0408.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import ttest_ind, chi2_contingency\n",
        "import rpy2.robjects.numpy2ri\n",
        "from rpy2.robjects.packages import importr\n",
        "rpy2.robjects.numpy2ri.activate()\n",
        "stats = importr('stats')"
      ],
      "metadata": {
        "id": "NhLDvNmNWXzL"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 미션1"
      ],
      "metadata": {
        "id": "Br8NF73lJBAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.read_excel('/content/drive/MyDrive/lsda02/data/customers.xlsx')\n",
        "raw_df.head()"
      ],
      "metadata": {
        "id": "I-6OufY1B17y",
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          "height": 206
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        "outputId": "f8012ef5-e0c9-4840-d39e-6b87355ff41a"
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      "execution_count": null,
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        {
          "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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              "      <th>CustID</th>\n",
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              "      <th>College</th>\n",
              "      <th>HouseholdSize</th>\n",
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              "      <th>Income</th>\n",
              "      <th>Spending2017</th>\n",
              "      <th>Spending2018</th>\n",
              "      <th>NumOfOrders</th>\n",
              "      <th>DaysSinceLast</th>\n",
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              "      <th>0</th>\n",
              "      <td>1530016</td>\n",
              "      <td>Female</td>\n",
              "      <td>Black</td>\n",
              "      <td>1986-12-16</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5</td>\n",
              "      <td>90047</td>\n",
              "      <td>53000</td>\n",
              "      <td>287</td>\n",
              "      <td>241</td>\n",
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              "      <td>Very Dissatisfied</td>\n",
              "      <td>SM</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1531136</td>\n",
              "      <td>Male</td>\n",
              "      <td>White</td>\n",
              "      <td>1993-05-09</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5</td>\n",
              "      <td>90026</td>\n",
              "      <td>94000</td>\n",
              "      <td>1227</td>\n",
              "      <td>843</td>\n",
              "      <td>12</td>\n",
              "      <td>262</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>TV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1532160</td>\n",
              "      <td>Male</td>\n",
              "      <td>Black</td>\n",
              "      <td>1966-05-22</td>\n",
              "      <td>Yes</td>\n",
              "      <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",
              "      <td>4</td>\n",
              "      <td>90029</td>\n",
              "      <td>60000</td>\n",
              "      <td>516</td>\n",
              "      <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",
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            "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.info()"
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        "id": "4RrsxeCf4vFw",
        "outputId": "ea7b2b42-138e-48e2-e58a-699d5ead3069"
      },
      "execution_count": null,
      "outputs": [
        {
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          "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": "4ae1f9e3-60ce-40ad-84b7-b7dccd50b92f"
      },
      "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": [
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              "      <td>1530016</td>\n",
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              "      <td>1993-05-09</td>\n",
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              "      <td>262</td>\n",
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              "      <th>2</th>\n",
              "      <td>1532160</td>\n",
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              "      <td>Black</td>\n",
              "      <td>1966-05-22</td>\n",
              "      <td>Yes</td>\n",
              "      <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",
              "      <td>4</td>\n",
              "      <td>90029</td>\n",
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              "      <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",
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              "      <td>845</td>\n",
              "      <td>7</td>\n",
              "      <td>97</td>\n",
              "      <td>Very Dissatisfied</td>\n",
              "      <td>Web</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      spin 1s steps(1) infinite;\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",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "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": 8
        }
      ]
    },
    {
      "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": "34ba0f7a-6467-4040-d91e-e6c3efcd1f87"
      },
      "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": 9
        }
      ]
    },
    {
      "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": "230350aa-5538-4dae-98c8-db8fb854ca5b"
      },
      "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": "b2f39712-7af5-4224-e1d0-296b942eb08e"
      },
      "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": "033332ce-5a00-4d36-d907-68dbebb13739"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('1982-01-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "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": "a2413c16-f218-4d48-a522-c71b399afb24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('1999-12-31 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "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": "3f3375b1-543d-4e3a-ddea-1ff048b40ebe"
      },
      "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": "157c0694-65f8-46e7-dcec-f107db677118"
      },
      "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": "e76c32db-6308-479a-9796-84e6eda91de0"
      },
      "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": "a6fa84f6-0122-4fcc-8034-e9d3155d6d65"
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 미션2"
      ],
      "metadata": {
        "id": "CCj_5OtKAAmi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 미션 1에서 만든 대학교 졸업한 82년~99년 출생 고객의 데이터를 이용\n",
        "# Sex, Race, HouseholdSize, Income, Satisfaction, Channel 변수를 선택하여 desc_df로 저장\n",
        "selected_columns = ['Sex', 'Race', 'HouseholdSize', 'Income', 'Satisfaction', 'Channel']\n",
        "desc_df = college_8299[selected_columns]\n",
        "desc_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxTAgjHqCwT4",
        "outputId": "9800190f-38d7-4c36-fbcf-488971b2effa"
      },
      "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 6 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Sex            59 non-null     object\n",
            " 1   Race           59 non-null     object\n",
            " 2   HouseholdSize  59 non-null     int64 \n",
            " 3   Income         59 non-null     int64 \n",
            " 4   Satisfaction   59 non-null     object\n",
            " 5   Channel        59 non-null     object\n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 3.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# desc_df를 이용하여 남녀 차이를 보기 위한 기술통계표를 작성하세요.\n",
        "print(pd.crosstab(desc_df.Race, desc_df.Sex))\n",
        "print(\"\\n\")\n",
        "print(pd.crosstab(desc_df.HouseholdSize, desc_df.Sex))\n",
        "print(\"\\n\")\n",
        "print(\"Female: \", pd.DataFrame(desc_df[desc_df.Sex == \"Female\"].Income.describe().loc[[\"mean\", \"std\"]]).T)\n",
        "print(\"Male: \", pd.DataFrame(desc_df[desc_df.Sex == \"Male\"].Income.describe().loc[[\"mean\", \"std\"]]).T)\n",
        "print(\"\\n\")\n",
        "print(pd.crosstab(desc_df.Satisfaction, desc_df.Sex))\n",
        "print(\"\\n\")\n",
        "print(pd.crosstab(desc_df.Channel, desc_df.Sex))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KB1AWnuQlbH",
        "outputId": "2b7c0630-b4c3-4f27-a200-94a933a748a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex               Female  Male\n",
            "Race                          \n",
            "American Indian        0     2\n",
            "Asian                  0     4\n",
            "Black                  7     6\n",
            "Hispanic               4     8\n",
            "Pacific Islander       0     1\n",
            "White                 10    17\n",
            "\n",
            "\n",
            "Sex            Female  Male\n",
            "HouseholdSize              \n",
            "1                   4     6\n",
            "2                   4     7\n",
            "3                   5     9\n",
            "4                   2     8\n",
            "5                   6     8\n",
            "\n",
            "\n",
            "Female:                  mean           std\n",
            "Income  61285.714286  19680.301972\n",
            "Male:                  mean          std\n",
            "Income  83157.894737  27514.64113\n",
            "\n",
            "\n",
            "Sex                    Female  Male\n",
            "Satisfaction                       \n",
            "Neutral                     1     6\n",
            "Somewhat Dissatisfied       3     3\n",
            "Somewhat Satisfied         11    21\n",
            "Very Dissatisfied           2     2\n",
            "Very Satisfied              4     6\n",
            "\n",
            "\n",
            "Sex       Female  Male\n",
            "Channel               \n",
            "Referral       6     6\n",
            "SM             3    16\n",
            "TV             4     9\n",
            "Web            8     7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "desc_df.Income.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4l5hybhMAW6",
        "outputId": "576042b3-0f6b-4727-b40c-e0a5b4fb33b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        59.000000\n",
              "mean      75372.881356\n",
              "std       26982.688131\n",
              "min       34000.000000\n",
              "25%       55000.000000\n",
              "50%       69000.000000\n",
              "75%       94000.000000\n",
              "max      167000.000000\n",
              "Name: Income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "desc_df.Income.describe().loc[[\"mean\", \"std\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkho6ZG9Tp0Q",
        "outputId": "b12be841-c73f-4e06-88fd-5f1f0aefedc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean    75372.881356\n",
              "std     26982.688131\n",
              "Name: Income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# t검정\n",
        "female_income = desc_df.query('Sex == \"Female\"')['Income']\n",
        "male_income = desc_df.query('Sex == \"Male\"')['Income']\n",
        "tv, pv = ttest_ind(female_income, male_income, equal_var = True)\n",
        "print(\"t-statitics: \", round(tv, 5))\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")"
      ],
      "metadata": {
        "id": "DvYAZeSuESLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba12cf7e-f8e6-4310-f2c8-4193c55b8226"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t-statitics:  -3.21161\n",
            "p-value:  0.00217\n",
            "significant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 카이제곱검정\n",
        "tab = pd.crosstab(desc_df.Race, desc_df.Sex)\n",
        "print(tab)\n",
        "print(\"\\n\")\n",
        "print(chi2_contingency(tab))\n",
        "print(\"\\n\")\n",
        "result = chi2_contingency(tab)\n",
        "chiv = result[0]\n",
        "pv = result[1]\n",
        "print(\"chi2-statitics: \", round(chiv, 5))\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")\n",
        "print(\"expected frequency\")\n",
        "print(result[3])\n",
        "f_pct = ((result[3] < 5).sum() / (result[3].shape[0] * result[3].shape[1])) * 100\n",
        "print(str(round(f_pct,2)) + \"%\")\n",
        "if f_pct >= 20:\n",
        "  print(\"Fisher Exact Test!\")\n",
        "else:\n",
        "  print(\"No Fisher Exact Test!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qk5qVPliEZlz",
        "outputId": "2924f766-7e29-4a3a-d46f-0fdba3d31f3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex               Female  Male\n",
            "Race                          \n",
            "American Indian        0     2\n",
            "Asian                  0     4\n",
            "Black                  7     6\n",
            "Hispanic               4     8\n",
            "Pacific Islander       0     1\n",
            "White                 10    17\n",
            "\n",
            "\n",
            "Chi2ContingencyResult(statistic=5.809045405536633, pvalue=0.32524532718433313, dof=5, expected_freq=array([[ 0.71186441,  1.28813559],\n",
            "       [ 1.42372881,  2.57627119],\n",
            "       [ 4.62711864,  8.37288136],\n",
            "       [ 4.27118644,  7.72881356],\n",
            "       [ 0.3559322 ,  0.6440678 ],\n",
            "       [ 9.61016949, 17.38983051]]))\n",
            "\n",
            "\n",
            "chi2-statitics:  5.80905\n",
            "p-value:  0.32525\n",
            "not significant\n",
            "expected frequency\n",
            "[[ 0.71186441  1.28813559]\n",
            " [ 1.42372881  2.57627119]\n",
            " [ 4.62711864  8.37288136]\n",
            " [ 4.27118644  7.72881356]\n",
            " [ 0.3559322   0.6440678 ]\n",
            " [ 9.61016949 17.38983051]]\n",
            "66.67%\n",
            "Fisher Exact Test!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 피셔 정확 검정\n",
        "tab = np.array(tab)\n",
        "result = stats.fisher_test(tab)\n",
        "print(result)\n",
        "print(result[0])\n",
        "pv = result[0][0]\n",
        "print(pv)\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1amfFNTnT_Kb",
        "outputId": "620573d6-ce8a-4ff0-a675-9a9b031cc500"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\tFisher's Exact Test for Count Data\n",
            "\n",
            "data:  structure(c(0L, 0L, 7L, 4L, 0L, 10L, 2L, 4L, 6L, 8L, 1L, 17L), dim = c(6L, 2L))\n",
            "p-value = 0.4008\n",
            "alternative hypothesis: two.sided\n",
            "\n",
            "\n",
            "[1] 0.400762\n",
            "\n",
            "0.400761953244126\n",
            "p-value:  0.40076\n",
            "not significant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab = pd.crosstab(desc_df.HouseholdSize, desc_df.Sex)\n",
        "print(tab)\n",
        "print(\"\\n\")\n",
        "print(chi2_contingency(tab))\n",
        "print(\"\\n\")\n",
        "result = chi2_contingency(tab)\n",
        "chiv = result[0]\n",
        "pv = result[1]\n",
        "print(\"chi2-statitics: \", round(chiv, 5))\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")\n",
        "print(\"expected frequency\")\n",
        "print(result[3])\n",
        "f_pct = ((result[3] < 5).sum() / (result[3].shape[0] * result[3].shape[1])) * 100\n",
        "print(str(round(f_pct,2)) + \"%\")\n",
        "if f_pct >= 20:\n",
        "  print(\"Fisher Exact Test!\")\n",
        "else:\n",
        "  print(\"No Fisher Exact Test!\")"
      ],
      "metadata": {
        "id": "jFY3maezzbRG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22ab8714-12f2-4d70-e91c-bf662632df98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex            Female  Male\n",
            "HouseholdSize              \n",
            "1                   4     6\n",
            "2                   4     7\n",
            "3                   5     9\n",
            "4                   2     8\n",
            "5                   6     8\n",
            "\n",
            "\n",
            "Chi2ContingencyResult(statistic=1.4705351039937513, pvalue=0.8318476716826708, dof=4, expected_freq=array([[3.55932203, 6.44067797],\n",
            "       [3.91525424, 7.08474576],\n",
            "       [4.98305085, 9.01694915],\n",
            "       [3.55932203, 6.44067797],\n",
            "       [4.98305085, 9.01694915]]))\n",
            "\n",
            "\n",
            "chi2-statitics:  1.47054\n",
            "p-value:  0.83185\n",
            "not significant\n",
            "expected frequency\n",
            "[[3.55932203 6.44067797]\n",
            " [3.91525424 7.08474576]\n",
            " [4.98305085 9.01694915]\n",
            " [3.55932203 6.44067797]\n",
            " [4.98305085 9.01694915]]\n",
            "50.0%\n",
            "Fisher Exact Test!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab = np.array(tab)\n",
        "result = stats.fisher_test(tab)\n",
        "print(result)\n",
        "print(result[0])\n",
        "pv = result[0][0]\n",
        "print(pv)\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c42d6a82-b819-449f-9d9e-4d89844ce5f0",
        "id": "XeuQL6E2VtIY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\tFisher's Exact Test for Count Data\n",
            "\n",
            "data:  structure(c(4L, 4L, 5L, 2L, 6L, 6L, 7L, 9L, 8L, 8L), dim = c(5L, 2L))\n",
            "p-value = 0.8503\n",
            "alternative hypothesis: two.sided\n",
            "\n",
            "\n",
            "[1] 0.850317\n",
            "\n",
            "0.8503170108227862\n",
            "p-value:  0.85032\n",
            "not significant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab = pd.crosstab(desc_df.Satisfaction, desc_df.Sex)\n",
        "print(tab)\n",
        "print(\"\\n\")\n",
        "print(chi2_contingency(tab))\n",
        "print(\"\\n\")\n",
        "result = chi2_contingency(tab)\n",
        "chiv = result[0]\n",
        "pv = result[1]\n",
        "print(\"chi2-statitics: \", round(chiv, 5))\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")\n",
        "print(\"expected frequency\")\n",
        "print(result[3])\n",
        "f_pct = ((result[3] < 5).sum() / (result[3].shape[0] * result[3].shape[1])) * 100\n",
        "print(str(round(f_pct,2)) + \"%\")\n",
        "if f_pct >= 20:\n",
        "  print(\"Fisher Exact Test!\")\n",
        "else:\n",
        "  print(\"No Fisher Exact Test!\")"
      ],
      "metadata": {
        "id": "eWk8POR0zeRH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70a72071-1240-46f6-9360-50a994e62b6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex                    Female  Male\n",
            "Satisfaction                       \n",
            "Neutral                     1     6\n",
            "Somewhat Dissatisfied       3     3\n",
            "Somewhat Satisfied         11    21\n",
            "Very Dissatisfied           2     2\n",
            "Very Satisfied              4     6\n",
            "\n",
            "\n",
            "Chi2ContingencyResult(statistic=2.397139052989616, pvalue=0.663144348129268, dof=4, expected_freq=array([[ 2.49152542,  4.50847458],\n",
            "       [ 2.13559322,  3.86440678],\n",
            "       [11.38983051, 20.61016949],\n",
            "       [ 1.42372881,  2.57627119],\n",
            "       [ 3.55932203,  6.44067797]]))\n",
            "\n",
            "\n",
            "chi2-statitics:  2.39714\n",
            "p-value:  0.66314\n",
            "not significant\n",
            "expected frequency\n",
            "[[ 2.49152542  4.50847458]\n",
            " [ 2.13559322  3.86440678]\n",
            " [11.38983051 20.61016949]\n",
            " [ 1.42372881  2.57627119]\n",
            " [ 3.55932203  6.44067797]]\n",
            "70.0%\n",
            "Fisher Exact Test!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab = np.array(tab)\n",
        "result = stats.fisher_test(tab)\n",
        "print(result)\n",
        "print(result[0])\n",
        "pv = result[0][0]\n",
        "print(pv)\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03d40ed5-3d2d-4ecb-f9c2-19759ed1ee00",
        "id": "PVGiMwMHV2IA"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\tFisher's Exact Test for Count Data\n",
            "\n",
            "data:  structure(c(1L, 3L, 11L, 2L, 4L, 6L, 3L, 21L, 2L, 6L), dim = c(5L, 2L))\n",
            "p-value = 0.6617\n",
            "alternative hypothesis: two.sided\n",
            "\n",
            "\n",
            "[1] 0.6617458\n",
            "\n",
            "0.6617457992615556\n",
            "p-value:  0.66175\n",
            "not significant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab = pd.crosstab(desc_df.Channel, desc_df.Sex)\n",
        "print(tab)\n",
        "print(\"\\n\")\n",
        "print(chi2_contingency(tab))\n",
        "print(\"\\n\")\n",
        "result = chi2_contingency(tab)\n",
        "chiv = result[0]\n",
        "pv = result[1]\n",
        "print(\"chi2-statitics: \", round(chiv, 5))\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")\n",
        "print(\"expected frequency\")\n",
        "print(result[3])\n",
        "f_pct = ((result[3] < 5).sum() / (result[3].shape[0] * result[3].shape[1])) * 100\n",
        "print(str(round(f_pct,2)) + \"%\")\n",
        "if f_pct >= 20:\n",
        "  print(\"Fisher Exact Test!\")\n",
        "else:\n",
        "  print(\"No Fisher Exact Test!\")"
      ],
      "metadata": {
        "id": "oiub4pjAzp3u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfa89c4b-d6b9-4e18-8abf-a42c5bde566c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex       Female  Male\n",
            "Channel               \n",
            "Referral       6     6\n",
            "SM             3    16\n",
            "TV             4     9\n",
            "Web            8     7\n",
            "\n",
            "\n",
            "Chi2ContingencyResult(statistic=6.528156761674767, pvalue=0.08855866046491273, dof=3, expected_freq=array([[ 4.27118644,  7.72881356],\n",
            "       [ 6.76271186, 12.23728814],\n",
            "       [ 4.62711864,  8.37288136],\n",
            "       [ 5.33898305,  9.66101695]]))\n",
            "\n",
            "\n",
            "chi2-statitics:  6.52816\n",
            "p-value:  0.08856\n",
            "not significant\n",
            "expected frequency\n",
            "[[ 4.27118644  7.72881356]\n",
            " [ 6.76271186 12.23728814]\n",
            " [ 4.62711864  8.37288136]\n",
            " [ 5.33898305  9.66101695]]\n",
            "25.0%\n",
            "Fisher Exact Test!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tab = np.array(tab)\n",
        "result = stats.fisher_test(tab)\n",
        "print(result)\n",
        "print(result[0])\n",
        "pv = result[0][0]\n",
        "print(pv)\n",
        "print(\"p-value: \", round(pv, 5))\n",
        "if pv < 0.05:\n",
        "  print(\"significant\")\n",
        "else:\n",
        "  print(\"not significant\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79551aa6-db2c-4010-e2bc-0701582cd12a",
        "id": "nAnn1ja6V-_b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\tFisher's Exact Test for Count Data\n",
            "\n",
            "data:  structure(c(6L, 3L, 4L, 8L, 6L, 16L, 9L, 7L), dim = c(4L, 2L))\n",
            "p-value = 0.08059\n",
            "alternative hypothesis: two.sided\n",
            "\n",
            "\n",
            "[1] 0.08059186\n",
            "\n",
            "0.08059185685217439\n",
            "p-value:  0.08059\n",
            "not significant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 분석 프로젝트"
      ],
      "metadata": {
        "id": "j52ftnE4fno8"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "440f9faf-b566-48ea-92d4-68da91c884e0"
      },
      "source": [
        "## 한국복지패널 데이터 분석 준비하기"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 한글 폰트 설치\n",
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf\n",
        "# 메뉴 - 런타임 - 세션 다시 시작"
      ],
      "metadata": {
        "id": "PrBAVAwmSwxL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94a1d4e8-5375-4683-eba0-78e3deefa9b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  fonts-nanum\n",
            "0 upgraded, 1 newly installed, 0 to remove and 45 not upgraded.\n",
            "Need to get 10.3 MB of archives.\n",
            "After this operation, 34.1 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 fonts-nanum all 20200506-1 [10.3 MB]\n",
            "Fetched 10.3 MB in 1s (18.8 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 121753 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20200506-1_all.deb ...\n",
            "Unpacking fonts-nanum (20200506-1) ...\n",
            "Setting up fonts-nanum (20200506-1) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 12 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/usr/share/fonts/truetype: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/liberation: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/nanum: skipping, looped directory detected\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d79d23d4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 한글 폰트 지정\n",
        "plt.rc(\"font\", family = \"NanumGothic\")"
      ],
      "metadata": {
        "id": "Q2LnuDjsSxeQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "2f2bc49f"
      },
      "outputs": [],
      "source": [
        "# 데이터 불러오기\n",
        "raw_welfare = pd.read_csv('/content/drive/MyDrive/lsda02/data/welfare_2015.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 복사본 만들기\n",
        "welfare = raw_welfare.copy()"
      ],
      "metadata": {
        "id": "smEXBzf8zSax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "4e362c2e-b484-4f8c-925e-275f3389daa6",
        "outputId": "19b2b64e-88d9-4308-a53c-d032b5e24b6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       h10_g3  h10_g4  h10_g10  h10_g11  h10_eco9  p1002_8aq1  h10_reg7\n",
            "0           2    1936        2        2       NaN         NaN         1\n",
            "1           2    1945        2        2       NaN         NaN         1\n",
            "2           1    1948        2        2     942.0       120.0         1\n",
            "3           1    1942        3        1     762.0       200.0         1\n",
            "4           2    1923        2        1       NaN         NaN         1\n",
            "...       ...     ...      ...      ...       ...         ...       ...\n",
            "16665       2    9999        5        1       NaN         NaN         6\n",
            "16666       1    1972        9        1       NaN         NaN         6\n",
            "16667       2    1969        1        9       NaN         NaN         2\n",
            "16668       2    1960        3        2    9999.0         NaN         6\n",
            "16669       1    1940        1        1       NaN      9999.0         5\n",
            "\n",
            "[16670 rows x 7 columns]\n",
            "   h10_g3  h10_g4  h10_g10  h10_g11  h10_eco9  p1002_8aq1  h10_reg7\n",
            "0       2    1936        2        2       NaN         NaN         1\n",
            "1       2    1945        2        2       NaN         NaN         1\n",
            "2       1    1948        2        2     942.0       120.0         1\n",
            "3       1    1942        3        1     762.0       200.0         1\n",
            "4       2    1923        2        1       NaN         NaN         1\n",
            "       h10_g3  h10_g4  h10_g10  h10_g11  h10_eco9  p1002_8aq1  h10_reg7\n",
            "16665       2    9999        5        1       NaN         NaN         6\n",
            "16666       1    1972        9        1       NaN         NaN         6\n",
            "16667       2    1969        1        9       NaN         NaN         2\n",
            "16668       2    1960        3        2    9999.0         NaN         6\n",
            "16669       1    1940        1        1       NaN      9999.0         5\n"
          ]
        }
      ],
      "source": [
        "# 데이터 앞부분, 뒷부분 출력\n",
        "print(welfare)\n",
        "print(welfare.head())\n",
        "print(welfare.tail())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "bc164146-4793-458d-9882-8da8190cf68f",
        "outputId": "f89d094b-c668-4277-fb2b-c80163f71d8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16670, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ],
      "source": [
        "welfare.shape       # 행, 열 개수 출력"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "872665e6-1dca-4339-8309-ef7c5ff423e9",
        "outputId": "ec65d523-67b0-4bfa-dd54-27a44ba0eb34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16670 entries, 0 to 16669\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   h10_g3      16670 non-null  int64  \n",
            " 1   h10_g4      16670 non-null  int64  \n",
            " 2   h10_g10     16670 non-null  int64  \n",
            " 3   h10_g11     16670 non-null  int64  \n",
            " 4   h10_eco9    7530 non-null   float64\n",
            " 5   p1002_8aq1  4635 non-null   float64\n",
            " 6   h10_reg7    16670 non-null  int64  \n",
            "dtypes: float64(2), int64(5)\n",
            "memory usage: 911.8 KB\n"
          ]
        }
      ],
      "source": [
        "welfare.info()      # 변수 속성 출력"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "d382ce64-310b-4910-84a9-41af3c710dad",
        "outputId": "4d9fed5f-379c-45a4-bef3-799bb7e35cf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             h10_g3        h10_g4       h10_g10       h10_g11     h10_eco9  \\\n",
              "count  16670.000000  16670.000000  16670.000000  16670.000000  7530.000000   \n",
              "mean       1.545711   1968.047391      1.648410      1.517516   592.493094   \n",
              "std        0.501283     66.738241      1.572843      0.503058   277.804427   \n",
              "min        1.000000   1907.000000      0.000000      1.000000   111.000000   \n",
              "25%        1.000000   1946.000000      1.000000      1.000000   314.000000   \n",
              "50%        2.000000   1966.000000      1.000000      2.000000   611.000000   \n",
              "75%        2.000000   1988.000000      2.000000      2.000000   863.000000   \n",
              "max        9.000000   9999.000000      9.000000      9.000000  9999.000000   \n",
              "\n",
              "        p1002_8aq1      h10_reg7  \n",
              "count  4635.000000  16670.000000  \n",
              "mean    243.724168      3.705099  \n",
              "std     233.549988      2.069236  \n",
              "min       0.000000      1.000000  \n",
              "25%     122.000000      2.000000  \n",
              "50%     192.500000      3.000000  \n",
              "75%     316.650000      6.000000  \n",
              "max    9999.000000      7.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22ce2d35-3281-41c2-93fa-c83c79750a9e\" 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>h10_g3</th>\n",
              "      <th>h10_g4</th>\n",
              "      <th>h10_g10</th>\n",
              "      <th>h10_g11</th>\n",
              "      <th>h10_eco9</th>\n",
              "      <th>p1002_8aq1</th>\n",
              "      <th>h10_reg7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>16670.000000</td>\n",
              "      <td>16670.000000</td>\n",
              "      <td>16670.000000</td>\n",
              "      <td>16670.000000</td>\n",
              "      <td>7530.000000</td>\n",
              "      <td>4635.000000</td>\n",
              "      <td>16670.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.545711</td>\n",
              "      <td>1968.047391</td>\n",
              "      <td>1.648410</td>\n",
              "      <td>1.517516</td>\n",
              "      <td>592.493094</td>\n",
              "      <td>243.724168</td>\n",
              "      <td>3.705099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.501283</td>\n",
              "      <td>66.738241</td>\n",
              "      <td>1.572843</td>\n",
              "      <td>0.503058</td>\n",
              "      <td>277.804427</td>\n",
              "      <td>233.549988</td>\n",
              "      <td>2.069236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1907.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1946.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>314.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1966.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>611.000000</td>\n",
              "      <td>192.500000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1988.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>863.000000</td>\n",
              "      <td>316.650000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>7.000000</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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              "      display:flex;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-22ce2d35-3281-41c2-93fa-c83c79750a9e 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-22ce2d35-3281-41c2-93fa-c83c79750a9e');\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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              "        element.appendChild(docLink);\n",
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              "    animation:\n",
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              "  }\n",
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              "  @keyframes spin {\n",
              "    0% {\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",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"welfare\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"h10_g3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5892.874649796094,\n        \"min\": 0.5012832729019862,\n        \"max\": 16670.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          16670.0,\n          1.5457108578284342,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h10_g4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5736.571239409469,\n        \"min\": 66.7382411717031,\n        \"max\": 16670.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1968.0473905218955,\n          1966.0,\n          16670.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h10_g10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5892.916386024116,\n        \"min\": 0.0,\n        \"max\": 16670.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          16670.0,\n          1.6484103179364127,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h10_g11\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5892.875984705669,\n        \"min\": 0.5030583281314346,\n        \"max\": 16670.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          16670.0,\n          1.5175164967006598,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h10_eco9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3906.688789475211,\n        \"min\": 111.0,\n        \"max\": 9999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          592.4930942895086,\n          611.0,\n          7530.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"p1002_8aq1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3600.6227784718867,\n        \"min\": 0.0,\n        \"max\": 9999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          243.72416828478964,\n          192.5,\n          4635.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"h10_reg7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5892.484083845242,\n        \"min\": 1.0,\n        \"max\": 16670.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.7050989802039593,\n          3.0,\n          16670.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 183
        }
      ],
      "source": [
        "welfare.describe()  # 요약 통계량"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1b17969d"
      },
      "outputs": [],
      "source": [
        "welfare = welfare.rename(columns={'h10_g3':'sex', #  성별\n",
        "                                  'h10_g4':'birth_year', #  태어난 연도\n",
        "                                  'h10_g10':'marital_status', #  혼인 상태\n",
        "                                  'h10_g11':'religion', #  종교\n",
        "                                  'h10_eco9':'job_code', #  월급\n",
        "                                  'p1002_8aq1':'income', #  직업 코드\n",
        "                                  'h10_reg7':'region_code'}) #  지역 코드"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BbRUhcGUyRUd",
        "outputId": "52e99b9d-3e0e-4d59-cde1-4a2e0a67db80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sex  birth_year  marital_status  religion  job_code  income  region_code\n",
              "0    2        1936               2         2       NaN     NaN            1\n",
              "1    2        1945               2         2       NaN     NaN            1\n",
              "2    1        1948               2         2     942.0   120.0            1\n",
              "3    1        1942               3         1     762.0   200.0            1\n",
              "4    2        1923               2         1       NaN     NaN            1"
            ],
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "welfare",
              "summary": "{\n  \"name\": \"welfare\",\n  \"rows\": 16670,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birth_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66,\n        \"min\": 1907,\n        \"max\": 9999,\n        \"num_unique_values\": 103,\n        \"samples\": [\n          1939,\n          1988,\n          1981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital_status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"religion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 277.80442675969294,\n        \"min\": 111.0,\n        \"max\": 9999.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          243.0,\n          855.0,\n          399.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 233.54998802862727,\n        \"min\": 0.0,\n        \"max\": 9999.0,\n        \"num_unique_values\": 1768,\n        \"samples\": [\n          380.4,\n          156.9,\n          471.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          7,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4973e90e-181a-496c-a1ce-c42419ae51a3"
      },
      "source": [
        "## 1 성별에 따른 월급 차이 - '성별에 따라 월급이 다를까?'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef8df942-a714-4fc5-814d-561ab884c3b1"
      },
      "source": [
        "### 성별 변수 검토 및 전처리하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "788bf87c-cebf-43cc-90de-a118bb7a7f19"
      },
      "source": [
        "#### 1. 변수 검토하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3a8f8601",
        "outputId": "6758ebd3-b63f-44a1-ed17-da3e25f2d443",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ],
      "source": [
        "welfare['sex'].dtypes  # 변수 타입 출력"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ea9a91f",
        "outputId": "63a08b13-1ec3-4388-ebeb-2cad63d33421",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "2    9089\n",
              "1    7580\n",
              "9       1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 187
        }
      ],
      "source": [
        "welfare['sex'].value_counts()  # 빈도 구하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "be0316e4"
      },
      "source": [
        "#### 2. 전처리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2fe3c910",
        "outputId": "11ccb4a9-0cf8-410c-a27d-3b1165313c29",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "2    9089\n",
              "1    7580\n",
              "9       1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ],
      "source": [
        "# 이상치 확인\n",
        "welfare['sex'].value_counts(dropna = False)\n",
        "# 9 => 코드북 상으로 모름/무응답(미상)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3412385c"
      },
      "outputs": [],
      "source": [
        "# 이상치 결측 처리\n",
        "welfare['sex'] = np.where(welfare['sex'] == 9, np.nan, welfare['sex'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이상치, 결측치 확인\n",
        "welfare['sex'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAa9wne5452T",
        "outputId": "af81f064-8003-43cd-9e38-637643ec95b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "2.0    9089\n",
              "1.0    7580\n",
              "NaN       1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['sex'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1kr5B4x4-cp",
        "outputId": "53ccd33d-ba5c-4afd-f6ff-69e7abdc4b5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 성별 항목 이름 부여\n",
        "welfare['sex'] = np.where(welfare['sex'] == 1, 'male',\n",
        "                          np.where(welfare['sex'] == 2, 'female', np.nan))"
      ],
      "metadata": {
        "id": "YnOQ8s81MrOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['sex'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7m2Qd5j5F2A",
        "outputId": "8f9c6d72-fc1a-4913-c09d-dd8d80be3ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    9089\n",
              "male      7580\n",
              "nan          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  nan 문자열을 NaN 으로 변환\n",
        "welfare['sex'] = np.where(welfare['sex'] == 'nan', np.nan, welfare['sex'])"
      ],
      "metadata": {
        "id": "Gy7bEDlC7YwE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['sex'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Exmy4THL7qXz",
        "outputId": "46feea93-5b5e-444c-b761-10c434ced81b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    9089\n",
              "male      7580\n",
              "NaN          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "3c897b65",
        "outputId": "f3447e43-9c3f-482f-d289-a1c985377e8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 빈도 막대 그래프 만들기\n",
        "sns.countplot(x = 'sex', data = welfare)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0c1c30c6"
      },
      "source": [
        "### 월급 변수 검토 및 전처리하기"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. 변수 검토하기"
      ],
      "metadata": {
        "id": "vyVEsUdG4pgX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4d16dd00",
        "outputId": "ffe9c33a-2530-4ada-a8d9-a417d3c9d599",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ],
      "source": [
        "welfare['income'].dtypes  # 변수 타입 출력"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "567a20e5",
        "outputId": "0aa7122b-a941-41b7-cb7b-f9282743063d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4635.000000\n",
              "mean      243.724168\n",
              "std       233.549988\n",
              "min         0.000000\n",
              "25%       122.000000\n",
              "50%       192.500000\n",
              "75%       316.650000\n",
              "max      9999.000000\n",
              "Name: income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ],
      "source": [
        "welfare['income'].describe()  # 요약 통계량 구하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30141494"
      },
      "source": [
        "#### 2. 전처리\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "929159da",
        "outputId": "eb9ef3e7-ae80-4ee0-ab84-c9b3bade3b0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4635.000000\n",
              "mean      243.724168\n",
              "std       233.549988\n",
              "min         0.000000\n",
              "25%       122.000000\n",
              "50%       192.500000\n",
              "75%       316.650000\n",
              "max      9999.000000\n",
              "Name: income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ],
      "source": [
        "welfare['income'].describe()  # 이상치 확인\n",
        "# 월급이 0 => 소득이 없음\n",
        "# 월급이 9999 => 코드북 상으로 모름/무응답(미상)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3f2999f0",
        "outputId": "c553213d-51ff-4f85-f846-61e639104466",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12035"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ],
      "source": [
        "welfare['income'].isnull().sum()  # 결측치 확인"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['income'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71ZG8A7vCuZj",
        "outputId": "2d195ddc-9bb7-4fb0-daaa-8e6b217f0099"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "income\n",
              "NaN        12035\n",
              "20.00        136\n",
              "150.00       128\n",
              "200.00        91\n",
              "250.00        74\n",
              "           ...  \n",
              "392.70         1\n",
              "515.00         1\n",
              "397.18         1\n",
              "304.16         1\n",
              "9999.00        1\n",
              "Name: count, Length: 1769, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "d5edafa9"
      },
      "outputs": [],
      "source": [
        "# 이상치 결측 처리 - 9999\n",
        "welfare['income'] = np.where(welfare['income'] == 9999, np.nan, welfare['income'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['income'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6miAKLk8YZt",
        "outputId": "9d621970-967d-49cf-8818-d25872784294"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12036"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['income'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYIS9EGbC2ve",
        "outputId": "86970953-d43d-4f7d-fd9c-a1cae1e862d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "income\n",
              "NaN       12036\n",
              "20.00       136\n",
              "150.00      128\n",
              "200.00       91\n",
              "250.00       74\n",
              "          ...  \n",
              "515.00        1\n",
              "397.18        1\n",
              "304.16        1\n",
              "487.50        1\n",
              "187.66        1\n",
              "Name: count, Length: 1768, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare['income'].describe()  # 이상치 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yG1aFY4e8dtS",
        "outputId": "13e3bd04-680b-40e8-85f3-665530e31436"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4634.000000\n",
              "mean      241.619016\n",
              "std       184.423869\n",
              "min         0.000000\n",
              "25%       122.000000\n",
              "50%       192.500000\n",
              "75%       316.600000\n",
              "max      2400.000000\n",
              "Name: income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 이상치 결측 처리 - 0\n",
        "welfare['income'] = np.where(welfare['income'] == 0, np.nan, welfare['income'])"
      ],
      "metadata": {
        "id": "A1V9dALL8jEX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['income'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uD0S9ntIBDHW",
        "outputId": "32cdf6c8-40b5-4563-95b9-76b96dc97647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12050"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['income'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESSYvW4zDIEi",
        "outputId": "88d74c65-a723-4771-d3e8-e53accdc8c94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "income\n",
              "NaN       12050\n",
              "20.00       136\n",
              "150.00      128\n",
              "200.00       91\n",
              "250.00       74\n",
              "          ...  \n",
              "515.00        1\n",
              "397.18        1\n",
              "304.16        1\n",
              "487.50        1\n",
              "187.66        1\n",
              "Name: count, Length: 1767, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "3adad7d1",
        "outputId": "e5c13146-be48-42a2-93ea-8fd3f69fd1ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(x = 'income', data = welfare)  # 히스토그램 만들기\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평균 월급 천만원 이내에 많이 모여 있으므로 자세히 보기 위해 축 범위 변경\n",
        "sns.histplot(x = 'income', data = welfare).set(xlim = (0,1000))  # 히스토그램 만들기\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "kyDHnsBN7P1_",
        "outputId": "8faa735e-f8ad-4776-d041-eaa535b21989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f29b769b"
      },
      "source": [
        "### 성별에 따른 월급 차이 분석하기"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. 성별 월급 평균표 만들기"
      ],
      "metadata": {
        "id": "iL1rlO-a4tCk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.info()"
      ],
      "metadata": {
        "id": "G2_uqyXCDUTc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eeddd148-f1aa-483f-fb16-6b4aa10f5550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16670 entries, 0 to 16669\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   sex             16669 non-null  object \n",
            " 1   birth_year      16670 non-null  int64  \n",
            " 2   marital_status  16670 non-null  int64  \n",
            " 3   religion        16670 non-null  int64  \n",
            " 4   job_code        7530 non-null   float64\n",
            " 5   income          4620 non-null   float64\n",
            " 6   region_code     16670 non-null  int64  \n",
            "dtypes: float64(2), int64(4), object(1)\n",
            "memory usage: 911.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.dropna(subset = ['sex','income'])"
      ],
      "metadata": {
        "id": "CP5c1Ox9Dgge",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "69a450fe-11c7-4d56-89b6-15b18199121c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          sex  birth_year  marital_status  religion  job_code  income  \\\n",
              "2        male        1948               2         2     942.0  120.00   \n",
              "3        male        1942               3         1     762.0  200.00   \n",
              "18       male        1962               5         2     999.0   40.00   \n",
              "19       male        1978               1         2     312.0  350.00   \n",
              "20     female        1975               1         2     254.0  200.00   \n",
              "...       ...         ...             ...       ...       ...     ...   \n",
              "16639    male        1956               3         1     873.0  162.00   \n",
              "16642    male        1966               1         1     873.0  188.00   \n",
              "16643  female        1962               1         1     899.0   90.00   \n",
              "16654    male        1956               1         2     743.0  187.66   \n",
              "16660  female        1992               5         1     314.0  302.50   \n",
              "\n",
              "       region_code  \n",
              "2                1  \n",
              "3                1  \n",
              "18               1  \n",
              "19               1  \n",
              "20               1  \n",
              "...            ...  \n",
              "16639            6  \n",
              "16642            6  \n",
              "16643            6  \n",
              "16654            6  \n",
              "16660            5  \n",
              "\n",
              "[4620 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>birth_year</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>religion</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>male</td>\n",
              "      <td>1948</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>942.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>male</td>\n",
              "      <td>1942</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>762.0</td>\n",
              "      <td>200.00</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>male</td>\n",
              "      <td>1962</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>999.0</td>\n",
              "      <td>40.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>male</td>\n",
              "      <td>1978</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>312.0</td>\n",
              "      <td>350.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>female</td>\n",
              "      <td>1975</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>254.0</td>\n",
              "      <td>200.00</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>16639</th>\n",
              "      <td>male</td>\n",
              "      <td>1956</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>873.0</td>\n",
              "      <td>162.00</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16642</th>\n",
              "      <td>male</td>\n",
              "      <td>1966</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>873.0</td>\n",
              "      <td>188.00</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16643</th>\n",
              "      <td>female</td>\n",
              "      <td>1962</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>899.0</td>\n",
              "      <td>90.00</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16654</th>\n",
              "      <td>male</td>\n",
              "      <td>1956</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>743.0</td>\n",
              "      <td>187.66</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16660</th>\n",
              "      <td>female</td>\n",
              "      <td>1992</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>314.0</td>\n",
              "      <td>302.50</td>\n",
              "      <td>5</td>\n",
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              "<p>4620 rows × 7 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"welfare\",\n  \"rows\": 4620,\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\": \"birth_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1927,\n        \"max\": 1996,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          1947,\n          1948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital_status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"religion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 290.24178923636566,\n        \"min\": 111.0,\n        \"max\": 1012.0,\n        \"num_unique_values\": 142,\n        \"samples\": [\n          742.0,\n          313.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 184.22206728611204,\n        \"min\": 0.46,\n        \"max\": 2400.0,\n        \"num_unique_values\": 1766,\n        \"samples\": [\n          405.5,\n          93.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 하나씩 해보자!\n",
        "sex_income_test = welfare.dropna(subset = ['sex','income'])"
      ],
      "metadata": {
        "id": "RG3ZFJ0MB3Zb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sex_income_test.sex.value_counts(dropna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JIWIcJZCH_6",
        "outputId": "b511bf66-b77d-4558-dbf2-a15162d04fc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "male      2452\n",
              "female    2168\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sex_income_test.income.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGTXpqeFCgPn",
        "outputId": "b5c2a647-e920-44b3-9a57-68b25fbe9a51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    4620.000000\n",
              "mean      242.351195\n",
              "std       184.222067\n",
              "min         0.460000\n",
              "25%       123.000000\n",
              "50%       193.000000\n",
              "75%       316.700000\n",
              "max      2400.000000\n",
              "Name: income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sex_income_test.income.value_counts(dropna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlSLWS-bD2xx",
        "outputId": "2cd83760-3f8b-4bbc-f389-2e7287209a00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "income\n",
              "20.00     136\n",
              "150.00    128\n",
              "200.00     91\n",
              "250.00     74\n",
              "120.00     71\n",
              "         ... \n",
              "515.00      1\n",
              "397.18      1\n",
              "304.16      1\n",
              "487.50      1\n",
              "187.66      1\n",
              "Name: count, Length: 1766, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# as_index = True => 그룹 이름을 행 인덱스로 사용\n",
        "grouped = sex_income_test.groupby('sex')\n",
        "print(grouped.agg({'income': 'mean'}))\n",
        "print(grouped.agg({'income': ['mean','std']}))\n",
        "grouped.agg(mean_income = ('income', 'mean'), sd_income = ('income', 'std')) # 튜플 형태"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "BFPibRb4D6PW",
        "outputId": "5a7dd236-6b36-4373-f9b1-8dc551af2150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            income\n",
            "sex               \n",
            "female  163.247085\n",
            "male    312.293165\n",
            "            income            \n",
            "              mean         std\n",
            "sex                           \n",
            "female  163.247085  126.053981\n",
            "male    312.293165  198.694969\n"
          ]
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              "        mean_income   sd_income\n",
              "sex                            \n",
              "female   163.247085  126.053981\n",
              "male     312.293165  198.694969"
            ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"grouped\",\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\": \"mean_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.39149380133622,\n        \"min\": 163.2470848708487,\n        \"max\": 312.2931647634584,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          312.2931647634584,\n          163.2470848708487\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sd_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.36493582411064,\n        \"min\": 126.05398053661833,\n        \"max\": 198.69496940949924,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          198.69496940949924,\n          126.05398053661833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# as_index = False => 그룹 이름을 행 인덱스로 사용하지 않게 설정\n",
        "grouped = sex_income_test.groupby('sex', as_index = False)\n",
        "print(grouped.agg({'income': 'mean'}))\n",
        "print(grouped.agg({'income': ['mean','std']}))\n",
        "grouped.agg(mean_income = ('income', 'mean'), sd_income = ('income', 'std')) # 튜플 형태"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "4_b2mhaAHPQM",
        "outputId": "b5d06b8d-a75d-44f6-9036-c018ca18a7cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      sex      income\n",
            "0  female  163.247085\n",
            "1    male  312.293165\n",
            "      sex      income            \n",
            "                 mean         std\n",
            "0  female  163.247085  126.053981\n",
            "1    male  312.293165  198.694969\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex  mean_income   sd_income\n",
              "0  female   163.247085  126.053981\n",
              "1    male   312.293165  198.694969"
            ],
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              "    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-c05edb14-4eb6-4879-8748-316a7d1b1a56 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\": 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\": \"mean_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.39149380133622,\n        \"min\": 163.2470848708487,\n        \"max\": 312.2931647634584,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          312.2931647634584,\n          163.2470848708487\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sd_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.36493582411064,\n        \"min\": 126.05398053661833,\n        \"max\": 198.69496940949924,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          198.69496940949924,\n          126.05398053661833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dfad869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "2c381ae5-0e62-457b-e20e-e613b4c52ac0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex  mean_income\n",
              "0  female   163.247085\n",
              "1    male   312.293165"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba5e188e-8fba-4937-b5ca-e58d922fa6d5\" 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>mean_income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>female</td>\n",
              "      <td>163.247085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>male</td>\n",
              "      <td>312.293165</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-ba5e188e-8fba-4937-b5ca-e58d922fa6d5')\"\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-ba5e188e-8fba-4937-b5ca-e58d922fa6d5 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-ba5e188e-8fba-4937-b5ca-e58d922fa6d5');\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-b5b167eb-9190-4150-920d-8ec3e1e97646\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5b167eb-9190-4150-920d-8ec3e1e97646')\"\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-b5b167eb-9190-4150-920d-8ec3e1e97646 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ec3d2947-447d-476d-a35c-b0f6d83ad450\">\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('sex_income')\"\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_ec3d2947-447d-476d-a35c-b0f6d83ad450 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sex_income');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sex_income",
              "summary": "{\n  \"name\": \"sex_income\",\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\": \"mean_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.39149380133622,\n        \"min\": 163.2470848708487,\n        \"max\": 312.2931647634584,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          312.2931647634584,\n          163.2470848708487\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 219
        }
      ],
      "source": [
        "# 하나씩 한 내용 종합하여 한번에 처리\n",
        "\n",
        "# 성별 월급 평균표 만들기\n",
        "# 그룹 이름을 행 인덱스로 사용하지 않게 설정\n",
        "sex_income = welfare.dropna(subset = ['sex','income']). \\\n",
        "                     groupby('sex', as_index = False). \\\n",
        "                     agg(mean_income = ('income', 'mean'))\n",
        "sex_income\n",
        "# income 결측치 제거\n",
        "# sex별 분리\n",
        "# income 평균 구하기"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sex_income_diff = sex_income.iloc[1,1] - sex_income.iloc[0,1]\n",
        "sex_income_diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBaUWTgdIvBu",
        "outputId": "a761cee8-b294-4a77-f6c3-b87b8fe3c050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "149.04607989260967"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a09e2e54"
      },
      "source": [
        "#### 2. 그래프 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "10f5d3f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "e6886fe4-753b-46b2-bbae-305833ce1982"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 막대 그래프 만들기\n",
        "sns.barplot(x = 'sex', y = 'mean_income', data = sex_income)\n",
        "plt.show()\n",
        "# 추가 분석 진행 - 독립표본 t검정 & 유의확률"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 막대 그래프 만들기\n",
        "sns.barplot(x = 'sex', y = 'mean_income', data = sex_income)\n",
        "for i, j in enumerate(sex_income[\"mean_income\"]):\n",
        "  plt.annotate(round(j), (i, j), xytext = (0, 2), textcoords = 'offset points', fontsize = 8, ha = 'center', color = 'black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8sO9AQ3Fa2B1",
        "outputId": "9c436a1c-1965-43bf-dec2-ba3582b65138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 중간 데이터 저장"
      ],
      "metadata": {
        "id": "4-OxoGZY3ShK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.to_csv('/content/drive/MyDrive/lsda02/data/welfare_2015_0408.csv', index = False)"
      ],
      "metadata": {
        "id": "C39LfYkb3Uxz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 불러오기\n",
        "welfare = pd.read_csv('/content/drive/MyDrive/lsda02/data/welfare_2015_0408.csv')"
      ],
      "metadata": {
        "id": "_9hiZHFr3reH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f86fab2e"
      },
      "source": [
        "## 2 나이와 월급의 관계 - '몇 살 때 월급을 가장 많이 받을까?'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 나이 변수 검토 및 전처리하기"
      ],
      "metadata": {
        "id": "v5uQtOcO4wsU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. 변수 검토하기"
      ],
      "metadata": {
        "id": "rK9IK99Z4yxh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "861c3bf4",
        "outputId": "f411c0be-8f01-431a-b49e-df063517d6da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ],
      "source": [
        "welfare['birth_year'].dtypes  # 변수 타입 출력"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7604c8ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb2b70d3-8c8f-4f34-b22d-b0918f17c483"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16670.000000\n",
              "mean      1968.047391\n",
              "std         66.738241\n",
              "min       1907.000000\n",
              "25%       1946.000000\n",
              "50%       1966.000000\n",
              "75%       1988.000000\n",
              "max       9999.000000\n",
              "Name: birth_year, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ],
      "source": [
        "welfare['birth_year'].describe()  # 요약 통계량 구하기"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['birth_year'].value_counts(dropna = False)"
      ],
      "metadata": {
        "id": "m2_-UxcfEjfD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2709e68d-ca69-4e1d-b9a2-c42f33f8de5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "birth_year\n",
              "1942    354\n",
              "1939    300\n",
              "1940    294\n",
              "1938    279\n",
              "1947    269\n",
              "       ... \n",
              "1914      1\n",
              "1911      1\n",
              "1917      1\n",
              "1915      1\n",
              "9999      1\n",
              "Name: count, Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "d79f323e",
        "outputId": "05f99fcc-b783-4c09-8547-73b05d29c41f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(x = 'birth_year', data = welfare).set(xlim = (1900,2020))  # 히스토그램 만들기\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "549fd2df"
      },
      "source": [
        "#### 2. 전처리"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "531b1090",
        "outputId": "2bcbfd31-4b8f-4b6d-c4af-a257eb81860c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16670.000000\n",
              "mean      1968.047391\n",
              "std         66.738241\n",
              "min       1907.000000\n",
              "25%       1946.000000\n",
              "50%       1966.000000\n",
              "75%       1988.000000\n",
              "max       9999.000000\n",
              "Name: birth_year, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ],
      "source": [
        "welfare['birth_year'].describe()  # 이상치 확인\n",
        "# 9999 => 코드북 상으로 모름/무응답(미상)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eb63165b",
        "outputId": "3604feec-8606-455a-b576-d7c2ca36a849",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ],
      "source": [
        "welfare['birth_year'].isnull().sum()  # 결측치 확인"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['birth_year'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "On6nVpRIE60S",
        "outputId": "374acdbe-ff87-4cab-9240-3501457fd607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "birth_year\n",
              "1942    354\n",
              "1939    300\n",
              "1940    294\n",
              "1938    279\n",
              "1947    269\n",
              "       ... \n",
              "1914      1\n",
              "1911      1\n",
              "1917      1\n",
              "1915      1\n",
              "9999      1\n",
              "Name: count, Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "87acb792"
      },
      "outputs": [],
      "source": [
        "# 이상치 결측 처리\n",
        "welfare['birth_year'] = np.where(welfare['birth_year'] == 9999, np.nan, welfare['birth_year'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['birth_year'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6Q10dPVTbAf",
        "outputId": "7d1aca5f-4cfa-4fcb-b7e9-4bcb9b851298"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['birth_year'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "834JFdZuFC3G",
        "outputId": "ba3863a5-53fe-4c5a-dd33-401d6ac5e403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "birth_year\n",
              "1942.0    354\n",
              "1939.0    300\n",
              "1940.0    294\n",
              "1938.0    279\n",
              "1947.0    269\n",
              "         ... \n",
              "1914.0      1\n",
              "1911.0      1\n",
              "1917.0      1\n",
              "1915.0      1\n",
              "NaN         1\n",
              "Name: count, Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare['birth_year'].describe()  # 이상치 확인"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMUAI-zCUBvE",
        "outputId": "26bb357a-0354-429c-817e-2c28fbded6b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16669.000000\n",
              "mean      1967.565601\n",
              "std         24.177789\n",
              "min       1907.000000\n",
              "25%       1946.000000\n",
              "50%       1966.000000\n",
              "75%       1988.000000\n",
              "max       2014.000000\n",
              "Name: birth_year, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "757ff7ce"
      },
      "source": [
        "#### 3. 파생변수 만들기 - 나이"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare['age'] = 2015 - welfare['birth_year'] + 1"
      ],
      "metadata": {
        "id": "i1DVisCqf8cy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.head()"
      ],
      "metadata": {
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        "id": "iFWc4FhBTsbv",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex  birth_year  marital_status  religion  job_code  income  \\\n",
              "0  female      1936.0               2         2       NaN     NaN   \n",
              "1  female      1945.0               2         2       NaN     NaN   \n",
              "2    male      1948.0               2         2     942.0   120.0   \n",
              "3    male      1942.0               3         1     762.0   200.0   \n",
              "4  female      1923.0               2         1       NaN     NaN   \n",
              "\n",
              "   region_code   age  \n",
              "0            1  80.0  \n",
              "1            1  71.0  \n",
              "2            1  68.0  \n",
              "3            1  74.0  \n",
              "4            1  93.0  "
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "welfare",
              "summary": "{\n  \"name\": \"welfare\",\n  \"rows\": 16670,\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\": \"birth_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.17778909850143,\n        \"min\": 1907.0,\n        \"max\": 2014.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          1939.0,\n          1988.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital_status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"religion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 277.80442675969294,\n        \"min\": 111.0,\n        \"max\": 9999.0,\n        \"num_unique_values\": 146,\n        \"samples\": [\n          243.0,\n          855.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 184.22206728611204,\n        \"min\": 0.46,\n        \"max\": 2400.0,\n        \"num_unique_values\": 1766,\n        \"samples\": [\n          405.5,\n          93.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.17778909850143,\n        \"min\": 2.0,\n        \"max\": 109.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          77.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['age'].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unThOWUmU0c0",
        "outputId": "035c27b0-d4c4-4681-bf15-49ff158d85d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['age'].value_counts(dropna = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGKy4PyzFWl9",
        "outputId": "6f82052e-953e-4ff7-89d6-c8512b171304"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age\n",
              "74.0     354\n",
              "77.0     300\n",
              "76.0     294\n",
              "78.0     279\n",
              "69.0     269\n",
              "        ... \n",
              "102.0      1\n",
              "105.0      1\n",
              "99.0       1\n",
              "101.0      1\n",
              "NaN        1\n",
              "Name: count, Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare['age'].describe() # 요약 통계량 구하기"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PiGddkf5TisT",
        "outputId": "a1c44bd3-b439-442f-a53c-36519a7b05bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16669.000000\n",
              "mean        48.434399\n",
              "std         24.177789\n",
              "min          2.000000\n",
              "25%         28.000000\n",
              "50%         50.000000\n",
              "75%         70.000000\n",
              "max        109.000000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "9d09266a",
        "outputId": "2cd8dc8c-0a51-4255-a3b1-b69af0d8acd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(x = 'age', data = welfare)  # 히스토그램 만들기\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "834ef6ee"
      },
      "source": [
        "### 나이와 월급의 관계 분석하기"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. 나이에 따른 월급 평균표 만들기"
      ],
      "metadata": {
        "id": "XPH8O1Xs4K-9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clIQsyfZF8b5",
        "outputId": "fd871fe1-9adb-4990-8c16-5115d5d67294"
      },
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16670 entries, 0 to 16669\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   sex             16669 non-null  object \n",
            " 1   birth_year      16669 non-null  float64\n",
            " 2   marital_status  16670 non-null  int64  \n",
            " 3   religion        16670 non-null  int64  \n",
            " 4   job_code        7530 non-null   float64\n",
            " 5   income          4620 non-null   float64\n",
            " 6   region_code     16670 non-null  int64  \n",
            " 7   age             16669 non-null  float64\n",
            "dtypes: float64(4), int64(3), object(1)\n",
            "memory usage: 1.0+ MB\n"
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              "    age  mean_income\n",
              "0  20.0   121.300000\n",
              "1  21.0   105.518519\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "age_income",
              "summary": "{\n  \"name\": \"age_income\",\n  \"rows\": 69,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.135887469748468,\n        \"min\": 20.0,\n        \"max\": 89.0,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          42.0,\n          20.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.90163903945384,\n        \"min\": 15.0,\n        \"max\": 318.6777477477478,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          72.06888888888889,\n          20.0,\n          121.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 243
        }
      ],
      "source": [
        "# 나이별 월급 평균표 만들기\n",
        "age_income = welfare.dropna(subset = ['age','income']) \\\n",
        "                    .groupby('age', as_index = False) \\\n",
        "                    .agg(mean_income = ('income', 'mean'))\n",
        "# 결측치 제거\n",
        "# age별 분리\n",
        "# income 평균 구하기\n",
        "age_income.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평균 월급 최대값 나이 확인\n",
        "max_age_income = age_income.mean_income.max()\n",
        "print(max_age_income)\n",
        "age_income[age_income['mean_income'] == max_age_income]\n",
        "# age_income[age_income.mean_income == age_income.mean_income.max()]"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 99
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        "id": "gd4tJUfeVLYs",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "318.6777477477478\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  mean_income\n",
              "33  53.0   318.677748"
            ],
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              "        const element = document.querySelector('#df-54c26d91-7a35-420c-9614-f41b395bcefc');\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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              "type": "dataframe",
              "summary": "{\n  \"name\": \"# age_income[age_income\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 53.0,\n        \"max\": 53.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          53.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 318.6777477477478,\n        \"max\": 318.6777477477478,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          318.6777477477478\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_age_income_index = np.argmax(age_income['mean_income'])\n",
        "print(max_age_income_index)\n",
        "age_income.iloc[max_age_income_index,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OpVBz7teWHyw",
        "outputId": "dcd9f1b3-e4a6-438c-b075-532eb07db11e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age             53.000000\n",
              "mean_income    318.677748\n",
              "Name: 33, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f2e5974"
      },
      "source": [
        "#### 2.  그래프 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "id": "8ba9470c",
        "outputId": "805b20ad-eeeb-42eb-a1e0-0568e9efc182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 선 그래프 만들기\n",
        "sns.lineplot(x = 'age', y = 'mean_income', data = age_income)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 중간 데이터 저장"
      ],
      "metadata": {
        "id": "8ni3TpDpPxfy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.to_csv('/content/drive/MyDrive/lsda02/data/welfare_2015_0408.csv', index = False)"
      ],
      "metadata": {
        "id": "RT88aQXmPxfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 불러오기\n",
        "welfare = pd.read_csv('/content/drive/MyDrive/lsda02/data/welfare_2015_0408.csv')"
      ],
      "metadata": {
        "id": "kMrkN8gcPxfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5dbddcd"
      },
      "source": [
        "## 3 연령대에 따른 월급 차이 - 어떤 연령대의 월급이 가장 많을까?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 연령대 변수 검토 및 전처리하기"
      ],
      "metadata": {
        "id": "-lWyeAgm5Qlu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 파생변수 만들기 - 연령대"
      ],
      "metadata": {
        "id": "kndeZqE55SW8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "434111bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b970a5a4-4e40-4f7f-fc17-c670c6ca5806"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    80.0\n",
              "1    71.0\n",
              "2    68.0\n",
              "3    74.0\n",
              "4    93.0\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ],
      "source": [
        "# 나이 변수 살펴보기\n",
        "welfare['age'].head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "welfare['age'].describe() # 요약 통계량 구하기"
      ],
      "metadata": {
        "id": "SvSxFlbxYWeu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "242b5e74-91da-42c7-e1a8-1c4797ac2f93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16669.000000\n",
              "mean        48.434399\n",
              "std         24.177789\n",
              "min          2.000000\n",
              "25%         28.000000\n",
              "50%         50.000000\n",
              "75%         70.000000\n",
              "max        109.000000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['age'].isnull().sum()"
      ],
      "metadata": {
        "id": "DlteBpkAYWe0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b48945e3-f130-4663-ce15-d9745ad1fc5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['age'].value_counts(dropna = False)"
      ],
      "metadata": {
        "id": "L1n05G5oGbyB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edccb758-d0b3-4588-b380-9af58d3ff72b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age\n",
              "74.0     354\n",
              "77.0     300\n",
              "76.0     294\n",
              "78.0     279\n",
              "69.0     269\n",
              "        ... \n",
              "102.0      1\n",
              "105.0      1\n",
              "99.0       1\n",
              "101.0      1\n",
              "NaN        1\n",
              "Name: count, Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "14d84e59"
      },
      "outputs": [],
      "source": [
        "# 연령대 변수 만들기\n",
        "# 60 세 이상 = old\n",
        "# 30 ~ 59 세 = middle\n",
        "# ~ 29 세 = young\n",
        "welfare['age_group'] = np.where(welfare['age'].isnull() == True, np.nan,\n",
        "                                np.where(welfare['age'] >=  60, 'old',\n",
        "                                         np.where(welfare['age'] >= 30, 'middle', 'young')))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['age_group'].value_counts(dropna = False)"
      ],
      "metadata": {
        "id": "leQnfd1aYDrc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6d850d3-e5fd-45b6-9bb4-8b5b8c77af32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age_group\n",
              "old       6283\n",
              "middle    6052\n",
              "young     4334\n",
              "nan          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['age_group'].isnull().sum()"
      ],
      "metadata": {
        "id": "HVW5zFbYaZY2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24712c39-e36b-41ff-b683-6c3fbbe77c1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 문자열 nan 을 NaN 으로 변환\n",
        "welfare['age_group'] = np.where(welfare['age_group'] == 'nan', np.nan, welfare['age_group'])"
      ],
      "metadata": {
        "id": "uznJaNl0fl8I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "welfare['age_group'].isnull().sum()"
      ],
      "metadata": {
        "id": "gufNcqxHh-3t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c38a200-a0f6-43aa-cd3f-5dee3f7f0e38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 빈도 구하기\n",
        "welfare['age_group'].value_counts(dropna = False)"
      ],
      "metadata": {
        "id": "PjkXf_9DG6yr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af6ce4a3-b9fc-4f8b-ec48-5b6c189d036d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age_group\n",
              "old       6283\n",
              "middle    6052\n",
              "young     4334\n",
              "NaN          1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "name": "p_ageg",
        "id": "17ab826c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "b53082c4-146d-4512-b661-7d8cb2f10fa7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 빈도 막대 그래프 만들기\n",
        "sns.countplot(x = 'age_group', data = welfare)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31542b89"
      },
      "source": [
        "### 연령대에 따른 월급 차이 분석하기"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. 연령대별 월급 평균표 만들기"
      ],
      "metadata": {
        "id": "s9qDY4pg4BaD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.info()"
      ],
      "metadata": {
        "id": "k3KMKHVtH4em",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fd029ec-4c0e-45eb-d510-62a2e30ec453"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16670 entries, 0 to 16669\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   sex             16669 non-null  object \n",
            " 1   birth_year      16669 non-null  float64\n",
            " 2   marital_status  16670 non-null  int64  \n",
            " 3   religion        16670 non-null  int64  \n",
            " 4   job_code        7530 non-null   float64\n",
            " 5   income          4620 non-null   float64\n",
            " 6   region_code     16670 non-null  int64  \n",
            " 7   age             16669 non-null  float64\n",
            " 8   age_group       16669 non-null  object \n",
            "dtypes: float64(4), int64(3), object(2)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1a48caa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "39ffe569-764d-469f-8025-40cf163ebad9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age_group  mean_income\n",
              "0    middle   281.887100\n",
              "1       old   125.329463\n",
              "2     young   163.595331"
            ],
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              "      --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-f91a4fa8-e186-4675-9b38-29c2cae57447 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7b03b16e-1b4f-4bfb-9992-d1f73884ba75\">\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('age_group_income')\"\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_7b03b16e-1b4f-4bfb-9992-d1f73884ba75 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('age_group_income');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "age_group_income",
              "summary": "{\n  \"name\": \"age_group_income\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"age_group\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"middle\",\n          \"old\",\n          \"young\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.61649467481386,\n        \"min\": 125.3294631710362,\n        \"max\": 281.8871004497751,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          281.8871004497751,\n          125.3294631710362,\n          163.5953305785124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 261
        }
      ],
      "source": [
        "# 연령대별 월급 평균표 만들기\n",
        "age_group_income = welfare.dropna(subset = ['age_group','income']) \\\n",
        "                     .groupby('age_group', as_index = False) \\\n",
        "                     .agg(mean_income = ('income', 'mean'))\n",
        "# 결측치 제거\n",
        "# age_group 별 분리\n",
        "# income 평균 구하기\n",
        "age_group_income"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "504ee27a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0131e3af-0b9c-41df-ce93-f518776c18cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "156.55763727873892\n",
            "118.29176987126272\n",
            "38.26586740747621\n"
          ]
        }
      ],
      "source": [
        "# middle 과 old 의 평균 월급 차이\n",
        "print(age_group_income.iloc[0,1] - age_group_income.iloc[1,1])\n",
        "# middle 과 young 의 평균 월급 차이\n",
        "print(age_group_income.iloc[0,1] - age_group_income.iloc[2,1])\n",
        "# young 과 old 의 평균 월급 차이\n",
        "print(age_group_income.iloc[2,1] - age_group_income.iloc[1,1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99b63226"
      },
      "source": [
        "#### 2. 그래프 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "name": "p_ageg_income",
        "id": "6bd6ae9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "bb6076e5-830b-44eb-8bed-2206333e87e4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 막대 그래프 만들기\n",
        "sns.barplot(x = 'age_group', y = 'mean_income', data = age_group_income)\n",
        "plt.show()\n",
        "# 추가 분석 진행 - 단순분산분석 & 사후검정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "eval": false,
        "name": "p_ageg_income_reorder",
        "id": "1c6e69b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "5da26211-cb77-42ed-8922-96142c8ed20b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 막대 정렬하기\n",
        "sns.barplot(x = 'age_group', y = 'mean_income',\n",
        "            data = age_group_income, order = ['young', 'middle', 'old'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 중간 데이터 저장"
      ],
      "metadata": {
        "id": "XKZD63qpKmqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "welfare.to_csv('/content/drive/MyDrive/lsda02/data/welfare_2015_0408.csv', index = False)"
      ],
      "metadata": {
        "id": "02K_7FOLKmqc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 불러오기\n",
        "welfare = pd.read_csv('/content/drive/MyDrive/lsda02/data/welfare_2015_0408.csv')"
      ],
      "metadata": {
        "id": "jXWrsh0cKmqc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Tf7ESedEPZTo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}