Практика_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/Keshabl/ML2025/blob/main/%D0%9F%D1%80%D0%B0%D0%BA%D1%82%D0%B8%D0%BA%D0%B0_1.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",
      "source": [
        "# МЛ Практика 1\n",
        "## Обзор занятий: Чем предстоит заниматься\n",
        "Цели сессии:\n",
        "\n",
        "1. Ознакомление с основными понятиями машинного обучения и структурой практических занятий.\n",
        "2. Представление доступных наборов данных для последующих упражнений.\n",
        "3. Формирование списка участников и распределение ролей (при необходимости).\n",
        "4. Практическое освоение методов описательного анализа данных.\n",
        "5. Понимание типов данных и их важности в контексте машинного обучения.\n",
        "План занятий:\n",
        "\n",
        "1. Введение и обзор целей курса.\n",
        "2. Представление наборов данных.\n",
        "3. Формирование списка студентов.\n",
        "4. Совместный разбор выбранного набора данных с обсуждением вопросов.\n",
        "5. Задача 1: Описание набора данных и определение типов данных.\n",
        "6. Пример статистического разбора на основе wine.csv.\n",
        "7. Обсуждение типов переменных и их определения по статистике.\n",
        "8. Вопросы и ответы, обсуждение домашних заданий."
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "# Наборы данных по вариантам\n",
        "\n",
        "https://disk.yandex.ru/i/nY93FrjzthRO2Q"
      ],
      "metadata": {
        "id": "rudyRMMW-U2i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[github](https://github.com/Letch49/ML-vvsu-2025)"
      ],
      "metadata": {
        "id": "NdlLoSu8EhPc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "# Наборы данных по вариантам\n",
        "\n",
        "| №  | Название файла      | Уровень сложности (1-5) | Обоснование                                                                                         | Ссылка                                                              |\n",
        "|----|---------------------|-------------------------|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------|\n",
        "| 1  | wine.csv            | 1                       | Базовый набор данных из scikit-learn с небольшим количеством признаков и простой структурой.          | [wine.csv](https://disk.yandex.ru/d/OELXhKkfsWTJuw)                 |\n",
        "| 2  | diamond.csv         | 2                       | Набор данных со средним количеством признаков (например, караты, цвет, огранка) и четкими целями.     | [diamond.csv](https://disk.yandex.ru/d/7u9rI1RqWPQD7Q)              |\n",
        "| 3  | employee.csv        | 2                       | Типичные данные о сотрудниках с сочетанием числовых и категориальных признаков.                      | [employee.csv](https://disk.yandex.ru/d/b9kxHcJ8vviXWg)             |\n",
        "| 4  | heart.csv           | 2                       | Стандартный медицинский набор данных для задач классификации, хорошо изучен и документирован.         | [heart.csv](https://disk.yandex.ru/d/QNT6pUh6G3Eeqw)                |\n",
        "| 5  | heart_disease.csv   | 3                       | Более специализированный медицинский набор данных, возможно с большим количеством признаков.          | [heart_disease.csv](https://disk.yandex.ru/d/jHLFkdLICQIilQ)        |\n",
        "| 6  | hepatitis.csv       | 3                       | Медицинский набор данных с потенциально сложными признаками и необходимостью продвинутой обработки.   | [hepatitis.csv](https://disk.yandex.ru/d/0fuKz_gWwMJGqg)            |\n",
        "| 7  | income.csv          | 3                       | Набор данных для прогнозирования дохода, может содержать разнообразные и сложные признаки.            | [income.csv](https://disk.yandex.ru/d/pjFb9JhG7AZ3Jg)               |\n",
        "| 8  | insurance.csv       | 3                       | Страховые данные с множеством признаков, включая категориальные и числовые, возможны сложности в обработке. | [insurance.csv](https://disk.yandex.ru/d/WNdgMUGvgsPKgA)           |\n",
        "| 9  | france.csv          | 3                       | Данные, связанные с Францией, вероятно, экономические или демографические, требующие комплексного анализа. | [france.csv](https://disk.yandex.ru/d/ts48WLo7TazEvg)             |\n",
        "| 10 | germany.csv         | 3                       | Аналогично france.csv, данные о Германии могут быть комплексными и многогранными.                     | [germany.csv](https://disk.yandex.ru/d/UDxaNz46HwcFsw)              |\n",
        "| 11 | jucy.csv            | 3                       | Предполагается, что это специализированный или менее известный набор данных, возможны неопределенности в структуре и сложности. |  [jucy.csv](https://disk.yandex.ru/d/omBOncVnzElRoA)                                                                 |\n"
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      "cell_type": "markdown",
      "source": [
        "**p.s. wine датасет не берем**, это будет читерство, такого не одобряем"
      ],
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Описание:\n",
        "В этом разделе вы загрузите набор данных wine.csv, ознакомитесь с его структурой и проведёте первичный обзор."
      ],
      "metadata": {
        "id": "Eho9OVlR_mar"
      }
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    {
      "cell_type": "code",
      "source": [
        "# импортируем библиотеки\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "cAklc2-4_hag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "```p.s. а если либы нет, делаем (ошибка импорта например) ```"
      ],
      "metadata": {
        "id": "nB8lKsq8FTCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seaborn # или название другой либы, загуглите how to install packages using pip"
      ],
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            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.26.4)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (2.1.4)\n",
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      "source": [
        "Описание:\n",
        "В этом разделе вы загрузите набор данных wine.csv, ознакомитесь с его структурой и проведёте первичный обзор."
      ],
      "metadata": {
        "id": "gkrZd1Sf_j-4"
      }
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    {
      "cell_type": "code",
      "source": [
        "PATH_TO_FILE: str = '/content/employee.csv' # Загрузка набора данных\n",
        "## p.s. в левой панеле нажать на папку и перетащить туда файл,\n",
        "## а wince.csv заменить на свой вариант (ПКМ -> копировать путь)\n",
        "\n",
        "df = pd.read_csv(PATH_TO_FILE) # функция превращение данных из CSV в DataFrame"
      ],
      "metadata": {
        "id": "_AxEco9xAAvN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Вывод первых 5 строк\n",
        "df.head(12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "BY0SAfx6AWXB",
        "outputId": "357dd768-709e-4cf7-89b2-5e94346fef7d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
              "0                 0.38             0.53               2                   157   \n",
              "1                 0.80             0.86               5                   262   \n",
              "2                 0.11             0.88               7                   272   \n",
              "3                 0.72             0.87               5                   223   \n",
              "4                 0.37             0.52               2                   159   \n",
              "5                 0.41             0.50               2                   153   \n",
              "6                 0.10             0.77               6                   247   \n",
              "7                 0.92             0.85               5                   259   \n",
              "8                 0.89             1.00               5                   224   \n",
              "9                 0.42             0.53               2                   142   \n",
              "10                0.45             0.54               2                   135   \n",
              "11                0.11             0.81               6                   305   \n",
              "\n",
              "    time_spend_company  Work_accident  promotion_last_5years department  \\\n",
              "0                    3              0                      0      sales   \n",
              "1                    6              0                      0      sales   \n",
              "2                    4              0                      0      sales   \n",
              "3                    5              0                      0      sales   \n",
              "4                    3              0                      0      sales   \n",
              "5                    3              0                      0      sales   \n",
              "6                    4              0                      0      sales   \n",
              "7                    5              0                      0      sales   \n",
              "8                    5              0                      0      sales   \n",
              "9                    3              0                      0      sales   \n",
              "10                   3              0                      0      sales   \n",
              "11                   4              0                      0      sales   \n",
              "\n",
              "    salary  left  \n",
              "0      low     1  \n",
              "1   medium     1  \n",
              "2   medium     1  \n",
              "3      low     1  \n",
              "4      low     1  \n",
              "5      low     1  \n",
              "6      low     1  \n",
              "7      low     1  \n",
              "8      low     1  \n",
              "9      low     1  \n",
              "10     low     1  \n",
              "11     low     1  "
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              "      <th></th>\n",
              "      <th>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.53</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
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              "        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",
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              "\n",
              "<style>\n",
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              "      --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",
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              "    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",
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              "    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",
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              "      border-color: transparent;\n",
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              "      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-d5792c99-7a53-4638-8e83-311a9a04f762 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 14999,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24863065106114257,\n        \"min\": 0.09,\n        \"max\": 1.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.83,\n          0.13,\n          0.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17116911062327533,\n        \"min\": 0.36,\n        \"max\": 1.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          0.66,\n          0.44,\n          0.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 96,\n        \"max\": 310,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          118,\n          112,\n          222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6,\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"department\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"marketing\",\n          \"accounting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"low\",\n          \"medium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "08E8yFAcFkgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Информация о наборе данных\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXF-l0y1AXNU",
        "outputId": "d056314f-e834-49c9-ff3c-53c5947ae3d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14999 entries, 0 to 14998\n",
            "Data columns (total 10 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   satisfaction_level     14999 non-null  float64\n",
            " 1   last_evaluation        14999 non-null  float64\n",
            " 2   number_project         14999 non-null  int64  \n",
            " 3   average_montly_hours   14999 non-null  int64  \n",
            " 4   time_spend_company     14999 non-null  int64  \n",
            " 5   Work_accident          14999 non-null  int64  \n",
            " 6   promotion_last_5years  14999 non-null  int64  \n",
            " 7   department             14999 non-null  object \n",
            " 8   salary                 14999 non-null  object \n",
            " 9   left                   14999 non-null  int64  \n",
            "dtypes: float64(2), int64(6), object(2)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Статистический разбор\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1eP4wqv1_tqN",
        "outputId": "0825c73c-6b60-4b46-8b95-a546ddc4741c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       satisfaction_level  last_evaluation  number_project  \\\n",
              "count        14999.000000     14999.000000    14999.000000   \n",
              "mean             0.612834         0.716102        3.803054   \n",
              "std              0.248631         0.171169        1.232592   \n",
              "min              0.090000         0.360000        2.000000   \n",
              "25%              0.440000         0.560000        3.000000   \n",
              "50%              0.640000         0.720000        4.000000   \n",
              "75%              0.820000         0.870000        5.000000   \n",
              "max              1.000000         1.000000        7.000000   \n",
              "\n",
              "       average_montly_hours  time_spend_company  Work_accident  \\\n",
              "count          14999.000000        14999.000000   14999.000000   \n",
              "mean             201.050337            3.498233       0.144610   \n",
              "std               49.943099            1.460136       0.351719   \n",
              "min               96.000000            2.000000       0.000000   \n",
              "25%              156.000000            3.000000       0.000000   \n",
              "50%              200.000000            3.000000       0.000000   \n",
              "75%              245.000000            4.000000       0.000000   \n",
              "max              310.000000           10.000000       1.000000   \n",
              "\n",
              "       promotion_last_5years          left  \n",
              "count           14999.000000  14999.000000  \n",
              "mean                0.021268      0.238083  \n",
              "std                 0.144281      0.425924  \n",
              "min                 0.000000      0.000000  \n",
              "25%                 0.000000      0.000000  \n",
              "50%                 0.000000      0.000000  \n",
              "75%                 0.000000      0.000000  \n",
              "max                 1.000000      1.000000  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        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>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>left</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.612834</td>\n",
              "      <td>0.716102</td>\n",
              "      <td>3.803054</td>\n",
              "      <td>201.050337</td>\n",
              "      <td>3.498233</td>\n",
              "      <td>0.144610</td>\n",
              "      <td>0.021268</td>\n",
              "      <td>0.238083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.248631</td>\n",
              "      <td>0.171169</td>\n",
              "      <td>1.232592</td>\n",
              "      <td>49.943099</td>\n",
              "      <td>1.460136</td>\n",
              "      <td>0.351719</td>\n",
              "      <td>0.144281</td>\n",
              "      <td>0.425924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.560000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.820000</td>\n",
              "      <td>0.870000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
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              "      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",
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              "    (() => {\n",
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              "        document.querySelector('#df-d31a0050-0217-46c7-8280-5d98a552b785 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\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.752785300377,\n        \"min\": 0.09,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6128335222348156,\n          0.64,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.725216503965,\n        \"min\": 0.17116911062327533,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7161017401160078,\n          0.72,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5301.632605184108,\n        \"min\": 1.2325923553183522,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.80305353690246,\n          4.0,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5240.043131004932,\n        \"min\": 49.94309937128408,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          201.0503366891126,\n          200.0,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5301.586356319515,\n        \"min\": 1.4601362305354812,\n        \"max\": 14999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14999.0,\n          3.498233215547703,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.871740631288,\n        \"min\": 0.0,\n        \"max\": 14999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1446096406427095,\n          1.0,\n          0.35171855238017985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.88844750851,\n        \"min\": 0.0,\n        \"max\": 14999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.021268084538969265,\n          1.0,\n          0.14428146457858232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.863271830312,\n        \"min\": 0.0,\n        \"max\": 14999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2380825388359224,\n          1.0,\n          0.4259240993802994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Вывод колонок в наборе данных\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "157IXtxKAjbv",
        "outputId": "e46f0852-173b-46fa-977a-d32189f10723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
              "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
              "       'promotion_last_5years', 'department', 'salary', 'left'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод датафрейма\n",
        "df # p.s. обычно переменную с набором данных называют df, придерживайтесь этого наименования в дальнейшем"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rnL3akt2Ao81",
        "outputId": "51d868cc-e101-4ab0-c822-d93ef572da49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       satisfaction_level  last_evaluation  number_project  \\\n",
              "0                    0.38             0.53               2   \n",
              "1                    0.80             0.86               5   \n",
              "2                    0.11             0.88               7   \n",
              "3                    0.72             0.87               5   \n",
              "4                    0.37             0.52               2   \n",
              "...                   ...              ...             ...   \n",
              "14994                0.40             0.57               2   \n",
              "14995                0.37             0.48               2   \n",
              "14996                0.37             0.53               2   \n",
              "14997                0.11             0.96               6   \n",
              "14998                0.37             0.52               2   \n",
              "\n",
              "       average_montly_hours  time_spend_company  Work_accident  \\\n",
              "0                       157                   3              0   \n",
              "1                       262                   6              0   \n",
              "2                       272                   4              0   \n",
              "3                       223                   5              0   \n",
              "4                       159                   3              0   \n",
              "...                     ...                 ...            ...   \n",
              "14994                   151                   3              0   \n",
              "14995                   160                   3              0   \n",
              "14996                   143                   3              0   \n",
              "14997                   280                   4              0   \n",
              "14998                   158                   3              0   \n",
              "\n",
              "       promotion_last_5years department  salary  left  \n",
              "0                          0      sales     low     1  \n",
              "1                          0      sales  medium     1  \n",
              "2                          0      sales  medium     1  \n",
              "3                          0      sales     low     1  \n",
              "4                          0      sales     low     1  \n",
              "...                      ...        ...     ...   ...  \n",
              "14994                      0    support     low     1  \n",
              "14995                      0    support     low     1  \n",
              "14996                      0    support     low     1  \n",
              "14997                      0    support     low     1  \n",
              "14998                      0    support     low     1  \n",
              "\n",
              "[14999 rows x 10 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
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              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>medium</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.87</td>\n",
              "      <td>5</td>\n",
              "      <td>223</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2</td>\n",
              "      <td>159</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>sales</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.57</td>\n",
              "      <td>2</td>\n",
              "      <td>151</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>support</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.48</td>\n",
              "      <td>2</td>\n",
              "      <td>160</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>support</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>143</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>support</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.96</td>\n",
              "      <td>6</td>\n",
              "      <td>280</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>support</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>14998</th>\n",
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              "      <td>0</td>\n",
              "      <td>support</td>\n",
              "      <td>low</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14999 rows × 10 columns</p>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b15137da-dde3-44e9-a8db-ef0cc0448f03 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b15137da-dde3-44e9-a8db-ef0cc0448f03');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </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",
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              "      --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",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      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-61a2ff9e-ce79-454c-9667-baa5641f41cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 14999,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24863065106114257,\n        \"min\": 0.09,\n        \"max\": 1.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          0.83,\n          0.13,\n          0.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17116911062327533,\n        \"min\": 0.36,\n        \"max\": 1.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          0.66,\n          0.44,\n          0.53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 96,\n        \"max\": 310,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          118,\n          112,\n          222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6,\n          8,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"department\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"marketing\",\n          \"accounting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"low\",\n          \"medium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Определение типов переменных"
      ],
      "metadata": {
        "id": "tm8i1hZ4A5lu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "0XVXA7LmA7fM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Список столбцов и их типов данных\n",
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOIfnW3OAbZ-",
        "outputId": "944bec28-45cb-4c95-c25a-46383e05cae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "satisfaction_level       float64\n",
            "last_evaluation          float64\n",
            "number_project             int64\n",
            "average_montly_hours       int64\n",
            "time_spend_company         int64\n",
            "Work_accident              int64\n",
            "promotion_last_5years      int64\n",
            "department                object\n",
            "salary                    object\n",
            "left                       int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Числовые переменные обычно имеют тип int64 или float64.\n",
        "- Категориальные переменные могут иметь тип object или category, но иногда целочисленные переменные также могут быть категориальными (например, метки классов)."
      ],
      "metadata": {
        "id": "D6K8rKQkBBTx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Почему целые числа могут быть категориальными**?"
      ],
      "metadata": {
        "id": "6ePNxVMIBNAX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "   ### Объяснение:\n",
        "   ```\n",
        "   Целочисленные переменные, такие как `Class`, могут представлять категории\n",
        "   (например, тип вина) вместо количественных значений.\n",
        "   В таких случаях они используются как метки классов, а не как числовые данные.\n",
        "   ```"
      ],
      "metadata": {
        "id": "pud5HW85BK4u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Статистический разбор данных"
      ],
      "metadata": {
        "id": "eiFQUgHECLP_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Посчитайте количество уникальных значений для каждой переменной:**"
      ],
      "metadata": {
        "id": "MbYwHh_gCseY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Количество уникальных значений\n",
        "for column in df.columns:\n",
        "    print(f'{column}: {df[column].nunique()} уникальных значений')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7YqAEKUBmhf",
        "outputId": "b7453ef7-ba85-4c7a-fbfd-be7d491ee28b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "satisfaction_level: 92 уникальных значений\n",
            "last_evaluation: 65 уникальных значений\n",
            "number_project: 6 уникальных значений\n",
            "average_montly_hours: 215 уникальных значений\n",
            "time_spend_company: 8 уникальных значений\n",
            "Work_accident: 2 уникальных значений\n",
            "promotion_last_5years: 2 уникальных значений\n",
            "department: 10 уникальных значений\n",
            "salary: 3 уникальных значений\n",
            "left: 2 уникальных значений\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **Определите, какие переменные являются непрерывными, а какие дискретными:**\n",
        "\n",
        "   - **Непрерывные переменные:** могут принимать любое значение в диапазоне.\n",
        "   - **Дискретные переменные:** принимают только определенные значения (обычно целые числа).\n",
        "\n",
        "   **Ваши ответы:**\n",
        "\n",
        "   - **Непрерывные переменные:**\n",
        "     - 'satisfaction_level,\n",
        "        last_evaluation,\n",
        "        average_montly_hours' ⏰\n",
        "   \n",
        "   - **Дискретные переменные:**\n",
        "     - 'number_project,\n",
        "        time_spend_company,\n",
        "        Work_accident,\n",
        "        promotion_last_5years,\n",
        "        department,\n",
        "        salary,\n",
        "        left' ⏰"
      ],
      "metadata": {
        "id": "qxTEAKDYCvkX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. **Посмотрите на статистические показатели и сделайте выводы о распределении переменных:**"
      ],
      "metadata": {
        "id": "UyZC2Uc1C0Zf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "   # Статистический разбор\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "KavTuUGxC28e",
        "outputId": "5186692b-5d21-435b-e48d-c39c274a9a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       satisfaction_level  last_evaluation  number_project  \\\n",
              "count        14999.000000     14999.000000    14999.000000   \n",
              "mean             0.612834         0.716102        3.803054   \n",
              "std              0.248631         0.171169        1.232592   \n",
              "min              0.090000         0.360000        2.000000   \n",
              "25%              0.440000         0.560000        3.000000   \n",
              "50%              0.640000         0.720000        4.000000   \n",
              "75%              0.820000         0.870000        5.000000   \n",
              "max              1.000000         1.000000        7.000000   \n",
              "\n",
              "       average_montly_hours  time_spend_company  Work_accident  \\\n",
              "count          14999.000000        14999.000000   14999.000000   \n",
              "mean             201.050337            3.498233       0.144610   \n",
              "std               49.943099            1.460136       0.351719   \n",
              "min               96.000000            2.000000       0.000000   \n",
              "25%              156.000000            3.000000       0.000000   \n",
              "50%              200.000000            3.000000       0.000000   \n",
              "75%              245.000000            4.000000       0.000000   \n",
              "max              310.000000           10.000000       1.000000   \n",
              "\n",
              "       promotion_last_5years          left  \n",
              "count           14999.000000  14999.000000  \n",
              "mean                0.021268      0.238083  \n",
              "std                 0.144281      0.425924  \n",
              "min                 0.000000      0.000000  \n",
              "25%                 0.000000      0.000000  \n",
              "50%                 0.000000      0.000000  \n",
              "75%                 0.000000      0.000000  \n",
              "max                 1.000000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e19dffc3-c738-42a9-aa0c-16941a2337b6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>satisfaction_level</th>\n",
              "      <th>last_evaluation</th>\n",
              "      <th>number_project</th>\n",
              "      <th>average_montly_hours</th>\n",
              "      <th>time_spend_company</th>\n",
              "      <th>Work_accident</th>\n",
              "      <th>promotion_last_5years</th>\n",
              "      <th>left</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.612834</td>\n",
              "      <td>0.716102</td>\n",
              "      <td>3.803054</td>\n",
              "      <td>201.050337</td>\n",
              "      <td>3.498233</td>\n",
              "      <td>0.144610</td>\n",
              "      <td>0.021268</td>\n",
              "      <td>0.238083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.248631</td>\n",
              "      <td>0.171169</td>\n",
              "      <td>1.232592</td>\n",
              "      <td>49.943099</td>\n",
              "      <td>1.460136</td>\n",
              "      <td>0.351719</td>\n",
              "      <td>0.144281</td>\n",
              "      <td>0.425924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.560000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.720000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.820000</td>\n",
              "      <td>0.870000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>310.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e19dffc3-c738-42a9-aa0c-16941a2337b6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e19dffc3-c738-42a9-aa0c-16941a2337b6 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-e19dffc3-c738-42a9-aa0c-16941a2337b6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e0e27d10-19d8-4fe4-8f04-c020cd69af3c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0e27d10-19d8-4fe4-8f04-c020cd69af3c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </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-e0e27d10-19d8-4fe4-8f04-c020cd69af3c 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\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"satisfaction_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.752785300377,\n        \"min\": 0.09,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6128335222348156,\n          0.64,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"last_evaluation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.725216503965,\n        \"min\": 0.17116911062327533,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.7161017401160078,\n          0.72,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"number_project\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5301.632605184108,\n        \"min\": 1.2325923553183522,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.80305353690246,\n          4.0,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_montly_hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5240.043131004932,\n        \"min\": 49.94309937128408,\n        \"max\": 14999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          201.0503366891126,\n          200.0,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spend_company\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5301.586356319515,\n        \"min\": 1.4601362305354812,\n        \"max\": 14999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14999.0,\n          3.498233215547703,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work_accident\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.871740631288,\n        \"min\": 0.0,\n        \"max\": 14999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1446096406427095,\n          1.0,\n          0.35171855238017985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"promotion_last_5years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.88844750851,\n        \"min\": 0.0,\n        \"max\": 14999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.021268084538969265,\n          1.0,\n          0.14428146457858232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"left\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5302.863271830312,\n        \"min\": 0.0,\n        \"max\": 14999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2380825388359224,\n          1.0,\n          0.4259240993802994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "   **Ваши наблюдения:**\n",
        "   \n",
        "   ВАЖНО! ⏰\n",
        "   **```тут пишем много текста почему мы так считаем, пойдет даже (ну я вижу там всего 4 уникальных значения, скорее всего категориальное ИЛИ ну там числа дробные, вероятно числоая переменная```**"
      ],
      "metadata": {
        "id": "pu48XMD9C5bo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Первые два столбца выдают информацию в формате процентного соотношения в виде от нуля до еденицы, можно заметить что только 25% людей удовлетворены своей работой меньше чем на 44%. Так же можно увидеть, что в среднем люди работают 200 часов в месяц, распределение довольно ровное. Так же можно сделать вывод, что 14% людей стаклкивались с несчастными случаями на производстве. Грустно. А по карьерной лестнице за 5 последних лет продвигались так и вовсе всего 2% опрошенных. Аж плакать хочется."
      ],
      "metadata": {
        "id": "tnN5_5ViFHk5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Визуализация распределения переменных**\n",
        "\n",
        "### **Описание:**\n",
        "Создайте визуализации для понимания распределения числовых и категориальных переменных.\n",
        "\n",
        "### **Шаги:**\n"
      ],
      "metadata": {
        "id": "YmWlNDdjDh7G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Гистограмма для числовой переменной `satisfaction_level`:**"
      ],
      "metadata": {
        "id": "7U-U04OFDlLr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "   sns.histplot(df['satisfaction_level'], kde=True)\n",
        "   plt.title('Распределение уровня удовлетворения')\n",
        "   plt.xlabel('satisfaction_level')\n",
        "   plt.ylabel('Количество')\n",
        "   plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "MyuMx_RlDl1y",
        "outputId": "b8c950d6-77ee-414d-e8bb-cef4d22d2fb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **Столбчатая диаграмма для категориальной переменной `salary`:**"
      ],
      "metadata": {
        "id": "xl44MN9qDnv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "   sns.countplot(x='salary', data=df)\n",
        "   plt.title('Распределение уровня зарплаты')\n",
        "   plt.xlabel('Зарплата')\n",
        "   plt.ylabel('Количество')\n",
        "   plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "S4PxrTMQDoyZ",
        "outputId": "31648bf5-36e4-4d81-b61f-ef191e66094e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Боксплот для числовой переменной `last_evaluation`:**"
      ],
      "metadata": {
        "id": "A_u0EaAxDpdY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "   sns.boxplot(x=df['last_evaluation'])\n",
        "   plt.title('Boxplot last_evaluation')\n",
        "   plt.xlabel('last_evaluation')\n",
        "   plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Au59-9x8DsYS",
        "outputId": "c9149bfb-6035-4ec9-ee7a-6c254d95798e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "   **Ваши наблюдения:**\n",
        "   \n",
        "   ВАЖНО! ⏰\n",
        "   **```тут пишем много текста почему мы так считаем, пойдет даже (Распределение по алкоголю сосредоточено не как нормальное распределение (alcohol), в среднем большинство значений в районе 6 (quality) ) ИЛИ на box plot chlorides видно много выбросов и медиана сосредоточена между 0 и 1``` **\n",
        "   \n",
        "   p.s. если знаете контекст, то можете описать"
      ],
      "metadata": {
        "id": "YkkhU8qNQiNu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "На box plot last_evaluation видно, что в основном рейтинг работников распределен между 0.56 и 0.87. Большинство людей получают зарплату иже среднего, немного меньшее количество получает среднюю, а высокую зарплату получают только избранные. Грустно. А вот уровень удовлетворенности работой распределен от 0.4 до 1 довольно равномерно. Однако мы можем увидеть очень резкий выброс в значении крайней неудовлетворенности. Аж плакать хочется."
      ],
      "metadata": {
        "id": "IBi2LsQbWvCI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. Дополнительное задание** ⏰ (не обязательное, но полезное)\n",
        "\n",
        "### **Описание:**\n",
        "Для тех, кто хочет углубить свои знания и навыки, выполните дополнительное задание ниже.\n",
        "\n",
        "### **Задание:**"
      ],
      "metadata": {
        "id": "B1bbdRrWD7iZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Постройте корреляционную матрицу для всех числовых переменных и визуализируйте её с помощью тепловой карты:**"
      ],
      "metadata": {
        "id": "f59wNH9vEBCj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "   # Корреляционная матрица\n",
        "   correlation_matrix = df.corr(numeric_only='true')\n",
        "\n",
        "   # Визуализация тепловой карты\n",
        "   sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "   plt.title('Корреляционная матрица')\n",
        "   plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "hjoJf7vbEDtY",
        "outputId": "4f0bfb52-c0c3-4bca-82f5-0267435418b3"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-a87a5602cb79>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Корреляционная матрица\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcorrelation_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'true'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Визуализация тепловой карты\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrelation_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coolwarm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Если будут ошибки ValueError из-за could not conver string to float\n",
        "df = df.drop('type', axis=1)\n",
        "# найти колонку, из-за которой ничего не получается,\n",
        "# можно найти по object из df.info()"
      ],
      "metadata": {
        "id": "6tI82fozG1wk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "d03973c2-1f58-4ca6-b5f3-5dcdca8585c9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-eee527658b54>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Если будут ошибки ValueError из-за could not conver string to float\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m# найти колонку, из-за которой ничего не получается,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# можно найти по object из df.info()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **Интерпретируйте полученную корреляционную матрицу:**\n",
        "   \n",
        "   - Какие переменные имеют сильную положительную корреляцию?\n",
        "   - Какие переменные имеют сильную отрицательную корреляцию?\n",
        "   - Какие переменные практически некоррелированы?\n",
        "\n",
        "   **Ваши ответы:**\n",
        "   \n",
        "   - **Сильная положительная корреляция:**\n",
        "     - `number_project \\ average_montly_hours`\n",
        "   \n",
        "   - **Сильная отрицательная корреляция:**\n",
        "     - `left \\ satisfaction_level`\n",
        "   \n",
        "   - **Практически некоррелированные переменные:**\n",
        "     - `time_spend_company \\ Work_accident`\n"
      ],
      "metadata": {
        "id": "Fv0XQxXrEGFb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Определите, какие переменные можно потенциально исключить из дальнейшего анализа из-за высокой корреляции:**\n",
        "   \n",
        "   **Ваши ответы:**\n",
        "   \n",
        "   - лучше всего никаких, так как корреляция нигде не показала достаточно большого уровня"
      ],
      "metadata": {
        "id": "9ks1q6K_EInx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Заключение**\n",
        "\n",
        "В этом задании вы научились загружать и исследовать набор данных, определять типы переменных, проводить статистический разбор и визуализировать распределение данных. Эти навыки являются фундаментальными для дальнейшего анализа данных и построения моделей машинного обучения."
      ],
      "metadata": {
        "id": "6hG7m031IMN_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## По желанию\n",
        "#### Опишите, что вы поняли из практики, и на чем стоит сфокусироваться, или общие впечатления\n",
        "\n",
        "## https://forms.yandex.ru/u/66e83a1ef47e73e9432f7e4f/"
      ],
      "metadata": {
        "id": "rpwjPg2gIPGa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "## **Рекомендации по выполнению задания:**\n",
        "\n",
        "- **Убедитесь, что все необходимые библиотеки установлены и импортированы.**\n",
        "- **Проверяйте правильность путей к файлам при загрузке данных.**\n",
        "- **Используйте комментарии для объяснения своих действий в коде.**\n",
        "- **Документируйте свои выводы и наблюдения в текстовых ячейках.**\n",
        "- **У вас могут быть ошибки, с которыми вы не сталкивались, в таком случае можете кинуть в чат и кто-нибудь из коллег (включая меня) могут помочь**\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "sVbfh3JeEUhA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Требования\n",
        "\n",
        "1. Не забываем про ⏰\n",
        "2. Итоговая тетрадка jupyter должна быть на вашем github (нет тетрадки, нет проверки)"
      ],
      "metadata": {
        "id": "NP_Sym3cQE3M"
      }
    }
  ]
}