0405.ipynb

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      "source": [
        "## 데이터프레임 합치기"
      ],
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      "source": [
        "### concat(axis, join) - 기본 outer"
      ],
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      }
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    {
      "cell_type": "markdown",
      "source": [
        "#### 데이터프레임과 데이터프레임 연결"
      ],
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      "source": [
        "# 위아래로 합치기 concat(axis = 0) => 행 방향으로 합침\n",
        "# 좌우로 합치기 concat(axis = 1) => 열 방향으로 합침\n",
        "import pandas as pd\n",
        "df1 = pd.DataFrame({'a': [1,2,3,4],\n",
        "                    'b': [11,12,13,14],\n",
        "                    'c': [21,22,23,24]})\n",
        "df2 = pd.DataFrame({'a': [9,8,7],\n",
        "                   'b': [19,18,17],\n",
        "                   'c': [29,28,27],\n",
        "                   'd': [39,38,37]})\n",
        "print(df1)\n",
        "print(df2)"
      ],
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          "text": [
            "   a   b   c\n",
            "0  1  11  21\n",
            "1  2  12  22\n",
            "2  3  13  23\n",
            "3  4  14  24\n",
            "   a   b   c   d\n",
            "0  9  19  29  39\n",
            "1  8  18  28  38\n",
            "2  7  17  27  37\n"
          ]
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        "# 기본값 => axis = 0, join = 'outer'\n",
        "# 위아래로 합치기 concat(axis = 0)\n",
        "# 모든 열\n",
        "res1 = pd.concat([df1,df2])\n",
        "# res1 = pd.concat([df1,df2], join = 'outer')\n",
        "res1"
      ],
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              "   a   b   c     d\n",
              "0  1  11  21   NaN\n",
              "1  2  12  22   NaN\n",
              "2  3  13  23   NaN\n",
              "3  4  14  24   NaN\n",
              "0  9  19  29  39.0\n",
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        "# axis = 0, join = 'inner'\n",
        "# 공통된 열\n",
        "res2 = pd.concat([df1,df2], join = 'inner')\n",
        "res2"
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              "   a   b   c\n",
              "0  1  11  21\n",
              "1  2  12  22\n",
              "2  3  13  23\n",
              "3  4  14  24\n",
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              "      <th>0</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('res2')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('res2');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "res2",
              "summary": "{\n  \"name\": \"res2\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          2,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 11,\n        \"max\": 19,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11,\n          12,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 21,\n        \"max\": 29,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          21,\n          22,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# axis = 0, join = 'inner'\n",
        "# 공통된 열 & 행 인덱스 초기화\n",
        "res3 = pd.concat([df1,df2], join = 'inner', ignore_index=True)\n",
        "res3"
      ],
      "metadata": {
        "id": "zlvocovrMhcE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "88c8ad3d-4898-4881-95a6-6963d74b2c67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a   b   c\n",
              "0  1  11  21\n",
              "1  2  12  22\n",
              "2  3  13  23\n",
              "3  4  14  24\n",
              "4  9  19  29\n",
              "5  8  18  28\n",
              "6  7  17  27"
            ],
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e6ed2953-5120-4b50-bc92-7620613503ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e6ed2953-5120-4b50-bc92-7620613503ba')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e6ed2953-5120-4b50-bc92-7620613503ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_03bd5b37-401e-4ff4-8e3d-aee97474237c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('res3')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_03bd5b37-401e-4ff4-8e3d-aee97474237c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('res3');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "res3",
              "summary": "{\n  \"name\": \"res3\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          2,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 11,\n        \"max\": 19,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11,\n          12,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 21,\n        \"max\": 29,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          21,\n          22,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 좌우로 합치기 concat(axis = 1)\n",
        "# 기본값 => join = 'outer'\n",
        "# 모든 행\n",
        "res4 = pd.concat([df1,df2], axis = 1)\n",
        "# res4 = pd.concat([df1,df2], axis = 1, join = 'outer')\n",
        "res4"
      ],
      "metadata": {
        "id": "674Z1rPGONSB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "f95b02b1-434a-4794-e0ce-46edf2c065d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a   b   c    a     b     c     d\n",
              "0  1  11  21  9.0  19.0  29.0  39.0\n",
              "1  2  12  22  8.0  18.0  28.0  38.0\n",
              "2  3  13  23  7.0  17.0  27.0  37.0\n",
              "3  4  14  24  NaN   NaN   NaN   NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-feb7a51a-086e-416a-a5ca-c329e5a38a52\" 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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>21</td>\n",
              "      <td>9.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>22</td>\n",
              "      <td>8.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>23</td>\n",
              "      <td>7.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>37.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-feb7a51a-086e-416a-a5ca-c329e5a38a52')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-feb7a51a-086e-416a-a5ca-c329e5a38a52 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-feb7a51a-086e-416a-a5ca-c329e5a38a52');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e10aa0e4-7b4a-4ce9-84ca-488db27c8002\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e10aa0e4-7b4a-4ce9-84ca-488db27c8002')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
              "  <div id=\"id_9ebf77ab-0bd3-4b76-a5f9-892d117b1967\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "res4",
              "summary": "{\n  \"name\": \"res4\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 11,\n        \"max\": 14,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          14,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 21,\n        \"max\": 24,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22,\n          24,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 7.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.0,\n          8.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 17.0,\n        \"max\": 19.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19.0,\n          18.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 27.0,\n        \"max\": 29.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          29.0,\n          28.0,\n          27.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 37.0,\n        \"max\": 39.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39.0,\n          38.0,\n          37.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 공통된 행\n",
        "res5 = pd.concat([df1,df2], axis = 1, join = 'inner')\n",
        "res5"
      ],
      "metadata": {
        "id": "d48TUHeEOoY5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "6ec2ba9c-c190-4ce8-e6d8-a0b1cef5f3d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a   b   c  a   b   c   d\n",
              "0  1  11  21  9  19  29  39\n",
              "1  2  12  22  8  18  28  38\n",
              "2  3  13  23  7  17  27  37"
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    <script>\n",
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              "\n",
              "    </div>\n",
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            ],
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              "type": "dataframe",
              "variable_name": "res5",
              "summary": "{\n  \"name\": \"res5\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 11,\n        \"max\": 13,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11,\n          12,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 21,\n        \"max\": 23,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          21,\n          22,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 7,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9,\n          8,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 17,\n        \"max\": 19,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19,\n          18,\n          17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 27,\n        \"max\": 29,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          29,\n          28,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 37,\n        \"max\": 39,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39,\n          38,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 데이터프레임과 시리즈 연결"
      ],
      "metadata": {
        "id": "1Z8NrqzKrvR2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 시리즈 만들기 & name = 열 이름\n",
        "sr1 = pd.Series([4,5,6,7], name = 'e')\n",
        "sr2 = pd.Series([14,15,16,17], name = 'f', index = [1,2,3,4])\n",
        "sr3 = pd.Series([24,25,26,27], name = 'g')\n",
        "print(sr1);print(sr2);print(sr3)"
      ],
      "metadata": {
        "id": "WgpTtcxWO5Iz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5154d177-1278-490d-d104-1c5360a81e14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    4\n",
            "1    5\n",
            "2    6\n",
            "3    7\n",
            "Name: e, dtype: int64\n",
            "1    14\n",
            "2    15\n",
            "3    16\n",
            "4    17\n",
            "Name: f, dtype: int64\n",
            "0    24\n",
            "1    25\n",
            "2    26\n",
            "3    27\n",
            "Name: g, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 시리즈를 axis = 1로 합치면 새로운 열을 합치는 것과 동일\n",
        "# 시리즈의 이름이 열 이름\n",
        "print(df1)\n",
        "pd.concat([df1,sr1],axis = 1)\n",
        "# pd.concat([df1,sr1],axis = 1, join = 'outer')"
      ],
      "metadata": {
        "id": "VLS2pO9_TTce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "044d29f4-d081-479d-fbdd-ba32798ae46b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   a   b   c\n",
            "0  1  11  21\n",
            "1  2  12  22\n",
            "2  3  13  23\n",
            "3  4  14  24\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a   b   c  e\n",
              "0  1  11  21  4\n",
              "1  2  12  22  5\n",
              "2  3  13  23  6\n",
              "3  4  14  24  7"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
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              "    .colab-df-buttons div {\n",
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              "    [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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              "        document.querySelector('#df-fa9eae84-d592-4274-a0a7-a2dc4b36db76 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-fa9eae84-d592-4274-a0a7-a2dc4b36db76');\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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "  }\n",
              "\n",
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              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "\n",
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            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"# pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 11,\n        \"max\": 14,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          14,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 21,\n        \"max\": 24,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22,\n          24,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"e\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          7,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기본적으로 행 인덱스가 같은 것끼리 연결\n",
        "# join = 'outer' => 모든 행\n",
        "# 없는 인덱스인 경우에는 NaN\n",
        "print(df2)\n",
        "pd.concat([df2,sr2],axis = 1)"
      ],
      "metadata": {
        "id": "Aaj7GZSvThwD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "1d0174ca-2046-4aee-b6c8-86e40201edbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   a   b   c   d\n",
            "0  9  19  29  39\n",
            "1  8  18  28  38\n",
            "2  7  17  27  37\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     a     b     c     d     f\n",
              "0  9.0  19.0  29.0  39.0   NaN\n",
              "1  8.0  18.0  28.0  38.0  14.0\n",
              "2  7.0  17.0  27.0  37.0  15.0\n",
              "3  NaN   NaN   NaN   NaN  16.0\n",
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              "\n",
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              "      <th>0</th>\n",
              "      <td>9.0</td>\n",
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              "      <td>16.0</td>\n",
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              "      <td>17.0</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f0e4eef-4e11-400c-b446-12c0db22b700')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7f0e4eef-4e11-400c-b446-12c0db22b700 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-7f0e4eef-4e11-400c-b446-12c0db22b700');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b2ccde6a-7373-479d-903e-f93d9ab8b6a3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b2ccde6a-7373-479d-903e-f93d9ab8b6a3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b2ccde6a-7373-479d-903e-f93d9ab8b6a3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 7.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9.0,\n          8.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 17.0,\n        \"max\": 19.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19.0,\n          18.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 27.0,\n        \"max\": 29.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          29.0,\n          28.0,\n          27.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 37.0,\n        \"max\": 39.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39.0,\n          38.0,\n          37.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 14.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          15.0,\n          17.0,\n          14.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기본적으로 행 인덱스가 같은 것끼리 연결\n",
        "# join = 'inner' => 공통된 행만\n",
        "print(df2)\n",
        "pd.concat([df2,sr2], axis = 1, join = 'inner')"
      ],
      "metadata": {
        "id": "ZvYxv-RKT_G1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "outputId": "05b5edeb-eb0a-4897-e54b-5641180def81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   a   b   c   d\n",
            "0  9  19  29  39\n",
            "1  8  18  28  38\n",
            "2  7  17  27  37\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a   b   c   d   f\n",
              "1  8  18  28  38  14\n",
              "2  7  17  27  37  15"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [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",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6ee3d0f8-592a-43fa-9b4e-a76a778910d6 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-6ee3d0f8-592a-43fa-9b4e-a76a778910d6');\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",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "type": "dataframe",
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            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 인덱스 번호를 랜덤하게 하여 데이터프레임 생성\n",
        "df3 = pd.DataFrame({'a': [9,8,7],\n",
        "                   'b': [19,18,17],\n",
        "                   'c': [29,28,27],\n",
        "                   'd': [39,38,37]}, index = [4,3,2])"
      ],
      "metadata": {
        "id": "wKg2Ze23VvSD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df3);print(sr2)\n",
        "# df3 인덱스 번호 순서대로 합쳐짐 = 4, 3, 2, 1\n",
        "pd.concat([df3,sr2], axis = 1)"
      ],
      "metadata": {
        "id": "Z2sOGFriV9bm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "4dc2008e-6120-481e-9fcd-f3c9dea8ac7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   a   b   c   d\n",
            "4  9  19  29  39\n",
            "3  8  18  28  38\n",
            "2  7  17  27  37\n",
            "1    14\n",
            "2    15\n",
            "3    16\n",
            "4    17\n",
            "Name: f, dtype: int64\n"
          ]
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              "     a     b     c     d   f\n",
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              "        document.querySelector('#df-5b25d438-37fc-4ff9-b51c-267ce0612ab5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5b25d438-37fc-4ff9-b51c-267ce0612ab5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart {\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df3);print(sr2)\n",
        "# sort = True 설정하면 인덱스 번호가 오름차순으로 정렬\n",
        "pd.concat([df3,sr2], axis = 1, sort = True)"
      ],
      "metadata": {
        "id": "zsqz4izaWWRk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "29b366ab-e62c-4095-d07f-3de155ec1f54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   a   b   c   d\n",
            "4  9  19  29  39\n",
            "3  8  18  28  38\n",
            "2  7  17  27  37\n",
            "1    14\n",
            "2    15\n",
            "3    16\n",
            "4    17\n",
            "Name: f, dtype: int64\n"
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a79c3eeb-6f59-42ec-a1b6-bf99fe6095a3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a79c3eeb-6f59-42ec-a1b6-bf99fe6095a3 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-a79c3eeb-6f59-42ec-a1b6-bf99fe6095a3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-be94bf29-0fe2-4119-9e32-b11a06d70046\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be94bf29-0fe2-4119-9e32-b11a06d70046')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-be94bf29-0fe2-4119-9e32-b11a06d70046 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"a\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 7.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.0,\n          8.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 17.0,\n        \"max\": 19.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17.0,\n          18.0,\n          19.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"c\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 27.0,\n        \"max\": 29.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          27.0,\n          28.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 37.0,\n        \"max\": 39.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.0,\n          38.0,\n          39.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 14,\n        \"max\": 17,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          15,\n          17,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 시리즈와 시리즈 연결"
      ],
      "metadata": {
        "id": "ako1p94ptiAA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# axis = 0 위아래로 합치기\n",
        "pd.concat([sr1,sr2], axis = 0)"
      ],
      "metadata": {
        "id": "uStaBFsuWds-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1205141-904e-48dc-a951-5c01ea6140c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     4\n",
              "1     5\n",
              "2     6\n",
              "3     7\n",
              "1    14\n",
              "2    15\n",
              "3    16\n",
              "4    17\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# join = 'outer'\n",
        "# 모든 행\n",
        "pd.concat([sr1,sr2], axis = 1)"
      ],
      "metadata": {
        "id": "3r0Gxt5TX9WR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1d48d67b-5aae-4055-f706-42f6c8649ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     e     f\n",
              "0  4.0   NaN\n",
              "1  5.0  14.0\n",
              "2  6.0  15.0\n",
              "3  7.0  16.0\n",
              "4  NaN  17.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-163cd532-3d0e-4c3a-bb1c-001e1be3c366\" 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>e</th>\n",
              "      <th>f</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-163cd532-3d0e-4c3a-bb1c-001e1be3c366')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-163cd532-3d0e-4c3a-bb1c-001e1be3c366 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-163cd532-3d0e-4c3a-bb1c-001e1be3c366');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bc2ef722-0cf3-4590-951c-8cf75d5d12f1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc2ef722-0cf3-4590-951c-8cf75d5d12f1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bc2ef722-0cf3-4590-951c-8cf75d5d12f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"e\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 4.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          7.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2909944487358056,\n        \"min\": 14.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          15.0,\n          17.0,\n          14.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 공통된 행\n",
        "pd.concat([sr1,sr2], axis = 1, join = 'inner')"
      ],
      "metadata": {
        "id": "WTWz-vMdYoha",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "6a496b3d-5bf6-4584-87b8-077bc3b58516"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   e   f\n",
              "1  5  14\n",
              "2  6  15\n",
              "3  7  16"
            ],
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              "\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>e</th>\n",
              "      <th>f</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53c98885-0c11-47d8-99c0-86c871cce857')\"\n",
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              "\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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              "      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",
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              "\n",
              "    .colab-df-buttons div {\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-53c98885-0c11-47d8-99c0-86c871cce857 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-53c98885-0c11-47d8-99c0-86c871cce857');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e1042ab6-2e7b-4acd-adba-8b964eea59fa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1042ab6-2e7b-4acd-adba-8b964eea59fa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1042ab6-2e7b-4acd-adba-8b964eea59fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"e\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 5,\n        \"max\": 7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5,\n          6,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 14,\n        \"max\": 16,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          14,\n          15,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# axis = 0 위아래로 합치기\n",
        "pd.concat([sr1,sr3], axis = 0)"
      ],
      "metadata": {
        "id": "sJZH9R5EWsXj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fc3916d-c817-4de7-f75d-943d0aa0fbdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     4\n",
              "1     5\n",
              "2     6\n",
              "3     7\n",
              "0    24\n",
              "1    25\n",
              "2    26\n",
              "3    27\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# join = 'outer'\n",
        "# 모든 행\n",
        "pd.concat([sr1,sr3], axis = 1)"
      ],
      "metadata": {
        "id": "ynGP1cNdYc7G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "2f4c8911-f554-4c23-a046-fe19e177beec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   e   g\n",
              "0  4  24\n",
              "1  5  25\n",
              "2  6  26\n",
              "3  7  27"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f687c20-e491-456e-940b-3692875393a9\" 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",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>e</th>\n",
              "      <th>g</th>\n",
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              "      <th>3</th>\n",
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              "      <td>27</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f687c20-e491-456e-940b-3692875393a9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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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-0f687c20-e491-456e-940b-3692875393a9 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-0f687c20-e491-456e-940b-3692875393a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-61f1f6ef-899a-45e0-b211-75b3ad70e7bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61f1f6ef-899a-45e0-b211-75b3ad70e7bc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61f1f6ef-899a-45e0-b211-75b3ad70e7bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"e\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          7,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 24,\n        \"max\": 27,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          25,\n          27,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### merge(how, on) - 기본 inner"
      ],
      "metadata": {
        "id": "Xi-q91R-Zo0s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# merge = SQL JOIN 과 유사 = 좌우로 합치기(병합)\n",
        "# 공통된 열, 인덱스 => 키\n",
        "# 라이브러리 불러오기\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "UyYdoSThZr3_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 불러오기\n",
        "df1 = pd.read_excel('/content/drive/MyDrive/lsda02/data/stock price.xlsx', engine = 'openpyxl')\n",
        "df2 = pd.read_excel('/content/drive/MyDrive/lsda02/data/stock valuation.xlsx', engine = 'openpyxl')\n",
        "print(df1.head())\n",
        "print(df2.head())"
      ],
      "metadata": {
        "id": "4ZZkq67Bac8z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f5d4b79-4b7b-407e-8a20-b3ae84812200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name          value   price\n",
            "0  128940       한미약품   59385.666667  421000\n",
            "1  130960     CJ E&M   58540.666667   98900\n",
            "2  138250      엔에스쇼핑   14558.666667   13200\n",
            "3  139480        이마트  239230.833333  254500\n",
            "4  142280     녹십자엠에스     468.833333   10200\n",
            "       id     name           eps     bps        per       pbr\n",
            "0  130960   CJ E&M   6301.333333   54068  15.695091  1.829178\n",
            "1  136480       하림    274.166667    3551  11.489362  0.887074\n",
            "2  138040  메리츠금융지주   2122.333333   14894   6.313806  0.899691\n",
            "3  139480      이마트  18268.166667  295780  13.931338  0.860437\n",
            "4  145990      삼양사   5741.000000  108090  14.283226  0.758627\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# INNER JOIN\n",
        "# 기본값 how = 'inner', on = None (공통열 = 여기서는 id)\n",
        "merge_inner = pd.merge(df1,df2)\n",
        "print(merge_inner.head())"
      ],
      "metadata": {
        "id": "g9P_1SP4a60o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "006278e7-6421-4d42-d7cd-fea492dd66fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name          value   price    name           eps     bps  \\\n",
            "0  130960     CJ E&M   58540.666667   98900  CJ E&M   6301.333333   54068   \n",
            "1  139480        이마트  239230.833333  254500     이마트  18268.166667  295780   \n",
            "2  145990        삼양사   82750.000000   82000     삼양사   5741.000000  108090   \n",
            "3  185750        종근당   40293.666667  100500     종근당   3990.333333   40684   \n",
            "4  204210     모두투어리츠    3093.333333    3475  모두투어리츠     85.166667    5335   \n",
            "\n",
            "         per       pbr  \n",
            "0  15.695091  1.829178  \n",
            "1  13.931338  0.860437  \n",
            "2  14.283226  0.758627  \n",
            "3  25.185866  2.470259  \n",
            "4  40.802348  0.651359  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# FULL OUTER JOIN\n",
        "merge_outer = pd.merge(df1,df2, how = 'outer', on = 'id')\n",
        "print(merge_outer.head())"
      ],
      "metadata": {
        "id": "pRUH1BOHb9am",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8eb35d5f-bd3e-463a-cfe9-1b6a41e260af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name          value     price    name           eps       bps  \\\n",
            "0  128940       한미약품   59385.666667  421000.0     NaN           NaN       NaN   \n",
            "1  130960     CJ E&M   58540.666667   98900.0  CJ E&M   6301.333333   54068.0   \n",
            "2  138250      엔에스쇼핑   14558.666667   13200.0     NaN           NaN       NaN   \n",
            "3  139480        이마트  239230.833333  254500.0     이마트  18268.166667  295780.0   \n",
            "4  142280     녹십자엠에스     468.833333   10200.0     NaN           NaN       NaN   \n",
            "\n",
            "         per       pbr  \n",
            "0        NaN       NaN  \n",
            "1  15.695091  1.829178  \n",
            "2        NaN       NaN  \n",
            "3  13.931338  0.860437  \n",
            "4        NaN       NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LEFT OUTER JOIN\n",
        "merge_left = pd.merge(df1,df2, how = 'left', on = 'id')\n",
        "print(merge_left.head())"
      ],
      "metadata": {
        "id": "5h22YDER35zP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70c42307-be93-4876-efd0-bc519e9b081c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name          value   price    name           eps       bps  \\\n",
            "0  128940       한미약품   59385.666667  421000     NaN           NaN       NaN   \n",
            "1  130960     CJ E&M   58540.666667   98900  CJ E&M   6301.333333   54068.0   \n",
            "2  138250      엔에스쇼핑   14558.666667   13200     NaN           NaN       NaN   \n",
            "3  139480        이마트  239230.833333  254500     이마트  18268.166667  295780.0   \n",
            "4  142280     녹십자엠에스     468.833333   10200     NaN           NaN       NaN   \n",
            "\n",
            "         per       pbr  \n",
            "0        NaN       NaN  \n",
            "1  15.695091  1.829178  \n",
            "2        NaN       NaN  \n",
            "3  13.931338  0.860437  \n",
            "4        NaN       NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RIGHT OUTER JOIN\n",
        "merge_right = pd.merge(df1,df2, how = 'right', on = 'id')\n",
        "print(merge_right.head())"
      ],
      "metadata": {
        "id": "F3CU0VX4uzJU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c54d87f-5827-4767-98ae-b801b1f44ae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name          value     price     name           eps     bps  \\\n",
            "0  130960     CJ E&M   58540.666667   98900.0   CJ E&M   6301.333333   54068   \n",
            "1  136480        NaN            NaN       NaN       하림    274.166667    3551   \n",
            "2  138040        NaN            NaN       NaN  메리츠금융지주   2122.333333   14894   \n",
            "3  139480        이마트  239230.833333  254500.0      이마트  18268.166667  295780   \n",
            "4  145990        삼양사   82750.000000   82000.0      삼양사   5741.000000  108090   \n",
            "\n",
            "         per       pbr  \n",
            "0  15.695091  1.829178  \n",
            "1  11.489362  0.887074  \n",
            "2   6.313806  0.899691  \n",
            "3  13.931338  0.860437  \n",
            "4  14.283226  0.758627  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 불 인덱스 => price < 100000 조건을 만족하는 데이터 추출\n",
        "df1_price = df1[df1['price'] < 100000]"
      ],
      "metadata": {
        "id": "Hk5-foDcu_9V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 불 인덱스 데이터와 INNER JOIN\n",
        "data = pd.merge(df1_price, df2)\n",
        "print(data.head())"
      ],
      "metadata": {
        "id": "NRg1DmSZvzGc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b647fab-3953-4c0e-ea6d-a59bea891d6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name         value  price    name          eps     bps  \\\n",
            "0  130960     CJ E&M  58540.666667  98900  CJ E&M  6301.333333   54068   \n",
            "1  145990        삼양사  82750.000000  82000     삼양사  5741.000000  108090   \n",
            "2  204210     모두투어리츠   3093.333333   3475  모두투어리츠    85.166667    5335   \n",
            "\n",
            "         per       pbr  \n",
            "0  15.695091  1.829178  \n",
            "1  14.283226  0.758627  \n",
            "2  40.802348  0.651359  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### join(how) - 기본 left"
      ],
      "metadata": {
        "id": "XdCm8prExlEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# join = merge 와 유사\n",
        "# 행 인덱스 기준으로 결합\n",
        "# 옵션을 지정하면 열 기준으로 결합 가능\n",
        "print(df1);print(df2)"
      ],
      "metadata": {
        "id": "-TcLQf45xncy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92b704d0-5475-47d3-f4f7-3793d0caf01c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id stock_name          value   price\n",
            "0  128940       한미약품   59385.666667  421000\n",
            "1  130960     CJ E&M   58540.666667   98900\n",
            "2  138250      엔에스쇼핑   14558.666667   13200\n",
            "3  139480        이마트  239230.833333  254500\n",
            "4  142280     녹십자엠에스     468.833333   10200\n",
            "5  145990        삼양사   82750.000000   82000\n",
            "6  185750        종근당   40293.666667  100500\n",
            "7  192400      쿠쿠홀딩스  179204.666667  177500\n",
            "8  199800         툴젠   -2514.333333  115400\n",
            "9  204210     모두투어리츠    3093.333333    3475\n",
            "       id       name           eps     bps        per       pbr\n",
            "0  130960     CJ E&M   6301.333333   54068  15.695091  1.829178\n",
            "1  136480         하림    274.166667    3551  11.489362  0.887074\n",
            "2  138040    메리츠금융지주   2122.333333   14894   6.313806  0.899691\n",
            "3  139480        이마트  18268.166667  295780  13.931338  0.860437\n",
            "4  145990        삼양사   5741.000000  108090  14.283226  0.758627\n",
            "5  161390      한국타이어   5648.500000   51341   7.453306  0.820007\n",
            "6  181710  NHN엔터테인먼트   2110.166667   78434  30.755864  0.827447\n",
            "7  185750        종근당   3990.333333   40684  25.185866  2.470259\n",
            "8  204210     모두투어리츠     85.166667    5335  40.802348  0.651359\n",
            "9  207940   삼성바이오로직스   4644.166667   60099  89.790059  6.938551\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 행 인덱스 변경\n",
        "df1 = df1.set_index('id')\n",
        "df2 = df2.set_index('id')\n",
        "print(df1);print(df2)"
      ],
      "metadata": {
        "id": "1wHL-Idsx31h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ae647f8-5ebd-44f1-a25e-d748e53ad70c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       stock_name          value   price\n",
            "id                                      \n",
            "128940       한미약품   59385.666667  421000\n",
            "130960     CJ E&M   58540.666667   98900\n",
            "138250      엔에스쇼핑   14558.666667   13200\n",
            "139480        이마트  239230.833333  254500\n",
            "142280     녹십자엠에스     468.833333   10200\n",
            "145990        삼양사   82750.000000   82000\n",
            "185750        종근당   40293.666667  100500\n",
            "192400      쿠쿠홀딩스  179204.666667  177500\n",
            "199800         툴젠   -2514.333333  115400\n",
            "204210     모두투어리츠    3093.333333    3475\n",
            "             name           eps     bps        per       pbr\n",
            "id                                                          \n",
            "130960     CJ E&M   6301.333333   54068  15.695091  1.829178\n",
            "136480         하림    274.166667    3551  11.489362  0.887074\n",
            "138040    메리츠금융지주   2122.333333   14894   6.313806  0.899691\n",
            "139480        이마트  18268.166667  295780  13.931338  0.860437\n",
            "145990        삼양사   5741.000000  108090  14.283226  0.758627\n",
            "161390      한국타이어   5648.500000   51341   7.453306  0.820007\n",
            "181710  NHN엔터테인먼트   2110.166667   78434  30.755864  0.827447\n",
            "185750        종근당   3990.333333   40684  25.185866  2.470259\n",
            "204210     모두투어리츠     85.166667    5335  40.802348  0.651359\n",
            "207940   삼성바이오로직스   4644.166667   60099  89.790059  6.938551\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LEFT OUTER JOIN\n",
        "# 기본값 how = 'left'\n",
        "join_left = df1.join(df2)\n",
        "print(join_left)"
      ],
      "metadata": {
        "id": "aHvuIIy5yOQI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d65a66d-d0ce-4b08-f4d2-ed097f7a8034"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       stock_name          value   price    name           eps       bps  \\\n",
            "id                                                                         \n",
            "128940       한미약품   59385.666667  421000     NaN           NaN       NaN   \n",
            "130960     CJ E&M   58540.666667   98900  CJ E&M   6301.333333   54068.0   \n",
            "138250      엔에스쇼핑   14558.666667   13200     NaN           NaN       NaN   \n",
            "139480        이마트  239230.833333  254500     이마트  18268.166667  295780.0   \n",
            "142280     녹십자엠에스     468.833333   10200     NaN           NaN       NaN   \n",
            "145990        삼양사   82750.000000   82000     삼양사   5741.000000  108090.0   \n",
            "185750        종근당   40293.666667  100500     종근당   3990.333333   40684.0   \n",
            "192400      쿠쿠홀딩스  179204.666667  177500     NaN           NaN       NaN   \n",
            "199800         툴젠   -2514.333333  115400     NaN           NaN       NaN   \n",
            "204210     모두투어리츠    3093.333333    3475  모두투어리츠     85.166667    5335.0   \n",
            "\n",
            "              per       pbr  \n",
            "id                           \n",
            "128940        NaN       NaN  \n",
            "130960  15.695091  1.829178  \n",
            "138250        NaN       NaN  \n",
            "139480  13.931338  0.860437  \n",
            "142280        NaN       NaN  \n",
            "145990  14.283226  0.758627  \n",
            "185750  25.185866  2.470259  \n",
            "192400        NaN       NaN  \n",
            "199800        NaN       NaN  \n",
            "204210  40.802348  0.651359  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LEFT OUTER JOIN\n",
        "# 기본값 how = 'left'\n",
        "join_right = df2.join(df1)\n",
        "print(join_right)"
      ],
      "metadata": {
        "id": "HDShx9DjylWx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53ea128e-33f5-4779-9cdb-d4e35f1a2a5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             name           eps     bps        per       pbr stock_name  \\\n",
            "id                                                                        \n",
            "130960     CJ E&M   6301.333333   54068  15.695091  1.829178     CJ E&M   \n",
            "136480         하림    274.166667    3551  11.489362  0.887074        NaN   \n",
            "138040    메리츠금융지주   2122.333333   14894   6.313806  0.899691        NaN   \n",
            "139480        이마트  18268.166667  295780  13.931338  0.860437        이마트   \n",
            "145990        삼양사   5741.000000  108090  14.283226  0.758627        삼양사   \n",
            "161390      한국타이어   5648.500000   51341   7.453306  0.820007        NaN   \n",
            "181710  NHN엔터테인먼트   2110.166667   78434  30.755864  0.827447        NaN   \n",
            "185750        종근당   3990.333333   40684  25.185866  2.470259        종근당   \n",
            "204210     모두투어리츠     85.166667    5335  40.802348  0.651359     모두투어리츠   \n",
            "207940   삼성바이오로직스   4644.166667   60099  89.790059  6.938551        NaN   \n",
            "\n",
            "                value     price  \n",
            "id                               \n",
            "130960   58540.666667   98900.0  \n",
            "136480            NaN       NaN  \n",
            "138040            NaN       NaN  \n",
            "139480  239230.833333  254500.0  \n",
            "145990   82750.000000   82000.0  \n",
            "161390            NaN       NaN  \n",
            "181710            NaN       NaN  \n",
            "185750   40293.666667  100500.0  \n",
            "204210    3093.333333    3475.0  \n",
            "207940            NaN       NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# merge 에서 right join\n",
        "# RIGHT OUTER JOIN\n",
        "# 기본값 how = 'left'\n",
        "join_left = df1.join(df2, how = 'right')\n",
        "print(join_left)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m06HXI93Fm4X",
        "outputId": "6f43b9b6-72fa-4b04-ccb9-dabffa503a60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       stock_name          value     price       name           eps     bps  \\\n",
            "id                                                                            \n",
            "130960     CJ E&M   58540.666667   98900.0     CJ E&M   6301.333333   54068   \n",
            "136480        NaN            NaN       NaN         하림    274.166667    3551   \n",
            "138040        NaN            NaN       NaN    메리츠금융지주   2122.333333   14894   \n",
            "139480        이마트  239230.833333  254500.0        이마트  18268.166667  295780   \n",
            "145990        삼양사   82750.000000   82000.0        삼양사   5741.000000  108090   \n",
            "161390        NaN            NaN       NaN      한국타이어   5648.500000   51341   \n",
            "181710        NaN            NaN       NaN  NHN엔터테인먼트   2110.166667   78434   \n",
            "185750        종근당   40293.666667  100500.0        종근당   3990.333333   40684   \n",
            "204210     모두투어리츠    3093.333333    3475.0     모두투어리츠     85.166667    5335   \n",
            "207940        NaN            NaN       NaN   삼성바이오로직스   4644.166667   60099   \n",
            "\n",
            "              per       pbr  \n",
            "id                           \n",
            "130960  15.695091  1.829178  \n",
            "136480  11.489362  0.887074  \n",
            "138040   6.313806  0.899691  \n",
            "139480  13.931338  0.860437  \n",
            "145990  14.283226  0.758627  \n",
            "161390   7.453306  0.820007  \n",
            "181710  30.755864  0.827447  \n",
            "185750  25.185866  2.470259  \n",
            "204210  40.802348  0.651359  \n",
            "207940  89.790059  6.938551  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# merge 에서 right join\n",
        "# RIGHT OUTER JOIN\n",
        "# 기본값 how = 'left'\n",
        "join_right = df2.join(df1, how = 'right')\n",
        "print(join_right)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idZeKoSSF74A",
        "outputId": "6874028e-44d0-4342-c3b2-f5a0de04ae8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          name           eps       bps        per       pbr stock_name  \\\n",
            "id                                                                       \n",
            "128940     NaN           NaN       NaN        NaN       NaN       한미약품   \n",
            "130960  CJ E&M   6301.333333   54068.0  15.695091  1.829178     CJ E&M   \n",
            "138250     NaN           NaN       NaN        NaN       NaN      엔에스쇼핑   \n",
            "139480     이마트  18268.166667  295780.0  13.931338  0.860437        이마트   \n",
            "142280     NaN           NaN       NaN        NaN       NaN     녹십자엠에스   \n",
            "145990     삼양사   5741.000000  108090.0  14.283226  0.758627        삼양사   \n",
            "185750     종근당   3990.333333   40684.0  25.185866  2.470259        종근당   \n",
            "192400     NaN           NaN       NaN        NaN       NaN      쿠쿠홀딩스   \n",
            "199800     NaN           NaN       NaN        NaN       NaN         툴젠   \n",
            "204210  모두투어리츠     85.166667    5335.0  40.802348  0.651359     모두투어리츠   \n",
            "\n",
            "                value   price  \n",
            "id                             \n",
            "128940   59385.666667  421000  \n",
            "130960   58540.666667   98900  \n",
            "138250   14558.666667   13200  \n",
            "139480  239230.833333  254500  \n",
            "142280     468.833333   10200  \n",
            "145990   82750.000000   82000  \n",
            "185750   40293.666667  100500  \n",
            "192400  179204.666667  177500  \n",
            "199800   -2514.333333  115400  \n",
            "204210    3093.333333    3475  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# INNER JOIN\n",
        "join_inner = df1.join(df2, how = 'inner')\n",
        "print(join_inner)"
      ],
      "metadata": {
        "id": "OUjo_D9Iy9fC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "714ae170-bb4d-4796-99f7-3b53a010514e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       stock_name          value   price    name           eps     bps  \\\n",
            "id                                                                       \n",
            "130960     CJ E&M   58540.666667   98900  CJ E&M   6301.333333   54068   \n",
            "139480        이마트  239230.833333  254500     이마트  18268.166667  295780   \n",
            "145990        삼양사   82750.000000   82000     삼양사   5741.000000  108090   \n",
            "185750        종근당   40293.666667  100500     종근당   3990.333333   40684   \n",
            "204210     모두투어리츠    3093.333333    3475  모두투어리츠     85.166667    5335   \n",
            "\n",
            "              per       pbr  \n",
            "id                           \n",
            "130960  15.695091  1.829178  \n",
            "139480  13.931338  0.860437  \n",
            "145990  14.283226  0.758627  \n",
            "185750  25.185866  2.470259  \n",
            "204210  40.802348  0.651359  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OUTER JOIN\n",
        "join_outer = df1.join(df2, how = \"outer\")\n",
        "print(join_outer)"
      ],
      "metadata": {
        "id": "RoM1Qjst3-oz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "982582e7-dfce-4ad4-8ebb-da2b5524642c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       stock_name          value     price       name           eps       bps  \\\n",
            "id                                                                              \n",
            "128940       한미약품   59385.666667  421000.0        NaN           NaN       NaN   \n",
            "130960     CJ E&M   58540.666667   98900.0     CJ E&M   6301.333333   54068.0   \n",
            "136480        NaN            NaN       NaN         하림    274.166667    3551.0   \n",
            "138040        NaN            NaN       NaN    메리츠금융지주   2122.333333   14894.0   \n",
            "138250      엔에스쇼핑   14558.666667   13200.0        NaN           NaN       NaN   \n",
            "139480        이마트  239230.833333  254500.0        이마트  18268.166667  295780.0   \n",
            "142280     녹십자엠에스     468.833333   10200.0        NaN           NaN       NaN   \n",
            "145990        삼양사   82750.000000   82000.0        삼양사   5741.000000  108090.0   \n",
            "161390        NaN            NaN       NaN      한국타이어   5648.500000   51341.0   \n",
            "181710        NaN            NaN       NaN  NHN엔터테인먼트   2110.166667   78434.0   \n",
            "185750        종근당   40293.666667  100500.0        종근당   3990.333333   40684.0   \n",
            "192400      쿠쿠홀딩스  179204.666667  177500.0        NaN           NaN       NaN   \n",
            "199800         툴젠   -2514.333333  115400.0        NaN           NaN       NaN   \n",
            "204210     모두투어리츠    3093.333333    3475.0     모두투어리츠     85.166667    5335.0   \n",
            "207940        NaN            NaN       NaN   삼성바이오로직스   4644.166667   60099.0   \n",
            "\n",
            "              per       pbr  \n",
            "id                           \n",
            "128940        NaN       NaN  \n",
            "130960  15.695091  1.829178  \n",
            "136480  11.489362  0.887074  \n",
            "138040   6.313806  0.899691  \n",
            "138250        NaN       NaN  \n",
            "139480  13.931338  0.860437  \n",
            "142280        NaN       NaN  \n",
            "145990  14.283226  0.758627  \n",
            "161390   7.453306  0.820007  \n",
            "181710  30.755864  0.827447  \n",
            "185750  25.185866  2.470259  \n",
            "192400        NaN       NaN  \n",
            "199800        NaN       NaN  \n",
            "204210  40.802348  0.651359  \n",
            "207940  89.790059  6.938551  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 열 기준으로 합치는 방법"
      ],
      "metadata": {
        "id": "PHniSncW61xf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# 데이터 새로 불러오기\n",
        "df1 = pd.read_excel('/content/drive/MyDrive/lsda02/data/stock price.xlsx', engine = 'openpyxl')\n",
        "df2 = pd.read_excel('/content/drive/MyDrive/lsda02/data/stock valuation.xlsx', engine = 'openpyxl')\n",
        "# 열 기준으로 합치는 방법\n",
        "# df1.join(df2, on='id')\n",
        "df1.join(df2.set_index('id'), on='id')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "IcMWJOeM6Llf",
        "outputId": "07e81b98-a519-4e07-edb6-9026500edc67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id stock_name          value   price    name           eps       bps  \\\n",
              "0  128940       한미약품   59385.666667  421000     NaN           NaN       NaN   \n",
              "1  130960     CJ E&M   58540.666667   98900  CJ E&M   6301.333333   54068.0   \n",
              "2  138250      엔에스쇼핑   14558.666667   13200     NaN           NaN       NaN   \n",
              "3  139480        이마트  239230.833333  254500     이마트  18268.166667  295780.0   \n",
              "4  142280     녹십자엠에스     468.833333   10200     NaN           NaN       NaN   \n",
              "5  145990        삼양사   82750.000000   82000     삼양사   5741.000000  108090.0   \n",
              "6  185750        종근당   40293.666667  100500     종근당   3990.333333   40684.0   \n",
              "7  192400      쿠쿠홀딩스  179204.666667  177500     NaN           NaN       NaN   \n",
              "8  199800         툴젠   -2514.333333  115400     NaN           NaN       NaN   \n",
              "9  204210     모두투어리츠    3093.333333    3475  모두투어리츠     85.166667    5335.0   \n",
              "\n",
              "         per       pbr  \n",
              "0        NaN       NaN  \n",
              "1  15.695091  1.829178  \n",
              "2        NaN       NaN  \n",
              "3  13.931338  0.860437  \n",
              "4        NaN       NaN  \n",
              "5  14.283226  0.758627  \n",
              "6  25.185866  2.470259  \n",
              "7        NaN       NaN  \n",
              "8        NaN       NaN  \n",
              "9  40.802348  0.651359  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79e2b9bd-e0ce-4bc6-9bda-a39285b46288\" 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>id</th>\n",
              "      <th>stock_name</th>\n",
              "      <th>value</th>\n",
              "      <th>price</th>\n",
              "      <th>name</th>\n",
              "      <th>eps</th>\n",
              "      <th>bps</th>\n",
              "      <th>per</th>\n",
              "      <th>pbr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>128940</td>\n",
              "      <td>한미약품</td>\n",
              "      <td>59385.666667</td>\n",
              "      <td>421000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>130960</td>\n",
              "      <td>CJ E&amp;M</td>\n",
              "      <td>58540.666667</td>\n",
              "      <td>98900</td>\n",
              "      <td>CJ E&amp;M</td>\n",
              "      <td>6301.333333</td>\n",
              "      <td>54068.0</td>\n",
              "      <td>15.695091</td>\n",
              "      <td>1.829178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>138250</td>\n",
              "      <td>엔에스쇼핑</td>\n",
              "      <td>14558.666667</td>\n",
              "      <td>13200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>139480</td>\n",
              "      <td>이마트</td>\n",
              "      <td>239230.833333</td>\n",
              "      <td>254500</td>\n",
              "      <td>이마트</td>\n",
              "      <td>18268.166667</td>\n",
              "      <td>295780.0</td>\n",
              "      <td>13.931338</td>\n",
              "      <td>0.860437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142280</td>\n",
              "      <td>녹십자엠에스</td>\n",
              "      <td>468.833333</td>\n",
              "      <td>10200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>145990</td>\n",
              "      <td>삼양사</td>\n",
              "      <td>82750.000000</td>\n",
              "      <td>82000</td>\n",
              "      <td>삼양사</td>\n",
              "      <td>5741.000000</td>\n",
              "      <td>108090.0</td>\n",
              "      <td>14.283226</td>\n",
              "      <td>0.758627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>185750</td>\n",
              "      <td>종근당</td>\n",
              "      <td>40293.666667</td>\n",
              "      <td>100500</td>\n",
              "      <td>종근당</td>\n",
              "      <td>3990.333333</td>\n",
              "      <td>40684.0</td>\n",
              "      <td>25.185866</td>\n",
              "      <td>2.470259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>192400</td>\n",
              "      <td>쿠쿠홀딩스</td>\n",
              "      <td>179204.666667</td>\n",
              "      <td>177500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>199800</td>\n",
              "      <td>툴젠</td>\n",
              "      <td>-2514.333333</td>\n",
              "      <td>115400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>204210</td>\n",
              "      <td>모두투어리츠</td>\n",
              "      <td>3093.333333</td>\n",
              "      <td>3475</td>\n",
              "      <td>모두투어리츠</td>\n",
              "      <td>85.166667</td>\n",
              "      <td>5335.0</td>\n",
              "      <td>40.802348</td>\n",
              "      <td>0.651359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30654,\n        \"min\": 128940,\n        \"max\": 204210,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          199800,\n          130960,\n          145990\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stock_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"\\ud234\\uc820\",\n          \"CJ E&M\",\n          \"\\uc0bc\\uc591\\uc0ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81263.94475464002,\n        \"min\": -2514.333333333333,\n        \"max\": 239230.8333333333,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -2514.333333333333,\n          58540.66666666666,\n          82750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129417,\n        \"min\": 3475,\n        \"max\": 421000,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          115400,\n          98900,\n          82000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\uc774\\ub9c8\\ud2b8\",\n          \"\\ubaa8\\ub450\\ud22c\\uc5b4\\ub9ac\\uce20\",\n          \"\\uc0bc\\uc591\\uc0ac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"eps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6816.201479033451,\n        \"min\": 85.16666666666667,\n        \"max\": 18268.16666666667,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          18268.16666666667,\n          85.16666666666667,\n          5741.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115088.79575701537,\n        \"min\": 5335.0,\n        \"max\": 295780.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          295780.0,\n          5335.0,\n          108090.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"per\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.489992910118012,\n        \"min\": 13.93133775511135,\n        \"max\": 40.80234833659491,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          13.93133775511135,\n          40.80234833659491,\n          14.28322591882947\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pbr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7993112910862892,\n        \"min\": 0.6513589503280225,\n        \"max\": 2.470258578310884,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8604368111434174,\n          0.6513589503280225,\n          0.7586270700342307\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 그룹 연산"
      ],
      "metadata": {
        "id": "Amhq-5V-1BYt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 그룹 객체 만들기(분할 단계)"
      ],
      "metadata": {
        "id": "7ObowPqj1Ids"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 라이브러리 불러오기\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "gfkmqbFWzUIg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 불러오기\n",
        "titanic = sns.load_dataset('titanic')\n",
        "titanic.head()"
      ],
      "metadata": {
        "id": "mXekn3El1TQ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "17b94ec4-068c-45c8-a16f-7433a7546818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b7ede2f-1b73-46e7-900c-7e33441c3bbb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6b7ede2f-1b73-46e7-900c-7e33441c3bbb 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-6b7ede2f-1b73-46e7-900c-7e33441c3bbb');\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",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-94abf9b9-73b5-42e2-9a0c-621712ebfdb7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = titanic.loc[:,['sex','age','fare','class','survived']]"
      ],
      "metadata": {
        "id": "smHGtpcI1c8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 개수\n",
        "len(df)"
      ],
      "metadata": {
        "id": "Wc_KcGKQ1xaq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "462fa888-9575-4194-d4eb-77f69386afdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1개의 열로 그룹 객체 만들기"
      ],
      "metadata": {
        "id": "XBl1sI4r9-5A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체 => groupby(그룹화에 사용할 열)\n",
        "grouped = df.groupby('sex')\n",
        "print(grouped) # => 그룹 객체"
      ],
      "metadata": {
        "id": "di0OlqXQ13_Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd62c6d2-4be7-41d1-82ea-0b274c6efc06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7c7ab0326110>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별 그룹 확인\n",
        "df['sex'].value_counts()"
      ],
      "metadata": {
        "id": "VxxTabfm2nWv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee63c47b-d3af-4700-8c0a-cad231b8695f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 반복문으로 그룹 객체 확인\n",
        "for i, j in grouped:\n",
        "  print(\"그룹의 이름: \", i) # 그룹의 이름\n",
        "  print(j.head()) # 그룹에 해당하는 데이터"
      ],
      "metadata": {
        "id": "sxllQGjD2ZtT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "967428ec-4fb7-41df-e032-6c686a19c920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "그룹의 이름:  female\n",
            "      sex   age     fare   class  survived\n",
            "1  female  38.0  71.2833   First         1\n",
            "2  female  26.0   7.9250   Third         1\n",
            "3  female  35.0  53.1000   First         1\n",
            "8  female  27.0  11.1333   Third         1\n",
            "9  female  14.0  30.0708  Second         1\n",
            "그룹의 이름:  male\n",
            "    sex   age     fare  class  survived\n",
            "0  male  22.0   7.2500  Third         0\n",
            "4  male  35.0   8.0500  Third         0\n",
            "5  male   NaN   8.4583  Third         0\n",
            "6  male  54.0  51.8625  First         0\n",
            "7  male   2.0  21.0750  Third         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체에 함수 적용 (예, 평균)\n",
        "gr_sex_mean = grouped.mean(numeric_only=True)\n",
        "print(gr_sex_mean)"
      ],
      "metadata": {
        "id": "kszgunZM3KEQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab3dc30f-91fa-491e-9b78-73c4deac2ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              age       fare  survived\n",
            "sex                                   \n",
            "female  27.915709  44.479818  0.742038\n",
            "male    30.726645  25.523893  0.188908\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체에서 원하는 그룹의 데이터를 가져오기\n",
        "df_female = grouped.get_group('female')\n",
        "print(df_female)"
      ],
      "metadata": {
        "id": "tnE1DTUw2WtH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42a24cc7-5fb2-4bf6-adaf-0d21240f9c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex   age     fare   class  survived\n",
            "1    female  38.0  71.2833   First         1\n",
            "2    female  26.0   7.9250   Third         1\n",
            "3    female  35.0  53.1000   First         1\n",
            "8    female  27.0  11.1333   Third         1\n",
            "9    female  14.0  30.0708  Second         1\n",
            "..      ...   ...      ...     ...       ...\n",
            "880  female  25.0  26.0000  Second         1\n",
            "882  female  22.0  10.5167   Third         0\n",
            "885  female  39.0  29.1250   Third         0\n",
            "887  female  19.0  30.0000   First         1\n",
            "888  female   NaN  23.4500   Third         0\n",
            "\n",
            "[314 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_male = grouped.get_group('male')\n",
        "print(df_male)"
      ],
      "metadata": {
        "id": "4P_Mx53U9Bbg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e368903f-3ac7-4fdf-bf25-8218d8505fb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      sex   age     fare   class  survived\n",
            "0    male  22.0   7.2500   Third         0\n",
            "4    male  35.0   8.0500   Third         0\n",
            "5    male   NaN   8.4583   Third         0\n",
            "6    male  54.0  51.8625   First         0\n",
            "7    male   2.0  21.0750   Third         0\n",
            "..    ...   ...      ...     ...       ...\n",
            "883  male  28.0  10.5000  Second         0\n",
            "884  male  25.0   7.0500   Third         0\n",
            "886  male  27.0  13.0000  Second         0\n",
            "889  male  26.0  30.0000   First         1\n",
            "890  male  32.0   7.7500   Third         0\n",
            "\n",
            "[577 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 여자 데이터가 314개, 남자 데이터가 577개인지 확인\n",
        "df.sex.value_counts()"
      ],
      "metadata": {
        "id": "oYKMTOmn8xU0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ff179c9-aa0e-4fd7-94b8-ffa2ad7a138a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "male      577\n",
              "female    314\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 여러 개의 열로 그룹 객체 만들기"
      ],
      "metadata": {
        "id": "ATcareM6-EFq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체 => groupby(그룹화에 사용할 열)\n",
        "grouped = df.groupby(['class','sex'])\n",
        "print(grouped) # => 그룹 객체"
      ],
      "metadata": {
        "id": "9F0Agthk2LrY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70c3347e-1739-48c7-be29-12057670d599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7c7ab00b9750>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 클래스와 성별 그룹 확인\n",
        "df[['class','sex']].value_counts()"
      ],
      "metadata": {
        "id": "9iHyKNFU-lOB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0637f591-a142-4f80-a291-ac061932e00d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class   sex   \n",
              "Third   male      347\n",
              "        female    144\n",
              "First   male      122\n",
              "Second  male      108\n",
              "First   female     94\n",
              "Second  female     76\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 반복문으로 그룹 객체 확인\n",
        "for i, j in grouped:\n",
        "  print(\"그룹의 이름: \", i) # 그룹의 이름 => 튜플 형태\n",
        "  print(j.head()) # 그룹에 해당하는 데이터"
      ],
      "metadata": {
        "id": "w9GrlXUI-lOB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52ca4140-2edd-48f9-a16a-04ff0b047e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "그룹의 이름:  ('First', 'female')\n",
            "       sex   age      fare  class  survived\n",
            "1   female  38.0   71.2833  First         1\n",
            "3   female  35.0   53.1000  First         1\n",
            "11  female  58.0   26.5500  First         1\n",
            "31  female   NaN  146.5208  First         1\n",
            "52  female  49.0   76.7292  First         1\n",
            "그룹의 이름:  ('First', 'male')\n",
            "     sex   age      fare  class  survived\n",
            "6   male  54.0   51.8625  First         0\n",
            "23  male  28.0   35.5000  First         1\n",
            "27  male  19.0  263.0000  First         0\n",
            "30  male  40.0   27.7208  First         0\n",
            "34  male  28.0   82.1708  First         0\n",
            "그룹의 이름:  ('Second', 'female')\n",
            "       sex   age     fare   class  survived\n",
            "9   female  14.0  30.0708  Second         1\n",
            "15  female  55.0  16.0000  Second         1\n",
            "41  female  27.0  21.0000  Second         0\n",
            "43  female   3.0  41.5792  Second         1\n",
            "53  female  29.0  26.0000  Second         1\n",
            "그룹의 이름:  ('Second', 'male')\n",
            "     sex   age  fare   class  survived\n",
            "17  male   NaN  13.0  Second         1\n",
            "20  male  35.0  26.0  Second         0\n",
            "21  male  34.0  13.0  Second         1\n",
            "33  male  66.0  10.5  Second         0\n",
            "70  male  32.0  10.5  Second         0\n",
            "그룹의 이름:  ('Third', 'female')\n",
            "       sex   age     fare  class  survived\n",
            "2   female  26.0   7.9250  Third         1\n",
            "8   female  27.0  11.1333  Third         1\n",
            "10  female   4.0  16.7000  Third         1\n",
            "14  female  14.0   7.8542  Third         0\n",
            "18  female  31.0  18.0000  Third         0\n",
            "그룹의 이름:  ('Third', 'male')\n",
            "     sex   age     fare  class  survived\n",
            "0   male  22.0   7.2500  Third         0\n",
            "4   male  35.0   8.0500  Third         0\n",
            "5   male   NaN   8.4583  Third         0\n",
            "7   male   2.0  21.0750  Third         0\n",
            "12  male  20.0   8.0500  Third         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체에 함수 적용 (예, 평균)\n",
        "gr_class_sex_mean = grouped.mean()\n",
        "print(gr_class_sex_mean)"
      ],
      "metadata": {
        "id": "IFMGmxlx1uXX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "faea089d-c3c1-4b83-81f2-67df6d0a6b8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     age        fare  survived\n",
            "class  sex                                    \n",
            "First  female  34.611765  106.125798  0.968085\n",
            "       male    41.281386   67.226127  0.368852\n",
            "Second female  28.722973   21.970121  0.921053\n",
            "       male    30.740707   19.741782  0.157407\n",
            "Third  female  21.750000   16.118810  0.500000\n",
            "       male    26.507589   12.661633  0.135447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체에서 원하는 그룹의 데이터를 가져오기\n",
        "df_th_male = grouped.get_group(('Third', 'male')) # 튜플 형태\n",
        "print(df_th_male)"
      ],
      "metadata": {
        "id": "m3kQuxuU_UNz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b4f2b2f-958c-4b22-8d8a-39ac7af4c759"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      sex   age     fare  class  survived\n",
            "0    male  22.0   7.2500  Third         0\n",
            "4    male  35.0   8.0500  Third         0\n",
            "5    male   NaN   8.4583  Third         0\n",
            "7    male   2.0  21.0750  Third         0\n",
            "12   male  20.0   8.0500  Third         0\n",
            "..    ...   ...      ...    ...       ...\n",
            "877  male  19.0   7.8958  Third         0\n",
            "878  male   NaN   7.8958  Third         0\n",
            "881  male  33.0   7.8958  Third         0\n",
            "884  male  25.0   7.0500  Third         0\n",
            "890  male  32.0   7.7500  Third         0\n",
            "\n",
            "[347 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_se_female = grouped.get_group(('Second', 'female')) # 튜플 형태\n",
        "print(df_se_female)"
      ],
      "metadata": {
        "id": "7oOE-opg_9Nf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8498c647-76d8-4225-f1fe-a6dca1b67e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex   age     fare   class  survived\n",
            "9    female  14.0  30.0708  Second         1\n",
            "15   female  55.0  16.0000  Second         1\n",
            "41   female  27.0  21.0000  Second         0\n",
            "43   female   3.0  41.5792  Second         1\n",
            "53   female  29.0  26.0000  Second         1\n",
            "..      ...   ...      ...     ...       ...\n",
            "854  female  44.0  26.0000  Second         0\n",
            "865  female  42.0  13.0000  Second         1\n",
            "866  female  27.0  13.8583  Second         1\n",
            "874  female  28.0  24.0000  Second         1\n",
            "880  female  25.0  26.0000  Second         1\n",
            "\n",
            "[76 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 클래스와 성별 그룹 확인\n",
        "df[['class','sex']].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2j5bLr5__4Sp",
        "outputId": "b58c2371-6fa6-4465-bbaf-7cf2c719cba8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class   sex   \n",
              "Third   male      347\n",
              "        female    144\n",
              "First   male      122\n",
              "Second  male      108\n",
              "First   female     94\n",
              "Second  female     76\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 그룹 연산 메소드(적용, 결합 단계)"
      ],
      "metadata": {
        "id": "1OHxq27KBnoL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 집계"
      ],
      "metadata": {
        "id": "-B-KiYRADX41"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 내장 함수 1개 적용"
      ],
      "metadata": {
        "id": "ooZ_HbMJI-y_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 클래스 그룹 객체 생성\n",
        "grouped = df.groupby('class')"
      ],
      "metadata": {
        "id": "GF3olNolBrgn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준편차 <= 내장함수\n",
        "gr_sd = grouped.std(numeric_only=True)\n",
        "print(gr_sd)\n",
        "print(type(gr_sd)) # 데이터프레임 = 여러 열"
      ],
      "metadata": {
        "id": "e8SGYlmzB_hi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c32a6f59-89c7-4ea6-a030-de9c5e683519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              age       fare  survived\n",
            "class                                 \n",
            "First   14.802856  78.380373  0.484026\n",
            "Second  14.001077  13.417399  0.500623\n",
            "Third   12.495398  11.778142  0.428949\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gr_age_sd = grouped.age.std()\n",
        "print(gr_age_sd)\n",
        "print(type(gr_age_sd)) # 시리즈 = 하나의 열"
      ],
      "metadata": {
        "id": "gGpJRaboCTlr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ec46426-1d51-407d-920d-34b40a7136a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "First     14.802856\n",
            "Second    14.001077\n",
            "Third     12.495398\n",
            "Name: age, dtype: float64\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 함수 2개 이상 적용(내장함수, 사용자 정의 함수)"
      ],
      "metadata": {
        "id": "b7R7ZxSwJGd-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_bc-gSu5BM0",
        "outputId": "284c7627-02c3-4049-a83c-b98c461a2f13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype   \n",
            "---  ------    --------------  -----   \n",
            " 0   sex       891 non-null    object  \n",
            " 1   age       714 non-null    float64 \n",
            " 2   fare      891 non-null    float64 \n",
            " 3   class     891 non-null    category\n",
            " 4   survived  891 non-null    int64   \n",
            "dtypes: category(1), float64(2), int64(1), object(1)\n",
            "memory usage: 29.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 사용자 정의 함수\n",
        "# 범위를 반환하는 함수\n",
        "def range_xy(x):\n",
        "  if x.dtype == 'O' or x.dtype == 'category':\n",
        "    pass\n",
        "  else:\n",
        "    return x.max() - x.min()"
      ],
      "metadata": {
        "id": "yJOZ86flC5Yb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체에 사용자 정의 함수 적용\n",
        "grouped.agg(range_xy)"
      ],
      "metadata": {
        "id": "J5xMgZrGEV-O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "e3603a8e-3d4f-4eeb-f83a-efae035e71b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         sex    age      fare  survived\n",
              "class                                  \n",
              "First   None  79.08  512.3292         1\n",
              "Second  None  69.33   73.5000         1\n",
              "Third   None  73.58   69.5500         1"
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              "    [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-185f6a6a-e12c-4070-b1c4-7695c7171ba9 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-185f6a6a-e12c-4070-b1c4-7695c7171ba9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-99d7d69c-f6b3-48cc-9720-bbabd1d2897c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-99d7d69c-f6b3-48cc-9720-bbabd1d2897c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-99d7d69c-f6b3-48cc-9720-bbabd1d2897c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 함수를 여러 개 적용\n",
        "grouped.agg(['min', 'max'])"
      ],
      "metadata": {
        "id": "BrpUMZ1MEgFV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "64211844-6077-4d02-f7d3-25816641e347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           sex         age       fare           survived    \n",
              "           min   max   min   max  min       max      min max\n",
              "class                                                       \n",
              "First   female  male  0.92  80.0  0.0  512.3292        0   1\n",
              "Second  female  male  0.67  70.0  0.0   73.5000        0   1\n",
              "Third   female  male  0.42  74.0  0.0   69.5500        0   1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-238d93ab-fa05-4d6b-baf6-59d43ce8d9b9\" 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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">sex</th>\n",
              "      <th colspan=\"2\" halign=\"left\">age</th>\n",
              "      <th colspan=\"2\" halign=\"left\">fare</th>\n",
              "      <th colspan=\"2\" halign=\"left\">survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>female</td>\n",
              "      <td>male</td>\n",
              "      <td>0.92</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>female</td>\n",
              "      <td>male</td>\n",
              "      <td>0.67</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>73.5000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>female</td>\n",
              "      <td>male</td>\n",
              "      <td>0.42</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.5500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-238d93ab-fa05-4d6b-baf6-59d43ce8d9b9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-238d93ab-fa05-4d6b-baf6-59d43ce8d9b9 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-238d93ab-fa05-4d6b-baf6-59d43ce8d9b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6e0173f7-131c-412c-8eb7-364bb8021b12\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e0173f7-131c-412c-8eb7-364bb8021b12')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6e0173f7-131c-412c-8eb7-364bb8021b12 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"grouped\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"class\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"First\",\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"sex\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"sex\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25,\n        \"min\": 0.42,\n        \"max\": 0.92,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.033222956847166,\n        \"min\": 70.0,\n        \"max\": 80.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          80.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"fare\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"fare\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 254.50608677242542,\n        \"min\": 69.55,\n        \"max\": 512.3292,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          512.3292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"survived\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"survived\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 함수를 여러 개 적용 & 내가 원하는 열에 사용 가능\n",
        "grouped.agg({'age': ['min', 'max'], # 나이의 최소값, 최대값\n",
        "             'fare': 'mean'}) # fare 의 평균"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wgsNF4XKE7Fe",
        "outputId": "0edfcfcb-b2f3-4bec-a2f3-535353acaa63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         age             fare\n",
              "         min   max       mean\n",
              "class                        \n",
              "First   0.92  80.0  84.154687\n",
              "Second  0.67  70.0  20.662183\n",
              "Third   0.42  74.0  13.675550"
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"             'fare': 'mean'}) # fare \\uc758 \\ud3c9\\uade0\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"class\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"First\",\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"min\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25,\n        \"min\": 0.42,\n        \"max\": 0.92,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.92,\n          0.67,\n          0.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        \"max\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.033222956847166,\n        \"min\": 70.0,\n        \"max\": 80.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          80.0,\n          70.0,\n          74.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"fare\",\n        \"mean\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.83173092143531,\n        \"min\": 13.675550101832993,\n        \"max\": 84.1546875,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          84.1546875,\n          20.662183152173913,\n          13.675550101832993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 변환"
      ],
      "metadata": {
        "id": "p0ie__a8MZxg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# transform = 그룹 연산 데이터 변환\n",
        "# 단일 행 함수 = 데이터 개수와 함수 적용 결과 개수가 동일\n",
        "age_mean = grouped.age.mean()\n",
        "age_sd = grouped.age.std()\n",
        "print(age_mean);print(age_sd)"
      ],
      "metadata": {
        "id": "Nrs-pODhSbln",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c18eb45f-a14a-4700-911e-7dee1e9393a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class\n",
            "First     38.233441\n",
            "Second    29.877630\n",
            "Third     25.140620\n",
            "Name: age, dtype: float64\n",
            "class\n",
            "First     14.802856\n",
            "Second    14.001077\n",
            "Third     12.495398\n",
            "Name: age, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, j in grouped.age:\n",
        "  z_score = (j - age_mean[i]) / age_sd[i]\n",
        "  print(\"그룹 이름: \", i)\n",
        "  print(z_score)"
      ],
      "metadata": {
        "id": "ReutTPlWKWH5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d810832-0b28-44b7-f623-6905bea28200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "그룹 이름:  First\n",
            "1     -0.015770\n",
            "3     -0.218434\n",
            "6      1.065103\n",
            "11     1.335321\n",
            "23    -0.691315\n",
            "         ...   \n",
            "871    0.592221\n",
            "872   -0.353543\n",
            "879    1.200212\n",
            "887   -1.299306\n",
            "889   -0.826424\n",
            "Name: age, Length: 216, dtype: float64\n",
            "그룹 이름:  Second\n",
            "9     -1.134029\n",
            "15     1.794317\n",
            "17          NaN\n",
            "20     0.365855\n",
            "21     0.294432\n",
            "         ...   \n",
            "866   -0.205529\n",
            "874   -0.134106\n",
            "880   -0.348375\n",
            "883   -0.134106\n",
            "886   -0.205529\n",
            "Name: age, Length: 184, dtype: float64\n",
            "그룹 이름:  Third\n",
            "0     -0.251342\n",
            "2      0.068776\n",
            "4      0.789041\n",
            "5           NaN\n",
            "7     -1.851931\n",
            "         ...   \n",
            "882   -0.251342\n",
            "884   -0.011254\n",
            "885    1.109159\n",
            "888         NaN\n",
            "890    0.548953\n",
            "Name: age, Length: 491, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def zscore(x):\n",
        "  if x.dtype == 'O'  or x.dtype == 'category':\n",
        "    pass\n",
        "  else:\n",
        "    return (x - x.mean()) / x.std()\n",
        "grouped.age.transform(zscore)\n",
        "# 전체 데이터 개수 891 개 = First 개수 + Second 개수 + Third 개수"
      ],
      "metadata": {
        "id": "dxVYNB4pLHyA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0296e17-53ed-4db1-c29c-05ddd12d893f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     -0.251342\n",
              "1     -0.015770\n",
              "2      0.068776\n",
              "3     -0.218434\n",
              "4      0.789041\n",
              "         ...   \n",
              "886   -0.205529\n",
              "887   -1.299306\n",
              "888         NaN\n",
              "889   -0.826424\n",
              "890    0.548953\n",
              "Name: age, Length: 891, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 필터링"
      ],
      "metadata": {
        "id": "V4YFsIC4Na8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 반복문으로 그룹 객체 확인\n",
        "for i, j in grouped:\n",
        "  print(\"그룹의 이름: \", i) # 그룹의 이름\n",
        "  print(j) # 그룹에 해당하는 데이터"
      ],
      "metadata": {
        "id": "7bKeXS7uNZd4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20cd6a73-4ee8-463f-8cc8-e4547e42a963"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "그룹의 이름:  First\n",
            "        sex   age     fare  class  survived\n",
            "1    female  38.0  71.2833  First         1\n",
            "3    female  35.0  53.1000  First         1\n",
            "6      male  54.0  51.8625  First         0\n",
            "11   female  58.0  26.5500  First         1\n",
            "23     male  28.0  35.5000  First         1\n",
            "..      ...   ...      ...    ...       ...\n",
            "871  female  47.0  52.5542  First         1\n",
            "872    male  33.0   5.0000  First         0\n",
            "879  female  56.0  83.1583  First         1\n",
            "887  female  19.0  30.0000  First         1\n",
            "889    male  26.0  30.0000  First         1\n",
            "\n",
            "[216 rows x 5 columns]\n",
            "그룹의 이름:  Second\n",
            "        sex   age     fare   class  survived\n",
            "9    female  14.0  30.0708  Second         1\n",
            "15   female  55.0  16.0000  Second         1\n",
            "17     male   NaN  13.0000  Second         1\n",
            "20     male  35.0  26.0000  Second         0\n",
            "21     male  34.0  13.0000  Second         1\n",
            "..      ...   ...      ...     ...       ...\n",
            "866  female  27.0  13.8583  Second         1\n",
            "874  female  28.0  24.0000  Second         1\n",
            "880  female  25.0  26.0000  Second         1\n",
            "883    male  28.0  10.5000  Second         0\n",
            "886    male  27.0  13.0000  Second         0\n",
            "\n",
            "[184 rows x 5 columns]\n",
            "그룹의 이름:  Third\n",
            "        sex   age     fare  class  survived\n",
            "0      male  22.0   7.2500  Third         0\n",
            "2    female  26.0   7.9250  Third         1\n",
            "4      male  35.0   8.0500  Third         0\n",
            "5      male   NaN   8.4583  Third         0\n",
            "7      male   2.0  21.0750  Third         0\n",
            "..      ...   ...      ...    ...       ...\n",
            "882  female  22.0  10.5167  Third         0\n",
            "884    male  25.0   7.0500  Third         0\n",
            "885  female  39.0  29.1250  Third         0\n",
            "888  female   NaN  23.4500  Third         0\n",
            "890    male  32.0   7.7500  Third         0\n",
            "\n",
            "[491 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 필터 조건 => 데이터 개수가 특정 개수 이상인 조건 = 200개 이상\n",
        "grouped.filter(lambda x: len(x) >= 200)\n",
        "# First 216개 + Third 491개 = 707개"
      ],
      "metadata": {
        "id": "AFtGIGoZNrIK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "2b228e47-ac8e-41c9-a671-d6e66f79df4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age     fare  class  survived\n",
              "0      male  22.0   7.2500  Third         0\n",
              "1    female  38.0  71.2833  First         1\n",
              "2    female  26.0   7.9250  Third         1\n",
              "3    female  35.0  53.1000  First         1\n",
              "4      male  35.0   8.0500  Third         0\n",
              "..      ...   ...      ...    ...       ...\n",
              "885  female  39.0  29.1250  Third         0\n",
              "887  female  19.0  30.0000  First         1\n",
              "888  female   NaN  23.4500  Third         0\n",
              "889    male  26.0  30.0000  First         1\n",
              "890    male  32.0   7.7500  Third         0\n",
              "\n",
              "[707 rows x 5 columns]"
            ],
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              "      <th>0</th>\n",
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              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>Third</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Third</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>707 rows × 5 columns</p>\n",
              "</div>\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "        document.querySelector('#df-88965ddd-52a9-4a80-ad83-9937a329e1e1 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-88965ddd-52a9-4a80-ad83-9937a329e1e1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"# First 216\\uac1c + Third 491\\uac1c = 707\\uac1c\",\n  \"rows\": 707,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.702744387522245,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          25.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.9782644008904,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          15.5,\n          7.8292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"First\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 람다 대신 사용자 정의 함수 만들어서 결과 동일하게 보기\n",
        "def over200(x):\n",
        "  return len(x) >= 200\n",
        "grouped.filter(lambda x: over200(x))"
      ],
      "metadata": {
        "id": "agbr4NqFOVmg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "52c34b6f-c128-4231-d52f-ce25cd81b9cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age     fare  class  survived\n",
              "0      male  22.0   7.2500  Third         0\n",
              "1    female  38.0  71.2833  First         1\n",
              "2    female  26.0   7.9250  Third         1\n",
              "3    female  35.0  53.1000  First         1\n",
              "4      male  35.0   8.0500  Third         0\n",
              "..      ...   ...      ...    ...       ...\n",
              "885  female  39.0  29.1250  Third         0\n",
              "887  female  19.0  30.0000  First         1\n",
              "888  female   NaN  23.4500  Third         0\n",
              "889    male  26.0  30.0000  First         1\n",
              "890    male  32.0   7.7500  Third         0\n",
              "\n",
              "[707 rows x 5 columns]"
            ],
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              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>class</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Third</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>707 rows × 5 columns</p>\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"grouped\",\n  \"rows\": 707,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.702744387522245,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          25.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.9782644008904,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          15.5,\n          7.8292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"First\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped.filter(over200)"
      ],
      "metadata": {
        "id": "t7g2rSKIO7qU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "003c583a-ce30-4ec7-a67c-70c93d84b646"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age     fare  class  survived\n",
              "0      male  22.0   7.2500  Third         0\n",
              "1    female  38.0  71.2833  First         1\n",
              "2    female  26.0   7.9250  Third         1\n",
              "3    female  35.0  53.1000  First         1\n",
              "4      male  35.0   8.0500  Third         0\n",
              "..      ...   ...      ...    ...       ...\n",
              "885  female  39.0  29.1250  Third         0\n",
              "887  female  19.0  30.0000  First         1\n",
              "888  female   NaN  23.4500  Third         0\n",
              "889    male  26.0  30.0000  First         1\n",
              "890    male  32.0   7.7500  Third         0\n",
              "\n",
              "[707 rows x 5 columns]"
            ],
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              "      <td>Third</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4562026d-af9d-4e7a-9300-cf7e0901b458 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-4562026d-af9d-4e7a-9300-cf7e0901b458');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8566f44d-c928-4cb9-b34d-b3aff41f429a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8566f44d-c928-4cb9-b34d-b3aff41f429a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8566f44d-c928-4cb9-b34d-b3aff41f429a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"grouped\",\n  \"rows\": 707,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.702744387522245,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          25.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54.9782644008904,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          15.5,\n          7.8292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"First\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 나이 평균 30세 미만인 그룹 객체 데이터 필터링\n",
        "grouped.filter(lambda x: x.age.mean() < 30)\n",
        "# Second 184개 + Third 491개 = 675개"
      ],
      "metadata": {
        "id": "6KaCcDZlPJj5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "e21459f9-0fec-45ec-c8cd-80bd14cd9a10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age     fare   class  survived\n",
              "0      male  22.0   7.2500   Third         0\n",
              "2    female  26.0   7.9250   Third         1\n",
              "4      male  35.0   8.0500   Third         0\n",
              "5      male   NaN   8.4583   Third         0\n",
              "7      male   2.0  21.0750   Third         0\n",
              "..      ...   ...      ...     ...       ...\n",
              "884    male  25.0   7.0500   Third         0\n",
              "885  female  39.0  29.1250   Third         0\n",
              "886    male  27.0  13.0000  Second         0\n",
              "888  female   NaN  23.4500   Third         0\n",
              "890    male  32.0   7.7500   Third         0\n",
              "\n",
              "[675 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d43bdba0-87e8-4d70-83c0-15c5ae67a79e\" 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>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>class</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Third</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>884</th>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>7.0500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>Second</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>675 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d43bdba0-87e8-4d70-83c0-15c5ae67a79e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d43bdba0-87e8-4d70-83c0-15c5ae67a79e 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-d43bdba0-87e8-4d70-83c0-15c5ae67a79e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dfe35ac7-93cd-4d15-9570-9c316d09485e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dfe35ac7-93cd-4d15-9570-9c316d09485e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dfe35ac7-93cd-4d15-9570-9c316d09485e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"# Second 184\\uac1c + Third 491\\uac1c = 675\\uac1c\",\n  \"rows\": 675,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.183784821309068,\n        \"min\": 0.42,\n        \"max\": 74.0,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.83,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.626406125567367,\n        \"min\": 0.0,\n        \"max\": 73.5,\n        \"num_unique_values\": 159,\n        \"samples\": [\n          22.025,\n          14.1083\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fare 표준편차 30보다 큰 그룹 객체 데이터 필터링\n",
        "grouped.filter(lambda x: x.fare.std() > 50)\n",
        "# First 216개"
      ],
      "metadata": {
        "id": "Q2sVnMOfQavv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f59411ae-2a4b-48aa-a002-5f69fc9e38ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age     fare  class  survived\n",
              "1    female  38.0  71.2833  First         1\n",
              "3    female  35.0  53.1000  First         1\n",
              "6      male  54.0  51.8625  First         0\n",
              "11   female  58.0  26.5500  First         1\n",
              "23     male  28.0  35.5000  First         1\n",
              "..      ...   ...      ...    ...       ...\n",
              "871  female  47.0  52.5542  First         1\n",
              "872    male  33.0   5.0000  First         0\n",
              "879  female  56.0  83.1583  First         1\n",
              "887  female  19.0  30.0000  First         1\n",
              "889    male  26.0  30.0000  First         1\n",
              "\n",
              "[216 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15d345da-1285-4ae7-83fa-f170611b3a74\" 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>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>class</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>First</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>35.5000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>871</th>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>52.5542</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>872</th>\n",
              "      <td>male</td>\n",
              "      <td>33.0</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>879</th>\n",
              "      <td>female</td>\n",
              "      <td>56.0</td>\n",
              "      <td>83.1583</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>216 rows × 5 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  </svg>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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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-15d345da-1285-4ae7-83fa-f170611b3a74 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-15d345da-1285-4ae7-83fa-f170611b3a74');\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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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-29d2be7a-91b0-4537-8aae-b48ce9c76d54 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"# First 216\\uac1c\",\n  \"rows\": 216,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.802855896450462,\n        \"min\": 0.92,\n        \"max\": 80.0,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          38.0,\n          19.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.38037264672882,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          135.6333,\n          66.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 함수 매핑"
      ],
      "metadata": {
        "id": "_BFVEhmlRYj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체의 데이터마다 함수 적용\n",
        "grouped.apply(lambda x: x.describe())"
      ],
      "metadata": {
        "id": "c6iK6SovRTjy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "2d54f6e6-530a-46ca-90de-6af4cd87f15d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     age        fare    survived\n",
              "class                                           \n",
              "First  count  186.000000  216.000000  216.000000\n",
              "       mean    38.233441   84.154687    0.629630\n",
              "       std     14.802856   78.380373    0.484026\n",
              "       min      0.920000    0.000000    0.000000\n",
              "       25%     27.000000   30.923950    0.000000\n",
              "       50%     37.000000   60.287500    1.000000\n",
              "       75%     49.000000   93.500000    1.000000\n",
              "       max     80.000000  512.329200    1.000000\n",
              "Second count  173.000000  184.000000  184.000000\n",
              "       mean    29.877630   20.662183    0.472826\n",
              "       std     14.001077   13.417399    0.500623\n",
              "       min      0.670000    0.000000    0.000000\n",
              "       25%     23.000000   13.000000    0.000000\n",
              "       50%     29.000000   14.250000    0.000000\n",
              "       75%     36.000000   26.000000    1.000000\n",
              "       max     70.000000   73.500000    1.000000\n",
              "Third  count  355.000000  491.000000  491.000000\n",
              "       mean    25.140620   13.675550    0.242363\n",
              "       std     12.495398   11.778142    0.428949\n",
              "       min      0.420000    0.000000    0.000000\n",
              "       25%     18.000000    7.750000    0.000000\n",
              "       50%     24.000000    8.050000    0.000000\n",
              "       75%     32.000000   15.500000    0.000000\n",
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              "      <th>fare</th>\n",
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              "      <th>class</th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.233441</td>\n",
              "      <td>84.154687</td>\n",
              "      <td>0.629630</td>\n",
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              "      <th>std</th>\n",
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              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>30.923950</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>60.287500</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>93.500000</td>\n",
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              "      <td>512.329200</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Second</th>\n",
              "      <th>count</th>\n",
              "      <td>173.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>184.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.877630</td>\n",
              "      <td>20.662183</td>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.001077</td>\n",
              "      <td>13.417399</td>\n",
              "      <td>0.500623</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.670000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>14.250000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>26.000000</td>\n",
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              "      <th rowspan=\"8\" valign=\"top\">Third</th>\n",
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              "      <th>mean</th>\n",
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              "      <td>13.675550</td>\n",
              "      <td>0.242363</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>7.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
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              "      <th>50%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>8.050000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>max</th>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"grouped\",\n  \"rows\": 24,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79.14932446481043,\n        \"min\": 0.42,\n        \"max\": 355.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          173.0,\n          355.0,\n          186.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 139.9079524260284,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          216.0,\n          26.0,\n          184.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.87502032273703,\n        \"min\": 0.0,\n        \"max\": 491.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          184.0,\n          216.0,\n          0.24236252545824846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체의 데이터마다 평균 나이가 30세 미만인지 확인\n",
        "age_mean_filter = grouped.apply(lambda x: x.age.mean() < 30)"
      ],
      "metadata": {
        "id": "gvzS3ApZR3Iw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "age_mean_filter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBOdqFiJ6Kni",
        "outputId": "6daab019-1c5a-4157-9d43-f9f07a372592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "First     False\n",
              "Second     True\n",
              "Third      True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 인덱스 값(클래스)이 True 인 데이터 가져오기\n",
        "for i in age_mean_filter.index:\n",
        "  if age_mean_filter[i] == True:\n",
        "    df_get = grouped.get_group(i)\n",
        "    print(i)\n",
        "    print(df_get)"
      ],
      "metadata": {
        "id": "GBYcg0fmS8Yj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e74db2da-607a-43ab-a976-a4ce4dc20a8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Second\n",
            "        sex   age     fare   class  survived\n",
            "9    female  14.0  30.0708  Second         1\n",
            "15   female  55.0  16.0000  Second         1\n",
            "17     male   NaN  13.0000  Second         1\n",
            "20     male  35.0  26.0000  Second         0\n",
            "21     male  34.0  13.0000  Second         1\n",
            "..      ...   ...      ...     ...       ...\n",
            "866  female  27.0  13.8583  Second         1\n",
            "874  female  28.0  24.0000  Second         1\n",
            "880  female  25.0  26.0000  Second         1\n",
            "883    male  28.0  10.5000  Second         0\n",
            "886    male  27.0  13.0000  Second         0\n",
            "\n",
            "[184 rows x 5 columns]\n",
            "Third\n",
            "        sex   age     fare  class  survived\n",
            "0      male  22.0   7.2500  Third         0\n",
            "2    female  26.0   7.9250  Third         1\n",
            "4      male  35.0   8.0500  Third         0\n",
            "5      male   NaN   8.4583  Third         0\n",
            "7      male   2.0  21.0750  Third         0\n",
            "..      ...   ...      ...    ...       ...\n",
            "882  female  22.0  10.5167  Third         0\n",
            "884    male  25.0   7.0500  Third         0\n",
            "885  female  39.0  29.1250  Third         0\n",
            "888  female   NaN  23.4500  Third         0\n",
            "890    male  32.0   7.7500  Third         0\n",
            "\n",
            "[491 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 멀티 인덱스"
      ],
      "metadata": {
        "id": "Wvnc1vxKX3Oy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 그룹 객체 => groupby(그룹화에 사용할 열)\n",
        "grouped = df.groupby(['class','sex'])\n",
        "print(grouped) # => 그룹 객체"
      ],
      "metadata": {
        "id": "b1tBomW6SySu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "735d9fde-c79a-4702-d907-9558971176e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7c7ab0637a90>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mg_mean = grouped.mean()\n",
        "print(mg_mean)\n",
        "print(type(mg_mean))"
      ],
      "metadata": {
        "id": "BguV3IjhYB22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39345221-fe7f-4450-c494-11a5d5a514e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                     age        fare  survived\n",
            "class  sex                                    \n",
            "First  female  34.611765  106.125798  0.968085\n",
            "       male    41.281386   67.226127  0.368852\n",
            "Second female  28.722973   21.970121  0.921053\n",
            "       male    30.740707   19.741782  0.157407\n",
            "Third  female  21.750000   16.118810  0.500000\n",
            "       male    26.507589   12.661633  0.135447\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스만 보기\n",
        "mg_mean.loc['First'] # 데이터프레임"
      ],
      "metadata": {
        "id": "2qPkeHEiYXIL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "405037b5-aa4a-4f3a-ae20-ab0aa9d4ff8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age        fare  survived\n",
              "sex                                    \n",
              "female  34.611765  106.125798  0.968085\n",
              "male    41.281386   67.226127  0.368852"
            ],
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              "        const element = document.querySelector('#df-49f35c62-dad4-4bb7-b2ee-b286a8ae7021');\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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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mg_mean\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.716134543031587,\n        \"min\": 34.61176470588235,\n        \"max\": 41.28138613861386,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          41.28138613861386,\n          34.61176470588235\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.506221024981002,\n        \"min\": 67.22612704918032,\n        \"max\": 106.12579787234043,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          67.22612704918032,\n          106.12579787234043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4237214684612796,\n        \"min\": 0.36885245901639346,\n        \"max\": 0.9680851063829787,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.36885245901639346,\n          0.9680851063829787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스, female 만 보기\n",
        "mg_mean.loc[('First', 'female')] # 튜플 형태\n",
        "# 시리즈"
      ],
      "metadata": {
        "id": "dWKC-R8lYoNa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e64cee06-9e2b-4b12-e166-b923b54697d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          34.611765\n",
              "fare        106.125798\n",
              "survived      0.968085\n",
              "Name: (First, female), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스만 보기\n",
        "mg_mean.xs('First', level = 'class') # 데이터프레임"
      ],
      "metadata": {
        "id": "4gRLosshYH_c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a565be86-bd6d-4a91-bedd-255266c179a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age        fare  survived\n",
              "sex                                    \n",
              "female  34.611765  106.125798  0.968085\n",
              "male    41.281386   67.226127  0.368852"
            ],
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\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",
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              "\n",
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              "        const element = document.querySelector('#df-556803f5-2035-45da-80a2-bb738c435ac2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9155216e-57f5-45bb-9376-2fdfb164e452\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9155216e-57f5-45bb-9376-2fdfb164e452')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9155216e-57f5-45bb-9376-2fdfb164e452 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mg_mean\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.716134543031587,\n        \"min\": 34.61176470588235,\n        \"max\": 41.28138613861386,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          41.28138613861386,\n          34.61176470588235\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.506221024981002,\n        \"min\": 67.22612704918032,\n        \"max\": 106.12579787234043,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          67.22612704918032,\n          106.12579787234043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4237214684612796,\n        \"min\": 0.36885245901639346,\n        \"max\": 0.9680851063829787,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.36885245901639346,\n          0.9680851063829787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스, female 만 보기\n",
        "mg_mean.xs(('First', 'female')) # 튜플 형태\n",
        "# 시리즈"
      ],
      "metadata": {
        "id": "PJQb013vZWEf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "285eba85-cd43-40a4-e3fd-ab789bb2a424"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          34.611765\n",
              "fare        106.125798\n",
              "survived      0.968085\n",
              "Name: (First, female), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# female 만 보기\n",
        "mg_mean.xs('female', level = 'sex') # 데이터프레임"
      ],
      "metadata": {
        "id": "W1i8miU3Zk1o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "83f0a16c-dc46-483c-dd22-2024209eaf4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age        fare  survived\n",
              "class                                  \n",
              "First   34.611765  106.125798  0.968085\n",
              "Second  28.722973   21.970121  0.921053\n",
              "Third   21.750000   16.118810  0.500000"
            ],
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              "\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>fare</th>\n",
              "      <th>survived</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>34.611765</td>\n",
              "      <td>106.125798</td>\n",
              "      <td>0.968085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>28.722973</td>\n",
              "      <td>21.970121</td>\n",
              "      <td>0.921053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>21.750000</td>\n",
              "      <td>16.118810</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d73aed2-b56f-4368-90f1-ab6ef25ee2c2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9d73aed2-b56f-4368-90f1-ab6ef25ee2c2 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-9d73aed2-b56f-4368-90f1-ab6ef25ee2c2');\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-615026a6-dc2e-43fd-b39a-542a985fd060 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "summary": "{\n  \"name\": \"mg_mean\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"First\",\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.438493761546955,\n        \"min\": 21.75,\n        \"max\": 34.61176470588235,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34.61176470588235,\n          28.722972972972972,\n          21.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.36148280369992,\n        \"min\": 16.118809722222224,\n        \"max\": 106.12579787234043,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          106.12579787234043,\n          21.97012105263158,\n          16.118809722222224\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25774698157408593,\n        \"min\": 0.5,\n        \"max\": 0.9680851063829787,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9680851063829787,\n          0.9210526315789473,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스, female 만 보기\n",
        "mg_mean.xs(('First', 'female'), level = ['class','sex']) # 튜플 형태\n",
        "# 데이터프레임"
      ],
      "metadata": {
        "id": "xwXsJbPCapwk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "d03534fa-7d86-4b0c-eb21-4c169869061f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    age        fare  survived\n",
              "class sex                                    \n",
              "First female  34.611765  106.125798  0.968085"
            ],
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              "\n",
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              "      <th>female</th>\n",
              "      <td>34.611765</td>\n",
              "      <td>106.125798</td>\n",
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              "    </button>\n",
              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9674c864-dbb7-422b-b6f2-98f9afb2a241 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-9674c864-dbb7-422b-b6f2-98f9afb2a241');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"# \\ub370\\uc774\\ud130\\ud504\\ub808\\uc784\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 34.61176470588235,\n        \"max\": 34.61176470588235,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          34.61176470588235\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 106.12579787234043,\n        \"max\": 106.12579787234043,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          106.12579787234043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9680851063829787,\n        \"max\": 0.9680851063829787,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9680851063829787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스, female 만 보기\n",
        "mg_mean.xs(('First', 'female'), level = [0,1]) # 튜플 형태\n",
        "# 데이터프레임"
      ],
      "metadata": {
        "id": "lCotsjHLbWeB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "acad5441-4509-4913-cb91-066fb3b66626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    age        fare  survived\n",
              "class sex                                    \n",
              "First female  34.611765  106.125798  0.968085"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7b0a269-0e9b-4118-a6dc-289937cf12f4\" 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>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <th>female</th>\n",
              "      <td>34.611765</td>\n",
              "      <td>106.125798</td>\n",
              "      <td>0.968085</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7b0a269-0e9b-4118-a6dc-289937cf12f4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7b0a269-0e9b-4118-a6dc-289937cf12f4 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-f7b0a269-0e9b-4118-a6dc-289937cf12f4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"# \\ub370\\uc774\\ud130\\ud504\\ub808\\uc784\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 34.61176470588235,\n        \"max\": 34.61176470588235,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          34.61176470588235\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 106.12579787234043,\n        \"max\": 106.12579787234043,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          106.12579787234043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9680851063829787,\n        \"max\": 0.9680851063829787,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9680851063829787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 피봇 테이블"
      ],
      "metadata": {
        "id": "sVEvnyZtbzlL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "FmsuKMlCBguZ",
        "outputId": "d1e5e71f-e647-4467-fabe-7f56b6bc0608"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex   age     fare   class  survived\n",
              "0      male  22.0   7.2500   Third         0\n",
              "1    female  38.0  71.2833   First         1\n",
              "2    female  26.0   7.9250   Third         1\n",
              "3    female  35.0  53.1000   First         1\n",
              "4      male  35.0   8.0500   Third         0\n",
              "..      ...   ...      ...     ...       ...\n",
              "886    male  27.0  13.0000  Second         0\n",
              "887  female  19.0  30.0000   First         1\n",
              "888  female   NaN  23.4500   Third         0\n",
              "889    male  26.0  30.0000   First         1\n",
              "890    male  32.0   7.7500   Third         0\n",
              "\n",
              "[891 rows x 5 columns]"
            ],
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              "\n",
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              "      <th>0</th>\n",
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              "      <td>Third</td>\n",
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              "      <th>1</th>\n",
              "      <td>female</td>\n",
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              "      <td>71.2833</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Third</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>Second</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>First</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Third</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cbe9e02-88d1-4377-a39b-242713dee51f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3cbe9e02-88d1-4377-a39b-242713dee51f 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-3cbe9e02-88d1-4377-a39b-242713dee51f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8ed1d43d-6a49-4a12-a200-d40ce123d4af\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8ed1d43d-6a49-4a12-a200-d40ce123d4af')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8ed1d43d-6a49-4a12-a200-d40ce123d4af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ca50a82a-350d-4894-80cc-ea643e70625b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ca50a82a-350d-4894-80cc-ea643e70625b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1개의 행, 1개의 열, 1개의 값, 1개의 함수\n",
        "pd.pivot_table(df, # 데이터\n",
        "               index = 'class', # 행\n",
        "               columns = 'sex', # 열\n",
        "               values = 'age', # 값\n",
        "               aggfunc = 'mean') # 집계 함수"
      ],
      "metadata": {
        "id": "0l6ez982b17C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "9f36ac9d-5b97-4a8f-e1b9-100f97f75ef2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex        female       male\n",
              "class                       \n",
              "First   34.611765  41.281386\n",
              "Second  28.722973  30.740707\n",
              "Third   21.750000  26.507589"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e18d34b-ec20-494c-95e6-3beeaed04630\" 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>sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>34.611765</td>\n",
              "      <td>41.281386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>28.722973</td>\n",
              "      <td>30.740707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>21.750000</td>\n",
              "      <td>26.507589</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7e18d34b-ec20-494c-95e6-3beeaed04630 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-7e18d34b-ec20-494c-95e6-3beeaed04630');\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",
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              "\n",
              "\n",
              "<div id=\"df-761ed3d5-ea03-4df5-a3cb-c38746f402d1\">\n",
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              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-761ed3d5-ea03-4df5-a3cb-c38746f402d1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"               aggfunc = 'mean') # \\uc9d1\\uacc4 \\ud568\\uc218\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"First\",\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.438493761546955,\n        \"min\": 21.75,\n        \"max\": 34.61176470588235,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34.61176470588235,\n          28.722972972972972,\n          21.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.608003332679335,\n        \"min\": 26.507588932806325,\n        \"max\": 41.28138613861386,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          41.28138613861386,\n          30.74070707070707,\n          26.507588932806325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1개의 행, 1개의 열, 1개의 값, 2개의 함수\n",
        "pd.pivot_table(df, # 데이터\n",
        "               index = 'class', # 행\n",
        "               columns = 'sex', # 열\n",
        "               values = 'age', # 값\n",
        "               aggfunc = ['mean', 'std']) # 집계 함수"
      ],
      "metadata": {
        "id": "E6M6TmByceAz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "28edbf4e-f8a7-4026-aed5-e831a48e47b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             mean                   std           \n",
              "sex        female       male     female       male\n",
              "class                                             \n",
              "First   34.611765  41.281386  13.612052  15.139570\n",
              "Second  28.722973  30.740707  12.872702  14.793894\n",
              "Third   21.750000  26.507589  12.729964  12.159514"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13584538-5374-4089-8df0-e73abe3d3903\" 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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
              "      <th colspan=\"2\" halign=\"left\">std</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>First</th>\n",
              "      <td>34.611765</td>\n",
              "      <td>41.281386</td>\n",
              "      <td>13.612052</td>\n",
              "      <td>15.139570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Second</th>\n",
              "      <td>28.722973</td>\n",
              "      <td>30.740707</td>\n",
              "      <td>12.872702</td>\n",
              "      <td>14.793894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Third</th>\n",
              "      <td>21.750000</td>\n",
              "      <td>26.507589</td>\n",
              "      <td>12.729964</td>\n",
              "      <td>12.159514</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13584538-5374-4089-8df0-e73abe3d3903')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-13584538-5374-4089-8df0-e73abe3d3903 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-13584538-5374-4089-8df0-e73abe3d3903');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8766c0e5-96e2-4a51-ad4c-b8dd9f50c763\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8766c0e5-96e2-4a51-ad4c-b8dd9f50c763')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8766c0e5-96e2-4a51-ad4c-b8dd9f50c763 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"               aggfunc = ['mean', 'std']) # \\uc9d1\\uacc4 \\ud568\\uc218\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": [\n        \"class\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"First\",\n          \"Second\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"female\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.438493761546955,\n        \"min\": 21.75,\n        \"max\": 34.61176470588235,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34.61176470588235,\n          28.722972972972972,\n          21.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"male\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.608003332679335,\n        \"min\": 26.507588932806325,\n        \"max\": 41.28138613861386,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          41.28138613861386,\n          30.74070707070707,\n          26.507588932806325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"female\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4734785776656171,\n        \"min\": 12.729963872612517,\n        \"max\": 13.612051835857546,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          13.612051835857546,\n          12.87270178767177,\n          12.729963872612517\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"male\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6299380006443207,\n        \"min\": 12.159513851309788,\n        \"max\": 15.139570471430352,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          15.139570471430352,\n          14.793893752223997,\n          12.159513851309788\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2개의 행, 1개의 열, 1개의 값, 2개의 함수\n",
        "pd.pivot_table(df, # 데이터\n",
        "               index = ['class', 'sex'], # 행\n",
        "               columns = 'survived', # 열\n",
        "               values = 'age', # 값\n",
        "               aggfunc = ['mean', 'std']) # 집계 함수"
      ],
      "metadata": {
        "id": "KT4VsVmpczTr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "6bc552ef-ac41-431c-e7a2-0bac7f54a971"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    mean                   std           \n",
              "survived               0          1          0          1\n",
              "class  sex                                               \n",
              "First  female  25.666667  34.939024  24.006943  13.223014\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"               aggfunc = ['mean', 'std']) # \\uc9d1\\uacc4 \\ud568\\uc218\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": [\n        \"mean\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.816079154192983,\n        \"min\": 23.818181818181817,\n        \"max\": 44.58196721311475,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          25.666666666666668,\n          44.58196721311475,\n          27.25581395348837\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.33136584742189,\n        \"min\": 16.022,\n        \"max\": 36.248000000000005,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          34.9390243902439,\n          36.248000000000005,\n          22.274210526315787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.612346281351681,\n        \"min\": 12.135706609773328,\n        \"max\": 24.00694344004112,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          24.00694344004112,\n          14.457748881905525,\n          12.135706609773328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9193381919007515,\n        \"min\": 11.555786109267055,\n        \"max\": 19.547121527222362,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          13.223013572001038,\n          14.936743749765334,\n          11.555786109267055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2개의 행, 1개의 열, 2개의 값, 2개의 함수\n",
        "pv_table = pd.pivot_table(df, # 데이터\n",
        "                          index = ['class', 'sex'], # 행\n",
        "                          columns = 'survived', # 열\n",
        "                          values = ['age', 'fare'], # 값\n",
        "                          aggfunc = ['mean', 'std']) # 집계 함수\n",
        "pv_table"
      ],
      "metadata": {
        "id": "Gc7JwpJ_b4oj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "c77c685d-ba01-49b1-b935-8be09c2fdeea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    mean                                           std  \\\n",
              "                     age                   fare                    age   \n",
              "survived               0          1           0           1          0   \n",
              "class  sex                                                               \n",
              "First  female  25.666667  34.939024  110.604167  105.978159  24.006943   \n",
              "       male    44.581967  36.248000   62.894910   74.637320  14.457749   \n",
              "Second female  36.000000  28.080882   18.250000   22.288989  12.915107   \n",
              "       male    33.369048  16.022000   19.488965   21.095100  12.158125   \n",
              "Third  female  23.818182  19.329787   19.773093   12.464526  12.833465   \n",
              "       male    27.255814  22.274211   12.204469   15.579696  12.135707   \n",
              "\n",
              "                                                 \n",
              "                               fare              \n",
              "survived               1          0           1  \n",
              "class  sex                                       \n",
              "First  female  13.223014  70.920264   74.738897  \n",
              "       male    14.936744  60.052545  101.091948  \n",
              "Second female  12.764693   6.969577   11.140937  \n",
              "       male    19.547122  15.720548    9.832542  \n",
              "Third  female  12.303246  14.572275    5.984714  \n",
              "       male    11.555786  10.984904   15.232388  "
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              "      <th rowspan=\"2\" valign=\"top\">First</th>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Second</th>\n",
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              "      <td>21.095100</td>\n",
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              "      <td>15.720548</td>\n",
              "      <td>9.832542</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Third</th>\n",
              "      <th>female</th>\n",
              "      <td>23.818182</td>\n",
              "      <td>19.329787</td>\n",
              "      <td>19.773093</td>\n",
              "      <td>12.464526</td>\n",
              "      <td>12.833465</td>\n",
              "      <td>12.303246</td>\n",
              "      <td>14.572275</td>\n",
              "      <td>5.984714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>27.255814</td>\n",
              "      <td>22.274211</td>\n",
              "      <td>12.204469</td>\n",
              "      <td>15.579696</td>\n",
              "      <td>12.135707</td>\n",
              "      <td>11.555786</td>\n",
              "      <td>10.984904</td>\n",
              "      <td>15.232388</td>\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pv_table')\"\n",
              "            title=\"Generate code using this dataframe.\"\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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              "    </button>\n",
              "    <script>\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pv_table",
              "summary": "{\n  \"name\": \"pv_table\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": [\n        \"mean\",\n        \"age\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.816079154192983,\n        \"min\": 23.818181818181817,\n        \"max\": 44.58196721311475,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          25.666666666666668,\n          44.58196721311475,\n          27.25581395348837\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"age\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.33136584742189,\n        \"min\": 16.022,\n        \"max\": 36.248000000000005,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          34.9390243902439,\n          36.248000000000005,\n          22.274210526315787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"fare\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.94325685851672,\n        \"min\": 12.204469333333334,\n        \"max\": 110.60416666666667,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          110.60416666666667,\n          62.89491038961039,\n          12.204469333333334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"fare\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.869820436246066,\n        \"min\": 12.464526388888888,\n        \"max\": 105.97815934065935,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          105.97815934065935,\n          74.63732,\n          15.579695744680853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"age\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.612346281351681,\n        \"min\": 12.135706609773328,\n        \"max\": 24.00694344004112,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          24.00694344004112,\n          14.457748881905525,\n          12.135706609773328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"age\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9193381919007515,\n        \"min\": 11.555786109267055,\n        \"max\": 19.547121527222362,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          13.223013572001038,\n          14.936743749765334,\n          11.555786109267055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"fare\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.96931812135785,\n        \"min\": 6.969576744681129,\n        \"max\": 70.92026369158066,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          70.92026369158066,\n          60.052544913426445,\n          10.984904129323462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"fare\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40.91919546705966,\n        \"min\": 5.984713781170595,\n        \"max\": 101.09194787129012,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          74.73889688443506,\n          101.09194787129012,\n          15.23238770744697\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스만 보기\n",
        "pv_table.xs('First')"
      ],
      "metadata": {
        "id": "LdduJ3Z6eKOt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a33d1bea-fe39-4465-f580-eaf057499494"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               mean                                           std             \\\n",
              "                age                   fare                    age              \n",
              "survived          0          1           0           1          0          1   \n",
              "sex                                                                            \n",
              "female    25.666667  34.939024  110.604167  105.978159  24.006943  13.223014   \n",
              "male      44.581967  36.248000   62.894910   74.637320  14.457749  14.936744   \n",
              "\n",
              "                                 \n",
              "               fare              \n",
              "survived          0           1  \n",
              "sex                              \n",
              "female    70.920264   74.738897  \n",
              "male      60.052545  101.091948  "
            ],
            "text/html": [
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              "      <th colspan=\"2\" halign=\"left\">age</th>\n",
              "      <th colspan=\"2\" halign=\"left\">fare</th>\n",
              "      <th colspan=\"2\" halign=\"left\">age</th>\n",
              "      <th colspan=\"2\" halign=\"left\">fare</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>survived</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>female</th>\n",
              "      <td>25.666667</td>\n",
              "      <td>34.939024</td>\n",
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              "      <td>105.978159</td>\n",
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              "      <td>74.738897</td>\n",
              "    </tr>\n",
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              "      <td>36.248000</td>\n",
              "      <td>62.894910</td>\n",
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              "      <td>14.457749</td>\n",
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              "</div>\n",
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              "        document.querySelector('#df-8ef7210a-de1f-4b4e-964e-455a2ad9c631 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e14f2df1-8897-4a93-9779-cb12c6a3394d button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pv_table\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": [\n        \"sex\",\n        \"\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"age\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.375137284575048,\n        \"min\": 25.666666666666668,\n        \"max\": 44.58196721311475,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          44.58196721311475,\n          25.666666666666668\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"age\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9255855300663368,\n        \"min\": 34.9390243902439,\n        \"max\": 36.248000000000005,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          36.248000000000005,\n          34.9390243902439\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"fare\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.73553863887336,\n        \"min\": 62.89491038961039,\n        \"max\": 110.60416666666667,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          62.89491038961039,\n          110.60416666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"mean\",\n        \"fare\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.161320025858345,\n        \"min\": 74.63732,\n        \"max\": 105.97815934065935,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          74.63732,\n          105.97815934065935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"age\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.752300226927356,\n        \"min\": 14.457748881905525,\n        \"max\": 24.00694344004112,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          14.457748881905525,\n          24.00694344004112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"age\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2117902298211611,\n        \"min\": 13.223013572001038,\n        \"max\": 14.936743749765334,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          14.936743749765334,\n          13.223013572001038\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"fare\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.684637644061225,\n        \"min\": 60.052544913426445,\n        \"max\": 70.92026369158066,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          60.052544913426445,\n          70.92026369158066\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"std\",\n        \"fare\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.634421057760047,\n        \"min\": 74.73889688443506,\n        \"max\": 101.09194787129012,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          101.09194787129012,\n          74.73889688443506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스, female 만 보기\n",
        "pv_table.xs(('First', 'female')) # 튜플 형태\n",
        "# 시리즈"
      ],
      "metadata": {
        "id": "Q-p03Ip2ej69",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd415c65-0fa2-4792-8cd9-8a5e082c2a76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            survived\n",
              "mean  age   0            25.666667\n",
              "            1            34.939024\n",
              "      fare  0           110.604167\n",
              "            1           105.978159\n",
              "std   age   0            24.006943\n",
              "            1            13.223014\n",
              "      fare  0            70.920264\n",
              "            1            74.738897\n",
              "Name: (First, female), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First 클래스, female 만 보기\n",
        "pv_table.xs(('First', 'female'), level = ['class', 'sex']) # 튜플 형태\n",
        "# 데이터프레임"
      ],
      "metadata": {
        "id": "TuI7Q8dte3NY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "92ab1df0-61d0-4a45-b81f-0d7bf3034aeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   mean                                           std  \\\n",
              "                    age                   fare                    age   \n",
              "survived              0          1           0           1          0   \n",
              "class sex                                                               \n",
              "First female  25.666667  34.939024  110.604167  105.978159  24.006943   \n",
              "\n",
              "                                               \n",
              "                              fare             \n",
              "survived              1          0          1  \n",
              "class sex                                      \n",
              "First female  13.223014  70.920264  74.738897  "
            ],
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              "      <th>survived</th>\n",
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              "    </tr>\n",
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              "      <th>class</th>\n",
              "      <th>sex</th>\n",
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              "      <th>First</th>\n",
              "      <th>female</th>\n",
              "      <td>25.666667</td>\n",
              "      <td>34.939024</td>\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "        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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          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평균만 보기\n",
        "pv_table.xs('mean', axis = 1)\n",
        "# 데이터프레임"
      ],
      "metadata": {
        "id": "RSefVkrtfOlL",
        "colab": {
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          "height": 300
        },
        "outputId": "93af240c-047c-4727-81b1-31a2fb44fac1"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
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              "                     age                   fare            \n",
              "survived               0          1           0           1\n",
              "class  sex                                                 \n",
              "First  female  25.666667  34.939024  110.604167  105.978159\n",
              "       male    44.581967  36.248000   62.894910   74.637320\n",
              "Second female  36.000000  28.080882   18.250000   22.288989\n",
              "       male    33.369048  16.022000   19.488965   21.095100\n",
              "Third  female  23.818182  19.329787   19.773093   12.464526\n",
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            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 표준편차만 보기\n",
        "pv_table.xs('std', axis = 1)\n",
        "# 데이터프레임"
      ],
      "metadata": {
        "id": "z4bAcJR3froj",
        "colab": {
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        "outputId": "e493b32a-f669-4784-e828-3b95b24891be"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     age                  fare            \n",
              "survived               0          1          0           1\n",
              "class  sex                                                \n",
              "First  female  24.006943  13.223014  70.920264   74.738897\n",
              "       male    14.457749  14.936744  60.052545  101.091948\n",
              "Second female  12.915107  12.764693   6.969577   11.140937\n",
              "       male    12.158125  19.547122  15.720548    9.832542\n",
              "Third  female  12.833465  12.303246  14.572275    5.984714\n",
              "       male    12.135707  11.555786  10.984904   15.232388"
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">First</th>\n",
              "      <th>female</th>\n",
              "      <td>24.006943</td>\n",
              "      <td>13.223014</td>\n",
              "      <td>70.920264</td>\n",
              "      <td>74.738897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>14.457749</td>\n",
              "      <td>14.936744</td>\n",
              "      <td>60.052545</td>\n",
              "      <td>101.091948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Second</th>\n",
              "      <th>female</th>\n",
              "      <td>12.915107</td>\n",
              "      <td>12.764693</td>\n",
              "      <td>6.969577</td>\n",
              "      <td>11.140937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>12.158125</td>\n",
              "      <td>19.547122</td>\n",
              "      <td>15.720548</td>\n",
              "      <td>9.832542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Third</th>\n",
              "      <th>female</th>\n",
              "      <td>12.833465</td>\n",
              "      <td>12.303246</td>\n",
              "      <td>14.572275</td>\n",
              "      <td>5.984714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>12.135707</td>\n",
              "      <td>11.555786</td>\n",
              "      <td>10.984904</td>\n",
              "      <td>15.232388</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "        document.querySelector('#df-dbcbffba-d779-4d2b-be55-4ff8c5c4386d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dbcbffba-d779-4d2b-be55-4ff8c5c4386d');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"# \\ub370\\uc774\\ud130\\ud504\\ub808\\uc784\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": [\n        \"age\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.612346281351681,\n        \"min\": 12.135706609773328,\n        \"max\": 24.00694344004112,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          24.00694344004112,\n          14.457748881905525,\n          12.135706609773328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"age\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9193381919007515,\n        \"min\": 11.555786109267055,\n        \"max\": 19.547121527222362,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          13.223013572001038,\n          14.936743749765334,\n          11.555786109267055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"fare\",\n        0\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.96931812135785,\n        \"min\": 6.969576744681129,\n        \"max\": 70.92026369158066,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          70.92026369158066,\n          60.052544913426445,\n          10.984904129323462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"fare\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40.91919546705966,\n        \"min\": 5.984713781170595,\n        \"max\": 101.09194787129012,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          74.73889688443506,\n          101.09194787129012,\n          15.23238770744697\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평균, 나이, 생존자만 보기\n",
        "pv_table.xs(('mean','age',1), level =  [0,1,2], axis = 1) # 튜플 형태\n",
        "# 데이터프레임"
      ],
      "metadata": {
        "id": "veE_-F7jf-Bn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "734bb045-347d-414e-bd05-fcecf0deb61a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    mean\n",
              "                     age\n",
              "survived               1\n",
              "class  sex              \n",
              "First  female  34.939024\n",
              "       male    36.248000\n",
              "Second female  28.080882\n",
              "       male    16.022000\n",
              "Third  female  19.329787\n",
              "       male    22.274211"
            ],
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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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-32609eda-1e7c-4cbe-922c-cec8ffe38607 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"# \\ub370\\uc774\\ud130\\ud504\\ub808\\uc784\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": [\n        \"mean\",\n        \"age\",\n        1\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.33136584742189,\n        \"min\": 16.022,\n        \"max\": 36.248000000000005,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          34.9390243902439,\n          36.248000000000005,\n          22.274210526315787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 미션1"
      ],
      "metadata": {
        "id": "Br8NF73lJBAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "JMc2GdsUZb3R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.read_excel('/content/drive/MyDrive/lsda02/data/customers.xlsx')\n",
        "raw_df.head()"
      ],
      "metadata": {
        "id": "I-6OufY1B17y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a0bb0b3f-e192-46ef-dc3e-c47211562e6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CustID     Sex      Race  BirthDate College  HouseholdSize  ZipCode  \\\n",
              "0  1530016  Female     Black 1986-12-16     Yes              5    90047   \n",
              "1  1531136    Male     White 1993-05-09     Yes              5    90026   \n",
              "2  1532160    Male     Black 1966-05-22     Yes              2    90027   \n",
              "3  1532307    Male     White 1964-09-16     Yes              4    90029   \n",
              "4  1532356  Female  Hispanic 1964-07-15      No              5    90017   \n",
              "\n",
              "   Income  Spending2017  Spending2018  NumOfOrders  DaysSinceLast  \\\n",
              "0   53000           287           241            3            101   \n",
              "1   94000          1227           843           12            262   \n",
              "2   64000           523           719            9            122   \n",
              "3   60000           516           582           13            129   \n",
              "4   47000           555           845            7             97   \n",
              "\n",
              "        Satisfaction Channel  \n",
              "0  Very Dissatisfied      SM  \n",
              "1            Neutral      TV  \n",
              "2     Very Satisfied      TV  \n",
              "3  Very Dissatisfied      SM  \n",
              "4  Very Dissatisfied     Web  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>HouseholdSize</th>\n",
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              "      <td>1530016</td>\n",
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              "      <td>1986-12-16</td>\n",
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              "      <td>101</td>\n",
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              "    <tr>\n",
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              "      <td>1531136</td>\n",
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              "      <td>1993-05-09</td>\n",
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              "      <td>94000</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2</td>\n",
              "      <td>90027</td>\n",
              "      <td>64000</td>\n",
              "      <td>523</td>\n",
              "      <td>719</td>\n",
              "      <td>9</td>\n",
              "      <td>122</td>\n",
              "      <td>Very Satisfied</td>\n",
              "      <td>TV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1532307</td>\n",
              "      <td>Male</td>\n",
              "      <td>White</td>\n",
              "      <td>1964-09-16</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>90029</td>\n",
              "      <td>60000</td>\n",
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              "      <td>582</td>\n",
              "      <td>13</td>\n",
              "      <td>129</td>\n",
              "      <td>Very Dissatisfied</td>\n",
              "      <td>SM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1532356</td>\n",
              "      <td>Female</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>1964-07-15</td>\n",
              "      <td>No</td>\n",
              "      <td>5</td>\n",
              "      <td>90017</td>\n",
              "      <td>47000</td>\n",
              "      <td>555</td>\n",
              "      <td>845</td>\n",
              "      <td>7</td>\n",
              "      <td>97</td>\n",
              "      <td>Very Dissatisfied</td>\n",
              "      <td>Web</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d9c7da5-d8a6-4ebd-8ecc-3be4c268047d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d9c7da5-d8a6-4ebd-8ecc-3be4c268047d 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-6d9c7da5-d8a6-4ebd-8ecc-3be4c268047d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7ee6b348-ac2a-45e4-af86-1c0a4a800922\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7ee6b348-ac2a-45e4-af86-1c0a4a800922')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7ee6b348-ac2a-45e4-af86-1c0a4a800922 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "raw_df",
              "summary": "{\n  \"name\": \"raw_df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13494,\n        \"min\": 1530016,\n        \"max\": 1579979,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          1553843,\n          1535406,\n          1540539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Black\",\n          \"White\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BirthDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1952-02-01 00:00:00\",\n        \"max\": \"2000-12-09 00:00:00\",\n        \"num_unique_values\": 199,\n        \"samples\": [\n          \"1999-08-05 00:00:00\",\n          \"1984-02-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"College\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseholdSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ZipCode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 90001,\n        \"max\": 90068,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          90031,\n          90064\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24056,\n        \"min\": 31000,\n        \"max\": 167000,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          47000,\n          95000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending2017\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 404,\n        \"min\": 42,\n        \"max\": 1851,\n        \"num_unique_values\": 187,\n        \"samples\": [\n          325,\n          105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending2018\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350,\n        \"min\": 50,\n        \"max\": 1250,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          210,\n          549\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumOfOrders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          27,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DaysSinceLast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103,\n        \"min\": 6,\n        \"max\": 360,\n        \"num_unique_values\": 145,\n        \"samples\": [\n          195,\n          136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Satisfaction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Neutral\",\n          \"Somewhat Dissatisfied\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"TV\",\n          \"Referral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RrsxeCf4vFw",
        "outputId": "8681c318-4db8-4a8c-f640-52f365d8acde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   CustID         200 non-null    int64         \n",
            " 1   Sex            200 non-null    object        \n",
            " 2   Race           200 non-null    object        \n",
            " 3   BirthDate      200 non-null    datetime64[ns]\n",
            " 4   College        200 non-null    object        \n",
            " 5   HouseholdSize  200 non-null    int64         \n",
            " 6   ZipCode        200 non-null    int64         \n",
            " 7   Income         200 non-null    int64         \n",
            " 8   Spending2017   200 non-null    int64         \n",
            " 9   Spending2018   200 non-null    int64         \n",
            " 10  NumOfOrders    200 non-null    int64         \n",
            " 11  DaysSinceLast  200 non-null    int64         \n",
            " 12  Satisfaction   200 non-null    object        \n",
            " 13  Channel        200 non-null    object        \n",
            "dtypes: datetime64[ns](1), int64(8), object(5)\n",
            "memory usage: 22.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 전처리할 사본 생성 - 문제가 생기면 원본으로 바로 재생성 가능하게\n",
        "df = raw_df.copy()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xsBs_EnZ4xhA",
        "outputId": "d6402a1d-ad37-42ab-a97e-6a09cb3bd4a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CustID     Sex      Race  BirthDate College  HouseholdSize  ZipCode  \\\n",
              "0  1530016  Female     Black 1986-12-16     Yes              5    90047   \n",
              "1  1531136    Male     White 1993-05-09     Yes              5    90026   \n",
              "2  1532160    Male     Black 1966-05-22     Yes              2    90027   \n",
              "3  1532307    Male     White 1964-09-16     Yes              4    90029   \n",
              "4  1532356  Female  Hispanic 1964-07-15      No              5    90017   \n",
              "\n",
              "   Income  Spending2017  Spending2018  NumOfOrders  DaysSinceLast  \\\n",
              "0   53000           287           241            3            101   \n",
              "1   94000          1227           843           12            262   \n",
              "2   64000           523           719            9            122   \n",
              "3   60000           516           582           13            129   \n",
              "4   47000           555           845            7             97   \n",
              "\n",
              "        Satisfaction Channel  \n",
              "0  Very Dissatisfied      SM  \n",
              "1            Neutral      TV  \n",
              "2     Very Satisfied      TV  \n",
              "3  Very Dissatisfied      SM  \n",
              "4  Very Dissatisfied     Web  "
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13494,\n        \"min\": 1530016,\n        \"max\": 1579979,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          1553843,\n          1535406,\n          1540539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Black\",\n          \"White\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BirthDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1952-02-01 00:00:00\",\n        \"max\": \"2000-12-09 00:00:00\",\n        \"num_unique_values\": 199,\n        \"samples\": [\n          \"1999-08-05 00:00:00\",\n          \"1984-02-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"College\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseholdSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ZipCode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 90001,\n        \"max\": 90068,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          90031,\n          90064\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24056,\n        \"min\": 31000,\n        \"max\": 167000,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          47000,\n          95000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending2017\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 404,\n        \"min\": 42,\n        \"max\": 1851,\n        \"num_unique_values\": 187,\n        \"samples\": [\n          325,\n          105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending2018\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350,\n        \"min\": 50,\n        \"max\": 1250,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          210,\n          549\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumOfOrders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          27,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DaysSinceLast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103,\n        \"min\": 6,\n        \"max\": 360,\n        \"num_unique_values\": 145,\n        \"samples\": [\n          195,\n          136\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Satisfaction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Neutral\",\n          \"Somewhat Dissatisfied\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Channel\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"TV\",\n          \"Referral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 대학교를 졸업한 1982년에서 1999년까지 태어난 고객의 데이터를 생성하세요.\n",
        "df['College'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBkk6EsjHrPt",
        "outputId": "b343e180-e985-42da-c45d-15a69d643901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "College\n",
              "Yes    168\n",
              "No      32\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.query('College == \"Yes\"'))\n",
        "print(df[df['College'] == 'Yes'])"
      ],
      "metadata": {
        "id": "fpUYEAGlXIUd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f5c03cc-0f10-446e-b74a-79b6ccea97d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      CustID     Sex             Race  BirthDate College  HouseholdSize  \\\n",
            "0    1530016  Female            Black 1986-12-16     Yes              5   \n",
            "1    1531136    Male            White 1993-05-09     Yes              5   \n",
            "2    1532160    Male            Black 1966-05-22     Yes              2   \n",
            "3    1532307    Male            White 1964-09-16     Yes              4   \n",
            "5    1532387    Male            White 1957-08-27     Yes              2   \n",
            "..       ...     ...              ...        ...     ...            ...   \n",
            "195  1578525    Male         Hispanic 1963-12-12     Yes              1   \n",
            "196  1579349    Male            Asian 1980-12-19     Yes              1   \n",
            "197  1579389  Female  American Indian 2000-05-21     Yes              1   \n",
            "198  1579857  Female            White 1991-01-26     Yes              1   \n",
            "199  1579979    Male            White 1999-07-05     Yes              5   \n",
            "\n",
            "     ZipCode  Income  Spending2017  Spending2018  NumOfOrders  DaysSinceLast  \\\n",
            "0      90047   53000           287           241            3            101   \n",
            "1      90026   94000          1227           843           12            262   \n",
            "2      90027   64000           523           719            9            122   \n",
            "3      90029   60000           516           582           13            129   \n",
            "5      90028   67000           631           452            9            150   \n",
            "..       ...     ...           ...           ...          ...            ...   \n",
            "195    90005   82000          1521          1121           25            302   \n",
            "196    90010   49000           217           397           10            200   \n",
            "197    90009   50000           625           514           10            233   \n",
            "198    90055   52000           325           586           13            219   \n",
            "199    90043  102000          1080           926           10            113   \n",
            "\n",
            "              Satisfaction   Channel  \n",
            "0        Very Dissatisfied        SM  \n",
            "1                  Neutral        TV  \n",
            "2           Very Satisfied        TV  \n",
            "3        Very Dissatisfied        SM  \n",
            "5                  Neutral        SM  \n",
            "..                     ...       ...  \n",
            "195     Somewhat Satisfied       Web  \n",
            "196     Somewhat Satisfied       Web  \n",
            "197  Somewhat Dissatisfied  Referral  \n",
            "198  Somewhat Dissatisfied  Referral  \n",
            "199     Somewhat Satisfied        SM  \n",
            "\n",
            "[168 rows x 14 columns]\n",
            "      CustID     Sex             Race  BirthDate College  HouseholdSize  \\\n",
            "0    1530016  Female            Black 1986-12-16     Yes              5   \n",
            "1    1531136    Male            White 1993-05-09     Yes              5   \n",
            "2    1532160    Male            Black 1966-05-22     Yes              2   \n",
            "3    1532307    Male            White 1964-09-16     Yes              4   \n",
            "5    1532387    Male            White 1957-08-27     Yes              2   \n",
            "..       ...     ...              ...        ...     ...            ...   \n",
            "195  1578525    Male         Hispanic 1963-12-12     Yes              1   \n",
            "196  1579349    Male            Asian 1980-12-19     Yes              1   \n",
            "197  1579389  Female  American Indian 2000-05-21     Yes              1   \n",
            "198  1579857  Female            White 1991-01-26     Yes              1   \n",
            "199  1579979    Male            White 1999-07-05     Yes              5   \n",
            "\n",
            "     ZipCode  Income  Spending2017  Spending2018  NumOfOrders  DaysSinceLast  \\\n",
            "0      90047   53000           287           241            3            101   \n",
            "1      90026   94000          1227           843           12            262   \n",
            "2      90027   64000           523           719            9            122   \n",
            "3      90029   60000           516           582           13            129   \n",
            "5      90028   67000           631           452            9            150   \n",
            "..       ...     ...           ...           ...          ...            ...   \n",
            "195    90005   82000          1521          1121           25            302   \n",
            "196    90010   49000           217           397           10            200   \n",
            "197    90009   50000           625           514           10            233   \n",
            "198    90055   52000           325           586           13            219   \n",
            "199    90043  102000          1080           926           10            113   \n",
            "\n",
            "              Satisfaction   Channel  \n",
            "0        Very Dissatisfied        SM  \n",
            "1                  Neutral        TV  \n",
            "2           Very Satisfied        TV  \n",
            "3        Very Dissatisfied        SM  \n",
            "5                  Neutral        SM  \n",
            "..                     ...       ...  \n",
            "195     Somewhat Satisfied       Web  \n",
            "196     Somewhat Satisfied       Web  \n",
            "197  Somewhat Dissatisfied  Referral  \n",
            "198  Somewhat Dissatisfied  Referral  \n",
            "199     Somewhat Satisfied        SM  \n",
            "\n",
            "[168 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 대학교 졸업한 사람\n",
        "college = df[df['College'] == 'Yes']\n",
        "college.info()"
      ],
      "metadata": {
        "id": "92vNTRJpDuy4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95cb9d28-b0cd-4f27-8982-1e47219cf682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 168 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   CustID         168 non-null    int64         \n",
            " 1   Sex            168 non-null    object        \n",
            " 2   Race           168 non-null    object        \n",
            " 3   BirthDate      168 non-null    datetime64[ns]\n",
            " 4   College        168 non-null    object        \n",
            " 5   HouseholdSize  168 non-null    int64         \n",
            " 6   ZipCode        168 non-null    int64         \n",
            " 7   Income         168 non-null    int64         \n",
            " 8   Spending2017   168 non-null    int64         \n",
            " 9   Spending2018   168 non-null    int64         \n",
            " 10  NumOfOrders    168 non-null    int64         \n",
            " 11  DaysSinceLast  168 non-null    int64         \n",
            " 12  Satisfaction   168 non-null    object        \n",
            " 13  Channel        168 non-null    object        \n",
            "dtypes: datetime64[ns](1), int64(8), object(5)\n",
            "memory usage: 19.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1982~1999년 출생한 고객을 추출하기 위한 타임스탬프 생성\n",
        "start_date = pd.to_datetime(\"1982-01-01\")\n",
        "start_date"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghR1Oz1V5UbM",
        "outputId": "13d15fec-6656-4d75-dd77-0ab7555ff0de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('1982-01-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "end_date = pd.to_datetime(\"1999-12-31\")\n",
        "end_date"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5M7wWWo50ag",
        "outputId": "ea6a1eb4-617d-4f92-f384-317af8e66a94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('1999-12-31 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터[(조건1) & (조건2)]\n",
        "college_8299 = college[(college['BirthDate'] >= start_date) & (college['BirthDate'] <= end_date)]\n",
        "college_8299.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rBaakgkXd0u",
        "outputId": "f7f5e514-78da-48f5-cbfc-0dbae9759bb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 59 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   CustID         59 non-null     int64         \n",
            " 1   Sex            59 non-null     object        \n",
            " 2   Race           59 non-null     object        \n",
            " 3   BirthDate      59 non-null     datetime64[ns]\n",
            " 4   College        59 non-null     object        \n",
            " 5   HouseholdSize  59 non-null     int64         \n",
            " 6   ZipCode        59 non-null     int64         \n",
            " 7   Income         59 non-null     int64         \n",
            " 8   Spending2017   59 non-null     int64         \n",
            " 9   Spending2018   59 non-null     int64         \n",
            " 10  NumOfOrders    59 non-null     int64         \n",
            " 11  DaysSinceLast  59 non-null     int64         \n",
            " 12  Satisfaction   59 non-null     object        \n",
            " 13  Channel        59 non-null     object        \n",
            "dtypes: datetime64[ns](1), int64(8), object(5)\n",
            "memory usage: 6.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 한번에 만들어보자!\n",
        "df = raw_df.copy()\n",
        "start_date = pd.to_datetime(\"1982-01-01\")\n",
        "end_date = pd.to_datetime(\"1999-12-31\")\n",
        "college_8299 = df[(df['College'] == 'Yes') & (df['BirthDate'] >= start_date) & (df['BirthDate'] <= end_date)]\n",
        "college_8299.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvZI2GVG58xL",
        "outputId": "2fd017ff-836a-49c4-d720-f26257cf248b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 59 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   CustID         59 non-null     int64         \n",
            " 1   Sex            59 non-null     object        \n",
            " 2   Race           59 non-null     object        \n",
            " 3   BirthDate      59 non-null     datetime64[ns]\n",
            " 4   College        59 non-null     object        \n",
            " 5   HouseholdSize  59 non-null     int64         \n",
            " 6   ZipCode        59 non-null     int64         \n",
            " 7   Income         59 non-null     int64         \n",
            " 8   Spending2017   59 non-null     int64         \n",
            " 9   Spending2018   59 non-null     int64         \n",
            " 10  NumOfOrders    59 non-null     int64         \n",
            " 11  DaysSinceLast  59 non-null     int64         \n",
            " 12  Satisfaction   59 non-null     object        \n",
            " 13  Channel        59 non-null     object        \n",
            "dtypes: datetime64[ns](1), int64(8), object(5)\n",
            "memory usage: 6.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 여성\n",
        "female = college_8299[college_8299['Sex'] == \"Female\"]\n",
        "female.info()\n",
        "female.to_csv(\"/content/drive/MyDrive/lsda02/data/female.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOK0fmXu9gxt",
        "outputId": "fc3fe9aa-5311-46d4-a8b7-191a795e951f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 21 entries, 0 to 198\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   CustID         21 non-null     int64         \n",
            " 1   Sex            21 non-null     object        \n",
            " 2   Race           21 non-null     object        \n",
            " 3   BirthDate      21 non-null     datetime64[ns]\n",
            " 4   College        21 non-null     object        \n",
            " 5   HouseholdSize  21 non-null     int64         \n",
            " 6   ZipCode        21 non-null     int64         \n",
            " 7   Income         21 non-null     int64         \n",
            " 8   Spending2017   21 non-null     int64         \n",
            " 9   Spending2018   21 non-null     int64         \n",
            " 10  NumOfOrders    21 non-null     int64         \n",
            " 11  DaysSinceLast  21 non-null     int64         \n",
            " 12  Satisfaction   21 non-null     object        \n",
            " 13  Channel        21 non-null     object        \n",
            "dtypes: datetime64[ns](1), int64(8), object(5)\n",
            "memory usage: 2.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 남성\n",
        "male = college_8299[college_8299['Sex'] == \"Male\"]\n",
        "male.info()\n",
        "male.to_csv(\"/content/drive/MyDrive/lsda02/data/male.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Zgl-tZv--tM",
        "outputId": "551876a2-290b-4ded-c2b0-d312f1642688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 38 entries, 1 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   CustID         38 non-null     int64         \n",
            " 1   Sex            38 non-null     object        \n",
            " 2   Race           38 non-null     object        \n",
            " 3   BirthDate      38 non-null     datetime64[ns]\n",
            " 4   College        38 non-null     object        \n",
            " 5   HouseholdSize  38 non-null     int64         \n",
            " 6   ZipCode        38 non-null     int64         \n",
            " 7   Income         38 non-null     int64         \n",
            " 8   Spending2017   38 non-null     int64         \n",
            " 9   Spending2018   38 non-null     int64         \n",
            " 10  NumOfOrders    38 non-null     int64         \n",
            " 11  DaysSinceLast  38 non-null     int64         \n",
            " 12  Satisfaction   38 non-null     object        \n",
            " 13  Channel        38 non-null     object        \n",
            "dtypes: datetime64[ns](1), int64(8), object(5)\n",
            "memory usage: 4.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 한번에 해보자!\n",
        "# 그룹 객체 => groupby(그룹화에 사용할 열)\n",
        "college_8299_grouped_sex = college_8299.groupby(by = 'Sex')\n",
        "college_8299_grouped_sex.get_group('Female').to_csv(\"/content/drive/MyDrive/lsda02/data/female.csv\")\n",
        "college_8299_grouped_sex.get_group('Male').to_csv(\"/content/drive/MyDrive/lsda02/data/male.csv\")"
      ],
      "metadata": {
        "id": "HvJN9iky7n6s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}