{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jD8fAts784du"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funciones"
      ],
      "metadata": {
        "id": "ycQh81M2zbKx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def truncar(df, column, bound='both'):\n",
        "    #imputamos con el percentil 99 las columnas con outliers altos\n",
        "    percentil_99=df[column].quantile(0.99)\n",
        "    #imputamos con el percentil 1 las columnas con outliers bajos\n",
        "    percentil_01=df[column].quantile(0.01)\n",
        "\n",
        "    if bound =='upper':\n",
        "        df[column] = np.where(df[column]> percentil_99,percentil_99, df[column] )\n",
        "    if bound =='lower':\n",
        "        df[column] = np.where(df[column]< percentil_01,percentil_01, df[column] )\n",
        "    if bound =='both':\n",
        "        df[column] = np.where(df[column]> percentil_99,percentil_99, df[column] )\n",
        "        df[column] = np.where(df[column]< percentil_01,percentil_01, df[column] )\n",
        "    return df"
      ],
      "metadata": {
        "id": "oMuk92SMzfjJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outliers Univariados"
      ],
      "metadata": {
        "id": "HYKvgN0c-cse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(45)"
      ],
      "metadata": {
        "id": "BOKTYgQS8-Kg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_normal = np.random.normal(loc = 50, scale =5, size = 200)"
      ],
      "metadata": {
        "id": "7PsIG15w9Gl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.Series(data_normal).describe(percentiles=[.01, .05, .25, .5, .75, .9, .95, .99])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "qKP5p5KS9Rqh",
        "outputId": "1a12a3a9-5e7f-4e79-c46d-872479a75bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    200.000000\n",
              "mean      49.840562\n",
              "std        4.875650\n",
              "min       37.015607\n",
              "1%        37.963278\n",
              "5%        41.723384\n",
              "25%       46.939341\n",
              "50%       49.805127\n",
              "75%       52.903723\n",
              "90%       56.074980\n",
              "95%       57.601096\n",
              "99%       61.250373\n",
              "max       64.968170\n",
              "dtype: float64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>49.840562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.875650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>37.015607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1%</th>\n",
              "      <td>37.963278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5%</th>\n",
              "      <td>41.723384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46.939341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.805127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>52.903723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90%</th>\n",
              "      <td>56.074980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95%</th>\n",
              "      <td>57.601096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99%</th>\n",
              "      <td>61.250373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.968170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outliers = [10,15, 80, 85]"
      ],
      "metadata": {
        "id": "RkgvxkqW9XHK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = np.concatenate([data_normal, outliers])"
      ],
      "metadata": {
        "id": "ZBSx7SyK9qI0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data, columns = ['valor'])"
      ],
      "metadata": {
        "id": "qLc0mdx690QM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "plt.boxplot(df['valor'])\n",
        "plt.title('Boxplot para detectar outliers univariados')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "FKfUmFiM9-Wm",
        "outputId": "21f6b036-6191-4993-aa6c-20fd11cbb264"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['valor'].describe(percentiles=[.01, .05, .25, .5, .75, .9, .95, .99])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "PPEWawYKyQIe",
        "outputId": "a26fbf73-fb08-4738-9cc6-14a0a2f6e991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    204.000000\n",
              "mean      49.794669\n",
              "std        6.904380\n",
              "min       10.000000\n",
              "1%        37.041083\n",
              "5%        41.573116\n",
              "25%       46.859699\n",
              "50%       49.805127\n",
              "75%       52.943591\n",
              "90%       56.299185\n",
              "95%       58.319333\n",
              "99%       64.886114\n",
              "max       85.000000\n",
              "Name: valor, dtype: float64"
            ],
            "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>valor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>204.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>49.794669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.904380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1%</th>\n",
              "      <td>37.041083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5%</th>\n",
              "      <td>41.573116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46.859699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.805127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>52.943591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90%</th>\n",
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              "count    204.000000\n",
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              "std        5.208834\n",
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      "cell_type": "markdown",
      "source": [
        "# Outlier Multivariado\n",
        "\n",
        "Matriz de covarianzas:\n",
        "\n",
        "|-|Cintura|Altura|Cabeza|\n",
        "|---|---|---|---|\n",
        "|Cintura|Var(C)|Cov(C,A)|Cov(C,H)|\n",
        "|Altura|Cov(A,C)|Var(A)|Cov(A,Ca)|\n",
        "|Cabeza|Cov(Ca,C)|Cov(Ca,A)|Var(Ca)|\n",
        "\n",
        "Donde:\n",
        "\n",
        "Var(X) = varianza de X (cuánto se alejan los valores de su media).\n",
        "\n",
        "Cov(X,Y) = medida de cómo varían juntas X e Y:\n",
        "\n",
        "Positiva → si X sube, Y tiende a subir.\n",
        "\n",
        "Negativa → si X sube, Y tiende a bajar.\n",
        "\n",
        "Cerca de 0 → poca relación lineal."
      ],
      "metadata": {
        "id": "Jkrp5QtU-pkd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Semilla para reproducibilidad\n",
        "np.random.seed(42)\n",
        "\n",
        "# Generar datos \"normales\"\n",
        "# Valores aproximados realistas\n",
        "media = [90, 170, 57]  # valores medios de cintura, altura, cabeza\n",
        "cov = [\n",
        "    [25, 15, 5],   # cintura vs altura vs cabeza\n",
        "    [15, 30, 6],\n",
        "    [5, 6, 4]\n",
        "]\n",
        "\n",
        "# 200 individuos con distribución normal multivariante\n",
        "data_normal = np.random.multivariate_normal(media, cov, size=200)\n",
        "\n",
        "# Generar outliers (ej: medidas inusuales o errores)\n",
        "outliers = np.array([\n",
        "    [140, 165, 65],   # cintura muy grande, altura baja\n",
        "    [60, 210, 48],    # cintura muy pequeña, altura muy alta\n",
        "    [130, 180, 75],   # cabeza extremadamente grande\n",
        "    [50, 140, 40]     # todo muy pequeño\n",
        "])\n",
        "\n",
        "# Combinar\n",
        "data = np.vstack([data_normal, outliers])\n",
        "\n",
        "# Crear DataFrame\n",
        "df = pd.DataFrame(data, columns=[\"Cintura_cm\", \"Altura_cm\", \"Cabeza_cm\"])\n",
        "\n"
      ],
      "metadata": {
        "id": "AInah-IL-qD-"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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          "height": 206
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        "id": "djd66g_X_hb2",
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      "execution_count": null,
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            "text/plain": [
              "   Cintura_cm   Altura_cm  Cabeza_cm\n",
              "0   87.413795  167.685171  57.375267\n",
              "1   82.992714  162.995997  54.712340\n",
              "2   85.482725  160.505842  54.255552\n",
              "3   86.557101  168.454584  55.601811\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 204,\n  \"fields\": [\n    {\n      \"column\": \"Cintura_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.277928878642668,\n        \"min\": 50.0,\n        \"max\": 140.0,\n        \"num_unique_values\": 204,\n        \"samples\": [\n          91.60493356871432,\n          86.86082234352597,\n          85.40467116700347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Altura_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.161333050832451,\n        \"min\": 140.0,\n        \"max\": 210.0,\n        \"num_unique_values\": 204,\n        \"samples\": [\n          174.37971280311004,\n          169.5554102332904,\n          172.45103291824168\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabeza_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7516346287136733,\n        \"min\": 40.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 204,\n        \"samples\": [\n          59.557589115750254,\n          56.08548035836056,\n          54.559806974642136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data= df, x = 'Cintura_cm', y= 'Altura_cm', size = 'Cabeza_cm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "-TXCAtA7NFyg",
        "outputId": "562fa3c0-95ce-4820-f236-ab3591569e68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Cintura_cm', ylabel='Altura_cm'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['Altura_cm'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "EAEK6wRuPSDl",
        "outputId": "23a32e09-7d31-41ea-83d4-fa48ddcee922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Altura_cm'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o866T10wQjdL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}