02_Escobedo_Jessica.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Synergy Logistics**\n",
    "#### Análisis de Datos\n",
    "\n",
    "#### _por Jessica Martínez Escobedo_\n",
    "\n",
    "\n",
    "##### Introducción a la Ciencia de Datos | _**EMTECH**_ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Contexto_\n",
    "\n",
    "La Dirección de Synergy Logistics ha solicitado al equipo operativo, realizar unapropuesta que permita enfocar las prioridades de la estrategia operativa 2021; para ello, se plantea analizar la viabilidad de 3 opciones de enfoque: rutas de importación y exportación, medio de transporte utilizado y valor total de importaciones y exportaciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Identificar los puntos en que debe enfocarse la estrategia de la empresa.*** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Llamar a las Bibliotecas a utilizar.\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Base de Datos\n",
    "df = pd.read_csv('synergy_logistics_database.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>register_id</th>\n",
       "      <th>direction</th>\n",
       "      <th>origin</th>\n",
       "      <th>destination</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>product</th>\n",
       "      <th>transport_mode</th>\n",
       "      <th>company_name</th>\n",
       "      <th>total_value</th>\n",
       "      <th>mes</th>\n",
       "      <th>anio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Exports</td>\n",
       "      <td>Japan</td>\n",
       "      <td>China</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>Cars</td>\n",
       "      <td>Sea</td>\n",
       "      <td>Honda</td>\n",
       "      <td>33000000</td>\n",
       "      <td>01</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Exports</td>\n",
       "      <td>Japan</td>\n",
       "      <td>China</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>Cars</td>\n",
       "      <td>Sea</td>\n",
       "      <td>Honda</td>\n",
       "      <td>16000000</td>\n",
       "      <td>01</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Exports</td>\n",
       "      <td>Japan</td>\n",
       "      <td>China</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-02-02</td>\n",
       "      <td>Cars</td>\n",
       "      <td>Sea</td>\n",
       "      <td>Honda</td>\n",
       "      <td>29000000</td>\n",
       "      <td>02</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Exports</td>\n",
       "      <td>Japan</td>\n",
       "      <td>China</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-03-02</td>\n",
       "      <td>Cars</td>\n",
       "      <td>Sea</td>\n",
       "      <td>Honda</td>\n",
       "      <td>14000000</td>\n",
       "      <td>03</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Exports</td>\n",
       "      <td>Japan</td>\n",
       "      <td>China</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-04-02</td>\n",
       "      <td>Cars</td>\n",
       "      <td>Sea</td>\n",
       "      <td>Honda</td>\n",
       "      <td>17000000</td>\n",
       "      <td>04</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   register_id direction origin destination  year       date product  \\\n",
       "0            1   Exports  Japan       China  2015 2015-01-31    Cars   \n",
       "1            2   Exports  Japan       China  2015 2015-01-02    Cars   \n",
       "2            3   Exports  Japan       China  2015 2015-02-02    Cars   \n",
       "3            4   Exports  Japan       China  2015 2015-03-02    Cars   \n",
       "4            5   Exports  Japan       China  2015 2015-04-02    Cars   \n",
       "\n",
       "  transport_mode company_name  total_value mes  anio  \n",
       "0            Sea        Honda     33000000  01  2015  \n",
       "1            Sea        Honda     16000000  01  2015  \n",
       "2            Sea        Honda     29000000  02  2015  \n",
       "3            Sea        Honda     14000000  03  2015  \n",
       "4            Sea        Honda     17000000  04  2015  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Crear DataFrame \n",
    "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "df['mes'] = df['date'].dt.strftime('%m')\n",
    "df['anio'] = df['date'].dt.strftime('%Y')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "register_id                int64\n",
       "direction                 object\n",
       "origin                    object\n",
       "destination               object\n",
       "year                       int64\n",
       "date              datetime64[ns]\n",
       "product                   object\n",
       "transport_mode            object\n",
       "company_name              object\n",
       "total_value                int64\n",
       "mes                       object\n",
       "anio                      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df. dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ### Rutas de Importación y Exportación a partir de los flujos de de las mismas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtienedf(datos): \n",
    "  dff = df[df['direction'] == datos]\n",
    "  dff = dff.groupby(by=['origin','destination'], as_index=False).count()\n",
    "  dff = dff.filter(['origin','destination','register_id'])\n",
    "  dff = dff.set_axis(['origen','destino','total'], axis=1)\n",
    "  dff = dff.sort_values(by='total', ascending=False).head(10)\n",
    "  dff[\"Origen_Destino\"] = dff[\"origen\"]+\"-\"+dff[\"destino\"]\n",
    "  dff = dff.filter(['Origen_Destino','total'])\n",
    "  return dff "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def graficabarras(datos, t):\n",
    "  df1.plot(kind = 'bar')\n",
    "  plt.xlabel(\"Origen - Destino\")\n",
    "  plt.title(\"Rutas de \"+ t )\n",
    "  plt.ylabel(\"Viajes\")\n",
    "  plt.xticks(range(10), df1[\"Origen_Destino\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Origen_Destino</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>South Korea-Vietnam</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Netherlands-Belgium</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>USA-Netherlands</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>China-Mexico</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Japan-Brazil</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Germany-France</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>South Korea-Japan</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia-Singapore</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Canada-Mexico</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>China-Spain</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Origen_Destino  total\n",
       "118  South Korea-Vietnam    497\n",
       "95   Netherlands-Belgium    437\n",
       "135      USA-Netherlands    436\n",
       "32          China-Mexico    330\n",
       "75          Japan-Brazil    306\n",
       "51        Germany-France    299\n",
       "115    South Korea-Japan    279\n",
       "3    Australia-Singapore    273\n",
       "24         Canada-Mexico    261\n",
       "35           China-Spain    250"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "df1 = obtienedf('Exports')\n",
    "df1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graficabarras(df1,\"exportación\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>Origen_Destino</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Singapore-Thailand</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Germany-China</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China-Japan</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Japan-Mexico</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>China-Thailand</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Malaysia-Thailand</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Spain-Germany</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Mexico-USA</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>China-United Arab Emirates</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil-China</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Origen_Destino  total\n",
       "33          Singapore-Thailand    273\n",
       "12               Germany-China    233\n",
       "4                  China-Japan    210\n",
       "20                Japan-Mexico    206\n",
       "7               China-Thailand    200\n",
       "26           Malaysia-Thailand    195\n",
       "38               Spain-Germany    142\n",
       "31                  Mexico-USA    122\n",
       "9   China-United Arab Emirates    114\n",
       "1                 Brazil-China    113"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = obtienedf('Imports')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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earnPuHxsOpL2ljRS0sgJEya8izDMzKynfi1ei4inehx6p906JwArAsOB8cAv3u4TRMRJETEiIkYMHjz4HYZhZmbt9CcpPCVpAyAkDZT0deDBd9JYRDwbEVMi4i3g90ztInoaWLblrsvkY2ZmVlB/ksJXgH1J3TlPkz7l7/tOGpO0ZMvVTwGNmUmXATtJmlvSCsAw4J/vpA0zM3vn+jP76HnSGoW3RdI5wKbA4pLGAd8HNpU0HAhgLPDl3Mb9ks4HHgDeBPb1zCMzs/J6TQqSvhERR0v6DelNvFWQZhadGRGPtXt8ROzc5vDJvbUXET8CfjTjkM3MrCp9nSk0xg1G9nL7YsBFwJozNSIzK2booVfO+E4zMPaorWdCJNYpek0KEXF5vjytt/tIermKoMzMrB4zHFOQNBj4JrAqMKhxPCI2j4jfVRibmZkV1p/ZR2eRupJWAI4gDRDfUWFMZmZWk/4khcUi4mTgjYi4MSI+D2xecVxmZlaDGXYfAW/ky/GStgb+DSxaXUhmZlaX/iSFIyUtBBwM/AZYEDio0qjMzKwW/Vm8dkX+diKwWbXhmJlZnd7p4jUioj97KpiZ2SykrzOFr0i6jd4Xr5mZ2Wymr6Twa+BnwJLA+cA5EXFXkajMzKwWvU5JjYhjI+JDwCakDXZOkfSQpO9JGlYsQjMzK2aG6xQi4omI+GlErAXsTCp5/VDlkZmZWXH92aN5TknbSjoL+DPwMLB95ZGZmVlxfc0++gjpzGAr0oY35wJ7R4SL4JmZzab6Gmj+FnA2cHBE/KdQPGbWhVzCu3P0VTrb9Y3MzLpMfwrimZlZl3BSMDOzJicFMzNrclIwM7Om/pTONjOb7XkGVOKkYGbWQepOTu4+MjOzJicFMzNrclIwM7MmJwUzM2tyUjAzsyYnBTMza6osKUg6RdJzku5rObaopGskPZovF8nHJenXksZIukfS2lXFZWZmvavyTOGPwMd7HDsUuC4ihgHX5esAWwLD8tfewAkVxmVmZr2oLClExE3Aiz0Obweclr8/Dfhky/HTI7kdWFjSklXFZmZm7ZUeUxgSEePz988AQ/L3SwNPtdxvXD42HUl7SxopaeSECROqi9TMrAvVNtAcEQHEO3jcSRExIiJGDB48uILIzMy6V+mk8GyjWyhfPpePPw0s23K/ZfIxMzMrqHRSuAzYM3+/J3Bpy/E98iyk9YGJLd1MZmZWSGVVUiWdA2wKLC5pHPB94CjgfElfAJ4Adsx3vwrYChgDTAY+V1VcZmbWu8qSQkTs3MtNW7S5bwD7VhWLmZn1j1c0m5lZk5OCmZk1OSmYmVmTk4KZmTU5KZiZWZOTgpmZNTkpmJlZk5OCmZk1OSmYmVmTk4KZmTU5KZiZWZOTgpmZNTkpmJlZk5OCmZk1OSmYmVmTk4KZmTU5KZiZWZOTgpmZNTkpmJlZk5OCmZk1OSmYmVmTk4KZmTU5KZiZWZOTgpmZNTkpmJlZk5OCmZk1OSmYmVmTk4KZmTU5KZiZWdOcdTQqaSwwCZgCvBkRIyQtCpwHDAXGAjtGxH/qiM/MrFvVeaawWUQMj4gR+fqhwHURMQy4Ll83M7OCOqn7aDvgtPz9acAn6wvFzKw71ZUUArha0ihJe+djQyJifP7+GWBIuwdK2lvSSEkjJ0yYUCJWM7OuUcuYAvDhiHha0nuAayQ91HpjRISkaPfAiDgJOAlgxIgRbe9jZmbvTC1nChHxdL58DrgYWA94VtKSAPnyuTpiMzPrZsWTgqT5JC3Q+B74KHAfcBmwZ77bnsClpWMzM+t2dXQfDQEultRo/+yI+IukO4DzJX0BeALYsYbYzMy6WvGkEBGPA2u2Of4CsEXpeMzMbKpOmpJqZmY1c1IwM7MmJwUzM2tyUjAzsyYnBTMza3JSMDOzJicFMzNrclIwM7MmJwUzM2tyUjAzsyYnBTMza3JSMDOzJicFMzNrclIwM7MmJwUzM2tyUjAzsyYnBTMza3JSMDOzJicFMzNrclIwM7MmJwUzM2tyUjAzsyYnBTMza3JSMDOzJicFMzNrclIwM7MmJwUzM2tyUjAzsyYnBTMza+q4pCDp45IeljRG0qF1x2Nm1k06KilIGgD8FtgSWBXYWdKq9UZlZtY9OiopAOsBYyLi8Yh4HTgX2K7mmMzMuoYiou4YmiR9Bvh4RHwxX98d+GBE7Ndyn72BvfPV9wMPv8tmFweef5fP8W51QgzQGXF0QgzQGXF0QgzQGXF0QgzQGXHMjBiWj4jB7W6Y810+cXERcRJw0sx6PkkjI2LEzHq+WTWGTomjE2LolDg6IYZOiaMTYuiUOKqOodO6j54Glm25vkw+ZmZmBXRaUrgDGCZpBUlzATsBl9Uck5lZ1+io7qOIeFPSfsBfgQHAKRFxf8XNzrSuqHehE2KAzoijE2KAzoijE2KAzoijE2KAzoij0hg6aqDZzMzq1WndR2ZmViMnBTMza3JSMDOzJicFq5WkgZIOkHRB/tpf0sC64+pmkraV1JHvDaVfG5I2lDRf/n43Sb+UtHzJGErrqoFmSWv3dXtE3FkqlgZJ7wEGtcTwZMG2BwNfAobSMhMtIj5fMIY/AAOB0/Kh3YEpjVXtBdr/DdDrP0FEHFAojntnEMcaJeLIsZwJfAi4kDQD8KFSbfcSj4DNgV2AbSJiSMG27wHWBNYA/gj8AdgxIjYpFUNLLKuRasK1vl+cPrPb6agpqQX8Il8OAkYAdwMi/cFHkv4RipD0iRzPUsBzwPLAg8AHSsUAXArcDFwLTCnYbqt1I2LNlut/k3R3wfZH5ssNSf9w5+XrOwAPFIxjm3y5b748I1/uWjAGACJiN0kLAjsDf5QUwKnAORExqVQcktYnJYJPAouSfjdfL9V+9mZEhKTtgOMi4mRJXygcA5K+D2xKeo1eRSoaegsw05MCEdF1X8BFwOot11cDLigcw93AYsBd+fpmwMmFYxjdAX+LO4EVW66/F7izhjhuB+ZsuT4QuL2GOO5q9zuq6W+zGPA1YCzwZ+BRYP8C7f44t3Ud8MUcx79q+h3cCHwrx7MEqcv93hriuDe3fXe+PgS4poq2OrLfsID3R8S9jSsRcR+wSuEY3oiIF4A5JM0REdeTzl5KukLSVoXb7OkQ4HpJN0i6EfgbcHANcSwCLNhyff58rDRJ2rDlygYUHvuT9AlJFwM3kJLjehGxJakbpcTf5ovAs8AJwBn5/6Sufu7PAq8Bn4+IZ0ild35WQxyvRMRbwJv5LO45pi0JNNN0W/dRwz25L/vMfH1X4J7CMbwkaX7gJuAsSc8BLxeO4UDgMEmvAW+QutIiIhbs+2EzT0RcJ2kYqeItwMMR8Vqp9lscBdwl6XrS72Fj4PAa4vgCcIqkhXIc/wGKjfFknwaOiYibWg9GxORCXSdLAh8hdV8dm/8m80iaMyLeLNB+U0Q8I+lCYFg+9DxwcckYspGSFgZ+D4wC/g/4exUNddVAc4OkQcBXSf/4kN6YT4iIVwvGMB/wKukff1dgIeCs/Kmoa0jal/Rzv5SvLwLsHBHH1xDLEsAH89V/5E+GtchJgYiYWFcMnUDS3KTxll2ADwPXRcQuBdv/EqlU/6IRsWL+AHNiRGxRKoY2MQ0FFoyISj7IdmVSsKnym/Awpp3RcFPvj5jp7Y+OiOE9jt0VEWuViqGl3aVJA/6tM7GK/S5yDHOTPqkP7RHHDwrGsD3wU+A9pA8txc8ge4lrAeBTUcGMmz7aHE3a/OsfjdekpHsjYvVSMbTEUuT12ZXdR7nP9nCm/wW/t2AMtf/jSfoiqQtpGWA0sD7plHTzUjEAAyQp8qeTvCXrXAXbJ7f7U1L/8f3AW/lwkM4iS7oUmEjqIqijGw3gaGDbiHiwjsYlrQs81ThTk7QHKVE+Qfkuvdci4vU0KxYkzUkN4xstr88HmDpTsJLXZ1eeKUh6CDiI9I/XnIpZsutG0hhq/MfLMdwLrEuaZTNc0srAjyNi+4Ix/IyUnH+XD32Z9IZQdLBZ0sPAGjWNZ7TGcV9ErFZzDLdGxIYzvmdl7d8J/E9EvChpY9K2vPsDw4FVIuIzBWM5GngJ2CPHsA/wQER8u1QMOY5ir8+uPFMAJkbEn2uO4dk6E0L2akS8KglJc0fEQ5LeP+OHzVTfJCWCr+br15AWCJX2OGmmTa1JAbhN0uqts+NqMFLSecAltPw+IuKiQu0PiIgX8/efBU6KiAuBC3N3TkmHkgb/7yW9Tq+KiN8XjgEKvj67NSlcnz+hXsS0L/qSK5rr/scDGJdnNFwCXCPpP6RT9GLyNLsT8ledJgOjJV3HtH+PIiuaW3wY2EvSv3IcjW7FYiuaSVNzJwMfbTkWpP+XEga0zDTagql7skP596z9I+JXpFk/AEg6MB8rqdjrs1u7j65vczgiolhfuqRTe4mh9PRDACRtQpoB9ZeIeL1Ae+dHxI69lXco/CaIpD3bHY+I09odrzCOtnV1IqJosq6TpG8DW5Gmfy4HrB0RIel9wGklu7Yk3RkRa/c4VnwiRMnXZ1cmBZsq14P6MOmN+dZSZ0uSloyI8X4TbK/mmliDSF0mH+gRQ8maWOuT1itcHREv52MrAfOXeI1K2pmp02BvbrlpAeCtOqekVq1bu4+QtDXTv+grn/Yn6RsRcXRvhdhKdldI+h6pxk+jW+BUSX+KiCOrbjsixudv54uIaWoMSdqUwt1Yef75T5i+4FixGWk5jk6oiXUG8BDwMeAHpHU0xca/JC0KPJK/5lbar/2liHikVAzAbcB4YHGm1kwDmETBha51nFF35ZmCpBOBeUn1hv4AfAb4Z0RUvlpT0rYRcXkndFfkGQ1rNhbtSZqHVA+p2GCzpPtIb0JHk96MjwZGRESx4oQ5jluA7wPHANsCnwPmiIjvFY7jbtKU4GsjYi1JmwG7lXhttsRwV277nohYQ6lc9c0RsX6h9v/F1DdA5cv5SfXCvhgRY0vE0QnqOKPu1jOFDfKL/Z6IOELSL0gFvyoXEZfny6J91b34N+mNuLGSe27g6cIxfJC0XuM20qn5WaSKpaXNE6nkhvI/2uGSRgFFkwK5JpakZk0sSceWjiFfvqRUrvkZ0nqaIiJihXbH89qeE4GPl4old2P9hlQbbS5gAPByqfVEjTPqkt2p3ZoUXsmXkyUtBbxA6r8sJvePfp3pV66WXDg2Ebhf0jWkT2YfAf4p6dc5lhJdWW+Q/h7zkBLUv/KMpNJeU9pY5lFJ+5GS4/w1xNEJNbFOyivdvwtcRvo9lE6O04mIiyR9p3CzxwE7AX8iFazcA1ipcAxFF7t2a/fRd0nZfwvgt6Q3xD9ExHcLxnA36VNPzwV0owrG0LYLqyWWys9m8u/hUuCHpP7bE4HXI2KHqtvuEce6pH7zhXMsCwFHR8TtheNwTaxe5GR5S/Qoi1JxmyMjYkSjKy0fq2P2UbHFrl2ZFFrlWjODonDhMUmjImKdkm12IkkjImJkj2O7R8QZvT3GqpXXruzB9GexpXah+982hxcBPkHa6KbY4jFJNwH/Qxp7fIY0+LxXTLsxVIk4iq0y76qkkE/BelVi4VieWQFwAGl2ycVMuxjlxXaPqyiW2mfcKBWV2RV4b0T8QNJywBIR8c9C7V9O39tgfqJQHJN6iaOOmli3kTYdupepdaCKjYMp7TLWKkhdvDeVXumdB3ifJY0nHEQ6czs+IsYUar/xnrUJaZOfS6h4sWu3JYV2C8Yaiiwca5lZoTY3R+E35Npn3Eg6gfTGs3lErJL7sq+OiHULtd/nXrsRcWOJODpJuwVbdZO0RBQuZa5UnPH0iCi+JWpLDMXfs7oqKdi0Gl1YaikFXLpbq/EG1NpPK+nu0qfndZO0YET8t+VMchqFzyAPIm3icgU1ncW2iamWRJU/OG0eBVb5d4qumn0kabeIOLOXPksi4pclYiAl4zN6HN8dmBIRZ1cdQ4tOmHHzRv5E1iidPZiWLouq1bE4qBdnkzaTGcX0Z5JB2ru6lNdJW05+m6m/k9Ix9NTuzLqEx4FbJV1GyyywEu8V0KwiPCYiftfj+JeBFSLi0JndZlclBWC+fLlAjTHsT5r11NNFpGmIJZPCgaRFfAeQZtxsDvQ5I6kCvyaNq7xH0o9ICwlLTjs8MF9uU7DN6UTENvmy7Rz9wg4G3hcRz9cdSIs6KpMCPJa/5qCe943NgW+0Of570srqmZ4U3H1UWF+nwa3T3rqJ0j4OW5A+DV5XYtpdJ1P9u+FdDXwyIiaXarOPWIaQ9vyAVHXguTrjKU197K8h6f6ImOnlT7rtTAGoveDXPJLmi1zkqyWmBSi049gMZty8Rvpk9NuIeKrCGFr7zp8Dzmm9rXT/dd0rV1vi6ITd8F4mlWm+nhrLiEvakdSNdQPpA8NvJB0SERcUaPvYiPhab/8rpWalAa9IGhYRj/aIbxhTF+HOVF2ZFKi34NfJwAWSvtJYuq60Efdv820l/LyP2+YkJcvzgSrrDz0PjAPezNfr7EOHDlm5SkoIjd3wNstnUT8uHMMl+atu3wbWbZwd5PGma4HKkwLpPQL6/l8p4XvAnyUdSRpvgvT6/BbwtUpajIiu+wLuypf35MuBpH/CUu1/hVQF9IX89QTw1bp/Lz1i/EPFz38sqcDZ8cBG5K7MGn/eka2vidbXSeE47siXo4G58/f3F2x/AHB9nX+Lllju7XF9jp7HuuELWA04jZQURuXvV6+qvW49U6i74NeJwIm5y4iImFSq7d5IOikimjtcRcQXq2wv0qm5gE2B3UldA1cDJ0TEv6psuxeTlUo0j1bal3c86U2otFp3w4uIKZLekrRQFF7l38ZfJP2VqV2Ln6VQ4coGSduQJmEsTzqLLr6YMCLuo2UCSNVrNrpyoDn3214IrAGcSi74ld+s64jnisizT+pS54Kl/Ca4E+mf77CoYQ/culeu9hJT0d3wWtq9FFiLtF926zTM0luTNlb0fjhfvTkiLi7c/hhge9IZSke8WVb9v9qVSaHT1FFgq00Mf4mIkiWJ5wO2I336G0yaknt+FNxhrFPldRtDmLbuUMmd12rf6yPHsQIwPqbd72NIFNxPIQ+2bxH1VO5tq+r3i65MCrkI3qeZvuBX5Tuv9RLPKVHT3sx1kfQy8Chwbr6c5oUYBepQ9YhnQ+BwpnYTNOIovfPa/qTSI88ydRFfRPk9q+cBlouIh0u22yOGkaS9T17P1+cibRlbpARKbnNd0hnsjUw7E6vI4rVeYtonIo6v6vm7dUzhUtJeAqNo+UOXlP/5z4yI/9SVEPJsjm8yfUG8EtMf/0RKBO/PX62CqVuElnIyqdtomlLmNTgQeH/UWCpb0rakWTdzAStIGg78IMpNw2yYs7XbLCJez4mhpB+RSn4MotCU8Va9lD05t3E8Kpi63a1JYZmSXSW9GALcIelO4BTgrzX0WZ4FnAdsTZoRtScwoUTDEbFXiXbehokRUXQQsxdPkT6w1OlwYD3S+gAiYrSkOkpcTJD0iYi4DEDSdqSpzCUtFb0sHiukt7InoqKp293afXQS8JsoXIa3TRwCPkqqTjqCtDbg5Ih4rFD7jYJ4rRuI3FHy9LxHPMUH3CU1Bux2JE3HvIhpuwnuLBRHox7XB0hnTldSU3eFpNsjYv0eRQqLr7aXtCLpg8tSpDfBp4A9Sg7+55lo10bE1aXarFtXnSm0FD2bE/icpMdJ/3iNaWZFX/QREZKeIU2JfZO0kcgFkq6JiHb1Tma2xtTc8ZK2Ju3Z3LZKZyFL19DmL3pcH9HyfVBuJXGjrs6T+WsuauiuyO6XtAswIK+cPYC0h3ZR+cPR+ko7rhER/1c6BuCrwNclvUb6fyk6JVXSyhHxUMuHl2lU8aGlq84U8gu816l9UXBzbEkHklbNPk/a1emSiHijUbU0IlYsEMM2wM3AsqQSDwsCRzRO10urY8Bd0qcj4sKSbfYSx48j4rC64wCQNC9pNfFHSW+CfwV+2JgFVKD92qsZd4rG+qE8C6qnqGL8r9uSQsdsHiLpCOCUdolI0irR5UXhSumU10SnxNEJJH05In6n6XdgAyAijigQw24RcWb+fsOIuLXltv0i4riqY6hLtyWF2tcDtOqA+eiDgS8x/dTcYp/W20wFbZyeFxnY7JQ3Y0l3k1Z3t903oIpZJm1i+DBpW9TT8/ULmNqdeGRE/K3qGGZE0lwlFvK1vi56vkbqeM1Iegz4WesC26rG4LpqTAEY3NspKRQfzNuP9GY4zXx00irrUi4ldR9dS33TMOueCrqypHvaHC89zrQy6XfQdptWyhQIPIK030fD+4G9SPuQHAYUTQqSbgD2aixWy2sG/gCU2JVPvXzf7noJbwCbSfog8OWcGCsZg+u2pDCAVNKirl2cWn2NmuejA/NGxDdrbB/qnwr6L9L+1HV7oAPOYheMiAdarj8aEaMAJP2khnh+Qqp/9GvSG+CWpJl6JUQv37e7XsLkiPispG8AN0vaoao4ui0pjK9r1XIbnTAf/QpJW0XEVTXGcL3SloO1TAUFXi85waDDLdx6JSK2b7k6pGwoEBF/lfQVUg2m54G1osJCcD00ziAFrNhyNinq2ZZUABFxdF7bdDUVzRTstqQw3RmCpG0i4ooaYnkcuEFSbfPRSatnD6trul32wXxZ11TQW3seUI+KsYX8qk0cpfuuH5K0dURc2SOObYDi5S4kfZe0fmRjUrfqDZIO7hlfRVYp0Mbb8b3GNxFxraSPkrr2ZrpuG2iebkevugYa65xZYX3roMHnohMjJL2PtGjuNqBxprYOsAGwTUQ8UiqWHM+xwLci4pV8fXnSPh8fKRlHp1ChbVq7Kim002kzkkqoY0HMDOLZmum3Rq2tm0+FK8b2EceREfGdwm3OTdqJsLH37/3A2aXWKLSJp/bCfJ1AvWzT6nUKFZC0XkT8s4Z2BwPfYPo3w8q7TepYENNHLCcC8wKbkWaWfIa0QfsXSsVgnam1MF9E1FmYr3a5GkNjm9bhytu09hj3mSm6bUwBaK7YPJj0CeRLeaXz+wuPLTSK0W1D+WJ0e+fLzUq0NwMbRMQaubbOEZJ+QeHdtaD2irGtcaxPWl2+CqnMxQDg5cLjPK3x1NmVdjgdUJhPqTLryqSxrodLrJNo49WIeFUSkubOZ/o9qwvPFF2ZFEi7rY1i6sb0T5NKOZdMCotFxMmSDoyIG4EbJd1RsH0AJG3A9IvXTi8Ywiv5crKkpUh7Vi9ZsP2G2irG9nAcaRe6P5EG3/cAVqohjoY6p2+/ERETpWlCKLrZTe7aPBF4jPS7WCGvuC79waXYNq3dmhRWzHN+dwaIiMnq8coroPZidJLOAFYk9VE2Fo4FUDIpXJFf7D8jDW4GUHw7TjokSQNExBhJAyJiCnCqpLuAb9URC2nguS6dUJjvF8BmkSuzKlVuvZLCZ7MR8an87eG523ch4C9VtNWtSeH1PIAV0PxDl95s50hJC5G6sRrF6A4qHMMIYNWocWApIn6Yv71Q0hXAoKhnw/jak3Q2OXdXjFYq2zwemKOGOAAoPdDdw/6kwnyvAeeQC/MVjmFSTFuq+3FgUskAcjmc+yNiZYD8oaW69rpxoFnSR4DvkPqPrwY2JC2nv6HOuEqT9CfggIgYX2MMg4B9SJuzB3ALcELp2S7qkIqxedrlc8BA0oeEhYDjo+weAtsDPwXeQ+oyqWP9Sq3y7wDgI6S6XOeTXp87AE9GxD6F47kU2L9EbbSuSwpKpak/A1xHmtYl0oh+0R2dlDYl35/p+/Mrn1kh6XLSC3wBYDjwT6ZdQFdsdoek80mfvM7Mh3YBFo6IHUrFYNOSNAbYNmqq1Cupz0Rc6H/k1BnEUKrcBgCSbgLWIv2vvtwSx0z/XXRdUgCQNDIiRsz4npXGcDepGNy9tAyeVX1qmNv+Eqlswc09btqIVArk5KpjaInlgYhYdUbHCsRRa8VYSedHxI6auhHUNAoW5kPSrRGxYan22rQ/gVQG5hzgH/QY7C7xP9JpJG3S7ngVv4tuHVO4VtLXSbNNWrNu5eWJW7waEb8u2F6r7UgrRafZjlTSi8CPScmqlDslrR8Rt+cYPgiMLNh+Q90VYw/Ml0W3I+3FSEnnkWa6tJ5BXlSo/SVI3TY7k84crwTOiYj7C7XflLs3v8D064mKbgbV+uYvaXHgharGArv1TOFfbQ5HFKrhn2PYhbRk/WoKF4JTH/swS7o3IlavOoaW9h4klWhu9JUuR6qz8yYFS1dLGh0Rw0u0NYM45gNeiYi3JK1Emh//54h4YwYPnZkxtOs6idJvhDmWuUnJ4WekMZ6im9vkcbeHSMnpB6TV3g9GxIF9PnDmtb8+cBTwImmQ/QxgcdLkgz0iYqbPQOrKpNAJlEoR706a/9zcT6HQiuZHI2JYL7eNiYj3VR1DS3vL93V7FKpgKulI4Laot2IskkaRuvEWIRXru4NUyXXXOuMqLSeDrUkJYShwGWmnwqcLx3FXRKyVF1euIWkgcHNErF+o/ZGkvSwWAk4CtoyI2/OK5nOqKNHTld1H+Q/7VVL1RUgrJn9X8tMYaRbDe2taHTlS0pciYpr1ALm+yqiSgTTe9CW9h2lPz4vtQJd1QsVYSB/UJkv6AmnW0dGSRhdpWPpGbu83tB/XOKBQHKcDqwFXkc4O7ivRbi8a7wkvSVoNeIY0K6uUOSPiagBJP2h0s+YVzdU0WMmzdr4TSFP+js/Xd8/HvlgwhvtI9eufK9hmw9eAiyXtytQkMIJUVuFTvT2oCpI+QVogtBTpd7E88CBTC7IVERELlGyvD5L0IVI3RaP+04BCbTdmG9UxptNqN9JY34HAAS1vfnUk6pOUqpN+h3S2Mj/w3YLtt67gfqXHbR5TmFkk3R0Ra87oWMUx3ECqEX8H9U0H3Yz0iQzS4pjie/DmWVibA9fm0/TNgN2iUEE8dV7F2I2BrwO3RsRPc62fr5X6lG5TNaavR8T5NcYwhZQgBcwDTG7cRFroOXCmt9mlSeFOYIeIeCxffy9wQRQs/FVyilkna0wPzslhrTzAWixBq4MqxuZ4Vu85K6w0dUhxwE7QCdPXS+vW7qNDSNtAPk7KuMtTbu/XxrL13zWWrXe5lyTNT5oOepak52iZJly16KyKsQDH50HWPwJn1VTyo1OKA3aCTpi+XlRXnilAc3ZDo/TswxFRtPZRyWXrnUypjPmrpOS8G6m8xFl1/NOp/oqxjThWIn1I2YG0gvWPjcHGQu2Pioh1GjNu8rFepzHPzjph+nppXXmm0Kbezs2SToyy9XYWIVWBrHzZeieSNInpB8oaI4rfk/QY8O2IuK5QPJ1QMTY1GvGIpO+QBnx/DaylNNp6WKEFZJ1SHLB2EbFCz2NKBQtnW115ptAJ9XY8ptC73L22GumMYbUZ3X8mtfkgNVeMzXGsQTpL2Bq4Bjg5Iu5U2mvi7xHR57qOmRRDRxQH7CQ5KW9Oeq/YJiKG1BxSZbo1KXRKvZ3lgWERcW3uRhkQEUXL8nYypc1MfleordorxuY4biRtS3pB5A3rW27bPSLOqCey7pRXFO8CfJJ0trQvcFlE/KfOuKrUrUnhTOC4HvV29o2IPQrG8CVgb2DRiFhRaROREyNii1IxWGdVjO0UeTber0g7E74F/B04KCIerzWwgiT9mFwmm1SY72JgZLvupNlNV44pAOsAt0mapt6OcoXKQvV29iXtP/sPUqOP5lW9VtZl9FExtnQw+cPBT5h+OmjJgc2zgd8ydSHjTqQ3xg8WjKFuXwQeIS1qvTwiXpPUFZ+guzUpfLzuAIDXIuL1xmpNSXNS0QpF61MnVYyFtH/494FjgM1I4wuld16bt0c31ZmSDikcQ92WZGql1mPzOpZ5JM0ZEW/WG1q1atvmr04R8USuufMK6Y040uHm8RJulHQY6YX2EdJG7ZcXatumGtJusVg+NrR8OMyTZ1wpvx4PJw06l/RnSYdKGippeUnfAK6StKikrpiFFBFTIuIvEbEnaVbaJaQChU9LOrvW4CrWrWMKbevtRESxejt5Cf0XgI/mQ3+NiD+Uat8SdVDF2NzmbaSp0hcAfwOeBo6KiPf3+cCZG0Njbn7jzaG18tpsPUd/RiQtCHyyjvUrpXTlmQKpLvn6wCN54GgL4PYSDUvaTtK+EfFWrlK6PKkY3WGSPlMiBpvGyDzoP406KsZmBwLzAgeQxr52J60orpykdSUtEREr5P+LI0iFGy8H1snHuzYhAETEf2fnhADde6ZQW70dSbcCO0XEU/n6aNL85/mBUz37qCxJQ0gzS16nTcXYiHimrthKyzXB/iciXsyF+c4l7SM+HFglIvyhpQt060Bzo97OTZSvtzNXIyFkt+SSDi8q7bplBUXEs8AGPSrGXlm6YmzL1Ni2Ck2NHdBSXuSzwEkRcSFwoQrt6WD169YzhfmYWm9nV9KuRmdFxAsF2u61n1rSYxGxYtUxWOfpbYV7Q4mV7pLuA4ZHxJuSHgL2joibGreVWl3eCSRt39fthcqN1KIrzxQiovWs4LTCzf9D7Xc9+zJp4ZR1oQ4pb3IOaVbc86SZeTcDSHofUEe11jptmy/fA2xAGvSHNE34NmC2TQrdeqbQrhjbRFIBsoOrXLmZF6hdQlo129jAZR1gbtKshmerats6X92L13JZhyWBqxsfnnLV1vmj8IZDnUDS1cCejfInkpYkVa39WL2RVadbk8IPgXGklZsirdhckfQm/dWI2LRADJszdcvJWnY9s84j6RamLl7blrx4LSK+V2tgXUrSgxGxSsv1OUj/r6v08bBZWrcmhXbbcY6OiOGlZiGZtdOyl8G9EbF667G6Y+tGko4DhpG61iANwI+JiP3ri6paXTmmAEyWtCNpgRDAZ0gDz+BSE1av1/Kn0Ucl7UdavDZ/zTF1rYjYT9KngI3zoZMi4uI6Y6pat54ptFaBDNLCtYNI/4DrRMQtNYZnXUzSusCDwMKkRZYLAUc3Kvpaed1W4r4rk4KZWX90Y4n7ruw+kjQY+BLT78f7+bpisu4mqc9dzbpxX4cO0XUl7rsyKQCXkuZgX8vU/XjN6vQh4CnSgOY/mLYIndWn60rcd2tSmDcivll3EGYtlmBq/f5dgCuBcyLi/lqjsp4l7vdhNi9x35VjCpKOBG6LiKvqjsWsJ0lzk5LDz4AjIuK4mkPqWj1K3ItU4v73fT9q1tatSWESMB9pVfEbpD92RMSCtQZmXS0ng61JCWEoaavQUyLi6Trj6maSDoyIX83o2OykK5OCWaeRdDqpSutVwLkRcV/NIRmpnHhErN3j2F0RsVZdMVWtq5KCpJUj4iFJa7e7vRtru1hnkPQWU8u3t/5T+iy2BpIaYzsfJhcGzBYA3vKU1NnH/5LmHP+i5VjrP+DmZcMxSyKiW3dB7FS3AeOBxZn2/WIScE8tERXSbWcK6wFPNnbTkrQn8GlgLHB4ywYjZmZdqds+nZxI2naRvN3gT0j7KUwETqoxLjPrILlaLZImSfpvy9ckSf+tO74qdduZQrMCqqTfAhMi4vB8fXREDK8xPDOz2nXbmMIASXNGxJvAFqTxhYZu+12YWT9IGgAMYdqSOE/WF1G1uu2N0NsNmlm/SdqftOnRs8Bb+XAAa9QWVMW6qvsIvN2gmfWfpDHAByPihbpjKaXbzhRoV5c+Ih6pIxYz63hP0WW9CF2XFMzM3obHgRskXUkqiwNARPyyvpCq5aRgZta7J/PXXPlrttd1YwpmZtY7nymYmfUg6XKmLYETwPPA9RFxZj1RleEzBTOzHiRt0ubwosBuwKMRcWjhkIpxUjAz66e8kG3U7Fz9oNtqH5mZvWMRMdvv6e4xBTOzHiQt2ubwIsAewGy9b7aTgpnZ9EaRBpeVrwfwAnA98NW6girBYwpmZtbkMQUzs36Q1BV7rjgpmJn1z4i6AyjBScHMrH+eqzuAEjymYGZmTZ59ZGbWC0mDgW8CqwKDGscjYvPagqqYu4/MzHp3FvAgsAJwBDAWuKPOgKrm7iMzs15IGhUR60i6JyLWyMfuiIh1646tKu4+MjPr3Rv5crykrYF/kwrjzbacFMzMenekpIWAg4HfAAsCB9UbUrXcfWRmZk0+UzAz60WeffQlYCgt75cR8fm6Yqqak4KZWe8uBW4GrgVm+7LZ4O4jM7NeSRo9O2+o047XKZiZ9e4KSVvVHURJPlMwM+uFpEnAfMBrpOmpAiIiFqw1sAo5KZiZWZMHms3MepC0ckQ8JGntdrdHxJ2lYyrFZwpmZj1IOiki9pZ0fZubY3YuiOekYGZmTe4+MjPrg6QNmH7x2um1BVQxJwUzs15IOgNYERjN1MVrAcy2ScHdR2ZmvZD0ILBqdNEbpRevmZn17j5gibqDKMndR2ZmPUi6nNRNtADwgKR/khawARARn6grtqo5KZiZTe/ndQdQFycFM7PpPQ0MiYhbWw9K+jAwvp6QyvCYgpnZ9I4F/tvm+MR822zLScHMbHpDIuLengfzsaHlwynHScHMbHoL93HbPKWCqIOTgpnZ9EZK+lLPg5K+CIyqIZ5ivHjNzKwHSUOAi4HXmZoERgBzAZ+KiGfqiq1qTgpmZr2QtBmwWr56f0T8rc54SnBSMDOzJo8pmJlZk5OCmZk1OSnYLEvSMpIulfSopMck/UrSXL3cdylJF5SOsaX9wyU9LWl0jvciSau+w+faNNf4b1z/iqQ9Zl601s2cFGyWJEnARcAlETEMWAmYH/hRm/vOGRH/jojPFA6zp2MiYniO9zzgb5IGv4Pn2RRoJoWIOHF23vTFynJSsFnV5sCrEXEqQERMAQ4CPi9pXkl7SbpM0t+A6yQNlXQfQL79fEkPSLpY0j8kjci3fVTS3yXdKelPkubPx8dKOiIfv1fSyu8m+Ig4D7ga2CU//zqSbpQ0StJfJS2Zjx+Q47xH0rmShgJfAQ7KZx0b5bOQr+f73yDpp5L+KekRSRvl44MknZpjvyvPqjGbjpOCzao+QI9FRBHxX+BJ4H350NrAZyJikx6P3Qf4T0SsCnwXWAdA0uLAd4D/iYi1gZHA/7Y87vl8/ATg6zPhZ7gTWFnSQOA3OdZ1gFOYesZzKLBWRKwBfCUixgInMvWs4+Y2zztnRKwHfA34fj62L2nD+dWBnYHTJA2aCT+DzWZcJdVmZ9dExIttjn8Y+BVARNwn6Z58fH1gVeDW1DvFXMDfWx53Ub4cBWw/E+JTvnw/aS78NbndAUytxHkPcJakS4BL+vm8rXEOzd9/mJR4iIiHJD1B6nK7Z7pHW1dzUrBZ1QPANGMEkhYElgPGkM4SXn6bzylSItm5l9sbm6xMoc3/jqRTgbWAf0fEVv1oby3S2YhIC6M+1OY+WwMbA9sC35a0ej+et884zfri7iObVV0HzNuYdSNpAPAL4I8RMXkGj70V2DE/blWg8UZ7O7ChpPfl2+aTtFJ/A4qIz+UunRkmBEmfBj4KnAM8DAyW9KF820BJH5A0B7BsRFwPfBNYiDSYPom0I9jbcTOwa37+lUjJ8+G3+RzWBZwUbJaUN1L/FLCDpEeBR4BXgcP68fDjSW/CDwBHAvcDEyNiArAXcE7uUvo78K4GlHtoDA4/CuwGbB4REyLiddJZz08l3Q2MJs0uGgCcKele4C7g1xHxEnA58KnGQHM/2z4emCM/13nAXhHx2gweY13IZS6s6+SzioER8aqkFYFrgffnN2ezrub+RutG8wLX51k/AvZxQjBLfKZgZmZNHlMwM7MmJwUzM2tyUjAzsyYnBTMza3JSMDOzpv8Hj+SnNAAETngAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graficabarras(df1, \"importación\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ### Medio de transporte utilizado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definir variable \n",
    "def medio_transporte(tipo):\n",
    "  mt = df[df['direction'] == tipo]\n",
    "  mt = mt.groupby(by=['transport_mode'], as_index=False).count()\n",
    "  mt = mt.filter(['transport_mode','register_id'])\n",
    "  mt = mt.set_axis(['transport_mode','total'], axis=1)\n",
    "  mt = mt.sort_values(by='total', ascending=False).head(10)\n",
    "  return mt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>transport_mode</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sea</td>\n",
       "      <td>8434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rail</td>\n",
       "      <td>2780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Road</td>\n",
       "      <td>2149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air</td>\n",
       "      <td>2045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  transport_mode  total\n",
       "3            Sea   8434\n",
       "1           Rail   2780\n",
       "2           Road   2149\n",
       "0            Air   2045"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "me = medio_transporte('Exports')\n",
    "me.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>transport_mode</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sea</td>\n",
       "      <td>2254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rail</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Road</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  transport_mode  total\n",
       "3            Sea   2254\n",
       "1           Rail    601\n",
       "2           Road    449\n",
       "0            Air    344"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Medio de importación más usado\n",
    "mi = medio_transporte('Imports')\n",
    "mi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ ### Valor total de Importaciones y Exportaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imp_exp(dir,pais):\n",
    "  ie = df[df['direction'] == dir]\n",
    "  ie = ie.groupby(by=[pais], as_index=False).count()\n",
    "  ie = ie.filter([pais,'register_id'])\n",
    "  ie = ie.set_axis([pais,'total'], axis=1)\n",
    "  ie = ie.sort_values(by='total', ascending=False)#.head(10)\n",
    "  suma = ie['total'].sum()\n",
    "  ie['acumulado'] = ie['total'].cumsum(axis = 0)\n",
    "  ie['porcentaje'] = (ie['acumulado']/suma)*100\n",
    "  ie = ie[ie['porcentaje'] < 80]\n",
    "  return ie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>destination</th>\n",
       "      <th>total</th>\n",
       "      <th>acumulado</th>\n",
       "      <th>porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1404</td>\n",
       "      <td>1404</td>\n",
       "      <td>9.112150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USA</td>\n",
       "      <td>1345</td>\n",
       "      <td>2749</td>\n",
       "      <td>17.841381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1248</td>\n",
       "      <td>3997</td>\n",
       "      <td>25.941070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>China</td>\n",
       "      <td>1008</td>\n",
       "      <td>5005</td>\n",
       "      <td>32.483126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>903</td>\n",
       "      <td>5908</td>\n",
       "      <td>38.343718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>France</td>\n",
       "      <td>893</td>\n",
       "      <td>6801</td>\n",
       "      <td>44.139408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Germany</td>\n",
       "      <td>824</td>\n",
       "      <td>7625</td>\n",
       "      <td>49.487279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>699</td>\n",
       "      <td>8324</td>\n",
       "      <td>54.023884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Japan</td>\n",
       "      <td>658</td>\n",
       "      <td>8982</td>\n",
       "      <td>58.294393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Spain</td>\n",
       "      <td>632</td>\n",
       "      <td>9614</td>\n",
       "      <td>62.396158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Canada</td>\n",
       "      <td>603</td>\n",
       "      <td>10217</td>\n",
       "      <td>66.309709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>531</td>\n",
       "      <td>10748</td>\n",
       "      <td>69.755971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>514</td>\n",
       "      <td>11262</td>\n",
       "      <td>73.091900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>497</td>\n",
       "      <td>11759</td>\n",
       "      <td>76.317497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Italy</td>\n",
       "      <td>422</td>\n",
       "      <td>12181</td>\n",
       "      <td>79.056334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       destination  total  acumulado  porcentaje\n",
       "15          Mexico   1404       1404    9.112150\n",
       "29             USA   1345       2749   17.841381\n",
       "2          Belgium   1248       3997   25.941070\n",
       "6            China   1008       5005   32.483126\n",
       "4           Brazil    903       5908   38.343718\n",
       "8           France    893       6801   44.139408\n",
       "9          Germany    824       7625   49.487279\n",
       "16     Netherlands    699       8324   54.023884\n",
       "13           Japan    658       8982   58.294393\n",
       "25           Spain    632       9614   62.396158\n",
       "5           Canada    603      10217   66.309709\n",
       "31  United Kingdom    531      10748   69.755971\n",
       "22       Singapore    514      11262   73.091900\n",
       "32         Vietnam    497      11759   76.317497\n",
       "12           Italy    422      12181   79.056334"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exportaciones = imp_exp('Exports','destination')\n",
    "exportaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>origin</th>\n",
       "      <th>total</th>\n",
       "      <th>acumulado</th>\n",
       "      <th>porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>615</td>\n",
       "      <td>615</td>\n",
       "      <td>16.858553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Japan</td>\n",
       "      <td>445</td>\n",
       "      <td>1060</td>\n",
       "      <td>29.057018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>USA</td>\n",
       "      <td>376</td>\n",
       "      <td>1436</td>\n",
       "      <td>39.364035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>369</td>\n",
       "      <td>1805</td>\n",
       "      <td>49.479167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>366</td>\n",
       "      <td>2171</td>\n",
       "      <td>59.512061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>273</td>\n",
       "      <td>2444</td>\n",
       "      <td>66.995614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>199</td>\n",
       "      <td>2643</td>\n",
       "      <td>72.450658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>183</td>\n",
       "      <td>2826</td>\n",
       "      <td>77.467105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       origin  total  acumulado  porcentaje\n",
       "3       China    615        615   16.858553\n",
       "7       Japan    445       1060   29.057018\n",
       "14        USA    376       1436   39.364035\n",
       "5     Germany    369       1805   49.479167\n",
       "9      Mexico    366       2171   59.512061\n",
       "11  Singapore    273       2444   66.995614\n",
       "8    Malaysia    199       2643   72.450658\n",
       "13      Spain    183       2826   77.467105"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importaciones = imp_exp('Imports','origin')\n",
    "importaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La base de la estrategia, de acuerdo con los datos, debería estar enfocada en atender a las exportaciones  que se realizan por el medio de transporte Sea, en las rutas South Korea-Vietnam, Netherlands-Belgium, USA-Netherlands, China-Mexico, Japan-Brazil, Germany- France, South Korea- Japan, Australia-Singapour, Canada-Mexico y China-Spain.\n",
    "\n",
    "Respecto a las Importaciones, que también tienen como principal medio de transporte Sea, en las rutas Singapore-Thailand, Germany-China, China-Japan, Japan-Mexico, China-Thailand, Malasya-Thailand, Spain-Germany, Mexico-USA, China-United Arab Emirates y Brazil-China. "
   ]
  }
 ],
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