src/PEC2_adonis_gonzalez.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "desperate-joint",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\", align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\">M2.859 · Visualización de datos aula 2</p>\n",
    "<p style=\"margin: 0; text-align:right;\">2020-2 · Máster universitario en Ciencia de datos (Data science)</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px; font-weight: bold;\">Nombre: Adonis González Godoy</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "handled-xerox",
   "metadata": {},
   "source": [
    "# PEC 2: Creación de visualizaciones de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-trademark",
   "metadata": {},
   "source": [
    "## 1. Título\n",
    "\n",
    "**Evolución de los tratados de paz a lo largo de los años en las diferentes regiones**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transsexual-fifteen",
   "metadata": {},
   "source": [
    "## 2. Descripción\n",
    "\n",
    "En este documento se ha utilizado una base de datos \"Peaceagreements database (PA-X)\" que contiene 1868 acuerdos de paz, se encuentran diferentes procesos de paz entre los años 1990 y mediados de 2020. El dataset esta disponible en la siguiente URL: (https://www.peaceagreements.org/search). Se trata de\n",
    "una base de datos creada y mantenida por la Law School de la Universidad de Edimburgo.\n",
    "    \n",
    "El dataset contienes diferentes conflictos que se han producidos entre estados, gobiernos, sociedad y/o grupos paramilitares a lo largo de los años y que a pesar de todo, muchos de ellos siguen teniendo problemas. En esta resolución de conflictos algunos de los temas que trataban para poner fin eran, los diferentes grupos de personas (niños, personas discapacitadas, ancianos, etc.), diferentes géneros (mujeres, hombres, niños, LGBTI y familia), la gobernanza (como reformar o establecer nuevas instituciones políticas), los derechos humanos y la igualdad, los repartos de poderes, reforma del sector de justicia, tierra, propiedad y medio ambiente, justicia transicional (amnistías o indultos) e implementación. \n",
    "\n",
    "En concreto en este análisis se va a profundizar sobe los acuerdos en los diferentes países a lo largo de los años, para intentar responder a las siguientes preguntas: ¿Cómo ha sido la evolución de los acuerdos de paz a lo largo de los años, en los diferentes países/regiones? ¿En qué regiones se ha llevado más acuerdos de paz? ¿Países con más/menos acuerdos de paz que han participado? ¿Cuáles son los tipos de acuerdos más comunes y su estado actual?\n",
    "\n",
    "Estas son algunas de las preguntas que se intentará responder en un conjunto de gráficas.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "economic-moore",
   "metadata": {},
   "source": [
    "\n",
    "## 3. Los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enormous-award",
   "metadata": {},
   "source": [
    "Cargamos las librerias necesarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adult-camping",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intellectual-element",
   "metadata": {},
   "source": [
    "Cargamos el conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ambient-attack",
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_DIR = os.path.abspath(\"../\")\n",
    "DATA_DIR = os.path.join(ROOT_DIR, \"data\")\n",
    "data_file = os.path.join(DATA_DIR, \"pax_all_agreements_data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "middle-carry",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xlsx = pd.ExcelFile(data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protective-survival",
   "metadata": {},
   "source": [
    "El conjunto de datos lo pasaremos a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "under-eagle",
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet_names = data_xlsx.sheet_names\n",
    "df = data_xlsx.parse(sheet_names[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blessed-camel",
   "metadata": {},
   "source": [
    "Tenemos un primer contacto con el dataset, mostramos las 2 primeras filas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "effective-import",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Con</th>\n",
       "      <th>Contp</th>\n",
       "      <th>PP</th>\n",
       "      <th>PPName</th>\n",
       "      <th>Reg</th>\n",
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       "      <th>Status</th>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Government/territory</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan: 2000s Post-intervention process</td>\n",
       "      <td>Europe and Eurasia</td>\n",
       "      <td>2232</td>\n",
       "      <td>3</td>\n",
       "      <td>Resolution of Intra Afghan Peace Conference in...</td>\n",
       "      <td>2019-07-08</td>\n",
       "      <td>Multiparty signed/agreed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Government</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan: 2000s Post-intervention process</td>\n",
       "      <td>Europe and Eurasia</td>\n",
       "      <td>1739</td>\n",
       "      <td>2</td>\n",
       "      <td>Agreement between the Islamic Republic of Afgh...</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>Multiparty signed/agreed</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>2 rows × 266 columns</p>\n",
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      ],
      "text/plain": [
       "           Con                 Contp  PP  \\\n",
       "0  Afghanistan  Government/territory   2   \n",
       "1  Afghanistan            Government   2   \n",
       "\n",
       "                                         PPName                 Reg  AgtId  \\\n",
       "0  Afghanistan: 2000s Post-intervention process  Europe and Eurasia   2232   \n",
       "1  Afghanistan: 2000s Post-intervention process  Europe and Eurasia   1739   \n",
       "\n",
       "   Ver                                                Agt         Dat  \\\n",
       "0    3  Resolution of Intra Afghan Peace Conference in...  2019-07-08   \n",
       "1    2  Agreement between the Islamic Republic of Afgh...  2016-09-22   \n",
       "\n",
       "                     Status  ...  TjRep  TjRSym TjRMa TjNR ImUN ImOth ImRef  \\\n",
       "0  Multiparty signed/agreed  ...      0       0     0    0    0     0     0   \n",
       "1  Multiparty signed/agreed  ...      0       0     0    0    0     0     0   \n",
       "\n",
       "  ImPK ImE ImSrc  \n",
       "0    0   0     1  \n",
       "1    0   1     1  \n",
       "\n",
       "[2 rows x 266 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "central-provincial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1868, 266)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprising-emperor",
   "metadata": {},
   "source": [
    "Como se puede ver la dimensión del dataset es de 1868 filas x 266 columnas. Es un dataset extenso y disponible para aplicar diferentes técnicas de análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promising-negotiation",
   "metadata": {},
   "source": [
    "A continuación se analizará la estructura y resumen de datos, de esta forma se entenderá mejor el dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "appointed-mirror",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PP</th>\n",
       "      <th>AgtId</th>\n",
       "      <th>Ver</th>\n",
       "      <th>Lgt</th>\n",
       "      <th>N_characters</th>\n",
       "      <th>Loc1GWNO</th>\n",
       "      <th>Loc2GWNO</th>\n",
       "      <th>UcdpAgr</th>\n",
       "      <th>PamAgr</th>\n",
       "      <th>CowWar</th>\n",
       "      <th>...</th>\n",
       "      <th>TjRep</th>\n",
       "      <th>TjRSym</th>\n",
       "      <th>TjRMa</th>\n",
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       "      <th>ImOth</th>\n",
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       "      <th>ImPK</th>\n",
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       "      <th>ImSrc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1857.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>2.970000e+02</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1115.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "      <td>1868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>84.870450</td>\n",
       "      <td>1155.105996</td>\n",
       "      <td>1.249465</td>\n",
       "      <td>6.274625</td>\n",
       "      <td>11951.505353</td>\n",
       "      <td>528.693592</td>\n",
       "      <td>439.719512</td>\n",
       "      <td>2.903701e+10</td>\n",
       "      <td>17.272727</td>\n",
       "      <td>759.324664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177195</td>\n",
       "      <td>0.011242</td>\n",
       "      <td>0.102248</td>\n",
       "      <td>0.300321</td>\n",
       "      <td>0.193790</td>\n",
       "      <td>0.238758</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>0.291756</td>\n",
       "      <td>0.383833</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.273883</td>\n",
       "      <td>663.722169</td>\n",
       "      <td>0.590838</td>\n",
       "      <td>17.194469</td>\n",
       "      <td>37644.455332</td>\n",
       "      <td>223.469441</td>\n",
       "      <td>195.075032</td>\n",
       "      <td>3.602522e+11</td>\n",
       "      <td>10.022702</td>\n",
       "      <td>254.762977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.553679</td>\n",
       "      <td>0.105459</td>\n",
       "      <td>0.303056</td>\n",
       "      <td>0.608157</td>\n",
       "      <td>0.395372</td>\n",
       "      <td>0.426439</td>\n",
       "      <td>0.097715</td>\n",
       "      <td>0.454692</td>\n",
       "      <td>0.486448</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>568.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2397.250000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>346.250000</td>\n",
       "      <td>1.107000e+03</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1135.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4354.000000</td>\n",
       "      <td>531.000000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>1.334000e+03</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>1736.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9420.500000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>1.523000e+03</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>2317.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>323.000000</td>\n",
       "      <td>908459.000000</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>770.000000</td>\n",
       "      <td>5.171991e+12</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 250 columns</p>\n",
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      ],
      "text/plain": [
       "                PP        AgtId          Ver          Lgt   N_characters  \\\n",
       "count  1868.000000  1868.000000  1868.000000  1868.000000    1868.000000   \n",
       "mean     84.870450  1155.105996     1.249465     6.274625   11951.505353   \n",
       "std      42.273883   663.722169     0.590838    17.194469   37644.455332   \n",
       "min       1.000000     1.000000     1.000000     1.000000     329.000000   \n",
       "25%      55.000000   568.750000     1.000000     1.000000    2397.250000   \n",
       "50%      90.000000  1135.500000     1.000000     2.000000    4354.000000   \n",
       "75%     123.000000  1736.250000     1.000000     5.000000    9420.500000   \n",
       "max     160.000000  2317.000000     4.000000   323.000000  908459.000000   \n",
       "\n",
       "          Loc1GWNO    Loc2GWNO       UcdpAgr     PamAgr       CowWar  ...  \\\n",
       "count  1857.000000  246.000000  2.970000e+02  33.000000  1115.000000  ...   \n",
       "mean    528.693592  439.719512  2.903701e+10  17.272727   759.324664  ...   \n",
       "std     223.469441  195.075032  3.602522e+11  10.022702   254.762977  ...   \n",
       "min      41.000000    2.000000  2.400000e+01   1.000000   139.000000  ...   \n",
       "25%     365.000000  346.250000  1.107000e+03   9.000000   817.000000  ...   \n",
       "50%     531.000000  371.000000  1.334000e+03  17.000000   857.000000  ...   \n",
       "75%     678.000000  625.000000  1.523000e+03  26.000000   905.000000  ...   \n",
       "max     940.000000  770.000000  5.171991e+12  34.000000   941.000000  ...   \n",
       "\n",
       "             TjRep       TjRSym        TjRMa         TjNR         ImUN  \\\n",
       "count  1868.000000  1868.000000  1868.000000  1868.000000  1868.000000   \n",
       "mean      0.177195     0.011242     0.102248     0.300321     0.193790   \n",
       "std       0.553679     0.105459     0.303056     0.608157     0.395372   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       3.000000     1.000000     1.000000     3.000000     1.000000   \n",
       "\n",
       "             ImOth        ImRef         ImPK          ImE   ImSrc  \n",
       "count  1868.000000  1868.000000  1868.000000  1868.000000  1868.0  \n",
       "mean      0.238758     0.009636     0.291756     0.383833     1.0  \n",
       "std       0.426439     0.097715     0.454692     0.486448     0.0  \n",
       "min       0.000000     0.000000     0.000000     0.000000     1.0  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     1.0  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     1.0  \n",
       "75%       0.000000     0.000000     1.000000     1.000000     1.0  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.0  \n",
       "\n",
       "[8 rows x 250 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "hundred-married",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Con  --> object\n",
      "Contp  --> object\n",
      "PP  --> int64\n",
      "PPName  --> object\n",
      "Reg  --> object\n",
      "AgtId  --> int64\n",
      "Ver  --> int64\n",
      "Agt  --> object\n",
      "Dat  --> object\n",
      "Status  --> object\n",
      "Lgt  --> int64\n",
      "N_characters  --> int64\n",
      "Agtp  --> object\n",
      "Stage  --> object\n",
      "StageSub  --> object\n",
      "Part  --> object\n",
      "ThrdPart  --> object\n",
      "OthAgr  --> object\n",
      "Loc1ISO  --> object\n",
      "Loc2ISO  --> object\n",
      "Loc1GWNO  --> float64\n",
      "Loc2GWNO  --> float64\n",
      "UcdpCon  --> object\n",
      "UcdpAgr  --> float64\n",
      "PamAgr  --> float64\n",
      "CowWar  --> float64\n",
      "GCh  --> int64\n",
      "GChRhet  --> int64\n",
      "GChAntid  --> int64\n",
      "GChSubs  --> int64\n",
      "GChOth  --> int64\n",
      "GDis  --> int64\n",
      "GDisRhet  --> int64\n",
      "GDisAntid  --> int64\n",
      "GDisSubs  --> int64\n",
      "GDisOth  --> int64\n",
      "GAge  --> int64\n",
      "GAgeRhet  --> int64\n",
      "GAgeAntid  --> int64\n",
      "GAgeSubs  --> int64\n",
      "GAgeOth  --> int64\n",
      "GMig  --> int64\n",
      "GMigRhet  --> int64\n",
      "GMigAntid  --> int64\n",
      "GMigSubs  --> int64\n",
      "GMigOth  --> int64\n",
      "GRa  --> int64\n",
      "GRaRhet  --> int64\n",
      "GRaAntid  --> int64\n",
      "GRaSubs  --> int64\n",
      "GRaOth  --> int64\n",
      "GRe  --> int64\n",
      "GReRhet  --> int64\n",
      "GReAntid  --> int64\n",
      "GReSubs  --> int64\n",
      "GReOth  --> int64\n",
      "GInd  --> int64\n",
      "GIndRhet  --> int64\n",
      "GIndAntid  --> int64\n",
      "GIndSubs  --> int64\n",
      "GIndOth  --> int64\n",
      "GOth  --> int64\n",
      "GOthRhet  --> int64\n",
      "GOthAntid  --> int64\n",
      "GOthSubs  --> int64\n",
      "GOthOth  --> int64\n",
      "GRef  --> int64\n",
      "GRefRhet  --> int64\n",
      "GRefSubs  --> int64\n",
      "GRefOth  --> int64\n",
      "GSoc  --> int64\n",
      "GSocRhet  --> int64\n",
      "GSocAntid  --> int64\n",
      "GSocSubs  --> int64\n",
      "GSocOth  --> int64\n",
      "GeWom  --> int64\n",
      "GeMe  --> int64\n",
      "GeMeNu  --> int64\n",
      "GeMeOth  --> int64\n",
      "GeLgbti  --> int64\n",
      "GeLgbtiPos  --> int64\n",
      "GeLgbtiNeg  --> int64\n",
      "GeFa  --> int64\n",
      "StDef  --> int64\n",
      "StGen  --> int64\n",
      "StCon  --> int64\n",
      "StSd  --> int64\n",
      "StRef  --> int64\n",
      "StSym  --> int64\n",
      "StInd  --> int64\n",
      "StUni  --> int64\n",
      "StBor  --> int64\n",
      "StXbor  --> int64\n",
      "Pol  --> int64\n",
      "PolGen  --> int64\n",
      "PolNewInd  --> int64\n",
      "PolNewTemp  --> int64\n",
      "ConRen  --> int64\n",
      "Cons  --> int64\n",
      "Ele  --> int64\n",
      "ElecComm  --> int64\n",
      "PolPar  --> int64\n",
      "PolParTrans  --> int64\n",
      "PolParOth  --> int64\n",
      "Civso  --> int64\n",
      "Tral  --> int64\n",
      "Pubad  --> int64\n",
      "Polps  --> int64\n",
      "PpsSt  --> int64\n",
      "PpsSub  --> int64\n",
      "PpsEx  --> int64\n",
      "PpsOro  --> int64\n",
      "PpsOthPr  --> int64\n",
      "PpsVet  --> int64\n",
      "PpsAut  --> int64\n",
      "PpsInt  --> int64\n",
      "PpsOth  --> int64\n",
      "Terps  --> int64\n",
      "TpsSub  --> int64\n",
      "TpsLoc  --> int64\n",
      "TpsAut  --> int64\n",
      "TpsOth  --> int64\n",
      "Eps  --> int64\n",
      "EpsRes  --> int64\n",
      "EpsFis  --> int64\n",
      "EpsOth  --> int64\n",
      "Mps  --> int64\n",
      "MpsMe  --> int64\n",
      "MpsJt  --> int64\n",
      "MpsPro  --> int64\n",
      "MpsOth  --> int64\n",
      "HrGen  --> int64\n",
      "EqGen  --> int64\n",
      "HrDem  --> int64\n",
      "Prot  --> int64\n",
      "ProtCiv  --> int64\n",
      "ProtGrp  --> int64\n",
      "ProtLgl  --> int64\n",
      "ProtOth  --> int64\n",
      "HrFra  --> int64\n",
      "HrfSp  --> int64\n",
      "HrfBor  --> int64\n",
      "HrfTinc  --> int64\n",
      "HrfOth  --> int64\n",
      "HrCp  --> int64\n",
      "CprLife  --> int64\n",
      "CprTort  --> int64\n",
      "CprEq  --> int64\n",
      "CprSlav  --> int64\n",
      "CprLib  --> int64\n",
      "CprDet  --> int64\n",
      "CprFmov  --> int64\n",
      "CprFspe  --> int64\n",
      "CprFass  --> int64\n",
      "CprTria  --> int64\n",
      "CprPriv  --> int64\n",
      "CprVote  --> int64\n",
      "CprReli  --> int64\n",
      "CprOth  --> int64\n",
      "HrSec  --> int64\n",
      "SerProp  --> int64\n",
      "SerWork  --> int64\n",
      "SerHeal  --> int64\n",
      "SerEdu  --> int64\n",
      "SerStdl  --> int64\n",
      "SerShel  --> int64\n",
      "SerSs  --> int64\n",
      "SerCult  --> int64\n",
      "SerOth  --> int64\n",
      "HrNi  --> int64\n",
      "HrNiMe  --> int64\n",
      "HrNiNe  --> int64\n",
      "HrNiOth  --> int64\n",
      "HrIi  --> int64\n",
      "HrIiMon  --> int64\n",
      "HrIiBod  --> int64\n",
      "HrIiOth  --> int64\n",
      "HrMob  --> int64\n",
      "HrDet  --> int64\n",
      "Med  --> int64\n",
      "MedGov  --> int64\n",
      "MedSubs  --> int64\n",
      "MedLog  --> int64\n",
      "MedOth  --> int64\n",
      "HrCit  --> int64\n",
      "CitGen  --> int64\n",
      "CitRights  --> int64\n",
      "CitDef  --> int64\n",
      "CitOth  --> int64\n",
      "JusCr  --> int64\n",
      "JusCrSp  --> int64\n",
      "JusCrSys  --> int64\n",
      "JusCrPow  --> int64\n",
      "JusEm  --> int64\n",
      "JusJu  --> int64\n",
      "JusPri  --> int64\n",
      "JusTra  --> int64\n",
      "Dev  --> int64\n",
      "DevSoc  --> int64\n",
      "DevHum  --> int64\n",
      "DevInfra  --> int64\n",
      "NEC  --> int64\n",
      "NatRes  --> int64\n",
      "IntFu  --> int64\n",
      "Bus  --> int64\n",
      "Tax  --> int64\n",
      "TaxPo  --> int64\n",
      "TaxRef  --> int64\n",
      "TaxOth  --> int64\n",
      "Ban  --> int64\n",
      "CenBan  --> int64\n",
      "BanPers  --> int64\n",
      "BanInt  --> int64\n",
      "BanXb  --> int64\n",
      "LaRef  --> int64\n",
      "LaRefMan  --> int64\n",
      "LaRefRet  --> int64\n",
      "LaRefOth  --> int64\n",
      "LaNom  --> int64\n",
      "LaCH  --> int64\n",
      "LaCHTa  --> int64\n",
      "LaCHIt  --> int64\n",
      "LaCHPro  --> int64\n",
      "LaCHOth  --> int64\n",
      "LaEn  --> int64\n",
      "Wat  --> int64\n",
      "SsrGua  --> int64\n",
      "Ce  --> int64\n",
      "CeProv  --> int64\n",
      "CeGen  --> int64\n",
      "SsrPol  --> int64\n",
      "SsrArm  --> int64\n",
      "SsrDdr  --> int64\n",
      "DdrDemil  --> int64\n",
      "DdrProg  --> int64\n",
      "SsrInt  --> int64\n",
      "SsrPsf  --> int64\n",
      "SsrFf  --> int64\n",
      "Cor  --> int64\n",
      "SsrCrOcr  --> int64\n",
      "SsrDrugs  --> int64\n",
      "Terr  --> int64\n",
      "TjGen  --> int64\n",
      "TjAm  --> int64\n",
      "TjAmPro  --> int64\n",
      "TjSan  --> int64\n",
      "TjPower  --> int64\n",
      "TjAmBan  --> int64\n",
      "TjCou  --> int64\n",
      "TjJaNc  --> int64\n",
      "TjJaIc  --> int64\n",
      "TjMech  --> int64\n",
      "TjPrire  --> int64\n",
      "TjVet  --> int64\n",
      "TjVic  --> int64\n",
      "TjMis  --> int64\n",
      "TjRep  --> int64\n",
      "TjRSym  --> int64\n",
      "TjRMa  --> int64\n",
      "TjNR  --> int64\n",
      "ImUN  --> int64\n",
      "ImOth  --> int64\n",
      "ImRef  --> int64\n",
      "ImPK  --> int64\n",
      "ImE  --> int64\n",
      "ImSrc  --> int64\n"
     ]
    }
   ],
   "source": [
    "# print(df.dtypes)\n",
    "obj = df.dtypes\n",
    "res = []\n",
    "for key,value in obj.iteritems():\n",
    "        print(key,\" -->\",value)\n",
    "#         res.append([key, value])\n",
    "# print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cordless-democracy",
   "metadata": {},
   "source": [
    "Como se puede observar la mayoría de datos son de tipo integer, por lo general las columnas con tipo de datos object son de tipo String."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "freelance-oregon",
   "metadata": {},
   "source": [
    "### Descripción de variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rubber-benjamin",
   "metadata": {},
   "source": [
    "A continuación se describe las variables que se han considerado para esta exploración."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "improved-transcription",
   "metadata": {},
   "source": [
    "|Campo|Descripción|\n",
    "|---|:---:|\n",
    "|Con| - Campo de tipo String que se refiere a Country, son los diferentes países en los que se han realizado acuerdos|\n",
    "|PPName| - Campo de tipo String que contiene el nombre de los acuerdos realizados|\n",
    "|Reg| - Campo de tipo String que se refiere a Region, contienes las diferentes regiones donde se han realizado los acuerdos|\n",
    "|Dat| - Campo de tipo Object que contiene fechas, contiene las fechas que en que se firmaron los acuerdos de paz|\n",
    "|Status  | - Campo de tipo String que contiene la descipción del los diferentes tipos de acuerdos|\n",
    "|Stage  | - Campo de tipo String que contiene información sobre la fase del tipo de acuerdo|"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "casual-personal",
   "metadata": {},
   "source": [
    "Miraremos si el dataset contiene campos con valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "looking-hearts",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 16, 892, 892, 35, 1622, 11, 1622, 155, 1571, 1835, 753, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fitted-cycling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StageSub  --> 25\n",
      "Part  --> 16\n",
      "ThrdPart  --> 892\n",
      "OthAgr  --> 892\n",
      "Loc1ISO  --> 35\n",
      "Loc2ISO  --> 1622\n",
      "Loc1GWNO  --> 11\n",
      "Loc2GWNO  --> 1622\n",
      "UcdpCon  --> 155\n",
      "UcdpAgr  --> 1571\n",
      "PamAgr  --> 1835\n",
      "CowWar  --> 753\n"
     ]
    }
   ],
   "source": [
    "obj = df.isnull().sum()\n",
    "for key,value in obj.iteritems():\n",
    "    if value > 0:\n",
    "        print(key,\" -->\",value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "banned-davis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9429\n",
      "9429\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().values.sum())\n",
    "print(df.isna().values.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affiliated-taylor",
   "metadata": {},
   "source": [
    "Estos son los campos con el número de total de valores nulos, estas variables no han sido seleccionadas por lo que se procede a la exploración de los datos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wireless-knight",
   "metadata": {},
   "source": [
    "\n",
    "## 4. Exploración de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-michigan",
   "metadata": {},
   "source": [
    "En la exploración de los datos nos centraremos en la evolución de los acuerdos a lo largo de los años dado las regiones/países, tipos de acuerdos y estado que se encuentran.\n",
    "\n",
    "Primero de todo miraremos desde un punto de vista general los acuerdos por las diferentes regiones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "irish-humanity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Africa (excl MENA)              584\n",
       "Europe and Eurasia              411\n",
       "Asia and Pacific                377\n",
       "Middle East and North Africa    263\n",
       "Americas                        195\n",
       "Cross-regional                   38\n",
       "Name: Reg, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Reg.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ignored-thesis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Número de acuerdos por región')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S4BLgEUPWJ0mSNBcM3UL2LuCVwG0j+zapqisB+vuN+/2bA5ePnLe033c7SQ5LsiTJkmuvvXaQoiVJksZpsECWZB/gmqo6e2WfMsO+usOOqqOqaueq2nmjjTa6RzVKkiTNBUNOe7E78NQkTwbWANZN8nHg6iSbVtWVSTYFrunPXwpsOfL8LYArBqxPkiRpThishayqXlNVW1TV1nSD9b9SVQcDJwKH9KcdAny+f3wicECS1ZNsAywGzhqqPkmSpLmixcSwbwWOT3IocBmwP0BVXZDkeOAHwK3A4VW1rEF9kiRJYzWWQFZVpwOn949/Duy5nPOOBI4cR02SJElzhTP1S5IkNWYgkyRJasxAJkmS1JiBTJIkqbEWV1lKGsDu79m9dQnzwtdf8vXWJUjSHdhCJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYysVyJKcujL7JEmSdNetuqKDSdYA1gI2TLI+kP7QusBmA9cmSZK0IKwwkAEvBo6gC19n84dAdj3w78OVJUmStHCsMJBV1buBdyd5SVW9Z0w1SZIkLSh31kIGQFW9J8mjgK1Hn1NVHx2oLkmSpAVjpQJZko8B2wLnAMv63QUYyCRJku6hlQpkwM7A9lVVQxYjSZK0EK3sPGTfB+47ZCGSJEkL1cq2kG0I/CDJWcDNUzur6qmDVCVJkrSArGwge/2QRUiSJC1kK3uV5RlDFyJJkrRQrezSSTckub6/3ZRkWZLr7+Q5ayQ5K8m5SS5I8oZ+/wZJTklycX+//shzXpPkkiQXJdnrnn1rkiRJ88NKBbKqWqeq1u1vawDPAN57J0+7GXh8Ve0I7ATsnWRX4NXAqVW1GDi13ybJ9sABwA7A3sD7kiy6G9+TJEnSvLKyV1neTlV9Dnj8nZxTVfXrfvNe/a2A/YBj+v3HAE/rH+8HHFdVN1fVpcAlwCPuTn2SJEnzycpODPv0kc1V6OYlu9M5yfoWrrOBPwb+vaq+nWSTqroSoKquTLJxf/rmwLdGnr603zf9NQ8DDgPYaqutVqZ8SZKkOW1lr7Lcd+TxrcCP6Vq0VqiqlgE7JVkPOCHJg1ZwembYd4fQV1VHAUcB7Lzzzk5UK0mS5r2Vvcry+ffki1TVL5OcTjc27Ookm/atY5sC1/SnLQW2HHnaFsAV9+TrSpIkzQcre5XlFklOSHJNkquTfCbJFnfynI36ljGSrAk8AbgQOBE4pD/tEODz/eMTgQOSrJ5kG2AxcNZd/o4kSZLmmZXtsjwa+CSwf799cL/viSt4zqbAMf04slWA46vqpCTfBI5Pcihw2dRrVtUFSY4HfkDXLXp43+UpSZI00VY2kG1UVUePbH8kyRErekJVnQc8dIb9Pwf2XM5zjgSOXMmaJEmSJsLKTnvxsyQHJ1nU3w4Gfj5kYZIkSQvFygayFwDPAq4CrgSeCdyjgf6SJEnqrGyX5ZuAQ6rqF9AtfwT8K11QkyRJ0j2wsi1kD5kKYwBVdR0zjA+TJEnSXbeygWyVaYuAb8DKt65JkiRpBVY2VL0d+EaST9PNnv8svBpSkiRpVqzsTP0fTbKEbkHxAE+vqh8MWpkkSdICsdLdjn0AM4RJkiTNspUdQyZJkqSBGMgkSZIa80pJSbqbznjMY1uXMC889swzWpcgzXm2kEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhobLJAl2TLJaUl+mOSCJC/r92+Q5JQkF/f364885zVJLklyUZK9hqpNkiRpLhmyhexW4G+r6k+AXYHDk2wPvBo4taoWA6f22/THDgB2APYG3pdk0YD1SZIkzQmDBbKqurKqvts/vgH4IbA5sB9wTH/aMcDT+sf7AcdV1c1VdSlwCfCIoeqTJEmaK8YyhizJ1sBDgW8Dm1TVldCFNmDj/rTNgctHnra03zf9tQ5LsiTJkmuvvXbQuiVJksZh8ECW5N7AZ4Ajqur6FZ06w766w46qo6pq56raeaONNpqtMiVJkpoZNJAluRddGPtEVX223311kk3745sC1/T7lwJbjjx9C+CKIeuTJEmaC1Yd6oWTBPgw8MOqesfIoROBQ4C39vefH9n/ySTvADYDFgNnDVWfJGn+ee/ffqF1CfPCX79939Yl6C4aLJABuwPPAc5Pck6/77V0Qez4JIcClwH7A1TVBUmOB35Ad4Xm4VW1bMD6JEmS5oTBAllVfY2Zx4UB7Lmc5xwJHDlUTZIkSXORM/VLkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqbHBAlmS/0xyTZLvj+zbIMkpSS7u79cfOfaaJJckuSjJXkPVJUmSNNcM2UL2EWDvafteDZxaVYuBU/ttkmwPHADs0D/nfUkWDVibJEnSnDFYIKuqM4Hrpu3eDzimf3wM8LSR/cdV1c1VdSlwCfCIoWqTJEmaS8Y9hmyTqroSoL/fuN+/OXD5yHlL+313kOSwJEuSLLn22msHLVaSJGkc5sqg/sywr2Y6saqOqqqdq2rnjTbaaOCyJEmShjfuQHZ1kk0B+vtr+v1LgS1HztsCuGLMtUmSJDUx7kB2InBI//gQ4PMj+w9IsnqSbYDFwFljrk2SJKmJVYd64STHAo8DNkyyFHgd8Fbg+CSHApcB+wNU1QVJjgd+ANwKHF5Vy4aqTZIkaS4ZLJBV1YHLObTncs4/EjhyqHokSZLmqrkyqF+SJGnBMpBJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKmxVVsXIEmS5q4jD35m6xLmhb//+Kfv0fNtIZMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxuZcIEuyd5KLklyS5NWt65EkSRranApkSRYB/w78KbA9cGCS7dtWJUmSNKw5FciARwCXVNWPquoW4Dhgv8Y1SZIkDSpV1bqG30vyTGDvqnphv/0c4JFV9dcj5xwGHNZvPgC4aOyFzo4NgZ+1LmKB8T0fP9/z8fM9Hz/f8/Gbr+/5/apqo5kOrDruSu5EZth3u8RYVUcBR42nnOEkWVJVO7euYyHxPR8/3/Px8z0fP9/z8ZvE93yudVkuBbYc2d4CuKJRLZIkSWMx1wLZd4DFSbZJshpwAHBi45okSZIGNae6LKvq1iR/DXwRWAT8Z1Vd0Lisocz7btd5yPd8/HzPx8/3fPx8z8dv4t7zOTWoX5IkaSGaa12WkiRJC46BTJIkqTEDmSRJUmNzalD/JEuyCrAjsBnwW+CCqrq6bVWTL8n6wGJgjal9VXVmu4omW5LDgU9U1S/77fWBA6vqfU0Lk6Q5zkH9A0uyLfAq4AnAxcC1dOFgO+A3wAeAY6rqtmZFTqgkLwReRjef3TnArsA3q+rxLeuaZEnOqaqdpu37XlU9tFFJEy/JYuAtdOv/jv7hcf9mRU24JP8CvJnuj+uT6f7YPqKqPt60sAmU5D1MmyB+VFW9dIzlDMoWsuG9GXg/8OKaln6TbAIcCDwHOKZBbZPuZcAuwLeqao8kDwTe0LimSbdKkkz9rCdZBKzWuKZJdzTwOuCdwB7A85l51RPNnidV1SuT/BndhOb7A6cBBrLZt6R1AeNiIBtYVR24gsPXVdW7xlXLAnRTVd2UhCSrV9WFSR7QuqgJ90Xg+CT/QfdX7V/QtSBoOGtW1al9EP4J8PokX6ULaRrGvfr7JwPHVtV1iRl4CFW1YBorDGRjlu5/7R7As4F9gU3aVjTRliZZD/gccEqSX+BSXEN7FfBi4C/pWmm+BHyoaUWT76Z+jOrF/cTaPwU2blzTpPtCkgvpuiz/KslGwE2Na5po/Xv8Ku7YNT8xQ1AcQzYmSR5JF8L+DNgAOBw4sap+0bSwBSLJY4H7ACdX1S2t65FmS5JdgB8C6wFvAtYF3lZV32pZ16TrL1i5vqqWJVkLWLeqrmpd16RK8iXgU8Ar6FreDwGurapXNS1sFhnIBpbkSOBZwGXAscAJwJKq2qZpYRMsybpVdX2SDWY6XlXXjbumSZfk+Kp6VpLzmWEAblU9pEFZ0mCSPIg7ttZ8tF1Fky3J2VX18CTnTX2eJDmjqh7burbZYpfl8A4DLqIb2H9SP6bJFDysTwL7AGfThYPRwR0FePXZ7Duiv9+nZRELUZJTgP2nTTVyXFXt1bSwCZbkdcDj6ALZ/wB/CnwNMJAN53f9/ZVJnkI3/GSLhvXMOlvIBtZfZfYkuqspH093Jc4TgC2r6taWtUmzJcl3q+phST5WVc9pXc9CMtO0Ik41Mqy+JXhH4HtVtWN/xfyHqmrfxqVNrCT7AF8FtgTeQ9c1/4aqOrFpYbPIFrKBVdUy4H+B/02yBl0LwlrAT5OcWlXPblrgBEuyO3BOVd2Y5GDgYcC7quqyxqVNotWSHAI8KsnTpx+sqs82qGmhuC3JVlM/10nuxwrmbdKs+G1V3Zbk1iTrAtdgy/ugquqk/uGv6C6MmzgGsjGqqpuATwOfTrIOcIdfXJpV7wd2TLIj8Ergw8DHgIkZczCH/AVwEN3A8umtBAUYyIbz98DXkpzRbz+GbqiEhrOkv4L7g3RDI34NnNW0ognXX2X5ImBrRrJLVb2gVU2zzS7LgSV57oqOOwh0OCPdaP8I/LSqPjy1r3VtkyrJoVX14dZ1LDRJNqRbiSJ0q1H8rHFJC0aSremusDyvdS2TLMk36LoszwaWTe2vqs80K2qWGcgG1i/7cIfddK0Im1eVrZQD6VsMTqabufwxdMtWnVNVD25a2ARK8viq+spM3ZVgl+UQkjywn+x4xj8wquq7465poehn6P9KVf2q314PeFxVfa5lXZNspmXZJo2BbIz6SWEPopvc7gfAkf5VNZwk96Wb++07VfXVJFvRfWjaKjnLkryhql6X5OgZDtckdSvMFUmOqqrDkpw2w+GapAkz5xrXbB2/JG8GvlFV/9O6lqEYyMYgyarA84C/Bb4NvKWqLmpalKR5r5+hf7eq+nrrWhaS0bmwRvadb+v7cJLcAKwN3MIfpsCoqlq3XVWza5XWBUy6JIfTtYY9HNi7qp5nGBuPJLsm+U6SXye5JcmyJL9qXdckS/JPfffN1Pb6/V+2GkBV3Qb8a+s6FqAlSd6RZNsk90/yTrqxTRpIVa1TVatU1Rr943UmKYyBLWSDS3Ib3SXR13L7S9FDl+6dwXwgSZYABwD/BewMPBdYXFWvbVrYBFvOnFheSDGgJG8AzgM+W36gj0WStYH/Qzen5NSarW+uqhubFjbhkjyVbjwwwOkjU2FMBAeUD88lkhqqqkuSLOrngzu6v1JHw1mUZPWquhkgyZrA6o1rmnQvp+vKuTXJTfzhj72Jaj2YS/rg9erWdSwkSd4K7AJ8ot/1siSPrqqJ+XcwkA1vzaq6EGD0F1W/vSvwk2aVTb7fJFkNOCfJvwBX0v3i0nA+DpzaD+4v4AXAMW1LmkxJdu/Hjm3Uz3GogSV5V1UdkeQLzLxm61MblLVQPBnYqe+mJ8kxwPeYoGBsl+XARrtrpnfd2JUzrH7G8quB1YC/Ae4DvK+qLmla2IRL8qfAnvRdOVX1xcYlTaSRxZb9HBmTJA+vqrOTzDi5dFWdMdN+3XNJzqO7Sv66fnsDum7LiRn2YwvZ8LKcxzNta5b0a4geWVUHAzcBb2hc0oJRVf9Lt1yYhvW7viVyiyT/Nv1gVb20QU0TrQ9ji4AX9Z8tGp+3AN/rp3kJ3Viy17QtaXYZyIZXy3k807ZmSVUtS7JRktWq6pbW9SwUfTf8e4A/oWuZXATc6HimQexDN6j88dzxCj8/WwbiZ0sbVXVsktPpxpEFeFVVXdW2qtllIBve1F+v4fZ/yQbYvF1ZC8KPga8nORH4/dVPVfWOZhVNvvdyxytb/7hpRROqXx7puCQ/rKpzp/YneTRwIOAEyMP5MX62jMUMK1Is7e83S7LZJK1IYSAb3t+NPF4y7dj0bc2uK/rbKsA6jWtZMLyydbyq6twkO9GtSvEs4FJgYtb3m6P8bBmflwOHAW+f4VjRtRBPBAf1S5o1Sc6k60b7EHAV3ZWtz6uqHZsWNoGSbEfXGnkg8HPgU8Arqup+TQtbQJKs7dxjmi0GsoH1TdrL5WXSw+kHf850afrE/EU11/TrhV6DV7YOrp90+qvAoVPvb5IfVdX921Y2+ZLsBnwYuHdVbZVkR+DFVfVXjUubWEmePsPuXwHnV9U1465nCHZZDm834HLgWLp1LL2ycnxeMfJ4DeAZwK2NaploSRbTLeGzLXA+XUuNV7YO6xl0LWSnJTkZOA4/X8blXcBewInw+27jx6zwGbqnDqX7fXpav/044FvAdkneWFUfa1XYbDGQDe++wBPpuhWeDfw3cGxVXdC0qgWgqqZfefb1JM4TNIz/pBtEfibwVLorLWf6i1azpKpOAE7ol/F5Gl2L5CZJ3g+cUFVfalnfpKuqy5Pb5d9lrWpZIG4D/qSqrgZIsgnwfuCRdJ878z6Qubj4wKpqWVWdXFWHALsClwCnJ3lJ49ImXpINRm4bJtmLLiBr9q1TVR+sqouq6m3A1q0LWiiq6saq+kRV7QNsAZzDBM1ePkddnuRRQCVZLckrgB+2LmrCbT0VxnrXANv1E8X+rlFNs8oWsjFIsjrwFLpWsq2BfwM+27KmBeJsujFkoeuqvJSu2Vuzb40kD+UPXWZrjm5P0qXpc1n/y+kD/U3D+Qvg3XRTFy2lW1z88KYVTb6vJjmJbkodgGcCZ/YtxL9sVtUsclD/wPr1th5EN3P5cVX1/cYlSbOuv4BiecoLKSTdE+n6h58OPJruD72vAZ+pCQoxBrKB9VdCTV0WPfpmh+4XlTOYz7Ikr6yqf+kf719V/zVy7J+q6rXtqpM03yXZBngJXY/H73uavGp+WP36xIur6stJ1gIWVdUNreuaLQYyTRwXdJc0pCTn0k17cT7dYHPAxcWHlORFdBPEblBV2/ZXdv9HVe3ZuLRZ4xgyTSIXdNeC0c/P9M/AxnQ/37a+D++mqrrDgu4a1OHAI+imj6KqLk6ycduSZpeBTJPIBd21kPwLsG9VeZXf+Lw7yevoBvPfPLXTi1cGdXNV3TI11UiSVZmwz3MDmSbRjkmup2spWLN/TL+9RruyJtfIwr8z8hfVoK42jI3dg4Hn0K2jONVlOVHrKs5BZyR5Ld1n+hOBvwK+0LimWeUYMkn32MhVlmsAOwPn0gXghwDfrqpHt6ptUo0sJfNYuvn1PsftW2ucWmcgSS4EHlJVt7SuZaHor7J8IfAkus+WLwIfmqSrLG0hG1iSG5i5WdVxHpoYVbUHQJLjgMOq6vx++0HcfgkrzZ59Rx7/hu4X1ZTCuQ6HdC6wHt3kpBpYklWA86rqQcAHW9czFAPZwKpqndY1SGP0wKkwBlBV30+yU8N6JlZVPR8gye5V9fXRY0l2b1PVgrEJcGGS7/CHVsmqqv0a1jSxquq2JOcm2aqqLmtdz1DsshyTJLsCF0zNmZLk3sAOVfXttpVJsyfJsXTz7n2crpXmYODeVXVg08Im2ExTuTi9y7CSPHZ0k26y0gOraodGJU28JF8BdgHO4g9ze07U3G+2kI3P+4HRD8jfzLBPmu+eD/wl8LJ++0y6n3PNsiS7AY8CNkry8pFD6wKL2lS1MFTVGX3L77OBZ9Ety/YfTYuafG9oXcDQDGTjk9HBh30TrO//AFYwbg8Ax+0Np6puAt7Z3zSs1YB7032Ojw6NuJ5unT/NsiTbAQfQrUv8c+BTdJ/tezQtbAEYnXQ3yT5VdVLLeoZgl+WYJPkscDp/aC34K2CPqnpaq5omXZI3AlcBH6PrVjgIWGdqWSXNvn7s0uuB+3H7JWXu36qmSZZkEfCpqjKAjUG/FN5XgUOr6pJ+34/8+R6vSe2SX6V1AQvIX9B1L/wUWAo8km4ZCA1nr6p6X1XdUFXXV9X7gWe0LmrCfRh4B92Yml1GbhpAVS0DNmhdxwLyDLo/8k5L8sEke+LqHy1M5Htul9n4XF9VB4zuSLJhq2IWiGVJDgKOo+vCPBBY1rakiferqvrf1kUsMN9LciLwX9x+sLPTXsyyqjoBOCHJ2sDTgL8BNknyfuCEqvpSy/oWkBe3LmAItpCNz3f6Ky0BSPIM4BsN61kIpgbcXt3f9u/3aTinJXlbkt2SPGzq1rqoCbcB3Ximx9PNTbYvsE/TiiZcVd1YVZ+oqn2ALYBzgFe3rWqyJdk/ydRYyb2SfHbSPlscQzYmSR4M/CfdOLLNgD8CXlhVS1vWJc2mkRn7R1VVuaSMpLstyXlV9ZAkjwb+CXg78NqqemTj0maNgWyMkjyNboD5DcBjpgaFahhJNgJeBGzN7QeYv6BVTdJsS7IF8B5gd7qu+a8BL/OPPU2SJN+rqocmeQtwflV9cmpf69pmi2PIxiTJh4Ft6db22w74QpL3VtW/t61son2e7oqoL+PYsbFJ8hRgB0YWcq+qN7araOIdDXySrkseusl4jwae2Kwiafb9NMkHgCcA/5xkdSZs2JUtZGOS5G+Ad03NRZbkPsA7qurQtpVNriTnVNVOretYSJL8B7AWsAfwIbr5sM7y53w4M/2c+7OvSZNkLWBvutaxi5NsCjx4ki6kmKh0OZdV1TuBrZI8od91C3BEu4oWhJOSPLl1EQvMo6rqucAvquoNwG7Alo1rmnQ/S3JwkkX97WC6Qf7SJNkU+O8+jD2OrkX4rKYVzTID2ZgkeRHwaeAD/a4tgM81K2hheBldKPttkuuT3JDk+tZFTbjf9ve/SbIZ8Dtgm4b1LAQvoLua+CrgSrpWScdJatJ8hm4qoz+mm+9wG7qu+onhGLLxORx4BPBtgD7lb9y2pMlWVevc+VmaZSclWQ94G/BdukHmH2xa0YSrqsuAiVlgWVqO26rq1iRPpxv+854k32td1GwykI3PzVV1S9JNMNyvY+kAvoElWR9YzO0HmJ/ZrqLJVlVv6h9+JslJwBpV9auWNU2qJP+4gsM18m8hTYLfJTkQeC7dXHsA92pYz6wzkI3PGUleC6yZ5Il0a1l+oXFNEy3JC+m6LacmbtwV+CbdBJoaWFXdDNzcuo4JduMM+9YGDqWb59BApknyfLolCI+sqkuTbAN8vHFNs8qrLMckySp0H5RPoluH64vAh8p/gMEkOZ9uHcVvVdVOSR4IvKGq/rxxadKs6mcwfxndZ8zxwNur6pq2VUmzK8lqdNNGAVxUVb9rWc9ss4VsTKrqNrqxNI6nGZ+bquqmJCRZvaouTPKA1kVJsyXJBsDLgYOAY4CHVdUv2lYlzb7+yspjgB/TNWpsmeSQSRqCYiAbWJIvAEcBJ09P80nuDzwP+HFV/WeD8ibd0n6A+eeAU5L8AriiaUULgOP2xiPJ24Cn032+PLiqft24JGlIbweeVFUXASTZDjgWeHjTqmaRXZYDS3Jfur9gnwFcB1xL94tqG+AS4L1V9fl2FS4MSR4L3IcuGN/Sup5Jtbxxe65lOfuS3EY3Ru9Wbn+BUOgG9a/bpDBpAFNrWd7ZvvnMQDZGSbamm9zut8D/X1W/aVuRNLsctydpCEmOBm6jWw8aum76Vavq+e2qml12WY5RVf2Yrv9bmlSO25M0hL+gm8/zpXStwGcC72ta0SwzkEmaTY7bkzSr+lkKzq6qBwHvaF3PUOyy1ERLcj9gcVV9OcmadE3cN7SuayFw3J6k2ZLkE8Br+pUpJpItZJpY/fqhhwEbANvSDTT/D2DPlnVNoiTrVtX1/TQMU87v7+9Nd0GLJN1dmwIXJDmLkUmRq2pilg2zhWxMkiwG3gJsz+2nA7h/s6ImXJJz6NcPraqH9vvOr6oHNy1sAiU5qar2SXIp3RV/GTlc/pzPviQ3sILl17zKUpOgX0x8E+7YgPRY4KdV9eHxVzUMW8jG52jgdcA7gT3oloHICp+he8r1Q8ekqvbp77dpXctCUVXrACR5I3AV3dVnobv6bJ2GpUmz6V3Aa6vqvNGdSW6k+506MYFsldYFLCBrVtWpdK2SP6mq1+OaikObvn7of+H6oYNKsnuStfvHByd5R5KtWtc14faqqvdV1Q1VdX1VvZ9u3kNpEmw9PYwBVNUSYOvxlzMcA9n43NRfKXJxkr9O8mfAxq2LmnCvppuI93zgxcD/AP/QtKLJ937gN0l2BF4J/IQ/zBukYSxLclCSRUlWSXIQsKx1UdIsWWMFx9YcWxVj4BiyMUmyC/BDYD3gTcC6wNuq6lst65p0/WK0D6TrqrzIq/2GleS7VfWwJP9IP75jal/r2iZVP+H0u4Hd6X7Ovw4c0c97KM1rSY4FvlJVH5y2/1C6pZQmZtJpA5kmVpKn0F1V+X/pxtZsA7y4qv63aWETLMkZwMl0YyQfQ9dCeY4XUki6O5JsApwA3AKc3e/eGVgN+LOquqpVbbPNQDYmSU4B9q+qX/bb6wPHVdVeTQubYEkuBPapqkv67W2B/66qB7atbHL1a7c+G/hOVX21Hz/2uKr6aOPSJlaSjYAX0Y2n+f2FWlX1glY1SbMtyR7Ag/rNC6rqKy3rGYJXWY7PhlNhDKCqfpHEMWTDumYqjPV+BFzTqpiFoP9rdXQm7a2ARwIGsuF8Hvgq8GUcO6YJVVWnAae1rmNIBrLxuS3JVlOzDPczyNs8OawLkvwPcDzde70/8J0kTweoqs+2LG5SJdmJrpXsWcClwGeaFjT51qqqV7UuQtI9YyAbn78HvtaPsYFufM1hDetZCNYArqabQBC68UwbAPvSBTQD2SxJsh1wAHAg8HPgU3RDIvZoWtjCcFKSJ1fV/7QuRNLd5xiyMUqyIbAr3QDzb1bVzxqXJM2KJLfRdZsdOjJm70fO0D+8fsb+tYGbgd/Rfb6UM/VL84vzkA0syQP7+4fRjae5AvgpsFW/TwNJskWSE5Jck+TqJJ9JskXruibUM+hmiz8tyQeT7IkrUYxFVa1TVatU1ZpVtW6/bRiT5hlbyAaW5KiqOizJTIMRq6qcrX8g/ZWtn+QPE5MeDBxUVU9sV9Vk62fpfxpd1+XjgWOAE6rqSy3rmnT9VduLuf06uWe2q0jSXWUgG4N+hv7dqurrrWtZSJKcU1U73dk+DSPJBnQXUvy5f3gMJ8kLgZcBWwDn0A2L+KbvuTS/2GU5BlV1G/CvretYgH7Wr6e4qL8dTDfgXGNQVddV1QcMBoN7GbAL8JP+IoqH0l3AImkeMZCNz5eSPCOJ42rG5wV0Uy9cBVwJPLPfJ02Sm6rqJoAkq1fVhcADGtck6S5y2ovxeTndlVC3JrkJr4QaVJJFwD9V1VNb1yINbGmS9YDPAack+QXdxUOS5hHHkA0sye5V9fUka0z9FavxSPJFYF8XFNdCkeSxwH2Ak/25l+YXA9nAkpxdVQ9P8t2qcpqLMUryAeBhwInAjVP7q+ody32SJEkN2GU5vN8lORrYIsm/TT9YVS9tUNNCcUV/WwVYp3EtkiQtl4FsePsAT6Cbk+nsacdsnhxQVb2hdQ2SJK0MuyzHJMmOVXXuyPajgQOr6vCGZU20fjLeO/yAOw2DJkmSf56+uPhM+yTNbQayMUqyE/BsuqkYLgU+U1XvbVrUBEvy8JHNNeiW97m1ql7ZqCRp1s00PjXJeVX1kFY1Sbrr7LIcWJLtgAPolpL5OfApuiC8R9PCFoCqmt5F/PUkZzQpRpplSf4S+Cvg/knOGzm0DuCqINI8YwvZwJLcBnwVOLSqLun3/aiq7t+2ssnXL90zZRXg4cC/VZWTZmreS3IfYH3gLcCrRw7dUFXXtalK0t1lC9nwnkHXQnZakpOB4+gmhdXwzqYbQxbgVrpu4kObViTNkqr6FfCrJP8AXFVVNyd5HPCQJB+tql+2rE/SXWML2ZgkWRt4Gl3X5eOBY4ATqupLLeuSNL8lOQfYGdga+CLdvHsPqKonNyxL0l3kWpZjUlU3VtUnqmofYAvgHG7fzaBZkuSVI4/3n3bsn8ZfkTSo26rqVuDpwLuq6m+ATRvXJOkuMpA1UFXXVdUHnH5hMAeMPH7NtGN7j7MQaQx+l+RA4LnASf2+ezWsR9LdYCDTJMpyHs+0Lc13zwd2A46sqkuTbAN8vHFNku4iB/VrEtVyHs+0Lc1rVfUD4KUj25cCb21XkaS7w0H9mjhJltEtJh5gTeA3U4eANarK7hxNjCSL6aa+2J5uAmQAnFpHml9sIdPEqapFrWuQxuho4HXAO4E96Low7ZqX5hnHkEnS/LZmVZ1K1+Pxk6p6Pd3UOpLmEVvIJGl+uynJKsDFSf4a+CmwceOaJN1FjiGTpHksyS7AD4H1gDcB6wJvq6pvtaxL0l1jIJOkCZNk1X6yWEnzhGPIJGkeSvK1kccfm3b4rDGXI+keMpBJ0vy09sjjHaYd8ypLaZ4xkEnS/LSi8SaORZHmGa+ylKT5ab0kf0b3h/V6SZ7e7w9wn3ZlSbo7HNQvSfNQkqNXdLyqnj+uWiTdcwYySZKkxhxDJkmS1JiBTJIkqTEDmSRJUmMGMkmax5KsleT/JPlgv704yT6t65J01xjIJGl+Oxq4Gdit314KvLldOZLuDgOZJM1v21bVvwC/A6iq3+JM/dK8YyCTpPntliRr0s/On2RbuhYzSfOIM/VL0vz2OuBkYMsknwB2B57XtCJJd5kTw0rSPJfkj4Bd6boqv1VVP2tckqS7yEAmSfNQkoet6HhVfXdctUi65wxkkjQPJTltBYerqh4/tmIk3WMGMkmSpMYc1C9J81CSp6/oeFV9dly1SLrnDGSSND/t299vDDwK+Eq/vQdwOmAgk+YRA5kkzUNV9XyAJCcB21fVlf32psC/t6xN0l3nxLCSNL9tPRXGelcD27UqRtLdYwuZJM1vpyf5InAs3Wz9BwArugJT0hzkVZaSNM/1A/z/v37zzKo6oWU9ku46A5kkSVJjdllK0jyU5Ab6BcVnUlXrjrEcSfeQgUyS5qGqWgcgyRuBq4CP0a1leRCwTsPSJN0NdllK0jyW5NtV9cg72ydpbnPaC0ma35YlOSjJoiSrJDkIWNa6KEl3jYFMkua3ZwPPopt/7Gpg/36fpHnELktJkqTGHNQvSfNQkldW1b8keQ8zXG1ZVS9tUJaku8lAJknz0w/7+yVNq5A0K+yylCRJaswWMkmah5KcuKLjVfXUcdUi6Z4zkEnS/LQbcDndouLfppsUVtI8ZZelJM1DSRYBTwQOBB4C/DdwbFVd0LQwSXeL85BJ0jxUVcuq6uSqOgTYFbgEOD3JSxqXJulusMtSkuapJKsDT6FrJdsa+Dfgsy1rknT32GUpSfNQkmOABwH/CxxXVd9vXJKke8BAJknzUJLbgBv7zdEP8gBVVeuOvypJd5eBTJIkqTEH9UuSJDVmIJMkSWrMQCZJktSY015IWnCSLAPOp/sMvBR4TlX9smlRkhY0W8gkLUS/raqdqupBwHXA4a0LkrSwGcgkLXTfBDYHSLJtkpOTnJ3kq0keOLL/W0m+k+SNSX7dtGJJE8dAJmnB6teD3BM4sd91FPCSqno48Argff3+dwPvrqpdgCvGXqikiec8ZJIWnJExZFsDZwNPAtYErgUuGjl19ar6kyQ/BzapqluTrAtcUVX3HnPZkiaYLWSSFqLfVtVOwP2A1ejGkK0C/LIfWzZ1+5OWRUpaOAxkkhasqvoV8FK67snfApcm2R8gnR37U78FPKN/fMDYC5U08Qxkkha0qvoecC5d0DoIODTJucAFwH79aUcAL09yFrAp8KsGpUqaYI4hk6Q7kWQtum7OSnIAcGBV7Xdnz5OkleXEsJJ05x4OvDdJgF8CL2hbjqRJYwuZJElSY44hkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMb+H+eNMTIjXaisAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart = sns.countplot(x = df.Reg, data = df, order=df.Reg.value_counts().iloc[:len(df.Reg.value_counts())].index)\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "chart.set_title(\"Número de acuerdos por región\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incoming-smoke",
   "metadata": {},
   "source": [
    "Como se puede ver la región  que más veces aparece con acuerdos es la región de África (excl MENA), seguida de Europa y Eurasia."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "editorial-broadcasting",
   "metadata": {},
   "source": [
    "Miramos las diferentes tipo de acuerdos que se han firmado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "worst-liberia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Multiparty signed/agreed            1725\n",
       "Unilateral document                  125\n",
       "Status unclear                        10\n",
       "Agreement with subsequent status       8\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ranging-enzyme",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Status de acuerdos realizados')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart = sns.countplot(x = df.Status, data = df, order=df.Status.value_counts().iloc[:len(df.Status.value_counts())].index)\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "chart.set_title(\"Status de acuerdos realizados\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hawaiian-captain",
   "metadata": {},
   "source": [
    "Se puede observar que los acuerdo de Multiparty son el tipo de acuerdo que más se ha realizado, en total 1725 acuerdos donde ha estado involucrado más de un grupo de protagonistas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proud-campus",
   "metadata": {},
   "source": [
    "Miramos la fase en la que se encuentra los diferentes tipos de acuerdos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "suitable-nirvana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pre        513\n",
       "SubPar     475\n",
       "Cea        388\n",
       "Imp        335\n",
       "SubComp    113\n",
       "Ren         43\n",
       "Oth          1\n",
       "Name: Stage, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fatty-opportunity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Stage de acuerdos realizados')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart = sns.countplot(x = df.Stage, data = df, order=df.Stage.value_counts().iloc[:len(df.Stage.value_counts())].index)\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "chart.set_title(\"Stage de acuerdos realizados\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informational-building",
   "metadata": {},
   "source": [
    "Se puede observar las 7 etapas en las que se pueden encontrar los diferentes acuerdos de paz. Mayoritariamente se observa que los acuerdos se encuentran en la etapa Pre y SubPar.\n",
    "\n",
    "En el stage Pre, los acuerdos pretenden que las partes lleguen a negociar las incompatibilidades que están en el centro del conflicto y en el stage Subpar son los acuerdos que afectan a las partes que están debatiendo y acordando temas importantes para resolver."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medical-commonwealth",
   "metadata": {},
   "source": [
    "A continuación, miramos los diferentes países con más acuerdos, en esta exploración, se ha configurado el gráfico para que se pueda visualizar correctamente estableciendo solo los 10 países con más acuerdos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "creative-sunrise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bosnia and Herzegovina/Yugoslavia (former)    133\n",
       "Colombia                                      128\n",
       "Philippines/Mindanao                          126\n",
       "Syria                                          93\n",
       "South Sudan                                    51\n",
       "Nepal                                          51\n",
       "Yemen                                          50\n",
       "Croatia/Yugoslavia (former)                    44\n",
       "South Sudan/Sudan                              44\n",
       "Central African Republic                       43\n",
       "Name: Con, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Con.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "liable-brooklyn",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Número de acuerdos por país')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "chart = sns.countplot(x = df.Con, data = df, order=df.Con.value_counts().iloc[:10].index)\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90)\n",
    "chart.set_title(\"Número de acuerdos por país\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "warming-season",
   "metadata": {},
   "source": [
    "Se puede ver que el país con más acuerdos es Bosnia and Herzegovina/Yugoslavia (former) con más de 130 acuerdos seguido de Colombia con 128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "junior-wednesday",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sudan/Uganda                                                           1\n",
      "Eritrea/Sudan                                                          1\n",
      "China/Japan/North Korea/Russia/South Korea/United States of America    1\n",
      "Afghanistan/Pakistan                                                   1\n",
      "Costa Rica/El Salvador/Guatemala/Honduras/Nicaragua                    1\n",
      "Indonesia/Portugal/(East Timor)                                        1\n",
      "Macedonia/Yugoslavia (former)                                          1\n",
      "Central African Republic/Chad/Sudan/(Darfur)                           1\n",
      "Central African Republic/Sudan                                         1\n",
      "Bangladesh/Chittagong Hill Tracts                                      1\n",
      "Name: Con, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "obj = df.Con.value_counts()\n",
    "print(obj[-10:len(obj)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "empty-violin",
   "metadata": {},
   "source": [
    "También podemos ver los diferentes países con menos acuerdos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "approximate-thing",
   "metadata": {},
   "source": [
    "Después de mirar las diferentes regiones y países, nos centraremos en la evolución a lo largo de los años que se han producido estos acuerdos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "human-rider",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-02-15\n",
      "2020-04-09\n"
     ]
    }
   ],
   "source": [
    "print(df.Dat.min())\n",
    "print(df.Dat.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latin-tongue",
   "metadata": {},
   "source": [
    "La fecha mínima registrada es en el año 1990 y la última en el 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "short-score",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Dat.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "charming-rainbow",
   "metadata": {},
   "source": [
    "Primero, antes que nada, haremos una transformación de la columna fecha (Dat) que contiene las fechas que se realizaron los acuerdos. Esta columna contiene las fechas en formato Object (O), la pasaremos a formato Date, pero no reemplazaremos, crearemos una nueva columna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "english-production",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df.Dat, infer_datetime_format= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "given-charlotte",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2019-07-08\n",
       "1      2016-09-22\n",
       "2      2014-09-21\n",
       "3      2012-07-08\n",
       "4      2011-12-05\n",
       "          ...    \n",
       "1863   1994-06-30\n",
       "1864   1990-04-22\n",
       "1865   2013-03-19\n",
       "1866   2008-09-15\n",
       "1867   2008-07-21\n",
       "Name: Date, Length: 1868, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-consistency",
   "metadata": {},
   "source": [
    "Como podemos ver ahora sí la tenemos en formato datetime, de esta fecha podemos extraer solo el año y añadiéndolo a una nueva columna en el dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "christian-clone",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2019\n",
       "1       2016\n",
       "2       2014\n",
       "3       2012\n",
       "4       2011\n",
       "        ... \n",
       "1863    1994\n",
       "1864    1990\n",
       "1865    2013\n",
       "1866    2008\n",
       "1867    2008\n",
       "Name: Year, Length: 1868, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'] = df['Date'].dt.year\n",
    "df.Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "surgical-pipeline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.title(\"Serie temporal de acuerdos de paz\")\n",
    "fig = df['Date'].value_counts().resample('Y').sum().plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "studied-paint",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "B:\\software\\miniConda\\envs\\models\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Acuerdos de paz por año')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,6))\n",
    "plt.subplot(121)\n",
    "sns.distplot(df['Year'], kde = True, bins = 50).set_title('Acuerdos de paz por año')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "close-aviation",
   "metadata": {},
   "source": [
    "Observando la serie podemos apreciar que tenemos una ligera tendencia lineal a la baja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "raised-geography",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions_evolution = pd.crosstab(df.Year,df.Reg)\n",
    "regions_evolution.plot(color=sns.color_palette('Set2',12), title='Peace Agreements per region over time')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(18,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opposite-russian",
   "metadata": {},
   "source": [
    "### Conclusiones\n",
    "\n",
    "- Mayoritariamente  los acuerdos se encuentran en el stage Pre, los acuerdos que pretenden que las partes lleguen a negociar las incompatibilidades que están en el centro del conflicto.\n",
    "\n",
    "- Los acuerdos de tipo Multiparty son el tipo de acuerdo que más se realizado, en total 1725 acuerdos donde ha estado involucrado más de un grupo de protagonistas.\n",
    "- Se ha observado que entre los años 91-95 se realizaron más acuerdos, aunque desde el 2014 al 2020 en la región Middle East y North África también se aprecia una cantidad significante de acuerdos realizados.\n",
    "\n",
    "- Se puede apreciar principalmente la curva Europea y Eurasia que en esta región, muchos acuerdos de paz se realizaron, sobre todo entre los años 91-95, mientras que después se ha firmado un número muy bajo de acuerdos, puede que se deba a que esta región no ha tenido muchos conflictos después de estos años.\n",
    "\n",
    "- Los países con más acuerdos son Bosnia and Herzegovina/Yugoslavia (former) con más de 130 acuerdos seguido de Colombia con 128.\n",
    "\n",
    "- También se ha observado que varias regiones tienen una ligera tendencia lineal a la baja, en otras palabras, con el pasar de los años no se han ido realizando más acuerdo de paz, se puede el caso de Asia y Europa. Se podría decir que la tendencia continuará de esta manera, aunque para hacer un análisis más exahustivo se tendría que ajustar una regresión lineal para tratar la tendencia y aplicar un modelo para intentar predecir cómo sería el número de acuerdos en los siguientes años.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thousand-therapist",
   "metadata": {},
   "source": [
    "## 5. Procedimiento y herramientas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organized-amazon",
   "metadata": {},
   "source": [
    "En este documento se presenta primer contacto con el dataset. Ha  sido realizado usando Jupyter Notebook en un conda enviroment que ejecuta la versión 3.6 de Python. Las librería que se han usado son las usadas en este documento, entre las principales se tiene que destacar, Padas, Numpy, Matplotlib.\n",
    "\n",
    "Lo primero que se ha realizado es la carga del dataset para realizar una primera inspección, dimensión, valores nulos, tipo de datos. Cuando se ha requerido se ha transformado para facilitar la manipulación y/o creación de visualizaciones.\n",
    "\n",
    "La herramienta que se ha utilizado para poder hacer las representaciones gráficas del dashboard es Tableau, esta herramienta permite expresar datos visualmente al transformar acciones de arrastrar y soltar en consultas de dados, en una interfaz intuitiva. Puede permitir incluso aprendizaje automático, estadísticos, lenguaje natural y preparación de los datos inteligentes para que sean más eficaces y permitan aumentar la creatividad de las personas que lo utilizan para hacer análisis. \n",
    "\n",
    "\n",
    "También se han realizado distintas pruebas con Tableau para poder asegurarse del diseño adecuado que se pretende seleccionar, para que el usuario final puede interactuar. En general, se ha tenido en cuenta que la visualización de todos los gráficos sean capaces de entenderse sin poner texto que los acompañen, pero utilizando herramientas elementales que mejora mucho la legibilidad de las visualizaciones como son las etiquetas y las leyendas. \n",
    "\n",
    "Por último, el panel de Tableu ha facilitado la realización de las visualizaciones en comparación con matplotlib. Usando esta herramienta resulta más fácil centrarse en lo que se quiere visualizar.\n",
    "\n",
    "En este repositorio se encuentra adjunto el dashboard de Tableau."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "diagnostic-david",
   "metadata": {},
   "source": [
    "## 6. Conjunto de vistas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complimentary-copper",
   "metadata": {},
   "source": [
    "En el siguiente [link](https://public.tableau.com/profile/adonis.gonz.lez#!/vizhome/Evolutionofthepeaceagreementsovertheyearsinthedifferentregions/Dashboard1) se muestra la representación de las diferentes visualizaciones. Es un dashboard que ha sido publicado en Tableu public."
   ]
  }
 ],
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