공모전_인구감소_그래프.ipynb

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      "authorship_tag": "ABX9TyOkpyPUHxMzdRXL87aY0lEL",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/2millionwon/Education_Data_Competition/blob/main/%EA%B3%B5%EB%AA%A8%EC%A0%84_%EC%9D%B8%EA%B5%AC%EA%B0%90%EC%86%8C_%EA%B7%B8%EB%9E%98%ED%94%84.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6EagNOS3b8E"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/시군구_출생아수__합계출산율_20240525130020.csv\")\n",
        "df"
      ],
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          "height": 1000
        },
        "id": "ofpjW7S33hu2",
        "outputId": "350d412c-74c7-4039-d46e-30f34b8833a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       시군구별    2015 2015.1    2016 2016.1    2017 2017.1    2018 2018.1  \\\n",
              "0      시군구별    출생아수  합계출산율    출생아수  합계출산율    출생아수  합계출산율    출생아수  합계출산율   \n",
              "1        전국  438420  1.239  406243  1.172  357771  1.052  326822  0.977   \n",
              "2     서울특별시   83005  1.001   75536  0.940   65389  0.836   58074  0.761   \n",
              "3     부산광역시   26645  1.139   24906  1.095   21480  0.976   19152  0.899   \n",
              "4     대구광역시   19438  1.216   18298  1.186   15946  1.067   14400  0.987   \n",
              "5     인천광역시   25491  1.216   23609  1.144   20445  1.007   20087  1.006   \n",
              "6     광주광역시   12441  1.207   11580  1.168   10120  1.053    9105  0.972   \n",
              "7     대전광역시   13774  1.277   12436  1.192   10851  1.075    9337  0.952   \n",
              "8     울산광역시   11732  1.486   10910  1.418    9381  1.261    8149  1.131   \n",
              "9   세종특별자치시    2708  1.893    3297  1.821    3504  1.668    3703  1.566   \n",
              "10      경기도  113495  1.272  105643  1.194   94088  1.069   88175  1.002   \n",
              "11  강원특별자치도   10929  1.311   10058  1.237    8958  1.123    8351  1.067   \n",
              "12     충청북도   13563  1.414   12742  1.358   11394  1.235   10586  1.172   \n",
              "13     충청남도   18604  1.480   17302  1.395   15670  1.276   14380  1.186   \n",
              "14     전라북도   14087  1.352   12698  1.251   11348  1.151   10001  1.044   \n",
              "15     전라남도   15061  1.549   13980  1.466   12354  1.325   11238  1.240   \n",
              "16     경상북도   22310  1.464   20616  1.396   17957  1.256   16079  1.167   \n",
              "17     경상남도   29537  1.437   27138  1.358   23849  1.227   21224  1.122   \n",
              "18  제주특별자치도    5600  1.477    5494  1.432    5037  1.305    4781  1.220   \n",
              "\n",
              "      2019 2019.1    2020 2020.1    2021 2021.1    2022 2022.1 2023 p)  \\\n",
              "0     출생아수  합계출산율    출생아수  합계출산율    출생아수  합계출산율    출생아수  합계출산율    출생아수   \n",
              "1   302676  0.918  272337  0.837  260562  0.808  249186  0.778  230000   \n",
              "2    53673  0.717   47445  0.642   45531  0.626   42602  0.593   39400   \n",
              "3    17049  0.827   15058  0.747   14446  0.728   14134  0.723   12900   \n",
              "4    13233  0.932   11193  0.807   10661  0.785   10134  0.757    9400   \n",
              "5    18522  0.940   16040  0.829   14947  0.778   14464  0.747   13700   \n",
              "6     8364  0.913    7318  0.811    7956  0.896    7446  0.844    6200   \n",
              "7     8410  0.883    7481  0.805    7414  0.810    7677  0.842    7200   \n",
              "8     7539  1.084    6617  0.984    6127  0.940    5399  0.848    5100   \n",
              "9     3819  1.472    3468  1.277    3570  1.277    3209  1.121    2800   \n",
              "10   83198  0.943   77737  0.878   76139  0.853   75323  0.839   68800   \n",
              "11    8283  1.082    7835  1.036    7357  0.979    7278  0.968    6700   \n",
              "12    9333  1.050    8607  0.983    8190  0.949    7452  0.871    7600   \n",
              "13   13228  1.112   11950  1.029   10984  0.963   10221  0.909    9400   \n",
              "14    8971  0.971    8165  0.909    7475  0.850    7032  0.817    6600   \n",
              "15   10832  1.234    9738  1.145    8430  1.017    7888  0.969    7800   \n",
              "16   14472  1.089   12873  1.003   12045  0.966   11311  0.930   10200   \n",
              "17   19250  1.046   16823  0.945   15562  0.903   14017  0.838   13100   \n",
              "18    4500  1.145    3989  1.021    3728  0.951    3599  0.919    3200   \n",
              "\n",
              "   2023 p).1  \n",
              "0      합계출산율  \n",
              "1      0.720  \n",
              "2      0.550  \n",
              "3      0.660  \n",
              "4      0.700  \n",
              "5      0.690  \n",
              "6      0.710  \n",
              "7      0.790  \n",
              "8      0.810  \n",
              "9      0.970  \n",
              "10     0.770  \n",
              "11     0.890  \n",
              "12     0.890  \n",
              "13     0.840  \n",
              "14     0.780  \n",
              "15     0.970  \n",
              "16     0.860  \n",
              "17     0.800  \n",
              "18     0.830  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>시군구별</th>\n",
              "      <th>2015</th>\n",
              "      <th>2015.1</th>\n",
              "      <th>2016</th>\n",
              "      <th>2016.1</th>\n",
              "      <th>2017</th>\n",
              "      <th>2017.1</th>\n",
              "      <th>2018</th>\n",
              "      <th>2018.1</th>\n",
              "      <th>2019</th>\n",
              "      <th>2019.1</th>\n",
              "      <th>2020</th>\n",
              "      <th>2020.1</th>\n",
              "      <th>2021</th>\n",
              "      <th>2021.1</th>\n",
              "      <th>2022</th>\n",
              "      <th>2022.1</th>\n",
              "      <th>2023 p)</th>\n",
              "      <th>2023 p).1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>시군구별</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>출생아수</td>\n",
              "      <td>합계출산율</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>전국</td>\n",
              "      <td>438420</td>\n",
              "      <td>1.239</td>\n",
              "      <td>406243</td>\n",
              "      <td>1.172</td>\n",
              "      <td>357771</td>\n",
              "      <td>1.052</td>\n",
              "      <td>326822</td>\n",
              "      <td>0.977</td>\n",
              "      <td>302676</td>\n",
              "      <td>0.918</td>\n",
              "      <td>272337</td>\n",
              "      <td>0.837</td>\n",
              "      <td>260562</td>\n",
              "      <td>0.808</td>\n",
              "      <td>249186</td>\n",
              "      <td>0.778</td>\n",
              "      <td>230000</td>\n",
              "      <td>0.720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>서울특별시</td>\n",
              "      <td>83005</td>\n",
              "      <td>1.001</td>\n",
              "      <td>75536</td>\n",
              "      <td>0.940</td>\n",
              "      <td>65389</td>\n",
              "      <td>0.836</td>\n",
              "      <td>58074</td>\n",
              "      <td>0.761</td>\n",
              "      <td>53673</td>\n",
              "      <td>0.717</td>\n",
              "      <td>47445</td>\n",
              "      <td>0.642</td>\n",
              "      <td>45531</td>\n",
              "      <td>0.626</td>\n",
              "      <td>42602</td>\n",
              "      <td>0.593</td>\n",
              "      <td>39400</td>\n",
              "      <td>0.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>부산광역시</td>\n",
              "      <td>26645</td>\n",
              "      <td>1.139</td>\n",
              "      <td>24906</td>\n",
              "      <td>1.095</td>\n",
              "      <td>21480</td>\n",
              "      <td>0.976</td>\n",
              "      <td>19152</td>\n",
              "      <td>0.899</td>\n",
              "      <td>17049</td>\n",
              "      <td>0.827</td>\n",
              "      <td>15058</td>\n",
              "      <td>0.747</td>\n",
              "      <td>14446</td>\n",
              "      <td>0.728</td>\n",
              "      <td>14134</td>\n",
              "      <td>0.723</td>\n",
              "      <td>12900</td>\n",
              "      <td>0.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>대구광역시</td>\n",
              "      <td>19438</td>\n",
              "      <td>1.216</td>\n",
              "      <td>18298</td>\n",
              "      <td>1.186</td>\n",
              "      <td>15946</td>\n",
              "      <td>1.067</td>\n",
              "      <td>14400</td>\n",
              "      <td>0.987</td>\n",
              "      <td>13233</td>\n",
              "      <td>0.932</td>\n",
              "      <td>11193</td>\n",
              "      <td>0.807</td>\n",
              "      <td>10661</td>\n",
              "      <td>0.785</td>\n",
              "      <td>10134</td>\n",
              "      <td>0.757</td>\n",
              "      <td>9400</td>\n",
              "      <td>0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>인천광역시</td>\n",
              "      <td>25491</td>\n",
              "      <td>1.216</td>\n",
              "      <td>23609</td>\n",
              "      <td>1.144</td>\n",
              "      <td>20445</td>\n",
              "      <td>1.007</td>\n",
              "      <td>20087</td>\n",
              "      <td>1.006</td>\n",
              "      <td>18522</td>\n",
              "      <td>0.940</td>\n",
              "      <td>16040</td>\n",
              "      <td>0.829</td>\n",
              "      <td>14947</td>\n",
              "      <td>0.778</td>\n",
              "      <td>14464</td>\n",
              "      <td>0.747</td>\n",
              "      <td>13700</td>\n",
              "      <td>0.690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>광주광역시</td>\n",
              "      <td>12441</td>\n",
              "      <td>1.207</td>\n",
              "      <td>11580</td>\n",
              "      <td>1.168</td>\n",
              "      <td>10120</td>\n",
              "      <td>1.053</td>\n",
              "      <td>9105</td>\n",
              "      <td>0.972</td>\n",
              "      <td>8364</td>\n",
              "      <td>0.913</td>\n",
              "      <td>7318</td>\n",
              "      <td>0.811</td>\n",
              "      <td>7956</td>\n",
              "      <td>0.896</td>\n",
              "      <td>7446</td>\n",
              "      <td>0.844</td>\n",
              "      <td>6200</td>\n",
              "      <td>0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>대전광역시</td>\n",
              "      <td>13774</td>\n",
              "      <td>1.277</td>\n",
              "      <td>12436</td>\n",
              "      <td>1.192</td>\n",
              "      <td>10851</td>\n",
              "      <td>1.075</td>\n",
              "      <td>9337</td>\n",
              "      <td>0.952</td>\n",
              "      <td>8410</td>\n",
              "      <td>0.883</td>\n",
              "      <td>7481</td>\n",
              "      <td>0.805</td>\n",
              "      <td>7414</td>\n",
              "      <td>0.810</td>\n",
              "      <td>7677</td>\n",
              "      <td>0.842</td>\n",
              "      <td>7200</td>\n",
              "      <td>0.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>울산광역시</td>\n",
              "      <td>11732</td>\n",
              "      <td>1.486</td>\n",
              "      <td>10910</td>\n",
              "      <td>1.418</td>\n",
              "      <td>9381</td>\n",
              "      <td>1.261</td>\n",
              "      <td>8149</td>\n",
              "      <td>1.131</td>\n",
              "      <td>7539</td>\n",
              "      <td>1.084</td>\n",
              "      <td>6617</td>\n",
              "      <td>0.984</td>\n",
              "      <td>6127</td>\n",
              "      <td>0.940</td>\n",
              "      <td>5399</td>\n",
              "      <td>0.848</td>\n",
              "      <td>5100</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>세종특별자치시</td>\n",
              "      <td>2708</td>\n",
              "      <td>1.893</td>\n",
              "      <td>3297</td>\n",
              "      <td>1.821</td>\n",
              "      <td>3504</td>\n",
              "      <td>1.668</td>\n",
              "      <td>3703</td>\n",
              "      <td>1.566</td>\n",
              "      <td>3819</td>\n",
              "      <td>1.472</td>\n",
              "      <td>3468</td>\n",
              "      <td>1.277</td>\n",
              "      <td>3570</td>\n",
              "      <td>1.277</td>\n",
              "      <td>3209</td>\n",
              "      <td>1.121</td>\n",
              "      <td>2800</td>\n",
              "      <td>0.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>경기도</td>\n",
              "      <td>113495</td>\n",
              "      <td>1.272</td>\n",
              "      <td>105643</td>\n",
              "      <td>1.194</td>\n",
              "      <td>94088</td>\n",
              "      <td>1.069</td>\n",
              "      <td>88175</td>\n",
              "      <td>1.002</td>\n",
              "      <td>83198</td>\n",
              "      <td>0.943</td>\n",
              "      <td>77737</td>\n",
              "      <td>0.878</td>\n",
              "      <td>76139</td>\n",
              "      <td>0.853</td>\n",
              "      <td>75323</td>\n",
              "      <td>0.839</td>\n",
              "      <td>68800</td>\n",
              "      <td>0.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>강원특별자치도</td>\n",
              "      <td>10929</td>\n",
              "      <td>1.311</td>\n",
              "      <td>10058</td>\n",
              "      <td>1.237</td>\n",
              "      <td>8958</td>\n",
              "      <td>1.123</td>\n",
              "      <td>8351</td>\n",
              "      <td>1.067</td>\n",
              "      <td>8283</td>\n",
              "      <td>1.082</td>\n",
              "      <td>7835</td>\n",
              "      <td>1.036</td>\n",
              "      <td>7357</td>\n",
              "      <td>0.979</td>\n",
              "      <td>7278</td>\n",
              "      <td>0.968</td>\n",
              "      <td>6700</td>\n",
              "      <td>0.890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>충청북도</td>\n",
              "      <td>13563</td>\n",
              "      <td>1.414</td>\n",
              "      <td>12742</td>\n",
              "      <td>1.358</td>\n",
              "      <td>11394</td>\n",
              "      <td>1.235</td>\n",
              "      <td>10586</td>\n",
              "      <td>1.172</td>\n",
              "      <td>9333</td>\n",
              "      <td>1.050</td>\n",
              "      <td>8607</td>\n",
              "      <td>0.983</td>\n",
              "      <td>8190</td>\n",
              "      <td>0.949</td>\n",
              "      <td>7452</td>\n",
              "      <td>0.871</td>\n",
              "      <td>7600</td>\n",
              "      <td>0.890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>충청남도</td>\n",
              "      <td>18604</td>\n",
              "      <td>1.480</td>\n",
              "      <td>17302</td>\n",
              "      <td>1.395</td>\n",
              "      <td>15670</td>\n",
              "      <td>1.276</td>\n",
              "      <td>14380</td>\n",
              "      <td>1.186</td>\n",
              "      <td>13228</td>\n",
              "      <td>1.112</td>\n",
              "      <td>11950</td>\n",
              "      <td>1.029</td>\n",
              "      <td>10984</td>\n",
              "      <td>0.963</td>\n",
              "      <td>10221</td>\n",
              "      <td>0.909</td>\n",
              "      <td>9400</td>\n",
              "      <td>0.840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>전라북도</td>\n",
              "      <td>14087</td>\n",
              "      <td>1.352</td>\n",
              "      <td>12698</td>\n",
              "      <td>1.251</td>\n",
              "      <td>11348</td>\n",
              "      <td>1.151</td>\n",
              "      <td>10001</td>\n",
              "      <td>1.044</td>\n",
              "      <td>8971</td>\n",
              "      <td>0.971</td>\n",
              "      <td>8165</td>\n",
              "      <td>0.909</td>\n",
              "      <td>7475</td>\n",
              "      <td>0.850</td>\n",
              "      <td>7032</td>\n",
              "      <td>0.817</td>\n",
              "      <td>6600</td>\n",
              "      <td>0.780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>전라남도</td>\n",
              "      <td>15061</td>\n",
              "      <td>1.549</td>\n",
              "      <td>13980</td>\n",
              "      <td>1.466</td>\n",
              "      <td>12354</td>\n",
              "      <td>1.325</td>\n",
              "      <td>11238</td>\n",
              "      <td>1.240</td>\n",
              "      <td>10832</td>\n",
              "      <td>1.234</td>\n",
              "      <td>9738</td>\n",
              "      <td>1.145</td>\n",
              "      <td>8430</td>\n",
              "      <td>1.017</td>\n",
              "      <td>7888</td>\n",
              "      <td>0.969</td>\n",
              "      <td>7800</td>\n",
              "      <td>0.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>경상북도</td>\n",
              "      <td>22310</td>\n",
              "      <td>1.464</td>\n",
              "      <td>20616</td>\n",
              "      <td>1.396</td>\n",
              "      <td>17957</td>\n",
              "      <td>1.256</td>\n",
              "      <td>16079</td>\n",
              "      <td>1.167</td>\n",
              "      <td>14472</td>\n",
              "      <td>1.089</td>\n",
              "      <td>12873</td>\n",
              "      <td>1.003</td>\n",
              "      <td>12045</td>\n",
              "      <td>0.966</td>\n",
              "      <td>11311</td>\n",
              "      <td>0.930</td>\n",
              "      <td>10200</td>\n",
              "      <td>0.860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>경상남도</td>\n",
              "      <td>29537</td>\n",
              "      <td>1.437</td>\n",
              "      <td>27138</td>\n",
              "      <td>1.358</td>\n",
              "      <td>23849</td>\n",
              "      <td>1.227</td>\n",
              "      <td>21224</td>\n",
              "      <td>1.122</td>\n",
              "      <td>19250</td>\n",
              "      <td>1.046</td>\n",
              "      <td>16823</td>\n",
              "      <td>0.945</td>\n",
              "      <td>15562</td>\n",
              "      <td>0.903</td>\n",
              "      <td>14017</td>\n",
              "      <td>0.838</td>\n",
              "      <td>13100</td>\n",
              "      <td>0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>제주특별자치도</td>\n",
              "      <td>5600</td>\n",
              "      <td>1.477</td>\n",
              "      <td>5494</td>\n",
              "      <td>1.432</td>\n",
              "      <td>5037</td>\n",
              "      <td>1.305</td>\n",
              "      <td>4781</td>\n",
              "      <td>1.220</td>\n",
              "      <td>4500</td>\n",
              "      <td>1.145</td>\n",
              "      <td>3989</td>\n",
              "      <td>1.021</td>\n",
              "      <td>3728</td>\n",
              "      <td>0.951</td>\n",
              "      <td>3599</td>\n",
              "      <td>0.919</td>\n",
              "      <td>3200</td>\n",
              "      <td>0.830</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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{\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ucd9c\\uc0dd\\uc544\\uc218\",\n          \"16040\",\n          \"7835\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.829\",\n          \"1.036\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ucd9c\\uc0dd\\uc544\\uc218\",\n          \"14947\",\n          \"7357\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.778\",\n          \"0.979\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ucd9c\\uc0dd\\uc544\\uc218\",\n          \"14464\",\n          \"7278\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.747\",\n          \"0.968\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2023 p)\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\ucd9c\\uc0dd\\uc544\\uc218\",\n          \"230000\",\n          \"5100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2023 p).1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.720\",\n          \"0.690\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['시군구별','2015.1','2016.1','2017.1','2018.1','2019.1','2020.1','2021.1','2022.1','2023 p).1']]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "wDfNxo_z3kwf",
        "outputId": "b2e9b2b3-a07b-4f6d-a2c0-fe7749ac4710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       시군구별 2015.1 2016.1 2017.1 2018.1 2019.1 2020.1 2021.1 2022.1 2023 p).1\n",
              "0      시군구별  합계출산율  합계출산율  합계출산율  합계출산율  합계출산율  합계출산율  합계출산율  합계출산율     합계출산율\n",
              "1        전국  1.239  1.172  1.052  0.977  0.918  0.837  0.808  0.778     0.720\n",
              "2     서울특별시  1.001  0.940  0.836  0.761  0.717  0.642  0.626  0.593     0.550\n",
              "3     부산광역시  1.139  1.095  0.976  0.899  0.827  0.747  0.728  0.723     0.660\n",
              "4     대구광역시  1.216  1.186  1.067  0.987  0.932  0.807  0.785  0.757     0.700\n",
              "5     인천광역시  1.216  1.144  1.007  1.006  0.940  0.829  0.778  0.747     0.690\n",
              "6     광주광역시  1.207  1.168  1.053  0.972  0.913  0.811  0.896  0.844     0.710\n",
              "7     대전광역시  1.277  1.192  1.075  0.952  0.883  0.805  0.810  0.842     0.790\n",
              "8     울산광역시  1.486  1.418  1.261  1.131  1.084  0.984  0.940  0.848     0.810\n",
              "9   세종특별자치시  1.893  1.821  1.668  1.566  1.472  1.277  1.277  1.121     0.970\n",
              "10      경기도  1.272  1.194  1.069  1.002  0.943  0.878  0.853  0.839     0.770\n",
              "11  강원특별자치도  1.311  1.237  1.123  1.067  1.082  1.036  0.979  0.968     0.890\n",
              "12     충청북도  1.414  1.358  1.235  1.172  1.050  0.983  0.949  0.871     0.890\n",
              "13     충청남도  1.480  1.395  1.276  1.186  1.112  1.029  0.963  0.909     0.840\n",
              "14     전라북도  1.352  1.251  1.151  1.044  0.971  0.909  0.850  0.817     0.780\n",
              "15     전라남도  1.549  1.466  1.325  1.240  1.234  1.145  1.017  0.969     0.970\n",
              "16     경상북도  1.464  1.396  1.256  1.167  1.089  1.003  0.966  0.930     0.860\n",
              "17     경상남도  1.437  1.358  1.227  1.122  1.046  0.945  0.903  0.838     0.800\n",
              "18  제주특별자치도  1.477  1.432  1.305  1.220  1.145  1.021  0.951  0.919     0.830"
            ],
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              "\n",
              "  <div id=\"df-5d7ba719-a898-4f6f-ac79-ff95a075a911\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>시군구별</th>\n",
              "      <th>2015.1</th>\n",
              "      <th>2016.1</th>\n",
              "      <th>2017.1</th>\n",
              "      <th>2018.1</th>\n",
              "      <th>2019.1</th>\n",
              "      <th>2020.1</th>\n",
              "      <th>2021.1</th>\n",
              "      <th>2022.1</th>\n",
              "      <th>2023 p).1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>시군구별</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "      <td>합계출산율</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>전국</td>\n",
              "      <td>1.239</td>\n",
              "      <td>1.172</td>\n",
              "      <td>1.052</td>\n",
              "      <td>0.977</td>\n",
              "      <td>0.918</td>\n",
              "      <td>0.837</td>\n",
              "      <td>0.808</td>\n",
              "      <td>0.778</td>\n",
              "      <td>0.720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>서울특별시</td>\n",
              "      <td>1.001</td>\n",
              "      <td>0.940</td>\n",
              "      <td>0.836</td>\n",
              "      <td>0.761</td>\n",
              "      <td>0.717</td>\n",
              "      <td>0.642</td>\n",
              "      <td>0.626</td>\n",
              "      <td>0.593</td>\n",
              "      <td>0.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>부산광역시</td>\n",
              "      <td>1.139</td>\n",
              "      <td>1.095</td>\n",
              "      <td>0.976</td>\n",
              "      <td>0.899</td>\n",
              "      <td>0.827</td>\n",
              "      <td>0.747</td>\n",
              "      <td>0.728</td>\n",
              "      <td>0.723</td>\n",
              "      <td>0.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>대구광역시</td>\n",
              "      <td>1.216</td>\n",
              "      <td>1.186</td>\n",
              "      <td>1.067</td>\n",
              "      <td>0.987</td>\n",
              "      <td>0.932</td>\n",
              "      <td>0.807</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.757</td>\n",
              "      <td>0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>인천광역시</td>\n",
              "      <td>1.216</td>\n",
              "      <td>1.144</td>\n",
              "      <td>1.007</td>\n",
              "      <td>1.006</td>\n",
              "      <td>0.940</td>\n",
              "      <td>0.829</td>\n",
              "      <td>0.778</td>\n",
              "      <td>0.747</td>\n",
              "      <td>0.690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>광주광역시</td>\n",
              "      <td>1.207</td>\n",
              "      <td>1.168</td>\n",
              "      <td>1.053</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.913</td>\n",
              "      <td>0.811</td>\n",
              "      <td>0.896</td>\n",
              "      <td>0.844</td>\n",
              "      <td>0.710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>대전광역시</td>\n",
              "      <td>1.277</td>\n",
              "      <td>1.192</td>\n",
              "      <td>1.075</td>\n",
              "      <td>0.952</td>\n",
              "      <td>0.883</td>\n",
              "      <td>0.805</td>\n",
              "      <td>0.810</td>\n",
              "      <td>0.842</td>\n",
              "      <td>0.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>울산광역시</td>\n",
              "      <td>1.486</td>\n",
              "      <td>1.418</td>\n",
              "      <td>1.261</td>\n",
              "      <td>1.131</td>\n",
              "      <td>1.084</td>\n",
              "      <td>0.984</td>\n",
              "      <td>0.940</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>세종특별자치시</td>\n",
              "      <td>1.893</td>\n",
              "      <td>1.821</td>\n",
              "      <td>1.668</td>\n",
              "      <td>1.566</td>\n",
              "      <td>1.472</td>\n",
              "      <td>1.277</td>\n",
              "      <td>1.277</td>\n",
              "      <td>1.121</td>\n",
              "      <td>0.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>경기도</td>\n",
              "      <td>1.272</td>\n",
              "      <td>1.194</td>\n",
              "      <td>1.069</td>\n",
              "      <td>1.002</td>\n",
              "      <td>0.943</td>\n",
              "      <td>0.878</td>\n",
              "      <td>0.853</td>\n",
              "      <td>0.839</td>\n",
              "      <td>0.770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>강원특별자치도</td>\n",
              "      <td>1.311</td>\n",
              "      <td>1.237</td>\n",
              "      <td>1.123</td>\n",
              "      <td>1.067</td>\n",
              "      <td>1.082</td>\n",
              "      <td>1.036</td>\n",
              "      <td>0.979</td>\n",
              "      <td>0.968</td>\n",
              "      <td>0.890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>충청북도</td>\n",
              "      <td>1.414</td>\n",
              "      <td>1.358</td>\n",
              "      <td>1.235</td>\n",
              "      <td>1.172</td>\n",
              "      <td>1.050</td>\n",
              "      <td>0.983</td>\n",
              "      <td>0.949</td>\n",
              "      <td>0.871</td>\n",
              "      <td>0.890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>충청남도</td>\n",
              "      <td>1.480</td>\n",
              "      <td>1.395</td>\n",
              "      <td>1.276</td>\n",
              "      <td>1.186</td>\n",
              "      <td>1.112</td>\n",
              "      <td>1.029</td>\n",
              "      <td>0.963</td>\n",
              "      <td>0.909</td>\n",
              "      <td>0.840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>전라북도</td>\n",
              "      <td>1.352</td>\n",
              "      <td>1.251</td>\n",
              "      <td>1.151</td>\n",
              "      <td>1.044</td>\n",
              "      <td>0.971</td>\n",
              "      <td>0.909</td>\n",
              "      <td>0.850</td>\n",
              "      <td>0.817</td>\n",
              "      <td>0.780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>전라남도</td>\n",
              "      <td>1.549</td>\n",
              "      <td>1.466</td>\n",
              "      <td>1.325</td>\n",
              "      <td>1.240</td>\n",
              "      <td>1.234</td>\n",
              "      <td>1.145</td>\n",
              "      <td>1.017</td>\n",
              "      <td>0.969</td>\n",
              "      <td>0.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>경상북도</td>\n",
              "      <td>1.464</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.256</td>\n",
              "      <td>1.167</td>\n",
              "      <td>1.089</td>\n",
              "      <td>1.003</td>\n",
              "      <td>0.966</td>\n",
              "      <td>0.930</td>\n",
              "      <td>0.860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>경상남도</td>\n",
              "      <td>1.437</td>\n",
              "      <td>1.358</td>\n",
              "      <td>1.227</td>\n",
              "      <td>1.122</td>\n",
              "      <td>1.046</td>\n",
              "      <td>0.945</td>\n",
              "      <td>0.903</td>\n",
              "      <td>0.838</td>\n",
              "      <td>0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>제주특별자치도</td>\n",
              "      <td>1.477</td>\n",
              "      <td>1.432</td>\n",
              "      <td>1.305</td>\n",
              "      <td>1.220</td>\n",
              "      <td>1.145</td>\n",
              "      <td>1.021</td>\n",
              "      <td>0.951</td>\n",
              "      <td>0.919</td>\n",
              "      <td>0.830</td>\n",
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              "    .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",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5d7ba719-a898-4f6f-ac79-ff95a075a911');\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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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    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",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  }\n",
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              "\n",
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              "    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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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
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              "\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": \"\\uc2dc\\uad70\\uad6c\\ubcc4\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\uc2dc\\uad70\\uad6c\\ubcc4\",\n          \"\\uc778\\ucc9c\\uad11\\uc5ed\\uc2dc\",\n          \"\\uac15\\uc6d0\\ud2b9\\ubcc4\\uc790\\uce58\\ub3c4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2015.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"1.239\",\n          \"1.893\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2016.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"1.172\",\n          \"1.418\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2017.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"1.007\",\n          \"1.123\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2018.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"1.006\",\n          \"1.067\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2019.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.940\",\n          \"1.082\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.829\",\n          \"1.036\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.778\",\n          \"0.979\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022.1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.747\",\n          \"0.968\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2023 p).1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"\\ud569\\uacc4\\ucd9c\\uc0b0\\uc728\",\n          \"0.720\",\n          \"0.690\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install fonts-nanum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7HaVUDR8-TyG",
        "outputId": "fd42fe3f-4f07-4cf8-b5d5-a89335827b9a"
      },
      "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 (17.2 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 ... 121918 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import font_manager, rc\n",
        "\n",
        "# 예제 시계열 데이터 생성\n",
        "data = {\n",
        "    'Year': [2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022,2023],\n",
        "    'Value': [1.24, 1.17, 1.05, 0.98, 0.92, 0.84, 0.81, 0.78,0.72]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# 독립 변수와 종속 변수 설정\n",
        "X = df[['Year']]  # 독립 변수\n",
        "y = df['Value']   # 종속 변수\n",
        "\n",
        "# 선형 회귀 모델 생성 및 학습\n",
        "model = LinearRegression()\n",
        "model.fit(X, y)\n",
        "\n",
        "# 회귀 계수와 절편 출력\n",
        "print(f\"회귀 계수: {model.coef_[0]}\")\n",
        "print(f\"절편: {model.intercept_}\")\n",
        "\n",
        "# 예측 값 생성\n",
        "y_pred = model.predict(X)\n",
        "\n",
        "# 2024년 추가 데이터 예측\n",
        "future_years = np.array([[2024]])\n",
        "future_pred = model.predict(future_years)\n",
        "\n",
        "# 시각화\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(df['Year'], df['Value'], color='blue', label='Actual Value')\n",
        "plt.plot(df['Year'], y_pred, color='red', marker='o', linestyle='-', label='Predicted Value')\n",
        "\n",
        "# 2024년 예측 값 추가\n",
        "plt.plot(future_years, future_pred, color='green', marker='o', linestyle='-', label='2024 Predicted Value')\n",
        "plt.scatter(future_years, future_pred, color='green', s=100)\n",
        "\n",
        "# 각 막대 위에 값 표시\n",
        "for i, v in enumerate(df['Value']):\n",
        "    plt.text(df['Year'][i], v + 0.05, f'{v:.2f}',fontsize=8, ha='center')\n",
        "\n",
        "# 2024년 예측 값 텍스트로 표시\n",
        "for i, txt in enumerate(future_pred):\n",
        "    plt.text(future_years[i], future_pred[i]+ 0.03, f'{txt:.2f}', fontsize=12, color='green', ha='center')\n",
        "\n",
        "plt.xticks(df['Year'])\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Count')\n",
        "plt.title(\"Total Fertility Rate (TFR)\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# 2024년 예측 값 출력\n",
        "print(f\"2024년 예측 값: {future_pred[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "72_RLkBh4ihm",
        "outputId": "8a4e4b5d-58a4-48f7-fedd-82f11aec0b80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "회귀 계수: -0.06450000000000002\n",
            "절편: 131.1710555555556\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n",
            "WARNING:matplotlib.font_manager:findfont: Font family 'NanumGothic' not found.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024년 예측 값: 0.6230555555555668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CyA81gFt_Qd_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}