KPI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ssl\n",
    "from urllib.request import urlopen\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "url='https://es.wikipedia.org/wiki/Buenos_Aires'\n",
    "pagina=pd.read_html(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_homicidios=pd.read_csv('Recursos_dashboard\\Datos_homicidios.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraer el mes de la fecha y calcular el semestre\n",
    "df_homicidios['FECHA']=pd.to_datetime(df_homicidios['FECHA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_homicidios['SEMESTRE'] = ((df_homicidios['FECHA'].dt.month-1)//6)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\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>ID</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "      <th>FECHA</th>\n",
       "      <th>AAAA</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>HORA</th>\n",
       "      <th>HH</th>\n",
       "      <th>LUGAR_DEL_HECHO</th>\n",
       "      <th>TIPO_DE_CALLE</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitud</th>\n",
       "      <th>Latitud</th>\n",
       "      <th>PARTICIPANTES</th>\n",
       "      <th>ACUSADO</th>\n",
       "      <th>ROL</th>\n",
       "      <th>VICTIMA</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>FECHA_FALLECIMIENTO</th>\n",
       "      <th>SEMESTRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2016-0142</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-05</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>23</td>\n",
       "      <td>AV DEL LIBERTADOR Y AV RAMOS MEJIA</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.37533517</td>\n",
       "      <td>-34.59276462</td>\n",
       "      <td>MOTO-PASAJEROS</td>\n",
       "      <td>PASAJEROS</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2016-11-05 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-0010</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-29</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>15:20:00</td>\n",
       "      <td>15</td>\n",
       "      <td>AV GENERAL PAZ Y AV DE LOS CORRALES</td>\n",
       "      <td>GRAL PAZ</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.50877521</td>\n",
       "      <td>-34.66977709</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2016-01-29 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2017-0016</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-02-03</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>05:12:21</td>\n",
       "      <td>5</td>\n",
       "      <td>AU 25 DE MAYO, KILOMETRO 8.3, SENTIDO HACIA PR...</td>\n",
       "      <td>AUTOPISTA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.38099494</td>\n",
       "      <td>-34.62288231</td>\n",
       "      <td>MOTO-CARGAS</td>\n",
       "      <td>CARGAS</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2017-02-04 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2020-0065</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>PAZ, GRAL. AV. Y GOYENECHE, ROBERTO PARQUE AV.</td>\n",
       "      <td>GRAL PAZ</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.49332664</td>\n",
       "      <td>-34.54836855</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2020-12-06 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>2019-0018</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>23:30:00</td>\n",
       "      <td>23</td>\n",
       "      <td>CANTILO, INT. Y UDAONDO, GUILLERMO AV.</td>\n",
       "      <td>AUTOPISTA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.44927253</td>\n",
       "      <td>-34.54048625</td>\n",
       "      <td>PEATON-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>PEATON</td>\n",
       "      <td>PEATON</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2019-03-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  N_VICTIMAS      FECHA  AAAA  MM  DD      HORA  HH  \\\n",
       "113  2016-0142           1 2016-11-05  2016  11   5  23:30:00  23   \n",
       "7    2016-0010           1 2016-01-29  2016   1  29  15:20:00  15   \n",
       "157  2017-0016           1 2017-02-03  2017   2   3  05:12:21   5   \n",
       "604  2020-0065           1 2020-12-05  2020  12   5  16:00:00  16   \n",
       "453  2019-0018           1 2019-03-03  2019   3   3  23:30:00  23   \n",
       "\n",
       "                                       LUGAR_DEL_HECHO TIPO_DE_CALLE  ...  \\\n",
       "113                 AV DEL LIBERTADOR Y AV RAMOS MEJIA       AVENIDA  ...   \n",
       "7                  AV GENERAL PAZ Y AV DE LOS CORRALES      GRAL PAZ  ...   \n",
       "157  AU 25 DE MAYO, KILOMETRO 8.3, SENTIDO HACIA PR...     AUTOPISTA  ...   \n",
       "604     PAZ, GRAL. AV. Y GOYENECHE, ROBERTO PARQUE AV.      GRAL PAZ  ...   \n",
       "453             CANTILO, INT. Y UDAONDO, GUILLERMO AV.     AUTOPISTA  ...   \n",
       "\n",
       "         Longitud       Latitud   PARTICIPANTES    ACUSADO        ROL VICTIMA  \\\n",
       "113  -58.37533517  -34.59276462  MOTO-PASAJEROS  PASAJEROS  CONDUCTOR    MOTO   \n",
       "7    -58.50877521  -34.66977709       MOTO-AUTO       AUTO  CONDUCTOR    MOTO   \n",
       "157  -58.38099494  -34.62288231     MOTO-CARGAS     CARGAS  CONDUCTOR    MOTO   \n",
       "604  -58.49332664  -34.54836855       MOTO-AUTO       AUTO  CONDUCTOR    MOTO   \n",
       "453  -58.44927253  -34.54048625     PEATON-AUTO       AUTO     PEATON  PEATON   \n",
       "\n",
       "          SEXO  EDAD  FECHA_FALLECIMIENTO SEMESTRE  \n",
       "113  MASCULINO  46.0  2016-11-05 00:00:00        2  \n",
       "7    MASCULINO  18.0  2016-01-29 00:00:00        1  \n",
       "157  MASCULINO  27.0  2017-02-04 00:00:00        1  \n",
       "604  MASCULINO  24.0  2020-12-06 00:00:00        2  \n",
       "453   FEMENINO  29.0  2019-03-03 00:00:00        1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_homicidios.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'N_VICTIMAS', 'FECHA', 'AAAA', 'MM', 'DD', 'HORA', 'HH',\n",
       "       'LUGAR_DEL_HECHO', 'TIPO_DE_CALLE', 'Calle', 'Altura', 'Cruce',\n",
       "       'Dirección Normalizada', 'COMUNA', 'XY (CABA)', 'Longitud', 'Latitud',\n",
       "       'PARTICIPANTES', 'ACUSADO', 'ROL', 'VICTIMA', 'SEXO', 'EDAD',\n",
       "       'FECHA_FALLECIMIENTO', 'SEMESTRE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_homicidios.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion=pagina[4].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Población histórica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Pob.</th>\n",
       "      <th>±%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1779</td>\n",
       "      <td>24 205</td>\n",
       "      <td>—</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1810</td>\n",
       "      <td>44 800</td>\n",
       "      <td>+85.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1869</td>\n",
       "      <td>177 797</td>\n",
       "      <td>+296.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1895</td>\n",
       "      <td>663 854</td>\n",
       "      <td>+273.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1914</td>\n",
       "      <td>1 575 814</td>\n",
       "      <td>+137.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1947</td>\n",
       "      <td>2 981 043</td>\n",
       "      <td>+89.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1960</td>\n",
       "      <td>2 966 634</td>\n",
       "      <td>−0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1970</td>\n",
       "      <td>2 972 453</td>\n",
       "      <td>+0.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1980</td>\n",
       "      <td>2 922 829</td>\n",
       "      <td>−1.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1991</td>\n",
       "      <td>2 965 403</td>\n",
       "      <td>+1.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2001</td>\n",
       "      <td>2 776 138</td>\n",
       "      <td>−6.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>2 890 151</td>\n",
       "      <td>+4.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2022</td>\n",
       "      <td>3 120 612</td>\n",
       "      <td>+8.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Población histórica                    \n",
       "                   Año       Pob.       ±%\n",
       "0                 1779     24 205        —\n",
       "1                 1810     44 800   +85.1%\n",
       "2                 1869    177 797  +296.9%\n",
       "3                 1895    663 854  +273.4%\n",
       "4                 1914  1 575 814  +137.4%\n",
       "5                 1947  2 981 043   +89.2%\n",
       "6                 1960  2 966 634    −0.5%\n",
       "7                 1970  2 972 453    +0.2%\n",
       "8                 1980  2 922 829    −1.7%\n",
       "9                 1991  2 965 403    +1.5%\n",
       "10                2001  2 776 138    −6.4%\n",
       "11                2010  2 890 151    +4.1%\n",
       "12                2022  3 120 612    +8.0%"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poblacion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "victimas_por_semestre="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>AAAA</th>\n",
       "      <th>SEMESTRE</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AAAA  SEMESTRE  N_VICTIMAS\n",
       "0   2016         1          65\n",
       "1   2016         2          81\n",
       "2   2017         1          69\n",
       "3   2017         2          71\n",
       "4   2018         1          70\n",
       "5   2018         2          79\n",
       "6   2019         1          57\n",
       "7   2019         2          47\n",
       "8   2020         1          31\n",
       "9   2020         2          50\n",
       "10  2021         1          55\n",
       "11  2021         2          42"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homicidios_semestre = df_homicidios.groupby(['AAAA', 'SEMESTRE'])['N_VICTIMAS'].sum().reset_index()\n",
    "homicidios_semestre\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion.columns=poblacion.columns.droplevel(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion['Pob.']=poblacion['Pob.'].str.replace(\"\\xa0\",'')\n",
    "poblacion=poblacion.rename(columns={'Pob.':'Poblacion'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=poblacion.Año.values.reshape(-1,1)\n",
    "y=poblacion.Poblacion.values.reshape(-1,1)\n",
    "X_selected = X.tolist()  # Convierte X_selected a lista\n",
    "y_selected = y.tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_selected,y_selected)\n",
    "plt.xlabel('Años')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=35)\n",
    "X_poly = poly.fit_transform(X_selected,y_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(X_poly,y_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_head2 = linear_regression.predict(X_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crear la gráfica\n",
    "plt.plot(X_selected,y_head2,color='green',label='poly')\n",
    "plt.legend()\n",
    "plt.scatter(X_selected, y_selected)\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Población')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9869452761268179"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y,y_head2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predicted_population(year):\n",
    "    X_pred = poly.transform([[year]])\n",
    "    predicted_population = linear_regression.predict(X_pred)\n",
    "    return int(predicted_population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "homicidios_semestre['Poblacion']=homicidios_semestre['AAAA'].apply(predicted_population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAAA</th>\n",
       "      <th>SEMESTRE</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "      <th>Poblacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>2899099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>2899099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>2927500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>2927500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2960408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>2960408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>2998148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>2998148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>3041063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>3041063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>3089508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>3089508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AAAA  SEMESTRE  N_VICTIMAS  Poblacion\n",
       "0   2016         1          65    2899099\n",
       "1   2016         2          81    2899099\n",
       "2   2017         1          69    2927500\n",
       "3   2017         2          71    2927500\n",
       "4   2018         1          70    2960408\n",
       "5   2018         2          79    2960408\n",
       "6   2019         1          57    2998148\n",
       "7   2019         2          47    2998148\n",
       "8   2020         1          31    3041063\n",
       "9   2020         2          50    3041063\n",
       "10  2021         1          55    3089508\n",
       "11  2021         2          42    3089508"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homicidios_semestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "homicidios_semestre['%_homicidios']=round((homicidios_semestre['N_VICTIMAS']/homicidios_semestre['Poblacion'])*100000,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAAA</th>\n",
       "      <th>SEMESTRE</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>%_homicidios</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>2899099</td>\n",
       "      <td>2.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>2899099</td>\n",
       "      <td>2.794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>2927500</td>\n",
       "      <td>2.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>2927500</td>\n",
       "      <td>2.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2960408</td>\n",
       "      <td>2.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>2960408</td>\n",
       "      <td>2.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>2998148</td>\n",
       "      <td>1.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>2998148</td>\n",
       "      <td>1.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>3041063</td>\n",
       "      <td>1.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>3041063</td>\n",
       "      <td>1.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>3089508</td>\n",
       "      <td>1.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>3089508</td>\n",
       "      <td>1.359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AAAA  SEMESTRE  N_VICTIMAS  Poblacion  %_homicidios\n",
       "0   2016         1          65    2899099         2.242\n",
       "1   2016         2          81    2899099         2.794\n",
       "2   2017         1          69    2927500         2.357\n",
       "3   2017         2          71    2927500         2.425\n",
       "4   2018         1          70    2960408         2.365\n",
       "5   2018         2          79    2960408         2.669\n",
       "6   2019         1          57    2998148         1.901\n",
       "7   2019         2          47    2998148         1.568\n",
       "8   2020         1          31    3041063         1.019\n",
       "9   2020         2          50    3041063         1.644\n",
       "10  2021         1          55    3089508         1.780\n",
       "11  2021         2          42    3089508         1.359"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homicidios_semestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage_difference(df,column:str,target):\n",
    "    df=df.copy()\n",
    "    for index, row in df.iterrows():\n",
    "        if index !=0:\n",
    "            df.at[index,'target']=round(pasado-((pasado*target)/100),3)\n",
    "        if index==0:\n",
    "            df.at[index,'target']=np.nan\n",
    "        pasado=row[column]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAAA</th>\n",
       "      <th>SEMESTRE</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>%_homicidios</th>\n",
       "      <th>target</th>\n",
       "      <th>AAAA-SEMESTRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>2899099</td>\n",
       "      <td>2.242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016 - 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>81</td>\n",
       "      <td>2899099</td>\n",
       "      <td>2.794</td>\n",
       "      <td>2.018</td>\n",
       "      <td>2016 - 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>2927500</td>\n",
       "      <td>2.357</td>\n",
       "      <td>2.515</td>\n",
       "      <td>2017 - 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>2927500</td>\n",
       "      <td>2.425</td>\n",
       "      <td>2.121</td>\n",
       "      <td>2017 - 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2960408</td>\n",
       "      <td>2.365</td>\n",
       "      <td>2.182</td>\n",
       "      <td>2018 - 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>2960408</td>\n",
       "      <td>2.669</td>\n",
       "      <td>2.129</td>\n",
       "      <td>2018 - 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>2998148</td>\n",
       "      <td>1.901</td>\n",
       "      <td>2.402</td>\n",
       "      <td>2019 - 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>2998148</td>\n",
       "      <td>1.568</td>\n",
       "      <td>1.711</td>\n",
       "      <td>2019 - 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>3041063</td>\n",
       "      <td>1.019</td>\n",
       "      <td>1.411</td>\n",
       "      <td>2020 - 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>3041063</td>\n",
       "      <td>1.644</td>\n",
       "      <td>0.917</td>\n",
       "      <td>2020 - 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>3089508</td>\n",
       "      <td>1.780</td>\n",
       "      <td>1.480</td>\n",
       "      <td>2021 - 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>3089508</td>\n",
       "      <td>1.359</td>\n",
       "      <td>1.602</td>\n",
       "      <td>2021 - 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AAAA  SEMESTRE  N_VICTIMAS  Poblacion  %_homicidios  target AAAA-SEMESTRE\n",
       "0   2016         1          65    2899099         2.242     NaN      2016 - 1\n",
       "1   2016         2          81    2899099         2.794   2.018      2016 - 2\n",
       "2   2017         1          69    2927500         2.357   2.515      2017 - 1\n",
       "3   2017         2          71    2927500         2.425   2.121      2017 - 2\n",
       "4   2018         1          70    2960408         2.365   2.182      2018 - 1\n",
       "5   2018         2          79    2960408         2.669   2.129      2018 - 2\n",
       "6   2019         1          57    2998148         1.901   2.402      2019 - 1\n",
       "7   2019         2          47    2998148         1.568   1.711      2019 - 2\n",
       "8   2020         1          31    3041063         1.019   1.411      2020 - 1\n",
       "9   2020         2          50    3041063         1.644   0.917      2020 - 2\n",
       "10  2021         1          55    3089508         1.780   1.480      2021 - 1\n",
       "11  2021         2          42    3089508         1.359   1.602      2021 - 2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homicidios_semestre = percentage_difference(homicidios_semestre,'%_homicidios',10)\n",
    "\n",
    "homicidios_semestre['AAAA-SEMESTRE']=homicidios_semestre['AAAA'].astype(str) + ' - ' + homicidios_semestre['SEMESTRE'].astype(str)\n",
    "homicidios_semestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "homicidios_semestre.to_csv('Recursos_dashboard/Homicidios_KPI.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3143854"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año2022=predicted_population(2022)\n",
    "año2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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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       "    }\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>ID</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "      <th>FECHA</th>\n",
       "      <th>AAAA</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>HORA</th>\n",
       "      <th>HH</th>\n",
       "      <th>LUGAR_DEL_HECHO</th>\n",
       "      <th>TIPO_DE_CALLE</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitud</th>\n",
       "      <th>Latitud</th>\n",
       "      <th>PARTICIPANTES</th>\n",
       "      <th>ACUSADO</th>\n",
       "      <th>ROL</th>\n",
       "      <th>VICTIMA</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>FECHA_FALLECIMIENTO</th>\n",
       "      <th>SEMESTRE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-0001</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>AV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZ</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.47533969</td>\n",
       "      <td>-34.68757022</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-0003</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>07:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>AV ENTRE RIOS 2034</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.39040293</td>\n",
       "      <td>-34.63189362</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2016-01-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-0004</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>AV LARRAZABAL Y GRAL VILLEGAS CONRADO</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.46503904</td>\n",
       "      <td>-34.68092974</td>\n",
       "      <td>MOTO-SD</td>\n",
       "      <td>SD</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>18.0</td>\n",
       "      <td>SD</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-0005</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-21</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>05:20:00</td>\n",
       "      <td>5</td>\n",
       "      <td>AV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑA</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.38718297</td>\n",
       "      <td>-34.62246630</td>\n",
       "      <td>MOTO-PASAJEROS</td>\n",
       "      <td>PASAJEROS</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2016-02-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-0008</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>18:30:00</td>\n",
       "      <td>18</td>\n",
       "      <td>AV 27 DE FEBRERO Y AV ESCALADA</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.44451316</td>\n",
       "      <td>-34.68475866</td>\n",
       "      <td>MOTO-OBJETO FIJO</td>\n",
       "      <td>OBJETO FIJO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2016-01-24 00:00:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2021-0089</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-02</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>01:10:00</td>\n",
       "      <td>1</td>\n",
       "      <td>AV. GAONA 3655</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.47633683</td>\n",
       "      <td>-34.62140594</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2021-12-11 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>2021-0091</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>BAIGORRIA Y VICTOR HUGO</td>\n",
       "      <td>CALLE</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.51989389</td>\n",
       "      <td>-34.62284918</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2021-12-11 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>2021-0093</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-13</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>17:10:00</td>\n",
       "      <td>17</td>\n",
       "      <td>AV. RIESTRA Y MOM</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.43353773</td>\n",
       "      <td>-34.64561636</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>PASAJERO_ACOMPAÑANTE</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2021-12-18 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>2021-0094</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-20</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>01:10:00</td>\n",
       "      <td>1</td>\n",
       "      <td>AU DELLEPIANE Y LACARRA</td>\n",
       "      <td>AUTOPISTA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.46739825</td>\n",
       "      <td>-34.65117757</td>\n",
       "      <td>MOTO-AUTO</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>PASAJERO_ACOMPAÑANTE</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>FEMENINO</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2021-12-20 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>2021-0095</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>00:43:00</td>\n",
       "      <td>0</td>\n",
       "      <td>AV. GAONA Y TERRADA</td>\n",
       "      <td>AVENIDA</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.47293407</td>\n",
       "      <td>-34.61984745</td>\n",
       "      <td>MOTO-CARGAS</td>\n",
       "      <td>CARGAS</td>\n",
       "      <td>CONDUCTOR</td>\n",
       "      <td>MOTO</td>\n",
       "      <td>MASCULINO</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2022-01-02 00:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  N_VICTIMAS      FECHA  AAAA  MM  DD      HORA  HH  \\\n",
       "0    2016-0001           1 2016-01-01  2016   1   1  04:00:00   4   \n",
       "2    2016-0003           1 2016-01-03  2016   1   3  07:00:00   7   \n",
       "3    2016-0004           1 2016-01-10  2016   1  10  00:00:00   0   \n",
       "4    2016-0005           1 2016-01-21  2016   1  21  05:20:00   5   \n",
       "5    2016-0008           1 2016-01-24  2016   1  24  18:30:00  18   \n",
       "..         ...         ...        ...   ...  ..  ..       ...  ..   \n",
       "708  2021-0089           1 2021-12-02  2021  12   2  01:10:00   1   \n",
       "710  2021-0091           1 2021-12-11  2021  12  11  23:00:00  23   \n",
       "712  2021-0093           1 2021-12-13  2021  12  13  17:10:00  17   \n",
       "713  2021-0094           1 2021-12-20  2021  12  20  01:10:00   1   \n",
       "714  2021-0095           1 2021-12-30  2021  12  30  00:43:00   0   \n",
       "\n",
       "                               LUGAR_DEL_HECHO TIPO_DE_CALLE  ...  \\\n",
       "0    AV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZ       AVENIDA  ...   \n",
       "2                           AV ENTRE RIOS 2034       AVENIDA  ...   \n",
       "3        AV LARRAZABAL Y GRAL VILLEGAS CONRADO       AVENIDA  ...   \n",
       "4    AV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑA        AVENIDA  ...   \n",
       "5               AV 27 DE FEBRERO Y AV ESCALADA       AVENIDA  ...   \n",
       "..                                         ...           ...  ...   \n",
       "708                             AV. GAONA 3655       AVENIDA  ...   \n",
       "710                    BAIGORRIA Y VICTOR HUGO         CALLE  ...   \n",
       "712                          AV. RIESTRA Y MOM       AVENIDA  ...   \n",
       "713                    AU DELLEPIANE Y LACARRA     AUTOPISTA  ...   \n",
       "714                        AV. GAONA Y TERRADA       AVENIDA  ...   \n",
       "\n",
       "         Longitud       Latitud     PARTICIPANTES      ACUSADO  \\\n",
       "0    -58.47533969  -34.68757022         MOTO-AUTO         AUTO   \n",
       "2    -58.39040293  -34.63189362         MOTO-AUTO         AUTO   \n",
       "3    -58.46503904  -34.68092974           MOTO-SD           SD   \n",
       "4    -58.38718297  -34.62246630    MOTO-PASAJEROS    PASAJEROS   \n",
       "5    -58.44451316  -34.68475866  MOTO-OBJETO FIJO  OBJETO FIJO   \n",
       "..            ...           ...               ...          ...   \n",
       "708  -58.47633683  -34.62140594         MOTO-AUTO         AUTO   \n",
       "710  -58.51989389  -34.62284918         MOTO-AUTO         AUTO   \n",
       "712  -58.43353773  -34.64561636         MOTO-AUTO         AUTO   \n",
       "713  -58.46739825  -34.65117757         MOTO-AUTO         AUTO   \n",
       "714  -58.47293407  -34.61984745       MOTO-CARGAS       CARGAS   \n",
       "\n",
       "                      ROL VICTIMA       SEXO  EDAD  FECHA_FALLECIMIENTO  \\\n",
       "0               CONDUCTOR    MOTO  MASCULINO  19.0  2016-01-01 00:00:00   \n",
       "2               CONDUCTOR    MOTO  MASCULINO  30.0  2016-01-03 00:00:00   \n",
       "3               CONDUCTOR    MOTO  MASCULINO  18.0                   SD   \n",
       "4               CONDUCTOR    MOTO  MASCULINO  29.0  2016-02-01 00:00:00   \n",
       "5               CONDUCTOR    MOTO  MASCULINO  30.0  2016-01-24 00:00:00   \n",
       "..                    ...     ...        ...   ...                  ...   \n",
       "708             CONDUCTOR    MOTO  MASCULINO  41.0  2021-12-11 00:00:00   \n",
       "710             CONDUCTOR    MOTO  MASCULINO  24.0  2021-12-11 00:00:00   \n",
       "712  PASAJERO_ACOMPAÑANTE    MOTO   FEMENINO  18.0  2021-12-18 00:00:00   \n",
       "713  PASAJERO_ACOMPAÑANTE    MOTO   FEMENINO  43.0  2021-12-20 00:00:00   \n",
       "714             CONDUCTOR    MOTO  MASCULINO  27.0  2022-01-02 00:00:00   \n",
       "\n",
       "    SEMESTRE  \n",
       "0          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "5          1  \n",
       "..       ...  \n",
       "708        2  \n",
       "710        2  \n",
       "712        2  \n",
       "713        2  \n",
       "714        2  \n",
       "\n",
       "[303 rows x 26 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpi_motos = df_homicidios[df_homicidios['VICTIMA']=='MOTO']\n",
    "kpi_motos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>AAAA</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AAAA  N_VICTIMAS\n",
       "0  2016          65\n",
       "1  2017          56\n",
       "2  2018          57\n",
       "3  2019          50\n",
       "4  2020          29\n",
       "5  2021          46"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpi_motos = kpi_motos.groupby('AAAA')['N_VICTIMAS'].sum().reset_index()\n",
    "kpi_motos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def last(df,column):\n",
    "    df=df.copy()\n",
    "    for index, row in df.iterrows():\n",
    "        if index !=0:\n",
    "            df.at[index,'last_year']=pasado\n",
    "        if index==0:\n",
    "            df.at[index,'last_year']=np.nan\n",
    "        pasado=row[column]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    .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>AAAA</th>\n",
       "      <th>N_VICTIMAS</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>%_homicidios</th>\n",
       "      <th>target</th>\n",
       "      <th>last_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>65</td>\n",
       "      <td>2899099</td>\n",
       "      <td>2.242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>56</td>\n",
       "      <td>2927500</td>\n",
       "      <td>1.913</td>\n",
       "      <td>2.085</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>57</td>\n",
       "      <td>2960408</td>\n",
       "      <td>1.925</td>\n",
       "      <td>1.779</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>50</td>\n",
       "      <td>2998148</td>\n",
       "      <td>1.668</td>\n",
       "      <td>1.790</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>29</td>\n",
       "      <td>3041063</td>\n",
       "      <td>0.954</td>\n",
       "      <td>1.551</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>46</td>\n",
       "      <td>3089508</td>\n",
       "      <td>1.489</td>\n",
       "      <td>0.887</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AAAA  N_VICTIMAS  Poblacion  %_homicidios  target  last_year\n",
       "0  2016          65    2899099         2.242     NaN        NaN\n",
       "1  2017          56    2927500         1.913   2.085       65.0\n",
       "2  2018          57    2960408         1.925   1.779       56.0\n",
       "3  2019          50    2998148         1.668   1.790       57.0\n",
       "4  2020          29    3041063         0.954   1.551       50.0\n",
       "5  2021          46    3089508         1.489   0.887       29.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpi_motos['Poblacion'] = kpi_motos['AAAA'].apply(predicted_population)\n",
    "kpi_motos['%_homicidios'] = round((kpi_motos['N_VICTIMAS']/kpi_motos['Poblacion'])*100000,3)\n",
    "kpi_motos = percentage_difference(kpi_motos,'%_homicidios',7)\n",
    "kpi_motos = last(kpi_motos, 'N_VICTIMAS')\n",
    "kpi_motos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "kpi_motos.to_csv('Recursos_dashboard/kpi_motos.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>AAAA</th>\n",
       "      <th>TIPO_DE_CALLE</th>\n",
       "      <th>Poblacion</th>\n",
       "      <th>%_homicidios</th>\n",
       "      <th>target</th>\n",
       "      <th>last_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>91</td>\n",
       "      <td>2899099</td>\n",
       "      <td>3.139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>81</td>\n",
       "      <td>2927500</td>\n",
       "      <td>2.767</td>\n",
       "      <td>2.982</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>93</td>\n",
       "      <td>2960408</td>\n",
       "      <td>3.141</td>\n",
       "      <td>2.629</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>64</td>\n",
       "      <td>2998148</td>\n",
       "      <td>2.135</td>\n",
       "      <td>2.984</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>52</td>\n",
       "      <td>3041063</td>\n",
       "      <td>1.710</td>\n",
       "      <td>2.028</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021</td>\n",
       "      <td>61</td>\n",
       "      <td>3089508</td>\n",
       "      <td>1.974</td>\n",
       "      <td>1.624</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AAAA  TIPO_DE_CALLE  Poblacion  %_homicidios  target  last_year\n",
       "0  2016             91    2899099         3.139     NaN        NaN\n",
       "1  2017             81    2927500         2.767   2.982       91.0\n",
       "2  2018             93    2960408         3.141   2.629       81.0\n",
       "3  2019             64    2998148         2.135   2.984       93.0\n",
       "4  2020             52    3041063         1.710   2.028       64.0\n",
       "5  2021             61    3089508         1.974   1.624       52.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpi_avenidas = df_homicidios[df_homicidios['TIPO_DE_CALLE']=='AVENIDA']\n",
    "kpi_avenidas = kpi_avenidas.groupby('AAAA')['TIPO_DE_CALLE'].count().reset_index()\n",
    "kpi_avenidas['Poblacion'] = kpi_avenidas['AAAA'].apply(predicted_population)\n",
    "kpi_avenidas['%_homicidios'] = round((kpi_avenidas['TIPO_DE_CALLE']/kpi_avenidas['Poblacion'])*100000,3)\n",
    "kpi_avenidas = percentage_difference(kpi_avenidas,'%_homicidios',5)\n",
    "kpi_avenidas = last(kpi_avenidas, 'TIPO_DE_CALLE')\n",
    "kpi_avenidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "kpi_avenidas.to_csv('Recursos_dashboard/kpi_avenidas.csv', index=False)"
   ]
  }
 ],
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