05-Procesamiento_datos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Procesando archivos y bases de datos con Python \n",
    "\n",
    "Taller \"Introducción a la Programación en Python\".\n",
    "\n",
    "[G.F. Rubilar](http://google.com/+GuillermoRubilar), Stefan Vogt y Esteban Vöhringer-Martinez\n",
    "\n",
    "La última versión de estos [notebooks de IPython](http://ipython.org/notebook.html) está disponible en [http://github.com/gfrubi/Tp17)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a trabajar con un archivo que contiene la velocidad de un auto medida en distintos puntos de la trayectoria.\n",
    "Este archivo se ve así:\n",
    "\n",
    "```\n",
    "0.0  30\n",
    "0.8  50\n",
    "1.5  43.2\n",
    "2.0  20.0\n",
    "2.6  42.1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero abrimos un objeto con la ubicación (ruta) en donde se encuentra el archivo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "str_archivo = 'Data/Clases/posicion_velocidad.dat'\n",
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtener información sobre el obejto que apunta al archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Data/Clases/posicion_velocidad.dat'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por defecto los archivos se abren en modo de lectura solamente(read). Otros modos son w (write=escritura), a (append=agregar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usamos el método read del objeto f para leer el contenido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si queremos volver a leer el contenido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El archivo se procesa una vez y después se llego al final del archivo.\n",
    "Cerramos el objeto y volvemos a abrirlo para partir de nuevo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ahora usamos método readline para leer el archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.readline?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "line = f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0  30\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.0', '30']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = line.split()[0]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('0.0', '30')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line.split()[0], line.split()[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volvemos a ejecutar readline para leer próxima linea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "line = f.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8  50\\n'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### También podemos leer todas las líneas de una vez en una lista de stings con la función readlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A diferencia de read esto crea una lista de strings y no un string entero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lines = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.0  30\\n', '0.8  50\\n', '1.5  43.2\\n', '2.0  20.0\\n', '2.6  42.1\\n']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0  30\\n'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.0', '30']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0].split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ahora vamos a leer el contenido en dos variables que muestran la posición (x) y la velocidad (v)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuando usamos un bucle sobre el objeto f se ejecuta el comando readline en cada vuelta del bucle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for line in f:\n",
    "    x = line.split()[0]\n",
    "    v = line.split()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.6'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De esta forma se leen linea por linea pero la variable se sobreescribe. Por eso debemos trabajar con una lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()\n",
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = []\n",
    "v = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for line in f:\n",
    "    x.append(line.split()[0])\n",
    "    v.append(line.split()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.0', '0.8', '1.5', '2.0', '2.6']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['30', '50', '43.2', '20.0', '42.1']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(v[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78487f0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DZbzLyeJAklQ1li+HH/8YjjwSXOOofCwOJElV45ZbYP58bymUm8WBJKlqTJ0KI0bAjjvm\nnaS29ag4iIhvR0RLRJy30vtnR8TLEfF2RNwVEVv3LKakSpYSvPpq3ilU6555Bu66y1GD3lB0cRAR\nnwaOBx5b6f3TgQmt23YBlgB3RoRT7DrR3Oyzuqp+3/oWjBrlgkgqr7594etfhy9+Me8kta+o4iAi\n1gKuAY4D3lxp86nAlJTSrSmlJ4GjgE2AsT0JWqumTIHddsvWqZeq1Z57wtNPw9y5eSdRLRsyBP7n\nf2D11fNOUvuKHTm4GLglpdSh7UpEDAE2An7b9l5KaTHwEDCi2JC1qm0lxNNOg7XXzjuNVLx99slm\njttrQaoNBRcHEXEYsBPwnU42bwQkYMFK7y9o3aZWtmNWLVltNTjoIIsDqVYUVBxExGbABcARKaXl\n5YlUH9q3Y3YlRNWC8ePhscfg2WfzTiKppwrtrTAcWB+YHe83zu4LjIqICcAngAA2pOPowYbAh66A\nPXHiRAYPHtzhvcbGRhobGwuMWPna2jGPG2c7ZtWOAw7I7gX/4hfZBEVJpdXU1ERTU1OH9xYtWlSW\n74pUwPTiiFgT2GKlt68E5gL/mVKaGxEvA/+VUjq/9TNrkxUKR6WUbujkmMOAWbNmzWLYsGHF/RZV\n5rTTskk1c+fCFiufTamKjR8PL7+cNQ+TVH6zZ89m+PDhAMNTSrNLddyCbiuklJaklOa0f5E9qvha\nSqltnvIFwOSIODgidgCuBl4CppcqdDVbuBD++7+zdswWBqo1DQ3w3HPZ6JhUCj4em49SrJDY4V9d\nSulc4CJgGtlTCqsDB6SU3i3Bd1W99deHhx92EqJq06GHZiMHrnmvUkgpWz/joovyTlJ/elwcpJT2\nTil9Y6X3zkopbZJSWiOltF9K6Zmefk8t+eQnnYSo2tS/f7ZQjVQKf/wj3Hdftr6Bepe9FSRJFWnq\nVNh882yyq3qXxYEkqeK8+SY0NcHxxzsalQeLA0lSxbn6ali+HL7ylbyT1CeLA0lSRUkpu6Uwbhxs\n5Nq6ubA4KLN3fUZDkgpy773ZOjC2Zs6PxUEZNTfD0KFw3XV5J5F637Jl2WxzqVD33APbbAN77ZV3\nkvplcVBGZ58N8+fDrrvmnUTqfZdcAp/9LLz1Vt5JVG3OOAMeeQT+b5F+9TqLgzKZMwcuuMCVEFW/\nxo7NRg9uvz3vJKpGtrHPl8VBGaQEEybYjln1bcgQGDbMNs5SNbI4KIPrr4e777Yds9TQALfdBkuX\n5p1EUiEsDkrMdszS+8aPhyVL4Ne/zjuJpEJYHJTYlCnwxhtw/vl5J5Hy94lPwLbbemtBqjb98g5Q\na8aPh+22cxKi1KahIbvF9u67MGBA3mkkdYcjByW2665w9NF5p5Aqx/jxsNlm8NJLeSdRpXrvvWy0\n9bXX8k6iNhYHkspqp53giSdgq63yTqJKdcst2VwtC8jKYXEgScrV1KnZqOuOO+adRG2ccyBJys0z\nz2RPs1x5Zd5J1J4jB5Kk3Fx2Gay7Lhx6aN5J1J7FgSQpF8uWwU9/CsccA6uvnncatWdx0AM33QTf\n/z60tOSdRJKqz403Zk8onHBC3km0MouDIjU3wymnZC1p+3gWJalgU6dmbZk//vG8k2hlTkgs0tln\nuxKiVKjXX4cZM+Dww23HW+9SyhrTrbNO3knUGYuDIrS1Yz7rLFdClArxyCNw5JHZKqI77ZR3GuUp\nAg45JO8U6ooD4gWyHbNUvL32yv6maK8FqbJZHBTIdsxS8QYMgDFj4Be/yDuJpA9jcVCAtnbMY8fa\njlkqVkNDdmvuqafyTiKpKxYHBZg/Hzbd1EmIUk/ssw+suaa3FqRKZnFQgG22gYceyuYbSCrO6qvD\n5z9vcSBVMouDAvn4ldRzDQ3w6KPw3HN5J1FvWroUli/PO4W6w+JAUq878MDstXhx3knUm378Y9h6\n66xIUGVznQNJvW6tteC22/JOod6UUrYi4i67wMCBeafRqjhyIEkqu/vuy55SOfHEvJOoOywOJEll\nN3VqNql7773zTqLusDjoQkpw3XXw3nt5J5Gk6rZwYdaB8YQTnNRdLSwOunD99XDYYXDvvXknkaTq\ndsUVWVFw9NF5J1F3WRx0om0lxHHjsrXgJUnFaWmBadPg0ENhvfXyTqPusjjoxJQptmOWpFJ46aVs\n1OCkk/JOokJYHKxkzpysKJg0yXbMUm949dWsIH/zzbyTqBw23xzmzYNdd807iQphcdBOSnDyybZj\nlnrTu+/CGWfArbfmnUTl0qePExGrjcVBO9dfDzNn2o5Z6k2bbQaf+YxtnKVKYnHQztSp2SRE2zFL\nvauhAe64A5YsyTuJJCiwOIiIEyPisYhY1Pq6PyL2b7f9iohoWel1e+ljl8cdd8Bll+WdQqo/DQ3w\nzjswY0beSSRB4SMHLwKnA8OA4cBMYHpEDG23zwxgQ2Cj1ldjCXL2itVWg49+NO8UUv3ZaivYaSfb\nOEuVoqDiIKV0W0rpjpTSsymlZ1JKk4G3gPbzUJellBamlF5tfS0qaWJJNWn8+GxSoh37pPwVPecg\nIvpExGHAGsD97TbtGRELIuKpiLgkIj7S45SSal5DA7z1FvzmN3knUU9ddlm2XLKqV8HFQURsHxHN\nwDLgEmBcSunp1s0zgKOAvYHTgD2A2yN8iEXSh9t2WzjtNNh007yTqCeWLYPJk7MujKpekVIq7AMR\n/YDNgcHAF4CvAqNSSk91su8Q4Fngcymlu7s43jBg1qhRoxg8eHCHbY2NjTQ2Vs2UBUmqez//OTQ2\nwty58IlP5J2mtjQ1NdHU1NThvUWLFvH73/8eYHhKaXapvqvg4uADB4i4C3gmpdTp4pgR8SowKaX0\n4y62DwNmzZo1i2HDhvUoSyFeew3WWQf69u21r5SkmrfHHtmiR3d3+tdBldrs2bMZPnw4lLg4KMU6\nB32ATpcMiojNgPWA+SX4npJJKWsCcsQReSeRpNoxZw78/vdw4ol5J1FP9Stk54j4Ptm8gr8Bg4Aj\nyOYV7BsRawJnAjcBrwBbAz8A5gF3ljBzj7WthOgz1ZJUOtOmwQYbZIvJqboVVBwAGwBXARsDi4DH\ngX1TSjMjYiDwSbIJiesAL5MVBWeklJaXLnLPtG/H7EqIklQaS5bAVVfB174GAwbknUY9VVBxkFI6\n7kO2LQUq/o9b2zFLUulddx0sXgxf/WreSVQKhY4cVLW2dsxnnWU7ZkkqpYaGbIXZIUPyTqJSqJvG\nSynBhAm2Y5aqwZtvZpPaHn007yTqrsGDYcyYvFOoVOpm5OAXv8gerZkxw3bMUqVba61shb3Bg+FT\nn8o7jVR/6mbkYP/94eqrnYQoVYN+/WDs2Kyo7+FSLJKKUDfFwZprwpe+lHcKSd3V0ADPPANPPJF3\nEqn+1E1xIKm67L03rL22bZylPFgcSKpIq60GBx+c3VqQ1LssDiRVrIYGePJJmDcv7yTqzDPPOCek\nVlkcSKpY++0Ha6zhrYVKtHAhbLcdXH553klUDnXzKKOk6rPGGnDZZbDTTnkn0cquvBIisqdKVHtq\ncuRg7txsMtPf/553Ekk9dcQR2d9QVTlaWrImS1/8YrYqompPzY0ctK2E+OKLXrSSVA6//S08+2zW\naEm1qeaKg/btmF0JUZJKb+pU2H572G23vJOoXGrqtkJbO+axY10JUZLK4e9/h+nT4aSTsjkHqk01\nVRzYjlmSyuvyy2HgQDjyyLyTqJxqpjhoa8c8aVLWeVGSVHoDBsDXvpatXqnaVRNzDlKCk0+2HbMk\nldu3v513AvWGmhg5WL4cdtwRLrrISYhSrVqxAsaPh2uvzTuJVPtqojgYMADOO89JiFIt69sX/vEP\naGrKO4lU+2qiOJBUHxoa4K67YPHivJNItc3iQFLVGDcO3n0Xbr017yRSbbM4kFQ1Nt8cPv1p2zhL\n5WZxIKmqNDRkK6C+/XbeSaTaZXEgqaqMH58VBnfckXeS+jB3btZgacGCvJOoN1VlcXDFFfD003mn\nkJSHj30MdtgBbrop7yT1Ydo0+N3vYN11806i3lR1xcGcOXD88XDjjXknkZSXyy+Hc8/NO0Xte/vt\nrPPiV76SPTKu+lFVKyS6EqIkyCYlqvyuuw4WLYKvfjXvJOptVVUc2I5ZknrP1Kmw336w1VZ5J1Fv\nq5rioK0d87hxroQoSeU2ezY8/DD86ld5J1EeqmbOge2YJan3TJsGm20Gn/983kmUh6ooDtq3Y95i\ni7zTSFJtW7oUfv7zbK5Bv6oZX1YpVcW/9pkz4Z//2UmIktQbBg6Exx+HNdfMO4nyUhUjBxMmwKOP\nOglRUudWrMg7Qe3ZYgv46EfzTqG8VEVxALD66nknkFSJxoyB00/PO4VUW6qmOJCkzmy2WbYoWkp5\nJ5Fqh8WBpKrW0AAvvJA9eiepNCwOJFW1UaPgIx+x14JUShYHkqpa//5wyCFZceCtBak0Kq44aG6G\n557LO4WkatLQAPPmZWuiqDhPPw3PP593ClWKiisOpkyBnXeGJUvyTiKpWoweDYMGeWuhJyZNyp78\nkKDCioO2lRC/8Q0X35DUfautBgcdBLfdlneS6vTyy1kPheOPzzuJKkVBxUFEnBgRj0XEotbX/RGx\n/0r7nB0RL0fE2xFxV0Rs3Z1jp5QtdmQ7ZknF+OEP4e67805RnS6/PCuwvvSlvJOoUhQ6cvAicDow\nDBgOzASmR8RQgIg4HZgAHA/sAiwB7oyIAas68OjRJ3L33Wfygx80uxKipIJtsgmssUbeKarPe+/B\nZZfB4YfD4MF5p1GlKKg4SCndllK6I6X0bErpmZTSZOAtYNfWXU4FpqSUbk0pPQkcBWwCjF3Vsd98\n81JgBGec0UBzc3Nhv4UkqSi33w4vvQQnnph3ElWSouccRESfiDgMWAO4PyKGABsBv23bJ6W0GHgI\nGNGNIwL7M3fuRCZP/lGxsSRJBZg6NZsEPnx43klUSQouDiJi+4hoBpYBlwDjUkpPkxUGCViw0kcW\ntG7rlpaW/bn55j8UGkuSVKDnnoM77nDUQB9UTMvmp4AdgcHAF4CrI2JUz6NMbD0kvPLKE4wZM4bG\nxkYaGxt7fmhJ0ge89x4ceSQcdljeSdQdTU1NNDU1dXhv0aJFZfmuSD1cUiwi7gKeAc4FngV2Sik9\n3m77PcCjKaWJXXx+GDALZpHNc0xsueU+PPfcb3qUS5KkWjd79myGZ/eEhqeUStZhpBTrHPQBVksp\nPQe8AnyubUNErA18Bri/2wfrcwdjxny2BLEk1auWFnjttbxTSNWroNsKEfF9YAbwN2AQcASwB7Bv\n6y4XAJMj4hngeWAK8BIwfdVHT/TpM4OhQ8/nnHNc5kxS8Q4/HF5/HX7967yTSNWp0JGDDYCryOYd\n/IZsrYN9U0ozAVJK5wIXAdPInlJYHTggpfTuqg688cZfY8KEh3jggZsYNGhQgbEk6X2jRmULIr3+\net5JpOrU4zkHPQ7QOudg1qxZDBs2LNcskmrD/Pmw6aZwxRVw9NF5p5HKp5LnHEhSRdl4Y9htNxsx\nScWyOJBUkxoasjkHLrgqFc7iQFJNGjcOli3LlgfW+w44IOulIH0YiwNJNWnLLbMlgb218L5HH81W\nRNxgg7yTqNJZHEiqWQ0N8Nhj2boHgmnTsomaBx2UdxJVOosDSTVr4kSYOxf6+F86Fi+Ga66Br34V\n+hWzcL7qipeIpJo1cGDeCSrHtdfC0qVw3HF5J1E1sJ6WpBqXElx6KRx8cHZbQVoViwNJqnEPPABP\nPAEnnZR3ElULiwNJqnG33QZbbQWjR+edRNXC4kCSatw552SjB07MVHd5qUhSjYtwbQMVxuJAUl14\n+214/PG8U0jVweJAUl2YPDlbOtgFkaRVsziQVBfGjoWXX4aHH847iVT5LA4k1YXdd8/uu9trQVo1\niwNJdaFv36xT4003ZYsCSeqaxYGkujF+PDz3HPzpT3knKa8VK+AnP8n6KUjFsDiQVDf22gvWXbf2\nby3MmJE1WJo3L+8kqlYWB5LqRv/+MGZM7RcHl14Kw4fDzjvnnUTVyuJAUl1paIA114Q338w7SXk8\n/3w2cnDiiXknUTWzOJBUVw46CP74R1hnnbyTlMePfwyDBkFjY95JVM0sDiTVlYi8E5TPu+/C5ZfD\nUUdloyO1NQvlAAANCUlEQVRSsSwOJKlGTJ8OCxZ4S0E9Z3EgSTXi0kth5EjYbru8k6ja9cs7gCSp\n51KCr3zF7osqDYsDSaoBEXDEEXmnUK3wtoIkSerA4kBS3XrlldpfEEkqhsWBpLp1yy1w6KHw6qt5\nJ5Eqi8WBpLo1dmz2v9On55tDqjQWB5Lq1vrrw6hR3lqQVmZxIKmuNTTAb39bnb0Wli+Hlpa8U6gW\nWRxIqmvjxsF772XzD6rN5ZfD0KGwbFneSVRrLA4k1bVNN4Vdd62+WwspwdSpsO22sNpqeadRrbE4\nkFT3GhrgzjvhrbfyTtJ9Dz0Ejz1mHwWVh8WBpLrX0ACjR8Nrr+WdpPsuvRSGDIF99sk7iWqRyydL\nqntDhlTXnIPXX4frroPvfhf6+Fc8lYGXlSRVmauuyp5SOPbYvJOoVlkcSFIVaZuI2NBgB0aVT0HF\nQUR8JyIejojFEbEgIn4ZEdustM8VEdGy0uv20saWpPr04ovQ3AwnnZR3EtWyQuccjAQuAv7Y+tn/\nAH4dEUNTSu+0228GcAwQrT/7FK4klcDmm8MLL0A/Z4ypjAq6vFJKB7b/OSKOAV4FhgP3tdu0LKW0\nsMfpJEkf0L9/3glU63o652AdIAGvr/T+nq23HZ6KiEsi4iM9/B5JktRLii4OIiKAC4D7Ukpz2m2a\nARwF7A2cBuwB3N66vyRVtBdegLPPhhUr8k4i5acnd60uAbYFdm//Zkrp+nY//jkingCeBfYE7u7B\n90lS2c2fD2eeCXvumXVslOpRUcVBRPwPcCAwMqU0/8P2TSk9FxH/ALbmQ4qDiRMnMnjw4A7vNTY2\n0tjYWExESSrKLrvAJptkvRYsDlRJmpqaaGpq6vDeokWLyvJdkVIq7ANZYXAIsEdK6a/d2H8z4AXg\nkJTSrZ1sHwbMmjVrFsOGDSsoiySVw8knw69+BX/7G3hDVJVs9uzZDB8+HGB4Sml2qY5b6DoHlwBH\nAIcDSyJiw9bXwNbta0bEuRHxmYjYIiI+B/wKmAfcWarQklRODQ3w0kvwyCN5J8n87GcwY0beKVRP\nCp2QeCKwNnAP8HK716Gt21cAnwSmA08DPwYeAUallJaXIK8kld3IkbD++pXRxnn5cvjWt+C22/JO\nonpS6DoHH1pMpJSWAvv3KJEk5axvXzjkkKw4+M//zPfWwvTpsGABnHBCfhlUf+ytIEmdaGiAZ5+F\nJ57IN8ell8Luu8MOO+SbQ/XFBTglqRN77w0TJsDAgfllePppmDkzm3Mg9SaLA0nqxIABcNFF+Wa4\n7DJYbz34whfyzaH6420FSapA77wDV1wBxx6b7+iF6pPFgSRVoBtugDfegOOPzzuJ6pHFgSRVoAMO\ngGuvhY99LO8kqkcWB5JUgdZfHw4/PO8UqlcWB5IkqQOLA0mS1IHFgSStwosvwpe/DC+/nHcSqXdY\nHEjSKqy5ZrYQ0S9/mXcSqXdYHEjSKnzkI7DXXvCLX+SdROodFgeS1A0NDfC738E//lG+75g/v3zH\nlgphcSBJ3TB2LLS0ZF0Sy+H112GrreDKK8tzfKkQFgeS1A0bbggjR5bv1sLVV8OKFdniR1LeLA4k\nqZsaGuCuu2DRotIeNyWYOhXGj8+KEClvFgeS1E3jx8Py5XDrraU97j33ZO2ZTzqptMeVimVxIEnd\ntNlmMG0a7LpraY87dSp84hMwalRpjysVq1/eASSpmpS6S+KCBdk8hh/+ECJKe2ypWI4cSFKOfvpT\n6N8fjjoq7yTS+ywOJClH77yTLc287rp5J5He520FScrR2WfnnUD6IEcOJElSBxYHkiSpA4sDSZLU\ngcWBJBXhzTdh//3h7rvzTiKVnsWBJBVh8OBsVcMbbsg7iVR6FgeSVISIbDnlX/4y69Yo1RKLA0kq\nUkMDvPIK3H9/9z/zl79k6xosXFi+XFJPWRxIUpF23RU23riwNs7TpsH06TBoUPlyST1lcSBJRerT\nB8aNy4qDlFa9/9KlcMUVcOyxMHBg+fNJxbI4kKQeaGiAF16AWbNWve+NN8Lrr5e+eZNUahYHktQD\no0bBeuvBTTetet9LL4XPfQ622ab8uaSesLeCJPVAv35ZYTB06Ifv9/jj2cTFG2/snVxST1gcSFIP\n7bHHqveZNg022gjGjCl/HqmnvK0gSWX2zjtwzTVw3HHQv3/eaaRVc+RAksps9dXhgQeyuQlSNbA4\nkKResO22eSeQus/bCpIkqQOLA0mS1IHFQRVramrKO0LV8ZwVx/NWmJYWz1mxPG+VoaDiICK+ExEP\nR8TiiFgQEb+MiA8s5xERZ0fEyxHxdkTcFRFbly6y2vh/osJ5zorjeeuelhbYZRe46CLPWbE8b5Wh\n0JGDkcBFwGeA0UB/4NcRsXrbDhFxOjABOB7YBVgC3BkRA0qSWJIq1JIlzSxceCannz6au+56mCFD\nRnPKKWfS3NycdzSpIAUVBymlA1NKP0spzU0pPQEcA2wODG+326nAlJTSrSmlJ4GjgE2AsSXKLEkV\np7m5mREjGnjhhREsW3YXS5fuwvPP38XFF49gxIgGCwRVlZ7OOVgHSMDrABExBNgI+G3bDimlxcBD\nwIgefpckVaxJk37I3LnfIKX9gWh9N2hp2Z+5cycyefKP8ownFaTodQ4iIoALgPtSSnNa396IrFhY\nsNLuC1q3dWYgwNy5c4uNUrcWLVrE7Nmz845RVTxnxfG8rdqNN86gpWUM0HaeFv3fP7e0bMANN8zg\n6KNdO3lVvNYK0+7PzpI2AY/UnSbknX0w4lJgP2D3lNL81vdGAPcBm6SUFrTb9zqgJaXU2MlxDgeu\nLSqEJEkCOCKl9L+lOlhRIwcR8T/AgcDItsKg1Stk42kb0nH0YEPg0S4OdydwBPA8sLSYPJIk1amB\nwJZkf5aWTMEjB62FwSHAHimlv3ay/WXgv1JK57f+vDZZoXBUSumGnkeWJEnlVNDIQURcAjQCY4Al\nEbFh66ZFKaW2v/VfAEyOiGfIRgOmAC8B00uSWJIklVVBIwcR0UI24XBlx6aUrm6331lk6xysA9wL\nfD2l9EzPokqSpN5Q9IRESZJUm+ytIEmSOrA4kCRJHfRKcRARX4+I5yLinYh4MCI+vYr994yIWRGx\nNCLmRcTRvZGz0hRy3iJij4hoWem1IiI26M3MeYqIkRFxc0T8vfX3X+WKM/V+rRV6zrzOut+ArpPP\n1fu1VvB5q/frLSJOjIjHImJR6+v+iNh/FZ8pyXVW9uIgIv4f8CPgTOBTwGNkjZg+2sX+WwK3ki3B\nvCNwIfCTiNin3FkrSaHnrVUCPka2GuVGwMYppVfLnbWCrAn8CfganU+c7cBrDSjwnLWq9+tslQ3o\nVua1BhRx3lrV8/X2InA6MIysh9FMYHpEDO1s55JeZymlsr6AB4EL2/0cZI82ntbF/j8AHl/pvSbg\n9nJnraRXEedtD2AFsHbe2SvhBbQAY1axj9da4efM6+yD5+Sjrefusx+yj9dacefN6+2D5+Q1sicE\nO9tWsuusrCMHEdGfrNpp34gpAb+h60ZMu7Zub+/OD9m/5hR53iArIP4UES9HxK8jYrfyJq16dX+t\nFcnrrKMODei64LX2Qd05b+D1BkBE9ImIw4A1gAe62K1k11m5byt8FOhLYY2YNupi/7UjYrXSxqtY\nxZy3+cAJQAMwnmw46p6I2KlcIWuA11rhvM7a6aIBXWe81top4LzV/fUWEdtHRDOwDLgEGJdSeqqL\n3Ut2nRXdlVGVJaU0D5jX7q0HI+KfgYlAXU18Uvl4nX3AJcC2wO55B6ky3TpvXm8APEU2f2Aw8AXg\n6ogY9SEFQkmUe+TgH2T3izZc6f0NyZo0deaVLvZfnFJaVtp4FauY89aZh4GtSxWqBnmtlUZdXmfx\nfgO6PVPHBnSd8VprVeB560xdXW8ppfdSSn9NKT2aUppENjn91C52L9l1VtbiIKW0HJgFfK7tvdbh\npM8B93fxsQfa799qX7q+x1JzijxvndmJbFhOnav7a61E6u46i/cb0O2VUvpbNz7itUZR560zdXe9\nraQP0NUtgtJdZ70ws/JQ4G3gKOATwDSy2Zbrt27/D+CqdvtvCTSTzbr8ONkjVu8Co/OeJdqbryLO\n26lkDbH+GdiO7H7ecrLqPPffp5fO2Zpkw287kc2C/pfWn//Ja61k58zrLBsSf4Ps0bwN270Gttvn\n+15rJTlvdX29tZ6PkcAWwPat/398D9i7dXvZ/pvWW7/g18g6NL5DVsHs3G7bFcDMlfYfRfY353eA\nvwBfyvtfUk4XRrfPG/Ct1nO1BFhI9qTDqLx/h14+X3u0/gG3YqXXT73WSnPOvM7+75HPlc/XCrK2\n9G37eK2V4LzV+/UG/AT4a+s18wrw67bCoNzXmY2XJElSB/ZWkCRJHVgcSJKkDiwOJElSBxYHkiSp\nA4sDSZLUgcWBJEnqwOJAkiR1YHEgSZI6sDiQJEkdWBxIkqQOLA4kSVIH/x8myzMn1qVhzgAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3fc04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,v,'o--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora queremos graficar velocidad pero en función de la posición en metros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_m = []\n",
    "for i in range(len(x)):\n",
    "    x_m.append(x[i]*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Debemos transformar la información leida a float para poder utilizar cualquier tratamiento númerico de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(str_archivo)\n",
    "x = []\n",
    "v = []\n",
    "for line in f:\n",
    "    x.append(float(line.split()[0]))\n",
    "    v.append(float(line.split()[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_m = []\n",
    "for i in range(len(x)):\n",
    "    x_m.append(x[i]*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 800.0, 1500.0, 2000.0, 2600.0]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora cambiamos las velocidades a m/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v_ms = []\n",
    "for i in range(len(v)):\n",
    "    v_ms.append(v[i] * 1000 / 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7cf9748>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4SJKkYgYHSZJUzOAgSZKKGRwkSVIxg4MkSSpmcJAkScUMDpIkqZjBQZIkFTM4SJKkYgYH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\nVMzgIEmSihkcJElSMYODJEkq9v8BBWnFh/f3cnMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x674d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_m,v_ms,'o',color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cómo guardar los valores en metros y metros por segundo a un archivo diferente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "str_archivo_out = 'Data/Clases/posicion_velocidad_metros.dat'\n",
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f_out.write(str(x_m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al observar el archivo vemos que se escribe todo junto.\n",
    "\n",
    "Cómo escribimos cada item por separado?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(x_m)):\n",
    "    f_out.write(str(x_m[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como escribir en forma de columna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(x_m)):\n",
    "    f_out.write(str(x_m[i])+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora escribimos la posición junto a la velocidad en el archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(x_m)):\n",
    "    f_out.write(str(x_m[i]) + '\\t' + str(v_ms[i]) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mejoremos el formato de los datos escritos.\n",
    "Para maoyor información ver https://docs.python.org/2/tutorial/inputoutput.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(x_m)):\n",
    "    f_out.write(\"%.2f\\t%.2f\\n\" % (x_m[i], v_ms[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Format Strings\n",
    "\n",
    "%d : integer\n",
    "\n",
    "%5d : %-5d : integer in a field of width 5 chars integer in a field of width 5 chars, but adjusted to the left\n",
    "\n",
    "%05d :integer in a field of width 5 chars, padded with zeroes from the left\n",
    "\n",
    "%g :  float variable in %f or %g notation\n",
    "\n",
    "%e : float variable in scientific notation\n",
    "\n",
    "%11.3e : float variable in scientific notation, with 3 decimals, field of width 11 chars \n",
    "\n",
    "%5.1f : float variable in fixed decimal notation, with one decimal, field of width 5 chars \n",
    "\n",
    "%.3f : float variable in fixed decimal form, with three decimals, field of min. width\n",
    "%s : string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La versión mas moderna es usar la función format de un string para dar formato a los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(x_m)):\n",
    "    f_out.write('{0:.2f}   {1:.2f}\\n'.format(x_m[i],v_ms[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alineamos los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()\n",
    "f_out = open(str_archivo_out,'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(x_m)):\n",
    "    f_out.write(\"%8.2f\\t%5.2f\\n\" % (x_m[i], v_ms[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agregar datos a un archivo existente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out = open(str_archivo_out,'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_out.mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Go to end of file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.write('END\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Procesar archivos con comentarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "str_archivo = 'Data/Clases/posicion_velocidad_coment.dat'\n",
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: #posicion",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-633bb8e706fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: #posicion"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "v = []\n",
    "for line in f:\n",
    "    x.append(float(line.split()[0]))\n",
    "    v.append(float(line.split()[1]))        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f.close()\n",
    "f = open(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = []\n",
    "v = []\n",
    "for line in f:\n",
    "    if line[0] is not \"#\":\n",
    "        x.append(float(line.split()[0]))\n",
    "        v.append(float(line.split()[1]))        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.8, 1.5, 2.0, 2.6]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esto ahora se puede funcionalizar para el formato específico que tengan sus datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Ahora usaremos la funciones de numpy para procesar archivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trabajaremos con el archivo posicion_velocidad_coment.dat que se muestra aquí:\n",
    "\n",
    "\n",
    "```#posicion velocidad\n",
    "0.0  30\n",
    "0.8  50\n",
    "1.5  43.2\n",
    "2.0  20.0\n",
    "2.6  42.1```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "str_archivo = 'Data/Clases/posicion_velocidad_coment.dat'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Función loadtxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos la función loadtxt de numpy que nos permite procesar archivos con datos númericos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.loadtxt?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existen las opciones de definir el tipo de información a procesar como float, string, double etc, el delimitador de los valores y el saltar los primeras n lineas con skiprows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0. ,  30. ],\n",
       "       [  0.8,  50. ],\n",
       "       [  1.5,  43.2],\n",
       "       [  2. ,  20. ],\n",
       "       [  2.6,  42.1]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.loadtxt(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.8,  1.5,  2. ,  2.6])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30. ,  50. ,  43.2,  20. ,  42.1])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = data[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v = data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0. ,  0.8,  1.5,  2. ,  2.6]),\n",
       " array([ 30. ,  50. ,  43.2,  20. ,  42.1]))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora importamos directamente en las dos variables con opción \"unpack\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x,v  = np.loadtxt(str_archivo,unpack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.8,  1.5,  2. ,  2.6])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Función genfromtxt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos la alternativa de genfromtxt para trabajar con archivos que incompletos de datos y para tener mayor flexibilidad en el procesamiento.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.genfromtxt?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El archivo posicion_velocidad_missing.dat le falta un valor en la velocidad:\n",
    "\n",
    "```@ Archivo obtenido el 30 diciembre 2016\n",
    "0.0  30\n",
    "0.8  50\n",
    "1.5  43.2\n",
    "2.0  Bla \n",
    "2.6  42.1```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: Bla",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-799ccd272074>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstr_archivo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Data/Clases/posicion_velocidad_missing.dat'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr_archivo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcomments\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'@'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda2\\lib\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m             \u001b[1;31m# Convert each value according to its column and store\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 930\u001b[0;31m             \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mconv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mconv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconverters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    931\u001b[0m             \u001b[1;31m# Then pack it according to the dtype's nesting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m             \u001b[0mitems\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpack_items\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpacking\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda2\\lib\\site-packages\\numpy\\lib\\npyio.py\u001b[0m in \u001b[0;36mfloatconv\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34mb'0x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromhex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    660\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m     \u001b[0mtyp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: Bla"
     ]
    }
   ],
   "source": [
    "str_archivo = 'Data/Clases/posicion_velocidad_missing.dat'\n",
    "data = np.loadtxt(str_archivo,comments='@')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "str_archivo = 'Data/Clases/posicion_velocidad_missing.dat'\n",
    "data = np.genfromtxt(str_archivo,comments='@')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0. ,  30. ],\n",
       "       [  0.8,  50. ],\n",
       "       [  1.5,  43.2],\n",
       "       [  2. ,   nan],\n",
       "       [  2.6,  42.1]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como cambiamos el valor del item ausente?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.genfromtxt(str_archivo,comments='@',filling_values='0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0. ,  30. ],\n",
       "       [  0.8,  50. ],\n",
       "       [  1.5,  43.2],\n",
       "       [  2. ,   0. ],\n",
       "       [  2.6,  42.1]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cómo podemos procesar la nombre de la variable de los valores que se esta leyendo. \n",
    "\n",
    "Como ejemplo se usará el archivo posicion_velocidad_names.dat:\n",
    "\n",
    "```@ Archivo obtenido el 30 diciembre 2016\n",
    "x    v\n",
    "0.0  30\n",
    "0.8  50\n",
    "1.5  43.2\n",
    "2.0  Bla \n",
    "2.6  42.1```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "str_archivo = 'Data/Clases/posicion_velocidad_names.dat'\n",
    "data1 = np.genfromtxt(str_archivo,skip_header=1,names=True,filling_values='0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0.0, 30.0), (0.8, 50.0), (1.5, 43.2), (2.0, 0.0), (2.6, 42.1)], \n",
       "      dtype=[('x', '<f8'), ('v', '<f8')])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30. ,  50. ,  43.2,   0. ,  42.1])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.8,  1.5,  2. ,  2.6])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.genfromtxt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Después de hacer un análisis numérico como guardar array de datos en archivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array([[0, 1, 2 ],[2, 3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open('Data/Clases/array.bin','w')\n",
    "x.tofile(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [2, 3, 4]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = np.fromfile('Data/Clases/array.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.12199579e-314,   4.24399158e-314,   8.48798317e-314])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Los datos se guardan en forma binaria sin información alguna sobre el tipo de variable. Aparte la forma de guardar los datos depende del sistema operativo que se usa. Por eso se recomienda usar la opción save y load de numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [2, 3, 4]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.save?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open('Data/Clases/array.bin','w')\n",
    "np.save(f,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open('Data/Clases/array.bin','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [2, 3, 4]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###También se puede guardar en forma de texto sacrificando precisión y espacio de disco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savetxt('Data/Clases/array.dat',x,fmt=\"%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.loadtxt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = np.loadtxt('Data/Clases/array.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int32"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.loadtxt('Data/Clases/array.dat',dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int32"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Cuando se tienen bases de datos que tienen distintos tipos de datos con formato diferente se remienda usar el módulo pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora procesaremos una base de datos bajada en internet bajo el link https://catalog.data.gov/dataset/most-popular-baby-names-by-sex-and-mothers-ethnic-group-new-york-city-8c742 que contiene información sobre los nombres de bebes según género y grupo etnico al cual pertenece la madre.\n",
    "\n",
    "Las primeras lineas se ven aquí:\n",
    "```\n",
    "\n",
    "BRTH_YR,GNDR,ETHCTY,NM,CNT,RNK\n",
    "\n",
    "2011,FEMALE,HISPANIC,GERALDINE,13,75\n",
    "\n",
    "2011,FEMALE,HISPANIC,GIA,21,67\n",
    "\n",
    "2011,FEMALE,HISPANIC,GIANNA,49,42\n",
    "\n",
    "2011,FEMALE,HISPANIC,GISELLE,38,51\n",
    "\n",
    "2011,FEMALE,HISPANIC,GRACE,36,53```\n",
    "\n",
    "\n",
    "La primera línea contiene el nombre de la variable que se muestra y las después los datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para eso usamos pandas que es un modulo de python que facilita el tratamiento de datos heterogéneos (strings, float etc.)\n",
    "\n",
    "\n",
    "Mayor información se puede obtener en estos dos tutoriales de introducción:\n",
    "\n",
    "http://nbviewer.jupyter.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Lesson.ipynb\n",
    "\n",
    "http://nbviewer.jupyter.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Lesson.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "str_archivo = 'Data/Clases/Most_Popular_Baby_Names_by_Sex_and_Mother_s_Ethnic_Group__New_York_City.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(str_archivo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BRTH_YR</th>\n",
       "      <th>GNDR</th>\n",
       "      <th>ETHCTY</th>\n",
       "      <th>NM</th>\n",
       "      <th>CNT</th>\n",
       "      <th>RNK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>GERALDINE</td>\n",
       "      <td>13</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>GIA</td>\n",
       "      <td>21</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>GIANNA</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>GISELLE</td>\n",
       "      <td>38</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>GRACE</td>\n",
       "      <td>36</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BRTH_YR    GNDR    ETHCTY         NM  CNT  RNK\n",
       "0     2011  FEMALE  HISPANIC  GERALDINE   13   75\n",
       "1     2011  FEMALE  HISPANIC        GIA   21   67\n",
       "2     2011  FEMALE  HISPANIC     GIANNA   49   42\n",
       "3     2011  FEMALE  HISPANIC    GISELLE   38   51\n",
       "4     2011  FEMALE  HISPANIC      GRACE   36   53"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2011\n",
       "1        2011\n",
       "2        2011\n",
       "3        2011\n",
       "4        2011\n",
       "5        2011\n",
       "6        2011\n",
       "7        2011\n",
       "8        2011\n",
       "9        2011\n",
       "10       2011\n",
       "11       2011\n",
       "12       2011\n",
       "13       2011\n",
       "14       2011\n",
       "15       2011\n",
       "16       2011\n",
       "17       2011\n",
       "18       2011\n",
       "19       2011\n",
       "20       2011\n",
       "21       2011\n",
       "22       2011\n",
       "23       2011\n",
       "24       2011\n",
       "25       2011\n",
       "26       2011\n",
       "27       2011\n",
       "28       2011\n",
       "29       2011\n",
       "         ... \n",
       "13932    2014\n",
       "13933    2014\n",
       "13934    2014\n",
       "13935    2014\n",
       "13936    2014\n",
       "13937    2014\n",
       "13938    2014\n",
       "13939    2014\n",
       "13940    2014\n",
       "13941    2014\n",
       "13942    2014\n",
       "13943    2014\n",
       "13944    2014\n",
       "13945    2014\n",
       "13946    2014\n",
       "13947    2014\n",
       "13948    2014\n",
       "13949    2014\n",
       "13950    2014\n",
       "13951    2014\n",
       "13952    2014\n",
       "13953    2014\n",
       "13954    2014\n",
       "13955    2014\n",
       "13956    2014\n",
       "13957    2014\n",
       "13958    2014\n",
       "13959    2014\n",
       "13960    2014\n",
       "13961    2014\n",
       "Name: BRTH_YR, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.BRTH_YR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas ofrece una serie de análisis estadísticos sobre cada serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "482131L"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.CNT.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora lo vamos a ordenar según el ranking que obtuvieron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BRTH_YR</th>\n",
       "      <th>GNDR</th>\n",
       "      <th>ETHCTY</th>\n",
       "      <th>NM</th>\n",
       "      <th>CNT</th>\n",
       "      <th>RNK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>ISABELLA</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3505</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13782</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9140</th>\n",
       "      <td>2012</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISP</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3850</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10981</th>\n",
       "      <td>2013</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9687</th>\n",
       "      <td>2012</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISP</td>\n",
       "      <td>JOSEPH</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13055</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>Jayden</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>ETHAN</td>\n",
       "      <td>177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13514</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>Liam</td>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2688</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8135</th>\n",
       "      <td>2012</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISP</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7393</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10343</th>\n",
       "      <td>2013</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>Isabella</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>SOPHIA</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>MICHAEL</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5430</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10008</th>\n",
       "      <td>2013</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>Sophia</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6369</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>ISABELLA</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>2013</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>Madison</td>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7141</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4982</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>ETHAN</td>\n",
       "      <td>177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11123</th>\n",
       "      <td>2013</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>Ethan</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5178</th>\n",
       "      <td>2011</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>JAYDEN</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11622</th>\n",
       "      <td>2013</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>David</td>\n",
       "      <td>304</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13205</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>Ethan</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13885</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Riley</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13880</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Reed</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Shloime</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13935</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Wesley</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13661</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Binyomin</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13917</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Silas</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13858</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Nikita</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13934</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Vincenzo</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13725</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Filip</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13882</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Reuben</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13862</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Nosson</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13632</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Anton</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13738</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Grant</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13651</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Aydin</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13658</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Berish</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13815</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Marcel</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13691</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Dashiell</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13949</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Yidel</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13746</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Hayden</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13757</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Imran</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13736</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Giuseppe</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13810</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Lucien</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13760</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Issac</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13719</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Erik</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13789</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Karas</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13915</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Shraga</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13844</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Mordche</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13850</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Myles</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13695</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Denis</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13856</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Nico</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13962 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BRTH_YR    GNDR                      ETHCTY        NM  CNT  RNK\n",
       "2906      2011  FEMALE                    HISPANIC  ISABELLA  331    1\n",
       "6151      2011  FEMALE          BLACK NON HISPANIC   MADISON  176    1\n",
       "1504      2011    MALE                    HISPANIC    JAYDEN  426    1\n",
       "3505      2011    MALE                    HISPANIC    JAYDEN  426    1\n",
       "13782     2014    MALE          WHITE NON HISPANIC    Joseph  300    1\n",
       "2266      2011    MALE          BLACK NON HISPANIC    JAYDEN  184    1\n",
       "9140      2012    MALE              BLACK NON HISP    JAYDEN  171    1\n",
       "3850      2011    MALE          WHITE NON HISPANIC   MICHAEL  292    1\n",
       "10981     2013    MALE  ASIAN AND PACIFIC ISLANDER    Jayden  220    1\n",
       "9687      2012    MALE              WHITE NON HISP    JOSEPH  300    1\n",
       "13055     2014    MALE  ASIAN AND PACIFIC ISLANDER    Jayden  187    1\n",
       "598       2011    MALE  ASIAN AND PACIFIC ISLANDER     ETHAN  177    1\n",
       "13514     2014    MALE                    HISPANIC      Liam  312    1\n",
       "794       2011    MALE          BLACK NON HISPANIC    JAYDEN  184    1\n",
       "2688      2011  FEMALE          BLACK NON HISPANIC   MADISON  176    1\n",
       "8135      2012  FEMALE              BLACK NON HISP   MADISON  159    1\n",
       "7393      2011    MALE                    HISPANIC    JAYDEN  426    1\n",
       "10343     2013  FEMALE                    HISPANIC  Isabella  326    1\n",
       "2561      2011  FEMALE  ASIAN AND PACIFIC ISLANDER    SOPHIA  119    1\n",
       "1849      2011    MALE          WHITE NON HISPANIC   MICHAEL  292    1\n",
       "5430      2011    MALE                    HISPANIC    JAYDEN  426    1\n",
       "10008     2013  FEMALE  ASIAN AND PACIFIC ISLANDER    Sophia  124    1\n",
       "6369      2011  FEMALE                    HISPANIC  ISABELLA  331    1\n",
       "10142     2013  FEMALE          BLACK NON HISPANIC   Madison  134    1\n",
       "7141      2011    MALE          BLACK NON HISPANIC    JAYDEN  184    1\n",
       "4982      2011    MALE  ASIAN AND PACIFIC ISLANDER     ETHAN  177    1\n",
       "11123     2013    MALE          BLACK NON HISPANIC     Ethan  146    1\n",
       "5178      2011    MALE          BLACK NON HISPANIC    JAYDEN  184    1\n",
       "11622     2013    MALE          WHITE NON HISPANIC     David  304    1\n",
       "13205     2014    MALE          BLACK NON HISPANIC     Ethan  138    1\n",
       "...        ...     ...                         ...       ...  ...  ...\n",
       "13885     2014    MALE          WHITE NON HISPANIC     Riley   11  101\n",
       "13880     2014    MALE          WHITE NON HISPANIC      Reed   11  101\n",
       "13908     2014    MALE          WHITE NON HISPANIC   Shloime   11  101\n",
       "13935     2014    MALE          WHITE NON HISPANIC    Wesley   11  101\n",
       "13661     2014    MALE          WHITE NON HISPANIC  Binyomin   11  101\n",
       "13917     2014    MALE          WHITE NON HISPANIC     Silas   11  101\n",
       "13858     2014    MALE          WHITE NON HISPANIC    Nikita   11  101\n",
       "13934     2014    MALE          WHITE NON HISPANIC  Vincenzo   11  101\n",
       "13725     2014    MALE          WHITE NON HISPANIC     Filip   11  101\n",
       "13882     2014    MALE          WHITE NON HISPANIC    Reuben   11  101\n",
       "13862     2014    MALE          WHITE NON HISPANIC    Nosson   11  101\n",
       "13632     2014    MALE          WHITE NON HISPANIC     Anton   11  101\n",
       "13738     2014    MALE          WHITE NON HISPANIC     Grant   11  101\n",
       "13651     2014    MALE          WHITE NON HISPANIC     Aydin   11  101\n",
       "13658     2014    MALE          WHITE NON HISPANIC    Berish   10  102\n",
       "13815     2014    MALE          WHITE NON HISPANIC    Marcel   10  102\n",
       "13691     2014    MALE          WHITE NON HISPANIC  Dashiell   10  102\n",
       "13949     2014    MALE          WHITE NON HISPANIC     Yidel   10  102\n",
       "13746     2014    MALE          WHITE NON HISPANIC    Hayden   10  102\n",
       "13757     2014    MALE          WHITE NON HISPANIC     Imran   10  102\n",
       "13736     2014    MALE          WHITE NON HISPANIC  Giuseppe   10  102\n",
       "13810     2014    MALE          WHITE NON HISPANIC    Lucien   10  102\n",
       "13760     2014    MALE          WHITE NON HISPANIC     Issac   10  102\n",
       "13719     2014    MALE          WHITE NON HISPANIC      Erik   10  102\n",
       "13789     2014    MALE          WHITE NON HISPANIC     Karas   10  102\n",
       "13915     2014    MALE          WHITE NON HISPANIC    Shraga   10  102\n",
       "13844     2014    MALE          WHITE NON HISPANIC   Mordche   10  102\n",
       "13850     2014    MALE          WHITE NON HISPANIC     Myles   10  102\n",
       "13695     2014    MALE          WHITE NON HISPANIC     Denis   10  102\n",
       "13856     2014    MALE          WHITE NON HISPANIC      Nico   10  102\n",
       "\n",
       "[13962 rows x 6 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values('RNK')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora según el ranking y el año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BRTH_YR</th>\n",
       "      <th>GNDR</th>\n",
       "      <th>ETHCTY</th>\n",
       "      <th>NM</th>\n",
       "      <th>CNT</th>\n",
       "      <th>RNK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>ISABELLA</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>ESTHER</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>SOPHIA</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>SOPHIA</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2688</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2906</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>ISABELLA</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3206</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>ESTHER</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>SOPHIA</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4188</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>ISABELLA</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4706</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>ESTHER</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6024</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>SOPHIA</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6151</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6369</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>ISABELLA</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6669</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>ESTHER</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>MIA</td>\n",
       "      <td>229</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>EMMA</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>OLIVIA</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>CHLOE</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>LONDON</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2465</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>CHLOE</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>LONDON</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>MIA</td>\n",
       "      <td>229</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3203</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>EMMA</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>OLIVIA</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3965</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>ASIAN AND PACIFIC ISLANDER</td>\n",
       "      <td>CHLOE</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4184</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>BLACK NON HISPANIC</td>\n",
       "      <td>LONDON</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4506</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>HISPANIC</td>\n",
       "      <td>MIA</td>\n",
       "      <td>229</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>2011</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>EMMA</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13858</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Nikita</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13862</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Nosson</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13873</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Pinchus</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13880</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Reed</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13882</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Reuben</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13885</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Riley</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13890</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Rory</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13908</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Shloime</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13917</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Silas</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13934</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Vincenzo</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13935</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Wesley</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13936</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Weston</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13938</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Winston</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13943</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Yahya</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13658</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Berish</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13691</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Dashiell</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13695</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Denis</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13719</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Erik</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13736</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Giuseppe</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13746</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Hayden</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13757</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Imran</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13760</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Issac</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13789</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Karas</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13810</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Lucien</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13815</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Marcel</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13844</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Mordche</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13850</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Myles</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13856</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Nico</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13915</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Shraga</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13949</th>\n",
       "      <td>2014</td>\n",
       "      <td>MALE</td>\n",
       "      <td>WHITE NON HISPANIC</td>\n",
       "      <td>Yidel</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13962 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BRTH_YR    GNDR                      ETHCTY        NM  CNT  RNK\n",
       "22        2011  FEMALE                    HISPANIC  ISABELLA  331    1\n",
       "322       2011  FEMALE          WHITE NON HISPANIC    ESTHER  224    1\n",
       "1089      2011  FEMALE  ASIAN AND PACIFIC ISLANDER    SOPHIA  119    1\n",
       "1216      2011  FEMALE          BLACK NON HISPANIC   MADISON  176    1\n",
       "2561      2011  FEMALE  ASIAN AND PACIFIC ISLANDER    SOPHIA  119    1\n",
       "2688      2011  FEMALE          BLACK NON HISPANIC   MADISON  176    1\n",
       "2906      2011  FEMALE                    HISPANIC  ISABELLA  331    1\n",
       "3206      2011  FEMALE          WHITE NON HISPANIC    ESTHER  224    1\n",
       "4061      2011  FEMALE  ASIAN AND PACIFIC ISLANDER    SOPHIA  119    1\n",
       "4188      2011  FEMALE          BLACK NON HISPANIC   MADISON  176    1\n",
       "4406      2011  FEMALE                    HISPANIC  ISABELLA  331    1\n",
       "4706      2011  FEMALE          WHITE NON HISPANIC    ESTHER  224    1\n",
       "6024      2011  FEMALE  ASIAN AND PACIFIC ISLANDER    SOPHIA  119    1\n",
       "6151      2011  FEMALE          BLACK NON HISPANIC   MADISON  176    1\n",
       "6369      2011  FEMALE                    HISPANIC  ISABELLA  331    1\n",
       "6669      2011  FEMALE          WHITE NON HISPANIC    ESTHER  224    1\n",
       "122       2011  FEMALE                    HISPANIC       MIA  229    2\n",
       "319       2011  FEMALE          WHITE NON HISPANIC      EMMA  213    2\n",
       "456       2011  FEMALE          WHITE NON HISPANIC    OLIVIA  213    2\n",
       "993       2011  FEMALE  ASIAN AND PACIFIC ISLANDER     CHLOE  106    2\n",
       "1212      2011  FEMALE          BLACK NON HISPANIC    LONDON   92    2\n",
       "2465      2011  FEMALE  ASIAN AND PACIFIC ISLANDER     CHLOE  106    2\n",
       "2684      2011  FEMALE          BLACK NON HISPANIC    LONDON   92    2\n",
       "3006      2011  FEMALE                    HISPANIC       MIA  229    2\n",
       "3203      2011  FEMALE          WHITE NON HISPANIC      EMMA  213    2\n",
       "3340      2011  FEMALE          WHITE NON HISPANIC    OLIVIA  213    2\n",
       "3965      2011  FEMALE  ASIAN AND PACIFIC ISLANDER     CHLOE  106    2\n",
       "4184      2011  FEMALE          BLACK NON HISPANIC    LONDON   92    2\n",
       "4506      2011  FEMALE                    HISPANIC       MIA  229    2\n",
       "4703      2011  FEMALE          WHITE NON HISPANIC      EMMA  213    2\n",
       "...        ...     ...                         ...       ...  ...  ...\n",
       "13858     2014    MALE          WHITE NON HISPANIC    Nikita   11  101\n",
       "13862     2014    MALE          WHITE NON HISPANIC    Nosson   11  101\n",
       "13873     2014    MALE          WHITE NON HISPANIC   Pinchus   11  101\n",
       "13880     2014    MALE          WHITE NON HISPANIC      Reed   11  101\n",
       "13882     2014    MALE          WHITE NON HISPANIC    Reuben   11  101\n",
       "13885     2014    MALE          WHITE NON HISPANIC     Riley   11  101\n",
       "13890     2014    MALE          WHITE NON HISPANIC      Rory   11  101\n",
       "13908     2014    MALE          WHITE NON HISPANIC   Shloime   11  101\n",
       "13917     2014    MALE          WHITE NON HISPANIC     Silas   11  101\n",
       "13934     2014    MALE          WHITE NON HISPANIC  Vincenzo   11  101\n",
       "13935     2014    MALE          WHITE NON HISPANIC    Wesley   11  101\n",
       "13936     2014    MALE          WHITE NON HISPANIC    Weston   11  101\n",
       "13938     2014    MALE          WHITE NON HISPANIC   Winston   11  101\n",
       "13943     2014    MALE          WHITE NON HISPANIC     Yahya   11  101\n",
       "13658     2014    MALE          WHITE NON HISPANIC    Berish   10  102\n",
       "13691     2014    MALE          WHITE NON HISPANIC  Dashiell   10  102\n",
       "13695     2014    MALE          WHITE NON HISPANIC     Denis   10  102\n",
       "13719     2014    MALE          WHITE NON HISPANIC      Erik   10  102\n",
       "13736     2014    MALE          WHITE NON HISPANIC  Giuseppe   10  102\n",
       "13746     2014    MALE          WHITE NON HISPANIC    Hayden   10  102\n",
       "13757     2014    MALE          WHITE NON HISPANIC     Imran   10  102\n",
       "13760     2014    MALE          WHITE NON HISPANIC     Issac   10  102\n",
       "13789     2014    MALE          WHITE NON HISPANIC     Karas   10  102\n",
       "13810     2014    MALE          WHITE NON HISPANIC    Lucien   10  102\n",
       "13815     2014    MALE          WHITE NON HISPANIC    Marcel   10  102\n",
       "13844     2014    MALE          WHITE NON HISPANIC   Mordche   10  102\n",
       "13850     2014    MALE          WHITE NON HISPANIC     Myles   10  102\n",
       "13856     2014    MALE          WHITE NON HISPANIC      Nico   10  102\n",
       "13915     2014    MALE          WHITE NON HISPANIC    Shraga   10  102\n",
       "13949     2014    MALE          WHITE NON HISPANIC     Yidel   10  102\n",
       "\n",
       "[13962 rows x 6 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(['BRTH_YR','GNDR','RNK'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Busquemos un nombre en especial, p.e. EMMA\n",
    "Esto se hace con la función: data.loc[df['column_name'] == some_value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRTH_YR                                                40224\n",
       "GNDR       FEMALEFEMALEFEMALEFEMALEFEMALEFEMALEFEMALEFEMA...\n",
       "ETHCTY     WHITE NON HISPANICASIAN AND PACIFIC ISLANDERBL...\n",
       "NM         EMMAEMMAEMMAEMMAEMMAEMMAEMMAEMMAEMMAEMMAEMMAEM...\n",
       "CNT                                                     2145\n",
       "RNK                                                      278\n",
       "dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=data.loc[data['NM'] == \"EMMA\"]\n",
    "a.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Para mayor información sobre los atributos y métodos de la clase Dataframe en pandas ver:\n",
    "http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GNDR</th>\n",
       "      <th>RNK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>FEMALE</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13932</th>\n",
       "      <td>MALE</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13933</th>\n",
       "      <td>MALE</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13934</th>\n",
       "      <td>MALE</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13935</th>\n",
       "      <td>MALE</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13936</th>\n",
       "      <td>MALE</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13937</th>\n",
       "      <td>MALE</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13938</th>\n",
       "      <td>MALE</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13939</th>\n",
       "      <td>MALE</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13940</th>\n",
       "      <td>MALE</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13941</th>\n",
       "      <td>MALE</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13942</th>\n",
       "      <td>MALE</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13943</th>\n",
       "      <td>MALE</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13944</th>\n",
       "      <td>MALE</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13945</th>\n",
       "      <td>MALE</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13946</th>\n",
       "      <td>MALE</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13947</th>\n",
       "      <td>MALE</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13948</th>\n",
       "      <td>MALE</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13949</th>\n",
       "      <td>MALE</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13950</th>\n",
       "      <td>MALE</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13951</th>\n",
       "      <td>MALE</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13952</th>\n",
       "      <td>MALE</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13953</th>\n",
       "      <td>MALE</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13954</th>\n",
       "      <td>MALE</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13955</th>\n",
       "      <td>MALE</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13956</th>\n",
       "      <td>MALE</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13957</th>\n",
       "      <td>MALE</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13958</th>\n",
       "      <td>MALE</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13959</th>\n",
       "      <td>MALE</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13960</th>\n",
       "      <td>MALE</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13961</th>\n",
       "      <td>MALE</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13962 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GNDR  RNK\n",
       "0      FEMALE   75\n",
       "1      FEMALE   67\n",
       "2      FEMALE   42\n",
       "3      FEMALE   51\n",
       "4      FEMALE   53\n",
       "5      FEMALE   62\n",
       "6      FEMALE    8\n",
       "7      FEMALE   74\n",
       "8      FEMALE   71\n",
       "9      FEMALE   71\n",
       "10     FEMALE   75\n",
       "11     FEMALE   78\n",
       "12     FEMALE   73\n",
       "13     FEMALE   73\n",
       "14     FEMALE   72\n",
       "15     FEMALE   75\n",
       "16     FEMALE   77\n",
       "17     FEMALE   77\n",
       "18     FEMALE   77\n",
       "19     FEMALE   78\n",
       "20     FEMALE   60\n",
       "21     FEMALE   78\n",
       "22     FEMALE    1\n",
       "23     FEMALE   70\n",
       "24     FEMALE   75\n",
       "25     FEMALE   61\n",
       "26     FEMALE   65\n",
       "27     FEMALE   58\n",
       "28     FEMALE   67\n",
       "29     FEMALE   41\n",
       "...       ...  ...\n",
       "13932    MALE   77\n",
       "13933    MALE   70\n",
       "13934    MALE  101\n",
       "13935    MALE  101\n",
       "13936    MALE  101\n",
       "13937    MALE   13\n",
       "13938    MALE  101\n",
       "13939    MALE  100\n",
       "13940    MALE   79\n",
       "13941    MALE   87\n",
       "13942    MALE   67\n",
       "13943    MALE  101\n",
       "13944    MALE   68\n",
       "13945    MALE  100\n",
       "13946    MALE   73\n",
       "13947    MALE   86\n",
       "13948    MALE   41\n",
       "13949    MALE  102\n",
       "13950    MALE   98\n",
       "13951    MALE   43\n",
       "13952    MALE   97\n",
       "13953    MALE   51\n",
       "13954    MALE  100\n",
       "13955    MALE   28\n",
       "13956    MALE   97\n",
       "13957    MALE   94\n",
       "13958    MALE   88\n",
       "13959    MALE   96\n",
       "13960    MALE   39\n",
       "13961    MALE   65\n",
       "\n",
       "[13962 rows x 2 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.filter(items=[\"GNDR\", \"RNK\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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