Clase 8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a Python para ciencias e ingenierías (clase 8)\n",
    "\n",
    "\n",
    "Ing. Martín Gaitán\n",
    "\n",
    "- Email: gaitan@gmail.com\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "\n",
    "Antes de empezar con pandas vamos a configurar [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), un paquete que, entre otras cosas, mejora la estética de gráficos de matplotlib\n",
    "\n",
    "Para instalar, como casi siempre\n",
    "\n",
    "    conda install seaborn\n",
    "      \n",
    "o \n",
    "\n",
    "    pip install seaborn\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos un gráfico default generado por matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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36n9Eu0dfPHsBqW5xxh7p79FKcEVFrYaTORUGi/T9LeJvuFi48N7B94zTkwkY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\n1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HsYa9rUtfP2nywm+0wFdwxRV+ttVtdBMH0F5CXAmtncFeLC2apathxtYqVyaS6suBUK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\nEruiKEoHoxK7oihKB/P/3bOl/Ela7x8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a29e5d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sinplot():\n",
    "    x = np.linspace(0, 14, 100)\n",
    "    for i in range(1, 7):\n",
    "        plt.plot(x, np.sin(x + i * .5) * (7 - i))\n",
    "        \n",
    "sinplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tin/.virtualenvs/curso/lib/python3.6/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fv3aIkKoLJDr7URqNxH7+i0Ff1x6fh++d/Fd8oo//tvGfbtuqc/PvKYoif//z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2K3pghBITh0Gr7VWcvX22qwaNS8t+zhv40FmLU+TjIZxT37UtbTaWZe5+PVsezb0DA/mbu0pVlj\not5cw5RvhnjqylI/TyzB304s8RfD8yyF0/2k/6C/iYOVNuoqzOi0qs3Et0KJhRbwZ9zN9Y8pHmd1\nyc/Kgp+mtjLsZbm3Hs2QikQID11AW9+Atir/cMjmprKskHmdnyLWJ4VJo+JLLVX8k+56KvRajq35\n+LOhFS6anyQhgXv2xazu577dG7k6eYrJFIOS8b0KURA4WGnnXw80c1eVHW88wa+9NfxK/iwjK+NE\n/NvLOrZ0OjFZtAwPLhPLsdPKJddz7vExURDptLeyHvXgiriBdG3j300s8edDc4z7I5tNwZ9orEC3\nUXtqNWlx2pR1OLqaVCKEe+6ldHZz0zOKspshfWo9+l66bEpJGQZcrnmbv2tOkiXGPBM4dHachvxj\nalMbIgS32sn3cgRBoK/MzB/0N/FwbRmRpMTL62aelz/HoDdKcP3sttdX19ny9uqY+gYQNJq85jWk\nN0BJOcWkbxoAbyzBC9Or/KfBaQbdQepNOr7T08CXW6uxatMbtUyHo0VXiEgsvw5HVyNJCdZnXgBk\nypueUexuhkunXqXzOnTuLHIyqUhYA9JJhGpBRZutOe9rJ29yBn+utFgM/PMdjTzVWIFKgLddIj+V\nv8C5oB7v8vbiGyaLjraedK7O/LTnE3riNCXjuwVGtYqnGiv4V31N7HCYWJYcvJh6hL8YW2No/Voh\ngwwqlUjfnjoS8RTD55Zyupe2uhptXT3hoQukIpGcn7FzYzd7bGWSn00s82fnZzjnDlJn1PF7XXV8\nu6uOauO1p8BMh6O1PDocXU1au/klpGQYe81DaPTKspsBlhf8LM55ae4ox+ZQlrAUOHkCQa3GNJC/\nlOVCcJlQMkyXoz1/NS5ZZmopQKXDgPkWiYtth0YUebiunD/sb2Kf04pPMvKGdDf/czrJmdWVbdu6\n7TyQ3+lX1Osx9Q0QX1oktpi7JygTUhl0TfPizCrfG5zh2JoPm1bDsy1V/H7P9dtattRY0x2Olgo7\n/XoX3yQZW8dScRC9pUXxOEF/lImLazicRuqblSUsBU+kvQbmffnnMYQSYeYDi7TYmtAqKI+aXPSj\nVYvUVxavk9GNQi0K3FVl51/3N3N/jYOorOEd6SD/Y97C+3PTm5Us12PgJpUdlYxvFpx6Ld9or+U7\nPQ206yMsSw7+etLFf7swy/E133U1dXt31aJWiwyeXEDKUWLPsncfcjJJ6NyZnN4vyTJ6TTMmwxN8\nsGbjjDuAU6/la63VfKe3gXbr1i6uy0UJlBJaP0XEN4LO3ISl8qDicQDOHU9ntu7cn18XoQzxlWXi\n83MYe3egMuRvvAtxza24w4RjSdpuUZfzVth1Gr7YUsUf9DfRb07gkS38bMbP/3tumncW3QSvo6nb\n0unEYtUxMrhMNJJb1yLzvvy8OrIsI6pqMBoe4bSvkaOrPqzatHvxD/qb2O20bplQlXGPThZgfKOB\nSYJrx1DrndhqH1I8DrDx9y+zc3+DokQ8KRoldP4c2ppadLX5x4vHPBPIyIrivdF4kgVXkKZqCyqF\nMrM3A71axWP1Tv5ooJl9doEwen6znOA/np3ijYX167YsrKyxUl1vY3bCjWs197ybQvn0fKs3mQaz\nnm/37eAbphN0CFOsRmL8cnqVPz0zyc8nl5n0hzfduHqDhs7+agK+KFOjuUlHZjJ0Aye2j48th2O8\nOufi/zk7zYuzYdTqOuTUMr/bUcN3dzQyUG7Jmu25uUgpjI8loi48868hqvSUN31esbsZwO+NMDXq\norrOSk2DstjSpSzn/F3OcMn4KqmDnFi4teNi2SjXa/ladw//0D7EDmGUcCLO6wvr/Iez0/xsYpmL\n3iCJjQ2kKIr076snmZS4kKPohmlgF4JanbXkyBNL8P6yhz87P8Nfj6+hUbeQSnl4utHBH24k1qiy\nzuv0BmgiDy3qy5GSkY3sZhFn0+cRReWejHgsydCZRQwmDZ078o/VwobLOZFQdOqFy+vW8ze+M8sB\nZPnWzWPIhk2r4Ysd7fx+1SJ7hUEkKcFbi27+w9kpfjiywJl1/xWn4czp9/THs5/YMxanyPMOQRBU\ndLZ+BuvI/+IQIyyUfYnTnhin1gOcWg9g0ajotJnosBnp2lPL0OlFznw8R2tXRdadr66uDm1NLeHB\nc0jRCKI+fYKLpSTGfWEmAmFGvSGWNuTVdCqRvU4ra8GjDK59iEXdjiDkpgjVWGVGJQqKklNkKYVr\n+hfIcpLyhs+j1hZmdAZPLiDLcOi+VsVlOoGNNmvmnflr3qakFGMKW63BJe9BW92nc5GCdDy4reUx\nTBf/BwcS51mq+ConvHDGHeCMO4BOFOmyG+l1mKnvrUDzwTTnTy2w62ADqiw61iqDAWPvDkLnzhJf\nXkZbXQ2kmx6shGNc9IYY8oY2m0KoBIFd5RaSiWE+XHgFi+rbqMTcar4dFh1l1ksdjvKZT7Issz77\na1IJP7aaB9Aa89P2vpqL55aIx1IcONCASq1sc5pJVLMo3FSOeMbRqbQ0W/P3KE3c4slWuVJbfz93\nhX/ArvDfs1j+Jc6HTIz5w4z5w2jFVTpsJtqtRlqbrPTsrKGu8ZNT8ioZ3zzR6Mtx1D2GPPcS/dHX\nebDvm8wEY5xy+Rn2hTnp8nPS5UcATPfWsL4e5eWRJTprbJTrNBjVKnQq8YrTqSzLpGQZ+cBhFo+f\nYOHcMMHqepbCMeZC0U1BcZUAPXYTu8otdNtNaESRjxZrGVxLF9LXmXMr0dFqVNRXmplZSXc4yqcR\ngHfpLRKRZUxluzA6evP67q4mHkty8ewSRpOWHbvqcHvyaxQBkHCtEZuewrijD5U5fznKQlqtQXqR\n0qhF6itufCejG4lKbaS8+Qukxp+jzf8id/X8E5aiAuc9Ac57gpxzp/8BGO6uRlgN8+LgPN3NZdi0\nGuxaNQaVeI3BS0gSyb2HWFl1s3ZhBF9cxXwoykI4RuKyed1pM7LDYabHbsKsUTPqCfLhQtqA9Dl7\ncv4crbU2Tgyvsu6L4sxD8CToOkHEN4zO3IS16p6cr7sekiQxeHwetVpkh4LuRQBSLEZo8ByajXyQ\nfPHGfKyE19hR3p13X2r49CRbZUMQ1Tibv8Dy8F/Q4vs1h7v/GV6pitOuAGfcfi54glzwpOe1o05L\nR3X+GelKKRlfBZjKdxMJTBDxXiSw/C6ttQ/RajUiyTKL4RijvjBjvhDzwSipGiNHAiGOBC4ZFoH0\nyVUjCsQlmURKQgKo7oanu9NvWvEiAA1WA01GHa0WI80Ww2bWcoZMnHLEM8aDDbkvGq21VmaWA8yt\nBnPO0o36JwmsfoRaV4aj/rM532srhs4skoin2H2oUfHpYFN2r4DTAShzOcfi6U5GbXW2T6yT0Y1E\nb2nBWnUP/pUP8M69Qn3zF2kw6/lsvXNzXs8GI8wEokSqjRxPxDg+dimpUC0IqAQBQUhXDUiyTDQl\ngdYJz/xu+k0b87rKoKXepKfNaqTLZkR/lf5ypnVmvjrPbbVWTgyvMrHoz9n4xsPLeBZeR1QbKW/+\nYkFhFIDJERcBf4wdu2vz7sqVITR4Fjkex7JvvyKP0Ihb+byGtEfHZtJSZs2/bO9WQ6OvwF73KJ75\n37A+8wsq23+XR+vLeaSuDHcswbg/zLg/zHQgylIwSp31k0kwKxlfBQiCQHnj0yyHl/GvfIDO1IDB\n1oEoCNSb9NSb9DxUW0ZKkvjpz86yFIrReV8zMTVEkhKRZIpISiIpyZjVAhqViFYUMahE1Ec/wLw8\nz47f+z0qbWYaqu2srW0tIVmmd1BhKGfMk9bDzXWX21pj5W0WmFz052R8k3E/rplfAiLlTV9QJC5/\nOamkxLnj86g1In17lLv4AieOgyBg3rVH0fXD7jEEBEWL1PSyH1nmU5dstR22mgeIBqcJey+gd7di\nLt+NIAjUmfSbDehlWeZXLw8xsRak82A9skmDL57EH0+mN5GyjKgWSSUlTBoVVo0azpxEOzNF75e/\nRGNt1TWbyKvRqDS02ZoZ8YwTiAdz7sazGfdd9HGwN3usVUrFcU3/PcgpyhufQa0prJ2mLMucPZZO\nIMzURishcGKjd68CtTa4tKnscuQvDuL2R/EG4+zucN4yim2FYnbuIxqYIuIbxrf4Fva6R9LruF5L\nuV7Lwcq0u7miwrLteltMSsZXIaJKj7PlyyyP/oD1mReo7v6n18Q/VaLIod31vPbLC1gnfNz/2a6s\n47rOq3APn8UxPoz+QG5ZxF2Odj5Y/JjZwDwttqacrtnsgbro4+G92y8S6Tjvz5GSIRz1n0VnUuZK\nu5yxoRVCwTgD++vR6ZWdDhLrLqKTExh7elFb8zeAsVScKd8MDZZazJr8d7u3S1zscgRBxNn8RZaG\n/yee+VfRGmvRGqqueo/A4T0NLP3VKYRBF09/dec141y9iPnmzay8coLynlZ0DU/k9Czdjg5GPOOM\nesbZW5VbCVlTlSWvfAb33CvpsqLKwwWpWGVYnvexuhSgub1ckagGbLicz51BU1WFtj7/eG1ap3wC\nk8ZInbk67+tvddEYJQiCQHnT51geWcW/egStuQGjLft6fCP59PvKbiJaYw2O+seQUhFc03+PLF+r\nD9rc4cRq1zMyuEw4FL/OKFdi2chszEcVqKssvWjk02KwqsyIUafeNCDb4Vn8LfHQPEZHH2ansp34\n5ciyzJmP5xBFgZ0FnQ4yNZAHFF0/4Z0iKacUnQ7g0iJ1O518Id37t7zxGWQpgWvyZ6SS19aeV9Va\nqW20MzflYW05+0nBvHsPiGLWbP7LyYRUht25u561GhUNlWZmVgIkktuX+QVcJwl7zqE11mKvKays\nKMPZY+la0Z0HlJXNAYTOD6ZdznuVuZzXIi48MS+d9ra8+1LDZR26PuXx3qsRVXqczV9GENSsz/yK\nZOyTFdW45nlu6t1vA8zlezHadxAPzeNdeOOa10VRYOeBBlIpOSfJSW1dPZrqakKD55BiubW66tjo\nVpJPfEwUBFprrax6IgTCW28KQu7zBNeOodFXUNbwVFHcUDPj63jWw7T3VmK2KlcPChw/BqKoWP1n\neEM/uLssf+MryzITCz7sZi0Oy6c/LnY1Rns31qp7SMY9rE//4royfbsPNQJwJofyDJXFgrGnl9j0\nFPG13LojNVjqMKoNDHvG8lJja621kkzJzK5uvSmIBmfwzP0GUW3E2fJlBAVJSVfjdoWYGnNRWWNR\nXDYHEDypXFgDLnM5K6hbB5hc8CEI0HybGV8ArbEaR8MTyKkoa1M/z6ld7I2iZHwLJN0j9SnUOieB\ntY8Jrl8rktHVX43eoOb8qQUS1ynyvno8y779yPE4oXPbS/5lsGjN1JlrmPRNX6OHux3ZdJ7jkVXc\nc79GELU4W75ScJw3Q6aWbtdB5aeD+NpqOsu5pxeVRVmcbsQ9jlpU06pAes/tj+ELxWmttd02cbGr\nsdU8gN7aTjQwge86uuYNLQ6clWYmhtfwebJLTlr2pz0UwePHcrq/KIh0OtpxXyahmgttmTr2Lep9\nk3EfrqnnAXA2fxm1tjjlJac/Ss/rPYebFM8JKR4nePYMmopKdA2NisbIeMCUiGskUxLTywHqnGYM\nutszKmku34WpbBeJyBLuuZc/cU3nDCXjWwRElY6Ktq8hqvS4514iGpi+4nWNRkXf3npi0SQXTmeX\nnLTsTS9SgRO5LVKQ/kNLSslNPdxcaLssOeVqUokQrsmfIksJypueQaMvz3nc7Via97E8nxaaLy+g\nPCezgGcW9HwJxIPMBxdpszWjVeUfc858Z7eby/lyBEHE2fRF1Loy/CsfXtN+UBAEdh9uRJbhzMfZ\n+++ad+8FlYrA8Y9zfobujOt5my5HV9O6UXN9PaUrSUqwNvlTpGQYR/1j6C3NOY+7HX5vhLGhFRxO\nI80dyv9WQucHkWMxzHv3KVPFkiVGPePYdTYqDPn3xJ5fCxJPSrR/iuvWc8HR8DhaQw0h91n8Kx/e\nlGcoGd8iodGV4Wz5CsjgmnqeROzKnXr/3jo0WhVnj82RTG7fO1JbX4+mKu16Tqfva40AACAASURB\nVEVz01/O7HLzcT1vpQiUXqD+jmTcg7X6Xoz23Osss5FxUe46pGxXnyFw4nhaWGO3MpdzRtWqu8B4\n7+2UlHI9RLUeZ8tXEEQN67O/Ih6+cvPY2pXOaRgeXCYU3D5MojKZMO3oIzY3R3w5N93zTDx+xJ27\n8a20p3W2Jxau3FTKsox75sV0nXr5HsxOZeVp1+PMsTlkGfYcaizIExI4lt6YKN1UzgcXCSXCdDs6\nFD3Hp12xLVdEUUNF29dQaaz4lt7Kqa910Z/hE7/jbYze0kxZwxNIqQhrk3+HlLpkOPUGDX17agmH\n4gyfW952nMtdz+7jJ3O6d7u9FZWgYjiPRcqo11BTbmRyyY+0IXggyxLr078kHl7A6BjAVv1AzuNl\nw+0KMT22TlWtlZp65X/c8ZVlYrMzmHb0oTIpq8nLJPEojost+hEFgebq29v4AmgNlZQ3fQFZSrA6\n8TdXJKqIosiug41IKZnBHITpLfvTGfyBHF3PFYZyyvQORjzjSFnaw2UQNvIZXL4ovsuSHH3L7xD2\nXkBnaqCs/vGihQvCwRjDZ5ew2PS09yrrZw0bWs7nzqCpqkbXmFvVwtVk/v57FOQxwGUendv85Aug\n0lioaPsdBFHL+swLxEKlxgqfaszOPVgqDpGMulibfP6KgP7A/rTU3Omjs6S26bIBYNkoM3K9/35O\n99WrdbTYGpkLLBBM5K4U1VZrIxZPsehKX+Nd+O2G0k8z5Y1PFzWeefLIDAC7Dxd4Osi4nBVmOcuy\nzLBnDIPaQIMl/7KpTFysvsKETlt4os6nAaO9G0f940jJEKsTPyF12Rzr6q/CYNJw4fRi1jaapl27\n01rPObqeBUGg29FOOBlhLpB7Z6TLS+kA/Ksf419+H7XWgbPl2aIkWGU4d2KeVEpm96EGxAKaEATP\nnk5nOR84qPjv4+KG8e1SanwXfJj0aqoUlkl92tAaqnA2fwlZTrE2+VNieeQWFErJ+N4A7HWPYLB1\nEgtO4Zr6+WYJktGkpXdnDUF/jLELK9uOoaurR1tXj+fkaVLh3IxpT1knMvKmuk0uZHa444s+Aqsf\nE1j7GLXeSUWRFyiPK8T40CrOKjPN7YXFjwPHj6XbB+7KX8sZwBVx44566HIoK8WYW03LcrbW3d6u\nuauxVOxPZ0DH3KxN/A1SKu1mVqtV7NzfQDyW4vyp7Q2kymDA1L+T+OIisYXcThqbpXR5zOtLxtdP\nyH0O78JrqNRmKtv/ASpN8aRAo5EE508tYjRp6erPv6b2cgIfHwXAmmN9/9XEU3EmvVPUm2tzFiW5\nHH8ozpo3SmutLWtzltsJg60jXTKaDLEy/c4ndt+S8b0BpIUKvoze0krEP8r69C83SzV2HWxAFAVO\nfTS76erdCuvBQ8jJ5KaEYjYyJTP5uJ4zmaFxzyk8C68hqk1Utn4dUa2sr+5WZE69++5WngkKEFtc\nJL4wj7GvH5VR2e48k7xTqu/NH1vNg5jKdxOPLOGaeh5ZSm8sd+yuRadXc/bYXNbTbyaemevpN5PP\nkFfS1UaZTNQ/yvrMrxBUeirav4Fap6yv7lYc/3CKRDzFwIF61Grlm9VUMEjownl0DY1oa5Qpvo1v\n1K0rKZ2DOyOJcCssFQdwtnyVqqb7PrF7lozvDUIQ1Thbv4rO3EjYO8T6zIvIsozZqqervxqfJ8LE\n8Pb1jpvxsY0dcTYaLfV510XWOk3c3bJMt/00otpEVfvvotYVt7OHZz3M+MVVyitNNHfkn4F5OcET\nhWU5w6WktG6F8d7MInW7J1tdD0EQKGt4EoO1k2hgMp3bICXQ6tTsOthALJrMGvs17dyFoNUSOHYs\np3lq0ZqpN9cykUcpnVGvYV9zhEM1JxFENZVtv3ONUlehxGNJjr47iVanZseuwrogBU+dhFRqM9yk\nhMymW6nx3dxU3mEenQxGexc6Y2HrUz6UjO8NRBQ1VLT+DlpjHWHPOdxzLyHLErsPNSIIcOqj2W0X\nH01FBZauLsLDF0n6vNnvJ4h0bdRFrkZy6yMcdB3j0c5xAjEN1sZvoDFU5Pz5cuXUkRlkGfbd3VzQ\nqVeWZQLHPkbQaDDvzE1u8GokWWLUPY5DZ1dUigHp+lGj7s6Ji12NIIiUt3wJvbWDaGCCtfGfIKWi\n9O2pQ29Qc/b4HNHI1kZS1OkwDewisbpCbHYmp3t2leVXShf2DPFE52kEQQbHU+hMymvKt+LciXki\n4QS7DjagLbAmNuMFKGRTOewZQy2qabO1KLp+YsGHADk3WilRGCXje4MRVToq276BxlBDaP00a5M/\nxWJV0dFbhXstxMTw2rbXO++7B2SZwPHcZPlydT3Lsox/5QjehdeISwZ+dKyfmXXlzcO3wusOMza0\nQlmFiZbOwnaVsZkZ4stLmHbu3ux3nC/zwUVCyTDdZcpKMfzhOKveCC211jsqLnY1oqihouUrGO07\niIVmWRl7DpUqzq6DjcRjKY6+N7nt9Zdcz7llPWdKwnIJqQRcJ3BN/xwEFT85uYMJd1lO98iHaCTB\n2WNzGE1a+vcWpnWe9HkJD19E39aOxqls8+uPB1gILtFua1FUt56SJKaWAtQ6TRj1t6e4xq1Gyfh+\nAohqPVUd30RvaSXqH2Nl7EfsOeREFAWOvTe1beaz8567QBAIHPsop3t1l3UC2y9SspTEPfcS3sU3\nUGksRCxfYD1svKYushic3Dz1FhbrBfAfPQKA9dBhxWMMr2fivQpdzhvfUccd6pq7HEFUUd78BUzl\nu0lEllgZ+xHdfSb0Rg0fvzdJLLr16dfUP4BoMBA4dhRZyl5C1GZvQS2oGNkm7ivLMr7l9/DMvYKo\nNqKq+ApTbvsNmddnj80Rj6W4+6H2Ipx6j4Ms31SX88JaiFgidUeGUm4WJeP7CSGq9FS0/c7GQrVM\neOUn9O8x4PNEGBncuu5Xa7dj7OklOjmZkyau01BGhaGcUc8EKelaMY9UIsDK+F8RWj+NxlBDVefv\n0dKQrinMpclCPnjdYcYupE+9rV2FubPlVIrAsaOIZjOmvn7F4wy5RxAQFC9S4xsLeVsBdcq3E4Ig\nUtbw1GZ53frkDzhwCGLR5GaTgesharWY9+4j6XYTGR3Jeh+dSkuLrYm5wCLB+LXZ/1Iqjnv2V/iW\n3kGltVPV8W3qalsx6FSMbyEzqZRwKM65E/MYzVr23aWsHvdyAsc/ho3afqVcMr6diq6fuMPjvTeD\nkvH9BBEEFWUNT2GreYhUwk+9802am5Y48eHUtqpXlgOHgEvqN9noLuskmooy7b9S8i8Wmmd55C83\nOhT1U9X5LdRaG2aDhiqHgclFP1IRdU6PvTdVtFNvePgiKb8fy/4DCGplJ41oMsbkRgtBJaUYABPz\n6bjY7dbxpRAEQcBe9yhlDU8hSXEsmjfp3zHD4MnZbWO/1kN3AZc8GtnoLetCRmbYPXrF/8fDSyyP\n/C9C7nSHoqrOb6PRlyOKAq21Nlbc4W2bh+TL6aOzJBMSew83odEWdupNuNaIToxj7O5BbVOW6CjL\nMsPuMcwak6IWgnDJo3MnZjrfLErG9xNGEARs1fekW1uJKnZ0j7Gj8yQXT41ueY15z960MEGOxrdn\nM+6bHlNKxfEs/JaV0R+SSgSx1z5CedPnEcVLsaG2OhuRWJKl9ewC+bmwsuhnYniNyhpLwadeuNzl\nfJfiMca8E6TkFL1lyvp4JlMSU8sB6ipuX9F5pQiCgNm5h+rOf4RaV0Zj/Qx7d57m3LGtZfsMnV2o\ny8oInjyBFM9uHHvK06e6oY15Lcsy/tWjLI/+b5IxN5bKw1R1fBu15lKjjfaNk9zVEqpKCQZiXDi1\ngNmqo2dnTcHjXZKTVO5yXgqt4Iv76S7rUFS3DumTr0GnosapTDGuRP6UjO9Nwujopab799Ga2qhw\nejEJv8C7fPy6rdtURmNamGBhnth8dgH7zg3xiGHPKGHvMEsX/zuB1Y9QaW1Utn8Da9Vd15xEMzve\nySLEx2RZ5qO30p1VDj/YVvCpV4rFCJ46iaaiAn1rm+JxhtbT7s2ecmXGd3YlSCIp0V5yOW+J1lhN\nddc/wVbRT5nDT6X5RVYmf4OUvFajXBBFLAcPI0UihM6czjp2nbkGi9bMkHuESGCKlbEf4l14HVFl\noKLtGzjqHr1GGCZjfMeLFPc9dWSGVEpm393NqNSFLZ+yLOM78gGCWo15n3Kd6c3WmArr1oORBCvu\nMK01d3YS4SdNyfjeRNRaK1UdX8cfvwtZBv/Sb1gc+v8IrB1Hkq5011kOpl3P/o+yu+j0Kj0HrFXs\nTa3hmvoZqWQQa9W91PR8B72l9brXZGI91+twlC/T4+sszftobi+ntrHwmuHgmVPIsRiWQ9duGvJh\nyD2KXqWjxaqsqUPGNXe7d3wpFFGlo23XN4mrHiIa0xLzHWdx6L8RWDt2Tf9U6+HcXc+iILLf3sjj\n2iRr488RD81jsPdQ0/3PMFivvylrrbUiUBzj61kPcfHsEjaHga7+wmuGo1OTJJaXMe/eg8qo/MRZ\neH1vRs+5tKn8JFHkOwsEAvzxH/8xwWCQRCLBn/zJn7B7tzKpvzsdQRDo2v0AP/+BQGP9FA31K3jm\nf4Nv+V0szv2YdLuRZTOmnbsQjSb8R4/g/OKXEVTXqumkEiFC7jMEXae4XwyAqCKhq6Cx9Vk0+u3L\nfOoqTOg0qoLdc5IkcfSdSQQBDj14fUOfL/6P0pne1oPKs5zXwuu4IuvsrOhDpVA2c3zT+JYWqWwI\ngkBb/9388jktJt0IPV0LeOZfxbv0DkZ7Dyb7DnSWZnS1degamwidHyQZ8KO2XLuxScZ9RHwjhL3D\n7E/MgkZNSG2ntfVL6Ezbl/kYdGrqKsxML/lJpiTUKuXnjSNvTiBJMocfbC1IwzmD/0i6lZ31rnsU\nj5GUkox5J6k2VuLQK9vojt8hnYxuNRQZ3x/+8IccOnSIb33rW0xOTvJHf/RH/PKXvyz2s90xaLRq\n+g908/7rKlKqfQzsdBNwHce3/C6+5XcRVHr05kYMT3QTvTCKe/BVDM2tgEAi6iIRWSYeWSa50cZQ\nENRI5jb+enGQ9qoq2rIYXgCVKNJSY2Fk1ks4msCoV1bze/HsMt71ML27anCUFx4/Svp8hIfOo2tu\nQVutXDv3onvD5awwGxTSxtdq1FBhL6705u2KIAgcfrCTF34SJpZs594HgoQ95wmtnya0nlZU05ka\n0D5cR/K4B8/pVzHv3E0qGSKVCJJKBokH54hHLrUfVBvr+OuVcbTmSv5VFsObob3exvxakLnVoGIB\niZmJdWYn3dQ12QtWaQOQEgkCxz5GZbNh7N2heJwJ7zTxVLygeZ3x6JTKjD5ZFBnfb33rW2i1WgBS\nqRQ6na6oD3Un0rurhqEzi1w466VzYC91O+4m4huBxAJe1xgR3yhUgaaqgpB8ktDUlXrPokqPztyM\nwdaFuWwAWdTiXRphyD2CLMs5uWvb620Mz3oZX/Az0JZ/84NEPMnxD6ZQa0T239Oc9/XXI3D8GEhS\nQYlWkC4xAuhVuEi5/VE8gRi7O5xF7fR0u1PTYKe1q4LJkTXW/Ttp2/EIsdAsYc8Fwt4hIr5hMILm\nficRholMDl81goje0orB1o3B1olaa0X2/1cmfNNEk1H0an3WZ2ivs/LO6QXGF3yKjG8qJfHhm+MI\nAtz9SHtRfv/Q2TNI4RCOxz57XS9WrlxYT39fO8q7FV2fkiQmF/3UOk2YDcUX2SmxNVmN7/PPP8+P\nf/zjK/7vT//0TxkYGGBtbY0//uM/5t/+23+b9UYOh7Eg4fHrUVFhyf6mTxFPP7uTH/35EY6+Pck/\n+u69iNV3b74Wi3gI+eaY/vGPiAd8NH3zdxB0avRGJwZLHVq9/ZpFYWdNL0fnTpHQh6mzZj817u2t\n4aUjMyx6Ijys4Lt98+WLREIJ7vtMJ00t+Z8Orvd7Lp78GESR5scfQmtX9nsnU2nXXK2lim6lfVLn\n0665XV2VBc+7223ebkXmcz7xxX7++398m+PvT7H/cDPqyj5o6UOWZZLxALGIh8mf/G/CrgVqP/cU\nxsp6NFoLGp0FnaEMleZKT8O+hn5mh+ZZkRbZV7Ez63Mc6Bf5y5cuMu8KK/ruj747gc8dYd9dzXT3\nXpvhrGTMtZNpvfamJx/DVMB8GDkxhk6l5VDHgCJlq/E5L7FEiv52Z9bPcafN2xtNVuP77LPP8uyz\nz17z/yMjI/zhH/4h/+bf/BsOHMiuR+rxFKeEJUNFhYW1tUBRx7zZGCxaOnZUMnZhlfffHKV3V+1l\nn1MNqhZMdYeIPP9TgmcC2B96hASQCALB4DXjtZvbOcop3h87ycON2bt1VJg1CMC50VXW9tXn9ezr\na0E+emcCi1VHZ19V3r/N9X7P2PwcwbFxTP0D+BIqUPh7j3omiCZjdNraFc+ZUxfTQijVdn1B8+52\nnLfX4+rP2b+3jrPH5nnzNxfZc/jyDZAAlGFqupfA6/+DUI0PwxceIQ7EYxCKJYErv68mfTMAH02d\npUmbPa9AJctYjRouTLry/u7DoTjvvDaCTq+mf3/dNdcr+T2TPh+ek6fRNTYRNjoIK5wP6xEP8/4l\n+sq78bmjwLUZ5dk4dn4RgPpy47af406dt8UYbysUZQ2Mj4/z3e9+l+9973vcf//9ih+sxLUcfqAN\njVbF0XcmrytQYD10GEQR30ayxnZk6lkzrqlsGPUa6ipMTC6mk1NyRZZl3nt1FEmSufexTjRFajDv\n++A9AGz3FTbHNkuMCoz3qkSB5uo7Y/dfbPbe1YTBqOHEhzP4rrMRN+/ajajX4//ow6xyky3WRgxq\nPUPrIzl1RRIEgbY6G25/DLc/PwN1/P0p4rEU++9pRl8kt2zg46PpUEoBiVYAQ+7CXM4AY/Mbcqml\n8rlPHEXG93vf+x7xeJx//+//Pd/85jf5zne+U+znumMxWXTsu7uJWDTJsfemrnldbbNj6usnNj2V\ntRm5TWeh0VLHuHeK6HXqLK9He72deFJiduXak/RWXDy7xPKCn7buCpoUxIqvh5SI4//oCCqrFVN/\ndtfits/nHkUtqOhwKKsRjsVTzK4Eaa62oCly6OROQafXcM+jHaSSEu/8ZvQaoynqdFgOHCLpdhO+\ncH7bsVSiii5HB+tRN2s5du9SUu+7PO9j6MwSjnIjvbsLaxl4Of6PPgCVCstB5cIacGlT3avQ+Mqy\nzPi8F6tJW0oivAkoMr7f//73eeutt3juued47rnn+P73v1/s57qj6d9Xj73cyIXTiyzMeq553Xp3\nesfsP/JB1rF6y7tJySlGPBM53TuzAx6fz97CECAcjPHR25NodSruflhZs4LrETx1CikUwnrXPYrl\nJCHd7WU+uEi7vRWdSqtojOnltOxmqQ6yMNq6K2huL2dx1svFc0vXvJ7xcPjeezfrWJnEuSH31spw\nV9w7T+ObSKR46+W0cbv/s52oCihRupzY3CyxuTlMAzuvW1aVKwkpyYhngipjBU6Dsq5N674o3mCc\njnpbKYnwJlAS2bgFUalE7vtMumD+hb85TSJxpe6zeeduRLMZ/0dHkFNba0LDJZdUrq7nTLeejDsq\nGx++NUE8luTg/a2YLMXLeve9n16Abfdmj1Vvx8X19OKckSZUQqm+tzgIgsC9j3Wi1an46K0JQoHY\nFa/rmprRNTYRPHuapHf7zV/m97y4nr0pA0BztQWVKOTc4ejYe1P4PBEG9tdT01C4UEwG34fvA2C7\n6+4s79yeCe8U8VS8OC7n0ry+KZSM7y1KXZODgf31rK+FOPr2ladWQa3GeuAQKb+f0PnBbcdptjZg\nUhu5sD6cU3ys3KbHbtYytuDL+v6ZiXXGh1aprLXQu6t4brn46iqR4YsYOrvQVimv7YXLXHMK9ZwB\nxudLCkDFwmzRceiBNuKxFO+/PnbFHBMEAdu994MkZfXqlOkdVBsrGfVMkEht3bwhg1ajoqnawuxK\nkHhi+w3r0pyXc8fnsTkMHLhPWWP66yHFYviPfIjKZis4lHLJ5ax8Xo9lNpX1xdtclMidkvG9hTl4\nfwsVVWbOn1pkdtJ9xWvWe+4FwPfu29uOIQoiPeWdeGM+FkNbty7MIAgC7fV2/KE4a97Ilu8LBWO8\n/fIwoihw/2NdiGLx3Fb+TKLVvYUlWqWkFEPuEcr1DmpMyuQAZVlmfMGH06bHUcST/Z1M764aahps\nTI25mBxZu+I1y8FDCFotvvfezZp41VveRVxKMOadzOm+7XU2UpLM9PLW2ayJRIq3X0mfph96shuN\npngx/sDxY0jhMLZ77ysolAJwYX0Eraih3a5cRW583otWLdJYpazDV4nCKBnfWxi1WsXnv74HURR4\n+5XhK7Kf9Y1N6FvbCA2eI7G2ts0ol1zPQzm66DJx361cz5Ik8+avLxIJJzj0YCvOIv7xyqkUvg8/\nQDQYMO9VLjYPMOGbIpKM0ufsURzTWnaHCUWTpVNvEREEgQce70KlFnn31VH8l23yVEYjlv0HSbjW\nCA9f3HacfmcvAIOu7d+XoX0zpLK1S/vjdyfxeSLsPFBPdZEzgL3vvAWCgO3eBwoaxxVxsxJepaus\nHY2ozIiHowkW1kK01loLktwsoZzSt36LU1NvY/+9zYSDcd577cosUfuDD4Ms481y+u0p60RAyD3u\nm8X4njoyw8KMl+aOcgbyrAfORmjwHCmfF8uhuxC1yhKkMmQW5f7yXsVjjM6lF+rOIsb9SoC9zMi9\nj3YQiyZ57ZcXSF7mCr6UePXOtmO02ZoxqA0MuoZyCql0bPyGo3PXn9fT4y4GTyxgLzNw4N7iuZsB\notPTxKanMA3sRFNeWEXA0GYoRXm8d3zBjwylDl03kZLx/RSw62Aj1fVWJobXGDqzuPn/5n37UJkt\n+D54DymxdT9Ui9ZMk7WBCd80keTWruQMDZVmdBrVdTNDF2Y8nPhwGrNVx4NPdBc9S3KztrfARCtZ\nlhl0DaFTaWl3KHfNbRrf0iJVdHp21tCzswbXSpD3fzu2+f/61ja0dfUET58i6d+60YdKVLGjvAtP\nzMtC8Nrs6auxmbRUlRkZX/AiSVcaa7crxBsvXkStFnnkc72oi+huho1TL2B/4KGCx7okKVlAHsNC\nel53lOK9N42S8f0UIIoCDz/Vg96g4f3Xxzbjv6JGi/Wee5GCQYInjm87Rm95F5IscdE9tu37IN1k\nobXWyqIrRPAyV3c4FOeNX19EEAQefaa3aKIDGRLr64TOnUXX1IxeoQxkhtXwGmuRdXrKuhS75iB9\nSjIbNKUm4zeIex5tp6LazPC5ZYbOpjeWm4lXqVTWxKv+8h4gd9dzZ72NSCzF3OqlOvZYNMGrf3+e\nRDzFg092U1FkIZVUOETg2FE0zgqMO/oKGiuRSjDimaDaWEm5whIjSCcRCkBbqZPRTaNkfD8lWO0G\nHv9SH6Io8PoLF1hfSy8e9gceBEHA+/ab217fl2/JUf2VdZGJeIrXfnGecDDOgftbqL4BMVDvW2+A\nJGF/6JGCxxpcTy/Gfc4exWO4fBHW/VE66m2lJuM3CLVaxWc+vwOdXs0Hr4+xtpEMZT10GEGjyZp4\n1VvehSiIDK4P5XS/zk3Xc/rkJ0kSr78whM8TYffhRtp7Kgv8RNfi/+gIcjyO7f4HEApsRTjiGSch\nJQrKck6m0s0U6ipMGPWFJX6VUE7J+H6KqK638dBTPSTiKV55fpBwMIbGWYGpf4Do5CTR6ektr22w\n1GHRmjnvuogkZ5eObN8U2/CRTKZ49RfnWV7w095bya4DDcX6SJukIhF8772DymrFcqAw5R+AQdcQ\nAsLmpkMJYxuxwVK898ZitRt45HM9pFIyr/x8EM96GJXZjOXAIRKrK4TOnd3yWqPGSJutmRn/HL5Y\ndk3eq43vR29NMj/toam9nINFLCvKIMsyvnfeBpVqs0KhEM65LgCws0L5CXp2JUg8KZVczjeZkvH9\nlNHeU8mB+1oI+mO88vO0q8z+0MMAeN/Z+vQrCiIDzl6CiRCTvpms92mrtSEIMD7n4Y1fXWR+2kNz\nezkPPVn8OC/A6lvvIEUi2B98GFFTmDs7lAgz6Zuh2dqIRas8E3t0vpRs9UnR2FrO3Y+0Ew7GefFv\nz+BZD+N49DMAeH772rbXDmxkPV9Yz+56zpSMjc55OP7BFOdOzONwGnnkaeUZ8dsRGR0hvrSIZe/+\nghStACRZ4pxrCLPGRKtNeVgmo15XSra6uZSM76eQPYcb6e6vZm05wK//7ixCcyeaigoCHx8lFQpt\ned2AM920+9zahaz3MOjUNDhNpBaDTI25qGuy8+jne4sms3c5siSx+NLLCGo1tvsfLHi8ofURJFkq\nyOUM6dORTqsq1UF+Qgzsq+fuhy8Z4LChHGPvDiIjw0Rnpre8ri+PkiNBEOiot2GPpDjxwQwWm54n\nvtyPVndj3K/et94AwPZA4fN62j9HIB6k39mLKCj/O8yIa5SUrW4uJeP7KUQQBO77bCedO6pYWfTz\ny78+g3DoYeREYjNb+Hp0lXWgU2k5u3Y+a2lGIpGiPgVlMlidRh7/Ul/R+zFnCA2eI7q4hOXgYdTW\nwk4HkHY5A/QXYHz9oThL62Ha62yoCozTlcidgf2XGeC/OYN416MAeF7f+vRbaXRSZazkonuUeBa1\nK0mSsPhiVCOgM2v5/D/YjfUGNRWIrywTPHUSXWMThg7l8qYZMpvmnRU7FI8hyzKjc14cFh3lNn3B\nz1RCOaVV5VOKSiXy0FPd7L27Cb83yluTJrzWerxvvI6UuP4CpBHV7CjvxhV1b6t25VkP8Yu/OkXC\nHSGIjLm9DI32xiVmeN94HQDHI48WPFZG1apM76DWpFyacqzkcr5pDOyvT7ugQ3FePRpkvWEPgRPH\nSLjdW17T7+whISUY9Yxv+Z5EPMnrvxzCvxggiEyyzoL5BqqWeV57FWSZsieeLIpL+6zrPFpRQ5ej\nQ/EYi+thAuEE3Y32UjOFm0zJ+H6KEQSBA/e28OATXSQSEqcrH2ZCrsX7pNQG8QAAIABJREFU4da9\nfndmcT2PXljh5z86iXstRNdANcPIjC7duCbasfk5wheHsA30o2toLHi8dC1zlP4CVK3gkhBDqb73\n5jCwr54HHu8ilZQ4oxvgvPNuVn+7dU7DJbWr62c9T426+Lu/PL4ZQlnQqxhb2rqGuFCSXi/+Ix+g\nqazCvKcwpTaA5dAqq2EXveVdaFXKcyJGNrqkdTU6Cn6mEoVRMr63Ad0DNTz5lQE0OjXjzv28eCTM\n2Pml67qWdzi7UQkqzrquNL6rS35ef+ECb27U8X7m87089EQ3tRUmJhZ8JFPZM6SV4HnjtwDUPv1k\nUcbLLL595YXHe9Uqgdbawt3gJZTRs7OGZ39vH5U1ZlYsrbw2bmLywuJ153WLtRGT2sj5qxqIBHxR\nfvP3g7y6USa353AjTz47QFuDnXV/DJcvu+iMEjxvvI6cTOJ47LMFlxfBpc1yJm9DKcOzaY9OV2PJ\no3OzKRV53SbUNzv4+u8f4v2/fIWJoJk3Xhrh3MlFduyupbrehs1hQBAEDGoDnY42LrpHWQu68c0l\nGTwxz/JC+hRQUW3mkc/1Yi8zAtDd4GBhLcTUkr/opQlJn5fA0SNoKqtw7NuLa33rZLFckGWZ06uD\n6FV6OhxtiseJxJLMrgboqLOhuUFx7hK5YS8z8oVv7uHDH73OhRU9r/16FPO7M3TuqKKzrxpHeXqe\nqkQVveXdHF85xfjaDLgMzEysMzXqIpmQqG2wcd9jnTg2xFI6G+ycHnMxNufDaStuzDcVDuN7921U\nVivWAlsHZjjruoAoiAUlEcqyzMisB4dFR+UNinOXyJ2S8b2N0Bs03P/Vw1T8n/83U/V3s7wEq0sj\nG6+pqaq1olKrMLs76PTV8fMT52DjQNvYVsbAvnrqmx1XuGu7Gu28eWqe4Vlv0Y2v+5WXi3o6mAnM\n4Yl5OVC9pyBVq/EFH7J8SQu4xM1FFEUOP3sXpn/3fzFfPsBqtJlTH81y6qNZ7GUGtHo1arUKndRA\ni1fLG8emgPQcNlt17P9MC119VVfM681633kvh/sKa1t5Nb5302VzzsefRNQUpk8O4I35mPbP0mlv\nw6QxKh4nE+89tKOqFO+9BSgZ39sMbWUlVXt6MX78G/Z/+1/g0VexsuBnecHPzEQ6YUUQATVIlgg7\n29vp21u3edK9ms4N99TIrIen72ou2nMm3G58776N2unEdnfh4gMAp1bPAbCncqCgcTICDF0l43vL\noLZYqTvYj/ntN7n7G9/CXdXDyPkVVhb8+H1RpFTa1WzEQdwa5O6d/TS3l1Neab6uoWmsMqPTqjZ/\n62IhJeJ43ngNUa8vSnkRXAqlDBSQ5QwwuhHv7S7Fe28JSsb3NqTs8ScJfHwU6b3f0Pcn/47+venO\nQ+FQHFmWMRi1/KdTf854YJ5/eM99mLfZTVuNWuqcJsY34r7Faj/mfvnXyMkk5U89U3BvU7jS5dxd\nVlhZx+icF0Gg1EbwFqP8yafxf/A+vldepO1P76Kj91KPZkmSSSVTPHfxeS6sn+YL/btxWrfWaFaJ\nIu11Ni5MufGH4lhNhZ9QIS0lmfL5cHz2CVTG4uiBn1vbML7FiveWNpW3BKWEq9sQXX0Dpl27iU6M\nExm91MPXaNJiMusQRYGBih1IssT5HIQJuhrtxBMS00XKek641vB98B6aqiqsh+8qypizgXncUQ/9\nzt6CXM6JZIqpJT+NVRYMN0h4oYQy1HYH9oceIelxpyUbL0MUBTRaNXtr+4FLXpDt6KzP3t83H6RE\nHPcrLyGo1Tge+UxRxowko4x4xmkw11JuUH5izcR77WYtlY5SvPdWoGR8b1PKHk9nD6+/8IvrZodm\nSo7O5qB2lXFTDW+4rQpl/aUXIZWi/OlnEFTFSWjKLLa7K/sLGmdiwU8yJdNZ0r29JSl7/ElEgwH3\nKy8hRa/NVO4p60Kn0nJq9VxWIZlM3DdzIiwU71tvknS5sD/8CGp7cebPoGuIlJwq2OW8tB7GH07Q\n3egoxXtvEUrG9zbF0NaOadduImOjBE+duOb1KlMl1aYqhtwjWXv8ZhapkSLEx+IrK/iPfIi2phbL\ngUMFjwcZl/M59CodvQW6nIdm0huMnuZSXOxWRGU24/jMZ0kFAnh++/o1r2tVGvqdvaxH3cwG5rcd\nq7XWhlYtcnGm8E1lKhjE/dKLiEYTZU88XfB4GU6snAFgb+XOgsbJ1Pd2lkqMbhlKxvc2puLZr4JK\nhev5nyEl4te8vr9qF0kpyZksp1+rSUut08TYvLfget/1X78AkkT55z5flAxngLnAAutRD33OHjQF\nCBAAXJxxIwpCKS52C+N49DOozBY8r79KKhi85vVMwt3p1cFtx9GoRTob7Cy6QniDsYKeaf2lXyFF\nIpQ//TlUpuLEeoPxEBfdozRY6qgyFdbqMLNxLiVb3TqUjO9tjLaqGsfDj5JwreHdELO4nH1VuwA4\nsXw661ibcd9l5XHf2NwcgY+Poq1vwLy3cNWfDMXKco7EkkwtBmipLcV7b2VEvYGyJ55CikRwv/rK\nNa/3brqez2Z1PWc8HIWcfuMry3jffgtNRSX2Bx9WPM7VnF47hyRLm3+nSpFlmeFZLzazlqpSvPeW\noWR8b3PKnnoaldmC++Vfk/Rd6TZ2GsppsTYy4hnP2gs1s2MeURj3lSWJled+CLJMxZefLdqpN+Ny\n1qm09JQpbzAOMDLrRZJleprKivJsJW4ctgcfRO0ow/vmb0m41q54TbPpevZkdT33bvzWQ9Nb60Zn\nw/WLn0MqhfNLzxYlcz/DiZUzCAgFu5yX3WH8oXgp3nuLUTK+tzkqo4nyZ76AFI3ieuEX17y+r2o3\nMjKnVrduWA6XyhNGFCan+N59m+jkJJb9BzD1FXZCvZy54AKuqJu+8p6CNG8BhmbSC/COUrz3lkfU\naHF+8cvIiQQrz/34mhPung2Dlc313FBlxqRXc3HGk/WUfD38F4cJnjyBvq29qN4cT9TLhHeaNnsz\nDn1hIZBSidGtScn43gHY7rsfbW0d/g/eJzo7c8Vre6oGEAWR4yvbu56tJi015UbG5vPXeU56Pbh+\n8XNEo5GKr3097+ffjsziWqjLGdKuR61apLW2VN/7acBy6DDGvn7CF87jP/LBFa/1lnWiV+myup5F\nQaC7yYHbH2PVk5/Os5xMMvkXPwCg4itfK+qp8uTqWWRk9lXtLnisS80USsb3VqJkfO8ABJWKiq/+\nDsgyq3/9V8ip1OZrVq2FLkc7M/45VsOubcfpbnIQS6TrYPNh9W9/kpbb+9JXUNuKtwBIssTx5dPo\nVFp6y7sLGssXjLGwFqKjwY5GXfqz+DQgCAJV3/wWgk7P2k//9oqwikaloc/Zk5vruXnD9Zxn3Hf9\n5V8TmpjAcvguDG3t+X+AbTixcgZREAsunZMy8V6TluotVOxK3BxKq8wdgmlHH5YDh4hOTrD+4gtX\nvLZ/Y3d9cqOsYSt2bCxSF6Zyj48Fz5xOu+XaO/j/27vv+KjKfPHjnynJpPdJJ40kJAFCDIYiRQQR\nRF2UpYnCWq7rul77b6+rLujeXXW9ey8v3eLLviri6rK4iwoWBEQ6AUIoCS2kk56QMmlTzu+PEKSX\nzJmZlO/7H1/JZJ7zPcx4vuc8z/d5Hv8JE68y6ks70lBAQ8dJRoaOsLvLubvgJi1Wupz7ErfgYIyz\n52BrbaX644/Oeq27UGl7xe5LttH9medfxbhvW8Ex6ld/gcEYQuidd11l1JdWZaqmtLmctKBkfNzs\nq5wuq26hydRJWlyQjPf2MpJ8B5DQuxfhFmKkfs2XtB76cWWrdONQ3LR6sqtyLtlFlxobiFajueLk\na2tv67og6nRdTygqFVl121aRDcCYiCy725L5vX2X//U34JmUTMvuXTTvzj79+7SgIfi5+5JdlYPZ\nar7o+0MDPQnyM5Bf3IDtCsZ9be3tVL7zFigKSY8/qtoykt1Oz+21s8oZfrxRHhYvRYS9jSTfAUTn\n5UX4z38BWi0V77yJtbmrwtlT78GwkDSqWmsobSm/6Ps9DXoGR/lxvKIJU/vFL2bQVd1c8e7bWOrr\nCJo+A0NUlKrn0mpuI7fmAKFeIST4x9rVlqIo5Bc14O2hJyb04usBi95Jo9US9rN70ej1VC9fhqWp\na1hEp9UxOnwkbZa28/avPuv9Gg2psYGY2i2UVp0/b/hc1Z9+jLmmmsBpN+M/zL6Vp86lKAq7qvbi\npnWzey1ngAOnkm+aJN9eR5LvAOOZMJiQmXdgPXmSyr+9c/pJN+v0nN/LdD3HB6EokF906fGxun9/\nhilnD55DUgi+baY6wZ9hd3UuZpuFMeHX2t2dVnOyjbqmdlJiA9FqpWuuL3IPjyD49llYm5o48edX\nsXV0LZoxNqKrAnnbiexLvf3HKUfFl+7VacnZTdOmHzAMiiF45h0qRH620uZyqttqSQ9Jw0NvsKut\njk4rR8tOEhPqg79KG0cI9UjyHYACp8/AK3Uopn25nDy1RF9acAqeek92Ve3FarNe9L3D4oOBH++o\nL6Rp21bq13yJW2gYkQ/9p6pzH7ttr9iFBg2jI0ba3VZekYz39geB027Gd+x1tBcep+LtN1BsNsK8\nQ0nwj+NwwzHq2i5+w5hyetz34n/TXlJM5d/eQ+PmRvgDD6J1s6/O4EK2VOwEICtchSrn0pNYrApD\nE+SptzeS5DsAabRawu9/AJ2vLzUrPuHkD9/jptWTFZZBY2cTB+ouvtNRXLgv3h56DhbWXXB8uK3g\nGFUfvIfW05OoRx5D5+OjevyVpiqKmkpIDUomwGD/tKAfx3vlItWXaTQawn92H16paZj25lDzyXIU\nRWFsRBYKCjsqz1/jvFugr4HIEG+OXGQJ1Y6yUsqW/hFbWythi+7BEKnuMApAu6Wd7Mo9BBoCGGpn\n9T7AgcI6AIbJ97pXkuQ7QOkDAoh64v+h8/ah+sP3afhuLROixgKwqXz7Rd+n1WpIjQuirqmDyvrW\ns17rrKrkxF/+hGKzEfGLh3GPiHRI7Nsqui6iYyLsX9TApigcKm4g0NcgS+/1Axq9noiH/hP3qGhO\nrl9Hw7dfkxk6HHedO9srdmFTLj5HPTU2kE6zjYLyxrN+33GinLL/+x9sLS2ELboHv7HjHBJ7dtVe\nOqydjIscjVZj/6X5YGE97m5aEmWHrl5Jku8A5hETS/Svfo3O35+aT5bjsWk3g/3jyK8/QnVrzUXf\n1105eWbVc8veHEp+/1uszU2Ezl+A99BhDonZarOys3IPnnpP0kPS7G6vrLqFljYzaXGy9F5/ofPy\nIuqxJ9AFBFC74lNaVn9FZsgw6tobONpw/KLv6x52yDuj67mzsoKy/30Fa3MzoQt/hv+E6x0Ss6Io\nbCrfhlaj5bpI+6v365vaqahrJSUmUOat91LyqQxwhqgoBv3XM+gDg6hduYKp+63oLAqby3dc9D3d\n830PFNaj2GzUfvZPTvzlNRSrlfD7HyBg8o0Oize//ghNnc1khWXYvYMRwP7jXV1zadI116+4BQUT\n/fhT6IOCqf9iFaP/dRBfk/X09LQLGRITiE6r4cCpIZWmbVspfeVlrE1NhC64m4Drb3BYvEVNpZS3\nVJAeMhR/g5/d7XXXZAyV73WvZVfyLSgoYOTIkXR02Lcdl3At97BwBj39DG4hRgybd3PvF/U0bNxA\nR2f7Bf8+2N+DiGAvqo4WUrr0f7uKq4yhxDyz2GFdct1+nNurzjq6uQV1aDQwPCFYlfZE72GIHkTs\nC/+Nz7WjoKiUu79qoHnXTkydrRf8ey8PPUnR/piKiil6+UUq330LW3sboXctcugNJcCm8m0ATIhS\nZ4/r0/N7pdiq1+pxGWpLSwuvvPIK7u5Swt4fuIUYiVnyAvVfrcG29msmbK/j+LFfEzHjdtyNoej8\n/dH7B2Bra6U5eydzDm/E62Q17YD3iIyuAi6VFxs4V11bPbk1B4n2iSTGN9ru9lrazBSUNzI4yh8f\nT/UrV4Xr6by8iXjwIZqGDaNi+YdM29RA8f6nCb4mC+9h6XilpGLr7KCzogJzZSVTyvbiXboXMwo+\nmSMxzp2PW4jRoTGazK3sqc7F6BlMcuBgu9uz2RTyiuoJ8jPIkpK9WI+Sr6IoLF68mCeffJJf/vKX\nasckXETn5Y3xp3Ng3LX88P7/MLTgJNUfvn/Bv/XU6jjqFY175ihuXHSr6qtXXciG0s0oKEyJmajK\n+Oz+43UoCowYLE+9/ZlGo8F//ERssVFs+9sfiakw0bhhPY0b1oNGA2dU7fsCdW5+FGVM5c4Hb3NK\nfDsqd2O2WRgfNUaVQquiymZM7RYyk41Sx9CLXTb5rlixgg8++OCs30VGRjJjxgxSUq68HD4w0Au9\nXnf1EV6C0TgwViNy9nkajel8PnMsyw7v5ZGgyfh3aulsaMDccBJFUQgenYV35rX83yubiNX7siBM\nnV2ALnWeLZ0mtlZmE+wZyLSh49Fr7f8uHS49DMANWbFO/TeW761rGI0ZbLTdwptHf+CRsJuIKmul\nKS8fN19fPKOj8IyKwjMqkl99VkyDycxDgd5XVKxkz3kqisK27J24afXcMmwSfgb7p+at33sCgOsy\nolT9DHrb5+kozjrPyybfOXPmMGfOnLN+N3XqVFauXMnKlSupqanhvvvuY/ny5Zdsp6HhwuMsPWU0\n+lJTc+kN4PsDV53naGMWeyoOsM7Yzl2pszl3Ek6bDZKi/ckvbqCgqA4/O1fQudx5flu0gQ5LBzPi\nbqShzv7vktVmY1d+FcF+Bjx1OO3fWL63rnWdcSzfFWzmX+15/Nf0R/G++ezV1zqAtAQT3+0qY0tO\n6WULluw9zyMNxzjRXEVWWCYdTQo12P9vtuNABRoNRAV6qvYZ9NbPU21qn+elEnmP+jjWrl3LsmXL\nWLZsGUajkffee6/HwYneKS14CMEegeyqyqGl03TBv+mectRdMewoZpuF78s246HzYFzkaFXaPFbW\nSGuHhfTEEOmaG0BCvUIYYRxGSXM5RxoKLvg3IxJDAMg9duktNtXwbfH3AEyMHqtKe111DE3ER/hJ\nHUMvJ1ONxAVpNVpuGDSBTpuZ70o2XvBvrknuKkTZc+Tic4LVsKtqL42dzYyLGoWn3kOVNnMLum4Y\nZLx34Lkxpmuu7tqS7y/4+pBBARjcdew7duFV3NRyvLGY/PojDAlMtHtzkG65x2qxKQrXJIWo0p5w\nHLuT7/r16zEY7FsAXPRO4yJH4+/ux8ayLTR3nr/bS3iQFxHBXhwsrKfDfPH1oO2hKArrSjZ23QxE\nj1et3X0FdbjrtaTEyHrOA028fwyJAfHk1x+hvKXivNf1Oi3D4oKoPtl23ipualpTuBaAGfFTVWsz\n52jX03pmsmMrtIX95MlXXJS7zo1pcZPptJlZe6p77FyZyUY6LbYr3uP3auXVH6bCVMXI0AwCPdRZ\nJq/mZBsnak2kxgbi7qZuEaDoG6bGTAJgbfGFe3XST/WI7CtwzJBK4amn3uTARBID4lVps8Ns5cDx\nulM3xY6d9ifsJ8lXXNJ1kaMIMPjzQ/k2GjvOL0TIdGDXs6Iop5P+lJiJqrXbfUHtHtsTA09a8BAi\nvMPYXb2X+vbzdzLqTr6OGvddU/gdADPi1Fu8I6+wnk6LTZ56+whJvuKS3LR6psdNxmwzs7Zkw3mv\nx4b7EuhrIPdYLVbbxRet74mDdYc4evI4aUFDGOSr3iYN3RfUdBnvHbC0Gi1TYq7Hptj4umj9ea/7\n+xiIC/flaFkjre0WVY9d2FhMXv1hkgMGkxSYoFq73TfA1yTLTWVfIMlXXNbYiCwCDQFsLt/OyY6z\nd3zRajRkJIVgardwpLTxIi1cPYvNwsqjX6DVaLkj8RbV2m3vtHCopIFBoT4E+alTvCX6plFh1xDu\nHcbWEzspaS477/URiSFYbQoHi9QdUjn91Buv3lOv1WZj77FaAnzciY+wf21o4XiSfMVl6bV6bo6b\ngtlmOT014kyZSep3PX9ftoXqtlomRI0h0idctXbzihqwWBV56hXotDrmJP0EBYUVR1adV9k8IrHr\nO7L3qHpdz4WNJeTVHyYpIIEkFZaS7HaktBFTu4VrkoxoZepcnyDJV1yRMRHXEuwRxJby7VS3nn0x\nGhITgKdBT87RGlWmZjR3tvBV4Tq89V7cEn+T3e2dadehagCuSZJxMQEpQUlkGIdzvLGY7Kqcs16L\nCfMlyM/A3mM1mC32D6lYbVZWHFkFwC0qVjgD5Jy68ZXx3r5Dkq+4IjqtjpmDp2NRrHyU/4+zNiXX\n67SMSAymvqmDkqrzpyRdrS+Of027tZ1bEm7C2029heE7zFZyjtZiDPAgPmJgLJUnLm9W4i24afX8\n+9hq2i0/7uSl1WgYlRJGW0dXFbG91pduori5lKywTFWfehVFIedoDZ4GPUNi1JkRIBxPkq+4Ypmh\nI8gwDqegsYjvSzef/dqpJ8nddnY9lzaXs/VENhHeYYxXaTWrbvsK6ugwWxmVGiarWonTgj2DmBp7\nA42dzXxVtO6s10alhQKwI7/KrmNUmqr5svBbfN19mJP8E7vaOldJVQt1TR2MSAxGr5NLel8hn5S4\nYhqNhvlD7sDHzZvPj39Nlan69GvDEoLQ67TkHO158rUptq6xNxRmJ/0EnQqbJ5xpR17XBXR0apiq\n7Yq+b2rMJII8AtlQuvms73VsmC+hgZ7sPVZLR2fPFpKxKTY+yl+BxWZh/pBZqvbmwI+1FpkylNKn\nSPIVV8XX3Yf5Q2Zhtln48IzuZw93PUPjAimvMVHVw000Pi/4moLGIjKMw0kJSlIzbFrbLewrqCMq\nxJvoUPt3jhH9i7vOjZ8m3opVsfJ+3id0WjuBrhvO0alhdJq7qol74vvSzRQ2FTMydAQZxmFqhg3A\nnqM1XatyJVx6EwjRu0jyFVftmtDhXBuWQVFTyVnrPmcO6brz3pl39V10PxTtYG3J94R5GbkrZbZq\nsXbLOVqDxWpjVGqo6m2L/mGEcRhjwq+lpLmMD/M+PX1jOSqtq6dkZw+6nqtba/n8+Df4uHkzJ3nm\n5d9wlcprTZTXmBgaF4iHe4+2ZxcuIslX9Mjc5Nvxc/dl9fFvOXpqd5iRyaG467VsOVB5VVXPRU0l\nvJn9EZ56Dx4c/jO83M7dwNB+3WN23RdSIc6l0Wi4M2UWiQHx5NTsZ/WptZejQryJNnqz/3gdre3m\nK26vsaOZN/e9j9lmZm7y7fi6q9/jsmV/19rU1w2PUL1t4ViSfEWPeLt5sSh1Hgrw19x3yas7jJeH\nnswhRqob2jhWfmULbpzsaOStfR9gUazcN/QuwrzVfzJtbu0kr7CBuHBfwgLVHW8T/Yteq+eBYYsI\n8Qzm66J17KzcA8DotDAsVuWKCwobO5p4LedNKlurmTxoApmh6arHarXZ2HagEm8PPRmJMm+9r5Hk\nK3osNTiZB9N/BsCb+94nt+YA407dgXffkV9KY0cTb+77gMbOZu5On0Va8BCHxLnrcA02RWG0PPWK\nK+Dj7s1D6ffiqfdgef4KjjQUkJXa3fVcfZl3/5h4q1qrmTJoIrMSb3VIdf3BwgYaTZ2MSgvDTS8b\nhPQ1knyFXYYGp/DLEfeh1ep458BHmAzFBPkZ2JlffcltBndX5fLijqWUNJcxJuJabh0yxWExdo9B\nZ6XIeK+4MuHeodw/7G5sKPwp5y221m4gLtKL/KIGmkydF33fyY5GXs15g6rWGqbGTOKOxFscNq2t\n+wZ3vHQ590mSfIXdkgMTeSTjAQw6dz7M/wSf1H10elSz+/D5TwkmcyvvHVjOeweX02kzMy/5du5O\nmeOwC1RDcwdHSk+SHO0vazmLq5IalMwjGQ8Q7BHI2pLvaRm0DnzqLvi9rm2r4x9HVvHb7X+kurWW\nm2JvYObgmx32vTa1m8k5WkNkiDdx4bJgTF8k5XFCFQn+sTx2zYMsz19BaUshhpRC/nHiECbfCdgU\nG7VtddS21VPWcoJWSxvxfrEsSptLqJdj5ybuyKtCAelyFj2SHDiY50Y/yRfHv2FD6WYMqTv5vKqQ\n4oODCDD4EVkfQm75YXJrDqCgEGDw547BM5gQNdahC7nszKvCYlUYNzxcFozpoyT5CtUM8o3i6azH\nKGoq4a+bvqTVo4R/F6w5628CDP5MjZnEjbHXo9U4tuPFpih8v7ccN7329JidEFfLXefOT5NuIzN0\nBH/avpxOQw3ZVacKr0q6/jPIN4opgyaSGZqu+uIwF7J5fyUaDYwdqt6mI8K5JPkKVWk0GuL9Y7kt\n+nY++G4/o0fDdakxhHgGE+wRhLvOzWmxHDheR3VDG+PTI/DxdN5xRf8U7x/DPYMf4LWVexk13J+b\nxoWiGDqh3Y0E/1inPYGW15oorGgifXAwAT4GpxxTqE/GfIVDZKWE4YYHRXn+DAtOJcI7zKmJF+C7\nXV17tN44MtqpxxX91/DBwRj9vcjJMxHqHsmYQZkMDohzatfv1lOFVuOk0KpPk+QrHMLLQ8/IZCNV\nVzHnV00VdSYOFNaTFO1PTJgUpAh1aDUaJmdGY7bY2JR7wunHt9psbD0oc3v7A0m+wmHGp3fdma89\n9QTqTOv3lAMwRZ56hcompEfg7qZl/Z5yrDb796++GrsP19DY0slomdvb50nyFQ6TGhtIbJgvuw9V\nU1Fnctpx2zosbNlfQYCPu2wuLlTn5eHGdUPDqWtqJzuv0mnHVRSFL7cWo9HATVmDnHZc4RiSfIXD\naDQabhkbiwJ8tb3EacfdeqCS9k4rk66Jkv1NhUNMPtWj8uXm40475r6COspqWhidGkaoLJPa58mV\nSThU5hAjEcFebDtYSV1ju8OPZ1MU1u0uQ6/TcH1GlMOPJwamaKMPKTEB5B6tpbzW8b06iqLw5bYi\nAGaMiXX48YTjSfIVDqXVaJgxJharTeGbnY5/+s0rqqeyvpWslFD8vd0dfjwxcE0Z2dX1u36342sa\nDpecpKC8iYzEENmPup+Q5CscbnRaGMF+Bn7IPXHJdXHtpSgKX278WJ6WAAAKxUlEQVQpAn68MArh\nKBlJwYQGerJ5f4XDe3VWbysC4Jbr5Km3v5DkKxxOr9MyfXQsnRYba3eVOuw4uw/XcKSskYzEEBIi\n/Rx2HCEAdFotd01PwWyxsXJjgcOOU1jRxMGiBlJjAxkc6e+w4wjnkuQrnGJCegR+Xm6s31NGa7tF\n9fbNFiv/2HAMnVbDvMmJqrcvxIVMyhxEXLgv2/OqKHDQfPbV24oBuHWsPPX2J5J8hVO4u+mYmjWI\ntg6rQ8Z+v80upbaxnSkjowkLkkpQ4RxarYb5U5IA+Pu6oyiKuvN+iyub2XOkhoRIP1JiA1VtW7iW\nJF/hNJMzown0NbBmezElVc2qtdvY0sHqbcX4eLrxk3FxqrUrxJVIHhRAVkoox080sePU3tFqsFht\nvLs6H4A7JibI7kX9jCRf4TSeBj333JyC1abw7up8LFabKu3+a9Nx2jut3DEhHi8P2UBBON+cSYPR\n67Ss+L6ADrNVlTY/31JEWU0L12dEMjQuSJU2Re8hyVc41fCEYCaOiKC0uoUvtxbZ3V5JVTObciuI\nCvFmYkak/QEK0QMhAZ5MGzWIhuYOvtlh/7BKYUUTa7YVE+znwdwbpIahP5LkK5xu3uQkgvwMrN5W\nTHFlz7uf2zosvLc6HwWYPyUJnVa+zsJ1ZoyJxc/bndXbi+0qvjJbrLy7Oh+bonDfjBQ8DbLza38k\nVyvhdJ4GPffOSD3V/ZyH2XL13c8Wq42//ms/JdUtTMqIZGi8dMsJ1/I06LlvRipWq8Jr/9xHZX1r\nj9r59+ZCTtSamJwZRap0N/dbknyFSwyNC2LSNVGU1Zj46NvDWK9i/FdRFP62Jp+8ogYyEkO466Zk\nB0YqxJVLHxzMoulDaGkzs/TTvTRe5aIye47U8PWOEowBHsyZJN3N/ZkkX+EycyYNJtrow6Z9Ffz3\nezto67iy+b8rNx5n28EqEiL9eHDmUOluFr3KxBGR/GRcHLWN7by6Ipf2zst/rxVF4esdJfz1s/24\n6bX8x61pGNxly8D+rEdXLavVyu9//3vmz5/PrFmz2LBhg9pxiQHA06DnmbszGZ4QzJ5D1bz00W5q\nG9su+vcdZiurNheyZnsxYYGePDY7HYObXKBE7zNzfDzj0yMormzmL5/tp+bkxb/XFquN9786xD82\nHCPA18Azd40kKTrAidEKV+jRSP6qVauwWCx88sknVFVV8dVXX6kdlxggPA16Hp09nFVbi/lycyG/\n/2AXcycnEh/hR1igF1qtBlO7mfW7y1i7q4yWNjP+3u48MS8DXy/ZOEH0ThqNhkXThtBk6mRfQR3P\nvLmd0WmhzBgTS5Sxa2OERlMnpVXNrNlezKGSk8SG+/LoT9MJ9DW4OHrhDD1Kvps3byYpKYmf//zn\nKIrC4sWL1Y5LDCA6rZYH70jH39ONj787wjtfdi0sYHDTEWX0przWREenFS+Dnluvi+PGa6Pxk8Qr\nejm9TssjPx1Odn41q7cVs+1gFdsOVhEf4UddU/tZm4yMTDbyH7elSU/OAKJRLrMe2ooVK/jggw/O\n+l1gYCDR0dG89NJLZGdn89prr7F8+fJLHshisaLXyxdLXFrhiUZyj9ZyvPwkx8sbKa1uIcDHnZkT\nE5k+NlYW0RB9ks2mkJ1XyYp1Rzlc0kBooCfxkf7ER/qTFBPAtSlhaLWygtVActnkeyFPPPEE06dP\nZ9q0aQCMGzeOLVu2XPI9NTXqLScIYDT6qt5mbzTQz9NssaHTadD2k6X1Bvrn2d/05DzNFitufexB\nRD7Pnrd3MT0quBo5ciQbN24E4NChQ0RERPQsMiEuw02v7TeJVwigzyVe4Rg9Sr5z585FURTmzp3L\n4sWL+e1vf6t2XEIIIUS/1aOCK3d3d15++WW1YxFCCCEGBFmdQAghhHAySb5CCCGEk0nyFUIIIZxM\nkq8QQgjhZJJ8hRBCCCeT5CuEEEI4mSRfIYQQwskk+QohhBBOJslXCCGEcLIebawghBBCiJ6TJ18h\nhBDCyST5CiGEEE4myVcIIYRwMkm+QgghhJNJ8hVCCCGcTJKvEEII4WR9LvnabDaWLFnCvHnzWLhw\nIcXFxa4OSXVms5lf/epXLFiwgNmzZ7Nu3TpXh+RQdXV1XH/99RQUFLg6FId58803mTdvHrNmzWLF\nihWuDschzGYzTz31FPPnz2fBggX98vPMzc1l4cKFABQXF3PnnXeyYMECnn/+eWw2m4ujU8+Z55mf\nn8+CBQtYuHAh999/P7W1tS6OTj1nnme3L774gnnz5jn82H0u+X733Xd0dnby6aef8tRTT/GHP/zB\n1SGp7vPPPycgIICPP/6Yd955h9/97neuDslhzGYzS5YswcPDw9WhOMyOHTvIycnh73//O8uWLaOy\nstLVITnExo0bsVgsfPLJJzz88MO8+uqrrg5JVW+//Ta/+c1v6OjoAODll1/m8ccf5+OPP0ZRlH5z\nk3zueb744ossXryYZcuWMXXqVN5++20XR6iOc88TIC8vj3/+8584Y/mLPpd8d+/ezYQJEwDIyMjg\nwIEDLo5IfdOnT+exxx4DQFEUdDqdiyNynFdeeYX58+cTGhrq6lAcZvPmzSQnJ/Pwww/zi1/8gkmT\nJrk6JIeIj4/HarVis9loaWlBr9e7OiRVxcTE8Oc///n0zwcPHmTUqFEATJw4ka1bt7oqNFWde55L\nly4lNTUVAKvVisFgcFVoqjr3PBsaGli6dCnPPvusU47f5/7vaGlpwcfH5/TPOp0Oi8XSr/5H9/b2\nBrrO9dFHH+Xxxx93cUSO8dlnnxEUFMSECRN46623XB2OwzQ0NHDixAneeOMNysrKeOihh/j666/R\naDSuDk1VXl5elJeXc/PNN9PQ0MAbb7zh6pBUNW3aNMrKyk7/rCjK6c/Q29ub5uZmV4WmqnPPs/vG\neM+ePXz00UcsX77cVaGp6szztFqtPPfcczzzzDNOu7noc0++Pj4+mEym0z/bbLZ+lXi7VVRUsGjR\nImbOnMltt93m6nAcYuXKlWzdupWFCxeSn5/P008/TU1NjavDUl1AQADjx4/H3d2dhIQEDAYD9fX1\nrg5Lde+//z7jx4/nm2++YdWqVfz6178+q0uvv9Fqf7x8mkwm/Pz8XBiNY61Zs4bnn3+et956i6Cg\nIFeHo7qDBw9SXFzMCy+8wJNPPsmxY8d48cUXHXrMPpe1MjMz2bBhAzNmzGDv3r0kJye7OiTV1dbW\nct9997FkyRLGjh3r6nAc5sw76IULF/LCCy9gNBpdGJFjjBw5kg8//JB7772X6upq2traCAgIcHVY\nqvPz88PNzQ0Af39/LBYLVqvVxVE5TlpaGjt27GD06NH88MMPjBkzxtUhOcSqVav49NNPWbZsWb/8\n3gKkp6ezevVqAMrKynjyySd57rnnHHrMPpd8p06dypYtW5g/fz6KovDSSy+5OiTVvfHGGzQ1NfH6\n66/z+uuvA13FAf25KKk/u+GGG8jOzmb27NkoisKSJUv65Tj+Pffcw7PPPsuCBQswm8088cQTeHl5\nuTosh3n66adZvHgxS5cuJSEhgWnTprk6JNVZrVZefPFFIiIieOSRRwDIysri0UcfdXFkfZ/saiSE\nEEI4WZ8b8xVCCCH6Okm+QgghhJNJ8hVCCCGcTJKvEEII4WSSfIUQQggnk+QrhBBCOJkkXyGEEMLJ\nJPkKIYQQTvb/AZFKyjC8vZoHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a29e45f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns   # magia !\n",
    "sinplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn permite configurar paletas, estilos de una manera muy fácil, globalmente o para un gráfico en particular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d4LLL1q8JjnviehbCxmZkNsd4Jk97NLgoGQWgoFsx0ohPmWtKsBxO42KVyJLE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qs8dVjKNfHKJkuoskQ4mXvc92z5DL/YdYRdRzpZ21DBJ6/dZEn+AkAwlmBvWy/lbgebluRvSWca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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a27af4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.color_palette(\"Blues\", desat=.7):\n",
    "    sinplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al ser un paquete especialmente diseñado para **estadística**, además de gráficos más lindos, tiene **gráficos especiales** e incorpora \"datesets\" de prueba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8Gd797vfiX//1/2HBgkV49tmnMl976qkncfx4d1afC+UXVviIiOisnnvuWbz+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\n4CMiIiJSuP8H7qvtEY/tv1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a1b9600f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "g = sns.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"reg\", xlim=(0, 50), ylim=(0, 10), size=9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Y qué ese dataset? Veamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tips)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Pandas\n",
    "\n",
    "\n",
    "[Pandas](http://pandas.pydata.org) es una biblioteca para manipulación y análisis de datos basada en Numpy. Aporta nuevas estructuras de datos de alto nivel que extienden datos almacenados en arrays, aportando más semántica y nuevas operaciones. \n",
    "\n",
    "Puede pensarse a Pandas como un **reemplazo pythonico a excel**\n",
    "\n",
    "\n",
    "Pandas introduce dos estructuras de datos principales: `Series` y `DataFrame`.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series\n",
    "\n",
    "Un Series es un objeto **unidemensional**, similar a un array, una lista o una columna en una tabla, que tiene **asociado una etiqueta** para cada elemento. Por defecto, esta etiqueta es un número de 0 a N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8]) # creamos una serie, analogo a un array de 1D\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.Series?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, es simplemente un envoltorio más lindo: el verdadero contenedor es un array de numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   3.,   5.,  nan,   6.,   8.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, (6,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values.ndim, s.values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También podemos ver que nos generó el **indice** con claves default. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=6, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.keys of 0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por eso, la manera más genérica y recomendada para indizar elementos es usar el `.ix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.ix[-1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Pero entonces una Serie es un array?. Bueno, en realidad es más parecido a un diccionario ordenado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-0e2107f91cbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    599\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2475\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2476\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2477\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2478\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4404)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4087)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14031)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13975)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "s[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2441\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14031)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13975)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   2031\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2032\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14031)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13975)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-7aa4e4202966>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1075\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1076\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1077\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1078\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1079\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    130\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'no slices here, handle elsewhere'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    599\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2475\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2476\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2477\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2478\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4404)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4087)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:14031)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13975)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "s.ix[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hay muchas operaciones que podemos hacer sobre una serie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.5999999999999996, 23.0, 2.7018512172212592)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.mean(), s.sum(), s.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E incluso ver un resumen general de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.000000\n",
       "mean     4.600000\n",
       "std      2.701851\n",
       "min      1.000000\n",
       "25%      3.000000\n",
       "50%      5.000000\n",
       "75%      6.000000\n",
       "max      8.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, es un poco más permisivo que Numpy con los datos faltantes (ese `nan` que hay por allí)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(nan, nan, nan)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values.mean(), s.values.sum(), s.values.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y hay operaciones que no están disponibles en numpy o se comportan distinto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'median'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-af75eb1f12b5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# np.median(s.values)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'median'"
     ]
    }
   ],
   "source": [
    "s.values.median() # np.median(s.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es una herramienta nacida en la industria de las finanzas, y por ello intenta hacer fácil las cosas tipicas. Una de ellas es **graficar datos**. Utiliza **matplotlib** por default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a191a40f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V16RVG3JVeLZKg3vHa8H0FEVHhFo9CwDgh2yGYRgtPaimpkbz58/XzJkzNXXq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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a191a4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como dijimos, los valores de una serie tienen una etiqueta y se pueden definir explicitamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gaitan     10\n",
       "messi      54\n",
       "suarez     31\n",
       "ronaldo     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goles = pd.Series([10, 54, 31, 0], index=['gaitan', 'messi', 'suarez', 'ronaldo'])\n",
    "goles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando el índice no es de enteros, se puede buscar un valor tanto por clave como por posición (con reglas de slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goles['messi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goles[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "messi     54\n",
       "suarez    31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goles.ix[1:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviamente, dado que se trata de claves y valores, se puede instanciar una serie directamente desde un diccionario. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gaitan     80\n",
       "messi     100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'gaitan': 80, 'messi': 100})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero a diferencia de un diccionario, las etiquetas se pueden repetir. Es decir que pueden haber multiples valores para una clave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "messi     6.814979\n",
       "messi    27.762713\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.rand(4) * 100, ['gaitan', 'messi', 'maradona', 'messi'])\n",
    "s['messi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f064c8b3e10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Hd953/COwcaRJKn9vMKVJEmFWb6SJBVm+UqSVJjlK0lSYZavJEmFWb6SJBVm+UqSVJjl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f064c896438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot('bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La series se utilizan mucho para representar datos en **función del tiempo**. Hay una función especial análoga a `range()` que permite generar índices temporales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01', '2010-01-02', '2010-01-03', '2010-01-04',\n",
       "               '2010-01-05', '2010-01-06', '2010-01-07', '2010-01-08',\n",
       "               '2010-01-09', '2010-01-10',\n",
       "               ...\n",
       "               '2012-09-17', '2012-09-18', '2012-09-19', '2012-09-20',\n",
       "               '2012-09-21', '2012-09-22', '2012-09-23', '2012-09-24',\n",
       "               '2012-09-25', '2012-09-26'],\n",
       "              dtype='datetime64[ns]', length=1000, freq='D')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('1/1/2010', periods=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a19202c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KzXj/WBtWVZXg0OlO5Fv1KB8oICco/jAQcZj2jd1CpSOxZ1ycZ8SVG2biTHNf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\nhgwAgAnQkAEAMAEaMgAAJvB/631XQwWRSXkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a191fd9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2010', periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe\n",
    "\n",
    "Un DataFrame es una estructura tabular de **filas y columnas** (¡como una hoja de cálculo!). También se puede pensar un DataFrame como un conjunto de Series que comparten el índice (es decir, la primera columna)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>-1.536303</td>\n",
       "      <td>-0.686149</td>\n",
       "      <td>0.359030</td>\n",
       "      <td>1.810788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-02</th>\n",
       "      <td>-0.357695</td>\n",
       "      <td>-0.952089</td>\n",
       "      <td>-0.525806</td>\n",
       "      <td>1.960201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-03</th>\n",
       "      <td>-0.419310</td>\n",
       "      <td>-1.484320</td>\n",
       "      <td>-1.296897</td>\n",
       "      <td>-0.536892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.297675</td>\n",
       "      <td>-1.988424</td>\n",
       "      <td>0.417766</td>\n",
       "      <td>-1.207143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1.001379</td>\n",
       "      <td>-0.384861</td>\n",
       "      <td>1.073841</td>\n",
       "      <td>-0.524555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2010-01-01 -1.536303 -0.686149  0.359030  1.810788\n",
       "2010-01-02 -0.357695 -0.952089 -0.525806  1.960201\n",
       "2010-01-03 -0.419310 -1.484320 -1.296897 -0.536892\n",
       "2010-01-04  0.297675 -1.988424  0.417766 -1.207143\n",
       "2010-01-05  1.001379 -0.384861  1.073841 -0.524555"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000,4), index=ts.index, columns=list('ABCD'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-09-22</th>\n",
       "      <td>0.355165</td>\n",
       "      <td>-1.717212</td>\n",
       "      <td>0.683035</td>\n",
       "      <td>-0.413115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-23</th>\n",
       "      <td>0.399204</td>\n",
       "      <td>0.170302</td>\n",
       "      <td>-1.953205</td>\n",
       "      <td>0.514796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-24</th>\n",
       "      <td>-0.326344</td>\n",
       "      <td>1.741530</td>\n",
       "      <td>0.737105</td>\n",
       "      <td>-0.557197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-25</th>\n",
       "      <td>2.254474</td>\n",
       "      <td>0.570247</td>\n",
       "      <td>-0.162486</td>\n",
       "      <td>-0.795563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-26</th>\n",
       "      <td>0.368155</td>\n",
       "      <td>-0.349530</td>\n",
       "      <td>-1.568959</td>\n",
       "      <td>0.190495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2012-09-22  0.355165 -1.717212  0.683035 -0.413115\n",
       "2012-09-23  0.399204  0.170302 -1.953205  0.514796\n",
       "2012-09-24 -0.326344  1.741530  0.737105 -0.557197\n",
       "2012-09-25  2.254474  0.570247 -0.162486 -0.795563\n",
       "2012-09-26  0.368155 -0.349530 -1.568959  0.190495"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las columnas son Series!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f064c6fc710>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GHwDg9gYn8KpovHRaqVZCT79vgq+EaGrgnHOWyZ2pAMBmHjo4A8CFq+uwan4lyp3mhOPG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f064c6ffa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"A\"].cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se puede instanciar un DataFrame a partir de un diccionario. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C    D    E\n",
       "0  1.0 2013-01-02  1.0  3.0  foo\n",
       "1  1.0 2013-01-02  1.0  2.0  foo\n",
       "2  1.0 2013-01-02  1.0  1.0  foo\n",
       "3  1.0 2013-01-02  1.0  0.9  foo"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({ 'A': 1.,\n",
    "                     'B': pd.Timestamp('20130102'),\n",
    "                     'C': pd.Series(1, index=range(4),dtype='float32'),\n",
    "                     'D': np.array([3, 2, 1, .9]),\n",
    "                     'E': 'foo' })\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           B    C\n",
       "0 2013-01-02  1.0\n",
       "1 2013-01-02  1.0\n",
       "2 2013-01-02  1.0\n",
       "3 2013-01-02  1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['B','C']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
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       "        vertical-align: top;\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C    D    E\n",
       "0  1.0 2013-01-02  1.0  3.0  foo\n",
       "1  1.0 2013-01-02  1.0  2.0  foo\n",
       "2  1.0 2013-01-02  1.0  1.0  foo\n",
       "3  1.0 2013-01-02  1.0  0.9  foo"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.loads(pickle.dumps(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas tambien puede importar archivos CSV o Excel, locales o remotos. Por ejemplo este [dataset](https://github.com/rjtavares/football-crunching/blob/master/datasets/cl-shots-2012.csv) de remates al arco en la Champions League"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/rjtavares/football-crunching/master/datasets/cl-shots-2012.csv'\n",
    "shots = pd.read_csv(url, index_col=0, na_values='N/A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dist', 'dx', 'dy', 'event_id', 'goal', 'play_id', 'player', 'shot',\n",
       "       'team', 'x', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist</th>\n",
       "      <th>dx</th>\n",
       "      <th>dy</th>\n",
       "      <th>event_id</th>\n",
       "      <th>goal</th>\n",
       "      <th>play_id</th>\n",
       "      <th>player</th>\n",
       "      <th>shot</th>\n",
       "      <th>team</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.989564</td>\n",
       "      <td>28.245</td>\n",
       "      <td>6.528</td>\n",
       "      <td>4fc58cdd-544b-4c05-8b2f-1cdc3d5a8164</td>\n",
       "      <td>False</td>\n",
       "      <td>699caaab-f4d6-411c-b7ef-737ec6d23196</td>\n",
       "      <td>James Rodríguez</td>\n",
       "      <td>True</td>\n",
       "      <td>Porto</td>\n",
       "      <td>76.755</td>\n",
       "      <td>28.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.254175</td>\n",
       "      <td>-18.585</td>\n",
       "      <td>5.032</td>\n",
       "      <td>764b87c7-426b-45da-9921-9e0556b90922</td>\n",
       "      <td>False</td>\n",
       "      <td>373c02f2-81ff-4b46-b2df-16629800c8fb</td>\n",
       "      <td>Sammir</td>\n",
       "      <td>True</td>\n",
       "      <td>Dinamo Zagreb</td>\n",
       "      <td>18.585</td>\n",
       "      <td>27.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25.772814</td>\n",
       "      <td>-20.790</td>\n",
       "      <td>-15.232</td>\n",
       "      <td>a276c188-492e-4ada-8168-5bee7e998cc7</td>\n",
       "      <td>False</td>\n",
       "      <td>e190760e-9912-4d36-99f7-a1ceb696b5da</td>\n",
       "      <td>Duje Čop</td>\n",
       "      <td>True</td>\n",
       "      <td>Dinamo Zagreb</td>\n",
       "      <td>20.790</td>\n",
       "      <td>48.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.180567</td>\n",
       "      <td>33.810</td>\n",
       "      <td>-9.724</td>\n",
       "      <td>b5f9dd79-14ff-4671-ad3a-a198abfa24a8</td>\n",
       "      <td>False</td>\n",
       "      <td>1204aee1-6925-48b1-92af-d70fa87b888f</td>\n",
       "      <td>Maicon</td>\n",
       "      <td>True</td>\n",
       "      <td>Porto</td>\n",
       "      <td>71.190</td>\n",
       "      <td>44.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.755000</td>\n",
       "      <td>13.755</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3a0178d9-67c8-4018-b18e-cceb3fbbdf48</td>\n",
       "      <td>False</td>\n",
       "      <td>a968c9e5-6f17-48ab-9081-7210ce10b3ed</td>\n",
       "      <td>Miguel Lopes</td>\n",
       "      <td>True</td>\n",
       "      <td>Porto</td>\n",
       "      <td>91.245</td>\n",
       "      <td>38.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dist      dx      dy                              event_id   goal  \\\n",
       "0   28.989564  28.245   6.528  4fc58cdd-544b-4c05-8b2f-1cdc3d5a8164  False   \n",
       "2   19.254175 -18.585   5.032  764b87c7-426b-45da-9921-9e0556b90922  False   \n",
       "8   25.772814 -20.790 -15.232  a276c188-492e-4ada-8168-5bee7e998cc7  False   \n",
       "10  35.180567  33.810  -9.724  b5f9dd79-14ff-4671-ad3a-a198abfa24a8  False   \n",
       "12  13.755000  13.755   0.000  3a0178d9-67c8-4018-b18e-cceb3fbbdf48  False   \n",
       "\n",
       "                                 play_id           player  shot  \\\n",
       "0   699caaab-f4d6-411c-b7ef-737ec6d23196  James Rodríguez  True   \n",
       "2   373c02f2-81ff-4b46-b2df-16629800c8fb           Sammir  True   \n",
       "8   e190760e-9912-4d36-99f7-a1ceb696b5da         Duje Čop  True   \n",
       "10  1204aee1-6925-48b1-92af-d70fa87b888f           Maicon  True   \n",
       "12  a968c9e5-6f17-48ab-9081-7210ce10b3ed     Miguel Lopes  True   \n",
       "\n",
       "             team       x       y  \n",
       "0           Porto  76.755  28.832  \n",
       "2   Dinamo Zagreb  18.585  27.404  \n",
       "8   Dinamo Zagreb  20.790  48.280  \n",
       "10          Porto  71.190  44.200  \n",
       "12          Porto  91.245  38.012  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos filtrar el dataset por múltiples criterios. Por ejemplo, para calcular la distancia promedio de los goles que hizo el genio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.951773001848336"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots[(shots.player == 'Lionel Messi') & shots.goal].dist.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos separar subconjuntos basado en uno o más criterios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>team</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ajax</th>\n",
       "      <td>51.0</td>\n",
       "      <td>19.386078</td>\n",
       "      <td>7.300035</td>\n",
       "      <td>4.085401</td>\n",
       "      <td>13.722455</td>\n",
       "      <td>20.317035</td>\n",
       "      <td>25.228054</td>\n",
       "      <td>33.838975</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.252059</td>\n",
       "      <td>...</td>\n",
       "      <td>91.14000</td>\n",
       "      <td>100.800</td>\n",
       "      <td>51.0</td>\n",
       "      <td>32.582667</td>\n",
       "      <td>9.684273</td>\n",
       "      <td>14.484</td>\n",
       "      <td>24.956</td>\n",
       "      <td>31.076</td>\n",
       "      <td>41.038</td>\n",
       "      <td>55.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anderlecht</th>\n",
       "      <td>37.0</td>\n",
       "      <td>18.542860</td>\n",
       "      <td>7.971910</td>\n",
       "      <td>6.424019</td>\n",
       "      <td>12.498227</td>\n",
       "      <td>16.192901</td>\n",
       "      <td>26.684157</td>\n",
       "      <td>34.941322</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.805946</td>\n",
       "      <td>...</td>\n",
       "      <td>90.72000</td>\n",
       "      <td>102.165</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.713081</td>\n",
       "      <td>9.527022</td>\n",
       "      <td>11.900</td>\n",
       "      <td>26.996</td>\n",
       "      <td>35.156</td>\n",
       "      <td>43.316</td>\n",
       "      <td>48.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arsenal</th>\n",
       "      <td>37.0</td>\n",
       "      <td>15.050964</td>\n",
       "      <td>6.232507</td>\n",
       "      <td>5.273269</td>\n",
       "      <td>10.322907</td>\n",
       "      <td>14.766801</td>\n",
       "      <td>19.390594</td>\n",
       "      <td>26.889665</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.175946</td>\n",
       "      <td>...</td>\n",
       "      <td>93.97500</td>\n",
       "      <td>99.960</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.918919</td>\n",
       "      <td>9.601529</td>\n",
       "      <td>9.588</td>\n",
       "      <td>26.452</td>\n",
       "      <td>34.884</td>\n",
       "      <td>41.752</td>\n",
       "      <td>51.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BATE</th>\n",
       "      <td>49.0</td>\n",
       "      <td>20.173918</td>\n",
       "      <td>8.508928</td>\n",
       "      <td>6.826355</td>\n",
       "      <td>13.439592</td>\n",
       "      <td>17.661409</td>\n",
       "      <td>27.055201</td>\n",
       "      <td>43.293600</td>\n",
       "      <td>49.0</td>\n",
       "      <td>-4.735714</td>\n",
       "      <td>...</td>\n",
       "      <td>87.57000</td>\n",
       "      <td>98.490</td>\n",
       "      <td>49.0</td>\n",
       "      <td>33.229796</td>\n",
       "      <td>11.015689</td>\n",
       "      <td>8.228</td>\n",
       "      <td>26.724</td>\n",
       "      <td>34.000</td>\n",
       "      <td>41.616</td>\n",
       "      <td>58.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>118.0</td>\n",
       "      <td>17.149734</td>\n",
       "      <td>7.541686</td>\n",
       "      <td>4.261234</td>\n",
       "      <td>10.554698</td>\n",
       "      <td>16.266352</td>\n",
       "      <td>22.695007</td>\n",
       "      <td>41.683173</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.460042</td>\n",
       "      <td>...</td>\n",
       "      <td>90.82500</td>\n",
       "      <td>100.275</td>\n",
       "      <td>118.0</td>\n",
       "      <td>34.578000</td>\n",
       "      <td>8.960725</td>\n",
       "      <td>8.024</td>\n",
       "      <td>27.472</td>\n",
       "      <td>35.020</td>\n",
       "      <td>41.667</td>\n",
       "      <td>56.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayern</th>\n",
       "      <td>128.0</td>\n",
       "      <td>17.517833</td>\n",
       "      <td>8.159604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.213396</td>\n",
       "      <td>17.042660</td>\n",
       "      <td>23.029633</td>\n",
       "      <td>44.005908</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.149606</td>\n",
       "      <td>...</td>\n",
       "      <td>94.02750</td>\n",
       "      <td>105.000</td>\n",
       "      <td>128.0</td>\n",
       "      <td>31.812313</td>\n",
       "      <td>9.023347</td>\n",
       "      <td>12.580</td>\n",
       "      <td>24.956</td>\n",
       "      <td>31.008</td>\n",
       "      <td>37.910</td>\n",
       "      <td>55.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benfica</th>\n",
       "      <td>74.0</td>\n",
       "      <td>19.007116</td>\n",
       "      <td>7.869186</td>\n",
       "      <td>4.922845</td>\n",
       "      <td>12.452546</td>\n",
       "      <td>19.879145</td>\n",
       "      <td>24.552495</td>\n",
       "      <td>36.973481</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.151824</td>\n",
       "      <td>...</td>\n",
       "      <td>86.49375</td>\n",
       "      <td>101.640</td>\n",
       "      <td>74.0</td>\n",
       "      <td>32.155730</td>\n",
       "      <td>8.577317</td>\n",
       "      <td>15.368</td>\n",
       "      <td>24.888</td>\n",
       "      <td>32.606</td>\n",
       "      <td>37.519</td>\n",
       "      <td>51.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Braga</th>\n",
       "      <td>69.0</td>\n",
       "      <td>18.941317</td>\n",
       "      <td>9.082840</td>\n",
       "      <td>3.796599</td>\n",
       "      <td>11.155296</td>\n",
       "      <td>17.709284</td>\n",
       "      <td>25.772078</td>\n",
       "      <td>36.081930</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.387826</td>\n",
       "      <td>...</td>\n",
       "      <td>89.46000</td>\n",
       "      <td>102.060</td>\n",
       "      <td>69.0</td>\n",
       "      <td>33.602841</td>\n",
       "      <td>10.229242</td>\n",
       "      <td>6.936</td>\n",
       "      <td>26.452</td>\n",
       "      <td>34.000</td>\n",
       "      <td>41.888</td>\n",
       "      <td>59.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFR Cluj</th>\n",
       "      <td>45.0</td>\n",
       "      <td>20.795136</td>\n",
       "      <td>11.522079</td>\n",
       "      <td>4.328055</td>\n",
       "      <td>11.510186</td>\n",
       "      <td>17.054152</td>\n",
       "      <td>28.336787</td>\n",
       "      <td>54.922115</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.022667</td>\n",
       "      <td>...</td>\n",
       "      <td>92.92500</td>\n",
       "      <td>101.220</td>\n",
       "      <td>45.0</td>\n",
       "      <td>32.502489</td>\n",
       "      <td>7.570459</td>\n",
       "      <td>19.176</td>\n",
       "      <td>26.656</td>\n",
       "      <td>32.368</td>\n",
       "      <td>38.760</td>\n",
       "      <td>53.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Celtic</th>\n",
       "      <td>57.0</td>\n",
       "      <td>19.422191</td>\n",
       "      <td>9.324724</td>\n",
       "      <td>2.584000</td>\n",
       "      <td>11.534265</td>\n",
       "      <td>18.101430</td>\n",
       "      <td>27.348737</td>\n",
       "      <td>48.384144</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.116053</td>\n",
       "      <td>...</td>\n",
       "      <td>87.57000</td>\n",
       "      <td>105.000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>31.062877</td>\n",
       "      <td>8.812781</td>\n",
       "      <td>12.036</td>\n",
       "      <td>25.092</td>\n",
       "      <td>31.824</td>\n",
       "      <td>34.612</td>\n",
       "      <td>53.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chelsea</th>\n",
       "      <td>64.0</td>\n",
       "      <td>16.749424</td>\n",
       "      <td>8.089951</td>\n",
       "      <td>1.840361</td>\n",
       "      <td>10.857455</td>\n",
       "      <td>16.038476</td>\n",
       "      <td>21.720067</td>\n",
       "      <td>40.794737</td>\n",
       "      <td>64.0</td>\n",
       "      <td>-1.847344</td>\n",
       "      <td>...</td>\n",
       "      <td>92.71500</td>\n",
       "      <td>103.425</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32.205438</td>\n",
       "      <td>8.474091</td>\n",
       "      <td>10.336</td>\n",
       "      <td>26.061</td>\n",
       "      <td>31.484</td>\n",
       "      <td>38.981</td>\n",
       "      <td>47.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinamo Zagreb</th>\n",
       "      <td>30.0</td>\n",
       "      <td>19.756942</td>\n",
       "      <td>8.474091</td>\n",
       "      <td>5.698686</td>\n",
       "      <td>11.484111</td>\n",
       "      <td>19.216867</td>\n",
       "      <td>26.198767</td>\n",
       "      <td>34.638485</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.027500</td>\n",
       "      <td>...</td>\n",
       "      <td>92.45250</td>\n",
       "      <td>103.635</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.646400</td>\n",
       "      <td>8.581043</td>\n",
       "      <td>20.536</td>\n",
       "      <td>27.897</td>\n",
       "      <td>32.708</td>\n",
       "      <td>38.522</td>\n",
       "      <td>49.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dortmund</th>\n",
       "      <td>138.0</td>\n",
       "      <td>18.099782</td>\n",
       "      <td>7.413309</td>\n",
       "      <td>5.560040</td>\n",
       "      <td>12.214629</td>\n",
       "      <td>16.563488</td>\n",
       "      <td>23.208222</td>\n",
       "      <td>41.040176</td>\n",
       "      <td>138.0</td>\n",
       "      <td>-1.273696</td>\n",
       "      <td>...</td>\n",
       "      <td>86.52000</td>\n",
       "      <td>102.690</td>\n",
       "      <td>138.0</td>\n",
       "      <td>33.209623</td>\n",
       "      <td>9.752949</td>\n",
       "      <td>2.856</td>\n",
       "      <td>26.163</td>\n",
       "      <td>33.490</td>\n",
       "      <td>39.950</td>\n",
       "      <td>61.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dynamo Kyiv</th>\n",
       "      <td>50.0</td>\n",
       "      <td>19.943924</td>\n",
       "      <td>8.295395</td>\n",
       "      <td>5.443827</td>\n",
       "      <td>11.806465</td>\n",
       "      <td>20.258975</td>\n",
       "      <td>27.029103</td>\n",
       "      <td>33.936203</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.631300</td>\n",
       "      <td>...</td>\n",
       "      <td>86.44125</td>\n",
       "      <td>100.695</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.062400</td>\n",
       "      <td>10.709127</td>\n",
       "      <td>7.548</td>\n",
       "      <td>24.446</td>\n",
       "      <td>30.804</td>\n",
       "      <td>37.536</td>\n",
       "      <td>63.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galatasaray</th>\n",
       "      <td>91.0</td>\n",
       "      <td>19.493112</td>\n",
       "      <td>8.267951</td>\n",
       "      <td>5.661238</td>\n",
       "      <td>12.888909</td>\n",
       "      <td>18.291736</td>\n",
       "      <td>25.817664</td>\n",
       "      <td>39.380871</td>\n",
       "      <td>91.0</td>\n",
       "      <td>-3.062308</td>\n",
       "      <td>...</td>\n",
       "      <td>82.42500</td>\n",
       "      <td>99.120</td>\n",
       "      <td>91.0</td>\n",
       "      <td>33.086857</td>\n",
       "      <td>8.530236</td>\n",
       "      <td>17.612</td>\n",
       "      <td>26.316</td>\n",
       "      <td>33.116</td>\n",
       "      <td>40.562</td>\n",
       "      <td>52.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juventus</th>\n",
       "      <td>118.0</td>\n",
       "      <td>18.029021</td>\n",
       "      <td>7.710819</td>\n",
       "      <td>2.176000</td>\n",
       "      <td>12.527100</td>\n",
       "      <td>17.094019</td>\n",
       "      <td>23.795291</td>\n",
       "      <td>37.066559</td>\n",
       "      <td>118.0</td>\n",
       "      <td>-0.966356</td>\n",
       "      <td>...</td>\n",
       "      <td>90.72000</td>\n",
       "      <td>104.370</td>\n",
       "      <td>118.0</td>\n",
       "      <td>32.447525</td>\n",
       "      <td>8.442428</td>\n",
       "      <td>16.184</td>\n",
       "      <td>26.010</td>\n",
       "      <td>30.906</td>\n",
       "      <td>38.284</td>\n",
       "      <td>52.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOSC</th>\n",
       "      <td>57.0</td>\n",
       "      <td>20.661852</td>\n",
       "      <td>7.298055</td>\n",
       "      <td>6.001171</td>\n",
       "      <td>14.357041</td>\n",
       "      <td>20.940939</td>\n",
       "      <td>25.882962</td>\n",
       "      <td>38.957090</td>\n",
       "      <td>57.0</td>\n",
       "      <td>-1.980263</td>\n",
       "      <td>...</td>\n",
       "      <td>81.79500</td>\n",
       "      <td>97.335</td>\n",
       "      <td>57.0</td>\n",
       "      <td>32.644772</td>\n",
       "      <td>9.257369</td>\n",
       "      <td>14.892</td>\n",
       "      <td>25.296</td>\n",
       "      <td>33.592</td>\n",
       "      <td>39.168</td>\n",
       "      <td>49.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man. City</th>\n",
       "      <td>53.0</td>\n",
       "      <td>16.032345</td>\n",
       "      <td>7.927974</td>\n",
       "      <td>3.527694</td>\n",
       "      <td>11.066026</td>\n",
       "      <td>13.404709</td>\n",
       "      <td>20.083812</td>\n",
       "      <td>45.980092</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.800377</td>\n",
       "      <td>...</td>\n",
       "      <td>94.60500</td>\n",
       "      <td>102.795</td>\n",
       "      <td>53.0</td>\n",
       "      <td>34.347698</td>\n",
       "      <td>8.386569</td>\n",
       "      <td>16.796</td>\n",
       "      <td>28.288</td>\n",
       "      <td>34.000</td>\n",
       "      <td>40.528</td>\n",
       "      <td>61.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man. United</th>\n",
       "      <td>87.0</td>\n",
       "      <td>15.808515</td>\n",
       "      <td>7.746846</td>\n",
       "      <td>3.964089</td>\n",
       "      <td>9.829987</td>\n",
       "      <td>13.676495</td>\n",
       "      <td>20.909913</td>\n",
       "      <td>34.955116</td>\n",
       "      <td>87.0</td>\n",
       "      <td>-0.797759</td>\n",
       "      <td>...</td>\n",
       "      <td>93.45000</td>\n",
       "      <td>102.900</td>\n",
       "      <td>87.0</td>\n",
       "      <td>33.841333</td>\n",
       "      <td>9.083352</td>\n",
       "      <td>10.540</td>\n",
       "      <td>27.506</td>\n",
       "      <td>35.496</td>\n",
       "      <td>39.780</td>\n",
       "      <td>50.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milan</th>\n",
       "      <td>67.0</td>\n",
       "      <td>18.183003</td>\n",
       "      <td>7.807676</td>\n",
       "      <td>2.680616</td>\n",
       "      <td>12.682053</td>\n",
       "      <td>18.175968</td>\n",
       "      <td>24.272487</td>\n",
       "      <td>34.098123</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-2.004403</td>\n",
       "      <td>...</td>\n",
       "      <td>88.56750</td>\n",
       "      <td>102.690</td>\n",
       "      <td>67.0</td>\n",
       "      <td>34.993612</td>\n",
       "      <td>10.298427</td>\n",
       "      <td>10.268</td>\n",
       "      <td>27.132</td>\n",
       "      <td>35.700</td>\n",
       "      <td>43.010</td>\n",
       "      <td>53.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montpellier</th>\n",
       "      <td>53.0</td>\n",
       "      <td>23.653237</td>\n",
       "      <td>8.926125</td>\n",
       "      <td>3.465000</td>\n",
       "      <td>16.813756</td>\n",
       "      <td>23.417468</td>\n",
       "      <td>29.249926</td>\n",
       "      <td>41.844881</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.511509</td>\n",
       "      <td>...</td>\n",
       "      <td>86.31000</td>\n",
       "      <td>101.535</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.911472</td>\n",
       "      <td>11.326532</td>\n",
       "      <td>13.124</td>\n",
       "      <td>24.684</td>\n",
       "      <td>34.000</td>\n",
       "      <td>41.072</td>\n",
       "      <td>61.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Málaga</th>\n",
       "      <td>68.0</td>\n",
       "      <td>17.683059</td>\n",
       "      <td>7.735159</td>\n",
       "      <td>1.289024</td>\n",
       "      <td>11.293821</td>\n",
       "      <td>17.009160</td>\n",
       "      <td>22.221774</td>\n",
       "      <td>37.327943</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.421544</td>\n",
       "      <td>...</td>\n",
       "      <td>89.85375</td>\n",
       "      <td>103.740</td>\n",
       "      <td>68.0</td>\n",
       "      <td>33.928000</td>\n",
       "      <td>8.484058</td>\n",
       "      <td>6.460</td>\n",
       "      <td>28.339</td>\n",
       "      <td>34.000</td>\n",
       "      <td>40.324</td>\n",
       "      <td>50.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordsjælland</th>\n",
       "      <td>31.0</td>\n",
       "      <td>20.345961</td>\n",
       "      <td>9.752031</td>\n",
       "      <td>3.992317</td>\n",
       "      <td>12.014537</td>\n",
       "      <td>19.543468</td>\n",
       "      <td>29.405007</td>\n",
       "      <td>35.700079</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-3.309194</td>\n",
       "      <td>...</td>\n",
       "      <td>77.80500</td>\n",
       "      <td>101.010</td>\n",
       "      <td>31.0</td>\n",
       "      <td>34.772129</td>\n",
       "      <td>8.287273</td>\n",
       "      <td>18.564</td>\n",
       "      <td>29.988</td>\n",
       "      <td>33.660</td>\n",
       "      <td>40.596</td>\n",
       "      <td>49.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olympiacos</th>\n",
       "      <td>50.0</td>\n",
       "      <td>19.001173</td>\n",
       "      <td>7.989388</td>\n",
       "      <td>4.797256</td>\n",
       "      <td>12.854162</td>\n",
       "      <td>16.977733</td>\n",
       "      <td>26.001416</td>\n",
       "      <td>35.325450</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-1.625400</td>\n",
       "      <td>...</td>\n",
       "      <td>91.24500</td>\n",
       "      <td>100.380</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32.796400</td>\n",
       "      <td>9.038476</td>\n",
       "      <td>15.028</td>\n",
       "      <td>24.990</td>\n",
       "      <td>34.952</td>\n",
       "      <td>39.627</td>\n",
       "      <td>52.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSG</th>\n",
       "      <td>106.0</td>\n",
       "      <td>16.955020</td>\n",
       "      <td>6.969359</td>\n",
       "      <td>3.624803</td>\n",
       "      <td>11.366603</td>\n",
       "      <td>16.614551</td>\n",
       "      <td>21.984564</td>\n",
       "      <td>36.370648</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.848915</td>\n",
       "      <td>...</td>\n",
       "      <td>93.00375</td>\n",
       "      <td>105.000</td>\n",
       "      <td>106.0</td>\n",
       "      <td>31.659774</td>\n",
       "      <td>8.724961</td>\n",
       "      <td>17.068</td>\n",
       "      <td>25.415</td>\n",
       "      <td>30.192</td>\n",
       "      <td>38.284</td>\n",
       "      <td>53.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porto</th>\n",
       "      <td>69.0</td>\n",
       "      <td>18.397419</td>\n",
       "      <td>6.664212</td>\n",
       "      <td>3.752497</td>\n",
       "      <td>14.007318</td>\n",
       "      <td>17.248097</td>\n",
       "      <td>23.537782</td>\n",
       "      <td>35.180567</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.031957</td>\n",
       "      <td>...</td>\n",
       "      <td>92.19000</td>\n",
       "      <td>101.850</td>\n",
       "      <td>69.0</td>\n",
       "      <td>35.212174</td>\n",
       "      <td>8.292901</td>\n",
       "      <td>13.736</td>\n",
       "      <td>29.172</td>\n",
       "      <td>34.884</td>\n",
       "      <td>42.432</td>\n",
       "      <td>49.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Madrid</th>\n",
       "      <td>166.0</td>\n",
       "      <td>18.082265</td>\n",
       "      <td>8.231794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.079421</td>\n",
       "      <td>18.282674</td>\n",
       "      <td>23.727117</td>\n",
       "      <td>36.295839</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0.308636</td>\n",
       "      <td>...</td>\n",
       "      <td>89.48625</td>\n",
       "      <td>103.740</td>\n",
       "      <td>166.0</td>\n",
       "      <td>33.550627</td>\n",
       "      <td>9.307351</td>\n",
       "      <td>12.444</td>\n",
       "      <td>26.656</td>\n",
       "      <td>33.932</td>\n",
       "      <td>40.647</td>\n",
       "      <td>52.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schalke</th>\n",
       "      <td>75.0</td>\n",
       "      <td>18.570815</td>\n",
       "      <td>7.851050</td>\n",
       "      <td>5.329180</td>\n",
       "      <td>12.481424</td>\n",
       "      <td>17.548985</td>\n",
       "      <td>24.245786</td>\n",
       "      <td>37.919084</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-0.354200</td>\n",
       "      <td>...</td>\n",
       "      <td>92.40000</td>\n",
       "      <td>100.590</td>\n",
       "      <td>75.0</td>\n",
       "      <td>33.406133</td>\n",
       "      <td>9.219641</td>\n",
       "      <td>15.776</td>\n",
       "      <td>27.030</td>\n",
       "      <td>32.912</td>\n",
       "      <td>39.678</td>\n",
       "      <td>53.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakhtar Donetsk</th>\n",
       "      <td>67.0</td>\n",
       "      <td>18.829678</td>\n",
       "      <td>9.676831</td>\n",
       "      <td>5.056374</td>\n",
       "      <td>12.339711</td>\n",
       "      <td>17.686763</td>\n",
       "      <td>25.378385</td>\n",
       "      <td>50.630296</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.248433</td>\n",
       "      <td>...</td>\n",
       "      <td>90.51000</td>\n",
       "      <td>103.530</td>\n",
       "      <td>67.0</td>\n",
       "      <td>33.494567</td>\n",
       "      <td>9.121807</td>\n",
       "      <td>13.804</td>\n",
       "      <td>27.370</td>\n",
       "      <td>33.320</td>\n",
       "      <td>40.970</td>\n",
       "      <td>49.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spartak Moskva</th>\n",
       "      <td>43.0</td>\n",
       "      <td>17.761703</td>\n",
       "      <td>7.484024</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>12.750133</td>\n",
       "      <td>17.620498</td>\n",
       "      <td>20.834700</td>\n",
       "      <td>36.071434</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.540814</td>\n",
       "      <td>...</td>\n",
       "      <td>83.26500</td>\n",
       "      <td>101.850</td>\n",
       "      <td>43.0</td>\n",
       "      <td>32.489767</td>\n",
       "      <td>8.969001</td>\n",
       "      <td>12.716</td>\n",
       "      <td>26.418</td>\n",
       "      <td>31.076</td>\n",
       "      <td>41.208</td>\n",
       "      <td>46.308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia</th>\n",
       "      <td>64.0</td>\n",
       "      <td>18.669912</td>\n",
       "      <td>7.497609</td>\n",
       "      <td>7.771786</td>\n",
       "      <td>13.374904</td>\n",
       "      <td>16.558904</td>\n",
       "      <td>23.491556</td>\n",
       "      <td>37.068992</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.858047</td>\n",
       "      <td>...</td>\n",
       "      <td>90.22125</td>\n",
       "      <td>100.800</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32.871625</td>\n",
       "      <td>8.629260</td>\n",
       "      <td>7.820</td>\n",
       "      <td>28.815</td>\n",
       "      <td>32.402</td>\n",
       "      <td>37.876</td>\n",
       "      <td>49.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zenit</th>\n",
       "      <td>61.0</td>\n",
       "      <td>18.893458</td>\n",
       "      <td>7.615732</td>\n",
       "      <td>4.330330</td>\n",
       "      <td>13.187103</td>\n",
       "      <td>18.807891</td>\n",
       "      <td>24.370591</td>\n",
       "      <td>34.626521</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.927787</td>\n",
       "      <td>...</td>\n",
       "      <td>84.21000</td>\n",
       "      <td>102.375</td>\n",
       "      <td>61.0</td>\n",
       "      <td>32.345705</td>\n",
       "      <td>9.766835</td>\n",
       "      <td>10.472</td>\n",
       "      <td>24.616</td>\n",
       "      <td>32.028</td>\n",
       "      <td>41.616</td>\n",
       "      <td>50.048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dist                                                        \\\n",
       "                  count       mean        std       min        25%        50%   \n",
       "team                                                                            \n",
       "Ajax               51.0  19.386078   7.300035  4.085401  13.722455  20.317035   \n",
       "Anderlecht         37.0  18.542860   7.971910  6.424019  12.498227  16.192901   \n",
       "Arsenal            37.0  15.050964   6.232507  5.273269  10.322907  14.766801   \n",
       "BATE               49.0  20.173918   8.508928  6.826355  13.439592  17.661409   \n",
       "Barcelona         118.0  17.149734   7.541686  4.261234  10.554698  16.266352   \n",
       "Bayern            128.0  17.517833   8.159604  0.000000  11.213396  17.042660   \n",
       "Benfica            74.0  19.007116   7.869186  4.922845  12.452546  19.879145   \n",
       "Braga              69.0  18.941317   9.082840  3.796599  11.155296  17.709284   \n",
       "CFR Cluj           45.0  20.795136  11.522079  4.328055  11.510186  17.054152   \n",
       "Celtic             57.0  19.422191   9.324724  2.584000  11.534265  18.101430   \n",
       "Chelsea            64.0  16.749424   8.089951  1.840361  10.857455  16.038476   \n",
       "Dinamo Zagreb      30.0  19.756942   8.474091  5.698686  11.484111  19.216867   \n",
       "Dortmund          138.0  18.099782   7.413309  5.560040  12.214629  16.563488   \n",
       "Dynamo Kyiv        50.0  19.943924   8.295395  5.443827  11.806465  20.258975   \n",
       "Galatasaray        91.0  19.493112   8.267951  5.661238  12.888909  18.291736   \n",
       "Juventus          118.0  18.029021   7.710819  2.176000  12.527100  17.094019   \n",
       "LOSC               57.0  20.661852   7.298055  6.001171  14.357041  20.940939   \n",
       "Man. City          53.0  16.032345   7.927974  3.527694  11.066026  13.404709   \n",
       "Man. United        87.0  15.808515   7.746846  3.964089   9.829987  13.676495   \n",
       "Milan              67.0  18.183003   7.807676  2.680616  12.682053  18.175968   \n",
       "Montpellier        53.0  23.653237   8.926125  3.465000  16.813756  23.417468   \n",
       "Málaga             68.0  17.683059   7.735159  1.289024  11.293821  17.009160   \n",
       "Nordsjælland       31.0  20.345961   9.752031  3.992317  12.014537  19.543468   \n",
       "Olympiacos         50.0  19.001173   7.989388  4.797256  12.854162  16.977733   \n",
       "PSG               106.0  16.955020   6.969359  3.624803  11.366603  16.614551   \n",
       "Porto              69.0  18.397419   6.664212  3.752497  14.007318  17.248097   \n",
       "Real Madrid       166.0  18.082265   8.231794  0.000000  11.079421  18.282674   \n",
       "Schalke            75.0  18.570815   7.851050  5.329180  12.481424  17.548985   \n",
       "Shakhtar Donetsk   67.0  18.829678   9.676831  5.056374  12.339711  17.686763   \n",
       "Spartak Moskva     43.0  17.761703   7.484024  3.150000  12.750133  17.620498   \n",
       "Valencia           64.0  18.669912   7.497609  7.771786  13.374904  16.558904   \n",
       "Zenit              61.0  18.893458   7.615732  4.330330  13.187103  18.807891   \n",
       "\n",
       "                                           dx             ...           x  \\\n",
       "                        75%        max  count      mean   ...         75%   \n",
       "team                                                      ...               \n",
       "Ajax              25.228054  33.838975   51.0  5.252059   ...    91.14000   \n",
       "Anderlecht        26.684157  34.941322   37.0 -0.805946   ...    90.72000   \n",
       "Arsenal           19.390594  26.889665   37.0  0.175946   ...    93.97500   \n",
       "BATE              27.055201  43.293600   49.0 -4.735714   ...    87.57000   \n",
       "Barcelona         22.695007  41.683173  118.0  0.460042   ...    90.82500   \n",
       "Bayern            23.029633  44.005908  127.0  2.149606   ...    94.02750   \n",
       "Benfica           24.552495  36.973481   74.0  0.151824   ...    86.49375   \n",
       "Braga             25.772078  36.081930   69.0  1.387826   ...    89.46000   \n",
       "CFR Cluj          28.336787  54.922115   45.0  4.022667   ...    92.92500   \n",
       "Celtic            27.348737  48.384144   57.0  0.116053   ...    87.57000   \n",
       "Chelsea           21.720067  40.794737   64.0 -1.847344   ...    92.71500   \n",
       "Dinamo Zagreb     26.198767  34.638485   30.0  3.027500   ...    92.45250   \n",
       "Dortmund          23.208222  41.040176  138.0 -1.273696   ...    86.52000   \n",
       "Dynamo Kyiv       27.029103  33.936203   50.0 -2.631300   ...    86.44125   \n",
       "Galatasaray       25.817664  39.380871   91.0 -3.062308   ...    82.42500   \n",
       "Juventus          23.795291  37.066559  118.0 -0.966356   ...    90.72000   \n",
       "LOSC              25.882962  38.957090   57.0 -1.980263   ...    81.79500   \n",
       "Man. City         20.083812  45.980092   53.0  0.800377   ...    94.60500   \n",
       "Man. United       20.909913  34.955116   87.0 -0.797759   ...    93.45000   \n",
       "Milan             24.272487  34.098123   67.0 -2.004403   ...    88.56750   \n",
       "Montpellier       29.249926  41.844881   53.0  5.511509   ...    86.31000   \n",
       "Málaga            22.221774  37.327943   68.0  0.421544   ...    89.85375   \n",
       "Nordsjælland      29.405007  35.700079   31.0 -3.309194   ...    77.80500   \n",
       "Olympiacos        26.001416  35.325450   50.0 -1.625400   ...    91.24500   \n",
       "PSG               21.984564  36.370648  106.0  0.848915   ...    93.00375   \n",
       "Porto             23.537782  35.180567   69.0  0.031957   ...    92.19000   \n",
       "Real Madrid       23.727117  36.295839  165.0  0.308636   ...    89.48625   \n",
       "Schalke           24.245786  37.919084   75.0 -0.354200   ...    92.40000   \n",
       "Shakhtar Donetsk  25.378385  50.630296   67.0  5.248433   ...    90.51000   \n",
       "Spartak Moskva    20.834700  36.071434   43.0 -1.540814   ...    83.26500   \n",
       "Valencia          23.491556  37.068992   64.0  0.858047   ...    90.22125   \n",
       "Zenit             24.370591  34.626521   61.0  0.927787   ...    84.21000   \n",
       "\n",
       "                               y                                        \\\n",
       "                      max  count       mean        std     min     25%   \n",
       "team                                                                     \n",
       "Ajax              100.800   51.0  32.582667   9.684273  14.484  24.956   \n",
       "Anderlecht        102.165   37.0  34.713081   9.527022  11.900  26.996   \n",
       "Arsenal            99.960   37.0  34.918919   9.601529   9.588  26.452   \n",
       "BATE               98.490   49.0  33.229796  11.015689   8.228  26.724   \n",
       "Barcelona         100.275  118.0  34.578000   8.960725   8.024  27.472   \n",
       "Bayern            105.000  128.0  31.812313   9.023347  12.580  24.956   \n",
       "Benfica           101.640   74.0  32.155730   8.577317  15.368  24.888   \n",
       "Braga             102.060   69.0  33.602841  10.229242   6.936  26.452   \n",
       "CFR Cluj          101.220   45.0  32.502489   7.570459  19.176  26.656   \n",
       "Celtic            105.000   57.0  31.062877   8.812781  12.036  25.092   \n",
       "Chelsea           103.425   64.0  32.205438   8.474091  10.336  26.061   \n",
       "Dinamo Zagreb     103.635   30.0  33.646400   8.581043  20.536  27.897   \n",
       "Dortmund          102.690  138.0  33.209623   9.752949   2.856  26.163   \n",
       "Dynamo Kyiv       100.695   50.0  31.062400  10.709127   7.548  24.446   \n",
       "Galatasaray        99.120   91.0  33.086857   8.530236  17.612  26.316   \n",
       "Juventus          104.370  118.0  32.447525   8.442428  16.184  26.010   \n",
       "LOSC               97.335   57.0  32.644772   9.257369  14.892  25.296   \n",
       "Man. City         102.795   53.0  34.347698   8.386569  16.796  28.288   \n",
       "Man. United       102.900   87.0  33.841333   9.083352  10.540  27.506   \n",
       "Milan             102.690   67.0  34.993612  10.298427  10.268  27.132   \n",
       "Montpellier       101.535   53.0  33.911472  11.326532  13.124  24.684   \n",
       "Málaga            103.740   68.0  33.928000   8.484058   6.460  28.339   \n",
       "Nordsjælland      101.010   31.0  34.772129   8.287273  18.564  29.988   \n",
       "Olympiacos        100.380   50.0  32.796400   9.038476  15.028  24.990   \n",
       "PSG               105.000  106.0  31.659774   8.724961  17.068  25.415   \n",
       "Porto             101.850   69.0  35.212174   8.292901  13.736  29.172   \n",
       "Real Madrid       103.740  166.0  33.550627   9.307351  12.444  26.656   \n",
       "Schalke           100.590   75.0  33.406133   9.219641  15.776  27.030   \n",
       "Shakhtar Donetsk  103.530   67.0  33.494567   9.121807  13.804  27.370   \n",
       "Spartak Moskva    101.850   43.0  32.489767   8.969001  12.716  26.418   \n",
       "Valencia          100.800   64.0  32.871625   8.629260   7.820  28.815   \n",
       "Zenit             102.375   61.0  32.345705   9.766835  10.472  24.616   \n",
       "\n",
       "                                          \n",
       "                     50%     75%     max  \n",
       "team                                      \n",
       "Ajax              31.076  41.038  55.964  \n",
       "Anderlecht        35.156  43.316  48.892  \n",
       "Arsenal           34.884  41.752  51.748  \n",
       "BATE              34.000  41.616  58.820  \n",
       "Barcelona         35.020  41.667  56.168  \n",
       "Bayern            31.008  37.910  55.896  \n",
       "Benfica           32.606  37.519  51.476  \n",
       "Braga             34.000  41.888  59.160  \n",
       "CFR Cluj          32.368  38.760  53.244  \n",
       "Celtic            31.824  34.612  53.244  \n",
       "Chelsea           31.484  38.981  47.804  \n",
       "Dinamo Zagreb     32.708  38.522  49.368  \n",
       "Dortmund          33.490  39.950  61.200  \n",
       "Dynamo Kyiv       30.804  37.536  63.444  \n",
       "Galatasaray       33.116  40.562  52.836  \n",
       "Juventus          30.906  38.284  52.904  \n",
       "LOSC              33.592  39.168  49.640  \n",
       "Man. City         34.000  40.528  61.064  \n",
       "Man. United       35.496  39.780  50.320  \n",
       "Milan             35.700  43.010  53.244  \n",
       "Montpellier       34.000  41.072  61.404  \n",
       "Málaga            34.000  40.324  50.728  \n",
       "Nordsjælland      33.660  40.596  49.436  \n",
       "Olympiacos        34.952  39.627  52.360  \n",
       "PSG               30.192  38.284  53.312  \n",
       "Porto             34.884  42.432  49.504  \n",
       "Real Madrid       33.932  40.647  52.156  \n",
       "Schalke           32.912  39.678  53.312  \n",
       "Shakhtar Donetsk  33.320  40.970  49.844  \n",
       "Spartak Moskva    31.076  41.208  46.308  \n",
       "Valencia          32.402  37.876  49.096  \n",
       "Zenit             32.028  41.616  50.048  \n",
       "\n",
       "[32 rows x 40 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots.groupby('team').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team              goal \n",
       "Ajax              False    20.321821\n",
       "                  True     13.504265\n",
       "Anderlecht        False    19.198889\n",
       "                  True     13.130618\n",
       "Arsenal           False    16.474430\n",
       "                  True     10.622403\n",
       "BATE              False    20.809256\n",
       "                  True     17.350193\n",
       "Barcelona         False    17.686370\n",
       "                  True     13.163291\n",
       "Bayern            False    18.953692\n",
       "                  True     12.146656\n",
       "Benfica           False    19.661054\n",
       "                  True      9.982772\n",
       "Braga             False    19.871390\n",
       "                  True     10.703532\n",
       "CFR Cluj          False    21.967072\n",
       "                  True     15.374932\n",
       "Celtic            False    20.581338\n",
       "                  True     12.322416\n",
       "Chelsea           False    17.485217\n",
       "                  True     14.345834\n",
       "Dinamo Zagreb     False    20.047741\n",
       "                  True     11.323766\n",
       "Dortmund          False    19.511815\n",
       "                  True     11.039619\n",
       "Dynamo Kyiv       False    20.660831\n",
       "                  True     13.491763\n",
       "Galatasaray       False    20.462527\n",
       "                  True     11.640850\n",
       "                             ...    \n",
       "Man. City         False    15.783804\n",
       "                  True     17.665613\n",
       "Man. United       False    16.475926\n",
       "                  True     10.669451\n",
       "Milan             False    18.659194\n",
       "                  True     13.341725\n",
       "Montpellier       False    24.754086\n",
       "                  True     15.029924\n",
       "Málaga            False    18.622637\n",
       "                  True     14.058971\n",
       "Nordsjælland      False    21.450126\n",
       "                  True     12.892849\n",
       "Olympiacos        False    20.608253\n",
       "                  True     11.680029\n",
       "PSG               False    18.066634\n",
       "                  True     12.175081\n",
       "Porto             False    19.192670\n",
       "                  True     13.705443\n",
       "Real Madrid       False    18.843789\n",
       "                  True     13.981747\n",
       "Schalke           False    19.267595\n",
       "                  True     14.041746\n",
       "Shakhtar Donetsk  False    19.878827\n",
       "                  True     14.021079\n",
       "Spartak Moskva    False    18.694015\n",
       "                  True     10.676129\n",
       "Valencia          False    20.009240\n",
       "                  True     13.886594\n",
       "Zenit             False    19.820624\n",
       "                  True     10.394430\n",
       "Name: dist, Length: 64, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots.groupby(['team', 'goal']).dist.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una alternativa aún más poderosa es hacer un pivot de datos, donde se definen una o múltples filas y columnas para agrupar datos, y la dimesiones ('values') que se quieren ver resumidas. La función de agregación por defecto es `np.mean`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goal</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ajax</th>\n",
       "      <td>20.321821</td>\n",
       "      <td>13.504265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anderlecht</th>\n",
       "      <td>19.198889</td>\n",
       "      <td>13.130618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arsenal</th>\n",
       "      <td>16.474430</td>\n",
       "      <td>10.622403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BATE</th>\n",
       "      <td>20.809256</td>\n",
       "      <td>17.350193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona</th>\n",
       "      <td>17.686370</td>\n",
       "      <td>13.163291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bayern</th>\n",
       "      <td>18.953692</td>\n",
       "      <td>12.146656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benfica</th>\n",
       "      <td>19.661054</td>\n",
       "      <td>9.982772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Braga</th>\n",
       "      <td>19.871390</td>\n",
       "      <td>10.703532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFR Cluj</th>\n",
       "      <td>21.967072</td>\n",
       "      <td>15.374932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Celtic</th>\n",
       "      <td>20.581338</td>\n",
       "      <td>12.322416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chelsea</th>\n",
       "      <td>17.485217</td>\n",
       "      <td>14.345834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinamo Zagreb</th>\n",
       "      <td>20.047741</td>\n",
       "      <td>11.323766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dortmund</th>\n",
       "      <td>19.511815</td>\n",
       "      <td>11.039619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dynamo Kyiv</th>\n",
       "      <td>20.660831</td>\n",
       "      <td>13.491763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galatasaray</th>\n",
       "      <td>20.462527</td>\n",
       "      <td>11.640850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juventus</th>\n",
       "      <td>18.859114</td>\n",
       "      <td>12.737179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOSC</th>\n",
       "      <td>21.062201</td>\n",
       "      <td>13.455570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man. City</th>\n",
       "      <td>15.783804</td>\n",
       "      <td>17.665613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man. United</th>\n",
       "      <td>16.475926</td>\n",
       "      <td>10.669451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milan</th>\n",
       "      <td>18.659194</td>\n",
       "      <td>13.341725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montpellier</th>\n",
       "      <td>24.754086</td>\n",
       "      <td>15.029924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Málaga</th>\n",
       "      <td>18.622637</td>\n",
       "      <td>14.058971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordsjælland</th>\n",
       "      <td>21.450126</td>\n",
       "      <td>12.892849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olympiacos</th>\n",
       "      <td>20.608253</td>\n",
       "      <td>11.680029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSG</th>\n",
       "      <td>18.066634</td>\n",
       "      <td>12.175081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porto</th>\n",
       "      <td>19.192670</td>\n",
       "      <td>13.705443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Madrid</th>\n",
       "      <td>18.843789</td>\n",
       "      <td>13.981747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schalke</th>\n",
       "      <td>19.267595</td>\n",
       "      <td>14.041746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakhtar Donetsk</th>\n",
       "      <td>19.878827</td>\n",
       "      <td>14.021079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spartak Moskva</th>\n",
       "      <td>18.694015</td>\n",
       "      <td>10.676129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia</th>\n",
       "      <td>20.009240</td>\n",
       "      <td>13.886594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zenit</th>\n",
       "      <td>19.820624</td>\n",
       "      <td>10.394430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dist           \n",
       "goal                  False      True \n",
       "team                                  \n",
       "Ajax              20.321821  13.504265\n",
       "Anderlecht        19.198889  13.130618\n",
       "Arsenal           16.474430  10.622403\n",
       "BATE              20.809256  17.350193\n",
       "Barcelona         17.686370  13.163291\n",
       "Bayern            18.953692  12.146656\n",
       "Benfica           19.661054   9.982772\n",
       "Braga             19.871390  10.703532\n",
       "CFR Cluj          21.967072  15.374932\n",
       "Celtic            20.581338  12.322416\n",
       "Chelsea           17.485217  14.345834\n",
       "Dinamo Zagreb     20.047741  11.323766\n",
       "Dortmund          19.511815  11.039619\n",
       "Dynamo Kyiv       20.660831  13.491763\n",
       "Galatasaray       20.462527  11.640850\n",
       "Juventus          18.859114  12.737179\n",
       "LOSC              21.062201  13.455570\n",
       "Man. City         15.783804  17.665613\n",
       "Man. United       16.475926  10.669451\n",
       "Milan             18.659194  13.341725\n",
       "Montpellier       24.754086  15.029924\n",
       "Málaga            18.622637  14.058971\n",
       "Nordsjælland      21.450126  12.892849\n",
       "Olympiacos        20.608253  11.680029\n",
       "PSG               18.066634  12.175081\n",
       "Porto             19.192670  13.705443\n",
       "Real Madrid       18.843789  13.981747\n",
       "Schalke           19.267595  14.041746\n",
       "Shakhtar Donetsk  19.878827  14.021079\n",
       "Spartak Moskva    18.694015  10.676129\n",
       "Valencia          20.009240  13.886594\n",
       "Zenit             19.820624  10.394430"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt1 = pd.pivot_table(shots, index=['team'], columns=['goal'], values=['dist'])\n",
    "pt1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "como siempre, podemos graficar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a1b9206d8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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kpYf2YsWKy9PTU+7uiSpTpmyG+xYvXkJBQY0VGjpEKSkpatq0hcvP7zihuJHdbpck/fHH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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a19501048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pt1.plot(kind='bar', figsize=(13,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En los dataframes resultantes de un pivot, índice y claves no son lo mismo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt1.index is pt1.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces esto no funciona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Barcelona'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2441\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2442\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Barcelona'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-ff061b54d8d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpt1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Barcelona'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1960\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1961\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_mi_columns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1962\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1963\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1964\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2005\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2006\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2007\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2008\u001b[0m             \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method)\u001b[0m\n\u001b[1;32m   1974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1976\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1977\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0m_maybe_to_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m   2237\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2239\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2240\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2241\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/curso/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2442\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2443\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2444\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5280)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5126)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20523)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20477)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Barcelona'"
     ]
    }
   ],
   "source": [
    "pt1['Barcelona']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>goal</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th>player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Ajax</th>\n",
       "      <th>Christian Eriksen</th>\n",
       "      <td>22.917869</td>\n",
       "      <td>20.386337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daley Blind</th>\n",
       "      <td>17.560331</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Daniel Hoesen</th>\n",
       "      <td>11.153286</td>\n",
       "      <td>21.340401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derk Boerrigter</th>\n",
       "      <td>21.101901</td>\n",
       "      <td>18.931250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jody Lukoki</th>\n",
       "      <td>22.134413</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Lasse Schøne</th>\n",
       "      <td>25.664372</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Niklas Moisander</th>\n",
       "      <td>8.951737</td>\n",
       "      <td>10.126408</td>\n",
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       "    <tr>\n",
       "      <th>Ricardo van Rhijn</th>\n",
       "      <td>18.820696</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Ryan Babel</th>\n",
       "      <td>21.957276</td>\n",
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       "    <tr>\n",
       "      <th>Siem de Jong</th>\n",
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       "      <td>6.809526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tobias Sana</th>\n",
       "      <td>16.238031</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktor Fischer</th>\n",
       "      <td>18.146298</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">Anderlecht</th>\n",
       "      <th>Cheikhou Kouyaté</th>\n",
       "      <td>30.318649</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dennis Praet</th>\n",
       "      <td>21.446210</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dieudonné Mbokani</th>\n",
       "      <td>19.764263</td>\n",
       "      <td>14.005145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guillaume Gillet</th>\n",
       "      <td>13.908516</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kanu</th>\n",
       "      <td>22.545872</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucas Biglia</th>\n",
       "      <td>19.917420</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marcin Wasilewski</th>\n",
       "      <td>9.912763</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massimo Bruno</th>\n",
       "      <td>14.733733</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milan Jovanović</th>\n",
       "      <td>20.431687</td>\n",
       "      <td>18.088162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olexandr Yakovenko</th>\n",
       "      <td>22.839317</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olivier Deschacht</th>\n",
       "      <td>9.851069</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sacha Kljestan</th>\n",
       "      <td>26.612876</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom De Sutter</th>\n",
       "      <td>14.062380</td>\n",
       "      <td>6.424019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Arsenal</th>\n",
       "      <th>Aaron Ramsey</th>\n",
       "      <td>24.900117</td>\n",
       "      <td>12.822007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Oxlade-Chamberlain</th>\n",
       "      <td>21.432514</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrey Arshavin</th>\n",
       "      <td>15.469432</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gervinho</th>\n",
       "      <td>12.983576</td>\n",
       "      <td>5.273269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack Wilshere</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.715528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Spartak Moskva</th>\n",
       "      <th>Kirill Kombarov</th>\n",
       "      <td>36.071434</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marek Suchý</th>\n",
       "      <td>13.783557</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Pareja</th>\n",
       "      <td>17.857045</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rafael Carioca</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.620419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romulo</th>\n",
       "      <td>20.316394</td>\n",
       "      <td>14.135129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welliton</th>\n",
       "      <td>18.092743</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Valencia</th>\n",
       "      <th>Adil Rami</th>\n",
       "      <td>16.901760</td>\n",
       "      <td>11.655198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrés Guardado</th>\n",
       "      <td>18.027033</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Parejo</th>\n",
       "      <td>34.346378</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Albelda</th>\n",
       "      <td>37.068992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fernando Gago</th>\n",
       "      <td>24.625403</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas</th>\n",
       "      <td>18.068141</td>\n",
       "      <td>16.828963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nelson Valdez</th>\n",
       "      <td>13.976845</td>\n",
       "      <td>9.071122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricardo Costa</th>\n",
       "      <td>8.402404</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roberto Soldado</th>\n",
       "      <td>15.854446</td>\n",
       "      <td>11.219796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sofiane Feghouli</th>\n",
       "      <td>18.233322</td>\n",
       "      <td>14.887335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tino Costa</th>\n",
       "      <td>28.166059</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Éver Banega</th>\n",
       "      <td>18.927973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Zenit</th>\n",
       "      <th>Aleksandr Kerzhakov</th>\n",
       "      <td>18.129066</td>\n",
       "      <td>11.478895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Axel Witsel</th>\n",
       "      <td>20.134777</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danny</th>\n",
       "      <td>12.416504</td>\n",
       "      <td>9.025218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domenico Criscito</th>\n",
       "      <td>30.378758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hulk</th>\n",
       "      <td>23.162441</td>\n",
       "      <td>9.308568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Igor Denisov</th>\n",
       "      <td>20.440771</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Lombaerts</th>\n",
       "      <td>9.647090</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roman Shirokov</th>\n",
       "      <td>19.396145</td>\n",
       "      <td>7.225680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergei Semak</th>\n",
       "      <td>21.584877</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomáš Hubočan</th>\n",
       "      <td>12.783965</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktor Fayzulin</th>\n",
       "      <td>28.379789</td>\n",
       "      <td>16.303000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vladimir Bystrov</th>\n",
       "      <td>20.214203</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>468 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             dist           \n",
       "goal                                        False      True \n",
       "team           player                                       \n",
       "Ajax           Christian Eriksen        22.917869  20.386337\n",
       "               Daley Blind              17.560331        NaN\n",
       "               Daniel Hoesen            11.153286  21.340401\n",
       "               Derk Boerrigter          21.101901  18.931250\n",
       "               Jody Lukoki              22.134413        NaN\n",
       "               Lasse Schøne             25.664372        NaN\n",
       "               Niklas Moisander          8.951737  10.126408\n",
       "               Ricardo van Rhijn        18.820696        NaN\n",
       "               Ryan Babel               21.957276        NaN\n",
       "               Siem de Jong             22.246804   6.809526\n",
       "               Tobias Sana              16.238031        NaN\n",
       "               Viktor Fischer           18.146298        NaN\n",
       "Anderlecht     Cheikhou Kouyaté         30.318649        NaN\n",
       "               Dennis Praet             21.446210        NaN\n",
       "               Dieudonné Mbokani        19.764263  14.005145\n",
       "               Guillaume Gillet         13.908516        NaN\n",
       "               Kanu                     22.545872        NaN\n",
       "               Lucas Biglia             19.917420        NaN\n",
       "               Marcin Wasilewski         9.912763        NaN\n",
       "               Massimo Bruno            14.733733        NaN\n",
       "               Milan Jovanović          20.431687  18.088162\n",
       "               Olexandr Yakovenko       22.839317        NaN\n",
       "               Olivier Deschacht         9.851069        NaN\n",
       "               Sacha Kljestan           26.612876        NaN\n",
       "               Tom De Sutter            14.062380   6.424019\n",
       "Arsenal        Aaron Ramsey             24.900117  12.822007\n",
       "               Alex Oxlade-Chamberlain  21.432514        NaN\n",
       "               Andrey Arshavin          15.469432        NaN\n",
       "               Gervinho                 12.983576   5.273269\n",
       "               Jack Wilshere                  NaN   7.715528\n",
       "...                                           ...        ...\n",
       "Spartak Moskva Kirill Kombarov          36.071434        NaN\n",
       "               Marek Suchý              13.783557        NaN\n",
       "               Nicolas Pareja           17.857045        NaN\n",
       "               Rafael Carioca                 NaN  10.620419\n",
       "               Romulo                   20.316394  14.135129\n",
       "               Welliton                 18.092743        NaN\n",
       "Valencia       Adil Rami                16.901760  11.655198\n",
       "               Andrés Guardado          18.027033        NaN\n",
       "               Daniel Parejo            34.346378        NaN\n",
       "               David Albelda            37.068992        NaN\n",
       "               Fernando Gago            24.625403        NaN\n",
       "               Jonas                    18.068141  16.828963\n",
       "               Nelson Valdez            13.976845   9.071122\n",
       "               Ricardo Costa             8.402404        NaN\n",
       "               Roberto Soldado          15.854446  11.219796\n",
       "               Sofiane Feghouli         18.233322  14.887335\n",
       "               Tino Costa               28.166059        NaN\n",
       "               Éver Banega              18.927973        NaN\n",
       "Zenit          Aleksandr Kerzhakov      18.129066  11.478895\n",
       "               Axel Witsel              20.134777        NaN\n",
       "               Danny                    12.416504   9.025218\n",
       "               Domenico Criscito        30.378758        NaN\n",
       "               Hulk                     23.162441   9.308568\n",
       "               Igor Denisov             20.440771        NaN\n",
       "               Nicolas Lombaerts         9.647090        NaN\n",
       "               Roman Shirokov           19.396145   7.225680\n",
       "               Sergei Semak             21.584877        NaN\n",
       "               Tomáš Hubočan            12.783965        NaN\n",
       "               Viktor Fayzulin          28.379789  16.303000\n",
       "               Vladimir Bystrov         20.214203        NaN\n",
       "\n",
       "[468 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt2 = pd.pivot_table(shots, index=['team', 'player'], columns=['goal'], values=['dist'])\n",
    "pt2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      goal \n",
       "dist  False    17.686370\n",
       "      True     13.163291\n",
       "Name: Barcelona, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt1.loc['Barcelona']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las tablas pivoteantes son mucho más poderosas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">dist</th>\n",
       "      <th colspan=\"2\" halign=\"left\">x</th>\n",
       "      <th colspan=\"2\" halign=\"left\">y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>goal</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th>player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Ajax</th>\n",
       "      <th>Christian Eriksen</th>\n",
       "      <td>22.917869</td>\n",
       "      <td>20.386337</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>84.6300</td>\n",
       "      <td>30.013500</td>\n",
       "      <td>33.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daley Blind</th>\n",
       "      <td>17.560331</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.610000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.796000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Hoesen</th>\n",
       "      <td>11.153286</td>\n",
       "      <td>21.340401</td>\n",
       "      <td>95.917500</td>\n",
       "      <td>92.1900</td>\n",
       "      <td>35.445000</td>\n",
       "      <td>19.856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Derk Boerrigter</th>\n",
       "      <td>21.101901</td>\n",
       "      <td>18.931250</td>\n",
       "      <td>65.555000</td>\n",
       "      <td>87.2550</td>\n",
       "      <td>34.793333</td>\n",
       "      <td>39.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jody Lukoki</th>\n",
       "      <td>22.134413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.050000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.432000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasse Schøne</th>\n",
       "      <td>25.664372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.017500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.384000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niklas Moisander</th>\n",
       "      <td>8.951737</td>\n",
       "      <td>10.126408</td>\n",
       "      <td>7.665000</td>\n",
       "      <td>97.9125</td>\n",
       "      <td>33.320000</td>\n",
       "      <td>40.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricardo van Rhijn</th>\n",
       "      <td>18.820696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.350000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.328000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryan Babel</th>\n",
       "      <td>21.957276</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.542500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.070000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siem de Jong</th>\n",
       "      <td>22.246804</td>\n",
       "      <td>6.809526</td>\n",
       "      <td>57.244091</td>\n",
       "      <td>1.8900</td>\n",
       "      <td>35.088000</td>\n",
       "      <td>35.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tobias Sana</th>\n",
       "      <td>16.238031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.040000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.420000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktor Fischer</th>\n",
       "      <td>18.146298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.772500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.280000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">Anderlecht</th>\n",
       "      <th>Cheikhou Kouyaté</th>\n",
       "      <td>30.318649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.135000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.252000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dennis Praet</th>\n",
       "      <td>21.446210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.160000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dieudonné Mbokani</th>\n",
       "      <td>19.764263</td>\n",
       "      <td>14.005145</td>\n",
       "      <td>62.965000</td>\n",
       "      <td>92.8725</td>\n",
       "      <td>28.582667</td>\n",
       "      <td>36.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guillaume Gillet</th>\n",
       "      <td>13.908516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.448000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.400800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kanu</th>\n",
       "      <td>22.545872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.162000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.997600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucas Biglia</th>\n",
       "      <td>19.917420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.887500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.846000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marcin Wasilewski</th>\n",
       "      <td>9.912763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.352500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.478000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massimo Bruno</th>\n",
       "      <td>14.733733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.892000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milan Jovanović</th>\n",
       "      <td>20.431687</td>\n",
       "      <td>18.088162</td>\n",
       "      <td>57.945000</td>\n",
       "      <td>90.3000</td>\n",
       "      <td>32.513714</td>\n",
       "      <td>25.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olexandr Yakovenko</th>\n",
       "      <td>22.839317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.420000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.740000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olivier Deschacht</th>\n",
       "      <td>9.851069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.335000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.052000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sacha Kljestan</th>\n",
       "      <td>26.612876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.627500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.544000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom De Sutter</th>\n",
       "      <td>14.062380</td>\n",
       "      <td>6.424019</td>\n",
       "      <td>11.865000</td>\n",
       "      <td>98.8050</td>\n",
       "      <td>36.924000</td>\n",
       "      <td>34.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Arsenal</th>\n",
       "      <th>Aaron Ramsey</th>\n",
       "      <td>24.900117</td>\n",
       "      <td>12.822007</td>\n",
       "      <td>11.340000</td>\n",
       "      <td>11.3400</td>\n",
       "      <td>51.748000</td>\n",
       "      <td>41.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Oxlade-Chamberlain</th>\n",
       "      <td>21.432514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.488000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrey Arshavin</th>\n",
       "      <td>15.469432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.290000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.864000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gervinho</th>\n",
       "      <td>12.983576</td>\n",
       "      <td>5.273269</td>\n",
       "      <td>54.390000</td>\n",
       "      <td>99.8550</td>\n",
       "      <td>35.972000</td>\n",
       "      <td>34.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jack Wilshere</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.715528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Spartak Moskva</th>\n",
       "      <th>Kirill Kombarov</th>\n",
       "      <td>36.071434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.755000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.268000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marek Suchý</th>\n",
       "      <td>13.783557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.345000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Pareja</th>\n",
       "      <td>17.857045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.380000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.076000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rafael Carioca</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.620419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romulo</th>\n",
       "      <td>20.316394</td>\n",
       "      <td>14.135129</td>\n",
       "      <td>84.840000</td>\n",
       "      <td>99.7500</td>\n",
       "      <td>37.468000</td>\n",
       "      <td>45.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welliton</th>\n",
       "      <td>18.092743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.940000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.320000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Valencia</th>\n",
       "      <th>Adil Rami</th>\n",
       "      <td>16.901760</td>\n",
       "      <td>11.655198</td>\n",
       "      <td>49.087500</td>\n",
       "      <td>11.6550</td>\n",
       "      <td>28.764000</td>\n",
       "      <td>35.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrés Guardado</th>\n",
       "      <td>18.027033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.147000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.374400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Parejo</th>\n",
       "      <td>34.346378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.335000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.952000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Albelda</th>\n",
       "      <td>37.068992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.935000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.164000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fernando Gago</th>\n",
       "      <td>24.625403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.377500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.172000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonas</th>\n",
       "      <td>18.068141</td>\n",
       "      <td>16.828963</td>\n",
       "      <td>82.385625</td>\n",
       "      <td>44.0160</td>\n",
       "      <td>36.227000</td>\n",
       "      <td>28.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nelson Valdez</th>\n",
       "      <td>13.976845</td>\n",
       "      <td>9.071122</td>\n",
       "      <td>8.225000</td>\n",
       "      <td>7.4550</td>\n",
       "      <td>43.792000</td>\n",
       "      <td>31.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ricardo Costa</th>\n",
       "      <td>8.402404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.455000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.524000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roberto Soldado</th>\n",
       "      <td>15.854446</td>\n",
       "      <td>11.219796</td>\n",
       "      <td>52.955000</td>\n",
       "      <td>73.8150</td>\n",
       "      <td>35.042667</td>\n",
       "      <td>36.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sofiane Feghouli</th>\n",
       "      <td>18.233322</td>\n",
       "      <td>14.887335</td>\n",
       "      <td>63.735000</td>\n",
       "      <td>13.7550</td>\n",
       "      <td>33.481500</td>\n",
       "      <td>27.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tino Costa</th>\n",
       "      <td>28.166059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.715000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.645778</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Éver Banega</th>\n",
       "      <td>18.927973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.310000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.640000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Zenit</th>\n",
       "      <th>Aleksandr Kerzhakov</th>\n",
       "      <td>18.129066</td>\n",
       "      <td>11.478895</td>\n",
       "      <td>37.666364</td>\n",
       "      <td>93.9750</td>\n",
       "      <td>27.026909</td>\n",
       "      <td>34.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Axel Witsel</th>\n",
       "      <td>20.134777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.485000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.606667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danny</th>\n",
       "      <td>12.416504</td>\n",
       "      <td>9.025218</td>\n",
       "      <td>31.325000</td>\n",
       "      <td>8.1375</td>\n",
       "      <td>35.156000</td>\n",
       "      <td>40.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domenico Criscito</th>\n",
       "      <td>30.378758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.020000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.256000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hulk</th>\n",
       "      <td>23.162441</td>\n",
       "      <td>9.308568</td>\n",
       "      <td>40.405909</td>\n",
       "      <td>7.3500</td>\n",
       "      <td>33.338545</td>\n",
       "      <td>41.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Igor Denisov</th>\n",
       "      <td>20.440771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.840000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicolas Lombaerts</th>\n",
       "      <td>9.647090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.862500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.660000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roman Shirokov</th>\n",
       "      <td>19.396145</td>\n",
       "      <td>7.225680</td>\n",
       "      <td>50.855000</td>\n",
       "      <td>102.3750</td>\n",
       "      <td>37.944000</td>\n",
       "      <td>27.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergei Semak</th>\n",
       "      <td>21.584877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.731250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.592000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomáš Hubočan</th>\n",
       "      <td>12.783965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.685000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.744000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viktor Fayzulin</th>\n",
       "      <td>28.379789</td>\n",
       "      <td>16.303000</td>\n",
       "      <td>65.695000</td>\n",
       "      <td>12.4950</td>\n",
       "      <td>22.848000</td>\n",
       "      <td>41.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vladimir Bystrov</th>\n",
       "      <td>20.214203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.450000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.381600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>468 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             dist                     x  \\\n",
       "goal                                        False      True       False   \n",
       "team           player                                                     \n",
       "Ajax           Christian Eriksen        22.917869  20.386337  63.000000   \n",
       "               Daley Blind              17.560331        NaN  92.610000   \n",
       "               Daniel Hoesen            11.153286  21.340401  95.917500   \n",
       "               Derk Boerrigter          21.101901  18.931250  65.555000   \n",
       "               Jody Lukoki              22.134413        NaN  85.050000   \n",
       "               Lasse Schøne             25.664372        NaN  56.017500   \n",
       "               Niklas Moisander          8.951737  10.126408   7.665000   \n",
       "               Ricardo van Rhijn        18.820696        NaN  49.350000   \n",
       "               Ryan Babel               21.957276        NaN  77.542500   \n",
       "               Siem de Jong             22.246804   6.809526  57.244091   \n",
       "               Tobias Sana              16.238031        NaN  89.040000   \n",
       "               Viktor Fischer           18.146298        NaN  69.772500   \n",
       "Anderlecht     Cheikhou Kouyaté         30.318649        NaN  30.135000   \n",
       "               Dennis Praet             21.446210        NaN  21.000000   \n",
       "               Dieudonné Mbokani        19.764263  14.005145  62.965000   \n",
       "               Guillaume Gillet         13.908516        NaN  56.448000   \n",
       "               Kanu                     22.545872        NaN  57.162000   \n",
       "               Lucas Biglia             19.917420        NaN  65.887500   \n",
       "               Marcin Wasilewski         9.912763        NaN   6.352500   \n",
       "               Massimo Bruno            14.733733        NaN  94.500000   \n",
       "               Milan Jovanović          20.431687  18.088162  57.945000   \n",
       "               Olexandr Yakovenko       22.839317        NaN  21.420000   \n",
       "               Olivier Deschacht         9.851069        NaN  97.335000   \n",
       "               Sacha Kljestan           26.612876        NaN  22.627500   \n",
       "               Tom De Sutter            14.062380   6.424019  11.865000   \n",
       "Arsenal        Aaron Ramsey             24.900117  12.822007  11.340000   \n",
       "               Alex Oxlade-Chamberlain  21.432514        NaN  84.000000   \n",
       "               Andrey Arshavin          15.469432        NaN  94.290000   \n",
       "               Gervinho                 12.983576   5.273269  54.390000   \n",
       "               Jack Wilshere                  NaN   7.715528        NaN   \n",
       "...                                           ...        ...        ...   \n",
       "Spartak Moskva Kirill Kombarov          36.071434        NaN  34.755000   \n",
       "               Marek Suchý              13.783557        NaN   9.345000   \n",
       "               Nicolas Pareja           17.857045        NaN  16.380000   \n",
       "               Rafael Carioca                 NaN  10.620419        NaN   \n",
       "               Romulo                   20.316394  14.135129  84.840000   \n",
       "               Welliton                 18.092743        NaN  86.940000   \n",
       "Valencia       Adil Rami                16.901760  11.655198  49.087500   \n",
       "               Andrés Guardado          18.027033        NaN  63.147000   \n",
       "               Daniel Parejo            34.346378        NaN  34.335000   \n",
       "               David Albelda            37.068992        NaN  67.935000   \n",
       "               Fernando Gago            24.625403        NaN  80.377500   \n",
       "               Jonas                    18.068141  16.828963  82.385625   \n",
       "               Nelson Valdez            13.976845   9.071122   8.225000   \n",
       "               Ricardo Costa             8.402404        NaN   7.455000   \n",
       "               Roberto Soldado          15.854446  11.219796  52.955000   \n",
       "               Sofiane Feghouli         18.233322  14.887335  63.735000   \n",
       "               Tino Costa               28.166059        NaN  43.715000   \n",
       "               Éver Banega              18.927973        NaN  86.310000   \n",
       "Zenit          Aleksandr Kerzhakov      18.129066  11.478895  37.666364   \n",
       "               Axel Witsel              20.134777        NaN  86.485000   \n",
       "               Danny                    12.416504   9.025218  31.325000   \n",
       "               Domenico Criscito        30.378758        NaN  76.020000   \n",
       "               Hulk                     23.162441   9.308568  40.405909   \n",
       "               Igor Denisov             20.440771        NaN  84.840000   \n",
       "               Nicolas Lombaerts         9.647090        NaN  54.862500   \n",
       "               Roman Shirokov           19.396145   7.225680  50.855000   \n",
       "               Sergei Semak             21.584877        NaN  33.731250   \n",
       "               Tomáš Hubočan            12.783965        NaN  34.685000   \n",
       "               Viktor Fayzulin          28.379789  16.303000  65.695000   \n",
       "               Vladimir Bystrov         20.214203        NaN  30.450000   \n",
       "\n",
       "                                                          y          \n",
       "goal                                       True       False   True   \n",
       "team           player                                                \n",
       "Ajax           Christian Eriksen         84.6300  30.013500  33.524  \n",
       "               Daley Blind                   NaN  16.796000     NaN  \n",
       "               Daniel Hoesen             92.1900  35.445000  19.856  \n",
       "               Derk Boerrigter           87.2550  34.793333  39.780  \n",
       "               Jody Lukoki                   NaN  42.432000     NaN  \n",
       "               Lasse Schøne                  NaN  26.384000     NaN  \n",
       "               Niklas Moisander          97.9125  33.320000  40.732  \n",
       "               Ricardo van Rhijn             NaN  30.328000     NaN  \n",
       "               Ryan Babel                    NaN  29.070000     NaN  \n",
       "               Siem de Jong               1.8900  35.088000  35.462  \n",
       "               Tobias Sana                   NaN  38.420000     NaN  \n",
       "               Viktor Fischer                NaN  31.280000     NaN  \n",
       "Anderlecht     Cheikhou Kouyaté              NaN  33.252000     NaN  \n",
       "               Dennis Praet                  NaN  25.160000     NaN  \n",
       "               Dieudonné Mbokani         92.8725  28.582667  36.652  \n",
       "               Guillaume Gillet              NaN  35.400800     NaN  \n",
       "               Kanu                          NaN  39.997600     NaN  \n",
       "               Lucas Biglia                  NaN  44.846000     NaN  \n",
       "               Marcin Wasilewski             NaN  29.478000     NaN  \n",
       "               Massimo Bruno                 NaN  48.892000     NaN  \n",
       "               Milan Jovanović           90.3000  32.513714  25.840  \n",
       "               Olexandr Yakovenko            NaN  37.740000     NaN  \n",
       "               Olivier Deschacht             NaN  23.052000     NaN  \n",
       "               Sacha Kljestan                NaN  34.544000     NaN  \n",
       "               Tom De Sutter             98.8050  36.924000  34.408  \n",
       "Arsenal        Aaron Ramsey              11.3400  51.748000  41.752  \n",
       "               Alex Oxlade-Chamberlain       NaN  38.488000     NaN  \n",
       "               Andrey Arshavin               NaN  33.864000     NaN  \n",
       "               Gervinho                  99.8550  35.972000  34.612  \n",
       "               Jack Wilshere              7.1400        NaN  31.008  \n",
       "...                                          ...        ...     ...  \n",
       "Spartak Moskva Kirill Kombarov               NaN  27.268000     NaN  \n",
       "               Marek Suchý                   NaN  25.500000     NaN  \n",
       "               Nicolas Pareja                NaN  31.076000     NaN  \n",
       "               Rafael Carioca             9.2400        NaN  42.364  \n",
       "               Romulo                    99.7500  37.468000  45.220  \n",
       "               Welliton                      NaN  33.320000     NaN  \n",
       "Valencia       Adil Rami                 11.6550  28.764000  35.360  \n",
       "               Andrés Guardado               NaN  33.374400     NaN  \n",
       "               Daniel Parejo                 NaN  34.952000     NaN  \n",
       "               David Albelda                 NaN  32.164000     NaN  \n",
       "               Fernando Gago                 NaN  29.172000     NaN  \n",
       "               Jonas                     44.0160  36.227000  28.084  \n",
       "               Nelson Valdez              7.4550  43.792000  31.280  \n",
       "               Ricardo Costa                 NaN  33.524000     NaN  \n",
       "               Roberto Soldado           73.8150  35.042667  36.159  \n",
       "               Sofiane Feghouli          13.7550  33.481500  27.608  \n",
       "               Tino Costa                    NaN  27.645778     NaN  \n",
       "               Éver Banega                   NaN  32.640000     NaN  \n",
       "Zenit          Aleksandr Kerzhakov       93.9750  27.026909  34.000  \n",
       "               Axel Witsel                   NaN  36.606667     NaN  \n",
       "               Danny                      8.1375  35.156000  40.596  \n",
       "               Domenico Criscito             NaN  23.256000     NaN  \n",
       "               Hulk                       7.3500  33.338545  41.616  \n",
       "               Igor Denisov                  NaN  34.000000     NaN  \n",
       "               Nicolas Lombaerts             NaN  33.660000     NaN  \n",
       "               Roman Shirokov           102.3750  37.944000  27.676  \n",
       "               Sergei Semak                  NaN  33.592000     NaN  \n",
       "               Tomáš Hubočan                 NaN  27.744000     NaN  \n",
       "               Viktor Fayzulin           12.4950  22.848000  41.004  \n",
       "               Vladimir Bystrov              NaN  32.381600     NaN  \n",
       "\n",
       "[468 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt2 = pd.pivot_table(shots, index=['team', 'player'], columns=['goal'], values=['dist', 'x', 'y'])\n",
    "pt2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notar que en este caso usamos un índice compuesto (team-player). Esto hace que el dataframe utilice un tipo de índice especial llamado `MultiIndex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">dist</th>\n",
       "      <th colspan=\"2\" halign=\"left\">x</th>\n",
       "      <th colspan=\"2\" halign=\"left\">y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goal</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adriano</th>\n",
       "      <td>25.493844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.733000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.476000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alex Song</th>\n",
       "      <td>7.928566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.667500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.368000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alexis Sánchez</th>\n",
       "      <td>13.244627</td>\n",
       "      <td>6.301468</td>\n",
       "      <td>51.043125</td>\n",
       "      <td>98.7000</td>\n",
       "      <td>39.856500</td>\n",
       "      <td>35.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrés Iniesta</th>\n",
       "      <td>21.089408</td>\n",
       "      <td>10.040548</td>\n",
       "      <td>65.185909</td>\n",
       "      <td>95.6550</td>\n",
       "      <td>32.986182</td>\n",
       "      <td>41.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cesc Fàbregas</th>\n",
       "      <td>19.930224</td>\n",
       "      <td>17.711724</td>\n",
       "      <td>71.321250</td>\n",
       "      <td>10.2900</td>\n",
       "      <td>40.443000</td>\n",
       "      <td>45.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christian Tello</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.150836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Alves</th>\n",
       "      <td>20.561624</td>\n",
       "      <td>20.567223</td>\n",
       "      <td>17.587500</td>\n",
       "      <td>87.6750</td>\n",
       "      <td>31.858000</td>\n",
       "      <td>41.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>David Villa</th>\n",
       "      <td>19.491869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.917500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.905600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gerard Deulofeu</th>\n",
       "      <td>20.202206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.530000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.592000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gerard Piqué</th>\n",
       "      <td>9.634535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.390000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.249000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jordi Alba</th>\n",
       "      <td>31.893448</td>\n",
       "      <td>10.801282</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.4100</td>\n",
       "      <td>52.632000</td>\n",
       "      <td>40.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lionel Messi</th>\n",
       "      <td>19.218590</td>\n",
       "      <td>10.951773</td>\n",
       "      <td>44.682273</td>\n",
       "      <td>65.3975</td>\n",
       "      <td>32.748182</td>\n",
       "      <td>33.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marc Bartra</th>\n",
       "      <td>9.377514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.890000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.665333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pedro Rodríguez</th>\n",
       "      <td>15.128710</td>\n",
       "      <td>16.723390</td>\n",
       "      <td>47.517273</td>\n",
       "      <td>16.5900</td>\n",
       "      <td>33.598182</td>\n",
       "      <td>38.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sergio Busquets</th>\n",
       "      <td>15.455097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.755000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.162667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thiago Alcántara</th>\n",
       "      <td>6.259553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.670000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.112000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xavi Hernández</th>\n",
       "      <td>19.316458</td>\n",
       "      <td>11.278964</td>\n",
       "      <td>63.334091</td>\n",
       "      <td>11.0250</td>\n",
       "      <td>37.226909</td>\n",
       "      <td>34.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dist                     x                   y        \n",
       "goal                  False      True       False    True       False   True \n",
       "player                                                                       \n",
       "Adriano           25.493844        NaN  47.733000      NaN  34.476000     NaN\n",
       "Alex Song          7.928566        NaN   6.667500      NaN  32.368000     NaN\n",
       "Alexis Sánchez    13.244627   6.301468  51.043125  98.7000  39.856500  35.564\n",
       "Andrés Iniesta    21.089408  10.040548  65.185909  95.6550  32.986182  41.004\n",
       "Cesc Fàbregas     19.930224  17.711724  71.321250  10.2900  40.443000  45.832\n",
       "Christian Tello         NaN  25.150836        NaN  87.0450        NaN  19.652\n",
       "Daniel Alves      20.561624  20.567223  17.587500  87.6750  31.858000  41.888\n",
       "David Villa       19.491869        NaN  53.917500      NaN  31.905600     NaN\n",
       "Gerard Deulofeu   20.202206        NaN  19.530000      NaN  33.592000     NaN\n",
       "Gerard Piqué       9.634535        NaN  96.390000      NaN  32.249000     NaN\n",
       "Jordi Alba        31.893448  10.801282  21.000000   4.4100  52.632000  40.324\n",
       "Lionel Messi      19.218590  10.951773  44.682273  65.3975  32.748182  33.728\n",
       "Marc Bartra        9.377514        NaN  50.890000      NaN  31.665333     NaN\n",
       "Pedro Rodríguez   15.128710  16.723390  47.517273  16.5900  33.598182  38.148\n",
       "Sergio Busquets   15.455097        NaN  41.755000      NaN  41.162667     NaN\n",
       "Thiago Alcántara   6.259553        NaN   5.670000      NaN  26.112000     NaN\n",
       "Xavi Hernández    19.316458  11.278964  63.334091  11.0250  37.226909  34.000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt2.loc['Barcelona']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos conseguir la fila particular para un índice compuesto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      goal \n",
       "dist  False    19.218590\n",
       "      True     10.951773\n",
       "x     False    44.682273\n",
       "      True     65.397500\n",
       "y     False    32.748182\n",
       "      True     33.728000\n",
       "Name: (Barcelona, Lionel Messi), dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt2.loc[('Barcelona', 'Lionel Messi')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ahora podemos volver al dataset `tips` importado de Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"4\" halign=\"left\">total_bill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Lunch</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Dinner</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Lunch</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Dinner</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
       "      <th>Male</th>\n",
       "      <td>3.058</td>\n",
       "      <td>2.941500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.171000</td>\n",
       "      <td>18.486500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2.990</td>\n",
       "      <td>2.437083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.218571</td>\n",
       "      <td>15.899167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
       "      <th>Male</th>\n",
       "      <td>1.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.246000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>11.386667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.892000</td>\n",
       "      <td>17.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2.660</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>13.260000</td>\n",
       "      <td>15.980000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>22.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
       "      <th>Male</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.879259</td>\n",
       "      <td>3.256563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.837778</td>\n",
       "      <td>19.929063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.868667</td>\n",
       "      <td>2.724615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.266667</td>\n",
       "      <td>19.003846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
       "      <th>Male</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.521333</td>\n",
       "      <td>3.115349</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.141333</td>\n",
       "      <td>20.403256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.329286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.540000</td>\n",
       "      <td>20.824286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tip                               total_bill             \\\n",
       "time         Lunch              Dinner                Lunch              \n",
       "smoker         Yes        No       Yes        No        Yes         No   \n",
       "day  sex                                                                 \n",
       "Thur Male    3.058  2.941500       NaN       NaN  19.171000  18.486500   \n",
       "     Female  2.990  2.437083       NaN  3.000000  19.218571  15.899167   \n",
       "Fri  Male    1.900       NaN  3.246000  2.500000  11.386667        NaN   \n",
       "     Female  2.660  3.000000  2.700000  3.250000  13.260000  15.980000   \n",
       "Sat  Male      NaN       NaN  2.879259  3.256563        NaN        NaN   \n",
       "     Female    NaN       NaN  2.868667  2.724615        NaN        NaN   \n",
       "Sun  Male      NaN       NaN  3.521333  3.115349        NaN        NaN   \n",
       "     Female    NaN       NaN  3.500000  3.329286        NaN        NaN   \n",
       "\n",
       "                                   \n",
       "time            Dinner             \n",
       "smoker             Yes         No  \n",
       "day  sex                           \n",
       "Thur Male          NaN        NaN  \n",
       "     Female        NaN  18.780000  \n",
       "Fri  Male    25.892000  17.475000  \n",
       "     Female  12.200000  22.750000  \n",
       "Sat  Male    21.837778  19.929063  \n",
       "     Female  20.266667  19.003846  \n",
       "Sun  Male    26.141333  20.403256  \n",
       "     Female  16.540000  20.824286  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(index=['day', 'sex'], columns=['time', 'smoker'], values=['total_bill', 'tip'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y ya que estamos, veamos los gráficos facetados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8/EIRiepE6FVaFNeWAXYNUhPi/W6mDWVzGGXpiRyN+1nb+n6tmgt49SP5AC1C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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a18de3438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(tips, col=\"sex\", hue=\"smoker\")\n",
    "g.map(plt.scatter, \"total_bill\", \"tip\")\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas es una herramienta poderosísima y sólo vimos un poquito. ¡A estudiarlo!\n",
    "\n",
    "------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python es infinito: Instalando paquetes\n",
    "\n",
    "\n",
    "Python es un lenguaje utilizado en muchísimas áreas. El repositorio donde se suelen compartir paquetes se llama PyPi, el Python Package Index. \n",
    "\n",
    "**Desafío**: elijan un tópico cualquiera y busquemos un paquete para instalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"400\"\n",
       "            src=\"http://pypi.python.org/pypi\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f8a18cc6a20>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('http://pypi.python.org/pypi', '100%', 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La herramienta oficial para instalar paquetes es [pip](https://pip.pypa.io/en/stable/). La forma canónica es\n",
    "\n",
    "    pip install --user <nombre_paquete>\n",
    "\n",
    "(el flag `--user` (opcional) instala el paquete a nivel usuario, sin requerir permisos de adminitracion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Usage:   \r\n",
      "  pip install [options] <requirement specifier> ...\r\n",
      "  pip install [options] -r <requirements file> ...\r\n",
      "  pip install [options] [-e] <vcs project url> ...\r\n",
      "  pip install [options] [-e] <local project path> ...\r\n",
      "  pip install [options] <archive url/path> ...\r\n",
      "\r\n",
      "Description:\r\n",
      "  Install packages from:\r\n",
      "  \r\n",
      "  - PyPI (and other indexes) using requirement specifiers.\r\n",
      "  - VCS project urls.\r\n",
      "  - Local project directories.\r\n",
      "  - Local or remote source archives.\r\n",
      "  \r\n",
      "  pip also supports installing from \"requirements files\", which provide\r\n",
      "  an easy way to specify a whole environment to be installed.\r\n",
      "\r\n",
      "Install Options:\r\n",
      "  -e, --editable <path/url>   Install a project in editable mode (i.e.\r\n",
      "                              setuptools \"develop mode\") from a local project\r\n",
      "                              path or a VCS url.\r\n",
      "  -r, --requirement <file>    Install from the given requirements file. This\r\n",
      "                              option can be used multiple times.\r\n",
      "  -b, --build <dir>           Directory to unpack packages into and build in.\r\n",
      "                              The default in a virtualenv is \"<venv\r\n",
      "                              path>/build\". The default for global installs is\r\n",
      "                              \"<OS temp dir>/pip_build_<username>\".\r\n",
      "  -t, --target <dir>          Install packages into <dir>.\r\n",
      "  -d, --download <dir>        Download packages into <dir> instead of\r\n",
      "                              installing them, regardless of what's already\r\n",
      "                              installed.\r\n",
      "  --download-cache <dir>      Cache downloaded packages in <dir>.\r\n",
      "  --src <dir>                 Directory to check out editable projects into.\r\n",
      "                              The default in a virtualenv is \"<venv\r\n",
      "                              path>/src\". The default for global installs is\r\n",
      "                              \"<current dir>/src\".\r\n",
      "  -U, --upgrade               Upgrade all packages to the newest available\r\n",
      "                              version. This process is recursive regardless of\r\n",
      "                              whether a dependency is already satisfied.\r\n",
      "  --force-reinstall           When upgrading, reinstall all packages even if\r\n",
      "                              they are already up-to-date.\r\n",
      "  -I, --ignore-installed      Ignore the installed packages (reinstalling\r\n",
      "                              instead).\r\n",
      "  --no-deps                   Don't install package dependencies.\r\n",
      "  --no-install                DEPRECATED. Download and unpack all packages,\r\n",
      "                              but don't actually install them.\r\n",
      "  --no-download               DEPRECATED. Don't download any packages, just\r\n",
      "                              install the ones already downloaded (completes\r\n",
      "                              an install run with --no-install).\r\n",
      "  --install-option <options>  Extra arguments to be supplied to the setup.py\r\n",
      "                              install command (use like --install-option\r\n",
      "                              =\"--install-scripts=/usr/local/bin\"). Use\r\n",
      "                              multiple --install-option options to pass\r\n",
      "                              multiple options to setup.py install. If you are\r\n",
      "                              using an option with a directory path, be sure\r\n",
      "                              to use absolute path.\r\n",
      "  --global-option <options>   Extra global options to be supplied to the\r\n",
      "                              setup.py call before the install command.\r\n",
      "  --user                      Install using the user scheme.\r\n",
      "  --egg                       Install packages as eggs, not 'flat', like pip\r\n",
      "                              normally does. This option is not about\r\n",
      "                              installing *from* eggs. (WARNING: Because this\r\n",
      "                              option overrides pip's normal install logic,\r\n",
      "                              requirements files may not behave as expected.)\r\n",
      "  --root <dir>                Install everything relative to this alternate\r\n",
      "                              root directory.\r\n",
      "  --compile                   Compile py files to pyc\r\n",
      "  --no-compile                Do not compile py files to pyc\r\n",
      "  --no-use-wheel              Do not Find and prefer wheel archives when\r\n",
      "                              searching indexes and find-links locations.\r\n",
      "  --pre                       Include pre-release and development versions. By\r\n",
      "                              default, pip only finds stable versions.\r\n",
      "  --no-clean                  Don't clean up build directories.\r\n",
      "\r\n",
      "Package Index Options:\r\n",
      "  -i, --index-url <url>       Base URL of Python Package Index (default\r\n",
      "                              https://pypi.python.org/simple/).\r\n",
      "  --extra-index-url <url>     Extra URLs of package indexes to use in addition\r\n",
      "                              to --index-url.\r\n",
      "  --no-index                  Ignore package index (only looking at --find-\r\n",
      "                              links URLs instead).\r\n",
      "  -f, --find-links <url>      If a url or path to an html file, then parse for\r\n",
      "                              links to archives. If a local path or file://\r\n",
      "                              url that's a directory, then look for archives\r\n",
      "                              in the directory listing.\r\n",
      "  --allow-external <package>  Allow the installation of externally hosted\r\n",
      "                              files\r\n",
      "  --allow-all-external        Allow the installation of all externally hosted\r\n",
      "                              files\r\n",
      "  --allow-unverified <package>\r\n",
      "                              Allow the installation of insecure and\r\n",
      "                              unverifiable files\r\n",
      "  --process-dependency-links  Enable the processing of dependency links.\r\n",
      "\r\n",
      "General Options:\r\n",
      "  -h, --help                  Show help.\r\n",
      "  -v, --verbose               Give more output. Option is additive, and can be\r\n",
      "                              used up to 3 times.\r\n",
      "  -V, --version               Show version and exit.\r\n",
      "  -q, --quiet                 Give less output.\r\n",
      "  --log-file <path>           Path to a verbose non-appending log, that only\r\n",
      "                              logs failures. This log is active by default at\r\n",
      "                              /home/tin/.pip/pip.log.\r\n",
      "  --log <path>                Path to a verbose appending log. This log is\r\n",
      "                              inactive by default.\r\n",
      "  --proxy <proxy>             Specify a proxy in the form\r\n",
      "                              [user:passwd@]proxy.server:port.\r\n",
      "  --timeout <sec>             Set the socket timeout (default 15 seconds).\r\n",
      "  --exists-action <action>    Default action when a path already exists:\r\n",
      "                              (s)witch, (i)gnore, (w)ipe, (b)ackup.\r\n",
      "  --cert <path>               Path to alternate CA bundle.\r\n"
     ]
    }
   ],
   "source": [
    "!pip install --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pip busca, baja, descomprime, e instala (y repite los pasos recursivamente para las dependencias). Por ejemplo, podemos instalar [pint](http://pint.readthedocs.org/en/0.6/tutorial.html), del amigo [Hernán Greco](http://www.conicet.gov.ar/hernan-grecco-la-alfabetizacion-cientifica-es-tan-importante-como-la-alfabetizacion-tradicional/)\n",
    "\n",
    "(Nota: esto no va a funcionar en `try.jupyter.org` debido a las limitaciones de salida de red por cuestiones de seguridad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading/unpacking pint\n",
      "  Using download cache from /home/tin/.pip_download_cache/https%3A%2F%2Fpypi.python.org%2Fpackages%2Fsource%2FP%2FPint%2FPint-0.6.zip\n",
      "  Running setup.py (path:/home/tin/.virtualenvs/curso/build/pint/setup.py) egg_info for package pint\n",
      "    \n",
      "    no previously-included directories found matching 'docs/_build'\n",
      "    no previously-included directories found matching 'docs/_themes/.git'\n",
      "    warning: no previously-included files matching '*.pyc' found anywhere in distribution\n",
      "    warning: no previously-included files matching '*~' found anywhere in distribution\n",
      "    warning: no previously-included files matching '.DS_Store' found anywhere in distribution\n",
      "    warning: no previously-included files matching '*__pycache__*' found anywhere in distribution\n",
      "    warning: no previously-included files matching '*.pyo' found anywhere in distribution\n",
      "Installing collected packages: pint\n",
      "  Running setup.py install for pint\n",
      "    \n",
      "    no previously-included directories found matching 'docs/_build'\n",
      "    no previously-included directories found matching 'docs/_themes/.git'\n",
      "    warning: no previously-included files matching '*.pyc' found anywhere in distribution\n",
      "    warning: no previously-included files matching '*~' found anywhere in distribution\n",
      "    warning: no previously-included files matching '.DS_Store' found anywhere in distribution\n",
      "    warning: no previously-included files matching '*__pycache__*' found anywhere in distribution\n",
      "    warning: no previously-included files matching '*.pyo' found anywhere in distribution\n",
      "Successfully installed pint\n",
      "Cleaning up...\n"
     ]
    }
   ],
   "source": [
    "!pip install pint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pint \n",
    "ur = pint.UnitRegistry()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "60.0 kilometer/hour"
      ],
      "text/latex": [
       "$60.0 \\frac{kilometer}{hour}$"
      ],
      "text/plain": [
       "<Quantity(60.0, 'kilometer / hour')>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vel = 60 * ur.km / ur.hr\n",
    "vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "16.666666666666668 meter/second"
      ],
      "text/latex": [
       "$16.666666666666668 \\frac{meter}{second}$"
      ],
      "text/plain": [
       "<Quantity(16.666666666666668, 'meter / second')>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vel.to('m/s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero **¿dónde se instala un paquete?**. Si no le decimos lo contrario con algun parámetro del instalador, el paquete se descromprime y se copia a un directorio especial para Python llamado `site-packages`, que a su vez se encuentra en una **lista de directorios** (definida como una variable de entorno del sistema) en la que la maquinaria de importanción de Python busca paquetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/pint/__init__.py'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pint.__file__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/home/tin/lab/curso-python-cientifico/test_pack',\n",
       " '/home/tin/.virtualenvs/curso/lib/python3.4',\n",
       " '/home/tin/.virtualenvs/curso/lib/python3.4/plat-x86_64-linux-gnu',\n",
       " '/home/tin/.virtualenvs/curso/lib/python3.4/lib-dynload',\n",
       " '/usr/lib/python3.4',\n",
       " '/usr/lib/python3.4/plat-x86_64-linux-gnu',\n",
       " '/home/tin/.virtualenvs/curso/lib/python3.4/site-packages',\n",
       " '/home/tin/.virtualenvs/curso/lib/python3.4/site-packages/IPython/extensions']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para desintalar paquetes, el subcomando de pip es `uninstall`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-f file:///home/tin/.pip_packages\n",
      "Cython==0.22\n",
      "Jinja2==2.7.3\n",
      "MarkupSafe==0.23\n",
      "Pint==0.6\n",
      "Pygments==2.0.2\n",
      "certifi==2015.04.28\n",
      "folium==0.1.4\n",
      "ipdb==0.8\n",
      "ipython==3.1.0\n",
      "jsonschema==2.4.0\n",
      "llvmlite==0.5.0-0.g93a41b1.dirty\n",
      "marquesina==0.3\n",
      "matplotlib==1.4.3\n",
      "mistune==0.5.1\n",
      "mpld3==0.2\n",
      "mpmath==0.19\n",
      "nose==1.3.6\n",
      "numba==0.18.2\n",
      "numpy==1.9.2\n",
      "pandas==0.16.0\n",
      "poliastro==0.3.0\n",
      "ptyprocess==0.4\n",
      "pyparsing==2.0.3\n",
      "python-dateutil==2.4.2\n",
      "pytz==2015.2\n",
      "pyzmq==14.6.0\n",
      "scipy==0.15.1\n",
      "seaborn==0.5.1\n",
      "six==1.9.0\n",
      "sympy==0.7.6\n",
      "terminado==0.5\n",
      "tornado==4.1\n",
      "virtualenv==13.0.3\n"
     ]
    }
   ],
   "source": [
    "!pip freeze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notaron ese directorio \".virtualenvs\" que se lee en mi sys.path?  Es que yo estoy usando un **entorno virtual**\n",
    "\n",
    "### Entornos virtuales\n",
    "\n",
    "Un entorno virtual (virtualenv) en python es una compartimentalización para tener multiples conjuntos de **dependencias por proyecto**, instalándolas en un directorio particular. Es decir, podemos tener un virtualenv en el que usamos numpy 1.8 y no vernos obligado a actualizarlo si comenzamos otro para el que necesitamos una feature que aparece en numpy 2.0. \n",
    "\n",
    "Python 3 incorpora built-in una herramienta llamada [pyenv](https://docs.python.org/3/library/venv.html) para manejar entornos virtuales, pero la forma canónica sigue siendo utilizar la herramienta [virtuelenv](https://virtualenv.pypa.io/en/latest/)\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero instalamos virtualenv\n",
    "\n",
    "        pip install virtualenv   #  o pip install --user virtualenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para crear un virtualenv basta usar \n",
    "\n",
    "    virtualenv <DIR>\n",
    "    \n",
    "Donde `<DIR>` es el directorio donde queremos instalar las dependencias exclusivas para nuestro proyecto. En mi caso es `~/.virtualenvs/curso`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez creado un virtualenv, cada vez que se quiere usarlo hay que activarlo. En sistemas linux/mac:\n",
    "\n",
    "    source <DIR>/bin/activate \n",
    "    \n",
    "o en sistemas windows: \n",
    "\n",
    "    <DIR>\\Scripts\\activate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando el virtualenv está activado, se denotará por un prefijo en el prompt. Luego, podemos instalar paquetes directamente via pip, sin permisos de adminitrador, ya que estamos instalando los paquetes en el `site-packages` definido dentro del virtualenv. \n",
    "\n",
    "\n",
    "Para facilitar la creacion y activacion de entronos con virtualenv se puede usar [virtualenvwrapper](https://virtualenvwrapper.readthedocs.org/en/latest/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para salir de un virtualenv (desactivarlo) se ejecuta\n",
    "\n",
    "    deactivate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conda \n",
    "\n",
    "Un problema del tandem virtualenv-pip es que, en general, los paquetes instalables son archivos comprimidos de **código fuente**. Eso no es ningun problema cuando se trata de paquetes python puro, porque Python se interpreta y no se compila, pero cuando es un paquete que tiene dependencias externas (fortran, cython, c, etc.) o requiere dependencias complejas en el sistema operativo, la instalación via pip requerirá compilar, eso implica compiladores, headers y un montón de cosas que complican la vida (además de ser un proceso lento). \n",
    "\n",
    "Por eso la herramienta [conda](http://conda.pydata.org/), que viene preinstalada si usamos la distribución de python Anaconda, es muy interesante. En vez de bajar código fuente, baja paquetes precompilados (binarios) en un formato y desde un [repositorio especial](https://binstar.org/) (distinto a PyPi). \n",
    "\n",
    "Además, conda sabe crear por sí mismo entornos virtuales, es decir que es una mezcla entre virtualenv y pip. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para crear un entorno virtual via conda\n",
    "\n",
    "    conda create -n <nombre_venv> <programas o paquetes basicos a instalar, opcionalmente con versiones>\n",
    "    \n",
    "Por ejemplo:\n",
    "\n",
    "    conda create -n curso python=3.4 numpy scipy matplotlib pandas sympy seaborn ipython ipython-notebook\n",
    "\n",
    "Para activar el entorno, es análogo a la manera de `virtualenv`\n",
    "\n",
    "    source activate curso   \n",
    "    \n",
    "O en Windows, directamente \n",
    "\n",
    "\n",
    "    activate curso\n",
    "    \n",
    "Para instalar paquetes dentro del entorno, \n",
    "\n",
    "    conda install <paquetes o programas>\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribuyendo tus paquetes (o módulos)\n",
    "\n",
    "Ya vimos algo de esto. Python usa un archivo especial llamado `setup.py` que invoca a una función llamada `setup()` con diversos parámetros que especifican de qué se trata el paquete, la versión, el autor, la lista explícita de  módulos y paquetes que debe incluir, etc.\n",
    "\n",
    "Este *script* `setup.py` es llamado por python (directamente o a través de pip) y nos presenta una lista de subcomandos. \n",
    "\n",
    "\n",
    "Se nuevo sucede que Python trae incoporada lo necesario para escribir este archivo de setup, en el paquete `distutils` de la biblioteca estandar, pero la opción canónica superadora es usar el paquete [setuptools](https://setuptools.pypa.io/en/latest/index.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/tin/lab/curso-python-cientifico\n"
     ]
    }
   ],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "%mkdir test_pack/\n",
    "%mkdir test_pack/marquesina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/tin/lab/curso-python-cientifico/test_pack\n"
     ]
    }
   ],
   "source": [
    "%cd test_pack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mmarquesina\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que queremos distribuir nuestra [función maquesina](https://gist.githubusercontent.com/mgaitan/6319640/raw/68d4fc1b55407ce9e358b6769699e536eed5a4e1/gistfile1.txt) de la clase 2. Vamos a crear un paquete. Como es  nuestro único código, lo podemos poner directamente en el `__init__.py` del proyecto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing marquesina/__init__.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile marquesina/__init__.py\n",
    "\n",
    "def marquesina(cadena, ancho=60, alto=1, caracter=\"*\"):\n",
    "    \"\"\"\n",
    "    Wraps ``cadena`` in a box of ``caracter`` symbols with padding ``ancho`` x ``alto``\n",
    "    \"\"\"\n",
    "    \n",
    "    cadena = cadena.center(ancho)\n",
    "    cadena = caracter + cadena[1:-1] + caracter\n",
    "    cadena += '\\n'\n",
    "    relleno = \" \" * ancho\n",
    "    relleno = caracter + relleno[1:-1] + caracter\n",
    "    relleno += '\\n'\n",
    "    tapa = caracter * ancho\n",
    "    return tapa + '\\n' + relleno * alto + cadena + relleno * alto + tapa\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sigiendo la [doc](https://setuptools.pypa.io/en/latest/setuptools.html), una versión mínima del `setup.py` se vería así"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting setup.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile setup.py\n",
    "from setuptools import setup, find_packages\n",
    "\n",
    "setup(\n",
    "    name='marquesina',\n",
    "    version = '0.1',\n",
    "    description='Marquesina, decorate your text',\n",
    "    long_description='A package with functions to decorate text',\n",
    "    author='Martín Gaitán',\n",
    "    author_email='curso@',\n",
    "    packages = find_packages(), \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así se ve el árbol de archivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34m.\u001b[00m\r\n",
      "├── \u001b[01;34mmarquesina\u001b[00m\r\n",
      "│   └── __init__.py\r\n",
      "└── setup.py\r\n",
      "\r\n",
      "1 directory, 2 files\r\n"
     ]
    }
   ],
   "source": [
    "!tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro `setup.py` es un programa de linea de comandos, con ayuda y todo!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common commands: (see '--help-commands' for more)\r\n",
      "\r\n",
      "  setup.py build      will build the package underneath 'build/'\r\n",
      "  setup.py install    will install the package\r\n",
      "\r\n",
      "Global options:\r\n",
      "  --verbose (-v)  run verbosely (default)\r\n",
      "  --quiet (-q)    run quietly (turns verbosity off)\r\n",
      "  --dry-run (-n)  don't actually do anything\r\n",
      "  --help (-h)     show detailed help message\r\n",
      "  --no-user-cfg   ignore pydistutils.cfg in your home directory\r\n",
      "\r\n",
      "Options for 'install' command:\r\n",
      "  --prefix                             installation prefix\r\n",
      "  --exec-prefix                        (Unix only) prefix for platform-\r\n",
      "                                       specific files\r\n",
      "  --home                               (Unix only) home directory to install\r\n",
      "                                       under\r\n",
      "  --install-base                       base installation directory (instead of\r\n",
      "                                       --prefix or --home)\r\n",
      "  --install-platbase                   base installation directory for\r\n",
      "                                       platform-specific files (instead of --\r\n",
      "                                       exec-prefix or --home)\r\n",
      "  --root                               install everything relative to this\r\n",
      "                                       alternate root directory\r\n",
      "  --install-purelib                    installation directory for pure Python\r\n",
      "                                       module distributions\r\n",
      "  --install-platlib                    installation directory for non-pure\r\n",
      "                                       module distributions\r\n",
      "  --install-lib                        installation directory for all module\r\n",
      "                                       distributions (overrides --install-\r\n",
      "                                       purelib and --install-platlib)\r\n",
      "  --install-headers                    installation directory for C/C++\r\n",
      "                                       headers\r\n",
      "  --install-scripts                    installation directory for Python\r\n",
      "                                       scripts\r\n",
      "  --install-data                       installation directory for data files\r\n",
      "  --compile (-c)                       compile .py to .pyc [default]\r\n",
      "  --no-compile                         don't compile .py files\r\n",
      "  --optimize (-O)                      also compile with optimization: -O1 for\r\n",
      "                                       \"python -O\", -O2 for \"python -OO\", and\r\n",
      "                                       -O0 to disable [default: -O0]\r\n",
      "  --force (-f)                         force installation (overwrite any\r\n",
      "                                       existing files)\r\n",
      "  --skip-build                         skip rebuilding everything (for\r\n",
      "                                       testing/debugging)\r\n",
      "  --record                             filename in which to record list of\r\n",
      "                                       installed files\r\n",
      "  --install-layout                     installation layout to choose (known\r\n",
      "                                       values: deb, unix)\r\n",
      "  --user                               install in user site-package\r\n",
      "                                       '/home/tin/.local/lib/python3.4/site-\r\n",
      "                                       packages'\r\n",
      "  --old-and-unmanageable               Try not to use this!\r\n",
      "  --single-version-externally-managed  used by system package builders to\r\n",
      "                                       create 'flat' eggs\r\n",
      "\r\n",
      "usage: setup.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]\r\n",
      "   or: setup.py --help [cmd1 cmd2 ...]\r\n",
      "   or: setup.py --help-commands\r\n",
      "   or: setup.py cmd --help\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!python setup.py install --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos instalar nuestro paquete, por ejemplo via `pip install -e` que instala el paquete sin copiar los archivos a `site-packages`, permitiendo que si modificamos el código impacte automaticamente en la versión importable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///home/tin/lab/curso-python-cientifico/test_pack\n",
      "  Running setup.py (path:/home/tin/lab/curso-python-cientifico/test_pack/setup.py) egg_info for package from file:///home/tin/lab/curso-python-cientifico/test_pack\n",
      "    \n",
      "Installing collected packages: marquesina\n",
      "  Running setup.py develop for marquesina\n",
      "    \n",
      "    Creating /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/marquesina.egg-link (link to .)\n",
      "    marquesina 0.1 is already the active version in easy-install.pth\n",
      "    \n",
      "    Installed /home/tin/lab/curso-python-cientifico/test_pack\n",
      "Successfully installed marquesina\n",
      "Cleaning up...\n"
     ]
    }
   ],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from marquesina import marquesina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************************************\n",
      "*                                                          *\n",
      "*                        hola mundo                        *\n",
      "*                                                          *\n",
      "************************************************************\n"
     ]
    }
   ],
   "source": [
    "print(marquesina('hola mundo'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supongamos que lanzamos una nueva versión que incopora otra función "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appending to marquesina/__init__.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile -a marquesina/__init__.py\n",
    "\n",
    "def boca():\n",
    "    print(marquesina('¡Aguante Boca!'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No haría falta cambiar el `setup.py` para importar el paquete o sus funciones, pero podemos usar una funcionalidad de `setuptools` que permite convertir funciones en simples programas de línea de comando instalables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting setup.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile setup.py\n",
    "from setuptools import setup, find_packages\n",
    "\n",
    "setup(\n",
    "    name='marquesina',\n",
    "    version = '0.2',\n",
    "    description='Marquesina, decorate your text',\n",
    "    author='Martín Gaitán',\n",
    "    author_email='curso@',\n",
    "    packages = find_packages(),    # ['marquesina']\n",
    "    entry_points = {\n",
    "        'console_scripts': [           # esto crear un script 'boquita'  que ejecuta marquesina.boca()\n",
    "            'boquita = marquesina:boca',\n",
    "        ]}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///home/tin/lab/curso-python-cientifico/test_pack\n",
      "  Running setup.py (path:/home/tin/lab/curso-python-cientifico/test_pack/setup.py) egg_info for package from file:///home/tin/lab/curso-python-cientifico/test_pack\n",
      "    \n",
      "Installing collected packages: marquesina\n",
      "  Running setup.py develop for marquesina\n",
      "    \n",
      "    Creating /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/marquesina.egg-link (link to .)\n",
      "    marquesina 0.2 is already the active version in easy-install.pth\n",
      "    Installing boquita script to /home/tin/.virtualenvs/curso/bin\n",
      "    \n",
      "    Installed /home/tin/lab/curso-python-cientifico/test_pack\n",
      "Successfully installed marquesina\n",
      "Cleaning up...\n"
     ]
    }
   ],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entonces **boquita** ahora es un programa en nuestro sistema!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************************************\r\n",
      "*                                                          *\r\n",
      "*                      ¡Aguante Boca!                      *\r\n",
      "*                                                          *\r\n",
      "************************************************************\r\n"
     ]
    }
   ],
   "source": [
    "!boquita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para escribir \"scripts\" de linea de comandos que reciban parámetros, se puede utilizar el paquete de las biblioteca estándar [argparse](https://docs.python.org/3.4/library/argparse.html) o bien usar [Docopt](http://docopt.org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos más allá"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing marquesina/plot.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile marquesina/plot.py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def marquesina_plt(text, x=5, y=2, text_size=80):\n",
    "    \"\"\"\n",
    "    return create a matplotlib's Text instance of `text`\n",
    "    \"\"\"\n",
    "    coef =  x/max(len(text), 6)\n",
    "    plt.figure(figsize=(x/coef, y/coef))\n",
    "    plt.tick_params(bottom=False, left=False, right=False, top=False, labelleft=False, labelbottom=False) \n",
    "    return plt.text(0.5, 0.5, text, size=text_size, ha=\"center\", va=\"center\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f06477dc358>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZT34xyRkL9vlMhq4Pr8/wWPoTGa6Ly2a4Lr4xQ4vsd2T8Bue/V9Uvt9Z+eqeFPlJVXSXD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\nAKATYRwAADr5/6nKGJurX9opAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f066c03bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "marquesina_plt('aguante boca!', x=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting setup.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile setup.py\n",
    "from setuptools import setup, find_packages\n",
    "\n",
    "setup(\n",
    "    name='marquesina',\n",
    "    version = '0.3',\n",
    "    description='Marquesina, decorate your text',\n",
    "    author='Martín Gaitán',\n",
    "    author_email='curso@',\n",
    "    packages = find_packages(),    # ['marquesina']\n",
    "    install_requires=['matplotlib'],\n",
    "    entry_points = {\n",
    "        'console_scripts': [           # esto crear un script 'boquita'  que ejecuta marquesina.boca()\n",
    "            'boquita = marquesina:boca',\n",
    "        ]}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///home/tin/lab/curso-python-cientifico/test_pack\n",
      "  Running setup.py (path:/home/tin/lab/curso-python-cientifico/test_pack/setup.py) egg_info for package from file:///home/tin/lab/curso-python-cientifico/test_pack\n",
      "    \n",
      "Requirement already satisfied (use --upgrade to upgrade): matplotlib in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from marquesina==0.3)\n",
      "Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6 in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from matplotlib->marquesina==0.3)\n",
      "Requirement already satisfied (use --upgrade to upgrade): six>=1.4 in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from matplotlib->marquesina==0.3)\n",
      "Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from matplotlib->marquesina==0.3)\n",
      "Requirement already satisfied (use --upgrade to upgrade): pytz in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from matplotlib->marquesina==0.3)\n",
      "Requirement already satisfied (use --upgrade to upgrade): pyparsing>=1.5.6,!=2.0.0 in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from matplotlib->marquesina==0.3)\n",
      "Requirement already satisfied (use --upgrade to upgrade): nose>=0.11.1 in /home/tin/.virtualenvs/curso/lib/python3.4/site-packages (from matplotlib->marquesina==0.3)\n",
      "Installing collected packages: marquesina\n",
      "  Running setup.py develop for marquesina\n",
      "    \n",
      "    Creating /home/tin/.virtualenvs/curso/lib/python3.4/site-packages/marquesina.egg-link (link to .)\n",
      "    marquesina 0.3 is already the active version in easy-install.pth\n",
      "    Installing boquita script to /home/tin/.virtualenvs/curso/bin\n",
      "    \n",
      "    Installed /home/tin/lab/curso-python-cientifico/test_pack\n",
      "Successfully installed marquesina\n",
      "Cleaning up...\n"
     ]
    }
   ],
   "source": [
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from marquesina.plot import marquesina_plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/tin/lab/curso-python-cientifico/test_pack\n"
     ]
    }
   ],
   "source": [
    "%cd test_pack/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Luego, podemos armar nuestro paquete distribuíble. Es un simple archivo comprimido con todo lo necesario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running sdist\n",
      "running egg_info\n",
      "writing entry points to marquesina.egg-info/entry_points.txt\n",
      "writing requirements to marquesina.egg-info/requires.txt\n",
      "writing marquesina.egg-info/PKG-INFO\n",
      "writing dependency_links to marquesina.egg-info/dependency_links.txt\n",
      "writing top-level names to marquesina.egg-info/top_level.txt\n",
      "reading manifest file 'marquesina.egg-info/SOURCES.txt'\n",
      "writing manifest file 'marquesina.egg-info/SOURCES.txt'\n",
      "warning: sdist: standard file not found: should have one of README, README.rst, README.txt\n",
      "\n",
      "running check\n",
      "warning: check: missing required meta-data: url\n",
      "\n",
      "creating marquesina-0.3\n",
      "creating marquesina-0.3/marquesina\n",
      "creating marquesina-0.3/marquesina.egg-info\n",
      "making hard links in marquesina-0.3...\n",
      "hard linking setup.py -> marquesina-0.3\n",
      "hard linking marquesina/__init__.py -> marquesina-0.3/marquesina\n",
      "hard linking marquesina/plot.py -> marquesina-0.3/marquesina\n",
      "hard linking marquesina.egg-info/PKG-INFO -> marquesina-0.3/marquesina.egg-info\n",
      "hard linking marquesina.egg-info/SOURCES.txt -> marquesina-0.3/marquesina.egg-info\n",
      "hard linking marquesina.egg-info/dependency_links.txt -> marquesina-0.3/marquesina.egg-info\n",
      "hard linking marquesina.egg-info/entry_points.txt -> marquesina-0.3/marquesina.egg-info\n",
      "hard linking marquesina.egg-info/requires.txt -> marquesina-0.3/marquesina.egg-info\n",
      "hard linking marquesina.egg-info/top_level.txt -> marquesina-0.3/marquesina.egg-info\n",
      "Writing marquesina-0.3/setup.cfg\n",
      "creating dist\n",
      "Creating tar archive\n",
      "removing 'marquesina-0.3' (and everything under it)\n"
     ]
    }
   ],
   "source": [
    "!python setup.py sdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "marquesina-0.3.tar.gz\r\n"
     ]
    }
   ],
   "source": [
    "!ls dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setuptools también sabe registrar y subir nuestro paquete a PyPi\n",
    "\n",
    "    # solo hace falta hacerlo la primera vez\n",
    "    python setup.py register\n",
    "    \n",
    "Luego cada vez que queramos publicar una nueva versión\n",
    "\n",
    "    python setup.py sdist upload \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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