Clas_02_06_Agosto_DatAnova.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/yeiscop/PYTHON/blob/master/Clas_02_06_Agosto_DatAnova.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "h1aGVILQrgqW",
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      "source": [
        "#      Clase 2: 06 de agosto <br/> Fundamentos de Estadística\n",
        "![texto alternativo](https://drive.google.com/uc?export=view&id=1dzCjzcQcqMKzlnKzLBlnthqHvvp2wVYL)\n",
        "\n",
        "## Requisitos \n",
        "- Condición sin ecuánime: Tener cuenta de Gmail <br/>\n",
        "- Los Scripts van estar alojados en la carpeta de formación del TEAMS y publicados en el canal <br/>\n",
        "- Repositorio: https://github.com/yeiscop/PYTHON/blob/master/Clas_02_06_Agosto_DatAnova.ipynb\n",
        "\n",
        "# Puede verse antes de clase\n",
        "## Tutorial inicio GOOGLE COLAB\n",
        "- https://www.youtube.com/watch?v=inN8seMm7UI\n",
        "\n",
        "## Qué es GOOGLE COLAB \n",
        "- https://research.google.com/colaboratory/faq.html\n",
        "\n",
        "## Datos a trabajar\n",
        "- https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv\n",
        "- https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.names\n",
        "- https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
        "- https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html\n",
        "\n",
        "#Temas: Estadística Descriptiva\n",
        "```\n",
        "- Noticias de la semana                             : yltabord            5 min\n",
        "- Tipos de Datos:                                   : yltabord dmrendon  20 min\n",
        "- Análisis Descriptivo                             : yltabord dmrendon  90 min\n",
        "- Quiz con SCORE o tarea                            : e tal               5 min\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOz_8Qp0j3bt",
        "colab_type": "text"
      },
      "source": [
        "# Estadística Descriptiva \n",
        "![texto alternativo](https://drive.google.com/uc?export=view&id=1IiZBJ1cDeIUPh9AprkqgUnBhGMampgOZ)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOr5hd5JMCkl",
        "colab_type": "text"
      },
      "source": [
        "## Tipos de datos\n",
        "* Númericas / Cuantitativas (discretas y continuas)\n",
        "> * Número de llamadas /hora que ingresan a un call center\n",
        "> * Duración (en min) de cada llamada\n",
        "\n",
        "* Categóricas / Cualitativas (Nominales Ordinales)\n",
        "\n",
        "> * Grupo Sanguíneo\n",
        "> * Ubicación (Rural/Urbano)\n",
        "> * Riesgo (Alto, Medio y Bajo)\n",
        "> * Fraude / No Fraude\n",
        "\n",
        "* Corte transversal: datos recopilados en el mismo instante\n",
        "* Longitudinales: datos recopilados durante un intervalo de tiempo \n",
        "> * Evolución de pacientes durante un tratamiento\n",
        "> * Valores mensuales de facturación (series de tiempo)\n",
        "* No estructurado (texto, imágenes, video, audio)\n",
        "\n",
        "![texto alternativo](https://drive.google.com/uc?export=view&id=1aPH7v32E8Wbefxku_-Srt8QqbmlkljuH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9qxXXUbOxUa",
        "colab_type": "text"
      },
      "source": [
        "### Númericas - Categóricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IqFJrmjO9tN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2007a62b-3754-4114-843d-61574a9fd822"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "iris"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "{'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
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              "        [6.9, 3.1, 4.9, 1.5],\n",
              "        [5.5, 2.3, 4. , 1.3],\n",
              "        [6.5, 2.8, 4.6, 1.5],\n",
              "        [5.7, 2.8, 4.5, 1.3],\n",
              "        [6.3, 3.3, 4.7, 1.6],\n",
              "        [4.9, 2.4, 3.3, 1. ],\n",
              "        [6.6, 2.9, 4.6, 1.3],\n",
              "        [5.2, 2.7, 3.9, 1.4],\n",
              "        [5. , 2. , 3.5, 1. ],\n",
              "        [5.9, 3. , 4.2, 1.5],\n",
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              "        [6. , 3.4, 4.5, 1.6],\n",
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              "        [6.1, 3. , 4.6, 1.4],\n",
              "        [5.8, 2.6, 4. , 1.2],\n",
              "        [5. , 2.3, 3.3, 1. ],\n",
              "        [5.6, 2.7, 4.2, 1.3],\n",
              "        [5.7, 3. , 4.2, 1.2],\n",
              "        [5.7, 2.9, 4.2, 1.3],\n",
              "        [6.2, 2.9, 4.3, 1.3],\n",
              "        [5.1, 2.5, 3. , 1.1],\n",
              "        [5.7, 2.8, 4.1, 1.3],\n",
              "        [6.3, 3.3, 6. , 2.5],\n",
              "        [5.8, 2.7, 5.1, 1.9],\n",
              "        [7.1, 3. , 5.9, 2.1],\n",
              "        [6.3, 2.9, 5.6, 1.8],\n",
              "        [6.5, 3. , 5.8, 2.2],\n",
              "        [7.6, 3. , 6.6, 2.1],\n",
              "        [4.9, 2.5, 4.5, 1.7],\n",
              "        [7.3, 2.9, 6.3, 1.8],\n",
              "        [6.7, 2.5, 5.8, 1.8],\n",
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              "        [6.5, 3.2, 5.1, 2. ],\n",
              "        [6.4, 2.7, 5.3, 1.9],\n",
              "        [6.8, 3. , 5.5, 2.1],\n",
              "        [5.7, 2.5, 5. , 2. ],\n",
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              "        [6. , 2.2, 5. , 1.5],\n",
              "        [6.9, 3.2, 5.7, 2.3],\n",
              "        [5.6, 2.8, 4.9, 2. ],\n",
              "        [7.7, 2.8, 6.7, 2. ],\n",
              "        [6.3, 2.7, 4.9, 1.8],\n",
              "        [6.7, 3.3, 5.7, 2.1],\n",
              "        [7.2, 3.2, 6. , 1.8],\n",
              "        [6.2, 2.8, 4.8, 1.8],\n",
              "        [6.1, 3. , 4.9, 1.8],\n",
              "        [6.4, 2.8, 5.6, 2.1],\n",
              "        [7.2, 3. , 5.8, 1.6],\n",
              "        [7.4, 2.8, 6.1, 1.9],\n",
              "        [7.9, 3.8, 6.4, 2. ],\n",
              "        [6.4, 2.8, 5.6, 2.2],\n",
              "        [6.3, 2.8, 5.1, 1.5],\n",
              "        [6.1, 2.6, 5.6, 1.4],\n",
              "        [7.7, 3. , 6.1, 2.3],\n",
              "        [6.3, 3.4, 5.6, 2.4],\n",
              "        [6.4, 3.1, 5.5, 1.8],\n",
              "        [6. , 3. , 4.8, 1.8],\n",
              "        [6.9, 3.1, 5.4, 2.1],\n",
              "        [6.7, 3.1, 5.6, 2.4],\n",
              "        [6.9, 3.1, 5.1, 2.3],\n",
              "        [5.8, 2.7, 5.1, 1.9],\n",
              "        [6.8, 3.2, 5.9, 2.3],\n",
              "        [6.7, 3.3, 5.7, 2.5],\n",
              "        [6.7, 3. , 5.2, 2.3],\n",
              "        [6.3, 2.5, 5. , 1.9],\n",
              "        [6.5, 3. , 5.2, 2. ],\n",
              "        [6.2, 3.4, 5.4, 2.3],\n",
              "        [5.9, 3. , 5.1, 1.8]]),\n",
              " 'feature_names': ['sepal length (cm)',\n",
              "  'sepal width (cm)',\n",
              "  'petal length (cm)',\n",
              "  'petal width (cm)'],\n",
              " 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/iris.csv',\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
              " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10')}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqCindiURz70",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "63adaf1b-7739-48ee-d132-2caa0a4cbfe6"
      },
      "source": [
        "type(iris)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0CVd_PFToMk",
        "colab_type": "text"
      },
      "source": [
        "![Petalo - Sepalo](https://drive.google.com/uc?export=view&id=1GKh6nxuVIgP0Mnq68iNiKtIgy2S-N340)\n",
        "\n",
        "![IRIS](https://drive.google.com/uc?export=view&id=1H8yh3lP0Hx4yfx2LdI515uvoGw4EcrDP)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfHrPCX5qo8r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "634af61c-67f1-44e5-fd34-241063973a5b"
      },
      "source": [
        "# Medidas desciptivas\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "\n",
        "# import some data to play with\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target       # labels\n",
        "\n",
        "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
        "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
        "\n",
        "plt.figure(2, figsize=(8, 6))\n",
        "plt.clf()\n",
        "\n",
        "# Plot the training points\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n",
        "            edgecolor='k')\n",
        "plt.xlabel('Sepal length')\n",
        "plt.ylabel('Sepal width')\n",
        "\n",
        "plt.xlim(x_min, x_max)\n",
        "plt.ylim(y_min, y_max)\n",
        "plt.xticks(())\n",
        "plt.yticks(())\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKQ5xP0T7Z3_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "94d67bf2-d8fa-4426-ca58-a3e8beac7a4f"
      },
      "source": [
        "import numpy as np\n",
        "np.corrcoef(X[:, 0], X[:, 1]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        , -0.11756978],\n",
              "       [-0.11756978,  1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQfqe9gg5-K_",
        "colab_type": "text"
      },
      "source": [
        "### Medidas de centralidad y dispersión"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPknBfNWovne",
        "colab_type": "text"
      },
      "source": [
        "* Centralidad: permiten establecer valores alrededor de los cuales se ubican/agrupan los datos\n",
        "> Media, Moda,  Mínimo, Mediana, Máximo, percentiles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpHAMPKyr2ZL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "35b53dbf-8a6d-4c75-eda1-3bbbba9a6498"
      },
      "source": [
        "X[:, 0].mean(), X[:, 0].min(), X[:, 0].max()   # Sepal_Length [cm]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5.843333333333334, 4.3, 7.9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAj1Z_-IpVOW",
        "colab_type": "text"
      },
      "source": [
        "* Dispersión: indican cómo están distribuidos o \"regados\" los datos respecto de alguna de las medidas de centralidad\n",
        "> Varianza, desviación estándar, rango, IQR, coeficiente de variación\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYZjf524pYib",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "ccef63a5-5e25-417c-e070-1b382111e300"
      },
      "source": [
        "X[:, 0].std(), X[:, 0].var(), X[:, 0].std()/X[:, 0].mean() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8253012917851409, 0.6811222222222223, 0.14123809899346393)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQLVQESO6D5A",
        "colab_type": "text"
      },
      "source": [
        "### Medidas de centralidad y dispersión con numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOEd_QS85GC8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d3f6b2a7-4357-4c2e-dc05-bc58c10c39a2"
      },
      "source": [
        "# Medidas de centralidad y dispersión con numpy\n",
        "import numpy as np\n",
        "np.min(X[:, 0]), np.max(X[:, 0]), np.mean(X[:, 0]), np.std(X[:, 0]), np.var(X[:, 0]), np.std(X[:, 0])/np.mean(X[:, 0]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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q+1C7DIuvQ7G/gzzKfpFrYwXiY/OAqQVtafJ7l7h+FVo+zw+bqGNytVOXYbwmiZHfIU4MTtooir+hx+hXPpYNtdcmdecSjVyacURonVhj5y7983VIHtLI1ZTu45HJnKsL9r/BGchJ2blfwQbZGJkxCvmE5f8Da6vehjHsmO8axtqB+bphrF2YzxuG0Y+TkPmUvfoVHIvMMvmsZdIUM5EvImg+JTbMrK16G8awY75rGGsH5uuGsXZhPm8YRhkbIK8sXeFbYR/gbPIXyzMMwzAMwzAMwzAMwzBGmIK8hjRpsGIYhmEYhmEYhmEYhmEYhmEYhmEYhmEYhmEYRlX+B4NU6tWiVrj8AAAAAElFTkSuQmCC\n",
            "text/latex": "$$\\left ( 4.3, \\quad 7.9, \\quad 5.843333333333334, \\quad 0.8253012917851409, \\quad 0.6811222222222223, \\quad 0.14123809899346393\\right )$$",
            "text/plain": [
              "(4.3, 7.9, 5.843333333333334, 0.8253012917851409, 0.6811222222222223, 0.141238\n",
              "09899346393)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoFnPKlx71hU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "4eaca969-b719-47f0-aa1c-2ae092f82ddb"
      },
      "source": [
        "from scipy import stats # importando scipy.stats\n",
        "import numpy as np\n",
        "np.median(X[:, 0]), stats.mode(X[:, 0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5.8, ModeResult(mode=array([5.]), count=array([10])))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lpJE9WJYtVYS",
        "colab_type": "text"
      },
      "source": [
        "### Gráficos\n",
        "Los gráficos son la representación visual de los datos; permiten observar, analizar, comparar y generar información acerca del problema o fenómeno de interés.\n",
        "Un buen gráfico estadístico se caracteriza por:\n",
        "* Tener título, etiquetas de los ejes, una clave\n",
        "* Ser simple: evitar elementos distractores que \"desorienten\" al lector\n",
        "* Manejo adecuado de escalas\n",
        "* Sombreado o coloreado uniforme\n",
        "* Ser bidimensional (en lo posible)\n",
        "\n",
        "#### Ejemplo: Uno de los \"mejores\" gráficos de la historia\n",
        "![texto alternativo](https://media.nationalgeographic.org/assets/photos/000/343/34313.jpg)\n",
        "\n",
        "## Gráficos Estadísticos\n",
        "### Histograma y Densidad"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtTS4Qy1794K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "0db3d3b3-bf64-4d94-e89c-13f6d2e50f95"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(131)\n",
        "sns.distplot((X[:, 0]), hist=True, kde=True, \n",
        "             bins=int(180/10), color = 'darkblue', \n",
        "             hist_kws={'edgecolor':'black'},\n",
        "             kde_kws={'linewidth': 4})\n",
        "plt.title(\"Kernel - Densidad\")\n",
        "plt.xlabel(\"sepal length\")\n",
        "plt.ylabel(\"Densidad\");\n",
        "\n",
        "plt.subplot(132)\n",
        "sns.distplot((X[:, 1]), hist=True, kde=True, \n",
        "             bins=int(180/10), color = 'darkgreen', \n",
        "             hist_kws={'edgecolor':'black'},\n",
        "             kde_kws={'linewidth': 4})\n",
        "plt.title(\"Kernel - Densidad\")\n",
        "plt.xlabel(\"sepal width\")\n",
        "plt.ylabel(\"Densidad\");\n",
        "\n",
        "plt.subplot(133)\n",
        "sns.distplot((iris.data[:, 2]), hist=True, kde=True, \n",
        "             bins=int(180/10), color = 'darkred', \n",
        "             hist_kws={'edgecolor':'black'},\n",
        "             kde_kws={'linewidth': 4})\n",
        "plt.title(\"Kernel - Densidad\")\n",
        "plt.xlabel(\"petal length\")\n",
        "plt.ylabel(\"Densidad\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6XhEbCQ-8Y5",
        "colab_type": "text"
      },
      "source": [
        "### Data Frame (pandas)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbUvTwaA_A9J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "4dde8e97-3b22-4329-e7eb-edfe41831a19"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "iris_1 = pd.read_csv(\"https://raw.githubusercontent.com/toneloy/data/master/iris.csv\")\n",
        "iris_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDgIdzwO_cVK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "03f9d837-94c2-4ed5-ec98-8a91d51fd345"
      },
      "source": [
        "iris_1.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.057333      3.758000     1.199333\n",
              "std        0.828066     0.435866      1.765298     0.762238\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsK7tbZLAAyJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "a2b18e7f-b572-4a9b-c1e1-fcd2a288e352"
      },
      "source": [
        "from plotnine import *\n",
        "ggplot\n",
        "(ggplot(iris_1) +\n",
        "  geom_boxplot(aes(x=\"species\", y=\"sepal_length\", fill = 'species')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363291971660484)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JITjXS7bAmNd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "47ad03f1-46ba-462e-c7b3-c9dd64e2a226"
      },
      "source": [
        "from plotnine import *\n",
        "(ggplot(iris_1) +\n",
        "  geom_boxplot(aes(x=\"species\", y=\"petal_length\", color = 'species')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363291986244152)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5L6vefyAyI5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "605cc278-fe98-48d2-de43-6bad347a5486"
      },
      "source": [
        "from plotnine import *\n",
        "(ggplot(iris_1) +\n",
        "  geom_boxplot(aes(x=\"species\", y=\"sepal_width\", fill = 'species')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363291989632112)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DOlM_9WA_u2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "4a081a11-fea8-47b8-dec4-f06435227227"
      },
      "source": [
        "from plotnine import *\n",
        "\n",
        "(ggplot(iris_1) +\n",
        "  geom_boxplot(aes(x=0, y=\"petal_length\")) +\n",
        "  labs(x=\"all iris\") +\n",
        "  theme(\n",
        "    axis_text_x=element_blank(),\n",
        "    axis_ticks_minor_x=element_blank(),\n",
        "    axis_ticks_major_x=element_blank()\n",
        "  ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8744865137956)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDyXowVsE2jL",
        "colab_type": "text"
      },
      "source": [
        "### Tablas de Frecuencias Variables Categóricas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Fuk6iC5EzvW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "2662f7be-17fa-4559-dee1-949288fe6db0"
      },
      "source": [
        "#Graficas por clases\n",
        "count_classes = pd.value_counts(iris_1.species, sort = True)\n",
        "count_classes.plot.bar()\n",
        "plt.title(\"Conteo por estado ENSAMBLE\")\n",
        "plt.xlabel(\"Cluster\")\n",
        "plt.ylabel(\"Frecuencia\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rg5TEDvFbKu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "4abecb31-1fe6-430d-b84f-c0776ee49b9c"
      },
      "source": [
        "count_classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "versicolor    50\n",
              "setosa        50\n",
              "virginica     50\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-SIcI0lFgCL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "outputId": "bb3f20a3-4414-4513-f1db-07cc51c35306"
      },
      "source": [
        "grouped_data = iris_1.groupby('species')\n",
        "grouped_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">sepal_length</th>\n",
              "      <th colspan=\"8\" halign=\"left\">sepal_width</th>\n",
              "      <th colspan=\"8\" halign=\"left\">petal_length</th>\n",
              "      <th colspan=\"8\" halign=\"left\">petal_width</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>species</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>setosa</th>\n",
              "      <td>50.0</td>\n",
              "      <td>5.006</td>\n",
              "      <td>0.352490</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4.800</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.8</td>\n",
              "      <td>50.0</td>\n",
              "      <td>3.428</td>\n",
              "      <td>0.379064</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.200</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.675</td>\n",
              "      <td>4.4</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.462</td>\n",
              "      <td>0.173664</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1.575</td>\n",
              "      <td>1.9</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.246</td>\n",
              "      <td>0.105386</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>versicolor</th>\n",
              "      <td>50.0</td>\n",
              "      <td>5.936</td>\n",
              "      <td>0.516171</td>\n",
              "      <td>4.9</td>\n",
              "      <td>5.600</td>\n",
              "      <td>5.9</td>\n",
              "      <td>6.3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2.770</td>\n",
              "      <td>0.313798</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.525</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.000</td>\n",
              "      <td>3.4</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4.260</td>\n",
              "      <td>0.469911</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.35</td>\n",
              "      <td>4.600</td>\n",
              "      <td>5.1</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.326</td>\n",
              "      <td>0.197753</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>virginica</th>\n",
              "      <td>50.0</td>\n",
              "      <td>6.588</td>\n",
              "      <td>0.635880</td>\n",
              "      <td>4.9</td>\n",
              "      <td>6.225</td>\n",
              "      <td>6.5</td>\n",
              "      <td>6.9</td>\n",
              "      <td>7.9</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2.974</td>\n",
              "      <td>0.322497</td>\n",
              "      <td>2.2</td>\n",
              "      <td>2.800</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.175</td>\n",
              "      <td>3.8</td>\n",
              "      <td>50.0</td>\n",
              "      <td>5.552</td>\n",
              "      <td>0.551895</td>\n",
              "      <td>4.5</td>\n",
              "      <td>5.1</td>\n",
              "      <td>5.55</td>\n",
              "      <td>5.875</td>\n",
              "      <td>6.9</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2.026</td>\n",
              "      <td>0.274650</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           sepal_length                               ... petal_width                    \n",
              "                  count   mean       std  min    25%  ...         min  25%  50%  75%  max\n",
              "species                                               ...                                \n",
              "setosa             50.0  5.006  0.352490  4.3  4.800  ...         0.1  0.2  0.2  0.3  0.6\n",
              "versicolor         50.0  5.936  0.516171  4.9  5.600  ...         1.0  1.2  1.3  1.5  1.8\n",
              "virginica          50.0  6.588  0.635880  4.9  6.225  ...         1.4  1.8  2.0  2.3  2.5\n",
              "\n",
              "[3 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-Zj-pyOYbcT",
        "colab_type": "text"
      },
      "source": [
        "### Filtro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeopWLhPX3hw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "226e9ab1-0e91-47b7-caa9-ab44004663ec"
      },
      "source": [
        "iris_mod = iris_1[iris_1.petal_length > 4]\n",
        "iris_mod.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>6.5</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length  sepal_width  petal_length  petal_width     species\n",
              "50           7.0          3.2           4.7          1.4  versicolor\n",
              "51           6.4          3.2           4.5          1.5  versicolor\n",
              "52           6.9          3.1           4.9          1.5  versicolor\n",
              "54           6.5          2.8           4.6          1.5  versicolor\n",
              "55           5.7          2.8           4.5          1.3  versicolor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FMpGLrtYYGw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "93654e4d-8cb1-4ddf-adaf-7fc366190ffa"
      },
      "source": [
        "#Graficas por clases\n",
        "count_classes = pd.value_counts(iris_mod.species, sort = True)\n",
        "count_classes.plot.bar()\n",
        "plt.title(\"Conteo por estado ENSAMBLE\")\n",
        "plt.xlabel(\"Cluster\")\n",
        "plt.ylabel(\"Frecuencia\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_N3JUBUfY0H5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "d4dce187-104e-47c3-fc65-de677ffd603d"
      },
      "source": [
        "count_classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "virginica     50\n",
              "versicolor    34\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sM7vUziA8z2E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "c17f47ec-a04a-4c9e-f435-3f142bd17a7c"
      },
      "source": [
        "iris_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ct_TmfP8vt3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "ee1660f6-a5d1-4893-dc6c-47f8dded9311"
      },
      "source": [
        "iris_mod_1 = iris_1[iris_1.species == \"setosa\"]\n",
        "iris_mod_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YXV8ZFq9OrI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c4de0fb6-014c-4be2-f4a7-77b0ce893bd1"
      },
      "source": [
        "import numpy as np\n",
        "np.corrcoef(iris_mod_1.sepal_length, iris_mod_1.sepal_width) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.74254669],\n",
              "       [0.74254669, 1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2qL-XLJ9su_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "4f9d63de-6de1-46b2-e805-f04c6aeeb9fa"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.subplot(131)\n",
        "plt.scatter(iris_mod_1.sepal_length, iris_mod_1.sepal_width, \n",
        "            edgecolor='k')\n",
        "plt.xlabel('Sepal length')\n",
        "plt.ylabel('Sepal width')\n",
        "plt.title('Setosa')\n",
        "\n",
        "plt.subplot(132)\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n",
        "            edgecolor='k')\n",
        "plt.xlabel('Sepal length')\n",
        "plt.ylabel('Sepal width')\n",
        "plt.title('All flowers')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'All flowers')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZFl2Sj3-x7_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "c95e802e-2cc9-4f60-9a16-ba3ae684bd5e"
      },
      "source": [
        "np.cov(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.28 , 1.52 , 1.2  , ..., 2.8  , 2.24 , 2.32 ],\n",
              "       [1.52 , 1.805, 1.425, ..., 3.325, 2.66 , 2.755],\n",
              "       [1.2  , 1.425, 1.125, ..., 2.625, 2.1  , 2.175],\n",
              "       ...,\n",
              "       [2.8  , 3.325, 2.625, ..., 6.125, 4.9  , 5.075],\n",
              "       [2.24 , 2.66 , 2.1  , ..., 4.9  , 3.92 , 4.06 ],\n",
              "       [2.32 , 2.755, 2.175, ..., 5.075, 4.06 , 4.205]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Gpted1G_mcE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "7ebac170-0f73-4c6f-db35-f1e6d2d321e1"
      },
      "source": [
        "np.cov(iris_1.sepal_length, iris_1.sepal_width)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.68569351, -0.042434  ],\n",
              "       [-0.042434  ,  0.18997942]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZncw6nCOfOp",
        "colab_type": "text"
      },
      "source": [
        "## Time series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqN33dKNNoHD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "31645a79-2a98-4584-8183-328c02d19af7"
      },
      "source": [
        "from vega_datasets import data\n",
        "stocks = data.stocks()\n",
        "\n",
        "import altair as alt\n",
        "alt.Chart(stocks).mark_line().encode(\n",
        "  x='date:T',\n",
        "  y='price',\n",
        "  color='symbol'\n",
        ").interactive(bind_y=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.Chart(...)"
            ],
            "text/html": [
              "\n",
              "<div id=\"altair-viz-fa439efe273f46b4a2da72128270188e\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-fa439efe273f46b4a2da72128270188e\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-fa439efe273f46b4a2da72128270188e\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function loadScript(lib) {\n",
              "      return new Promise(function(resolve, reject) {\n",
              "        var s = document.createElement('script');\n",
              "        s.src = paths[lib];\n",
              "        s.async = true;\n",
              "        s.onload = () => resolve(paths[lib]);\n",
              "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "      });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else if (typeof vegaEmbed === \"function\") {\n",
              "      displayChart(vegaEmbed);\n",
              "    } else {\n",
              "      loadScript(\"vega\")\n",
              "        .then(() => loadScript(\"vega-lite\"))\n",
              "        .then(() => loadScript(\"vega-embed\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-96e857a61c6b623bafe23440d582a500\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"symbol\"}, \"x\": {\"type\": \"temporal\", \"field\": \"date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"price\"}}, \"selection\": {\"selector004\": {\"type\": \"interval\", \"bind\": \"scales\", \"encodings\": [\"x\"]}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-96e857a61c6b623bafe23440d582a500\": [{\"symbol\": \"MSFT\", \"date\": \"2000-01-01T00:00:00\", \"price\": 39.81}, {\"symbol\": \"MSFT\", \"date\": \"2000-02-01T00:00:00\", \"price\": 36.35}, {\"symbol\": \"MSFT\", \"date\": \"2000-03-01T00:00:00\", \"price\": 43.22}, {\"symbol\": \"MSFT\", \"date\": \"2000-04-01T00:00:00\", \"price\": 28.37}, {\"symbol\": \"MSFT\", \"date\": \"2000-05-01T00:00:00\", \"price\": 25.45}, {\"symbol\": \"MSFT\", \"date\": \"2000-06-01T00:00:00\", \"price\": 32.54}, {\"symbol\": \"MSFT\", \"date\": \"2000-07-01T00:00:00\", \"price\": 28.4}, {\"symbol\": \"MSFT\", \"date\": \"2000-08-01T00:00:00\", \"price\": 28.4}, {\"symbol\": \"MSFT\", \"date\": \"2000-09-01T00:00:00\", \"price\": 24.53}, {\"symbol\": \"MSFT\", \"date\": \"2000-10-01T00:00:00\", \"price\": 28.02}, {\"symbol\": \"MSFT\", \"date\": \"2000-11-01T00:00:00\", \"price\": 23.34}, {\"symbol\": \"MSFT\", \"date\": \"2000-12-01T00:00:00\", \"price\": 17.65}, {\"symbol\": \"MSFT\", \"date\": \"2001-01-01T00:00:00\", \"price\": 24.84}, {\"symbol\": \"MSFT\", \"date\": \"2001-02-01T00:00:00\", \"price\": 24.0}, {\"symbol\": \"MSFT\", \"date\": \"2001-03-01T00:00:00\", \"price\": 22.25}, {\"symbol\": \"MSFT\", \"date\": \"2001-04-01T00:00:00\", \"price\": 27.56}, {\"symbol\": \"MSFT\", \"date\": \"2001-05-01T00:00:00\", \"price\": 28.14}, {\"symbol\": \"MSFT\", \"date\": \"2001-06-01T00:00:00\", \"price\": 29.7}, 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{\"symbol\": \"MSFT\", \"date\": \"2002-08-01T00:00:00\", \"price\": 19.97}, {\"symbol\": \"MSFT\", \"date\": \"2002-09-01T00:00:00\", \"price\": 17.79}, {\"symbol\": \"MSFT\", \"date\": \"2002-10-01T00:00:00\", \"price\": 21.75}, {\"symbol\": \"MSFT\", \"date\": \"2002-11-01T00:00:00\", \"price\": 23.46}, {\"symbol\": \"MSFT\", \"date\": \"2002-12-01T00:00:00\", \"price\": 21.03}, {\"symbol\": \"MSFT\", \"date\": \"2003-01-01T00:00:00\", \"price\": 19.31}, {\"symbol\": \"MSFT\", \"date\": \"2003-02-01T00:00:00\", \"price\": 19.34}, {\"symbol\": \"MSFT\", \"date\": \"2003-03-01T00:00:00\", \"price\": 19.76}, {\"symbol\": \"MSFT\", \"date\": \"2003-04-01T00:00:00\", \"price\": 20.87}, {\"symbol\": \"MSFT\", \"date\": \"2003-05-01T00:00:00\", \"price\": 20.09}, {\"symbol\": \"MSFT\", \"date\": \"2003-06-01T00:00:00\", \"price\": 20.93}, {\"symbol\": \"MSFT\", \"date\": \"2003-07-01T00:00:00\", \"price\": 21.56}, {\"symbol\": \"MSFT\", \"date\": \"2003-08-01T00:00:00\", \"price\": 21.65}, 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\"date\": \"2000-10-01T00:00:00\", \"price\": 9.78}, {\"symbol\": \"AAPL\", \"date\": \"2000-11-01T00:00:00\", \"price\": 8.25}, {\"symbol\": \"AAPL\", \"date\": \"2000-12-01T00:00:00\", \"price\": 7.44}, {\"symbol\": \"AAPL\", \"date\": \"2001-01-01T00:00:00\", \"price\": 10.81}, {\"symbol\": \"AAPL\", \"date\": \"2001-02-01T00:00:00\", \"price\": 9.12}, {\"symbol\": \"AAPL\", \"date\": \"2001-03-01T00:00:00\", \"price\": 11.03}, {\"symbol\": \"AAPL\", \"date\": \"2001-04-01T00:00:00\", \"price\": 12.74}, {\"symbol\": \"AAPL\", \"date\": \"2001-05-01T00:00:00\", \"price\": 9.98}, {\"symbol\": \"AAPL\", \"date\": \"2001-06-01T00:00:00\", \"price\": 11.62}, {\"symbol\": \"AAPL\", \"date\": \"2001-07-01T00:00:00\", \"price\": 9.4}, {\"symbol\": \"AAPL\", \"date\": \"2001-08-01T00:00:00\", \"price\": 9.27}, {\"symbol\": \"AAPL\", \"date\": \"2001-09-01T00:00:00\", \"price\": 7.76}, {\"symbol\": \"AAPL\", \"date\": \"2001-10-01T00:00:00\", \"price\": 8.78}, {\"symbol\": \"AAPL\", \"date\": \"2001-11-01T00:00:00\", \"price\": 10.65}, {\"symbol\": \"AAPL\", \"date\": \"2001-12-01T00:00:00\", \"price\": 10.95}, {\"symbol\": \"AAPL\", \"date\": \"2002-01-01T00:00:00\", \"price\": 12.36}, {\"symbol\": \"AAPL\", \"date\": \"2002-02-01T00:00:00\", \"price\": 10.85}, {\"symbol\": \"AAPL\", \"date\": \"2002-03-01T00:00:00\", \"price\": 11.84}, {\"symbol\": \"AAPL\", \"date\": \"2002-04-01T00:00:00\", \"price\": 12.14}, {\"symbol\": \"AAPL\", \"date\": \"2002-05-01T00:00:00\", \"price\": 11.65}, {\"symbol\": \"AAPL\", \"date\": \"2002-06-01T00:00:00\", \"price\": 8.86}, {\"symbol\": \"AAPL\", \"date\": \"2002-07-01T00:00:00\", \"price\": 7.63}, {\"symbol\": \"AAPL\", \"date\": \"2002-08-01T00:00:00\", \"price\": 7.38}, {\"symbol\": \"AAPL\", \"date\": \"2002-09-01T00:00:00\", \"price\": 7.25}, {\"symbol\": \"AAPL\", \"date\": \"2002-10-01T00:00:00\", \"price\": 8.03}, {\"symbol\": \"AAPL\", \"date\": \"2002-11-01T00:00:00\", \"price\": 7.75}, {\"symbol\": \"AAPL\", \"date\": \"2002-12-01T00:00:00\", \"price\": 7.16}, {\"symbol\": \"AAPL\", \"date\": \"2003-01-01T00:00:00\", \"price\": 7.18}, {\"symbol\": \"AAPL\", \"date\": \"2003-02-01T00:00:00\", \"price\": 7.51}, {\"symbol\": \"AAPL\", \"date\": \"2003-03-01T00:00:00\", \"price\": 7.07}, {\"symbol\": \"AAPL\", \"date\": \"2003-04-01T00:00:00\", \"price\": 7.11}, {\"symbol\": \"AAPL\", \"date\": \"2003-05-01T00:00:00\", \"price\": 8.98}, {\"symbol\": \"AAPL\", \"date\": \"2003-06-01T00:00:00\", \"price\": 9.53}, {\"symbol\": \"AAPL\", \"date\": \"2003-07-01T00:00:00\", \"price\": 10.54}, {\"symbol\": \"AAPL\", \"date\": \"2003-08-01T00:00:00\", \"price\": 11.31}, {\"symbol\": \"AAPL\", \"date\": \"2003-09-01T00:00:00\", \"price\": 10.36}, {\"symbol\": \"AAPL\", \"date\": \"2003-10-01T00:00:00\", \"price\": 11.44}, {\"symbol\": \"AAPL\", \"date\": \"2003-11-01T00:00:00\", \"price\": 10.45}, {\"symbol\": \"AAPL\", \"date\": \"2003-12-01T00:00:00\", \"price\": 10.69}, {\"symbol\": \"AAPL\", \"date\": \"2004-01-01T00:00:00\", \"price\": 11.28}, {\"symbol\": \"AAPL\", \"date\": \"2004-02-01T00:00:00\", \"price\": 11.96}, {\"symbol\": \"AAPL\", \"date\": \"2004-03-01T00:00:00\", \"price\": 13.52}, {\"symbol\": \"AAPL\", \"date\": \"2004-04-01T00:00:00\", \"price\": 12.89}, {\"symbol\": \"AAPL\", \"date\": \"2004-05-01T00:00:00\", \"price\": 14.03}, {\"symbol\": \"AAPL\", \"date\": \"2004-06-01T00:00:00\", \"price\": 16.27}, {\"symbol\": \"AAPL\", \"date\": \"2004-07-01T00:00:00\", \"price\": 16.17}, {\"symbol\": \"AAPL\", \"date\": \"2004-08-01T00:00:00\", \"price\": 17.25}, {\"symbol\": \"AAPL\", \"date\": \"2004-09-01T00:00:00\", \"price\": 19.38}, {\"symbol\": \"AAPL\", \"date\": \"2004-10-01T00:00:00\", \"price\": 26.2}, {\"symbol\": \"AAPL\", \"date\": \"2004-11-01T00:00:00\", \"price\": 33.53}, {\"symbol\": \"AAPL\", \"date\": \"2004-12-01T00:00:00\", \"price\": 32.2}, {\"symbol\": \"AAPL\", \"date\": \"2005-01-01T00:00:00\", \"price\": 38.45}, {\"symbol\": \"AAPL\", \"date\": \"2005-02-01T00:00:00\", \"price\": 44.86}, {\"symbol\": \"AAPL\", \"date\": \"2005-03-01T00:00:00\", \"price\": 41.67}, {\"symbol\": \"AAPL\", \"date\": \"2005-04-01T00:00:00\", \"price\": 36.06}, {\"symbol\": \"AAPL\", \"date\": \"2005-05-01T00:00:00\", \"price\": 39.76}, {\"symbol\": \"AAPL\", \"date\": \"2005-06-01T00:00:00\", \"price\": 36.81}, {\"symbol\": \"AAPL\", \"date\": \"2005-07-01T00:00:00\", \"price\": 42.65}, {\"symbol\": \"AAPL\", \"date\": \"2005-08-01T00:00:00\", \"price\": 46.89}, {\"symbol\": \"AAPL\", \"date\": \"2005-09-01T00:00:00\", \"price\": 53.61}, {\"symbol\": \"AAPL\", \"date\": \"2005-10-01T00:00:00\", \"price\": 57.59}, {\"symbol\": \"AAPL\", \"date\": \"2005-11-01T00:00:00\", \"price\": 67.82}, {\"symbol\": \"AAPL\", \"date\": \"2005-12-01T00:00:00\", \"price\": 71.89}, {\"symbol\": \"AAPL\", \"date\": \"2006-01-01T00:00:00\", \"price\": 75.51}, {\"symbol\": \"AAPL\", \"date\": \"2006-02-01T00:00:00\", \"price\": 68.49}, {\"symbol\": \"AAPL\", \"date\": \"2006-03-01T00:00:00\", \"price\": 62.72}, {\"symbol\": \"AAPL\", \"date\": \"2006-04-01T00:00:00\", \"price\": 70.39}, {\"symbol\": \"AAPL\", \"date\": \"2006-05-01T00:00:00\", \"price\": 59.77}, {\"symbol\": \"AAPL\", \"date\": \"2006-06-01T00:00:00\", \"price\": 57.27}, {\"symbol\": \"AAPL\", \"date\": \"2006-07-01T00:00:00\", \"price\": 67.96}, {\"symbol\": \"AAPL\", \"date\": \"2006-08-01T00:00:00\", \"price\": 67.85}, {\"symbol\": \"AAPL\", \"date\": \"2006-09-01T00:00:00\", \"price\": 76.98}, {\"symbol\": \"AAPL\", \"date\": \"2006-10-01T00:00:00\", \"price\": 81.08}, {\"symbol\": \"AAPL\", \"date\": \"2006-11-01T00:00:00\", \"price\": 91.66}, {\"symbol\": \"AAPL\", \"date\": \"2006-12-01T00:00:00\", \"price\": 84.84}, {\"symbol\": \"AAPL\", \"date\": \"2007-01-01T00:00:00\", \"price\": 85.73}, {\"symbol\": \"AAPL\", \"date\": \"2007-02-01T00:00:00\", \"price\": 84.61}, {\"symbol\": \"AAPL\", \"date\": \"2007-03-01T00:00:00\", \"price\": 92.91}, {\"symbol\": \"AAPL\", \"date\": \"2007-04-01T00:00:00\", \"price\": 99.8}, {\"symbol\": \"AAPL\", \"date\": \"2007-05-01T00:00:00\", \"price\": 121.19}, {\"symbol\": \"AAPL\", \"date\": \"2007-06-01T00:00:00\", \"price\": 122.04}, {\"symbol\": \"AAPL\", \"date\": \"2007-07-01T00:00:00\", \"price\": 131.76}, {\"symbol\": \"AAPL\", \"date\": \"2007-08-01T00:00:00\", \"price\": 138.48}, {\"symbol\": \"AAPL\", \"date\": \"2007-09-01T00:00:00\", \"price\": 153.47}, {\"symbol\": \"AAPL\", \"date\": \"2007-10-01T00:00:00\", \"price\": 189.95}, {\"symbol\": \"AAPL\", \"date\": \"2007-11-01T00:00:00\", \"price\": 182.22}, {\"symbol\": \"AAPL\", \"date\": \"2007-12-01T00:00:00\", \"price\": 198.08}, {\"symbol\": \"AAPL\", \"date\": \"2008-01-01T00:00:00\", \"price\": 135.36}, {\"symbol\": \"AAPL\", \"date\": \"2008-02-01T00:00:00\", \"price\": 125.02}, {\"symbol\": \"AAPL\", \"date\": \"2008-03-01T00:00:00\", \"price\": 143.5}, {\"symbol\": \"AAPL\", \"date\": \"2008-04-01T00:00:00\", \"price\": 173.95}, {\"symbol\": \"AAPL\", \"date\": \"2008-05-01T00:00:00\", \"price\": 188.75}, {\"symbol\": \"AAPL\", \"date\": \"2008-06-01T00:00:00\", \"price\": 167.44}, {\"symbol\": \"AAPL\", \"date\": \"2008-07-01T00:00:00\", \"price\": 158.95}, {\"symbol\": \"AAPL\", \"date\": \"2008-08-01T00:00:00\", \"price\": 169.53}, {\"symbol\": \"AAPL\", \"date\": \"2008-09-01T00:00:00\", \"price\": 113.66}, {\"symbol\": \"AAPL\", \"date\": \"2008-10-01T00:00:00\", \"price\": 107.59}, {\"symbol\": \"AAPL\", \"date\": \"2008-11-01T00:00:00\", \"price\": 92.67}, {\"symbol\": \"AAPL\", \"date\": \"2008-12-01T00:00:00\", \"price\": 85.35}, {\"symbol\": \"AAPL\", \"date\": \"2009-01-01T00:00:00\", \"price\": 90.13}, {\"symbol\": \"AAPL\", \"date\": \"2009-02-01T00:00:00\", \"price\": 89.31}, {\"symbol\": \"AAPL\", \"date\": \"2009-03-01T00:00:00\", \"price\": 105.12}, {\"symbol\": \"AAPL\", \"date\": \"2009-04-01T00:00:00\", \"price\": 125.83}, {\"symbol\": \"AAPL\", \"date\": \"2009-05-01T00:00:00\", \"price\": 135.81}, {\"symbol\": \"AAPL\", \"date\": \"2009-06-01T00:00:00\", \"price\": 142.43}, {\"symbol\": \"AAPL\", \"date\": \"2009-07-01T00:00:00\", \"price\": 163.39}, {\"symbol\": \"AAPL\", \"date\": \"2009-08-01T00:00:00\", \"price\": 168.21}, {\"symbol\": \"AAPL\", \"date\": \"2009-09-01T00:00:00\", \"price\": 185.35}, {\"symbol\": \"AAPL\", \"date\": \"2009-10-01T00:00:00\", \"price\": 188.5}, {\"symbol\": \"AAPL\", \"date\": \"2009-11-01T00:00:00\", \"price\": 199.91}, {\"symbol\": \"AAPL\", \"date\": \"2009-12-01T00:00:00\", \"price\": 210.73}, {\"symbol\": \"AAPL\", \"date\": \"2010-01-01T00:00:00\", \"price\": 192.06}, {\"symbol\": \"AAPL\", \"date\": \"2010-02-01T00:00:00\", \"price\": 204.62}, {\"symbol\": \"AAPL\", \"date\": \"2010-03-01T00:00:00\", \"price\": 223.02}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ]
          },
          "metadata": {
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          "execution_count": 11
        }
      ]
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      "cell_type": "code",
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      "source": [
        "stocks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
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              "<IPython.core.display.HTML object>"
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              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>symbol</th>\n",
              "      <th>date</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>2000-01-01</td>\n",
              "      <td>39.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>2000-02-01</td>\n",
              "      <td>36.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>2000-03-01</td>\n",
              "      <td>43.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>2000-04-01</td>\n",
              "      <td>28.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MSFT</td>\n",
              "      <td>2000-05-01</td>\n",
              "      <td>25.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2009-11-01</td>\n",
              "      <td>199.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2009-12-01</td>\n",
              "      <td>210.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2010-01-01</td>\n",
              "      <td>192.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>558</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2010-02-01</td>\n",
              "      <td>204.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>559</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2010-03-01</td>\n",
              "      <td>223.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>560 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    symbol       date   price\n",
              "0     MSFT 2000-01-01   39.81\n",
              "1     MSFT 2000-02-01   36.35\n",
              "2     MSFT 2000-03-01   43.22\n",
              "3     MSFT 2000-04-01   28.37\n",
              "4     MSFT 2000-05-01   25.45\n",
              "..     ...        ...     ...\n",
              "555   AAPL 2009-11-01  199.91\n",
              "556   AAPL 2009-12-01  210.73\n",
              "557   AAPL 2010-01-01  192.06\n",
              "558   AAPL 2010-02-01  204.62\n",
              "559   AAPL 2010-03-01  223.02\n",
              "\n",
              "[560 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "9kbkUowyOJS5",
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          "height": 84
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        "outputId": "11f2c6f8-dc02-4616-af33-7f4506a03497"
      },
      "source": [
        "stocks.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
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              "<script src='https://www.gstatic.com/external_hosted/mathjax/latest/MathJax.js?config=default'></script>"
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              "<IPython.core.display.HTML object>"
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "symbol            object\n",
              "date      datetime64[ns]\n",
              "price            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr3VRFkIq7hl",
        "colab_type": "text"
      },
      "source": [
        "## Tratamiento de datos\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKyAt32Rt8o9",
        "colab_type": "text"
      },
      "source": [
        "* Na's\n",
        "* Duplicados\n",
        "* Codificación\n",
        "* Normalizar variables\n",
        "* One - Encoding\n",
        "* ACP: Análisis de componentes principales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVVFIM5rtLgv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "4b4aa952-c545-4dd4-a472-9977ba8bef46"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn import datasets\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# import some data to play with\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\n",
        "\n",
        "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
        "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
        "\n",
        "\n",
        "# To getter a better understanding of interaction of the dimensions\n",
        "# plot the first three PCA dimensions\n",
        "fig = plt.figure(1, figsize=(8, 6))\n",
        "ax = Axes3D(fig, elev=-150, azim=110)\n",
        "X_reduced = PCA(n_components=3).fit_transform(iris.data)\n",
        "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,\n",
        "           cmap=plt.cm.Set1, edgecolor='k', s=40)\n",
        "ax.set_title(\"First three PCA directions\")\n",
        "ax.set_xlabel(\"1st eigenvector\")\n",
        "ax.w_xaxis.set_ticklabels([])\n",
        "ax.set_ylabel(\"2nd eigenvector\")\n",
        "ax.w_yaxis.set_ticklabels([])\n",
        "ax.set_zlabel(\"3rd eigenvector\")\n",
        "ax.w_zaxis.set_ticklabels([])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOKdfpAXu_Mt",
        "colab_type": "text"
      },
      "source": [
        "Imputaciones Na's <br/>\n",
        "https://machinelearningmastery.com/statistical-imputation-for-missing-values-in-machine-learning/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bRCUq7fwo4v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "976c6c33-96d9-40e7-b0c6-0a53d04e470f"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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              "     0    1    2\n",
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              "1  4.0  NaN  NaN\n",
              "2  NaN  NaN  9.0"
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          },
          "metadata": {
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          },
          "execution_count": 34
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-hhBAquyB5N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "07bcecd5-b72a-4067-d6b5-1c942c3a1893"
      },
      "source": [
        "df_1 = df.fillna(method='ffill')\n",
        "df_1"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <th>2</th>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
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              "     0    1    2\n",
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      "cell_type": "code",
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        "colab_type": "code",
        "colab": {
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          "height": 137
        },
        "outputId": "8b5469d5-40d8-4a82-8bb2-f54c741472fb"
      },
      "source": [
        "df.fillna(method='ffill', inplace=True)\n",
        "df"
      ],
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      "outputs": [
        {
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          "data": {
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        "colab_type": "code",
        "colab": {
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          "height": 137
        },
        "outputId": "f4bda0a6-7a1a-41ab-ac01-1c91c1675a2c"
      },
      "source": [
        "df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "     0    1    2\n",
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              "1  4.0  NaN  NaN\n",
              "2  NaN  NaN  9.0"
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          "execution_count": 42
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8UgWNdgyxc1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "cc279c1e-d00e-4fd8-a747-7bc556bca240"
      },
      "source": [
        "df.fillna(0, inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
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              "     0    1    2\n",
              "0  1.0  2.0  3.0\n",
              "1  4.0  0.0  0.0\n",
              "2  0.0  0.0  9.0"
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          },
          "metadata": {
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          "execution_count": 44
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pB-Zsjg2vUgT",
        "colab_type": "text"
      },
      "source": [
        "# Mi primer modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7g1f20QAVlX",
        "colab_type": "text"
      },
      "source": [
        "$$Y = \\alpha + \\sum_{i=1}^{k}\\beta_{i}X_{i}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzIhQ1reAGNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "b0f31130-aacf-4474-ca8f-089936f032d5"
      },
      "source": [
        "# importanto la api de statsmodels\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as sm\n",
        "iris_mod_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iT30P4d7A-lY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "d5d8061a-62d3-4398-9dfa-d634f8933755"
      },
      "source": [
        "# utilizando la api de formula de statsmodels\n",
        "est = smf.ols(formula='sepal_length ~ sepal_width', data=iris_mod_1).fit()\n",
        "est.summary() # Employed se estima en base a GNP."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>sepal_length</td>   <th>  R-squared:         </th> <td>   0.551</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.542</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   58.99</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Wed, 05 Aug 2020</td> <th>  Prob (F-statistic):</th> <td>6.71e-10</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>05:25:44</td>     <th>  Log-Likelihood:    </th> <td>  1.7341</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>  0.5319</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    48</td>      <th>  BIC:               </th> <td>   4.356</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>   <td>    2.6390</td> <td>    0.310</td> <td>    8.513</td> <td> 0.000</td> <td>    2.016</td> <td>    3.262</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sepal_width</th> <td>    0.6905</td> <td>    0.090</td> <td>    7.681</td> <td> 0.000</td> <td>    0.510</td> <td>    0.871</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 0.274</td> <th>  Durbin-Watson:     </th> <td>   2.542</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.872</td> <th>  Jarque-Bera (JB):  </th> <td>   0.464</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.041</td> <th>  Prob(JB):          </th> <td>   0.793</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.535</td> <th>  Cond. No.          </th> <td>    34.3</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:           sepal_length   R-squared:                       0.551\n",
              "Model:                            OLS   Adj. R-squared:                  0.542\n",
              "Method:                 Least Squares   F-statistic:                     58.99\n",
              "Date:                Wed, 05 Aug 2020   Prob (F-statistic):           6.71e-10\n",
              "Time:                        05:25:44   Log-Likelihood:                 1.7341\n",
              "No. Observations:                  50   AIC:                            0.5319\n",
              "Df Residuals:                      48   BIC:                             4.356\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "===============================================================================\n",
              "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-------------------------------------------------------------------------------\n",
              "Intercept       2.6390      0.310      8.513      0.000       2.016       3.262\n",
              "sepal_width     0.6905      0.090      7.681      0.000       0.510       0.871\n",
              "==============================================================================\n",
              "Omnibus:                        0.274   Durbin-Watson:                   2.542\n",
              "Prob(Omnibus):                  0.872   Jarque-Bera (JB):                0.464\n",
              "Skew:                          -0.041   Prob(JB):                        0.793\n",
              "Kurtosis:                       2.535   Cond. No.                         34.3\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTFoVb2qB4Wn",
        "colab_type": "text"
      },
      "source": [
        "$Y = 2.64 + 0.69*sepal\\_width$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "za822X3LBxt5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b7d4e16f-08ea-47e3-83c9-7af76e438a6d"
      },
      "source": [
        "# grafico de regresion. que tanto se ajusta el modelo a los datos.\n",
        "y = iris_mod_1.sepal_length  # Respuesta\n",
        "X = iris_mod_1.sepal_width  # Predictor\n",
        "X = sm.add_constant(X) # agrega constante\n",
        "\n",
        "X_1 = pd.DataFrame({'sepal_width': np.linspace(X.sepal_width.min(), X.sepal_width.max(), 5)})\n",
        "X_1 = sm.add_constant(X_1) \n",
        "\n",
        "y_reg = est.predict(X_1) # estimacion\n",
        "\n",
        "plt.scatter(X.sepal_width, y, alpha=0.3)  # grafica los puntos de datos\n",
        "plt.ylim(4, 6)  # limite de eje y\n",
        "plt.xlabel(\"sepal_width\") # leyenda eje x\n",
        "plt.ylabel(\"sepal_length\") # leyenda eje y\n",
        "plt.title(\"Ajuste de regresion\") # titulo del grafico\n",
        "reg = plt.plot(X_1.sepal_width, y_reg, 'r', alpha=0.9)  # linea de regresion"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzYjSjH6F44w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "f304c697-332b-429d-8a95-18892c49baa6"
      },
      "source": [
        "Y = 2.64 + 0.69*3\n",
        "Y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.71"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_H78fCh6JH-",
        "colab_type": "text"
      },
      "source": [
        "# Algunas librerías\n",
        "\n",
        "- numpy: El popular paquete matemático de Python, se utiliza tanto que mucha gente ya lo considera parte integral del lenguaje. Nos proporciona algunas funciones estadísticas que podemos aplicar fácilmente sobre los arrays de Numpy.\n",
        "\n",
        "- scipy.stats: Este submodulo del paquete científico Scipy es el complemento perfecto para Numpy, las funciones estadisticas que no encontremos en uno, las podemos encontrar en el otro.\n",
        "\n",
        "- statsmodels: Esta librería nos brinda un gran número de herramientas para explorar datos, estimar modelos estadísticos, realizar pruebas estadísticas y muchas cosas más.\n",
        "\n",
        "- matplotlib: Es la librería más popular en Python para visualizaciones y gráficos. Ella nos va a permitir realizar los gráficos de las distintas distribuciones de datos.\n",
        "\n",
        "- seaborn: Esta librería es un complemento ideal de matplotlib para realizar gráficos estadísticos.\n",
        "\n",
        "- pandas: Esta es la librería más popular para análisis de datos y financieros. Posee algunas funciones muy útiles para realizar estadística descriptiva sobre nuestros datos y nos facilita sobremanera el trabajar con series de tiempo.\n",
        "\n",
        "- pyMC: pyMC es un módulo de Python que implementa modelos estadísticos bayesianos, incluyendo la cadena de Markov Monte Carlo(MCMC). pyMC ofrece funcionalidades para hacer el análisis bayesiano lo mas simple posible.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVvHLljF1D5n",
        "colab_type": "text"
      },
      "source": [
        "# Referencias\n",
        "```\n",
        "- https://relopezbriega.github.io/blog/2015/06/27/probabilidad-y-estadistica-con-python/\n",
        "\n",
        "```"
      ]
    }
  ]
}