Solucion_Into_Python.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1YSHNyFyX_8LLoNpbB1tSqDxUbv6eKsTQ",
      "authorship_tag": "ABX9TyPDrJy48eOafSZcThhTN7II",
      "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/github/atalianb/ShePlusPlusIV/blob/main/Solucion_Into_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Palabras reservadas**"
      ],
      "metadata": {
        "id": "IhGx3-iZoa-9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "and           as              assert         break\n",
        "class         continue        def            del\n",
        "elif          else            except         exec\n",
        "finally       for             from           global\n",
        "if            import          in             is\n",
        "lambda        not             or             pass\n",
        "print         raise           return         try\n",
        "while         yield           False          None\n",
        "True"
      ],
      "metadata": {
        "id": "3UkIcP-moZka"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Tipos de datos en Python**\n",
        "\n",
        "Las variables pueden guardar datos de diferentes tipos y diferentes tipos pueden hacer distintas cosas.\n",
        "Python tiene los siguientes tipos de datos por default."
      ],
      "metadata": {
        "id": "IxJZEWxHeqfh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "FNNiJoqcKY8B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cff40f6d-4a7d-48ed-8082-0ae8e47758fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "int"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "x = 5\n",
        "type(x)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 3.1416\n",
        "type(x)"
      ],
      "metadata": {
        "id": "Y9wRuNJDozzK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27e8a7d2-bac2-4653-cca8-3a0bfb7a4ccc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 1j\n",
        "type(x)"
      ],
      "metadata": {
        "id": "LW_XWO9SpIYK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b936bd90-e472-4f78-9977-d4133cffcc2b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "complex"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = complex(1.5, 0.5)\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X17-mfnw97YU",
        "outputId": "11e05409-ef0f-4917-cae8-1a25390bf2fe"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.5+0.5j)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(c.real,c.imag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVfJT55j9-5q",
        "outputId": "3341c3d2-16ce-496e-c52d-ebb1af4fb623"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5 0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 'Hola Mundo'\n",
        "type(x)"
      ],
      "metadata": {
        "id": "UrlfoEh6o6E8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e415831b-4df9-479b-f490-5b696c637f1e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = ['manzanas','peras','fresas']\n",
        "type(x)"
      ],
      "metadata": {
        "id": "VDt9itHupJEL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bef8d68a-397e-4429-abca-f212bb49831f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = ('manzanas','peras','fresas')\n",
        "type(x)"
      ],
      "metadata": {
        "id": "c2bWnoIppn7C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33cc121d-d78b-4ede-e80d-4d9616b152d3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = {\"Nombre\" : \"Alicia\", \"Edad\" : 30}\n",
        "type(x)"
      ],
      "metadata": {
        "id": "TJDlKZvVo_ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3c2ae0e-1692-478c-bcd1-beeb1531914d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = True\n",
        "type(x)"
      ],
      "metadata": {
        "id": "prTykK01ptIR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53e6f9d8-408f-49ab-9e96-c26e261707af"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bool"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "from IPython.core.display import HTML \n",
        "Image(url= \"https://ipcisco.com/wp-content/uploads/2021/03/Python-Tuple-vs-List-ipcisco.com_.jpg\")#, width=950, height=1000)"
      ],
      "metadata": {
        "id": "5WCw6Jze-hxL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "891a89b1-9442-4979-a5b5-6c00dcc44b4b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://ipcisco.com/wp-content/uploads/2021/03/Python-Tuple-vs-List-ipcisco.com_.jpg\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.w3schools.com/python/python_datatypes.asp"
      ],
      "metadata": {
        "id": "6MCj-W0ZeohE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Operaciones"
      ],
      "metadata": {
        "id": "rnGbg1TxnlYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Suma\n",
        "5+5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a0KkED3Bnm3l",
        "outputId": "f8fa4b67-0b94-4110-c54d-b06304d57fa1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Resta\n",
        "3-2"
      ],
      "metadata": {
        "id": "ZTkqvj2TwegP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d81b3d00-84e6-4de3-b52c-218e49652e28"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Multiplicación\n",
        "6*5"
      ],
      "metadata": {
        "id": "hil4wshIwgxF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ab74bc5-1960-4b72-d469-09d1b15d4588"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#División\n",
        "5/6"
      ],
      "metadata": {
        "id": "R4uDXTb_wYcp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f06fc9b-9c74-43e9-d05b-eb9fef9293e0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Potencia\n",
        "#Dos formas\n",
        "2**3"
      ],
      "metadata": {
        "id": "EuOyyub3wWNj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19aa7393-d4eb-45f2-b6c0-2efcf15c0970"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pow(2.,3.)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZcq0OOzvqk2",
        "outputId": "5d594271-5f9e-47ad-f41b-e09ab7c241b0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#flor division\n",
        "\n",
        "11 // 3                 # what's the result of  11//3"
      ],
      "metadata": {
        "id": "okcaqFbu9vGS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40b31ced-78c2-4ecd-b23c-7f54c57b22e8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "11/3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z94se7J9v5Ko",
        "outputId": "7b439120-3f39-4780-944e-29562bd87a07"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.6666666666666665"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#module\n",
        "\n",
        "11%3                     # what's the result of  11%3"
      ],
      "metadata": {
        "id": "WEsZ0ipN9wRG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a28ce5b-d6b1-47a3-8350-af15f21a932c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Operaciones boleanas\n",
        "\n",
        "\n",
        "*   **>**  mayor que (estricto)\n",
        "*   **<**  menor que (estricto)\n",
        "*   **>=**  mayor o igual que\n",
        "*   **<=**  menor o igual que\n",
        "*   **==**  identicamente igual\n",
        "*   **!=**  no es\n"
      ],
      "metadata": {
        "id": "SOHiFiT6AE9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=5\n",
        "y=6"
      ],
      "metadata": {
        "id": "dTc1sK6AAEFv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x>y"
      ],
      "metadata": {
        "id": "0gUln7f7EJwf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edc2a18d-0a94-45ec-9e3d-e3c3ed972f84"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x<=y"
      ],
      "metadata": {
        "id": "9W6G0I-YEJlx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecfa1607-e41c-436a-dd01-62f419e23ea3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = []\n",
        "y = []"
      ],
      "metadata": {
        "id": "u2vYB3cVEJUS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x is x"
      ],
      "metadata": {
        "id": "Xs58_0M_ENKh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd04e204-d63c-406b-e9c7-d774916fcbd5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x is not x"
      ],
      "metadata": {
        "id": "d_YN6kNjEOi0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "756f3bf8-7ce1-4217-fffd-9431db29094f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x is y"
      ],
      "metadata": {
        "id": "z5BEnZr8EQXq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e2a277d-8a4a-40ef-9462-017f8c92fe8c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x is not y"
      ],
      "metadata": {
        "id": "R8p22eYDESxZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "308fc29d-d8d2-436e-fb4e-7cf3984ab720"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Otras operaciones"
      ],
      "metadata": {
        "id": "bNbHnYpNEWcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('hola '+ 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "oO0MZeMYoE4K",
        "outputId": "6071624d-8d7c-41bf-d279-f093370eb2cf"
      },
      "execution_count": 31,
      "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-31-671ecd36ce13>\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[0;34m'hola '\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0;36m10\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('hola '* 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hm4vPM9UoG6k",
        "outputId": "f4ed4bb1-19f8-4d01-b1b7-ae32119ff883"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hola hola hola hola hola hola hola hola hola hola \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = [1,3,2]"
      ],
      "metadata": {
        "id": "jkggKzDPqJna"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b.sort()"
      ],
      "metadata": {
        "id": "LZ3Z-st8xHSa"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e0Kbaosx2Qi",
        "outputId": "0a725113-539f-4463-f472-b85aae989a91"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.pop()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z6W0GPM5xV11",
        "outputId": "fcee75ad-4091-43e5-fd7d-953e072819a6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLfXCdRGxh2w",
        "outputId": "24e45f2c-03b3-48cf-d219-2aac769e9a14"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.clear()"
      ],
      "metadata": {
        "id": "rgbFjoB5tWJw"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWWTPjSqtdrc",
        "outputId": "dd091fc0-70ab-4a9e-e2e8-1e2efa077973"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b.append(5)"
      ],
      "metadata": {
        "id": "HVZ6FZZBzgBv"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b?"
      ],
      "metadata": {
        "id": "rQTrnVU3teP4"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "También podemos usar el notebook con comandos de la terminal usando ! o %\n",
        "\n",
        "\n",
        "*   **pwd:** print working directory\n",
        "*   **ls:** list files and directories\n",
        "*   **cd:** change directory\n",
        "*   **hist:** returns the commands **history**\n",
        "\n",
        "Otros comandos que pueden ser útiles\n",
        "*   **pip install library_name:** Te permite instalar la librería que necesites y no tengas instalada, solamente tienes que cambiar **library_name** por el nombre de la librería.\n",
        "\n"
      ],
      "metadata": {
        "id": "PXmKZXn9w2Ta"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFNnS30Cw3ME",
        "outputId": "6ac1c351-8c32-4a17-c484-2bf79ddf2f79"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Otros comandos útiles"
      ],
      "metadata": {
        "id": "01T6_kFc2KgX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b = [1,3,2]"
      ],
      "metadata": {
        "id": "drHvyZ_z2jdt"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# The previous result\n",
        "_"
      ],
      "metadata": {
        "id": "-CPLQ-G_2QGM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ca2b089-1d65-44a5-f809-311592e9836d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The result obtained in cell 43\n",
        "\n",
        "_43"
      ],
      "metadata": {
        "id": "AgH-Vvte2Qgc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "e7192868-f881-4e9a-cee4-dc07057b201c"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-96be42df9b3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# The result obtained in cell 29\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0m_43\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name '_43' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The command you used in cell 43\n",
        "\n",
        "_i43"
      ],
      "metadata": {
        "id": "Znvgv9Qq2Q9o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "1ac0f64e-9f5f-4b36-ea8b-6dd56ee4c24e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'b = [1,3,2]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Funciones**"
      ],
      "metadata": {
        "id": "MKxfB34HvP-0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def suma(a,b):\n",
        "  return a+b"
      ],
      "metadata": {
        "id": "UZzQ7_ME1ez_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def suma(a,b=5):\n",
        "    \"\"\" suma dos números\n",
        "        Returns\n",
        "        ----\n",
        "        la_suma :  tipo de argumentos\n",
        "        Ejemplos\n",
        "        ----\n",
        "        \"\"\"\n",
        "    suma_de_numeros = a+b\n",
        "    return suma_de_numeros"
      ],
      "metadata": {
        "id": "k1VhnSuQuhMh"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = suma(5,b=2)"
      ],
      "metadata": {
        "id": "tMQrzYZjvst3"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwkdXaSI2wzr",
        "outputId": "af45125d-5d1d-4238-b0ba-efc755d7d3ea"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "suma(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIMZYwE40BNf",
        "outputId": "980040e6-131f-4bc3-de04-2558784fcb4c"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Una breve introducción a Numpy\n",
        "te recomiendo revisar la documentación https://numpy.org/doc/stable/"
      ],
      "metadata": {
        "id": "H1JW2rpO5Gqr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "rZIVqJQ85GTA"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Esta es otra forma de hacer una suma\n",
        "#Suma todos los elementos del arreglo, lista, tupla\n",
        "np.sum([5,2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yGqNIjIuh9T",
        "outputId": "2a7bd1ff-6bb6-4aa3-c532-86961f491e93"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Un tipo de dato muy importante y usado son los arreglos (arrays en inglés) y numpy nos ayuda a trabajar con ellos"
      ],
      "metadata": {
        "id": "KiFxukYt5a0K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([5,2,4])\n",
        "print(x)\n",
        "type(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjV9I8he5Bce",
        "outputId": "a06ec7c9-cf4a-4278-fcea-55c15272b0c8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 2 4]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CazBxWZw38WQ",
        "outputId": "c2681e28-f390-43f4-d898-be3e665f7ebe"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDVvzwMk3_x-",
        "outputId": "a92cb0e3-ab57-4c52-edb6-e32b6c92e569"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.arange(6)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Zvw7kp0vrj5",
        "outputId": "218fb3ca-934f-4091-fc66-9909811a4e4e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1, 2, 3, 4], \n",
        "              [5, 6, 7, 8], \n",
        "              [9, 10, 11, 12]])\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQ5OD3Ag4vcf",
        "outputId": "d83f7a06-e8f4-44da-d94f-a2ed94fd823d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4],\n",
              "       [ 5,  6,  7,  8],\n",
              "       [ 9, 10, 11, 12]])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvUs7wDe0Sn_",
        "outputId": "192a72b0-4b66-4a3e-cc65-fb8b5ffe9049"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(0,10,10)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oEBEC6V6U5W",
        "outputId": "e4c185d6-938f-478d-92f7-4cae4d67c65a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  1.11111111,  2.22222222,  3.33333333,  4.44444444,\n",
              "        5.55555556,  6.66666667,  7.77777778,  8.88888889, 10.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#primer número, último número, tamaño del paso\n",
        "x = np.arange(2, 9, 2)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxKn8pg46lys",
        "outputId": "58d30eed-ae2b-4095-c842-642427d4dd79"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 4, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# default np.float64\n",
        "x = np.zeros(2, dtype=np.int64)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpjG3f4i6pag",
        "outputId": "1fd6dcc5-7b88-4858-dd5a-ad14be3a89d0"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([2, 1, 5, 3, 7, 4, 6, 8])"
      ],
      "metadata": {
        "id": "r8pC-X6k7ZLR"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1H8iC-cb7ejZ",
        "outputId": "c8e557b1-6349-4b77-a7f1-d2a02dcdf979"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sort(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuey8CAr7jZR",
        "outputId": "b000d808-c112-4924-b76a-0ae1983b72e9"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Indices\n",
        "`array[start:stop]`\n",
        "\n",
        "donde *start* es el índice del primer elemento a incluir y *stop* es el índice del elemento en el que detenerse sin incluirlo en el segmento(slice)"
      ],
      "metadata": {
        "id": "4dwyiQW_Whnw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###y esto también se aplica para listas y tuplas https://realpython.com/lessons/indexing-and-slicing/ \n",
        "\n",
        "*  Omitir el primer índice `mylist[:n]` inicia el *slice* al principio de la lista.\n",
        "* Omitir el último índice `mylist[m:]` extiende el *slice* del primer índice  `m` al final de la lista.\n",
        "* Omitir ambos índices `mylist[:]` regresa una copia de la lista entera, pero a diferencia de una cadena, es una copia, no una referencia al mismo objeto."
      ],
      "metadata": {
        "id": "Xs-XvWrYbcGW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOTA: En los arreglos y listas, los índices empiezan a contar desde 0. Si queremos acceder a la última posición del arreglo, entonces utilizamos en índice -1."
      ],
      "metadata": {
        "id": "I_HB2RyW0g24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([2, 1, 5, 3, 7, 4, 6, 8])"
      ],
      "metadata": {
        "id": "nO-26L-T5Mtu"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr[0::]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWJQHH_WdNLy",
        "outputId": "a49c2d24-bf2c-4ed3-f2ce-9ca40b436bd3"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 5, 3, 7, 4, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1SzH3KksWKZt",
        "outputId": "2349b716-1b84-475b-8d0e-a0599fc41873"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYUwnYrvWgqg",
        "outputId": "8eca34e8-ab48-40df-ca92-ff78485da90f"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr[-3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNEo_GAuWLBV",
        "outputId": "570ed44b-bafd-44c3-9cb2-834a04e4446c"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1, 2, 3, 4], \n",
        "              [5, 6, 7, 8], \n",
        "              [9, 10, 11, 12]])"
      ],
      "metadata": {
        "id": "fq2FWZOJXAfb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwNOR-sZXDro",
        "outputId": "38aa8b01-9419-4711-8173-1a2cb194afd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[1,2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4CXz2aDXEAU",
        "outputId": "798e7462-44c2-44ae-a02a-c1f3853b38ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = ['spam', 'egg', 'bacon', 'tomato', 'ham', 'lobster']"
      ],
      "metadata": {
        "id": "F47Vad8hbYYb"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a[::2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-h-S3haIcVzx",
        "outputId": "d7578385-14d9-4110-eef6-7e1938b41301"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['spam', 'bacon', 'ham']"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Realiza las siguientes funciones:\n",
        "### Conversión Celsius-Fahrenheit \n",
        "  \n",
        "  $F = \\frac{9}{5}C + 32$\n",
        "   \n",
        "### Calcular sinh x para una x dada\n",
        "  \n",
        "  $\\sinh(x) = \\frac{1}{2}(e^x - e^{-x})$\n",
        "\n",
        "  Hint: x puede ser un arreglo\n",
        " \n",
        "### Calcular\n",
        "  sqrt(-1) \n",
        "\n",
        "  Hint: from cmath import sqrt\n",
        "\n",
        "###  Revisa la equivalencia\n",
        "\n",
        "  $sin^2(x) + cos^2(x) = 1$"
      ],
      "metadata": {
        "id": "fR4XxutrwwgC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def CelsiusFahrenheit(TC):\n",
        "  F = 9.*TC/5. + 32.\n",
        "  return print('La temperatura',TC,'ºC en ºF es',F)"
      ],
      "metadata": {
        "id": "ROlPszvUvr_s"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CelsiusFahrenheit(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHLuLEFW1OZ5",
        "outputId": "59ab1b90-2da2-4712-9454-52ec5522fb76"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La temperatura 25 ºC en ºF es 77.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ejercicio 2: Carcular sinh x"
      ],
      "metadata": {
        "id": "kjRDiHrl1k_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sinh(x):\n",
        "  sinhyper = (np.exp(x) - np.exp(-x))/2.\n",
        "  return sinhyper"
      ],
      "metadata": {
        "id": "W4YRQldW1thO"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_pi = np.linspace(0,2.*np.pi,20)\n",
        "sinh(x_pi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5Ms_aQB7JdZ",
        "outputId": "99c5ffd6-024e-4e1e-fa22-278c2c5483e0"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0.        ,   0.33675437,   0.71067253,   1.16301963,\n",
              "         1.7437161 ,   2.5168469 ,   3.56773376,   5.01235123,\n",
              "         7.01012549,   9.78152841,  13.63240851,  18.9877441 ,\n",
              "        26.43854328,  36.8070669 ,  51.2375722 ,  71.32259163,\n",
              "        99.27868271, 138.19104455, 192.35399654, 267.74489404])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ejercicio 3: Calcular la raíz cuadrada (sqrt) de -1"
      ],
      "metadata": {
        "id": "WN0zIb1S7YGk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.sqrt(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TyRjcR9Z7d59",
        "outputId": "a555c726-64a8-4c9c-8a29-e7ab4c30e83d"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-79-597592b72a04>:1: RuntimeWarning: invalid value encountered in sqrt\n",
            "  np.sqrt(-1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nan"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Numpy tiene integrada la función que calcula raíces, sin embargo no puede obtener la raíz de un número negativo (por definición, de números complejos)"
      ],
      "metadata": {
        "id": "kgGizb_L755p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from cmath import sqrt"
      ],
      "metadata": {
        "id": "w_gJ9nf073R-"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sqrt(-1)\n",
        "#En programación se suele usar la j para indicar el número complejo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qNMDJv079gw",
        "outputId": "9525837e-8012-424f-9986-14a8e75f2f83"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1j"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ejercicio 4: \n",
        "Revisar la equivalencia de la identidad trigonométrica\n",
        "$sin^{2}(x) + cos^{2}(x) = 1$"
      ],
      "metadata": {
        "id": "udCuH2a98Na8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para este ejercicio podemos utilizar el arreglo que definimos hace rato de 0 a 2$π$ y las funciones de numpy sin y cos"
      ],
      "metadata": {
        "id": "YQrCLsgy8iPX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pow(np.sin(x_pi),2) + pow(np.cos(x_pi),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8932QVJ8gae",
        "outputId": "b478f722-4789-4a50-f50f-b48059eb946e"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sin(x_pi)**2. + np.cos(x_pi)**2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xd2tSKuQ9Hb6",
        "outputId": "4ad6c9e2-a38a-481b-8b72-19f17dbf2d1d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "       1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ahora graficaremos\n",
        "\n",
        "https://matplotlib.org\n"
      ],
      "metadata": {
        "id": "p8FLlA6lehI-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "5svBL0mJehwV"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(0.,6.*np.pi,100)"
      ],
      "metadata": {
        "id": "LaAKCmXESjSq"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x,np.sin(x),'o',label='sin')\n",
        "plt.plot(x,np.cos(x),'*',label = 'cos')\n",
        "plt.plot(x,np.tan(x),'+',label = 'tan')\n",
        "plt.legend(loc='lower left')\n",
        "plt.ylabel('angulo')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "l_ivW72wSfp2",
        "outputId": "41eda98b-a750-4ec3-ec63-11a0c7110047"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'angulo')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ClZWPcu6WlYysfASAI20Z/+iduu20xY1EB2ILPajgUz7xKnqyk7cYxpXbv83kXn9mYNMHXPSz1Rint60c67Y08+sXVre9VmJ8qqOeVDpuCMG8nNbp4k8pXU4a5sCbi2L5yDEv8/3otOf1dz0nsaXnHmtuySUvW5p3dnjVkHMCBd9WknOy18obWL2zmn6tmyOzrWSSE8g8L32qKph2cv66gTNtVvoRsY7h/0ds3ZgEfB54CZjq7sflLaLY+1US65AeT6wo/AP4lru/lmr+rpytlDiM21j5GANbTi/8ry3T7r3GO2yTNoT6vru3O5c/lalbPmR2vz2Y70fTuGYL/3u/19vOJ/9w4D/YY2PsMliZ7um0eHzFJrZiA3xMFb3YyXIfytU7r+CbFU+yl23lsp1XZ/3xu7o3Wmp5AfKWk2bvyXrvz3DbwCdU0ivPeelOOVky4ETmDf0unz5+TaS3lV2dFJPvvOTjdw6L3b22w7hGdx+dalo+mNmpwM+Incp6t7v/ON28JfdPcMkrf9KZCIkNodOVfOVne+XJPz7q+EOkdNrt6XRYyRMr9m1VsXOlL9t5TU4FobO2+FC/fNLJQ17ymZMqWljjA3nm01rubx2fl7x095ykepxOsbaVXfWPFDIv+SgOzwP/F/htfNTZwDXuPjZRJLocXR6UXHFIob6xnsuX/RfsPpiaTYtYsm23tpU/+demS1auad9Onu7Xq0nDn1KB0dp2uAv5PSpIZ1d7ppH48tmFzPOyOnXzh3KSd13KSUVv2H0QfNQUOwKJUF7S5SRxBFHIvOTc5wCcS+wHafXEmsdeAM4zsz5Ayk5iyc7sxbOZDRA/3bmmejtUx/eK+u0PwNTPnwkDtn3WTp58qN3JcI/4uey9kn44c/HHV7Wt5Et9f37QcmFBPldixU4+n3z7bn/Ivb26SDLOy7YFweYP5aQgupST1k+g6nPQui6SeUnOyR7rfx7r3F/fxMYmQstLJM9WylY5HDmk+xVmyh/dpDvUTjfc4fTShFSnNGZy5dJUEns21b2r+KR6Qcp5SvH6/RnnpQA5yeTMr10xoPd+v6LycytTTu82OWmYA8t+D4ec1nmOirStRCUn+WhWGgRcAgwn6WjD3Quza5OlUi0O9Y31af9LN8yNNt219dMNJ05b/F+vTEh7JdGMfvEbEeWcF+Ukv0o9J50VB9x9lzfgOeBm4Bzg64lbJs8txm3MmDFe6kbdM6pt+Jcv/zLESLou+TOkelyKyi0vykk0RCUnQIOn+V7NtM9hN3f/Xu51SjJRKntzENyjS/5nrHL70/VyyEvd4NQ7iaVKOSmgdFUj+QbcBJyaybxh3Er1yCF5r6dU94CSlcNeqbvyEkXKSWHQyZFDRhfeA74DPG5mzWb2oZltM7MPC1WwuovkvYhS2gMqd8pL9CgnxZdRs5K7V5tZf+BAYn/2IxJQbs1I5UJ5iZ5SyEmmZytdTOzoYRjQCIwFnnP38QWNLkOldLZSurMuptZO1R5RiJSX6FFOCi8fp7IuAb4EvOCxS2YcAvybu5+V31C7ppSKQ7K8/EG55J3yEj3KSWHk/Gc/wA533xF/sV7uvgwoyL/ASWmpb6wPOwRJQXmJnlLLSabFYW38f6PnAYvMbD6Q+b/rSEql0O64K+l+mFTKlJfoUU6KL9MO6TPjgz80s6eBvkDqayRIxtRuGk3KS/QoJ8WnaytJ1tRRGE3KS/REPScl9zeh2VJxCI86CqNJeYmeKOYkHx3SIiLSjag4SE7KoaOwHCkv0VNqOVGzkohIN6VmJRERyYqKg4iIBKg4iIhIgIqDiIgEqDiIiEiAioOIiASoOBRZqV2ZsbtQXqJHOQmXikORldqVGbsL5SV6lJNwqTiIiEiAfiFdBFG/MmN3pbxEj3JSXLoqa4RE8cqMorxEkXJSeLp8hoiIZEXFochK7cqM3YXyEj3KSbjUrCQi0k2pWUlERLKi4iAiIgEqDiIiEqDiICIiASoOIiISoOIgIiIBoRQHM/uGmb1mZp+aWV2Hadeb2ZtmttzMTg4jPhGR7q4ypPd9FTgL+FXySDP7IjAJOBQYAjxhZge5e2vxQxQR6b5COXJw96XuvjzFpDOAB9z9Y3dfCbwJHFnc6EREJGp9DkOBNUmP18bHBZjZpWbWYGYNTU1NRQlORKS7KFizkpk9AeydYtIMd5+f6+u7++3A7RC7fEauryciIp8pWHFw9xO68LR1wL5Jj4fFx4mISBFFrVnpMWCSmfUysxHAgcDfQ45JRKTbCetU1jPNbC3wZeD3ZrYQwN1fA34DvA4sAL6tM5VERIovlFNZ3f1R4NE0034M/Li4EYmISLKoNSuJiEgEqDiIiEiAioOIiASEdfmMgtu5cydr165lx44dYYeSF71792bYsGFUVVWFHYqIdANlWxzWrl1LdXU1w4cPx8zCDicn7s6mTZtYu3YtI0aMCDscEekGyrZZaceOHQwYMKDkCwOAmTFgwICyOQoSkegr2+IAlEVhSCinzyIi0VfWxUFERLpGxSFu3svrGDfzKUZM/z3jZj7FvJcLc0mniy++mNdff70gry0iki9l2yGdjXkvr+P6R5bQvDN2pY51W5q5/pElAHzt8JRXDO+yO++8M6+vJyJSCDpyAGYtXN5WGBKad7Yya2Gq/yPK3EcffcRpp51GbW0to0aN4sEHH+S4446joaEBgN13350ZM2ZQW1vL2LFj2bBhQ07vJyKSLyoOwLtbmrMan6kFCxYwZMgQFi9ezKuvvsqECRPaTf/oo48YO3Ysixcv5phjjuGOO+7I6f1ERPJFxQEY0q9PVuMzVVNTw6JFi/je977HX/7yF/r27dtues+ePZk4cSIAY8aMYdWqVTm9n4hIvqg4ANNOPpg+VRXtxvWpqmDayQfn9LoHHXQQL730EjU1Ndxwww3ceOON7aZXVVW1naJaUVFBS0tLTu8nIpIv6pDms07nWQuX8+6WZob068O0kw/OuTP63XffpX///px33nn069dPndEiUjJUHOK+dvjQvJ+ZtGTJEqZNm0aPHj2oqqpi9uzZXHvttXl9DxGRQjB3DzuGnNXV1XniDKCEpUuXMnLkyJAiKoxy/EwiEh4ze9Hd61JNU5+DiIgEqDiIiEiAioOIiASoOIiISICKg4iIBKg4iIhIgIpDsm3vwZxTYJsugCci3ZuKQ7JnfgKrX4Bnbg47EhGRUKk4ANy0F/ywLzTcBf5p7P6HfWPjc3Tfffdx2GGHUVtby/nnn8+qVas4/vjjOeywwxg/fjyrV68G4KGHHmLUqFHU1tZyzDHH5Py+IiK50OUzAL7zCiy8AZY9Di3NUNkHRk6Ek36c08u+9tpr3HTTTTz33HMMHDiQzZs3M3ny5Lbb3XffzZVXXsm8efO48cYbWbhwIUOHDmXLli35+VwiIl2kIweA6r2hVzW0fgyVvWP3vfaA6sE5vexTTz3FN77xDQYOHAhA//79ef755/nWt74FwPnnn89f//pXAMaNG8cFF1zAHXfcQWtra9rXFBEpBhWHhI/ehzFT4OInYvf/LG6n9G233cZNN93EmjVrGDNmDJs2bSrq+4uIJFNxSJg0FybeAnvXxO4nzc35JY8//ngeeuihti/6zZs3c/TRR/PAAw8AMHfuXL761a8C8NZbb3HUUUdx4403MmjQINasWZPz+4uIdJX6HAro0EMPZcaMGRx77LFUVFRw+OGHc+uttzJlyhRmzZrFoEGDmDNnDgDTpk1jxYoVuDvjx4+ntrY25OhFpDvTJbtLSDl+JhEJjy7ZLSIiWVFxEBGRABUHEREJUHEQEZEAFQcREQlQcRARkYBQioOZzTKzZWb2ipk9amb9kqZdb2ZvmtlyMzu52LHVN9bn5XW2bNlCfX1+XktEpNjCOnJYBIxy98OAN4DrAczsi8Ak4FBgAlBvZhXFDGz24tl5eR0VBxEpZaEUB3f/k7u3xB++AAyLD58BPODuH7v7SuBN4MgwYszV9OnTeeuttxg9ejRXX30148eP54gjjqCmpob58+cDsGrVKkaOHMkll1zCoYceykknnURzc3PIkYuIROPyGRcCD8aHhxIrFglr4+MCzOxS4FKA/fbbL6cA6hvr2x0x1NxbA8DU2qlcPvryLr3mzJkzefXVV2lsbKSlpYXt27ezxx57sHHjRsaOHcvpp58OwIoVK7j//vu54447OOecc3j44Yc577zzcvo8IiK5KlhxMLMngL1TTJrh7vPj88wAWoCsr3Ln7rcDt0Ps8hk5hMrloy9vKwI199awZPKSXF4uwN35/ve/z7PPPkuPHj1Yt24dGzbErvo6YsQIRo8eDcCYMWNYtWpVXt9bRKQrCtas5O4nuPuoFLdEYbgAmAic659d4GkdsG/SywyLjytpc+fOpampiRdffJHGxkYGDx7Mjh07AOjVq1fbfBUVFbS0tKR7mcjIV6e95JfyEj2lnJOwzlaaAFwHnO7u25MmPQZMMrNeZjYCOBD4ezFjm1o7NS+vU11dzbZt2wDYunUre+21F1VVVTz99NO88847eXmPsOSr017yS3mJnlLOSVh9Dr8AegGLzAzgBXe/zN1fM7PfAK8Ta276trsX9W/RutrH0NGAAQMYN24co0aN4ktf+hLLli2jpqaGuro6DjnkkLy8h4hIoeiS3SUkzM/UsdM+IZdOe8md8hI9pZSTzi7ZreJQQqLymQrRaS+5U16iJ+o50f85iIhIVsq6OJTDUVFClD5LvjrtJb+Ul+gp5ZyUbbPSypUrqa6uZsCAAcQ7vUuWu7Np0ya2bdvGiBEjwg5HRMpEZ81KUfiFdEEMGzaMtWvX0tTUFHYoedG7d2+GDRu26xlFRPKgbItDVVWV9rJFRLqorPscRESka1QcREQkQMVBREQCyuJsJTNrArp6waKBwMY8hlMIUY8x6vFB9GNUfLmLeoxRjG9/dx+UakJZFIdcmFlDulO5oiLqMUY9Poh+jIovd1GPMerxdaRmJRERCVBxEBGRABWH+L/JRVzUY4x6fBD9GBVf7qIeY9Tja6fb9zmIiEiQjhxERCRAxUFERAK6TXEwswlmttzM3jSz6Smm9zKzB+PT/2Zmw4sc375m9rSZvW5mr5nZd1LMc5yZbTWzxvjtB0WOcZWZLYm/d0OK6WZm/xFfhq+Y2RFFjO3gpOXSaGYfmtlVHeYp+vIzs7vN7H0zezVpXH8zW2RmK+L3e6Z57uT4PCvMbHIR45tlZsviOXzUzPqleW6n60OBY/yhma1LyuWpaZ7b6XZfwPgeTIptlZk1pnluUZZhl7h72d+ACuAt4ACgJ7AY+GKHeS4HbosPTwIeLHKM+wBHxIergTdSxHgc8HiIy3EVMLCT6acCfwQMGAv8LcR8v0fsBz6hLj/gGOAI4NWkcT8BpseHpwM3p3hef+Dt+P2e8eE9ixTfSUBlfPjmVPFlsj4UOMYfAtdmsB50ut0XKr4O038K/CDMZdiVW3c5cjgSeNPd33b3T4AHgDM6zHMGcG98+LfAeCviH0G4+3p3fyk+vA1YCgwt1vvnyRnAfR7zAtDPzPYJIY7xwFvu3tVfzeeNuz8LbO4wOnlduxf4WoqnngwscvfN7v4BsAiYUIz43P1P7t4Sf/gCEOq14tMsw0xkst3nrLP44t8h5wD35/t9C627FIehwJqkx2sJfvG2zRPfMLYCA4oSXQfxJq3Dgb+lmPxlM1tsZn80s0OLGxkO/MnMXjSzS1NMz2Q5F8Mk0m+MYS6/hMHuvj4+/B4wOMU8UVmWFxI7GkxlV+tDoV0Rb/q6O03TXBSW4VeBDe6+Is30sJdhWt2lOJQMM9sdeBi4yt0/7DD5JWJNJbXArcC8Iof3FXc/AjgF+LaZHVPk998lM+sJnA48lGJy2MsvwGNtC5E8n9zMZgAtwNw0s4S5PswGPg+MBtYTa7qJom/S+VFDZLep7lIc1gH7Jj0eFh+Xch4zqwT6ApuKEl2cmVURKwxz3f2RjtPd/UN3/2d8+A9AlZkNLFZ87r4ufv8+8Cixw/ZkmSznQjsFeMndN3ScEPbyS7Ih0dwWv38/xTyhLkszuwCYCJwbL2ABGawPBePuG9y91d0/Be5I895hL8NK4CzgwXTzhLkMd6W7FId/AAea2Yj4nuUk4LEO8zwGJM4IORt4Kt1GUQjxtsm7gKXufkuaefZO9IOY2ZHE8leUAmZmnzOz6sQwsU7LVzvM9hjwL/GzlsYCW5OaT4ol7Z5amMuvg+R1bTIwP8U8C4GTzGzPeJPJSfFxBWdmE4DrgNPdfXuaeTJZHwoZY3Jf1plp3juT7b6QTgCWufvaVBPDXoa7FHaPeLFuxM6keYPY2Qsz4uNuJLYBAPQm1hTxJvB34IAix/cVYs0LrwCN8dupwGXAZfF5rgBeI3bWxQvA0UWM74D4+y6Ox5BYhsnxGfDL+DJeAtQVeRl+jtiXfd+kcaEuP2KFaj2wk1ib90XE+rKeBFYATwD94/PWAXcmPffC+Pr4JjCliPG9SaytPrEeJs7iGwL8obP1oYgx/md8HXuF2Bf+Ph1jjD8ObPfFiC8+/p7Eupc0byjLsCs3XT5DREQCukuzkoiIZEHFQUREAlQcREQkQMVBREQCVBxERCRAxUFERAJUHEREJOD/A2mUP1juL9llAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.random.normal(loc=0.,scale=1.,size=1000)"
      ],
      "metadata": {
        "id": "qcam2qxyvdc8"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x,bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "HTveQwKzvYiI",
        "outputId": "b5e11821-7472-4b6c-cb9a-4102130df82a"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  5.,   8.,  16.,  32.,  27.,  49.,  69., 103., 103., 144.,  94.,\n",
              "        108.,  86.,  54.,  38.,  33.,  18.,   9.,   2.,   2.]),\n",
              " array([-2.84229335, -2.54332956, -2.24436577, -1.94540198, -1.64643819,\n",
              "        -1.34747439, -1.0485106 , -0.74954681, -0.45058302, -0.15161923,\n",
              "         0.14734456,  0.44630835,  0.74527214,  1.04423593,  1.34319972,\n",
              "         1.64216352,  1.94112731,  2.2400911 ,  2.53905489,  2.83801868,\n",
              "         3.13698247]),\n",
              " <BarContainer object of 20 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.style as style\n",
        "style.use('seaborn-colorblind')\n",
        "#style.use('tableau-colorblind10')"
      ],
      "metadata": {
        "id": "Dn3IlAyWUuCC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0309208-3851-431b-f20e-d7024a466a47"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-89-8c24df2f280a>:2: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  style.use('seaborn-colorblind')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Ejercicio:** Grafica otras funciones trigonométricas y/o la conversión en la temperatura usando la función que construiste arriba\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "d-DJ9rTRTFy0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Nota: Esta función se llama igual que la de la celda 73, sin embargo, regresa el valor de la conversión, en vez de imprimir \n",
        "def CelsiusFahrenheit(TC):\n",
        "  F = 9.*TC/5. + 32.\n",
        "  return F"
      ],
      "metadata": {
        "id": "EWDOmtkTMUxb"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tempC = np.linspace(-10,100,120)"
      ],
      "metadata": {
        "id": "DbgXzY3wK7II"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CelsiusFahrenheit(tempC[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtH3qJi-M_pu",
        "outputId": "42f6087c-a295-420a-9df3-db7329c5215d"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.0"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(tempC,CelsiusFahrenheit(tempC),'k','o')\n",
        "plt.xlabel('Temperatura en ºC')\n",
        "plt.ylabel('Temperatura en ºF')\n",
        "plt.xlim(-10,100)\n",
        "plt.ylim(14,212)\n",
        "#plt.savefig('Nombre_de_tu_imagen.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "xOsY7W3hL5p_",
        "outputId": "0687ac36-ba9e-4bf0-d15a-0cad16ae2c1c"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14.0, 212.0)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x,np.sin(x),'o',label='sin')\n",
        "plt.plot(x,np.cos(x),'*',label = 'cos')\n",
        "plt.plot(x,np.tan(x),'+',label = 'tan')\n",
        "plt.plot(x,np.sinh(x),'^',label='sinh')\n",
        "plt.plot(x,np.cosh(x),'<',label='cosh')\n",
        "plt.legend(loc='lower left')\n",
        "plt.ylim(-2,2)"
      ],
      "metadata": {
        "id": "5yVBFWsiS_cM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "94bc5ef8-7087-4693-912f-36b83b78c957"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-2.0, 2.0)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Condicionales"
      ],
      "metadata": {
        "id": "gvtM5Hl7Pb3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = 33\n",
        "b = 200"
      ],
      "metadata": {
        "id": "GcZb3O-lQH3y"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if b > a:\n",
        "  print(\"b es mayor que a\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sfD2FQ8LQGk1",
        "outputId": "2cef8787-7f90-4dab-fa2d-0206263a9d61"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b es mayor que a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if b > a: print(\"b es mayor que a\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3iz2bPZQK9L",
        "outputId": "f9c352d1-58fc-42a8-97fe-39efe4f9c091"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b es mayor que a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = 200\n",
        "b = 200\n",
        "c = 500"
      ],
      "metadata": {
        "id": "sCSW5F10RfOj"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if a > b or a > c:\n",
        "  print(\"At least one of the conditions is True\")"
      ],
      "metadata": {
        "id": "kufnJZuQRf4A"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Podemos añadir más condiciones con **elif** y **else**"
      ],
      "metadata": {
        "id": "ILIz7e80QZey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if b > a:\n",
        "  print(\"b es mayor que a\")\n",
        "elif b==a:\n",
        "  print(\"a y b son iguales\")\n",
        "else:\n",
        "  print(\"a es mayor b\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncqc8H3ZQLe4",
        "outputId": "f8eb9903-8bf6-42e4-e6fb-0673471f2180"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a y b son iguales\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Otra forma de usar condicionales\n",
        "Usando palíndromos"
      ],
      "metadata": {
        "id": "M5y_TgQQRBnd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#texto=\"Anita lava la tina\"\n",
        "texto = \"Odiseo es ido\""
      ],
      "metadata": {
        "id": "LryWVRUCRAX1"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "texto=texto.lower()\n",
        "texto"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "pfHeVw6RPqIS",
        "outputId": "3f4aa04f-c3d1-4211-a86a-5cfab92725b3"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'odiseo es ido'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#texto=texto.lower()                #Cambia mayúsculas por minúsculas\n",
        "texto=texto.replace(\" \", \"\")       #Elimina espacios en blanco\n",
        "texto"
      ],
      "metadata": {
        "id": "li_LtMTVQ77I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "7a4c0d8a-d1db-4bb9-be1f-77b66b9d8f38"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'odiseoesido'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texto[::-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "Hn1ub7agP-_4",
        "outputId": "6f1d0530-2761-4161-dcd0-17ad0c24d27a"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'odiseoesido'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verficiar palindromos\n",
        "if texto == texto[::-1]:\n",
        "    print(\"Es palindromo\")\n",
        "else:\n",
        "    print(\"No es palindromo\")"
      ],
      "metadata": {
        "id": "XWWoVm4DPryz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb0a4369-7abf-444c-ddea-059eae014244"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Es palindromo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Ejercicio: Revisa si el primer y último número de las siguientes listas son los mismos"
      ],
      "metadata": {
        "id": "eCEhcWGtTIoz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = [10, 20, 30, 40, 10]\n",
        "y = [75, 65, 35, 75, 30]"
      ],
      "metadata": {
        "id": "znu3LnOCSfOY"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def primero_ultimo_mismos(lista):\n",
        "  print('La lista recibida es',lista)\n",
        "  ultimo = lista[-1]\n",
        "  primero = lista[0]\n",
        "    \n",
        "  if ultimo==primero:\n",
        "      return True\n",
        "  else:\n",
        "      return False"
      ],
      "metadata": {
        "id": "9FFo-KzQSgHM"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "primero_ultimo_mismos(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ikA1P-XUlbO",
        "outputId": "b11a179a-5af4-4148-cb26-072126b7bbc4"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La lista recibida es [10, 20, 30, 40, 10]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "primero_ultimo_mismos(y)"
      ],
      "metadata": {
        "id": "22iEACLCVwGt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd86e719-730a-4aa5-9977-4b4d31e476f6"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La lista recibida es [75, 65, 35, 75, 30]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ahora aprenderemos a hacer ciclos\n",
        "###Hay dos formas de hacerlos\n",
        "\n",
        "\n",
        "1.   Usando **for**\n",
        "2.   Usando **while**\n",
        "\n"
      ],
      "metadata": {
        "id": "8cCXxP4s7w7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(10):\n",
        "    print(i,i**2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FEcmgFJ8HDl",
        "outputId": "f1e67acf-7ff6-40bc-edf3-ce8dd4a271e6"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0\n",
            "1 1\n",
            "2 4\n",
            "3 9\n",
            "4 16\n",
            "5 25\n",
            "6 36\n",
            "7 49\n",
            "8 64\n",
            "9 81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ciclos cortos se pueden escribir en una línea\n",
        "for i in range(10):print(i**2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrP21aD68G1C",
        "outputId": "daad5068-8aff-4a43-896e-0dfbc5f2fcc9"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "4\n",
            "9\n",
            "16\n",
            "25\n",
            "36\n",
            "49\n",
            "64\n",
            "81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Algunos comandos últiles son: **%time** y **%timeit**"
      ],
      "metadata": {
        "id": "YD3fYPUu8aWd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%time for i in range(10):i**2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVCn-iPy8W70",
        "outputId": "a89e8ca6-6f15-403c-d57c-7e7b9941b024"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 13 µs, sys: 0 ns, total: 13 µs\n",
            "Wall time: 16 µs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Run the command several times and presents the info\n",
        "\n",
        "%timeit for i in range(10): i**2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Cvtm1H98GgH",
        "outputId": "308a9238-eaf2-4691-a282-d0c4bb1b36ff"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.84 µs ± 64.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**while** generalmente se usa cuando tenemos una condición"
      ],
      "metadata": {
        "id": "ghT7Q6dr3Vsm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 1"
      ],
      "metadata": {
        "id": "CtrVff_pLtAs"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "while i < 6:\n",
        "  print(i)\n",
        "  i += 1\n",
        "  #i = i+1"
      ],
      "metadata": {
        "id": "Oj8fBgWeLsxt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b468d0ff-22ba-4f40-80f8-6917eeee2aec"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = 1\n",
        "while i < 6:\n",
        "  print(i)\n",
        "  if i == 3:\n",
        "    break\n",
        "  i += 1"
      ],
      "metadata": {
        "id": "ngmFHCwIWQul",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17cc33d4-72fa-40c5-f707-ad719faea8a3"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ejercicio\n",
        "Dado un número que desees, has la suma de los números pares, desde cero hasta este número "
      ],
      "metadata": {
        "id": "Ky1rVT2LYWUN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0#esta variable será nuestro contador\n",
        "sum = 0#Esta variable guardará la suma\n",
        "n = int(input(\"Ingresa el número n: \"))#Queremos que n sea entero para que sea usado en la condición"
      ],
      "metadata": {
        "id": "Qz2wjp7aWS4_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d31fc629-693c-4843-972c-41841a570640"
      },
      "execution_count": 129,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ingresa el número n: 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "while  i <= n:\n",
        "  #queremos sumar aquellos números pares, por eso nuestra condición es que el módulo 2 del número sea cero\n",
        "    if i % 2 == 0:\n",
        "      #si se cumple que i es un número par, entonces lo suma a la variable suma \n",
        "        sum += i# Esto es analogo a hacer sum = sum+i\n",
        "    #Afuera de nuestra condición aumentamos nuestro contador de i\n",
        "    i += 1\n",
        "        \n",
        "print(\"La suma de números pares hasta n:\",sum)"
      ],
      "metadata": {
        "id": "zVv9L3r4Yhso",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5699bd84-f761-48b8-8d6d-d8cb7c2d4f61"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La suma de números pares hasta n: 12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#¿Cómo leer archivos?"
      ],
      "metadata": {
        "id": "NWwnTqSZxCdS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://pandas.pydata.org/docs/"
      ],
      "metadata": {
        "id": "meugAcnpVrDz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "epD-woqexB53"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#En el path va la ruta de donde está tu archivo\n",
        "path_file = '/content/drive/MyDrive/Iris_data/'\n",
        "df = pd.read_csv(path_file+'Iris.csv')"
      ],
      "metadata": {
        "id": "3wrkbgC8xAOl"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "truYfQlGxBYG",
        "outputId": "111ce687-a597-4cf7-a4ab-85f84dacfb07"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdb069f0-21ea-4ad1-b75f-9fd81ae73aa0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdb069f0-21ea-4ad1-b75f-9fd81ae73aa0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cdb069f0-21ea-4ad1-b75f-9fd81ae73aa0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cdb069f0-21ea-4ad1-b75f-9fd81ae73aa0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQQ52Yau13dB",
        "outputId": "9bb55a90-76a0-4363-b08b-6f30ab76cfca"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.SepalLengthCm.hist(grid=False,bins=20,legend=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "cIjpalc102N3",
        "outputId": "84ea073d-508d-4667-8a29-2c8b10d9f1b9"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Species.hist(grid=False,bins=20,legend=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6p1Vtytt02nB",
        "outputId": "83dd91b4-5d68-44bb-f363-69d80f0b0c76"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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mI64BRMT8zBy6TFtrZi7s88ZXcr9aPQ3UGGr2/iLifuDLmTm5zv4tmbmokTXo3Qbw/WqV+7dt6CmgiBgZEQ9ExG3Ak1Xb/Opxo4j4RUQ8FhEzImKvbtbfPiIeqfo8HhFbVe2f6dJ+eUS0RMQYYK2q7bqq35eqbc+IiNFV29oR8ZOImFa1j6ra/ykiJlVtYyMiGvlaqHf6MoYiYt2IeD4i1qjm146I30bEoIj4UETcGRFTqu1vU/W5JiIui4iHge9ExN7V9h+LiEcjYp2IaI+IGVX/loj4l2r/j0fEaVX7flX/6RFxdUQM7ua5HVstnxERF3Zpnx8R34uIacDuTXlh9R5NHGvXRMRRVftzEXFhREwFPh0RB0fEb6pxeElETKz6nRgRP6imr6mW/ToiZnXZVj3jcOXe1zKzzz/A/OpxJPAmsHk3y84Evl5NtwDrdLOdS4Hjquk1gbWAbYHbgUFV+78Bx3fddjX9l8B0YG1gKPAE8FHgSOCKLv3WrR7X79L2n8ChjXgt/BnwMXQrsE81PQq4spq+B9iqmt4NuLeavgaYCLRU87cDe1TTQ6ndKt0OzKjaPg/cBLQuGUfAEGp/FmXrqu1aYHQ1fT/QAWwMvAC0Vdu8Fzii6pPA0QP9b1DKTz+MtWuAo6rp54Czqukl42Tzav56YGI1fSLwgy7r30jtP+jbUfuba/Q0Drs+VtM9vq814yLwI5n5bDftk4CTIuKbwIjMfKObPg8CX4uIs4HNMvMPwH7U3twnRcRj1fwW3ay7J3BLZr6ZmfOBm4G9qIXCAVUK75WZv6v67xMRD0fEdGBfYPvePmE1XF/G0ARqv4xQ+3DihIgYCnwcuLEaQ5cDG3VZ58b806H5r4CLIuKLwHr53tMC+wOXL2nPzNeADwPPZub/Vn3GAZ9YZr1dgPszs7Na97oufRYBP+7muaj5GjrWlrOPJe3bALO67O/6FdT135m5ODOfBDbsZnl34xBW8n2tGQHwZneNmfkLagP+ReCaiDg+Ij7V5XC7IzPHA4cBfwDuiIh9gQDGZeZO1c+HM/Ob9RZT/VLuTC0IzqsOkYZQO5I4KjNHAFdQS2etGno9hqh9Gv2giFif2n8c7qU2zud1GUM7Zea23e0vM8cAn6N29PmrJaeKmmxBrmLnhgvS6LFW9z568Mcu03Wdnu7N+1q/3QYaEZsBL2fmFcCVwM6ZeUuXX8jJEbEFtYS8hNrh1Y7UDt2Pioi/qLazfrUtgHciYlA1/QBwRES8LyLWBj4FPBARGwNvZeZ/Ad+lFgZLXpS51f8Oj2r6C6A+q2cMVUd/k4DvUzu8XpSZvweejYhPV9uJiPjIcvbxocycnpkXVttZNgDuBv4+Ilqr/usDTwPtEbFl1eezwM+XWe8RYO+IGBa179I4tps+WkX0dqz1sNmngS0ior2aH7WCvj3pbhyu9Pta0/8aaBcjga9ExDvAfOD4bvocDXy26vMS8O3MfC0ivgH8tLrg8g5wKvA8tY9XPx4RUzPzuIi4htovGtTOxz0aEX8FfDciFlfrfj4z50XEFcCMaj+TmvSc1Vgj6XkMQe2Q+8aq/xLHAf9ejaVB1L6/Ylo3646OiH2AxdSuI/0P7z5ddCWwNbVx9w6160s/iIiTqJ1iaqU2ni7rutHMnBO12/zuo/Y/up9k5q31PnH1u5H0fqx1KzP/EBH/CNwZEW/St/ed5Y3DlXpf809BSFI/iYihmTm/ujvnh8AzmXnxQNXjJ4Elqf/8XXUjwhPAutRuSBgwHgFIUqE8ApCkQhkAklQoA0CSCmUASFKhDABJKtT/A5coCj7AqFReAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Species\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86oGFuWgXFp4",
        "outputId": "915cb35a-9d3e-4217-a4ed-ceea42a65117"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df[\"column_name\"].sum()\n",
        "\n",
        "sum_df = df[\"SepalLengthCm\"].sum()\n",
        "mean_df = df[\"SepalLengthCm\"].mean()\n",
        "median_df = df[\"SepalLengthCm\"].median()\n",
        "\n",
        "print(\"Sum:\",sum_df, \"\\nMean:\", mean_df, \"\\nMedian:\",median_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFPfP6aKWubB",
        "outputId": "92ddee2b-403e-4e80-912f-c637bc92818a"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sum: 876.5 \n",
            "Mean: 5.843333333333334 \n",
            "Median: 5.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_df=df[\"SepalLengthCm\"].min()\n",
        "max_df=df[\"SepalLengthCm\"].max()\n",
        "\n",
        "print(\"Minimum:\",min_df, \"\\nMaximum:\", max_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRS2hHwIWu0h",
        "outputId": "9090d3c4-e909-4c5f-e58e-ee5daee64211"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum: 4.3 \n",
            "Maximum: 7.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr(method='pearson')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "etHZDCfXWvMg",
        "outputId": "f18fc588-ddaf-4a38-e26b-9d6eb2d2a813"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  \\\n",
              "Id             1.000000       0.716676     -0.397729       0.882747   \n",
              "SepalLengthCm  0.716676       1.000000     -0.109369       0.871754   \n",
              "SepalWidthCm  -0.397729      -0.109369      1.000000      -0.420516   \n",
              "PetalLengthCm  0.882747       0.871754     -0.420516       1.000000   \n",
              "PetalWidthCm   0.899759       0.817954     -0.356544       0.962757   \n",
              "\n",
              "               PetalWidthCm  \n",
              "Id                 0.899759  \n",
              "SepalLengthCm      0.817954  \n",
              "SepalWidthCm      -0.356544  \n",
              "PetalLengthCm      0.962757  \n",
              "PetalWidthCm       1.000000  "
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
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              "    </tr>\n",
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              "      <th>SepalLengthCm</th>\n",
              "      <td>0.716676</td>\n",
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              "    </tr>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc362bc2-f83e-4ec4-97b1-e28ee56b8036 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc362bc2-f83e-4ec4-97b1-e28ee56b8036');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Otra forma de leer archivos\n",
        "galaxia = np.loadtxt('/content/drive/MyDrive/data_LBSG/DDO168_rotmod.dat')"
      ],
      "metadata": {
        "id": "oXI6FTgdzcrl"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "galaxia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6cdt-UNzrU4",
        "outputId": "6f1df4a1-bf51-4b40-dff6-95bbf959eb14"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.41, 15.  ,  4.1 , 10.41,  8.28,  0.  , 20.43,  0.  ],\n",
              "       [ 0.82, 23.4 ,  1.3 , 18.08, 12.66,  0.  , 14.59,  0.  ],\n",
              "       [ 1.24, 29.2 ,  1.5 , 20.09, 15.11,  0.  ,  8.79,  0.  ],\n",
              "       [ 1.64, 36.2 ,  1.1 , 20.2 , 15.99,  0.  ,  5.9 ,  0.  ],\n",
              "       [ 2.06, 44.  ,  1.1 , 22.  , 16.89,  0.  ,  4.09,  0.  ],\n",
              "       [ 2.47, 52.1 ,  1.2 , 22.63, 17.03,  0.  ,  2.87,  0.  ],\n",
              "       [ 2.89, 52.9 ,  1.7 , 22.95, 16.99,  0.  ,  2.09,  0.  ],\n",
              "       [ 3.3 , 54.8 ,  2.2 , 22.74, 17.19,  0.  ,  1.56,  0.  ],\n",
              "       [ 3.71, 55.  ,  2.5 , 22.74, 17.16,  0.  ,  0.82,  0.  ],\n",
              "       [ 4.12, 52.  ,  2.7 , 22.74, 16.37,  0.  ,  0.  ,  0.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A diferencia de pandas, loadtxt de numpy no me dice nada de que es cada columna.\n",
        "En el caso del archivo que estoy leyendo los encabezados son:\n",
        "\n",
        " Rad\t   Vobs\t   errV\t  Vgas\t  Vdisk\tVbul\tSBdisk\tSBbul\t\t\n",
        " [kpc]\t[km/s]\t[km/s]\t[km/s]\t[km/s]\t[km/s]\t[L/pc^2]\t[L/pc^2]"
      ],
      "metadata": {
        "id": "q0vD18eTQe8_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a leer las primeras tres columnas, la primera corresponde a la distancia radial desde el centro, la segunda es la velocidad observada y la tercerra el error de esta velocidad"
      ],
      "metadata": {
        "id": "3q2w7thXRhdO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vecRp = np.array([row[0] for row in galaxia])# galactocentric distance [kpc]\n",
        "vecvRp = np.array([row[1] for row in galaxia])# rotation velocity [km/s]\n",
        "vecerrvRp = np.array([row[2] for row in galaxia])# error in rotation velocity [km/s]"
      ],
      "metadata": {
        "id": "6m4dynhIQdQp"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.errorbar(vecRp,vecvRp,yerr=vecerrvRp,fmt='.',label='data')\n",
        "plt.ylabel(r'$v_{c}(r)$[km/s]')\n",
        "plt.xlabel(\"r[kpc]\")\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('DDO168')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "NDyL-0d4Qb3V",
        "outputId": "f2693e18-e309-4212-9f4e-2cb4015208bd"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'DDO168')"
            ]
          },
          "metadata": {},
          "execution_count": 146
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "E65_evOLY_Lr"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Noten que seaborn tomó el estilo que cargamos de matplotlib"
      ],
      "metadata": {
        "id": "-J63KNJyS21G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.pairplot(df,hue=\"Species\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        },
        "id": "-b5-ikLRYSrv",
        "outputId": "33472539-261b-4dbb-a802-450d22f601df"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1003.25x900 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Links de interés\n",
        "\n",
        "\n",
        "\n",
        "*   https://www.educative.io/courses/python-101-interactively-learn-how-to-program-with-python-3\n",
        "*   https://www.codecademy.com/catalog/language/python\n",
        "*   https://www.freecodecamp.org/news/python-projects-for-beginners/\n",
        "*  https://www.kaggle.com/datasets/saurabh00007/iriscsv\n",
        "*  https://www.geeksforgeeks.org/python-basics-of-pandas-using-iris-dataset/\n",
        "*  https://www.geeksforgeeks.org/plotting-graph-for-iris-dataset-using-seaborn-and-matplotlib/?ref=rp\n",
        "*  https://aloa.co/blog/what-is-code-repository-best-code-repo-hosting-2021\n",
        "\n"
      ],
      "metadata": {
        "id": "jHY1I-plP49y"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pomcvnyFw_Au"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}