Análisis De Resultados en Mesas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1fc37dc4",
   "metadata": {},
   "source": [
    "# Proyecto Veritas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52731678",
   "metadata": {},
   "source": [
    "Objetivo: analizar de forma objetiva los resultados en las mesas de votacion en las elecciones presidenciales 2021 de Peru para determinar si hay irregularidades estadisticas y que pueden significar. Se esta utilizando la data descargada desde la pagina oficial de la ONPE ( https://www.datosabiertos.gob.pe/dataset/resultados-por-mesa-de-las-elecciones-presidenciales-2021-segunda-vuelta-oficina-nacional-de )."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542f794e",
   "metadata": {},
   "source": [
    "## Importando Librerias y Cargando Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3eed532",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.503657Z",
     "start_time": "2021-06-20T14:24:01.520162Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import scipy\n",
    "import math\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "import seaborn as sns\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "plt.style.use(\"default\")\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.float', '{:.5f}'.format)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d93904d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.659681Z",
     "start_time": "2021-06-20T14:24:02.505651Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Resultados_2da_vuelta_Version_PCM.csv\", sep = ';', index_col=False, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34bf19e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.690048Z",
     "start_time": "2021-06-20T14:24:02.661677Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UBIGEO</th>\n",
       "      <th>DEPARTAMENTO</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>DISTRITO</th>\n",
       "      <th>TIPO_ELECCION</th>\n",
       "      <th>MESA_DE_VOTACION</th>\n",
       "      <th>DESCRIP_ESTADO_ACTA</th>\n",
       "      <th>TIPO_OBSERVACION</th>\n",
       "      <th>N_CVAS</th>\n",
       "      <th>N_ELEC_HABIL</th>\n",
       "      <th>VOTOS_P1</th>\n",
       "      <th>VOTOS_P2</th>\n",
       "      <th>VOTOS_VB</th>\n",
       "      <th>VOTOS_VN</th>\n",
       "      <th>VOTOS_VI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43463</th>\n",
       "      <td>921315</td>\n",
       "      <td>AMERICA</td>\n",
       "      <td>ESTADOS UNIDOS DE AMERICA</td>\n",
       "      <td>LOS ANGELES</td>\n",
       "      <td>PRESIDENCIAL</td>\n",
       "      <td>81157</td>\n",
       "      <td>CONTABILIZADA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.00000</td>\n",
       "      <td>298</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>63.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26152</th>\n",
       "      <td>150301</td>\n",
       "      <td>LORETO</td>\n",
       "      <td>LORETO</td>\n",
       "      <td>NAUTA</td>\n",
       "      <td>PRESIDENCIAL</td>\n",
       "      <td>62468</td>\n",
       "      <td>CONTABILIZADA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177.00000</td>\n",
       "      <td>300</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       UBIGEO DEPARTAMENTO                  PROVINCIA     DISTRITO  \\\n",
       "43463  921315      AMERICA  ESTADOS UNIDOS DE AMERICA  LOS ANGELES   \n",
       "26152  150301       LORETO                     LORETO        NAUTA   \n",
       "\n",
       "      TIPO_ELECCION  MESA_DE_VOTACION DESCRIP_ESTADO_ACTA  TIPO_OBSERVACION  \\\n",
       "43463  PRESIDENCIAL             81157       CONTABILIZADA               NaN   \n",
       "26152  PRESIDENCIAL             62468       CONTABILIZADA               NaN   \n",
       "\n",
       "         N_CVAS  N_ELEC_HABIL  VOTOS_P1  VOTOS_P2  VOTOS_VB  VOTOS_VN  \\\n",
       "43463  83.00000           298  15.00000  63.00000       NaN   5.00000   \n",
       "26152 177.00000           300 100.00000  66.00000   2.00000   9.00000   \n",
       "\n",
       "       VOTOS_VI  \n",
       "43463       NaN  \n",
       "26152       NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e291b09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.706069Z",
     "start_time": "2021-06-20T14:24:02.691046Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data.rename(columns={'VOTOS_P1': 'PL', 'VOTOS_P2': 'FP'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d39285a",
   "metadata": {},
   "source": [
    "## Analisis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a413894",
   "metadata": {},
   "source": [
    "La metodologia utilizada va a ser de calcular si una muestra pertenece a una poblacion y luego graficar la distribucion que obtenemos sobrepuesta sobre la distribucion esperada para ver si hay desviaciones significativas. En este caso la muestra va a ser la mesa de votacion y la poblacion el distrito."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2b19532",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.721820Z",
     "start_time": "2021-06-20T14:24:02.708035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86488, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84558ef",
   "metadata": {},
   "source": [
    "Remplazando celdas vacias de votos por 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "157f6b4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.737478Z",
     "start_time": "2021-06-20T14:24:02.723816Z"
    }
   },
   "outputs": [],
   "source": [
    "data[\"PL\"].fillna(0, inplace = True)\n",
    "data[\"FP\"].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3408fd6d",
   "metadata": {},
   "source": [
    "Calculando la cantidad de votos validos emitidos para cualquier partido y la porporcion de estos que fueron para FP en cada mesa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "203cdf65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.753023Z",
     "start_time": "2021-06-20T14:24:02.739459Z"
    }
   },
   "outputs": [],
   "source": [
    "data['VOTOS'] = (data['FP']+data['PL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbeb826c",
   "metadata": {},
   "source": [
    "Eliminando filas con total de votos en 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56031a85",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.784551Z",
     "start_time": "2021-06-20T14:24:02.756015Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86254, 16)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data.VOTOS != 0]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02171083",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.815011Z",
     "start_time": "2021-06-20T14:24:02.787279Z"
    }
   },
   "outputs": [],
   "source": [
    "data['RESULTADO'] = (data['FP'])/(data['FP']+data['PL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94dc8374",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.846682Z",
     "start_time": "2021-06-20T14:24:02.816974Z"
    }
   },
   "outputs": [],
   "source": [
    "data['DISTRITO_2'] = data['DEPARTAMENTO']+data['PROVINCIA']+data['DISTRITO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6300aa70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:02.862006Z",
     "start_time": "2021-06-20T14:24:02.848048Z"
    }
   },
   "outputs": [],
   "source": [
    "data['DISTRITO_2'] = data['DISTRITO_2'].astype('str')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58101cca",
   "metadata": {},
   "source": [
    "Eliminando distritos donde solo hay una mesa en ese distrito porque no se puede hacer el analisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07e4567a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:03.033640Z",
     "start_time": "2021-06-20T14:24:02.864006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86172, 18)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.groupby('DISTRITO_2').filter(lambda x : len(x)>1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "869e0188",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:03.065306Z",
     "start_time": "2021-06-20T14:24:03.034638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DISTRITO</th>\n",
       "      <th>CANTIDAD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>SAN MARTINRIOJAPARDO MIGUEL</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DISTRITO  CANTIDAD\n",
       "296  SAN MARTINRIOJAPARDO MIGUEL        48"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distritos = data['DISTRITO_2'].value_counts().reset_index()\n",
    "distritos.columns = ['DISTRITO','CANTIDAD']\n",
    "distritos.sample(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1bfc1c",
   "metadata": {},
   "source": [
    "Formula para calcular la desviacion estandar y promedio del distrito ponderado por la cantidad de votos validos de la mesa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eea5182c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:03.080647Z",
     "start_time": "2021-06-20T14:24:03.067190Z"
    }
   },
   "outputs": [],
   "source": [
    "def weighted_avg_and_std(values, weights):\n",
    "    \"\"\"\n",
    "    Return the weighted average and standard deviation.\n",
    "\n",
    "    values, weights -- Numpy ndarrays with the same shape.\n",
    "    \"\"\"\n",
    "    average = np.average(values, weights=weights)\n",
    "    # Fast and numerically precise:\n",
    "    variance = np.average((values-average)**2, weights=weights)\n",
    "    return (average, math.sqrt(variance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7884c478",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:18.015049Z",
     "start_time": "2021-06-20T14:24:03.082654Z"
    }
   },
   "outputs": [],
   "source": [
    "data2 = []\n",
    "for distrito in distritos.DISTRITO:\n",
    "    df = data[data['DISTRITO_2']==distrito].copy()\n",
    "    df['PROM_DIST'] = weighted_avg_and_std(df['RESULTADO'],df['VOTOS'])[0]\n",
    "    df['STD_DIST'] = weighted_avg_and_std(df['RESULTADO'],df['VOTOS'])[1]\n",
    "    df['VALOR_Z'] = (df['RESULTADO'] - df['PROM_DIST'])/df['STD_DIST']\n",
    "    df['VALOR_P'] = scipy.stats.norm.sf(abs(df['VALOR_Z']))*2  \n",
    "    data2.append(df)\n",
    "    \n",
    "data2 = pd.concat(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e0fa74e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:18.062124Z",
     "start_time": "2021-06-20T14:24:18.016405Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Las mesas con un valor p menor a  0.01  fueron 1149\n",
      "con 86172 mesas en total se esperaria que hubieran sido  862\n",
      "Fuerza Popular tuvo 29916 votos mas en estas mesas\n",
      "\n",
      "Las mesas con un valor p menor a  0.001  fueron 290\n",
      "con 86172 mesas en total se esperaria que hubieran sido  86\n",
      "Fuerza Popular tuvo 644 votos mas en estas mesas\n",
      "\n",
      "Las mesas con un valor p menor a  0.0001  fueron 119\n",
      "con 86172 mesas en total se esperaria que hubieran sido  9\n",
      "Peru Libre tuvo 2745 votos mas en estas mesas\n",
      "\n"
     ]
    }
   ],
   "source": [
    "values = [0.01,0.001,0.0001]\n",
    "\n",
    "data.shape[0]\n",
    "\n",
    "for value in values:\n",
    "\n",
    "    mesas_menor = data2[data2['VALOR_P']<value]\n",
    "    print('Las mesas con un valor p menor a ',str(value),' fueron',str(mesas_menor['VALOR_P'].count()))\n",
    "    print('con', str(data.shape[0]), 'mesas en total se esperaria que hubieran sido ',\n",
    "          str(round(data.shape[0]*value)))\n",
    "    if mesas_menor['FP'].sum() > mesas_menor['PL'].sum():\n",
    "        print(\"Fuerza Popular tuvo\", str(round(mesas_menor['FP'].sum()-mesas_menor['PL'].sum())),'votos mas en estas mesas\\n')\n",
    "    else:\n",
    "        print(\"Peru Libre tuvo\", str(round(mesas_menor['PL'].sum()-mesas_menor['FP'].sum())),'votos mas en estas mesas\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b084ad9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-20T14:24:18.974809Z",
     "start_time": "2021-06-20T14:24:18.064117Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.kdeplot(data2['VALOR_Z'], shade='fill')\n",
    "ax.set(xlabel='Desviaciones Estandar de la Mesa')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}