notebooks/recursos_humanos_uni_barplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cálculo promedio de remuneración UNRC\n",
    "\n",
    "Según datos oficiales extraídos del sistema de información de la UNRC y declaraciones públicas varias.\n",
    "\n",
    "Se extrae de **Recursos humanos UNRC**: [Estadísticas Sireh](https://sisinfo.unrc.edu.ar/estadisticas/estadisticas_sireh.php) la cantidad de personal clasificados según *categoría* y *horas semanales*:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**AUTORIDADES**\n",
    "\n",
    "| **Dedicación**        | Exclusiva | Simple | Tiempo Completo | Tiempo Parcial |\n",
    "|-----------------------|-----------|--------|-----------------|----------------|\n",
    "| **Cantidad personas** | 41        | 39     | 2               | 1              |\n",
    "| **Horas semanales**   | 40        | 20     | 40              | 20             |\n",
    "\n",
    "**DOCENTES**\n",
    "\n",
    "| **Dedicación**        | Exclusiva | Otra | Semi-Exclusiva | Simple |\n",
    "|-----------------------|-----------|------|----------------|--------|\n",
    "| **Cantidad personas** | 705       | 171  | 581            | 418    |\n",
    "| **Horas semanales**   | 40        | 20   | 20             | 10     |\n",
    "\n",
    "**NO DOCENTES**\n",
    "\n",
    "| **Categoría**         | C1 | C2 | C3  | C4  | C5  | C6 | C7  |\n",
    "|-----------------------|----|----|-----|-----|-----|----|-----|\n",
    "| **Cantidad personas** | 16 | 45 | 110 | 104 | 144 | 49 | 122 |\n",
    "| **Horas semanales**   | 40 | 40 | 40  | 40  | 40  | 40 | 40  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x = [0, 3, 4, 7, 8, 9]\n",
    "y = [418, 752, 40, 705, 43, 590]\n",
    "colors = ['green', 'green', 'blue', 'green', 'blue', 'orange']\n",
    "\n",
    "bars = plt.bar(x, y, color=colors)\n",
    "plt.xticks([0, 3.5, 8], [10, 20, 40], fontsize=12)\n",
    "plt.yticks([40] + list(range(100, 800, 100)), fontsize=12)\n",
    "plt.xlim(-1, 10)\n",
    "plt.xlabel('Cantidad de horas semanales', fontsize=14)\n",
    "plt.ylabel('Cantidad de personas', fontsize=14)\n",
    "plt.title('Recursos humanos UNRC', fontsize=20)\n",
    "plt.legend([bars[0], bars[2], bars[-1]], ['Docente', 'Autoridad', 'No docente'])\n",
    "#plt.hlines(40, -1, 7.6, linestyles='--', alpha=0.3)\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.savefig('../content/img/rrhh_unrc.png', dpi=100, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El sitio oficial de la UNRC **no publica** presupuesto para 2018, la última publicación al respecto data de 2016 *([Presupuesto UNRC](https://www.unrc.edu.ar/unrc/presupuesto.php))*. Una [noticia de puntal](http://www.puntal.com.ar/noticia/UNRC-el-presupuesto-para-2018-crece-25-y-llega-a--1.478-millones-20170920-0017.html) informa el monto de **$1.478** millones para el presupuesto 2018.\n",
    "\n",
    "Según declaraciones públicas de autoridades de la UNRC en una [nota](https://www.unrc.edu.ar/unrc/n_comp.cdc?nota=32358) en el sitio oficial: **\"*Los gastos de funcionamiento, que insumen entre el 10 y el 15 por ciento del presupuesto de la UNRC (el resto es para sueldos) fueron otro de los tópicos.*\"**\n",
    "\n",
    "Para un **85%** del presupuesto destinado a sueldos **(aproximadamente $1200 millones)**, se calcula un promedio por hora y así la supuesta remuneración por personal según su dedicación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "presupuesto_2018 = 1_478_000_000\n",
    "presupuesto_sueldos_2018 = presupuesto_2018 * 0.85\n",
    "total_horas = 3803800"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pago_hora = presupuesto_sueldos_2018 / total_horas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "330.274988169725"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pago_hora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pago_semana = []\n",
    "pago_mes = []\n",
    "horas = [10, 20, 30, 40]\n",
    "for hora in horas:\n",
    "    semana = round(hora * pago_hora, 2)\n",
    "    pago_semana.append(semana)\n",
    "    mes = round(4 * hora * pago_hora, 2)\n",
    "    pago_mes.append(mes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3302.75, 6605.5, 9908.25, 13211.0]\n",
      "[13211.0, 26422.0, 39633.0, 52844.0]\n"
     ]
    }
   ],
   "source": [
    "print(pago_semana)\n",
    "print(pago_mes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pp+YygGfqyr/YaIGS9gH2ARg4cCDjx4/v5iqY9W0Hrzm9t0OYSSv+T86t27hskrmLtDdzUn5d25vZlzRGM9si4mFJxwLXAu8A9wPTgVOBo/OyjgZOAPYC1Gg2NO76a3h4dUSMJZ1gyogRI6Ktra07q2DW542e086T2bWtt0Nourl1G5dNMj8BrpG0ep7moPx8PWCj7gYREacDpwNI+hUwNSJerNVLOo00/gNpD2XZwuSDSRftpINyMzPrBaUG/iPiVuA/gAHAZNIl/58D1o+Ie7sbhKTP5r/LATsAf5W0dKHJ9qRuNYDLgJ0kzSdpKDAMuJO0tzVM0lBJA0gHB1zW3djMzGz2lb6fTET8C9ijojguymMyHwHfj4jXJZ0raTipy2sKqWuOiHhI0vmkAf3puf3HAJL2B64B+gNnRMRDFcVrZmYllE4yVYqILzco+3YH7ccAYxqUjwPGNTc6MzObXb79spmZVcZJxszMKuMkY2ZmlXGSMTOzypQe+Jf0LdKhy5+lLjlFxDZNjsvMzFpAqSQj6TjgQOBG0vkxvlGZmZl1quyezO7AzhFxYZXBmJlZayk7JtOPGbdfNjMzK6VskhkL7FZlIGZm1nrKdpctCuwiaTPgAdLlX/4tIn7Q7MDMzKzvK5tkVmNGd9kqdXU+CMDMzBoqlWQiYuOqAzEzs9bTpQtkSpofWJG09zI5Ij6oJCozM2sJpQb+Jc2bz5V5nXTnyn8Br0v6taR5qwzQzMz6roZJRtK+ktYtFB1LOrrsu8BKpBuF7Qd8G/ifqoM0M7O+qb3usonAJZL2jYirgF2AvfL9WmomS3oZ+CNwSMVxmplZH9RwTyYibgY2An6aixYh3Xa53mTS4c1mZmazaHdMJiKmAG355f1Ao3NhDsBXAjAzs3Z0eHRZRNROuvwRMC6fjHkb6eiy9YFlgC0rjdDMzPqsUkeXRcRNpAH/C4CFgIXz85Uj4pbqwjMzs76s9HkyEfEccHiFsZiZWYtpN8lIWgeYEBGf5Oftioh7mx6ZmZn1eR3tydwNLAW8lJ8HoAbtAujf/NDMzKyv6yjJDAVeLjw3MzPrknaTTEQ81ei5mZlZWWWvXba/pFluWiZpN0nfa35YZmbWCsreGfNA4JkG5VOAHzYtGjMzayllk8xgoFGX2dRcZ2ZmNouySeYFYHiD8nWAV5oXjpmZtZKyJ2P+BfhfSe8C43PZxsBJwJ8riMvMzFpA2SRzJOkw5muAj3NZP9KlZX5WQVxmZtYCSiWZfKHMnSUdQeo2E3BvREyqMjgzM+vbSl+7DCAiHgcerygWMzNrMaWTjKSVgG8AywEDinURsVeT4zIzsxZQKslIGgVcBNwHrAvcBXwOmA+4ubLozMysTyt7CPMvgJ9HxPrANODbwBDgOmYcbWZmZjaTsklmZeBv+flHwIIR8QEp+RxYRWBmZtb3lU0ybwPz5+fPAyvm5/MAizU7KDMzaw1lk8wdwIb5+ZXACZKOBM4EbutuEJIWlXShpEckPSxpfUlHSXpW0oT82KrQ/jBJkyQ9KmmLQvnIXDZJ0qHdjcvMzLqn7NFlBwEL5edHAZ8Gvg48luu662Tg6oj4hqQBwILAFsCJEXF8saGk1YCdgNWBZYDr8pFvAL8HNiNdU+0uSZdFxMQmxGe9ZMihV/Z2CDOZcsyo3g7BrE/pNMlImgdYhbQ3Q0S8B+zXrAAkLQxsBIzO8/8Q+FBqdBNOALYFzouIacCTkiYB6+W6SRHxRJ7vebmtk4yZWS/pNMlExHRJF5MSzasVxLAC6Q6cZ0paC7gHOCDX7S9pd9Ltnw+OiNeBQcDthemn5jKY+XYEU4EvNlqgpH2AfQAGDhzI+PHjm7Mm1nQHrzm9t0OYSat+Vrydqze3buOy3WX3kwb7p1QUwzrAf0XEHZJOBg4FfgccDUT+ewKwF+mSNvWCxuNL0WiBETEWGAswYsSIaGtr6+YqWFVGz2ndZbu29XYIlfB2rt7cuo3LDvwfRRrs307SspIWLz66GcNUYGpE3JFfXwisExEvRsTHEfEJcBozusSmAssWph8MPNdBuZmZ9ZKyezK1FHwxM+8dKL/uP7sBRMQLkp6RtHJEPApsCkyUtHREPJ+bbQ88mJ9fBvxF0m9IA//DgDtzLMMkDQWeJR0csMvsxmVmZt1XNslsXGkU8F/An/ORZU8Ae5LuXzOclMSmAPsCRMRDks4nDehPB74fER8DSNqfdDuC/sAZEfFQxXGbmVkHyl7q/x9VBhERE4ARdcXf7qD9GGBMg/JxwLjmRmdmZrOr7JgMktaU9DtJV0laOpdtJ2nt6sIzM7O+rFSSkbQ56crLg4BNgAVy1edId800MzObRdk9maOBgyJie+DDQvl4Zhz1ZWZmNpOySWZ1Go91vAZ09xBmMzNrUWWTTO1M+3rrkM5PMTMzm0XZJPMX4DhJg0mHFM8j6SvA8cA5VQVnZmZ9W9kk81PgSeAp0tWYJwI3ALfQ4FBiMzMzKH+ezEfArpKOANYmJaf7IuLxKoMzM7O+rewZ/wBExGRgckWxmJlZiymdZCR9i3Rdsc9S180WEds0OS4zM2sBpZKMpOOAA4EbSVc2bngJfTMzs6KyezK7AztHxIVVBmNmZq2l7NFl/YAJVQZiZmatp2ySGQvsVmUgZmbWesp2ly0K7CJpM+AB4KNiZUT8oNmBmZlZ31c2yazGjO6yVerqfBCAmZk1VPZkzKrvjGlmZi2o9E3LzMzMuqrseTKXdVTvkzHNzKyRsmMyr9a9nhdYC1gWuLipEZmZWcsoOyazZ6NySScAbzc1IjMzaxndHZP5P+B7zQjEzMxaT3eTzMpNicLMzFpS2YH//60vApYGtgTOaHZQZmbWGsoO/K9Z9/oT4GXghzjJmJlZO3wyppmZVcYnY5qZWWXKjsnMRzqKbGMa3xlzveaHZmZmfV3ZMZnTgK2BS4GJ+KKYZmZWQtkksw2wbUT8o8pgzMystZQdk3kJeKXKQMzMrPWUTTI/AX4labEqgzEzs9ZStrvs78A+wEuSXmDWO2Ou0OzAzMys7yubZM4BVgdOAl7EA/9mZlZC2SSzGbBJRNxRZTBmZtZayo7JPA1MqzIQMzNrPWWTzA+BX0tascpgzMystZTtLrsAmA94VNI0YHqxMiIWbnZgZmbW95VNMvtXGoWZmbWksldhPrvqQMzMrPWUvgqzpIGSDpF0qqQlc9kGkoZWF56ZmfVlpZKMpHWBR4Fdgb2B2hjMZsCYakIzM7O+ruyezPHAyRGxNjMfynwNsEHTozIzs5ZQNsmsCzQal3keGNi8cMzMrJWUPbrsfaDRxTFXIV2hea405NArezuEmUw5ZlRvh2BmNpOyezKXAkfmO2QChKQhwLHARRXEZWZmLaBskjkEWBx4GVgQuAWYBLwB/LQ7AUiaX9Kdku6X9JCkn+fyoZLukPS4pL9JGpDL58uvJ+X6IYV5HZbLH5W0RXfiMjOz7it7nsxbwIaSNgHWISWneyPiuibEMI108c13JM0L3CLpKuAg4MSIOE/SH0hHtZ2a/74eEStK2om0N/UtSasBO5GuFr0McJ2klSLi4ybEaGZms6H0eTIAEXFDRBwfEb+uJRhJy3YngEjeyS/nzY8ANgEuzOVnA9vl59sy4yCEC4FNJSmXnxcR0yLiSdKe1nrdic3MzLqn7MD/LCQtBfwM2AtYoDtBSOoP3AOsCPwemAy8ERG1a6RNBQbl54OAZwAiYrqkN4ElcvnthdkWp6lf3j6km7AxcOBAxo8fP1txH7zm9M4b9aDZXY85mbdxz/B2rt7cuo07TDKSFiV96W9OuhvmMcBvgSOAHwMPkZJMt+QureF5eZcAqzZqVgurnbr2yhstbywwFmDEiBHR1tbW1ZABGD2nHV22a1tvh9B03sY9w9u5enPrNu5sT+ZXwEak7qmRwImks/w/BWwZEf9oZjAR8Yak8cCXgEUlzZP3ZgYDz+VmU4FlgamS5gEWAV4rlNcUpzEzs17Q2ZjMKGDPiDgE2Ia0tzA5IjZpVoKR9Jm8B4OkBYCvAg8DNwLfyM32IB1GDXBZfk2uvyEiIpfvlI8+GwoMA+5sRoxmZjZ7OtuTWQaYCBART0j6ADityTEsDZydx2X6AedHxBWSJgLnSfolcB9wem5/OnCupEmkPZidcnwPSTo/xzsd+L6PLDMz612dJZl+pLGYmo+B95oZQEQ8AKzdoPwJGhwdFhEfADu2M68x+IKdZmZzjM6SjIA/5bthAswPnCZppkQTEdtUEZyZmfVtnSWZ+oti/qmqQMzMrPV0mGQiYs+eCsTMzFpPl874NzMz6wonGTMzq4yTjJmZVcZJxszMKuMkY2ZmlXGSMTOzyjjJmJlZZZxkzMysMk4yZmZWGScZMzOrjJOMmZlVxknGzMwq4yRjZmaVcZIxM7PKOMmYmVllnGTMzKwyTjJmZlYZJxkzM6uMk4yZmVXGScbMzCrjJGNmZpVxkjEzs8o4yZiZWWWcZMzMrDJOMmZmVhknGTMzq4yTjJmZVcZJxszMKuMkY2ZmlXGSMTOzyjjJmJlZZZxkzMysMk4yZmZWGScZMzOrjJOMmZlVxknGzMwq4yRjZmaVcZIxM7PKKCJ6O4ZeJell4KleDmNJ4JVejqHVeRv3DG/n6s0J23j5iPhMmYZzfZKZE0i6OyJG9HYcrczbuGd4O1evr21jd5eZmVllnGTMzKwyTjJzhrG9HcBcwNu4Z3g7V69PbWOPyZiZWWW8J2NmZpVxkjEzs8o4yfQwSWdIeknSg4WyxSVdK+nx/Hex3oyxr5O0rKQbJT0s6SFJB+Ryb+cmkTS/pDsl3Z+38c9z+VBJd+Rt/DdJA3o71r5OUn9J90m6Ir/uU9vYSabnnQWMrCs7FLg+IoYB1+fXNvumAwdHxKrAl4DvS1oNb+dmmgZsEhFrAcOBkZK+BBwLnJi38evA3r0YY6s4AHi48LpPbWMnmR4WETcBr9UVbwucnZ+fDWzXo0G1mIh4PiLuzc/fJv2DDsLbuWkieSe/nDc/AtgEuDCXext3k6TBwCjgj/m16GPb2ElmzjAwIp6H9AUJfLaX42kZkoYAawN34O3cVLkbZwLwEnAtMBl4IyKm5yZTScndZt9JwI+AT/LrJehj29hJxlqWpIWAi4ADI+Kt3o6n1UTExxExHBgMrAes2qhZz0bVOiRtDbwUEfcUixs0naO38Ty9HYAB8KKkpSPieUlLk34ZWjdImpeUYP4cERfnYm/nCkTEG5LGk8a/FpU0T/6lPRh4rleD69s2ALaRtBUwP7Awac+mT21j78nMGS4D9sjP9wAu7cVY+rzcb3068HBE/KZQ5e3cJJI+I2nR/HwB4Kuksa8bgW/kZt7G3RARh0XE4IgYAuwE3BARu9LHtrHP+O9hkv4KtJEu1/0icCTw/4DzgeWAp4EdI6L+4AArSdKGwM3Av5jRl/0T0riMt3MTSPo8adC5P+nH6vkR8QtJKwDnAYsD9wG7RcS03ou0NUhqAw6JiK372jZ2kjEzs8q4u8zMzCrjJGNmZpVxkjEzs8o4yZiZWWWcZMzMrDJOMtarJE2RdEgnbd6RNLqbyzlE0pQuTrOkpMiHj87ucofkeYyY3XlY80g6q3Y1Y+sZTjJzOUkDJZ0sabKkaZKelXRVPsu4mcs5qnh7g4IvAKc0c1lmNufwZWXmYvnikf8E3gYOA+4n/fDYFPgD6aTFSkXEy1UvoxVJGhARH/Z2HGad8Z7M3O0U0gX3RkTE+RHxaEQ8HBG/A9aqNZJ0kKQHJL2b93T+WLukSK4fnbu0NpX0YG53o6ShtXrSlQ1Wz11HUev+qu8uk7SipPGSPpD0aL5I4EwkHZPr3s/T/1rS/HVtfiTphRzXOcBCnW0MSV+QdE9e9n3AFxu0WU3SlZLeVrr53F8lLdXZvIHl843S3pM0UdJmdfPdKN+I6gNJL0o6sXgzqrxNTpV0vKSXST8Oyrw3i0g6N8f6gaQnJB3YwTZYVtKlkl7LsT4iaadC/SBJ50l6PT+ulDSsUH9U/gzskd+bdySdKWmApO9JekbSq5J+I6lfYbrdJN1V2K4XSBpUqG/Ln5tN83Z6T9LdktYptFkivx9T82fjIUl7dvSmKPlR3pN/X9K/JO2aLIOtAAAHD0lEQVRW1+YISU8p7em/kD9PVlZE+DEXPkiXpPgE+EmJtgeS7mExBPgK8ABwbqF+NPARcB3paryfJ13u4ppcvwBwPPAIsFR+LJDrppAulwHpR8+/gJtIl+ffALg7z3t0YXk/y3VDgK1Il4g5ulD/TeBDYF9gJeBw4C1gSgfr+CnSBTMvANYAtiBdiyuAttxmaeAV0k2jVs3reTlwJ9CvnfkOyfN4BPgaMIx0OZZXgYVym0HAu6S9x1WBrYEXgBMK8xlP2uM8AVgFWLXke/NbYEJ+X4aQLmm0Ywfb4XLSZfvXAoaSbrA3MtctCDxGuvHe53McfwSeAhbMbY4C3gEuLmzHd4CrgDPz+m2f39OvF5a7V34vV8ix3gjcVKhvy9vxTmDjvOxr8nukwnb8b9JN1FYA9smfg00L8zkLuKLwegzwaF7PocAu+b0Yleu/TvrsjCLt2Y8A9u/t/9++9Oj1APzopTc+/SMHsP1sTDuSdGfEfvn16DyvlQttds3/4LU2RwEPNpjXFGYkmc2Bj4HlCvUb5nmP7iCe7wKTCq9vBU6ra3MdHSeZfYA3yF/8uWw3Zk4yvyDdWbM43WK5zXrtzHdIrt+3UDYol22YX48BJlFIVHmbTmPGl/d44IHZeG8uA87swnv7AHBkO3V7AY/XvtRzWX9Swvxm4X1+H1ik0OZC4GVgQKFsPPC7DuJYJW+jwfl1W369RaHNBsU27cznPOCPhddnkZMM6YfF+8CX66Y5CRiXnx9ESkLzdvd/bm59uLts7tXovhSNG0qb5K6eqZLeJv1KHUDaI6mZFhGPFl4/R7pb4qKUtyrwbEQ8XSi7gxkXuazF8w1Jt9S6w4ATmXn8aFXgtrp5179utOwHYsbdHhtNsy6wUe4Ceicv+5lc97lO5v9A4Xnt0uy1m6atCtwWEcX1vIW0jVcslBXvKwKUem9OBb4p6f7c1faVTuI8GfippNsk/VLSuoW6dUm/9t8urP+bpERbXP+nI+LNwusXgcdi5jGkFwvrj6R1cjfdU3k97s5V9eOC7W5HpZuoHZ67D1/N8e3QYB41q5EuoX913Xu6X2F9LshtnpR0uqQdJc3XzvysASeZudfjpF+BjW409W+SlgeuJHVL7Ej6otkrVw8oNJ1eN2ntyqtd+Yx1mviU7iN/Hqmr5GukbrWfkhJad5RJuv1I22J43WMY0NlhsR/VnkT+icyMbSPav/FUsfzdmQIu8d5ExFXA8qTuyiWBKyWd2V6QEXE6KZGcSepqvFXSUYV4JzDr+q8E/F+jdS2sQ6Oyfnk9PkV6P98Dvk064nBkcT3amXf9djwEOBg4jnTwynDSFc7r50HddF+rW5/VSXvVRMQzwMqkrte3SN2V9+SYrQQnmblUpEvcXwPsr3QHyZkUBo9HkP5JfxgRt0XEY8Ays7HID0ldKx2ZCAyStGyhbD1m/pxuQNrbOToi7oqIx0lfokUPk26gVVT/utGy16z78qif5l7SF9BTETGp7vF2J/PvbNnrFwfCSd2EH5JuadyeUu9NRLwSEedGxGhgb2CPjn6NR8TUiBgbEd8EjiB1JUJa/xWBVxqsf3dumbAKKQH+JCJuiohHmL1bY28IXJ7XdQJp263UQfuJpK7F5Rusz1O1RhHxQURcGRE/JCXA1UmfQyvBSWbu9j3Sr+i7czfAypJWkbQfM7olHid9Tg6UNFTSzqTB5q6aQjrCah2lkxwbfcldRxogP0fScEnrk7rCintJj5ES0a6SVsix7lw3n5NJX6T/KWmYpMNocKRYnb/k5ZwhaXWlo78Or2vze2AR4G+SvpiX/1VJYyV9uvNN0K5TSMnhFEmrShoFHEMas3ivg+k6fW8k/ULSdnk7rErqPnoi2rn/iNI5UyPzug0n7VFMzNV/JnVzXSrpK3mZG0k6QYUjzGbD06Qv+/3zckcBR8/GfB4DNpW0oaRVgN+R9soayj8MjgeOl7SX0pGNwyV9V9I+8O8jJ78jaU2loyX3JO1NPT4b8c2VnGTmYhHxJLAO6WiiY0mJ5QZgG1L3ABHxAHAAaQB0IvAdUrdEV10EjAOuJw0C1ycG8pjE9qTP5R3AOcAvSV9AtTaXk7pDTsrxbkb6tV2cz99IA9BjSEe5rQkU75A5izwWszWp6+te0pfPj+vaPEf6BfsJcDXwECnxTCvG2FUR8SywJanrbwJwBvBX0o3WOpquzHszjbQd7icd9vxpUvdQe/qRjkibSPpcvEi+m2hOeBsBT5DGKh4hHSm3GPB6mXVtZz1ezsvYLi/3yLxOXfVL0tFnV5GOUHyXlBg78jPSZ+UQ0vt5LemIsidz/Rukvb+bgQdz3Q75f8dK8E3LzMysMt6TMTOzyjjJmJlZZZxkzMysMk4yZmZWGScZMzOrjJOMmZlVxknGzMwq4yRjZmaV+f8pAJoOrffLDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [10, 20, 30, 40]\n",
    "y1 = pago_semana\n",
    "y2 = pago_mes\n",
    "#colors = ['green', 'green', 'blue', 'green', 'blue', 'orange']\n",
    "\n",
    "bars = plt.bar(x, y1, width=4)#, color=colors)\n",
    "#plt.xlim(-1, 45)\n",
    "plt.xlabel('Cantidad de horas semanales', fontsize=14)\n",
    "plt.ylabel('Remuneración en $', fontsize=14)\n",
    "plt.title('Pago por semana', fontsize=20)\n",
    "#plt.legend([bars[0], bars[2], bars[-1]], ['Docente', 'Autoridad', 'No docente'])\n",
    "plt.xticks([10, 20, 30, 40])\n",
    "plt.yticks([3000, 6500, 9500, 13000])\n",
    "\n",
    "\n",
    "plt.grid(axis='y')\n",
    "plt.savefig('../content/img/pago_semanal.png', dpi=100, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Horas semanales  | 10       | 20       | 30       | 40       |\n",
    "|------------------|----------|----------|----------|----------|\n",
    "| **Pago mensual** | \\$13211.0 | \\$26422.0 | \\$39633.0 | \\$52844.0 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "bars = plt.bar(x, y2, width=4)#, color=colors)\n",
    "#plt.xlim(-1, 45)\n",
    "plt.xlabel('Cantidad de horas semanales', fontsize=14)\n",
    "plt.ylabel('Remuneración en $', fontsize=14)\n",
    "plt.title('Pago por mes', fontsize=20)\n",
    "#plt.legend([bars[0], bars[2], bars[-1]], ['Docente', 'Autoridad', 'No docente'])\n",
    "plt.xticks([10, 20, 30, 40])\n",
    "plt.yticks([13000, 25000, 40000, 50000])\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.savefig('../content/img/pago_mensual.png', dpi=100, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cantidad de personas por dedicación exclusiva, semi-exclusiva, simple\n",
    "autoridades = [43, 1, 40]\n",
    "docentes = [705, 581, 418+171]\n",
    "no_docentes = [16, 45, 110, 104, 144, 49, 122]\n",
    "# Remuneración por dedicación/categoría\n",
    "exclusiva_max = 77_410\n",
    "exclusiva_promedio = (77_410 + 42_335) / 2\n",
    "\n",
    "semi_exclusiva_max = 38_689\n",
    "semi_exclusiva_promedio = (38_689 + 21_152) / 2\n",
    "\n",
    "simple_max = 19_326\n",
    "simple_promedio = (19_326 + 10_557) / 2\n",
    "\n",
    "\n",
    "cat_no_docentes_max = [\n",
    "    52699 + 3074 + 10540 + 13175 + 527 + 5270 + 13649,\n",
    "    43916 + 3074 + 8783 + 10979 + 439 + 4391 + 6148,\n",
    "    36538 + 3074 + 7307 + 9134 + 365 + 3653 + 5164,\n",
    "    30390 + 3074 + 6078 + 7597 + 607 + 3039 + 4304,\n",
    "    25296 + 500 + 3074 + 5059 + 6324 + 505 +2529 + 3566,\n",
    "    21079 + 2500 + 3074 + 4216 + 5270 + 421 + 2108 + 2951,\n",
    "    17566 + 2500 + 3074 + 3513 + 4391 + 351 + 1756 + 2459\n",
    "]\n",
    "cat_no_docentes_promedio = [\n",
    "    52699 + 3074 + 10540 + 13175 + 527 + 5270 + ((13649 + 1949)/2),\n",
    "    43916 + 3074 + 8783 + 10979 + 439 + 4391 + ((6148 + 878)/2),\n",
    "    36538 + 3074 + 7307 + 9134 + 365 + 3653 + ((5164 + 737) /2),\n",
    "    30390 + 3074 + 6078 + 7597 + 607 + 3039 + ((4304 + 614) / 2),\n",
    "    25296 + 500 + 3074 + 5059 + 6324 + 505 +2529 + ((3566 + 509) /2),\n",
    "    21079 + 2500 + 3074 + 4216 + 5270 + 421 + 2108 + ((2951 + 421) /2),\n",
    "    17566 + 2500 + 3074 + 3513 + 4391 + 351 + 1756 + ((2459 + 351) /2)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "remuneracion_autoridades_max = []\n",
    "remuneracion_autoridades_promedio = []\n",
    "\n",
    "remuneracion_docentes_max = []\n",
    "remuneracion_docentes_promedio = []\n",
    "\n",
    "remuneracion_no_docentes_max = []\n",
    "remuneracion_no_docentes_promedio = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total autoridades: $ 38425176.0\n",
      "Total docentes: $ 820733598.0\n",
      "Total no docentes: $ 360699192.0\n",
      "Total sueldos: $ 1219857966.0\n",
      "Presupuesto sueldos 2018: $ 1256300000.0\n",
      "Resto: $ 36442034.0\n"
     ]
    }
   ],
   "source": [
    "# Aproximación para remuneración mensual promedio\n",
    "remuneracion_autoridades_promedio.append(autoridades[0] * exclusiva_promedio * 12)\n",
    "remuneracion_autoridades_promedio.append(autoridades[1] * semi_exclusiva_promedio * 12)\n",
    "remuneracion_autoridades_promedio.append(autoridades[2] * simple_promedio * 12)\n",
    "\n",
    "remuneracion_docentes_promedio.append(docentes[0] * exclusiva_promedio * 12)\n",
    "remuneracion_docentes_promedio.append(docentes[1] * semi_exclusiva_promedio * 12)\n",
    "remuneracion_docentes_promedio.append(docentes[2] * simple_promedio * 12)\n",
    "    \n",
    "for i, cant in enumerate(no_docentes):\n",
    "    remuneracion_no_docentes_promedio.append(cant * cat_no_docentes_promedio[i] * 12)\n",
    "    \n",
    "total_autoridades = sum(remuneracion_autoridades_promedio)\n",
    "total_docentes = sum(remuneracion_docentes_promedio)\n",
    "total_no_docentes = sum(remuneracion_no_docentes_promedio)\n",
    "\n",
    "print('Total autoridades: $', total_autoridades)\n",
    "print('Total docentes: $', total_docentes)\n",
    "print('Total no docentes: $', total_no_docentes)\n",
    "\n",
    "total_sueldos = total_autoridades + total_docentes + total_no_docentes\n",
    "print('Total sueldos: $', total_sueldos)\n",
    "\n",
    "presupuesto_2018 = 1_478_000_000\n",
    "presupuesto_sueldos_2018 = presupuesto_2018 * 0.85\n",
    "print(f'Presupuesto sueldos 2018: $ {presupuesto_sueldos_2018}')\n",
    "\n",
    "resto = presupuesto_sueldos_2018 - total_sueldos\n",
    "print('Resto: $', resto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def div(a):\n",
    "    return a/1_000_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y0 = [total_autoridades, total_docentes, total_no_docentes, resto]\n",
    "y1 = [presupuesto_sueldos_2018]\n",
    "y0 = list(map(div, y0))\n",
    "y1 = list(map(div, y1))\n",
    "#y_millones = list(map(div, y))\n",
    "#y_millones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-3e6b78b5f8b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m plt.legend([bars[0], bars[1], bars[2], bars[3], bars[4]], \n\u001b[0m\u001b[1;32m     20\u001b[0m            ['Autoridad', 'Docente', 'No docente', 'Presupuesto para sueldos 2018', 'Resto'])\n\u001b[1;32m     21\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../content/img/balance_promedio.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
     ]
    },
    {
     "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": [
    "y0_cum = np.cumsum(y0)\n",
    "y0_cum_shift = np.zeros_like(y0_cum)\n",
    "y0_cum_shift[1:] = y0_cum[:-1]\n",
    "colors = ['b', 'g', 'orange', 'r']\n",
    "bars0 = plt.bar(x=0, height=y0, width=0.7, bottom=y0_cum_shift, color=colors)\n",
    "bars1 = plt.bar(x=1, height=y1, color=['purple'])\n",
    "\n",
    "plt.xlim(-3.25, 1.5)\n",
    "plt.xlabel('Balance', fontsize=14)\n",
    "plt.ylabel('Monto en millones de $', fontsize=14)\n",
    "plt.title('Balance de sueldos con salario promedio', fontsize=20)\n",
    "\n",
    "plt.yticks(y0_cum)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.legend([bars[0], bars[1], bars[2], bars[3], bars[4]], \n",
    "           ['Autoridad', 'Docente', 'No docente', 'Presupuesto para sueldos 2018', 'Resto'])\n",
    "plt.savefig('../content/img/balance_promedio.png', dpi=100, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total autoridades: $ 49684308\n",
      "Total docentes: $ 1061224476\n",
      "Total no docentes: $ 371477796\n",
      "Total sueldos: $ 1482386580\n",
      "Presupuesto sueldos 2018: $ 1256300000.0\n",
      "Resto: $ -226086580.0\n"
     ]
    }
   ],
   "source": [
    "# Aproximación para remuneración mensual maximo\n",
    "remuneracion_autoridades_max.append(autoridades[0] * exclusiva_max * 12)\n",
    "remuneracion_autoridades_max.append(autoridades[1] * semi_exclusiva_max * 12)\n",
    "remuneracion_autoridades_max.append(autoridades[2] * simple_max * 12)\n",
    "\n",
    "remuneracion_docentes_max.append(docentes[0] * exclusiva_max * 12)\n",
    "remuneracion_docentes_max.append(docentes[1] * semi_exclusiva_max * 12)\n",
    "remuneracion_docentes_max.append(docentes[2] * simple_max * 12)\n",
    "    \n",
    "for i, cant in enumerate(no_docentes):\n",
    "    remuneracion_no_docentes_max.append(cant * cat_no_docentes_max[i] * 12)\n",
    "    \n",
    "total_autoridades = sum(remuneracion_autoridades_max)\n",
    "total_docentes = sum(remuneracion_docentes_max)\n",
    "total_no_docentes = sum(remuneracion_no_docentes_max)\n",
    "\n",
    "print('Total autoridades: $', total_autoridades)\n",
    "print('Total docentes: $', total_docentes)\n",
    "print('Total no docentes: $', total_no_docentes)\n",
    "\n",
    "total_sueldos = total_autoridades + total_docentes + total_no_docentes\n",
    "print('Total sueldos: $', total_sueldos)\n",
    "\n",
    "presupuesto_2018 = 1_478_000_000\n",
    "presupuesto_sueldos_2018 = presupuesto_2018 * 0.85\n",
    "print(f'Presupuesto sueldos 2018: $ {presupuesto_sueldos_2018}')\n",
    "\n",
    "resto = presupuesto_sueldos_2018 - total_sueldos\n",
    "print('Resto: $', resto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 'g', 'orange', 'r']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "tuple index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-2cb276aab48b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m plt.legend([bars[0], bars[1], bars[2], bars[3], bars[4]], \n\u001b[0m\u001b[1;32m     26\u001b[0m            ['Autoridad', 'Docente', 'No docente', 'Presupuesto para sueldos 2018', 'Resto'])\n\u001b[1;32m     27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../content/img/balance_maximo.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
     ]
    },
    {
     "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": [
    "y0 = [total_autoridades, total_docentes, total_no_docentes, resto]\n",
    "y1 = [presupuesto_sueldos_2018]\n",
    "y0 = list(map(div, y0))\n",
    "y1 = list(map(div, y1))\n",
    "#y_millones = list(map(div, y))\n",
    "#y_millones\n",
    "\n",
    "y0_cum = np.cumsum(y0)\n",
    "y0_cum_shift = np.zeros_like(y0_cum)\n",
    "y0_cum_shift[1:] = y0_cum[:-1]\n",
    "colors = ['b', 'g', 'orange', 'r']\n",
    "bars0 = plt.bar(x=0, height=y0, width=0.7, bottom=y0_cum_shift, color=colors)\n",
    "bars1 = plt.bar(x=1, height=y1, color=['purple'])\n",
    "\n",
    "plt.xlim(-3.25, 1.5)\n",
    "plt.xlabel('Balance', fontsize=14)\n",
    "plt.ylabel('Monto en millones de $', fontsize=14)\n",
    "plt.title('Balance de sueldos con salario promedio', fontsize=20)\n",
    "\n",
    "plt.yticks(y0_cum)\n",
    "plt.xticks([])\n",
    "\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.legend([bars[0], bars[1], bars[2], bars[3], bars[4]], \n",
    "           ['Autoridad', 'Docente', 'No docente', 'Presupuesto para sueldos 2018', 'Resto'])\n",
    "plt.savefig('../content/img/balance_maximo.png', dpi=100, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}