GET Final Report Visualization (Food Import to SG).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 1367,
     "status": "ok",
     "timestamp": 1666691467825,
     "user": {
      "displayName": "Wang Mingzhe",
      "userId": "03239930408549291405"
     },
     "user_tz": -480
    },
    "id": "LxpA-LAyVrx4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import networkprocessing as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excel file is manually created based off the ratio on WorldBank food import data (per thousand USD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "OH1LVrPNVwLb",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('2000_ratio.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>To</th>\n",
       "      <th>From</th>\n",
       "      <th>Nearest</th>\n",
       "      <th>Exact Ratio</th>\n",
       "      <th>Rounded Ratio to 0.5</th>\n",
       "      <th>Estimate Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>United States</td>\n",
       "      <td>342</td>\n",
       "      <td>4.329114</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>342</td>\n",
       "      <td>4.329114</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>France</td>\n",
       "      <td>291</td>\n",
       "      <td>3.683544</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>265</td>\n",
       "      <td>3.354430</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>China</td>\n",
       "      <td>120</td>\n",
       "      <td>1.518987</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United States</td>\n",
       "      <td>Canada</td>\n",
       "      <td>4351</td>\n",
       "      <td>55.075949</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>United States</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2102</td>\n",
       "      <td>26.607595</td>\n",
       "      <td>26.5</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>United States</td>\n",
       "      <td>France</td>\n",
       "      <td>1855</td>\n",
       "      <td>23.481013</td>\n",
       "      <td>23.5</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>United States</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1219</td>\n",
       "      <td>15.430380</td>\n",
       "      <td>15.5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>United States</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1084</td>\n",
       "      <td>13.721519</td>\n",
       "      <td>13.5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>239</td>\n",
       "      <td>3.025316</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>United States</td>\n",
       "      <td>180</td>\n",
       "      <td>2.278481</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>114</td>\n",
       "      <td>1.443038</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>110</td>\n",
       "      <td>1.392405</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>79</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>France</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1820</td>\n",
       "      <td>23.037975</td>\n",
       "      <td>23.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>France</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1680</td>\n",
       "      <td>21.265823</td>\n",
       "      <td>21.5</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>France</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>1458</td>\n",
       "      <td>18.455696</td>\n",
       "      <td>18.5</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>France</td>\n",
       "      <td>Italy</td>\n",
       "      <td>904</td>\n",
       "      <td>11.443038</td>\n",
       "      <td>11.5</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>France</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>869</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>France</td>\n",
       "      <td>2197</td>\n",
       "      <td>27.810127</td>\n",
       "      <td>28.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>1348</td>\n",
       "      <td>17.063291</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1342</td>\n",
       "      <td>16.987342</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1198</td>\n",
       "      <td>15.164557</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1111</td>\n",
       "      <td>14.063291</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>China</td>\n",
       "      <td>Peru</td>\n",
       "      <td>395</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>China</td>\n",
       "      <td>United States</td>\n",
       "      <td>247</td>\n",
       "      <td>3.126582</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>China</td>\n",
       "      <td>Japan</td>\n",
       "      <td>105</td>\n",
       "      <td>1.329114</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>China</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>99</td>\n",
       "      <td>1.253165</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>China</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>85</td>\n",
       "      <td>1.075949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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      ],
      "text/plain": [
       "                To            From  Nearest   Exact Ratio  \\\n",
       "0        Singapore   United States       342     4.329114   \n",
       "1        Singapore        Malaysia       342     4.329114   \n",
       "2        Singapore          France       291     3.683544   \n",
       "3        Singapore  United Kingdom       265     3.354430   \n",
       "4        Singapore           China       120     1.518987   \n",
       "5    United States          Canada      4351    55.075949   \n",
       "6    United States          Mexico      2102    26.607595   \n",
       "7    United States          France      1855    23.481013   \n",
       "8    United States  United Kingdom      1219    15.430380   \n",
       "9    United States        Thailand      1084    13.721519   \n",
       "10        Malaysia       Australia       239     3.025316   \n",
       "11        Malaysia   United States       180     2.278481   \n",
       "12        Malaysia       Indonesia       114     1.443038   \n",
       "13        Malaysia        Thailand       110     1.392405   \n",
       "14        Malaysia       Argentina        79     1.000000   \n",
       "15          France     Netherlands      1820    23.037975   \n",
       "16          France         Germany      1680    21.265823   \n",
       "17          France         Belgium      1458    18.455696   \n",
       "18          France           Italy       904    11.443038   \n",
       "19          France  United Kingdom       869    11.000000   \n",
       "20  United Kingdom          France      2197    27.810127   \n",
       "21  United Kingdom         Ireland      1348    17.063291   \n",
       "22  United Kingdom     Netherlands      1342    16.987342   \n",
       "23  United Kingdom         Germany      1198    15.164557   \n",
       "24  United Kingdom           Italy      1111    14.063291   \n",
       "25           China            Peru       395     5.000000   \n",
       "26           China   United States       247     3.126582   \n",
       "27           China           Japan       105     1.329114   \n",
       "28           China        Zimbabwe        99     1.253165   \n",
       "29           China          Brazil        85     1.075949   \n",
       "\n",
       "    Rounded Ratio to 0.5  Estimate Ratio  \n",
       "0                    4.5               9  \n",
       "1                    4.5               9  \n",
       "2                    3.5               7  \n",
       "3                    3.5               7  \n",
       "4                    1.5               3  \n",
       "5                   55.0             110  \n",
       "6                   26.5              53  \n",
       "7                   23.5              47  \n",
       "8                   15.5              31  \n",
       "9                   13.5              27  \n",
       "10                   3.0               6  \n",
       "11                   2.5               5  \n",
       "12                   1.5               3  \n",
       "13                   1.5               3  \n",
       "14                   1.0               2  \n",
       "15                  23.0              46  \n",
       "16                  21.5              43  \n",
       "17                  18.5              37  \n",
       "18                  11.5              23  \n",
       "19                  11.0              22  \n",
       "20                  28.0              56  \n",
       "21                  17.0              34  \n",
       "22                  17.0              34  \n",
       "23                  15.0              30  \n",
       "24                  14.0              28  \n",
       "25                   5.0              10  \n",
       "26                   3.0               6  \n",
       "27                   1.5               3  \n",
       "28                   1.5               3  \n",
       "29                   1.0               2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_excel('2020_ratio.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>To</th>\n",
       "      <th>From</th>\n",
       "      <th>Nearest</th>\n",
       "      <th>Exact Ratio</th>\n",
       "      <th>Rounded Ratio to 0.5</th>\n",
       "      <th>Estimate Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>France</td>\n",
       "      <td>1164</td>\n",
       "      <td>2.414938</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>919</td>\n",
       "      <td>1.906639</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>China</td>\n",
       "      <td>632</td>\n",
       "      <td>1.311203</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>624</td>\n",
       "      <td>1.294606</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>571</td>\n",
       "      <td>1.184647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>4930</td>\n",
       "      <td>10.228216</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4428</td>\n",
       "      <td>9.186722</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3764</td>\n",
       "      <td>7.809129</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>France</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2974</td>\n",
       "      <td>6.170124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2923</td>\n",
       "      <td>6.064315</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>651</td>\n",
       "      <td>1.350622</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>646</td>\n",
       "      <td>1.340249</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>China</td>\n",
       "      <td>581</td>\n",
       "      <td>1.205394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>503</td>\n",
       "      <td>1.043568</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>482</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>China</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2323</td>\n",
       "      <td>4.819502</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>China</td>\n",
       "      <td>France</td>\n",
       "      <td>2306</td>\n",
       "      <td>4.784232</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>China</td>\n",
       "      <td>United States</td>\n",
       "      <td>2184</td>\n",
       "      <td>4.531120</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>China</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2103</td>\n",
       "      <td>4.363071</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>China</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1723</td>\n",
       "      <td>3.574689</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>France</td>\n",
       "      <td>4119</td>\n",
       "      <td>8.545643</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3731</td>\n",
       "      <td>7.740664</td>\n",
       "      <td>7.5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3646</td>\n",
       "      <td>7.564315</td>\n",
       "      <td>7.5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2941</td>\n",
       "      <td>6.101660</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>2886</td>\n",
       "      <td>5.987552</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1643</td>\n",
       "      <td>3.408714</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>975</td>\n",
       "      <td>2.022822</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>901</td>\n",
       "      <td>1.869295</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>United States</td>\n",
       "      <td>819</td>\n",
       "      <td>1.699170</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>China</td>\n",
       "      <td>732</td>\n",
       "      <td>1.518672</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                To            From  Nearest   Exact Ratio  \\\n",
       "0        Singapore          France      1164     2.414938   \n",
       "1        Singapore        Malaysia       919     1.906639   \n",
       "2        Singapore           China       632     1.311203   \n",
       "3        Singapore  United Kingdom       624     1.294606   \n",
       "4        Singapore       Indonesia       571     1.184647   \n",
       "5           France         Belgium      4930    10.228216   \n",
       "6           France         Germany      4428     9.186722   \n",
       "7           France     Netherlands      3764     7.809129   \n",
       "8           France           Italy      2974     6.170124   \n",
       "9           France           Spain      2923     6.064315   \n",
       "10        Malaysia        Thailand       651     1.350622   \n",
       "11        Malaysia       Indonesia       646     1.340249   \n",
       "12        Malaysia           China       581     1.205394   \n",
       "13        Malaysia       Argentina       503     1.043568   \n",
       "14        Malaysia          Brazil       482     1.000000   \n",
       "15           China     Netherlands      2323     4.819502   \n",
       "16           China          France      2306     4.784232   \n",
       "17           China   United States      2184     4.531120   \n",
       "18           China       Australia      2103     4.363071   \n",
       "19           China     New Zealand      1723     3.574689   \n",
       "20  United Kingdom          France      4119     8.545643   \n",
       "21  United Kingdom         Germany      3731     7.740664   \n",
       "22  United Kingdom     Netherlands      3646     7.564315   \n",
       "23  United Kingdom           Italy      2941     6.101660   \n",
       "24  United Kingdom         Ireland      2886     5.987552   \n",
       "25       Indonesia          Brazil      1643     3.408714   \n",
       "26       Indonesia        Thailand       975     2.022822   \n",
       "27       Indonesia       Argentina       901     1.869295   \n",
       "28       Indonesia   United States       819     1.699170   \n",
       "29       Indonesia           China       732     1.518672   \n",
       "\n",
       "    Rounded Ratio to 0.5  Estimate Ratio  \n",
       "0                    2.5               5  \n",
       "1                    2.0               4  \n",
       "2                    1.5               3  \n",
       "3                    1.5               3  \n",
       "4                    1.0               2  \n",
       "5                   10.0              20  \n",
       "6                    9.0              18  \n",
       "7                    8.0              16  \n",
       "8                    6.0              12  \n",
       "9                    6.0              12  \n",
       "10                   1.5               3  \n",
       "11                   1.5               3  \n",
       "12                   1.0               2  \n",
       "13                   1.0               2  \n",
       "14                   1.0               2  \n",
       "15                   5.0              10  \n",
       "16                   5.0              10  \n",
       "17                   4.5               9  \n",
       "18                   4.5               9  \n",
       "19                   3.5               7  \n",
       "20                   8.5              17  \n",
       "21                   7.5              15  \n",
       "22                   7.5              15  \n",
       "23                   6.0              12  \n",
       "24                   6.0              12  \n",
       "25                   3.5               7  \n",
       "26                   2.0               4  \n",
       "27                   2.0               4  \n",
       "28                   1.5               3  \n",
       "29                   1.5               3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make to create 2 text files in a folder (I used \"dataNew\" folder) to store info for networkprocessing.py (2000.txt and 2020.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 0\n",
    "with open('dataNew/2000.txt', 'w') as f:\n",
    "    for index, row in df.iterrows():\n",
    "        while x < int(row.loc['Estimate Ratio']):\n",
    "            f.write(row.loc['To'] + \",\" +row.loc['From']+\"\\n\")\n",
    "            x += 1\n",
    "        x = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 0\n",
    "with open('dataNew/2020.txt', 'w') as f:\n",
    "    for index, row in df2.iterrows():\n",
    "        while x < int(row.loc['Estimate Ratio']):\n",
    "            f.write(row.loc['To'] + \",\" +row.loc['From']+\"\\n\")\n",
    "            x += 1\n",
    "        x = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataNew\\2000.txt\n",
      "Processing dataNew\\2020.txt\n"
     ]
    }
   ],
   "source": [
    "np.make_network(\"dataNew\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>label</th>\n",
       "      <th>degree</th>\n",
       "      <th>weighted_degree</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>normalized_betweenness</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>closeness</th>\n",
       "      <th>network</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>0.218421</td>\n",
       "      <td>2</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>8</td>\n",
       "      <td>288</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>0.478947</td>\n",
       "      <td>2</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>0.286842</td>\n",
       "      <td>3</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>7</td>\n",
       "      <td>281</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>0.218421</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>7</td>\n",
       "      <td>242</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>0.218421</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>China</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>3</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.425532</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Peru</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>27.166667</td>\n",
       "      <td>0.199755</td>\n",
       "      <td>2</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>France</td>\n",
       "      <td>8</td>\n",
       "      <td>110</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.143382</td>\n",
       "      <td>3</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>China</td>\n",
       "      <td>8</td>\n",
       "      <td>53</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>0.409314</td>\n",
       "      <td>3</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "      <td>22.166667</td>\n",
       "      <td>0.162990</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>3</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.395349</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.018382</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.395349</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.361702</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.361702</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.361702</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>United States</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.435897</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.395349</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.395349</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             label  degree  weighted_degree  betweenness  \\\n",
       "0        Singapore       5               35    41.500000   \n",
       "1    United States       8              288    91.000000   \n",
       "2         Malaysia       6               28    54.500000   \n",
       "3           France       7              281    41.500000   \n",
       "4   United Kingdom       7              242    41.500000   \n",
       "5            China       6               27    70.000000   \n",
       "6           Canada       1              110     0.000000   \n",
       "7           Mexico       1               53     0.000000   \n",
       "8         Thailand       2               30     0.000000   \n",
       "9        Australia       1                6     0.000000   \n",
       "10       Indonesia       1                3     0.000000   \n",
       "11       Argentina       1                2     0.000000   \n",
       "12     Netherlands       2               80     0.000000   \n",
       "13         Germany       2               73     0.000000   \n",
       "14         Belgium       1               37     0.000000   \n",
       "15           Italy       2               51     0.000000   \n",
       "16         Ireland       1               34     0.000000   \n",
       "17            Peru       1               10     0.000000   \n",
       "18           Japan       1                3     0.000000   \n",
       "19        Zimbabwe       1                3     0.000000   \n",
       "20          Brazil       1                2     0.000000   \n",
       "21       Singapore       5               17    27.166667   \n",
       "22          France       8              110    51.000000   \n",
       "23        Malaysia       6               16    19.500000   \n",
       "24           China       8               53    55.666667   \n",
       "25  United Kingdom       6               74    22.166667   \n",
       "26       Indonesia       7               26    24.000000   \n",
       "27         Belgium       1               20     0.000000   \n",
       "28         Germany       2               33     0.000000   \n",
       "29     Netherlands       3               41     2.500000   \n",
       "30           Italy       2               24     0.000000   \n",
       "31           Spain       1               12     0.000000   \n",
       "32        Thailand       2                7     0.000000   \n",
       "33       Argentina       2                6     0.000000   \n",
       "34          Brazil       2                9     0.000000   \n",
       "35   United States       2               12     0.000000   \n",
       "36       Australia       1                9     0.000000   \n",
       "37     New Zealand       1                7     0.000000   \n",
       "38         Ireland       1               12     0.000000   \n",
       "\n",
       "    normalized_betweenness  eccentricity  closeness  network  \n",
       "0                 0.218421             2   0.571429     2000  \n",
       "1                 0.478947             2   0.625000     2000  \n",
       "2                 0.286842             3   0.465116     2000  \n",
       "3                 0.218421             3   0.500000     2000  \n",
       "4                 0.218421             3   0.500000     2000  \n",
       "5                 0.368421             3   0.476190     2000  \n",
       "6                 0.000000             3   0.392157     2000  \n",
       "7                 0.000000             3   0.392157     2000  \n",
       "8                 0.000000             3   0.425532     2000  \n",
       "9                 0.000000             4   0.322581     2000  \n",
       "10                0.000000             4   0.322581     2000  \n",
       "11                0.000000             4   0.322581     2000  \n",
       "12                0.000000             4   0.350877     2000  \n",
       "13                0.000000             4   0.350877     2000  \n",
       "14                0.000000             4   0.338983     2000  \n",
       "15                0.000000             4   0.350877     2000  \n",
       "16                0.000000             4   0.338983     2000  \n",
       "17                0.000000             4   0.327869     2000  \n",
       "18                0.000000             4   0.327869     2000  \n",
       "19                0.000000             4   0.327869     2000  \n",
       "20                0.000000             4   0.327869     2000  \n",
       "21                0.199755             2   0.586207     2020  \n",
       "22                0.375000             3   0.586207     2020  \n",
       "23                0.143382             3   0.515152     2020  \n",
       "24                0.409314             3   0.629630     2020  \n",
       "25                0.162990             3   0.500000     2020  \n",
       "26                0.176471             3   0.531250     2020  \n",
       "27                0.000000             4   0.377778     2020  \n",
       "28                0.000000             4   0.395349     2020  \n",
       "29                0.018382             3   0.500000     2020  \n",
       "30                0.000000             4   0.395349     2020  \n",
       "31                0.000000             4   0.377778     2020  \n",
       "32                0.000000             4   0.361702     2020  \n",
       "33                0.000000             4   0.361702     2020  \n",
       "34                0.000000             4   0.361702     2020  \n",
       "35                0.000000             4   0.435897     2020  \n",
       "36                0.000000             4   0.395349     2020  \n",
       "37                0.000000             4   0.395349     2020  \n",
       "38                0.000000             4   0.340000     2020  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodeinfo = pd.read_excel(\"dataNew_analysis/nodeInfo.xlsx\",index_col=0)\n",
    "nodeinfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y2000 = nodeinfo[\"network\"] == \"2000\"\n",
    "y2020 = nodeinfo[\"network\"] == \"2020\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nodeinfo.loc[\"y2020\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>nodes</th>\n",
       "      <th>edges</th>\n",
       "      <th>avg path length</th>\n",
       "      <th>avg degree</th>\n",
       "      <th>avg weighted degree</th>\n",
       "      <th>diameter</th>\n",
       "      <th>radius</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>network_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>2.619048</td>\n",
       "      <td>2.761905</td>\n",
       "      <td>66.571429</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.138095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>2.320261</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>27.111111</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.196078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            nodes  edges  avg path length  avg degree  avg weighted degree  \\\n",
       "network_id                                                                   \n",
       "2000           21     29         2.619048    2.761905            66.571429   \n",
       "2020           18     30         2.320261    3.333333            27.111111   \n",
       "\n",
       "            diameter  radius   density  \n",
       "network_id                              \n",
       "2000               4       2  0.138095  \n",
       "2020               4       2  0.196078  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwinfo = pd.read_excel(\"dataNew_analysis/networkInfo.xlsx\",index_col=0)\n",
    "nwinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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