Ejercicio 3 sin comentar

6b0bc5da · Jennifer Grisales Casadiegos · ae0f3ea6 · 6b0bc5da · 6b0bc5da
Commit 6b0bc5da authored 4 years ago by Jennifer Grisales Casadiegos
--- a/.ipynb_checkpoints/ejercicio3-checkpoint.ipynb
+++ b/.ipynb_checkpoints/ejercicio3-checkpoint.ipynb
@@ -2,9 +2,20 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n@author: jennifer\\n\\nEste programa entrega los números de una fila del\\ntriángulo de Pascal.\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
@@ -55,7 +66,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -81,115 +92,217 @@
    "          \"sirias\": {\"Nombre\":\"Siria\",\"Apellido\":\"Sadeddin\",\"Edad\":30,\"País\": \"Venezuela\",\n",
    "                     \"Ciudad\": \"Colombia\",\"Institución\": \"Universidad Simón Bolivar\", \"Hobbie\": \"Data Science\",  \"Tipo\": \"Estudiante\"},\n",
    "          \"teofilo\": {\"Nombre\":\"Teofilo\",\"Apellido\":\"Vargas\",\"Edad\":54,\"País\": \"Perú\",\n",
-    "                     \"Ciudad\": \"Lima\",\"Institución\": \"Universidad Nacional Mayor de San Marcos\", \"Hobbie\": \"Wing Chun Kuen\",  \"Tipo\": \"Profesor\"},\n",
-    "          \"sirias\": {\"Nombre\":\"Siria\",\"Apellido\":\"Sadeddin\",\"Edad\":30,\"País\": \"Venezuela\",\n",
-    "                     \"Ciudad\": \"Colombia\",\"Institución\": \"Universidad Simón Bolivar\", \"Hobbie\": \"Data Science\",  \"Tipo\": \"Profesor\"}}"
+    "                     \"Ciudad\": \"Lima\",\"Institución\": \"Universidad Nacional Mayor de San Marcos\", \"Hobbie\": \"Wing Chun Kuen\",  \"Tipo\": \"Profesor\"}\n",
+    "         }\n"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Perú'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compas[\"teofilo\"][\"País\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Promediar edades"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statistics as st"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Pais: Colombia\n",
-      "Pais: Colombia\n",
-      "Pais: Perú\n",
-      "Pais: Colombia\n",
-      "Pais: Colombia\n",
-      "Pais: Venezuela\n",
-      "Pais: Venezuela\n",
-      "Pais: Colombia\n",
-      "Pais: Colombia\n",
-      "Pais: Venezuela\n",
-      "Pais: Perú\n"
+      "[29, 23, 25, 25, 24, 22, 27, 24, 20, 30, 54]\n"
     ]
    }
   ],
   "source": [
+    "edades = []\n",
    "for item in compas:\n",
-    "#    print('Nombre:',compas[item][\"Nombre\"])\n",
-    "    print('Pais:',compas[item][\"País\"])"
+    "    value = compas[item][\"Edad\"]\n",
+    "    edades.append(value)\n",
+    "print(edades)"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27.545454545454547"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.mean(edades)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mostrar las instituciones sin repetición"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad Nacional de Colombia', 'Universidad del Valle', 'Universidad Simón Bolivar', 'Universidad Nacional Mayor de San Marcos']\n"
+     ]
+    }
+   ],
+   "source": [
+    "univer = []\n",
+    "for item in compas:\n",
+    "    value = compas[item][\"Institución\"]\n",
+    "    univer.append(value)\n",
+    "print(univer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad del Valle', 'Universidad Nacional Mayor de San Marcos']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Eliminar elementos repetidos de la lista de Institutos\n",
+    "InstitutoT = [] \n",
+    "for i in univer: \n",
+    "    if i not in InstitutoT: \n",
+    "        InstitutoT.append(i) \n",
+    "        \n",
+    "print(InstitutoT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tabular datos por país común"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
-    "# Create a nested dictionary\n",
-    "courses={ 'bash': {'classes': 10, 'hours': 2, 'fee': 500},\n",
-    "          'PHP': {'classes': 30, 'hours': 2, 'fee': 1500},\n",
-    "          'Angular': {'classes': 10, 'hours': 2, 'fee': 1000}}"
+    "from tabulate import _table_formats, tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country = \"Perú\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Course Name: bash\n",
-      "Total classes: 10\n",
-      "Hours: 2\n",
-      "Fee: $ 500\n",
-      "Course Name: PHP\n",
-      "Total classes: 30\n",
-      "Hours: 2\n",
-      "Fee: $ 1500\n",
-      "Course Name: Angular\n",
-      "Total classes: 10\n",
-      "Hours: 2\n",
-      "Fee: $ 1000\n"
+      "['omarasto', 'teofilo']\n"
     ]
    }
   ],
   "source": [
-    "# Print the keys and values of the dictionary\n",
-    "for course in courses:\n",
-    "    print('Course Name:',course)\n",
-    "    print('Total classes:',courses[course]['classes'])\n",
-    "    print('Hours:',courses[course]['hours'])\n",
-    "    print('Fee: $',courses[course]['fee'])"
+    "order = {}\n",
+    "keys = []\n",
+    "values = []\n",
+    "for item in compas:\n",
+    "    if compas[item][\"País\"]== country:\n",
+    "        keys.append(item)\n",
+    "print(keys)"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
-     "ename": "KeyError",
-     "evalue": "'Apellido'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-c2695fec8d9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Apellido\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 'Apellido'"
-     ]
+     "data": {
+      "text/plain": [
+       "'Colombia'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
    }
   ],
-   "source": []
+   "source": [
+    "compas[\"perezy\"][\"País\"]"
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
-    "def congueros(dicci,country):\n",
-    "    for item in compas:\n",
-    "#    print('Nombre:',compas[item][\"Nombre\"])\n",
-    "        print('Pais:',compas[item][\"País\"])\n",
-    "    "
+    "Nombre = []\n",
+    "Apellido = []\n",
+    "Edad = []\n",
+    "Instituto = []\n",
+    "Hobbie = []"
   ]
  },
  {
@@ -201,19 +314,135 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]\n"
+      "['Omar', 'Teofilo']\n"
     ]
    }
   ],
   "source": [
-    "newlist = []\n",
-    "for x in range(10):\n",
-    "    innerlist = []\n",
-    "    for y in range(10):\n",
-    "        innerlist.append(y)\n",
-    "    newlist.append(innerlist)\n",
-    "\n",
-    "print(newlist)"
+    "for word in keys:\n",
+    "    element = compas[word][\"Nombre\"]\n",
+    "    Nombre.append(element)\n",
+    "print(Nombre)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "elements = [\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['Omar', 'Asto', 25, 'Universidad Nacional de Ingeniería', 'Leer periódico'], ['Teofilo', 'Vargas', 54, 'Universidad Nacional Mayor de San Marcos', 'Wing Chun Kuen']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "user = []\n",
+    "details = []\n",
+    "for word2 in keys:\n",
+    "    for word in elements:\n",
+    "        element = compas[word2][word]\n",
+    "        details.append(element)\n",
+    "    user.append(details)\n",
+    "    details = []\n",
+    "print(user)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nombre    Apellido      Edad  Institución                               Hobbie\n",
+      "--------  ----------  ------  ----------------------------------------  --------------\n",
+      "Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico\n",
+      "Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tabulate(user, headers=[\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Crearé la función"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def congueros(dic,country):\n",
+    "    from tabulate import _table_formats, tabulate\n",
+    "    #\n",
+    "    keys = []\n",
+    "    values = []\n",
+    "    for item in dic:\n",
+    "        if dic[item][\"País\"]== country:\n",
+    "            keys.append(item)\n",
+    "#\n",
+    "    Nombre = []\n",
+    "    Apellido = []\n",
+    "    Edad = []\n",
+    "    Instituto = []\n",
+    "    Hobbie = []\n",
+    "#\n",
+    "    for word in keys:\n",
+    "        element = dic[word][\"Nombre\"]\n",
+    "        Nombre.append(element)\n",
+    "#\n",
+    "    elements = [\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]\n",
+    "#\n",
+    "    user = []\n",
+    "    details = []\n",
+    "    for word2 in keys:\n",
+    "        for word in elements:\n",
+    "            element = dic[word2][word]\n",
+    "            details.append(element)\n",
+    "        user.append(details)\n",
+    "        details = []\n",
+    "#\n",
+    "    print(tabulate(user, headers=[\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nombre    Apellido      Edad  Institución                               Hobbie\n",
+      "--------  ----------  ------  ----------------------------------------  --------------\n",
+      "Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico\n",
+      "Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen\n"
+     ]
+    }
+   ],
+   "source": [
+    "congueros(compas,\"Perú\")"
   ]
  },
  {

 %% Cell type:code id: tags:

 ``` python
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
 @author: jennifer

 Este programa entrega los números de una fila del
 triángulo de Pascal.
 """
 ```

+%% Output
+
+    '\n@author: jennifer\n\nEste programa entrega los números de una fila del\ntriángulo de Pascal.\n'
+
 %% Cell type:markdown id: tags:

 ### Ejercicio 3: Amigos congueros

 %% Cell type:markdown id: tags:

 Entre en contacto con 10 estudiantes del curso de datos y 2 profesores o personal de soporte
 de LaConga, uno del curso de datos y otro de afuera, y consulte su nombre completo, su
 nombre de usuario en mattermost, edad, pais de origen, ciudad donde residen, su especialidad
 científica, nombre del instituto en que estudian/laboran, y un hobbie o afición.

 %% Cell type:markdown id: tags:

 Cree un diccionario llamado “compas”, donde la llave sea el nombre de usuario en mattermost,
 y si depliego el valor almacenado, por ejemplo en compas[“juan-pineda”], lo que obtengo es
 a la vez otro diccionario, con las llaves “nombre”, “apellido”, “país”, “residencia”, “edad”,
 “institución”, “hobbie”.

 %% Cell type:markdown id: tags:

 Cree una función que reciba como entrada el diccionario y un país de origen, y retorne las
 informaciones completas de todas las personas de ese país, tabuladas en una forma fácil de
 entender. -Busque una forma de calcular, a partir del diccionario, el promedio de edad de
 todas las personas en él, y una forma de mostrar todas las instituciones (sin repetición)

 %% Cell type:code id: tags:

 ``` python
 d = {'dict1': {'foo': 1, 'bar': 2}, 'dict2': {'baz': 3, 'quux': 4}}
 compas = {"perezy": {"Nombre":"Yineth","Apellido":"Perez","Edad":29,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Voleibol y ficción", "Tipo": "Estudiante"},
          "acerot": {"Nombre":"Tatiana","Apellido":"Acero","Edad":23,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Ver anime y disfrutar la naturaleza", "Tipo": "Estudiante"},
          "omarasto":{"Nombre":"Omar","Apellido":"Asto","Edad":25,"País": "Perú",
                     "Ciudad": "Lima","Institución": "Universidad Nacional de Ingeniería", "Hobbie": "Leer periódico", "Tipo": "Estudiante"},
          "carrilloj": {"Nombre":"Juan","Apellido":"Carrillo","Edad":25,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Videojuegos", "Tipo": "Estudiante"},
          "ladinoj": {"Nombre":"JoseM","Apellido":"Ladino","Edad":24,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Skate, ciclismo, guitarra", "Tipo": "Estudiante"},
          "martinezj": {"Nombre":"Jocabed","Apellido":"Martinez","Edad":22,"País": "Venezuela",
                     "Ciudad": "Caracas","Institución": "Universidad Central de Venezuela", "Hobbie": "Música", "Tipo": "Estudiante"},
          "gomezc":{"Nombre":"Carla","Apellido":"Gomez","Edad":27,"País": "Venezuela",
                     "Ciudad": "Caracas","Institución": "Universidad Simón Bolivar", "Hobbie": "Bici, electrónica y la gastronomía", "Tipo": "Estudiante"},
          "navasa":{"Nombre":"Alfonso","Apellido":"Navas","Edad":24,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Crossfit", "Tipo": "Estudiante"},
          "vargass":{"Nombre":"Sasiri","Apellido":"Vargas","Edad":20,"País": "Colombia",
                     "Ciudad": "Cali","Institución": "Universidad del Valle", "Hobbie": "Danza y canto",  "Tipo": "Estudiante"},
          "sirias": {"Nombre":"Siria","Apellido":"Sadeddin","Edad":30,"País": "Venezuela",
                     "Ciudad": "Colombia","Institución": "Universidad Simón Bolivar", "Hobbie": "Data Science",  "Tipo": "Estudiante"},
          "teofilo": {"Nombre":"Teofilo","Apellido":"Vargas","Edad":54,"País": "Perú",
-                     "Ciudad": "Lima","Institución": "Universidad Nacional Mayor de San Marcos", "Hobbie": "Wing Chun Kuen",  "Tipo": "Profesor"},
-          "sirias": {"Nombre":"Siria","Apellido":"Sadeddin","Edad":30,"País": "Venezuela",
-                     "Ciudad": "Colombia","Institución": "Universidad Simón Bolivar", "Hobbie": "Data Science",  "Tipo": "Profesor"}}
+                     "Ciudad": "Lima","Institución": "Universidad Nacional Mayor de San Marcos", "Hobbie": "Wing Chun Kuen",  "Tipo": "Profesor"}
+         }
+```
+
+%% Cell type:code id: tags:
+
+``` python
+compas["teofilo"]["País"]
+```
+
+%% Output
+
+    'Perú'
+
+%% Cell type:markdown id: tags:
+
+# Promediar edades
+
+%% Cell type:code id: tags:
+
+``` python
+import statistics as st
+```
+
+%% Cell type:code id: tags:
+
+``` python
+edades = []
+for item in compas:
+    value = compas[item]["Edad"]
+    edades.append(value)
+print(edades)
 ```

+%% Output
+
+    [29, 23, 25, 25, 24, 22, 27, 24, 20, 30, 54]
+
 %% Cell type:code id: tags:

 ``` python
+st.mean(edades)
+```
+
+%% Output
+
+    27.545454545454547
+
+%% Cell type:markdown id: tags:
+
+# Mostrar las instituciones sin repetición
+
+%% Cell type:code id: tags:
+
+``` python
+univer = []
 for item in compas:
-#    print('Nombre:',compas[item]["Nombre"])
-    print('Pais:',compas[item]["País"])
+    value = compas[item]["Institución"]
+    univer.append(value)
+print(univer)
+```
+
+%% Output
+
+    ['Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad Nacional de Colombia', 'Universidad del Valle', 'Universidad Simón Bolivar', 'Universidad Nacional Mayor de San Marcos']
+
+%% Cell type:code id: tags:
+
+``` python
+#Eliminar elementos repetidos de la lista de Institutos
+InstitutoT = []
+for i in univer:
+    if i not in InstitutoT:
+        InstitutoT.append(i)
+
+print(InstitutoT)
 ```

 %% Output

-    Pais: Colombia
-    Pais: Colombia
-    Pais: Perú
-    Pais: Colombia
-    Pais: Colombia
-    Pais: Venezuela
-    Pais: Venezuela
-    Pais: Colombia
-    Pais: Colombia
-    Pais: Venezuela
-    Pais: Perú
+    ['Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad del Valle', 'Universidad Nacional Mayor de San Marcos']
+
+%% Cell type:markdown id: tags:
+
+# Tabular datos por país común

 %% Cell type:code id: tags:

 ``` python
-# Create a nested dictionary
-courses={ 'bash': {'classes': 10, 'hours': 2, 'fee': 500},
-          'PHP': {'classes': 30, 'hours': 2, 'fee': 1500},
-          'Angular': {'classes': 10, 'hours': 2, 'fee': 1000}}
+from tabulate import _table_formats, tabulate
 ```

 %% Cell type:code id: tags:

 ``` python
-# Print the keys and values of the dictionary
-for course in courses:
-    print('Course Name:',course)
-    print('Total classes:',courses[course]['classes'])
-    print('Hours:',courses[course]['hours'])
-    print('Fee: $',courses[course]['fee'])
+country = "Perú"
+```
+
+%% Cell type:code id: tags:
+
+``` python
+order = {}
+keys = []
+values = []
+for item in compas:
+    if compas[item]["País"]== country:
+        keys.append(item)
+print(keys)
 ```

 %% Output

-    Course Name: bash
-    Total classes: 10
-    Hours: 2
-    Fee: $ 500
-    Course Name: PHP
-    Total classes: 30
-    Hours: 2
-    Fee: $ 1500
-    Course Name: Angular
-    Total classes: 10
-    Hours: 2
-    Fee: $ 1000
+    ['omarasto', 'teofilo']
+
+%% Cell type:code id: tags:
+
+``` python
+compas["perezy"]["País"]
+```
+
+%% Output
+
+    'Colombia'
+
+%% Cell type:code id: tags:
+
+``` python
+Nombre = []
+Apellido = []
+Edad = []
+Instituto = []
+Hobbie = []
+```

 %% Cell type:code id: tags:

 ``` python
+for word in keys:
+    element = compas[word]["Nombre"]
+    Nombre.append(element)
+print(Nombre)
 ```

 %% Output

-    ---------------------------------------------------------------------------
-    KeyError                                  Traceback (most recent call last)
-    <ipython-input-18-c2695fec8d9c> in <module>
-    ----> 1 print(compas["Apellido"])
+    ['Omar', 'Teofilo']
+
+%% Cell type:code id: tags:

-    KeyError: 'Apellido'
+``` python
+elements = ["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]
+```

 %% Cell type:code id: tags:

 ``` python
-def congueros(dicci,country):
-    for item in compas:
-#    print('Nombre:',compas[item]["Nombre"])
-        print('Pais:',compas[item]["País"])
+user = []
+details = []
+for word2 in keys:
+    for word in elements:
+        element = compas[word2][word]
+        details.append(element)
+    user.append(details)
+    details = []
+print(user)
+```
+
+%% Output
+
+    [['Omar', 'Asto', 25, 'Universidad Nacional de Ingeniería', 'Leer periódico'], ['Teofilo', 'Vargas', 54, 'Universidad Nacional Mayor de San Marcos', 'Wing Chun Kuen']]
+
+%% Cell type:code id: tags:

+``` python
+print(tabulate(user, headers=["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]))
 ```

+%% Output
+
+    Nombre    Apellido      Edad  Institución                               Hobbie
+    --------  ----------  ------  ----------------------------------------  --------------
+    Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico
+    Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen
+
+%% Cell type:markdown id: tags:
+
+## Crearé la función
+
 %% Cell type:code id: tags:

 ``` python
-newlist = []
-for x in range(10):
-    innerlist = []
-    for y in range(10):
-        innerlist.append(y)
-    newlist.append(innerlist)
+def congueros(dic,country):
+    from tabulate import _table_formats, tabulate
+    #
+    keys = []
+    values = []
+    for item in dic:
+        if dic[item]["País"]== country:
+            keys.append(item)
+#
+    Nombre = []
+    Apellido = []
+    Edad = []
+    Instituto = []
+    Hobbie = []
+#
+    for word in keys:
+        element = dic[word]["Nombre"]
+        Nombre.append(element)
+#
+    elements = ["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]
+#
+    user = []
+    details = []
+    for word2 in keys:
+        for word in elements:
+            element = dic[word2][word]
+            details.append(element)
+        user.append(details)
+        details = []
+#
+    print(tabulate(user, headers=["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]))
+```

-print(newlist)
+%% Cell type:code id: tags:
+
+``` python
+congueros(compas,"Perú")
 ```

 %% Output

-    [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
+    Nombre    Apellido      Edad  Institución                               Hobbie
+    --------  ----------  ------  ----------------------------------------  --------------
+    Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico
+    Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen

 %% Cell type:code id: tags:

 ``` python
 ```

 %% Cell type:code id: tags:

 ``` python
 ```

--- a/ejercicio3.ipynb
+++ b/ejercicio3.ipynb
@@ -2,9 +2,20 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n@author: jennifer\\n\\nEste programa entrega los números de una fila del\\ntriángulo de Pascal.\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
@@ -55,7 +66,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -81,115 +92,217 @@
    "          \"sirias\": {\"Nombre\":\"Siria\",\"Apellido\":\"Sadeddin\",\"Edad\":30,\"País\": \"Venezuela\",\n",
    "                     \"Ciudad\": \"Colombia\",\"Institución\": \"Universidad Simón Bolivar\", \"Hobbie\": \"Data Science\",  \"Tipo\": \"Estudiante\"},\n",
    "          \"teofilo\": {\"Nombre\":\"Teofilo\",\"Apellido\":\"Vargas\",\"Edad\":54,\"País\": \"Perú\",\n",
-    "                     \"Ciudad\": \"Lima\",\"Institución\": \"Universidad Nacional Mayor de San Marcos\", \"Hobbie\": \"Wing Chun Kuen\",  \"Tipo\": \"Profesor\"},\n",
-    "          \"sirias\": {\"Nombre\":\"Siria\",\"Apellido\":\"Sadeddin\",\"Edad\":30,\"País\": \"Venezuela\",\n",
-    "                     \"Ciudad\": \"Colombia\",\"Institución\": \"Universidad Simón Bolivar\", \"Hobbie\": \"Data Science\",  \"Tipo\": \"Profesor\"}}"
+    "                     \"Ciudad\": \"Lima\",\"Institución\": \"Universidad Nacional Mayor de San Marcos\", \"Hobbie\": \"Wing Chun Kuen\",  \"Tipo\": \"Profesor\"}\n",
+    "         }\n"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Perú'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compas[\"teofilo\"][\"País\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Promediar edades"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statistics as st"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Pais: Colombia\n",
-      "Pais: Colombia\n",
-      "Pais: Perú\n",
-      "Pais: Colombia\n",
-      "Pais: Colombia\n",
-      "Pais: Venezuela\n",
-      "Pais: Venezuela\n",
-      "Pais: Colombia\n",
-      "Pais: Colombia\n",
-      "Pais: Venezuela\n",
-      "Pais: Perú\n"
+      "[29, 23, 25, 25, 24, 22, 27, 24, 20, 30, 54]\n"
     ]
    }
   ],
   "source": [
+    "edades = []\n",
    "for item in compas:\n",
-    "#    print('Nombre:',compas[item][\"Nombre\"])\n",
-    "    print('Pais:',compas[item][\"País\"])"
+    "    value = compas[item][\"Edad\"]\n",
+    "    edades.append(value)\n",
+    "print(edades)"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27.545454545454547"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.mean(edades)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mostrar las instituciones sin repetición"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad Nacional de Colombia', 'Universidad del Valle', 'Universidad Simón Bolivar', 'Universidad Nacional Mayor de San Marcos']\n"
+     ]
+    }
+   ],
+   "source": [
+    "univer = []\n",
+    "for item in compas:\n",
+    "    value = compas[item][\"Institución\"]\n",
+    "    univer.append(value)\n",
+    "print(univer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad del Valle', 'Universidad Nacional Mayor de San Marcos']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Eliminar elementos repetidos de la lista de Institutos\n",
+    "InstitutoT = [] \n",
+    "for i in univer: \n",
+    "    if i not in InstitutoT: \n",
+    "        InstitutoT.append(i) \n",
+    "        \n",
+    "print(InstitutoT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tabular datos por país común"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
-    "# Create a nested dictionary\n",
-    "courses={ 'bash': {'classes': 10, 'hours': 2, 'fee': 500},\n",
-    "          'PHP': {'classes': 30, 'hours': 2, 'fee': 1500},\n",
-    "          'Angular': {'classes': 10, 'hours': 2, 'fee': 1000}}"
+    "from tabulate import _table_formats, tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country = \"Perú\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Course Name: bash\n",
-      "Total classes: 10\n",
-      "Hours: 2\n",
-      "Fee: $ 500\n",
-      "Course Name: PHP\n",
-      "Total classes: 30\n",
-      "Hours: 2\n",
-      "Fee: $ 1500\n",
-      "Course Name: Angular\n",
-      "Total classes: 10\n",
-      "Hours: 2\n",
-      "Fee: $ 1000\n"
+      "['omarasto', 'teofilo']\n"
     ]
    }
   ],
   "source": [
-    "# Print the keys and values of the dictionary\n",
-    "for course in courses:\n",
-    "    print('Course Name:',course)\n",
-    "    print('Total classes:',courses[course]['classes'])\n",
-    "    print('Hours:',courses[course]['hours'])\n",
-    "    print('Fee: $',courses[course]['fee'])"
+    "order = {}\n",
+    "keys = []\n",
+    "values = []\n",
+    "for item in compas:\n",
+    "    if compas[item][\"País\"]== country:\n",
+    "        keys.append(item)\n",
+    "print(keys)"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
-     "ename": "KeyError",
-     "evalue": "'Apellido'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-c2695fec8d9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Apellido\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 'Apellido'"
-     ]
+     "data": {
+      "text/plain": [
+       "'Colombia'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
    }
   ],
-   "source": []
+   "source": [
+    "compas[\"perezy\"][\"País\"]"
+   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
-    "def congueros(dicci,country):\n",
-    "    for item in compas:\n",
-    "#    print('Nombre:',compas[item][\"Nombre\"])\n",
-    "        print('Pais:',compas[item][\"País\"])\n",
-    "    "
+    "Nombre = []\n",
+    "Apellido = []\n",
+    "Edad = []\n",
+    "Instituto = []\n",
+    "Hobbie = []"
   ]
  },
  {
@@ -201,19 +314,135 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]\n"
+      "['Omar', 'Teofilo']\n"
     ]
    }
   ],
   "source": [
-    "newlist = []\n",
-    "for x in range(10):\n",
-    "    innerlist = []\n",
-    "    for y in range(10):\n",
-    "        innerlist.append(y)\n",
-    "    newlist.append(innerlist)\n",
-    "\n",
-    "print(newlist)"
+    "for word in keys:\n",
+    "    element = compas[word][\"Nombre\"]\n",
+    "    Nombre.append(element)\n",
+    "print(Nombre)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "elements = [\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['Omar', 'Asto', 25, 'Universidad Nacional de Ingeniería', 'Leer periódico'], ['Teofilo', 'Vargas', 54, 'Universidad Nacional Mayor de San Marcos', 'Wing Chun Kuen']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "user = []\n",
+    "details = []\n",
+    "for word2 in keys:\n",
+    "    for word in elements:\n",
+    "        element = compas[word2][word]\n",
+    "        details.append(element)\n",
+    "    user.append(details)\n",
+    "    details = []\n",
+    "print(user)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nombre    Apellido      Edad  Institución                               Hobbie\n",
+      "--------  ----------  ------  ----------------------------------------  --------------\n",
+      "Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico\n",
+      "Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tabulate(user, headers=[\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Crearé la función"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def congueros(dic,country):\n",
+    "    from tabulate import _table_formats, tabulate\n",
+    "    #\n",
+    "    keys = []\n",
+    "    values = []\n",
+    "    for item in dic:\n",
+    "        if dic[item][\"País\"]== country:\n",
+    "            keys.append(item)\n",
+    "#\n",
+    "    Nombre = []\n",
+    "    Apellido = []\n",
+    "    Edad = []\n",
+    "    Instituto = []\n",
+    "    Hobbie = []\n",
+    "#\n",
+    "    for word in keys:\n",
+    "        element = dic[word][\"Nombre\"]\n",
+    "        Nombre.append(element)\n",
+    "#\n",
+    "    elements = [\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]\n",
+    "#\n",
+    "    user = []\n",
+    "    details = []\n",
+    "    for word2 in keys:\n",
+    "        for word in elements:\n",
+    "            element = dic[word2][word]\n",
+    "            details.append(element)\n",
+    "        user.append(details)\n",
+    "        details = []\n",
+    "#\n",
+    "    print(tabulate(user, headers=[\"Nombre\", \"Apellido\", \"Edad\", \"Institución\", \"Hobbie\"]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nombre    Apellido      Edad  Institución                               Hobbie\n",
+      "--------  ----------  ------  ----------------------------------------  --------------\n",
+      "Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico\n",
+      "Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen\n"
+     ]
+    }
+   ],
+   "source": [
+    "congueros(compas,\"Perú\")"
   ]
  },
  {
@@ -247,7 +476,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.3"
  }
 },
 "nbformat": 4,

 %% Cell type:code id: tags:

 ``` python
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
 @author: jennifer

 Este programa entrega los números de una fila del
 triángulo de Pascal.
 """
 ```

+%% Output
+
+    '\n@author: jennifer\n\nEste programa entrega los números de una fila del\ntriángulo de Pascal.\n'
+
 %% Cell type:markdown id: tags:

 ### Ejercicio 3: Amigos congueros

 %% Cell type:markdown id: tags:

 Entre en contacto con 10 estudiantes del curso de datos y 2 profesores o personal de soporte
 de LaConga, uno del curso de datos y otro de afuera, y consulte su nombre completo, su
 nombre de usuario en mattermost, edad, pais de origen, ciudad donde residen, su especialidad
 científica, nombre del instituto en que estudian/laboran, y un hobbie o afición.

 %% Cell type:markdown id: tags:

 Cree un diccionario llamado “compas”, donde la llave sea el nombre de usuario en mattermost,
 y si depliego el valor almacenado, por ejemplo en compas[“juan-pineda”], lo que obtengo es
 a la vez otro diccionario, con las llaves “nombre”, “apellido”, “país”, “residencia”, “edad”,
 “institución”, “hobbie”.

 %% Cell type:markdown id: tags:

 Cree una función que reciba como entrada el diccionario y un país de origen, y retorne las
 informaciones completas de todas las personas de ese país, tabuladas en una forma fácil de
 entender. -Busque una forma de calcular, a partir del diccionario, el promedio de edad de
 todas las personas en él, y una forma de mostrar todas las instituciones (sin repetición)

 %% Cell type:code id: tags:

 ``` python
 d = {'dict1': {'foo': 1, 'bar': 2}, 'dict2': {'baz': 3, 'quux': 4}}
 compas = {"perezy": {"Nombre":"Yineth","Apellido":"Perez","Edad":29,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Voleibol y ficción", "Tipo": "Estudiante"},
          "acerot": {"Nombre":"Tatiana","Apellido":"Acero","Edad":23,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Ver anime y disfrutar la naturaleza", "Tipo": "Estudiante"},
          "omarasto":{"Nombre":"Omar","Apellido":"Asto","Edad":25,"País": "Perú",
                     "Ciudad": "Lima","Institución": "Universidad Nacional de Ingeniería", "Hobbie": "Leer periódico", "Tipo": "Estudiante"},
          "carrilloj": {"Nombre":"Juan","Apellido":"Carrillo","Edad":25,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Videojuegos", "Tipo": "Estudiante"},
          "ladinoj": {"Nombre":"JoseM","Apellido":"Ladino","Edad":24,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Skate, ciclismo, guitarra", "Tipo": "Estudiante"},
          "martinezj": {"Nombre":"Jocabed","Apellido":"Martinez","Edad":22,"País": "Venezuela",
                     "Ciudad": "Caracas","Institución": "Universidad Central de Venezuela", "Hobbie": "Música", "Tipo": "Estudiante"},
          "gomezc":{"Nombre":"Carla","Apellido":"Gomez","Edad":27,"País": "Venezuela",
                     "Ciudad": "Caracas","Institución": "Universidad Simón Bolivar", "Hobbie": "Bici, electrónica y la gastronomía", "Tipo": "Estudiante"},
          "navasa":{"Nombre":"Alfonso","Apellido":"Navas","Edad":24,"País": "Colombia",
                     "Ciudad": "Bogotá","Institución": "Universidad Nacional de Colombia", "Hobbie": "Crossfit", "Tipo": "Estudiante"},
          "vargass":{"Nombre":"Sasiri","Apellido":"Vargas","Edad":20,"País": "Colombia",
                     "Ciudad": "Cali","Institución": "Universidad del Valle", "Hobbie": "Danza y canto",  "Tipo": "Estudiante"},
          "sirias": {"Nombre":"Siria","Apellido":"Sadeddin","Edad":30,"País": "Venezuela",
                     "Ciudad": "Colombia","Institución": "Universidad Simón Bolivar", "Hobbie": "Data Science",  "Tipo": "Estudiante"},
          "teofilo": {"Nombre":"Teofilo","Apellido":"Vargas","Edad":54,"País": "Perú",
-                     "Ciudad": "Lima","Institución": "Universidad Nacional Mayor de San Marcos", "Hobbie": "Wing Chun Kuen",  "Tipo": "Profesor"},
-          "sirias": {"Nombre":"Siria","Apellido":"Sadeddin","Edad":30,"País": "Venezuela",
-                     "Ciudad": "Colombia","Institución": "Universidad Simón Bolivar", "Hobbie": "Data Science",  "Tipo": "Profesor"}}
+                     "Ciudad": "Lima","Institución": "Universidad Nacional Mayor de San Marcos", "Hobbie": "Wing Chun Kuen",  "Tipo": "Profesor"}
+         }
+```
+
+%% Cell type:code id: tags:
+
+``` python
+compas["teofilo"]["País"]
+```
+
+%% Output
+
+    'Perú'
+
+%% Cell type:markdown id: tags:
+
+# Promediar edades
+
+%% Cell type:code id: tags:
+
+``` python
+import statistics as st
+```
+
+%% Cell type:code id: tags:
+
+``` python
+edades = []
+for item in compas:
+    value = compas[item]["Edad"]
+    edades.append(value)
+print(edades)
 ```

+%% Output
+
+    [29, 23, 25, 25, 24, 22, 27, 24, 20, 30, 54]
+
 %% Cell type:code id: tags:

 ``` python
+st.mean(edades)
+```
+
+%% Output
+
+    27.545454545454547
+
+%% Cell type:markdown id: tags:
+
+# Mostrar las instituciones sin repetición
+
+%% Cell type:code id: tags:
+
+``` python
+univer = []
 for item in compas:
-#    print('Nombre:',compas[item]["Nombre"])
-    print('Pais:',compas[item]["País"])
+    value = compas[item]["Institución"]
+    univer.append(value)
+print(univer)
+```
+
+%% Output
+
+    ['Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Nacional de Colombia', 'Universidad Nacional de Colombia', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad Nacional de Colombia', 'Universidad del Valle', 'Universidad Simón Bolivar', 'Universidad Nacional Mayor de San Marcos']
+
+%% Cell type:code id: tags:
+
+``` python
+#Eliminar elementos repetidos de la lista de Institutos
+InstitutoT = []
+for i in univer:
+    if i not in InstitutoT:
+        InstitutoT.append(i)
+
+print(InstitutoT)
 ```

 %% Output

-    Pais: Colombia
-    Pais: Colombia
-    Pais: Perú
-    Pais: Colombia
-    Pais: Colombia
-    Pais: Venezuela
-    Pais: Venezuela
-    Pais: Colombia
-    Pais: Colombia
-    Pais: Venezuela
-    Pais: Perú
+    ['Universidad Nacional de Colombia', 'Universidad Nacional de Ingeniería', 'Universidad Central de Venezuela', 'Universidad Simón Bolivar', 'Universidad del Valle', 'Universidad Nacional Mayor de San Marcos']
+
+%% Cell type:markdown id: tags:
+
+# Tabular datos por país común

 %% Cell type:code id: tags:

 ``` python
-# Create a nested dictionary
-courses={ 'bash': {'classes': 10, 'hours': 2, 'fee': 500},
-          'PHP': {'classes': 30, 'hours': 2, 'fee': 1500},
-          'Angular': {'classes': 10, 'hours': 2, 'fee': 1000}}
+from tabulate import _table_formats, tabulate
 ```

 %% Cell type:code id: tags:

 ``` python
-# Print the keys and values of the dictionary
-for course in courses:
-    print('Course Name:',course)
-    print('Total classes:',courses[course]['classes'])
-    print('Hours:',courses[course]['hours'])
-    print('Fee: $',courses[course]['fee'])
+country = "Perú"
+```
+
+%% Cell type:code id: tags:
+
+``` python
+order = {}
+keys = []
+values = []
+for item in compas:
+    if compas[item]["País"]== country:
+        keys.append(item)
+print(keys)
 ```

 %% Output

-    Course Name: bash
-    Total classes: 10
-    Hours: 2
-    Fee: $ 500
-    Course Name: PHP
-    Total classes: 30
-    Hours: 2
-    Fee: $ 1500
-    Course Name: Angular
-    Total classes: 10
-    Hours: 2
-    Fee: $ 1000
+    ['omarasto', 'teofilo']
+
+%% Cell type:code id: tags:
+
+``` python
+compas["perezy"]["País"]
+```
+
+%% Output
+
+    'Colombia'
+
+%% Cell type:code id: tags:
+
+``` python
+Nombre = []
+Apellido = []
+Edad = []
+Instituto = []
+Hobbie = []
+```

 %% Cell type:code id: tags:

 ``` python
+for word in keys:
+    element = compas[word]["Nombre"]
+    Nombre.append(element)
+print(Nombre)
 ```

 %% Output

-    ---------------------------------------------------------------------------
-    KeyError                                  Traceback (most recent call last)
-    <ipython-input-18-c2695fec8d9c> in <module>
-    ----> 1 print(compas["Apellido"])
+    ['Omar', 'Teofilo']
+
+%% Cell type:code id: tags:

-    KeyError: 'Apellido'
+``` python
+elements = ["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]
+```

 %% Cell type:code id: tags:

 ``` python
-def congueros(dicci,country):
-    for item in compas:
-#    print('Nombre:',compas[item]["Nombre"])
-        print('Pais:',compas[item]["País"])
+user = []
+details = []
+for word2 in keys:
+    for word in elements:
+        element = compas[word2][word]
+        details.append(element)
+    user.append(details)
+    details = []
+print(user)
+```
+
+%% Output
+
+    [['Omar', 'Asto', 25, 'Universidad Nacional de Ingeniería', 'Leer periódico'], ['Teofilo', 'Vargas', 54, 'Universidad Nacional Mayor de San Marcos', 'Wing Chun Kuen']]
+
+%% Cell type:code id: tags:

+``` python
+print(tabulate(user, headers=["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]))
 ```

+%% Output
+
+    Nombre    Apellido      Edad  Institución                               Hobbie
+    --------  ----------  ------  ----------------------------------------  --------------
+    Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico
+    Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen
+
+%% Cell type:markdown id: tags:
+
+## Crearé la función
+
 %% Cell type:code id: tags:

 ``` python
-newlist = []
-for x in range(10):
-    innerlist = []
-    for y in range(10):
-        innerlist.append(y)
-    newlist.append(innerlist)
+def congueros(dic,country):
+    from tabulate import _table_formats, tabulate
+    #
+    keys = []
+    values = []
+    for item in dic:
+        if dic[item]["País"]== country:
+            keys.append(item)
+#
+    Nombre = []
+    Apellido = []
+    Edad = []
+    Instituto = []
+    Hobbie = []
+#
+    for word in keys:
+        element = dic[word]["Nombre"]
+        Nombre.append(element)
+#
+    elements = ["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]
+#
+    user = []
+    details = []
+    for word2 in keys:
+        for word in elements:
+            element = dic[word2][word]
+            details.append(element)
+        user.append(details)
+        details = []
+#
+    print(tabulate(user, headers=["Nombre", "Apellido", "Edad", "Institución", "Hobbie"]))
+```

-print(newlist)
+%% Cell type:code id: tags:
+
+``` python
+congueros(compas,"Perú")
 ```

 %% Output

-    [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]
+    Nombre    Apellido      Edad  Institución                               Hobbie
+    --------  ----------  ------  ----------------------------------------  --------------
+    Omar      Asto            25  Universidad Nacional de Ingeniería        Leer periódico
+    Teofilo   Vargas          54  Universidad Nacional Mayor de San Marcos  Wing Chun Kuen

 %% Cell type:code id: tags:

 ``` python
 ```

 %% Cell type:code id: tags:

 ``` python
 ```