titan.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/titan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1313 entries, 0 to 1312\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerID  1313 non-null   int64  \n",
            " 1   Name         1313 non-null   object \n",
            " 2   PClass       1313 non-null   object \n",
            " 3   Age          756 non-null    float64\n",
            " 4   Sex          1313 non-null   object \n",
            " 5   Survived     1313 non-null   int64  \n",
            " 6   SexCode      1313 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 71.9+ KB\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df_all = pd.read_csv('/content/titanic (1).csv')\n",
        "df_all.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna"
      ],
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              "<bound method DataFrame.isna of   Column1 Column2 Column3\n",
              "0      10       0     1st\n",
              "1      25    NULL     3rd\n",
              "2    NULL       1     3rd\n",
              "3      70       0     2nd\n",
              "4      18       1     1st>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find columns with NaN values\n",
        "columns_with_nan = df.columns[df.isna().any()].tolist()\n",
        "\n",
        "# Print the column names\n",
        "print(', '.join(columns_with_nan))"
      ],
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            "\n"
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        "df_all.describe()"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerID         Age     Survived      SexCode\n",
              "count  1313.000000  756.000000  1313.000000  1313.000000\n",
              "mean    657.000000   30.397989     0.342727     0.351866\n",
              "std     379.174762   14.259049     0.474802     0.477734\n",
              "min       1.000000    0.170000     0.000000     0.000000\n",
              "25%     329.000000   21.000000     0.000000     0.000000\n",
              "50%     657.000000   28.000000     0.000000     0.000000\n",
              "75%     985.000000   39.000000     1.000000     1.000000\n",
              "max    1313.000000   71.000000     1.000000     1.000000"
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              "      <th>count</th>\n",
              "      <td>1313.000000</td>\n",
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              "      <td>1313.000000</td>\n",
              "      <td>1313.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>657.000000</td>\n",
              "      <td>30.397989</td>\n",
              "      <td>0.342727</td>\n",
              "      <td>0.351866</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.474802</td>\n",
              "      <td>0.477734</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>985.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>71.000000</td>\n",
              "      <td>1.000000</td>\n",
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      "source": [
        "average_age_male = df_all[df_all['Sex'] == 'male']['Age'].mean()\n",
        "rounded_average_age_male = round(average_age_male)\n",
        "rounded_average_age_male"
      ],
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              "31"
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        "df_all.head()"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerID                                           Name PClass    Age  \\\n",
              "0            1                   Allen, Miss Elisabeth Walton    1st  29.00   \n",
              "1            2                    Allison, Miss Helen Loraine    1st   2.00   \n",
              "2            3            Allison, Mr Hudson Joshua Creighton    1st  30.00   \n",
              "3            4  Allison, Mrs Hudson JC (Bessie Waldo Daniels)    1st  25.00   \n",
              "4            5                  Allison, Master Hudson Trevor    1st   0.92   \n",
              "\n",
              "      Sex  Survived  SexCode  \n",
              "0  female         1        1  \n",
              "1  female         0        1  \n",
              "2    male         0        0  \n",
              "3  female         0        1  \n",
              "4    male         1        0  "
            ],
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              "      <td>Allen, Miss Elisabeth Walton</td>\n",
              "      <td>1st</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Allison, Master Hudson Trevor</td>\n",
              "      <td>1st</td>\n",
              "      <td>0.92</td>\n",
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      "source": [
        "\n",
        "average_age_female = df_all[(df_all['Sex'] == 'female') & (df_all['Survived'] == 1)]['Age'].mean()\n",
        "\n",
        "average_age_female"
      ],
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        "id": "3dBhUDMoc-eR",
        "outputId": "e2d32dae-4aaa-421b-b073-827c20354ef8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30.86714285714286"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_passengers = df_all.sort_values('Age')\n",
        "passenger_pclass = sorted_passengers.iloc[957]['PClass']\n",
        "passenger_pclass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "JAbGqRJnds7c",
        "outputId": "c8d4d9fc-d11a-41dc-fbf4-dec5e33a9933"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3rd'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame({\n",
        "    'Column1': [10, 25, 'NULL', 70, 18],\n",
        "    'Column2': [0, 'NULL', 1, 0, 1],\n",
        "    'Column3': ['1st', '3rd', '3rd', '2nd', '1st']})\n",
        "df"
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Column1 Column2 Column3\n",
              "0      10       0     1st\n",
              "1      25    NULL     3rd\n",
              "2    NULL       1     3rd\n",
              "3      70       0     2nd\n",
              "4      18       1     1st"
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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        "df.drop({'Column1','Column2'}, axis=1)"
      ],
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        "colab": {
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          "height": 206
        },
        "id": "gpX1wsiUBdVN",
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      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Column3\n",
              "0     1st\n",
              "1     3rd\n",
              "2     3rd\n",
              "3     2nd\n",
              "4     1st"
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv('/content/task4_1_939291.csv')\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fAtb7IUif1J",
        "outputId": "ab33d3c2-6530-4cc0-9783-d00520d746a2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype\n",
            "---  ------      --------------  -----\n",
            " 0   ID          200 non-null    int64\n",
            " 1   DISTANCE    200 non-null    int64\n",
            " 2   STOP_COUNT  200 non-null    int64\n",
            " 3   COST        200 non-null    int64\n",
            " 4   FITNESS     200 non-null    int64\n",
            " 5   INTERNET    200 non-null    int64\n",
            "dtypes: int64(6)\n",
            "memory usage: 9.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Присвоение имен столбцам\n",
        "data.columns = ['ID', 'Расстояние до метро (мин)', 'Количество остановок на метро', 'Стоимость аренды', 'Расстояние до фитнес-клуба (мин)', 'Количество интернет-провайдеров']\n",
        "\n",
        "# Экспоненциальная нормировка\n",
        "normalized_distance = data['Расстояние до метро (мин)'].apply(lambda x: 1 / (2 ** x))\n",
        "normalized_stops = data['Количество остановок на метро'].apply(lambda x: 1 / (2 ** x))\n",
        "normalized_rent = data['Стоимость аренды'].apply(lambda x: 1 / x)\n",
        "normalized_fitness = data['Расстояние до фитнес-клуба (мин)'].apply(lambda x: 1 / (2 ** x))\n",
        "normalized_providers = -0.4 * data['Количество интернет-провайдеров']\n",
        "\n",
        "# Вычисление целевой функции\n",
        "target_function = normalized_distance + normalized_stops + normalized_rent + 0.2 * normalized_fitness + normalized_providers\n",
        "\n",
        "# Выбор трех лучших вариантов\n",
        "best_options = target_function.nsmallest(3)\n",
        "\n",
        "# Получение идентификаторов трех лучших вариантов\n",
        "best_ids = best_options.index.tolist()\n",
        "\n",
        "# Вывод результатов\n",
        "print('Идентификаторы трех лучших вариантов:', ', '.join(map(str, best_ids)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uB8Kv6IHUzjJ",
        "outputId": "781304fc-30aa-44df-9540-ba1fbbdfed9d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Идентификаторы трех лучших вариантов: 125, 4, 44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "data = pd.read_csv('/content/task4_1_939291.csv')\n",
        "\n",
        "data['distance_walk_norm'] = 1 - np.exp(1 - data['DISTANCE'] / data['DISTANCE'].min())\n",
        "data['metro_stations_norm'] = 1 - np.exp(1 - data['STOP_COUNT'] / data['STOP_COUNT'].min())\n",
        "data['rent_norm'] = 1 - np.exp(1 - data['COST'] / data['COST'].min())\n",
        "data['fitness_distance_norm'] = 1 - np.exp(1 - data['FITNESS'] / data['FITNESS'].min())\n",
        "data['internet_providers_norm'] = -1 * np.exp(1 - data['INTERNET'] / data['INTERNET'].max())\n",
        "\n",
        "data['target_value'] = (\n",
        "    data['distance_walk_norm'] +\n",
        "    data['metro_stations_norm'] +\n",
        "    0.2 * data['fitness_distance_norm'] +\n",
        "    0.4 * data['internet_providers_norm']\n",
        ")\n",
        "\n",
        "best_options = data.nsmallest(3, 'target_value')['ID'].tolist()\n",
        "\n",
        "best_options_str = \",\".join(map(str, best_options))\n",
        "print(best_options_str)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPOx3Bz2nYwi",
        "outputId": "f4eb3ace-9150-4a03-83f1-d0025474315d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "180,17,9\n"
          ]
        }
      ]
    }
  ]
}