{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ba4d7f1a"
      },
      "source": [
        "# Генерация данных\n",
        "В этом блоке создаём искусственный набор данных с выбросами. Мы используем нормальное распределение и добавляем несколько точек, сильно отличающихся от остальных. Это позволяет визуализировать и проверить методы обнаружения выбросов."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s0pmC8eEYFVW",
        "outputId": "a69b1445-ddd6-42ff-9507-9b3cc53f06c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   feature_1  feature_2\n",
              "0   4.634094   5.734306\n",
              "1   4.491004   4.826737\n",
              "2  -0.517258  -0.865238\n",
              "3  -0.481365   1.481823\n",
              "4   4.397711   4.730811"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.634094</td>\n",
              "      <td>5.734306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.491004</td>\n",
              "      <td>4.826737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.517258</td>\n",
              "      <td>-0.865238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.481365</td>\n",
              "      <td>1.481823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.397711</td>\n",
              "      <td>4.730811</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-094e7d47-07a4-4a0e-a748-0bb0a4fb3e14 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-094e7d47-07a4-4a0e-a748-0bb0a4fb3e14');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "    </g>\n",
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              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
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              "\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-391864c1-8c3a-414c-8c6d-b3cdafd89300 button');\n",
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              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 510,\n  \"fields\": [\n    {\n      \"column\": \"feature_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7793117645704077,\n        \"min\": -9.588310114083951,\n        \"max\": 6.8013518622749025,\n        \"num_unique_values\": 510,\n        \"samples\": [\n          6.8013518622749025,\n          -0.6050805962274427,\n          4.942494175125202\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.746801702794539,\n        \"min\": -6.880109593275947,\n        \"max\": 9.398197043239886,\n        \"num_unique_values\": 510,\n        \"samples\": [\n          5.041452903810142,\n          -1.1378029676781394,\n          5.78210708211169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Генерируем данные: 2 кластера и добавим шум (выбросы)\n",
        "X, y = make_blobs(\n",
        "    n_samples=500,\n",
        "    centers=[(0, 0), (5, 5)],\n",
        "    cluster_std=[0.8, 0.7],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Добавим 10 выбросов вручную\n",
        "rng = np.random.RandomState(42)\n",
        "outliers = rng.uniform(low=-10, high=10, size=(10, 2))\n",
        "X = np.vstack([X, outliers])\n",
        "\n",
        "df = pd.DataFrame(X, columns=[\"feature_1\", \"feature_2\"])\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8cedd9b"
      },
      "source": [
        "# Обнаружение выбросов с помощью Z-score\n",
        "Метод **Z-score** показывает, насколько далеко значение находится от среднего в единицах стандартного отклонения.  \n",
        "Если точка имеет |Z| > 3 (или другое пороговое значение), она считается выбросом.  \n",
        "Аналогия: если рост людей в группе — около 170 см ±10, то человек ростом 210 см — это редкий случай (выброс)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "CNLxSdrBYJs-",
        "outputId": "ca5471a1-3f4e-4e06-aa57-6d1a734a8a8f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(df[\"feature_1\"], df[\"feature_2\"], s=20)\n",
        "plt.title(\"Сырые данные с выбросами\")\n",
        "plt.xlabel(\"feature_1\")\n",
        "plt.ylabel(\"feature_2\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68301bf6"
      },
      "source": [
        "# Trim (удаление выбросов)\n",
        "Здесь мы удаляем (обрезаем) наблюдения, выходящие за пределы допустимого диапазона по Z-score. Метод подходит для больших выборок, где потеря нескольких наблюдений не искажает картину. Однако при малом объёме данных он может удалить важную информацию."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "KaIVOaxOYLCv",
        "outputId": "efc4193e-61e3-465f-c64a-ecb39faea0e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Удалено наблюдений: 5 из 510\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from scipy.stats import zscore\n",
        "\n",
        "z_scores = np.abs(zscore(df))\n",
        "filtered = df[(z_scores < 3).all(axis=1)]\n",
        "\n",
        "print(f\"Удалено наблюдений: {len(df) - len(filtered)} из {len(df)}\")\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(filtered[\"feature_1\"], filtered[\"feature_2\"], s=20, color=\"blue\")\n",
        "plt.title(\"После trim по Z-score — выбросы остались\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a44d1462"
      },
      "source": [
        "# Визуализация результата\n",
        "Отобразим данные до и после удаления выбросов, чтобы увидеть, как метод Trim повлиял на распределение."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "QWzNhpnHYUBT",
        "outputId": "d466bcdf-4222-494d-aa5a-5b78862463ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(df)\n",
        "\n",
        "iso = IsolationForest(contamination=0.02, random_state=42)\n",
        "df[\"is_outlier\"] = iso.fit_predict(X_scaled)\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(df[\"feature_1\"], df[\"feature_2\"],\n",
        "            c=df[\"is_outlier\"].map({1: \"blue\", -1: \"red\"}),\n",
        "            s=20)\n",
        "plt.title(\"Isolation Forest — красные точки = выбросы\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "BvYV1GNeYXMv",
        "outputId": "fe095628-78fd-4861-d06d-b3290eef837a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "\n",
        "lof = LocalOutlierFactor(n_neighbors=20, contamination=0.02)\n",
        "labels = lof.fit_predict(X_scaled)\n",
        "df[\"is_outlier_lof\"] = labels\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(df[\"feature_1\"], df[\"feature_2\"],\n",
        "            c=df[\"is_outlier_lof\"].map({1: \"blue\", -1: \"red\"}),\n",
        "            s=20)\n",
        "plt.title(\"Local Outlier Factor — локальные выбросы\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "JiPxt_kPYaiv",
        "outputId": "8d0406cf-b94f-42eb-c3e5-e2ea11e8ec0e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df_imputed = df.copy()\n",
        "\n",
        "for col in [\"feature_1\", \"feature_2\"]:\n",
        "    median_value = df_imputed.loc[df_imputed[\"is_outlier_lof\"] == 1, col].median()\n",
        "    df_imputed.loc[df_imputed[\"is_outlier_lof\"] == -1, col] = median_value\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(df_imputed[\"feature_1\"], df_imputed[\"feature_2\"], s=20, color=\"green\")\n",
        "plt.title(\"После импутации выбросов медианой\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "IQApmb_rYcHu",
        "outputId": "94e759b3-bf7d-4295-dec0-028e9b27b28f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import zscore\n",
        "\n",
        "# Генерируем 200 \"нормальных\" наблюдений\n",
        "np.random.seed(42)\n",
        "normal_data = np.random.normal(loc=50, scale=5, size=200)\n",
        "\n",
        "# Добавим несколько экстремумов\n",
        "outliers = np.array([20, 120, 130])\n",
        "data = np.concatenate([normal_data, outliers])\n",
        "\n",
        "df = pd.DataFrame(data, columns=[\"value\"])\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.boxplot(df[\"value\"])\n",
        "plt.title(\"До фильтрации — выбросы явно видны\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "wyh6unJQYnyW",
        "outputId": "12e60811-f588-4930-d0ef-408f9d9377a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Найдено выбросов: 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     value   z_score\n",
              "200   20.0  3.371928\n",
              "201  120.0  7.723485\n",
              "202  130.0  8.833026"
            ],
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-7291c162-12a8-4814-8c0b-4bf28cdba7c7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df[outlier_mask])\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.8276253029822,\n        \"min\": 20.0,\n        \"max\": 130.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          20.0,\n          120.0,\n          130.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8864832462900045,\n        \"min\": 3.371928251024839,\n        \"max\": 8.833025953189587,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.371928251024839,\n          7.723484661897366,\n          8.833025953189587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "z_scores = np.abs(zscore(df[\"value\"]))\n",
        "df[\"z_score\"] = z_scores\n",
        "\n",
        "# Определим выбросы при |z| > 3\n",
        "outlier_mask = df[\"z_score\"] > 3\n",
        "print(\"Найдено выбросов:\", outlier_mask.sum())\n",
        "\n",
        "display(df[outlier_mask])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "u6VaEmjyYqSE",
        "outputId": "000355d2-721f-491b-be6f-636c830c9353"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Размер до: 203 → после: 200\n",
            "Среднее до: 50.39029081196017 → после: 49.796145174139575\n"
          ]
        }
      ],
      "source": [
        "filtered = df[~outlier_mask][\"value\"]\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.boxplot(filtered)\n",
        "plt.title(\"После удаления выбросов по Z-score\")\n",
        "plt.show()\n",
        "\n",
        "print(\"Размер до:\", len(df), \"→ после:\", len(filtered))\n",
        "print(\"Среднее до:\", df['value'].mean(), \"→ после:\", filtered.mean())\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}