Python para Data Science.ipynb

python-para-data-science.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNdT4ftGJJWOged1SLN1j3N",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/Rafaelowsky/c0abccd112a3a15b617e72439389cdc5/python-para-data-science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tipos de datos"
      ],
      "metadata": {
        "id": "NUCeY_j-pA4k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entre los tipos de datos podemos encontrar los booleanos que estos se encuentran cuando hacemos una comparación entre variables o datos, una vez que se ejecuta esta comparación se puede apreciar que el resultado puede ser \"**True**\", esto significa que la comparación era verdadera, mientras que si esta no es verdadera lanzara de resultado \"**False**\""
      ],
      "metadata": {
        "id": "xjvbkG6ypE1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "edad = 19\n"
      ],
      "metadata": {
        "id": "_aTKPkvYpl6z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "edad > 18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gxjn-Bhip940",
        "outputId": "69d1db24-6efe-4166-a1cc-12591cdd9efb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edad >= 18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GotuRxzYqCpk",
        "outputId": "7480e442-f29e-4b5b-f7a9-51b0c6878268"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edad < 18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEIHG4S7qFd0",
        "outputId": "8e4e550d-9469-48c9-be0f-785d37d62385"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edad <= 18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBJ9364MqH8S",
        "outputId": "743a3aa6-0e62-45c6-95e1-9be85aa41889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edad == 18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AWoN1YKqK8o",
        "outputId": "986d7b59-0905-456e-fcc8-835494bb7299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "edad != 18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3Dzw7P9qN-s",
        "outputId": "c8cf2235-1578-4b5f-9703-2c4a7b43ba2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Variables"
      ],
      "metadata": {
        "id": "pdc-QoIky6i3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para poder declarar una variable ponemos primero el nombre de la variable, despues de esté, un \"=\", para después poner el valor que le vamos a asignar a esta variable"
      ],
      "metadata": {
        "id": "43Um1J253KuN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nombre = 'Rafael'\n",
        "edad = 23"
      ],
      "metadata": {
        "id": "QJpcWOxD3Ecq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para después imprimir este valor en consola ocupamos el comando print"
      ],
      "metadata": {
        "id": "gqthOuke3gqw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (nombre);\n",
        "print (edad);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nwpGnM63oGj",
        "outputId": "1d92599a-5807-4e4e-841f-dc7a1679b1c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rafael\n",
            "23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No solamente podemos imprimir un valor de alguna variable tambien podemos imprimir directamente un texto que vayamos a escribir dentro del parametro despues del comando print"
      ],
      "metadata": {
        "id": "mXb0_Uv-3wqQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"Este comando nos ayuda para poder imprimir mensajes en pantalla\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uy33tE2w35PQ",
        "outputId": "880674e5-81e8-4652-919f-cc6ed8e7ac1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Este comando nos ayuda para poder imprimir mensajes en pantalla\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tambien podemos concatenar nuestras variables con texto para poder ocuparlas dentro de nuestra función print"
      ],
      "metadata": {
        "id": "R4Gntpud45fx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (f\"Mi nombre es: {nombre} y mi edad es: {edad} años\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "gZOY7Rvp44LU",
        "outputId": "95c409c3-6200-4ea4-b37e-a026f1d78c92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mi nombre es: Rafael y mi edad es: 23 años\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-973c6f744e31>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34mf\"Mi nombre es: {nombre} y mi edad es: {edad} años\"\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[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Mi nombre es: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnombre\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"y mi edad es: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0medad\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" años\"\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;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Nota: cuando se quieren concatenar datos estos deben de ser del mismo tipo o el codigo no compilará*"
      ],
      "metadata": {
        "id": "S2gjv9HQ4AdD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"Mi nombre es: \" + nombre + \"y mi edad es: \" + edad + \" años\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "pZ7aM8Gx56ye",
        "outputId": "a2389e4f-c766-4033-8984-2fbc8df1d14b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-f68097255a91>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"Mi nombre es: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnombre\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"y mi edad es: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0medad\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" años\"\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;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"Mi nombre es: \" + nombre);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1vkpV3j58iv",
        "outputId": "bf3c3e68-4aa9-4d90-eaff-9cfc9fc1414c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mi nombre es: Rafael\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "HdJmTTTDo-Xv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funciones"
      ],
      "metadata": {
        "id": "HquPrspKzB88"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con la \"def\" podemos inicializar/crear una función. *cabe recalcar que siempre se tienen que respetar las identaciones que son dos espacios después de escribir una función.*\n",
        "\n",
        "La función **input** nos ayuda con lanzar un mensaje en pantalla el cual podemos pedir datos que nos pida el usuario para asi declarar una variable"
      ],
      "metadata": {
        "id": "gIdjhx136J0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def saludar():\n",
        "  nombre = input ('Ingrese su nombre por favor: ')\n",
        "  print (f'Sea bienvenido {nombre} al programa!!!')"
      ],
      "metadata": {
        "id": "Wz5UOXDL6Or9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora para poder lanzar esta función tenemos que escrbir la función y después de un parentesis que es aqui donde vamos a colocar los datos de entrada si es que esta necesita alguno."
      ],
      "metadata": {
        "id": "6AiKuMGE7wq5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "saludar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqTg6VbC7tLH",
        "outputId": "be5d3a04-6325-4807-dde4-ae108d86083e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ingrese su nombre por favor: Rafael\n",
            "Sea bienvenido Rafael al programa!!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tambien podemos crear funciones con parametro, esto quiere decir que al momento de llamar la función, tendremos que brindarle de un parametro que va a ocupar para poder realizar la acción que le hayamos asignado."
      ],
      "metadata": {
        "id": "0Nea19GtXgG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nombre = 'Rafael'\n",
        "def saludar_con_parametro (nombre):\n",
        "  print (f\"Bienvenido {nombre}\")\n",
        "\n",
        "saludar_con_parametro(nombre)"
      ],
      "metadata": {
        "id": "1fJ2J0Fx77oI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf9c57e8-1785-452b-da95-440b89a85e36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bienvenido Rafael\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aquí podemos apreciar como es que podemos ocupar condicionales dentro de una función para saber que si una persona es apta para poder conducir, en este caso con **input** recibimos un string, lo que debemos hacer es transformar este dato en un dato tipo entero para que no haya problemas al realizar la logica con la condicional"
      ],
      "metadata": {
        "id": "cZ3uQj2kZk4j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def puede_conducir_sin_parametro ():\n",
        "  edad = int(input(\"Ingrese su edad: \"))\n",
        "  if edad >= 18:\n",
        "    print('Puede conducir')\n",
        "  else:\n",
        "    print('No tiene la edad para conducir')\n",
        "\n",
        "puede_conducir_sin_parametro()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGgl7kIpXeZ5",
        "outputId": "48b04fcd-08bd-47da-a15b-371021ab973a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ingrese su edad: 18\n",
            "Puede conducir\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Listas"
      ],
      "metadata": {
        "id": "KT4vt_g5um5T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tambien para almacenar varios datos no necesariamente tenemos que crear muchas variables, tambien podemos crear listas en las cuales podemos almacenar varios datos de varios tipos, tambien es importante conocer como es que sirven los indices de las listas"
      ],
      "metadata": {
        "id": "nAy8a0zevHAz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "edades = [12, 56, 36, 67, True, 'Eduardo', False, 'Mariana']\n",
        "#indices  0,  1,  2,   3,   4,       5\n",
        "#indices -6, -5, -4,  -3,  -2,      -1\n",
        "\n",
        "print (edades)\n",
        "print (edades[-1])\n",
        "print(edades[-6])\n",
        "\n",
        "for indice, elemento in enumerate(edades):\n",
        "  print(f'El elemento de la lista {indice} que contiene {elemento} es de tipo', type(elemento))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQ522CIyswfj",
        "outputId": "5423dd9d-211a-4d24-cbea-0062b946ebfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12, 56, 36, 67, True, 'Eduardo', False, 'Mariana']\n",
            "Mariana\n",
            "36\n",
            "El elemento de la lista 0 que contiene 12 es de tipo <class 'int'>\n",
            "El elemento de la lista 1 que contiene 56 es de tipo <class 'int'>\n",
            "El elemento de la lista 2 que contiene 36 es de tipo <class 'int'>\n",
            "El elemento de la lista 3 que contiene 67 es de tipo <class 'int'>\n",
            "El elemento de la lista 4 que contiene True es de tipo <class 'bool'>\n",
            "El elemento de la lista 5 que contiene Eduardo es de tipo <class 'str'>\n",
            "El elemento de la lista 6 que contiene False es de tipo <class 'bool'>\n",
            "El elemento de la lista 7 que contiene Mariana es de tipo <class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ciclos"
      ],
      "metadata": {
        "id": "vMLkPsit3TkY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos tambien crear ciclos que van a iterar dependiendo de los parametros que nosotros le vayamos a dar, en el siguiente ejemplo vamos crear dos variables donde vamos a alojar indices y los valores dentro de una lista, para realizar esto ocupamos el comando **enumerate**, este nos entrega el indice y el valor, el cual podemos imprimir gracias a la iteración **for**, que esta va a iterar tantas veces como elementos contenga la lista."
      ],
      "metadata": {
        "id": "PsLI798hqG8z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "edades = [12,4,13,16,64,23,75,72,34]\n",
        "\n",
        "for indice, edad in enumerate(edades):\n",
        "  print(f'El número que esta en el indice {indice} es {edad}')"
      ],
      "metadata": {
        "id": "JA58__S33XRf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6de67ca9-5e81-4c63-f516-f0366bd0e8d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El número que esta en el indice 0 es 12\n",
            "El número que esta en el indice 1 es 4\n",
            "El número que esta en el indice 2 es 13\n",
            "El número que esta en el indice 3 es 16\n",
            "El número que esta en el indice 4 es 64\n",
            "El número que esta en el indice 5 es 23\n",
            "El número que esta en el indice 6 es 75\n",
            "El número que esta en el indice 7 es 72\n",
            "El número que esta en el indice 8 es 34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora con el conocimiento en bucles y en listas podemos crear un codigo para poder verificar las edades en una lista para ver si son aptos para poder conseguir una licencia para conducir si es que cumplen con la mayoría de edad"
      ],
      "metadata": {
        "id": "sxI87vMDt1C7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "edades = [12,4,13,16,64,23,75,72,34]\n",
        "\n",
        "def comprobacion_edad(edades):\n",
        "  for edad in edades:\n",
        "    if edad >= 18:\n",
        "      print(\"Puedes conseguir tu lincencia para conducir\")\n",
        "    else:\n",
        "      print(\"Aún no puedes conseguir tu licencia para conducir\")\n",
        "\n",
        "comprobacion_edad(edades)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPednDWCt0S1",
        "outputId": "9309e53a-f6a0-4c3d-dc33-3e296ec2fb44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aún no puedes conseguir tu licencia para conducir\n",
            "Aún no puedes conseguir tu licencia para conducir\n",
            "Aún no puedes conseguir tu licencia para conducir\n",
            "Aún no puedes conseguir tu licencia para conducir\n",
            "Puedes conseguir tu lincencia para conducir\n",
            "Puedes conseguir tu lincencia para conducir\n",
            "Puedes conseguir tu lincencia para conducir\n",
            "Puedes conseguir tu lincencia para conducir\n",
            "Puedes conseguir tu lincencia para conducir\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importando bibliotecas"
      ],
      "metadata": {
        "id": "VlqIXACS7hIs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Es muy conocido que python se esta haciendo un lenguaje bastante conocido a base de sus librerias, ya que la comunidad crea codigos, que estan disponibles para todos y que se pueden ocupar para la realizacion de algun proyecto, solamente hay que importar estas librerias para poder utilizar sus funciones"
      ],
      "metadata": {
        "id": "J00SHfCP7lQd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from random import randrange"
      ],
      "metadata": {
        "id": "VLr2qNL3ubjT"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora podemos ver una aplicación de esta librería que nos va a dar números aleatorios"
      ],
      "metadata": {
        "id": "oOtZU4TBBC0-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numeros_aleatorios = []\n",
        "\n",
        "for numeros in range (6):\n",
        "  numeros_aleatorios.append(randrange(0,11))\n",
        "\n",
        "print (numeros_aleatorios)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWBbRhQYALJQ",
        "outputId": "637f0815-efb5-4d6c-a581-6dd1774318a1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4, 10, 0, 7, 3, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creación de gráficos"
      ],
      "metadata": {
        "id": "cD2yKOvmEbbq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "GBX3bQ_qEiWS"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " x = list(range(1,7))\n",
        " y = numeros_aleatorios"
      ],
      "metadata": {
        "id": "J7knL2ovFOp3"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x,y, marker = ('o'))\n",
        "plt.plot(x,y)\n",
        "plt.xlabel('Examenes')\n",
        "plt.ylabel('Calificaciones')\n",
        "plt.title('Gráfica de calificaciones')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HMKGg59CFboQ",
        "outputId": "3b032f13-de81-4b9a-8119-3fbd61510f97"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qqO2Wi1mFf2P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}