kruby/undergrad-uh401
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
a6e6a51041e9883c8aa2e068d1e4269528ea1423
undergrad-uh401/explore-basic.ipynb
kruby a6e6a51041 feat: new dataset
2025-11-08 05:42:28 +00:00

2582 lines
243 KiB
Plaintext
Raw Blame History

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e024287e-559f-4ab8-af6f-4bc648c6bb69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting awkward (from -r requirements.txt (line 1))\n",
" Downloading awkward-2.8.10-py3-none-any.whl.metadata (7.5 kB)\n",
"Requirement already satisfied: pandas in /opt/conda/lib/python3.12/site-packages (from -r requirements.txt (line 2)) (2.3.0)\n",
"Requirement already satisfied: numpy in /opt/conda/lib/python3.12/site-packages (from -r requirements.txt (line 3)) (2.2.6)\n",
"Requirement already satisfied: tqdm in /opt/conda/lib/python3.12/site-packages (from -r requirements.txt (line 5)) (4.67.1)\n",
"Collecting awkward-cpp==50 (from awkward->-r requirements.txt (line 1))\n",
" Using cached awkward_cpp-50-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.2 kB)\n",
"Requirement already satisfied: fsspec>=2022.11.0 in /opt/conda/lib/python3.12/site-packages (from awkward->-r requirements.txt (line 1)) (2025.5.1)\n",
"Requirement already satisfied: packaging in /opt/conda/lib/python3.12/site-packages (from awkward->-r requirements.txt (line 1)) (25.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.12/site-packages (from pandas->-r requirements.txt (line 2)) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.12/site-packages (from pandas->-r requirements.txt (line 2)) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.12/site-packages (from pandas->-r requirements.txt (line 2)) (2025.2)\n",
"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas->-r requirements.txt (line 2)) (1.17.0)\n",
"Downloading awkward-2.8.10-py3-none-any.whl (907 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m908.0/908.0 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hUsing cached awkward_cpp-50-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (655 kB)\n",
"Installing collected packages: awkward-cpp, awkward\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [awkward]m1/2\u001b[0m [awkward]\n",
"\u001b[1A\u001b[2KSuccessfully installed awkward-2.8.10 awkward-cpp-50\n"
]
}
],
"source": [
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "eeee645e-48b5-4bdf-a386-e3354c8ac46a",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import numpy as np\n",
"import awkward as ak\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e18b60a6-be26-473b-9938-c9d18c28b8aa",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.12/site-packages/openpyxl/styles/stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
" warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
]
},
{
"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>Field</th>\n",
" <th>All R&amp;D expenditures</th>\n",
" <th>Federal government</th>\n",
" <th>State and local government</th>\n",
" <th>Institution funds</th>\n",
" <th>Business</th>\n",
" <th>Nonprofit organizations</th>\n",
" <th>All other sources</th>\n",
" <th>Federal government Percent</th>\n",
" <th>State and local government Percent</th>\n",
" <th>Institution funds Percent</th>\n",
" <th>Business Percent</th>\n",
" <th>Nonprofit organizations Percent</th>\n",
" <th>All other sources Percent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>All R&amp;D fields</td>\n",
" <td>184880.0</td>\n",
" <td>86244.0</td>\n",
" <td>16660.0</td>\n",
" <td>77554.0</td>\n",
" <td>1973.0</td>\n",
" <td>1753.0</td>\n",
" <td>696.0</td>\n",
" <td>46.648637</td>\n",
" <td>9.011251</td>\n",
" <td>41.948291</td>\n",
" <td>1.067179</td>\n",
" <td>0.948183</td>\n",
" <td>0.376460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Science</td>\n",
" <td>92834.0</td>\n",
" <td>44257.0</td>\n",
" <td>12309.0</td>\n",
" <td>34954.0</td>\n",
" <td>570.0</td>\n",
" <td>570.0</td>\n",
" <td>174.0</td>\n",
" <td>47.673266</td>\n",
" <td>13.259151</td>\n",
" <td>37.652153</td>\n",
" <td>0.613999</td>\n",
" <td>0.613999</td>\n",
" <td>0.187431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Computer and information sciences</td>\n",
" <td>18813.0</td>\n",
" <td>5584.0</td>\n",
" <td>9591.0</td>\n",
" <td>3595.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>40.0</td>\n",
" <td>29.681603</td>\n",
" <td>50.980705</td>\n",
" <td>19.109127</td>\n",
" <td>0.000000</td>\n",
" <td>0.015946</td>\n",
" <td>0.212619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Geosciences, atmospheric sciences, and ocean s...</td>\n",
" <td>16822.0</td>\n",
" <td>12947.0</td>\n",
" <td>1327.0</td>\n",
" <td>2324.0</td>\n",
" <td>106.0</td>\n",
" <td>102.0</td>\n",
" <td>16.0</td>\n",
" <td>76.964689</td>\n",
" <td>7.888479</td>\n",
" <td>13.815242</td>\n",
" <td>0.630127</td>\n",
" <td>0.606349</td>\n",
" <td>0.095114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Atmospheric science and meteorology</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>5</th>\n",
" <td>Geological and earth sciences</td>\n",
" <td>14248.0</td>\n",
" <td>12945.0</td>\n",
" <td>21.0</td>\n",
" <td>1058.0</td>\n",
" <td>106.0</td>\n",
" <td>102.0</td>\n",
" <td>16.0</td>\n",
" <td>90.854857</td>\n",
" <td>0.147389</td>\n",
" <td>7.425604</td>\n",
" <td>0.743964</td>\n",
" <td>0.715890</td>\n",
" <td>0.112296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Ocean sciences and marine sciences</td>\n",
" <td>2574.0</td>\n",
" <td>2.0</td>\n",
" <td>1306.0</td>\n",
" <td>1266.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.077700</td>\n",
" <td>50.738151</td>\n",
" <td>49.184149</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Geosciences, atmospheric sciences, and ocean s...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>8</th>\n",
" <td>Life sciences</td>\n",
" <td>23853.0</td>\n",
" <td>11858.0</td>\n",
" <td>0.0</td>\n",
" <td>11487.0</td>\n",
" <td>141.0</td>\n",
" <td>292.0</td>\n",
" <td>75.0</td>\n",
" <td>49.712824</td>\n",
" <td>0.000000</td>\n",
" <td>48.157464</td>\n",
" <td>0.591121</td>\n",
" <td>1.224165</td>\n",
" <td>0.314426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Agricultural sciences</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>63.636364</td>\n",
" <td>0.000000</td>\n",
" <td>36.363636</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Biological and biomedical sciences</td>\n",
" <td>10055.0</td>\n",
" <td>4436.0</td>\n",
" <td>0.0</td>\n",
" <td>5540.0</td>\n",
" <td>1.0</td>\n",
" <td>62.0</td>\n",
" <td>16.0</td>\n",
" <td>44.117355</td>\n",
" <td>0.000000</td>\n",
" <td>55.096967</td>\n",
" <td>0.009945</td>\n",
" <td>0.616609</td>\n",
" <td>0.159125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Health sciences</td>\n",
" <td>13736.0</td>\n",
" <td>7366.0</td>\n",
" <td>0.0</td>\n",
" <td>5942.0</td>\n",
" <td>140.0</td>\n",
" <td>230.0</td>\n",
" <td>58.0</td>\n",
" <td>53.625510</td>\n",
" <td>0.000000</td>\n",
" <td>43.258591</td>\n",
" <td>1.019220</td>\n",
" <td>1.674432</td>\n",
" <td>0.422248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Natural resources and conservation</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>13</th>\n",
" <td>Life sciences nec</td>\n",
" <td>51.0</td>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>96.078431</td>\n",
" <td>0.000000</td>\n",
" <td>1.960784</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.960784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Mathematics and statistics</td>\n",
" <td>2918.0</td>\n",
" <td>453.0</td>\n",
" <td>1019.0</td>\n",
" <td>1418.0</td>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>15.524332</td>\n",
" <td>34.921179</td>\n",
" <td>48.594928</td>\n",
" <td>0.000000</td>\n",
" <td>0.959561</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Physical sciences</td>\n",
" <td>11124.0</td>\n",
" <td>7960.0</td>\n",
" <td>0.0</td>\n",
" <td>3148.0</td>\n",
" <td>4.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>71.556994</td>\n",
" <td>0.000000</td>\n",
" <td>28.299173</td>\n",
" <td>0.035958</td>\n",
" <td>0.107875</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Astronomy and astrophysics</td>\n",
" <td>271.0</td>\n",
" <td>271.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Chemistry</td>\n",
" <td>7938.0</td>\n",
" <td>5109.0</td>\n",
" <td>0.0</td>\n",
" <td>2817.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>64.361300</td>\n",
" <td>0.000000</td>\n",
" <td>35.487528</td>\n",
" <td>0.000000</td>\n",
" <td>0.151172</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Materials science</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>Physics</td>\n",
" <td>2911.0</td>\n",
" <td>2580.0</td>\n",
" <td>0.0</td>\n",
" <td>327.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>88.629337</td>\n",
" <td>0.000000</td>\n",
" <td>11.233253</td>\n",
" <td>0.137410</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Physical sciences nec</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Psychology</td>\n",
" <td>5690.0</td>\n",
" <td>3001.0</td>\n",
" <td>13.0</td>\n",
" <td>2656.0</td>\n",
" <td>0.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>52.741652</td>\n",
" <td>0.228471</td>\n",
" <td>46.678383</td>\n",
" <td>0.000000</td>\n",
" <td>0.351494</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Social sciences</td>\n",
" <td>13350.0</td>\n",
" <td>2454.0</td>\n",
" <td>359.0</td>\n",
" <td>10062.0</td>\n",
" <td>319.0</td>\n",
" <td>113.0</td>\n",
" <td>43.0</td>\n",
" <td>18.382022</td>\n",
" <td>2.689139</td>\n",
" <td>75.370787</td>\n",
" <td>2.389513</td>\n",
" <td>0.846442</td>\n",
" <td>0.322097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Anthropology</td>\n",
" <td>792.0</td>\n",
" <td>270.0</td>\n",
" <td>0.0</td>\n",
" <td>465.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>43.0</td>\n",
" <td>34.090909</td>\n",
" <td>0.000000</td>\n",
" <td>58.712121</td>\n",
" <td>0.000000</td>\n",
" <td>1.767677</td>\n",
" <td>5.429293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Economics</td>\n",
" <td>3707.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3707.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Political science and government</td>\n",
" <td>898.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>883.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>0.111359</td>\n",
" <td>0.000000</td>\n",
" <td>98.329621</td>\n",
" <td>0.000000</td>\n",
" <td>1.559020</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Sociology, demography, and population studies</td>\n",
" <td>2908.0</td>\n",
" <td>84.0</td>\n",
" <td>183.0</td>\n",
" <td>2641.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.888583</td>\n",
" <td>6.292985</td>\n",
" <td>90.818432</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Social sciences nec</td>\n",
" <td>5045.0</td>\n",
" <td>2099.0</td>\n",
" <td>176.0</td>\n",
" <td>2366.0</td>\n",
" <td>319.0</td>\n",
" <td>85.0</td>\n",
" <td>0.0</td>\n",
" <td>41.605550</td>\n",
" <td>3.488603</td>\n",
" <td>46.897919</td>\n",
" <td>6.323092</td>\n",
" <td>1.684836</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Sciences nec</td>\n",
" <td>264.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>264.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Engineering</td>\n",
" <td>51029.0</td>\n",
" <td>32570.0</td>\n",
" <td>3216.0</td>\n",
" <td>12977.0</td>\n",
" <td>1162.0</td>\n",
" <td>670.0</td>\n",
" <td>434.0</td>\n",
" <td>63.826452</td>\n",
" <td>6.302299</td>\n",
" <td>25.430637</td>\n",
" <td>2.277137</td>\n",
" <td>1.312979</td>\n",
" <td>0.850497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Aerospace, aeronautical, and astronautical eng...</td>\n",
" <td>4665.0</td>\n",
" <td>3291.0</td>\n",
" <td>50.0</td>\n",
" <td>1324.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>70.546624</td>\n",
" <td>1.071811</td>\n",
" <td>28.381565</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Bioengineering and biomedical engineering</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>Chemical engineering</td>\n",
" <td>6647.0</td>\n",
" <td>3946.0</td>\n",
" <td>20.0</td>\n",
" <td>2164.0</td>\n",
" <td>325.0</td>\n",
" <td>192.0</td>\n",
" <td>0.0</td>\n",
" <td>59.365127</td>\n",
" <td>0.300888</td>\n",
" <td>32.556040</td>\n",
" <td>4.889424</td>\n",
" <td>2.888521</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Civil engineering</td>\n",
" <td>15198.0</td>\n",
" <td>8770.0</td>\n",
" <td>2872.0</td>\n",
" <td>3068.0</td>\n",
" <td>96.0</td>\n",
" <td>119.0</td>\n",
" <td>273.0</td>\n",
" <td>57.704961</td>\n",
" <td>18.897223</td>\n",
" <td>20.186867</td>\n",
" <td>0.631662</td>\n",
" <td>0.782998</td>\n",
" <td>1.796289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Electrical, electronic, and communications eng...</td>\n",
" <td>10299.0</td>\n",
" <td>7380.0</td>\n",
" <td>109.0</td>\n",
" <td>1985.0</td>\n",
" <td>391.0</td>\n",
" <td>346.0</td>\n",
" <td>88.0</td>\n",
" <td>71.657442</td>\n",
" <td>1.058355</td>\n",
" <td>19.273716</td>\n",
" <td>3.796485</td>\n",
" <td>3.359549</td>\n",
" <td>0.854452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Industrial and manufacturing engineering</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>36</th>\n",
" <td>Mechanical engineering</td>\n",
" <td>7056.0</td>\n",
" <td>4641.0</td>\n",
" <td>144.0</td>\n",
" <td>2096.0</td>\n",
" <td>157.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>65.773810</td>\n",
" <td>2.040816</td>\n",
" <td>29.705215</td>\n",
" <td>2.225057</td>\n",
" <td>0.000000</td>\n",
" <td>0.255102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Metallurgical and materials engineering</td>\n",
" <td>6473.0</td>\n",
" <td>3896.0</td>\n",
" <td>21.0</td>\n",
" <td>2308.0</td>\n",
" <td>193.0</td>\n",
" <td>0.0</td>\n",
" <td>55.0</td>\n",
" <td>60.188475</td>\n",
" <td>0.324425</td>\n",
" <td>35.655801</td>\n",
" <td>2.981616</td>\n",
" <td>0.000000</td>\n",
" <td>0.849683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Engineering nec</td>\n",
" <td>691.0</td>\n",
" <td>646.0</td>\n",
" <td>0.0</td>\n",
" <td>32.0</td>\n",
" <td>0.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>93.487699</td>\n",
" <td>0.000000</td>\n",
" <td>4.630970</td>\n",
" <td>0.000000</td>\n",
" <td>1.881331</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Non-S&amp;E</td>\n",
" <td>41017.0</td>\n",
" <td>9417.0</td>\n",
" <td>1135.0</td>\n",
" <td>29623.0</td>\n",
" <td>241.0</td>\n",
" <td>513.0</td>\n",
" <td>88.0</td>\n",
" <td>22.958773</td>\n",
" <td>2.767145</td>\n",
" <td>72.221274</td>\n",
" <td>0.587561</td>\n",
" <td>1.250701</td>\n",
" <td>0.214545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Business management and business administration</td>\n",
" <td>11919.0</td>\n",
" <td>1114.0</td>\n",
" <td>717.0</td>\n",
" <td>9954.0</td>\n",
" <td>123.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>9.346422</td>\n",
" <td>6.015605</td>\n",
" <td>83.513718</td>\n",
" <td>1.031966</td>\n",
" <td>0.092290</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Communication and communications technologies</td>\n",
" <td>2675.0</td>\n",
" <td>47.0</td>\n",
" <td>0.0</td>\n",
" <td>2612.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>1.757009</td>\n",
" <td>0.000000</td>\n",
" <td>97.644860</td>\n",
" <td>0.000000</td>\n",
" <td>0.523364</td>\n",
" <td>0.074766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Education</td>\n",
" <td>9731.0</td>\n",
" <td>4520.0</td>\n",
" <td>379.0</td>\n",
" <td>4688.0</td>\n",
" <td>2.0</td>\n",
" <td>142.0</td>\n",
" <td>0.0</td>\n",
" <td>46.449491</td>\n",
" <td>3.894769</td>\n",
" <td>48.175933</td>\n",
" <td>0.020553</td>\n",
" <td>1.459254</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Humanities</td>\n",
" <td>4813.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>4685.0</td>\n",
" <td>0.0</td>\n",
" <td>51.0</td>\n",
" <td>59.0</td>\n",
" <td>0.373987</td>\n",
" <td>0.000000</td>\n",
" <td>97.340536</td>\n",
" <td>0.000000</td>\n",
" <td>1.059630</td>\n",
" <td>1.225847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Law</td>\n",
" <td>2076.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1876.0</td>\n",
" <td>0.0</td>\n",
" <td>200.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>90.366089</td>\n",
" <td>0.000000</td>\n",
" <td>9.633911</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Social work</td>\n",
" <td>4557.0</td>\n",
" <td>3565.0</td>\n",
" <td>20.0</td>\n",
" <td>914.0</td>\n",
" <td>0.0</td>\n",
" <td>45.0</td>\n",
" <td>13.0</td>\n",
" <td>78.231293</td>\n",
" <td>0.438885</td>\n",
" <td>20.057055</td>\n",
" <td>0.000000</td>\n",
" <td>0.987492</td>\n",
" <td>0.285275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Visual and performing arts</td>\n",
" <td>2803.0</td>\n",
" <td>0.0</td>\n",
" <td>17.0</td>\n",
" <td>2762.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>14.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.606493</td>\n",
" <td>98.537281</td>\n",
" <td>0.000000</td>\n",
" <td>0.356761</td>\n",
" <td>0.499465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Non-S&amp;E nec</td>\n",
" <td>2443.0</td>\n",
" <td>153.0</td>\n",
" <td>2.0</td>\n",
" <td>2132.0</td>\n",
" <td>116.0</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" <td>6.262792</td>\n",
" <td>0.081867</td>\n",
" <td>87.269750</td>\n",
" <td>4.748260</td>\n",
" <td>1.637331</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Field All R&D expenditures \\\n",
"0 All R&D fields 184880.0 \n",
"1 Science 92834.0 \n",
"2 Computer and information sciences 18813.0 \n",
"3 Geosciences, atmospheric sciences, and ocean s... 16822.0 \n",
"4 Atmospheric science and meteorology 0.0 \n",
"5 Geological and earth sciences 14248.0 \n",
"6 Ocean sciences and marine sciences 2574.0 \n",
"7 Geosciences, atmospheric sciences, and ocean s... 0.0 \n",
"8 Life sciences 23853.0 \n",
"9 Agricultural sciences 11.0 \n",
"10 Biological and biomedical sciences 10055.0 \n",
"11 Health sciences 13736.0 \n",
"12 Natural resources and conservation 0.0 \n",
"13 Life sciences nec 51.0 \n",
"14 Mathematics and statistics 2918.0 \n",
"15 Physical sciences 11124.0 \n",
"16 Astronomy and astrophysics 271.0 \n",
"17 Chemistry 7938.0 \n",
"18 Materials science 0.0 \n",
"19 Physics 2911.0 \n",
"20 Physical sciences nec 4.0 \n",
"21 Psychology 5690.0 \n",
"22 Social sciences 13350.0 \n",
"23 Anthropology 792.0 \n",
"24 Economics 3707.0 \n",
"25 Political science and government 898.0 \n",
"26 Sociology, demography, and population studies 2908.0 \n",
"27 Social sciences nec 5045.0 \n",
"28 Sciences nec 264.0 \n",
"29 Engineering 51029.0 \n",
"30 Aerospace, aeronautical, and astronautical eng... 4665.0 \n",
"31 Bioengineering and biomedical engineering 0.0 \n",
"32 Chemical engineering 6647.0 \n",
"33 Civil engineering 15198.0 \n",
"34 Electrical, electronic, and communications eng... 10299.0 \n",
"35 Industrial and manufacturing engineering 0.0 \n",
"36 Mechanical engineering 7056.0 \n",
"37 Metallurgical and materials engineering 6473.0 \n",
"38 Engineering nec 691.0 \n",
"39 Non-S&E 41017.0 \n",
"40 Business management and business administration 11919.0 \n",
"41 Communication and communications technologies 2675.0 \n",
"42 Education 9731.0 \n",
"43 Humanities 4813.0 \n",
"44 Law 2076.0 \n",
"45 Social work 4557.0 \n",
"46 Visual and performing arts 2803.0 \n",
"47 Non-S&E nec 2443.0 \n",
"\n",
" Federal government State and local government Institution funds \\\n",
"0 86244.0 16660.0 77554.0 \n",
"1 44257.0 12309.0 34954.0 \n",
"2 5584.0 9591.0 3595.0 \n",
"3 12947.0 1327.0 2324.0 \n",
"4 0.0 0.0 0.0 \n",
"5 12945.0 21.0 1058.0 \n",
"6 2.0 1306.0 1266.0 \n",
"7 0.0 0.0 0.0 \n",
"8 11858.0 0.0 11487.0 \n",
"9 7.0 0.0 4.0 \n",
"10 4436.0 0.0 5540.0 \n",
"11 7366.0 0.0 5942.0 \n",
"12 0.0 0.0 0.0 \n",
"13 49.0 0.0 1.0 \n",
"14 453.0 1019.0 1418.0 \n",
"15 7960.0 0.0 3148.0 \n",
"16 271.0 0.0 0.0 \n",
"17 5109.0 0.0 2817.0 \n",
"18 0.0 0.0 0.0 \n",
"19 2580.0 0.0 327.0 \n",
"20 0.0 0.0 4.0 \n",
"21 3001.0 13.0 2656.0 \n",
"22 2454.0 359.0 10062.0 \n",
"23 270.0 0.0 465.0 \n",
"24 0.0 0.0 3707.0 \n",
"25 1.0 0.0 883.0 \n",
"26 84.0 183.0 2641.0 \n",
"27 2099.0 176.0 2366.0 \n",
"28 0.0 0.0 264.0 \n",
"29 32570.0 3216.0 12977.0 \n",
"30 3291.0 50.0 1324.0 \n",
"31 0.0 0.0 0.0 \n",
"32 3946.0 20.0 2164.0 \n",
"33 8770.0 2872.0 3068.0 \n",
"34 7380.0 109.0 1985.0 \n",
"35 0.0 0.0 0.0 \n",
"36 4641.0 144.0 2096.0 \n",
"37 3896.0 21.0 2308.0 \n",
"38 646.0 0.0 32.0 \n",
"39 9417.0 1135.0 29623.0 \n",
"40 1114.0 717.0 9954.0 \n",
"41 47.0 0.0 2612.0 \n",
"42 4520.0 379.0 4688.0 \n",
"43 18.0 0.0 4685.0 \n",
"44 0.0 0.0 1876.0 \n",
"45 3565.0 20.0 914.0 \n",
"46 0.0 17.0 2762.0 \n",
"47 153.0 2.0 2132.0 \n",
"\n",
" Business Nonprofit organizations All other sources \\\n",
"0 1973.0 1753.0 696.0 \n",
"1 570.0 570.0 174.0 \n",
"2 0.0 3.0 40.0 \n",
"3 106.0 102.0 16.0 \n",
"4 0.0 0.0 0.0 \n",
"5 106.0 102.0 16.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 141.0 292.0 75.0 \n",
"9 0.0 0.0 0.0 \n",
"10 1.0 62.0 16.0 \n",
"11 140.0 230.0 58.0 \n",
"12 0.0 0.0 0.0 \n",
"13 0.0 0.0 1.0 \n",
"14 0.0 28.0 0.0 \n",
"15 4.0 12.0 0.0 \n",
"16 0.0 0.0 0.0 \n",
"17 0.0 12.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"19 4.0 0.0 0.0 \n",
"20 0.0 0.0 0.0 \n",
"21 0.0 20.0 0.0 \n",
"22 319.0 113.0 43.0 \n",
"23 0.0 14.0 43.0 \n",
"24 0.0 0.0 0.0 \n",
"25 0.0 14.0 0.0 \n",
"26 0.0 0.0 0.0 \n",
"27 319.0 85.0 0.0 \n",
"28 0.0 0.0 0.0 \n",
"29 1162.0 670.0 434.0 \n",
"30 0.0 0.0 0.0 \n",
"31 0.0 0.0 0.0 \n",
"32 325.0 192.0 0.0 \n",
"33 96.0 119.0 273.0 \n",
"34 391.0 346.0 88.0 \n",
"35 0.0 0.0 0.0 \n",
"36 157.0 0.0 18.0 \n",
"37 193.0 0.0 55.0 \n",
"38 0.0 13.0 0.0 \n",
"39 241.0 513.0 88.0 \n",
"40 123.0 11.0 0.0 \n",
"41 0.0 14.0 2.0 \n",
"42 2.0 142.0 0.0 \n",
"43 0.0 51.0 59.0 \n",
"44 0.0 200.0 0.0 \n",
"45 0.0 45.0 13.0 \n",
"46 0.0 10.0 14.0 \n",
"47 116.0 40.0 0.0 \n",
"\n",
" Federal government Percent State and local government Percent \\\n",
"0 46.648637 9.011251 \n",
"1 47.673266 13.259151 \n",
"2 29.681603 50.980705 \n",
"3 76.964689 7.888479 \n",
"4 NaN NaN \n",
"5 90.854857 0.147389 \n",
"6 0.077700 50.738151 \n",
"7 NaN NaN \n",
"8 49.712824 0.000000 \n",
"9 63.636364 0.000000 \n",
"10 44.117355 0.000000 \n",
"11 53.625510 0.000000 \n",
"12 NaN NaN \n",
"13 96.078431 0.000000 \n",
"14 15.524332 34.921179 \n",
"15 71.556994 0.000000 \n",
"16 100.000000 0.000000 \n",
"17 64.361300 0.000000 \n",
"18 NaN NaN \n",
"19 88.629337 0.000000 \n",
"20 0.000000 0.000000 \n",
"21 52.741652 0.228471 \n",
"22 18.382022 2.689139 \n",
"23 34.090909 0.000000 \n",
"24 0.000000 0.000000 \n",
"25 0.111359 0.000000 \n",
"26 2.888583 6.292985 \n",
"27 41.605550 3.488603 \n",
"28 0.000000 0.000000 \n",
"29 63.826452 6.302299 \n",
"30 70.546624 1.071811 \n",
"31 NaN NaN \n",
"32 59.365127 0.300888 \n",
"33 57.704961 18.897223 \n",
"34 71.657442 1.058355 \n",
"35 NaN NaN \n",
"36 65.773810 2.040816 \n",
"37 60.188475 0.324425 \n",
"38 93.487699 0.000000 \n",
"39 22.958773 2.767145 \n",
"40 9.346422 6.015605 \n",
"41 1.757009 0.000000 \n",
"42 46.449491 3.894769 \n",
"43 0.373987 0.000000 \n",
"44 0.000000 0.000000 \n",
"45 78.231293 0.438885 \n",
"46 0.000000 0.606493 \n",
"47 6.262792 0.081867 \n",
"\n",
" Institution funds Percent Business Percent \\\n",
"0 41.948291 1.067179 \n",
"1 37.652153 0.613999 \n",
"2 19.109127 0.000000 \n",
"3 13.815242 0.630127 \n",
"4 NaN NaN \n",
"5 7.425604 0.743964 \n",
"6 49.184149 0.000000 \n",
"7 NaN NaN \n",
"8 48.157464 0.591121 \n",
"9 36.363636 0.000000 \n",
"10 55.096967 0.009945 \n",
"11 43.258591 1.019220 \n",
"12 NaN NaN \n",
"13 1.960784 0.000000 \n",
"14 48.594928 0.000000 \n",
"15 28.299173 0.035958 \n",
"16 0.000000 0.000000 \n",
"17 35.487528 0.000000 \n",
"18 NaN NaN \n",
"19 11.233253 0.137410 \n",
"20 100.000000 0.000000 \n",
"21 46.678383 0.000000 \n",
"22 75.370787 2.389513 \n",
"23 58.712121 0.000000 \n",
"24 100.000000 0.000000 \n",
"25 98.329621 0.000000 \n",
"26 90.818432 0.000000 \n",
"27 46.897919 6.323092 \n",
"28 100.000000 0.000000 \n",
"29 25.430637 2.277137 \n",
"30 28.381565 0.000000 \n",
"31 NaN NaN \n",
"32 32.556040 4.889424 \n",
"33 20.186867 0.631662 \n",
"34 19.273716 3.796485 \n",
"35 NaN NaN \n",
"36 29.705215 2.225057 \n",
"37 35.655801 2.981616 \n",
"38 4.630970 0.000000 \n",
"39 72.221274 0.587561 \n",
"40 83.513718 1.031966 \n",
"41 97.644860 0.000000 \n",
"42 48.175933 0.020553 \n",
"43 97.340536 0.000000 \n",
"44 90.366089 0.000000 \n",
"45 20.057055 0.000000 \n",
"46 98.537281 0.000000 \n",
"47 87.269750 4.748260 \n",
"\n",
" Nonprofit organizations Percent All other sources Percent \n",
"0 0.948183 0.376460 \n",
"1 0.613999 0.187431 \n",
"2 0.015946 0.212619 \n",
"3 0.606349 0.095114 \n",
"4 NaN NaN \n",
"5 0.715890 0.112296 \n",
"6 0.000000 0.000000 \n",
"7 NaN NaN \n",
"8 1.224165 0.314426 \n",
"9 0.000000 0.000000 \n",
"10 0.616609 0.159125 \n",
"11 1.674432 0.422248 \n",
"12 NaN NaN \n",
"13 0.000000 1.960784 \n",
"14 0.959561 0.000000 \n",
"15 0.107875 0.000000 \n",
"16 0.000000 0.000000 \n",
"17 0.151172 0.000000 \n",
"18 NaN NaN \n",
"19 0.000000 0.000000 \n",
"20 0.000000 0.000000 \n",
"21 0.351494 0.000000 \n",
"22 0.846442 0.322097 \n",
"23 1.767677 5.429293 \n",
"24 0.000000 0.000000 \n",
"25 1.559020 0.000000 \n",
"26 0.000000 0.000000 \n",
"27 1.684836 0.000000 \n",
"28 0.000000 0.000000 \n",
"29 1.312979 0.850497 \n",
"30 0.000000 0.000000 \n",
"31 NaN NaN \n",
"32 2.888521 0.000000 \n",
"33 0.782998 1.796289 \n",
"34 3.359549 0.854452 \n",
"35 NaN NaN \n",
"36 0.000000 0.255102 \n",
"37 0.000000 0.849683 \n",
"38 1.881331 0.000000 \n",
"39 1.250701 0.214545 \n",
"40 0.092290 0.000000 \n",
"41 0.523364 0.074766 \n",
"42 1.459254 0.000000 \n",
"43 1.059630 1.225847 \n",
"44 9.633911 0.000000 \n",
"45 0.987492 0.285275 \n",
"46 0.356761 0.499465 \n",
"47 1.637331 0.000000 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel(\"data/ua.xlsx\", header=3)\n",
"df = df[:-4][[field for field in df[:-4] if 'Unnamed' not in field]]\n",
"key_all = \"All R&D expenditures\"\n",
"key_federal = \"Federal government\"\n",
"key_state = \"State and local government\"\n",
"key_inst = \"Institution funds\"\n",
"key_business = \"Business\"\n",
"key_nonprofit = \"Nonprofit organizations\"\n",
"key_other = \"All other sources\"\n",
"keys = [key_federal, key_state, key_inst, key_business, key_nonprofit, key_other]\n",
"for key in keys:\n",
" key_percent = key + \" Percent\"\n",
" df[key_percent] = df[key] / df[key_all] * 100\n",
"df.sort_values(key_all)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "45b02475-f4cb-4830-8367-05ef5eefaaee",
"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>Field</th>\n",
" <th>All R&amp;D expenditures</th>\n",
" <th>Federal government</th>\n",
" <th>State and local government</th>\n",
" <th>Institution funds</th>\n",
" <th>Business</th>\n",
" <th>Nonprofit organizations</th>\n",
" <th>All other sources</th>\n",
" <th>Federal government Percent</th>\n",
" <th>State and local government Percent</th>\n",
" <th>Institution funds Percent</th>\n",
" <th>Business Percent</th>\n",
" <th>Nonprofit organizations Percent</th>\n",
" <th>All other sources Percent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Economics</td>\n",
" <td>3707.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3707.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Physical sciences nec</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Sciences nec</td>\n",
" <td>264.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>264.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Visual and performing arts</td>\n",
" <td>2803.0</td>\n",
" <td>0.0</td>\n",
" <td>17.0</td>\n",
" <td>2762.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>14.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.606493</td>\n",
" <td>98.537281</td>\n",
" <td>0.000000</td>\n",
" <td>0.356761</td>\n",
" <td>0.499465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Political science and government</td>\n",
" <td>898.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>883.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
" <td>0.111359</td>\n",
" <td>0.000000</td>\n",
" <td>98.329621</td>\n",
" <td>0.000000</td>\n",
" <td>1.559020</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Communication and communications technologies</td>\n",
" <td>2675.0</td>\n",
" <td>47.0</td>\n",
" <td>0.0</td>\n",
" <td>2612.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>1.757009</td>\n",
" <td>0.000000</td>\n",
" <td>97.644860</td>\n",
" <td>0.000000</td>\n",
" <td>0.523364</td>\n",
" <td>0.074766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Humanities</td>\n",
" <td>4813.0</td>\n",
" <td>18.0</td>\n",
" <td>0.0</td>\n",
" <td>4685.0</td>\n",
" <td>0.0</td>\n",
" <td>51.0</td>\n",
" <td>59.0</td>\n",
" <td>0.373987</td>\n",
" <td>0.000000</td>\n",
" <td>97.340536</td>\n",
" <td>0.000000</td>\n",
" <td>1.059630</td>\n",
" <td>1.225847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Sociology, demography, and population studies</td>\n",
" <td>2908.0</td>\n",
" <td>84.0</td>\n",
" <td>183.0</td>\n",
" <td>2641.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.888583</td>\n",
" <td>6.292985</td>\n",
" <td>90.818432</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Law</td>\n",
" <td>2076.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1876.0</td>\n",
" <td>0.0</td>\n",
" <td>200.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>90.366089</td>\n",
" <td>0.000000</td>\n",
" <td>9.633911</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Non-S&amp;E nec</td>\n",
" <td>2443.0</td>\n",
" <td>153.0</td>\n",
" <td>2.0</td>\n",
" <td>2132.0</td>\n",
" <td>116.0</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" <td>6.262792</td>\n",
" <td>0.081867</td>\n",
" <td>87.269750</td>\n",
" <td>4.748260</td>\n",
" <td>1.637331</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Business management and business administration</td>\n",
" <td>11919.0</td>\n",
" <td>1114.0</td>\n",
" <td>717.0</td>\n",
" <td>9954.0</td>\n",
" <td>123.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>9.346422</td>\n",
" <td>6.015605</td>\n",
" <td>83.513718</td>\n",
" <td>1.031966</td>\n",
" <td>0.092290</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Social sciences</td>\n",
" <td>13350.0</td>\n",
" <td>2454.0</td>\n",
" <td>359.0</td>\n",
" <td>10062.0</td>\n",
" <td>319.0</td>\n",
" <td>113.0</td>\n",
" <td>43.0</td>\n",
" <td>18.382022</td>\n",
" <td>2.689139</td>\n",
" <td>75.370787</td>\n",
" <td>2.389513</td>\n",
" <td>0.846442</td>\n",
" <td>0.322097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Non-S&amp;E</td>\n",
" <td>41017.0</td>\n",
" <td>9417.0</td>\n",
" <td>1135.0</td>\n",
" <td>29623.0</td>\n",
" <td>241.0</td>\n",
" <td>513.0</td>\n",
" <td>88.0</td>\n",
" <td>22.958773</td>\n",
" <td>2.767145</td>\n",
" <td>72.221274</td>\n",
" <td>0.587561</td>\n",
" <td>1.250701</td>\n",
" <td>0.214545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Anthropology</td>\n",
" <td>792.0</td>\n",
" <td>270.0</td>\n",
" <td>0.0</td>\n",
" <td>465.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>43.0</td>\n",
" <td>34.090909</td>\n",
" <td>0.000000</td>\n",
" <td>58.712121</td>\n",
" <td>0.000000</td>\n",
" <td>1.767677</td>\n",
" <td>5.429293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Biological and biomedical sciences</td>\n",
" <td>10055.0</td>\n",
" <td>4436.0</td>\n",
" <td>0.0</td>\n",
" <td>5540.0</td>\n",
" <td>1.0</td>\n",
" <td>62.0</td>\n",
" <td>16.0</td>\n",
" <td>44.117355</td>\n",
" <td>0.000000</td>\n",
" <td>55.096967</td>\n",
" <td>0.009945</td>\n",
" <td>0.616609</td>\n",
" <td>0.159125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Ocean sciences and marine sciences</td>\n",
" <td>2574.0</td>\n",
" <td>2.0</td>\n",
" <td>1306.0</td>\n",
" <td>1266.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.077700</td>\n",
" <td>50.738151</td>\n",
" <td>49.184149</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Mathematics and statistics</td>\n",
" <td>2918.0</td>\n",
" <td>453.0</td>\n",
" <td>1019.0</td>\n",
" <td>1418.0</td>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>15.524332</td>\n",
" <td>34.921179</td>\n",
" <td>48.594928</td>\n",
" <td>0.000000</td>\n",
" <td>0.959561</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Education</td>\n",
" <td>9731.0</td>\n",
" <td>4520.0</td>\n",
" <td>379.0</td>\n",
" <td>4688.0</td>\n",
" <td>2.0</td>\n",
" <td>142.0</td>\n",
" <td>0.0</td>\n",
" <td>46.449491</td>\n",
" <td>3.894769</td>\n",
" <td>48.175933</td>\n",
" <td>0.020553</td>\n",
" <td>1.459254</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Life sciences</td>\n",
" <td>23853.0</td>\n",
" <td>11858.0</td>\n",
" <td>0.0</td>\n",
" <td>11487.0</td>\n",
" <td>141.0</td>\n",
" <td>292.0</td>\n",
" <td>75.0</td>\n",
" <td>49.712824</td>\n",
" <td>0.000000</td>\n",
" <td>48.157464</td>\n",
" <td>0.591121</td>\n",
" <td>1.224165</td>\n",
" <td>0.314426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Social sciences nec</td>\n",
" <td>5045.0</td>\n",
" <td>2099.0</td>\n",
" <td>176.0</td>\n",
" <td>2366.0</td>\n",
" <td>319.0</td>\n",
" <td>85.0</td>\n",
" <td>0.0</td>\n",
" <td>41.605550</td>\n",
" <td>3.488603</td>\n",
" <td>46.897919</td>\n",
" <td>6.323092</td>\n",
" <td>1.684836</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Psychology</td>\n",
" <td>5690.0</td>\n",
" <td>3001.0</td>\n",
" <td>13.0</td>\n",
" <td>2656.0</td>\n",
" <td>0.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>52.741652</td>\n",
" <td>0.228471</td>\n",
" <td>46.678383</td>\n",
" <td>0.000000</td>\n",
" <td>0.351494</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Health sciences</td>\n",
" <td>13736.0</td>\n",
" <td>7366.0</td>\n",
" <td>0.0</td>\n",
" <td>5942.0</td>\n",
" <td>140.0</td>\n",
" <td>230.0</td>\n",
" <td>58.0</td>\n",
" <td>53.625510</td>\n",
" <td>0.000000</td>\n",
" <td>43.258591</td>\n",
" <td>1.019220</td>\n",
" <td>1.674432</td>\n",
" <td>0.422248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>All R&amp;D fields</td>\n",
" <td>184880.0</td>\n",
" <td>86244.0</td>\n",
" <td>16660.0</td>\n",
" <td>77554.0</td>\n",
" <td>1973.0</td>\n",
" <td>1753.0</td>\n",
" <td>696.0</td>\n",
" <td>46.648637</td>\n",
" <td>9.011251</td>\n",
" <td>41.948291</td>\n",
" <td>1.067179</td>\n",
" <td>0.948183</td>\n",
" <td>0.376460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Science</td>\n",
" <td>92834.0</td>\n",
" <td>44257.0</td>\n",
" <td>12309.0</td>\n",
" <td>34954.0</td>\n",
" <td>570.0</td>\n",
" <td>570.0</td>\n",
" <td>174.0</td>\n",
" <td>47.673266</td>\n",
" <td>13.259151</td>\n",
" <td>37.652153</td>\n",
" <td>0.613999</td>\n",
" <td>0.613999</td>\n",
" <td>0.187431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Agricultural sciences</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>63.636364</td>\n",
" <td>0.000000</td>\n",
" <td>36.363636</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Metallurgical and materials engineering</td>\n",
" <td>6473.0</td>\n",
" <td>3896.0</td>\n",
" <td>21.0</td>\n",
" <td>2308.0</td>\n",
" <td>193.0</td>\n",
" <td>0.0</td>\n",
" <td>55.0</td>\n",
" <td>60.188475</td>\n",
" <td>0.324425</td>\n",
" <td>35.655801</td>\n",
" <td>2.981616</td>\n",
" <td>0.000000</td>\n",
" <td>0.849683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Chemistry</td>\n",
" <td>7938.0</td>\n",
" <td>5109.0</td>\n",
" <td>0.0</td>\n",
" <td>2817.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>64.361300</td>\n",
" <td>0.000000</td>\n",
" <td>35.487528</td>\n",
" <td>0.000000</td>\n",
" <td>0.151172</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Chemical engineering</td>\n",
" <td>6647.0</td>\n",
" <td>3946.0</td>\n",
" <td>20.0</td>\n",
" <td>2164.0</td>\n",
" <td>325.0</td>\n",
" <td>192.0</td>\n",
" <td>0.0</td>\n",
" <td>59.365127</td>\n",
" <td>0.300888</td>\n",
" <td>32.556040</td>\n",
" <td>4.889424</td>\n",
" <td>2.888521</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Mechanical engineering</td>\n",
" <td>7056.0</td>\n",
" <td>4641.0</td>\n",
" <td>144.0</td>\n",
" <td>2096.0</td>\n",
" <td>157.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>65.773810</td>\n",
" <td>2.040816</td>\n",
" <td>29.705215</td>\n",
" <td>2.225057</td>\n",
" <td>0.000000</td>\n",
" <td>0.255102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Aerospace, aeronautical, and astronautical eng...</td>\n",
" <td>4665.0</td>\n",
" <td>3291.0</td>\n",
" <td>50.0</td>\n",
" <td>1324.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>70.546624</td>\n",
" <td>1.071811</td>\n",
" <td>28.381565</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Physical sciences</td>\n",
" <td>11124.0</td>\n",
" <td>7960.0</td>\n",
" <td>0.0</td>\n",
" <td>3148.0</td>\n",
" <td>4.0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>71.556994</td>\n",
" <td>0.000000</td>\n",
" <td>28.299173</td>\n",
" <td>0.035958</td>\n",
" <td>0.107875</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Engineering</td>\n",
" <td>51029.0</td>\n",
" <td>32570.0</td>\n",
" <td>3216.0</td>\n",
" <td>12977.0</td>\n",
" <td>1162.0</td>\n",
" <td>670.0</td>\n",
" <td>434.0</td>\n",
" <td>63.826452</td>\n",
" <td>6.302299</td>\n",
" <td>25.430637</td>\n",
" <td>2.277137</td>\n",
" <td>1.312979</td>\n",
" <td>0.850497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Civil engineering</td>\n",
" <td>15198.0</td>\n",
" <td>8770.0</td>\n",
" <td>2872.0</td>\n",
" <td>3068.0</td>\n",
" <td>96.0</td>\n",
" <td>119.0</td>\n",
" <td>273.0</td>\n",
" <td>57.704961</td>\n",
" <td>18.897223</td>\n",
" <td>20.186867</td>\n",
" <td>0.631662</td>\n",
" <td>0.782998</td>\n",
" <td>1.796289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Social work</td>\n",
" <td>4557.0</td>\n",
" <td>3565.0</td>\n",
" <td>20.0</td>\n",
" <td>914.0</td>\n",
" <td>0.0</td>\n",
" <td>45.0</td>\n",
" <td>13.0</td>\n",
" <td>78.231293</td>\n",
" <td>0.438885</td>\n",
" <td>20.057055</td>\n",
" <td>0.000000</td>\n",
" <td>0.987492</td>\n",
" <td>0.285275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Electrical, electronic, and communications eng...</td>\n",
" <td>10299.0</td>\n",
" <td>7380.0</td>\n",
" <td>109.0</td>\n",
" <td>1985.0</td>\n",
" <td>391.0</td>\n",
" <td>346.0</td>\n",
" <td>88.0</td>\n",
" <td>71.657442</td>\n",
" <td>1.058355</td>\n",
" <td>19.273716</td>\n",
" <td>3.796485</td>\n",
" <td>3.359549</td>\n",
" <td>0.854452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Computer and information sciences</td>\n",
" <td>18813.0</td>\n",
" <td>5584.0</td>\n",
" <td>9591.0</td>\n",
" <td>3595.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>40.0</td>\n",
" <td>29.681603</td>\n",
" <td>50.980705</td>\n",
" <td>19.109127</td>\n",
" <td>0.000000</td>\n",
" <td>0.015946</td>\n",
" <td>0.212619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Geosciences, atmospheric sciences, and ocean s...</td>\n",
" <td>16822.0</td>\n",
" <td>12947.0</td>\n",
" <td>1327.0</td>\n",
" <td>2324.0</td>\n",
" <td>106.0</td>\n",
" <td>102.0</td>\n",
" <td>16.0</td>\n",
" <td>76.964689</td>\n",
" <td>7.888479</td>\n",
" <td>13.815242</td>\n",
" <td>0.630127</td>\n",
" <td>0.606349</td>\n",
" <td>0.095114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Physics</td>\n",
" <td>2911.0</td>\n",
" <td>2580.0</td>\n",
" <td>0.0</td>\n",
" <td>327.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>88.629337</td>\n",
" <td>0.000000</td>\n",
" <td>11.233253</td>\n",
" <td>0.137410</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Geological and earth sciences</td>\n",
" <td>14248.0</td>\n",
" <td>12945.0</td>\n",
" <td>21.0</td>\n",
" <td>1058.0</td>\n",
" <td>106.0</td>\n",
" <td>102.0</td>\n",
" <td>16.0</td>\n",
" <td>90.854857</td>\n",
" <td>0.147389</td>\n",
" <td>7.425604</td>\n",
" <td>0.743964</td>\n",
" <td>0.715890</td>\n",
" <td>0.112296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Engineering nec</td>\n",
" <td>691.0</td>\n",
" <td>646.0</td>\n",
" <td>0.0</td>\n",
" <td>32.0</td>\n",
" <td>0.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>93.487699</td>\n",
" <td>0.000000</td>\n",
" <td>4.630970</td>\n",
" <td>0.000000</td>\n",
" <td>1.881331</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Life sciences nec</td>\n",
" <td>51.0</td>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>96.078431</td>\n",
" <td>0.000000</td>\n",
" <td>1.960784</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.960784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Astronomy and astrophysics</td>\n",
" <td>271.0</td>\n",
" <td>271.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>100.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Atmospheric science and meteorology</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>7</th>\n",
" <td>Geosciences, atmospheric sciences, and ocean s...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>12</th>\n",
" <td>Natural resources and conservation</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>Materials science</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>Bioengineering and biomedical engineering</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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>35</th>\n",
" <td>Industrial and manufacturing engineering</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</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",
"</table>\n",
"</div>"
],
"text/plain": [
" Field All R&D expenditures \\\n",
"24 Economics 3707.0 \n",
"20 Physical sciences nec 4.0 \n",
"28 Sciences nec 264.0 \n",
"46 Visual and performing arts 2803.0 \n",
"25 Political science and government 898.0 \n",
"41 Communication and communications technologies 2675.0 \n",
"43 Humanities 4813.0 \n",
"26 Sociology, demography, and population studies 2908.0 \n",
"44 Law 2076.0 \n",
"47 Non-S&E nec 2443.0 \n",
"40 Business management and business administration 11919.0 \n",
"22 Social sciences 13350.0 \n",
"39 Non-S&E 41017.0 \n",
"23 Anthropology 792.0 \n",
"10 Biological and biomedical sciences 10055.0 \n",
"6 Ocean sciences and marine sciences 2574.0 \n",
"14 Mathematics and statistics 2918.0 \n",
"42 Education 9731.0 \n",
"8 Life sciences 23853.0 \n",
"27 Social sciences nec 5045.0 \n",
"21 Psychology 5690.0 \n",
"11 Health sciences 13736.0 \n",
"0 All R&D fields 184880.0 \n",
"1 Science 92834.0 \n",
"9 Agricultural sciences 11.0 \n",
"37 Metallurgical and materials engineering 6473.0 \n",
"17 Chemistry 7938.0 \n",
"32 Chemical engineering 6647.0 \n",
"36 Mechanical engineering 7056.0 \n",
"30 Aerospace, aeronautical, and astronautical eng... 4665.0 \n",
"15 Physical sciences 11124.0 \n",
"29 Engineering 51029.0 \n",
"33 Civil engineering 15198.0 \n",
"45 Social work 4557.0 \n",
"34 Electrical, electronic, and communications eng... 10299.0 \n",
"2 Computer and information sciences 18813.0 \n",
"3 Geosciences, atmospheric sciences, and ocean s... 16822.0 \n",
"19 Physics 2911.0 \n",
"5 Geological and earth sciences 14248.0 \n",
"38 Engineering nec 691.0 \n",
"13 Life sciences nec 51.0 \n",
"16 Astronomy and astrophysics 271.0 \n",
"4 Atmospheric science and meteorology 0.0 \n",
"7 Geosciences, atmospheric sciences, and ocean s... 0.0 \n",
"12 Natural resources and conservation 0.0 \n",
"18 Materials science 0.0 \n",
"31 Bioengineering and biomedical engineering 0.0 \n",
"35 Industrial and manufacturing engineering 0.0 \n",
"\n",
" Federal government State and local government Institution funds \\\n",
"24 0.0 0.0 3707.0 \n",
"20 0.0 0.0 4.0 \n",
"28 0.0 0.0 264.0 \n",
"46 0.0 17.0 2762.0 \n",
"25 1.0 0.0 883.0 \n",
"41 47.0 0.0 2612.0 \n",
"43 18.0 0.0 4685.0 \n",
"26 84.0 183.0 2641.0 \n",
"44 0.0 0.0 1876.0 \n",
"47 153.0 2.0 2132.0 \n",
"40 1114.0 717.0 9954.0 \n",
"22 2454.0 359.0 10062.0 \n",
"39 9417.0 1135.0 29623.0 \n",
"23 270.0 0.0 465.0 \n",
"10 4436.0 0.0 5540.0 \n",
"6 2.0 1306.0 1266.0 \n",
"14 453.0 1019.0 1418.0 \n",
"42 4520.0 379.0 4688.0 \n",
"8 11858.0 0.0 11487.0 \n",
"27 2099.0 176.0 2366.0 \n",
"21 3001.0 13.0 2656.0 \n",
"11 7366.0 0.0 5942.0 \n",
"0 86244.0 16660.0 77554.0 \n",
"1 44257.0 12309.0 34954.0 \n",
"9 7.0 0.0 4.0 \n",
"37 3896.0 21.0 2308.0 \n",
"17 5109.0 0.0 2817.0 \n",
"32 3946.0 20.0 2164.0 \n",
"36 4641.0 144.0 2096.0 \n",
"30 3291.0 50.0 1324.0 \n",
"15 7960.0 0.0 3148.0 \n",
"29 32570.0 3216.0 12977.0 \n",
"33 8770.0 2872.0 3068.0 \n",
"45 3565.0 20.0 914.0 \n",
"34 7380.0 109.0 1985.0 \n",
"2 5584.0 9591.0 3595.0 \n",
"3 12947.0 1327.0 2324.0 \n",
"19 2580.0 0.0 327.0 \n",
"5 12945.0 21.0 1058.0 \n",
"38 646.0 0.0 32.0 \n",
"13 49.0 0.0 1.0 \n",
"16 271.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"12 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"31 0.0 0.0 0.0 \n",
"35 0.0 0.0 0.0 \n",
"\n",
" Business Nonprofit organizations All other sources \\\n",
"24 0.0 0.0 0.0 \n",
"20 0.0 0.0 0.0 \n",
"28 0.0 0.0 0.0 \n",
"46 0.0 10.0 14.0 \n",
"25 0.0 14.0 0.0 \n",
"41 0.0 14.0 2.0 \n",
"43 0.0 51.0 59.0 \n",
"26 0.0 0.0 0.0 \n",
"44 0.0 200.0 0.0 \n",
"47 116.0 40.0 0.0 \n",
"40 123.0 11.0 0.0 \n",
"22 319.0 113.0 43.0 \n",
"39 241.0 513.0 88.0 \n",
"23 0.0 14.0 43.0 \n",
"10 1.0 62.0 16.0 \n",
"6 0.0 0.0 0.0 \n",
"14 0.0 28.0 0.0 \n",
"42 2.0 142.0 0.0 \n",
"8 141.0 292.0 75.0 \n",
"27 319.0 85.0 0.0 \n",
"21 0.0 20.0 0.0 \n",
"11 140.0 230.0 58.0 \n",
"0 1973.0 1753.0 696.0 \n",
"1 570.0 570.0 174.0 \n",
"9 0.0 0.0 0.0 \n",
"37 193.0 0.0 55.0 \n",
"17 0.0 12.0 0.0 \n",
"32 325.0 192.0 0.0 \n",
"36 157.0 0.0 18.0 \n",
"30 0.0 0.0 0.0 \n",
"15 4.0 12.0 0.0 \n",
"29 1162.0 670.0 434.0 \n",
"33 96.0 119.0 273.0 \n",
"45 0.0 45.0 13.0 \n",
"34 391.0 346.0 88.0 \n",
"2 0.0 3.0 40.0 \n",
"3 106.0 102.0 16.0 \n",
"19 4.0 0.0 0.0 \n",
"5 106.0 102.0 16.0 \n",
"38 0.0 13.0 0.0 \n",
"13 0.0 0.0 1.0 \n",
"16 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"12 0.0 0.0 0.0 \n",
"18 0.0 0.0 0.0 \n",
"31 0.0 0.0 0.0 \n",
"35 0.0 0.0 0.0 \n",
"\n",
" Federal government Percent State and local government Percent \\\n",
"24 0.000000 0.000000 \n",
"20 0.000000 0.000000 \n",
"28 0.000000 0.000000 \n",
"46 0.000000 0.606493 \n",
"25 0.111359 0.000000 \n",
"41 1.757009 0.000000 \n",
"43 0.373987 0.000000 \n",
"26 2.888583 6.292985 \n",
"44 0.000000 0.000000 \n",
"47 6.262792 0.081867 \n",
"40 9.346422 6.015605 \n",
"22 18.382022 2.689139 \n",
"39 22.958773 2.767145 \n",
"23 34.090909 0.000000 \n",
"10 44.117355 0.000000 \n",
"6 0.077700 50.738151 \n",
"14 15.524332 34.921179 \n",
"42 46.449491 3.894769 \n",
"8 49.712824 0.000000 \n",
"27 41.605550 3.488603 \n",
"21 52.741652 0.228471 \n",
"11 53.625510 0.000000 \n",
"0 46.648637 9.011251 \n",
"1 47.673266 13.259151 \n",
"9 63.636364 0.000000 \n",
"37 60.188475 0.324425 \n",
"17 64.361300 0.000000 \n",
"32 59.365127 0.300888 \n",
"36 65.773810 2.040816 \n",
"30 70.546624 1.071811 \n",
"15 71.556994 0.000000 \n",
"29 63.826452 6.302299 \n",
"33 57.704961 18.897223 \n",
"45 78.231293 0.438885 \n",
"34 71.657442 1.058355 \n",
"2 29.681603 50.980705 \n",
"3 76.964689 7.888479 \n",
"19 88.629337 0.000000 \n",
"5 90.854857 0.147389 \n",
"38 93.487699 0.000000 \n",
"13 96.078431 0.000000 \n",
"16 100.000000 0.000000 \n",
"4 NaN NaN \n",
"7 NaN NaN \n",
"12 NaN NaN \n",
"18 NaN NaN \n",
"31 NaN NaN \n",
"35 NaN NaN \n",
"\n",
" Institution funds Percent Business Percent \\\n",
"24 100.000000 0.000000 \n",
"20 100.000000 0.000000 \n",
"28 100.000000 0.000000 \n",
"46 98.537281 0.000000 \n",
"25 98.329621 0.000000 \n",
"41 97.644860 0.000000 \n",
"43 97.340536 0.000000 \n",
"26 90.818432 0.000000 \n",
"44 90.366089 0.000000 \n",
"47 87.269750 4.748260 \n",
"40 83.513718 1.031966 \n",
"22 75.370787 2.389513 \n",
"39 72.221274 0.587561 \n",
"23 58.712121 0.000000 \n",
"10 55.096967 0.009945 \n",
"6 49.184149 0.000000 \n",
"14 48.594928 0.000000 \n",
"42 48.175933 0.020553 \n",
"8 48.157464 0.591121 \n",
"27 46.897919 6.323092 \n",
"21 46.678383 0.000000 \n",
"11 43.258591 1.019220 \n",
"0 41.948291 1.067179 \n",
"1 37.652153 0.613999 \n",
"9 36.363636 0.000000 \n",
"37 35.655801 2.981616 \n",
"17 35.487528 0.000000 \n",
"32 32.556040 4.889424 \n",
"36 29.705215 2.225057 \n",
"30 28.381565 0.000000 \n",
"15 28.299173 0.035958 \n",
"29 25.430637 2.277137 \n",
"33 20.186867 0.631662 \n",
"45 20.057055 0.000000 \n",
"34 19.273716 3.796485 \n",
"2 19.109127 0.000000 \n",
"3 13.815242 0.630127 \n",
"19 11.233253 0.137410 \n",
"5 7.425604 0.743964 \n",
"38 4.630970 0.000000 \n",
"13 1.960784 0.000000 \n",
"16 0.000000 0.000000 \n",
"4 NaN NaN \n",
"7 NaN NaN \n",
"12 NaN NaN \n",
"18 NaN NaN \n",
"31 NaN NaN \n",
"35 NaN NaN \n",
"\n",
" Nonprofit organizations Percent All other sources Percent \n",
"24 0.000000 0.000000 \n",
"20 0.000000 0.000000 \n",
"28 0.000000 0.000000 \n",
"46 0.356761 0.499465 \n",
"25 1.559020 0.000000 \n",
"41 0.523364 0.074766 \n",
"43 1.059630 1.225847 \n",
"26 0.000000 0.000000 \n",
"44 9.633911 0.000000 \n",
"47 1.637331 0.000000 \n",
"40 0.092290 0.000000 \n",
"22 0.846442 0.322097 \n",
"39 1.250701 0.214545 \n",
"23 1.767677 5.429293 \n",
"10 0.616609 0.159125 \n",
"6 0.000000 0.000000 \n",
"14 0.959561 0.000000 \n",
"42 1.459254 0.000000 \n",
"8 1.224165 0.314426 \n",
"27 1.684836 0.000000 \n",
"21 0.351494 0.000000 \n",
"11 1.674432 0.422248 \n",
"0 0.948183 0.376460 \n",
"1 0.613999 0.187431 \n",
"9 0.000000 0.000000 \n",
"37 0.000000 0.849683 \n",
"17 0.151172 0.000000 \n",
"32 2.888521 0.000000 \n",
"36 0.000000 0.255102 \n",
"30 0.000000 0.000000 \n",
"15 0.107875 0.000000 \n",
"29 1.312979 0.850497 \n",
"33 0.782998 1.796289 \n",
"45 0.987492 0.285275 \n",
"34 3.359549 0.854452 \n",
"2 0.015946 0.212619 \n",
"3 0.606349 0.095114 \n",
"19 0.000000 0.000000 \n",
"5 0.715890 0.112296 \n",
"38 1.881331 0.000000 \n",
"13 0.000000 1.960784 \n",
"16 0.000000 0.000000 \n",
"4 NaN NaN \n",
"7 NaN NaN \n",
"12 NaN NaN \n",
"18 NaN NaN \n",
"31 NaN NaN \n",
"35 NaN NaN "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sort_values(key_inst + \" Percent\", ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "2455fa7a-60cc-44e3-9682-76012682503b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7d90e330f4a0>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_m = data[mask]\n",
"for key in data_m.academics.program_percentage.fields:\n",
" mean = ak.mean(data_m.academics.program_percentage[key], axis=0)\n",
" if ak.mean(mean) < 0.03:\n",
" continue\n",
" plt.plot(mean * 100, label=key)\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Percent Enrollment\")\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "8253df27-7f36-4bf4-9e8f-b6462e0a774c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAHFCAYAAADxOP3DAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAARGtJREFUeJzt3XlclPX+///nCDIgAoEkiLKplZI7mlsmZu6pWXlscy2PC2ZmZXr8lsupsEWzTmhHT2lmlpVpnrQMc/9oxz23TqahmIqkKbgkKrx/f/RjTgNoDA6MeD3ut9vcbs37el/X9Zr3aPP0uq73ddmMMUYAAAAWVc7TBQAAAHgSYQgAAFgaYQgAAFgaYQgAAFgaYQgAAFgaYQgAAFgaYQgAAFgaYQgAAFgaYQgAAFgaYQhlzuzZs2Wz2S77WrVqlUfqSkhIUEJCglObzWbT+PHjPVJPUe3Zs0fjx4/XgQMHitQ///j7+voqPDxcbdq0UVJSkjIyMgqsM378eNlsNpfqOnfunMaPH+/y91nYvmJiYnT33Xe7tJ0/M2/ePE2dOrXQZZ7+3idOnKi4uDjl5uYWWHb8+HHZ7XbZbDZt3rzZA9VdG3r37q177rnH02XgGuHt6QKA4po1a5Zq1apVoD0uLs4D1ZRde/bs0YQJE5SQkKCYmJgir5c3/hcvXlRGRobWrVunl19+Wa+99prmz5+vu+66y9H3scceU8eOHV2q69y5c5owYYIkFQiZV1KcfRXHvHnztGvXLo0YMaLAsg0bNqhatWolXkNhjhw5oldeeUWzZ89WuXIF/737/vvv68KFC5Kkd955R40bNy7tEq8J48ePV61atbRixQrdeeedni4HHkYYQplVp06dEv0f+blz51ShQoUS235Zl3/877vvPj355JO6/fbbde+99+rHH39UWFiYJKlatWolHg7yvq/S2Nefadasmcf2/cYbb+iGG27QvffeW+jyd999V5UrV1Z0dLQ+/PBDTZkyRX5+fqVa47Xwd6tGjRrq2LGjJk2aRBgCp8lwfbPZbBo2bJjef/991a5dWxUqVFD9+vX1xRdfOPXLO7WydetW3X///QoODlaNGjUkSefPn9eYMWMUGxsrHx8fVa1aVYmJiTp16lSxakpPT9egQYNUrVo1+fj4KDY2VhMmTNClS5ccfQ4cOCCbzaZXX31VL7/8smJiYuTn56eEhATt3btXFy9e1OjRoxUREaGgoCD16NGjwOmpvFNDX331lRo1aiQ/Pz/VqlVL7777rqPP7Nmz1bNnT0lSmzZtHKe+Zs+eXazPFhUVpcmTJ+v06dP65z//6Wgv7NTVihUrlJCQoEqVKsnPz09RUVG67777dO7cOR04cEA33nijJGnChAmOuvr16+e0vcK+ryudklu4cKHq1asnX19fVa9eXW+++abT8rxTgPlPGa5atcrpFGxCQoKWLFmigwcPOp0yzFPYabJdu3ape/fuCg4Olq+vrxo0aKD33nuv0P18+OGHGjt2rCIiIhQYGKi77rpLP/zww+UH/v934cIFvfPOO3rooYcKPSr0n//8R7t27VLv3r01cOBAZWZmasGCBY7lI0aMkL+/v7Kysgqs26tXL4WFhenixYuOtvnz56t58+by9/dXxYoV1aFDB23bts1pvX79+qlixYrauXOn2rdvr4CAALVt21aSlJKSou7du6tatWry9fVVzZo1NWjQIB0/frzA/j///HPVq1dPdrtd1atX1xtvvFHod22M0bRp09SgQQP5+fkpODhY999/v3766acC2+zdu7eWL1+u/fv3/8nI4rpngDJm1qxZRpL59ttvzcWLF51ely5dcuorycTExJjbbrvNfPzxx2bp0qUmISHBeHt7m/379zv6jRs3zkgy0dHR5tlnnzUpKSlm0aJFJjc313To0MF4e3ub5557znz99dfmtddeM/7+/qZhw4bm/Pnzjm20bt3atG7dusD+x40b53h/9OhRExkZaaKjo80///lPs3z5cvP3v//d2O12069fP0e/1NRURz1du3Y1X3zxhZk7d64JCwszN998s+ndu7cZMGCA+fLLL83bb79tKlasaLp27eq07+joaFOtWjUTFxdn5syZY5YtW2Z69uxpJJnVq1cbY4zJyMgwL730kpFkkpOTzYYNG8yGDRtMRkbGn47/pk2bCl1+5swZ4+XlZdq2bVtgfP/4+Xx9fU27du3MokWLzKpVq8wHH3xgevfubU6ePGnOnz9vvvrqKyPJPProo4669u3bd8Xvq7B95Y1F1apVTVRUlHn33XfN0qVLzcMPP2wkmVdffbXAZ0tNTXVaf+XKlUaSWblypTHGmN27d5uWLVua8PBwR20bNmxw9M//vf/3v/81AQEBpkaNGmbOnDlmyZIl5sEHHzSSzMsvv1xgPzExMebhhx82S5YsMR9++KGJiooyN910U4E/3/mtWbPGSDJLly4tdPnAgQONJLN7926TlZVlKlSoYBISEhzLv/vuOyPJzJw502m9kydPGrvdbkaOHOloe/HFF43NZjMDBgwwX3zxhfnss89M8+bNjb+/v9m9e7ejX9++fU358uVNTEyMSUpKMt98841ZtmyZMcaY6dOnm6SkJLN48WKzevVq895775n69eubW265xVy4cMGxjS+//NKUK1fOJCQkmIULF5pPPvnENG3a1MTExBT4rgcOHGjKly9vnnrqKfPVV1+ZefPmmVq1apmwsDCTnp7u1PfYsWNGknnzzTevOK64/hGGUObk/WAV9vLy8nLqK8mEhYWZrKwsR1t6eropV66cSUpKcrTl/YA+//zzTuvn/SC/8sorTu3z5883ksyMGTMcbUUJQ4MGDTIVK1Y0Bw8edOr32muvOX6kjPlfGKpfv77Jyclx9Js6daqRZLp16+a0/ogRI4wkk5mZ6WiLjo42vr6+Tvv67bffTEhIiBk0aJCj7ZNPPnH6of8zfxaGjDEmLCzM1K5d2/E+f0D59NNPjSSzffv2y27jl19+KTB++beX//sqbF/G/D4WNputwP7atWtnAgMDzdmzZ50+25+FIWOM6dKli4mOji609vx1P/DAA8Zut5u0tDSnfp06dTIVKlQwp06dctpP586dnfp9/PHHRpJT4CrMyy+/bCQV+NE3xpizZ8+awMBA06xZM0db3759jc1mc4RMY4xp1KiRadGihdO606ZNM5LMzp07jTHGpKWlGW9vb/P444879Tt9+rQJDw83f/nLX5z2Icm8++67V6w9NzfXXLx40Rw8eNBIMp9//rljWZMmTUxkZKTJzs522lelSpWcvusNGzYYSWby5MlO2z506JDx8/Mzo0aNKrDfqlWrml69el2xNlz/OE2GMmvOnDnatGmT0+s///lPgX5t2rRRQECA431YWJgqV66sgwcPFuh73333Ob1fsWKFJDlOz+Tp2bOn/P399c0337hU8xdffKE2bdooIiJCly5dcrw6deokSVq9erVT/86dOzud7qhdu7YkqUuXLk798trT0tKc2hs0aKCoqCjHe19fX918882FfnZ3MsZccXmDBg3k4+Ojv/71r3rvvfcKPYVRFPm/ryu59dZbVb9+fae2hx56SFlZWdq6dWux9l9UK1asUNu2bRUZGenU3q9fP507d04bNmxwau/WrZvT+3r16knSn35vR44ckc1mU2hoaIFlH3/8sbKysjRgwABH24ABA2SM0axZsxxt/fv31/r1651Oy82aNUtNmjRRnTp1JEnLli3TpUuX1KdPH6c/x76+vmrdunWhMwAL+64yMjI0ePBgRUZGytvbW+XLl1d0dLQk6fvvv5cknT17Vps3b9Y999wjHx8fx7oVK1ZU165dnbb3xRdfyGaz6ZFHHnGqKzw8XPXr1y+0rsqVK+vw4cOFDScshAuoUWbVrl27SBdQV6pUqUCb3W7Xb7/9VqC9SpUqTu9PnDghb29vx/UreWw2m8LDw3XixAmXaj527Jj+/e9/q3z58oUuz3+tREhIiNP7vB+Dy7WfP3/eqd2Vz+4uZ8+e1YkTJ1S3bt3L9qlRo4aWL1+uV155RYmJiTp79qyqV6+u4cOH64knnijyvvJ/X1cSHh5+2TZXv0dXnThxotBaIyIiCt1//u/NbrdL0p9+b7/99pvKly8vLy+vAsveeecd+fr6qmPHjo7r3erVq6eYmBjNnj1bEyZMkJeXlx5++GE9/fTTmj17tpKSkrRnzx5t2rRJ06ZNc2zr2LFjkqQmTZoUWkf+65UqVKigwMBAp7bc3Fy1b99eR44c0XPPPae6devK399fubm5atasmeOznjx5UsYYx8X4f5S/7dixY5ftK0nVq1cv0Obr61uifx9QNhCGgD/IfzFmpUqVdOnSJf3yyy9OgcgYo/T09Mv+GFxOaGio6tWrpxdffLHQ5Xk/jmXZkiVLlJOT86fT4Vu1aqVWrVopJydHmzdv1j/+8Q+NGDFCYWFheuCBB4q0L1fuXZSenn7Ztrzw4evrK0nKzs526lfYBb2uqFSpko4ePVqg/ciRI5JU6JGc4ggNDdWFCxd09uxZ+fv7O9r37t2rdevWSZLTkcI/WrZsmTp37qzg4GB1795dc+bM0QsvvKBZs2bJ19dXDz74oNN+JOnTTz91HMm5ksK+p127dum7777T7Nmz1bdvX0f7vn37nPoFBwfLZrM5Atgf5f9OQ0NDZbPZtHbtWkeA/KPC2n799VeXbimB6xOnyYAryJv1MnfuXKf2BQsW6OzZs47lRXX33Xdr165dqlGjhho3blzg5YkwVNSjDkWRlpamp59+WkFBQRo0aFCR1vHy8lLTpk2VnJwsSY5TVu6sS5J2796t7777zqlt3rx5CggIUKNGjSTJ8aO4Y8cOp36LFy8usD1XjrC1bdtWK1ascISfPHPmzFGFChXcNhU/775b+WdHvfPOO5KkmTNnauXKlU6vpUuXqnz58k6zDPv3768jR45o6dKlmjt3rnr06KEbbrjBsbxDhw7y9vbW/v37C/1zXJQjtnkBKX9A+eMsREny9/dX48aNtWjRIsf9kSTpzJkzBWaF3n333TLG6PDhw4XWlP9o5aVLl3To0CHuTQaODKHs2rVrl9N09Dw1atQocFqruNq1a6cOHTro2WefVVZWllq2bKkdO3Zo3LhxatiwoXr37u3S9iZOnKiUlBS1aNFCw4cP1y233KLz58/rwIEDWrp0qd5+++1Sv0dO3nUgM2bMUEBAgHx9fRUbG1voKbY/yhv/S5cuKSMjQ2vXrtWsWbPk5eWlhQsXXvE7ePvtt7VixQp16dJFUVFROn/+vOPHOO9mjQEBAYqOjtbnn3+utm3bKiQkRKGhocX+V3xERIS6deum8ePHq0qVKpo7d65SUlL08ssvO+5506RJE91yyy16+umndenSJQUHB2vhwoWOoyp/VLduXX322WeaPn264uPjVa5cucuGgHHjxjmuF3v++ecVEhKiDz74QEuWLNErr7yioKCgYn2m/PKOxn377beO64wuXbqkOXPmqHbt2nrssccKXa9r165avHix4who+/btVa1aNQ0dOlTp6enq37+/U/+YmBhNnDhRY8eO1U8//aSOHTsqODhYx44d08aNG+Xv7++4Yebl1KpVSzVq1NDo0aNljFFISIj+/e9/KyUlpUDfiRMnqkuXLurQoYOeeOIJ5eTk6NVXX1XFihX166+/Ovq1bNlSf/3rX9W/f39t3rxZd9xxh/z9/XX06FGtW7dOdevW1ZAhQxz9d+zYoXPnzqlNmzZFGl9cxzx48TZQLFeaTaZ804IlmcTExALbiI6ONn379nW8z5uB9MsvvxTo+9tvv5lnn33WREdHm/Lly5sqVaqYIUOGmJMnTzr1K8psMmN+nyU1fPhwExsba8qXL29CQkJMfHy8GTt2rDlz5owx5n+zyf447duY/802+uSTTwodkz/O8IqOjjZdunQp8HkKq3Pq1KkmNjbWeHl5GUlm1qxZBdbLv6+8l4+Pj6lcubJp3bq1eemllwqdlp9/hteGDRtMjx49THR0tLHb7aZSpUqmdevWZvHixU7rLV++3DRs2NDY7XYjyfGdXen7utxssi5duphPP/3U3HrrrcbHx8fExMSYKVOmFFh/7969pn379iYwMNDceOON5vHHHzdLliwpMJvs119/Nffff7+54YYbjM1mc9pnYd/7zp07TdeuXU1QUJDx8fEx9evXLzDOl/t+8/48XOl7ydOqVSun2WiLFi0ykszUqVMvu07erMk/zsL629/+ZiSZyMhIpxmNf7Ro0SLTpk0bExgYaOx2u4mOjjb333+/Wb58uaNP3759jb+/f6Hr79mzx7Rr184EBASY4OBg07NnT5OWllbo+C1cuNDUrVvX+Pj4mKioKDNp0iQzfPhwExwcXGC77777rmnatKnx9/c3fn5+pkaNGqZPnz5m8+bNTv2ee+45Exoa6nSLDFiTzZg/mfYBACgzFixYoF69eungwYOqWrWqp8spMRcvXlSDBg1UtWpVff311y6vn5OTo5o1a+qhhx667DV8sA7CEABcR4wxatGiheLj4/XWW295uhy3efTRR9WuXTtVqVJF6enpevvtt7V69Wp9/fXXTs/BK6r33ntPTz/9tH788Uen66FgTVwzBADXEZvNppkzZ2rx4sXKzc0t9LEcZdHp06f19NNP65dfflH58uXVqFEjLV26tFhBSPp9av8HH3xAEIIkjgwBAACLuz7+yQAAAFBMhCEAAGBphCEAAGBplr+AOjc3V0eOHFFAQIBLt/YHAACeY4zR6dOnFRERcdUTBSwfho4cOVLgSdIAAKBsOHTo0FXfud/yYSggIEDS74OZ/6nKAADg2pSVlaXIyEjH7/jVsHwYyjs1FhgYSBgCAKCMccclLlxADQAALI0wBAAALI0wBAAALI0wBAAALI0wBAAALI0wBAAALI0wBAAALI0wBAAALI0wBAAALK3Mh6HTp0+rSZMmatCggerWrauZM2d6uiQAAFCGlPnHcVSoUEGrV69WhQoVdO7cOdWpU0f33nuvKlWq5OnSAABAGVDmjwx5eXmpQoUKkqTz588rJydHxhgPVwUAAMoKj4ehNWvWqGvXroqIiJDNZtOiRYsK9Jk2bZpiY2Pl6+ur+Ph4rV271mn5qVOnVL9+fVWrVk2jRo1SaGhoKVUPAADKOo+HobNnz6p+/fp66623Cl0+f/58jRgxQmPHjtW2bdvUqlUrderUSWlpaY4+N9xwg7777julpqZq3rx5OnbsWGmVDwAAyjibuYbOKdlsNi1cuFD33HOPo61p06Zq1KiRpk+f7mirXbu27rnnHiUlJRXYxpAhQ3TnnXeqZ8+ehe4jOztb2dnZjvdZWVmKjIxUZmamAgMD3fdhJMWMXlKg7cCkLm7dBwAAVpSVlaWgoCC3/H57/MjQlVy4cEFbtmxR+/btndrbt2+v9evXS5KOHTumrKwsSb8PzJo1a3TLLbdcdptJSUkKCgpyvCIjI0vuAwAAgGveNR2Gjh8/rpycHIWFhTm1h4WFKT09XZL0888/64477lD9+vV1++23a9iwYapXr95ltzlmzBhlZmY6XocOHSrRzwAAAK5tZWJqvc1mc3pvjHG0xcfHa/v27UXelt1ul91ud2d5AACgDLumjwyFhobKy8vLcRQoT0ZGRoGjRQAAAMVxTYchHx8fxcfHKyUlxak9JSVFLVq08FBVAADgeuLx02RnzpzRvn37HO9TU1O1fft2hYSEKCoqSiNHjlTv3r3VuHFjNW/eXDNmzFBaWpoGDx58VftNTk5WcnKycnJyrvYjAACAMszjU+tXrVqlNm3aFGjv27evZs+eLen3my6+8sorOnr0qOrUqaPXX39dd9xxh1v2786pefkxtR4AgJLhzt9vj4chTyMMAQBQ9ljmPkMAAAAljTAEAAAszbJhKDk5WXFxcWrSpImnSwEAAB5k2TCUmJioPXv2aNOmTZ4uBQAAeJBlwxAAAIBEGAIAABZHGAIAAJZGGAIAAJZGGAIAAJZm2TDE1HoAACBZOAwxtR4AAEgWDkMAAAASYQgAAFgcYQgAAFgaYQgAAFgaYQgAAFiaZcMQU+sBAIBk4TDE1HoAACBZOAwBAABIhCEAAGBxhCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBplg1D3HQRAABIks0YYzxdhCdlZWUpKChImZmZCgwMdOu2Y0Yv+dM+ByZ1ces+AQCwAnf+flv2yBAAAIBEGAIAABZHGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZm2TDE4zgAAIBk4TCUmJioPXv2aNOmTZ4uBQAAeJBlwxAAAIBEGAIAABZHGAIAAJZGGAIAAJbm7ekCrC5m9JICbQcmdfFAJQAAWBNHhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKVZNgzx1HoAACBZOAzx1HoAACBZOAwBAABIhCEAAGBxhCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBp3p4uAAXFjF7i9P7ApC4eqgQAgOsfR4YAAIClEYYAAIClEYYAAIClWTYMJScnKy4uTk2aNPF0KQAAwIMsG4YSExO1Z88ebdq0ydOlAAAAD7JsGAIAAJAIQwAAwOIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNJ4an0ZkP8p9hJPsgcAwF04MgQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyN+wyVUfnvPcR9hwAAKB6ODAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsr82Ho0KFDSkhIUFxcnOrVq6dPPvnE0yUBAIAypMzfgdrb21tTp05VgwYNlJGRoUaNGqlz587y9/f3dGkAAKAMKPNhqEqVKqpSpYokqXLlygoJCdGvv/5quTCU//EcEo/oAACgKDx+mmzNmjXq2rWrIiIiZLPZtGjRogJ9pk2bptjYWPn6+io+Pl5r164tdFubN29Wbm6uIiMjS7hqAABwvfB4GDp79qzq16+vt956q9Dl8+fP14gRIzR27Fht27ZNrVq1UqdOnZSWlubU78SJE+rTp49mzJhRGmUDAIDrhMthaM6cOcrOzi7QfuHCBc2ZM8flAjp16qQXXnhB9957b6HLp0yZokcffVSPPfaYateuralTpyoyMlLTp0939MnOzlaPHj00ZswYtWjR4or7y87OVlZWltMLAABYl8thqH///srMzCzQfvr0afXv398tReW5cOGCtmzZovbt2zu1t2/fXuvXr5ckGWPUr18/3Xnnnerdu/efbjMpKUlBQUGOF6fUAACwNpfDkDFGNputQPvPP/+soKAgtxSV5/jx48rJyVFYWJhTe1hYmNLT0yVJ//d//6f58+dr0aJFatCggRo0aKCdO3dedptjxoxRZmam43Xo0CG31gwAAMqWIs8ma9iwoWw2m2w2m9q2bStv7/+tmpOTo9TUVHXs2LFEiswfvv4YyG6//Xbl5uYWeVt2u112u92t9QEAgLKryGHonnvukSRt375dHTp0UMWKFR3LfHx8FBMTo/vuu8+txYWGhsrLy8txFChPRkZGgaNFAAAAxVHkMDRu3DhJUkxMjHr16iVfX98SKyqPj4+P4uPjlZKSoh49ejjaU1JS1L179xLfPwAAuP65fNPFvn37Svr94uaMjIwCp6iioqJc2t6ZM2e0b98+x/vU1FRt375dISEhioqK0siRI9W7d281btxYzZs314wZM5SWlqbBgwe7WjoAAEABLoehH3/8UQMGDHDM5sqTdx1PTk6OS9vbvHmz2rRp43g/cuRISb+HrtmzZ6tXr146ceKEJk6cqKNHj6pOnTpaunSpoqOjXS3dSXJyspKTk12uFwAAXF9sxhjjygotW7aUt7e3Ro8erSpVqhS4uLl+/fpuLbCkZWVlKSgoSJmZmQoMDHTrtgt7REZp4nEcAIDrlTt/v10+MrR9+3Zt2bJFtWrVuqodAwAAXAtcvs9QXFycjh8/XhK1AAAAlDqXw9DLL7+sUaNGadWqVTpx4gSPtgAAAGWay6fJ7rrrLklS27ZtndqLewG1p1j1Aur81zFxXREAwOpcDkMrV64siTpKXWJiohITEx0XYF2PPH0BNwAAZYHLYah169YlUQcAAIBHuByG1qxZc8Xld9xxR7GLAQAAKG0uh6GEhIQCbX+815DVrsEBAABlm8uzyU6ePOn0ysjI0FdffaUmTZro66+/LokaAQAASozLR4YKu9i4Xbt2stvtevLJJ7Vlyxa3FAYAAFAaXD4ydDk33nijfvjhB3dtrsQlJycrLi5OTZo08XQpAADAg1w+MrRjxw6n98YYHT16VJMmTSpTzyWzwtR6AADw51wOQw0aNJDNZlP+57s2a9ZM7777rtsKAwAAKA0uh6HU1FSn9+XKldONN94oX19ftxUFAABQWlwOQ9HR0SVRBwAAgEcU6wLq1atXq2vXrqpZs6ZuuukmdevWTWvXrnV3bQAAACXO5TA0d+5c3XXXXapQoYKGDx+uYcOGyc/PT23bttW8efNKokYAAIASYzP5r4T+E7Vr19Zf//pXPfnkk07tU6ZM0cyZM/X999+7tcCS8sen1u/du1eZmZkKDAx06z7KwoNSeWo9AKAsypsN7o7fb5fDkN1u1+7du1WzZk2n9n379qlOnTo6f/78VRVU2tw5mPmVhTBUGAISAOBa587fb5dPk0VGRuqbb74p0P7NN98oMjLyqooBAAAobS7PJnvqqac0fPhwbd++XS1atJDNZtO6des0e/ZsvfHGGyVRIwAAQIlxOQwNGTJE4eHhmjx5sj7++GNJv19HNH/+fHXv3t3tBcLzCjvdx6k0AMD1wuUwJEk9evRQjx493F0LAABAqSvyNUMnT57UP/7xD2VlZRVYlpmZedllAAAA17IiHxl66623tGPHDj3++OMFlgUFBWnt2rXKysrS2LFj3VogSl9ZnQUHAEBxFPnI0IIFCzR48ODLLh80aJA+/fRTtxQFAABQWoochvbv36+bbrrpsstvuukm7d+/3y1FlYbk5GTFxcWpSZMmni4FAAB4UJHDkJeXl44cOXLZ5UeOHFG5csV61JlHJCYmas+ePdq0aZOnSwEAAB5U5PTSsGFDLVq06LLLFy5cqIYNG7qjJgAAgFJT5Auohw0bpgceeEDVqlXTkCFD5OXlJUnKycnRtGnT9Prrr/OgVgAAUOYUOQzdd999GjVqlIYPH66xY8eqevXqstls2r9/v86cOaNnnnlG999/f0nWCgAA4HYu3XTxxRdfVPfu3fXBBx9o3759Msbojjvu0EMPPaTbbrutpGoEAAAoMS7fgfq2224j+KAAHtkBACirys70LwAAgBJAGAIAAJZGGAIAAJZWrKfWAzy/DABwvXD5yND48eN18ODBkqilVPE4DgAAIBUjDP373/9WjRo11LZtW82bN0/nz58vibpKHI/jAAAAUjHC0JYtW7R161bVq1dPTz75pKpUqaIhQ4YQKlBAzOglTi8AAK5FxbqAul69enr99dd1+PBhvfvuuzp8+LBatmypunXr6o033lBmZqa76wQAACgRVzWbLDc3VxcuXFB2draMMQoJCdH06dMVGRmp+fPnu6tGAACAElOsMLRlyxYNGzZMVapU0ZNPPqmGDRvq+++/1+rVq/Xf//5X48aN0/Dhw91dKwAAgNu5HIbq1aunZs2aKTU1Ve+8844OHTqkSZMmqWbNmo4+ffr00S+//OLWQgEAAEqCy/cZ6tmzpwYMGKCqVatets+NN96o3NzcqyoMAACgNLh8ZMgYo+Dg4ALtv/32myZOnOiWogAAAEqLy2FowoQJOnPmTIH2c+fOacKECW4pCgAAoLQU68iQzWYr0P7dd98pJCTELUUBAACUliJfMxQcHCybzSabzaabb77ZKRDl5OTozJkzGjx4cIkUCQAAUFKKHIamTp0qY4wGDBigCRMmKCgoyLHMx8dHMTExat68eYkUietDYXehPjCpiwcqAQDgf4ochvr27StJio2NVcuWLeXtzQPvAQBA2edyogkICND333+vunXrSpI+//xzzZo1S3FxcRo/frx8fHzcXmRJSE5OVnJysnJycjxdiqVxtAgA4GkuX0A9aNAg7d27V5L0008/qVevXqpQoYI++eQTjRo1yu0FlhSeWg8AAKRihKG9e/eqQYMGkqRPPvlErVu31rx58zR79mwtWLDA3fUBAACUqGJNrc+7u/Ty5cvVuXNnSVJkZKSOHz/u3uoAAABKmMthqHHjxnrhhRf0/vvva/Xq1erS5ffrO1JTUxUWFub2AgEAAEqSy2Fo6tSp2rp1q4YNG6axY8c6HtD66aefqkWLFm4vEAAAoCS5PJusXr162rlzZ4H2V199VV5eXm4pCgAAoLS47WZBvr6+7toU4Bb5p+0zZR8AUJgihaGQkBDt3btXoaGhjsdyXM6vv/7qtuIAAABKWpHC0Ouvv66AgABJv18zBAAAcL0oUhjKexTHpUuXJEkdOnRQeHh4yVUFAABQSlyaTebt7a0hQ4YoOzu7pOoBAAAoVS5PrW/atKm2bdtWErUAAACUOpdnkw0dOlRPPfWUfv75Z8XHx8vf399peb169dxWHAAAQElzOQz16tVLkjR8+HBHm81mkzFGNpuNp8ADAIAyxeUwlJqaWhJ1AFcl/z2FAAAoKpfDUHR0dEnUAQAA4BHFugP1+++/r7ffflupqanasGGDoqOjNXXqVMXGxqp79+7urhEWV9hRH+4mDQBwF5dnk02fPl0jR45U586dderUKcc1QjfccAM3ZAQAAGWOy2HoH//4h2bOnKmxY8c6PZi1cePGhT7AFQAA4FrmchhKTU1Vw4YNC7Tb7XadPXvWLUWVhuTkZMXFxalJkyaeLgUAAHiQy2EoNjZW27dvL9D+5ZdfKi4uzh01lYrExETt2bNHmzZt8nQpAADAg1y+gPqZZ55RYmKizp8/L2OMNm7cqA8//FBJSUn617/+VRI1wmKKMk2eqfQAAHdxOQz1799fly5d0qhRo3Tu3Dk99NBDqlq1qt544w098MADJVEjAABAiSnW1PqBAwdq4MCBOn78uHJzc1W5cmV31wUAAFAqXA5DEyZM0COPPKIaNWooNDS0JGoCrinc5wgArm8uX0C9YMEC3XzzzWrWrJneeust/fLLLyVRFwAAQKlwOQzt2LFDO3bs0J133qkpU6aoatWq6ty5s+bNm6dz586VRI3ANSdm9BKnFwCg7HI5DEnSrbfeqpdeekk//fSTVq5cqdjYWI0YMULh4eHurg8AAKBEFSsM/ZG/v7/8/Pzk4+OjixcvuqMmAACAUlOsMJSamqoXX3xRcXFxaty4sbZu3arx48crPT3d3fUBAACUKJdnkzVv3lwbN25U3bp11b9/f8d9hgAAAMoil8NQmzZt9K9//Uu33nprSdQDAABQqlwOQy+99FJJ1AEAAOARRQpDI0eO1N///nf5+/tr5MiRV+w7ZcoUtxQGAABQGooUhrZt2+aYKbZt27bL9rPZbO6pCgAAoJQUKQytXLmy0P8GAAAo64r1oFagLOIZYwCAwrgchs6ePatJkybpm2++UUZGhnJzc52W//TTT24rDgAAoKS5HIYee+wxrV69Wr1791aVKlW4TggAAJRpLoehL7/8UkuWLFHLli1Loh4AAIBS5fLjOIKDgxUSElIStQAAAJQ6l8PQ3//+dz3//PM6d+5cSdQDAABQqlw+TTZ58mTt379fYWFhiomJUfny5Z2Wb9261W3FAQAAlDSXw9A999xTAmUAAAB4hsthaNy4cSVRBwAAgEe4fM0QAADA9aTIYahcuXLy8vIq8AoODlazZs302WeflWSdV9SjRw8FBwfr/vvv91gNAACgbCryabKFCxcW2n7q1Clt3LhRjzzyiN577z317NnTbcUV1fDhwzVgwAC99957pb5vAABQthU5DHXv3v2yy/r27au4uDi99tprHglDbdq00apVq0p9v7j+FPb8spLcNs9GAwDPc9s1Q+3bt9fevXtdXm/NmjXq2rWrIiIiZLPZtGjRogJ9pk2bptjYWPn6+io+Pl5r1651Q8UAAABuDEO//fabfH19XV7v7Nmzql+/vt56661Cl8+fP18jRozQ2LFjtW3bNrVq1UqdOnVSWlra1ZYMAADg+tT6y5k5c6YaNmzo8nqdOnVSp06dLrt8ypQpevTRR/XYY49JkqZOnaply5Zp+vTpSkpKcnl/2dnZys7OdrzPyspyeRsAAOD6UeQwNHLkyELbMzMztXnzZu3fv9/tp68uXLigLVu2aPTo0U7t7du31/r164u1zaSkJE2YMMEd5QEOXA8EAGVXkcPQtm3bCm0PDAxUx44dNXToUEVHR7utMEk6fvy4cnJyFBYW5tQeFham9PR0x/sOHTpo69atOnv2rKpVq6aFCxeqSZMmhW5zzJgxTsEuKytLkZGRbq0bAACUHUUOQytXrizJOq7IZrM5vTfGOLUtW7asyNuy2+2y2+1uqw0AAJRt1/QdqENDQ+Xl5eV0FEiSMjIyChwtAgAAKI5rOgz5+PgoPj5eKSkpTu0pKSlq0aKFh6oCAADXE7fNJiuuM2fOaN++fY73qamp2r59u0JCQhQVFaWRI0eqd+/eaty4sZo3b64ZM2YoLS1NgwcPvqr9JicnKzk5WTk5OVf7EYBSVdSLtYtyA0ku8gaAayAMbd68WW3atHG8z7u4uW/fvpo9e7Z69eqlEydOaOLEiTp69Kjq1KmjpUuXXvXF2omJiUpMTFRWVpaCgoKualsAAKDs8ngYSkhIkDHmin2GDh2qoUOHllJFAADASq7pa4YAAABKGmEIAABYGmEIAABYmmXDUHJysuLi4i57p2oAAGANlg1DiYmJ2rNnjzZt2uTpUgAAgAdZNgwBAABIhCEAAGBxhCEAAGBphCEAAGBpHr8DtafwbDKUtKI8Gyx/H54VBgClz7JHhphNBgAAJAuHIQAAAIkwBAAALI4wBAAALI0wBAAALI0wBAAALI0wBAAALI37DHGfIUsryr2AAADXN8seGeI+QwAAQLJwGAIAAJAIQwAAwOIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNK46SI3XcQ1pLCbQB6Y1KXU9leS+wKAa5Vljwxx00UAACBZOAwBAABIhCEAAGBxhCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBpPI6Dx3HgGlfYIzpwZaX9WBMAZZtljwzxOA4AACBZOAwBAABIhCEAAGBxhCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBphCEAAGBpPLWep9YDDoU97T2/wp7+nn+9ovQprJ+7+gCAKyx7ZIin1gMAAMnCYQgAAEAiDAEAAIsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEvz9nQBnpKcnKzk5GTl5OR4uhTgqsWMXnJd7gsASoNljwwlJiZqz5492rRpk6dLAQAAHmTZMAQAACARhgAAgMURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKVdF2Hoiy++0C233KKbbrpJ//rXvzxdDgAAKEO8PV3A1bp06ZJGjhyplStXKjAwUI0aNdK9996rkJAQT5cGAADKgDJ/ZGjjxo269dZbVbVqVQUEBKhz585atmyZp8sCAABlhMfD0Jo1a9S1a1dFRETIZrNp0aJFBfpMmzZNsbGx8vX1VXx8vNauXetYduTIEVWtWtXxvlq1ajp8+HBplA4AAK4DHg9DZ8+eVf369fXWW28Vunz+/PkaMWKExo4dq23btqlVq1bq1KmT0tLSJEnGmALr2Gy2Eq0ZAABcPzx+zVCnTp3UqVOnyy6fMmWKHn30UT322GOSpKlTp2rZsmWaPn26kpKSVLVqVacjQT///LOaNm162e1lZ2crOzvb8T4rK8sNnwIAAJRVHg9DV3LhwgVt2bJFo0ePdmpv37691q9fL0m67bbbtGvXLh0+fFiBgYFaunSpnn/++ctuMykpSRMmTCjRugGrixm9xG393NWnuOscmNTF5W0Xd/9F2Vdx1yuO0txXURXleyvJGvPvv7j7ctd24B4eP012JcePH1dOTo7CwsKc2sPCwpSeni5J8vb21uTJk9WmTRs1bNhQzzzzjCpVqnTZbY4ZM0aZmZmO16FDh0r0MwAAgGvbNX1kKE/+a4CMMU5t3bp1U7du3Yq0LbvdLrvd7tb6AABA2XVNHxkKDQ2Vl5eX4yhQnoyMjAJHiwAAAIrjmg5DPj4+io+PV0pKilN7SkqKWrRo4aGqAADA9cTjp8nOnDmjffv2Od6npqZq+/btCgkJUVRUlEaOHKnevXurcePGat68uWbMmKG0tDQNHjz4qvabnJys5ORk5eTkXO1HAAAAZZjHw9DmzZvVpk0bx/uRI0dKkvr27avZs2erV69eOnHihCZOnKijR4+qTp06Wrp0qaKjo69qv4mJiUpMTFRWVpaCgoKualsAAKDs8ngYSkhIKPTGiX80dOhQDR06tJQqAgAAVnJNXzMEAABQ0ghDAADA0ghDAADA0iwbhpKTkxUXF6cmTZp4uhQAAOBBlg1DiYmJ2rNnjzZt2uTpUgAAgAdZNgwBAABIhCEAAGBxhCEAAGBpHr/poqfl3fAxKyvL7dvOzT7n9m0CKJ78f8eL+vezJP7fcLn9F2VfxV2vOEpzX0VVlO+tJGvMv//i7std27GyvDH7sxs3F4XNuGMrZdjPP/+syMhIT5cBAACK4dChQ6pWrdpVbcPyYSg3N1dHjhxRQECAbDab27ablZWlyMhIHTp0SIGBgW7bLgpirEsPY116GOvSxXiXHneNtTFGp0+fVkREhMqVu7qrfix/mqxcuXJXnSivJDAwkL9YpYSxLj2MdelhrEsX41163DHW7nrQOhdQAwAASyMMAQAASyMMlRC73a5x48bJbrd7upTrHmNdehjr0sNYly7Gu/Rci2Nt+QuoAQCAtXFkCAAAWBphCAAAWBphCAAAWBphCAAAWBphqIRMmzZNsbGx8vX1VXx8vNauXevpkq4ZSUlJatKkiQICAlS5cmXdc889+uGHH5z6GGM0fvx4RUREyM/PTwkJCdq9e7dTn+zsbD3++OMKDQ2Vv7+/unXrpp9//tmpz8mTJ9W7d28FBQUpKChIvXv31qlTp5z6pKWlqWvXrvL391doaKiGDx+uCxculMhn97SkpCTZbDaNGDHC0cZYu8/hw4f1yCOPqFKlSqpQoYIaNGigLVu2OJYz1u5x6dIl/b//9/8UGxsrPz8/Va9eXRMnTlRubq6jD2NdfGvWrFHXrl0VEREhm82mRYsWOS2/1sZ2586dat26tfz8/FS1alVNnDjR9eeVGbjdRx99ZMqXL29mzpxp9uzZY5544gnj7+9vDh486OnSrgkdOnQws2bNMrt27TLbt283Xbp0MVFRUebMmTOOPpMmTTIBAQFmwYIFZufOnaZXr16mSpUqJisry9Fn8ODBpmrVqiYlJcVs3brVtGnTxtSvX99cunTJ0adjx46mTp06Zv369Wb9+vWmTp065u6773Ysv3TpkqlTp45p06aN2bp1q0lJSTERERFm2LBhpTMYpWjjxo0mJibG1KtXzzzxxBOOdsbaPX799VcTHR1t+vXrZ/7zn/+Y1NRUs3z5crNv3z5HH8baPV544QVTqVIl88UXX5jU1FTzySefmIoVK5qpU6c6+jDWxbd06VIzduxYs2DBAiPJLFy40Gn5tTS2mZmZJiwszDzwwANm586dZsGCBSYgIMC89tprLn1mwlAJuO2228zgwYOd2mrVqmVGjx7toYqubRkZGUaSWb16tTHGmNzcXBMeHm4mTZrk6HP+/HkTFBRk3n77bWOMMadOnTLly5c3H330kaPP4cOHTbly5cxXX31ljDFmz549RpL59ttvHX02bNhgJJn//ve/xpjf/9KXK1fOHD582NHnww8/NHa73WRmZpbchy5lp0+fNjfddJNJSUkxrVu3doQhxtp9nn32WXP77bdfdjlj7T5dunQxAwYMcGq79957zSOPPGKMYazdKX8YutbGdtq0aSYoKMicP3/e0ScpKclERESY3NzcIn9OTpO52YULF7Rlyxa1b9/eqb19+/Zav369h6q6tmVmZkqSQkJCJEmpqalKT093GkO73a7WrVs7xnDLli26ePGiU5+IiAjVqVPH0WfDhg0KCgpS06ZNHX2aNWumoKAgpz516tRRRESEo0+HDh2UnZ3tdHqjrEtMTFSXLl101113ObUz1u6zePFiNW7cWD179lTlypXVsGFDzZw507GcsXaf22+/Xd9884327t0rSfruu++0bt06de7cWRJjXZKutbHdsGGDWrdu7XQDxw4dOujIkSM6cOBAkT+X5R/U6m7Hjx9XTk6OwsLCnNrDwsKUnp7uoaquXcYYjRw5Urfffrvq1KkjSY5xKmwMDx486Ojj4+Oj4ODgAn3y1k9PT1flypUL7LNy5cpOffLvJzg4WD4+PtfN9/XRRx9p69at2rRpU4FljLX7/PTTT5o+fbpGjhypv/3tb9q4caOGDx8uu92uPn36MNZu9OyzzyozM1O1atWSl5eXcnJy9OKLL+rBBx+UxJ/rknStjW16erpiYmIK7CdvWWxsbJE+F2GohNhsNqf3xpgCbZCGDRumHTt2aN26dQWWFWcM8/cprH9x+pRVhw4d0hNPPKGvv/5avr6+l+3HWF+93NxcNW7cWC+99JIkqWHDhtq9e7emT5+uPn36OPox1ldv/vz5mjt3rubNm6dbb71V27dv14gRIxQREaG+ffs6+jHWJedaGtvCarncupfDaTI3Cw0NlZeXV4F/EWRkZBRIuFb3+OOPa/HixVq5cqWqVavmaA8PD5ekK45heHi4Lly4oJMnT16xz7Fjxwrs95dffnHqk38/J0+e1MWLF6+L72vLli3KyMhQfHy8vL295e3trdWrV+vNN9+Ut7e307+g/oixdl2VKlUUFxfn1Fa7dm2lpaVJ4s+1Oz3zzDMaPXq0HnjgAdWtW1e9e/fWk08+qaSkJEmMdUm61sa2sD4ZGRmSCh69uhLCkJv5+PgoPj5eKSkpTu0pKSlq0aKFh6q6thhjNGzYMH322WdasWJFgcOYsbGxCg8PdxrDCxcuaPXq1Y4xjI+PV/ny5Z36HD16VLt27XL0ad68uTIzM7Vx40ZHn//85z/KzMx06rNr1y4dPXrU0efrr7+W3W5XfHy8+z98KWvbtq127typ7du3O16NGzfWww8/rO3bt6t69eqMtZu0bNmywC0i9u7dq+joaEn8uXanc+fOqVw5558vLy8vx9R6xrrkXGtj27x5c61Zs8Zpuv3XX3+tiIiIAqfPrqjIl1qjyPKm1r/zzjtmz549ZsSIEcbf398cOHDA06VdE4YMGWKCgoLMqlWrzNGjRx2vc+fOOfpMmjTJBAUFmc8++8zs3LnTPPjgg4VO3axWrZpZvny52bp1q7nzzjsLnbpZr149s2HDBrNhwwZTt27dQqdutm3b1mzdutUsX77cVKtWrUxPi/0zf5xNZgxj7S4bN2403t7e5sUXXzQ//vij+eCDD0yFChXM3LlzHX0Ya/fo27evqVq1qmNq/WeffWZCQ0PNqFGjHH0Y6+I7ffq02bZtm9m2bZuRZKZMmWK2bdvmuD3MtTS2p06dMmFhYebBBx80O3fuNJ999pkJDAxkav21Ijk52URHRxsfHx/TqFEjx7Rx/D5Vs7DXrFmzHH1yc3PNuHHjTHh4uLHb7eaOO+4wO3fudNrOb7/9ZoYNG2ZCQkKMn5+fufvuu01aWppTnxMnTpiHH37YBAQEmICAAPPwww+bkydPOvU5ePCg6dKli/Hz8zMhISFm2LBhTtM0rzf5wxBj7T7//ve/TZ06dYzdbje1atUyM2bMcFrOWLtHVlaWeeKJJ0xUVJTx9fU11atXN2PHjjXZ2dmOPox18a1cubLQ/0f37dvXGHPtje2OHTtMq1atjN1uN+Hh4Wb8+PEuTas3xhibMa7ephEAAOD6wTVDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAADA0ghDAEpMQkKCRowY4ekyrikHDhyQzWbT9u3bJUmrVq2SzWbTqVOnrmq7MTExmjp16lXXB1gRYQiwkIyMDA0aNEhRUVGy2+0KDw9Xhw4dtGHDBkcfm82mRYsWea7IP9GvXz/dc889V7WN8ePHy2azFXjVqlXLPUUCKFO8PV0AgNJz33336eLFi3rvvfdUvXp1HTt2TN98841+/fVXT5dW6m699VYtX77cqc3b++r+l3jx4kWVL1/+qrYBoPRxZAiwiFOnTmndunV6+eWX1aZNG0VHR+u2227TmDFj1KVLF0lyPOW5R48estlsjveFHY0ZMWKEEhISHO/Pnj2rPn36qGLFiqpSpYomT55coIYLFy5o1KhRqlq1qvz9/dW0aVOtWrXKsXz27Nm64YYbtGzZMtWuXVsVK1ZUx44dHU+tHj9+vN577z19/vnnjqM5q1at0oULFzRs2DBVqVJFvr6+iomJUVJS0hXHw9vbW+Hh4U6v0NBQx/KYmBi99NJLGjBggAICAhQVFaUZM2Y4lued7vr444+VkJAgX19fzZ07V7m5uZo4caKqVasmu92uBg0a6Kuvvvqzr8fJ+vXrdccdd8jPz0+RkZEaPny4zp4961iekZGhrl27ys/PT7Gxsfrggw9c2j4AZ4QhwCIqVqyoihUratGiRcrOzi60z6ZNmyRJs2bN0tGjRx3vi+KZZ57RypUrtXDhQn399ddatWqVtmzZ4tSnf//++r//+z999NFH2rFjh3r27KmOHTvqxx9/dPQ5d+6cXnvtNb3//vtas2aN0tLS9PTTT0uSnn76af3lL39xBKSjR4+qRYsWevPNN7V48WJ9/PHH+uGHHzR37lxHkLsakydPVuPGjbVt2zYNHTpUQ4YM0X//+1+nPs8++6yGDx+u77//Xh06dNAbb7yhyZMn67XXXtOOHTvUoUMHdevWzekzXsnOnTvVoUMH3XvvvdqxY4fmz5+vdevWadiwYY4+/fr104EDB7RixQp9+umnmjZtmjIyMq768wKW5dJjXQGUaZ9++qkJDg42vr6+pkWLFmbMmDHmu+++c+ojySxcuNCprW/fvqZ79+5ObU888YRp3bq1McaY06dPGx8fH/PRRx85lp84ccL4+fmZJ554whhjzL59+4zNZjOHDx922k7btm3NmDFjjDHGzJo1y0gy+/btcyxPTk42YWFhV6zl8ccfN3feeWeRn1Q9btw4U65cOePv7+/0evTRRx19oqOjzSOPPOJ4n5ubaypXrmymT59ujDEmNTXVSDJTp0512nZERIR58cUXndqaNGlihg4d6rTetm3bjDH/e0J43tO6e/fubf761786rb927VpTrlw589tvv5kffvjBSDLffvutY/n3339vJJnXX3+9SJ8fgDOuGQIs5L777lOXLl20du1abdiwQV999ZVeeeUV/etf/1K/fv2Kvd39+/frwoULat68uaMtJCREt9xyi+P91q1bZYzRzTff7LRudna2KlWq5HhfoUIF1ahRw/G+SpUqf3rUo1+/fmrXrp1uueUWdezYUXfffbfat29/xXVuueUWLV682KktICDA6X29evUc/22z2RQeHl6glsaNGzv+OysrS0eOHFHLli2d+rRs2VLffffdFevJs2XLFu3bt8/p1JcxRrm5uUpNTdXevXvl7e3ttN9atWrphhtuKNL2ARREGAIsxtfXV+3atVO7du30/PPP67HHHtO4ceOuGIbKlSsnY4xT28WLFx3/nX9ZYXJzc+Xl5aUtW7bIy8vLaVnFihUd/53/AmSbzfan22/UqJFSU1P15Zdfavny5frLX/6iu+66S59++ull1/Hx8VHNmjWvuN3CasnNzXVq8/f3L7CezWZzem+MKdB2Obm5uRo0aJCGDx9eYFlUVJR++OGHQvcBoPi4ZgiwuLi4OKeLc8uXL6+cnBynPjfeeKPjIuY8effJkaSaNWuqfPny+vbbbx1tJ0+e1N69ex3vGzZsqJycHGVkZKhmzZpOr/Dw8CLX6+PjU6A+SQoMDFSvXr00c+ZMzZ8/XwsWLCj1WXKBgYGKiIjQunXrnNrXr1+v2rVrF2kbjRo10u7duwuMUc2aNeXj46PatWvr0qVL2rx5s2OdH3744arvUwRYGUeGAIs4ceKEevbsqQEDBqhevXoKCAjQ5s2b9corr6h79+6OfjExMfrmm2/UsmVL2e12BQcH684779Srr76qOXPmqHnz5po7d6527dqlhg0bSvr9yM6jjz6qZ555RpUqVVJYWJjGjh2rcuX+9++tm2++WQ8//LD69OmjyZMnq2HDhjp+/LhWrFihunXrqnPnzkX6HDExMVq2bJl++OEHVapUSUFBQXrrrbdUpUoVNWjQQOXKldMnn3yi8PDwK546unTpktLT053abDabwsLCXBjVgp555hmNGzdONWrUUIMGDTRr1ixt3769yDO+nn32WTVr1kyJiYkaOHCg/P399f333yslJUX/+Mc/HKcCBw4cqBkzZsjb21sjRoyQn5/fVdUNWBlhCLCIihUrqmnTpnr99de1f/9+Xbx4UZGRkRo4cKD+9re/OfpNnjxZI0eO1MyZM1W1alUdOHBAHTp00HPPPadRo0bp/PnzGjBggPr06aOdO3c61nv11Vd15swZdevWTQEBAXrqqaeUmZnpVMOsWbP0wgsv6KmnntLhw4dVqVIlNW/evMhBSJIGDhyoVatWqXHjxjpz5oxWrlypihUr6uWXX9aPP/4oLy8vNWnSREuXLnUKY/nt3r1bVapUcWqz2+06f/58kWspzPDhw5WVlaWnnnpKGRkZiouL0+LFi3XTTTcVaf169epp9erVGjt2rFq1aiVjjGrUqKFevXo5+syaNUuPPfaYWrdurbCwML3wwgt67rnnrqpuwMpspign+wEAAK5TXDMEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAsjTAEAAAs7f8DjJfdn2BG7CgAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"avg_students = ak.mean(data.student.enrollment.undergrad_12_month, axis=1)\n",
"plt.hist(avg_students, bins=np.arange(0, 100_000, 1_000))\n",
"plt.xlabel(\"Students Enrolled\")\n",
"plt.ylabel(\"Univeristy Count\")\n",
"plt.title(\"Enrollemnt Distribution (Average)\")\n",
"plt.yscale(\"log\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "e44f0fcc-1643-4830-82af-afb53eeed47c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data.student.enrollment.undergrad_12_month[:, -1], bins=np.arange(0, 100_000, 1_000))\n",
"plt.xlabel(\"Students Enrolled\")\n",
"plt.ylabel(\"Univeristy Count\")\n",
"plt.title(\"Enrollemnt Distribution (Last Year)\")\n",
"plt.yscale(\"log\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "eaf92130-15b0-466e-8fa4-a9540b27d1f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 35., 48., 40., 75., 118., 158., 227., 334., 389., 522.]),\n",
" array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
" <BarContainer object of 10 artists>)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data.admissions.admission_rate.overall[:, -1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afba0d1b-3dc4-4750-b61b-8ebbedf110a7",
"metadata": {},
"outputs": [],
"source": [
"data.school.fields"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue View Git Blame Copy Permalink