데이터_핸들링_판다스.ipynb

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        "<a href=\"https://colab.research.google.com/github/aliciawill/python-data/blob/master/%E1%84%83%E1%85%A6%E1%84%8B%E1%85%B5%E1%84%90%E1%85%A5_%E1%84%92%E1%85%A2%E1%86%AB%E1%84%83%E1%85%B3%E1%86%AF%E1%84%85%E1%85%B5%E1%86%BC_%E1%84%91%E1%85%A1%E1%86%AB%E1%84%83%E1%85%A1%E1%84%89%E1%85%B3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8syrvwP3uW9y"
      },
      "source": [
        "### Pandas 시작- 파일을 DataFrame 로딩, 기본 API"
      ]
    },
    {
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      "execution_count": 1,
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        "id": "Embsr9XUuW91"
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      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
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          "text": [
            "titanic 변수 type: <class 'pandas.core.frame.DataFrame'>\n"
          ]
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              "     pclass  survived                                     name     sex    age  \\\n",
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              "1         2         1                        Wells, Miss. Joan  female   4.00   \n",
              "2         2         1           Duran y More, Miss. Florentina  female  30.00   \n",
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              "4         3         1             Bradley, Miss. Bridget Delia  female  22.00   \n",
              "..      ...       ...                                      ...     ...    ...   \n",
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              "912       3         0                        Guest, Mr. Robert    male    NaN   \n",
              "913       3         1                       Smyth, Miss. Julia  female    NaN   \n",
              "914       2         1                     Sincock, Miss. Maude  female  20.00   \n",
              "915       3         0            Bourke, Mrs. John (Catherine)  female  32.00   \n",
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              "     sibsp  parch         ticket     fare cabin embarked  body  \\\n",
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              "1        1      1          29103  23.0000   NaN        S   NaN   \n",
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              "..                                                 ...  \n",
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              "913                                                NaN  \n",
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              "          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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          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "titanic_df = pd.read_csv('titanic_train.csv')\n",
        "print('titanic 변수 type:',type(titanic_df))\n",
        "titanic_df\n"
      ]
    },
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              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
              "1       2         1                  Wells, Miss. Joan  female   4.0      1   \n",
              "2       2         1     Duran y More, Miss. Florentina  female  30.0      1   \n",
              "\n",
              "   parch         ticket     fare cabin embarked  body  \\\n",
              "0      1         250644  19.5000   NaN        S   NaN   \n",
              "1      1          29103  23.0000   NaN        S   NaN   \n",
              "2      0  SC/PARIS 2148  13.8583   NaN        C   NaN   \n",
              "\n",
              "                         home.dest  \n",
              "0         England / Bennington, VT  \n",
              "1             Cornwall / Akron, OH  \n",
              "2  Barcelona, Spain / Havana, Cuba  "
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              "              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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e6c6c7fb-44f9-44f8-a43b-b1008b72ad29 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-e6c6c7fb-44f9-44f8-a43b-b1008b72ad29');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1D7mEU5uW94",
        "outputId": "d0c7510a-5fd8-49e5-df69-889bc7d2a875"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame 크기:  (916, 13)\n"
          ]
        }
      ],
      "source": [
        "print('DataFrame 크기: ', titanic_df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVKZCqvEuW94",
        "outputId": "cbd6ca8f-11dc-488a-9bf9-4cfb4b2c115c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 916 entries, 0 to 915\n",
            "Data columns (total 13 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   pclass     916 non-null    int64  \n",
            " 1   survived   916 non-null    int64  \n",
            " 2   name       916 non-null    object \n",
            " 3   sex        916 non-null    object \n",
            " 4   age        741 non-null    float64\n",
            " 5   sibsp      916 non-null    int64  \n",
            " 6   parch      916 non-null    int64  \n",
            " 7   ticket     916 non-null    object \n",
            " 8   fare       916 non-null    float64\n",
            " 9   cabin      214 non-null    object \n",
            " 10  embarked   914 non-null    object \n",
            " 11  body       85 non-null     float64\n",
            " 12  home.dest  527 non-null    object \n",
            "dtypes: float64(3), int64(4), object(6)\n",
            "memory usage: 93.2+ KB\n"
          ]
        }
      ],
      "source": [
        "titanic_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "FqQPBm9euW95",
        "outputId": "334a9799-e68f-4cf8-ab4b-2086a76c65e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           pclass    survived         age       sibsp       parch        fare  \\\n",
              "count  916.000000  916.000000  741.000000  916.000000  916.000000  916.000000   \n",
              "mean     2.292576    0.385371   30.231444    0.532751    0.371179   32.532050   \n",
              "std      0.842576    0.486949   13.934240    1.125957    0.837508   47.416524   \n",
              "min      1.000000    0.000000    0.170000    0.000000    0.000000    0.000000   \n",
              "25%      1.000000    0.000000   21.000000    0.000000    0.000000    7.895800   \n",
              "50%      3.000000    0.000000   28.000000    0.000000    0.000000   14.454200   \n",
              "75%      3.000000    1.000000   39.000000    1.000000    0.000000   31.275000   \n",
              "max      3.000000    1.000000   74.000000    8.000000    9.000000  512.329200   \n",
              "\n",
              "             body  \n",
              "count   85.000000  \n",
              "mean   157.870588  \n",
              "std     98.257653  \n",
              "min      1.000000  \n",
              "25%     68.000000  \n",
              "50%    148.000000  \n",
              "75%    249.000000  \n",
              "max    328.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f7ab300-0081-4d60-9b28-87c546b1d16c\">\n",
              "    <div 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>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>916.000000</td>\n",
              "      <td>916.000000</td>\n",
              "      <td>741.000000</td>\n",
              "      <td>916.000000</td>\n",
              "      <td>916.000000</td>\n",
              "      <td>916.000000</td>\n",
              "      <td>85.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.292576</td>\n",
              "      <td>0.385371</td>\n",
              "      <td>30.231444</td>\n",
              "      <td>0.532751</td>\n",
              "      <td>0.371179</td>\n",
              "      <td>32.532050</td>\n",
              "      <td>157.870588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.842576</td>\n",
              "      <td>0.486949</td>\n",
              "      <td>13.934240</td>\n",
              "      <td>1.125957</td>\n",
              "      <td>0.837508</td>\n",
              "      <td>47.416524</td>\n",
              "      <td>98.257653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>68.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>148.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.275000</td>\n",
              "      <td>249.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>328.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f7ab300-0081-4d60-9b28-87c546b1d16c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-8f7ab300-0081-4d60-9b28-87c546b1d16c 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-8f7ab300-0081-4d60-9b28-87c546b1d16c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "titanic_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e83KlYFuW95",
        "outputId": "1f934c82-8d5e-4fac-8209-ae98dd809293"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3    498\n",
            "1    230\n",
            "2    188\n",
            "Name: pclass, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "value_counts = titanic_df['pclass'].value_counts()\n",
        "print(value_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2E7QyXyuW96",
        "outputId": "26fe666b-95a2-4bd0-ad97-569d35ea1108"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "source": [
        "titanic_pclass = titanic_df['pclass']\n",
        "print(type(titanic_pclass))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECMWUd_JuW97",
        "outputId": "d09832ed-3fc4-4e5a-87f2-735efd8e3ae3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2\n",
              "1    2\n",
              "2    2\n",
              "3    3\n",
              "4    3\n",
              "Name: pclass, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "titanic_pclass.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abDb7ChruW97",
        "outputId": "89c66931-fad9-4e4b-c3f1-b622a238a688"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "3    498\n",
            "1    230\n",
            "2    188\n",
            "Name: pclass, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "value_counts = titanic_df['pclass'].value_counts()\n",
        "print(type(value_counts))\n",
        "print(value_counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2qLPwO9_uW98"
      },
      "source": [
        "### DataFrame과 리스트, 딕셔너리, 넘파이 ndarray 상호 변환\n",
        "\n",
        "* 넘파이 ndarray, 리스트, 딕셔너리를 DataFrame으로 변환하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WrtNbqcwuW98",
        "outputId": "12675601-601c-4885-e84e-35f0dff92387"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array1 shape: (3,)\n",
            "1차원 리스트로 만든 DataFrame:\n",
            "    col1\n",
            "0     1\n",
            "1     2\n",
            "2     3\n",
            "1차원 ndarray로 만든 DataFrame:\n",
            "    col1\n",
            "0     1\n",
            "1     2\n",
            "2     3\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "col_name1=['col1']\n",
        "list1 = [1, 2, 3]\n",
        "array1 = np.array(list1)\n",
        "\n",
        "print('array1 shape:', array1.shape )\n",
        "df_list1 = pd.DataFrame(list1, columns=col_name1)\n",
        "print('1차원 리스트로 만든 DataFrame:\\n', df_list1)\n",
        "df_array1 = pd.DataFrame(array1, columns=col_name1)\n",
        "print('1차원 ndarray로 만든 DataFrame:\\n', df_array1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAHjOHDMuW98",
        "outputId": "41d7e601-f30b-4b2c-ed20-410e847bd48a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array2 shape: (2, 3)\n",
            "2차원 리스트로 만든 DataFrame:\n",
            "    col1  col2  col3\n",
            "0     1     2     3\n",
            "1    11    12    13\n",
            "2차원 ndarray로 만든 DataFrame:\n",
            "    col1  col2  col3\n",
            "0     1     2     3\n",
            "1    11    12    13\n"
          ]
        }
      ],
      "source": [
        "# 3개의 컬럼명이 필요함. \n",
        "col_name2=['col1', 'col2', 'col3']\n",
        "\n",
        "# 2행x3열 형태의 리스트와 ndarray 생성 한 뒤 이를 DataFrame으로 변환. \n",
        "list2 = [[1, 2, 3],\n",
        "         [11, 12, 13]]\n",
        "array2 = np.array(list2)\n",
        "print('array2 shape:', array2.shape )\n",
        "df_list2 = pd.DataFrame(list2, columns=col_name2)\n",
        "print('2차원 리스트로 만든 DataFrame:\\n', df_list2)\n",
        "df_array2 = pd.DataFrame(array2, columns=col_name2)\n",
        "print('2차원 ndarray로 만든 DataFrame:\\n', df_array2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3MHyyKDuW99",
        "outputId": "fc2b57e4-edad-4768-a282-4fc8426926a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "딕셔너리로 만든 DataFrame:\n",
            "    col1  col2  col3\n",
            "0     1     2     3\n",
            "1    11    22    33\n"
          ]
        }
      ],
      "source": [
        "# Key는 컬럼명으로 매핑, Value는 리스트 형(또는 ndarray)\n",
        "dict = {'col1':[1, 11], 'col2':[2, 22], 'col3':[3, 33]}\n",
        "df_dict = pd.DataFrame(dict)\n",
        "print('딕셔너리로 만든 DataFrame:\\n', df_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Thip0Ny8uW99"
      },
      "source": [
        "* DataFrame을 넘파이 ndarray, 리스트, 딕셔너리로 변환하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZarjzzEuW9-",
        "outputId": "80eeee9a-720d-4c37-ff2e-ec9df77a772b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "df_dict.values 타입: <class 'numpy.ndarray'> df_dict.values shape: (2, 3)\n",
            "[[ 1  2  3]\n",
            " [11 22 33]]\n"
          ]
        }
      ],
      "source": [
        "# DataFrame을 ndarray로 변환\n",
        "array3 = df_dict.values\n",
        "print('df_dict.values 타입:', type(array3), 'df_dict.values shape:', array3.shape)\n",
        "print(array3)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ks7hI2BRuW9-",
        "outputId": "d8676c0d-9e01-4cd8-9d64-194efc900fb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "df_dict.values.tolist() 타입: <class 'list'>\n",
            "[[1, 2, 3], [11, 22, 33]]\n",
            "\n",
            " df_dict.to_dict() 타입: <class 'dict'>\n",
            "{'col1': [1, 11], 'col2': [2, 22], 'col3': [3, 33]}\n"
          ]
        }
      ],
      "source": [
        "# DataFrame을 리스트로 변환\n",
        "list3 = df_dict.values.tolist()\n",
        "print('df_dict.values.tolist() 타입:', type(list3))\n",
        "print(list3)\n",
        "\n",
        "# DataFrame을 딕셔너리로 변환\n",
        "dict3 = df_dict.to_dict('list')\n",
        "print('\\n df_dict.to_dict() 타입:', type(dict3))\n",
        "print(dict3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bf1mDqG4uW9-"
      },
      "source": [
        "### DataFrame의 컬럼 데이터 셋 Access"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "78R7d8RFuW9-",
        "outputId": "29975b1d-3695-48ec-c007-0cb4fc6a3e18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
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              "2      0  SC/PARIS 2148  13.8583   NaN        C   NaN   \n",
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        "titanic_df['Age_0']=0\n",
        "titanic_df.head(3)"
      ]
    },
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      "execution_count": 28,
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            "text/plain": [
              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
              "1       2         1                  Wells, Miss. Joan  female   4.0      1   \n",
              "2       2         1     Duran y More, Miss. Florentina  female  30.0      1   \n",
              "\n",
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              "1      1          29103  23.0000   NaN        S   NaN   \n",
              "2      0  SC/PARIS 2148  13.8583   NaN        C   NaN   \n",
              "\n",
              "                         home.dest  Age_0  Age_by_10  Family_No  \n",
              "0         England / Bennington, VT      0      130.0          2  \n",
              "1             Cornwall / Akron, OH      0       40.0          3  \n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9110936-e748-4416-a4fb-ca1f9a1cf85c 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-f9110936-e748-4416-a4fb-ca1f9a1cf85c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "titanic_df['Age_by_10'] = titanic_df['age']*10\n",
        "titanic_df['Family_No'] = titanic_df['sibsp'] + titanic_df['parch']+1\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "wgNRv051uW9_",
        "outputId": "adee56ff-9d2a-41cb-b64d-05e4a4ceac87"
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          "output_type": "execute_result",
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              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
              "1       2         1                  Wells, Miss. Joan  female   4.0      1   \n",
              "2       2         1     Duran y More, Miss. Florentina  female  30.0      1   \n",
              "\n",
              "   parch         ticket     fare cabin embarked  body  \\\n",
              "0      1         250644  19.5000   NaN        S   NaN   \n",
              "1      1          29103  23.0000   NaN        S   NaN   \n",
              "2      0  SC/PARIS 2148  13.8583   NaN        C   NaN   \n",
              "\n",
              "                         home.dest  Age_0  Age_by_10  Family_No  \n",
              "0         England / Bennington, VT      0      230.0          2  \n",
              "1             Cornwall / Akron, OH      0      140.0          3  \n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-3e2aae4a-b0a4-4ac0-a5fd-78d7c241ce07');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "titanic_df['Age_by_10'] = titanic_df['Age_by_10'] + 100\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjFM4tRuuW9_"
      },
      "source": [
        "### DataFrame 데이터 삭제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "eNZtA7_ZuW9_",
        "outputId": "dfc57ca8-0560-4511-9fe9-46070fea913d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
              "1       2         1                  Wells, Miss. Joan  female   4.0      1   \n",
              "2       2         1     Duran y More, Miss. Florentina  female  30.0      1   \n",
              "\n",
              "   parch         ticket     fare cabin embarked  body  \\\n",
              "0      1         250644  19.5000   NaN        S   NaN   \n",
              "1      1          29103  23.0000   NaN        S   NaN   \n",
              "2      0  SC/PARIS 2148  13.8583   NaN        C   NaN   \n",
              "\n",
              "                         home.dest  Age_by_10  Family_No  \n",
              "0         England / Bennington, VT      230.0          2  \n",
              "1             Cornwall / Akron, OH      140.0          3  \n",
              "2  Barcelona, Spain / Havana, Cuba      400.0          2  "
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>ticket</th>\n",
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              "      <th>embarked</th>\n",
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              "      <th>1</th>\n",
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              "      <td>Duran y More, Miss. Florentina</td>\n",
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              "      <td>30.0</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07d24c68-6d23-40fa-8b6a-23640a1689f6 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-07d24c68-6d23-40fa-8b6a-23640a1689f6');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "titanic_drop_df = titanic_df.drop('Age_0', axis=1 )\n",
        "titanic_drop_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "zxMCIpL5uW-A",
        "outputId": "f4943ba7-f97f-4cde-b247-026ae9db7e70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
              "1       2         1                  Wells, Miss. Joan  female   4.0      1   \n",
              "2       2         1     Duran y More, Miss. Florentina  female  30.0      1   \n",
              "\n",
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              "1      1          29103  23.0000   NaN        S   NaN   \n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "1SvAHztLuW-A",
        "outputId": "f9bddefd-aa40-4943-ed4d-af5fdada5adf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " inplace=True 로 drop 후 반환된 값: None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived                               name     sex   age  sibsp  \\\n",
              "0       2         1  Mellinger, Miss. Madeleine Violet  female  13.0      0   \n",
              "1       2         1                  Wells, Miss. Joan  female   4.0      1   \n",
              "2       2         1     Duran y More, Miss. Florentina  female  30.0      1   \n",
              "\n",
              "   parch         ticket     fare cabin embarked  body  \\\n",
              "0      1         250644  19.5000   NaN        S   NaN   \n",
              "1      1          29103  23.0000   NaN        S   NaN   \n",
              "2      0  SC/PARIS 2148  13.8583   NaN        C   NaN   \n",
              "\n",
              "                         home.dest  \n",
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              "                                                     [key], {});\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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          },
          "metadata": {},
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        }
      ],
      "source": [
        "drop_result = titanic_df.drop(['Age_0', 'Age_by_10', 'Family_No'], axis=1, inplace=True)\n",
        "print(' inplace=True 로 drop 후 반환된 값:',drop_result)\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "UQ8S_3zRzfjE",
        "outputId": "70fbc5b0-b90e-4c88-8d45-ad9748cb8d81"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     pclass  survived            name     sex    age  sibsp  parch      ticket     fare cabin embarked  body       home.dest\n",
              "3         3         0  Scanlan, Mr...    male    NaN      0      0       36209   7.7250   NaN        Q   NaN             NaN\n",
              "4         3         1  Bradley, Mi...  female  22.00      0      0      334914   7.7250   NaN        Q   NaN  Kingwilliam...\n",
              "5         3         0  Linehan, Mr...    male    NaN      0      0      330971   7.8792   NaN        Q   NaN             NaN\n",
              "6         1         1  Francatelli...  female  30.00      0      0    PC 17485  56.9292   E36        C   NaN             NaN\n",
              "7         2         1  Quick, Miss...  female   2.00      1      1       26360  26.0000   NaN        S   NaN  Plymouth, D...\n",
              "..      ...       ...             ...     ...    ...    ...    ...         ...      ...   ...      ...   ...             ...\n",
              "911       3         1  Dean, Miss....  female   0.17      1      2   C.A. 2315  20.5750   NaN        S   NaN  Devon, Engl...\n",
              "912       3         0  Guest, Mr. ...    male    NaN      0      0      376563   8.0500   NaN        S   NaN             NaN\n",
              "913       3         1  Smyth, Miss...  female    NaN      0      0      335432   7.7333   NaN        Q   NaN             NaN\n",
              "914       2         1  Sincock, Mi...  female  20.00      0      0  C.A. 33112  36.7500   NaN        S   NaN  Cornwall / ...\n",
              "915       3         0  Bourke, Mrs...  female  32.00      1      1      364849  15.5000   NaN        Q   NaN  Ireland Chi...\n",
              "\n",
              "[913 rows x 13 columns]"
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              "      <td>335432</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>914</th>\n",
              "      <td>2</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Ireland Chi...</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.columns"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "Z16HkCNnzkC0",
        "outputId": "1c1ee94f-649f-40af-8e5f-83f9cd31ade5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked', 'body', 'home.dest'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df2 = titanic_df\n",
        "titanic_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "b_pnjJHyzzPJ",
        "outputId": "7d9829cd-32c1-48ff-9265-4e15a76f8d3a"
      },
      "execution_count": 41,
      "outputs": [
        {
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              "     pclass  survived            name     sex    age  sibsp  parch      ticket     fare cabin embarked  body       home.dest\n",
              "3         3         0  Scanlan, Mr...    male    NaN      0      0       36209   7.7250   NaN        Q   NaN             NaN\n",
              "4         3         1  Bradley, Mi...  female  22.00      0      0      334914   7.7250   NaN        Q   NaN  Kingwilliam...\n",
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              "913       3         1  Smyth, Miss...  female    NaN      0      0      335432   7.7333   NaN        Q   NaN             NaN\n",
              "914       2         1  Sincock, Mi...  female  20.00      0      0  C.A. 33112  36.7500   NaN        S   NaN  Cornwall / ...\n",
              "915       3         0  Bourke, Mrs...  female  32.00      1      1      364849  15.5000   NaN        Q   NaN  Ireland Chi...\n",
              "\n",
              "[913 rows x 13 columns]"
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d54a6711-88a8-488b-9c7b-ea35ae0d5e99')\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d54a6711-88a8-488b-9c7b-ea35ae0d5e99 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-d54a6711-88a8-488b-9c7b-ea35ae0d5e99');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.index"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eKrCKWRuz_4X",
        "outputId": "06cd218b-2a4e-40d2-d7ea-f8da03c136e8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
              "            ...\n",
              "            906, 907, 908, 909, 910, 911, 912, 913, 914, 915], dtype='int64', length=913)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.drop(index=[6])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "MVk9Ip7Bz3T7",
        "outputId": "3efece26-0dfa-493d-ec74-2e0694b4a168"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
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              "915       3         0  Bourke, Mrs...  female  32.00      1      1      364849   15.5000   NaN        Q   NaN  Ireland Chi...\n",
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              "<p>909 rows × 13 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03ab6d18-385c-4a1b-9e49-9425a8512624')\"\n",
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              "  </svg>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-03ab6d18-385c-4a1b-9e49-9425a8512624 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-03ab6d18-385c-4a1b-9e49-9425a8512624');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9pKVHabuW-A",
        "outputId": "88749095-a297-4c78-9c9a-0af427411559"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### before axis 0 drop ####\n",
            "   pclass  survived            name     sex   age  sibsp  parch  ticket    fare cabin embarked  body       home.dest\n",
            "3       3         0  Scanlan, Mr...    male   NaN      0      0   36209  7.7250   NaN        Q   NaN             NaN\n",
            "4       3         1  Bradley, Mi...  female  22.0      0      0  334914  7.7250   NaN        Q   NaN  Kingwilliam...\n",
            "5       3         0  Linehan, Mr...    male   NaN      0      0  330971  7.8792   NaN        Q   NaN             NaN\n",
            "#### after axis 0 drop ####\n",
            "   pclass  survived            name     sex   age  sibsp  parch    ticket      fare cabin embarked  body       home.dest\n",
            "6       1         1  Francatelli...  female  30.0      0      0  PC 17485   56.9292   E36        C   NaN             NaN\n",
            "7       2         1  Quick, Miss...  female   2.0      1      1     26360   26.0000   NaN        S   NaN  Plymouth, D...\n",
            "8       1         0  Thayer, Mr....    male  49.0      1      1     17421  110.8833   C68        C   NaN   Haverford, PA\n"
          ]
        }
      ],
      "source": [
        "pd.set_option('display.width', 1000)\n",
        "pd.set_option('display.max_colwidth', 15)\n",
        "print('#### before axis 0 drop ####')\n",
        "print(titanic_df.head(3))\n",
        "\n",
        "titanic_df.drop([3,4,5], axis=0, inplace=True)\n",
        "\n",
        "print('#### after axis 0 drop ####')\n",
        "print(titanic_df.head(3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qu_OzzSauW-A"
      },
      "source": [
        "### Index 객체"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25Ndb2QMuW-A",
        "outputId": "221a0b53-09bb-424f-9b7b-f3570b3f6709"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RangeIndex(start=0, stop=916, step=1)\n",
            "Index 객체 array값:\n",
            " [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
            "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
            "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
            "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
            "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
            " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
            " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
            " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
            " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
            " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
            " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
            " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
            " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
            " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
            " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
            " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
            " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
            " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
            " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
            " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
            " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
            " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
            " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
            " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
            " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
            " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
            " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
            " 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
            " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
            " 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593\n",
            " 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611\n",
            " 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629\n",
            " 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647\n",
            " 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665\n",
            " 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683\n",
            " 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701\n",
            " 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719\n",
            " 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737\n",
            " 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755\n",
            " 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773\n",
            " 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791\n",
            " 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809\n",
            " 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827\n",
            " 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845\n",
            " 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863\n",
            " 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881\n",
            " 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899\n",
            " 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915]\n"
          ]
        }
      ],
      "source": [
        "# 원본 파일 재 로딩 \n",
        "titanic_df = pd.read_csv('titanic_train.csv')\n",
        "# Index 객체 추출\n",
        "indexes = titanic_df.index\n",
        "print(indexes)\n",
        "# Index 객체를 실제 값 arrray로 변환 \n",
        "print('Index 객체 array값:\\n',indexes.values)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6nNKAvauW-B",
        "outputId": "38b6ce89-91f3-47bd-f0be-2c283e099f04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "(916,)\n",
            "[0 1 2 3 4]\n",
            "[0 1 2 3 4]\n",
            "6\n"
          ]
        }
      ],
      "source": [
        "print(type(indexes.values))\n",
        "print(indexes.values.shape)\n",
        "print(indexes[:5].values)\n",
        "print(indexes.values[:5])\n",
        "print(indexes[6])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indexes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zg_AQ1IU08D8",
        "outputId": "d15a98d0-529c-42ef-cd3d-e97172c9c9a4"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=916, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indexes[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ouDtM6mt1DXn",
        "outputId": "f639f74c-55d1-4921-c6a9-d28750cfddb5"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mCy9ljpuW-B",
        "outputId": "7fcc9e48-c1c1-4dfb-9457-93a9712b6335"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "indexes[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dvNYL7luW-B",
        "outputId": "3f6e612a-1ff5-4427-e7f1-3588819fd723"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fair Series max 값: 512.3292\n",
            "Fair Series sum 값: 29799.358099999998\n",
            "sum() Fair Series: 29799.35809999998\n",
            "Fair Series + 3:\n",
            " 0    22.5000\n",
            "1    26.0000\n",
            "2    16.8583\n",
            "Name: fare, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "series_fair = titanic_df['fare']\n",
        "print('Fair Series max 값:', series_fair.max())\n",
        "print('Fair Series sum 값:', series_fair.sum())\n",
        "print('sum() Fair Series:', sum(series_fair))\n",
        "print('Fair Series + 3:\\n',(series_fair + 3).head(3) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "cMfHsaBhuW-B",
        "outputId": "15a00d40-0815-4dba-f350-44535ed9f6d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  pclass  survived            name     sex   age  sibsp  parch         ticket     fare cabin embarked  body       home.dest\n",
              "0      0       2         1  Mellinger, ...  female  13.0      0      1         250644  19.5000   NaN        S   NaN  England / B...\n",
              "1      1       2         1  Wells, Miss...  female   4.0      1      1          29103  23.0000   NaN        S   NaN  Cornwall / ...\n",
              "2      2       2         1  Duran y Mor...  female  30.0      1      0  SC/PARIS 2148  13.8583   NaN        C   NaN  Barcelona, ..."
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\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>index</th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
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              "      <td>England / B...</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Duran y Mor...</td>\n",
              "      <td>female</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>SC/PARIS 2148</td>\n",
              "      <td>13.8583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Barcelona, ...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f796f6eb-2c44-4b26-a5c6-0c85184a111a')\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "        buttonEl.style.display =\n",
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              "          if (!dataTable) return;\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "titanic_reset_df = titanic_df.reset_index(inplace=False)\n",
        "titanic_reset_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of1020l2uW-B",
        "outputId": "6ca2e475-ff6b-4670-8d9e-a3f843f8ec60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### before reset_index ###\n",
            "3    498\n",
            "1    230\n",
            "2    188\n",
            "Name: pclass, dtype: int64\n",
            "value_counts 객체 변수 타입: <class 'pandas.core.series.Series'>\n",
            "### After reset_index ###\n",
            "   index  pclass\n",
            "0      3     498\n",
            "1      1     230\n",
            "2      2     188\n",
            "new_value_counts 객체 변수 타입: <class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ],
      "source": [
        "print('### before reset_index ###')\n",
        "value_counts = titanic_df['pclass'].value_counts()\n",
        "print(value_counts)\n",
        "print('value_counts 객체 변수 타입:',type(value_counts))\n",
        "\n",
        "new_value_counts = value_counts.reset_index(inplace=False)\n",
        "print('### After reset_index ###')\n",
        "print(new_value_counts)\n",
        "print('new_value_counts 객체 변수 타입:',type(new_value_counts))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_LUA0LauW-C"
      },
      "source": [
        "### 데이터 셀렉션 및 필터링"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioFlZAIxuW-C"
      },
      "source": [
        "* DataFrame의 [ ] 연산자"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgXw7MxeuW-C",
        "outputId": "64d81a65-2134-488f-f5f8-b0f2ac2fb029"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "단일 컬럼 데이터 추출:\n",
            " 0    2\n",
            "1    2\n",
            "2    2\n",
            "Name: pclass, dtype: int64\n",
            "\n",
            "여러 컬럼들의 데이터 추출:\n",
            "    survived  pclass\n",
            "0         1       2\n",
            "1         1       2\n",
            "2         1       2\n"
          ]
        }
      ],
      "source": [
        "print('단일 컬럼 데이터 추출:\\n', titanic_df[ 'pclass' ].head(3))\n",
        "print('\\n여러 컬럼들의 데이터 추출:\\n', titanic_df[ ['survived', 'pclass'] ].head(3))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " titanic_df.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blbU7Hn8HMVx",
        "outputId": "eef6c15c-1b1c-412b-81db-9b2918609144"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass                    2\n",
              "survived                  1\n",
              "name         Mellinger, ...\n",
              "sex                  female\n",
              "age                    13.0\n",
              "sibsp                     0\n",
              "parch                     1\n",
              "ticket               250644\n",
              "fare                   19.5\n",
              "cabin                   NaN\n",
              "embarked                  S\n",
              "body                    NaN\n",
              "home.dest    England / B...\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('[ ] 안에 숫자 index는 KeyError 오류 발생:\\n', titanic_df.iloc[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4ePT0vs1c01",
        "outputId": "8a2e419d-9e3e-4d3a-e9c2-fcaee8553381"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ ] 안에 숫자 index는 KeyError 오류 발생:\n",
            " pclass                    2\n",
            "survived                  1\n",
            "name         Mellinger, ...\n",
            "sex                  female\n",
            "age                    13.0\n",
            "sibsp                     0\n",
            "parch                     1\n",
            "ticket               250644\n",
            "fare                   19.5\n",
            "cabin                   NaN\n",
            "embarked                  S\n",
            "body                    NaN\n",
            "home.dest    England / B...\n",
            "Name: 0, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "m_vGuKlHuW-C",
        "outputId": "3a5e63a0-e931-4f84-b482-e0c0f69cdf3d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived            name     sex   age  sibsp  parch  ticket  fare cabin embarked  body       home.dest\n",
              "0       2         1  Mellinger, ...  female  13.0      0      1  250644  19.5   NaN        S   NaN  England / B...\n",
              "1       2         1  Wells, Miss...  female   4.0      1      1   29103  23.0   NaN        S   NaN  Cornwall / ..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a2541340-32a6-4358-ad13-4b98fce26585\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\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>pclass</th>\n",
              "      <th>survived</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",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Mellinger, ...</td>\n",
              "      <td>female</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>250644</td>\n",
              "      <td>19.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>England / B...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Wells, Miss...</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>29103</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cornwall / ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2541340-32a6-4358-ad13-4b98fce26585')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      fill: #174EA6;\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",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a2541340-32a6-4358-ad13-4b98fce26585');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "titanic_df[0:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "mCwf-5BvuW-C",
        "outputId": "e596d022-de67-49b7-9d44-c0f6f10dbdfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived            name     sex   age  sibsp  parch  ticket    fare cabin embarked  body       home.dest\n",
              "3       3         0  Scanlan, Mr...    male   NaN      0      0   36209  7.7250   NaN        Q   NaN             NaN\n",
              "4       3         1  Bradley, Mi...  female  22.0      0      0  334914  7.7250   NaN        Q   NaN  Kingwilliam...\n",
              "5       3         0  Linehan, Mr...    male   NaN      0      0  330971  7.8792   NaN        Q   NaN             NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d71308ac-b176-408c-a5c4-f0be548cf8ab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>pclass</th>\n",
              "      <th>survived</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",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Scanlan, Mr...</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36209</td>\n",
              "      <td>7.7250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Bradley, Mi...</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>334914</td>\n",
              "      <td>7.7250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kingwilliam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Linehan, Mr...</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330971</td>\n",
              "      <td>7.8792</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d71308ac-b176-408c-a5c4-f0be548cf8ab')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\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-d71308ac-b176-408c-a5c4-f0be548cf8ab 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-d71308ac-b176-408c-a5c4-f0be548cf8ab');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "titanic_df[ titanic_df['pclass'] == 3].head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B33PI6cquW-D"
      },
      "source": [
        "* DataFrame ix[] 연산자"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "nz4EzHg_uW-D",
        "outputId": "1802ab30-04a4-4dbe-e2f7-0285d7f51fac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Name  Year  Gender\n",
              "one     Chulmin  2011    Male\n",
              "two    Eunkyung  2016  Female\n",
              "three  Jinwoong  2015    Male\n",
              "four    Soobeom  2015    Male"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a669146-23cb-40df-aece-c56b8f95dfdc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
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              "      <th>one</th>\n",
              "      <td>Chulmin</td>\n",
              "      <td>2011</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>Eunkyung</td>\n",
              "      <td>2016</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>three</th>\n",
              "      <td>Jinwoong</td>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>four</th>\n",
              "      <td>Soobeom</td>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a669146-23cb-40df-aece-c56b8f95dfdc')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "    .colab-df-container {\n",
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              "      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",
              "    [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-6a669146-23cb-40df-aece-c56b8f95dfdc 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-6a669146-23cb-40df-aece-c56b8f95dfdc');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "data = {'Name': ['Chulmin', 'Eunkyung','Jinwoong','Soobeom'],\n",
        "        'Year': [2011, 2016, 2015, 2015],\n",
        "        'Gender': ['Male', 'Female', 'Male', 'Male']\n",
        "       }\n",
        "data_df = pd.DataFrame(data, index=['one','two','three','four'])\n",
        "data_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lu5jBaPCuW-D",
        "outputId": "d6f2aa89-e654-44bd-fc9b-db7500af7a4c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " loc[0,0] Chulmin\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n loc[0,0]\", data_df.iloc[0,0])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n loc['one', 0]\", data_df.loc['one','Name'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LD0KZYfYICoP",
        "outputId": "13461b27-a759-466b-9334-2dbbfa651a12"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " loc['one', 0] Chulmin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "I6Xup2aVIeCq"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "B7NJdCf2IqOU",
        "outputId": "ac892e31-5607-44f7-8580-d0262fcdd652"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index      Name  Year  Gender\n",
              "0    one   Chulmin  2011    Male\n",
              "1    two  Eunkyung  2016  Female\n",
              "2  three  Jinwoong  2015    Male\n",
              "3   four   Soobeom  2015    Male"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3569e990-79ba-4572-83d6-95ef58ef8b09\">\n",
              "    <div 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",
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              "      <th>0</th>\n",
              "      <td>one</td>\n",
              "      <td>Chulmin</td>\n",
              "      <td>2011</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>two</td>\n",
              "      <td>Eunkyung</td>\n",
              "      <td>2016</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>three</td>\n",
              "      <td>Jinwoong</td>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>four</td>\n",
              "      <td>Soobeom</td>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3569e990-79ba-4572-83d6-95ef58ef8b09')\"\n",
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              "    }\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-3569e990-79ba-4572-83d6-95ef58ef8b09 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-3569e990-79ba-4572-83d6-95ef58ef8b09');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.set_index(keys='index', inplace=True)\n",
        "data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bF5JsBr9Is18",
        "outputId": "15bfcd49-081b-476c-cb3e-2a900611bb21"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Name  Year  Gender\n",
              "index                        \n",
              "one     Chulmin  2011    Male\n",
              "two    Eunkyung  2016  Female\n",
              "three  Jinwoong  2015    Male\n",
              "four    Soobeom  2015    Male"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8df19602-972a-4331-b82e-7f1904820db4\">\n",
              "    <div 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>Name</th>\n",
              "      <th>Year</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>one</th>\n",
              "      <td>Chulmin</td>\n",
              "      <td>2011</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>Eunkyung</td>\n",
              "      <td>2016</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>three</th>\n",
              "      <td>Jinwoong</td>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>four</th>\n",
              "      <td>Soobeom</td>\n",
              "      <td>2015</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8df19602-972a-4331-b82e-7f1904820db4')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\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",
              "    [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-8df19602-972a-4331-b82e-7f1904820db4 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-8df19602-972a-4331-b82e-7f1904820db4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['Name']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cs6nKMy-I65C",
        "outputId": "44bfe63e-2ebb-4859-db73-a007b2da352e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index\n",
              "one       Chulmin\n",
              "two      Eunkyung\n",
              "three    Jinwoong\n",
              "four      Soobeom\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.iloc[0, 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "ykVYBytKJHMQ",
        "outputId": "68fc43aa-8fb7-487f-8241-460ad355bcbe"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Chulmin'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.loc['one', 'Name']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "KkxoR7yiJPeg",
        "outputId": "993d0452-de83-43c5-f92d-dfade81320ee"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Chulmin'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "ncnRKvOrJZuc"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n loc[3, 'Name']\",data_df.loc[3, 'Name'],\"\\n\")\n",
        "\n",
        "print(\"\\n loc[0:2, [0,1]]\\n\", data_df.iloc[0:2, [0,1]])\n",
        "print(\"\\n loc[0:2, [0:3]]\\n\", data_df.iloc[0:2, 0:3])\n",
        "print(\"\\n loc[0:3, ['Name', 'Year']]\\n\", data_df.loc[0:3, ['Name', 'Year']], \"\\n\")\n",
        "print(\"\\n loc[:] \\n\", data_df.loc[:])\n",
        "print(\"\\n loc[:, :] \\n\", data_df.loc[:, :])\n",
        "\n",
        "print(\"\\n loc[data_df.Year >= 2014] \\n\", data_df.loc[data_df.Year >= 2014])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9KX4o7mCIH54",
        "outputId": "cd4a18ff-ab25-4bab-ce75-c880df250049"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " loc[3, 'Name'] Soobeom \n",
            "\n",
            "\n",
            " loc[0:2, [0,1]]\n",
            "   index      Name\n",
            "0   one   Chulmin\n",
            "1   two  Eunkyung\n",
            "\n",
            " loc[0:2, [0:3]]\n",
            "   index      Name  Year\n",
            "0   one   Chulmin  2011\n",
            "1   two  Eunkyung  2016\n",
            "\n",
            " loc[0:3, ['Name', 'Year']]\n",
            "        Name  Year\n",
            "0   Chulmin  2011\n",
            "1  Eunkyung  2016\n",
            "2  Jinwoong  2015\n",
            "3   Soobeom  2015 \n",
            "\n",
            "\n",
            " loc[:] \n",
            "    index      Name  Year  Gender\n",
            "0    one   Chulmin  2011    Male\n",
            "1    two  Eunkyung  2016  Female\n",
            "2  three  Jinwoong  2015    Male\n",
            "3   four   Soobeom  2015    Male\n",
            "\n",
            " loc[:, :] \n",
            "    index      Name  Year  Gender\n",
            "0    one   Chulmin  2011    Male\n",
            "1    two  Eunkyung  2016  Female\n",
            "2  three  Jinwoong  2015    Male\n",
            "3   four   Soobeom  2015    Male\n",
            "\n",
            " loc[data_df.Year >= 2014] \n",
            "    index      Name  Year  Gender\n",
            "1    two  Eunkyung  2016  Female\n",
            "2  three  Jinwoong  2015    Male\n",
            "3   four   Soobeom  2015    Male\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mHEq6LQiuW-D",
        "outputId": "49967227-caac-4327-89f6-44c39b999f41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "one    Chulmin\n",
            "Name: Name, dtype: object\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\KwonChulmin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \n",
            ".ix is deprecated. Please use\n",
            ".loc for label based indexing or\n",
            ".iloc for positional indexing\n",
            "\n",
            "See the documentation here:\n",
            "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ],
      "source": [
        "print(data_df.ix[0:1 , 'Name'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQYDkFz0uW-D"
      },
      "source": [
        "* 명칭 기반 인덱싱과 위치 기반 인덱싱의 구분"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "CSC3cRbeuW-D",
        "outputId": "149f0053-81ba-432d-bfa0-ef691fa0a279"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   level_0 old_index      Name  Year  Gender\n",
              "1        0       one   Chulmin  2011    Male\n",
              "2        1       two  Eunkyung  2016  Female\n",
              "3        2     three  Jinwoong  2015    Male\n",
              "4        3      four   Soobeom  2015    Male"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    }\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",
              "    [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",
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              "  </style>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-b21e4e66-531e-44f2-a9a6-118b6dc31171 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-b21e4e66-531e-44f2-a9a6-118b6dc31171');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "# data_df 를 reset_index() 로 새로운 숫자형 인덱스를 생성\n",
        "data_df_reset = data_df.reset_index()\n",
        "data_df_reset = data_df_reset.rename(columns={'index':'old_index'})\n",
        "\n",
        "# index 값에 1을 더해서 1부터 시작하는 새로운 index값 생성\n",
        "data_df_reset.index = data_df_reset.index+1\n",
        "data_df_reset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "xzxeBpYquW-E",
        "outputId": "9baa7485-f2ba-4124-9afe-2508b29c44b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "# 아래 코드는 오류를 발생합니다. \n",
        "data_df_reset.iloc[0,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "RaebckXCuW-E",
        "outputId": "9aa79013-640d-4278-a3b0-dcf80725ee8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'two'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "data_df_reset.iloc[1,1] #iloc는 기본 처음부터 가졌던 인덱스만 가능"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ollWQW7suW-E"
      },
      "source": [
        "* DataFrame iloc[ ] 연산자"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "lm-sIVzZuW-E",
        "outputId": "956593ce-3394-4043-a1e9-72fa660e859a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "data_df.iloc[0, 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4ZebJjjuW-E",
        "outputId": "63c8b161-a358-41b5-9839-56138e9b810a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2011"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "# 아래 코드는 오류를 발생합니다. \n",
        "data_df.iloc[0, 2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "_x6HmjQbuW-E",
        "outputId": "20ec1202-11ee-4ad3-8e52-8137b8ea4fe2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "# 아래 코드는 오류를 발생합니다. \n",
        "data_df.iloc[0, 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "iTDvtBfyuW-F",
        "outputId": "94831712-4417-4d89-a670-9bbad157d213"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'one'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ],
      "source": [
        "data_df_reset.iloc[0, 1]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-vEs8dpTKCtq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_deXBqMouW-F"
      },
      "source": [
        "* DataFrame loc[ ] 연산자"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "8SPWCiIRKHe4",
        "outputId": "35ba130c-b8e0-43f1-d0be-e88f47deecbd"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index      Name  Year  Gender\n",
              "0    one   Chulmin  2011    Male\n",
              "1    two  Eunkyung  2016  Female\n",
              "2  three  Jinwoong  2015    Male\n",
              "3   four   Soobeom  2015    Male"
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              "      <div>\n",
              "<style scoped>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1634bf89-afca-4f9c-9543-836ef756527e 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-1634bf89-afca-4f9c-9543-836ef756527e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.set_index(keys='index', inplace=True)"
      ],
      "metadata": {
        "id": "VDzHpFFfKMEZ"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "j8wNHaQFuW-F",
        "outputId": "9f19db3f-95a8-4deb-826c-54fd6520e829"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Chulmin'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "data_df.loc['one', 'Name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "DfTfQFxQuW-F",
        "outputId": "a447534b-72d7-44d6-f58b-cfc90a9b4d22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Chulmin'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "data_df_reset.loc[1, 'Name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "jFEvs6GsuW-F",
        "outputId": "e8d48108-535e-4855-a890-f8871423fe96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Chulmin'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "# 아래 코드는 오류를 발생합니다. \n",
        "data_df.loc[\"one\", 'Name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvWEgfuzuW-F",
        "outputId": "43b72976-280c-4715-dac8-e00bd1d5402b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "위치기반 iloc slicing\n",
            " index\n",
            "one    Chulmin\n",
            "Name: Name, dtype: object \n",
            "\n",
            "명칭기반 loc slicing\n",
            " index\n",
            "one     Chulmin\n",
            "two    Eunkyung\n",
            "Name: Name, dtype: object\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print('위치기반 iloc slicing\\n', data_df.iloc[0:1, 0],'\\n')\n",
        "print('명칭기반 loc slicing\\n', data_df.loc['one':'two', 'Name'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-Vyu3SsuW-G",
        "outputId": "dfe099f6-7dfe-4e6a-fd75-190b60f6efa9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1     Chulmin\n",
            "2    Eunkyung\n",
            "Name: Name, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(data_df_reset.loc[1:2 , 'Name'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_cnY-InuW-G",
        "outputId": "a96ae6cf-ee69-42ca-88c2-fd477c61faf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "index\n",
            "two    2016\n",
            "Name: Year, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(data_df.iloc[1:2 , 1])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMZBbsCNuW-G"
      },
      "source": [
        "* 불린 인덱싱"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q_Vf7GxSuW-G",
        "outputId": "39b29232-eda9-4fe5-fcdc-8af8c1c6b85e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     pclass  survived            name     sex   age  sibsp  parch      ticket      fare        cabin embarked   body       home.dest\n",
              "49        1         0  Stead, Mr. ...    male  62.0      0      0      113514   26.5500          C87        S    NaN  Wimbledon P...\n",
              "114       2         0  Lingane, Mr...    male  61.0      0      0      235509   12.3500          NaN        Q    NaN             NaN\n",
              "143       1         1  Crosby, Mrs...  female  64.0      1      1      112901   26.5500          B26        S    NaN   Milwaukee, WI\n",
              "193       1         1  Andrews, Mi...  female  63.0      1      0       13502   77.9583           D7        S    NaN      Hudson, NY\n",
              "222       1         0  Nicholson, ...    male  64.0      0      0         693   26.0000          NaN        S  263.0  Isle of Wig...\n",
              "253       1         0  Straus, Mr....    male  67.0      1      0    PC 17483  221.7792      C55 C57        S   96.0    New York, NY\n",
              "315       1         1  Compton, Mr...  female  64.0      0      2    PC 17756   83.1583          E45        C    NaN    Lakewood, NJ\n",
              "358       1         0  Wright, Mr....    male  62.0      0      0      113807   26.5500          NaN        S    NaN     Halifax, NS\n",
              "418       1         0  Millet, Mr....    male  65.0      0      0       13509   26.5500          E38        S  249.0  East Bridge...\n",
              "476       1         0  Goldschmidt...    male  71.0      0      0    PC 17754   34.6542           A5        C    NaN    New York, NY\n",
              "491       1         1  Stone, Mrs....  female  62.0      0      0      113572   80.0000          B28      NaN    NaN  Cincinatti, OH\n",
              "504       3         1  Turkula, Mr...  female  63.0      0      0        4134    9.5875          NaN        S    NaN             NaN\n",
              "595       1         0  Van der hoe...    male  61.0      0      0      111240   33.5000          B19        S  245.0    Brooklyn, NY\n",
              "634       3         0  Svensson, M...    male  74.0      0      0      347060    7.7750          NaN        S    NaN             NaN\n",
              "651       2         0  Howard, Mr....    male  63.0      1      0       24065   26.0000          NaN        S    NaN  Swindon, En...\n",
              "688       1         0  Ostby, Mr. ...    male  65.0      0      1      113509   61.9792          B30        C  234.0  Providence, RI\n",
              "711       3         0  Nysveen, Mr...    male  61.0      0      0      345364    6.2375          NaN        S    NaN             NaN\n",
              "735       3         0  Connors, Mr...    male  70.5      0      0      370369    7.7500          NaN        Q  171.0             NaN\n",
              "755       1         0  Sutton, Mr....    male  61.0      0      0       36963   32.3208          D50        S   46.0  Haddenfield...\n",
              "785       1         0  Straus, Mrs...  female  63.0      1      0    PC 17483  221.7792      C55 C57        S    NaN    New York, NY\n",
              "804       3         0  Duane, Mr. ...    male  65.0      0      0      336439    7.7500          NaN        Q    NaN             NaN\n",
              "809       2         0  Wheadon, Mr...    male  66.0      0      0  C.A. 24579   10.5000          NaN        S    NaN  Guernsey, E...\n",
              "895       1         0  Fortune, Mr...    male  64.0      1      4       19950  263.0000  C23 C25 C27        S    NaN    Winnipeg, MB"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-545793e8-9b8f-496d-9af0-f965048b8ba1\">\n",
              "    <div 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>pclass</th>\n",
              "      <th>survived</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",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Stead, Mr. ...</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113514</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C87</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wimbledon P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Lingane, Mr...</td>\n",
              "      <td>male</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>235509</td>\n",
              "      <td>12.3500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Crosby, Mrs...</td>\n",
              "      <td>female</td>\n",
              "      <td>64.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>112901</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>B26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Milwaukee, WI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Andrews, Mi...</td>\n",
              "      <td>female</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13502</td>\n",
              "      <td>77.9583</td>\n",
              "      <td>D7</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Hudson, NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Nicholson, ...</td>\n",
              "      <td>male</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>693</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>263.0</td>\n",
              "      <td>Isle of Wig...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Straus, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17483</td>\n",
              "      <td>221.7792</td>\n",
              "      <td>C55 C57</td>\n",
              "      <td>S</td>\n",
              "      <td>96.0</td>\n",
              "      <td>New York, NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Compton, Mr...</td>\n",
              "      <td>female</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>PC 17756</td>\n",
              "      <td>83.1583</td>\n",
              "      <td>E45</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lakewood, NJ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Wright, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113807</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Halifax, NS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Millet, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13509</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>E38</td>\n",
              "      <td>S</td>\n",
              "      <td>249.0</td>\n",
              "      <td>East Bridge...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Goldschmidt...</td>\n",
              "      <td>male</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17754</td>\n",
              "      <td>34.6542</td>\n",
              "      <td>A5</td>\n",
              "      <td>C</td>\n",
              "      <td>NaN</td>\n",
              "      <td>New York, NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>491</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Stone, Mrs....</td>\n",
              "      <td>female</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0000</td>\n",
              "      <td>B28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cincinatti, OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Turkula, Mr...</td>\n",
              "      <td>female</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4134</td>\n",
              "      <td>9.5875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Van der hoe...</td>\n",
              "      <td>male</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111240</td>\n",
              "      <td>33.5000</td>\n",
              "      <td>B19</td>\n",
              "      <td>S</td>\n",
              "      <td>245.0</td>\n",
              "      <td>Brooklyn, NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>634</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Svensson, M...</td>\n",
              "      <td>male</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>347060</td>\n",
              "      <td>7.7750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>651</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Howard, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>24065</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Swindon, En...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>688</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Ostby, Mr. ...</td>\n",
              "      <td>male</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>113509</td>\n",
              "      <td>61.9792</td>\n",
              "      <td>B30</td>\n",
              "      <td>C</td>\n",
              "      <td>234.0</td>\n",
              "      <td>Providence, RI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Nysveen, Mr...</td>\n",
              "      <td>male</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>345364</td>\n",
              "      <td>6.2375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "            '<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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "titanic_df = pd.read_csv('titanic_train.csv')\n",
        "titanic_boolean = titanic_df[titanic_df['age'] > 60]\n",
        "print(type(titanic_boolean))\n",
        "titanic_boolean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "5zeuTmyYuW-G",
        "outputId": "b1e27c01-1572-4a37-804d-64833d5ab2a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               name   age\n",
              "49   Stead, Mr. ...  62.0\n",
              "114  Lingane, Mr...  61.0\n",
              "143  Crosby, Mrs...  64.0"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\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>name</th>\n",
              "      <th>age</th>\n",
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              "      <td>Lingane, Mr...</td>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Crosby, Mrs...</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18d9b552-c233-40d1-8235-039610eea2d3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "       width=\"24px\">\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-18d9b552-c233-40d1-8235-039610eea2d3 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-18d9b552-c233-40d1-8235-039610eea2d3');\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",
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              "    </div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "titanic_df[titanic_df['age'] > 60][['name','age']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "4mgXmwdbuW-G",
        "outputId": "b1074f2b-e25c-495d-a7c8-a7ba661e3ef0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               name   age\n",
              "49   Stead, Mr. ...  62.0\n",
              "114  Lingane, Mr...  61.0\n",
              "143  Crosby, Mrs...  64.0"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "      <td>Crosby, Mrs...</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c6dfd53-04be-4a56-b9e8-e2a1d9ff0bd8')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\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",
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              "          document.querySelector('#df-1c6dfd53-04be-4a56-b9e8-e2a1d9ff0bd8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c6dfd53-04be-4a56-b9e8-e2a1d9ff0bd8');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ],
      "source": [
        "titanic_df.loc[titanic_df['age'] > 60, ['name','age']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "FHtLzcDGuW-G",
        "outputId": "081a11f0-ebd1-4cbe-94d5-e3708c1f82ed"
      },
      "outputs": [
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              "     pclass  survived            name     sex   age  sibsp  parch    ticket      fare    cabin embarked  body       home.dest\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "titanic_df[ (titanic_df['age'] > 60) & (titanic_df['pclass']==1) & (titanic_df['sex']=='female')]"
      ]
    },
    {
      "cell_type": "code",
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        "id": "b3Tx5odEuW-H",
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              "     pclass  survived            name     sex   age  sibsp  parch    ticket      fare    cabin embarked  body       home.dest\n",
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              "491       1         1  Stone, Mrs....  female  62.0      0      0    113572   80.0000      B28      NaN   NaN  Cincinatti, OH\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "cond1 = titanic_df['age'] > 60\n",
        "cond2 = titanic_df['pclass']==1\n",
        "cond3 = titanic_df['sex']=='female'\n",
        "titanic_df[ cond1 & cond2 & cond3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSxuYQN9uW-H"
      },
      "source": [
        "### 정렬, Aggregation함수, GroupBy 적용\n",
        "\n",
        "* DataFrame, Series의 정렬 - sort_values()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "9hZJLDQRuW-H",
        "outputId": "08030c51-de64-4748-ee7d-c252b9fc9caf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     pclass  survived            name     sex   age  sibsp  parch     ticket   fare cabin embarked   body       home.dest\n",
              "906       3         0  Abbing, Mr....    male  42.0      0      0  C.A. 5547   7.55   NaN        S    NaN             NaN\n",
              "405       3         0  Abbott, Mr....    male  16.0      1      1  C.A. 2673  20.25   NaN        S  190.0  East Provid...\n",
              "519       3         1  Abbott, Mrs...  female  35.0      1      1  C.A. 2673  20.25   NaN        S    NaN  East Provid..."
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</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",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>906</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbing, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>C.A. 5547</td>\n",
              "      <td>7.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbott, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>C.A. 2673</td>\n",
              "      <td>20.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>190.0</td>\n",
              "      <td>East Provid...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>519</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Abbott, Mrs...</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>C.A. 2673</td>\n",
              "      <td>20.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>East Provid...</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-78baed14-f917-4bf1-a89a-26ab25856ef1');\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "titanic_sorted = titanic_df.sort_values(by=['name'])\n",
        "titanic_sorted.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "8dv9xd01uW-H",
        "outputId": "282ecc0e-c498-4130-a022-e5b193cc2b99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     pclass  survived            name   sex   age  sibsp  parch    ticket  fare cabin embarked   body home.dest\n",
              "109       3         0  van Melkebe...  male   NaN      0      0    345777   9.5   NaN        S    NaN       NaN\n",
              "243       3         0  van Billiar...  male  40.5      0      2  A/5. 851  14.5   NaN        S  255.0       NaN\n",
              "710       3         0  van Billiar...  male  11.5      1      1  A/5. 851  14.5   NaN        S    1.0       NaN"
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              "\n",
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              "\n",
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              "\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "\n",
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              "          const element = document.querySelector('#df-bbf81890-2ce9-4e01-8628-0f8f68470540');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "titanic_sorted = titanic_df.sort_values(by=['pclass', 'name'], ascending=False)\n",
        "titanic_sorted.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeUBv3H0uW-H"
      },
      "source": [
        "* Aggregation 함수 적용"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoWdqrXVuW-H",
        "outputId": "f9e64854-7e30-4117-8d2c-ab4a1579d5a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass       916\n",
              "survived     916\n",
              "name         916\n",
              "sex          916\n",
              "age          741\n",
              "sibsp        916\n",
              "parch        916\n",
              "ticket       916\n",
              "fare         916\n",
              "cabin        214\n",
              "embarked     914\n",
              "body          85\n",
              "home.dest    527\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ],
      "source": [
        "titanic_df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3x-mfBrQuW-I",
        "outputId": "d84cf15a-716b-4e36-8d0e-dbc94b6780f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age     30.231444\n",
              "fare    32.532050\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ],
      "source": [
        "titanic_df[['age', 'fare']].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIwTIhj6uW-I"
      },
      "source": [
        "* groupby() 이용하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxAaBEgJuW-I",
        "outputId": "9986cabb-9bb5-4975-d1b8-14e50f356a26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
          ]
        }
      ],
      "source": [
        "titanic_groupby = titanic_df.groupby(by='pclass')\n",
        "print(type(titanic_groupby))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "eKM8bsFiuW-I",
        "outputId": "3141ec14-6f72-4d95-84ac-551d501428cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        survived  name  sex  age  sibsp  parch  ticket  fare  cabin  embarked  body  home.dest\n",
              "pclass                                                                                        \n",
              "1            230   230  230  204    230    230     230   230    185       228    28        205\n",
              "2            188   188  188  178    188    188     188   188     17       188    18        178\n",
              "3            498   498  498  359    498    498     498   498     12       498    39        144"
            ],
            "text/html": [
              "\n",
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              "      <th>pclass</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>1</th>\n",
              "      <td>230</td>\n",
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              "      <th>2</th>\n",
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              "      <td>178</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>188</td>\n",
              "      <td>17</td>\n",
              "      <td>188</td>\n",
              "      <td>18</td>\n",
              "      <td>178</td>\n",
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              "      <th>3</th>\n",
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              "      <td>498</td>\n",
              "      <td>498</td>\n",
              "      <td>359</td>\n",
              "      <td>498</td>\n",
              "      <td>498</td>\n",
              "      <td>498</td>\n",
              "      <td>498</td>\n",
              "      <td>12</td>\n",
              "      <td>498</td>\n",
              "      <td>39</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "titanic_groupby = titanic_df.groupby('pclass').count()\n",
        "titanic_groupby"
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            "text/plain": [
              "        ticket  survived\n",
              "pclass                  \n",
              "1          230       230\n",
              "2          188       188\n",
              "3          498       498"
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      "source": [
        "titanic_groupby = titanic_df.groupby('pclass')[['ticket', 'survived']].count()\n",
        "titanic_groupby"
      ]
    },
    {
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              "         max   min\n",
              "pclass            \n",
              "1       71.0  2.00\n",
              "2       66.0  1.00\n",
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      ],
      "source": [
        "titanic_df.groupby('pclass')['age'].agg([max, min])"
      ]
    },
    {
      "cell_type": "code",
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              "         age  sibsp       fare\n",
              "pclass                        \n",
              "1       71.0    103  83.944911\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6d73490c-0326-4819-8f05-f3aa3e1eac50 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-6d73490c-0326-4819-8f05-f3aa3e1eac50');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ],
      "source": [
        "agg_format={'age':'max', 'sibsp':'sum', 'fare':'mean'}\n",
        "titanic_df.groupby('pclass').agg(agg_format)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8ZH5XFiuW-J"
      },
      "source": [
        "### 결손 데이터 처리하기\n",
        "* isna()로 결손 데이터 여부 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "TjpXABXRuW-J",
        "outputId": "cb9144b5-b579-44d1-9362-5a2c36f19d17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived   name    sex    age  sibsp  parch  ticket   fare  cabin  embarked  body  home.dest\n",
              "0   False     False  False  False  False  False  False   False  False   True     False  True      False\n",
              "1   False     False  False  False  False  False  False   False  False   True     False  True      False\n",
              "2   False     False  False  False  False  False  False   False  False   True     False  True      False"
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            "text/html": [
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-fa22a395-8bb6-4a79-b3bd-265f7f1b63e0 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-fa22a395-8bb6-4a79-b3bd-265f7f1b63e0');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ],
      "source": [
        "titanic_df.isna().head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejOguMiquW-J",
        "outputId": "fc53538c-ffb2-4bbc-d5db-23c980bcae74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass         0\n",
              "survived       0\n",
              "name           0\n",
              "sex            0\n",
              "age          175\n",
              "sibsp          0\n",
              "parch          0\n",
              "ticket         0\n",
              "fare           0\n",
              "cabin        702\n",
              "embarked       2\n",
              "body         831\n",
              "home.dest    389\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ],
      "source": [
        "titanic_df.isna( ).sum( )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIrcAmQiuW-J"
      },
      "source": [
        "* fillna( ) 로 Missing 데이터 대체하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "xt6SqglAuW-J",
        "outputId": "38a19db9-838a-49ac-ddef-7327cff7c154"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pclass  survived            name     sex   age  sibsp  parch         ticket     fare cabin embarked  body       home.dest\n",
              "0       2         1  Mellinger, ...  female  13.0      0      1         250644  19.5000  C000        S   NaN  England / B...\n",
              "1       2         1  Wells, Miss...  female   4.0      1      1          29103  23.0000  C000        S   NaN  Cornwall / ...\n",
              "2       2         1  Duran y Mor...  female  30.0      1      0  SC/PARIS 2148  13.8583  C000        C   NaN  Barcelona, ..."
            ],
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              "                                                     [key], {});\n",
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ],
      "source": [
        "titanic_df['cabin'] = titanic_df['cabin'].fillna('C000')\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WRWu3L6xuW-J",
        "outputId": "132248e1-2790-4b65-c97a-690ccfe31393"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass         0\n",
              "survived       0\n",
              "name           0\n",
              "sex            0\n",
              "age            0\n",
              "sibsp          0\n",
              "parch          0\n",
              "ticket         0\n",
              "fare           0\n",
              "cabin          0\n",
              "embarked       0\n",
              "body         831\n",
              "home.dest    389\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ],
      "source": [
        "titanic_df['age'] = titanic_df['age'].fillna(titanic_df['age'].mean())\n",
        "titanic_df['embarked'] = titanic_df['embarked'].fillna('S')\n",
        "titanic_df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dq4clXduW-K"
      },
      "source": [
        "### apply lambda 식으로 데이터 가공"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dg7AmoiWuW-K",
        "outputId": "1ae18a6d-1a7d-4542-87f8-19214c567bbf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3의 제곱은: 9\n"
          ]
        }
      ],
      "source": [
        "def get_square(a):\n",
        "    return a**2\n",
        "\n",
        "print('3의 제곱은:',get_square(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfivxL34uW-K",
        "outputId": "a2b24f02-30f1-474a-cb96-6ca8cab15a30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3의 제곱은: 9\n"
          ]
        }
      ],
      "source": [
        "lambda_square = lambda x : x ** 2\n",
        "print('3의 제곱은:',lambda_square(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G99i01hEuW-K",
        "outputId": "d6ace7c5-601c-491a-fd1c-dea3e126e73d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 4, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ],
      "source": [
        "a=[1,2,3]\n",
        "squares = map(lambda x : x**2, a)\n",
        "list(squares)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "2vtygSWAuW-K",
        "outputId": "195759fc-c08a-4b74-fd17-1b5fed67c390"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             name  name_len\n",
              "0  Mellinger, ...        33\n",
              "1  Wells, Miss...        17\n",
              "2  Duran y Mor...        30"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-195c8560-51b6-4edd-a796-926b53084f59\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>name_len</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Duran y Mor...</td>\n",
              "      <td>30</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-195c8560-51b6-4edd-a796-926b53084f59')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-195c8560-51b6-4edd-a796-926b53084f59 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-195c8560-51b6-4edd-a796-926b53084f59');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ],
      "source": [
        "titanic_df['name_len']= titanic_df['name'].apply(lambda x : len(x))\n",
        "titanic_df[['name','name_len']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_len(x):\n",
        "  return len(x)"
      ],
      "metadata": {
        "id": "VSEzBptJMp9V"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_len1(x):\n",
        "  return len(x) + 1"
      ],
      "metadata": {
        "id": "JIFyekU5M1Uq"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['name_len']= titanic_df['name'].apply(get_len)\n",
        "titanic_df[['name','name_len']].head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "nxJzUS8cMmmn",
        "outputId": "b92a0071-7c34-43e8-db2d-9cfedd310f1f"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             name  name_len\n",
              "0  Mellinger, ...        33\n",
              "1  Wells, Miss...        17\n",
              "2  Duran y Mor...        30"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95203a76-c083-4c02-8da4-023fcb4539cb\">\n",
              "    <div 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",
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              "\n",
              "    .dataframe thead th {\n",
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              "  <thead>\n",
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              "      <th>name_len</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Wells, Miss...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Duran y Mor...</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95203a76-c083-4c02-8da4-023fcb4539cb')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\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-95203a76-c083-4c02-8da4-023fcb4539cb 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-95203a76-c083-4c02-8da4-023fcb4539cb');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lSb5qm-BuW-K",
        "outputId": "3fb09dda-2e86-49d8-e9e7-383d1cbc96e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age child_adult\n",
              "0  13.000000       child\n",
              "1   4.000000       child\n",
              "2  30.000000       adult\n",
              "3  30.231444       adult\n",
              "4  22.000000       adult\n",
              "5  30.231444       adult\n",
              "6  30.000000       adult\n",
              "7   2.000000       child"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>child</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>child</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30.231444</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>30.231444</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>adult</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>child</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78755206-9616-4df0-a2f3-efea338acf6c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-78755206-9616-4df0-a2f3-efea338acf6c 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-78755206-9616-4df0-a2f3-efea338acf6c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ],
      "source": [
        "titanic_df['child_adult'] = titanic_df['age'].apply(lambda x : 'child' if x <=15 else 'adult' )\n",
        "titanic_df[['age','child_adult']].head(8)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def classfication(x):\n",
        "  result = ''\n",
        "  if x <=15: result = 'child'\n",
        "  else: result = 'adult'\n",
        "  return result"
      ],
      "metadata": {
        "id": "0xqrkjuaSZtT"
      },
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['age'].apply(classfication)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNdceZ3RSrM7",
        "outputId": "6bcf47ff-6076-4193-c360-664ba588c5f0"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      child\n",
              "1      child\n",
              "2      adult\n",
              "3      adult\n",
              "4      adult\n",
              "       ...  \n",
              "911    child\n",
              "912    adult\n",
              "913    adult\n",
              "914    adult\n",
              "915    adult\n",
              "Name: age, Length: 916, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['age'].apply(classfication).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cj-y9JNcSvvT",
        "outputId": "ac958fce-7a46-408b-e411-8f1b337750fa"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "916"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['age'].apply(classfication).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iz6fJRdISxnU",
        "outputId": "07c8a319-848b-4949-e401-65611ccdc5fc"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adult    845\n",
              "child     71\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(titanic_df['age'].apply(classfication))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GV-tIKYNS16e",
        "outputId": "1b1f0834-b6c6-439a-9cbb-12bbc0f4bbd0"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result1 = titanic_df['age'].apply(classfication).value_counts()\n",
        "result1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F51XpspNTbNV",
        "outputId": "a9324cc9-c2e5-4102-a74a-e40f107fa440"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adult    845\n",
              "child     71\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.area.html"
      ],
      "metadata": {
        "id": "2Y6EtOLgVskF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.bar(result1.index, result1.values, data=result1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "rT_a_03vTK0q",
        "outputId": "79bc4364-76df-4c50-bc07-ce5385bd9e03"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result1_df = result1.to_frame()\n",
        "result1_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "eUFn00DbUX3v",
        "outputId": "b4b8b3df-fc2a-47fe-96fa-f03f7a653a97"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age\n",
              "adult  845\n",
              "child   71"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dec6590f-06af-48ad-912c-806f79865264\">\n",
              "    <div 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>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>adult</th>\n",
              "      <td>845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>child</th>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dec6590f-06af-48ad-912c-806f79865264')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-dec6590f-06af-48ad-912c-806f79865264 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-dec6590f-06af-48ad-912c-806f79865264');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "result1_df.boxplot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "JJ_7avSrUB3k",
        "outputId": "3621e636-462f-4c8c-d007-910548b55126"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['name'].apply(get_len, get_len1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuBMNzqVNHBQ",
        "outputId": "fffd50cc-9496-4e64-87cd-4731d9f2e4e2"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      33\n",
              "1      17\n",
              "2      30\n",
              "3      18\n",
              "4      28\n",
              "       ..\n",
              "911    39\n",
              "912    17\n",
              "913    18\n",
              "914    20\n",
              "915    29\n",
              "Name: name, Length: 916, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[titanic_df['name'].apply(get_len)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1qhv3MmNPmT",
        "outputId": "b4f1617c-74d3-4c64-968a-009f4a64bb58"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0      33\n",
              " 1      17\n",
              " 2      30\n",
              " 3      18\n",
              " 4      28\n",
              "        ..\n",
              " 911    39\n",
              " 912    17\n",
              " 913    18\n",
              " 914    20\n",
              " 915    29\n",
              " Name: name, Length: 916, dtype: int64]"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[titanic_df['name'].apply(get_len1)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9Zesv93R-R6",
        "outputId": "4f428ced-58f3-4bc4-8950-1aac2c4fcac7"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0      34\n",
              " 1      18\n",
              " 2      31\n",
              " 3      19\n",
              " 4      29\n",
              "        ..\n",
              " 911    40\n",
              " 912    18\n",
              " 913    19\n",
              " 914    21\n",
              " 915    30\n",
              " Name: name, Length: 916, dtype: int64]"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INMlcpibuW-L",
        "outputId": "d639c764-ed4e-48a8-b513-98f3895f218f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "adult      822\n",
              "child       71\n",
              "elderly     23\n",
              "Name: Age_cat, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ],
      "source": [
        "titanic_df['Age_cat'] = titanic_df['age'].apply(lambda x : 'child' \n",
        "                                                if x<=15 \n",
        "                                                else \n",
        "                                                ('adult' \n",
        "                                                 if x <= 60 \n",
        "                                                 else 'elderly'\n",
        "                                                 )\n",
        "                                                )\n",
        "titanic_df['Age_cat'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5Bvrgof7uW-L",
        "outputId": "063c717a-9cb2-4275-f82c-4aa30908f779"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age      Age_cat\n",
              "0  13.000000     Teenager\n",
              "1   4.000000         Baby\n",
              "2  30.000000  Young Adult\n",
              "3  30.231444  Young Adult\n",
              "4  22.000000      Student"
            ],
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a4340be-078e-4fc0-8c39-fd7a22cf5464');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ],
      "source": [
        "# 나이에 따라 세분화된 분류를 수행하는 함수 생성. \n",
        "def get_category(age):\n",
        "    cat = ''\n",
        "    if age <= 5: cat = 'Baby'\n",
        "    elif age <= 12: cat = 'Child'\n",
        "    elif age <= 18: cat = 'Teenager'\n",
        "    elif age <= 25: cat = 'Student'\n",
        "    elif age <= 35: cat = 'Young Adult'\n",
        "    elif age <= 60: cat = 'Adult'\n",
        "    else : cat = 'Elderly'\n",
        "    \n",
        "    return cat\n",
        "\n",
        "# lambda 식에 위에서 생성한 get_category( ) 함수를 반환값으로 지정. \n",
        "# get_category(X)는 입력값으로 ‘Age’ 컬럼 값을 받아서 해당하는 cat 반환\n",
        "titanic_df['Age_cat'] = titanic_df['age'].apply(lambda x : get_category(x))\n",
        "titanic_df[['age','Age_cat']].head()\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVZdLuQ_uW-L"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "1.4 데이터 핸들링 - 판다스.ipynb",
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      "include_colab_link": true
    }
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  "nbformat_minor": 0
}