ASPAC_시각화.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kimbob123/SNU_FinTech/blob/main/ASPAC_%EC%8B%9C%EA%B0%81%ED%99%94.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "metadata": {
        "id": "t1z5LYqWTrg9"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/SNU Big Data Fintec/data_from_image.csv')\n",
        "df"
      ],
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          "base_uri": "https://localhost:8080/",
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        "outputId": "b2fe2b88-4af1-45a6-c2a2-cc87d8fcf111"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Contry     2018     2019     2020     2021     2022  YoY (21/22)  \\\n",
              "0         India   595.00   636.30   689.20   741.70   822.10         10.8   \n",
              "1     Australia    69.70    67.90    68.70    79.10    83.50          5.5   \n",
              "2     Indonesia   186.80   202.90   208.50   230.50   243.90          5.8   \n",
              "3     Hong Kong    26.10    26.40    27.60    28.90    29.90          3.4   \n",
              "4     Singapore    10.60    10.80    10.90    11.00    11.40          3.8   \n",
              "5         Japan   581.10   584.80   604.00   621.00   626.10          0.8   \n",
              "6          Laos     4.81     4.89     5.18     5.39     5.87          9.0   \n",
              "7      Cambodia     5.90     6.40     6.70     7.10     7.60          5.7   \n",
              "8      Malaysia    42.80    44.20    45.10    50.50    53.90          6.8   \n",
              "9      Mongolia     3.23     3.40     3.62     3.91     4.63         18.2   \n",
              "10  Philippines    87.20    93.10   103.50   113.70   123.90          9.0   \n",
              "11     Thailand    52.50    57.30    56.30    60.30    64.00          6.1   \n",
              "12      Vietnam    64.90    70.30    76.50    81.70    87.70          7.4   \n",
              "13       Taiwan    42.40    42.20    45.30    49.40    51.50          4.1   \n",
              "14        Korea   401.00   412.00   466.00   505.00   558.00         10.5   \n",
              "15        China   966.20  1028.00  1158.00  1291.00  1387.00          7.4   \n",
              "16        Total  3140.24  3290.89  3575.10  3880.20  4161.00          NaN   \n",
              "17          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "18          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "19          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "20          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "21          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "22          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "23          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "24          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "25          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "26          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "27          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "28          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "29          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "30          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "31          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "32          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "33          NaN      NaN      NaN      NaN      NaN      NaN          NaN   \n",
              "\n",
              "    CAGR (18/22)  Unnamed: 8  Unnamed: 9  Unnamed: 10  \n",
              "0            8.4         NaN         NaN          NaN  \n",
              "1            4.6         NaN         NaN          NaN  \n",
              "2            6.9         NaN         NaN          NaN  \n",
              "3            3.5         NaN         NaN          NaN  \n",
              "4            1.8         NaN         NaN          NaN  \n",
              "5            1.9         NaN         NaN          NaN  \n",
              "6            5.1         NaN         NaN          NaN  \n",
              "7            6.4         NaN         NaN          NaN  \n",
              "8            5.9         NaN         NaN          NaN  \n",
              "9            9.4         NaN         NaN          NaN  \n",
              "10           9.2         NaN         NaN          NaN  \n",
              "11           5.1         NaN         NaN          NaN  \n",
              "12           7.9         NaN         NaN          NaN  \n",
              "13           4.9         NaN         NaN          NaN  \n",
              "14           8.6         NaN         NaN          NaN  \n",
              "15           9.5         NaN         NaN          NaN  \n",
              "16           NaN         NaN         NaN          NaN  \n",
              "17           NaN         NaN         NaN          NaN  \n",
              "18           NaN         NaN         NaN          NaN  \n",
              "19           NaN         NaN         NaN          NaN  \n",
              "20           NaN         NaN         NaN          NaN  \n",
              "21           NaN         NaN         NaN          NaN  \n",
              "22           NaN         NaN         NaN          NaN  \n",
              "23           NaN         NaN         NaN          NaN  \n",
              "24           NaN         NaN         NaN          NaN  \n",
              "25           NaN         NaN         NaN          NaN  \n",
              "26           NaN         NaN         NaN          NaN  \n",
              "27           NaN         NaN         NaN          NaN  \n",
              "28           NaN         NaN         NaN          NaN  \n",
              "29           NaN         NaN         NaN          NaN  \n",
              "30           NaN         NaN         NaN          NaN  \n",
              "31           NaN         NaN         NaN          NaN  \n",
              "32           NaN         NaN         NaN          NaN  \n",
              "33           NaN         NaN         NaN          NaN  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Contry</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>YoY (21/22)</th>\n",
              "      <th>CAGR (18/22)</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "      <th>Unnamed: 9</th>\n",
              "      <th>Unnamed: 10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>595.00</td>\n",
              "      <td>636.30</td>\n",
              "      <td>689.20</td>\n",
              "      <td>741.70</td>\n",
              "      <td>822.10</td>\n",
              "      <td>10.8</td>\n",
              "      <td>8.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Australia</td>\n",
              "      <td>69.70</td>\n",
              "      <td>67.90</td>\n",
              "      <td>68.70</td>\n",
              "      <td>79.10</td>\n",
              "      <td>83.50</td>\n",
              "      <td>5.5</td>\n",
              "      <td>4.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>186.80</td>\n",
              "      <td>202.90</td>\n",
              "      <td>208.50</td>\n",
              "      <td>230.50</td>\n",
              "      <td>243.90</td>\n",
              "      <td>5.8</td>\n",
              "      <td>6.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>26.10</td>\n",
              "      <td>26.40</td>\n",
              "      <td>27.60</td>\n",
              "      <td>28.90</td>\n",
              "      <td>29.90</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.40</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Japan</td>\n",
              "      <td>581.10</td>\n",
              "      <td>584.80</td>\n",
              "      <td>604.00</td>\n",
              "      <td>621.00</td>\n",
              "      <td>626.10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Laos</td>\n",
              "      <td>4.81</td>\n",
              "      <td>4.89</td>\n",
              "      <td>5.18</td>\n",
              "      <td>5.39</td>\n",
              "      <td>5.87</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>5.90</td>\n",
              "      <td>6.40</td>\n",
              "      <td>6.70</td>\n",
              "      <td>7.10</td>\n",
              "      <td>7.60</td>\n",
              "      <td>5.7</td>\n",
              "      <td>6.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>42.80</td>\n",
              "      <td>44.20</td>\n",
              "      <td>45.10</td>\n",
              "      <td>50.50</td>\n",
              "      <td>53.90</td>\n",
              "      <td>6.8</td>\n",
              "      <td>5.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.40</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.91</td>\n",
              "      <td>4.63</td>\n",
              "      <td>18.2</td>\n",
              "      <td>9.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>87.20</td>\n",
              "      <td>93.10</td>\n",
              "      <td>103.50</td>\n",
              "      <td>113.70</td>\n",
              "      <td>123.90</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>52.50</td>\n",
              "      <td>57.30</td>\n",
              "      <td>56.30</td>\n",
              "      <td>60.30</td>\n",
              "      <td>64.00</td>\n",
              "      <td>6.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>64.90</td>\n",
              "      <td>70.30</td>\n",
              "      <td>76.50</td>\n",
              "      <td>81.70</td>\n",
              "      <td>87.70</td>\n",
              "      <td>7.4</td>\n",
              "      <td>7.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Taiwan</td>\n",
              "      <td>42.40</td>\n",
              "      <td>42.20</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.40</td>\n",
              "      <td>51.50</td>\n",
              "      <td>4.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Korea</td>\n",
              "      <td>401.00</td>\n",
              "      <td>412.00</td>\n",
              "      <td>466.00</td>\n",
              "      <td>505.00</td>\n",
              "      <td>558.00</td>\n",
              "      <td>10.5</td>\n",
              "      <td>8.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>966.20</td>\n",
              "      <td>1028.00</td>\n",
              "      <td>1158.00</td>\n",
              "      <td>1291.00</td>\n",
              "      <td>1387.00</td>\n",
              "      <td>7.4</td>\n",
              "      <td>9.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Total</td>\n",
              "      <td>3140.24</td>\n",
              "      <td>3290.89</td>\n",
              "      <td>3575.10</td>\n",
              "      <td>3880.20</td>\n",
              "      <td>4161.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_0ad68438-1282-44b0-955a-573138d52626\">\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0ad68438-1282-44b0-955a-573138d52626 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 34,\n  \"fields\": [\n    {\n      \"column\": \"Contry\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"India\",\n          \"Australia\",\n          \"Japan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2018\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 765.9025866339008,\n        \"min\": 3.23,\n        \"max\": 3140.24,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          595.0,\n          69.7,\n          581.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2019\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 803.2132682851204,\n        \"min\": 3.4,\n        \"max\": 3290.89,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          636.3,\n          67.9,\n          584.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 874.8169864605968,\n        \"min\": 3.62,\n        \"max\": 3575.1,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          689.2,\n          68.7,\n          604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 950.6415031312988,\n        \"min\": 3.91,\n        \"max\": 3880.2,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          741.7,\n          79.1,\n          621.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1019.9060281189549,\n        \"min\": 4.63,\n        \"max\": 4161.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          822.1,\n          83.5,\n          626.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YoY (21/22)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.96988140041152,\n        \"min\": 0.8,\n        \"max\": 18.2,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          18.2,\n          7.4,\n          10.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAGR (18/22)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.530472617252885,\n        \"min\": 1.8,\n        \"max\": 9.5,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          9.4,\n          7.9,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unnamed: 8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unnamed: 9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unnamed: 10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 레코드가 있는 행만 선택합니다.\n",
        "df = df.dropna(how='all')\n",
        "\n",
        "# 레코드가 있는 열만 선택합니다.\n",
        "df = df.dropna(axis=1, how='all')\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "lWr85e1zUB5J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "f56abaad-a1de-4d21-8f2b-00269f2f9cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Contry     2018     2019     2020     2021     2022  YoY (21/22)  \\\n",
              "0         India   595.00   636.30   689.20   741.70   822.10         10.8   \n",
              "1     Australia    69.70    67.90    68.70    79.10    83.50          5.5   \n",
              "2     Indonesia   186.80   202.90   208.50   230.50   243.90          5.8   \n",
              "3     Hong Kong    26.10    26.40    27.60    28.90    29.90          3.4   \n",
              "4     Singapore    10.60    10.80    10.90    11.00    11.40          3.8   \n",
              "5         Japan   581.10   584.80   604.00   621.00   626.10          0.8   \n",
              "6          Laos     4.81     4.89     5.18     5.39     5.87          9.0   \n",
              "7      Cambodia     5.90     6.40     6.70     7.10     7.60          5.7   \n",
              "8      Malaysia    42.80    44.20    45.10    50.50    53.90          6.8   \n",
              "9      Mongolia     3.23     3.40     3.62     3.91     4.63         18.2   \n",
              "10  Philippines    87.20    93.10   103.50   113.70   123.90          9.0   \n",
              "11     Thailand    52.50    57.30    56.30    60.30    64.00          6.1   \n",
              "12      Vietnam    64.90    70.30    76.50    81.70    87.70          7.4   \n",
              "13       Taiwan    42.40    42.20    45.30    49.40    51.50          4.1   \n",
              "14        Korea   401.00   412.00   466.00   505.00   558.00         10.5   \n",
              "15        China   966.20  1028.00  1158.00  1291.00  1387.00          7.4   \n",
              "16        Total  3140.24  3290.89  3575.10  3880.20  4161.00          NaN   \n",
              "\n",
              "    CAGR (18/22)  \n",
              "0            8.4  \n",
              "1            4.6  \n",
              "2            6.9  \n",
              "3            3.5  \n",
              "4            1.8  \n",
              "5            1.9  \n",
              "6            5.1  \n",
              "7            6.4  \n",
              "8            5.9  \n",
              "9            9.4  \n",
              "10           9.2  \n",
              "11           5.1  \n",
              "12           7.9  \n",
              "13           4.9  \n",
              "14           8.6  \n",
              "15           9.5  \n",
              "16           NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca4c5581-7676-404d-be62-c4806b566f04\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Contry</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>YoY (21/22)</th>\n",
              "      <th>CAGR (18/22)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>595.00</td>\n",
              "      <td>636.30</td>\n",
              "      <td>689.20</td>\n",
              "      <td>741.70</td>\n",
              "      <td>822.10</td>\n",
              "      <td>10.8</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Australia</td>\n",
              "      <td>69.70</td>\n",
              "      <td>67.90</td>\n",
              "      <td>68.70</td>\n",
              "      <td>79.10</td>\n",
              "      <td>83.50</td>\n",
              "      <td>5.5</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>186.80</td>\n",
              "      <td>202.90</td>\n",
              "      <td>208.50</td>\n",
              "      <td>230.50</td>\n",
              "      <td>243.90</td>\n",
              "      <td>5.8</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>26.10</td>\n",
              "      <td>26.40</td>\n",
              "      <td>27.60</td>\n",
              "      <td>28.90</td>\n",
              "      <td>29.90</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.40</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Japan</td>\n",
              "      <td>581.10</td>\n",
              "      <td>584.80</td>\n",
              "      <td>604.00</td>\n",
              "      <td>621.00</td>\n",
              "      <td>626.10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Laos</td>\n",
              "      <td>4.81</td>\n",
              "      <td>4.89</td>\n",
              "      <td>5.18</td>\n",
              "      <td>5.39</td>\n",
              "      <td>5.87</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>5.90</td>\n",
              "      <td>6.40</td>\n",
              "      <td>6.70</td>\n",
              "      <td>7.10</td>\n",
              "      <td>7.60</td>\n",
              "      <td>5.7</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>42.80</td>\n",
              "      <td>44.20</td>\n",
              "      <td>45.10</td>\n",
              "      <td>50.50</td>\n",
              "      <td>53.90</td>\n",
              "      <td>6.8</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.40</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.91</td>\n",
              "      <td>4.63</td>\n",
              "      <td>18.2</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>87.20</td>\n",
              "      <td>93.10</td>\n",
              "      <td>103.50</td>\n",
              "      <td>113.70</td>\n",
              "      <td>123.90</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>52.50</td>\n",
              "      <td>57.30</td>\n",
              "      <td>56.30</td>\n",
              "      <td>60.30</td>\n",
              "      <td>64.00</td>\n",
              "      <td>6.1</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>64.90</td>\n",
              "      <td>70.30</td>\n",
              "      <td>76.50</td>\n",
              "      <td>81.70</td>\n",
              "      <td>87.70</td>\n",
              "      <td>7.4</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Taiwan</td>\n",
              "      <td>42.40</td>\n",
              "      <td>42.20</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.40</td>\n",
              "      <td>51.50</td>\n",
              "      <td>4.1</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Korea</td>\n",
              "      <td>401.00</td>\n",
              "      <td>412.00</td>\n",
              "      <td>466.00</td>\n",
              "      <td>505.00</td>\n",
              "      <td>558.00</td>\n",
              "      <td>10.5</td>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>966.20</td>\n",
              "      <td>1028.00</td>\n",
              "      <td>1158.00</td>\n",
              "      <td>1291.00</td>\n",
              "      <td>1387.00</td>\n",
              "      <td>7.4</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Total</td>\n",
              "      <td>3140.24</td>\n",
              "      <td>3290.89</td>\n",
              "      <td>3575.10</td>\n",
              "      <td>3880.20</td>\n",
              "      <td>4161.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "        google.colab.notebook.generateWithVariable('df');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": \"Contry\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"India\",\n          \"Australia\",\n          \"Japan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2018\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 765.9025866339008,\n        \"min\": 3.23,\n        \"max\": 3140.24,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          595.0,\n          69.7,\n          581.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2019\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 803.2132682851203,\n        \"min\": 3.4,\n        \"max\": 3290.89,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          636.3,\n          67.9,\n          584.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 874.8169864605968,\n        \"min\": 3.62,\n        \"max\": 3575.1,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          689.2,\n          68.7,\n          604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 950.6415031312988,\n        \"min\": 3.91,\n        \"max\": 3880.2,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          741.7,\n          79.1,\n          621.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1019.9060281189549,\n        \"min\": 4.63,\n        \"max\": 4161.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          822.1,\n          83.5,\n          626.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YoY (21/22)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.96988140041152,\n        \"min\": 0.8,\n        \"max\": 18.2,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          18.2,\n          7.4,\n          10.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAGR (18/22)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.530472617252885,\n        \"min\": 1.8,\n        \"max\": 9.5,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          9.4,\n          7.9,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWCpyxmgVv6I",
        "outputId": "c769e348-87a3-4d20-b433-c6507ec57e90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 17 entries, 0 to 16\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Contry        17 non-null     object \n",
            " 1   2018          17 non-null     float64\n",
            " 2   2019          17 non-null     float64\n",
            " 3   2020          17 non-null     float64\n",
            " 4   2021          17 non-null     float64\n",
            " 5   2022          17 non-null     float64\n",
            " 6   YoY (21/22)   16 non-null     float64\n",
            " 7   CAGR (18/22)  16 non-null     float64\n",
            "dtypes: float64(7), object(1)\n",
            "memory usage: 1.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df.drop(['YoY (21/22)','CAGR (18/22)' ], axis=1)\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "2trejOd5WgJw",
        "outputId": "e50cacf0-decf-4a58-91d2-54717e08b2c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Contry     2018     2019     2020     2021     2022\n",
              "0         India   595.00   636.30   689.20   741.70   822.10\n",
              "1     Australia    69.70    67.90    68.70    79.10    83.50\n",
              "2     Indonesia   186.80   202.90   208.50   230.50   243.90\n",
              "3     Hong Kong    26.10    26.40    27.60    28.90    29.90\n",
              "4     Singapore    10.60    10.80    10.90    11.00    11.40\n",
              "5         Japan   581.10   584.80   604.00   621.00   626.10\n",
              "6          Laos     4.81     4.89     5.18     5.39     5.87\n",
              "7      Cambodia     5.90     6.40     6.70     7.10     7.60\n",
              "8      Malaysia    42.80    44.20    45.10    50.50    53.90\n",
              "9      Mongolia     3.23     3.40     3.62     3.91     4.63\n",
              "10  Philippines    87.20    93.10   103.50   113.70   123.90\n",
              "11     Thailand    52.50    57.30    56.30    60.30    64.00\n",
              "12      Vietnam    64.90    70.30    76.50    81.70    87.70\n",
              "13       Taiwan    42.40    42.20    45.30    49.40    51.50\n",
              "14        Korea   401.00   412.00   466.00   505.00   558.00\n",
              "15        China   966.20  1028.00  1158.00  1291.00  1387.00\n",
              "16        Total  3140.24  3290.89  3575.10  3880.20  4161.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-98d691d8-0566-47a1-8f03-9c93f8d8145d\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Contry</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>595.00</td>\n",
              "      <td>636.30</td>\n",
              "      <td>689.20</td>\n",
              "      <td>741.70</td>\n",
              "      <td>822.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Australia</td>\n",
              "      <td>69.70</td>\n",
              "      <td>67.90</td>\n",
              "      <td>68.70</td>\n",
              "      <td>79.10</td>\n",
              "      <td>83.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>186.80</td>\n",
              "      <td>202.90</td>\n",
              "      <td>208.50</td>\n",
              "      <td>230.50</td>\n",
              "      <td>243.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>26.10</td>\n",
              "      <td>26.40</td>\n",
              "      <td>27.60</td>\n",
              "      <td>28.90</td>\n",
              "      <td>29.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Japan</td>\n",
              "      <td>581.10</td>\n",
              "      <td>584.80</td>\n",
              "      <td>604.00</td>\n",
              "      <td>621.00</td>\n",
              "      <td>626.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Laos</td>\n",
              "      <td>4.81</td>\n",
              "      <td>4.89</td>\n",
              "      <td>5.18</td>\n",
              "      <td>5.39</td>\n",
              "      <td>5.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>5.90</td>\n",
              "      <td>6.40</td>\n",
              "      <td>6.70</td>\n",
              "      <td>7.10</td>\n",
              "      <td>7.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>42.80</td>\n",
              "      <td>44.20</td>\n",
              "      <td>45.10</td>\n",
              "      <td>50.50</td>\n",
              "      <td>53.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.40</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.91</td>\n",
              "      <td>4.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>87.20</td>\n",
              "      <td>93.10</td>\n",
              "      <td>103.50</td>\n",
              "      <td>113.70</td>\n",
              "      <td>123.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>52.50</td>\n",
              "      <td>57.30</td>\n",
              "      <td>56.30</td>\n",
              "      <td>60.30</td>\n",
              "      <td>64.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>64.90</td>\n",
              "      <td>70.30</td>\n",
              "      <td>76.50</td>\n",
              "      <td>81.70</td>\n",
              "      <td>87.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Taiwan</td>\n",
              "      <td>42.40</td>\n",
              "      <td>42.20</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.40</td>\n",
              "      <td>51.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Korea</td>\n",
              "      <td>401.00</td>\n",
              "      <td>412.00</td>\n",
              "      <td>466.00</td>\n",
              "      <td>505.00</td>\n",
              "      <td>558.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>966.20</td>\n",
              "      <td>1028.00</td>\n",
              "      <td>1158.00</td>\n",
              "      <td>1291.00</td>\n",
              "      <td>1387.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Total</td>\n",
              "      <td>3140.24</td>\n",
              "      <td>3290.89</td>\n",
              "      <td>3575.10</td>\n",
              "      <td>3880.20</td>\n",
              "      <td>4161.00</td>\n",
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              "        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-f380df6c-5db5-44ba-b2c5-925d739b41b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4d2377bc-7070-4e05-8946-47aa63d992cf\">\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('df3')\"\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_4d2377bc-7070-4e05-8946-47aa63d992cf button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df3');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df3",
              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": \"Contry\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"India\",\n          \"Australia\",\n          \"Japan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2018\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 765.9025866339008,\n        \"min\": 3.23,\n        \"max\": 3140.24,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          595.0,\n          69.7,\n          581.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2019\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 803.2132682851203,\n        \"min\": 3.4,\n        \"max\": 3290.89,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          636.3,\n          67.9,\n          584.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 874.8169864605968,\n        \"min\": 3.62,\n        \"max\": 3575.1,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          689.2,\n          68.7,\n          604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 950.6415031312988,\n        \"min\": 3.91,\n        \"max\": 3880.2,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          741.7,\n          79.1,\n          621.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1019.9060281189549,\n        \"min\": 4.63,\n        \"max\": 4161.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          822.1,\n          83.5,\n          626.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create the data dictionary\n",
        "data = {\n",
        "    'Year': [2018, 2019, 2020, 2021, 2022],\n",
        "    'Market': [2739.00, 2879.00, 3109.00, 3375.00, 3603.00]\n",
        "}\n",
        "\n",
        "# Create the DataFrame\n",
        "data = pd.DataFrame(data)\n",
        "\n",
        "# Set the Seaborn style\n",
        "sns.set(style=\"white\")\n",
        "\n",
        "# Create the plot\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.lineplot(x='Year', y='Market', data=data, marker='o', color='darkblue',linewidth=5, markersize = 10)\n",
        "plt.title('Total Food Market Size in Major ASPAC Countries',fontsize=20)\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Market Size (in billions of dollars)')\n",
        "plt.xticks(data['Year'])  # Set X-axis labels to the exact years\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "D6ieOuGUgQIY",
        "outputId": "3e5a4df1-a422-4fa7-c0f8-3b83a9af71ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "iEE3S_yNg1D4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "9bc4b787-0549-4f18-d730-588ff733534c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Contry     2018     2019     2020     2021     2022  YoY (21/22)  \\\n",
              "0         India   595.00   636.30   689.20   741.70   822.10         10.8   \n",
              "1     Australia    69.70    67.90    68.70    79.10    83.50          5.5   \n",
              "2     Indonesia   186.80   202.90   208.50   230.50   243.90          5.8   \n",
              "3     Hong Kong    26.10    26.40    27.60    28.90    29.90          3.4   \n",
              "4     Singapore    10.60    10.80    10.90    11.00    11.40          3.8   \n",
              "5         Japan   581.10   584.80   604.00   621.00   626.10          0.8   \n",
              "6          Laos     4.81     4.89     5.18     5.39     5.87          9.0   \n",
              "7      Cambodia     5.90     6.40     6.70     7.10     7.60          5.7   \n",
              "8      Malaysia    42.80    44.20    45.10    50.50    53.90          6.8   \n",
              "9      Mongolia     3.23     3.40     3.62     3.91     4.63         18.2   \n",
              "10  Philippines    87.20    93.10   103.50   113.70   123.90          9.0   \n",
              "11     Thailand    52.50    57.30    56.30    60.30    64.00          6.1   \n",
              "12      Vietnam    64.90    70.30    76.50    81.70    87.70          7.4   \n",
              "13       Taiwan    42.40    42.20    45.30    49.40    51.50          4.1   \n",
              "14        Korea   401.00   412.00   466.00   505.00   558.00         10.5   \n",
              "15        China   966.20  1028.00  1158.00  1291.00  1387.00          7.4   \n",
              "16        Total  3140.24  3290.89  3575.10  3880.20  4161.00          NaN   \n",
              "\n",
              "    CAGR (18/22)  \n",
              "0            8.4  \n",
              "1            4.6  \n",
              "2            6.9  \n",
              "3            3.5  \n",
              "4            1.8  \n",
              "5            1.9  \n",
              "6            5.1  \n",
              "7            6.4  \n",
              "8            5.9  \n",
              "9            9.4  \n",
              "10           9.2  \n",
              "11           5.1  \n",
              "12           7.9  \n",
              "13           4.9  \n",
              "14           8.6  \n",
              "15           9.5  \n",
              "16           NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7558ab84-2409-4e99-8add-caf67295c761\" 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>Contry</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>YoY (21/22)</th>\n",
              "      <th>CAGR (18/22)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>595.00</td>\n",
              "      <td>636.30</td>\n",
              "      <td>689.20</td>\n",
              "      <td>741.70</td>\n",
              "      <td>822.10</td>\n",
              "      <td>10.8</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Australia</td>\n",
              "      <td>69.70</td>\n",
              "      <td>67.90</td>\n",
              "      <td>68.70</td>\n",
              "      <td>79.10</td>\n",
              "      <td>83.50</td>\n",
              "      <td>5.5</td>\n",
              "      <td>4.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>186.80</td>\n",
              "      <td>202.90</td>\n",
              "      <td>208.50</td>\n",
              "      <td>230.50</td>\n",
              "      <td>243.90</td>\n",
              "      <td>5.8</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>26.10</td>\n",
              "      <td>26.40</td>\n",
              "      <td>27.60</td>\n",
              "      <td>28.90</td>\n",
              "      <td>29.90</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>10.60</td>\n",
              "      <td>10.80</td>\n",
              "      <td>10.90</td>\n",
              "      <td>11.00</td>\n",
              "      <td>11.40</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Japan</td>\n",
              "      <td>581.10</td>\n",
              "      <td>584.80</td>\n",
              "      <td>604.00</td>\n",
              "      <td>621.00</td>\n",
              "      <td>626.10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Laos</td>\n",
              "      <td>4.81</td>\n",
              "      <td>4.89</td>\n",
              "      <td>5.18</td>\n",
              "      <td>5.39</td>\n",
              "      <td>5.87</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>5.90</td>\n",
              "      <td>6.40</td>\n",
              "      <td>6.70</td>\n",
              "      <td>7.10</td>\n",
              "      <td>7.60</td>\n",
              "      <td>5.7</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>42.80</td>\n",
              "      <td>44.20</td>\n",
              "      <td>45.10</td>\n",
              "      <td>50.50</td>\n",
              "      <td>53.90</td>\n",
              "      <td>6.8</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.40</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.91</td>\n",
              "      <td>4.63</td>\n",
              "      <td>18.2</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>87.20</td>\n",
              "      <td>93.10</td>\n",
              "      <td>103.50</td>\n",
              "      <td>113.70</td>\n",
              "      <td>123.90</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>52.50</td>\n",
              "      <td>57.30</td>\n",
              "      <td>56.30</td>\n",
              "      <td>60.30</td>\n",
              "      <td>64.00</td>\n",
              "      <td>6.1</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>64.90</td>\n",
              "      <td>70.30</td>\n",
              "      <td>76.50</td>\n",
              "      <td>81.70</td>\n",
              "      <td>87.70</td>\n",
              "      <td>7.4</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Taiwan</td>\n",
              "      <td>42.40</td>\n",
              "      <td>42.20</td>\n",
              "      <td>45.30</td>\n",
              "      <td>49.40</td>\n",
              "      <td>51.50</td>\n",
              "      <td>4.1</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Korea</td>\n",
              "      <td>401.00</td>\n",
              "      <td>412.00</td>\n",
              "      <td>466.00</td>\n",
              "      <td>505.00</td>\n",
              "      <td>558.00</td>\n",
              "      <td>10.5</td>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>966.20</td>\n",
              "      <td>1028.00</td>\n",
              "      <td>1158.00</td>\n",
              "      <td>1291.00</td>\n",
              "      <td>1387.00</td>\n",
              "      <td>7.4</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Total</td>\n",
              "      <td>3140.24</td>\n",
              "      <td>3290.89</td>\n",
              "      <td>3575.10</td>\n",
              "      <td>3880.20</td>\n",
              "      <td>4161.00</td>\n",
              "      <td>NaN</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": \"Contry\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"India\",\n          \"Australia\",\n          \"Japan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2018\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 765.9025866339008,\n        \"min\": 3.23,\n        \"max\": 3140.24,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          595.0,\n          69.7,\n          581.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2019\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 803.2132682851203,\n        \"min\": 3.4,\n        \"max\": 3290.89,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          636.3,\n          67.9,\n          584.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 874.8169864605968,\n        \"min\": 3.62,\n        \"max\": 3575.1,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          689.2,\n          68.7,\n          604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 950.6415031312988,\n        \"min\": 3.91,\n        \"max\": 3880.2,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          741.7,\n          79.1,\n          621.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1019.9060281189549,\n        \"min\": 4.63,\n        \"max\": 4161.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          822.1,\n          83.5,\n          626.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YoY (21/22)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.96988140041152,\n        \"min\": 0.8,\n        \"max\": 18.2,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          18.2,\n          7.4,\n          10.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAGR (18/22)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.530472617252885,\n        \"min\": 1.8,\n        \"max\": 9.5,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          9.4,\n          7.9,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_CAGR = { 'Country' : df['Contry'], 'CAGR': df['CAGR (18/22)']  }\n",
        "df_CAGR = pd.DataFrame(data_CAGR)\n",
        "df_CAGR.dropna(inplace=True)\n",
        "df_CAGR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "GR_MO6xBnYR5",
        "outputId": "afa01bb6-7042-4f9c-f071-434138e5c1cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Country  CAGR\n",
              "0         India   8.4\n",
              "1     Australia   4.6\n",
              "2     Indonesia   6.9\n",
              "3     Hong Kong   3.5\n",
              "4     Singapore   1.8\n",
              "5         Japan   1.9\n",
              "6          Laos   5.1\n",
              "7      Cambodia   6.4\n",
              "8      Malaysia   5.9\n",
              "9      Mongolia   9.4\n",
              "10  Philippines   9.2\n",
              "11     Thailand   5.1\n",
              "12      Vietnam   7.9\n",
              "13       Taiwan   4.9\n",
              "14        Korea   8.6\n",
              "15        China   9.5"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>CAGR</th>\n",
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              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>8.4</td>\n",
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              "      <th>1</th>\n",
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              "      <th>2</th>\n",
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              "      <td>6.9</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
              "      <td>Japan</td>\n",
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              "    </tr>\n",
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              "      <th>6</th>\n",
              "      <td>Laos</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>6.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>5.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>9.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Taiwan</td>\n",
              "      <td>4.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Korea</td>\n",
              "      <td>8.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>9.5</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_CAGR')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_CAGR",
              "summary": "{\n  \"name\": \"df_CAGR\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"India\",\n          \"Australia\",\n          \"Japan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAGR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.530472617252885,\n        \"min\": 1.8,\n        \"max\": 9.5,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          9.4,\n          7.9,\n          8.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Calculate the mean CAGR\n",
        "mean_CAGR = df_CAGR['CAGR'].mean()\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x='Country', y='CAGR', data=df_CAGR, palette='crest')\n",
        "plt.axhline(y=mean_CAGR, color='red', linestyle='--', label=f'Mean CAGR: {mean_CAGR:.2f}',linewidth=5)\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('CAGR (%)')\n",
        "plt.title('CAGR(18/22) in Major ASPAC Countries',fontsize=20)\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        },
        "id": "V-ByCuAqntao",
        "outputId": "b9f614ef-f9a7-451a-dcf8-9947b0dd768e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-80-1f12fbd2d56b>:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Country', y='CAGR', data=df_CAGR, palette='crest')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = pd.read_csv('/content/drive/MyDrive/SNU Big Data Fintec/ALL ASCAP.csv')\n",
        "\n",
        "totals = all_data.sum(numeric_only=True)\n",
        "\n",
        "# Add a new row for the totals\n",
        "totals_row = pd.DataFrame(totals).T\n",
        "totals_row['Country'] = 'Total'\n",
        "all_data_with_totals = pd.concat([all_data, totals_row], ignore_index=True)\n",
        "all_data_with_totals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1kLxDedMyzIu",
        "outputId": "1c9a9b20-7dd0-4c95-aa28-f6ed953a4fd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Country      2020      2021      2022      2023      2024  \\\n",
              "0              China  1482.125  1724.281  1752.615  1825.028  1918.528   \n",
              "1              Japan   410.769   400.989   349.402   358.671   379.454   \n",
              "2              India   180.565   214.435   233.587   256.123   276.754   \n",
              "3          Australia   107.063   122.098   122.533   128.986   134.109   \n",
              "4        South Korea   111.344   116.816   110.873   112.963   116.856   \n",
              "5          Indonesia    76.005    83.559    86.729    88.867    92.743   \n",
              "6        Philippines    62.457    67.356    69.024    70.529    76.184   \n",
              "7            Vietnam    62.735    64.290    73.009    72.685    75.925   \n",
              "8           Thailand    46.404    50.946    57.036    59.005    73.063   \n",
              "9             Taiwan    35.836    38.456    39.450    39.119    41.046   \n",
              "10          Pakistan    16.171    17.788    18.003    17.088    22.792   \n",
              "11         Hong Kong    22.082    23.481    23.481    24.308    25.759   \n",
              "12          Malaysia    18.964    20.384    20.321    21.760    24.215   \n",
              "13        Uzbekistan    19.127    20.043    21.741    22.206    22.787   \n",
              "14       New Zealand    20.345    22.141    22.430    22.340    23.687   \n",
              "15        Bangladesh    17.275    19.352    19.631    19.822    21.185   \n",
              "16        Kazakhstan    13.230    14.415    14.802    15.097    15.671   \n",
              "17         Singapore    12.039    12.982    13.804    14.677    15.475   \n",
              "18           Myanmar    11.222    10.977    11.037    11.112    11.712   \n",
              "19      Turkmenistan     6.587     7.360     8.372     9.126     9.993   \n",
              "20        Azerbaijan     5.998     7.382     8.024     8.246     9.241   \n",
              "21           Georgia     5.166     5.475     6.912     7.305     7.891   \n",
              "22         Sri Lanka     6.136     6.744     6.504     6.203     7.255   \n",
              "23             Nepal     4.174     4.944     5.208     5.680     5.911   \n",
              "24          Cambodia     3.414     3.857     4.597     4.909     5.088   \n",
              "25        Kyrgyzstan     3.306     3.432     4.097     4.777     4.777   \n",
              "26           Armenia     2.484     2.595     2.927     3.297     3.387   \n",
              "27       North Korea     3.670     3.728     3.844     3.844     3.961   \n",
              "28              Laos     2.342     2.418     2.522     2.541     2.642   \n",
              "29  Papua New Guinea     2.047     2.152     2.302     2.487     2.487   \n",
              "30        Tajikistan     2.025     2.288     2.529     2.562     2.672   \n",
              "31             Macau     1.572     1.748     1.850     2.019     2.102   \n",
              "32          Mongolia     1.136     1.278     1.311     1.289     1.354   \n",
              "33              Fiji     0.488     0.540     0.550     0.557     0.608   \n",
              "34        East Timor     0.256     0.272     0.298     0.325     0.350   \n",
              "35            Bhutan     0.331     0.353     0.356     0.370     0.385   \n",
              "36          Maldives     0.244     0.266     0.285     0.304     0.325   \n",
              "37         Polynesia     0.167     0.164     0.173     0.182     0.196   \n",
              "38     New Caledonia     0.134     0.154     0.148     0.167     0.184   \n",
              "39    American Samoa     0.106     0.148     0.158     0.159     0.190   \n",
              "40             Samoa     0.077     0.085     0.089     0.093     0.096   \n",
              "41        Micronesia     0.033     0.037     0.040     0.040     0.042   \n",
              "42          Kiribati     0.003     0.006     0.007     0.007     0.007   \n",
              "43  Marshall Islands     0.016     0.018     0.020     0.020     0.022   \n",
              "44             Tonga     0.014     0.016     0.018     0.018     0.019   \n",
              "45             Palau     0.011     0.013     0.014     0.014     0.014   \n",
              "46      Cook Islands     0.007     0.008     0.008     0.008     0.009   \n",
              "47              Niue     0.006     0.006     0.006     0.006     0.007   \n",
              "48             Total  2777.708  3102.276  3122.677  3246.941  3439.160   \n",
              "\n",
              "        2025      2026  \n",
              "0   2025.981  2143.819  \n",
              "1    405.344   429.824  \n",
              "2    299.535   323.634  \n",
              "3    138.822   143.949  \n",
              "4    121.309   125.819  \n",
              "5     97.049   101.422  \n",
              "6     81.479    86.408  \n",
              "7     79.757    87.017  \n",
              "8     73.063    77.316  \n",
              "9     42.135    43.344  \n",
              "10    22.792    27.384  \n",
              "11    26.617    27.512  \n",
              "12    25.236    25.326  \n",
              "13    23.646    24.903  \n",
              "14    24.465    24.903  \n",
              "15    22.611    23.985  \n",
              "16    16.290    16.808  \n",
              "17    16.216    16.968  \n",
              "18    12.328    12.817  \n",
              "19    10.809    11.468  \n",
              "20    10.241    11.168  \n",
              "21     8.447     9.027  \n",
              "22     7.712     8.123  \n",
              "23     5.925     6.614  \n",
              "24     5.222     6.089  \n",
              "25     4.767     5.432  \n",
              "26     3.790     4.151  \n",
              "27     4.048     4.211  \n",
              "28     2.742     2.979  \n",
              "29     2.642     2.950  \n",
              "30     2.805     2.927  \n",
              "31     2.176     2.248  \n",
              "32     1.428     1.501  \n",
              "33     0.668     0.688  \n",
              "34     0.377     0.406  \n",
              "35     0.400     0.415  \n",
              "36     0.348     0.374  \n",
              "37     0.206     0.219  \n",
              "38     0.196     0.199  \n",
              "39     0.157     0.159  \n",
              "40     0.159     0.104  \n",
              "41     0.042     0.045  \n",
              "42     0.007     0.008  \n",
              "43     0.022     0.022  \n",
              "44     0.019     0.021  \n",
              "45     0.014     0.014  \n",
              "46     0.009     0.009  \n",
              "47     0.007     0.008  \n",
              "48  3630.060  3844.737  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b8bd8a9-34dd-4758-911b-3806ec9286e1\" 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>Country</th>\n",
              "      <th>2020</th>\n",
              "      <th>2021</th>\n",
              "      <th>2022</th>\n",
              "      <th>2023</th>\n",
              "      <th>2024</th>\n",
              "      <th>2025</th>\n",
              "      <th>2026</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>China</td>\n",
              "      <td>1482.125</td>\n",
              "      <td>1724.281</td>\n",
              "      <td>1752.615</td>\n",
              "      <td>1825.028</td>\n",
              "      <td>1918.528</td>\n",
              "      <td>2025.981</td>\n",
              "      <td>2143.819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Japan</td>\n",
              "      <td>410.769</td>\n",
              "      <td>400.989</td>\n",
              "      <td>349.402</td>\n",
              "      <td>358.671</td>\n",
              "      <td>379.454</td>\n",
              "      <td>405.344</td>\n",
              "      <td>429.824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>India</td>\n",
              "      <td>180.565</td>\n",
              "      <td>214.435</td>\n",
              "      <td>233.587</td>\n",
              "      <td>256.123</td>\n",
              "      <td>276.754</td>\n",
              "      <td>299.535</td>\n",
              "      <td>323.634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Australia</td>\n",
              "      <td>107.063</td>\n",
              "      <td>122.098</td>\n",
              "      <td>122.533</td>\n",
              "      <td>128.986</td>\n",
              "      <td>134.109</td>\n",
              "      <td>138.822</td>\n",
              "      <td>143.949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>111.344</td>\n",
              "      <td>116.816</td>\n",
              "      <td>110.873</td>\n",
              "      <td>112.963</td>\n",
              "      <td>116.856</td>\n",
              "      <td>121.309</td>\n",
              "      <td>125.819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>76.005</td>\n",
              "      <td>83.559</td>\n",
              "      <td>86.729</td>\n",
              "      <td>88.867</td>\n",
              "      <td>92.743</td>\n",
              "      <td>97.049</td>\n",
              "      <td>101.422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Philippines</td>\n",
              "      <td>62.457</td>\n",
              "      <td>67.356</td>\n",
              "      <td>69.024</td>\n",
              "      <td>70.529</td>\n",
              "      <td>76.184</td>\n",
              "      <td>81.479</td>\n",
              "      <td>86.408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Vietnam</td>\n",
              "      <td>62.735</td>\n",
              "      <td>64.290</td>\n",
              "      <td>73.009</td>\n",
              "      <td>72.685</td>\n",
              "      <td>75.925</td>\n",
              "      <td>79.757</td>\n",
              "      <td>87.017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Thailand</td>\n",
              "      <td>46.404</td>\n",
              "      <td>50.946</td>\n",
              "      <td>57.036</td>\n",
              "      <td>59.005</td>\n",
              "      <td>73.063</td>\n",
              "      <td>73.063</td>\n",
              "      <td>77.316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Taiwan</td>\n",
              "      <td>35.836</td>\n",
              "      <td>38.456</td>\n",
              "      <td>39.450</td>\n",
              "      <td>39.119</td>\n",
              "      <td>41.046</td>\n",
              "      <td>42.135</td>\n",
              "      <td>43.344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>16.171</td>\n",
              "      <td>17.788</td>\n",
              "      <td>18.003</td>\n",
              "      <td>17.088</td>\n",
              "      <td>22.792</td>\n",
              "      <td>22.792</td>\n",
              "      <td>27.384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>22.082</td>\n",
              "      <td>23.481</td>\n",
              "      <td>23.481</td>\n",
              "      <td>24.308</td>\n",
              "      <td>25.759</td>\n",
              "      <td>26.617</td>\n",
              "      <td>27.512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>18.964</td>\n",
              "      <td>20.384</td>\n",
              "      <td>20.321</td>\n",
              "      <td>21.760</td>\n",
              "      <td>24.215</td>\n",
              "      <td>25.236</td>\n",
              "      <td>25.326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Uzbekistan</td>\n",
              "      <td>19.127</td>\n",
              "      <td>20.043</td>\n",
              "      <td>21.741</td>\n",
              "      <td>22.206</td>\n",
              "      <td>22.787</td>\n",
              "      <td>23.646</td>\n",
              "      <td>24.903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>20.345</td>\n",
              "      <td>22.141</td>\n",
              "      <td>22.430</td>\n",
              "      <td>22.340</td>\n",
              "      <td>23.687</td>\n",
              "      <td>24.465</td>\n",
              "      <td>24.903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>17.275</td>\n",
              "      <td>19.352</td>\n",
              "      <td>19.631</td>\n",
              "      <td>19.822</td>\n",
              "      <td>21.185</td>\n",
              "      <td>22.611</td>\n",
              "      <td>23.985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>13.230</td>\n",
              "      <td>14.415</td>\n",
              "      <td>14.802</td>\n",
              "      <td>15.097</td>\n",
              "      <td>15.671</td>\n",
              "      <td>16.290</td>\n",
              "      <td>16.808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>12.039</td>\n",
              "      <td>12.982</td>\n",
              "      <td>13.804</td>\n",
              "      <td>14.677</td>\n",
              "      <td>15.475</td>\n",
              "      <td>16.216</td>\n",
              "      <td>16.968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Myanmar</td>\n",
              "      <td>11.222</td>\n",
              "      <td>10.977</td>\n",
              "      <td>11.037</td>\n",
              "      <td>11.112</td>\n",
              "      <td>11.712</td>\n",
              "      <td>12.328</td>\n",
              "      <td>12.817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>6.587</td>\n",
              "      <td>7.360</td>\n",
              "      <td>8.372</td>\n",
              "      <td>9.126</td>\n",
              "      <td>9.993</td>\n",
              "      <td>10.809</td>\n",
              "      <td>11.468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>5.998</td>\n",
              "      <td>7.382</td>\n",
              "      <td>8.024</td>\n",
              "      <td>8.246</td>\n",
              "      <td>9.241</td>\n",
              "      <td>10.241</td>\n",
              "      <td>11.168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>5.166</td>\n",
              "      <td>5.475</td>\n",
              "      <td>6.912</td>\n",
              "      <td>7.305</td>\n",
              "      <td>7.891</td>\n",
              "      <td>8.447</td>\n",
              "      <td>9.027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>6.136</td>\n",
              "      <td>6.744</td>\n",
              "      <td>6.504</td>\n",
              "      <td>6.203</td>\n",
              "      <td>7.255</td>\n",
              "      <td>7.712</td>\n",
              "      <td>8.123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Nepal</td>\n",
              "      <td>4.174</td>\n",
              "      <td>4.944</td>\n",
              "      <td>5.208</td>\n",
              "      <td>5.680</td>\n",
              "      <td>5.911</td>\n",
              "      <td>5.925</td>\n",
              "      <td>6.614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Cambodia</td>\n",
              "      <td>3.414</td>\n",
              "      <td>3.857</td>\n",
              "      <td>4.597</td>\n",
              "      <td>4.909</td>\n",
              "      <td>5.088</td>\n",
              "      <td>5.222</td>\n",
              "      <td>6.089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Kyrgyzstan</td>\n",
              "      <td>3.306</td>\n",
              "      <td>3.432</td>\n",
              "      <td>4.097</td>\n",
              "      <td>4.777</td>\n",
              "      <td>4.777</td>\n",
              "      <td>4.767</td>\n",
              "      <td>5.432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2.484</td>\n",
              "      <td>2.595</td>\n",
              "      <td>2.927</td>\n",
              "      <td>3.297</td>\n",
              "      <td>3.387</td>\n",
              "      <td>3.790</td>\n",
              "      <td>4.151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>North Korea</td>\n",
              "      <td>3.670</td>\n",
              "      <td>3.728</td>\n",
              "      <td>3.844</td>\n",
              "      <td>3.844</td>\n",
              "      <td>3.961</td>\n",
              "      <td>4.048</td>\n",
              "      <td>4.211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Laos</td>\n",
              "      <td>2.342</td>\n",
              "      <td>2.418</td>\n",
              "      <td>2.522</td>\n",
              "      <td>2.541</td>\n",
              "      <td>2.642</td>\n",
              "      <td>2.742</td>\n",
              "      <td>2.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>2.047</td>\n",
              "      <td>2.152</td>\n",
              "      <td>2.302</td>\n",
              "      <td>2.487</td>\n",
              "      <td>2.487</td>\n",
              "      <td>2.642</td>\n",
              "      <td>2.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Tajikistan</td>\n",
              "      <td>2.025</td>\n",
              "      <td>2.288</td>\n",
              "      <td>2.529</td>\n",
              "      <td>2.562</td>\n",
              "      <td>2.672</td>\n",
              "      <td>2.805</td>\n",
              "      <td>2.927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Macau</td>\n",
              "      <td>1.572</td>\n",
              "      <td>1.748</td>\n",
              "      <td>1.850</td>\n",
              "      <td>2.019</td>\n",
              "      <td>2.102</td>\n",
              "      <td>2.176</td>\n",
              "      <td>2.248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>1.136</td>\n",
              "      <td>1.278</td>\n",
              "      <td>1.311</td>\n",
              "      <td>1.289</td>\n",
              "      <td>1.354</td>\n",
              "      <td>1.428</td>\n",
              "      <td>1.501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>0.488</td>\n",
              "      <td>0.540</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.557</td>\n",
              "      <td>0.608</td>\n",
              "      <td>0.668</td>\n",
              "      <td>0.688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>East Timor</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.272</td>\n",
              "      <td>0.298</td>\n",
              "      <td>0.325</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.377</td>\n",
              "      <td>0.406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Bhutan</td>\n",
              "      <td>0.331</td>\n",
              "      <td>0.353</td>\n",
              "      <td>0.356</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.385</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Maldives</td>\n",
              "      <td>0.244</td>\n",
              "      <td>0.266</td>\n",
              "      <td>0.285</td>\n",
              "      <td>0.304</td>\n",
              "      <td>0.325</td>\n",
              "      <td>0.348</td>\n",
              "      <td>0.374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Polynesia</td>\n",
              "      <td>0.167</td>\n",
              "      <td>0.164</td>\n",
              "      <td>0.173</td>\n",
              "      <td>0.182</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.206</td>\n",
              "      <td>0.219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>New Caledonia</td>\n",
              "      <td>0.134</td>\n",
              "      <td>0.154</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.167</td>\n",
              "      <td>0.184</td>\n",
              "      <td>0.196</td>\n",
              "      <td>0.199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>American Samoa</td>\n",
              "      <td>0.106</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.190</td>\n",
              "      <td>0.157</td>\n",
              "      <td>0.159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Samoa</td>\n",
              "      <td>0.077</td>\n",
              "      <td>0.085</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.093</td>\n",
              "      <td>0.096</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Micronesia</td>\n",
              "      <td>0.033</td>\n",
              "      <td>0.037</td>\n",
              "      <td>0.040</td>\n",
              "      <td>0.040</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Kiribati</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Marshall Islands</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Tonga</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.019</td>\n",
              "      <td>0.021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Palau</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.013</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Cook Islands</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Niue</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Total</td>\n",
              "      <td>2777.708</td>\n",
              "      <td>3102.276</td>\n",
              "      <td>3122.677</td>\n",
              "      <td>3246.941</td>\n",
              "      <td>3439.160</td>\n",
              "      <td>3630.060</td>\n",
              "      <td>3844.737</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "summary": "{\n  \"name\": \"all_data_with_totals\",\n  \"rows\": 49,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"Uzbekistan\",\n          \"Palau\",\n          \"Niue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.52418344005866,\n        \"min\": 0.003,\n        \"max\": 2777.7079999999996,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          19.127,\n          0.011,\n          0.006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 501.4561624764306,\n        \"min\": 0.006,\n        \"max\": 3102.276,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          3.728,\n          0.085,\n          2.595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 505.3187416799412,\n        \"min\": 0.006,\n        \"max\": 3122.6769999999997,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          21.741,\n          0.014,\n          0.006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2023\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 525.6587342986091,\n        \"min\": 0.006,\n        \"max\": 3246.9410000000003,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          22.206,\n          0.014,\n          0.006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2024\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 555.780332893485,\n        \"min\": 0.007,\n        \"max\": 3439.1600000000003,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          3.961,\n          0.096,\n          3.387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2025\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 586.8079395316807,\n        \"min\": 0.007,\n        \"max\": 3630.0600000000004,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          4.048,\n          0.159,\n          3.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2026\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 621.4192593011702,\n        \"min\": 0.008,\n        \"max\": 3844.737,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          2.979,\n          0.104,\n          4.211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create the data dictionary\n",
        "all_data = {\n",
        "    'Year': [2020, 2021, 2022, 2023, 2024, 2025, 2026],\n",
        "    'Market': [2778, 3102, 3123, 3247, 3439, 3630, 3845]\n",
        "}\n",
        "\n",
        "# Create the DataFrame\n",
        "all_data = pd.DataFrame(all_data)\n",
        "\n",
        "# Set the Seaborn style\n",
        "sns.set(style=\"white\")\n",
        "\n",
        "# Create the plot\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Plot the solid line for years up to and including 2024\n",
        "sns.lineplot(x='Year', y='Market', data=all_data[all_data['Year'] <= 2024], marker='o', color='darkred', linewidth=3, markersize=10)\n",
        "\n",
        "# Plot the dotted line for years after 2024\n",
        "sns.lineplot(x='Year', y='Market', data=all_data, marker='o', color='darkred', linestyle='--', linewidth=3, markersize=10)\n",
        "\n",
        "plt.title('Total Food Market Size in the ASPAC', fontsize=20)\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Market Size (in billions of dollars)')\n",
        "plt.xticks(all_data['Year'])  # Set X-axis labels to the exact years\n",
        "\n",
        "# Add vertical dotted line after 2024\n",
        "plt.axvline(x=2024, color='grey', linestyle='--', linewidth=2)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "fhzOoX0coGAW",
        "outputId": "f3cd12c5-da63-4108-c7bb-41d1011e4262"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터 설정\n",
        "data1 = {\n",
        "    'Year': [2020, 2021, 2022],\n",
        "    'Size': [1468, 81625, 249648]\n",
        "}\n",
        "\n",
        "data2 = {\n",
        "    'Year': [2018, 2019, 2020, 2021, 2022],\n",
        "    'Size': [34500, 101700, 188200, 300300, 376600]\n",
        "}\n",
        "\n",
        "data3 = {\n",
        "    'Year': [2023, 2024, 2025, 2026, 2027],\n",
        "    'Size': [448500, 507900, 561000, 610500, 660100]\n",
        "}\n",
        "\n",
        "# CAGR 계산 함수\n",
        "def calculate_cagr(start_value, end_value, periods):\n",
        "    return (end_value / start_value) ** (1 / periods) - 1\n",
        "\n",
        "# 데이터프레임 생성\n",
        "df1 = pd.DataFrame(data1)\n",
        "df2 = pd.DataFrame(data2)\n",
        "df3 = pd.DataFrame(data3)\n",
        "\n",
        "# CAGR 계산\n",
        "cagr1 = calculate_cagr(df1['Size'].iloc[0], df1['Size'].iloc[-1], len(df1['Year']) - 1)\n",
        "cagr2 = calculate_cagr(df2['Size'].iloc[0], df2['Size'].iloc[-1], len(df2['Year']) - 1)\n",
        "cagr3 = calculate_cagr(df3['Size'].iloc[0], df3['Size'].iloc[-1], len(df3['Year']) - 1)\n",
        "\n",
        "# 시각화\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "def plot_data(df, cagr, title):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.barplot(x='Year', y='Size', data=df, palette=sns.dark_palette(\"#69d\", reverse=True, as_cmap=True))\n",
        "    plt.title(title, fontsize=20)\n",
        "    plt.xlabel('Year')\n",
        "    plt.ylabel('Market Size (in one million won)')\n",
        "    plt.legend(labels=[f'CAGR: {cagr:.2%}'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "# 데이터1 그래프\n",
        "plot_data(df1, cagr1, 'Market Size over Years')\n",
        "\n",
        "# 데이터2 그래프\n",
        "plot_data(df2, cagr2, 'Market Size over Years')\n",
        "\n",
        "# 데이터3 그래프\n",
        "plot_data(df3, cagr3, 'Market Size and Forecast')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s6cqb2Nq-qby",
        "outputId": "86d46269-5030-434d-914b-63857a551375"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-e7763892ff42>:40: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Year', y='Size', data=df, palette=sns.dark_palette(\"#69d\", reverse=True, as_cmap=True))\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'LinearSegmentedColormap' object is not iterable",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-e7763892ff42>\u001b[0m in \u001b[0;36m<cell line: 48>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;31m# 데이터1 그래프\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0mplot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcagr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Market Size over Years'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;31m# 데이터2 그래프\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-6-e7763892ff42>\u001b[0m in \u001b[0;36mplot_data\u001b[0;34m(df, cagr, title)\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcagr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Size'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdark_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"#69d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mbarplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, seed, units, weights, orient, color, palette, saturation, fill, hue_norm, width, dodge, gap, log_scale, native_scale, formatter, legend, capsize, err_kws, ci, errcolor, errwidth, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m     \u001b[0msaturation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaturation\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfill\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2369\u001b[0;31m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_hue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2370\u001b[0m     \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_default_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/_base.py\u001b[0m in \u001b[0;36mmap_hue\u001b[0;34m(self, palette, order, norm, saturation)\u001b[0m\n\u001b[1;32m    836\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    837\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmap_hue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 838\u001b[0;31m         \u001b[0mmapping\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHueMapping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    839\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hue_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/_base.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, plotter, palette, order, norm, saturation)\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m                 \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m                 levels, lookup_table = self.categorical_mapping(\n\u001b[0m\u001b[1;32m    151\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/_base.py\u001b[0m in \u001b[0;36mcategorical_mapping\u001b[0;34m(self, data, palette, order)\u001b[0m\n\u001b[1;32m    246\u001b[0m                 \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_list_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"palette\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m                 \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m             \u001b[0mlookup_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mcolor_palette\u001b[0;34m(palette, n_colors, desat, as_cmap)\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0;31m# Always return as many colors as we asked for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mpal_cycle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m         \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpal_cycle\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'LinearSegmentedColormap' object is not iterable"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# 데이터 설정\n",
        "data1 = {\n",
        "    'Year': [2020, 2021, 2022],\n",
        "    'Size': [14.68, 816.25, 2496.48]\n",
        "}\n",
        "\n",
        "data2 = {\n",
        "    'Year': [2018, 2019, 2020, 2021, 2022],\n",
        "    'Size': [345, 1017, 1882, 3003, 3766]\n",
        "}\n",
        "\n",
        "data3 = {\n",
        "    'Year': [2023, 2024, 2025, 2026, 2027],\n",
        "    'Size': [4485, 5079, 5610, 6105, 6601]\n",
        "}\n",
        "\n",
        "# CAGR 계산 함수\n",
        "def calculate_cagr(start_value, end_value, periods):\n",
        "    return (end_value / start_value) ** (1 / periods) - 1\n",
        "\n",
        "# 데이터프레임 생성\n",
        "df1 = pd.DataFrame(data1)\n",
        "df2 = pd.DataFrame(data2)\n",
        "df3 = pd.DataFrame(data3)\n",
        "\n",
        "# CAGR 계산\n",
        "cagr1 = calculate_cagr(df1['Size'].iloc[0], df1['Size'].iloc[-1], len(df1['Year']) - 1)\n",
        "cagr2 = calculate_cagr(df2['Size'].iloc[0], df2['Size'].iloc[-1], len(df2['Year']) - 1)\n",
        "cagr3 = calculate_cagr(df3['Size'].iloc[0], df3['Size'].iloc[-1], len(df3['Year']) - 1)\n",
        "\n",
        "# 시각화\n",
        "sns.set(style=\"white\")\n",
        "\n",
        "def plot_data(df, cagr, title):\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    ax = sns.barplot(x='Year', y='Size', hue='Year', dodge=False, data=df, palette='Blues', legend=False)\n",
        "    for p in ax.patches:\n",
        "        ax.annotate(format(p.get_height(), ',.0f'),\n",
        "                    (p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "                    ha = 'center', va = 'center',\n",
        "                    xytext = (0, 6),\n",
        "                    textcoords = 'offset points')\n",
        "    plt.title(title, fontsize=20)\n",
        "    plt.xlabel('Year')\n",
        "    plt.ylabel('Market Size (in one billion won)')\n",
        "    plt.legend(labels=[f'CAGR: {cagr:.2%}'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "# 데이터1 그래프\n",
        "plot_data(df1, cagr1, 'Meal Kits Market Size_Korea MFDS')\n",
        "\n",
        "# 데이터2 그래프\n",
        "plot_data(df2, cagr2, 'Meal Kits Market Size_Euromonitor')\n",
        "\n",
        "# 데이터3 그래프\n",
        "plot_data(df3, cagr3, 'Meal Kits Market Size and Forecast')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qqEMvTOJ_AqT",
        "outputId": "a6963eca-a903-454d-e5ac-20b870ab350e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ns1cCIJbA39H"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}