            self.__create_gif('3d', velocity)\r\n",
    "\r\n",
    "title = 'Nearest words to: [' + ', '.join(topics) + '] ' + 'from AMLO speeches'\r\n",
    "word_vectors = ft_model.wv[all_words]\r\n",
    "ptsne = plot_tsne(word_vectors, all_words,topics, title, number_similar_words, list(range(10,36)))\r\n",
    "ptsne.plot_nearests_words_3d(gif=True, velocity = 0.3, annotations = False)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "source": [
    "from sentiment_analysis_spanish import sentiment_analysis"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "source": [
    "# # df_cleaned_date = df_cleaned.groupby(['date'])['content'].apply(' '.join).reset_index()\r\n",
    "# df_cleaned_date['year'] = pd.DatetimeIndex(df_cleaned_date['date']).year\r\n",
    "# df_cleaned_date['month'] = pd.DatetimeIndex(df_cleaned_date['date']).month\r\n",
    "df_cleaned['date'] = df_cleaned['date'].astype('datetime64[ns]')\r\n",
    "# df_cleaned_date"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "source": [
    "sentiment = sentiment_analysis.SentimentAnalysisSpanish()\r\n",
    "def detect_sentiment(text):\r\n",
    "    return sentiment.sentiment(text)\r\n",
    "\r\n",
    "s = lambda x: detect_sentiment(x)\r\n",
    "df_cleaned['sentiment'] = pd.DataFrame(df_cleaned['content'].apply(s))\r\n",
    "df_cleaned = df_cleaned.sort_values([\"date\"], ascending = (True))\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "source": [
    "df_cleaned_date_positive = df_cleaned.loc[(df_cleaned['sentiment'] >= 0.6) ]\r\n",
    "df_cleaned_date_negative = df_cleaned.loc[(df_cleaned['sentiment'] <= 0.4) ]\r\n",
    "df_cleaned_date_neutral = df_cleaned.loc[(df_cleaned['sentiment']  > 0.4 ) & (df_cleaned['sentiment']  < 0.6 )]\r\n",
    "fig, ax = plt.subplots(figsize=(30, 10))\r\n",
    "fig.suptitle('Sentiment analysis of AMLO speeches (2011 - 2021)')\r\n",
    "ax.scatter(df_cleaned_date_positive.date, df_cleaned_date_positive.sentiment, color= 'steelblue', alpha=0.6, s=15, label= 'positive')\r\n",
    "ax.scatter(df_cleaned_date_negative.date, df_cleaned_date_negative.sentiment, color= 'lightcoral', alpha=0.6, s=15, label= 'negative')\r\n",
    "ax.scatter(df_cleaned_date_neutral.date, df_cleaned_date_neutral.sentiment, color= 'darkseagreen', alpha=0.6, s=15, label= 'neutral')\r\n",
    "ax.set_ylabel('Negative <--- Probability ---> Positive')\r\n",
    "ax.set_xlabel('Dates')\r\n",
    "plt.axvspan('2012', '2018', alpha=0.06, facecolor='b', label='Peña Nieto Presidential term')\r\n",
    "plt.axvspan('2018', '2022', alpha=0.06, facecolor='r', label='AMLO Presidential term' )\r\n",
    "ax.legend(loc='center right')\r\n",
    "plt.xticks(rotation=90,fontsize=10)\r\n",
    "\r\n",
    "plt.plot()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "execution_count": 91
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
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