1_ETL_LoadData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# 1 - Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "En este primer paso cargamos los datos desde una base de datos georeferenciada GDB, utilizando geoPandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "data_path = 'D:\\\\Data Science\\\\_Diplo\\\\Mentorias\\\\Calidad_Serv_Electrico\\\\BDGD\\\\2017-12-31\\\\ENF_6612_2017-12-31_M10_20180820-1410.gdb'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Cargar Layers de interes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "Cada tabla de datos se conoce como \"layer\" para nuestro analisis utilizaremos los siguientes datos:\n",
    "\n",
    "    * UCBT, consumidores de baja tension\n",
    "    * UNTRD, unidades transformadoras de media tension\n",
    "    * SSDMT, linea de transmicion de media tension\n",
    "    * UNTRS, unidades transformadoras de segmentacion\n",
    "    * SUB, ubicacion de subestaciones\n",
    "    * SEGCON, informacion de conductores electricos utilizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "layer_list = ['UCBT','UNTRD','UNTRS', 'SSDMT','CTMT','PONNOT','SUB','SEGCON']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "GDB = {layer: gpd.read_file(data_path, driver='FileGDB', layer= layer) for layer in layer_list}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Chequear tipos y codificacion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Generar variable de salida FIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21ed0cf7be0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xDX3nXNDTP41lGE4mE87NnxP6yYRm74hI24ht6Jd9h3MsfE+/LvTV0xeRdhLb0C+Vg0n1p7PK5slkK+2dusXYkqaTs0SkbcQ39P1g7nz9RVTO1LwDuar0RaSNxDf0vSD0G10L90zMO5Cb1ECuiLSP+IZ+2N5pTU/fwzldHF1Elr8Yh37U3ln8St93UPC0FIOILH+xDf1ieYF7+pXllBtfVUstHhFpB7EN/ajSz7Sg0geY1gweEWkDsQ19b4GnbA50pzn/rF42ruyp2Z6uVPo6QUtElr/UUh/AYlno9k46meB9r9w0Z7suji4i7SS2lX5lyuYCVfrz0SUTRaSdxDf0oymbCzRPfz66OLqItJMYh/7CTtmcT1rtHRFpI7EN/YXu6c+nMmVTs3dEpA3ENvRbV+kHHyqavSMi7SC2oe8t8DIM89FAroi0k6YS0cyuMbMdZrbTzD7a4PHXmNlDZuaZ2dvrHrvRzJ4K/7lxoQ78VFrV3lFPX0TaySlD38ySwGeBa4ELgV8zswvrdtsHvA+4te65Q8DHgauBq4CPm9ng8z/sU1voM3LnkzAjl06opy8ibaGZRLwK2Omc2+WcKwK3AddX7+Cc2+Oc+zlQv+rYm4HvOefGnHPHgO8B1yzAcZ9SNE9/sds7AN2ZlC6ZKCJtoZlEXAvsr7p/INzWjKaea2Y3mdlWM9s6Ojra5EufXKvm6QN0pZNq74hIW2gmERs1xZtdPL6p5zrnvuCcu8I5d8XIyEiTL31yrerpA3Rnkjo5S0TaQjOhfwBYX3V/HfBsk6//fJ77vFRm7yRa0d6ZrfQPHs9zz/bDi/47RUTORDOJuAU4z8w2mVkGuAHY3OTr3wW8ycwGwwHcN4XbFl2p7JNMGInE4lf6XVWV/j/cu5vf/OetlH1dSUtElp9Thr5zzgM+RBDW24DbnXOPm9nNZnYdgJldaWYHgHcAnzezx8PnjgF/RvDBsQW4Ody26EplvyWtHQgHckvBQO6+sWk83zE2VWzJ7xYROR1NLa3snLsDuKNu28eqbm8haN00eu4twC3P4xjPSLHst2TmDgSVftTeOXBsGoDRiQIjfdmW/H4RkWbF9ozcUtlf9Dn6ke70bHvnmWN5AI5MFlryu0VETkd8Q99zLav0o4Hc4/kSE4WgzTM6odAXkeUnvqHv+6RTrenpd2VS5IvlSmsHYFSVvogsQ/EN/bJryXRNCCr9Ytln79Gq0FelLyLLUHxD32vdQO72gycA+OaDBwDIpRNs2TN3kpJX9tnaYLuISKvEN/TLrWvvREs9HJ4okEklWNWXY7Iwdy2e7z1xiLd/7qfsOTLVkuMSEakX29Bv5ZTNaJbQoRMzDHSl6culmJyZG/qHTszU/BQRabXYhn6phaEf/Z6jk0UGuzP0ZlNMNAj9Y9Olmp8iIq0W49B3LZunH109q+wcgz1BpZ8vlSl4tYuwjU8HZ+kem9bZuiKyNGIb+l4Ll2Go/nAZ7M7Ql00DQeVfbTwfVfoKfRFZGrEN/WLZkWpxpQ8w0J2hNxesblF/Vm7U1hlXe0dElkhTa++0o1Yuw1Bb6adx4QKb9XP1j0ftHS3GJiJLJNah36r2Trqu0vfCC7jUh/7sQK5CX0SWRnxDv4UnZ0WVfjpp9GSSlP3gfn3ozw7kqr0jIksjtqFfLLuWXB8XZnv6g90ZzIxU0uhKJ2vW3/HKPifCaZyq9EVkqcQ29D2/dT39ZMJImjHYnals682leGjvMW69fx8AU+EZuoYGckVk6cR29k7J80m14FKJkd5ciuHe2dDvy6YqyywDlYusDPZkGJ8u4utyiiKyBGJb6Zda2N4B+MAvbaInO/t29uZSlQuqAEwXgw+A4d4MY1NFTsyUGKj6ZiAi0gqxrPSdcy1dewdgZW+WXDpZuV9f6UdX1lrZE1xCUYO5IrIUYhn6Xtg6ybRoymYjvbk0Rc+vLMUQtXeiFpAGc0VkKcQy9EvhPPlWVvr1+sJWT7Ta5nQpCv2g0h9X6IvIEohn6HtBpb+UoR8txRCtqz9d9DBgqCeo9Mem1N4RkdaLZ+j7YaXfwoHcen1h6EdLLOeLZboySbozwXZV+iKyFOIZ+lF7p4VTNusNdNX27qeLZbozSXLpBMmEqacvIksinqG/DNo7XZkkPdlUZSmG6aJHdyaFmTHYndbsHRFZErEM/WJ56ds7ACO92cpSDPlima5wSudAd0YrbYrIkohl6EftnaWcsgkw0pepqvSD9g4QVvoKfRFpvViH/lK2dyCo9KeLZaYKHtOl2dAf6M5o/R0RWRIxDf2l7+kDjPQFc/KfOzFD0fPpCmfuDHVnVOmLyJKIaegvk0q/LwfAvrFpgNlKvyfNsakSzmnRNRFprZiH/tL29Ae606QSxr6jtaE/2J2hWPYrSzOIiLRKzEN/af+8hBnDvdmqSj9o7wx2pwGtvyMirRfL0C8ug3n6keHeDPlw3Z2uqoFc0MVURKT1lj4VF0FlymZqads7MDuYC7PtnWj9HVX6ItJqsQx9z18e7R1oHPpRe2es6gQtr+zzN9/dwdHJ2oupi4gspKVPxUWwHJZhiIz0BjN4kmaVa/Y2au88cfAE//vunXz3iUOtP0gR6RhLn4qLoLhMBnIBhvuCgO/OJDEL2k0DXXMHcqNLK0Zn8IqILIamUtHMrjGzHWa208w+2uDxrJl9PXz8fjPbGG7faGZ5M3sk/OdzC3v4jS2XKZsA2VSS/lyqMogLkEomGOxO1wT8M+NB6B+emGn5MYpI5zjlhdHNLAl8FngjcADYYmabnXNPVO32fuCYc+6FZnYD8EngneFjTzvnLl3g4z6p5TJlM7JhqBu/7jys9UPd7K+6cHol9E+o0heRxXPK0AeuAnY653YBmNltwPVAdehfD/xpePsbwGcs6mUsgeWyDEPkHVesn7Nt/VA3jz9zvHL/2Uqlr9AXkcXTTCquBfZX3T8Qbmu4j3POA44DK8PHNpnZw2b2IzN79fM83qYUveXT3oHgw6f+A2jDUDcHjuUph18Bnh0P2jrq6YvIYmom9BslZ/2iMfPtcxDY4Jy7DPgwcKuZ9c/5BWY3mdlWM9s6OjraxCGdnOf7pJPGEn7ZOKUNQ914vuPg8aDCj9o7oxMFrckjIoummdA/AFT3J9YBz863j5mlgBXAmHOu4Jw7CuCcexB4Gji//hc4577gnLvCOXfFyMjI6f8VdUplt2xaO/PZMNQNBIux5YtlxqaKrOwJ1uTRmboisliaScYtwHlmtsnMMsANwOa6fTYDN4a33w7c7ZxzZjYSDgRjZucC5wG7FubQ51f0fFJLeH3cZkShv39sulLlX7p+AFBfX0QWzykHcp1znpl9CLgLSAK3OOceN7Obga3Ouc3AF4Evm9lOYIzggwHgNcDNZuYBZeC3nXNji/GHVCuVfTJLfKnEU1m9IheswDk2zeoVXQAkwg+qr2/ZzwtX9Vb2fdfVG5bkGEUkfpqZvYNz7g7gjrptH6u6PQO8o8Hzvgl883ke42krlf1l395JJROsHexi31ie9YNBpb9+MKj+J2bU3hGRxbG8k/EMtUNPH4IWz76wvZMwWDsQVPwTM94SH5mIxNXyT8YzEFT6y7unD+EJWmHon92foyuTJJNMqNIXkUUTy9Avesu/vQNBpT82VWTHcxOsHQyq/L5cihOq9EVkkSz/ZDwDkwWPvlxTwxVLKprB88TBE6wZmA39Ru2doufj16/lICJymmIZ+uPTJVaEK1kuZ1HoO0dV6KfntHecc7zur3/IF3+8u+XHKCLxEsvQP54v0d8Oob+yu3J7bXWlX6it9A9PFHhmPM8j+8dbenwiEj+xDf2BrsxSH8Yp9efSDIRX0aqEfjZF0fMpeOXKfk+PTgKwd2yq4et848EDbH60/iRpEZG5Yhf6XtlnsuC1RXsHZls81e0dgMmqvv7uI0HY7z063XBdnk//4Ek+/6OnF/tQRSQGYhf60cyXFV3LfyAXgmmbAGsGgssqRgPQ1TN4do8GoT8x43Gsbl2eyYLH/rE8e45MaaE2ETml2IX+eHgJwug6tMvd1ZuGuGhNf6XCj35WD+buOjLb1tl7tLbF8+ShCQCmimUtyywipxS70D+eD8KyXdo7v/6KjXz792YvMxBV+hN17Z3zzwrW4tk3Nl3z/B3PTdTsJyJyMu3RAzkN41Hod7dH6NfrziRJmlVCv+w79h6d4pUvGObJQ5P826PPMlWYHeT9t58/ixFcvODrW/Zz9bkrG7+wiAgxrPRPtFmlX8/M6M2lKu2dsakivoOzV+Toz6UYmyrW7H/o+AxrBrpImnFkUu0dETm52IV+dAGSdg19gP5cqvKNJQry4d4sK3uzHJ2cDX3nHM+dmGH1ihxDPRmOTBYbvp6ISCR2od9uPf1GNg73sPfoFNMFryr0Mwz1ZGoq/cmCx3SxzFn9OVb2Zjg6pUpfRE4ulqHfk0m2xYJr87lk3QC+g188e4IjkwW6M0m6MylW9mSYKHiVC78fOhGE/NkrcgyH3wK0Po+InEz7JuM8xqdLbTNdcz6rwxB/9MA4RyaLjPRmARjqCf6uqNp/7sQMQKXS93zHwXBbxCv75ItlREQghqHfLuvunIyZccn6Few5MsWz43mG54R+UOEfOj5DbzZFbzZV2WdP3bTNv/zOdq759L06cUtEgFiGfrFtzsY9mUvWDeCAgucz3BuE/cqeINiPVlX6Z/fnwseCfapP5Cp6Pt986AB7j05z4Fi+hUcvIstV+6djneP5EucO9556x2VuuDfL2oEunhnPM9wXhH1XJklXOsnRqSK+cxyemOGqjUMA9HelSSeNOx87SNKCq4ZtO3iiMpvp4f3jlSUfRKRzxa7Sb5e19JtxyfoBAFb15SrbVvZmeO74DHf+4jlKZcfZK4LHEmas7MlWvgUAPLJ/nO5MklTCeGSflmUWkZhW+u16Nm69V5y7ktUrcoyElT4Eff2fHzjO/rFpLls/wEvXDVQeW9mb4VA4kDtTKrPt4AkuP2eQg8dneGT/sZYfv4gsP7EK/ZlSmYLnx6bSTyaMF4zUtqouDadz/vL5I5U1+CPDvVm2HTyB5/s8cfAEnu+4dP0AqcRxtuw9Fl4wPnZf7kTkNMQq9ONwYtapvHh1Py9e3d/wsbUDXfgO/urOHaSTxmB3mg1D3RzPl/jJ00fZfnCCi9etaPERi8hyEquyLwr9gZi0d07XRWv6ec/VGypBf+XGIcyM9YPBAK5aPCISq0o/DuvuPB9mxoVrVnDhmhWUyj6pRDCLZ6A7zXBvhof3j/PeV8zuf+cvDvLPP93L37/n8jnv2d98dwdPj05x5cZBsqlkZfu7rt7Qkr9FRBZHrEK/E9o7zaru3ZsZw71Z7n3yCLfevw8I1t6/5Se7KfuO27fs5zdfcy4A//H0EW7+tyfYHq7TXy77/PKLVrX+DxCRRRGr9k7lqlltcFH0Vls/1M2RyQLHporsPTrFl3+2h6HuDJesH+BLP91D2Xfki2U+/PVHmSp6XHfJGs4Z6uaBPWP4TZzNOzpR4Pdve5inDk2ccl8RWTqq9DtE1Nf/1Hd3ANCXTfG+V27k3JEefuerD/H9bYd48rkJnjsxw+2/9Qp2Hp6kO5Pkti37eerQJC86u4/HnjnOlX/+fV62YYCrNq2kKzPb9rnjsYP8eOcR7nvqCL/72hfQnUmpFSSyDMUq9E/kS5jNXnJQZm0a7uHal5yNAT3ZFOeO9LKiK80bLzyLtQNdfObunewaneTNF53FVZuG2Hl4kgvX9NOTTfHA7qMM92b45kMHSCWMu544xD1PjvK2l63j4rUrmC54PLB7jA1D3TxzLM9tW/Zz4ys2LvWfLCINxCodx/Ml+nNpEuEApsxKJoxXnzcyZ3sqmeDXX3EOf/md7aQSxkevvWD2sUSCK84Z5N4nRzk6VSRh8KHXvZDpYpl/feQZvvHgflb1ZfnFM8cpln3eetla9o9N862Hn+H72w7x3lecU3mtHz91hJes7W+4AmrRCwad9e9NZPHFqqd/PB+fJRha6Z1Xrqc/F7R7Ng331DwWre1zeKLA2162joHuDGsGunjPy88hm0ry1fv38R9PH+XFZ/dxdn+OKzcOcfmGQe57apTHnz0OBLOE3vPF+7npyw9Srlt+iGDmAAALIklEQVTvv+j5XPeZH/Nfv/Zww2P70ZOjfO2BfYvwV4t0pliFfrCWvkL/dA10Z7jvD1/PH//KBXMeG+zJ8JrzR3jThWdx0ZrZE7v6c2luuHI9RycL5EtlXnv+7LeIX7l4NV2ZFL/15Qf5+x8+zYdvf5TebIoHdo/xO195sOb1v/KzvWx/boJvP3aQe58crXlssuDx4a8/wp986zF21y0ZLSJnJlahr0r/zK3onr8t9uaLzua1DaZtnjvSy3952Vpe/cJhNqyc/YbQlUnyqxev5sCxPH/3w52Uyj4fePUmLl67gu9vO8Qnv7OdW+/fxxfv282n7trBpuEehnoy/Pm3n8Ar+5XX+Yd7d3F0qkgqkeAzd+9c+D9apAPFqqd/PF9i7WDXqXeUBXP5OUMNt7903Qoe2neMpw5Pct0la1jVl+OtlwY9/y/9dA+vf/Eqjk0VmSmVecvFqzk2XeSr9+/ja1v2896Xn8PoRIF/uG8Xb7l4NePTRb718AE2ruxmZe/s4nOaHSRy+mIX+gOq9JcFM+MdV6xn5+FJLgnX++nKJLnxlRvZ/Oiz/PvPDwJw2foB1gx0sXpFjpefO8Qnv7Od7QdPcHSySMHz+cibX8Rdjz/H/bvH+OGOUd52+brK73hmPM/WPWNcd8kazDQILNKM2IS+c07tnWWmN5vi0vUDNdvO6s/xgV/axFOHJ3l0/zhvvuhsIPiQ+NTbL+Evvr2Nbz38DNPFMu95+QY2DffQn0tz5aYh7t91lAtW93Phmn6eOz7Df/7sTzg8UWDbwQk+eu2La37PniNTfPOhA7zvlRtrvh2IdLrYhP5kwaPsOw3ktgEz4/yz+jj/rL6a7euHuvncey9nplTm0f3jlYvIALzuRavYPTrFV+7fywVn97H76BSZZIKXrlvB5370NHuOTPGa80fwncMr+3zizu3MlHzueOwgX/nA1axeobafCDQZ+mZ2DfBpIAn8o3PuE3WPZ4F/Bi4HjgLvdM7tCR/7I+D9QBn4PefcXQt29FVKZccrzl3JOSt7Tr2zLGu5dJKrz11Zs603m+J3X/cC7nvqCHdvP8xAV5rfeNUmVnSncQ7ufPw5fvTkKMWyT9l3nH9WL5efM8S/PHSAa//2Pj7xtotZM9BF2Xf8bNcYj+4f51UvXMk7r9xAJhWr+QwiJ3XK0DezJPBZ4I3AAWCLmW12zj1Rtdv7gWPOuRea2Q3AJ4F3mtmFwA3ARcAa4Ptmdr5zrrzQf8hQT4av3fTyhX5ZWUZSiQSve9EqLt8wSDaVIJsOloF4xxXrWNWfZbpQJp001gx0cfHaFZgZQz0Z/uknu/ntrzxU81r9uRR3Pv4cn/vRLt544VmMTxc5OlXkyGSRY1NFNgx186aLgmmqe45OsWt0klLZkUwYq1fkePV5I5x/Vi++g6NTBVKJBCu60iSrZkD5vmPXkSkKXpkXjPSSSycRWWrNVPpXATudc7sAzOw24HqgOvSvB/40vP0N4DMWjKxdD9zmnCsAu81sZ/h6P12Yw5dO1F83bpNKJPhPLz6r4b5rB7r4yJtexOhkgYmZoAV4zspuerMpnjo8yQ+2HeLW+/fRk03Sm03Rk02xZiDHRMHjz7+9rfI62VSCTCpBoeRTLPvANrKpBEXPJzrdLGHBOQ9DPRn6cil2Hp5kYsYDwAw2DHVz3qpeXrCql6QZ4/kSXtlnVV+OVf1ZMskEiYRxbKrI7iNTHJ4osG6wi03DPazqy9GXS5FKGMemS5yYCSYtrOrPMdSToSeTJJtK4vk+pbLDDFIJI5VMkEpY5cPIdw7fhT/9eW47h+9X3Q63A+RSSXLpBLlMklwqSTo5+yEXrcvnCMbYotvVj5V9x0ypTLHsh8eYIBkenwGlcvD+GhYcdzL4mTALHvOCKb2pZIJ0MnheOpHALPgds7/PUX0aoBG0FS38d7FYA/+Vv9vNvg+u5r1xs7frtjmC/4a6M4vbdW/m1dcC+6vuHwCunm8f55xnZseBleH2n9U9d+0ZH63IGcimk6wLF5yr1mhcIfKuqzewf2yap0cnecFIL2sHukgkjFvv38f4dJGdhyd5ZjxPdyZJby6Nc46pgsdUocxU0eP4dIkLzu5n/VAX6WSC0YkChycKPPbMce7ZMYpzjq5MiqQF41F1JyrTk0nSl0vzk51HKHh+w2OU5ycRhn/1z0T4YRCEsKv5IKFuW/UHWxML0Tbl0vUD/OsHX7UwLzaPZkK/0Udi/Z843z7NPBczuwm4Kbw7aWY7mjiu0zUMHFmE1z1t717qA6jybr0vjQy/e5m8J8vIsvnvZJlZ0PdlL2AfOuOnn3PqXZoL/QPA+qr764Bn59nngJmlgBXAWJPPxTn3BeALzRzwmTKzrc65Kxbzd7QjvS9z6T2ZS+9JY+34vjQzbWELcJ6ZbTKzDMHA7Oa6fTYDN4a33w7c7YLvPpuBG8wsa2abgPOABxbm0EVE5HSdstIPe/QfAu4imLJ5i3PucTO7GdjqnNsMfBH4cjhQO0bwwUC43+0Eg74e8MHFmLkjIiLNMbdQIxDLnJndFLaRpIrel7n0nsyl96SxdnxfOib0RUQkZksri4jIyXVE6JvZNWa2w8x2mtlHl/p4loKZ3WJmh83sF1Xbhszse2b2VPhzcCmPsdXMbL2Z3WNm28zscTP7/XB7p78vOTN7wMweDd+X/xFu32Rm94fvy9fDiR0dxcySZvawmf17eL/t3pPYh37VMhLXAhcCvxYuD9Fp/gm4pm7bR4EfOOfOA34Q3u8kHvDfnHMXAC8HPhj+t9Hp70sBeL1z7hLgUuAaM3s5wfIq/yt8X44RLL/SaX4f2FZ1v+3ek9iHPlXLSDjnikC0jERHcc7dSzCzqtr1wJfC218C3trSg1pizrmDzrmHwtsTBP8zr0Xvi3POTYZ30+E/Dng9wTIr0IHvi5mtA94C/GN432jD96QTQr/RMhJaCiJwlnPuIAQBCMy9JmKHMLONwGXA/eh9idoYjwCHge8BTwPjzjkv3KUT/z/6W+APgWhdjJW04XvSCaHf1FIQ0rnMrBf4JvAHzrkTS308y4Fzruycu5TgLPqrgAsa7dbao1o6ZvarwGHn3IPVmxvsuuzfk9hcROUkmloKokMdMrPVzrmDZraaoKrrKGaWJgj8rzrn/iXc3PHvS8Q5N25mPyQY8xgws1RY2Xba/0evAq4zs18BckA/QeXfdu9JJ1T6zSwj0amql8+4Efh/S3gsLRf2ZL8IbHPO/c+qhzr9fRkxs4HwdhfwBoLxjnsIllmBDntfnHN/5Jxb55zbSJAhdzvn3k0bvicdcXJW+On8t8wuI/EXS3xILWdmXwNeS7Aq4CHg48C/ArcDG4B9wDucc/WDvbFlZr8E3Ac8xmyf9o8J+vqd/L68lGBQMklQGN7unLvZzM4lmAgxBDwMvCe8VkZHMbPXAh9xzv1qO74nHRH6IiIS6IT2joiIhBT6IiIdRKEvItJBFPoiIh1EoS8i0kEU+iJ1zKxsZo9U/bPRzF4brawY7nOtmW0NV+jcbmZ/vZTHLNKsTjgjV+R05cMlCCrCtXmi2y8BPgO8xTm33cxSwE0tPUKRM6TQFzl9fwj8hXNuOwTXkQb+bmkPSaQ5au+IzNVV1dr5VoPHXwI82GC7yLKnSl9krjntHZG4UKUvcvoeBy5f6oMQORMKfZHT9yngj83sfAAzS5jZh5f4mESaovaOyGlyzv3czP4A+JqZdRNcOOPbS3xYIk3RKpsiIh1E7R0RkQ6i0BcR6SAKfRGRDqLQFxHpIAp9EZEOotAXEekgCn0RkQ6i0BcR6SD/H9PXLw9CiuRCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(GDB['UCBT'].FIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "UNTRD_FIC = GDB['UCBT'].groupby('UNI_TR_D')['FIC'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNI_TR_D\n",
       "              0.0\n",
       "101415715    15.0\n",
       "102321736     2.0\n",
       "103455355    12.0\n",
       "105138826    10.0\n",
       "Name: FIC, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UNTRD_FIC.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Analisar Variable de salida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21ed0e13da0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(UNTRD_FIC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Exportar Exit Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "outfile = open('./data/1_GDB_Layers.pkl','wb')\n",
    "pickle.dump(GDB, outfile)\n",
    "outfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "outfile = open('./data/1_OUTFT_UNTRD_FIC.pkl','wb')\n",
    "pickle.dump(UNTRD_FIC, outfile)\n",
    "outfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}