ES_TA_Capítulo_02_Python_para_Data_Science.ipynb

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      "name": "ES-TA Capítulo 02: Python para Data Science.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "oYh-hMSTB1Ur",
        "KE9L44nED-JK"
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/joanby/trading-algoritmico-a-z-con-python/blob/main/ES_TA_Cap%C3%ADtulo_02_Python_para_Data_Science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwIk5lNgltIS"
      },
      "source": [
        "<p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://www.quantreo.com/wp-content/uploads/2021/10/Original-on-Transparent.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\">\n",
        "<img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://static-881c.kxcdn.com/wp-content/uploads/2016/03/frogamesLogoFull4.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
        "\n",
        "\n",
        "# PRERREQUISITOS  - **Python para Data Science**\n",
        "\n",
        "Este capítulo te dará los fundamentos de python para ciencia de  datos que necesitas para seguir el resto del curso. Estos son sólo recordatorios de lo que será útil. Por eso te aconsejo que sigas una formación rápida y gratuita de Python para tus proyectos, porque si eres totalmente ajeno a él, puede ser difícil hacer tu proyecto. Nosotros te recomendamos el curso de más de 50 horas de programación, [Python de la A a la Z que puedes obtener desde nuestra web de frogames](https://cursos.frogamesformacion.com/courses/python-az/).\n",
        "\n",
        "\n",
        "* Numpy:\n",
        "> * Creación de tablas con Numpy\n",
        "> * Aleatoriedad con Numpy\n",
        "> * Indexación y Slicing\n",
        "\n",
        "* Pandas:\n",
        "> * Series y dataframe \n",
        "> * Funciones útiles de pandas\n",
        "> * Indexación y Slicing\n",
        "\n",
        "* Matplotlib\n",
        "> * Gráficos\n",
        "> * Nubes de puntos\n",
        "> * Funciones útiles\n",
        "\n",
        "\n",
        "💰 Únete a la comunidad de [Discord](https://discord.gg/z3dx5XpkX4)\n",
        "\n",
        "📚 Puedes leer nuestro libro en [Amazon](https://www.amazon.es/Python-para-finanzas-trading-algor%C3%ADtmico-ebook/dp/B0BT4ZS9Q3/)\n",
        "\n",
        "🖥️ El canal de [YouTube de Quantreo's](https://www.youtube.com/channel/UCp7jckfiEglNf_Gj62VR0pw) (en inglés) y el de [Frogames](https://www.youtube.com/channel/UCMUxXNYrVCv6-bQakhomvBg) en español\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6gHyLh1Bzh5"
      },
      "source": [
        "### Numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYh-hMSTB1Ur"
      },
      "source": [
        "##### Creación de tablas con Numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy75aQjfBqei",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0347ee9-5b33-40c4-9b65-146abd86b211"
      },
      "source": [
        "# Import de numpy\n",
        "import numpy as np\n",
        "\n",
        "# 1D array\n",
        "arr = np.array([1,2,3])\n",
        "arr"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO2_F5w7krek",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd54a9db-b101-4181-9f0f-95ed65834b2f"
      },
      "source": [
        "# Función Linspace\n",
        "np.linspace(0, 50, 51)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
              "       13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
              "       26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,\n",
              "       39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50.])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfR1z0KWk6H7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff78649f-80ac-42ae-9f07-2ef44e856f30"
      },
      "source": [
        "# Función Arange \n",
        "np.arange(0,51)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOMnsG3ZCWXi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f180cd4e-a9c8-4347-ca3a-6ee82b7c2619"
      },
      "source": [
        "# 2D array\n",
        "arr_bis = np.array([[1,2,3],\n",
        "                    [4,5,6],\n",
        "                    [7,8,9]])\n",
        "arr_bis"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTRSkxrJCtgC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3c96c4e-8660-4657-9c68-f8d096932c50"
      },
      "source": [
        "# Matriz de Ceros\n",
        "O = np.zeros([3,5])\n",
        "O"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVFbAMe9C_fw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87bfabdb-7257-4b16-f41a-726512fe7b18"
      },
      "source": [
        "# Matriz Identidad\n",
        "I = np.identity(5)\n",
        "I"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiNy3cSuDM9r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b5b9fbc-012c-4285-ade9-8043f59254d7"
      },
      "source": [
        "# Matriz de Unos\n",
        "One = np.ones([3,5])\n",
        "One"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_D2LA0o9DXA6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc11100a-9adf-4065-fc81-5bd580afa107"
      },
      "source": [
        "# Sumar dos matrices\n",
        "O + One"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYAJNct5DoYy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a934858-248f-4c73-d347-b6c8eb9176e3"
      },
      "source": [
        "# Restar dos matrices\n",
        "One-O"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwQe3BGYDuYx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42bba7ab-0c0d-44ac-fd14-7620c9a72232"
      },
      "source": [
        "# Producto por escalar\n",
        "One * 10"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 10., 10., 10., 10.],\n",
              "       [10., 10., 10., 10., 10.],\n",
              "       [10., 10., 10., 10., 10.]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE9L44nED-JK"
      },
      "source": [
        "##### Random"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXJBsigRENMh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f4ba3ce-6edf-4563-93d5-7df4cee0892c"
      },
      "source": [
        "# Matriz aleatoria 1d con valores entre 0 y 1 (Ideal para simular un peso aleatorio de una cartera por ejemplo)\n",
        "ran = np.random.rand(6)\n",
        "ran"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.32662702, 0.76859722, 0.29800789, 0.44973423, 0.0467521 ,\n",
              "       0.40029   ])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwdBtqLAE3gi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dcde4dc-e198-40ef-a8a1-7e6cd23d67cc"
      },
      "source": [
        "# BONUS: Cómo encontrar la forma de un array y transformar un array 1d en un array 2d\n",
        "print(f\"Shape es {np.shape(ran)}\")\n",
        "\n",
        "# Reshape\n",
        "ran = ran.reshape(-1,1)\n",
        "\n",
        "# Nueva forma\n",
        "print(f\"Shape es {np.shape(ran)}\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape es (6,)\n",
            "Shape es (6, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ran"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NLwJDd9Wzfwb",
        "outputId": "73907693-2968-4932-d466-57c50bb06fb7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.32662702],\n",
              "       [0.76859722],\n",
              "       [0.29800789],\n",
              "       [0.44973423],\n",
              "       [0.0467521 ],\n",
              "       [0.40029   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxwK8rYLFGkC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "158f8802-5ce2-411f-d3a8-b0d0afc34d03"
      },
      "source": [
        "# Valores aleatorios enteros\n",
        "arr_int = np.random.randint(100, size=(5,5))\n",
        "arr_int"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[31, 68, 60, 15,  4],\n",
              "       [19, 28, 88, 64, 84],\n",
              "       [89, 88, 28, 55, 46],\n",
              "       [88, 67, 56, 56, 99],\n",
              "       [67, 65, 10, 65, 78]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8wZZsWzlgHG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a644687e-5862-443e-9870-2be5c55aae0a"
      },
      "source": [
        "# Distribución normal 1 Dim\n",
        "np.random.normal(loc=0, scale=0.1, size=(6,))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.0317041 , -0.00369426,  0.07927536,  0.08888987, -0.02801485,\n",
              "        0.0061748 ])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BYKsDhxlz14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7aa1380d-5acd-40c9-bb11-1fa8ceb87d1f"
      },
      "source": [
        "# Distribución normal 2 Dim\n",
        "np.random.normal(loc=0, scale=0.1, size=(6,6))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.18217544,  0.11776452,  0.08301567, -0.00486639, -0.03066252,\n",
              "        -0.19389907],\n",
              "       [ 0.33269801, -0.14701157, -0.00718461,  0.02619011, -0.03061681,\n",
              "         0.07615358],\n",
              "       [ 0.04384155,  0.07425683, -0.06842404, -0.1067052 , -0.00276035,\n",
              "        -0.06787927],\n",
              "       [-0.04390115, -0.04891286, -0.01618534,  0.14619283,  0.02678329,\n",
              "         0.19601582],\n",
              "       [ 0.08948065, -0.02855578,  0.07938803, -0.07630312,  0.1058048 ,\n",
              "        -0.08884365],\n",
              "       [ 0.03616659, -0.06043576, -0.0274361 ,  0.01434024,  0.11709511,\n",
              "         0.11765874]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PP4qi331l_Lt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1423f378-1874-4b3c-d16b-c881723803ce"
      },
      "source": [
        "# Establecer la semilla de la aleatoriedad\n",
        "print(np.random.rand(3))\n",
        "print(np.random.rand(3))\n",
        "\n",
        "np.random.seed(seed=56)\n",
        "print(np.random.rand(3))\n",
        "\n",
        "np.random.seed(seed=56)\n",
        "print(np.random.rand(3))\n",
        "\n",
        "np.random.seed(seed=56)\n",
        "print(np.random.rand(3))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.73784304 0.25688482 0.05579651]\n",
            "[0.37668479 0.44209279 0.41497444]\n",
            "[0.98419185 0.33341227 0.67370162]\n",
            "[0.98419185 0.33341227 0.67370162]\n",
            "[0.98419185 0.33341227 0.67370162]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qy6T6HOiGHaN"
      },
      "source": [
        "##### Indices, Slicing y Transformaciones"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74oL7u6IGJMo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b04e8647-7e66-45b7-eb1d-c3485aa924c0"
      },
      "source": [
        "# Elegir un valor en una matriz\n",
        "arr_bis = np.array([[1,2,3],\n",
        "                    [7,1,6],\n",
        "                    [9,6,3]])\n",
        "arr_bis[0][0]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkhGlrwiO0HK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09f225e-4a95-4c06-ac73-7b16e12a03d7"
      },
      "source": [
        "arr_bis[0,0]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSF6ccF1Ggx6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a9ca4d1-182b-4a30-fad1-0c9d586a5e93"
      },
      "source": [
        "# Elija la submatriz en la matriz\n",
        "arr_bis[0:2,0:1]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [7]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks__IvR_GqHI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbbebf96-d430-4cbc-f2c7-cf4ce161f3a4"
      },
      "source": [
        "# Elija una columna o una fila\n",
        "print(arr_bis[1,:])\n",
        "print(arr_bis[:,1])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7 1 6]\n",
            "[2 1 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AG2UdRAng9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f1e5674-7aa8-462f-8581-d5eeebf35958"
      },
      "source": [
        "# Máximo\n",
        "# De la matriz\n",
        "print(arr_bis.max())\n",
        "\n",
        "# Por Filas\n",
        "print(arr_bis.max(axis=1))\n",
        "\n",
        "# Por Columnas\n",
        "print(arr_bis.max(axis=0))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n",
            "[3 7 9]\n",
            "[9 6 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omomB6aDoBnN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dd846b8-b068-461b-f226-5c24f0eadc0d"
      },
      "source": [
        "# Mínimo\n",
        "# De la matriz\n",
        "print(arr_bis.min())\n",
        "\n",
        "# Por Filas\n",
        "print(arr_bis.min(axis=1))\n",
        "\n",
        "# Por Columnas\n",
        "print(arr_bis.min(axis=0))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "[1 1 3]\n",
            "[1 1 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-xixa41oE4X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fe93199-80ac-42ee-ca55-4e94ac5fcc27"
      },
      "source": [
        "# Media\n",
        "# De la matriz\n",
        "print(arr_bis.mean())\n",
        "\n",
        "# Por Filas\n",
        "print(arr_bis.mean(axis=1))\n",
        "\n",
        "# Por Columnas\n",
        "print(arr_bis.mean(axis=0))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.222222222222222\n",
            "[2.         4.66666667 6.        ]\n",
            "[5.66666667 3.         4.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzvvyXXJoOeW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e00e752c-82ec-4da2-f6e2-855fd17f4836"
      },
      "source": [
        "# Desviación Típica\n",
        "# De la matriz\n",
        "print(arr_bis.std())\n",
        "\n",
        "# Por Filas\n",
        "print(arr_bis.std(axis=1))\n",
        "\n",
        "# Por Columnas\n",
        "print(arr_bis.std(axis=0))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.698879511442471\n",
            "[0.81649658 2.62466929 2.44948974]\n",
            "[3.39934634 2.1602469  1.41421356]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QRCdS8BoYL_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d6f1d28-dec8-434b-b7d2-beea9a52f36a"
      },
      "source": [
        "# Otras formas\n",
        "print(np.mean(arr_bis, axis=0))\n",
        "print(np.std(arr_bis, axis=0))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5.66666667 3.         4.        ]\n",
            "[3.39934634 2.1602469  1.41421356]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeSWgJteotgR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be708f4d-8790-4a4a-8144-035f4c729cb5"
      },
      "source": [
        "# Logaritmo\n",
        "np.log(arr_bis)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.69314718, 1.09861229],\n",
              "       [1.94591015, 0.        , 1.79175947],\n",
              "       [2.19722458, 1.79175947, 1.09861229]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvtkDDK9o4DB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eefc40c-8e24-4268-9e32-b501b3ee511a"
      },
      "source": [
        "# Exponencial\n",
        "np.exp(arr_bis)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.71828183e+00, 7.38905610e+00, 2.00855369e+01],\n",
              "       [1.09663316e+03, 2.71828183e+00, 4.03428793e+02],\n",
              "       [8.10308393e+03, 4.03428793e+02, 2.00855369e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPpAz_CWo4eH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd2b9873-c78e-4204-e239-f54a77d4ee06"
      },
      "source": [
        "# Raíz Cuadrada\n",
        "np.sqrt(arr_bis)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 1.41421356, 1.73205081],\n",
              "       [2.64575131, 1.        , 2.44948974],\n",
              "       [3.        , 2.44948974, 1.73205081]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fo7zIRfn39RL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ee73e6e-bf7e-457d-c2a4-c0b071d0a4ea"
      },
      "source": [
        "# Concatenar \n",
        "# Parámetros: tupla de arrays y el eje de la concatenación\n",
        "# Concatenar 1 dimensión\n",
        "arr1 = arr_bis[:,1]\n",
        "arr2 = arr_bis[:,2]\n",
        "\n",
        "print(f\"ARR1 {arr1}\")\n",
        "print(f\"ARR2 {arr2}\")\n",
        "\n",
        "print(np.concatenate((arr1, arr2), axis=0))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARR1 [2 1 6]\n",
            "ARR2 [3 6 3]\n",
            "[2 1 6 3 6 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSZV-n-X8-xx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8435d1f3-ad0e-4b96-ce9b-97ea27038139"
      },
      "source": [
        "# Concatenar 2 dimensión\n",
        "arr1 = arr1.reshape(-1,1)\n",
        "arr2 = arr2.reshape(-1,1)\n",
        "\n",
        "print(f\"ARR1 {arr1}\")\n",
        "print(f\"ARR2 {arr2}\")\n",
        "\n",
        "print(np.concatenate((arr1, arr2), axis=0))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARR1 [[2]\n",
            " [1]\n",
            " [6]]\n",
            "ARR2 [[3]\n",
            " [6]\n",
            " [3]]\n",
            "[[2]\n",
            " [1]\n",
            " [6]\n",
            " [3]\n",
            " [6]\n",
            " [3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aq0-jNt38_eq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e32268e-9f94-4cb4-eb0b-fb68c1d1fa3a"
      },
      "source": [
        "# Concatenar 2 dimensión\n",
        "print(np.concatenate((arr1, arr2), axis=1))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2 3]\n",
            " [1 6]\n",
            " [6 3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ilLgwf1HIMw"
      },
      "source": [
        "### Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxZBaJicHJBJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLRfDnNGHUkI"
      },
      "source": [
        "##### Series y Dataframes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdYvgavpJPGK"
      },
      "source": [
        "###### **Series**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YG-fUIdLIuaA"
      },
      "source": [
        "labels = [\"label 1\", \"label 2\", \"label 3\"]\n",
        "list_values = [1,2,3]\n",
        "arr_values = np.array(list_values)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNo-p9PIJA3x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b425e3b-9522-4109-eb58-60ce58209e45"
      },
      "source": [
        "# Crear una serie usando una lista\n",
        "pd.Series(list_values, index=labels)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label 1    1\n",
              "label 2    2\n",
              "label 3    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_trjK8YSJHqR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15caceb1-d79d-4830-ef38-ad3cc5a50434"
      },
      "source": [
        "# Crear una serie usando un array\n",
        "pd.Series(arr_values, index=labels)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label 1    1\n",
              "label 2    2\n",
              "label 3    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLPPB2NOJUSq"
      },
      "source": [
        "###### **Dataframes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSqejMNzJJ2h"
      },
      "source": [
        "list_columns = [\"col_1\", \"col_2\", \"col_3\"]\n",
        "list_index = [\"row_1\", \"row_2\", \"row_3\"]\n",
        "\n",
        "arr_bis = np.array([[1,2,3],\n",
        "                    [7,1,6],\n",
        "                    [9,6,9]])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8If1H15K8UA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "89052fa9-50b6-42fe-efe6-a74b41eca3ea"
      },
      "source": [
        "# Crear un dataframe desde un array\n",
        "pd.DataFrame(arr_bis)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0  1  2\n",
              "0  1  2  3\n",
              "1  7  1  6\n",
              "2  9  6  9"
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80985680-ffbf-4f49-a12c-ffaf7b78aee0')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          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",
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(arr_bis).values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqGshLu-l-fy",
        "outputId": "52c59ef3-e5d0-4b7e-dd46-ec0fe3d420d9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [7, 1, 6],\n",
              "       [9, 6, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0B8yIH3vQ6i-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "88ce9cb2-d3f0-4fc1-fa5f-48523e70763e"
      },
      "source": [
        "# Especificar filas/columnas\n",
        "pd.DataFrame(arr_bis, index=list_index, columns=list_columns)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       col_1  col_2  col_3\n",
              "row_1      1      2      3\n",
              "row_2      7      1      6\n",
              "row_3      9      6      9"
            ],
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              "\n",
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              "      <th>col_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>row_1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_2</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_3</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-398eb3f0-4b6f-4e70-9185-57c8d62746e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XM6mdryCRCAm"
      },
      "source": [
        "##### Cleaning and selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLc1A64XRGgz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "outputId": "fd429c65-8fc6-4b77-b211-1a73d56239f4"
      },
      "source": [
        "# Importar un CSV\n",
        "assets = pd.read_csv(\"/content/assets.csv\", parse_dates=True, index_col=\"time\")\n",
        "assets"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            Open MSFT  High MSFT  Low MSFT  Close MSFT  Volume MSFT  \\\n",
              "time                                                                  \n",
              "2020-03-18      138.0   146.0000    135.02      140.40   80794307.0   \n",
              "2020-03-17      140.0   147.4998    135.00      146.57   81059817.0   \n",
              "2020-03-16      140.0   149.3500    135.00      135.42   87905868.0   \n",
              "2020-03-13      147.5   161.9100    140.73      158.83   92727446.0   \n",
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              "\n",
              "            Open GOOG  High GOOG  Low GOOG  Close GOOG  Volume GOOG  ...  \\\n",
              "time                                                                 ...   \n",
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              "\n",
              "            Open EUROSTOCK50  High EUROSTOCK50  Low EUROSTOCK50  \\\n",
              "time                                                              \n",
              "2020-03-18         2507.3301         2507.3301        2365.7000   \n",
              "2020-03-17         2475.1799         2555.4099        2376.2300   \n",
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              "2020-03-13         2584.3601         2793.2700        2537.2700   \n",
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              "\n",
              "            Close EUROSTOCK50  Volume EUROSTOCK50  Open INTEL  High INTEL  \\\n",
              "time                                                                        \n",
              "2020-03-18          2385.8201          98214900.0       30.24     32.4550   \n",
              "2020-03-17          2530.5000         106905300.0       29.06     33.8000   \n",
              "2020-03-16          2450.3701         137137000.0       30.11     32.8450   \n",
              "2020-03-13          2586.0200         136547100.0       33.87     35.0077   \n",
              "2020-03-12          2545.2300         167329900.0       35.29     36.0432   \n",
              "...                       ...                 ...         ...         ...   \n",
              "2000-07-04          5185.8701                 0.0         NaN         NaN   \n",
              "2000-05-29          5084.6899                 0.0         NaN         NaN   \n",
              "2000-04-21          5170.3844            722995.0         NaN         NaN   \n",
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              "2000-01-17          4884.5000                 0.0         NaN         NaN   \n",
              "\n",
              "            Low INTEL  Close INTEL  Volume INTEL  \n",
              "time                                              \n",
              "2020-03-18      28.01        29.20      192337.0  \n",
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              "2020-03-12      32.33        32.43      145336.0  \n",
              "...               ...          ...           ...  \n",
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              "2000-01-17        NaN          NaN           NaN  \n",
              "\n",
              "[5732 rows x 80 columns]"
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              "      <th>time</th>\n",
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              "      <td>140.40</td>\n",
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              "      <td>2385.8201</td>\n",
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              "      <td>146.57</td>\n",
              "      <td>81059817.0</td>\n",
              "      <td>1093.11</td>\n",
              "      <td>1130.86</td>\n",
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              "      <td>2555.4099</td>\n",
              "      <td>2376.2300</td>\n",
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              "      <td>106905300.0</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.8000</td>\n",
              "      <td>28.79</td>\n",
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              "      <td>273967.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>140.0</td>\n",
              "      <td>149.3500</td>\n",
              "      <td>135.00</td>\n",
              "      <td>135.42</td>\n",
              "      <td>87905868.0</td>\n",
              "      <td>1096.00</td>\n",
              "      <td>1152.27</td>\n",
              "      <td>1074.44</td>\n",
              "      <td>1084.33</td>\n",
              "      <td>4252365.0</td>\n",
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              "      <td>2557.7100</td>\n",
              "      <td>2557.7100</td>\n",
              "      <td>2302.8401</td>\n",
              "      <td>2450.3701</td>\n",
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              "      <td>32.8450</td>\n",
              "      <td>28.62</td>\n",
              "      <td>29.01</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>147.5</td>\n",
              "      <td>161.9100</td>\n",
              "      <td>140.73</td>\n",
              "      <td>158.83</td>\n",
              "      <td>92727446.0</td>\n",
              "      <td>1179.00</td>\n",
              "      <td>1219.76</td>\n",
              "      <td>1117.14</td>\n",
              "      <td>1219.73</td>\n",
              "      <td>3700125.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2584.3601</td>\n",
              "      <td>2793.2700</td>\n",
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              "      <th>2020-03-12</th>\n",
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              "      <td>5132.2034</td>\n",
              "      <td>5170.3844</td>\n",
              "      <td>722995.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2000-02-21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2000-01-17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5732 rows × 80 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d2bde7c-715a-4b88-a865-afdbb931e1cb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      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-6d2bde7c-715a-4b88-a865-afdbb931e1cb 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-6d2bde7c-715a-4b88-a865-afdbb931e1cb');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryhsmwSMRvNn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "outputId": "8948f77b-e315-4d98-e147-92a79635b751"
      },
      "source": [
        "# Ordenar\n",
        "assets = assets.sort_index(ascending=True)\n",
        "assets"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            Open MSFT  High MSFT  Low MSFT  Close MSFT  Volume MSFT  \\\n",
              "time                                                                  \n",
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              "time                                                              \n",
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              "time                                                                        \n",
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              "2020-03-19                NaN                 NaN         NaN         NaN   \n",
              "\n",
              "            Low INTEL  Close INTEL  Volume INTEL  \n",
              "time                                              \n",
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              "      <td>147.4998</td>\n",
              "      <td>135.00</td>\n",
              "      <td>146.57</td>\n",
              "      <td>81059817.0</td>\n",
              "      <td>1093.11</td>\n",
              "      <td>1130.86</td>\n",
              "      <td>1056.01</td>\n",
              "      <td>1119.80</td>\n",
              "      <td>3861489.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2475.1799</td>\n",
              "      <td>2555.4099</td>\n",
              "      <td>2376.2300</td>\n",
              "      <td>2530.5000</td>\n",
              "      <td>106905300.0</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.800</td>\n",
              "      <td>28.79</td>\n",
              "      <td>32.40</td>\n",
              "      <td>273967.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>138.0</td>\n",
              "      <td>146.0000</td>\n",
              "      <td>135.02</td>\n",
              "      <td>140.40</td>\n",
              "      <td>80794307.0</td>\n",
              "      <td>1056.51</td>\n",
              "      <td>1106.50</td>\n",
              "      <td>1037.28</td>\n",
              "      <td>1096.80</td>\n",
              "      <td>4220935.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2507.3301</td>\n",
              "      <td>2507.3301</td>\n",
              "      <td>2365.7000</td>\n",
              "      <td>2385.8201</td>\n",
              "      <td>98214900.0</td>\n",
              "      <td>30.24</td>\n",
              "      <td>32.455</td>\n",
              "      <td>28.01</td>\n",
              "      <td>29.20</td>\n",
              "      <td>192337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5732 rows × 80 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b698dcc2-b2c5-43da-ae04-5308563eee1a')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-b698dcc2-b2c5-43da-ae04-5308563eee1a 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-b698dcc2-b2c5-43da-ae04-5308563eee1a');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ByJ7yg-TeOD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "7486f42c-60fb-4b79-fdb6-56b74133e289"
      },
      "source": [
        "# Seleccionar una columna\n",
        "assets = assets[[\"Close DJI30\", \"Close CAC40\", \"Close SP500\"]]\n",
        "assets"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500\n",
              "time                                             \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700\n",
              "...                 ...          ...          ...\n",
              "2020-03-15          NaN          NaN          NaN\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001\n",
              "2020-03-19          NaN          NaN          NaN\n",
              "\n",
              "[5732 rows x 3 columns]"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
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              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5732 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9844702-7e0d-4e31-b148-2ee995ccc269')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      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",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-a9844702-7e0d-4e31-b148-2ee995ccc269');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppgUESj5SAKm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "b66bd464-932a-44c1-dc3b-21f4923c75c4"
      },
      "source": [
        "# Eliminar los valores faltantes o NAN\n",
        "assets = assets.dropna()\n",
        "assets"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500\n",
              "time                                             \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700\n",
              "...                 ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001\n",
              "\n",
              "[5047 rows x 3 columns]"
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              "      <th>time</th>\n",
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              "  <tbody>\n",
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              "      <td>11357.5098</td>\n",
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              "      <td>1455.2200</td>\n",
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              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
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              "      <td>1399.4200</td>\n",
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              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
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              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
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              "      <td>1403.4500</td>\n",
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              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
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              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27a008e3-e450-4a96-b29c-b473f5d8a47e')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-27a008e3-e450-4a96-b29c-b473f5d8a47e 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-27a008e3-e450-4a96-b29c-b473f5d8a47e');\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSbtnwn8R4P3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ed8e138c-8427-4c77-8b72-b59498bf2e8e"
      },
      "source": [
        "# Reiniciar los índices\n",
        "assets.reset_index(drop=True)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Close DJI30  Close CAC40  Close SP500\n",
              "0      11357.5098    5917.3701    1455.2200\n",
              "1      10997.9297    5672.0200    1399.4200\n",
              "2      11122.6504    5479.7002    1402.1100\n",
              "3      11253.2598    5450.1099    1403.4500\n",
              "4      11522.5596    5539.6099    1441.4700\n",
              "...           ...          ...          ...\n",
              "5042   21200.6191    4044.2600    2420.8827\n",
              "5043   23185.6191    4118.3599    2418.0191\n",
              "5044   20188.5195    3881.4600    2426.6181\n",
              "5045   21237.3809    3991.7800    2436.5000\n",
              "5046   19898.9199    3754.8401    2398.1001\n",
              "\n",
              "[5047 rows x 3 columns]"
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              "      <th>Close DJI30</th>\n",
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              "  <tbody>\n",
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              "      <td>1399.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
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              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
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              "      <th>5042</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
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              "    <tr>\n",
              "      <th>5043</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
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              "    <tr>\n",
              "      <th>5044</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5045</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
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              "    <tr>\n",
              "      <th>5046</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b5b5e44-0f0e-4ecc-b8ad-79b9fa051131')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
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              "      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-4b5b5e44-0f0e-4ecc-b8ad-79b9fa051131 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-4b5b5e44-0f0e-4ecc-b8ad-79b9fa051131');\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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaUD8C16_Y_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "8d73bacc-6c8b-4fc7-d1ab-61ad2f249dc2"
      },
      "source": [
        "# Rolling \n",
        "assets[\"SMA 15\"] = assets[[\"Close SP500\"]].rolling(15).mean()\n",
        "assets"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200          NaN\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200          NaN\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100          NaN\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500          NaN\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700          NaN\n",
              "...                 ...          ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827  2445.100287\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191  2444.029520\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181  2443.003900\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000  2442.964567\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001  2440.551540\n",
              "\n",
              "[5047 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9497151-e690-4c72-b32c-ab69c267c8cc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
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              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2445.100287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2444.029520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2443.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2442.964567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>2440.551540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9497151-e690-4c72-b32c-ab69c267c8cc')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-f9497151-e690-4c72-b32c-ab69c267c8cc 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-f9497151-e690-4c72-b32c-ab69c267c8cc');\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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0V3RpnD_bxL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "878394e6-a266-44f6-f0d4-773d63cbf5dd"
      },
      "source": [
        "# Shift\n",
        "assets[\"Close SP500\"].shift(1)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "time\n",
              "2000-01-03          NaN\n",
              "2000-01-04    1455.2200\n",
              "2000-01-05    1399.4200\n",
              "2000-01-06    1402.1100\n",
              "2000-01-07    1403.4500\n",
              "                ...    \n",
              "2020-03-12    2423.1280\n",
              "2020-03-13    2420.8827\n",
              "2020-03-16    2418.0191\n",
              "2020-03-17    2426.6181\n",
              "2020-03-18    2436.5000\n",
              "Name: Close SP500, Length: 5047, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFzrct4WA-7i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "923b917c-7b15-4a09-b5de-dce9f58a7465"
      },
      "source": [
        "arr = [[1,11],\n",
        "       [1,10],\n",
        "       [3,15],\n",
        "       [3,15],\n",
        "       [3,10]]\n",
        "\n",
        "df = pd.DataFrame(arr, columns=[\"num\",\"value\"])\n",
        "\n",
        "df"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   num  value\n",
              "0    1     11\n",
              "1    1     10\n",
              "2    3     15\n",
              "3    3     15\n",
              "4    3     10"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-69f060d1-11ce-4795-971e-2c5a72f99aa1\">\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",
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              "      <td>11</td>\n",
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              "      <td>10</td>\n",
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              "      <td>3</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69f060d1-11ce-4795-971e-2c5a72f99aa1')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      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-69f060d1-11ce-4795-971e-2c5a72f99aa1 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-69f060d1-11ce-4795-971e-2c5a72f99aa1');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Agrupar por media\n",
        "df.groupby(by=\"num\").mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "kb_gwB4vzWuc",
        "outputId": "1f505e7c-c485-4b65-e597-1a694677faa5"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         value\n",
              "num           \n",
              "1    10.500000\n",
              "3    13.333333"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>value</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aeef2121-759a-4379-bb81-adfea0af7797')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
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              "\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-aeef2121-759a-4379-bb81-adfea0af7797 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-aeef2121-759a-4379-bb81-adfea0af7797');\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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg9Z3NYyCvbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "5b2dd00d-a8d9-44f8-b2e5-ad8208cb7711"
      },
      "source": [
        "# Agrupar por suma\n",
        "df.groupby(by=\"num\").sum()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     value\n",
              "num       \n",
              "1       21\n",
              "3       40"
            ],
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              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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",
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              "      <th></th>\n",
              "      <th>value</th>\n",
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              "      <th>num</th>\n",
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              "      <th>1</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b19c657-d129-4ef9-8d59-dd58dca90c80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      \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-1b19c657-d129-4ef9-8d59-dd58dca90c80 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-1b19c657-d129-4ef9-8d59-dd58dca90c80');\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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l-xwc-HC2MU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "0b7033da-8172-47ba-857a-5fc6e14302bb"
      },
      "source": [
        "# Agrupar por desviación típica\n",
        "df.groupby(by=\"num\").std()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        value\n",
              "num          \n",
              "1    0.707107\n",
              "3    2.886751"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a023f24-1ac9-49bd-9481-3ae94121e842\">\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>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.707107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.886751</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a023f24-1ac9-49bd-9481-3ae94121e842')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-4a023f24-1ac9-49bd-9481-3ae94121e842 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-4a023f24-1ac9-49bd-9481-3ae94121e842');\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": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5KoCwIFT27I"
      },
      "source": [
        "##### Iloc & Loc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4Jy9QxKT5R9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "0b01aa4d-4f16-4f58-9031-d69677f510e4"
      },
      "source": [
        "# Slicing\n",
        "assets.iloc[0:1500, 0:10]"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200          NaN\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200          NaN\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100          NaN\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500          NaN\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700          NaN\n",
              "...                 ...          ...          ...          ...\n",
              "2005-12-13   10823.7197    4693.3999    1267.4301  1261.032680\n",
              "2005-12-14   10883.5098    4674.8501    1272.7400  1261.800013\n",
              "2005-12-15   10881.6699    4673.1401    1270.9399  1262.155340\n",
              "2005-12-16   10875.5898    4704.4102    1267.3199  1262.093333\n",
              "2005-12-19   10836.5303    4694.8599    1259.9200  1262.257333\n",
              "\n",
              "[1500 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2731f2d0-57de-4748-aa9e-38c9c12d306e\">\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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-13</th>\n",
              "      <td>10823.7197</td>\n",
              "      <td>4693.3999</td>\n",
              "      <td>1267.4301</td>\n",
              "      <td>1261.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-14</th>\n",
              "      <td>10883.5098</td>\n",
              "      <td>4674.8501</td>\n",
              "      <td>1272.7400</td>\n",
              "      <td>1261.800013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-15</th>\n",
              "      <td>10881.6699</td>\n",
              "      <td>4673.1401</td>\n",
              "      <td>1270.9399</td>\n",
              "      <td>1262.155340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-16</th>\n",
              "      <td>10875.5898</td>\n",
              "      <td>4704.4102</td>\n",
              "      <td>1267.3199</td>\n",
              "      <td>1262.093333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-19</th>\n",
              "      <td>10836.5303</td>\n",
              "      <td>4694.8599</td>\n",
              "      <td>1259.9200</td>\n",
              "      <td>1262.257333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1500 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2731f2d0-57de-4748-aa9e-38c9c12d306e')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-2731f2d0-57de-4748-aa9e-38c9c12d306e 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-2731f2d0-57de-4748-aa9e-38c9c12d306e');\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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0KS0q6zUCTn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "c4f43e47-14f9-4fdb-a25e-e887a904a7f9"
      },
      "source": [
        "# Filtrado condicional por fechas\n",
        "assets.loc[\"2010\":\"2012\"]"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2010-01-04   10583.9600    4013.9700    1132.9900  1116.254013\n",
              "2010-01-05   10572.0195    4012.9099    1136.5200  1118.261347\n",
              "2010-01-06   10573.6797    4017.6699    1137.1400  1119.796680\n",
              "2010-01-07   10606.8604    4024.8000    1141.6899  1122.047333\n",
              "2010-01-08   10618.1904    4045.1399    1144.9800  1124.433993\n",
              "...                 ...          ...          ...          ...\n",
              "2012-12-19   13251.9697    3664.5901    1435.8101  1420.719333\n",
              "2012-12-20   13311.7197    3666.7300    1443.6899  1422.568660\n",
              "2012-12-21   13190.8398    3661.3999    1430.1500  1423.499987\n",
              "2012-12-27   13096.3096    3674.2600    1418.1000  1424.075987\n",
              "2012-12-28   12938.1104    3620.2500    1402.4301  1423.767993\n",
              "\n",
              "[746 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-be9d7862-1579-423a-b8cd-bd197335472b\">\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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>10583.9600</td>\n",
              "      <td>4013.9700</td>\n",
              "      <td>1132.9900</td>\n",
              "      <td>1116.254013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>10572.0195</td>\n",
              "      <td>4012.9099</td>\n",
              "      <td>1136.5200</td>\n",
              "      <td>1118.261347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>10573.6797</td>\n",
              "      <td>4017.6699</td>\n",
              "      <td>1137.1400</td>\n",
              "      <td>1119.796680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>10606.8604</td>\n",
              "      <td>4024.8000</td>\n",
              "      <td>1141.6899</td>\n",
              "      <td>1122.047333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>10618.1904</td>\n",
              "      <td>4045.1399</td>\n",
              "      <td>1144.9800</td>\n",
              "      <td>1124.433993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-19</th>\n",
              "      <td>13251.9697</td>\n",
              "      <td>3664.5901</td>\n",
              "      <td>1435.8101</td>\n",
              "      <td>1420.719333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-20</th>\n",
              "      <td>13311.7197</td>\n",
              "      <td>3666.7300</td>\n",
              "      <td>1443.6899</td>\n",
              "      <td>1422.568660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-21</th>\n",
              "      <td>13190.8398</td>\n",
              "      <td>3661.3999</td>\n",
              "      <td>1430.1500</td>\n",
              "      <td>1423.499987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-27</th>\n",
              "      <td>13096.3096</td>\n",
              "      <td>3674.2600</td>\n",
              "      <td>1418.1000</td>\n",
              "      <td>1424.075987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-28</th>\n",
              "      <td>12938.1104</td>\n",
              "      <td>3620.2500</td>\n",
              "      <td>1402.4301</td>\n",
              "      <td>1423.767993</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>746 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be9d7862-1579-423a-b8cd-bd197335472b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      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-be9d7862-1579-423a-b8cd-bd197335472b 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-be9d7862-1579-423a-b8cd-bd197335472b');\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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CU9dKTkVUMF8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "008b8a98-646d-45df-f395-24bcf83ef4f7"
      },
      "source": [
        "# Filtrado condicional por valores\n",
        "assets.loc[assets[\"Close DJI30\"] > 15000]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2013-05-07   15056.2002    3921.3201    1625.9600  1583.837320\n",
              "2013-05-08   15105.1201    3956.2800    1632.6899  1587.711987\n",
              "2013-05-09   15082.6201    3928.5801    1626.6700  1592.689320\n",
              "2013-05-10   15118.4902    3953.8301    1633.7000  1598.828653\n",
              "2013-05-13   15091.6797    3945.2000    1633.7700  1604.063320\n",
              "...                 ...          ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827  2445.100287\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191  2444.029520\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181  2443.003900\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000  2442.964567\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001  2440.551540\n",
              "\n",
              "[1687 rows x 4 columns]"
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              "      <th>2013-05-07</th>\n",
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              "      <td>3921.3201</td>\n",
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              "      <th>2013-05-08</th>\n",
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              "      <td>1626.6700</td>\n",
              "      <td>1592.689320</td>\n",
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              "      <th>2013-05-10</th>\n",
              "      <td>15118.4902</td>\n",
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              "      <th>2013-05-13</th>\n",
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              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2445.100287</td>\n",
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              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
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              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
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              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2442.964567</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>2440.551540</td>\n",
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              "  </tbody>\n",
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              "<p>1687 rows × 4 columns</p>\n",
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              "          const element = document.querySelector('#df-cbc375f0-5d4d-49b6-8206-b1a876e8f97f');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oXYx23B-wmb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "5e960e55-7208-496a-9298-568cfe81d53b"
      },
      "source": [
        "# Concatenar data frames\n",
        "df1 = assets.iloc[:,:3]\n",
        "df2 = assets.iloc[:,3:]\n",
        "\n",
        "pd.concat((df1,df2), axis=1)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200          NaN\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200          NaN\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100          NaN\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500          NaN\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700          NaN\n",
              "...                 ...          ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827  2445.100287\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191  2444.029520\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181  2443.003900\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000  2442.964567\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001  2440.551540\n",
              "\n",
              "[5047 rows x 4 columns]"
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              "\n",
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              "      <th>time</th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2445.100287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2444.029520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2443.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2442.964567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>2440.551540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84bb4b0f-cbc3-49e1-9239-0e90fcf21856')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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-84bb4b0f-cbc3-49e1-9239-0e90fcf21856 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-84bb4b0f-cbc3-49e1-9239-0e90fcf21856');\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YAkHm8yCm28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "70884f0a-0e79-466c-fc77-cb04bf1091e5"
      },
      "source": [
        "# Concatenar data frames\n",
        "df1 = assets.iloc[1500:,:]\n",
        "df2 = assets.iloc[:1500,:]\n",
        "\n",
        "pd.concat((df1,df2), axis=0)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2005-12-20   10805.5498    4703.4800    1259.6200  1262.400000\n",
              "2005-12-21   10833.7305    4752.4102    1262.7900  1263.287333\n",
              "2005-12-22   10889.4404    4751.9600    1268.1200  1263.517333\n",
              "2005-12-23   10883.2695    4757.7402    1268.6600  1263.756000\n",
              "2005-12-27   10777.7695    4769.3799    1256.5400  1263.386000\n",
              "...                 ...          ...          ...          ...\n",
              "2005-12-13   10823.7197    4693.3999    1267.4301  1261.032680\n",
              "2005-12-14   10883.5098    4674.8501    1272.7400  1261.800013\n",
              "2005-12-15   10881.6699    4673.1401    1270.9399  1262.155340\n",
              "2005-12-16   10875.5898    4704.4102    1267.3199  1262.093333\n",
              "2005-12-19   10836.5303    4694.8599    1259.9200  1262.257333\n",
              "\n",
              "[5047 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dabe8405-7d7f-4b61-9596-6090cb094c75\">\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",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-12-20</th>\n",
              "      <td>10805.5498</td>\n",
              "      <td>4703.4800</td>\n",
              "      <td>1259.6200</td>\n",
              "      <td>1262.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-21</th>\n",
              "      <td>10833.7305</td>\n",
              "      <td>4752.4102</td>\n",
              "      <td>1262.7900</td>\n",
              "      <td>1263.287333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-22</th>\n",
              "      <td>10889.4404</td>\n",
              "      <td>4751.9600</td>\n",
              "      <td>1268.1200</td>\n",
              "      <td>1263.517333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-23</th>\n",
              "      <td>10883.2695</td>\n",
              "      <td>4757.7402</td>\n",
              "      <td>1268.6600</td>\n",
              "      <td>1263.756000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-27</th>\n",
              "      <td>10777.7695</td>\n",
              "      <td>4769.3799</td>\n",
              "      <td>1256.5400</td>\n",
              "      <td>1263.386000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-13</th>\n",
              "      <td>10823.7197</td>\n",
              "      <td>4693.3999</td>\n",
              "      <td>1267.4301</td>\n",
              "      <td>1261.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-14</th>\n",
              "      <td>10883.5098</td>\n",
              "      <td>4674.8501</td>\n",
              "      <td>1272.7400</td>\n",
              "      <td>1261.800013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-15</th>\n",
              "      <td>10881.6699</td>\n",
              "      <td>4673.1401</td>\n",
              "      <td>1270.9399</td>\n",
              "      <td>1262.155340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-16</th>\n",
              "      <td>10875.5898</td>\n",
              "      <td>4704.4102</td>\n",
              "      <td>1267.3199</td>\n",
              "      <td>1262.093333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-19</th>\n",
              "      <td>10836.5303</td>\n",
              "      <td>4694.8599</td>\n",
              "      <td>1259.9200</td>\n",
              "      <td>1262.257333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dabe8405-7d7f-4b61-9596-6090cb094c75')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-dabe8405-7d7f-4b61-9596-6090cb094c75 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-dabe8405-7d7f-4b61-9596-6090cb094c75');\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": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aH-i3Tn3UjJ_"
      },
      "source": [
        "### Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvdLwfXmk91w"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKpXgOj6k99N"
      },
      "source": [
        "import matplotlib as mpl\n",
        "from matplotlib import cycler\n",
        "\n",
        "colors = cycler('color',\n",
        "                ['#669FEE', '#66EE91', '#9988DD',\n",
        "                 '#EECC55', '#88BB44', '#FFBBBB'])\n",
        "plt.rc('figure', facecolor='#313233')\n",
        "plt.rc('axes', facecolor=\"#313233\", edgecolor='none',\n",
        "       axisbelow=True, grid=True, prop_cycle=colors,\n",
        "       labelcolor='gray')\n",
        "plt.rc('grid', color='474A4A', linestyle='solid')\n",
        "plt.rc('xtick', color='gray')\n",
        "plt.rc('ytick', direction='out', color='gray')\n",
        "plt.rc('legend', facecolor=\"#313233\", edgecolor=\"#313233\")\n",
        "plt.rc(\"text\", color=\"#C9C9C9\")\n",
        "plt.rc('figure', facecolor='#313233')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6_cmiswU2AW"
      },
      "source": [
        "##### Representación gráfica"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "e-jNCUbBrpZN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBorfFcRwi42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85bc403b-1e35-4dee-d649-621b432fb20c"
      },
      "source": [
        "# Simulación de datos\n",
        "arr = np.random.randint(0,15,size=(15,))\n",
        "\n",
        "# Cuadrados del array\n",
        "square = arr**2\n",
        "\n",
        "# Productos del array\n",
        "mut = arr*3\n",
        "\n",
        "print(arr)\n",
        "print(square)\n",
        "print(mut)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 9  7  3 11  2 14  9  0 12  4 13 14  2 10  8]\n",
            "[ 81  49   9 121   4 196  81   0 144  16 169 196   4 100  64]\n",
            "[27 21  9 33  6 42 27  0 36 12 39 42  6 30 24]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noAG7_QvvTk1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "7f76bd22-6793-4cb1-d55d-4f85508fa36d"
      },
      "source": [
        "# Representación de los datos\n",
        "plt.plot(arr)\n",
        "plt.plot(square)\n",
        "plt.plot(mut)\n",
        "\n",
        "# Cambiar etiqueta del eje x\n",
        "plt.xlabel(\"Números Aleatorios\")\n",
        "\n",
        "# Cambiar etiqueta del eje y\n",
        "plt.ylabel(\"Valores\")\n",
        "\n",
        "# Cambiar etiqueta del título\n",
        "plt.title(\"Gráfico\")\n",
        "\n",
        "# Colocar una leyenda\n",
        "plt.legend([\"Normales\", \"Cuadrados\", \"Productos\"])\n",
        "\n",
        "# Mostrar el gráfico\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asuSI8Jg0EaC"
      },
      "source": [
        "##### Scatter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6Nrf3rvwCk7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "917494e4-16b0-4d22-b214-662fdbbe6397"
      },
      "source": [
        "# Nube de puntos\n",
        "plt.scatter(arr, mut)\n",
        "plt.scatter(arr, square)\n",
        "\n",
        "# Cambiar etiqueta del eje x\n",
        "plt.xlabel(\"Random Numbers\")\n",
        "\n",
        "# Cambiar etiqueta del eje y\n",
        "plt.ylabel(\"Values\")\n",
        "\n",
        "# Cambiar etiqueta del título\n",
        "plt.title(\"Scatter\")\n",
        "\n",
        "# Colocar una leyenda\n",
        "plt.legend([\"Producto\", \"Cuadrado\"])\n",
        "\n",
        "# Mostrar el gráfico\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppony41B0G1i"
      },
      "source": [
        "##### Más herramientas de Matplotlib "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO9JX-7fzXL0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "06bcb8a9-1866-491e-8435-6714783d5cd9"
      },
      "source": [
        "# Adaptar el tamaño\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "# Representar una línea\n",
        "plt.plot(arr, alpha=0.6, color=\"turquoise\")\n",
        "\n",
        "# Representar una línea\n",
        "plt.plot(square, \"-o\", linewidth=3)\n",
        "\n",
        "# Representar una línea\n",
        "plt.plot(mut, \"o\")\n",
        "\n",
        "# Cambiar etiqueta del eje y\n",
        "plt.ylabel(\"Values\")\n",
        "\n",
        "# Cambiar el título\n",
        "plt.title(\"Graph\")\n",
        "\n",
        "# Colocar una leyenda\n",
        "plt.legend([\"Normal\", \"Cuadrados\", \"Productos\"])\n",
        "\n",
        "# Mostrar el gráfico\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}