6_Addition_ipynb".ipynb

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      "name": "6.Addition.ipynb\"",
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    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kkattt51/ArtInt/blob/main/6_Addition_ipynb%22.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bewj4fAsLky1"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTNSZvvDLqDZ"
      },
      "source": [
        "#df_1 = pd.read_csv('titanic_data.csv')\n",
        "#pass_link = 'https://www.dropbox.com/s/lyzcuxu1pdrw5qb/titanic_data.csv?dl=1'\n",
        "#data = pd.read_csv(pass_link, ) #index_col=?\n",
        "data = pd.read_csv('https://www.dropbox.com/s/lyzcuxu1pdrw5qb/titanic_data.csv?dl=1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "L8F0FQKDVezD",
        "outputId": "942bf146-3e9c-43c5-f571-9e315301719a"
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0            1       3  ...   NaN        S\n",
              "1            2       1  ...   C85        C\n",
              "2            3       3  ...   NaN        S\n",
              "3            4       1  ...  C123        S\n",
              "4            5       3  ...   NaN        S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 3
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      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7GV2ks3Lupl"
      },
      "source": [
        "Задание 1\n",
        "Опишите данный датасет: какое расределение женщин/мужчин в нем? Сколько пассажиров ехало в каждом классе? Какой средний/минимальный/максимальный возраст пассажиров?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9nqS7XVVim6",
        "outputId": "c9468819-59b5-443e-8317-847b4f25f839"
      },
      "source": [
        "#data.info()\n",
        "#data.shape[0]\n",
        "#data.size\n",
        "#data['Sex'].value_counts()\n",
        "data.Age.max(), data.Age.min(), data.Age.mean()\n",
        "data.Pclass.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    491\n",
              "1    216\n",
              "2    184\n",
              "Name: Pclass, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74cy_yc2LwDB"
      },
      "source": [
        "Задание 2. Сгруппируйте записи по классам пассажиров, в каждой группе посчитайте средний возраст. Используйте метод pandas.DataFrame.groupby."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_JmLyKDW6yu",
        "outputId": "f1f28fb8-2a65-4abd-bdb0-4e45779cbee6"
      },
      "source": [
        "data.groupby('Pclass')['Age'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    38.233441\n",
              "2    29.877630\n",
              "3    25.140620\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYeF9yMkXkeo",
        "outputId": "136c3051-86fc-4b00-da5f-b5517d0ed304"
      },
      "source": [
        "data.groupby('Pclass')['Fare'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    512.3292\n",
              "2     73.5000\n",
              "3     69.5500\n",
              "Name: Fare, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDRfnPCOL1qp"
      },
      "source": [
        "Задание 3. Слияние таблиц Слейте два датасета по колонке индекса."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p9Qp2w7nMEN0"
      },
      "source": [
        "#df_2 = pd.read_csv('titanic_surv.csv')\n",
        "surv_link = 'https://www.dropbox.com/s/v35x9i6a1tc7emm/titanic_surv.csv?dl=1'\n",
        "df_2 = pd.read_csv(surv_link)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0o9W0N5L_jP"
      },
      "source": [
        "Задание Сколько всего выживших пассажиров? Выживших пассажиров по каждому из полов? Постройте матрицу корреляций факта выживания, пола и возраста."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "jD66SMCtZiAX",
        "outputId": "da74ceee-5985-4070-da82-51a5d11dcbfd"
      },
      "source": [
        "df_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>...</th>\n",
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              "<p>891 rows × 1 columns</p>\n",
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            "text/plain": [
              "     Survived\n",
              "0           0\n",
              "1           1\n",
              "2           1\n",
              "3           1\n",
              "4           0\n",
              "..        ...\n",
              "886         0\n",
              "887         1\n",
              "888         0\n",
              "889         1\n",
              "890         0\n",
              "\n",
              "[891 rows x 1 columns]"
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          "metadata": {
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          "execution_count": 8
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        "outputId": "f522d644-cb94-4efd-d998-dcb9c3a51ba6"
      },
      "source": [
        "data['survided'] = df_2\n",
        "df_2.index = np.arange(0, 891)\n",
        "#df_2.tail(5)\n",
        "df_2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "   Survived\n",
              "0         0\n",
              "1         1\n",
              "2         1\n",
              "3         1\n",
              "4         0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "nGgncjdqZwG3",
        "outputId": "ff10a8e3-8dd3-4ec1-c597-c4a210dbaba4"
      },
      "source": [
        "data['survided'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: survided, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "InlPJsfZZyG6",
        "outputId": "96fd42c6-9995-4540-f15b-81f91e551fae"
      },
      "source": [
        "data.groupby('survided')['Sex'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survided  Sex   \n",
              "0         male      468\n",
              "          female     81\n",
              "1         female    233\n",
              "          male      109\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "uaXUu_Otb_c8",
        "outputId": "b6b20f75-c705-48c0-c482-5bef7eb7a00a"
      },
      "source": [
        "data[data['Sex'] == 'male']['survided'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    468\n",
              "1    109\n",
              "Name: survided, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcTbHDfLcd8D",
        "outputId": "dccd6c70-057f-4fc3-f7d3-efab3191dbd1"
      },
      "source": [
        "data[data['Sex'] == 'female']['survided'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    233\n",
              "0     81\n",
              "Name: survided, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rGb2kvrwcj56",
        "outputId": "f826ad2d-419d-4b30-f008-28b43dc76e6a"
      },
      "source": [
        "data[data['survided'] == 1]['Sex'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "female    233\n",
              "male      109\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
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      "cell_type": "code",
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          "height": 404
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        "id": "-KEA6EZ8csx6",
        "outputId": "5e40418f-ad01-4aff-ef23-c784ac5f3617"
      },
      "source": [
        "data_cor = data[['survided', 'Age', 'Sex']]\n",
        "data_cor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survided</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>22.0</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survided   Age     Sex\n",
              "0           0  22.0    male\n",
              "1           1  38.0  female\n",
              "2           1  26.0  female\n",
              "3           1  35.0  female\n",
              "4           0  35.0    male\n",
              "..        ...   ...     ...\n",
              "886         0  27.0    male\n",
              "887         1  19.0  female\n",
              "888         0   NaN  female\n",
              "889         1  26.0    male\n",
              "890         0  32.0    male\n",
              "\n",
              "[891 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbFfAxq5c9tt",
        "outputId": "b6a5f1ce-cc71-4301-bab0-157a74f7cd96"
      },
      "source": [
        "data_cor.loc[data_cor['Sex'] == 'male', 'Sex'] = 0\n",
        "data_cor.loc[data_cor['Sex'] == 'female', 'Sex'] = 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  isetter(loc, value)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8WEoM1gdaNG",
        "outputId": "5efae64f-8074-4219-af34-7a0827490e5f"
      },
      "source": [
        "data_cor['Sex'] = data_cor['Sex'].astype('int')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "5leNQc-bdv34",
        "outputId": "121e6b62-997f-40ff-8a97-f935e2e80562"
      },
      "source": [
        "data_cor.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survided</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>survided</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.077221</td>\n",
              "      <td>0.543351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.077221</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.093254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.543351</td>\n",
              "      <td>-0.093254</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          survided       Age       Sex\n",
              "survided  1.000000 -0.077221  0.543351\n",
              "Age      -0.077221  1.000000 -0.093254\n",
              "Sex       0.543351 -0.093254  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KZyMF4seB_I"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}