Extra.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Material extra escuela 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les envió este notebook con un poco de material extra que les pueda servir, algunos links interesantes par profundizar un poco sobre Python, unos ejercicios resueltos, y lo que les mostré el último día sobre pandas, el paquete de Python para trabajar con datos y data frames similares a lo que vieron en R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la página de Python Argentina pueden encontrar mucha información, documentación y tutoriales (hasta ofertas laborales) sobre obviamente Python.<br>\n",
    "\n",
    "+ http://www.python.org.ar/\n",
    "+ http://www.python.org.ar/aprendiendo-python/<br>\n",
    "<br>\n",
    "Y en especial esto ya se lo mandamos pero para que lo tengan acá igual, el tutorial inicial de Python 3, en español, que está bastante bien explicado sin entrar en muchos detalles.\n",
    "+ http://docs.python.org.ar/tutorial/3/index.html\n",
    "+ http://docs.python.org.ar/tutorial/pdfs/TutorialPython3.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biopython\n",
    "\n",
    "El sitio oficial de biopython es: <br>\n",
    "+ https://biopython.org/ <br>\n",
    "Y el tutorial lo pueden encontrar en: <br>\n",
    "+ http://biopython.org/DIST/docs/tutorial/Tutorial.html <br>\n",
    "\n",
    "    +  Este tutorial es mas complicado que el de Python anterior, pero tb es mas completo. Está dividido por capitulos y si necesitan utilizar algo de Biopython no sería necesario que lean o entiendan todos los temas (aunque algunas cosas generales sí estaría bueno entenderlas). Por ejemplo distintos temas podrían ser manejo de Secuencias, Alineamientos, BLAST, Estructuras PDB, Filogenia, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### StackOverflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la parte de R lo nombramos y vimos un ejemplo, StackOverflow es sumermegautilizado como ayuda para programar, sea el lenguaje que sea. Esta disponible en varios idiomas\n",
    "\n",
    "+ https://stackoverflow.com\n",
    "+ https://es.stackoverflow.com\n",
    "\n",
    "Aunque la forma más usual de utilizarla es escribir en google una consulta (poner la palabra \"python\" para que busque en ese lenguaje) y que ahí nos redirija hacia los post de SO relacionados. Si salen muchas páginas falopa primero, agreguen Stak Overflow a la consulta y listo.<br> \n",
    "Salvo que sea muy muy particular lo que quieran consultar, siempre alguien ya intentó hacer algo parecido, y en base a las respuestas nos podemos ahorrar mucho tiempo y dolores de cabeza.\n",
    "Por ejemplo, si no se acuerdan como agregar una nueva pareja clave/valor a un diccionario, lo ecriben en google y muy probablemente los mande a este post:<br>\n",
    "https://stackoverflow.com/questions/1024847/add-new-keys-to-a-dictionary\n",
    "y ahí van viendo las explicaciones y los ejemplos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intento de repaso del bucle FOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos costó un poco entender el bucle for, seguramente practicando (a los que les interese) les va a quedar mas que claro, peor las dudas repasamos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando un bucle **for** lo que hacemos es repetir un bloque de código para cada elemento de un conjunto.<br>\n",
    "Supongamos que nuestro conjunto es una lista con 5 colores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = ['azul', 'amarillo', 'marron', 'rojo', 'verde']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y por ejemplo yo quiera contar la cantidad de letras de cada calor, tendría que ir uno por uno utilizando la funcion **len()** para cada uno. Así:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La verdad que hacer esto es medio una paja, y además si tenemos muchos elementos en la lista directamente no vamos a poder. Por eso utilizamos el bucle **for**. <br>\n",
    "Más allá de como hay que escribirlo, la idea es que si nosotros queremos saber lo mismo de cada elemento de la lista(en este caso la cantidad de letras), vamos a escribir una sola vez qué es eso que queremos, y python se va a encargar de hacer eso para todos los elementos. Si recuerdan la sintaxis del bucle for era:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "8\n",
      "6\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for elemento in lista: # para cada elemento de la lista\n",
    "    print(len(elemento)) # imprimir el largo del elemento."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A nivel código, queremos aplicar la función len() a cada elemento, por eso el bloque que se va a repetir es justamente la funcion len(), y vamos a tener que poner entre los parentesis de len(), a qué le queremos contar en este caso los caracteres."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El valor de esa variable, tiene que ir cambiando, porque nosotros queremos ir contando los caracteres de los distintos elementos de la lista. Entonces lo que va a hacer python, es buscar el primer elemento de la lista, y asignarselo a la variable elemento. Va a imprimir el largo, y luego va a a buscar el segundo elemento de la lista, va a asignarselo a la variable elemento nuevamente, que ahora ya no va a tener el valor del primer elemento sino del segundo. Por ejemplo, Python hace esto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ciclo 1: \n",
      "azul\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "#ciclo 1\n",
    "print('primera ciclo: ')\n",
    "elemento = lista[0]\n",
    "print(elemento)\n",
    "print(len(elemento))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despues:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "segundo ciclo: \n",
      "amarillo\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "#ciclo 2\n",
    "print('segundo ciclo: ')\n",
    "elemento = lista[1]\n",
    "print(elemento)\n",
    "print(len(elemento))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tercer ciclo: \n",
      "marron\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "#ciclo 3\n",
    "print('tercer ciclo: ')\n",
    "elemento = lista[2]\n",
    "print(elemento)\n",
    "print(len(elemento))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vean como la variable elemento la reasigna en cada ciclo, es decir, siempre se llama elemento pero va cambiando el valor que tiene asociado. Y el nombre de esa variable lo inventamos nosotros, puede ser cualquiera. Por ej, cambiando el nombre todo funcional igual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azul\n",
      "amarillo\n",
      "marron\n",
      "rojo\n",
      "verde\n"
     ]
    }
   ],
   "source": [
    "for elemento in lista:\n",
    "    print(elemento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azul\n",
      "amarillo\n",
      "marron\n",
      "rojo\n",
      "verde\n"
     ]
    }
   ],
   "source": [
    "for color in lista:\n",
    "    print(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azul\n",
      "amarillo\n",
      "marron\n",
      "rojo\n",
      "verde\n"
     ]
    }
   ],
   "source": [
    "for asdfghsfsdjklsdfsdmahashjasdnajsdnajkdhhaskdajsdhnaksjdhw3123123123123 in lista:  # alto nombre perri\n",
    "    print(asdfghsfsdjklsdfsdmahashjasdnajsdnajkdhhaskdajsdhnaksjdhw3123123123123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Funciones piolas para usar en bucles for:\n",
    "\n",
    "Hay dos funciones que facilitan hacer ciertas cosas con los bucles for, Se las muestro brevemente pero **si no queda claro no se frustren porque es más avanzado que lo que vimos hasta ahora**. Si les interesa aprenderas hay que sentarse y practicar.\n",
    "\n",
    "+ enumerate()\n",
    "+ zip()\n",
    "\n",
    "Enumerate les devuelve una tupla (qué es eso? no importa, algo muuy similar a una lista) con dos elementos por cade elemento que había en la lista original. El primero es el indice, y el segundo el elemento propiamente dicho. Así puesto es chino básico, pero en el ejemplo queda claro.\n",
    "<br>\n",
    "Zip hace algo parecido pero lo que hace es emparejar varias listas( 2 o +) del mismo tamaño, y nos devuelve tb una tupla, con los elementos de cada lista con el mismo índice. De nuevo chino basico pero con el ejemplo van a entender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si partimos de nuevo de la lista de colores, y queremos imprimir en pantalla cada elemento y su indice, haríamos algo asi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 azul\n",
      "1 amarillo\n",
      "2 marron\n",
      "3 rojo\n",
      "4 verde\n"
     ]
    }
   ],
   "source": [
    "n = 0\n",
    "for elemento in lista:\n",
    "    print(n, elemento)\n",
    "    n = n + 1 #aumento 1 a n asi va siendo igual a los indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pero utilizando la funcion **enumerate()** es más facil:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 azul\n",
      "1 amarillo\n",
      "2 marron\n",
      "3 rojo\n",
      "4 verde\n"
     ]
    }
   ],
   "source": [
    "for indice, elemento in enumerate(lista):\n",
    "    print(indice, elemento)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, eso de indice, elemento es medio raro. Por qué hay dos variables despues del for. En primer lugar Python permite asignar variabes utilizando la coma, para separarlas, seguidas del = y dos valores, tb separados por coma, por ej:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "b = 2\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es similar a escribir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2\n"
     ]
    }
   ],
   "source": [
    "a,b = 1,2\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo loco es que pueden hacer esto por ejemplo si le pasamos una lista con la misma cantidad de elementos que de variables querramos definir, y lo va a hacer siguiendo el orden en que las escbiramos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(a)\n",
    "print(b)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "si en enumerate definimos una sola variable en el for, nos devuelve esto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'azul')\n",
      "(1, 'amarillo')\n",
      "(2, 'marron')\n",
      "(3, 'rojo')\n",
      "(4, 'verde')\n"
     ]
    }
   ],
   "source": [
    "for variable in enumerate(lista):\n",
    "    print(variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "como ven enumerate() nos devuelve una tupla (casi lo mismo que una lista pero con parentesis en vez de corchetes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "en el primer ciclo nos devuelve (0, 'azul')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y python hace algo como esto, que vimos recien que funcionaba:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "azul\n"
     ]
    }
   ],
   "source": [
    "indice, elemento =  (0, 'azul')\n",
    "print(indice)\n",
    "print(elemento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "amarillo\n"
     ]
    }
   ],
   "source": [
    "# para el segundo ciclo, reemplaza los valores:\n",
    "indice, elemento = (1, 'amarillo')\n",
    "print(indice)\n",
    "print(elemento)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y etc... De nuevo, podriamos poner los nombres que quisieramos, o hacer más cosas en cada ciclo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 azul . cantidad de letras:  4\n",
      "1 amarillo . cantidad de letras:  8\n",
      "2 marron . cantidad de letras:  6\n",
      "3 rojo . cantidad de letras:  4\n",
      "4 verde . cantidad de letras:  5\n"
     ]
    }
   ],
   "source": [
    "for i, color in enumerate(lista):\n",
    "    print(i, color, '. cantidad de letras: ',len(color))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recuerden que se puede hacer cosas sobre las dos variables, elementos e indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 AZUL\n",
      "20 AMARILLO\n",
      "30 MARRON\n",
      "40 ROJO\n",
      "50 VERDE\n"
     ]
    }
   ],
   "source": [
    "for indice, elemento in enumerate(lista):\n",
    "    print((indice + 1) * 10, elemento.upper()) # metodo .upper() devuelve un string en mayusculas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La otra funcion, es similar, solo qeu en vez de devolvernos los elementos de una lista y los indices, devuelve todos los primeros elementos de las listas, luego los segundos, luego los terceros, etc. Si tenemos otra lista con nombres de los colores en ingles, con zip podemos hacer esto:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista2 = ['blue', 'yellow', 'brown', 'red', 'green']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azul blue\n",
      "amarillo yellow\n",
      "marron brown\n",
      "rojo red\n",
      "verde green\n"
     ]
    }
   ],
   "source": [
    "for color_spa, color_eng in zip(lista, lista2):\n",
    "    print(color_spa, color_eng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('azul', 'blue')\n",
      "('amarillo', 'yellow')\n",
      "('marron', 'brown')\n",
      "('rojo', 'red')\n",
      "('verde', 'green')\n"
     ]
    }
   ],
   "source": [
    "# para que devuelve zip:\n",
    "for variable in zip(lista,lista2):\n",
    "    print(variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "obviamente para que tenga sentido tienen que ordenar las listas con algun criterio que relacione el orden de ambas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "esto que vimos sería similar a hacer esto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azul blue\n",
      "amarillo yellow\n",
      "marron brown\n",
      "rojo red\n",
      "verde green\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for color_spa in lista:\n",
    "    color_eng = lista2[i]\n",
    "    print(color_spa, color_eng)\n",
    "    i = i + 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "o algo más logico, que tambien hace lo mismo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "azul blue\n",
      "amarillo yellow\n",
      "marron brown\n",
      "rojo red\n",
      "verde green\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(lista)):\n",
    "    color_spa = lista[i]\n",
    "    color_eng = lista2[i]\n",
    "    print(color_spa, color_eng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como ven, se puede hacer lo mismo de muchas formas, mientras sirva, todo bien."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biopython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les dejo un ejercicio resuelto que pudo haber sido el ejercicio del parcial pero no quisimos quedar como los malos de la película.\n",
    "Estaría bueno qeu lo pienen y traten de resolverlo por ustedes y si no les sale mirar la solución (puede haber otras soluciones, de nuevo, mientras sirva, todo bien)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partiendo de un transcripto de TP53 humano (archivo tp53.gb), generar un diccionario con las secuencias de cada exon. Como podríamos obtener la secuencia de la proteína?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#COMPLETAR \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una solución posible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO  # importamos el modulo necesario para leer el archivo.\n",
    "\n",
    "with open('tp53.gb', 'r') as handle:  \n",
    "    secuencia = SeqIO.read(handle, 'gb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeqRecord(seq=Seq('GATGGGATTGGGGTTTTCCCCTCCCATGTGCTCAAGACTGGCGCTAAAAGTTTT...GTG', IUPACAmbiguousDNA()), id='NM_000546.5', name='NM_000546', description='Homo sapiens tumor protein p53 (TP53), transcript variant 1, mRNA', dbxrefs=[])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "secuencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creamos una lista con todas las features asociadas a la secuencia que corresponden los exones\n",
    "posiciones_exones = []\n",
    "for feature in secuencia.features:\n",
    "    if feature.type == 'exon':\n",
    "        posiciones_exones.append(feature)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\t0\t174\n",
      "2\t174\t276\n",
      "3\t276\t298\n",
      "4\t298\t577\n",
      "5\t577\t761\n",
      "6\t761\t874\n",
      "7\t874\t984\n",
      "8\t984\t1121\n",
      "9\t1121\t1195\n",
      "10\t1195\t1302\n",
      "11\t1302\t2591\n"
     ]
    }
   ],
   "source": [
    "#vemos las posiciones para cada exon\n",
    "n = 1\n",
    "for pos in posiciones_exones: # se podria hacer mejor con enumerate()\n",
    "    print(n, pos.location.start, pos.location.end, sep=\"\\t\")\n",
    "    n = n + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "exones = {}\n",
    "n = 1\n",
    "for pos in posiciones_exones: # se podria hacer mejor con enumerate()\n",
    "    start = pos.location.start  # posicion inicial del exon\n",
    "    stop = pos.location.end     # posicion final del exon\n",
    "    exones[n] = secuencia.seq[start:stop]\n",
    "    n = n + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exon 1:\n",
      "GATGGGATTGGGGTTTTCCCCTCCCATGTGCTCAAGACTGGCGCTAAAAGTTTTGAGCTTCTCAAAAGTCTAGAGCCACCGTCCAGGGAGCAGGTAGCTGCTGGGCTCCGGGGACACTTTGCGTTCGGGCTGGGAGCGTGCTTTCCACGACGGTGACACGCTTCCCTGGATTGG\n",
      "Exon 2:\n",
      "CAGCCAGACTGCCTTCCGGGTCACTGCCATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAACT\n",
      "Exon 3:\n",
      "ACTTCCTGAAAACAACGTTCTG\n",
      "Exon 4:\n",
      "TCCCCCTTGCCGTCCCAAGCAATGGATGATTTGATGCTGTCCCCGGACGATATTGAACAATGGTTCACTGAAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAGAGGCTGCTCCCCCCGTGGCCCCTGCACCAGCAGCTCCTACACCGGCGGCCCCTGCACCAGCCCCCTCCTGGCCCCTGTCATCTTCTGTCCCTTCCCAGAAAACCTACCAGGGCAGCTACGGTTTCCGTCTGGGCTTCTTGCATTCTGGGACAGCCAAGTCTGTGACTTGCACG\n",
      "Exon 5:\n",
      "TACTCCCCTGCCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACATGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATG\n",
      "Exon 6:\n",
      "GTCTGGCCCCTCCTCAGCATCTTATCCGAGTGGAAGGAAATTTGCGTGTGGAGTATTTGGATGACAGAAACACTTTTCGACATAGTGTGGTGGTGCCCTATGAGCCGCCTGAG\n",
      "Exon 7:\n",
      "GTTGGCTCTGACTGTACCACCATCCACTACAACTACATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGAGGCCCATCCTCACCATCATCACACTGGAAGACTCCAG\n",
      "Exon 8:\n",
      "TGGTAATCTACTGGGACGGAACAGCTTTGAGGTGCGTGTTTGTGCCTGTCCTGGGAGAGACCGGCGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGCCTCACCACGAGCTGCCCCCAGGGAGCACTAAGCGAG\n",
      "Exon 9:\n",
      "CACTGCCCAACAACACCAGCTCCTCTCCCCAGCCAAAGAAGAAACCACTGGATGGAGAATATTTCACCCTTCAG\n",
      "Exon 10:\n",
      "ATCCGTGGGCGTGAGCGCTTCGAGATGTTCCGAGAGCTGAATGAGGCCTTGGAACTCAAGGATGCCCAGGCTGGGAAGGAGCCAGGGGGGAGCAGGGCTCACTCCAG\n",
      "Exon 11:\n",
      "CCACCTGAAGTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGACAGAAGGGCCTGACTCAGACTGACATTCTCCACTTCTTGTTCCCCACTGACAGCCTCCCACCCCCATCTCTCCCTCCCCTGCCATTTTGGGTTTTGGGTCTTTGAACCCTTGCTTGCAATAGGTGTGCGTCAGAAGCACCCAGGACTTCCATTTGCTTTGTCCCGGGGCTCCACTGAACAAGTTGGCCTGCACTGGTGTTTTGTTGTGGGGAGGAGGATGGGGAGTAGGACATACCAGCTTAGATTTTAAGGTTTTTACTGTGAGGGATGTTTGGGAGATGTAAGAAATGTTCTTGCAGTTAAGGGTTAGTTTACAATCAGCCACATTCTAGGTAGGGGCCCACTTCACCGTACTAACCAGGGAAGCTGTCCCTCACTGTTGAATTTTCTCTAACTTCAAGGCCCATATCTGTGAAATGCTGGCATTTGCACCTACCTCACAGAGTGCATTGTGAGGGTTAATGAAATAATGTACATCTGGCCTTGAAACCACCTTTTATTACATGGGGTCTAGAACTTGACCCCCTTGAGGGTGCTTGTTCCCTCTCCCTGTTGGTCGGTGGGTTGGTAGTTTCTACAGTTGGGCAGCTGGTTAGGTAGAGGGAGTTGTCAAGTCTCTGCTGGCCCAGCCAAACCCTGTCTGACAACCTCTTGGTGAACCTTAGTACCTAAAAGGAAATCTCACCCCATCCCACACCCTGGAGGATTTCATCTCTTGTATATGATGATCTGGATCCACCAAGACTTGTTTTATGCTCAGGGTCAATTTCTTTTTTCTTTTTTTTTTTTTTTTTTCTTTTTCTTTGAGACTGGGTCTCGCTTTGTTGCCCAGGCTGGAGTGGAGTGGCGTGATCTTGGCTTACTGCAGCCTTTGCCTCCCCGGCTCGAGCAGTCCTGCCTCAGCCTCCGGAGTAGCTGGGACCACAGGTTCATGCCACCATGGCCAGCCAACTTTTGCATGTTTTGTAGAGATGGGGTCTCACAGTGTTGCCCAGGCTGGTCTCAAACTCCTGGGCTCAGGCGATCCACCTGTCTCAGCCTCCCAGAGTGCTGGGATTACAATTGTGAGCCACCACGTCCAGCTGGAAGGGTCAACATCTTTTACATTCTGCAAGCACATCTGCATTTTCACCCCACCCTTCCCCTCCTTCTCCCTTTTTATATCCCATTTTTATATCGATCTCTTATTTTACAATAAAACTTTGCTGCCACCTGTGTGTCTGAGGGGTG\n"
     ]
    }
   ],
   "source": [
    "# imprimimos \n",
    "for i in range(1, len(exones) + 1): \n",
    "    print('Exon ' + str(i) + \":\")\n",
    "    print(exones[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtener las secuencias de la proteina:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type: CDS\n",
      "location: [202:1384](+)\n",
      "qualifiers:\n",
      "    Key: codon_start, Value: ['1']\n",
      "    Key: db_xref, Value: ['GI:120407068', 'CCDS:CCDS11118.1', 'GeneID:7157', 'HGNC:HGNC:11998', 'MIM:191170']\n",
      "    Key: gene, Value: ['TP53']\n",
      "    Key: gene_synonym, Value: ['BCC7; BMFS5; LFS1; P53; TRP53']\n",
      "    Key: note, Value: ['isoform a is encoded by transcript variant 1; tumor protein 53; mutant tumor protein 53; cellular tumor antigen p53; phosphoprotein p53; transformation-related protein 53; p53 tumor suppressor; antigen NY-CO-13; tumor supressor p53']\n",
      "    Key: product, Value: ['cellular tumor antigen p53 isoform a']\n",
      "    Key: protein_id, Value: ['NP_000537.3']\n",
      "    Key: translation, Value: ['MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for feature in secuencia.features:\n",
    "    if feature.type == 'CDS':\n",
    "        cds = feature\n",
    "        print(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWF...SD*', HasStopCodon(ExtendedIUPACProtein(), '*'))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cds_seq = secuencia.seq[cds.location.start:cds.location.end]  # cortamos la secuencia original quedandonos con el cds\n",
    "cds_prot_seq = cds_seq.translate()  # traducimos\n",
    "cds_prot_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_q = cds.qualifiers['translation']\n",
    "seq_q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cds.qualifiers['translation'][0] == str(cds_prot_seq)[:-1]\n",
    "#recordemos que qualifiers devuelve una lista, por agregamos el [0], y en cds_prot_seq tenemos el * por el stop, que se lo \"quitamos\" con el :-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib - Graficos en Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib es un paquete de Python que nos permite hacer gráficos. Esto ya lo tienen, pero cuando lo corrimos en clase habia dado error, asi que se los paso de nuevo corrido nuevamente.<br>\n",
    "**La idea es que vean que se pueden hacer graficos con python con codigo no un codigo relativamente simple** <br>\n",
    "Pueden ir cambiando variables y parámetros para ver como van variando los gráficos si quieren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]\n",
      "['0.03', '0.12', '0.46', '1.25', '2.04', '2.38', '2.47', '2.49', '2.50', '2.50']\n",
      "['0.01', '0.03', '0.12', '0.46', '1.25', '2.04', '2.38', '2.47', '2.49', '2.50']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fdee22160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "from numpy.random import normal\n",
    "import math\n",
    "\n",
    "#De aqui salen los datos, tiempos va a ser la variable X\n",
    "# y crecimiento las variables Y1 e Y2\n",
    "tiempo = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]\n",
    "crecimiento_A = [0.03, 0.12, 0.46, 1.25, 2.04, 2.38, 2.47, 2.49, 2.50, 2.50]\n",
    "crecimiento_B = [0.01, 0.03, 0.12, 0.46, 1.25, 2.04, 2.38, 2.47, 2.49, 2.50]\n",
    "\n",
    "print(tiempo)\n",
    "print([\"{0:.2f}\".format(x) for x in crecimiento_A])\n",
    "print([\"{0:.2f}\".format(x) for x in crecimiento_B])\n",
    "\n",
    "pyplot.rcParams[\"figure.figsize\"] = [10,7] # tamaño del grafico\n",
    "pyplot.rcParams['axes.grid']= False\n",
    "pyplot.xlabel(\"Tiempo(hs)\") # leyenda eje X\n",
    "pyplot.ylabel(\"DO_600\") # leyenda eje Y\n",
    "pyplot.plot(tiempo, crecimiento_A, label=\"Cultivo A\", marker=\"o\", markersize=10, linewidth=4, color=\"green\") #estilo de lineas\n",
    "pyplot.plot(tiempo, crecimiento_B, label=\"Cultivo B\", marker=\"s\", linewidth=1, linestyle=\"--\")\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algunos cambios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fe6f4dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.rcParams[\"figure.figsize\"] = [14,8] # tamaño del grafico\n",
    "\n",
    "pyplot.rcParams[\"figure.facecolor\"] = \"gray\"\n",
    "pyplot.xlabel(\"Tiempo(hs)\".upper()) # leyenda eje X\n",
    "pyplot.ylabel(\"DO_600\") # leyenda eje Y\n",
    "pyplot.plot(tiempo, crecimiento_A, label=\"CULTIVO A\", marker=\"o\",  linewidth=2, color=\"blue\", linestyle= '-.') #estilo de lineas\n",
    "pyplot.plot(tiempo, crecimiento_B, label=\"CULTIVO B\", marker=\"s\",  markersize=10, linewidth=4, color=\"red\")\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Desafío**: Cree una nueva serie de datos con la misma cantidad de puntos y agreguela al plot. Esta seria debe ser de color rojo y no tener un marcador."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráficos de barras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fe703f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "from numpy.random import normal\n",
    "import numpy\n",
    "import math\n",
    "\n",
    "goles_de_messi = [60, 59, 91, 45, 58, 52, 59, 50, 51]\n",
    "goles_de_ronaldo = [48, 60, 63, 69, 61, 57, 55, 53, 49]\n",
    "year = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018]\n",
    "\n",
    "pyplot.rcParams[\"figure.facecolor\"] = \"white\"\n",
    "pyplot.rcParams[\"figure.figsize\"] = [10,7]\n",
    "\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "index = numpy.arange(9)\n",
    "\n",
    "rects1 = ax.bar(index-0.125, goles_de_messi, 0.25, color='SkyBlue', label='Messi')\n",
    "rects2 = ax.bar(index+0.125, goles_de_ronaldo, 0.25, color='IndianRed', label='Ronaldo')\n",
    "\n",
    "\n",
    "pyplot.ylabel('Goles')\n",
    "pyplot.xlabel('Año')\n",
    "pyplot.title('Goles de Messi vs Ronaldo')\n",
    "\n",
    "ax.set_xticklabels(year)\n",
    "pyplot.xticks(range(0,9))\n",
    "pyplot.legend()\n",
    "pyplot.tight_layout()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Desafío:** Cambie el ancho de las columnas, tal que no quede espacio en blanco en medio.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Desafío:** Haga otro boxplot con los datos del año 2011 a 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fe70e2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contacts_per_residue = [\n",
    "    6, 12, 16, 21, 18, 20, 19, 21, 11, 15, 16, 17, 16, 19,\n",
    "    18, 15, 17, 20, 12, 5, 5, 7, 13, 13, 12, 16, 10, 13,\n",
    "    9,6, 6, 12, 16, 13, 18, 14, 18, 21, 17, 13, 18, 9, 8,\n",
    "    7, 9, 9, 12, 12, 16, 13, 18, 9, 14, 17, 12, 13, 16, 13,\n",
    "    8, 11, 11, 11, 14, 16, 16, 17, 14, 14, 20, 18, 18, 16,\n",
    "    10, 16, 10, 16, 9, 9, 8, 13, 16, 10, 10, 13, 13, 7, 8,\n",
    "    12, 11, 17, 10, 13, 11, 10, 9, 5, 9, 10, 16, 16, 18, 14,\n",
    "    23, 18, 22, 20, 18, 14, 22, 24, 31, 21, 27, 28, 25, 24,\n",
    "    24, 24, 26, 22, 27, 27, 30, 33, 31, 36, 36, 21, 31, 33,\n",
    "    26, 20, 27, 29, 15, 18, 18, 24, 18, 18, 17, 24, 19, 19,\n",
    "    16, 14, 12, 15, 13, 9, 7, 10, 16, 17, 12, 13, 20, 12, 13,\n",
    "    20, 15, 11, 13, 13, 11, 17, 10, 16, 14, 16, 13, 20, 18,\n",
    "    20, 18, 16, 18, 18, 16, 17, 16, 21, 9, 18, 15, 7, 13, 9,\n",
    "    9, 9, 13, 14, 17, 17, 19, 19, 16, 20, 16, 12, 16, 16, 20,\n",
    "    16, 16, 15, 18, 19, 12, 18, 15, 13, 15, 16, 18, 13, 15, 9,\n",
    "    11, 12, 14, 9, 12, 12, 13, 18, 16, 14, 18, 14, 21, 16, 12,\n",
    "    16, 17, 15, 12, 14, 22, 17, 10, 13, 20, 11, 9, 9, 11, 7, 10,\n",
    "    10, 15, 8, 7, 14, 12, 11, 13, 16, 17, 14, 19, 17, 14, 14, 17,\n",
    "    13, 12, 14, 18, 12, 13, 16, 12, 8, 9, 10, 10, 16, 11, 15, 11,\n",
    "    15, 14, 13, 8, 9, 9, 5, 9, 8, 13, 9, 11, 11, 18, 11, 16, 8,\n",
    "    10, 17, 15, 11, 13, 15, 13, 11, 14, 17, 11, 12, 19, 15, 10,\n",
    "    14, 16, 11, 11, 19, 17, 9, 12, 10, 8, 12, 8, 11, 7, 9, 15, 11,\n",
    "    14, 18, 16, 19, 19, 12, 12, 17, 14, 14, 16, 13, 11, 20, 14, 11,\n",
    "    15, 18, 14, 11, 13, 16, 9, 8, 12, 8, 10, 11, 7, 5, 11, 11, 14,\n",
    "    19, 14, 16, 22, 19, 20, 19, 19, 19, 15, 20, 17, 12, 11, 13, 9]\n",
    "\n",
    "num_bins = 15\n",
    "\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = ax.hist(contacts_per_residue, num_bins)\n",
    "\n",
    "ax.set_xlabel('Número de contactos')\n",
    "ax.set_ylabel('Cantidad de residuos')\n",
    "ax.set_title('Histograma de número de contactos por residuo ')\n",
    "pyplot.tight_layout()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fe7272630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "labels = ['control', 'trat 2h', 'trat 4h']\n",
    "\n",
    "control_data = [5, 3, 2 , 2, 5, 6 , 2, 2, 3, 3 , 3, 1, 3, 4, 3]\n",
    "trat2h_data  = [5, 8, 6 , 7, 4, 10, 6, 9, 6, 5 , 6, 5, 3, 6, 9]\n",
    "trat4h_data  = [8, 6, 10, 9, 8, 9 , 8, 6, 6, 12, 6, 7, 6, 9, 8]\n",
    "\n",
    "fig, axes = pyplot.subplots()\n",
    "\n",
    "all_data = [control_data, trat2h_data, trat4h_data]\n",
    "\n",
    "\n",
    "bplot1 = axes.boxplot(all_data,\n",
    "                      notch=True, # Corte (o muesca) en el boxplot\n",
    "                      vert=True,  # plot horizontal o vertical\n",
    "                      patch_artist=True,  # necesario para rellenar con color\n",
    "                      labels=labels)  # Etiquetas en el eje X\n",
    "\n",
    "axes.set_title('Box Plot control vs trat.')\n",
    "\n",
    "# Cambiar el color de relleno\n",
    "bplot1['boxes'][0].set_facecolor('pink')\n",
    "bplot1['boxes'][1].set_facecolor('lightblue')\n",
    "bplot1['boxes'][2].set_facecolor('lightgreen')\n",
    "\n",
    "# Agrega líneas horizontales\n",
    "#axes.yaxis.grid(True)\n",
    "\n",
    "# Agrega subtítulos a los ejes\n",
    "axes.set_xlabel('Muestras')\n",
    "axes.set_ylabel('Valor observado')\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas... como R pero en python.\n",
    "\n",
    "Antes que nada les aclaro que esto es una introducción a pandas/python y una comparación con cosas que vimos en R sumamente informal.\n",
    "Por eso la onda es ver un poquito como podemos hacer algunas cosas que hicimos en R con python, con una dificultad similar.\n",
    "Pero las definiciones no necesariamente van a estar técnicamente correctas ni las comparaciones con R son exactas.\n",
    "\n",
    "**Pandas** es una librería de Python que nos permite realizar Análisis de datos. Aporta estructuras de datos que Python base no tiene. Entre ellas\n",
    "los Data Frame y las Series (que son como los vetores de R, (No son lo mismo, pero más o menos)))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # estamos importando la libreria pandas, y la \"renombramos\" pd para no tener que escribir pandas cada vez qeu la queramos usar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si recuerdan, en Python tenemos listas, y hay que recorrerlas con el temible bucle **for**, por ej si queremos imprimir el doble de cada elemento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = [1,2,3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "for numero in lista:\n",
    "    print(numero * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En pandas, podemos convertir las listas en objetos Series (arrays de una dimensión):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "5    6\n",
      "6    7\n",
      "dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "serie = pd.Series(lista)\n",
    "print(serie)\n",
    "print(type(serie))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora tenemos un vector o array, y las operaciones matematicas para las series están justamente vectorizadas, por lo que se puede sumar, restar, dividir, etc. Sin tener que usar el temible bucle **for** como teníamos que hacer con las listas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "doble = serie * 2\n",
    "triple = serie * 3\n",
    "suma10 = serie + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EL doble:\n",
      " 0     2\n",
      "1     4\n",
      "2     6\n",
      "3     8\n",
      "4    10\n",
      "5    12\n",
      "6    14\n",
      "dtype: int64\n",
      "El triple:\n",
      " 0     3\n",
      "1     6\n",
      "2     9\n",
      "3    12\n",
      "4    15\n",
      "5    18\n",
      "6    21\n",
      "dtype: int64\n",
      "Sumando 10:\n",
      " 0    11\n",
      "1    12\n",
      "2    13\n",
      "3    14\n",
      "4    15\n",
      "5    16\n",
      "6    17\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('EL doble:\\n', doble) # recordemos que el \\n hace que me imprima en el siguiente renglon la serie.\n",
    "print('El triple:\\n', triple)\n",
    "print('Sumando 10:\\n', suma10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     35\n",
       "1     76\n",
       "2    123\n",
       "3    176\n",
       "4    235\n",
       "5    300\n",
       "6    371\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mas complejo\n",
    "doble + suma10 * triple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los elementos de las series se acceden igual que las listas, utilzando corchetes, o los dos puntos : para hacer subseries (sublistas):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie[2:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último con las series, los elementos pueden ser nombrados y acceder a cada uno por el nombre, como en R, sin tener que usar diccionarios. Para eso se utiliza se usa el atributo .index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombres = ['a', 'b', 'c', 'd', 'e', 'f', 'g']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "e    5\n",
      "f    6\n",
      "g    7\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "serie.index = nombres\n",
    "print(serie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tambien podemos pasarle los nombres al crear las series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "f    5\n",
       "g    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nueva_serie = pd.Series(range(7), index=nombres ) # Creamos una serie a partir de la funcion range que vimos en el curso\n",
    "# y en el argumento index le pasamos la lista de nombres.\n",
    "nueva_serie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y por último, tambien como en R podemos acceder a un elemento de la serie utilizando el nombre del índice en lugar del número."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hacer cuentas, slicing, etc.\n",
    "serie['e']+ serie['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "f    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie[['c', 'f']] #notar que le estoy pasando una lista con los nombre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Eso es todo con las series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pasamos a lo importante, los **Data Frames**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La idea es ver brevemente algunas de las cosas que hicimos en R en el día 2, como se pueden hacer fácilmente también en Python con Pandas. Es solo para mostrarle qué se puede hacer sin que sea mucho bardo. Y si les interesa puedan buscar mas info en internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargamos el dataset de Iris, que tenemos en un archivo csv.\n",
    "iris = pd.read_csv('iris.csv')\n",
    "#como ven, no hace falta leer el archivo y separarlo por comas, generar listas, diccionarios, ni esas cosas horribles que vimso en el curso.\n",
    "# pandas lee directamente el csv, y lo guarda como un data frame en la variable iris (en este caso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recuerden que en Python las funciones suelen ser métodos que estan asociados a un objeto y la sintaxis suele ser objeto.funcion(). En R era más comun que las funciones estén por fuera, de la forma funcion(objeto).\n",
    "Un claro ejemplo es la funcion head, que en R sería head(iris) y en python es iris.head()\n",
    "Va a pasar lo mismo en casi todos los metodos asociados a los data frame en pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           4.6          3.4           1.4          0.3  setosa\n",
       "7           5.0          3.4           1.5          0.2  setosa\n",
       "8           4.4          2.9           1.4          0.2  setosa\n",
       "9           4.9          3.1           1.5          0.1  setosa"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(10) # 10 primeras filas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal_length  sepal_width  petal_length  petal_width species\n",
      "0           5.1          3.5           1.4          0.2  setosa\n",
      "1           4.9          3.0           1.4          0.2  setosa\n",
      "2           4.7          3.2           1.3          0.2  setosa\n",
      "3           4.6          3.1           1.5          0.2  setosa\n",
      "4           5.0          3.6           1.4          0.2  setosa\n"
     ]
    }
   ],
   "source": [
    "#vista simple de consola:\n",
    "print(iris.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Para ver la estructura de un data frame, como la funcion str() de R\n",
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#nombres de las columnas del data frame\n",
    "list(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n",
      "5\n",
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "#numero de filas, numero de columnas, dimension\n",
    "print(   len(iris)    )\n",
    "print(   len(iris.columns)  )\n",
    "print(    iris.shape    ) # .shape es similar a dim() de R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# para ver rapidamente estadisticas del data frame utilizamos el metodo .describe\n",
    "iris.describe() # similar a summary() en R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "5      5.4\n",
       "6      4.6\n",
       "7      5.0\n",
       "8      4.4\n",
       "9      4.9\n",
       "10     5.4\n",
       "11     4.8\n",
       "12     4.8\n",
       "13     4.3\n",
       "14     5.8\n",
       "15     5.7\n",
       "16     5.4\n",
       "17     5.1\n",
       "18     5.7\n",
       "19     5.1\n",
       "20     5.4\n",
       "21     5.1\n",
       "22     4.6\n",
       "23     5.1\n",
       "24     4.8\n",
       "25     5.0\n",
       "26     5.0\n",
       "27     5.2\n",
       "28     5.2\n",
       "29     4.7\n",
       "      ... \n",
       "120    6.9\n",
       "121    5.6\n",
       "122    7.7\n",
       "123    6.3\n",
       "124    6.7\n",
       "125    7.2\n",
       "126    6.2\n",
       "127    6.1\n",
       "128    6.4\n",
       "129    7.2\n",
       "130    7.4\n",
       "131    7.9\n",
       "132    6.4\n",
       "133    6.3\n",
       "134    6.1\n",
       "135    7.7\n",
       "136    6.3\n",
       "137    6.4\n",
       "138    6.0\n",
       "139    6.9\n",
       "140    6.7\n",
       "141    6.9\n",
       "142    5.8\n",
       "143    6.8\n",
       "144    6.7\n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#podemos quedarnos con unsa sola columna\n",
    "iris['sepal_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tb podemos hacer un subset pasando una lista con los nombres de las columnas entre los corchetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width\n",
       "0             5.1          3.5\n",
       "1             4.9          3.0\n",
       "2             4.7          3.2\n",
       "3             4.6          3.1\n",
       "4             5.0          3.6\n",
       "5             5.4          3.9\n",
       "6             4.6          3.4\n",
       "7             5.0          3.4\n",
       "8             4.4          2.9\n",
       "9             4.9          3.1\n",
       "10            5.4          3.7\n",
       "11            4.8          3.4\n",
       "12            4.8          3.0\n",
       "13            4.3          3.0\n",
       "14            5.8          4.0\n",
       "15            5.7          4.4\n",
       "16            5.4          3.9\n",
       "17            5.1          3.5\n",
       "18            5.7          3.8\n",
       "19            5.1          3.8\n",
       "20            5.4          3.4\n",
       "21            5.1          3.7\n",
       "22            4.6          3.6\n",
       "23            5.1          3.3\n",
       "24            4.8          3.4\n",
       "25            5.0          3.0\n",
       "26            5.0          3.4\n",
       "27            5.2          3.5\n",
       "28            5.2          3.4\n",
       "29            4.7          3.2\n",
       "..            ...          ...\n",
       "120           6.9          3.2\n",
       "121           5.6          2.8\n",
       "122           7.7          2.8\n",
       "123           6.3          2.7\n",
       "124           6.7          3.3\n",
       "125           7.2          3.2\n",
       "126           6.2          2.8\n",
       "127           6.1          3.0\n",
       "128           6.4          2.8\n",
       "129           7.2          3.0\n",
       "130           7.4          2.8\n",
       "131           7.9          3.8\n",
       "132           6.4          2.8\n",
       "133           6.3          2.8\n",
       "134           6.1          2.6\n",
       "135           7.7          3.0\n",
       "136           6.3          3.4\n",
       "137           6.4          3.1\n",
       "138           6.0          3.0\n",
       "139           6.9          3.1\n",
       "140           6.7          3.1\n",
       "141           6.9          3.1\n",
       "142           5.8          2.7\n",
       "143           6.8          3.2\n",
       "144           6.7          3.3\n",
       "145           6.7          3.0\n",
       "146           6.3          2.5\n",
       "147           6.5          3.0\n",
       "148           6.2          3.4\n",
       "149           5.9          3.0\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[['sepal_length', 'sepal_width']] # Nos devuelve otro data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para guardarlo:\n",
    "iris_sepalos = iris[['sepal_length', 'sepal_width']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width\n",
       "0           5.1          3.5\n",
       "1           4.9          3.0\n",
       "2           4.7          3.2\n",
       "3           4.6          3.1\n",
       "4           5.0          3.6"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_sepalos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al igual que vimos en R, podemos hacer subsets quedandonos solo con las filas que cumplen ciertas condiciones. Por ejemplo si queremos quedarnos con las flores setosas con largo del sepalo mayores a 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#el metodo .loc se usa para acceder a los valores de filas y columnas\n",
    "#en R seria iris[fila, columna] y en pandas iris.loc[filas,columnas]\n",
    "\n",
    "iris_subset = iris.loc[(iris['species'] == 'setosa') & (iris['sepal_length'] > 5) , ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.4</td>\n",
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       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width species\n",
       "0            5.1          3.5           1.4          0.2  setosa\n",
       "5            5.4          3.9           1.7          0.4  setosa\n",
       "10           5.4          3.7           1.5          0.2  setosa\n",
       "14           5.8          4.0           1.2          0.2  setosa\n",
       "15           5.7          4.4           1.5          0.4  setosa\n",
       "16           5.4          3.9           1.3          0.4  setosa\n",
       "17           5.1          3.5           1.4          0.3  setosa\n",
       "18           5.7          3.8           1.7          0.3  setosa\n",
       "19           5.1          3.8           1.5          0.3  setosa\n",
       "20           5.4          3.4           1.7          0.2  setosa\n",
       "21           5.1          3.7           1.5          0.4  setosa\n",
       "23           5.1          3.3           1.7          0.5  setosa\n",
       "27           5.2          3.5           1.5          0.2  setosa\n",
       "28           5.2          3.4           1.4          0.2  setosa\n",
       "31           5.4          3.4           1.5          0.4  setosa\n",
       "32           5.2          4.1           1.5          0.1  setosa\n",
       "33           5.5          4.2           1.4          0.2  setosa\n",
       "36           5.5          3.5           1.3          0.2  setosa\n",
       "39           5.1          3.4           1.5          0.2  setosa\n",
       "44           5.1          3.8           1.9          0.4  setosa\n",
       "46           5.1          3.8           1.6          0.2  setosa\n",
       "48           5.3          3.7           1.5          0.2  setosa"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tb podemos agrupar valores para simplificar los data frame, por ejemplo, podriamos agrupar por las especies, setosa, virginica, y versicolor, y quedarnos con el promedio del largo y ancho de los petalos y sepalos para cada muy facilmente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.418         1.464        0.244\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(['species']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fded22a20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5fdecb96a0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fdec73470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5fdec2b390>]], dtype=object)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p+++/18aNGzVs2DDddNNNatu2rW677TYtXLhQQUFB6t+/v/bu3at58+YpPDy8RPsplW78HnzwQfdRxcWLF5eo/27dumnIkCHq0KGD6tevr2+++UZLlixR9+7dZbfb3e2Cg4P13HPP6cKFC+ratau2bdump59+WoMGDVLPnj0lST169NDvfvc73X777dq9e7d69+4th8Oh48ePa+vWrWrfvr3uu+8+hYWF6bnnntNdd92l/v376+6771bjxo114MABffnll/mWvs/TsmVLPfXUU/rjH/+ob7/91n09spMnT2rnzp3uo2NfffWVfv/73+vmm2/WZZddpuDgYH3yySf66quv9Oijj5b4tQeAKlOlS0kAQCUoaNW7unXrmk6dOpn58+fnW0Y7LS3N3HvvvaZp06YmMDDQREdHm8cee8yj3W233WbsdrvHUsjGGPPuu+8aSWbBggXGmF9WTZs9e7ZJSEgwl1xyiQkODjZXXXWV+fDDDwus03t1uNdff9106NDBBAcHm7p165phw4ble97s7Gxz1113mYYNGxo/P78C+/m13Nxc8/TTT7vr6dChg0lMTDQdO3Y0N910k7td3sp07777boH97NmzxwwdOtTUrVvXBAcHm44dO5rFixfna7d//35z/fXXm/DwcNOwYUNz//33mzVr1hS46t2VV15pNm3aZLp06WJsNptp2rSpefzxx43T6fTo0+l0mnnz5pmOHTuakJAQExYWZmJjY80999xj/ve//3m8NlOmTDGNGjUyISEh5pprrjHbt2830dHRJV71riTj92stW7Y0bdu2LbLvX3v00UdNly5dTP369Y3NZjOXXnqpefjhh01qaqq7zfjx443D4TBfffWViY+PN6GhoaZBgwbmvvvuMxcuXMjX5xtvvGG6detmHA6HCQ0NNa1atTLjxo0zu3fv9mi3du1a06dPH+NwOIzdbjdXXHGFx/LuBS0Pbowxq1evNn379jXh4eHGZrOZ6OhoM3LkSPPRRx8ZY4w5efKkmTBhgomNjTUOh8OEhYWZDh06mAULFnisDAkA1ZWfMaU43g4AKJXDhw8rJiZGc+fO1dSpU6u6nCIdOnRIsbGxmj59uh5//PEqqSE+Pl6pqanau3dvlTx/efjqq6/UsWNHvfTSS5o4cWK59TthwgStXLmy0MU/AADli1PvAMCCvvzySy1btkzXXnutwsPDtW/fPs2ZM0fh4eG68847q7q8GungwYM6cuSIHn/8cTVt2jTfsu4AgJqFoAQAFuRwOLR792797W9/09mzZ1W3bl3Fx8dr5syZhS4RjqL9+c9/1pIlS9S2bVu9++67Ht8tAgDUPJx6BwAAAABeWB4cAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJtcratWs1Y8YMn/qYMGGCWrZsWarHzJgxQ35+fj49b3kq6nXw8/PT73//+8otCADgk6qa38qqZcuWmjBhQrHtNm3aJD8/P23atMm9benSpVq4cGG+tocPH5afn5/mzZtXjpUChSMooVZZu3atEhISqrqMKsfrAAC1S037vf7+++/rySefLNNjCwtKQGULrOoCAAAAULtcddVVVV0C4DOOKKFK5Z2y9vnnn2vEiBEKDw9X3bp1ddttt+n06dMebZcvX67u3bvL4XAoLCxMN9xwgz7//HP3/RMmTNBLL70k6efTy/L+HT58WJL00ksvqXfv3mrUqJEcDofat2+vOXPmyOl0Vtj+FVdzXt1hYWE6cOCAbrzxRoWFhal58+aaMmWKsrOzPdp+//33GjlypOrUqaN69erp1ltv1a5du+Tn56e///3vJXod8ixZskRt27aV3W5Xx44dlZiYWGGvAwBYTW2Y39asWSM/Pz/t2rXLve29996Tn5+fBg8e7NG2Q4cO+u1vf+u+XdCpdykpKRo4cKDsdrsiIyN177336vz58x5t4uPjtWbNGh05csRjX73Nnz9fMTExCgsLU/fu3bVjxw6f9hUoCEEJ1cJNN92k1q1ba+XKlZoxY4ZWr16tG264wf1L/plnntGYMWN0xRVXaMWKFVqyZInOnz+vXr166euvv5YkPfnkkxo5cqQkafv27e5/TZs2lSQdPHhQY8eO1ZIlS5SYmKg777xTc+fO1T333FMh+1SSmvM4nU795je/Ub9+/fTBBx/ojjvu0IIFCzR79mx3m4yMDPXt21cbN27U7NmztWLFCjVu3FijR4/26Ku410H6efJ78cUX9dRTT+m9995TgwYNdNNNN+nbb7+tkNcCAKyqJs9vffr0UVBQkD766CP3to8++kihoaHavHmzex9OnTqlvXv3qn///oX2dfLkSfXp00d79+7VokWLtGTJEl24cCHfd2YXLVqkHj16qEmTJh77+msvvfSSkpKStHDhQr399tvKyMjQjTfeqHPnzvm0v0A+BqhC06dPN5LMww8/7LH97bffNpLMP/7xD3P06FETGBho7r//fo8258+fN02aNDGjRo1yb5s0aZIpyY91Tk6OcTqd5q233jIBAQHmxx9/dN83fvx4Ex0dXab9yFOamsePH28kmRUrVni0vfHGG02bNm3ct1966SUjyaxbt86j3T333GMkmcWLF7u3FfU6SDKNGzc26enp7m0nTpww/v7+ZtasWSXfaQBAoWrL/NazZ09z3XXXuW+3bt3aTJs2zfj7+5vNmzd77NP+/fvd7aKjo8348ePdt//whz8YPz8/88UXX3j0P2DAACPJbNy40b1t8ODBBdZ56NAhI8m0b9/euFwu9/adO3caSWbZsmWl2jegOBxRQrVw6623etweNWqUAgMDtXHjRn344YdyuVwaN26cXC6X+19ISIj69OnjsVJOUT7//HP95je/UUREhAICAhQUFKRx48YpJydH+/fvL9f9KW3Nfn5+Gjp0qMe2Dh066MiRI+7bmzdvVp06dTRw4ECPdmPGjCl1fX379lWdOnXctxs3bqxGjRp5PB8AwHc1fX7r16+f/v3vfysrK0tHjhzRgQMHdMstt6hTp05KSkqS9PNRphYtWuiyyy4rtJ+NGzfqyiuvVMeOHT22jx07ttQ1DR48WAEBAe7bHTp0kCTmMJQ7FnNAtdCkSROP24GBgYqIiFBaWppOnjwpSeratWuBj/X3Lz7vHz16VL169VKbNm30l7/8RS1btlRISIh27typSZMmKSsry/ed+JXS1my32xUSEuKxzWaz6eLFi+7baWlpaty4cb6+CtpWnIiIiHzbbDZbub8OAGB1NX1+69+/vxISErR161YdOXJEkZGRuuqqq9S/f3999NFH+vOf/6yPP/64yNPupJ/nsJiYmHzbvV+fkvCew2w2myQxh6HcEZRQLZw4cUJRUVHu2y6XS2lpaYqIiFBkZKQkaeXKlYqOji5T/6tXr1ZGRoZWrVrl0ccXX3zhW+GFKI+avUVERGjnzp35tp84caJc+gcAlL+aPr9169ZNYWFh+uijj3T48GH169dPfn5+6tevn5577jnt2rVLR48eLTYoRUREFDhfMYehOiMooVp4++23FRcX5769YsUKuVwuxcfHq2fPngoMDNTBgwc9VtQpyK//qhQaGurenrdiTt79kmSM0WuvvVaeu+F2ww03lLjmkurTp49WrFihdevWadCgQe7t77zzTr62hb0OAIDKVdPnt6CgIPXu3VtJSUn67rvv9Oyzz0qSevXqpcDAQD3xxBPu4FSUvn37as6cOfryyy89Tr9bunRpvrac4YDqgqCEamHVqlUKDAzUgAED9N///ldPPvmkOnbsqFGjRik4OFhPPfWU/vjHP+rbb7/VwIEDVb9+fZ08eVI7d+6Uw+FwX4Svffv2kqTZs2dr0KBBCggIUIcOHTRgwAAFBwdrzJgxeuSRR3Tx4kW9/PLLOnPmTIXsT8uWLUtcc0mNHz9eCxYs0G233aann35arVu31rp16/Thhx9K8jxFo7DXITg4uPx2EgBQrNowv/Xr109TpkyRJPeRo9DQUF177bXasGGDOnTooEaNGhXZx0MPPaQ33nhDgwcP1tNPP63GjRvr7bffVkpKSr627du316pVq/Tyyy8rLi5O/v7+6tKlS7ntD1BiVb2aBKwtb1Wg5ORkM3ToUBMWFmbq1KljxowZY06ePOnRdvXq1aZv374mPDzc2Gw2Ex0dbUaOHGk++ugjd5vs7Gxz1113mYYNGxo/Pz8jyRw6dMgYY8y//vUv07FjRxMSEmKioqLMtGnTzLp16/KttlMeq96Vpubx48cbh8NRoj6PHj1qRowY4X6dfvvb35q1a9caSeaDDz4o0esgyUyaNCnf83mvUAQAKLvaMr8ZY8yXX35pJJnLLrvMY/vMmTONJDN58uR8jyloTvn666/NgAEDTEhIiGnQoIG58847zQcffJCvzh9//NGMHDnS1KtXz72vxvyy6t3cuXPzPZ8kM3369FLvG1AUP2OMqfR0BvyfGTNmKCEhQadPn3afq43SeeaZZ/TEE0/o6NGjuuSSS6q6HACAmN+A2oBT74Aa5MUXX5QkxcbGyul06pNPPtHzzz+v2267jZAEAABQjghKQBFyc3OVm5tbZJvAwMp7G9ntdi1YsECHDx9Wdna2WrRooT/84Q964oknKq0GAEDNV93mN6A64tQ7oAgTJkzQm2++WWQb3kIAgJqG+Q0oHkEJKMLhw4eVmppaZBtW4gEA1DTMb0DxCEoAAAAA4MW/+CYAAAAAYC2V/i293NxcHTt2THXq1HFfTRoAUL0YY3T+/Hk1a9bM42LG+AXzGQDUDGWd0yo9KB07dkzNmzev7KcFAJTBd999x9LzhWA+A4CapbRzWqUHpTp16kj6udDw8PDKfvpK53Q6tWHDBl1//fUKCgqq6nJQARhja7DaOKenp6t58+bu39nIz2rzmWS994EVMca1nxXHuKxzWqUHpbzTE8LDwy0xsTidTtntdoWHh1vmh9FqGGNrsOo4c0pZ4aw2n0nWfR9YCWNc+1l5jEs7p3HiOQAAAAB4KVVQcrlceuKJJxQTE6PQ0FBdeumleuqpp4q9sjMAAAAA1CSlOvVu9uzZeuWVV/Tmm2/qyiuv1O7du3X77berbt26evDBByuqRgAAAACoVKUKStu3b9ewYcM0ePBgSVLLli21bNky7d69u0KKAwAAAICqUKqg1LNnT73yyivav3+/Lr/8cn355ZfaunWrFi5cWOhjsrOzlZ2d7b6dnp4u6ecvkjmdzjKWXXPk7aMV9tWqGGNrsNo4W2U/S8Pq85lkvfeBFTHGtZ8Vx7is++pnjDElbWyM0eOPP67Zs2crICBAOTk5mjlzph577LFCHzNjxgwlJCTk27506VLZ7fYyFQ0AqFiZmZkaO3aszp07Z5kV3YrDfAYANVNZ57RSBaV33nlH06ZN09y5c3XllVfqiy++0EMPPaT58+dr/PjxBT6moL/ANW/eXKmpqZaYfJ1Op5KSkjRgwADLLcFoFYyxNVhtnNPT0xUZGUlQ+hWrz2eS9d4HVsQY135WHOOyzmmlOvVu2rRpevTRR3XLLbdIktq3b68jR45o1qxZhQYlm80mm82Wb3tQUJBlBkey3v5aEWNsDVYZZyvsY2kxn/3CivtsNYxx7WelMS7rfpZqefDMzEz5+3s+JCAggOXBAQAAANQqpTqiNHToUM2cOVMtWrTQlVdeqc8//1zz58/XHXfcUVH1AQAAAEClK1VQeuGFF/Tkk09q4sSJOnXqlJo1a6Z77rlHf/rTnyqqPgAAAACodKUKSnXq1NHChQuLXA4cAAAAAGq6Un1HCQAAAACsgKAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF7/zgPeAAAgAElEQVQISgAAAADghaAEAAAAAF4ISgAAAADghaAEAAAAAF4Cq7oAAKhqmZmZSklJKbLNhaxsbdtzUPUjdyss1FZk29jYWNnt9vIsEQAAVDKCEgDLS0lJUVxcXInazilBm+TkZHXu3Nm3ogAAQJUiKAGwvNjYWCUnJxfZZt/xs5r87h7Nv7m92jStV2x/AACgZiMoAbA8u91e7BEg/yNpsm3JUtt2HdUpOqKSKgMAAFWFxRwAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8EJQAAAAAwAtBCQAAAAC8lDoo/fDDD7rtttsUEREhu92uTp06KTk5uSJqAwAAAIAqEViaxmfOnFGPHj3Ut29frVu3To0aNdLBgwdVr169iqoPAAAAACpdqYLS7Nmz1bx5cy1evNi9rWXLluVdEwAAAABUqVIFpX/+85+64YYbdPPNN2vz5s2KiorSxIkTdffddxf6mOzsbGVnZ7tvp6enS5KcTqecTmcZy6458vbRCvtqVYyxNbhcLvf/VhhrK+xjaVl9PpP4fWcFjHHtZ8UxLuu++hljTEkbh4SESJImT56sm2++WTt37tRDDz2kv/71rxo3blyBj5kxY4YSEhLybV+6dKnsdnuZigaAyvbdBWnenkBNbe9S87CqrqbiZWZmauzYsTp37pzCw8OrupxqgfkMAGqmss5ppQpKwcHB6tKli7Zt2+be9sADD2jXrl3avn17gY8p6C9wzZs3V2pqqiUmX6fTqaSkJA0YMEBBQUFVXQ4qAGNsDV8e/VEjX9utlXd3UccWDaq6nAqXnp6uyMhIgtKvWH0+k/h9ZwWMce1nxTEu65xWqlPvmjZtqiuuuMJjW9u2bfXee+8V+hibzSabzZZve1BQkGUGR7Le/loRY1y7BQYGuv+3wjhbYR9Li/nsF1bcZ6thjGs/K41xWfezVEGpR48e2rdvn8e2/fv3Kzo6ukxPDgAV7VBqhjKyXT73c/B0hvv/vNBUVg5boGIiHT7XBAAAKk6pZvuHH35Y1157rZ555hmNGjVKO3fu1KuvvqpXX321ouoDgDI7lJqhvvM2lWufU1buKZd+Nk6NJywBAFCNlSoode3aVe+//74ee+wxPfXUU4qJidHChQt16623VlR9AFBmeUeSFo7upNaNfFuBISMrW4mbtmtIfHc5QvOfflVSB05d0EPLvyiXo1wAAKDilPr8kSFDhmjIkCEVUQsAVIjWjcLULqquT304nU6daCh1jq5vmXO6AQCwMv+qLgAAAAAAqhuCEgAAAAB4ISgBAAAAgBeCEgAAAAB4ISgBAAAAgBffrpoIANVYds5F+Yf8oEPp++Qf4tvy4C6XS8dcx/TNj9/4dMHZQ+kX5B/yg7JzLkrybSU+AABQcQhKAGqtYxlH5Ih5QY/vLL8+F61f5HMfjhjpWEYnxalxOVQEAAAqAkEJQK3VzBGtjEP36y+jO6mVjxecdblc+vfWf6tHzx4+HVE6eOqCHlz+hZr1jfapHgAAULEISgBqLVtAiHIvRikmvI2uiPD9grOHAg+pbYO2Pl1wNvfiOeVePC1bQIhP9QAAgIrFYg4AAAAA4IWgBAAAAABeCEoAAAAA4IWgBAAAAABeCEoAAAAA4IWgBAAAAABeCEoAAAAA4IWgBAAAAABeuOAsUITMzEylpKQU2eZCVra27Tmo+pG7FRZqK7bP2NhY2e328ioRRchy5kiS9v5wzue+MrKytfu01OTIGTlKMM6FOXDqgs+1AACAikdQAoqQkpKiuLi4ErWdU8I+k5OT1blz57IXhRI7+H+h5NFVe8qpx0AtObCrXHpy2Pj1CwBAdcZMDRQhNjZWycnJRbbZd/ysJr+7R/Nvbq82TeuVqE9UjuuvbCJJatUoTKFBAT71te/4OU1ZuUfPjWyvNk3r+tSXwxaomEiHT30AAICKRVACimC324s9+uN/JE22LVlq266jOkVHVFJlKIkGjmDdcnWLcunL5XJJklo1dKhdlG9BCQAAVH8s5gAAAAAAXghKAAAAAOCFoAQAAAAAXghKAAAAAOCFoAQAAAAAXghKAAAAAOCFoAQAAAAAXghKAAAAAOCFoAQAAAAAXghKAAAAAOAlsKoLAKrSodQMZWS7fOrj4OkM9/+Bgb6/pRy2QMVEOnzuBwAAAGVHUIJlHUrNUN95m8qtvykr95RbXxunxhOWAAAAqhBBCZaVdyRp4ehOat0orOz9ZGUrcdN2DYnvLkeozaeaDpy6oIeWf+HzUS4AAAD4hqAEy2vdKEztouqW+fFOp1MnGkqdo+srKCioHCtDZcnMzFRKSkqRbfYdP6vsEwf0zd5Q5abVK7JtbGys7HZ7eZYIAAAqGUEJgOWlpKQoLi6uRG3Hvll8m+TkZHXu3NnHqgAAQFUiKAGwvNjYWCUnJxfZ5kJWttZs3K7BfbsrrJhTLGNjY8uzPAAAUAUISgAsz263F3sEyOl06kzqKXW/ugunWAIAYAEEJVhWds5F+Yf8oEPp++QfUvbFHFwul465jumbH7/xeXnwQ+kX5B/yg7JzLkoq+/emAAAA4BuCEizrWMYROWJe0OM7y6e/ResXlUs/jhjpWEYnxalxufQHAACA0iMowbKaOaKVceh+/WV0J7XyYXlwl8ulf2/9t3r07OHzEaWDpy7oweVfqFnfaJ/6AQAAgG8ISrAsW0CIci9GKSa8ja6I8G158EOBh9S2QVufv7uSe/Gcci+eli0gxKd+AAAA4Bv/qi4AAAAAAKobghIAAAAAeCEoAQAAAIAXn4LSrFmz5Ofnp4ceeqi86gEAAACAKlfmoLRr1y69+uqr6tChQ3nWAwAAAABVrkxB6cKFC7r11lv12muvqX79+uVdEwAAAABUqTItDz5p0iQNHjxY/fv319NPP11k2+zsbGVnZ7tvp6enS/p5SWWn01mWp69R8vbRCvta05zP+vnn8sujP8rlcpW5n4yL2dp9Wor89rQcITafajpwOkPSz9dm4memerHae9kq+1kaVp/PJOu9D6yIMa79rDjGZd3XUgeld955R5999pl27dpVovazZs1SQkJCvu0bNmyQ3W4v7dPXWElJSVVdArxsP+knKUB//ODrcugtUEsOfF4O/fxs1/atOhJabt2hHFnlvZyZmVnVJVQ7zGe/sMr7wMoY49rPSmNc1jnNzxhjStr4u+++U5cuXbRhwwZ17NhRkhQfH69OnTpp4cKFBT6moL/ANW/eXKmpqQoPDy9T0TWJ0+lUUlKSBgwY4PPFSFG+fsz4SR99c0qXNnQoNCigzP3sP3FOj7z/jebc1FaXNyn7hWvzOGwBahnh8LkflC+rvZfT09MVGRmpc+fOWeJ3dUlYfT6TrPc+sCLGuPaz4hiXdU4r1RGl5ORknTp1SnFxce5tOTk5+vTTT/Xiiy8qOztbAQGeHzhtNptstvynIwUFBVlmcCTr7W9N0LhekG7tHlNu/V3epK46RUeUW3+onqzyXrbCPpYW89kvrLjPVsMY135WGuOy7mepglK/fv20Z88ej2233367YmNj9Yc//CFfSAIAAACAmqhUQalOnTpq166dxzaHw6GIiIh82wEAAACgpvLpgrMAAAAAUBuVaXnwX9u0aVM5lAEAAAAA1QdHlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwQlAAAAADAC0EJAAAAALwEVnUBQHWWmZmplJSUItvsO35W2ScO6Ju9ocpNq1dsn7GxsbLb7eVVIgAAACoAQQkoQkpKiuLi4krUduybJeszOTlZnTt39qEqAAAAVDSCElCE2NhYJScnF9nmQla21mzcrsF9uyss1FaiPgEAAFC9EZSAItjt9mKP/jidTp1JPaXuV3dRUFBQJVUGAACAisRiDoAPcnJytHnzZn366afavHmzcnJyqrokAAAAlAOCElBGq1atUuvWrTVgwADNnz9fAwYMUOvWrbVq1aqqLg0AAAA+IigBZbBq1SqNHDlS7du315YtW7Rs2TJt2bJF7du318iRIwlLAAAANRxBCSilnJwcTZkyRUOGDNHq1avVrVs3hYaGqlu3blq9erWGDBmiqVOnchoeAABADUZQAkppy5YtOnz4sB5//HH5+3u+hfz9/fXYY4/p0KFD2rJlSxVVCAAAAF8RlIBSOn78uCSpXbt2Bd6ftz2vHQAAAGoeghJQSk2bNpUk7d27t8D787bntQMAAEDNQ1ACSqlXr15q2bKlnnnmGeXm5nrcl5ubq1mzZikmJka9evWqogoBAADgK4ISUEoBAQF67rnnlJiYqOHDh2vHjh3KysrSjh07NHz4cCUmJmrevHkKCAio6lIBAABQRoFVXQBQE40YMUIrV67UlClT1Lt3b/f2mJgYrVy5UiNGjKjC6gAAAOArghJQRiNGjNCwYcO0ceNGrVu3ToMGDVLfvn05kgQAAFALEJQAHwQEBKhPnz7KyMhQnz59CEkAAAC1BN9RAgAAAAAvBCUAAAAA8EJQAgAAAAAvBCUAAAAA8EJQAgAAAAAvBCUAAAAA8MLy4AAAAEAtkJmZqZSUlCLbXMjK1rY9B1U/crfCQm1Fto2NjZXdbi/PEmsUghIAAABQC6SkpCguLq5EbeeUoE1ycrI6d+7sW1E1GEEJAAAAqAViY2OVnJxcZJt9x89q8rt7NP/m9mrTtF6x/VkZQQkAAACoBex2e7FHgPyPpMm2JUtt23VUp+iISqqsZmIxBwAAAADwwhElAAAAoAY4lJqhjGyXT30cPJ3h/j8w0Lco4LAFKibS4VMf1RlBCQAAAKjmDqVmqO+8TeXW35SVe8qln41T42ttWCIoAQAAANVc3pGkhaM7qXWjsLL3k5WtxE3bNSS+uxzFLA9elAOnLuih5V/4fISrOiMoAQAAADVE60ZhahdVt8yPdzqdOtFQ6hxdX0FBQeVYWe3DYg4AAAAA4KVUQWnWrFnq2rWr6tSpo0aNGmn48OHat29fRdUGAAAAAFWiVEFp8+bNmjRpknbs2KGkpCS5XC5df/31ysjIqKj6AAAAAKDSleo7SuvXr/e4vXjxYjVq1EjJycnq3bt3uRZWG/z000964YUX9Mknn+jAgQO6//77FRwcXNVlAQAAACiGT99ROnfunCSpQYMG5VJMbfLII4/I4XBo6tSpWrt2raZOnSqHw6FHHnmkqksDAAAAUIwyr3pnjNHkyZPVs2dPtWvXrtB22dnZys7Odt9OT0+X9POKG06ns6xPX609+uijmj9/vho1aqTp06fLbrcrMzNTCQkJmjt3rnJycvTss89WdZkoJ3k/x7X15xk/s9o4W2U/S8OK85k3q70PrIgxrr4ysi/IP+QHHTjztXIDy37dIpfLpWOuY9pzao9PF5z99kyG/EN+UEb2BTmd9jL3UxnK+vPsZ4wxZXngpEmTtGbNGm3dulWXXHJJoe1mzJihhISEfNuXLl0qu716v6hl8dNPP+mWW25ReHi4Xn/9dY8fQJfLpbvuukvnz5/XsmXLOA0PQLWVmZmpsWPH6ty5cwoPD6/qcqoFq81nAKqXz84f06qcRVVdRj4jAiaqc51mVV1Gkco6p5UpKN1///1avXq1Pv30U8XExBTZtqC/wDVv3lypqam1cvJ9/vnnNXXqVL3yyiu644475HQ6lZSUpAEDBigoKEivv/66Jk6cqHnz5umBBx6o6nJRDrzHGLWT1cY5PT1dkZGRBKVfsdp8VhCrvQ+siDGuvj777pTGvLVG80e216UNfTui9J8d/1G3a7r5dkTpdIYmr9yjZeMGq3PzRmXupzKUdU4r1atjjNH999+v999/X5s2bSo2JEmSzWaTzZb/qr9BQUG18g14+PBhSdKwYcM89i9vf4cNG6aJEyfq8OHDtXL/ray2/kzDk1XG2Qr7WFpWm8+KYsV9thrGuPpx2MKUezFKretfoXaNfbvg7HeB36l9o/Y+jbG/65xyL/4ohy2s2v+slLW+Ui3mMGnSJP3jH//Q0qVLVadOHZ04cUInTpxQVlZWmZ68NmrVqpUkKTExscD787bntQMAAABQ/ZTqiNLLL78sSYqPj/fYvnjxYk2YMKG8aqrRJk6cqGnTpumJJ57I95q4XC796U9/UmBgoCZOnFg1BQIAAEvKzMxUSkpKkW0uZGVr256Dqh+5W2Gh+Y+g/lpsbCzfz6tEWc4cSdLeH8751E9GVrZ2n5aaHDkjRzFjXJQDpy74VEdNUOpT71C04OBgPfzww5o7d64uueQSTZ8+XSEhIXr99deVkJCgkydPatq0aSzkAAAAKlVKSori4uJK1HZOCdokJyerc+fOvhWFEjv4f8Hk0VV7yqG3QC05sKsc+pEctrJ/z6m6q717VoXmzPn518uCBQs8jhwFBgZq2rRp7vsBAAAqS2xsrJKTk4tss+/4WU1+d4/m39xebZrWK7Y/VJ7rr2wiSWrVKEyhQQFl7mff8XOasnKPnhvZXm2alv27TtLPISkmsuwLS1R3BCUfFHUI+5ZbbtGIESP0j6XLtG1nsq69Ok63jR2j4OBgffbZZwU+hkPYAACgotjt9mKPAPkfSZNtS5batuuoTtERlVQZSqKBI1i3XN3C535cLpckqVVDh9pF+RaUajuCkg9Kcwj78//8Wy+98HyRbTiEDQAAAFQPBCUfcAgbAAAAqJ0ISkU4lJqhjGxXkW2CGxe9zHdw7jnZmmQpuHFrBRez5v23Z5zSmcJXMqnt54ECAAAA1QVBqRCHUjPUd96mcutvysryWKFE2jg1nrAEAAAAVDCCUiHyjiQtHN1JrRuFlb2frGwlbtquIfHdfV6r/qHlXxR7hAsAAACA7whKhcjOuSj/kB8UENJQ/iFlD0qhgS41q39MoXVOyD+w7C93QMgF+Yf8oOyci5JYoQQAAHgqyVcGinPwdIb7/0AfPrdIfGUANR9BqRDHMo7IEfOCHt9ZPv0tWr/I5z4cMdKxjE6KU+NyqAgAANQWfGUAKH8EpULUD75EGYfu1/19W/t06l1W9k/asnuPenVpr1BbcJn7+e7HTM1L2q9mfaPL3AcAAKid+MoApKKv8Zln3/Gzyj5xQN/sDVVuWvErMlv5Gp8EpUJ8n+ZS7sUo/WVdlqQsH3troX8dKHw1u5KLUgN72X/5AQCA2q11ozCfLiLqdDp1oqHUObq+goKCyrEyVIbSXONz7JvFt7H6NT4JSoW4/somkqRWjcIUGhRQ5n72HT+nKSv36LmR7dWmqW/fLeJcXwAAABSmJNf4vJCVrTUbt2tw3+4KK+aoodWv8UlQKkQDR7BuubqFz/24XD8fcm7V0OHTX3gAAAAKk7cI1aH0fT4tQuVyuXTMdUzf/PiNT4s5HEpnEaqqYLfbiz0C5HQ6dSb1lLpf3YWjhsUgKAEAANRwLEIFlD+CUgXKycnR7u1blfH1v7V7u0vtLxmigICyn8YHAABQkGaOaGUcul9/Gd1JrXxYzMHlcunfW/+tHj17+HRE6eCpC3pw+RcsQoUajaDkg6JWFvnkk0+0YMECHTt2TJJ097/mavqUZnr44Yd13XXXFfgYq68sAgAAyiY3N0i5F6OUcb6JcsPLfqpbVla2jp1ppqzzTXxa9S7n4gXlXjwtW0BImfsAqhpByQelWVlEko4dO6Zp06YVer/VVxYBAABlc/DUBUnSo6vK4/pHgVpyYFc59PPzQlRATcVPrw8KWlkkJydHw4cPV7169XT27Fn3ESVJatasmerVq6dz587p/fffz3cantVXFgEAAGXDar1A+SMo+aCglUU2bdqkY8eO6fjx4xoyZIiWL1+u77//XpdcconmzJmjxMREGWOUkZGh+Pj4qikcAADUKqzWC5Q//6ouoLb54YcfJEkDBw7U6tWr1a1bN4WGhqpbt25avXq1Bg4c6NEOAAAAQPXDEaVydvr0aUnSiBEj5O/vr5ycHPd9/v7+Gj58uNatW+duBwAAUBmKWoQqz77jZ5V94oC+2Ruq3LR6RbZlESrUdgSlctawYUNJ0qpVq3THHXd43Jebm6vVq1d7tAMAAKgMpVmEauybxbdhESrUdgSlchYVFSVJWrdunYYPH65p06YpKytLO3bs0Ny5c7Vu3TqPdgAAAJWhoEWovF3Iytaajds1uG93hRWzPDiLUKG2IyiVs169eqlly5aKjIzUV199pd69e7vva9mypbp06aK0tDT16tWrCqsEAABWU9AiVN6cTqfOpJ5S96u7KCgoqJIqA6onglI5CwgI0HPPPaeRI0dq8ODBmjx5sv73v//psssuU1JSktasWaOVK1fmWxocAAAAQPVBUKoAI0aM0MqVKzVlyhQlJia6t8fExGjlypUaMWJEFVYHAAAAoDgEpQoyYsQIDRs2TBs3btS6des0aNAg9e3blyNJAAAAQA1AUKpAAQEB6tOnjzIyMtSnTx9CEgAAAFBDcMFZAABQrJycHG3evFmffvqpNm/e7HGdQACojQhKAACgSKtWrVLr1q01YMAAzZ8/XwMGDFDr1q21atWqqi4NACoMp94BAIBCrVq1yr2S68MPP+yxkuvIkSNZpAhArUVQAgAABcrJydGUKVMUFxenPXv2eKzkGh0drbi4OE2dOlXDhg3je7gAah1OvQMAAAXasmWLDh8+rN27d6tDhw7asmWLli1bpi1btqhDhw7avXu3Dh06pC1btlR1qQBQ7ghKAACgQD/88IMkadCgQVq9erW6deum0NBQdevWTatXr9agQYM82gFAbUJQAgAABTp9+rSkn68N6O/v+ZHB399fw4cP92gHALUJQQkAABSoYcOGkn5e0CE3N9fjvtzcXK1evdqjHQDUJgQlAABQoKioKEnS+vXrNXz4cO3YsUNZWVnasWOHhg8frvXr13u0A4DahFXvAACwuMzMTKWkpOTb7nA41KxZM9WrV0/Jycnq3bu3+75mzZqpbdu2OnfunBwOhz777DOPx8bGxsput1d47QBQUQhKAABYXEpKiuLi4gq9/9ixYwVuy9t+9dVX57s/OTlZnTt3Lr8iAaCSEZQAALC42NhYJScnF3r/J598ogULFngEpqioKD300EO67rrrCu0TAGoyghIAALXcodQMZWS7imwT3LhVofcNHNNKA0bdodXrkvTimmT9fnCchg8aUORFZr8945TOnCv0foctUDGRjuKLB4AqQlACAKAWO5Saob7zNpVTb2FyXNFHiw9Jixdt97m3jVPjCUsAqi2CEgAAtVjekaSFozupdaMw3/rKylbipu0aEt9djlBbmfs5cOqCHlr+RbFHuQCgKhGUAACoxc5ezJB/yA86lB6qgBDfglJW9k86q2M6cmG/Qp3BZe7nWGam/EN+UHbORUl1faoJACoKQQkAgFps1/cpcsS8oDcOSzpcDh0GSZu+9L0bR4x0xhknqbHvnQFABShTUFq0aJHmzp2r48eP68orr9TChQvVq1ev8q4NAAD4aHSnOEl/UfMGdtkCfbvO/OHUC1rw8QE93K+1Wkb6dnQqNDhAPaKv8KkPAKhIpQ5Ky5cv10MPPaRFixapR48e+utf/6pBgwbp66+/VosWLSqiRgAAUEbN6tbVw30KXsI7T2EXnPV2IV3KOpylyHQpylH0RwguOAugpit1UJo/f77uvPNO3XXXXZKkhQsX6sMPP9TLL7+sWbNmlXuBAACgYhV3wVlvY98svg0XnAVQ05UqKP30009KTk7Wo48+6rH9+uuv17Zt2wp8THZ2trKzs92309PTJUlOp1NOp7O09dY4eftohX21KsbYGqw2zlbZz9KozfNZq1at9J///KfYdheysvXhll26oVdXhRWz6l2rVq1q/OtiRVb7XWdFVhzjsu5rqYJSamqqcnJy1Lix5xcvGzdurBMnThT4mFmzZikhISHf9g0bNljqkHxSUlJVl4AKxhhbg1XGOTMzs6pLqHaYz352bftWOn/2R50/W3S748ePV05BqBBW+V1nZVYa47LOaX7GGFPSxseOHVNUVJS2bdum7t27u7fPnDlTS5YsKfD85oL+Ate8eXOlpqYqPDy8TEXXJE6nU0lJSRowYICCgoKquhxUAMbYGqw2zunp6YqMjNS5c+cs8bu6JKw+n0nWex9YEWNc+1lxjMs6p5XqiFJkZKQCAgLyHT06depUvqNMeWw2m2y2/Ifng4KCLDM4kvX214oYY2uwyjhbYR9Li/nsF1bcZ6thjGs/K41xWfezVOuEBgcHKy4uLt+huqSkJF177bVlKgAAAAAAqptSr3o3efJk/b//9//UpUsXde/eXa+++qqOHj2qe++9tyLqAwAAAIBKV+qgNHr0aKWlpempp57S8ePH1a5dO61du1bR0dEVUR8AAAAAVLpSByVJmjhxoiZOnFjetQAAAABAtVCmoOSLvEX28q4/Uds5nU5lZmYqPT3dMl+YsxrG2BqsNs55v6NLsTCq5VhtPpOs9z6wIsa49rPiGJd1Tqv0oHT+/HlJUvPmzSv7qQEApXT+/HnVrVu3qsuolpjPAKBmKe2cVqrrKJWH3NxcXX755UpOTpafn19lPnWVyLvOxnfffWeZ62xIUteuXbVr166qLqNSMMbWYLVxNsYoLi5O+/fvl79/qRZItQyrzWeS9d4HEr/rrIAxrv3KOqdV+hElf39/BQcHW+4vlOHh4Zb5YZSkgIAAS+2vxBhbhZXGOTg4mJBUBKvOZ5K13gf8rqv9GGNrKMucViUz4KRJk6riaVGJGOPajzGu/Rjj4vEa1X6Mce3HGFtDWca50k+9s5r09HTVrVtX586ds1RqtxLG2BoYZ/reYiAAACAASURBVID3gRUwxrUfY1xyATNmzJhR1UXUdgEBAYqPj1dgYKWf6YhKwhhbA+MM8D6wAsa49mOMS4YjSgAAAADghW/pAgAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIAAAAAeCEoAQAAAIAXghIA4P+3d+fhTVb5//9fXdOdrSwFCnRAKALSL0UUECnDoigIMriBK44zCi4oouKMLIoiotD5MYKDOugoKMqmg8oACogsChUcQIpDKQi0yCq1CyFpz+8PPo02lG5Jt9zPx3VxQe6cnLzvnCRv3rnPfW4AAOCGQgkAAAAA3FAoAcWYPHmy/Pz8yvWYt956S35+fjpw4EDlBFVOmzZt0uTJk/Xzzz9fcF+rVq00aNCgaogKAFCdKpLfKiopKUlJSUmltjtw4ID8/Pz01ltvubZ9+umnmjx5crHt/fz89OCDD3onSKAEFEqAj9q0aZOmTJlSbKEEAEBlmzNnjubMmVOhx3766aeaMmWKlyMCyiewugMAAACA77n00kurOwTAIxxRQrU5fvy4/vSnPyk2NlY2m00NGzZUz549tWbNGlebNWvWqG/fvoqKilJYWJh69uypzz//vEg/hdMItm/frmHDhikqKkp16tTR7bffruPHjxdpu2jRIg0YMEAxMTEKDQ1V+/bt9dRTTyknJ6fS9rM8+7B7927ddtttqlOnjho3bqxRo0bpzJkzRdr+/PPPuvfee1W/fn1FRETo+uuv1/79++Xn5+eapjB58mSNHz9ekhQXFyc/Pz/5+flp3bp1RfpauXKlunTpotDQUMXHx+uf//xnpb0OAGAVvpTfdu/eLT8/P3344YeubSkpKfLz81OHDh2KtL3hhhuUmJjoul3c1LuMjAzdfPPNioyMVJ06dXTLLbfo6NGjRdrcfffdevXVVyXJlb+Km9r+zjvvqH379goLC1Pnzp21YsUKj/YVcEehhGpzxx13aPny5Zo4caJWrVqlN954Q/369dPJkyclSe+++64GDBigqKgovf322/rggw9Uv359XXPNNRckE0m68cYb1aZNGy1evFiTJ0/W8uXLdc0118jhcLja/O9//9N1112nN998UytXrtTYsWP1wQcfaPDgwZWyj+Xdhz/84Q9q27atlixZoqeeekoLFy7Uo48+6rq/oKBAgwcP1sKFC/Xkk09q2bJluuKKK3TttdcW6eePf/yjHnroIUnS0qVLtXnzZm3evFldunRxtfnuu+80btw4Pfroo/roo4902WWX6d5779WXX35ZKa8FAFiFL+W3Dh06KCYm5oIiLzQ0VN9//70yMjIkSU6nU+vXr1e/fv0u2ldeXp769eunVatWadq0afrwww/VpEkT3XLLLUXaPfPMMxo+fLgkufLX5s2bFRMT42rzySef6O9//7ueffZZLVmyRPXr19eNN96o/fv3e7S/QBEGqCYRERFm7Nixxd6Xk5Nj6tevbwYPHlxke35+vuncubPp1q2ba9ukSZOMJPPoo48WabtgwQIjybz77rvFPkdBQYFxOBxm/fr1RpL57rvvLuizPObPn28kmfT09Arvw0svvVSk7ejRo01ISIgpKCgwxhjzySefGElm7ty5RdpNmzbNSDKTJk1ybZsxY0aReH6rZcuWJiQkxBw8eNC1LS8vz9SvX9/8+c9/Ltd+AwCK8rX8dvvtt5vf/e53rtv9+vUz9913n6lXr555++23jTHGbNy40Ugyq1atcrXr3bu36d27t+v23LlzjSTz0UcfFen/vvvuM5LM/PnzXdvGjBlz0TglmcaNG5usrCzXtqNHjxp/f38zbdq0cu0bUBKOKKHadOvWTW+99ZamTp2qLVu2FPllbNOmTTp16pTuuusuOZ1O15+CggJde+212rp16wXTCUaOHFnk9s0336zAwECtXbvWtW3//v0aMWKEmjRpooCAAAUFBal3796SpD179nh1/yqyDzfccEOR25dddpnOnj2rY8eOSZLWr1/v2rffuu2228odX0JCglq0aOG6HRISorZt2+rgwYPl7gsA8Ctfy299+/bV/v37lZ6errNnz+qrr77Stddeqz59+mj16tWSzh9lstlsuuqqqy7az9q1axUZGXlBrhsxYkS5Y+rTp48iIyNdtxs3bqxGjRqRw+BVLOaAarNo0SJNnTpVb7zxhp555hlFREToxhtv1EsvvaSffvpJklyH3otz6tQphYeHu243adKkyP2BgYFq0KCBa6pDdna2evXqpZCQEE2dOlVt27ZVWFiYDh06pGHDhikvL8+r+1eRfWjQoEGR+202myS5Yjt58qQCAwNVv379Iu0aN25c7vjcn6vw+bz9OgCA1fhafiucTrdmzRrFxcXJ4XDo97//vX766Sc999xzrvt69uyp0NDQi/Zz8uTJYvOV+/6VBTkMVYFCCdUmOjpaycnJSk5O1o8//qiPP/5YTz31lI4dO+Y6L2f27Nm68sori328+5ft0aNH1axZM9dtp9OpkydPur5Mv/jiC2VkZGjdunWuX9kkVdry2dHR0ZLKtw+ladCggZxOp06dOlWkWHI/ERYAUH18Lb81b95cbdu21Zo1a9SqVSt17dpVdevWVd++fTV69Gh9/fXX2rJlS6nLeTdo0EDffPPNBdvJYaipKJRQI7Ro0UIPPvigPv/8c23cuFE9e/ZU3bp19f3335f5onILFiwostrOBx98IKfT6Vpxp/ACe4VHaQr94x//8M5OuKnIPpSmd+/eeumll7Ro0SI98MADru3vv//+BW3dj0YBAKqer+S3fv366YMPPlBsbKyuv/56SVLbtm3VokULTZw4UQ6Ho8SFHKTz0+U++OADffzxx0Wm3y1cuPCCtr/NYSUdpQIqE4USqsWZM2fUp08fjRgxQvHx8YqMjNTWrVu1cuVKDRs2TBEREZo9e7buuusunTp1SsOHD1ejRo10/Phxfffddzp+/Ljmzp1bpM+lS5cqMDBQ/fv31+7du/XMM8+oc+fOrvN5evTooXr16un+++/XpEmTFBQUpAULFui7776rlH2syD6U5tprr1XPnj01btw4ZWVlKTExUZs3b9a//vUvSZK//6+nHXbq1EmS9Le//U133XWXgoKC1K5duyJzugEA3uWr+a1v376aM2eOTpw4oeTk5CLb58+fr3r16hUp5opz5513atasWbrzzjv1/PPP65JLLtGnn36q//znPxe0Lcxh06dP18CBAxUQEKDLLrtMwcHBXtsnoDQs5oBqERISoiuuuELvvPOORo4cqYEDB+qNN97Qk08+qddff12SdPvtt2vt2rXKzs7Wn//8Z/Xr10+PPPKIvv32W/Xt2/eCPpcuXarU1FQNGzZMEydO1ODBg7Vq1SrXl2qDBg30ySefKCwsTLfffrtGjRqliIgILVq0qNL2s7z7UBp/f3/9+9//1q233qoXX3xRQ4YM0YYNG/Tuu+9KkurWretqm5SUpAkTJujf//63rrrqKl1++eVKSUnx2r4BAC7kq/nt97//vfz9/RUeHq7u3bu7thceRerTp0+RH+uKExYWpi+++EL9+vXTU089peHDh+vw4cPFzooYMWKE/vjHP2rOnDnq3r27Lr/8ctdS5EBV8TPGmOoOAvDE5MmTNWXKFB0/ftx1XpDVLFy4UCNHjtTGjRvVo0eP6g4HAOAF5DegejH1Dqhl3nvvPR05ckSdOnWSv7+/tmzZohkzZujqq6+mSAIAAPASCiWgFAUFBSooKCixTWBg1X2UIiMj9f7772vq1KnKyclRTEyM7r77bk2dOrXKYgAA1H41Lb8BNQ1T74BSFE59KEl6erpatWpVNQEBAOAF5DegZBRKQCkyMjJKPYGUlXgAALUN+Q0oGYUSAAAAALhheXAAAAAAcFPlZ+gVFBQoIyNDkZGRritJAwBqFmOMfvnlFzVt2rTUa6NYFfkMAGqHiua0Ki+UMjIyFBsbW9VPCwCogEOHDql58+bVHUaNRD4DgNqlvDmtygulyMhISecDjYqKquqnr3IOh0OrVq3SgAEDFBQUVN3hoBIwxtZgtXHOyspSbGys6zsbF7JaPpOs9zmwIsbY91lxjCua06q8UCqcnhAVFWWJxOJwOBQWFqaoqCjLvBmthjG2BquOM1PKLs5q+Uyy7ufAShhj32flMS5vTmPiOQAAAAC4KVeh5HQ69de//lVxcXEKDQ3V7373Oz377LOlXtUZAAAAAGqTck29mz59ul577TW9/fbb6tChg7Zt26Z77rlHderU0SOPPFJZMQIAAABAlSpXobR582YNGTJE119/vSSpVatWeu+997Rt27ZKCQ4AAAAAqkO5CqWrrrpKr732mn744Qe1bdtW3333nb766islJydf9DF2u112u911OysrS9L5E8kcDkcFw649CvfRCvtqVYyxNVhtnK2yn+Vh9XwmWe9zYEWMse+z4hhXdF/9jDGmrI2NMXr66ac1ffp0BQQEKD8/X88//7wmTJhw0cdMnjxZU6ZMuWD7woULFRYWVqGgAQCVKzc3VyNGjNCZM2css6JbachnAFA7VTSnlatQev/99zV+/HjNmDFDHTp00I4dOzR27FjNnDlTd911V7GPKe4XuNjYWJ04ccISydfhcGj16tXq37+/5ZZgtArG2BqsNs5ZWVmKjo6mUPoNq+czyXqfAytijH2fFce4ojmtXFPvxo8fr6eeekq33nqrJKlTp046ePCgpk2bdtFCyWazyWazXbA9KCjIMoMjWW9/rYgxrr1yc3OVmppaYpvsPLs27UxTvehGigi98Dvtt+Lj42v9EQbeyxcin/3KivtsNYyx77PSGFd0P8tVKOXm5srfv+iK4gEBASwPDqBWS01NVWJiYpnavlSGNikpKerSpYtnQQEAgGpVrkJp8ODBev7559WiRQt16NBB27dv18yZMzVq1KjKig8AKl18fLxSUlJKbLM382c99uFOzbypk9rF1C21PwAAULuVq1CaPXu2nnnmGY0ePVrHjh1T06ZN9ec//1kTJ06srPgAoNKFhYWVegTI/+BJ2TbkqX3Hzkpo2aCKIgMAANWlXIVSZGSkkpOTS1wOHAAAAABqO//SmwAAAACAtVAoAQAAAIAbCiUAAAAAcEOhBAAAAABuKJQAAAAAwA2FEgAAAAC4oVACAAAAADcUSgAAAADghkIJAAAAANxQKAEAAACAGwolAAAAAHBDoQQAAAAAbiiUAAAAAMANhRIAAAAAuKFQAgAAAAA3FEoAAAAA4IZCCQAAAADcUCgBAAAAgBsKJQAAAABwQ6EEAAAAAG4CqzsAoCbLzc1VampqiW2y8+zatDNN9aK3KSLUVmqf8fHxCgsL81aIAAAAqAQUSkAJUlNTlZiYWKa2L5Wxz5SUFHXp0qXiQQEAAKDSUSgBJYiPj1dKSkqJbfZm/qzHPtypmTd1UruYumXqEwAAADUbhRJQgrCwsFKP/vgfPCnbhjy179hZCS0bVFFkKKv0EznKsTs97ifteI7r78BAz746w22BiosO9zgmAABQeSiUAPis9BM56vPyOq/2OW7xTq/0s/bxJIolAABqMAolAD6r8EhS8i0JatMowrO+8uxasW6zBiV1V3gZFu24mH3HsjV20Q6vHOUCAACVh0IJgM9r0yhCHZvV8agPh8Ohow2lLi3rKSgoyEuRAQCAmorrKAEAAACAGwolAAAAAHBDoQQAAAAAbiiUAAAAAMANhRIAAAAAuKFQAgAAAAA3FEoAAAAA4IbrKAHwWfb8s/IPOaL0rL3yD/HsgrNOp1MZzgztObVHgYEV/+pMz8qWf8gR2fPPSvLs2k4AAKDyUCgB8FkZOQcVHjdbT3/jvT7nrJzjcR/hcVJGToIS1dgLEQEAgMpAoQTAZzUNb6mc9If0t1sS1LqR50eUNn61UT2v6unREaW0Y9l6ZNEONe3T0qN4AABA5aJQAuCzbAEhKjjbTHFR7XRpA8+muTkcDqUHpqt9/fYKCgqqcD8FZ8+o4Oxx2QJCPIoHAABULhZzAAAAAAA3HFGCpaWfyFGO3elRH2nHc1x/ezIlq1C4LVBx0eEe9wMAAICKo1CCZaWfyFGfl9d5rb9xi3d6ra+1jydRLAEAAFQjCiVYVuGRpORbEtTGgxP9c/LsWrFuswYldVd4qM2jmPYdy9bYRTs8PsoFAAAAz1AowfLaNIpQx2YVP9Hf4XDoaEOpS8t6Hp3kD+/Lc+RLknYdOeNxXzl5dm07LjU5eNqjgnjfsWyPYwEAAJWPQgmAz0r7v6LkqaXemhYZqHf2bfVKT+E2vn4BAKjJyNQAfNaADk0kSa0bRSg0KMCjvvZmntG4xTv1yvBOahfj2VLjLNgBAEDNV+5C6ciRI3ryySf12WefKS8vT23bttWbb76pxMTEyogPACqsfniwbu3Wwit9OZ3nzxtr3TDco6maAACgdihXoXT69Gn17NlTffr00WeffaZGjRopLS1NdevWraz4AAAAAKDKlatQmj59umJjYzV//nzXtlatWnk7JgAAAACoVuUqlD7++GNdc801uummm7R+/Xo1a9ZMo0eP1n333XfRx9jtdtntdtftrKwsSedXCnM4HBUMu/Yo3Ecr7GttUziVyul0ejQ+3hxjb8UE77Pa2FhhH8vL6vlMIqdZAWPs+6w4xhXd13IVSvv379fcuXP12GOP6emnn9Y333yjhx9+WDabTXfeeWexj5k2bZqmTJlywfZVq1YpLCysQkHXRqtXr67uEODmULYkBeqrr77SwYpfRsnFG2Ps7ZjgPYVjs2XLFh3ZVd3RVL7c3NzqDqHGIZ/9ipzm+xhj32elMa5oTvMzxpiyNg4ODlbXrl21adMm17aHH35YW7du1ebNm4t9THG/wMXGxurEiROKioqqUNC1icPh0OrVq9W/f3+usVPD7M7I0tC5W7T8gSvVoWnF34veHGNvxQTv++7HUxr++jYtvq+rOreoX93hVLqsrCxFR0frzJkzlviuLgur5zOJnGYFjLHvs+IYVzSnleuIUkxMjC699NIi29q3b68lS5Zc9DE2m00224UXZwwKCrLM4EjW29/aIN/PKf+QIzqUm6bgrIofvnE6ncpwZmjfL/sUGOjZivuHcrPlH3JE+X5O3i81TOHYBgYGWmJsrLCP5UU++5UV99lqGGPfZ6Uxruh+lut/dT179tTevXuLbPvhhx/UsmXLCj05UJ0ycg4qPG62nv7GO/3NWTnHK/2Ex0kZOQlKVGOv9AcAAIDyK1eh9Oijj6pHjx564YUXdPPNN+ubb77RvHnzNG/evMqKr0bLzc1VampqiW2y8+zatDNN9aK3KSL0wl8ifys+Pt5y89yrU9PwlspJf0h/uyVBrRt5dkRp41cb1fOqnh4fUUo7lq1HFu1Q0z78+AAAAFCdyvW/ussvv1zLli3ThAkT9OyzzyouLk7JyckaOXJkZcVXo6Wmppb5QrsvlaFNSkqKunTp4llQKDNbQIgKzjZTXFQ7Xdqg4hcQdTgcSg9MV/v67T0+hF1w9owKzh6XLSDEo34AAADgmXL//D1o0CANGjSoMmKpdeLj45WSklJim72ZP+uxD3dq5k2d1C6m5AvzxsfHezM8AAAAABXk2TwhiwsLCyv1CJD/wZOybchT+46dldCyQRVFBgAAAMAT/tUdAAAAAADUNBRKAAAAAOCGqXewrDxHviRp15EzHvWTk2fXtuNSk4OnFV7Kyoal2Xcs26PHo2LKsoLl3syfZT+6T3t2hargZOnnG7KCJQAAtRuFEiwr7f+KkqeW7vRCb4F6Z99WL/RzXriNj2ZVKs8KliPeLr0NK1gCAFD78b8xWNaADk0kSa0bRSg0KKDC/ezNPKNxi3fqleGd1C6m4suMFwq3BSouOtzjflB2ZVnBMjvPrk/Wbtb1fbqX6ZpoAACgdqNQgmXVDw/Wrd1aeNyP0+mUJLVuGK6OzTwvlFD1yrKCpcPh0OkTx9S9W1ePr5cFAABqPhZzAAAAAAA3FEoAAAAA4IZCCQAAAADcUCgBAAAAgBsKJQAAAABwQ6EEAAAAAG4olAAAAADADddRKkH6iRzl2J0e9ZF2PMf1d2CgZy83FyIFAAAAqgaF0kWkn8hRn5fXea2/cYt3eqWftY8nUSwBAAAAlYxC6SIKjyQl35KgNo0iKt5Pnl0r1m3WoKTuCg+1VbiffceyNXbRDo+PcAEAAAAoHYVSKdo0ilDHZnUq/HiHw6GjDaUuLespKCjIi5EBAAAAqCws5gAApcjPz9f69ev15Zdfav369crPz6/ukAAAQCWjUAKAEixdulRt2rRR//79NXPmTPXv319t2rTR0qVLqzs0AABQiZh6dxH2/LPyDzmi9Ky98g+p+DlKTqdTGc4M7Tm1x6NV79KzsuUfckT2/LOSKj4VEEDZLV26VMOHD9egQYP0zjvv6PDhw2revLleeuklDR8+XIsXL9awYcOqO0wAAFAJKJQuIiPnoMLjZuvpb7zT35yVczzuIzxOyshJUKIaeyEiACXJz8/XuHHjNGjQIC1fvlz5+fk6efKkrrjiCi1fvlxDhw7V448/riFDhiggIKC6wwUAAF5GoXQRTcNbKif9If3tlgS19mDVO6fTqY1fbVTPq3p6dEQp7Vi2Hlm0Q037tKxwHyi/3Nxcpaamlthmb+bPsh/dpz27QlVwsm6pfcbHxyssLMxbIaKSbNiwQQcOHNB7770nf3//Iucl+fv7a8KECerRo4c2bNigpKSk6gsUAABUCgqli7AFhKjgbDPFRbXTpQ08W/UuPTBd7eu392jVu4KzZ1Rw9rhsASEV7gPll5qaqsTExDK1HfF22fpMSUlRly5dPIgKVSEzM1OS1LFjx2LvL9xe2A4AAPgWCiWgBPHx8UpJSSmxTXaeXZ+s3azr+3RXRBmulRUfH++t8FCJYmJiJEm7du3SlVdeecH9u3btKtIOAAD4Fgqli8hznJ9ms+vIGY/6ycmza9txqcnB0x5fcBZVLywsrNSjPw6HQ6dPHFP3bl25VpYP6dWrl1q1aqUXXnhBy5cvL3JfQUGBpk2bpri4OPXq1auaIgQAAJWJQuki0v6vMHlq6U4v9Baod/Zt9UI/UriNIQOqQkBAgF555RUNHz5cQ4cO1fjx45WXl6ctW7ZoxowZWrFihRYvXsxCDgAA+Cj+130RAzo0kSS1bhSh0KCK/0dob+YZjVu8U68M76R2MZ4t6x1uC1RcdLhHfQAou2HDhmnx4sUaN26crr76atf2uLg4lgYHAMDHUShdRP3wYN3arYXH/TidTklS64bh6tiM6x8Btc2wYcM0ZMgQrV27Vp999pkGDhyoPn36cCQJAAAfR6EEAKUICAhQ7969lZOTo969e1MkAQBgAf7VHQAAAAAA1DQUSgAAAADghkIJAAAAANxQKAEAAACAGwolAAAAAHBDoQQAAAAAblgeHAAAi8vNzVVqamqp7bLz7Nq0M031orcpItRWYtv4+HiFhYV5K0QAqHIUSgAAWFxqaqoSExPL3P6lMrRJSUlRly5dKh4UAFQzCiUAACwuPj5eKSkppbbbm/mzHvtwp2be1EntYuqW2icA1GYUSh4oy1SFvZk/y350n/bsClXBydKTCtMUAABVLSwsrExHf/wPnpRtQ57ad+yshJYNqiAyAKg+FEoeKM9UhRFvl96GaQoAAABAzUCh5IGyTFXIzrPrk7WbdX2f7mU68RUAAABA9aNQ8kBZpio4HA6dPnFM3bt1VVBQUBVFBgAAAMATXEcJAAAAANx4VChNmzZNfn5+Gjt2rLfiAQAAAIBqV+FCaevWrZo3b54uu+wyb8YDAAAAANWuQoVSdna2Ro4cqddff1316tXzdkwAAAAAUK0qtJjDmDFjdP3116tfv36aOnVqiW3tdrvsdrvrdlZWlqTzixw4HI6KPH2tUriPVthXq2KMrcFq42yV/SwPq+czSXI6na6/rbLPVmO17zorsuIYV3Rfy10ovf/++/r222+1devWMrWfNm2apkyZcsH2VatWWeriqqtXr67uEFDJGGNrsMo45+bmVncINQ75TDqULUmB2rJli47squ5oUJms8l1nZVYa44rmND9jjClr40OHDqlr165atWqVOnfuLElKSkpSQkKCkpOTi31Mcb/AxcbG6sSJE4qKiqpQ0LWJw+HQ6tWr1b9/f5YH91GMsTVYbZyzsrIUHR2tM2fOWOK7uiysns8k6bsfT2n469u0+L6u6tyifnWHg0pgte86qzl37pxeffVVrV+/Xr1799aYMWMUHBxc3WFVuormtHIdUUpJSdGxY8eUmJjo2pafn68vv/xSf//732W32xUQEFDkMTabTTbbhRdaDQoKstQH0Gr7a0WMsTVYZZytsI/lRT6TAgMDXX9bZZ+tykrva6t44oknNGvWLNcU2k8//VR/+ctf9Oijj+qll16q5ugqV0Xfy+UqlPr27audO3cW2XbPPfcoPj5eTz755AVFEgAAAIDq9cQTT2jGjBlq3LixpkyZIpvNJrvdrkmTJmnGjBmS5PPFUkWUa9W7yMhIdezYscif8PBwNWjQQB07dqysGAEAAABUwLlz5zRr1iw1btxYhw8f1qhRo1SvXj2NGjVKhw8fVuPGjTVr1iydO3euukOtcTy64CwAAACAmmvOnDlyOp2aOnWqa/psocDAQD377LNyOp2aM2dONUVYc1VoefDfWrdunRfCAAAAAOBtaWlpkqRBgwYVe3/h9sJ2+BVHlAAAAAAf1bp1a0nSihUrir2/cHthO/zK4yNKAACgZks/kaMcu9PjftKO57j+dp/CU17htkDFRYd7HBOAko0ePVrjx4/XX//6V9196W0SCwAAIABJREFU991F7nM6nZo4caICAwM1evTo6gmwBqNQAgDAh6WfyFGfl9d5tc9xi3eW3qgM1j6eRLEEVLLg4GA9+uijmjFjhpo3b65JkyYpJCREb7zxhqZMmaKffvpJ48ePt8T1lMqLQgkAAB9WeCQp+ZYEtWkU4VlfeXatWLdZg5K6Kzz0wmtKldW+Y9kau2iHV45yAShd4dLfs2bNKnLkKDAwUOPHj2dp8IugUAIAwALaNIpQx2Z1POrD4XDoaEOpS8t6XIwUqIFyc3OVmppa7H233nqrhg0bpncXvqdN36SoR7dE3T7iNgUHB+vbb78t9jHx8fEKCwurzJBrNAolAAAAwAekpqYqMTGxTG23f71Rr87+/0psk5KSoi5dungjtFqJQgkAAADwAfHx8UpJSSmxzd7Mn/XYhzs186ZOahdTt9T+rIxCCQAAAPABYWFhpR4B8j94UrYNeWrfsbMSWjaooshqJ66jBAAAAABuOKIEAIAPs+eflX/IEaVn7ZV/iGer3jmdTmU4M7Tn1B6PrqOUnpUt/5AjsuefleTZAhMou5JO9C+UnWfXpp1pqhe9TRGlrGxo9RP94fsolAAA8GEZOQcVHjdbT3/jvT7nrJzjcR/hcVJGToIS1dgLEaEsynOif1kWi7b6if7wfRRKAAD4sKbhLZWT/pD+dkuCWnt4HSWn06mNX21Uz6t6enREKe1Yth5ZtENN+7T0KB6UDyf6A+VDoQQAgA+zBYSo4GwzxUW106UNPL+OUnpgutrXb+/RdZQKzp5RwdnjsgWEeBQPyocT/YHyYTEHAAAAAHBDoQQAAAAAbph6BwAAANQC6SdylGN3etRH2vEc19+enGsoSeG2QMVFh3vUR01GoQQAAADUcOknctTn5XVe62/c4p1e6Wft40k+WyxRKAEA4MPyHPmSpF1HznjcV06eXduOS00OnlZ4KdfYKcm+Y9kexwJYTeGRpORbEtTGgxUsc/LsWrFuswYldff4czx20Q6Pj3DVZBRKAAD4sLT/K0qeWuqdX4+lQL2zb6tXegq38d8QoLzaNIpQx2YVX8HS4XDoaEOpS8t6Hq1eaQV8QwEA4MMGdGgiSWrdKEKhQQEe9bU384zGLd6pV4Z3UrsYz5Ya9/VzGwDUfhRKAAD4sPrhwbq1Wwuv9OV0np9i07phuEe/aKNycKK/b7Pnn5V/yBGlZ+2Vf0jFp945nU5lODO059Qej8Y4PStb/iFHZM8/K8k3vw8olAAAAGo5TvT3fRk5BxUeN1tPf+Od/uasnONxH+FxUkZOghLV2AsR1TwUSgAAALUcJ/r7vqbhLZWT/pD+dkuCWnswxk6nUxu/2qieV/X06IhS2rFsPbJoh5r2aVnhPmo6CiUAAAAfwYn+vssWEKKCs80UF9VOlzbwbIzTA9PVvn57j8a44OwZFZw9LltASIX7qOn8qzsAAAAAAKhpKJQAAAAAwA1T7wAAAGo5VkTzfd66eDQXji47CiUAAIBajhXRfJ93Lx7NhaPLwnf3DAAAwCJYEc33eevi0Vw4uuwolAAAAGo5VkTzfd66eDQXji47FnMAAAAAADcUSgAAAADghkIJAAAAANxwjhIAAEAtx9LRgPdRKAEAANRyLB0NScrNzVVqamqJbfZm/iz70X3asytUBSfrltg2Pj5eYWFh3gyxVuHdCwAAUMuxdDQkKTU1VYmJiWVqO+Lt0tukpKSoS5cuHkZVe1EoAQAA1HIsHQ3p/BGglJSUEttk59n1ydrNur5Pd0WUMr0yPj7em+HVOhRKAAAAgA8ICwsr9QiQw+HQ6RPH1L1bV4+ulWUFrHoHAAAAWEB+fr7Wr1+vL7/8UuvXr1d+fn51h1SjUSgBAAAAPm7p0qVq06aN+vfvr5kzZ6p///5q06aNli5dWt2h1VhMvQMAwOLKslKWxGpZQG21dOlSDR8+XIMGDdI777yjw4cPq3nz5nrppZc0fPhwLV68WMOGDavuMGscCiUAACyuPCtlSayWBdQm+fn5GjdunAYNGqTly5crPz9fJ0+e1BVXXKHly5dr6NChevzxxzVkyBAFBFR8xURfRKEEAIDFlWWlLInVsoDaaMOGDTpw4IDee+89+fv7Fzkvyd/fXxMmTFCPHj20YcMGJSUlVV+gNRCFEgAAFleWlbIkVssCaqPMzExJUseOHYu9v3B7YTv8isUcAAAAAB8VExMjSdq1a1ex9xduL2yHX5WrUJo2bZouv/xyRUZGqlGjRho6dKj27t1bWbEBAAAA8ECvXr3UqlUrvfDCCyooKChyX0FBgaZNm6a4uDj16tWrmiKsucpVKK1fv15jxozRli1btHr1ajmdTg0YMEA5OTmVFR8AAACACgoICNArr7yiFStWaOjQodqyZYvy8vK0ZcsWDR06VCtWrNDLL7/MQg7FKNc5SitXrixye/78+WrUqJFSUlJ09dVXezUwAAAAAJ4bNmyYFi9erHHjxhX5P3tcXBxLg5fAo8Uczpw5I0mqX7/+RdvY7XbZ7XbX7aysLEnnTwh1OByePH2tULiPVthXq2KMrcFq42yV/SwPq+czyXqfA1+Tm5tb6ikTP2Sekf3oPu3aEaxzP9UpsW27du24VlYtMnjwYF133XVat26dVq9erf79+yspKUkBAQE+/5mu6P75GWNMRR5ojNGQIUN0+vRpbdiw4aLtJk+erClTplywfeHChXy4AKCGys3N1YgRI3TmzBlFRUVVdzg1AvkMtV1aWprGjRvntf5eeeUVtW7d2mv9AZWlojmtwoXSmDFj9Mknn+irr75S8+bNL9quuF/gYmNjdeLECUskX4fD4araWUrVNzHG1mC1cc7KylJ0dDSF0m9YPZ9J1vsc+JqyHFHKzrPrPxu26ppel5d6rSyOKNVOVvwcVzSnVWjq3UMPPaSPP/5YX375ZYlFkiTZbDbZbBd+0IKCgiwzOJL19teKGGNrsMo4W2Efy4t89isr7rMvqFOnjrp161ZiG4fDoV9+PqVePa5kjH2clT7HFd3PchVKxhg99NBDWrZsmdatW6e4uLgKPSkAAAAA1GTlKpTGjBmjhQsX6qOPPlJkZKSOHj0q6fwvFKGhoZUSIAAAAABUtXJdR2nu3Lk6c+aMkpKSFBMT4/qzaNGiyooPAAAAAKpcuafeAQAAAICvK9cRJQAAAACwAgolAAAAAHBDoQQAAAAAbiiUAAAAAMANhRIAAAAAuKFQAgAAAAA3FEoAAAAA4IZCCQAAAADcUCgBAAAAgBsKJQAAAABwQ6EEAAAAAG4olAAAAADADYUSAAAAALihUAIAAAAANxRKAAAAAOCGQgkAAAAA3FAoAQAAAIAbCiUAAAAAcEOhBAAAAABuKJQAAAAAwA2FEgAAAAC4oVACAAAAADcUSgAAAADghkIJAAAAANxQKAEAAACAGwolAAAAAHBDoQQAAAAAbiiUAAAAAMANhRIAAAAAuKFQAgAAAAA3FEoAAAAA4IZCCQAAAADcUCgBAAAAgBsKJQAAAABwQ6EEAAAAAG4olAAAAADADYUSAAAAALihUAIAAAAANxRKAAAAAOCGQgkAAAAA3FAoAQAAAIAbCiUAAAAAcEOhBAAAAABuKJQAAAAAwE2FCqU5c+YoLi5OISEhSkxM1IYNG7wdFwAAAABUm3IXSosWLdLYsWP1l7/8Rdu3b1evXr00cOBA/fjjj5URHwAAAABUuXIXSjNnztS9996rP/7xj2rfvr2Sk5MVGxuruXPnVkZ8AAAAAFDlylUonTt3TikpKRowYECR7QMGDNCmTZu8GhgAAAAAVJfA8jQ+ceKE8vPz1bhx4yLbGzdurKNHjxb7GLvdLrvd7rqdlZUlSXI4HHI4HOWNt9Yp3Ecr7KtVMcbWYLVxtsp+lofV85lkvc+BFTHGvs+KY1zRfS1XoVTIz8+vyG1jzAXbCk2bNk1Tpky5YPuqVasUFhZWkaevlVavXl3dIaCSMcbWYJVxzs3Nre4Qahzy2a+s8jmwMsbY91lpjCua0/yMMaasjc+dO6ewsDB9+OGHuvHGG13bH3nkEe3YsUPr16+/4DHF/QIXGxurEydOKCoqqkJB1yYOh0OrV69W//79FRQUVN3hoBIwxtZgtXHOyspSdHS0zpw5Y4nv6rKwej6TrPc5sCLG2PdZcYwrmtPKdUQpODhYiYmJWr16dZFCafXq1RoyZEixj7HZbLLZbBdsDwoKsszgSNbbXytijK3BKuNshX0sL/LZr6y4z1bDGPs+K41xRfez3FPvHnvsMd1xxx3q2rWrunfvrnnz5unHH3/U/fffX6EAAAAAAKCmKXehdMstt+jkyZN69tlnlZmZqY4dO+rTTz9Vy5YtKyM+AAAAAKhyFVrMYfTo0Ro9erS3YwEAAACAGqHcF5wFAAAAAF9XoSNKnihcZK/w+hO+zuFwKDc3V1lZWZY5Yc5qGGNrsNo4F35Hl2NhVMuxWj6TrPc5sCLG2PdZcYwrmtOqvFD65ZdfJEmxsbFV/dQAgHL65ZdfVKdOneoOo0YinwFA7VLenFau6yh5Q0FBgdq2bauUlJSLXqTWlxReZ+PQoUOWuc6GJF1++eXaunVrdYdRJRhja7DaOBtjlJiYqB9++EH+/szSLo7V8plkvc+BxHedFTDGvq+iOa3Kjyj5+/srODjYcr9QRkVFWebNKEkBAQGW2l+JMbYKK41zcHAwRVIJrJrPJGt9Dviu832MsTVUJKdVSwYcM2ZMdTwtqhBj7PsYY9/HGJeO18j3Mca+jzG2hoqMc5VPvbOarKws1alTR2fOnLFU1W4ljLE1MM4AnwMrYIx9H2NcdgGTJ0+eXN1B+LqAgAAlJSUpMLDKZzqiijDG1sA4A3wOrIAx9n2McdlwRAkAAAAA3HCWLgAAAAC4oVACAAAAADcUSgAAAADghkIJqIADBw7Iz89PO3bsqJH9Wd3kyZOVkJDglb78/Py0fPnyMrd/6623VLduXa88NwBUNvJZzUdOqz4s5uAlBw4cUFxcnLZv3+61NzNqrvz8fB0/flzR0dFeWTGG9493ZWdny263q0GDBh73dfToUdWrV082m61M7fPy8vTLL7+oUaNGHj83UF34TrIO8lnNR06rPqwJCBTD4XAoKCjoovcHBASoSZMmVRhR6c6dO6fg4ODqDqNGiIiIUERExEXvL89rVd5xDg0NVWhoaLkeAwCVhXxW+5HTqg9T79wsXrxYnTp1UmhoqBo0aKB+/fopJydHkjR//ny1b99eISEhio+P15w5c1yPi4uLkyT9v//3/+Tn56ekpCRJUkFBgZ599lk1b95cNptNCQkJWrlypetx586d04MPPqiYmBiFhISoVatWmjZtmuv+mTNnqlOnTgoPD1dsbKxGjx6t7OzsKnglao9//OMfatasmQoKCopsv+GGG3TXXXdJkv79738rMTFRISEh+t3vfqcpU6bI6XS62vr5+em1117TkCFDFB4erqlTp+r06dMaOXKkGjZsqNDQUF1yySWaP3++pOKnFuzevVvXX3+9oqKiFBkZqV69eiktLU1S6e+D4qxfv17dunWTzWZTTEyMnnrqqSIxJyUl6cEHH9Rjjz2m6Oho9e/f37MXshYpbczdpyncfffdGjp0qKZNm6amTZuqbdu2kqTMzExdf/31Cg0NVVxcnBYuXKhWrVopOTnZ9djfTlMoHPelS5eqT58+CgsLU+fOnbV582ZX++KmKXz88cfq2rWrQkJCFB0drWHDhrnue/fdd9W1a1dFRkaqSZMmGjFihI4dO+a9FwuWRk6rXchn1stnEjmtRjNwycjIMIGBgWbmzJkmPT3d/Pe//zWvvvqq+eWXX8y8efNMTEyMWbJkidm/f79ZsmSJqV+/vnnrrbeMMcZ88803RpJZs2aNyczMNCdPnjTGGDNz5kwTFRVl3nvvPZOammqeeOIJExQUZH744QdjjDEzZswwsbGx5ssvvzQHDhwwGzZsMAsXLnTFNGvWLPPFF1+Y/fv3m88//9y0a9fOPPDAA1X/4tRgJ0+eNMHBwWbNmjWubadOnTLBwcHmP//5j1m5cqWJiooyb731lklLSzOrVq0yrVq1MpMnT3a1l2QaNWpk3nzzTZOWlmYOHDhgxowZYxISEszWrVtNenq6Wb16tfn444+NMcakp6cbSWb79u3GGGMOHz5s6tevb4YNG2a2bt1q9u7da/75z3+a1NRUY0zp74Pi+gsLCzOjR482e/bsMcuWLTPR0dFm0qRJrph79+5tIiIizPjx401qaqrZs2dPpb7ONUlpYz5p0iTTuXNn13133XWXiYiIMHfccYfZtWuX2blzpzHGmH79+pmEhASzZcsWk5KSYnr37m1CQ0PNrFmzXI+VZJYtW2aM+XWc4uPjzYoVK8zevXvN8OHDTcuWLY3D4TDGGDN//nxTp04d1+NXrFhhAgICzMSJE833339vduzYYZ5//nnX/W+++ab59NNPTVpamtm8ebO58sorzcCBAyvnhYOlkNNqH/KZ9fKZMeS0moxC6TdSUlKMJHPgwIEL7ouNjS3yZW+MMc8995zp3r27MebCL4ZCTZs2LfIGMsaYyy+/3IwePdoYY8xDDz1kfv/735uCgoIyxfjBBx+YBg0alHmfrOKGG24wo0aNct3+xz/+YZo0aWKcTqfp1auXeeGFF4q0f+edd0xMTIzrtiQzduzYIm0GDx5s7rnnnmKfz328J0yYYOLi4sy5c+eKbV/a+8C9v6efftq0a9euyPvi1VdfNRERESY/P98Ycz6xJCQkXPxF8XEljXlxSaVx48bGbre7tu3Zs8dIMlu3bnVt+9///mcklZpU3njjDdf9u3fvNpJcid09qXTv3t2MHDmyzPtV+B/UX375pcyPAYpDTqudyGfWRE6rmZh69xudO3dW37591alTJ9100016/fXXdfr0aR0/flyHDh3Svffe65onGhERoalTp7oORRcnKytLGRkZ6tmzZ5HtPXv21J49eySdP3y6Y8cOtWvXTg8//LBWrVpVpO3atWvVv39/NWvWTJGRkbrzzjt18uRJ19QJnDdy5EgtWbJEdrtdkrRgwQLdeuutCggIUEpKip599tkiY3ffffcpMzNTubm5rj66du1apM8HHnhA77//vhISEvTEE09o06ZNF33+HTt2qFevXsXOAy/L+8Ddnj171L17d/n5+RVpn52drcOHD180ZispacyL06lTpyJzuPfu3avAwEB16dLFta1NmzaqV69eqc992WWXuf4dExMjSRedWrBjxw717dv3on1t375dQ4YMUcuWLRUZGema4vTjjz+WGgdQEnJa7UQ+syZyWs1EofQbAQEBWr16tT777DNdeumlmj17ttq1a6f9+/dLkl5//XXt2LHD9WfXrl3asmVLqf3+9stBkowxrm1dunRRenq6nnvuOeXl5enmm2/W8OHDJUkHDx7Uddddp44dO2rJkiVKSUnRq6++Kun8yZn41eDBg1VQUKBPPvlEhw4d0oYNG3T77bdLOj+fesqUKUXGbufOnfrf//6nkJAQVx/h4eFF+hw4cKAOHjyosWPHKiMjQ3379tXjjz9e7POX5UTHkt4H7oq7z/zfApW/3e4es5WUNObFcX+tzEUW/LzY9t/67X8gCsfDfW55oZLeGzk5ORowYIAiIiL07rvvauvWrVq2bJmk8+d6AJ4gp9VO5DNrIqfVTBRKbvz8/NSzZ09NmTJF27dvV3BwsDZu3KhmzZpp//79atOmTZE/hSe8Flb1+fn5rr6ioqLUtGlTffXVV0WeY9OmTWrfvn2Rdrfccotef/11LVq0SEuWLNGpU6e0bds2OZ1OvfLKK7ryyivVtm1bZWRkVMGrUPuEhoZq2LBhWrBggd577z21bdtWiYmJks4n7r17914wdm3atJG/f8kfgYYNG+ruu+/Wu+++q+TkZM2bN6/Ydpdddpk2bNhQbLIv6/vgty699FJt2rSpyBfcpk2bFBkZqWbNmpUYs1WUNOZlER8fL6fTqe3bt7u27du3Tz///LNX47zsssv0+eefF3tfamqqTpw4oRdffFG9evVSfHx87T7pFTUOOa32IZ9ZEzmtZmJ58N/4+uuv9fnnn2vAgAFq1KiRvv76ax0/flzt27fX5MmT9fDDDysqKkoDBw6U3W7Xtm3bdPr0aT322GNq1KiRQkNDtXLlSjVv3lwhISGqU6eOxo8fr0mTJql169ZKSEjQ/PnztWPHDi1YsECSNGvWLMXExCghIUH+/v768MMP1aRJE9WtW1etW7eW0+nU7NmzNXjwYG3cuFGvvfZaNb9KNdfIkSM1ePBg7d69u8ivMBMnTtSgQYMUGxurm266Sf7+/vrvf/+rnTt3aurUqRftb+LEiUpMTFSHDh1kt9u1YsWKiyaCBx98ULNnz9att96qCRMmqE6dOtqyZYu6deumdu3alfo+cDd69GglJyfroYce0oMPPqi9e/dq0qRJeuyxx0pNhlZysTEvi/j4ePXr109/+tOfNHfuXAUFBWncuHEKDQ296C+jFTFp0iT17dtXrVu31q233iqn06nPPvtMTzzxhFq0aKHg4GDNnj1b999/v3bt2qXnnnvOa88NayOn1V7kM2sip9VA1XFiVE31/fffm2uuucY0bNjQ2Gw207ZtWzN79mzX/QsWLDAJCQkmODjY1KtXz1x99dVm6dKlrvtff/11Exsba/z9/U3v3r2NMcbk5+ebKVOmmGbNmpmgoCDTuXNn89lnn7keM2/ePJOQkGDCw8NNVFSU6du3r/n2229d98+cOdPExMSY0NBQc80115h//etfRpI5ffp05b8gtYzT6TQxMTFGkklLSyty38qVK02PHj1MaGioiYqKMt26dTPz5s1z3a/fnNxY6LnnnjPt27c3oaGhpn79+mbIkCFm//79xpjiT3T+7rvvzIABA0xYWJiJjIw0vXr1csVR2vuguP7WrVtnLr/8chMcHGyaNGlinnzySdcqNMacP/n1kUce8cIrV3tdbMyLO/F1yJAhFzw+IyPDDBw40NhsNtOyZUuzcOFC06hRI/Paa6+52qiYE19/O06nT582kszatWuNMRee+GqMMUuWLHF9d0RHR5thw4a57lu4cKFp1aqVsdlspnv37ubjjz8u9iR6oLzIabUX+cyayGk1j58xZZi8CAAWcPjwYcXGxmrNmjUlnqwKAEBNR07zHIUSAMv64osvlJ2drU6dOikzM1NPPPGEjhw5oh9++KHEK9kDAFDTkNO8j3OUAFiWw+HQ008/rf379ysyMlI9evTQggULSCgAgFqHnOZ9HFECAAAAADcsNwIAAAAAbiiUAAAAAMANhRIAAAAAuKFQAgAAAAA3FErwKUlJSfLz85Ofn5927NhR3eHowIEDXo1l8uTJrv1LTk72Sp8AgJqJnAZULwol+Jz77rtPmZmZ6tixY3WHotjYWK/G8vjjjyszM1PNmzf3Sn8AgJqNnAZUH66jBJ8TFhamJk2aVHcYkqSAgACvxhIREaGIiAgFBAR4rU8AQM1FTgOqD0eU4NNOnz6tkSNHqmHDhgoNDdUll1yi+fPnS/p1CsH777+vHj16KCQkRB06dNC6deuK9PH999/ruuuuU0REhBo3bqw77rhDJ06ccN1fUFCg6dOnq02bNrLZbGrRooWef/75Is/x22kKpfW3ePFiderUSaGhoWrQoIH69eunnJycSnyVAAC1ATkNqFoUSvBpzzzzjL7//nt99tln2rNnj+bOnavo6OgibcaPH69x48Zp+/bt6tGjh2644QadPHlSkpSZmanevXsrISFB27Zt08qVK/XTTz/p5ptvdj1+woQJmj59uuu5Fi5cqMaNGxcbT2n9ZWZm6rbbbtOoUaO0Z88erVu3TsOGDRPXhQYAkNOAKmYAH9K7d2/zyCOPuG4PHjzY3HPPPcW2TU9PN5LMiy++6NrmcDhM8+bNzfTp040xxjzzzDNmwIABRR536NAhI8ns3bvXZGVlGZvNZl5//fUSn2P79u1l6i8lJcVIMgcOHChxP1u2bGlmzZpVYhsAQO1GTgOqF+cowac98MAD+sMf/qBvv/1WAwYM0NChQ9WjR48ibbp37+76d2BgoLp27ao9e/ZIklJSUrR27VpFRERc0HdaWpp+/vln2e129e3bt0zxlNbfgAED1LdvX3Xq1EnXXHONBgwYoOHDh6tevXrl2W0AgA8ipwFVi0IJPm3gwIE6ePCgPvnkE61Zs0Z9+/bVmDFj9PLLL5f4OD8/P0nn52oPHjxY06dPv6BNTEyM9u/fX654SusvICBAq1ev1qZNm7Rq1SrNnj1bf/nLX/T1118rLi6uXM8FAPAt5DSganGOEnxew4YNdffdd+vdd99VcnKy5s2bV+T+LVu2uP7tdDqVkpKi+Ph4SVKXLl20e/dutWrVSm3atCnyJzw8XJdccolCQ0P1+eeflymW0vqTzie0nj17asqUKdq+fbuCg4O1bNkyL70aAIDajJwGVB0KJfi0iRMn6qOPPtK+ffu0e/durVixQu3bty/S5tVXX9WyZcuUmpqqMWPG6PTp0xo1apQkacyYMTp16pRuu+02ffPNN9q/f79WrVqlUaNGKT8/XyEhIXryySf1xBNP6F//+pfS0tK0ZcsWvfnmm8XGU1p/X3/9tV544QVt27ZNP/74o5YuXarjx49fEDMAwHrIaUDVYuodfFpwcLAmTJigAwcOKDQ0VL169dL7779fpM2LL76o6dOna/v27WrdurU++ugj1ypCTZs21caNG/Xkk0/qmmuukd1uV8uWLXXttdfK3//87wzPPPOMAgMDNXHiRGVkZCgmJkb3339/sfGU1l9UVJS+/PJLJScnKysrSy1bttQrr7yigQMHVu4LBQCo8chpQNXyM4Y1GuE7kpKSlJCQoOTk5FLbHjhwQHFxcdq+fbsSEhKqIDrvadWqlcaOHauxY8dWdygAgEqCx0+wAAAAtElEQVRCTgOqF1Pv4HPmzJmjiIgI7dy5s7pD8boXXnhBERER+vHHH6s7FABAFSCnAdWHI0rwKUeOHFFeXp4kqUWLFgoODr5o29r469upU6d06tQpSedP6K1Tp041RwQAqCzkNKB6USgBAAAAgBum3gEAAACAGwolAAAAAHBDoQQAAAAAbiiUAAAAAMANhRIAAAAAuKFQAgAAAAA3FEoAAAAA4IZCCQAAAADcUCgBAAAAgJv/H/OkNpWz1yh3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fded22358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graficamos los boxplot para las 4 medidas, separadas por el tipo de especie\n",
    "iris.boxplot(by='species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5fdf4ddef0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fdf3eb0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#solo graficamos el largo de los petalos\n",
    "iris.boxplot('petal_length', by = 'species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fdee89630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['petal_length'].hist(color='green',  edgecolor='black', linewidth=1.2).grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fde358c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5fde8ad6a0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fde89efd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5fde8f8438>]], dtype=object)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fde298f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.hist('petal_length', by='species', edgecolor='black', linewidth=1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fddc23470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5fddc0f160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fddbc9160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5fddb801d0>]], dtype=object)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fddd6f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.hist('petal_length', by='species', edgecolor='black', linewidth=1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por ultimo vamos a generar un grafico con 3 histogramas juntos, como hicimos en R.\n",
    "Esto es un poco más complejo pero no tanto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bbrstd911V5Lka1/7Wu68885Mnjw5jz32WKZPn55p06bl61//+ibXPu6447Lvvvtm9OjReeihh/LAAw/kjDPOyDHHHJMDDjjgXX9OlSJyAABgGxg3blxefPHFDB8+vFWI7LHHHvn1r3+ddevW5dhjj82AAQNywQUXbPG3LPX19fnJT36SIUOGZNCgQZk7d25+/vOfp0uXLpvc/+abb86BBx6Yk08+Of37989FF13U8nubQw89NDfccEOuvfba7L///pk0aVImT568yZsOJMkOO+yQW2+9NR07dsyRRx6Z4447Lvvss0+uv/76d/npVFZN85/ey2470NjYmPr6+jQ0NKRz587VHqdYfS76xTve9+lLR7bhJAAAW/b6669n2bJl6du3b3baaadqj0MFvd13+27bwJkcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAqmjOnDmpqanJSy+99J6PNXTo0FxwwQXveP+nn346NTU1Wbx48Xtee3tSW+0BAADgvRgwc8A2XW/JmCXveN9Ro0bltddey913373Ra7/5zW8yZMiQ/Pa3v82zzz6b+vr69zzbzTffnA996EPveP9evXrl2WefzYc//OH3vPb2xJkcAABoI+PGjcs999yT5cuXb/Ta1VdfnQMOOCCHHnpoevTokZqamk0eY/369dmwYcM7Wq9r167p1KnTO56vXbt26dGjR2pryzr3IXIAAKCNHH/88enevXtmzJjRavurr76aG2+8MePGjdvocrUZM2akS5cu+fnPf57+/funrq4uy5cvz5tvvpnzzjsvXbp0Sbdu3XLhhRdmzJgxOfHEE1uO++eXq/Xp0yeTJ0/O2LFj06lTp/Tu3TtXXnlly+ubulztkUceyciRI9O5c+d06tQpH//4x/Pkk08mSebPn59jjz02H/7wh1NfX5+jjz46ixYtaouP7j0ROQAA0EZqa2tz+umnZ8aMGWlubm7ZftNNN2XdunUZPXr0Jt/36quvZsqUKfnXf/3XPPLII+nevXsuu+yy/Md//EemT5+eX//612lsbMysWbO2OMMPfvCDDB48OA899FC+/OUv52//9m/zn//5n5vc95lnnslRRx2VnXbaKffcc08WLlyYsWPH5s0330ySvPzyyxkzZkzmzZuXBx54IHvvvXc+9alP5eWXX34Xn07bKeu8FAAAbGfGjh2b733ve5kzZ06GDRuW5H8uVfvMZz6TXXbZZZPveeONN/LjH/84gwYNatl2xRVXZMKECfnrv/7rJMnUqVNz2223bXH9T33qU/nyl7+cJLnwwgvzox/9KHPmzEm/fv022vf//J//k/r6+txwww0tv+3ZZ599Wl7/xCc+0Wr/f/mXf8kuu+ySuXPn5vjjj9/iLNuKMzkAANCG+vXrlyFDhuTqq69Okjz55JOZN29exo4du9n37Ljjjhk4cGDL84aGhjz33HM59NBDW7a1a9cuBx988BbX/9Pj1NTUpEePHlmzZs0m9128eHE+/vGPb/bmBWvWrMnZZ5+dffbZJ/X19amvr8/atWuzYsWKLc6xLYkcAABoY+PGjctPf/rTNDY2Zvr06dlzzz1zzDHHbHb/9u3bb/JGBH++7U8vgducPw+Wmpqazd7IoH379m97rDPOOCMLFy7M5Zdfnvvvvz+LFy9Ot27dsm7dui3OsS2JHAAAaGMnnXRS2rVrl+uuuy4zZ87MmWeeudm7qW1KfX19dttttzz44IMt29avX5+HHnqoonMOHDgw8+bNyxtvvLHJ1+fNm5fzzjsvn/rUp7Lffvulrq4uf/zjHys6QyWIHAAAaGMdO3bMySefnIsvvjirVq3KGWecsdXHOPfcczNlypT87Gc/y9KlS3P++efnxRdf3KpY2pKvfOUraWxszCmnnJIFCxbk8ccfzzXXXJOlS5cmST72sY/lmmuuyaOPPprf/va3GT169BbP/lSDyAEAgG1g3LhxefHFFzN8+PD07t17q99/4YUX5tRTT83pp5+eww8/PB07dsxxxx2XnXbaqWIzduvWLffcc0/Wrl2bo48+OgcffHCuuuqqlkverr766rz44os58MADc9ppp+W8885L9+7dK7Z+pdQ0v5ML+bahxsbG1NfXp6GhIZ07d672OMXqc9Ev3vG+T186sg0nAQDYstdffz3Lli1L3759K/pf6t/PNmzYkH333TcnnXRSvvOd71R7nHft7b7bd9sGbiENAADvA8uXL89dd92Vo48+Ok1NTZk6dWqWLVuWz33uc9UebbvjcjUAAHgf2GGHHTJjxowccsghOeKII7JkyZLcfffd2Xfffas92nbHmRwAAHgf6NWrV379619Xe4z3BWdyAACAoogcAADeN7aze2ZRAW3xnYocAAC2e2/dwvjVV1+t8iRU2rp165Ik7dq1q9gx/SYHAIDtXrt27dKlS5esWbMmSdKhQ4eK/hFMqmPDhg357//+73To0CG1tZVLE5EDAMD7Qo8ePZKkJXQoww477JDevXtXNFpFDgAA7ws1NTXZfffd071797zxxhvVHocK2XHHHbPDDpX9FY3IAQDgfaVdu3YV/f0G5XHjAQAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoylZHzr333ptRo0alZ8+eqampyaxZs1pee+ONN3LhhRdmwIAB2XnnndOzZ8+cfvrpWbVqVUWHBgAA2JytjpxXXnklgwYNytSpUzd67dVXX82iRYtyySWXZNGiRbn55pvz2GOP5dOf/nRFhgUAANiS2q19w4gRIzJixIhNvlZfX5/Zs2e32nbFFVfk0EMPzYoVK9K7d+93NyUAAMA7tNWRs7UaGhpSU1OTLl26bPL1pqamNDU1tTxvbGxs65EAAICCtWnkvP7667nooovyuc99Lp07d97kPlOmTMmkSZPacowPlD4X/aLaIwDAVhswc8A2X3PJmCXbfE1g22izu6u98cYbOeWUU7Jhw4b8+Mc/3ux+EyZMSENDQ8tj5cqVbTUSAADwAdAmZ3LeeOONnHTSSVm2bFnuueeezZ7FSZK6urrU1dW1xRgAAMAHUMUj563Aefzxx/OrX/0q3bp1q/QSAAAAm7XVkbN27do88cQTLc+XLVuWxYsXp2vXrunZs2f+1//6X1m0aFF+/vOfZ/369Vm9enWSpGvXrtlxxx0rNzkAAMAmbHXkLFiwIMOGDWt5Pn78+CTJmDFjMnHixNx6661JkgMOOKDV+371q19l6NCh72FUAACALdvqyBk6dGiam5s3+/rbvQYAANDW2uzuagAAANUgcgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKJsdeTce++9GTVqVHr27JmamprMmjWr1evNzc2ZOHFievbsmfbt22fo0KF55JFHKjYwAADA29nqyHnllVcyaNCgTJ06dZOv/+M//mN++MMfZurUqZk/f3569OiRY489Ni+//PJ7HhYAAGBLarf2DSNGjMiIESM2+Vpzc3Muv/zyfPOb38xnPvOZJMnMmTOz22675brrrstZZ5313qYFAADYgor+JmfZsmVZvXp1PvnJT7Zsq6ury9FHH53777+/kksBAABs0lafyXk7q1evTpLsttturbbvtttuWb58+Sbf09TUlKamppbnjY2NlRwJAAD4gKlo5Lylpqam1fPm5uaNtr1lypQpmTRpUluMAQDvTxPrt8EaDW2/BkCVVPRytR49eiT5/2d03rJmzZqNzu68ZcKECWloaGh5rFy5spIjAQAAHzAVjZy+ffumR48emT17dsu2devWZe7cuRkyZMgm31NXV5fOnTu3egAAALxbW3252tq1a/PEE0+0PF+2bFkWL16crl27pnfv3rngggsyefLk7L333tl7770zefLkdOjQIZ/73OcqOjgAAMCmbHXkLFiwIMOGDWt5Pn78+CTJmDFjMmPGjPzd3/1dXnvttXz5y1/Oiy++mMMOOyx33XVXOnXqVLmpAQAANmOrI2fo0KFpbm7e7Os1NTWZOHFiJk6c+F7mAgAAeFcq+pscAACAahM5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRaqs9AADAB8GAmQO2+ZpLxizZ5mvC9sCZHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiVDxy3nzzzXzrW99K37590759++y11175+7//+2zYsKHSSwEAAGykttIHvOyyy/KTn/wkM2fOzH777ZcFCxbkzDPPTH19fc4///xKLwcAANBKxSPnN7/5TU444YSMHDkySdKnT59cf/31WbBgQaWXAgAA2EjFL1c78sgj88tf/jKPPfZYkuR3v/td7rvvvnzqU5/a5P5NTU1pbGxs9QAAAHi3Kn4m58ILL0xDQ0P69euXdu3aZf369fnud7+bU089dZP7T5kyJZMmTar0GABsbybWb4M1Gtp+DXgfGTBzwDZdb8mYJdt0Pdicip/JufHGG3Pttdfmuuuuy6JFizJz5sx8//vfz8yZMze5/4QJE9LQ0NDyWLlyZaVHAgAAPkAqfibnG9/4Ri666KKccsopSZIBAwZk+fLlmTJlSsaMGbPR/nV1damrq6v0GAAAwAdUxc/kvPrqq9lhh9aHbdeunVtIAwAA20TFz+SMGjUq3/3ud9O7d+/st99+eeihh/LDH/4wY8eOrfRSAAAAG6l45FxxxRW55JJL8uUvfzlr1qxJz549c9ZZZ+Xb3/52pZcCAADYSMUjp1OnTrn88stz+eWXV/rQAAAAW1Tx3+QAAABUk8gBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIpSW+0B2LI+F/3ifbP+05eObMNJALZgYv02WKOh7dfYFnxWQMGcyQEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAooifdj9DAAAR7UlEQVQcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKG0SOc8880w+//nPp1u3bunQoUMOOOCALFy4sC2WAgAAaKW20gd88cUXc8QRR2TYsGG5/fbb07179zz55JPp0qVLpZcCAADYSMUj57LLLkuvXr0yffr0lm19+vSp9DIAAACbVPHL1W699dYMHjw4n/3sZ9O9e/cceOCBueqqqza7f1NTUxobG1s9AAAA3q2Kn8l56qmnMm3atIwfPz4XX3xxHnzwwZx33nmpq6vL6aefvtH+U6ZMyaRJkyo9BgC0jYn11Z4AgC2o+JmcDRs25KCDDsrkyZNz4IEH5qyzzsoXv/jFTJs2bZP7T5gwIQ0NDS2PlStXVnokAADgA6TikbP77runf//+rbbtu+++WbFixSb3r6urS+fOnVs9AAAA3q2KR84RRxyRpUuXttr22GOPZc8996z0UgAAABupeOR89atfzQMPPJDJkyfniSeeyHXXXZcrr7wy55xzTqWXAgAA2EjFI+eQQw7JLbfckuuvvz77779/vvOd7+Tyyy/P6NGjK70UAADARip+d7UkOf7443P88ce3xaEBAADeVsXP5AAAAFSTyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAilJb7QEAgO3LgL69K3OgmQMqcxzeNwZU4TtfMmbJNl+T7Z8zOQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUpc0jZ8qUKampqckFF1zQ1ksBAAC0beTMnz8/V155ZQYOHNiWywAAALRos8hZu3ZtRo8enauuuiq77LJLWy0DAADQSptFzjnnnJORI0dm+PDhbbUEAADARmrb4qA33HBDFi1alPnz529x36ampjQ1NbU8b2xsbIuRAACAD4iKR87KlStz/vnn56677spOO+20xf2nTJmSSZMmVXoMqqTPRb94x/s+fenINpwEAN7egJkDqj0C0EYqfrnawoULs2bNmhx88MGpra1NbW1t5s6dm3/+539ObW1t1q9f32r/CRMmpKGhoeWxcuXKSo8EAAB8gFT8TM4xxxyTJUuWtNp25plnpl+/frnwwgvTrl27Vq/V1dWlrq6u0mMAAAAfUBWPnE6dOmX//fdvtW3nnXdOt27dNtoOAABQaW3+x0ABAAC2pTa5u9qfmzNnzrZYBgAAwJkcAACgLCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAAChKbbUHAGALJtZvgzUa2n4NAN6VATMHbPM1l4xZss3XrCRncgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKUvHImTJlSg455JB06tQp3bt3z4knnpilS5dWehkAAIBNqnjkzJ07N+ecc04eeOCBzJ49O2+++WY++clP5pVXXqn0UgAAABuprfQB77jjjlbPp0+fnu7du2fhwoU56qijKr0cAABAKxWPnD/X0NCQJOnatesmX29qakpTU1PL88bGxrYeCQAAKFibRk5zc3PGjx+fI488Mvvvv/8m95kyZUomTZrUlmNsl/pc9Itqj/C+sTWf1dOXjmzDSeDPTKyv9gSVU9I/C/CBMmDmgG2+5pIxS7b5mmydNr272le+8pX8/ve/z/XXX7/ZfSZMmJCGhoaWx8qVK9tyJAAAoHBtdibn3HPPza233pp77703e+yxx2b3q6urS11dXVuNAQAAfMBUPHKam5tz7rnn5pZbbsmcOXPSt2/fSi8BAACwWRWPnHPOOSfXXXddfvazn6VTp05ZvXp1kqS+vj7t27ev9HIAAACtVPw3OdOmTUtDQ0OGDh2a3XffveVx4403VnopAACAjbTJ5WoAAADV0qZ3VwMAANjWRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUUQOAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUJTaag/wftDnol9UewRga02sr/YEUDED+vau9gjAnxgwc0C1R2ALnMkBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoCgiBwAAKIrIAQAAiiJyAACAoogcAACgKCIHAAAoisgBAACKInIAAICiiBwAAKAoIgcAACiKyAEAAIoicgAAgKKIHAAAoChtFjk//vGP07dv3+y00045+OCDM2/evLZaCgAAoEWbRM6NN96YCy64IN/85jfz0EMP5eMf/3hGjBiRFStWtMVyAAAALdokcn74wx9m3Lhx+cIXvpB99903l19+eXr16pVp06a1xXIAAAAtait9wHXr1mXhwoW56KKLWm3/5Cc/mfvvv3+j/ZuamtLU1NTyvKGhIUnS2NhY6dHetQ1Nr1Z7hCK90+94az7/7en/b6iypuZqTwAVs/619dUeAfiA2V7+O9VbczQ3b91/rlc8cv74xz9m/fr12W233Vpt32233bJ69eqN9p8yZUomTZq00fZevXpVejS2M/WXvz+OCVB9j1Z7AOADpv5v66s9Qisvv/xy6uvf+UwVj5y31NTUtHre3Ny80bYkmTBhQsaPH9/yfMOGDXnhhRfSrVu3Te6/rTU2NqZXr15ZuXJlOnfuXO1xqCDfbbl8t2Xz/ZbLd1s232+52vK7bW5uzssvv5yePXtu1fsqHjkf/vCH065du43O2qxZs2ajsztJUldXl7q6ulbbunTpUumx3rPOnTv7F7JQvtty+W7L5vstl++2bL7fcrXVd7s1Z3DeUvEbD+y44445+OCDM3v27FbbZ8+enSFDhlR6OQAAgFba5HK18ePH57TTTsvgwYNz+OGH58orr8yKFSty9tlnt8VyAAAALdpNnDhxYqUPuv/++6dbt26ZPHlyvv/97+e1117LNddck0GDBlV6qW2iXbt2GTp0aGpr2+wnTFSJ77Zcvtuy+X7L5bstm++3XNvbd1vTvLX3YwMAANiOtckfAwUAAKgWkQMAABRF5AAAAEUROQAAQFFEzmbce++9GTVqVHr27JmamprMmjWr2iNRIVOmTMkhhxySTp06pXv37jnxxBOzdOnSao9FBUybNi0DBw5s+WNkhx9+eG6//fZqj0UbmDJlSmpqanLBBRdUexQqYOLEiampqWn16NGjR7XHokKeeeaZfP7zn0+3bt3SoUOHHHDAAVm4cGG1x6IC+vTps9G/uzU1NTnnnHOqPZrI2ZxXXnklgwYNytSpU6s9ChU2d+7cnHPOOXnggQcye/bsvPnmm/nkJz+ZV155pdqj8R7tscceufTSS7NgwYIsWLAgn/jEJ3LCCSfkkUceqfZoVND8+fNz5ZVXZuDAgdUehQrab7/98uyzz7Y8lixZUu2RqIAXX3wxRxxxRD70oQ/l9ttvzx/+8If84Ac/SJcuXao9GhUwf/78Vv/ezp49O0ny2c9+tsqTtdEfAy3BiBEjMmLEiGqPQRu44447Wj2fPn16unfvnoULF+aoo46q0lRUwqhRo1o9/+53v5tp06blgQceyH777VelqaiktWvXZvTo0bnqqqvyD//wD9Uehwqqra119qZAl112WXr16pXp06e3bOvTp0/1BqKidt1111bPL7300nz0ox/N0UcfXaWJ/j9ncvjAa2hoSJJ07dq1ypNQSevXr88NN9yQV155JYcffni1x6FCzjnnnIwcOTLDhw+v9ihU2OOPP56ePXumb9++OeWUU/LUU09VeyQq4NZbb83gwYPz2c9+Nt27d8+BBx6Yq666qtpj0QbWrVuXa6+9NmPHjk1NTU21xxE5fLA1Nzdn/PjxOfLII7P//vtXexwqYMmSJenYsWPq6upy9tln55Zbbkn//v2rPRYVcMMNN2TRokWZMmVKtUehwg477LD8+7//e+68885cddVVWb16dYYMGZLnn3++2qPxHj311FOZNm1a9t5779x55505++yzc9555+Xf//3fqz0aFTZr1qy89NJLOeOMM6o9ShKXq/EB95WvfCW///3vc99991V7FCrkL/7iL7J48eK89NJL+elPf5oxY8Zk7ty5Qud9buXKlTn//PNz1113Zaeddqr2OFTYn14ePmDAgBx++OH56Ec/mpkzZ2b8+PFVnIz3asOGDRk8eHAmT56cJDnwwAPzyCOPZNq0aTn99NOrPB2V9G//9m8ZMWJEevbsWe1RkjiTwwfYueeem1tvvTW/+tWvsscee1R7HCpkxx13zMc+9rEMHjw4U6ZMyaBBg/JP//RP1R6L92jhwoVZs2ZNDj744NTW1qa2tjZz587NP//zP6e2tjbr16+v9ohU0M4775wBAwbk8ccfr/YovEe77777Rv8j07777psVK1ZUaSLawvLly3P33XfnC1/4QrVHaeFMDh84zc3NOffcc3PLLbdkzpw56du3b7VHog01Nzenqamp2mPwHh1zzDEb3W3rzDPPTL9+/XLhhRemXbt2VZqMttDU1JRHH300H//4x6s9Cu/REUccsdGfaXjsscey5557Vmki2sJbN3EaOXJktUdpIXI2Y+3atXniiSdani9btiyLFy9O165d07t37ypOxnt1zjnn5LrrrsvPfvazdOrUKatXr06S1NfXp3379lWejvfi4osvzogRI9KrV6+8/PLLueGGGzJnzpyN7qjH+0+nTp02+t3czjvvnG7duvk9XQG+/vWvZ9SoUendu3fWrFmTf/iHf0hjY2PGjBlT7dF4j7761a9myJAhmTx5ck466aQ8+OCDufLKK3PllVdWezQqZMOGDZk+fXrGjBmT2trtJy22n0m2MwsWLMiwYcNanr91TfCYMWMyY8aMKk1FJUybNi1JMnTo0Fbbp0+fvt38WI5357nnnstpp52WZ599NvX19Rk4cGDuuOOOHHvssdUeDXgb//Vf/5VTTz01f/zjH7PrrrvmL//yL/PAAw/4X/sLcMghh+SWW27JhAkT8vd///fp27dvLr/88owePbrao1Ehd999d1asWJGxY8dWe5RWapqbm5urPQQAAECluPEAAABQFJEDAAAUReQAAABFETkAAEBRRA4AAFAUkQMAABRF5AAAAEUROQAAQFFEDgAAUBSRAwAAFEXkAAAARRE5AABAUf4f+RDqRmllm+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fdd4e5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#primero generamos los subsets segun especies\n",
    "setosa = iris.loc[iris['species'] == 'setosa', 'petal_length']\n",
    "versicolor = iris.loc[iris['species'] == 'versicolor', 'petal_length']\n",
    "virginica  = iris.loc[iris['species'] == 'virginica', 'petal_length']\n",
    "#luego preparamos los 3 histogramas\n",
    "pyplot.hist(setosa,  label='Setosa')\n",
    "pyplot.hist(versicolor, label='Versicolor')\n",
    "pyplot.hist(virginica, label='Virginica')\n",
    "# y graficamos\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bueno como ven, utilizando Pandas podemos hacer análisis de datos y tablas en Python sin volvernos locos con listas ni diccionarios ni nada. Si les interesa pueden googlear pandas python en google que hay muchisimos tutoriales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}