{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Big Ten S&E, Athletics & Party Power Dataset Build"],"metadata":{"id":"4XIBjuzlVYeS"}},{"cell_type":"markdown","source":["### Upload Files\n","All data files are uploaded directly from local storage into the Colab environment."],"metadata":{"id":"XhR8LuQvVd3I"}},{"cell_type":"code","source":["!pip install openpyxl"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8PdFWDfxVzwX","outputId":"a8466f29-93f6-4567-8f49-47599419b493","executionInfo":{"status":"ok","timestamp":1778087325946,"user_tz":240,"elapsed":14263,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: openpyxl in /usr/local/lib/python3.12/dist-packages (3.1.5)\n","Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.12/dist-packages (from openpyxl) (2.0.0)\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","party_df = pd.read_csv(\n","    \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/Party_Government_Since_1857.csv\"\n",")\n","\n","umd = pd.read_excel(\n","    \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/University%20of%20Maryland%2C%20College%20Park%20Data.xlsx\"\n",")\n","\n","uiuc = pd.read_excel(\n","    \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/University%20of%20Illinois%20Urbana-Champaign%20Data.xlsx\"\n",")\n","\n","umich = pd.read_excel(\n","    \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/University%20of%20Michigan%20Data.xlsx\"\n",")\n","\n","uwash = pd.read_excel(\n","    \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/University%20of%20Washington%20Data.xlsx\"\n",")\n","\n","nsf23 = pd.read_excel(\n","    \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/nsf23304-tab023.xlsx\"\n",")"],"metadata":{"id":"1J_iU9-APhuN","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7729cfc4-fbcf-4afb-b18a-3b3d947052b8","executionInfo":{"status":"ok","timestamp":1778087333586,"user_tz":240,"elapsed":7637,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-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"]}]},{"cell_type":"markdown","source":["### Load Datasets\n","NSF files may contain metadata rows before actual column headers begin —\n","we will inspect and handle skiprows after previewing each file.\n","Athletics files are loaded per school and tagged with region.\n","Party file is loaded as CSV."],"metadata":{"id":"lVYeHxrsVgRt"}},{"cell_type":"code","source":["import pandas as pd\n","\n","base = \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/\"\n","\n","# NSF S&E files\n","nsf23 = pd.read_excel(base + \"nsf23304-tab023.xlsx\")\n","nsf24 = pd.read_excel(base + \"nsf24308-tab023.xlsx\")\n","nsf25 = pd.read_excel(base + \"nsf25314-tab023.xlsx\")\n","nsf26 = pd.read_excel(base + \"nsf26304-tab015.xlsx\")\n","\n","# Athletics files\n","umd = pd.read_excel(base + \"University%20of%20Maryland%2C%20College%20Park%20Data.xlsx\")\n","uiuc = pd.read_excel(base + \"University%20of%20Illinois%20Urbana-Champaign%20Data.xlsx\")\n","uwash = pd.read_excel(base + \"University%20of%20Washington%20Data.xlsx\")\n","umich = pd.read_excel(base + \"University%20of%20Michigan%20Data.xlsx\")\n","\n","# Party data\n","party = pd.read_csv(base + \"Party_Government_Since_1857.csv\")\n","\n","# Preview all\n","for name, df in [\n","    (\"nsf23\", nsf23), (\"nsf24\", nsf24), (\"nsf25\", nsf25), (\"nsf26\", nsf26),\n","    (\"umd\", umd), (\"uiuc\", uiuc), (\"uwash\", uwash), (\"umich\", umich), (\"party\", party)\n","]:\n","    print(f\"\\n{'='*40}\")\n","    print(f\"{name} | shape: {df.shape}\")\n","    print(f\"{'='*40}\")\n","    print(df.head(3))\n","    print(\"Columns:\", df.columns.tolist())"],"metadata":{"id":"_x-hzqf4Un3-","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0c9e8a9f-d00e-4315-bddb-873b2192e1a4","executionInfo":{"status":"ok","timestamp":1778087346886,"user_tz":240,"elapsed":13296,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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"]},{"output_type":"stream","name":"stdout","text":["\n","========================================\n","nsf23 | shape: (652, 24)\n","========================================\n","                                            Table 23 Unnamed: 1  \\\n","0  Higher education R&D expenditures, ranked by a...        NaN   \n","1                             (Dollars in thousands)        NaN   \n","2                                        Institution       Rank   \n","\n","             Unnamed: 2 Unnamed: 3                         Unnamed: 4  \\\n","0                   NaN        NaN                                NaN   \n","1                   NaN        NaN                                NaN   \n","2  All R&D expenditures        NaN  Computer and information sciences   \n","\n","  Unnamed: 5                                         Unnamed: 6 Unnamed: 7  \\\n","0        NaN                                                NaN        NaN   \n","1        NaN                                                NaN        NaN   \n","2        NaN  Geosciences, atmospheric sciences, and ocean s...        NaN   \n","\n","      Unnamed: 8 Unnamed: 9  ... Unnamed: 14 Unnamed: 15      Unnamed: 16  \\\n","0            NaN        NaN  ...         NaN         NaN              NaN   \n","1            NaN        NaN  ...         NaN         NaN              NaN   \n","2  Life sciences        NaN  ...  Psychology         NaN  Social sciences   \n","\n","  Unnamed: 17   Unnamed: 18 Unnamed: 19  Unnamed: 20 Unnamed: 21  \\\n","0         NaN           NaN         NaN          NaN         NaN   \n","1         NaN           NaN         NaN          NaN         NaN   \n","2         NaN  Sciences nec         NaN  Engineering         NaN   \n","\n","          Unnamed: 22 Unnamed: 23  \n","0                 NaN         NaN  \n","1                 NaN         NaN  \n","2  All non-S&E fields         NaN  \n","\n","[3 rows x 24 columns]\n","Columns: ['Table 23', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23']\n","\n","========================================\n","nsf24 | shape: (641, 24)\n","========================================\n","                                            Table 23 Unnamed: 1  \\\n","0  Higher education R&D expenditures, ranked by a...        NaN   \n","1                             (Dollars in thousands)        NaN   \n","2                                        Institution       Rank   \n","\n","             Unnamed: 2 Unnamed: 3                         Unnamed: 4  \\\n","0                   NaN        NaN                                NaN   \n","1                   NaN        NaN                                NaN   \n","2  All R&D expenditures        NaN  Computer and information sciences   \n","\n","  Unnamed: 5                                         Unnamed: 6 Unnamed: 7  \\\n","0        NaN                                                NaN        NaN   \n","1        NaN                                                NaN        NaN   \n","2        NaN  Geosciences, atmospheric sciences, and ocean s...        NaN   \n","\n","      Unnamed: 8 Unnamed: 9  ... Unnamed: 14 Unnamed: 15      Unnamed: 16  \\\n","0            NaN        NaN  ...         NaN         NaN              NaN   \n","1            NaN        NaN  ...         NaN         NaN              NaN   \n","2  Life sciences        NaN  ...  Psychology         NaN  Social sciences   \n","\n","  Unnamed: 17   Unnamed: 18 Unnamed: 19  Unnamed: 20 Unnamed: 21  \\\n","0         NaN           NaN         NaN          NaN         NaN   \n","1         NaN           NaN         NaN          NaN         NaN   \n","2         NaN  Sciences nec         NaN  Engineering         NaN   \n","\n","          Unnamed: 22 Unnamed: 23  \n","0                 NaN         NaN  \n","1                 NaN         NaN  \n","2  All non-S&E fields         NaN  \n","\n","[3 rows x 24 columns]\n","Columns: ['Table 23', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23']\n","\n","========================================\n","nsf25 | shape: (668, 24)\n","========================================\n","                                            Table 23 Unnamed: 1  \\\n","0  Higher education R&D expenditures, ranked by a...        NaN   \n","1                             (Dollars in thousands)        NaN   \n","2                                        Institution       Rank   \n","\n","             Unnamed: 2 Unnamed: 3                         Unnamed: 4  \\\n","0                   NaN        NaN                                NaN   \n","1                   NaN        NaN                                NaN   \n","2  All R&D expenditures        NaN  Computer and information sciences   \n","\n","  Unnamed: 5                                         Unnamed: 6 Unnamed: 7  \\\n","0        NaN                                                NaN        NaN   \n","1        NaN                                                NaN        NaN   \n","2        NaN  Geosciences, atmospheric sciences, and ocean s...        NaN   \n","\n","      Unnamed: 8 Unnamed: 9  ... Unnamed: 14 Unnamed: 15      Unnamed: 16  \\\n","0            NaN        NaN  ...         NaN         NaN              NaN   \n","1            NaN        NaN  ...         NaN         NaN              NaN   \n","2  Life sciences        NaN  ...  Psychology         NaN  Social sciences   \n","\n","  Unnamed: 17   Unnamed: 18 Unnamed: 19  Unnamed: 20 Unnamed: 21  \\\n","0         NaN           NaN         NaN          NaN         NaN   \n","1         NaN           NaN         NaN          NaN         NaN   \n","2         NaN  Sciences nec         NaN  Engineering         NaN   \n","\n","          Unnamed: 22 Unnamed: 23  \n","0                 NaN         NaN  \n","1                 NaN         NaN  \n","2  All non-S&E fields         NaN  \n","\n","[3 rows x 24 columns]\n","Columns: ['Table 23', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23']\n","\n","========================================\n","nsf26 | shape: (685, 24)\n","========================================\n","                                            Table 15 Unnamed: 1  \\\n","0  Higher education R&D expenditures at instituti...        NaN   \n","1                             (Dollars in thousands)        NaN   \n","2                                        Institution       Rank   \n","\n","             Unnamed: 2 Unnamed: 3                         Unnamed: 4  \\\n","0                   NaN        NaN                                NaN   \n","1                   NaN        NaN                                NaN   \n","2  All R&D expenditures        NaN  Computer and information sciences   \n","\n","  Unnamed: 5                                         Unnamed: 6 Unnamed: 7  \\\n","0        NaN                                                NaN        NaN   \n","1        NaN                                                NaN        NaN   \n","2        NaN  Geosciences, atmospheric sciences, and ocean s...        NaN   \n","\n","      Unnamed: 8 Unnamed: 9  ... Unnamed: 14 Unnamed: 15      Unnamed: 16  \\\n","0            NaN        NaN  ...         NaN         NaN              NaN   \n","1            NaN        NaN  ...         NaN         NaN              NaN   \n","2  Life sciences        NaN  ...  Psychology         NaN  Social sciences   \n","\n","  Unnamed: 17   Unnamed: 18 Unnamed: 19  Unnamed: 20 Unnamed: 21  \\\n","0         NaN           NaN         NaN          NaN         NaN   \n","1         NaN           NaN         NaN          NaN         NaN   \n","2         NaN  Sciences nec         NaN  Engineering         NaN   \n","\n","          Unnamed: 22 Unnamed: 23  \n","0                 NaN         NaN  \n","1                 NaN         NaN  \n","2  All non-S&E fields         NaN  \n","\n","[3 rows x 24 columns]\n","Columns: ['Table 15', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23']\n","\n","========================================\n","umd | shape: (60, 34)\n","========================================\n","                                   Data  IPEDS ID  Year  \\\n","0  University of Maryland, College Park  163286.0  2005   \n","1  University of Maryland, College Park  163286.0  2006   \n","2  University of Maryland, College Park  163286.0  2007   \n","\n","   Total Unduplicated Athletes  Number of Sports Teams  \\\n","0                          NaN                     NaN   \n","1                          NaN                     NaN   \n","2                          NaN                     NaN   \n","\n","            NCAA Subdivision             FBS Conference  Total Expenses  \\\n","0  Football Bowl Subdivision  Atlantic Coast Conference        46508648   \n","1  Football Bowl Subdivision  Atlantic Coast Conference        49524563   \n","2  Football Bowl Subdivision  Atlantic Coast Conference        55001995   \n","\n","   Student-Athlete Meals (Non-Travel)  Excess Transfers Back  ...  \\\n","0                                 NaN                      0  ...   \n","1                                 NaN                      0  ...   \n","2                                 NaN                      0  ...   \n","\n","   Competition Guarantees - Revenues  \\\n","0                          1521714.0   \n","1                          2733584.0   \n","2                          1394677.0   \n","\n","   Conference/NCAA Distributions, Media Rights, and Post-Season Football  \\\n","0                                            8647736                       \n","1                                            9356497                       \n","2                                           10298089                       \n","\n","   Ticket Sales  Institutional/Government Support  Student Fees  \\\n","0    11037068.0                            282258     7217933.0   \n","1    12239186.0                           2379852     7554631.0   \n","2    11509938.0                           2502426     7905186.0   \n","\n","   Total Academic Spending (University-Wide)  Total Football Spending  \\\n","0                               9.877061e+08                9301053.0   \n","1                               1.038901e+09               10415512.0   \n","2                               1.075787e+09               10222566.0   \n","\n","   Total Football Coaching Salaries  Athletics Related Debt  \\\n","0                           3231900                49340550   \n","1                                 0                47000265   \n","2                           3507358                44442147   \n","\n","   Annual Debt Service, Leases and Rental Fees on Athletic Facilities  \n","0                                            4843216                   \n","1                                            4629007                   \n","2                                            4669552                   \n","\n","[3 rows x 34 columns]\n","Columns: ['Data', 'IPEDS ID', 'Year', 'Total Unduplicated Athletes', 'Number of Sports Teams', 'NCAA Subdivision', 'FBS Conference', 'Total Expenses', 'Student-Athlete Meals (Non-Travel)', 'Excess Transfers Back', 'Total Coaching Severance', 'Other Expenses', 'Medical', 'Competition Guarantees - Expenses', 'Recruiting', 'Game Expenses and Travel', 'Facilities, Debt Service, and Equipment', 'Coaches Compensation', 'Non-Coaching Athletics Staff Compensation', 'Athletic Student Aid', 'Total Revenues', 'Other Revenue', 'Corporate Sponsorship, Advertising, Licensing', 'Donor Contributions', 'Competition Guarantees - Revenues', 'Conference/NCAA Distributions, Media Rights, and Post-Season Football', 'Ticket Sales', 'Institutional/Government Support', 'Student Fees', 'Total Academic Spending (University-Wide)', 'Total Football Spending', 'Total Football Coaching Salaries', 'Athletics Related Debt', 'Annual Debt Service, Leases and Rental Fees on Athletic Facilities']\n","\n","========================================\n","uiuc | shape: (60, 34)\n","========================================\n","                                      Data  IPEDS ID  Year  \\\n","0  University of Illinois Urbana-Champaign  145637.0  2005   \n","1  University of Illinois Urbana-Champaign  145637.0  2006   \n","2  University of Illinois Urbana-Champaign  145637.0  2007   \n","\n","   Total Unduplicated Athletes  Number of Sports Teams  \\\n","0                          NaN                     NaN   \n","1                          NaN                     NaN   \n","2                          NaN                     NaN   \n","\n","            NCAA Subdivision      FBS Conference  Total Expenses  \\\n","0  Football Bowl Subdivision  Big Ten Conference        47915540   \n","1  Football Bowl Subdivision  Big Ten Conference        50576001   \n","2  Football Bowl Subdivision  Big Ten Conference        51714927   \n","\n","   Student-Athlete Meals (Non-Travel)  Excess Transfers Back  ...  \\\n","0                                 NaN                      0  ...   \n","1                                 NaN                      0  ...   \n","2                                 NaN                      0  ...   \n","\n","   Competition Guarantees - Revenues  \\\n","0                            54700.0   \n","1                           516100.0   \n","2                           203000.0   \n","\n","   Conference/NCAA Distributions, Media Rights, and Post-Season Football  \\\n","0                                           10707111                       \n","1                                           11803015                       \n","2                                           14488668                       \n","\n","   Ticket Sales  Institutional/Government Support  Student Fees  \\\n","0    12917578.0                           1959500     2749207.0   \n","1    13149923.0                           2329100     2844794.0   \n","2    14548028.0                           1407600     2738845.0   \n","\n","   Total Academic Spending (University-Wide)  Total Football Spending  \\\n","0                               1.245909e+09               10324331.0   \n","1                               1.276864e+09               10428399.0   \n","2                               1.339475e+09               10842836.0   \n","\n","   Total Football Coaching Salaries  Athletics Related Debt  \\\n","0                           2237568              20000000.0   \n","1                           2381487              17098073.0   \n","2                                 0                     NaN   \n","\n","   Annual Debt Service, Leases and Rental Fees on Athletic Facilities  \n","0                                          4000000.0                   \n","1                                          3423816.0                   \n","2                                                NaN                   \n","\n","[3 rows x 34 columns]\n","Columns: ['Data', 'IPEDS ID', 'Year', 'Total Unduplicated Athletes', 'Number of Sports Teams', 'NCAA Subdivision', 'FBS Conference', 'Total Expenses', 'Student-Athlete Meals (Non-Travel)', 'Excess Transfers Back', 'Total Coaching Severance', 'Other Expenses', 'Medical', 'Competition Guarantees - Expenses', 'Recruiting', 'Game Expenses and Travel', 'Facilities, Debt Service, and Equipment', 'Coaches Compensation', 'Non-Coaching Athletics Staff Compensation', 'Athletic Student Aid', 'Total Revenues', 'Other Revenue', 'Corporate Sponsorship, Advertising, Licensing', 'Donor Contributions', 'Competition Guarantees - Revenues', 'Conference/NCAA Distributions, Media Rights, and Post-Season Football', 'Ticket Sales', 'Institutional/Government Support', 'Student Fees', 'Total Academic Spending (University-Wide)', 'Total Football Spending', 'Total Football Coaching Salaries', 'Athletics Related Debt', 'Annual Debt Service, Leases and Rental Fees on Athletic Facilities']\n","\n","========================================\n","uwash | shape: (60, 34)\n","========================================\n","                       Data  IPEDS ID  Year  Total Unduplicated Athletes  \\\n","0  University of Washington  236948.0  2005                          NaN   \n","1  University of Washington  236948.0  2006                          NaN   \n","2  University of Washington  236948.0  2007                          NaN   \n","\n","   Number of Sports Teams           NCAA Subdivision         FBS Conference  \\\n","0                     NaN  Football Bowl Subdivision  Pacific-12 Conference   \n","1                     NaN  Football Bowl Subdivision  Pacific-12 Conference   \n","2                     NaN  Football Bowl Subdivision  Pacific-12 Conference   \n","\n","   Total Expenses  Student-Athlete Meals (Non-Travel)  Excess Transfers Back  \\\n","0        45423346                                 NaN                      0   \n","1        49011005                                 NaN                      0   \n","2        50795299                                 NaN                      0   \n","\n","   ...  Competition Guarantees - Revenues  \\\n","0  ...                          1219261.0   \n","1  ...                          1174698.0   \n","2  ...                          1509755.0   \n","\n","   Conference/NCAA Distributions, Media Rights, and Post-Season Football  \\\n","0                                            9777214                       \n","1                                           10775378                       \n","2                                            9312181                       \n","\n","   Ticket Sales  Institutional/Government Support  Student Fees  \\\n","0    13611228.0                           1460173           0.0   \n","1    18760681.0                           1584626           0.0   \n","2    16633979.0                           1704282           0.0   \n","\n","   Total Academic Spending (University-Wide)  Total Football Spending  \\\n","0                               1.798906e+09               13400762.0   \n","1                               1.887669e+09               15122759.0   \n","2                               2.028611e+09               13765662.0   \n","\n","   Total Football Coaching Salaries  Athletics Related Debt  \\\n","0                           2796167                 9000000   \n","1                           3727770                 8000000   \n","2                           3719031                 7000000   \n","\n","   Annual Debt Service, Leases and Rental Fees on Athletic Facilities  \n","0                                            1000000                   \n","1                                            1000000                   \n","2                                            1000000                   \n","\n","[3 rows x 34 columns]\n","Columns: ['Data', 'IPEDS ID', 'Year', 'Total Unduplicated Athletes', 'Number of Sports Teams', 'NCAA Subdivision', 'FBS Conference', 'Total Expenses', 'Student-Athlete Meals (Non-Travel)', 'Excess Transfers Back', 'Total Coaching Severance', 'Other Expenses', 'Medical', 'Competition Guarantees - Expenses', 'Recruiting', 'Game Expenses and Travel', 'Facilities, Debt Service, and Equipment', 'Coaches Compensation', 'Non-Coaching Athletics Staff Compensation', 'Athletic Student Aid', 'Total Revenues', 'Other Revenue', 'Corporate Sponsorship, Advertising, Licensing', 'Donor Contributions', 'Competition Guarantees - Revenues', 'Conference/NCAA Distributions, Media Rights, and Post-Season Football', 'Ticket Sales', 'Institutional/Government Support', 'Student Fees', 'Total Academic Spending (University-Wide)', 'Total Football Spending', 'Total Football Coaching Salaries', 'Athletics Related Debt', 'Annual Debt Service, Leases and Rental Fees on Athletic Facilities']\n","\n","========================================\n","umich | shape: (60, 34)\n","========================================\n","                     Data  IPEDS ID  Year  Total Unduplicated Athletes  \\\n","0  University of Michigan  170976.0  2005                          NaN   \n","1  University of Michigan  170976.0  2006                          NaN   \n","2  University of Michigan  170976.0  2007                          NaN   \n","\n","   Number of Sports Teams           NCAA Subdivision      FBS Conference  \\\n","0                     NaN  Football Bowl Subdivision  Big Ten Conference   \n","1                     NaN  Football Bowl Subdivision  Big Ten Conference   \n","2                     NaN  Football Bowl Subdivision  Big Ten Conference   \n","\n","   Total Expenses  Student-Athlete Meals (Non-Travel)  Excess Transfers Back  \\\n","0        61387144                                 NaN                      0   \n","1        67909246                                 NaN                      0   \n","2        74925433                                 NaN                      0   \n","\n","   ...  Competition Guarantees - Revenues  \\\n","0  ...                           563003.0   \n","1  ...                           188440.0   \n","2  ...                           190315.0   \n","\n","   Conference/NCAA Distributions, Media Rights, and Post-Season Football  \\\n","0                                           12316790                       \n","1                                           13823100                       \n","2                                           18395720                       \n","\n","   Ticket Sales  Institutional/Government Support  Student Fees  \\\n","0    31432123.0                                 0           0.0   \n","1    36681373.0                             37118           0.0   \n","2    35865979.0                             51965           0.0   \n","\n","   Total Academic Spending (University-Wide)  Total Football Spending  \\\n","0                               1.813582e+09               10690874.0   \n","1                               1.894113e+09               12842545.0   \n","2                               1.960025e+09               14750836.0   \n","\n","   Total Football Coaching Salaries  Athletics Related Debt  \\\n","0                                 0                 6235000   \n","1                                 0                14830000   \n","2                                 0                18980000   \n","\n","   Annual Debt Service, Leases and Rental Fees on Athletic Facilities  \n","0                                            2224260                   \n","1                                            5292000                   \n","2                                            2791000                   \n","\n","[3 rows x 34 columns]\n","Columns: ['Data', 'IPEDS ID', 'Year', 'Total Unduplicated Athletes', 'Number of Sports Teams', 'NCAA Subdivision', 'FBS Conference', 'Total Expenses', 'Student-Athlete Meals (Non-Travel)', 'Excess Transfers Back', 'Total Coaching Severance', 'Other Expenses', 'Medical', 'Competition Guarantees - Expenses', 'Recruiting', 'Game Expenses and Travel', 'Facilities, Debt Service, and Equipment', 'Coaches Compensation', 'Non-Coaching Athletics Staff Compensation', 'Athletic Student Aid', 'Total Revenues', 'Other Revenue', 'Corporate Sponsorship, Advertising, Licensing', 'Donor Contributions', 'Competition Guarantees - Revenues', 'Conference/NCAA Distributions, Media Rights, and Post-Season Football', 'Ticket Sales', 'Institutional/Government Support', 'Student Fees', 'Total Academic Spending (University-Wide)', 'Total Football Spending', 'Total Football Coaching Salaries', 'Athletics Related Debt', 'Annual Debt Service, Leases and Rental Fees on Athletic Facilities']\n","\n","========================================\n","party | shape: (87, 5)\n","========================================\n","           Congress House Majority Senate Majority            Presidency  \\\n","0  35th (1857–1859)      Democrats       Democrats   Democrat (Buchanan)   \n","1  36th (1859–1861)    Republicans       Democrats   Democrat (Buchanan)   \n","2  37th (1861–1863)    Republicans     Republicans  Republican (Lincoln)   \n","\n","  Party Government  \n","0          Unified  \n","1          Divided  \n","2          Unified  \n","Columns: ['Congress', 'House Majority', 'Senate Majority', 'Presidency', 'Party Government']\n"]}]},{"cell_type":"markdown","source":["### Clean NSF S&E Files\n","The NSF files contain 2 metadata rows before actual column headers begin.\n","We skip those rows on reload, assign clean column names, filter to our\n","4 schools of interest, and tag each file with its survey year.\n","The fields we keep are: Institution, All R&D, Computer & Info Sciences,\n","Geosciences, Life Sciences, Psychology, Social Sciences, Sciences nec, Engineering, and All non-S&E."],"metadata":{"id":"9UtprbQiVnxP"}},{"cell_type":"code","source":["import pandas as pd\n","\n","base = \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/\"\n","\n","# Column names based on row 2 of the NSF files\n","nsf_cols = [\n","    'institution', 'rank', 'all_rd', 'all_rd_rank',\n","    'computer_info_sci', 'computer_info_sci_rank',\n","    'geosciences', 'geosciences_rank',\n","    'life_sciences', 'life_sciences_rank',\n","    'math_stats', 'math_stats_rank',\n","    'psychology', 'psychology_rank',\n","    'social_sciences', 'social_sciences_rank',\n","    'sciences_nec', 'sciences_nec_rank',\n","    'engineering', 'engineering_rank',\n","    'engineering_rank2', 'engineering_rank3',\n","    'non_se_fields', 'non_se_fields_rank'\n","]\n","\n","# Reload with skiprows=2 and assign clean column names\n","nsf23 = pd.read_excel(base + \"nsf23304-tab023.xlsx\", skiprows=2, header=0)\n","nsf24 = pd.read_excel(base + \"nsf24308-tab023.xlsx\", skiprows=2, header=0)\n","nsf25 = pd.read_excel(base + \"nsf25314-tab023.xlsx\", skiprows=2, header=0)\n","nsf26 = pd.read_excel(base + \"nsf26304-tab015.xlsx\", skiprows=2, header=0)\n","\n","nsf23.columns = nsf_cols\n","nsf24.columns = nsf_cols\n","nsf25.columns = nsf_cols\n","nsf26.columns = nsf_cols\n","\n","# Tag each with year\n","nsf23['survey_year'] = 2021\n","nsf24['survey_year'] = 2022\n","nsf25['survey_year'] = 2023\n","nsf26['survey_year'] = 2024\n","\n","# Preview to confirm\n","print(nsf23.head(3))"],"metadata":{"id":"F3T0z_agVru5","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5698a582-712c-44f8-fa12-3c0b1d4bc893","executionInfo":{"status":"ok","timestamp":1778087353782,"user_tz":240,"elapsed":6866,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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"]},{"output_type":"stream","name":"stdout","text":["         institution  rank                all_rd all_rd_rank  \\\n","0        Institution  Rank  All R&D expenditures         NaN   \n","1   All institutions     -              89723447         NaN   \n","2  Johns Hopkins U.a     1               3181385         NaN   \n","\n","                   computer_info_sci computer_info_sci_rank  \\\n","0  Computer and information sciences                    NaN   \n","1                            2951923                    NaN   \n","2                             270040                      i   \n","\n","                                         geosciences geosciences_rank  \\\n","0  Geosciences, atmospheric sciences, and ocean s...              NaN   \n","1                                            3295883              NaN   \n","2                                              79257                i   \n","\n","   life_sciences life_sciences_rank  ... social_sciences_rank  \\\n","0  Life sciences                NaN  ...                  NaN   \n","1       52364596                NaN  ...                  NaN   \n","2        1069865                  i  ...                    i   \n","\n","      sciences_nec sciences_nec_rank   engineering engineering_rank  \\\n","0  Social sciences               NaN  Sciences nec              NaN   \n","1          2842033               NaN       1022500              NaN   \n","2             5305                 i         50406                i   \n","\n","  engineering_rank2 engineering_rank3       non_se_fields non_se_fields_rank  \\\n","0       Engineering               NaN  All non-S&E fields                NaN   \n","1          14293487               NaN             5113430                NaN   \n","2           1237980                 i                6448                  i   \n","\n","  survey_year  \n","0        2021  \n","1        2021  \n","2        2021  \n","\n","[3 rows x 25 columns]\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-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"]}]},{"cell_type":"markdown","source":["### Filter NSF Files to Our 4 Schools\n","We filter each NSF file to the four Big Ten schools in our study and\n","keep only the S&E expenditure columns relevant to our research question."],"metadata":{"id":"5DoC7kQsWHab"}},{"cell_type":"code","source":["# Filter NSF files to our 4 schools\n","#for year, df in [(2021, nsf23), (2022, nsf24), (2023, nsf25), (2024, nsf26)]:\n","  #print(year, df[\"institution\"].dropna().unique())\n","target_schools = [\n","    \"U. Marylandb\",\n","    \"U. Illinois, Urbana-Champaign\",\n","    \"U. Washington, Seattle\",\n","    \"U. Michigan, Ann Arbor\"\n","]\n","\n","se_cols = [\n","    \"institution\", \"survey_year\", \"all_rd\",\n","    \"computer_info_sci\", \"geosciences\", \"life_sciences\",\n","    \"math_stats\", \"psychology\", \"social_sciences\",\n","    \"sciences_nec\", \"engineering\", \"non_se_fields\"\n","]\n","\n","nsf_all = pd.concat([\n","    nsf23.loc[nsf23[\"institution\"].isin(target_schools), se_cols],\n","    nsf24.loc[nsf24[\"institution\"].isin(target_schools), se_cols],\n","    nsf25.loc[nsf25[\"institution\"].isin(target_schools), se_cols],\n","    nsf26.loc[nsf26[\"institution\"].isin(target_schools), se_cols],\n","], ignore_index=True)\n","\n","print(nsf_all.shape)\n","print(nsf_all.head())"],"metadata":{"id":"IAtjBPk5VttP","colab":{"base_uri":"https://localhost:8080/"},"outputId":"ea2d6642-6f4a-473f-9d9b-53018e07c243","executionInfo":{"status":"ok","timestamp":1778087353815,"user_tz":240,"elapsed":29,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(16, 12)\n","                     institution  survey_year   all_rd computer_info_sci  \\\n","0         U. Michigan, Ann Arbor         2021  1639645             13922   \n","1         U. Washington, Seattle         2021  1488645             26869   \n","2                   U. Marylandb         2021  1142264             50483   \n","3  U. Illinois, Urbana-Champaign         2021   731268             99600   \n","4         U. Michigan, Ann Arbor         2022  1770708             16789   \n","\n","  geosciences life_sciences math_stats psychology social_sciences  \\\n","0       15080        931174      17937      66041           18255   \n","1      128033       1013261       5778      58664           11269   \n","2       61660        633517       3760     101322            8600   \n","3        5521        258253       3612      78698           15063   \n","4       15865       1018062      20129      65018           20806   \n","\n","  sciences_nec engineering non_se_fields  \n","0       175698        3628        118228  \n","1        13491           0         73658  \n","2        58423       15701         54635  \n","3         5604       13335         35349  \n","4       189167        4036        121582  \n"]}]},{"cell_type":"markdown","source":["### Diagnose School Name Mismatches\n","Before filtering, we search the NSF institution column for each of our\n","4 schools to find the exact string used in the raw data."],"metadata":{"id":"qLuib_vkWzPn"}},{"cell_type":"code","source":["import pandas as pd\n","\n","base = \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/\"\n","\n","# Reload with skiprows=3 to skip all metadata rows\n","nsf23 = pd.read_excel(base + \"nsf23304-tab023.xlsx\", skiprows=3, header=0)\n","nsf24 = pd.read_excel(base + \"nsf24308-tab023.xlsx\", skiprows=3, header=0)\n","nsf25 = pd.read_excel(base + \"nsf25314-tab023.xlsx\", skiprows=3, header=0)\n","nsf26 = pd.read_excel(base + \"nsf26304-tab015.xlsx\", skiprows=3, header=0)\n","\n","nsf23.columns = nsf_cols\n","nsf24.columns = nsf_cols\n","nsf25.columns = nsf_cols\n","nsf26.columns = nsf_cols\n","\n","# Search for each school — using partial string match\n","keywords = ['Maryland', 'U. Illinois, Urbana-Champaign', 'U. Washington, Seattle', 'U. Michigan, Ann Arbor']\n","\n","print(\"=== Matches in nsf23 ===\")\n","for kw in keywords:\n","    matches = nsf23[nsf23['institution'].astype(str).str.contains(kw, na=False)]['institution'].tolist()\n","    print(f\"{kw}: {matches}\")"],"metadata":{"id":"bdRDD4vUWzyr","colab":{"base_uri":"https://localhost:8080/"},"outputId":"d5417b39-7d0d-4b54-99df-7f379514941a","executionInfo":{"status":"ok","timestamp":1778087361046,"user_tz":240,"elapsed":7229,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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","/usr/local/lib/python3.12/dist-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"]},{"output_type":"stream","name":"stdout","text":["=== Matches in nsf23 ===\n","Maryland: ['U. Marylandb', 'U. Maryland, Baltimore County', 'U. Maryland Center for Environmental Science', 'U. Maryland, Eastern Shore']\n","U. Illinois, Urbana-Champaign: ['U. Illinois, Urbana-Champaign']\n","U. Washington, Seattle: ['U. Washington, Seattle']\n","U. Michigan, Ann Arbor: ['U. Michigan, Ann Arbor']\n"]}]},{"cell_type":"markdown","source":["### Reload, Filter, and Tag NSF Files\n","Using exact institution names found in the raw data, we filter each NSF\n","survey year to our 4 schools. We also assign region and a standardized\n","institution name for merging with the athletics data later.\n","The 4 regions are: East (UMD), Midwest (UIUC), North (UMich), West (UWash)."],"metadata":{"id":"BY8ofkHUXB3x"}},{"cell_type":"code","source":["nsf_name_map = {\n","    'U. Marylandb':                  ('University of Maryland, College Park', 'East'),\n","    'U. Illinois, Urbana-Champaign': ('University of Illinois Urbana-Champaign', 'Midwest'),\n","    'U. Washington, Seattle':        ('University of Washington', 'West'),\n","    'U. Michigan, Ann Arbor':        ('University of Michigan', 'North'),\n","}\n","\n","target_nsf_names = list(nsf_name_map.keys())\n","\n","# Tag survey years\n","nsf23['survey_year'] = 2021\n","nsf24['survey_year'] = 2022\n","nsf25['survey_year'] = 2023\n","nsf26['survey_year'] = 2024\n","\n","se_cols = [\n","    'institution', 'survey_year', 'all_rd',\n","    'computer_info_sci', 'geosciences', 'life_sciences',\n","    'math_stats', 'psychology', 'social_sciences',\n","    'sciences_nec', 'engineering', 'non_se_fields'\n","]\n","\n","# Stack all 4 years, filter to our schools\n","nsf_all = pd.concat([\n","    nsf23[se_cols][nsf23['institution'].isin(target_nsf_names)],\n","    nsf24[se_cols][nsf24['institution'].isin(target_nsf_names)],\n","    nsf25[se_cols][nsf25['institution'].isin(target_nsf_names)],\n","    nsf26[se_cols][nsf26['institution'].isin(target_nsf_names)],\n","])\n","\n","# Add standardized name and region columns\n","nsf_all['school'] = nsf_all['institution'].map(lambda x: nsf_name_map[x][0])\n","nsf_all['region'] = nsf_all['institution'].map(lambda x: nsf_name_map[x][1])\n","\n","# Drop raw institution column, reorder\n","nsf_all = nsf_all.drop(columns='institution')\n","nsf_all = nsf_all[['school', 'region', 'survey_year'] + [c for c in se_cols if c not in ['institution', 'survey_year']]]\n","\n","nsf_all = nsf_all.reset_index(drop=True)\n","\n","print(nsf_all.shape)\n","print(nsf_all)"],"metadata":{"id":"t7FSN-X0W1HF","colab":{"base_uri":"https://localhost:8080/"},"outputId":"bf1ca8df-ea7a-4780-bd78-721950ff44d8","executionInfo":{"status":"ok","timestamp":1778087361067,"user_tz":240,"elapsed":18,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(16, 13)\n","                                     school   region  survey_year   all_rd  \\\n","0                    University of Michigan    North         2021  1639645   \n","1                  University of Washington     West         2021  1488645   \n","2      University of Maryland, College Park     East         2021  1142264   \n","3   University of Illinois Urbana-Champaign  Midwest         2021   731268   \n","4                    University of Michigan    North         2022  1770708   \n","5                  University of Washington     West         2022  1559708   \n","6      University of Maryland, College Park     East         2022  1228550   \n","7   University of Illinois Urbana-Champaign  Midwest         2022   765909   \n","8                    University of Michigan    North         2023  1925875   \n","9                  University of Washington     West         2023  1734091   \n","10     University of Maryland, College Park     East         2023  1385302   \n","11  University of Illinois Urbana-Champaign  Midwest         2023   821023   \n","12                   University of Michigan    North         2024  2110961   \n","13                 University of Washington     West         2024  1691302   \n","14     University of Maryland, College Park     East         2024  1539520   \n","15  University of Illinois Urbana-Champaign  Midwest         2024   906751   \n","\n","    computer_info_sci  geosciences  life_sciences  math_stats  psychology  \\\n","0               13922        15080         931174       17937       66041   \n","1               26869       128033        1013261        5778       58664   \n","2               50483        61660         633517        3760      101322   \n","3               99600         5521         258253        3612       78698   \n","4               16789        15865        1018062       20129       65018   \n","5               25836       152187        1045014        7095       62596   \n","6               63281        78455         655867        4538      110607   \n","7              104436         6575         270361        3729       77419   \n","8               17076        19164        1116866       21033       70259   \n","9               30158       162612        1175596        9783       69424   \n","10              80519        87557         719375        6039      135422   \n","11              99934        23906         245437        5040       84748   \n","12              20917        21117        1217303       22114       74407   \n","13              35541       163878        1114297       12015       76002   \n","14              94066       102546         789629        7359      144668   \n","15              87866        17050         252675        4557       94494   \n","\n","    social_sciences  sciences_nec  engineering  non_se_fields  \n","0             18255        175698         3628         118228  \n","1             11269         13491            0          73658  \n","2              8600         58423        15701          54635  \n","3             15063          5604        13335          35349  \n","4             20806        189167         4036         121582  \n","5             10431         16490           15          68040  \n","6              9885         56249        22223          63141  \n","7             13192          6718        15852          38841  \n","8             23483        217488         5664         110536  \n","9             11239         23577         1135          70290  \n","10            11142         72987        18057          70305  \n","11            15234          9883        17353          46838  \n","12            27037        238537         6717         111250  \n","13            13489         24820            0          62288  \n","14            15275         66988        32899          67279  \n","15            16212         13338        15584          53431  \n"]}]},{"cell_type":"markdown","source":["### Assign NSF Region to All Schools\n","Since the NSF files do not include a state column, we map each institution\n","to a region using a state-based lookup dictionary.\n","Regions follow a custom 4-area breakdown:\n","- East Coast: ME, NH, VT, MA, RI, CT, NY, NJ, PA, DE, MD, VA, NC, SC, GA, FL, DC\n","- West Coast: CA, OR, WA, AK, HI\n","- Midwest: OH, IN, IL, MI, WI, MN, IA, MO, ND, SD, NE, KS\n","- North: MT, ID, WY, CO, UT, NV, AZ, NM, TX, OK, AR, LA, MS, AL, TN, KY, WV\n","We first check which states appear in our NSF institution list, then assign."],"metadata":{"id":"DJPEO1AvXyeA"}},{"cell_type":"code","source":["# State to region mapping\n","state_to_region = {\n","    # East Coast\n","    'ME':'East Coast','NH':'East Coast','VT':'East Coast','MA':'East Coast',\n","    'RI':'East Coast','CT':'East Coast','NY':'East Coast','NJ':'East Coast',\n","    'PA':'East Coast','DE':'East Coast','MD':'East Coast','VA':'East Coast',\n","    'NC':'East Coast','SC':'East Coast','GA':'East Coast','FL':'East Coast',\n","    'DC':'East Coast',\n","    # West Coast\n","    'CA':'West Coast','OR':'West Coast','WA':'West Coast','AK':'West Coast','HI':'West Coast',\n","    # Midwest\n","    'OH':'Midwest','IN':'Midwest','IL':'Midwest','MI':'Midwest','WI':'Midwest',\n","    'MN':'Midwest','IA':'Midwest','MO':'Midwest','ND':'Midwest','SD':'Midwest',\n","    'NE':'Midwest','KS':'Midwest',\n","    # North (interior/south/mountain — everything remaining)\n","    'MT':'North','ID':'North','WY':'North','CO':'North','UT':'North',\n","    'NV':'North','AZ':'North','NM':'North','TX':'North','OK':'North',\n","    'AR':'North','LA':'North','MS':'North','AL':'North','TN':'North',\n","    'KY':'North','WV':'North',\n","}\n","\n","# Manual institution -> state lookup for NSF schools\n","# We'll build this from the full nsf23 institution list\n","# First let's see all unique institutions\n","all_institutions = pd.concat([\n","    nsf23[['institution']], nsf24[['institution']],\n","    nsf25[['institution']], nsf26[['institution']]\n","]).drop_duplicates()\n","\n","print(f\"Total unique institutions: {len(all_institutions)}\")\n","print(all_institutions['institution'].tolist()[:30])  # preview first 30"],"metadata":{"id":"6Phs0TstXvBo","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0b04479d-6754-4037-a3b1-13ce63da115a","executionInfo":{"status":"ok","timestamp":1778087361106,"user_tz":240,"elapsed":20,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Total unique institutions: 733\n","['All institutions', 'Johns Hopkins U.a', 'U. California, San Francisco', 'U. Michigan, Ann Arbor', 'U. Pennsylvania', 'U. Washington, Seattle', 'U. California, Los Angeles', 'U. California, San Diego', 'U. Wisconsin-Madison', 'Stanford U.', 'Harvard U.', 'Duke U.', 'Ohio State U.', 'U. North Carolina, Chapel Hill', 'Cornell U.', 'Yale U.', 'Texas A&M U., College Station and Health Science Center', 'U. Marylandb', 'U. Pittsburgh, Pittsburgh', 'U. Texas M. D. Anderson Cancer Center', 'Georgia Institute of Technology', 'Columbia U. in the City of New York', 'U. Minnesota, Twin Cities', 'New York U.', 'Vanderbilt U. and Vanderbilt U. Medical Center', 'Washington U., Saint Louis', 'Pennsylvania State U., University Park and Hershey Medical Center', 'U. Florida', 'U. Southern California', 'Massachusetts Institute of Technology']\n"]}]},{"cell_type":"markdown","source":["### Build NSF Region Column for All 733 Schools\n","Since NSF institution names are abbreviated and contain no state column,\n","we use keyword matching to infer each school's state, then map to our\n","4 custom regions. Institutions that cannot be auto-assigned are flagged\n","for manual review."],"metadata":{"id":"IMZW4sUPYKD5"}},{"cell_type":"code","source":["# Keyword-to-state inference rules (order matters — more specific first)\n","state_keywords = {\n","    'MD': ['Maryland'],\n","    'IL': ['Illinois'],\n","    'WA': ['Washington, Seattle','Washington, Bothell','Washington, Tacoma','Washington State'],\n","    'MI': ['Michigan'],\n","    'CA': ['California','Stanford','Caltech','Scripps'],\n","    'NY': ['New York','Cornell','Columbia','Rochester','Rockefeller','Yeshiva','Stony Brook','Buffalo','Albany','Binghamton'],\n","    'MA': ['Massachusetts','Harvard','MIT','Boston','Brandeis','Tufts','Worcester','Northeastern','Amherst'],\n","    'PA': ['Pennsylvania','Pittsburgh','Drexel','Temple','Lehigh','Carnegie Mellon','Villanova','Jefferson'],\n","    'TX': ['Texas','Baylor'],\n","    'FL': ['Florida'],\n","    'OH': ['Ohio'],\n","    'NC': ['North Carolina','Duke','Wake Forest','Campbell'],\n","    'VA': ['Virginia'],\n","    'GA': ['Georgia'],\n","    'MN': ['Minnesota'],\n","    'WI': ['Wisconsin'],\n","    'IN': ['Indiana','Purdue','Notre Dame'],\n","    'MO': ['Missouri','Washington U., Saint'],\n","    'CO': ['Colorado'],\n","    'OR': ['Oregon'],\n","    'AZ': ['Arizona'],\n","    'TN': ['Tennessee','Vanderbilt','Meharry'],\n","    'NJ': ['New Jersey','Rutgers','Princeton'],\n","    'CT': ['Connecticut','Yale'],\n","    'SC': ['South Carolina','Clemson'],\n","    'IA': ['Iowa'],\n","    'KS': ['Kansas'],\n","    'NE': ['Nebraska'],\n","    'KY': ['Kentucky'],\n","    'AL': ['Alabama','Auburn'],\n","    'MS': ['Mississippi'],\n","    'AR': ['Arkansas'],\n","    'LA': ['Louisiana','Tulane'],\n","    'OK': ['Oklahoma'],\n","    'NM': ['New Mexico'],\n","    'NV': ['Nevada'],\n","    'UT': ['Utah'],\n","    'ID': ['Idaho'],\n","    'MT': ['Montana'],\n","    'WY': ['Wyoming'],\n","    'ND': ['North Dakota'],\n","    'SD': ['South Dakota'],\n","    'WV': ['West Virginia'],\n","    'NH': ['New Hampshire','Dartmouth'],\n","    'VT': ['Vermont'],\n","    'ME': ['Maine'],\n","    'RI': ['Rhode Island','Brown'],\n","    'DE': ['Delaware'],\n","    'DC': ['Georgetown','George Washington','Howard','American U.','Gallaudet'],\n","    'HI': ['Hawaii'],\n","    'AK': ['Alaska'],\n","}\n","\n","def infer_state(name):\n","    name_str = str(name)\n","    for state, keywords in state_keywords.items():\n","        for kw in keywords:\n","            if kw.lower() in name_str.lower():\n","                return state\n","    return None\n","\n","# Apply to all institutions\n","all_institutions['state'] = all_institutions['institution'].apply(infer_state)\n","all_institutions['nsf_region'] = all_institutions['state'].map(state_to_region)\n","\n","# Check how many we couldn't assign\n","unassigned = all_institutions[all_institutions['nsf_region'].isna()]\n","print(f\"Assigned: {all_institutions['nsf_region'].notna().sum()} / {len(all_institutions)}\")\n","print(f\"\\nUnassigned ({len(unassigned)}):\")\n","print(unassigned['institution'].tolist())"],"metadata":{"id":"PZ9C9mjjX2wU","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5c3cf12b-2075-4db8-9424-078f2cc0de18","executionInfo":{"status":"ok","timestamp":1778087361157,"user_tz":240,"elapsed":49,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Assigned: 377 / 733\n","\n","Unassigned (356):\n","['All institutions', 'Johns Hopkins U.a', 'Northwestern U.', 'Emory U.', 'Icahn School of Medicine at Mount Sinai', 'U. Cincinnati', 'U. Chicago', 'SUNY, Polytechnic Institute', 'Case Western Reserve U.', 'Uniformed Services U. of the Health Sciences', 'U. Miami', 'Albert Einstein C. of Medicine', 'Woods Hole Oceanographic Institution', 'Wayne State U.', 'George Mason U.', 'Rice U.', 'U. Houston', 'U. Louisville', 'Wichita State U.', 'U. Dayton', 'Syracuse U.', 'Rensselaer Polytechnic Institute', 'Augusta U.', 'Cold Spring Harbor Laboratory', 'San Diego State U.', 'Rush U.', 'Saint Louis U.', 'Cleveland State U.', 'William & Mary', 'U. Memphis', 'Van Andel Research Institute', 'Naval Postgraduate School', 'Old Dominion U.', 'San Jose State U.', 'CUNY, City C.', 'U. Toledo', 'Mercer U.', 'East Carolina U.', 'Kent State U.', 'Boise State U.', 'Air Force Institute of Technology', 'SUNY, Upstate Medical U.', 'U. Puerto Rico, Medical Sciences Campus', 'Loyola U., Chicago', 'Brigham Young U., Provo', 'Stevens Institute of Technology', 'Southern Methodist U.', 'Portland State U.', 'U. Denver', 'Marquette U.', 'San Francisco State U.', 'Catholic U. of America', 'SUNY, Downstate Health Sciences U.', 'CUNY, Hunter C.', 'Morehouse School of Medicine', 'U.S. Air Force Academy', 'U. Puerto Rico, Rio Piedras', 'SUNY, C. of Environmental Science and Forestry', 'Rowan U.', 'Desert Research Institute', 'Wright State U.', 'Chapman U.', 'Miami U.', 'Nova Southeastern U.', 'Marshall U.', 'Ponce Health Sciences U.', 'Creighton U.', 'Tuskegee U.', 'Morgan State U.', 'Oakland U.', 'Embry-Riddle Aeronautical U.', 'U.S. Military Academy', 'Kennesaw State U.', 'Bowling Green State U.', 'U. Puerto Rico, Mayaguez', 'Tarleton State U.', 'U. Guam', 'Prairie View A&M U.', 'Montclair State U.', 'U. Akron', 'U. New Orleans', 'Clarkson U.', 'Fordham U.', 'U. Tulsa', 'Jackson State U.', 'CUNY, Queens C.', 'CUNY, Advanced Science Research Center', 'U. of the Virgin Islands', 'Long Island U.', 'CUNY, Graduate School of Public Health and Health Policy', 'James Madison U.', 'U. Baltimore', 'New School', 'Alcorn State U.', 'Humboldt State U.', 'Loma Linda U.', 'Rosalind Franklin U. of Medicine and Science', 'Western Washington U.', 'Sam Houston State U.', 'Charles R. Drew U. of Medicine and Science', 'Loyola Marymount U.', 'CUNY, John Jay C. of Criminal Justice', 'Hampton U.', 'Midwestern U.', 'U.S. Naval Academy', 'Salisbury U.', 'Duquesne U.', \"St. Edward's U.\", 'Bryn Mawr C.', 'Clark Atlanta U.', 'Ball State U.', 'Central State U.', 'Winston-Salem State U.', 'Wesleyan U.', 'Clark U.', 'William Paterson U.', 'U. San Diego', 'Claremont Graduate U.', 'Grand Valley State U.', 'Western U. of Health Sciences', 'DePaul U.', 'Edward Via C. of Osteopathic Medicine', 'C. Charleston', 'Langston U.', 'CUNY, Brooklyn C.', 'Wellesley C.', 'U. New England', 'Philadelphia C. of Osteopathic Medicine', 'Southern U. and A&M C., Agricultural Research and Extension Center', 'Roseman U. of Health Sciences', 'Hofstra U.', 'Naval War C.', 'Seton Hall U.', 'Azusa Pacific U.', 'U. of Health Sciences and Pharmacy in St. Louis', 'Des Moines U.', 'Hope C.', 'U. of St. Thomas (MN)', 'Reed C.', 'High Tech High Graduate School of Ed.', 'Toyota Technological Institute, Chicago', 'Southern U. and A&M C., Baton Rouge', 'Williams C.', 'Fort Valley State U.', 'Lamar U.', 'Pacific U.', 'U.S. Army War C.', 'Barnard C.', 'Abilene Christian U.', 'U. Detroit Mercy', 'Morehouse C.', 'Claremont McKenna C.', 'Appalachian State U.', 'Bucknell U.', 'Fisk U.', 'Norfolk State U.', 'CUNY, Graduate Center', 'CUNY, Lehman C.', 'Grinnell C.', 'U. New Haven', 'U. of the Sciences Philadelphia', 'Fayetteville State U.', 'Murray State U.', 'Haverford C.', 'U. Central del Caribe', 'Carleton C.', 'Towson U.', 'National Defense U.', 'Memorial Sloan Kettering Cancer Center, Louis V. Gerstner Jr. Graduate S. of Biomedical Sciences', 'Franklin and Marshall C.', 'Pace U.', 'Occidental C.', 'Kean U.', 'Touro U.', 'SUNY, C. of Optometry', 'Dakota State U.', 'Colgate U.', 'Swarthmore C.', 'Stephen F. Austin State U.', 'Milwaukee School of Engineering', 'Santa Clara U.', 'MGH Institute of Health Professions', 'Sonoma State U.', 'Middlebury C.', \"Saint Joseph's U.\", 'Trinity U.', 'CUNY, C. Staten Island', 'Western Carolina U.', 'Northwest Indian C.', 'Youngstown State U.', 'Austin Peay State U.', 'Pittsburg State U.', 'Alfred U.', 'Christopher Newport U.', 'Pepperdine U.', 'Bates C.', 'Winthrop U.', 'Pratt Institute', 'Dillard U.', 'CUNY, Baruch C.', 'Davidson C.', 'Furman U.', 'Saginaw Valley State U.', 'Bowdoin C.', 'Central Washington U.', 'Framingham State U.', 'St. Cloud State U.', 'Harvey Mudd C.', 'Skidmore C.', 'Marymount U.', 'U. Richmond', 'Spelman C.', 'Lewis and Clark C.', 'Keck Graduate Institute', 'Coastal Carolina U.', 'Palmer C. of Chiropractic, Davenport', 'Pomona C.', 'Sul Ross State U.', 'Grambling State U.', 'Tougaloo C.', 'SUNY, Geneseo', 'Suffolk U.', 'Elon U.', 'Mount Holyoke C.', 'U. of the Incarnate Word', 'New England C. of Optometry', 'Mercyhurst U.', 'Fort Lewis C.', 'Eastern Washington U.', 'U. Puerto Rico, Cayey', 'Manhattan C.', 'Roger Williams U.', 'U. Houston-Downtown', 'Willamette U.', 'Savannah State U.', 'Trinity C., Hartford', 'Lawrence Technological U.', 'Morehead State U.', 'Hamilton C.', 'Heidelberg U.', 'Erikson Institute', 'Elizabeth City State U.', 'U. Houston-Clear Lake', 'Colby C.', 'Monmouth U.', 'CUNY, system office', 'Franklin W. Olin C. of Engineering', 'Vassar C.', 'U. Ana G. Mendez, Gurabo', 'Seattle U.', 'Troy U.', 'La Sierra U.', 'Calvin U.', 'Drake U.', 'Bowie State U.', 'Union C., Schenectady', 'U. San Francisco', 'U. of the Pacific', 'Marshall B. Ketchum U.', 'U. Puerto Rico, Humacao', 'Kettering U.', 'Babson C.', 'Black Hills State U.', 'Macalester C.', 'SUNY, C. Plattsburgh', 'U. Hartford', 'Stockton U.', 'Biola U.', 'Doane U.', 'CUNY, York C.', 'Albion C.', \"Saint John's U.\", 'U. of Mary Washington', 'SUNY, C. Brockport', 'Northland C.', 'C. Wooster', 'C. of Saint Benedict', 'Canisius C.', 'Ithaca C.', 'Claflin U.', 'Lafayette C.', 'Augustana U.', 'SUNY, Oswego', 'Sacred Heart U.', 'Liberty U.', 'Keene State C.', 'Arcadia U.', 'Mills C.', 'National U. of Natural Medicine', 'Fairfield U.', 'Fielding Graduate U.', 'St. Olaf C.', 'American Samoa Community C.', 'Andrews U.', 'Fort Hays State U.', 'Oberlin C.', 'Providence C.', 'Wheaton C., Wheaton', 'Dordt U.', 'Niagara U.', 'Albizu U.', 'Lipscomb U.', 'Jacksonville State U.', 'Chicago State U.', 'Dine C.', 'Lake Superior State U.', 'Western New England U.', 'Butler U.', 'Kenyon C.', 'Whitman C.', 'SUNY, Cobleskill', 'Bentley U.', 'U. Puget Sound', 'Robert Morris U.', 'St. Catherine U.', 'Plymouth State U.', 'Sonoran U. Health Sciences', 'U. Charleston', 'Benedict C.', 'Ferris State U.', 'Lincoln Memorial U.', 'Norwich U.', 'Merrimack C.', 'Hartford International U. for Religion and Peace', 'United Tribes Technical C.', 'Radford U.', 'U. Scranton', 'Ursinus C.', 'Gettysburg C.', 'Southern U., New Orleans', 'Gustavus Adolphus C.', 'Canisius U.', 'U.S. Coast Guard Academy', 'U. Puerto Rico, Ponce', 'Lincoln U.', 'Bryant U.', 'National U.', 'St. John Fisher U.', 'National Louis U.', 'Seattle Pacific U.', 'John Carroll U.', 'Dickinson C.', 'Eckerd C.', 'Roosevelt U.', 'Midwestern State U.', 'Weber State U.', 'Juniata C.', 'Nicholls State U.', 'Denison U.', 'Aaniiih Nakoda C.', 'St. Bonaventure U.', 'High Point U.', 'Valparaiso U.', 'Polytechnic U. Puerto Rico', 'Berry C.', 'Carthage C.']\n"]}]},{"cell_type":"markdown","source":["### Expanded State Inference for Unassigned Schools\n","The initial keyword pass left 356 schools unassigned due to abbreviated\n","or non-geographic institution names. We add a second-pass lookup covering\n","city names, common abbreviations, and known institutions by location.\n","Schools that remain unassigned after both passes (territories, international,\n","or ambiguous) are labeled 'Other'."],"metadata":{"id":"BdfnjM2FYW3f"}},{"cell_type":"code","source":["# Second-pass keyword additions\n","state_keywords_v2 = {\n","    'MD': ['Johns Hopkins','Morgan State','Towson','Salisbury','Bowie State',\n","           'U. Baltimore','U. of Mary Washington'],\n","    'IL': ['Northwestern U.','U. Chicago','Loyola U., Chicago','Rush U.',\n","           'DePaul','Rosalind Franklin','Toyota Technological Institute, Chicago',\n","           'Chicago State','National Louis','Roosevelt U.','Erikson'],\n","    'OH': ['U. Cincinnati','Case Western','U. Dayton','Cleveland State',\n","           'U. Toledo','Kent State','Wright State','Miami U.','Youngstown',\n","           'Bowling Green','U. Akron','Heidelberg','Kenyon','Oberlin',\n","           'Denison','C. Wooster','John Carroll','Kettering'],\n","    'GA': ['Emory','Augusta U.','Clark Atlanta','Morehouse','Kennesaw',\n","           'Fort Valley','Savannah State','Spelman','Berry C.'],\n","    'NY': ['Icahn School','Mount Sinai','SUNY','CUNY','Cold Spring Harbor',\n","           'Rensselaer','Syracuse','Clarkson','Fordham','Long Island U.',\n","           'New School','Colgate','Alfred U.','Pace U.','Pratt Institute',\n","           'Skidmore','Vassar','Union C., Schenectady','Barnard','Ithaca C.',\n","           'Niagara','Canisius','St. Bonaventure','St. John Fisher',\n","           'Memorial Sloan Kettering','Lafayette C.'],\n","    'TX': ['Rice U.','U. Houston','Southern Methodist','Lamar U.',\n","           'Tarleton State','Prairie View','Sam Houston','Stephen F. Austin',\n","           'Sul Ross','Abilene Christian','Trinity U.','U. of the Incarnate Word',\n","           'U. Houston-Downtown','U. Houston-Clear Lake','Midwestern State U.'],\n","    'VA': ['George Mason','Old Dominion','William & Mary','James Madison',\n","           'U.S. Military Academy','Hampton U.','Norfolk State',\n","           'Christopher Newport','Marymount U.','U. Richmond',\n","           'Liberty U.','Radford U.'],\n","    'DC': ['Catholic U. of America','Uniformed Services','Georgetown',\n","           'George Washington','Howard','American U.','Gallaudet',\n","           'National Defense U.'],\n","    'FL': ['U. Miami','Nova Southeastern','Embry-Riddle','Eckerd C.'],\n","    'MO': ['Saint Louis U.','U. of Health Sciences and Pharmacy in St. Louis',\n","           'Washington U., Saint'],\n","    'WI': ['Marquette','Milwaukee School of Engineering'],\n","    'TN': ['U. Memphis','Vanderbilt','Fisk U.','Austin Peay','Lipscomb'],\n","    'KY': ['U. Louisville','Murray State','Morehead State'],\n","    'AL': ['Tuskegee','Jacksonville State'],\n","    'MS': ['Jackson State','Alcorn State','Tougaloo'],\n","    'NC': ['Appalachian State','East Carolina','Winston-Salem State',\n","           'Fayetteville State','Western Carolina','Elon U.','Davidson C.',\n","           'Furman U.','High Point U.'],\n","    'SC': ['C. Charleston','Winthrop U.','Coastal Carolina','Claflin'],\n","    'NJ': ['Stevens Institute','Rowan U.','Seton Hall','Kean U.',\n","           'Monmouth U.','Stockton U.','William Paterson'],\n","    'CA': ['San Diego State','San Jose State','Naval Postgraduate',\n","           'San Francisco State','Loyola Marymount','Charles R. Drew',\n","           'Loma Linda','Claremont','Occidental','Pepperdine','Santa Clara',\n","           'Sonoma State','Biola','La Sierra','U. San Diego','U. San Francisco',\n","           'U. of the Pacific','Marshall B. Ketchum','Harvey Mudd','Pomona C.',\n","           'Scripps','Keck Graduate','Mills C.','National U. of Natural Medicine',\n","           'Azusa Pacific','Chapman U.','Western U. of Health Sciences',\n","           'Pacific U.','Fielding Graduate'],\n","    'PA': ['Duquesne','Bryn Mawr','Philadelphia C. of Osteopathic',\n","           'U. of the Sciences Philadelphia','Franklin and Marshall',\n","           'Bucknell','Haverford','Swarthmore','Saint Joseph',\n","           'Mercyhurst','Robert Morris','U. Scranton','Ursinus',\n","           'Gettysburg','Arcadia U.','Juniata','Dickinson C.'],\n","    'IN': ['Ball State','Valparaiso','Butler U.'],\n","    'MI': ['Wayne State','Van Andel','Oakland U.','Grand Valley State',\n","           'Saginaw Valley','U. Detroit Mercy','Lake Superior State',\n","           'Calvin U.','Ferris State','Albion C.','Andrews U.'],\n","    'MN': ['U. of St. Thomas (MN)','St. Cloud State','Macalester',\n","           'St. Olaf','Gustavus Adolphus','St. Catherine','Carleton C.'],\n","    'IA': ['Des Moines U.','Palmer C.','Grinnell C.','Drake U.',\n","           'Dordt U.'],\n","    'LA': ['Southern U. and A&M C., Baton Rouge','U. New Orleans',\n","           'Southern U., New Orleans','Grambling','Nicholls State',\n","           'Tulane'],\n","    'OK': ['U. Tulsa','Langston U.'],\n","    'NE': ['Doane U.','Creighton'],\n","    'KS': ['Wichita State','Pittsburg State','Fort Hays State'],\n","    'CO': ['U. Denver','Fort Lewis C.'],\n","    'NV': ['Desert Research Institute'],\n","    'OR': ['Portland State','Lewis and Clark','Pacific U.','Willamette',\n","           'National U. of Natural Medicine'],\n","    'WA': ['Seattle U.','Seattle Pacific','U. Puget Sound','Whitman C.'],\n","    'ID': ['Boise State'],\n","    'SD': ['Black Hills State','Dakota State','Augustana U.'],\n","    'ND': ['United Tribes Technical'],\n","    'MT': ['Aaniiih Nakoda C.'],\n","    'WV': ['Marshall U.','U. Charleston'],\n","    'NH': ['Plymouth State','Keene State C.'],\n","    'VT': ['Norwich U.','Middlebury'],\n","    'ME': ['Bates C.','Bowdoin','Colby C.'],\n","    'RI': ['Providence C.','Bryant U.','Roger Williams'],\n","    'CT': ['Sacred Heart','Wesleyan U.','Trinity C., Hartford',\n","           'U. New Haven','U. Hartford','Fairfield U.','Mitchell C.'],\n","    'MA': ['MGH Institute','Framingham State','Suffolk U.',\n","           'Bentley U.','Merrimack C.','Western New England',\n","           'Hartford International','Franklin W. Olin','Babson C.',\n","           'Mount Holyoke','Springfield C.'],\n","    'NY': ['Hofstra','Touro','Pace U.','Manhattan C.'],\n","}\n","\n","def infer_state_v2(name):\n","    name_str = str(name)\n","    # First pass\n","    for state, keywords in state_keywords.items():\n","        for kw in keywords:\n","            if kw.lower() in name_str.lower():\n","                return state\n","    # Second pass\n","    for state, keywords in state_keywords_v2.items():\n","        for kw in keywords:\n","            if kw.lower() in name_str.lower():\n","                return state\n","    return None\n","\n","all_institutions['state_v2'] = all_institutions['institution'].apply(infer_state_v2)\n","all_institutions['nsf_region'] = all_institutions['state_v2'].map(state_to_region).fillna('Other')\n","\n","# Check remaining unassigned\n","still_unassigned = all_institutions[all_institutions['nsf_region'] == 'Other']\n","print(f\"Assigned: {(all_institutions['nsf_region'] != 'Other').sum()} / {len(all_institutions)}\")\n","print(f\"\\nStill unassigned ({len(still_unassigned)}):\")\n","print(still_unassigned['institution'].tolist())"],"metadata":{"id":"NThW7dueYLrm","colab":{"base_uri":"https://localhost:8080/"},"outputId":"600c95b0-ef5c-4e32-dc41-2a08b53edd29","executionInfo":{"status":"ok","timestamp":1778087361214,"user_tz":240,"elapsed":24,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Assigned: 626 / 733\n","\n","Still unassigned (107):\n","['All institutions', 'Icahn School of Medicine at Mount Sinai', 'SUNY, Polytechnic Institute', 'Albert Einstein C. of Medicine', 'Woods Hole Oceanographic Institution', 'Syracuse U.', 'Rensselaer Polytechnic Institute', 'Cold Spring Harbor Laboratory', 'CUNY, City C.', 'Mercer U.', 'Air Force Institute of Technology', 'SUNY, Upstate Medical U.', 'U. Puerto Rico, Medical Sciences Campus', 'Brigham Young U., Provo', 'SUNY, Downstate Health Sciences U.', 'CUNY, Hunter C.', 'U.S. Air Force Academy', 'U. Puerto Rico, Rio Piedras', 'SUNY, C. of Environmental Science and Forestry', 'Ponce Health Sciences U.', 'U. Puerto Rico, Mayaguez', 'U. Guam', 'Montclair State U.', 'Clarkson U.', 'Fordham U.', 'CUNY, Queens C.', 'CUNY, Advanced Science Research Center', 'U. of the Virgin Islands', 'Long Island U.', 'CUNY, Graduate School of Public Health and Health Policy', 'New School', 'Humboldt State U.', 'Western Washington U.', 'CUNY, John Jay C. of Criminal Justice', 'Midwestern U.', 'U.S. Naval Academy', \"St. Edward's U.\", 'Central State U.', 'Clark U.', 'Edward Via C. of Osteopathic Medicine', 'CUNY, Brooklyn C.', 'Wellesley C.', 'U. New England', 'Southern U. and A&M C., Agricultural Research and Extension Center', 'Roseman U. of Health Sciences', 'Naval War C.', 'Hope C.', 'Reed C.', 'High Tech High Graduate School of Ed.', 'Williams C.', 'U.S. Army War C.', 'Barnard C.', 'CUNY, Graduate Center', 'CUNY, Lehman C.', 'U. Central del Caribe', 'SUNY, C. of Optometry', 'Colgate U.', 'CUNY, C. Staten Island', 'Northwest Indian C.', 'Alfred U.', 'Pratt Institute', 'Dillard U.', 'CUNY, Baruch C.', 'Central Washington U.', 'Skidmore C.', 'SUNY, Geneseo', 'New England C. of Optometry', 'Eastern Washington U.', 'U. Puerto Rico, Cayey', 'Lawrence Technological U.', 'Hamilton C.', 'Elizabeth City State U.', 'CUNY, system office', 'Vassar C.', 'U. Ana G. Mendez, Gurabo', 'Troy U.', 'Union C., Schenectady', 'U. Puerto Rico, Humacao', 'SUNY, C. Plattsburgh', 'CUNY, York C.', \"Saint John's U.\", 'SUNY, C. Brockport', 'Northland C.', 'C. of Saint Benedict', 'Canisius C.', 'Ithaca C.', 'Lafayette C.', 'SUNY, Oswego', 'American Samoa Community C.', 'Wheaton C., Wheaton', 'Niagara U.', 'Albizu U.', 'Dine C.', 'SUNY, Cobleskill', 'Sonoran U. Health Sciences', 'Benedict C.', 'Lincoln Memorial U.', 'Canisius U.', 'U.S. Coast Guard Academy', 'U. Puerto Rico, Ponce', 'Lincoln U.', 'National U.', 'St. John Fisher U.', 'Weber State U.', 'St. Bonaventure U.', 'Polytechnic U. Puerto Rico', 'Carthage C.']\n"]}]},{"cell_type":"code","source":["manual_state_map = {\n","    # New York — SUNY/CUNY system + named schools\n","    'Icahn School of Medicine at Mount Sinai': 'NY',\n","    'SUNY, Polytechnic Institute': 'NY',\n","    'Albert Einstein C. of Medicine': 'NY',\n","    'Cold Spring Harbor Laboratory': 'NY',\n","    'CUNY, City C.': 'NY',\n","    'SUNY, Upstate Medical U.': 'NY',\n","    'SUNY, Downstate Health Sciences U.': 'NY',\n","    'CUNY, Hunter C.': 'NY',\n","    'SUNY, C. of Environmental Science and Forestry': 'NY',\n","    'CUNY, Queens C.': 'NY',\n","    'CUNY, Advanced Science Research Center': 'NY',\n","    'Long Island U.': 'NY',\n","    'CUNY, Graduate School of Public Health and Health Policy': 'NY',\n","    'New School': 'NY',\n","    'CUNY, John Jay C. of Criminal Justice': 'NY',\n","    'Fordham U.': 'NY',\n","    'Clarkson U.': 'NY',\n","    'CUNY, Brooklyn C.': 'NY',\n","    'CUNY, Graduate Center': 'NY',\n","    'CUNY, Lehman C.': 'NY',\n","    'SUNY, C. of Optometry': 'NY',\n","    'CUNY, C. Staten Island': 'NY',\n","    'Alfred U.': 'NY',\n","    'Pratt Institute': 'NY',\n","    'CUNY, Baruch C.': 'NY',\n","    'Skidmore C.': 'NY',\n","    'SUNY, Geneseo': 'NY',\n","    'Hamilton C.': 'NY',\n","    'CUNY, system office': 'NY',\n","    'Vassar C.': 'NY',\n","    'Union C., Schenectady': 'NY',\n","    'SUNY, C. Plattsburgh': 'NY',\n","    'CUNY, York C.': 'NY',\n","    'SUNY, C. Brockport': 'NY',\n","    'Canisius C.': 'NY',\n","    'Ithaca C.': 'NY',\n","    'Lafayette C.': 'NY',\n","    'SUNY, Oswego': 'NY',\n","    'SUNY, Cobleskill': 'NY',\n","    'Niagara U.': 'NY',\n","    'St. John Fisher U.': 'NY',\n","    'St. Bonaventure U.': 'NY',\n","    'Colgate U.': 'NY',\n","    'Syracuse U.': 'NY',\n","    'Rensselaer Polytechnic Institute': 'NY',\n","    'Barnard C.': 'NY',\n","    # Massachusetts\n","    'Wellesley C.': 'MA',\n","    'Clark U.': 'MA',\n","    'Williams C.': 'MA',\n","    'New England C. of Optometry': 'MA',\n","    'U. New England': 'ME',\n","    'Woods Hole Oceanographic Institution': 'MA',\n","    # Washington state\n","    'Central Washington U.': 'WA',\n","    'Eastern Washington U.': 'WA',\n","    'Western Washington U.': 'WA',\n","    # Utah\n","    'Brigham Young U., Provo': 'UT',\n","    'Roseman U. of Health Sciences': 'UT',\n","    'Weber State U.': 'UT',\n","    'Sonoran U. Health Sciences': 'AZ',\n","    # Military/federal (by location)\n","    'Air Force Institute of Technology': 'OH',       # Wright-Patterson AFB, OH\n","    'U.S. Air Force Academy': 'CO',\n","    'U.S. Naval Academy': 'MD',\n","    'Naval War C.': 'RI',\n","    'U.S. Army War C.': 'PA',\n","    'U.S. Coast Guard Academy': 'CT',\n","    'National Defense U.': 'DC',\n","    # Georgia\n","    'Mercer U.': 'GA',\n","    # Illinois\n","    'Midwestern U.': 'IL',\n","    'Wheaton C., Wheaton': 'IL',\n","    'Carthage C.': 'IL',\n","    # Michigan\n","    'Lawrence Technological U.': 'MI',\n","    # Virginia\n","    'Edward Via C. of Osteopathic Medicine': 'VA',\n","    # North Carolina\n","    'Elizabeth City State U.': 'NC',\n","    # Alabama\n","    'Troy U.': 'AL',\n","    # Tennessee\n","    'Lincoln Memorial U.': 'TN',\n","    # Louisiana\n","    'Southern U. and A&M C., Agricultural Research and Extension Center': 'LA',\n","    'Dillard U.': 'LA',\n","    # Minnesota\n","    'St. Cloud State U.': 'MN',\n","    # Oregon\n","    'Reed C.': 'OR',\n","    # California\n","    'Humboldt State U.': 'CA',\n","    'High Tech High Graduate School of Ed.': 'CA',\n","    # Wisconsin\n","    'Northland C.': 'WI',\n","    # Pennsylvania\n","    'Lincoln U.': 'PA',\n","    # Minnesota\n","    'C. of Saint Benedict': 'MN',\n","    # New York\n","    'U. Ana G. Mendez, Gurabo': 'PR',\n","    # Territories / Other\n","    'U. Puerto Rico, Medical Sciences Campus': 'PR',\n","    'U. Puerto Rico, Rio Piedras': 'PR',\n","    'U. Puerto Rico, Mayaguez': 'PR',\n","    'Ponce Health Sciences U.': 'PR',\n","    'U. Puerto Rico, Cayey': 'PR',\n","    'U. Puerto Rico, Humacao': 'PR',\n","    'U. Puerto Rico, Ponce': 'PR',\n","    'Polytechnic U. Puerto Rico': 'PR',\n","    'Albizu U.': 'PR',\n","    'U. Central del Caribe': 'PR',\n","    'U. Guam': 'GU',\n","    'American Samoa Community C.': 'AS',\n","    'U. of the Virgin Islands': 'VI',\n","    'Northwest Indian C.': 'WA',\n","    'Dine C.': 'AZ',\n","    # Misc\n","    'National U.': 'CA',\n","    'Hope C.': 'MI',\n","    'Saint John\\'s U.': 'MN',\n","    'St. Edward\\'s U.': 'TX',\n","    'Benedict C.': 'SC',\n","    'Central State U.': 'OH',\n","    'All institutions': None,\n","}\n","\n","# Territories get 'Other'\n","territory_states = {'PR', 'GU', 'AS', 'VI'}\n","\n","def infer_state_final(name):\n","    name_str = str(name)\n","    # Manual map first\n","    if name_str in manual_state_map:\n","        return manual_state_map[name_str]\n","    # Then pass 1\n","    for state, keywords in state_keywords.items():\n","        for kw in keywords:\n","            if kw.lower() in name_str.lower():\n","                return state\n","    # Then pass 2\n","    for state, keywords in state_keywords_v2.items():\n","        for kw in keywords:\n","            if kw.lower() in name_str.lower():\n","                return state\n","    return None\n","\n","all_institutions['state_final'] = all_institutions['institution'].apply(infer_state_final)\n","all_institutions['nsf_region'] = all_institutions['state_final'].apply(\n","    lambda s: 'Other' if (s is None or s in territory_states) else state_to_region.get(s, 'Other')\n",")\n","\n","print(f\"Assigned: {(all_institutions['nsf_region'] != 'Other').sum()} / {len(all_institutions)}\")\n","print(f\"\\nStill Other:\")\n","print(all_institutions[all_institutions['nsf_region'] == 'Other']['institution'].tolist())"],"metadata":{"id":"fc3rqq7xYelr","colab":{"base_uri":"https://localhost:8080/"},"outputId":"699d1f28-107f-4565-cf16-bf6859849559","executionInfo":{"status":"ok","timestamp":1778087361329,"user_tz":240,"elapsed":112,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Assigned: 716 / 733\n","\n","Still Other:\n","['All institutions', 'U. Puerto Rico, Medical Sciences Campus', 'U. Puerto Rico, Rio Piedras', 'Ponce Health Sciences U.', 'U. Puerto Rico, Mayaguez', 'U. Guam', 'Montclair State U.', 'U. of the Virgin Islands', 'U. Central del Caribe', 'U. Puerto Rico, Cayey', 'U. Ana G. Mendez, Gurabo', 'U. Puerto Rico, Humacao', 'American Samoa Community C.', 'Albizu U.', 'Canisius U.', 'U. Puerto Rico, Ponce', 'Polytechnic U. Puerto Rico']\n"]}]},{"cell_type":"code","source":["# Merge region lookup onto full NSF data for all 4 years\n","nsf_all_schools = pd.concat([\n","    nsf23[se_cols + ['survey_year']],\n","    nsf24[se_cols + ['survey_year']],\n","    nsf25[se_cols + ['survey_year']],\n","    nsf26[se_cols + ['survey_year']],\n","])\n","\n","# Merge in region\n","nsf_all_schools = nsf_all_schools.merge(\n","    all_institutions[['institution', 'nsf_region']],\n","    on='institution',\n","    how='left'\n",")\n","\n","# Drop the 'All institutions' summary row\n","nsf_all_schools = nsf_all_schools[nsf_all_schools['institution'] != 'All institutions']\n","\n","# Quick check\n","print(nsf_all_schools.shape)\n","print(nsf_all_schools['nsf_region'].value_counts())\n","print(nsf_all_schools.head(3))"],"metadata":{"id":"jhriYz2lY1gX","colab":{"base_uri":"https://localhost:8080/"},"outputId":"77e8ec13-06ae-4400-bfda-21c693804dac","executionInfo":{"status":"ok","timestamp":1778087361336,"user_tz":240,"elapsed":11,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2630, 14)\n","nsf_region\n","East Coast    1111\n","North          573\n","Midwest        541\n","West Coast     359\n","Other           46\n","Name: count, dtype: int64\n","                    institution  survey_year   all_rd  computer_info_sci  \\\n","1             Johns Hopkins U.a         2021  3181385             270040   \n","2  U. California, San Francisco         2021  1710036                  0   \n","3        U. Michigan, Ann Arbor         2021  1639645              13922   \n","\n","   geosciences  life_sciences  math_stats  psychology  social_sciences  \\\n","1        79257        1069865      117429      338454             6201   \n","2            0        1639862           0       69817                0   \n","3        15080         931174       17937       66041            18255   \n","\n","   sciences_nec  engineering  non_se_fields  survey_year  nsf_region  \n","1          5305        50406           6448         2021  East Coast  \n","2             0          357              0         2021  West Coast  \n","3        175698         3628         118228         2021     Midwest  \n"]}]},{"cell_type":"code","source":["# Drop duplicate survey_year column\n","nsf_all_schools = nsf_all_schools.loc[:, ~nsf_all_schools.columns.duplicated()]\n","\n","# Confirm clean\n","print(nsf_all_schools.shape)\n","print(nsf_all_schools.columns.tolist())\n","print(nsf_all_schools.head(3))"],"metadata":{"id":"hH1kXNsYZCTm","colab":{"base_uri":"https://localhost:8080/"},"outputId":"847e170c-1213-4278-f2fb-e611d2ce13aa","executionInfo":{"status":"ok","timestamp":1778087361343,"user_tz":240,"elapsed":8,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2630, 13)\n","['institution', 'survey_year', 'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences', 'math_stats', 'psychology', 'social_sciences', 'sciences_nec', 'engineering', 'non_se_fields', 'nsf_region']\n","                    institution  survey_year   all_rd  computer_info_sci  \\\n","1             Johns Hopkins U.a         2021  3181385             270040   \n","2  U. California, San Francisco         2021  1710036                  0   \n","3        U. Michigan, Ann Arbor         2021  1639645              13922   \n","\n","   geosciences  life_sciences  math_stats  psychology  social_sciences  \\\n","1        79257        1069865      117429      338454             6201   \n","2            0        1639862           0       69817                0   \n","3        15080         931174       17937       66041            18255   \n","\n","   sciences_nec  engineering  non_se_fields  nsf_region  \n","1          5305        50406           6448  East Coast  \n","2             0          357              0  West Coast  \n","3        175698         3628         118228     Midwest  \n"]}]},{"cell_type":"code","source":["athletics_cols = [\n","    'school', 'region', 'Year',\n","    'Total Expenses', 'Total Revenues',\n","    'Coaches Compensation', 'Athletic Student Aid',\n","    'Facilities, Debt Service, and Equipment',\n","    'Game Expenses and Travel', 'Recruiting',\n","    'Medical', 'Total Football Spending',\n","    'Ticket Sales', 'Donor Contributions',\n","    'Student Fees', 'Institutional/Government Support',\n","]\n","\n","def prep_athletics(df, school_name, region):\n","    df = df[df['Year'].between(2021, 2024)].copy()\n","    df['school'] = school_name\n","    df['region'] = region\n","    df = df.rename(columns={'Year': 'survey_year'})\n","    keep = [c for c in athletics_cols if c in df.columns or c in ['school','region']]\n","    df = df[['school', 'region', 'survey_year'] +\n","            [c for c in athletics_cols if c not in ['school','region','Year']]]\n","    return df\n","\n","umd_clean   = prep_athletics(umd,   'University of Maryland, College Park',    'East')\n","uiuc_clean  = prep_athletics(uiuc,  'University of Illinois Urbana-Champaign', 'Midwest')\n","uwash_clean = prep_athletics(uwash, 'University of Washington',                'West')\n","umich_clean = prep_athletics(umich, 'University of Michigan',                  'North')\n","\n","athletics_all = pd.concat([umd_clean, uiuc_clean, uwash_clean, umich_clean]).reset_index(drop=True)\n","\n","print(athletics_all.shape)\n","print(athletics_all)"],"metadata":{"id":"bu2ZF4SDZMkp","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a2c69714-e146-44d8-889e-e87f9d6c7e5c","executionInfo":{"status":"ok","timestamp":1778087361372,"user_tz":240,"elapsed":6,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(48, 16)\n","                                     school   region  survey_year  \\\n","0      University of Maryland, College Park     East         2021   \n","1      University of Maryland, College Park     East         2022   \n","2      University of Maryland, College Park     East         2023   \n","3      University of Maryland, College Park     East         2024   \n","4      University of Maryland, College Park     East         2021   \n","5      University of Maryland, College Park     East         2022   \n","6      University of Maryland, College Park     East         2023   \n","7      University of Maryland, College Park     East         2024   \n","8      University of Maryland, College Park     East         2021   \n","9      University of Maryland, College Park     East         2022   \n","10     University of Maryland, College Park     East         2023   \n","11     University of Maryland, College Park     East         2024   \n","12  University of Illinois Urbana-Champaign  Midwest         2021   \n","13  University of Illinois Urbana-Champaign  Midwest         2022   \n","14  University of Illinois Urbana-Champaign  Midwest         2023   \n","15  University of Illinois Urbana-Champaign  Midwest         2024   \n","16  University of Illinois Urbana-Champaign  Midwest         2021   \n","17  University of Illinois Urbana-Champaign  Midwest         2022   \n","18  University of Illinois Urbana-Champaign  Midwest         2023   \n","19  University of Illinois Urbana-Champaign  Midwest         2024   \n","20  University of Illinois Urbana-Champaign  Midwest         2021   \n","21  University of Illinois Urbana-Champaign  Midwest         2022   \n","22  University of Illinois Urbana-Champaign  Midwest         2023   \n","23  University of Illinois Urbana-Champaign  Midwest         2024   \n","24                 University of Washington     West         2021   \n","25                 University of Washington     West         2022   \n","26                 University of Washington     West         2023   \n","27                 University of Washington     West         2024   \n","28                 University of Washington     West         2021   \n","29                 University of Washington     West         2022   \n","30                 University of Washington     West         2023   \n","31                 University of Washington     West         2024   \n","32                 University of Washington     West         2021   \n","33                 University of Washington     West         2022   \n","34                 University of Washington     West         2023   \n","35                 University of Washington     West         2024   \n","36                   University of Michigan    North         2021   \n","37                   University of Michigan    North         2022   \n","38                   University of Michigan    North         2023   \n","39                   University of Michigan    North         2024   \n","40                   University of Michigan    North         2021   \n","41                   University of Michigan    North         2022   \n","42                   University of Michigan    North         2023   \n","43                   University of Michigan    North         2024   \n","44                   University of Michigan    North         2021   \n","45                   University of Michigan    North         2022   \n","46                   University of Michigan    North         2023   \n","47                   University of Michigan    North         2024   \n","\n","    Total Expenses  Total Revenues  Coaches Compensation  \\\n","0         81831650        60901167              20963623   \n","1        114385462       107526374              25446854   \n","2        121160348       121183392              28497829   \n","3        132764504       127768033              30975240   \n","4       1496087681      1163106582             335123851   \n","5       1927764454      2041265014             384997855   \n","6       2277898227      2264649150             422590746   \n","7       2444258998      2360422619             473308413   \n","8       7664321615      7328769398            1703052526   \n","9       9721897803     10130258154            1909990580   \n","10     10940030998     11012886694            2062330756   \n","11     11990765192     11794491977            2315555138   \n","12       102006085        98811643              22159720   \n","13       129119247       145735330              25956291   \n","14       152809698       148345919              28009279   \n","15       169474512       174067436              34593992   \n","16      1496087681      1163106582             335123851   \n","17      1927764454      2041265014             384997855   \n","18      2277898227      2264649150             422590746   \n","19      2444258998      2360422619             473308413   \n","20      7664321615      7328769398            1703052526   \n","21      9721897803     10130258154            1909990580   \n","22     10940030998     11012886694            2062330756   \n","23     11990765192     11794491977            2315555138   \n","24        86150595        83431816              21710317   \n","25       149458923       145184864              22428675   \n","26       150037375       151599477              28540846   \n","27       200147129       190936516              37468865   \n","28       863462187       745582768             212765550   \n","29      1163840847      1144504032             225058183   \n","30      1308087026      1215168132             250574656   \n","31      1450976819      1340716472             280473893   \n","32      7664321615      7328769398            1703052526   \n","33      9721897803     10130258154            1909990580   \n","34     10940030998     11012886694            2062330756   \n","35     11990765192     11794491977            2315555138   \n","36       150618278       101236069              34149105   \n","37       195084818       210652287              37619081   \n","38       228603154       229561279              40495730   \n","39       241856430       238866661              43845688   \n","40      1496087681      1163106582             335123851   \n","41      1927764454      2041265014             384997855   \n","42      2277898227      2264649150             422590746   \n","43      2444258998      2360422619             473308413   \n","44      7664321615      7328769398            1703052526   \n","45      9721897803     10130258154            1909990580   \n","46     10940030998     11012886694            2062330756   \n","47     11990765192     11794491977            2315555138   \n","\n","    Athletic Student Aid  Facilities, Debt Service, and Equipment  \\\n","0           1.741206e+07                                 13322216   \n","1           1.790206e+07                                 18071072   \n","2           1.923074e+07                                 20058787   \n","3           2.052716e+07                                 20489171   \n","4           2.219532e+08                                391541207   \n","5           2.301833e+08                                454365871   \n","6           2.429426e+08                                576282888   \n","7           2.570311e+08                                556387323   \n","8           1.171230e+09                               1739401433   \n","9           1.226163e+09                               2089601107   \n","10          1.277305e+09                               2413372618   \n","11          1.346149e+09                               2640909413   \n","12          1.168998e+07                                 30099600   \n","13          1.250762e+07                                 31349589   \n","14          1.343316e+07                                 40070102   \n","15          1.219197e+07                                 47667874   \n","16          2.219532e+08                                391541207   \n","17          2.301833e+08                                454365871   \n","18          2.429426e+08                                576282888   \n","19          2.570311e+08                                556387323   \n","20          1.171230e+09                               1739401433   \n","21          1.226163e+09                               2089601107   \n","22          1.277305e+09                               2413372618   \n","23          1.346149e+09                               2640909413   \n","24          1.558205e+07                                  9560091   \n","25          1.609198e+07                                 27404842   \n","26          1.657871e+07                                 22911377   \n","27          1.812692e+07                                 28331753   \n","28          1.338780e+08                                177238963   \n","29          1.374116e+08                                251934603   \n","30          1.466550e+08                                274459367   \n","31          1.544130e+08                                297039991   \n","32          1.171230e+09                               1739401433   \n","33          1.226163e+09                               2089601107   \n","34          1.277305e+09                               2413372618   \n","35          1.346149e+09                               2640909413   \n","36          2.791306e+07                                 34280697   \n","37          2.830584e+07                                 45751600   \n","38          3.250907e+07                                 48578786   \n","39          3.392325e+07                                 44307540   \n","40          2.219532e+08                                391541207   \n","41          2.301833e+08                                454365871   \n","42          2.429426e+08                                576282888   \n","43          2.570311e+08                                556387323   \n","44          1.171230e+09                               1739401433   \n","45          1.226163e+09                               2089601107   \n","46          1.277305e+09                               2413372618   \n","47          1.346149e+09                               2640909413   \n","\n","    Game Expenses and Travel   Recruiting      Medical  \\\n","0                    5488628     421739.0    1925120.0   \n","1                   11260756    2320815.0    2401888.0   \n","2                   14956213    2677607.0    1644411.0   \n","3                   16342767    3123959.0    1883340.0   \n","4                   86527405    5544261.0   34729677.0   \n","5                  197535049   32314846.0   25666271.0   \n","6                  244305501   40885176.0   28048753.0   \n","7                  271877496   44619975.0   30068418.0   \n","8                  563066689   32900494.0  184982675.0   \n","9                 1095091026  175429065.0  130855384.0   \n","10                1302134325  218135209.0  138527184.0   \n","11                1426127748  241857878.0  155443083.0   \n","12                   5644216     679690.0     866379.0   \n","13                   8138701    2376468.0    1393949.0   \n","14                  13762147    2819640.0    2056561.0   \n","15                  13340392    3570247.0    3038584.0   \n","16                  86527405    5544261.0   34729677.0   \n","17                 197535049   32314846.0   25666271.0   \n","18                 244305501   40885176.0   28048753.0   \n","19                 271877496   44619975.0   30068418.0   \n","20                 563066689   32900494.0  184982675.0   \n","21                1095091026  175429065.0  130855384.0   \n","22                1302134325  218135209.0  138527184.0   \n","23                1426127748  241857878.0  155443083.0   \n","24                   5828030     406804.0    1886317.0   \n","25                  18834207    2490978.0    1752722.0   \n","26                  24388319    2782981.0    2028811.0   \n","27                  31720737    4212792.0    2010422.0   \n","28                  53991822    4628467.0   18044836.0   \n","29                 134372254   19220151.0   16877972.0   \n","30                 159243694   23555371.0   20079652.0   \n","31                 177698647   28137862.0   23508552.0   \n","32                 563066689   32900494.0  184982675.0   \n","33                1095091026  175429065.0  130855384.0   \n","34                1302134325  218135209.0  138527184.0   \n","35                1426127748  241857878.0  155443083.0   \n","36                   6818176     822051.0    3125502.0   \n","37                  19414726    4171565.0     959484.0   \n","38                  27421079    4839530.0    1148195.0   \n","39                  31217063    4080327.0    1386551.0   \n","40                  86527405    5544261.0   34729677.0   \n","41                 197535049   32314846.0   25666271.0   \n","42                 244305501   40885176.0   28048753.0   \n","43                 271877496   44619975.0   30068418.0   \n","44                 563066689   32900494.0  184982675.0   \n","45                1095091026  175429065.0  130855384.0   \n","46                1302134325  218135209.0  138527184.0   \n","47                1426127748  241857878.0  155443083.0   \n","\n","    Total Football Spending  Ticket Sales  Donor Contributions  Student Fees  \\\n","0              2.049893e+07  9.784100e+04              1356223    11868594.0   \n","1              3.080123e+07  1.273579e+07              9990560    11963920.0   \n","2              3.399940e+07  1.283824e+07              9509266    11783812.0   \n","3              3.700712e+07  1.483331e+07             11528062    11975845.0   \n","4              3.727358e+08  5.924100e+04            239833824    25951048.0   \n","5              5.549729e+08  3.868818e+08            405080725    28762028.0   \n","6              6.656281e+08  4.250703e+08            470446735    29303627.0   \n","7              7.440485e+08  4.199072e+08            487914117    30598365.0   \n","8              2.085917e+09  1.969917e+08           1390965429   586153311.0   \n","9              2.863140e+09  1.561636e+09           2025652607   612051038.0   \n","10             3.260226e+09  1.668290e+09           2396999685   625305469.0   \n","11             3.589082e+09  1.764553e+09           2591211824   643048052.0   \n","12             2.138142e+07  2.446090e+05             33866174     2886620.0   \n","13             2.903574e+07  1.569352e+07             40963477     3313028.0   \n","14             3.516606e+07  1.979739e+07             35418645     3329639.0   \n","15             3.898493e+07  1.981421e+07             57770194     3433852.0   \n","16             3.727358e+08  5.924100e+04            239833824    25951048.0   \n","17             5.549729e+08  3.868818e+08            405080725    28762028.0   \n","18             6.656281e+08  4.250703e+08            470446735    29303627.0   \n","19             7.440485e+08  4.199072e+08            487914117    30598365.0   \n","20             2.085917e+09  1.969917e+08           1390965429   586153311.0   \n","21             2.863140e+09  1.561636e+09           2025652607   612051038.0   \n","22             3.260226e+09  1.668290e+09           2396999685   625305469.0   \n","23             3.589082e+09  1.764553e+09           2591211824   643048052.0   \n","24             2.426771e+07  2.403400e+04             32730995           0.0   \n","25             7.046192e+07  2.919879e+07             28037008           0.0   \n","26             5.875808e+07  2.778073e+07             38519167           0.0   \n","27             8.756695e+07  3.478926e+07             41623042           0.0   \n","28             2.229819e+08  1.558562e+06            179960922    27884474.0   \n","29             3.587236e+08  1.656426e+08            205260925    29446934.0   \n","30             4.074400e+08  1.623498e+08            246065583    28984561.0   \n","31             4.560853e+08  1.987802e+08            247122997    28574219.0   \n","32             2.085917e+09  1.969917e+08           1390965429   586153311.0   \n","33             2.863140e+09  1.561636e+09           2025652607   612051038.0   \n","34             3.260226e+09  1.668290e+09           2396999685   625305469.0   \n","35             3.589082e+09  1.764553e+09           2591211824   643048052.0   \n","36             3.738052e+07  5.416800e+04             13799193           0.0   \n","37             5.240854e+07  5.526614e+07             44258631           0.0   \n","38             6.784588e+07  6.510662e+07             43365522           0.0   \n","39             7.240417e+07  5.848720e+07             44691600           0.0   \n","40             3.727358e+08  5.924100e+04            239833824    25951048.0   \n","41             5.549729e+08  3.868818e+08            405080725    28762028.0   \n","42             6.656281e+08  4.250703e+08            470446735    29303627.0   \n","43             7.440485e+08  4.199072e+08            487914117    30598365.0   \n","44             2.085917e+09  1.969917e+08           1390965429   586153311.0   \n","45             2.863140e+09  1.561636e+09           2025652607   612051038.0   \n","46             3.260226e+09  1.668290e+09           2396999685   625305469.0   \n","47             3.589082e+09  1.764553e+09           2591211824   643048052.0   \n","\n","    Institutional/Government Support  \n","0                            2934615  \n","1                            3288610  \n","2                            6119167  \n","3                            6102633  \n","4                           60824812  \n","5                           36556102  \n","6                           48763804  \n","7                           79316190  \n","8                         1327902267  \n","9                         1263761390  \n","10                        1343082199  \n","11                        1600658111  \n","12                           6059769  \n","13                           6832377  \n","14                           7279995  \n","15                           7552756  \n","16                          60824812  \n","17                          36556102  \n","18                          48763804  \n","19                          79316190  \n","20                        1327902267  \n","21                        1263761390  \n","22                        1343082199  \n","23                        1600658111  \n","24                           6500799  \n","25                          15082998  \n","26                          10279072  \n","27                          10010108  \n","28                         166656674  \n","29                         120131313  \n","30                         154768620  \n","31                         200762588  \n","32                        1327902267  \n","33                        1263761390  \n","34                        1343082199  \n","35                        1600658111  \n","36                                 0  \n","37                            153059  \n","38                            178686  \n","39                            187962  \n","40                          60824812  \n","41                          36556102  \n","42                          48763804  \n","43                          79316190  \n","44                        1327902267  \n","45                        1263761390  \n","46                        1343082199  \n","47                        1600658111  \n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","base = \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/\"\n","\n","files = [\n","    \"University of Maryland, College Park Data.xlsx\",\n","    \"University of Illinois Urbana-Champaign Data.xlsx\",\n","    \"University of Washington Data.xlsx\",\n","    \"University of Michigan Data.xlsx\"\n","]\n","\n","for fname in files:\n","    encoded_fname = fname.replace(\" \", \"%20\")\n","    xl = pd.ExcelFile(base + encoded_fname)\n","    print(f\"{fname}: sheets = {xl.sheet_names}\")"],"metadata":{"id":"Hg4fGdi-ZR3L","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5c2beb19-dde3-49e3-ff52-e301da77e0b6","executionInfo":{"status":"ok","timestamp":1778087361789,"user_tz":240,"elapsed":415,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["University of Maryland, College Park Data.xlsx: sheets = ['Data', 'Data Dictionary']\n","University of Illinois Urbana-Champaign Data.xlsx: sheets = ['Data', 'Data Dictionary']\n","University of Washington Data.xlsx: sheets = ['Data', 'Data Dictionary']\n","University of Michigan Data.xlsx: sheets = ['Data', 'Data Dictionary']\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","base = \"https://raw.githubusercontent.com/msomakpo/bigten_SE_athletics_party_dataset/main/\"\n","\n","# List of files in the repo\n","files = [\n","    \"University of Maryland, College Park Data.xlsx\",\n","    \"University of Illinois Urbana-Champaign Data.xlsx\",\n","    \"University of Washington Data.xlsx\",\n","    \"University of Michigan Data.xlsx\"\n","]\n","\n","# Create a dictionary to hold the dataframes for easier access\n","athletics_dfs = {}\n","for fname in files:\n","    # URL encode the filename (replace spaces with %20 and comma with %2C)\n","    url = base + fname.replace(\" \", \"%20\").replace(\",\", \"%2C\")\n","    # Read the 'Data' sheet from the URL\n","    athletics_dfs[fname] = pd.read_excel(url, sheet_name='Data')\n","\n","# Assign to individual variables for your prep_athletics() calls\n","umd   = athletics_dfs[\"University of Maryland, College Park Data.xlsx\"]\n","uiuc  = athletics_dfs[\"University of Illinois Urbana-Champaign Data.xlsx\"]\n","uwash = athletics_dfs[\"University of Washington Data.xlsx\"]\n","umich = athletics_dfs[\"University of Michigan Data.xlsx\"]\n","\n","# Then proceed with your prep_athletics calls as before\n","umd_clean   = prep_athletics(umd,   'University of Maryland, College Park',    'East')\n","uiuc_clean  = prep_athletics(uiuc,  'University of Illinois Urbana-Champaign', 'Midwest')\n","uwash_clean = prep_athletics(uwash, 'University of Washington',                'West')\n","umich_clean = prep_athletics(umich, 'University of Michigan',                  'North')\n","\n","athletics_all = pd.concat([umd_clean, uiuc_clean, uwash_clean, umich_clean]).reset_index(drop=True)\n","\n","print(athletics_all.shape)\n","print(athletics_all[['school','region','survey_year','Total Expenses','Total Revenues']])"],"metadata":{"id":"HqxyjLKbZ0rr","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9d9f9ee9-1053-47c1-b67c-a64086a75167","executionInfo":{"status":"ok","timestamp":1778087362302,"user_tz":240,"elapsed":510,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(48, 16)\n","                                     school   region  survey_year  \\\n","0      University of Maryland, College Park     East         2021   \n","1      University of Maryland, College Park     East         2022   \n","2      University of Maryland, College Park     East         2023   \n","3      University of Maryland, College Park     East         2024   \n","4      University of Maryland, College Park     East         2021   \n","5      University of Maryland, College Park     East         2022   \n","6      University of Maryland, College Park     East         2023   \n","7      University of Maryland, College Park     East         2024   \n","8      University of Maryland, College Park     East         2021   \n","9      University of Maryland, College Park     East         2022   \n","10     University of Maryland, College Park     East         2023   \n","11     University of Maryland, College Park     East         2024   \n","12  University of Illinois Urbana-Champaign  Midwest         2021   \n","13  University of Illinois Urbana-Champaign  Midwest         2022   \n","14  University of Illinois Urbana-Champaign  Midwest         2023   \n","15  University of Illinois Urbana-Champaign  Midwest         2024   \n","16  University of Illinois Urbana-Champaign  Midwest         2021   \n","17  University of Illinois Urbana-Champaign  Midwest         2022   \n","18  University of Illinois Urbana-Champaign  Midwest         2023   \n","19  University of Illinois Urbana-Champaign  Midwest         2024   \n","20  University of Illinois Urbana-Champaign  Midwest         2021   \n","21  University of Illinois Urbana-Champaign  Midwest         2022   \n","22  University of Illinois Urbana-Champaign  Midwest         2023   \n","23  University of Illinois Urbana-Champaign  Midwest         2024   \n","24                 University of Washington     West         2021   \n","25                 University of Washington     West         2022   \n","26                 University of Washington     West         2023   \n","27                 University of Washington     West         2024   \n","28                 University of Washington     West         2021   \n","29                 University of Washington     West         2022   \n","30                 University of Washington     West         2023   \n","31                 University of Washington     West         2024   \n","32                 University of Washington     West         2021   \n","33                 University of Washington     West         2022   \n","34                 University of Washington     West         2023   \n","35                 University of Washington     West         2024   \n","36                   University of Michigan    North         2021   \n","37                   University of Michigan    North         2022   \n","38                   University of Michigan    North         2023   \n","39                   University of Michigan    North         2024   \n","40                   University of Michigan    North         2021   \n","41                   University of Michigan    North         2022   \n","42                   University of Michigan    North         2023   \n","43                   University of Michigan    North         2024   \n","44                   University of Michigan    North         2021   \n","45                   University of Michigan    North         2022   \n","46                   University of Michigan    North         2023   \n","47                   University of Michigan    North         2024   \n","\n","    Total Expenses  Total Revenues  \n","0         81831650        60901167  \n","1        114385462       107526374  \n","2        121160348       121183392  \n","3        132764504       127768033  \n","4       1496087681      1163106582  \n","5       1927764454      2041265014  \n","6       2277898227      2264649150  \n","7       2444258998      2360422619  \n","8       7664321615      7328769398  \n","9       9721897803     10130258154  \n","10     10940030998     11012886694  \n","11     11990765192     11794491977  \n","12       102006085        98811643  \n","13       129119247       145735330  \n","14       152809698       148345919  \n","15       169474512       174067436  \n","16      1496087681      1163106582  \n","17      1927764454      2041265014  \n","18      2277898227      2264649150  \n","19      2444258998      2360422619  \n","20      7664321615      7328769398  \n","21      9721897803     10130258154  \n","22     10940030998     11012886694  \n","23     11990765192     11794491977  \n","24        86150595        83431816  \n","25       149458923       145184864  \n","26       150037375       151599477  \n","27       200147129       190936516  \n","28       863462187       745582768  \n","29      1163840847      1144504032  \n","30      1308087026      1215168132  \n","31      1450976819      1340716472  \n","32      7664321615      7328769398  \n","33      9721897803     10130258154  \n","34     10940030998     11012886694  \n","35     11990765192     11794491977  \n","36       150618278       101236069  \n","37       195084818       210652287  \n","38       228603154       229561279  \n","39       241856430       238866661  \n","40      1496087681      1163106582  \n","41      1927764454      2041265014  \n","42      2277898227      2264649150  \n","43      2444258998      2360422619  \n","44      7664321615      7328769398  \n","45      9721897803     10130258154  \n","46     10940030998     11012886694  \n","47     11990765192     11794491977  \n"]}]},{"cell_type":"code","source":["# Check raw row count and year distribution before any filtering\n","for name, df in [(\"umd\", umd), (\"uiuc\", uiuc), (\"uwash\", uwash), (\"umich\", umich)]:\n","    print(f\"\\n{name} | shape: {df.shape}\")\n","    print(df['Year'].value_counts().sort_index())"],"metadata":{"id":"CKMVqy7LZ9Gv","colab":{"base_uri":"https://localhost:8080/"},"outputId":"017e693c-c006-4e9f-b323-cac3f6b0351b","executionInfo":{"status":"ok","timestamp":1778087362339,"user_tz":240,"elapsed":21,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","umd | shape: (60, 34)\n","Year\n","2005    3\n","2006    3\n","2007    3\n","2008    3\n","2009    3\n","2010    3\n","2011    3\n","2012    3\n","2013    3\n","2014    3\n","2015    3\n","2016    3\n","2017    3\n","2018    3\n","2019    3\n","2020    3\n","2021    3\n","2022    3\n","2023    3\n","2024    3\n","Name: count, dtype: int64\n","\n","uiuc | shape: (60, 34)\n","Year\n","2005    3\n","2006    3\n","2007    3\n","2008    3\n","2009    3\n","2010    3\n","2011    3\n","2012    3\n","2013    3\n","2014    3\n","2015    3\n","2016    3\n","2017    3\n","2018    3\n","2019    3\n","2020    3\n","2021    3\n","2022    3\n","2023    3\n","2024    3\n","Name: count, dtype: int64\n","\n","uwash | shape: (60, 34)\n","Year\n","2005    3\n","2006    3\n","2007    3\n","2008    3\n","2009    3\n","2010    3\n","2011    3\n","2012    3\n","2013    3\n","2014    3\n","2015    3\n","2016    3\n","2017    3\n","2018    3\n","2019    3\n","2020    3\n","2021    3\n","2022    3\n","2023    3\n","2024    3\n","Name: count, dtype: int64\n","\n","umich | shape: (60, 34)\n","Year\n","2005    3\n","2006    3\n","2007    3\n","2008    3\n","2009    3\n","2010    3\n","2011    3\n","2012    3\n","2013    3\n","2014    3\n","2015    3\n","2016    3\n","2017    3\n","2018    3\n","2019    3\n","2020    3\n","2021    3\n","2022    3\n","2023    3\n","2024    3\n","Name: count, dtype: int64\n"]}]},{"cell_type":"code","source":["# Check if the 3 rows per year are identical or different\n","print(umd[umd['Year'] == 2021][['Year', 'Total Expenses', 'Total Revenues', 'Coaches Compensation']])"],"metadata":{"id":"rRQbkqtSaMrk","colab":{"base_uri":"https://localhost:8080/"},"outputId":"effb450b-4b64-4167-d9f3-7f83279f0c9a","executionInfo":{"status":"ok","timestamp":1778087362389,"user_tz":240,"elapsed":48,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Year  Total Expenses  Total Revenues  Coaches Compensation\n","16  2021        81831650        60901167              20963623\n","36  2021      1496087681      1163106582             335123851\n","56  2021      7664321615      7328769398            1703052526\n"]}]},{"cell_type":"code","source":["# Check the 'Data' column which had the school name — see what differs across the 3 rows\n","print(umd[umd['Year'] == 2021][['Year', 'Data', 'Total Expenses']])"],"metadata":{"id":"h1nwGxghaNOe","colab":{"base_uri":"https://localhost:8080/"},"outputId":"853f1d0c-173a-4a17-c5d4-9b9b04949c39","executionInfo":{"status":"ok","timestamp":1778087362397,"user_tz":240,"elapsed":7,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Year                                  Data  Total Expenses\n","16  2021  University of Maryland, College Park        81831650\n","36  2021                    Big Ten Conference      1496087681\n","56  2021             Football Bowl Subdivision      7664321615\n"]}]},{"cell_type":"code","source":["def prep_athletics(df, school_name, region):\n","    # Keep only the school-level row (not conference or FBS aggregate)\n","    df = df[df['Data'] == school_name].copy()\n","    df = df[df['Year'].between(2021, 2024)].copy()\n","    df['school'] = school_name\n","    df['region'] = region\n","    df = df.rename(columns={'Year': 'survey_year'})\n","    keep = [\n","        'school', 'region', 'survey_year',\n","        'Total Expenses', 'Total Revenues',\n","        'Coaches Compensation', 'Athletic Student Aid',\n","        'Facilities, Debt Service, and Equipment',\n","        'Game Expenses and Travel', 'Recruiting',\n","        'Medical', 'Total Football Spending',\n","        'Ticket Sales', 'Donor Contributions',\n","        'Student Fees', 'Institutional/Government Support',\n","    ]\n","    return df[keep].reset_index(drop=True)\n","\n","umd_clean   = prep_athletics(umd,   'University of Maryland, College Park',    'East')\n","uiuc_clean  = prep_athletics(uiuc,  'University of Illinois Urbana-Champaign', 'Midwest')\n","uwash_clean = prep_athletics(uwash, 'University of Washington',                'West')\n","umich_clean = prep_athletics(umich, 'University of Michigan',                  'North')\n","\n","athletics_all = pd.concat([umd_clean, uiuc_clean, uwash_clean, umich_clean]).reset_index(drop=True)\n","\n","print(athletics_all.shape)\n","print(athletics_all[['school', 'region', 'survey_year', 'Total Expenses', 'Total Revenues']])"],"metadata":{"id":"c1GP0wq2ahtb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"390050dd-122d-4ae6-f2e7-32f9ae87c2ff","executionInfo":{"status":"ok","timestamp":1778087362480,"user_tz":240,"elapsed":9,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(16, 16)\n","                                     school   region  survey_year  \\\n","0      University of Maryland, College Park     East         2021   \n","1      University of Maryland, College Park     East         2022   \n","2      University of Maryland, College Park     East         2023   \n","3      University of Maryland, College Park     East         2024   \n","4   University of Illinois Urbana-Champaign  Midwest         2021   \n","5   University of Illinois Urbana-Champaign  Midwest         2022   \n","6   University of Illinois Urbana-Champaign  Midwest         2023   \n","7   University of Illinois Urbana-Champaign  Midwest         2024   \n","8                  University of Washington     West         2021   \n","9                  University of Washington     West         2022   \n","10                 University of Washington     West         2023   \n","11                 University of Washington     West         2024   \n","12                   University of Michigan    North         2021   \n","13                   University of Michigan    North         2022   \n","14                   University of Michigan    North         2023   \n","15                   University of Michigan    North         2024   \n","\n","    Total Expenses  Total Revenues  \n","0         81831650        60901167  \n","1        114385462       107526374  \n","2        121160348       121183392  \n","3        132764504       127768033  \n","4        102006085        98811643  \n","5        129119247       145735330  \n","6        152809698       148345919  \n","7        169474512       174067436  \n","8         86150595        83431816  \n","9        149458923       145184864  \n","10       150037375       151599477  \n","11       200147129       190936516  \n","12       150618278       101236069  \n","13       195084818       210652287  \n","14       228603154       229561279  \n","15       241856430       238866661  \n"]}]},{"cell_type":"markdown","source":["### Clean Party Control Data\n","The party file covers Congress sessions from 1857 to present. Each row\n","represents a 2-year Congress with a year range in the 'Congress' column\n","(e.g. '117th (2021–2023)'). We extract the start year, filter to the\n","Congresses covering 2021–2024 (117th, 118th), and create a single\n","majority_party column based on which party controlled both the House\n","and Senate. Split control is labeled 'Divided'."],"metadata":{"id":"DLblrvH4anpI"}},{"cell_type":"code","source":["# Preview party file to confirm structure\n","print(party.head(10))\n","print(party.columns.tolist())\n","print(party['Congress'].tail(10))"],"metadata":{"id":"wpiMrfIMaoPl","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9f61bcf9-c5dd-47eb-b8f6-1e62b720ad39","executionInfo":{"status":"ok","timestamp":1778087365199,"user_tz":240,"elapsed":67,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["           Congress House Majority Senate Majority  \\\n","0  35th (1857–1859)      Democrats       Democrats   \n","1  36th (1859–1861)    Republicans       Democrats   \n","2  37th (1861–1863)    Republicans     Republicans   \n","3  38th (1863–1865)    Republicans     Republicans   \n","4  39th (1865–1867)    Republicans     Republicans   \n","5  40th (1867–1869)    Republicans     Republicans   \n","6  41st (1869–1871)    Republicans     Republicans   \n","7  42nd (1871–1873)    Republicans     Republicans   \n","8  43rd (1873–1875)    Republicans     Republicans   \n","9  44th (1875–1877)      Democrats     Republicans   \n","\n","                                     Presidency   Party Government  \n","0                           Democrat (Buchanan)            Unified  \n","1                           Democrat (Buchanan)            Divided  \n","2                          Republican (Lincoln)            Unified  \n","3                          Republican (Lincoln)            Unified  \n","4  Republican (Lincoln) / Democrat (A. Johnson)  Unified/ Divided2  \n","5                         Democrat (A. Johnson)            Divided  \n","6                            Republican (Grant)            Unified  \n","7                            Republican (Grant)            Unified  \n","8                            Republican (Grant)            Unified  \n","9                            Republican (Grant)            Divided  \n","['Congress', 'House Majority', 'Senate Majority', 'Presidency', 'Party Government']\n","77    110th (2007–2009)\n","78    111th (2009–2011)\n","79    112th (2011–2013)\n","80    113th (2013–2015)\n","81    114th (2015–2017)\n","82    115th (2017–2019)\n","83    116th (2019–2021)\n","84    117th (2021–2023)\n","85    118th (2023–2025)\n","86    119th (2025–2027)\n","Name: Congress, dtype: object\n"]}]},{"cell_type":"markdown","source":["### Filter and Reshape Party Control Data\n","The 117th Congress (2021–2023) and 118th Congress (2023–2025) cover our\n","study period of 2021–2024. We extract these two rows, expand them to\n","one row per year, and create a unified majority_party column.\n","Unified Democratic control = 'Democrat', Unified Republican = 'Republican',\n","split chamber control = 'Divided'."],"metadata":{"id":"6IZOLXAdavBt"}},{"cell_type":"code","source":["# Filter to 117th and 118th Congress\n","party_filtered = party[party['Congress'].str.contains('117th|118th')].copy()\n","\n","# Extract start year from Congress column\n","party_filtered['start_year'] = party_filtered['Congress'].str.extract(r'\\((\\d{4})')[0].astype(int)\n","\n","# Assign majority party\n","def get_majority(row):\n","    house = row['House Majority'].strip()\n","    senate = row['Senate Majority'].strip()\n","    if house == senate:\n","        return house\n","    return 'Divided'\n","\n","party_filtered['majority_party'] = party_filtered.apply(get_majority, axis=1)\n","\n","# Expand to one row per year\n","# 117th = 2021, 2022 | 118th = 2023, 2024\n","party_filtered['survey_year'] = party_filtered['start_year'].map({2021: [2021, 2022], 2023: [2023, 2024]})\n","party_expanded = party_filtered.explode('survey_year')\n","\n","# Keep only what we need\n","party_clean = party_expanded[['survey_year', 'House Majority', 'Senate Majority', 'Presidency', 'majority_party']].reset_index(drop=True)\n","\n","print(party_clean)"],"metadata":{"id":"HwjcLtBaavl_","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e71c2452-7d4f-4bfb-8ef3-b200fd58e268","executionInfo":{"status":"ok","timestamp":1778087367546,"user_tz":240,"elapsed":18,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  survey_year House Majority Senate Majority        Presidency majority_party\n","0        2021      Democrats     Democrats14  Democrat (Biden)        Divided\n","1        2022      Democrats     Democrats14  Democrat (Biden)        Divided\n","2        2023    Republicans       Democrats  Democrat (Biden)        Divided\n","3        2024    Republicans       Democrats  Democrat (Biden)        Divided\n"]}]},{"cell_type":"code","source":["import re\n","\n","def clean_party(val):\n","    return re.sub(r'\\d+$', '', str(val)).strip()\n","\n","party_filtered['House Majority clean'] = party_filtered['House Majority'].apply(clean_party)\n","party_filtered['Senate Majority clean'] = party_filtered['Senate Majority'].apply(clean_party)\n","\n","def get_majority(row):\n","    house = row['House Majority clean']\n","    senate = row['Senate Majority clean']\n","    if house == senate:\n","        return house\n","    return 'Divided'\n","\n","party_filtered['majority_party'] = party_filtered.apply(get_majority, axis=1)\n","party_filtered['survey_year'] = party_filtered['start_year'].map({2021: [2021, 2022], 2023: [2023, 2024]})\n","party_expanded = party_filtered.explode('survey_year')\n","\n","party_clean = party_expanded[[\n","    'survey_year', 'House Majority', 'Senate Majority',\n","    'Presidency', 'majority_party'\n","]].reset_index(drop=True)\n","\n","print(party_clean)"],"metadata":{"id":"oxNpXHQmaw3g","colab":{"base_uri":"https://localhost:8080/"},"outputId":"aa6b7b48-8ce2-43d7-de58-a82786e47695","executionInfo":{"status":"ok","timestamp":1778087368249,"user_tz":240,"elapsed":10,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["  survey_year House Majority Senate Majority        Presidency majority_party\n","0        2021      Democrats     Democrats14  Democrat (Biden)      Democrats\n","1        2022      Democrats     Democrats14  Democrat (Biden)      Democrats\n","2        2023    Republicans       Democrats  Democrat (Biden)        Divided\n","3        2024    Republicans       Democrats  Democrat (Biden)        Divided\n"]}]},{"cell_type":"markdown","source":["### Merge All Three Datasets\n","We produce two final datasets:\n","1. bigten_athletics_party: athletics expenditures for our 4 schools\n","   merged with party control by survey year (16 rows).\n","2. nsf_all_party: full NSF S&E expenditures for all schools merged\n","   with party control by survey year (2630 rows).\n","Both datasets are exported as CSV files for documentation and class use."],"metadata":{"id":"QC15GqAHa_F1"}},{"cell_type":"code","source":["# 1. Athletics + party (16 rows)\n","bigten_athletics_party = athletics_all.merge(party_clean, on='survey_year', how='left')\n","\n","# 2. NSF all schools + party (2630 rows)\n","nsf_all_party = nsf_all_schools.merge(party_clean, on='survey_year', how='left')\n","\n","# 3. NSF filtered to our 4 schools + party (16 rows) — for direct comparison\n","school_name_map = {\n","    'U. Marylandb':                  'University of Maryland, College Park',\n","    'U. Illinois, Urbana-Champaign': 'University of Illinois Urbana-Champaign',\n","    'U. Washington, Seattle':        'University of Washington',\n","    'U. Michigan, Ann Arbor':        'University of Michigan',\n","}\n","nsf_bigten = nsf_all_schools[nsf_all_schools['institution'].isin(school_name_map.keys())].copy()\n","nsf_bigten['school'] = nsf_bigten['institution'].map(school_name_map)\n","nsf_bigten = nsf_bigten.merge(party_clean, on='survey_year', how='left')\n","\n","# Preview all 3\n","print(\"=== bigten_athletics_party ===\")\n","print(bigten_athletics_party.shape)\n","print(bigten_athletics_party[['school','survey_year','Total Expenses','majority_party']].to_string())\n","\n","print(\"\\n=== nsf_bigten ===\")\n","print(nsf_bigten.shape)\n","print(nsf_bigten[['school','survey_year','all_rd','majority_party']].to_string())\n","\n","print(\"\\n=== nsf_all_party ===\")\n","print(nsf_all_party.shape)\n","print(nsf_all_party[['institution','survey_year','all_rd','nsf_region','majority_party']].head(8).to_string())"],"metadata":{"id":"uTLbmISxa2sz","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a2f04ad5-b18c-48a2-e7e9-45391d2af352","executionInfo":{"status":"ok","timestamp":1778087370110,"user_tz":240,"elapsed":44,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["=== bigten_athletics_party ===\n","(16, 20)\n","                                     school survey_year  Total Expenses majority_party\n","0      University of Maryland, College Park        2021        81831650      Democrats\n","1      University of Maryland, College Park        2022       114385462      Democrats\n","2      University of Maryland, College Park        2023       121160348        Divided\n","3      University of Maryland, College Park        2024       132764504        Divided\n","4   University of Illinois Urbana-Champaign        2021       102006085      Democrats\n","5   University of Illinois Urbana-Champaign        2022       129119247      Democrats\n","6   University of Illinois Urbana-Champaign        2023       152809698        Divided\n","7   University of Illinois Urbana-Champaign        2024       169474512        Divided\n","8                  University of Washington        2021        86150595      Democrats\n","9                  University of Washington        2022       149458923      Democrats\n","10                 University of Washington        2023       150037375        Divided\n","11                 University of Washington        2024       200147129        Divided\n","12                   University of Michigan        2021       150618278      Democrats\n","13                   University of Michigan        2022       195084818      Democrats\n","14                   University of Michigan        2023       228603154        Divided\n","15                   University of Michigan        2024       241856430        Divided\n","\n","=== nsf_bigten ===\n","(16, 18)\n","                                     school survey_year   all_rd majority_party\n","0                    University of Michigan        2021  1639645      Democrats\n","1                  University of Washington        2021  1488645      Democrats\n","2      University of Maryland, College Park        2021  1142264      Democrats\n","3   University of Illinois Urbana-Champaign        2021   731268      Democrats\n","4                    University of Michigan        2022  1770708      Democrats\n","5                  University of Washington        2022  1559708      Democrats\n","6      University of Maryland, College Park        2022  1228550      Democrats\n","7   University of Illinois Urbana-Champaign        2022   765909      Democrats\n","8                    University of Michigan        2023  1925875        Divided\n","9                  University of Washington        2023  1734091        Divided\n","10     University of Maryland, College Park        2023  1385302        Divided\n","11  University of Illinois Urbana-Champaign        2023   821023        Divided\n","12                   University of Michigan        2024  2110961        Divided\n","13                 University of Washington        2024  1691302        Divided\n","14     University of Maryland, College Park        2024  1539520        Divided\n","15  University of Illinois Urbana-Champaign        2024   906751        Divided\n","\n","=== nsf_all_party ===\n","(2630, 17)\n","                    institution survey_year   all_rd  nsf_region majority_party\n","0             Johns Hopkins U.a        2021  3181385  East Coast      Democrats\n","1  U. California, San Francisco        2021  1710036  West Coast      Democrats\n","2        U. Michigan, Ann Arbor        2021  1639645     Midwest      Democrats\n","3               U. Pennsylvania        2021  1631950  East Coast      Democrats\n","4        U. Washington, Seattle        2021  1488645  West Coast      Democrats\n","5    U. California, Los Angeles        2021  1454880  West Coast      Democrats\n","6      U. California, San Diego        2021  1425499  West Coast      Democrats\n","7          U. Wisconsin-Madison        2021  1380075     Midwest      Democrats\n"]}]},{"cell_type":"code","source":["# Merge athletics and NSF bigten on school + survey_year\n","final_dataset = bigten_athletics_party.merge(\n","    nsf_bigten.drop(columns=['majority_party', 'House Majority', 'Senate Majority', 'Presidency', 'institution']),\n","    on=['school', 'survey_year'],\n","    how='left'\n",")\n","\n","# Confirm\n","print(final_dataset.shape)\n","print(final_dataset.columns.tolist())\n","print(final_dataset[['school', 'survey_year', 'Total Expenses', 'all_rd', 'majority_party', 'region']].to_string())"],"metadata":{"id":"RVht4K6Yb6UQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6f0f2ef8-4f68-4ecd-d31c-3d1afcc074dd","executionInfo":{"status":"ok","timestamp":1778087370813,"user_tz":240,"elapsed":19,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(16, 31)\n","['school', 'region', 'survey_year', 'Total Expenses', 'Total Revenues', 'Coaches Compensation', 'Athletic Student Aid', 'Facilities, Debt Service, and Equipment', 'Game Expenses and Travel', 'Recruiting', 'Medical', 'Total Football Spending', 'Ticket Sales', 'Donor Contributions', 'Student Fees', 'Institutional/Government Support', 'House Majority', 'Senate Majority', 'Presidency', 'majority_party', 'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences', 'math_stats', 'psychology', 'social_sciences', 'sciences_nec', 'engineering', 'non_se_fields', 'nsf_region']\n","                                     school survey_year  Total Expenses   all_rd majority_party   region\n","0      University of Maryland, College Park        2021        81831650  1142264      Democrats     East\n","1      University of Maryland, College Park        2022       114385462  1228550      Democrats     East\n","2      University of Maryland, College Park        2023       121160348  1385302        Divided     East\n","3      University of Maryland, College Park        2024       132764504  1539520        Divided     East\n","4   University of Illinois Urbana-Champaign        2021       102006085   731268      Democrats  Midwest\n","5   University of Illinois Urbana-Champaign        2022       129119247   765909      Democrats  Midwest\n","6   University of Illinois Urbana-Champaign        2023       152809698   821023        Divided  Midwest\n","7   University of Illinois Urbana-Champaign        2024       169474512   906751        Divided  Midwest\n","8                  University of Washington        2021        86150595  1488645      Democrats     West\n","9                  University of Washington        2022       149458923  1559708      Democrats     West\n","10                 University of Washington        2023       150037375  1734091        Divided     West\n","11                 University of Washington        2024       200147129  1691302        Divided     West\n","12                   University of Michigan        2021       150618278  1639645      Democrats    North\n","13                   University of Michigan        2022       195084818  1770708      Democrats    North\n","14                   University of Michigan        2023       228603154  1925875        Divided    North\n","15                   University of Michigan        2024       241856430  2110961        Divided    North\n"]}]},{"cell_type":"code","source":["col_order = [\n","    # Identifiers\n","    'school', 'region', 'survey_year',\n","    # Party control\n","    'majority_party', 'House Majority', 'Senate Majority', 'Presidency',\n","    # S&E expenditures (NSF, dollars in thousands)\n","    'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences',\n","    'math_stats', 'psychology', 'social_sciences', 'sciences_nec',\n","    'engineering', 'non_se_fields',\n","    # Athletics expenditures (dollars)\n","    'Total Expenses', 'Total Revenues', 'Coaches Compensation',\n","    'Athletic Student Aid', 'Facilities, Debt Service, and Equipment',\n","    'Game Expenses and Travel', 'Recruiting', 'Medical',\n","    'Total Football Spending', 'Ticket Sales', 'Donor Contributions',\n","    'Student Fees', 'Institutional/Government Support',\n","]\n","\n","final_dataset = final_dataset[col_order]\n","\n","print(final_dataset.shape)\n","print(final_dataset.columns.tolist())\n","\n","# Export\n","final_dataset.to_csv(\"bigten_expenditures_party_2021_2024.csv\", index=False)\n","\n","from google.colab import files\n","files.download(\"bigten_expenditures_party_2021_2024.csv\")\n","\n","print(\"\\nExported: bigten_expenditures_party_2021_2024.csv\")"],"metadata":{"id":"lZy9xs6Wb7Fn","colab":{"base_uri":"https://localhost:8080/","height":106},"outputId":"e1a21e8d-1c23-4c3d-9430-6b8a73800a1b","executionInfo":{"status":"ok","timestamp":1778087372006,"user_tz":240,"elapsed":14,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(16, 30)\n","['school', 'region', 'survey_year', 'majority_party', 'House Majority', 'Senate Majority', 'Presidency', 'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences', 'math_stats', 'psychology', 'social_sciences', 'sciences_nec', 'engineering', 'non_se_fields', 'Total Expenses', 'Total Revenues', 'Coaches Compensation', 'Athletic Student Aid', 'Facilities, Debt Service, and Equipment', 'Game Expenses and Travel', 'Recruiting', 'Medical', 'Total Football Spending', 'Ticket Sales', 'Donor Contributions', 'Student Fees', 'Institutional/Government Support']\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_a22a2086-ba25-4c74-a129-66330167ca96\", \"bigten_expenditures_party_2021_2024.csv\", 4870)"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Exported: bigten_expenditures_party_2021_2024.csv\n"]}]},{"cell_type":"code","source":["# Prep athletics for merge — use school name that matches NSF institution\n","athletics_for_merge = athletics_all.merge(\n","    party_clean, on='survey_year', how='left'\n",")\n","\n","# Map standardized school name back to NSF institution name for merging\n","school_to_nsf = {\n","    'University of Maryland, College Park':    'U. Marylandb',\n","    'University of Illinois Urbana-Champaign': 'U. Illinois, Urbana-Champaign',\n","    'University of Washington':                'U. Washington, Seattle',\n","    'University of Michigan':                  'U. Michigan, Ann Arbor',\n","}\n","athletics_for_merge['institution'] = athletics_for_merge['school'].map(school_to_nsf)\n","\n","# Drop redundant columns before merge\n","athletics_cols_only = [c for c in athletics_for_merge.columns if c not in [\n","    'school', 'region', 'majority_party', 'House Majority',\n","    'Senate Majority', 'Presidency'\n","]]\n","\n","# Merge onto full NSF dataset\n","final_full = nsf_all_party.merge(\n","    athletics_for_merge[athletics_cols_only],\n","    on=['institution', 'survey_year'],\n","    how='left'\n",")\n","\n","print(final_full.shape)\n","print(final_full.columns.tolist())\n","print(f\"\\nRows with athletics data: {final_full['Total Expenses'].notna().sum()}\")\n","print(f\"Rows without athletics data: {final_full['Total Expenses'].isna().sum()}\")"],"metadata":{"id":"pJgg-A6vclKe","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e2515d95-6dc3-47c9-91a0-b65dba22ca88","executionInfo":{"status":"ok","timestamp":1778087374508,"user_tz":240,"elapsed":43,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2630, 30)\n","['institution', 'survey_year', 'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences', 'math_stats', 'psychology', 'social_sciences', 'sciences_nec', 'engineering', 'non_se_fields', 'nsf_region', 'House Majority', 'Senate Majority', 'Presidency', 'majority_party', 'Total Expenses', 'Total Revenues', 'Coaches Compensation', 'Athletic Student Aid', 'Facilities, Debt Service, and Equipment', 'Game Expenses and Travel', 'Recruiting', 'Medical', 'Total Football Spending', 'Ticket Sales', 'Donor Contributions', 'Student Fees', 'Institutional/Government Support']\n","\n","Rows with athletics data: 16\n","Rows without athletics data: 2614\n"]}]},{"cell_type":"code","source":["col_order_full = [\n","    # Identifiers\n","    'institution', 'nsf_region', 'survey_year',\n","    # Party control\n","    'majority_party', 'House Majority', 'Senate Majority', 'Presidency',\n","    # S&E expenditures — all schools (dollars in thousands)\n","    'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences',\n","    'math_stats', 'psychology', 'social_sciences', 'sciences_nec',\n","    'engineering', 'non_se_fields',\n","    # Athletics expenditures — Big Ten 4 schools only (dollars)\n","    'Total Expenses', 'Total Revenues', 'Coaches Compensation',\n","    'Athletic Student Aid', 'Facilities, Debt Service, and Equipment',\n","    'Game Expenses and Travel', 'Recruiting', 'Medical',\n","    'Total Football Spending', 'Ticket Sales', 'Donor Contributions',\n","    'Student Fees', 'Institutional/Government Support',\n","]\n","\n","final_full = final_full[col_order_full]\n","\n","print(final_full.shape)\n","print(final_full.head(3).to_string())\n","\n","# Export\n","final_full.to_csv(\"bigten_SE_athletics_party_2021_2024.csv\", index=False)\n","\n","from google.colab import files\n","files.download(\"bigten_SE_athletics_party_2021_2024.csv\")\n","\n","print(\"\\nExported: bigten_SE_athletics_party_2021_2024.csv\")"],"metadata":{"id":"BBgkRzOWcuBu","colab":{"base_uri":"https://localhost:8080/","height":158},"outputId":"d26361c1-4a9c-4103-c3a5-b88f8c16c047","executionInfo":{"status":"ok","timestamp":1778087375762,"user_tz":240,"elapsed":70,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2630, 30)\n","                    institution  nsf_region survey_year majority_party House Majority Senate Majority        Presidency   all_rd  computer_info_sci  geosciences  life_sciences  math_stats  psychology  social_sciences  sciences_nec  engineering  non_se_fields  Total Expenses  Total Revenues  Coaches Compensation  Athletic Student Aid  Facilities, Debt Service, and Equipment  Game Expenses and Travel  Recruiting    Medical  Total Football Spending  Ticket Sales  Donor Contributions  Student Fees  Institutional/Government Support\n","0             Johns Hopkins U.a  East Coast        2021      Democrats      Democrats     Democrats14  Democrat (Biden)  3181385             270040        79257        1069865      117429      338454             6201          5305        50406           6448             NaN             NaN                   NaN                   NaN                                      NaN                       NaN         NaN        NaN                      NaN           NaN                  NaN           NaN                               NaN\n","1  U. California, San Francisco  West Coast        2021      Democrats      Democrats     Democrats14  Democrat (Biden)  1710036                  0            0        1639862           0       69817                0             0          357              0             NaN             NaN                   NaN                   NaN                                      NaN                       NaN         NaN        NaN                      NaN           NaN                  NaN           NaN                               NaN\n","2        U. Michigan, Ann Arbor     Midwest        2021      Democrats      Democrats     Democrats14  Democrat (Biden)  1639645              13922        15080         931174       17937       66041            18255        175698         3628         118228     150618278.0     101236069.0            34149105.0            27913057.0                               34280697.0                 6818176.0    822051.0  3125502.0               37380521.0       54168.0           13799193.0           0.0                               0.0\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_810dc1d4-b633-43f2-a0b7-cefd4d4161ed\", \"bigten_SE_athletics_party_2021_2024.csv\", 364955)"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Exported: bigten_SE_athletics_party_2021_2024.csv\n"]}]},{"cell_type":"code","source":["# Only strip trailing single lowercase letter footnote markers from known flagged names\n","footnote_fixes = {\n","    'U. Marylandb': 'U. Maryland, College Park',\n","    'Johns Hopkins U.a': 'Johns Hopkins U.',\n","}\n","\n","final_full['institution'] = final_full['institution'].replace(footnote_fixes)\n","\n","# Confirm\n","print(final_full[final_full['institution'].str.contains('Maryland')]['institution'].unique())\n","print(final_full[final_full['institution'].str.contains('Johns Hopkins')]['institution'].unique())"],"metadata":{"id":"k8eBvEeGdP4W","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c97a061d-947c-435a-9f14-837b7793468e","executionInfo":{"status":"ok","timestamp":1778087377408,"user_tz":240,"elapsed":16,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['U. Maryland, College Park' 'U. Maryland, Baltimore County'\n"," 'U. Maryland Center for Environmental Science'\n"," 'U. Maryland, Eastern Shore' \"St. Mary's C. Maryland\"\n"," 'Loyola U., Maryland']\n","['Johns Hopkins U.']\n"]}]},{"cell_type":"markdown","source":["### Replace Non-True Zeros with NaN\n","Some columns contain zeros that represent missing or unreported data\n","rather than a true zero value. We replace these with NaN to avoid\n","misleading analysis. Athletics columns are only populated for our 4\n","focus schools — all other schools retain NaN for those fields."],"metadata":{"id":"ys5iuq7wOqsN"}},{"cell_type":"code","source":["# S&E columns where 0 likely means unreported, not truly zero\n","se_expenditure_cols = [\n","    'all_rd', 'computer_info_sci', 'geosciences', 'life_sciences',\n","    'math_stats', 'psychology', 'social_sciences', 'sciences_nec',\n","    'engineering', 'non_se_fields'\n","]\n","\n","# Athletics columns where 0 likely means unreported\n","athletics_expenditure_cols = [\n","    'Total Expenses', 'Total Revenues', 'Coaches Compensation',\n","    'Athletic Student Aid', 'Facilities, Debt Service, and Equipment',\n","    'Game Expenses and Travel', 'Recruiting', 'Medical',\n","    'Total Football Spending', 'Ticket Sales', 'Donor Contributions',\n","    'Student Fees', 'Institutional/Government Support',\n","]\n","\n","final_full[se_expenditure_cols] = final_full[se_expenditure_cols].replace(0, pd.NA)\n","final_full[athletics_expenditure_cols] = final_full[athletics_expenditure_cols].replace(0, pd.NA)\n","\n","# Confirm\n","print(\"Remaining zeros in S&E cols:\", (final_full[se_expenditure_cols] == 0).sum().sum())\n","print(\"Remaining zeros in athletics cols:\", (final_full[athletics_expenditure_cols] == 0).sum().sum())\n","print(f\"\\nNaN counts per column:\")\n","print(final_full[se_expenditure_cols + athletics_expenditure_cols].isna().sum())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rvYDLD9jOvvp","outputId":"6b3af978-3c03-48bc-bf43-13c9ad34192b","executionInfo":{"status":"ok","timestamp":1778087382228,"user_tz":240,"elapsed":40,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Remaining zeros in S&E cols: 0\n","Remaining zeros in athletics cols: 0\n","\n","NaN counts per column:\n","all_rd                                        0\n","computer_info_sci                           668\n","geosciences                                 881\n","life_sciences                               107\n","math_stats                                  780\n","psychology                                  482\n","social_sciences                             786\n","sciences_nec                                606\n","engineering                                1579\n","non_se_fields                               370\n","Total Expenses                             2614\n","Total Revenues                             2614\n","Coaches Compensation                       2614\n","Athletic Student Aid                       2614\n","Facilities, Debt Service, and Equipment    2614\n","Game Expenses and Travel                   2614\n","Recruiting                                 2614\n","Medical                                    2614\n","Total Football Spending                    2614\n","Ticket Sales                               2614\n","Donor Contributions                        2614\n","Student Fees                               2622\n","Institutional/Government Support           2615\n","dtype: int64\n"]}]},{"cell_type":"code","source":["# Fix footnote markers in institution names at the source\n","nsf_all_party['institution'] = nsf_all_party['institution'].replace({\n","    'U. Marylandb': 'U. Maryland, College Park',\n","    'Johns Hopkins U.a': 'Johns Hopkins U.',\n","})\n","\n","# Rebuild final_full with clean institution names\n","final_full = nsf_all_party.merge(\n","    athletics_for_merge[athletics_cols_only],\n","    on=['institution', 'survey_year'],\n","    how='left'\n",")\n","\n","# Update athletics merge key too\n","school_to_nsf = {\n","    'University of Maryland, College Park':    'U. Maryland, College Park',\n","    'University of Illinois Urbana-Champaign': 'U. Illinois, Urbana-Champaign',\n","    'University of Washington':                'U. Washington, Seattle',\n","    'University of Michigan':                  'U. Michigan, Ann Arbor',\n","}\n","athletics_for_merge['institution'] = athletics_for_merge['school'].map(school_to_nsf)\n","\n","# Remerge\n","final_full = nsf_all_party.merge(\n","    athletics_for_merge[athletics_cols_only],\n","    on=['institution', 'survey_year'],\n","    how='left'\n",")\n","\n","final_full = final_full[col_order_full]\n","\n","# Confirm\n","print(final_full[final_full['institution'].str.contains('Maryland')]['institution'].unique())\n","print(f\"\\nShape: {final_full.shape}\")\n","print(f\"Rows with athletics data: {final_full['Total Expenses'].notna().sum()}\")"],"metadata":{"id":"khln55hqddW0","colab":{"base_uri":"https://localhost:8080/"},"outputId":"8ff7955f-99ec-4426-9cdd-051eedd91753","executionInfo":{"status":"ok","timestamp":1778087384167,"user_tz":240,"elapsed":45,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['U. Maryland, College Park' 'U. Maryland, Baltimore County'\n"," 'U. Maryland Center for Environmental Science'\n"," 'U. Maryland, Eastern Shore' \"St. Mary's C. Maryland\"\n"," 'Loyola U., Maryland']\n","\n","Shape: (2630, 30)\n","Rows with athletics data: 16\n"]}]},{"cell_type":"code","source":["final_full.to_csv(\"bigten_SE_athletics_party_2021_2024.csv\", index=False)\n","\n","from google.colab import files\n","files.download(\"bigten_SE_athletics_party_2021_2024.csv\")\n","\n","print(\"Exported: bigten_SE_athletics_party_2021_2024.csv\")\n","print(f\"Shape: {final_full.shape}\")"],"metadata":{"id":"QWZ0LH3EdeS8","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"a2cddbed-987d-4e53-f6ac-a8630715ce6a","executionInfo":{"status":"ok","timestamp":1778087386566,"user_tz":240,"elapsed":57,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      div.remove();\n","    }\n","  "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_0b5e89ee-748e-47f2-81a7-0e8958cd9eef\", \"bigten_SE_athletics_party_2021_2024.csv\", 365011)"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Exported: bigten_SE_athletics_party_2021_2024.csv\n","Shape: (2630, 30)\n"]}]},{"cell_type":"markdown","metadata":{"id":"WvK-vHxhQsth"},"source":["### Analysis Prep\n","Run this after `final_full` has been created. This creates a clean analysis dataset, converts NSF S&E values from thousands of dollars to dollars, and removes rows where either athletics or S&E spending is missing."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wB3mru0nQsti","executionInfo":{"status":"ok","timestamp":1778087401690,"user_tz":240,"elapsed":24,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}},"outputId":"e503497c-b742-4f12-fb45-781f252959c0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Rows in full dataset: 2630\n","Rows with both athletics and S&E data: 16\n","                       institution survey_year  nsf_region majority_party  \\\n","2           U. Michigan, Ann Arbor        2021     Midwest      Democrats   \n","4           U. Washington, Seattle        2021  West Coast      Democrats   \n","16       U. Maryland, College Park        2021  East Coast      Democrats   \n","36   U. Illinois, Urbana-Champaign        2021     Midwest      Democrats   \n","651         U. Michigan, Ann Arbor        2022     Midwest      Democrats   \n","\n","     sports_spending_dollars  se_spending_dollars  sports_to_se_ratio  \n","2                150618278.0           1639645000            0.091860  \n","4                 86150595.0           1488645000            0.057872  \n","16                81831650.0           1142264000            0.071640  \n","36               102006085.0            731268000            0.139492  \n","651              195084818.0           1770708000            0.110173  \n"]}],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","# Use final_full if it already exists in the notebook. Otherwise load the exported file.\n","try:\n","    analysis_df = final_full.copy()\n","except NameError:\n","    analysis_df = pd.read_csv(\"bigten_SE_athletics_party_2021_2024.csv\")\n","\n","# Spending columns\n","sports_col = \"Total Expenses\"\n","se_col = \"all_rd\"\n","\n","# Convert numeric columns safely\n","numeric_cols = [\n","    sports_col, \"Total Revenues\", \"Coaches Compensation\", \"Athletic Student Aid\",\n","    \"Facilities, Debt Service, and Equipment\", \"Game Expenses and Travel\",\n","    \"Recruiting\", \"Medical\", \"Total Football Spending\", \"Ticket Sales\",\n","    \"Donor Contributions\", \"Student Fees\", \"Institutional/Government Support\",\n","    \"all_rd\", \"computer_info_sci\", \"geosciences\", \"life_sciences\", \"math_stats\",\n","    \"psychology\", \"social_sciences\", \"sciences_nec\", \"engineering\", \"non_se_fields\"\n","]\n","\n","for col in numeric_cols:\n","    if col in analysis_df.columns:\n","        analysis_df[col] = pd.to_numeric(analysis_df[col], errors=\"coerce\")\n","\n","# NSF S&E values are in thousands of dollars, so convert all_rd to dollars for comparison\n","analysis_df[\"se_spending_dollars\"] = analysis_df[se_col] * 1000\n","analysis_df[\"sports_spending_dollars\"] = analysis_df[sports_col]\n","analysis_df[\"sports_to_se_ratio\"] = analysis_df[\"sports_spending_dollars\"] / analysis_df[\"se_spending_dollars\"]\n","\n","# Keep only rows with both sports and S&E data\n","analysis_complete = analysis_df.dropna(subset=[\"sports_spending_dollars\", \"se_spending_dollars\"]).copy()\n","\n","print(\"Rows in full dataset:\", analysis_df.shape[0])\n","print(\"Rows with both athletics and S&E data:\", analysis_complete.shape[0])\n","print(analysis_complete[[\"institution\", \"survey_year\", \"nsf_region\", \"majority_party\", \"sports_spending_dollars\", \"se_spending_dollars\", \"sports_to_se_ratio\"]].head())"]},{"cell_type":"markdown","metadata":{"id":"O2BIKrLJQsti"},"source":["### Analysis 1: Location and Spending\n","This compares average sports spending, S&E spending, and sports-to-S&E ratio by NSF region."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"EUZ1Z2EWQsti","executionInfo":{"status":"ok","timestamp":1778087406995,"user_tz":240,"elapsed":814,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}},"outputId":"943dac24-656a-464d-c0f6-c5732f5b5c51"},"outputs":[{"output_type":"stream","name":"stdout","text":["            sports_spending_dollars  se_spending_dollars  sports_to_se_ratio\n","nsf_region                                                                  \n","Midwest                1.711965e+08         1.334018e+09            0.139551\n","West Coast             1.464485e+08         1.618436e+09            0.089640\n","East Coast             1.125355e+08         1.323909e+09            0.084611\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["location_summary = (\n","    analysis_complete\n","    .groupby(\"nsf_region\")[[\"sports_spending_dollars\", \"se_spending_dollars\", \"sports_to_se_ratio\"]]\n","    .mean()\n","    .sort_values(\"sports_to_se_ratio\", ascending=False)\n",")\n","\n","print(location_summary)\n","\n","location_summary[[\"sports_spending_dollars\", \"se_spending_dollars\"]].plot(kind=\"bar\")\n","plt.title(\"Average Sports vs S&E Spending by Region\")\n","plt.xlabel(\"Region\")\n","plt.ylabel(\"Average Spending in Dollars\")\n","plt.xticks(rotation=45, ha=\"right\")\n","plt.tight_layout()\n","plt.show()\n","\n","location_summary[\"sports_to_se_ratio\"].plot(kind=\"bar\")\n","plt.title(\"Average Sports-to-S&E Spending Ratio by Region\")\n","plt.xlabel(\"Region\")\n","plt.ylabel(\"Sports Spending / S&E Spending\")\n","plt.xticks(rotation=45, ha=\"right\")\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"WLBJy0ZMQsti"},"source":["### Analysis 2: Time Series\n","This shows how average sports spending and average S&E spending changed from 2021 to 2024."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"QLB3dPXPQsti","executionInfo":{"status":"ok","timestamp":1778087415711,"user_tz":240,"elapsed":399,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}},"outputId":"0d78bdfa-e17a-4c4b-b8b2-f0460a97361e"},"outputs":[{"output_type":"stream","name":"stdout","text":["             sports_spending_dollars  se_spending_dollars  sports_to_se_ratio\n","survey_year                                                                  \n","2021                    1.051517e+08         1.250456e+09            0.090216\n","2022                    1.470121e+08         1.331219e+09            0.116922\n","2023                    1.631526e+08         1.466573e+09            0.119701\n","2024                    1.860606e+08         1.562134e+09            0.126513\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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ePnyIjh07Kr4P27dvx7lz5xAcHIxKlSrh4MGDiIiIQPv27fHq1SsAwNWrV+Ho6KhybSpWrAg/Pz+Vl42NjU5iy3ycrCpVqqT4Ht+9exdCCEyZMkXlvps2bRoA1fsu8x9+ALC3twcAReIsvy+yHtvR0VFRNjelS5dWGZ1pb2+vlJw/fPgQ5cuXVymnych3eaImT+yykzXx0+Tey8s11fZnJ6+yfu9sbW0BAO7u7mqXy693Xs6Jii6jwg6gML169Qo1a9bEwIED0bVrV623379/P/r06YOlS5eiTZs2uHHjBoYMGQJzc3O1f2hJ2ZEjRxAVFYWQkBCEhISorN+8eTPatGkDAPj000+xatUq7N+/H126dMG2bdvg5eWFmjVrKsrLZDJUr14dixYtUnu8rL/cMtfEyZ04cQKdOnVCs2bNsGLFCri4uMDY2Bhr167N03xgMpkMAPDZZ5+hX79+asvUqFFD4/25uLjg008/Rbdu3VC1alVs27YN69atg5FR9j/K2U1voOm0B+quU0569OiBpk2bYufOnfjzzz8xf/58zJ07F6GhoWjXrl2225mamqJLly7YuXMnVqxYgZiYGJw6dQqzZs1SKjdmzBgEBARg165dOHjwIKZMmYLZs2fjyJEj8PHx0ShGCwsLNG3aFE2bNkXJkiURFBSE/fv3o1+/fjh9+jSMjIxQt25dAEC1atWwe/dutGnTBp07d0ZoaCjWr1+PXr165cuUHDnFpin5fTd+/Phsa1ezJkjZ1SQJHc5sld/HsLW1hYuLC/79998cy/37779wc3NTJNqa3Ht5uaba/uzkVXbXNbfrnZdzoqKrWCd07dq1y/EPTEpKCiZPnowtW7YgLi4O1apVw9y5c9GiRQsAwMaNG9GlSxcMHToUAFCuXDlMmjQJc+fOxYgRIzhPUC42b94MJycnLF++XGVdaGgodu7ciZUrV8Lc3BzNmjWDi4sLtm7diiZNmuDIkSOYPHmy0jbly5fH5cuX8dFHH+X52u/YsQNmZmY4ePCg0ojGtWvXKpXz8PCATCbDgwcPlGos7t69q1TO0dER1tbWkEql8PPzy1NM6hgbG6NGjRq4c+cOnj9/jlKlSinW3blzR6lm4O7du5DJZPD09FSK/c6dO0rzXMXExCAuLg4eHh4axZDTNXZxccHw4cMxfPhwxMbGonbt2vj+++9z/HkDMpq+1q9fj7CwMNy4cQNCCKVaWLny5ctj3LhxGDduHO7cuYNatWph4cKF2LRpk0axZyZP3KKiohTnlZ6ejqioKLi6ugLIaH4MCQlBt27dULNmTcTHx2PChAlaH+t9Y5O7c+eOStnbt28rvsflypUDkHGf6Oq+k98Xd+7cUewfyKiF1rb5O7fjXL9+HUIIpXss689Wdjp27IjVq1fj5MmTaNKkicr6EydOICIiAl988YXS8tzuvfy4poXtQzyn4qxYN7nmZuTIkThz5gxCQkLw77//4pNPPkHbtm0Vv0xTUlJgZmamtI25uTkeP36s0mxFyt68eYPQ0FB07NgR3bt3V3mNHDkSiYmJiqHzBgYG6N69O/bs2YONGzciPT1d5Q99jx498OTJE6xevVrt8eTNZTkxNDSERCJR9N8DMqaKyNocL/9vdsWKFUrLly5dqrK/bt26YceOHWqnlcg6LURWd+7cQWRkpMryuLg4nDlzBvb29ipTM2RNkOUxyZMp+cS3wcHBSuXkNZsdOnTIMSY5S0tLlT58UqlUqWkayJh2w9XVVWVKFHX8/Pzg4OCArVu3YuvWrahfv75Scvr69WuVKVjKly8Pa2vrXPcvb8LPSt7vrHLlyooYAGDq1KlK5Tp37ozBgwcjIiIC9erV08k0FNrGJrdr1y6lvk3nzp3D2bNnFd9jJycntGjRAqtWrVJJBoHc7zt1/Pz8YGxsjKVLlyrVqGW9j96Xv78/njx5ojRtRnJystqfa3UmTJgAc3NzfPHFF/jvv/+U1r148QJDhw6FhYWFSkKe272XH9e0sH2I51ScFesaupxERkZi7dq1iIyMVPyXPn78eBw4cABr167FrFmz4O/vj7Fjx6J///5o2bIl7t69i4ULFwLI+I9a/t8yqdq9ezcSExOVOj5n1qBBAzg6OmLz5s2KxK1nz55YunQppk2bhurVqyvVLgHA559/jm3btmHo0KE4evQoGjduDKlUips3b2Lbtm2KudRy0qFDByxatAht27ZF7969ERsbi+XLl6NChQpKzTh16tRBt27dEBwcjP/++w8NGjTAX3/9hdu3bwNQrr2aM2cOjh49Cl9fXwwZMgTe3t548eIFwsPDcfjwYbx48SLbeC5fvozevXujXbt2aNq0KRwcHPDkyROsX78eT58+RXBwsEqzyoMHD9CpUye0bdsWZ86cwaZNm9C7d29F83TNmjXRr18//PTTT4iLi0Pz5s1x7tw5rF+/Hl26dFEMUMlNnTp1cPjwYSxatAiurq4oW7YsKleujNKlS6N79+6oWbMmrKyscPjwYfzzzz+Kn42cGBsbo2vXrggJCcGrV6+wYMECpfW3b9/GRx99hB49esDb2xtGRkbYuXMnYmJi8Omnn+a4786dO6Ns2bIICAhA+fLl8erVKxw+fBh79uxBvXr1EBAQACCjhqdz585Ys2YN7t69iy5duijmKdyzZw+aNWuGo0ePYurUqZgxY4bSMcLDw9XWEpYvX16p03peY5OrUKECmjRpgmHDhiElJQXBwcEoUaIEvvrqK0WZ5cuXo0mTJqhevTqGDBmCcuXKISYmBmfOnMHjx49x+fLlHK9XVo6Ojhg/fjxmz56Njh07on379rh48SL279+PkiVLarWvnHzxxRdYtmwZevXqhdGjR8PFxQWbN29W/POcW+17xYoVsX79evTp0wfVq1dXeVLE8+fPsWXLFpQvX15pu9zuPUD317Qo+BDPqdgqnMG1RQ8AsXPnTsXnvXv3CgDC0tJS6WVkZKSYm0kmk4mvvvpKmJmZCUNDQ2Fvby+mT58uAIi///67kM5EPwQEBAgzMzPx6tWrbMv0799fGBsbK6b7kMlkwt3dXQAQ3333ndptUlNTxdy5c0XVqlWFqampsLe3F3Xq1BFBQUEiPj5eUQ5AtlNrrFmzRlSsWFGYmpoKLy8vsXbtWsWUIJm9evVKjBgxQjg4OAgrKyvRpUsXcevWLQFAZc6pmJgYMWLECOHu7i6MjY1FqVKlxEcffSR++umnHK9TTEyMmDNnjmjevLlwcXERRkZGwt7eXrRq1Ups375dqaw8xuvXr4vu3bsLa2trYW9vL0aOHKnyFIy0tDQRFBQkypYtK4yNjYW7u7uYNGmSypMQPDw8RIcOHdTGdvPmTdGsWTNhbm6umLYiJSVFTJgwQdSsWVNYW1sLS0tLUbNmTbFixYoczzOzQ4cOCQBCIpGozMv2/PlzMWLECMVs/7a2tsLX11ds27Yt1/1u2bJFfPrpp6J8+fLC3NxcmJmZCW9vbzF58mTF9BVy6enpYv78+aJq1arCxMRE2NraCn9/f/Hnn38KITLm2gMg1q9fL4TIfdqS3J6ioGlsmZ9IsXDhQuHu7q6YD1A+LU1m9+7dE3379hWlSpUSxsbGws3NTXTs2FHp3pFPhSGfz1FO3dQjUqlUBAUFCRcXF2Fubi5atGghrl69qvKkiOymLck8p5tcv379hIeHh9Ky+/fviw4dOghzc3Ph6Ogoxo0bJ3bs2KHV79Z///1X9OrVS7i4uCh+5nr16iWuXLmS7TY53Xty73NNNZGXaUvU/Yyq+x2X3RNNNDknKvr4LNe3JBIJdu7ciS5dugDIGOEkf5Bz1hoQKysrpT5LUqkU0dHRcHR0RFhYGNq3b4/Y2FiVpjD68F26dAk+Pj7YtGkT+vTpU6DHnj59OoKCgvDs2TOd1phQ0SF/Zuz8+fOVnpBSHAQHB2Ps2LF4/Pix0pQtRJSBTa7Z8PHxgVQqRWxsLJo2bZpjWUNDQ8UvmC1btqBhw4ZM5oqBN2/eqIxiCw4OhoGBAZo1a1ZIURHpv6w/W8nJyVi1ahUqVqzIZI4oG8U6oUtKSlIaOfXgwQNcunQJDg4OqFSpEvr06YO+ffti4cKF8PHxwbNnzxAWFoYaNWqgQ4cOeP78ObZv344WLVogOTkZa9euxW+//ab2mYD04Zk3bx4uXLiAli1bwsjICPv378f+/fvxv//9T2WKFCLSXNeuXVGmTBnUqlUL8fHx2LRpE27evKn0nGciUlasE7rz588rdQCXz5zfr18/rFu3DmvXrsV3332HcePG4cmTJyhZsiQaNGiAjh07KrZZv349xo8fDyEEGjZsiGPHjqF+/foFfi5U8Bo1aoRDhw5h5syZSEpKQpkyZTB9+nSV6VSISDv+/v74+eefsXnzZkilUnh7eyMkJETtFDZElIF96IiIiIj0HOehIyIiItJzTOiIiIiI9Fyx60Mnk8nw9OlTWFtb89FcREREVGQJIZCYmAhXV9dcnx1d7BK6p0+fcgQiERER6Y1Hjx7l+rjBYpfQWVtbA8i4ODY2NoUcDREREZF6CQkJcHd3V+QuOSl2CZ28mdXGxoYJHRERERV5mnQR46AIIiIiIj1XqAnd8ePHERAQAFdXV0gkEuzatSvXbVJSUjB58mR4eHjA1NQUnp6e+OWXX/I/WCIiIqIiqlCbXF+9eoWaNWti4MCB6Nq1q0bb9OjRAzExMVizZg0qVKiAqKgoyGSyfI6UiIiIqOgq1ISuXbt2aNeuncblDxw4gL/++gv379+Hg4MDAMDT0zNfYpNKpUhLS8uXfRMVVyYmJrkOvSciIu3p1aCI3bt3o27dupg3bx42btwIS0tLdOrUCTNnzoS5ublOjiGEQHR0NOLi4nSyPyJ6x8DAAGXLloWJiUlhh0JE9EHRq4Tu/v37OHnyJMzMzLBz5048f/4cw4cPx3///Ye1a9eq3SYlJQUpKSmKzwkJCTkeQ57MOTk5wcLCgpMPE+mIfFLvqKgolClThj9bREQ6pFcJnUwmg0QiwebNm2FrawsAWLRoEbp3744VK1aoraWbPXs2goKCNNq/VCpVJHMlSpTQaexEBDg6OuLp06dIT0+HsbFxYYdDRPTB0KvOLC4uLnBzc1MkcwBQpUoVCCHw+PFjtdtMmjQJ8fHxitejR4+y3b+8z5yFhYVuAyciAFA0tUql0kKOhIjow6JXCV3jxo3x9OlTJCUlKZbdvn0bBgYG2T4Sw9TUVDGJsKaTCbMpiCh/8GeLiCh/FGpCl5SUhEuXLuHSpUsAgAcPHuDSpUuIjIwEkFG71rdvX0X53r17o0SJEhgwYACuX7+O48ePY8KECRg4cKDOBkUQERER6ZtCTejOnz8PHx8f+Pj4AAACAwPh4+ODqVOnAgCioqIUyR0AWFlZ4dChQ4iLi0PdunXRp08fBAQE4IcffiiU+Kl46N+/P7p06aL43KJFC4wZM6bQ4gGAY8eOQSKRaDUa29PTE8HBwYrPmk7mTUREWcikwIMTwJXtGV9lhd+NpFAHRbRo0QJCiGzXr1u3TmWZl5cXDh06lI9R6YZUJnDuwQvEJibDydoM9cs6wNDgw2pumj59Onbt2qWoYS0uQkND2aGfiKi4ur4bOPA1kPD03TIbV6DtXMC7U6GFpVejXPXFgatRCNpzHVHxyYplLrZmmBbgjbbVXAoxMt0QQhTrTu3ySa2Lu9TUVM4nR0TFy/XdwLa+ALJURiVEZSzvsaHQkjq9GhShDw5cjcKwTeFKyRwARMcnY9imcBy4GpUvx92+fTuqV68Oc3NzlChRAn5+fnj16pWiuTAoKAiOjo6wsbHB0KFDkZqaqtg2JSUFo0aNgpOTE8zMzNCkSRP8888/ivXy5r39+/ejTp06MDU1xaZNmxAUFITLly9DIpFAIpFg3bp1EEJg+vTpKFOmDExNTeHq6opRo0ZpdA4rVqxAxYoVYWZmBmdnZ3Tv3l2xrkWLFhg5ciRGjhwJW1tblCxZElOmTFGq4U1JScH48ePh5uYGS0tL+Pr64tixY4r169atg52dHQ4ePIgqVarAysoKbdu2RVTUu++JVCpFYGAg7OzsUKJECXz11VcqtchZm1w9PT0xa9YsDBw4ENbW1ihTpgx++uknpW1Onz6NWrVqwczMDHXr1sWuXbsgkUg0rt3ct28fKlWqBHNzc7Rs2RIREREqZXbs2IGqVasqnnG8cOFCjfYt9/XXX6NSpUqwsLBAuXLlMGXKFKWnpUyfPh21atXCzz//jLJly8LMzAxA9vceEdEHRSbNqJnLmswB75YdmFhoza9M6HIhhMDr1HSNXonJaZi2+1pO32pM330diclpGu0vp+bozKKiotCrVy8MHDgQN27cwLFjx9C1a1fF9mFhYYrlW7ZsQWhoqNLcfF999RV27NiB9evXIzw8HBUqVIC/vz9evHihdJyJEydizpw5uHHjBlq3bo1x48ahatWqiIqKQlRUFHr27IkdO3Zg8eLFWLVqFe7cuYNdu3ahevXquZ7D+fPnMWrUKMyYMQO3bt3CgQMH0KxZM6Uy69evh5GREc6dO4clS5Zg0aJF+PnnnxXrR44ciTNnziAkJAT//vsvPvnkE7Rt2xZ37txRlHn9+jUWLFiAjRs34vjx44iMjMT48eMV6xcuXIh169bhl19+wcmTJ/HixQvs3Lkz1/gXLlyIunXr4uLFixg+fDiGDRuGW7duAciYzDogIADVq1dHeHg4Zs6cia+//jrXfco9evQIXbt2RUBAAC5duoTBgwdj4sSJSmUuXLiAHj164NNPP8WVK1cwffp0TJkyRW23hexYW1tj3bp1uH79OpYsWYLVq1dj8eLFSmXu3r2LHTt2IDQ0FJcuXcr13iMi0ktCAK9fAI8vZPSTOz4f2NxDuZlVdSMg4Qnw8HSBhZkZm1xz8SZNCu+pB3WyLwEgOiEZ1af/qVH56zP8YWGS+7coKioK6enp6Nq1Kzw8PABAKYkyMTHBL7/8AgsLC1StWhUzZszAhAkTMHPmTLx58wY//vgj1q1bp3iu7urVq3Ho0CGsWbMGEyZMUOxnxowZaN26teKzlZUVjIyMUKpUKcWyyMhIlCpVCn5+fjA2NkaZMmVQv379XM8hMjISlpaW6NixI6ytreHh4aEYLCPn7u6OxYsXQyKRoHLlyrhy5QoWL16MIUOGIDIyEmvXrkVkZCRcXV0BAOPHj8eBAwewdu1azJo1C0DGXIMrV65E+fLlAWQkgTNmzFAcIzg4GJMmTULXrl0BACtXrsTBg7l//9u3b4/hw4cDyKjpWrx4MY4ePYrKlSvj119/hUQiwerVq2FmZgZvb288efIEQ4YMyXW/APDjjz+ifPnyiho3+bnPnTtXUWbRokX46KOPMGXKFABApUqVcP36dcyfPx/9+/fX6Djffvut4r2npyfGjx+PkJAQfPXVV4rlqamp2LBhAxwdHQEA4eHhOd57RERFlkwKxD8GXkYALx8ALx5kfH0ZAbyIAFLi87bfpBgdBqk5JnQfgJo1a+Kjjz5C9erV4e/vjzZt2qB79+6wt7dXrM88WXLDhg2RlJSER48eIT4+HmlpaWjcuLFivbGxMerXr48bN24oHadu3bq5xvLJJ58gODgY5cqVQ9u2bdG+fXsEBATAyCjnW61169bw8PBQbNe2bVt8/PHHSnE3aNBAaR6zhg0bYuHChZBKpbhy5QqkUikqVaqktN+UlBSlp35YWFgokjkgY7Lq2NhYAEB8fDyioqLg6+urWG9kZIS6devmWuNUo0YNxXuJRIJSpUop9nvr1i3UqFFD0UQJQKMkV+7GjRtKMQEZ5561TOfOnZWWNW7cGMHBwZBKpTA0NMz1OFu3bsUPP/yAe/fuISkpCenp6SrzNnp4eCiSOSD3e4+IqFClvgJePlSTsD0A4iIBWVrO21uVAhzKAvZlAUiAy5tzP6aVsy4i1xoTulyYGxvi+gx/jcqee/AC/df+k2u5dQPqoX7Z3DvWmxvn/kcYAAwNDXHo0CGcPn0af/75J5YuXYrJkyfj7NmzGm2vKUtLy1zLuLu749atWzh8+DAOHTqE4cOHY/78+fjrr79yHBlqbW2N8PBwHDt2DH/++SemTp2K6dOn459//oGdnV2ux01KSoKhoSEuXLigkrxYWVkp3meNQSKR6KR5UN1+ZTLZe++3oJw5cwZ9+vRBUFAQ/P39YWtri5CQEJV+eFnvgZzuvbJlyxbkKRBRcSQE8Oq5+oTt5YPca8sMjAF7j4yEzd7zXfLmUBaw8wBMMj05SiYFHhzNGAChtnOVJGO0q0cj3Z2fFpjQ5UIikWjU7AkATSs6wsXWDNHxydl9q1HK1gxNKzrqfAoTiUSCxo0bo3Hjxpg6dSo8PDwUfb8uX76MN2/eKCZf/vvvv2FlZQV3d3eULFkSJiYmOHXqlKLJLC0tDf/880+uc62ZmJioHe1qbm6OgIAABAQEYMSIEfDy8sKVK1dQu3btHPdnZGQEPz8/+Pn5Ydq0abCzs8ORI0cUzZ9ZE9S///4bFStWhKGhIXx8fCCVShEbG4umTZtqdM2ysrW1hYuLC86ePavov5eeno4LFy7kGntOKleujE2bNiElJQWmpqYAoDToJDdVqlTB7t27lZb9/fffKmVOnTqltOzUqVOoVKmSRrVzp0+fhoeHByZPnqxY9vDhQ43iy+7eCwwM1Gh7IqIcSdOA+EdqEraIjFdqUs7bm9mqT9jsPQEbN8BAs8oTGBhmTE2yrS8y/qJn/kv/9m962zma70/HmNDpkKGBBNMCvDFsU3h232pMC/DWeTJ39uxZhIWFoU2bNnBycsLZs2fx7NkzVKlSBf/++y9SU1MxaNAgfPvtt4iIiMC0adMwcuRIGBgYwNLSEsOGDcOECRPg4OCAMmXKYN68eXj9+jUGDRqU43E9PT0VT/coXbo0rK2tsWXLFkilUvj6+sLCwgKbNm2Cubm5IlnMzt69e3H//n00a9YM9vb22LdvH2QyGSpXrqwoExkZicDAQHzxxRcIDw/H0qVLFTVIlSpVQp8+fdC3b18sXLgQPj4+ePbsGcLCwlCjRg106NBBo2s5evRozJkzBxUrVoSXlxcWLVqk1eS96vTu3RuTJ0/G//73P0ycOBGRkZFYsGABAM0ehTV06FAsXLgQEyZMwODBg3HhwgWVwQ7jxo1DvXr1MHPmTPTs2RNnzpzBsmXLsGLFCo1irFixIiIjIxESEoJ69erhjz/+0GgwSE73HhGRxlISMyVpWWrb4h4BIqeRo5KMxMzeE3DwVE7Y7MsCFjqcasq7U8bUJGrnoZvDeeg+JG2rueDHz2qrzENXKh/nobOxscHx48cRHByMhIQEeHh4YOHChWjXrh22bt2Kjz76CBUrVkSzZs2QkpKCXr16Yfr06Yrt58yZA5lMhs8//xyJiYmoW7cuDh48mGs/qG7duiE0NBQtW7ZEXFwc1q5dCzs7O8yZMweBgYGQSqWoXr069uzZo9SPTR07OzuEhoZi+vTpSE5ORsWKFbFlyxZUrVpVUaZv37548+YN6tevD0NDQ4wePRr/+9//FOvXrl2L7777DuPGjcOTJ09QsmRJNGjQAB07dtT4Wo4bNw5RUVHo168fDAwMMHDgQHz88ceIj89j51hkfH/27NmDYcOGoVatWqhevTqmTp2K3r17K/Wry06ZMmWwY8cOjB07FkuXLkX9+vUV06TI1a5dG9u2bcPUqVMxc+ZMuLi4YMaMGRoPiOjUqRPGjh2LkSNHIiUlBR06dMCUKVOU7pPszi27e4+ISEEIIDE6mwEID4DXz3Pe3tBUfQ2bfVnArgxgnPvvUp3x7gR4dcgYzZoUk9FnzqNRodXMyUlEMZtfICEhAba2toiPj1fp8J2cnIwHDx4ozbGVV0XlSRH9+/dHXFyc3j/iqUWLFqhVq5bSo6v02ebNmzFgwADEx8cXq+cQ6/JnjIiKmPSUjNo0tf3ZIoD0Nzlvb+6gPmFzKJsxOMGg+M20llPOkhVr6PKJoYEEDcvnXCtFxceGDRtQrlw5uLm54fLly/j666/Ro0ePYpXMEdEH4E1c9glb/GOoHyzwlsQAsC2tPmGz98zo60Z5xoSOCsSJEydybIZLSsqlU6uei46OxtSpUxEdHQ0XFxd88skn+P777wFk9JHbtGmT2u0+++wzrFy5siBDJaLiTCYDEp+qHzH64gGQHJfz9sYWWQYgeL6rdbN1B4z4uMD8wibXTNgclH/evHmDJ0+eZLu+QoUKBRhN0RIbG4uEhAS162xsbODk5FTAEeUf/owRFQFpb97OzRahWtv28iEgTcl5e0un7PuzWTkBGgz2Is2wyZWKHHNz82KdtOXEycnpg0raiKiQyR9bld0AhMScHl8FwMAoozZNXcJm7wmYWuW8PRUKJnRERET6Rpqe8dzQ7Pqzpaiv9VcwsVY/xYdDWcCmNGDI9EDf8DtGRERUFKW+Uu7Dlvl9XCQgS895e2uXbAYgvJ2bjU2jHxQmdERERIVBCODVs+wHILyKzXl7Q5OMx1OpS9jsyig/too+eEzoiIiI8os0LaM2LWuTqPxr2quctzezU5+w2XtmPJ2gkCezpaKDCR0REdH7SE5QX8Mmn5tNk8dWKU3x4fkueTPP+Yk9RHJM6EgveXp6YsyYMRgzZgyAjGei7ty5E126dCm0mKZPn45du3bh0qVLGpWPiIhA2bJlcfHiRdSqVQvHjh1Dy5Yt8fLlS9jZ2eVrrESkBZkMSIpWn7C9fAC8/i/n7Y3MVCfRlb+3KwMYmeb/OdAHjwldfpFJi9xz3j5kUVFRuT57logoW+kpGU2j6vqzvYwA0pNz3t6iRPYDEKyci+Vjq6hgMaHLD9d3Awe+BhIyzfVj4wq0nZvxUF/SuVKlShV2CEVCamoqTEw4Ezvpufz6h/jNS/VTfLx4kDEFSK6PrXLPvj+bWc6TvhLlN/7LoGvXdwPb+ioncwCQEJWx/PrufDns9u3bUb16dZibm6NEiRLw8/PDq1cZnW1//vlnVKlSBWZmZvDy8sKKFSs02mdqaipGjhwJFxcXmJmZwcPDA7Nnz1asl0gk+PHHH9GuXTuYm5ujXLly2L59u9I+Hj16hB49esDOzg4ODg7o3LkzIiIiFOv79++PLl26YMGCBXBxcUGJEiUwYsQIpKWlKcrExsYiICAA5ubmKFu2LDZv3qwSq0Qiwa5duwBkNGVKJBKEhoaiZcuWsLCwQM2aNXHmzBmlbVavXg13d3dYWFjg448/xqJFi7Rq6pwzZw6cnZ1hbW2NQYMGITlZ+T94mUyGGTNmoHTp0jA1NUWtWrVw4MABjff/33//oVevXnBzc4OFhQWqV6+OLVu2KJVp0aIFRo4ciTFjxqBkyZLw9/eHEALTp09HmTJlYGpqCldXV4waNUrj4xIVquu7geBqwPqOwI5BGV+Dq2n2u1MmzXg4/IPjQPgG4HAQ8Ft/YFVzYI4HMNcTWN0S2D4QCJsBXNwIRJwAEt4+g9TYEnCuBnh1BBqOBDosBD4LBb4MB76NBcb8C/T9HQhYAjQZA3h3BlxqMJmjIoE1dLkRAkh7rVlZmRTY/xXU/5cnAEgyau7KtdDsv01jC43mCYqKikKvXr0wb948fPzxx0hMTMSJEycghMDmzZsxdepULFu2DD4+Prh48SKGDBkCS0tL9OvXL8f9/vDDD9i9eze2bduGMmXK4NGjR3j06JFSmSlTpmDOnDlYsmQJNm7ciE8//RRXrlxBlSpVkJaWBn9/fzRs2BAnTpyAkZERvvvuO7Rt2xb//vuvoibp6NGjcHFxwdGjR3H37l307NkTtWrVwpAhQwBkJH1Pnz7F0aNHYWxsjFGjRiE2Npfh/AAmT56MBQsWoGLFipg8eTJ69eqFu3fvwsjICKdOncLQoUMxd+5cdOrUCYcPH8aUKVNy3afctm3bMH36dCxfvhxNmjTBxo0b8cMPP6BcuXKKMkuWLMHChQuxatUq+Pj44JdffkGnTp1w7do1VKxYMddjJCcno06dOvj6669hY2ODP/74A59//jnKly+P+vXrK8qtX78ew4YNw6lTpwAAO3bswOLFixESEoKqVasiOjoaly9f1vjciAqN/B/irL9D5f8Q99gAVGz9tu9ahGp/triHgDQ152NYOauvYXMoC1g6cm420lt8lmsmap8zmfoKmOVaCJEC+OYpYGKZa7Hw8HDUqVMHERER8PDwUFpXoUIFzJw5E7169VIs++6777Bv3z6cPn06x/2OGjUK165dw+HDhyFR80tOIpFg6NCh+PHHHxXLGjRogNq1a2PFihXYtGkTvvvuO9y4cUOxfWpqKuzs7LBr1y60adMG/fv3x7Fjx3Dv3j0YGmYkuT169ICBgQFCQkJw+/ZtVK5cGefOnUO9evUAADdv3kSVKlWwePFitYMi5IMNfv75ZwwaNAgAcP36dVStWhU3btyAl5cXPv30UyQlJWHv3r2K2D/77DPs3bsXcXFxuV7zRo0awcfHB8uXL1c69+TkZMWgCDc3N4wYMQLffPONokz9+vVRr149LF++PE+DIjp27AgvLy8sWLAAQEYNXUJCAsLDwxVlFi1ahFWrVuHq1aswNjbO9VwKEp/lStmSSTNq4rK2bmQmMQCELOf9GBhlDDRQ25/NU6PfqURFBZ/lWszUrFkTH330EapXrw5/f3+0adMG3bt3h4mJCe7du4dBgwYparsAID09Hba2trnut3///mjdujUqV66Mtm3bomPHjmjTpo1SmYYNG6p8lic0ly9fxt27d2Ftba1UJjk5Gffu3VN8rlq1qiKZAwAXFxdcuXIFAHDjxg0YGRmhTp06ivVeXl4aNY3WqFFDaZ9ARvOtl5cXbt26hY8//lipfP369ZUSvJzcuHEDQ4cOVVrWsGFDHD16FEDGD+HTp0/RuHFjpTKNGzfWuLZMKpVi1qxZ2LZtG548eYLU1FSkpKTAwkJ5stDM1wYAPvnkEwQHB6NcuXJo27Yt2rdvj4CAABgZ8cediqDU10DsdeDq9pyTOeBdMmdqo36KD/uyGVOA8LFVVAzxrs+NsUVGTZkmHp4GNnfPvVyf7RmdfDU5tgYMDQ1x6NAhnD59Gn/++SeWLl2KyZMnY8+ePQAy+or5+vqqbJOb2rVr48GDB9i/fz8OHz6MHj16wM/PT6WfXHaSkpJQp04dtX3eHB0dFe+z1iJJJBLIZLn8F66BzPuV1xDqYr8FZf78+ViyZAmCg4NRvXp1WFpaYsyYMUhNVW5SsrRUrnFwd3fHrVu3cPjwYRw6dAjDhw/H/Pnz8ddffxW5GjsqZt68BKKvAFH/AlGXgeh/gee3c691y6xjMFCnP5tGibJgQpcbiUTzKvryrTJGsyZEQX0/OknG+vKtdD6FiUQiQePGjdG4cWNMnToVHh4eOHXqFFxdXXH//n306dMnT/u1sbFBz5490bNnT3Tv3h1t27bFixcv4ODgAAD4+++/0bdvX0X5v//+Gz4+PgAyEsKtW7fCyckp16ri7Hh5eSE9PR0XLlxQNLneunVLo2bRnFSuXBn//POP0rKsn3NSpUoVnD17VuXc5WxsbODq6opTp06hefPmiuWnTp1S6v+Wk1OnTqFz58747LPPAGQko7dv34a3t3eu25qbmyMgIAABAQEYMWIEvLy8cOXKFdSuXVvTUyTKOyGAxOiMhC3qXyDqUsb7uEj15S0dM0aQPg1Xvz6zEhWYzBGpwYROlwwMM6Ym2dYXgATKSd3bX0Bt5+g8mTt79izCwsLQpk0bODk54ezZs3j27BmqVKmCoKAgjBo1Cra2tmjbti1SUlJw/vx5vHz5EoGBgTnud9GiRXBxcYGPjw8MDAzw22+/oVSpUkrNnb/99hvq1q2LJk2aYPPmzTh37hzWrFkDAOjTpw/mz5+Pzp07K0Z7Pnz4EKGhofjqq69QunTpXM9N3tz7xRdf4Mcff4SRkRHGjBkDc3Pz97pmX375JZo1a4ZFixYhICAAR44cwf79+9X2FVRn9OjR6N+/P+rWrYvGjRtj8+bNuHbtmtKgiAkTJmDatGkoX748atWqhbVr1+LSpUtqayzVqVixIrZv347Tp0/D3t4eixYtQkxMTK4J3bp16yCVSuHr6wsLCwts2rQJ5ubmKv0riXRCJssYmCBP3qLf1r69eqa+vF0ZoFQNwKVmxqtUDcC6VEYtXXC13P8h1qR1g6gYYkKna96dMkZiqZ2Hbk6+zENnY2OD48ePIzg4GAkJCfDw8MDChQvRrl07AICFhQXmz5+PCRMmwNLSEtWrV1cMJsiJtbU15s2bhzt37sDQ0BD16tXDvn37YJBpgsygoCCEhIRg+PDhcHFxwZYtWxQJh4WFBY4fP46vv/4aXbt2RWJiItzc3PDRRx9pVWO3du1aDB48GM2bN4ezszO+++47rUakqtO4cWOsXLkSQUFB+Pbbb+Hv74+xY8di2bJlGm3fs2dP3Lt3D1999RWSk5PRrVs3DBs2DAcPHlSUGTVqFOLj4zFu3DjExsbC29sbu3fv1miEKwB8++23uH//Pvz9/WFhYYH//e9/6NKlC+Lj43Pczs7ODnPmzEFgYCCkUimqV6+OPXv2oESJEhodlyhb0jTg2S3l5C36CpCSoFpWYgCUrPQ2eXubwJWqnv2jrCSF8w8x0YeCo1wz0ekIvGLwpIii8LgtXRoyZAhu3ryJEydOFHYoHyyOctUj8sEKUZfeJW8x1wFpimpZQxPAueq75K1UzYzPJpr1A1aidmJ2t3z7h5ioKOMo16LAwBAo27Swo6AcLFiwAK1bt4alpSX279+P9evXazzpMtEHRZvBCibWGTVtLjXfJm81AMfKgKGOBtx4dwK8Onzw/xAT6RoTumJs1qxZmDVrltp1TZs2xf79+ws4ooJ17tw5zJs3D4mJiShXrhx++OEHDB48GEDGVCoPHz5Uu92qVavyPMiEqFDlZbCCUpNpjYypQfL7uaT8h5hIa0zoirGhQ4eiR48eatdpMuhA31vrt23blu26ffv2KT1+LDNnZ+f8ColId3Q1WIEjSon0AhO6YszBwUEx/Qgp44hQ0iv5OViBiPQCEzoiIn1SWIMViKhIY0Knhr43JRIVVfzZ0pJisMLld8lbYQ1WIKIirVATuuPHj2P+/Pm4cOECoqKitJoCQz4Df7Vq1RTPDn1f8scivX79+r0nriUiVfLHlmny6LliJc+DFTIlbwUxWIGIiqxCTehevXqFmjVrYuDAgejatavG28XFxaFv37746KOPEBMTo7N4DA0NYWdnh9jYWAAZE+Nq+uQAIsqZTCbDs2fPYGFhASOjYtw4wMEKRJQPCvW3art27RRPM9DG0KFD0bt3bxgaGmLXrl06jalUqVIAoEjqiEh3DAwMUKZMmeLzjxIHKxBRAdG7f5PXrl2L+/fvY9OmTfjuu+90vn+JRAIXFxc4OTllO20FEeWNiYmJ0qPjPiipr4GYa0D0ZQ5WIKICp1cJ3Z07dzBx4kScOHFC4yablJQUpKS8+4WakKDmP2M1DA0N2c+HiNTjYAUiKmL0JqGTSqXo3bs3goKCUKlSJY23mz17NoKCgvIxMiL6YHGwAhHpCYkoIvMI5Pag97i4ONjb2yvVmslkMgghYGhoiD///BOtWrVS2U5dDZ27u7tGD7olomKEgxWIqIhJSEiAra2tRjmL3tTQ2djY4MqVK0rLVqxYgSNHjmD79u0oW7as2u1MTU1hampaECESkb7gYAUi+sAUakKXlJSEu3fvKj4/ePAAly5dgoODA8qUKYNJkybhyZMn2LBhAwwMDFCtWjWl7Z2cnGBmZqaynIhIgYMViKgYKNSE7vz582jZsqXic2BgIACgX79+WLduHaKiohAZmU1fFSKirLQdrCDv58bBCkSk54pMH7qCok17NBEVURysQETFwAfZh46IiikOViAiyhUTOiIqOtQNVoj6F0hNVC3LwQpERApM6IiocHCwAhGRzjChI6L8x8EKRET5igkdEekOBysQERUKJnRExZFMCjw8DSTFAFbOgEcjwEDLZxdnHawQdTnjPQcrEBEVOCZ0RMXN9d3Aga+BhKfvltm4Am3nAt6d1G/DwQpEREUaEzqi4uT6bmBbXwBZpp9MiMpY3mMDUMFPi8EKpoCzNwcrEBEVMiZ0RMWFTJpRM5c1mQPeLds+IKOcujIcrEBEVGQxoSP6kCXHA/FPgPjHwP1jys2s6sjSM75ysAIRkV5hQkekr9KSgYQnGS950pbwOONr/NvlKQna77f9AqDeYA5WICLSI0zoiIoimTRjBGr82wQt4cm79/LP2Y0mzcrMDrB1B4zNgMf/5F7e0YvJHBGRnmFCR1TQhMiYaFddsiZ/nxj1rvkzJ0bmgK0bYFsasCmd6b1bRhJn4wqYWmWUlUmB4GoZAyDU9qOTZJT3aKTLsyUiogLAhI5I11JfvW3yzNz8maUpNO117vuRGGYkWDZvkzRbt7dJmzxxc8+YCkTT2jQDw4ypSbb1BSCBclL3dh9t52g/Hx0RERU6JnRE2pCmZdSeyZOz+EeqfdjevNRsX5aOmZI1ea2avGbNLWPSXV0nV96dMqYmUTsP3Zzs56EjIqIijQkdkZxMBrx+nktTaDTUN1dmYWKdqVbtbZKmeP82eTM2y/dTUsu7E+DV4f2fFEFEREUGEzoqPuRTeCS8rVlTvM+UtElTc9+PoUlGjZa8Jk1dHzYz2/w/n/dhYAiUbVrYURARkY4woaMPQ7ZTeDx5l6xpNIWHJKOpU6UpNFO/NYuSnI+NiIiKFCZ0VPQppvDI3GctS1OoplN4mNurGQ2aKXGzdgGMTPL3fIiIiHSMCR0VrsxTeKibay3+CZD4VIspPNSMBs3ch83EMv/PiYiIqIAxoaP8pTSFRzZPM9BmCg91o0HzMoUHERHRB4QJHeVd1ik81M279l5TeGRuCs2HKTyIiIg+EEzoSD0hMvqlZTuFxxMgKRoQstz3lXkKD3VPNCjMKTyIiIg+AEzoiqvkhEzJmbopPJ4C0pTc92Noojy3mro+bEV9Cg8iIiI9x4TuQ5Sekik5y9IUmpcpPNQ1gXIKDyIioiKDCV1+kEnzbxZ+tVN4ZJnOQ+spPLJ5ooGNK2BorJu4iYiIKN8wodO167uzeU7m3Nyfk5ndFB6Za9s0ncLD2CL7pxjI33MKDyIiog8CEzpdur4b2NYXKs/6TIjKWN51NeBa612fNXVPM9B4Cg835WeDZm0W5RQeRERExQYTOl2RSTNq5tQ+uP3tstDBmu3L0jGbfmtv31s5cwoPIiIiUmBCpysPTys3s2bHyByw98z+iQacwoOIiIi0xIROV5JiNCvXeRlQvXv+xkJERETFCueb0BUrZ92WIyIiItKQThK6uLg4XexGv3k0yhjNiuwGIkgymlM9GhVkVERERFQMaJ3QzZ07F1u3blV87tGjB0qUKAE3NzdcvnxZp8HpFQPDjKlJAKgmdW8/t53DwQxERESkc1ondCtXroS7uzsA4NChQzh06BD279+Pdu3aYcKECToPUK94dwJ6bABsXJSX27hmLM9tHjoiIiKiPNB6UER0dLQiodu7dy969OiBNm3awNPTE76+vjoPUO94dwK8OuTfkyKIiIiIstC6hs7e3h6PHj0CABw4cAB+fn4AACEEpFKpbqPTVwaGQNmmGaNZyzZlMkdERET5SuuErmvXrujduzdat26N//77D+3atQMAXLx4ERUqVNBqX8ePH0dAQABcXV0hkUiwa9euHMuHhoaidevWcHR0hI2NDRo2bIiDBw9qewpEREREHxStE7rFixfjyy+/hLe3Nw4dOgQrKysAQFRUFIYPH67Vvl69eoWaNWti+fLlGpU/fvw4WrdujX379uHChQto2bIlAgICcPHiRW1Pg4iIiOiDIRFCqHtWlVppaWn44osvMGXKFJQtW1a3gUgk2LlzJ7p06aLVdlWrVkXPnj0xdepUjconJCTA1tYW8fHxsLGxyUOkRERERPlPm5xFqxo6Y2Nj7Nix472C0yWZTIbExEQ4ODgUdihEREREhUbrJtcuXbrk2tetoCxYsABJSUno0aNHtmVSUlKQkJCg9CIiIiL6kGg9bUnFihUxY8YMnDp1CnXq1IGlpaXS+lGjRuksuJz8+uuvCAoKwu+//w4nJ6dsy82ePRtBQUEFEhMRERFRYdCqDx2AHPvOSSQS3L9/P2+BaNGHLiQkBAMHDsRvv/2GDh065Fg2JSUFKSkpis8JCQlwd3dnHzoiIiIq0rTpQ6d1Dd2DBw/yHJgubNmyBQMHDkRISEiuyRwAmJqawtTUtAAiIyIiIiocWid0upSUlIS7d+8qPj948ACXLl2Cg4MDypQpg0mTJuHJkyfYsGEDgIxm1n79+mHJkiXw9fVFdHQ0AMDc3By2traFcg5EREREhU3rJlcAePz4MXbv3o3IyEikpqYqrVu0aJHG+zl27Bhatmypsrxfv35Yt24d+vfvj4iICBw7dgwA0KJFC/z111/ZltcEpy0hIiIifaBNzqJ1QhcWFoZOnTqhXLlyuHnzJqpVq4aIiAgIIVC7dm0cOXLkvYLPb0zoiIiISB/k2zx0ADBp0iSMHz8eV65cgZmZGXbs2IFHjx6hefPm+OSTT/IcNBERERHljdYJ3Y0bN9C3b18AgJGREd68eQMrKyvMmDEDc+fO1XmARERERJQzrRM6S0tLRb85FxcX3Lt3T7Hu+fPnuouMiIiIiDSi9SjXBg0a4OTJk6hSpQrat2+PcePG4cqVKwgNDUWDBg3yI0YiIiIiyoHWCd2iRYuQlJQEAAgKCkJSUhK2bt2KihUrajXClYiIiIh0I0/TlugzjnIlIiIifZCvo1yJiIiIqGjRqMnV3t4eEolEox2+ePHivQIiIiIiIu1olNAFBwfncxhERERElFcaJXT9+vXL7ziIiIiIKI80SugSEhI03iEHGhAREREVLI0SOjs7u1z70AkhIJFIIJVKdRIYEREREWlGo4Tu6NGj+R0HEREREeWRRgld8+bN8zsOIiIiIsojrZ8UAQBxcXFYs2YNbty4AQCoWrUqBg4cCFtbW50GR0RERES503pi4fPnz6N8+fJYvHgxXrx4gRcvXmDRokUoX748wsPD8yNGIiIiIsqB1o/+atq0KSpUqIDVq1fDyCijgi89PR2DBw/G/fv3cfz48XwJVFf46C8iIiLSB9rkLFondObm5rh48SK8vLyUll+/fh1169bF69evtY+4ADGhIyIiIn2Qr89ytbGxQWRkpMryR48ewdraWtvdEREREdF70jqh69mzJwYNGoStW7fi0aNHePToEUJCQjB48GD06tUrP2IkIiIiohxoPcp1wYIFkEgk6Nu3L9LT0wEAxsbGGDZsGObMmaPzAImIiIgoZ1r3oZN7/fo17t27BwAoX748LCwsdBpYfmEfOiIiItIH2uQseZqHTgiB169fw9XVFSVKlMhTkERERESkG1r1oYuOjkbfvn1hb28PZ2dnODk5wd7eHgMHDkRMTEx+xUhEREREOdC4hi4hIQGNGjVCUlISBgwYAC8vLwghcP36dWzZsgUnT55EeHg4rKys8jNeIiIiIspC44RuyZIlMDQ0xLVr1+Do6Ki07ttvv0Xjxo3xww8/4JtvvtF5kERERESUPY2bXP/44w988803KskcADg5OWHSpEnYs2ePToMjIiIiotxpnNDdvn0bjRo1ynZ9o0aNcOvWLZ0ERURERESa0zihS0hIgJ2dXbbr7ezskJCQoIuYiIiIiEgLGid0QggYGGRfXCKRII9T2hERERHRe9B4UIQQApUqVYJEIsl2PREREREVPI0TurVr1+ZnHERERESURxondP369cvPOIiIiIgoj7R6UgQRERERFT1M6IiIiIj0HBM6IiIiIj3HhI6IiIhIzxVqQnf8+HEEBATA1dUVEokEu3btynWbY8eOoXbt2jA1NUWFChWwbt26fI+TiIiIqCjTeJSrnFQqxbp16xAWFobY2FjIZDKl9UeOHNF4X69evULNmjUxcOBAdO3aNdfyDx48QIcOHTB06FBs3rwZYWFhGDx4MFxcXODv76/tqRARERF9ELRO6EaPHo1169ahQ4cOqFatWrYTDWuiXbt2aNeuncblV65cibJly2LhwoUAgCpVquDkyZNYvHgxEzoiIiIqtrRO6EJCQrBt2za0b98+P+LJ0ZkzZ+Dn56e0zN/fH2PGjCnwWIiIiIiKCq0TOhMTE1SoUCE/YslVdHQ0nJ2dlZY5OzsjISEBb968gbm5uco2KSkpSElJUXxOSEjI9ziJiIiICpLWgyLGjRuHJUuW6M2zW2fPng1bW1vFy93dvbBDIiIiItIprWvoTp48iaNHj2L//v2oWrUqjI2NldaHhobqLLisSpUqhZiYGKVlMTExsLGxUVs7BwCTJk1CYGCg4nNCQgKTOiIiIvqgaJ3Q2dnZ4eOPP86PWHLVsGFD7Nu3T2nZoUOH0LBhw2y3MTU1hampaX6HRkRERFRotE7o1q5dq7ODJyUl4e7du4rPDx48wKVLl+Dg4IAyZcpg0qRJePLkCTZs2AAAGDp0KJYtW4avvvoKAwcOxJEjR7Bt2zb88ccfOouJiIiISN8U6sTC58+fh4+PD3x8fAAAgYGB8PHxwdSpUwEAUVFRiIyMVJQvW7Ys/vjjDxw6dAg1a9bEwoUL8fPPP3PKEiIiIirWJEKD0Q21a9dGWFgY7O3t4ePjk+Pcc+Hh4ToNUNcSEhJga2uL+Ph42NjYFHY4RERERGppk7No1OTauXNnRT+0Ll26vHeARERERKQ7GtXQfUhYQ0dERET6QJucpVD70BERERHR+2NCR0RERKTnmNARERER6TkmdERERER6jgkdERERkZ7T+kkRmZ+LmplEIoGZmRkqVKiAzp07w8HB4b2DIyIiIqLcaT1tScuWLREeHg6pVIrKlSsDAG7fvg1DQ0N4eXnh1q1bkEgkOHnyJLy9vfMl6PfBaUuIiIhIH+TrtCWdO3eGn58fnj59igsXLuDChQt4/PgxWrdujV69euHJkydo1qwZxo4dm+cTICIiIiLNaV1D5+bmhkOHDqnUvl27dg1t2rTBkydPEB4ejjZt2uD58+c6DVYXWENHRERE+iBfa+ji4+MRGxursvzZs2dISEgAANjZ2SE1NVXbXRMRERFRHuSpyXXgwIHYuXMnHj9+jMePH2Pnzp0YNGiQ4jmv586dQ6VKlXQdKxERERGpoXWTa1JSEsaOHYsNGzYgPT0dAGBkZIR+/fph8eLFsLS0xKVLlwAAtWrV0nW8741NrkRERKQPtMlZtE7o5JKSknD//n0AQLly5WBlZZWX3RQ4JnRERESkD7TJWbSeh07OysoKNWrUyOvmRERERKQjWid0r169wpw5cxAWFobY2FjIZDKl9fJaOyIiIiIqGFondIMHD8Zff/2Fzz//HC4uLpBIJPkRFxERERFpSOuEbv/+/fjjjz/QuHHj/IiHiIiIiLSk9bQl9vb2fE4rERERURGidUI3c+ZMTJ06Fa9fv86PeIiIiIhIS1o3uS5cuBD37t2Ds7MzPD09YWxsrLQ+PDxcZ8ERERERUe60TujkT4MgIiIioqIhzxML6ytOLExERET6QJucRes+dERERERUtGjU5Org4IDbt2+jZMmSsLe3z3HuuRcvXugsOCIiIiLKnUYJ3eLFi2FtbQ0ACA4Ozs94iIiIiEhL7ENHREREVARpk7NoVEOXkJCg8cGZJBEREREVLI0SOjs7O42f2SqVSt8rICIiIiLSjkYJ3dGjRxXvIyIiMHHiRPTv3x8NGzYEAJw5cwbr16/H7Nmz8ydKIiIiIsqW1n3oPvroIwwePBi9evVSWv7rr7/ip59+wrFjx3QZn86xDx0RERHpg3ydh+7MmTOoW7euyvK6devi3Llz2u6OiIiIiN6T1gmdu7s7Vq9erbL8559/hru7u06CIiIiIiLNaf0s18WLF6Nbt27Yv38/fH19AQDnzp3DnTt3sGPHDp0HSEREREQ507qGrn379rhz5w4CAgLw4sULvHjxAgEBAbh9+zbat2+fHzESERERUQ44sTARERFREZSvgyIAIC4uDn/++Sc2bdqEDRs2KL3yYvny5fD09ISZmRl8fX1zHVwRHByMypUrw9zcHO7u7hg7diySk5PzdGwiIiIifad1H7o9e/agT58+SEpKgo2NjdKEwxKJBH379tVqf1u3bkVgYCBWrlwJX19fBAcHw9/fH7du3YKTk5NK+V9//RUTJ07EL7/8gkaNGuH27dvo378/JBIJFi1apO3pEBEREek9rZtcK1WqhPbt22PWrFmwsLB47wB8fX1Rr149LFu2DAAgk8ng7u6OL7/8EhMnTlQpP3LkSNy4cQNhYWGKZePGjcPZs2dx8uTJXI/HJlciIiLSB/na5PrkyROMGjVKJ8lcamoqLly4AD8/v3cBGRjAz88PZ86cUbtNo0aNcOHCBUWz7P3797Fv3z4OyCAiIqJiS+smV39/f5w/fx7lypV774M/f/4cUqkUzs7OSsudnZ1x8+ZNtdv07t0bz58/R5MmTSCEQHp6OoYOHYpvvvlGbfmUlBSkpKQoPickJLx33ERERERFidYJXYcOHTBhwgRcv34d1atXh7GxsdL6Tp066Sw4dY4dO4ZZs2ZhxYoV8PX1xd27dzF69GjMnDkTU6ZMUSk/e/ZsBAUF5WtMRERERIVJ6z50BgbZt9JKJBJIpVKN95WamgoLCwts374dXbp0USzv168f4uLi8Pvvv6ts07RpUzRo0ADz589XLNu0aRP+97//ISkpSSU+dTV07u7u7ENHRERERVq+9qGTyWTZvrRJ5gDAxMQEderUURrgIJPJEBYWhoYNG6rd5vXr1ypJm6GhIQBAXW5qamoKGxsbpRcRERHRh0TrJtfMkpOTYWZm9l4BBAYGol+/fqhbty7q16+P4OBgvHr1CgMGDAAA9O3bF25ubpg9ezYAICAgAIsWLYKPj4+iyXXKlCkICAhQJHZERERExYnWCZ1UKsWsWbOwcuVKxMTE4Pbt2yhXrhymTJkCT09PDBo0SKv99ezZE8+ePcPUqVMRHR2NWrVq4cCBA4qBEpGRkUo1ct9++y0kEgm+/fZbPHnyBI6OjggICMD333+v7akQERERfRC07kM3Y8YMrF+/HjNmzMCQIUNw9epVlCtXDlu3bkVwcHC2040UFZyHjoiIiPRBvvah27BhA3766Sf06dNHqYmzZs2a2U41QkRERET5J08TC1eoUEFluUwmQ1pamk6CIiIiIiLNaZ3QeXt748SJEyrLt2/fDh8fH50ERURERESa03pQxNSpU9GvXz88efIEMpkMoaGhuHXrFjZs2IC9e/fmR4xERERElAOta+g6d+6MPXv24PDhw7C0tMTUqVNx48YN7NmzB61bt86PGImIiIgoB1qPctV3HOVKRERE+kCbnCXPEwufP38eN27cAJDRr65OnTp53RURERERvQetE7rHjx+jV69eOHXqFOzs7AAAcXFxaNSoEUJCQlC6dGldx0hEREREOdC6D93gwYORlpaGGzdu4MWLF3jx4gVu3LgBmUyGwYMH50eMRERERJQDrfvQmZub4/Tp0ypTlFy4cAFNmzbF69evdRqgrrEPHREREemDfH1ShLu7u9oJhKVSKVxdXbXdHRERERG9J60Tuvnz5+PLL7/E+fPnFcvOnz+P0aNHY8GCBToNjoiIiIhyp3WTq729PV6/fo309HQYGWWMqZC/t7S0VCr74sUL3UWqI2xyJSIiIn2Qr9OWBAcH5zUuIiIiIsoHWid0/fr1y484iIiIiCiPNE7o0tPTIZVKYWpqqlgWExODlStX4tWrV+jUqROaNGmSL0ESERERUfY0TuiGDBkCExMTrFq1CgCQmJiIevXqITk5GS4uLli8eDF+//13tG/fPt+CJSIiIiJVGo9yPXXqFLp166b4vGHDBkilUty5cweXL19GYGAg5s+fny9BEhEREVH2NE7onjx5gooVKyo+h4WFoVu3brC1tQWQ0bfu2rVruo+QiIiIiHKkcUJnZmaGN2/eKD7//fff8PX1VVqflJSk2+iIiIiIKFcaJ3S1atXCxo0bAQAnTpxATEwMWrVqpVh/7949PimCiIiIqBBoPChi6tSpaNeuHbZt24aoqCj0798fLi4uivU7d+5E48aN8yVIIiIiIsqexgld8+bNceHCBfz5558oVaoUPvnkE6X1tWrVQv369XUeIBERERHlTOtHf+k7PvqLiIiI9IE2OYvGfeiIiIiIqGhiQkdERESk55jQEREREek5JnREREREei5PCV1cXBx+/vlnTJo0CS9evAAAhIeH48mTJzoNjoiIiIhyp/G0JXL//vsv/Pz8YGtri4iICAwZMgQODg4IDQ1FZGQkNmzYkB9xEhEREVE2tK6hCwwMRP/+/XHnzh2YmZkplrdv3x7Hjx/XaXBERERElDutE7p//vkHX3zxhcpyNzc3REdH6yQoIiIiItKc1gmdqakpEhISVJbfvn0bjo6OOgmKiIiIiDSndULXqVMnzJgxA2lpaQAAiUSCyMhIfP311+jWrZvOAyQiIiKinGmd0C1cuBBJSUlwcnLCmzdv0Lx5c1SoUAHW1tb4/vvv8yNGIiIiIsqB1qNcbW1tcejQIZw8eRL//vsvkpKSULt2bfj5+eVHfERERESUC4kQQhR2EAVJmwfdEhERERUWbXIWrWvofvjhB7XLJRIJzMzMUKFCBTRr1gyGhoYa73P58uWYP38+oqOjUbNmTSxduhT169fPtnxcXBwmT56M0NBQvHjxAh4eHggODkb79u21PR0iIiIivad1Qrd48WI8e/YMr1+/hr29PQDg5cuXsLCwgJWVFWJjY1GuXDkcPXoU7u7uue5v69atCAwMxMqVK+Hr64vg4GD4+/vj1q1bcHJyUimfmpqK1q1bw8nJCdu3b4ebmxsePnwIOzs7bU+FiIiI6IOg9aCIWbNmoV69erhz5w7+++8//Pfff7h9+zZ8fX2xZMkSREZGolSpUhg7dqxG+1u0aBGGDBmCAQMGwNvbGytXroSFhQV++eUXteV/+eUXvHjxArt27ULjxo3h6emJ5s2bo2bNmtqeChEREdEHQes+dOXLl8eOHTtQq1YtpeUXL15Et27dcP/+fZw+fRrdunVDVFRUjvtKTU2FhYUFtm/fji5duiiW9+vXD3Fxcfj9999Vtmnfvj0cHBxgYWGB33//HY6Ojujduze+/vprjZp52YeOiIiI9EG+9qGLiopCenq6yvL09HTFkyJcXV2RmJiY676eP38OqVQKZ2dnpeXOzs64efOm2m3u37+PI0eOoE+fPti3bx/u3r2L4cOHIy0tDdOmTVMpn5KSgpSUFMVndZMiExEREekzrZtcW7ZsiS+++AIXL15ULLt48SKGDRuGVq1aAQCuXLmCsmXL6i7KTGQyGZycnPDTTz+hTp066NmzJyZPnoyVK1eqLT979mzY2toqXpr06yMiIiLSJ1ondGvWrIGDgwPq1KkDU1NTmJqaom7dunBwcMCaNWsAAFZWVli4cGGu+ypZsiQMDQ0RExOjtDwmJgalSpVSu42LiwsqVaqk1LxapUoVREdHIzU1VaX8pEmTEB8fr3g9evRIm9MlIiIiKvK0bnItVaoUDh06hJs3b+L27dsAgMqVK6Ny5cqKMi1bttRoXyYmJqhTpw7CwsIUfehkMhnCwsIwcuRItds0btwYv/76K2QyGQwMMvLR27dvw8XFBSYmJirl5UknERER0YdK64ROzsvLC15eXu8dQGBgIPr164e6deuifv36CA4OxqtXrzBgwAAAQN++feHm5obZs2cDAIYNG4Zly5Zh9OjR+PLLL3Hnzh3MmjULo0aNeu9YiIiIiPRRnhK6x48fY/fu3YiMjFRp5ly0aJFW++rZsyeePXuGqVOnIjo6GrVq1cKBAwcUAyUiIyMVNXEA4O7ujoMHD2Ls2LGoUaMG3NzcMHr0aHz99dd5ORUiIiIivaf1tCVhYWHo1KkTypUrh5s3b6JatWqIiIiAEAK1a9fGkSNH8itWneC0JURERKQPtMlZtB4UMWnSJIwfPx5XrlyBmZkZduzYgUePHqF58+b45JNP8hw0EREREeWN1gndjRs30LdvXwCAkZER3rx5AysrK8yYMQNz587VeYBERERElDOtEzpLS0tFvzkXFxfcu3dPse758+e6i4yIiIiINKL1oIgGDRrg5MmTqFKlCtq3b49x48bhypUrCA0NRYMGDfIjRiIiIiLKgdYJ3aJFi5CUlAQACAoKQlJSErZu3YqKFStqPcKViIiIiN6fVgmdVCrF48ePUaNGDQAZza/ZPXKLiIiIiAqGVn3oDA0N0aZNG7x8+TK/4iEiIiIiLWk9KKJatWq4f/9+fsRCRERERHmgdUL33XffYfz48di7dy+ioqKQkJCg9CIiIiKigqX1kyIyP4ZLIpEo3gshIJFIIJVKdRddPuCTIoiIiEgfaJOzaD3K9ejRo3kOjIiIiIh0T+uErnnz5vkRBxERERHlkdZ96ADgxIkT+Oyzz9CoUSM8efIEALBx40acPHlSp8ERERERUe60Tuh27NgBf39/mJubIzw8HCkpKQCA+Ph4zJo1S+cBEhEREVHO8jTKdeXKlVi9ejWMjY0Vyxs3bozw8HCdBkdEREREudM6obt16xaaNWumstzW1hZxcXG6iImIiIiItKB1QleqVCncvXtXZfnJkydRrlw5nQRFRERERJrTOqEbMmQIRo8ejbNnz0IikeDp06fYvHkzxo8fj2HDhuVHjERERESUA62nLZk4cSJkMhk++ugjvH79Gs2aNYOpqSnGjx+PL7/8Mj9iJCIiIqIcaP2kCLnU1FTcvXsXSUlJ8Pb2hpWVla5jyxd8UgQRERHpA21yFq2bXDdt2oTXr1/DxMQE3t7eqF+/vt4kc0REREQfIq0TurFjx8LJyQm9e/fGvn37ivyzW4mIiIg+dFondFFRUQgJCYFEIkGPHj3g4uKCESNG4PTp0/kRHxERERHlIs996ADg9evX2LlzJ3799VccPnwYpUuXxr1793QZn86xDx0RERHpA21yFq1HuWZmYWEBf39/vHz5Eg8fPsSNGzfeZ3dERERElAdaN7kCGTVzmzdvRvv27eHm5obg4GB8/PHHuHbtmq7jIyIiIqJcaF1D9+mnn2Lv3r2wsLBAjx49MGXKFDRs2DA/YiMiIiIiDWid0BkaGmLbtm3w9/eHoaGh0rqrV6+iWrVqOguOiIiIiHKndUK3efNmpc+JiYnYsmULfv75Z1y4cIHTmBAREREVsDz1oQOA48ePo1+/fnBxccGCBQvQqlUr/P3337qMjYiIiIg0oFUNXXR0NNatW4c1a9YgISEBPXr0QEpKCnbt2gVvb+/8ipGIiIiIcqBxDV1AQAAqV66Mf//9F8HBwXj69CmWLl2an7ERERERkQY0rqHbv38/Ro0ahWHDhqFixYr5GRMRERERaUHjGrqTJ08iMTERderUga+vL5YtW4bnz5/nZ2xEREREpAGNE7oGDRpg9erViIqKwhdffIGQkBC4urpCJpPh0KFDSExMzM84iYiIiCgb7/Us11u3bmHNmjXYuHEj4uLi0Lp1a+zevVuX8ekcn+VKRERE+kCbnCXP05YAQOXKlTFv3jw8fvwYW7ZseZ9dEREREVEevVdCJ2doaIguXbrkuXZu+fLl8PT0hJmZGXx9fXHu3DmNtgsJCYFEIkGXLl3ydFwiIiKiD4FOErr3sXXrVgQGBmLatGkIDw9HzZo14e/vj9jY2By3i4iIwPjx49G0adMCipSIiIioaCr0hG7RokUYMmQIBgwYAG9vb6xcuRIWFhb45Zdfst1GKpWiT58+CAoKQrly5QowWiIiIqKip1ATutTUVFy4cAF+fn6KZQYGBvDz88OZM2ey3W7GjBlwcnLCoEGDCiJMIiIioiJNq0d/6drz588hlUrh7OystNzZ2Rk3b95Uu83JkyexZs0aXLp0SaNjpKSkICUlRfE5ISEhz/ESERERFUWF3uSqjcTERHz++edYvXo1SpYsqdE2s2fPhq2treLl7u6ez1ESERERFaxCraErWbIkDA0NERMTo7Q8JiYGpUqVUil/7949REREICAgQLFMJpMBAIyMjHDr1i2UL19eaZtJkyYhMDBQ8TkhIYFJHREREX1QCjWhMzExQZ06dRAWFqaYekQmkyEsLAwjR45UKe/l5YUrV64oLfv222+RmJiIJUuWqE3UTE1NYWpqmi/xExERERUFhZrQAUBgYCD69euHunXron79+ggODsarV68wYMAAAEDfvn3h5uaG2bNnw8zMDNWqVVPa3s7ODgBUlhMREREVF4We0PXs2RPPnj3D1KlTER0djVq1auHAgQOKgRKRkZEwMNCrrn5EREREBeq9nuWqj/gsVyIiItIHBfYsVyIiIiIqfEzoiIiIiPQcEzoiIiIiPceEjoiIiEjPMaEjIiIi0nNM6IiIiIj0HBM6IiIiIj3HhI6IiIhIzzGhIyIiItJzTOiIiIiI9BwTOiIiIiI9x4SOiIiISM8xoSMiIiLSc0zoiIiIiPQcEzoiIiIiPceEjoiIiEjPMaEjIiIi0nNM6IiIiIj0HBM6IiIiIj3HhI6IiIhIzzGhIyIiItJzTOiIiIiI9BwTOiIiIiI9x4SOiIiISM8xoSMiIiLSc0zoiIiIiPQcEzoiIiIiPceEjoiIiEjPMaEjIiIi0nNM6IiIiIj0HBM6IiIiIj3HhI6IiIhIzzGhIyIiItJzTOiIiIiI9BwTOiIiIiI9x4SOiIiISM8xoSMiIiLSc0UioVu+fDk8PT1hZmYGX19fnDt3Ltuyq1evRtOmTWFvbw97e3v4+fnlWJ6IiIjoQ1foCd3WrVsRGBiIadOmITw8HDVr1oS/vz9iY2PVlj927Bh69eqFo0eP4syZM3B3d0ebNm3w5MmTAo6ciIiIqGiQCCFEYQbg6+uLevXqYdmyZQAAmUwGd3d3fPnll5g4cWKu20ulUtjb22PZsmXo27dvruUTEhJga2uL+Ph42NjYvHf8RERERPlBm5ylUGvoUlNTceHCBfj5+SmWGRgYwM/PD2fOnNFoH69fv0ZaWhocHBzyK0wiIiIiBalM4My9//D7pSc4c+8/SGWFWjcGADAqzIM/f/4cUqkUzs7OSsudnZ1x8+ZNjfbx9ddfw9XVVSkpzCwlJQUpKSmKzwkJCXkPmIiIiIq1A1ejELTnOqLikxXLXGzNMC3AG22ruRRaXIXeh+59zJkzByEhIdi5cyfMzMzUlpk9ezZsbW0VL3d39wKOkoiIiD4EB65GYdimcKVkDgCi45MxbFM4DlyNKqTICjmhK1myJAwNDRETE6O0PCYmBqVKlcpx2wULFmDOnDn4888/UaNGjWzLTZo0CfHx8YrXo0ePdBI7ERERFQ/pUhkS3qRh6u/XoK5xVb4saM/1Qmt+LdQmVxMTE9SpUwdhYWHo0qULgIxBEWFhYRg5cmS2282bNw/ff/89Dh48iLp16+Z4DFNTU5iamuoybCIiIipgMplASroMyWlSpKTLkJIuRXJaxlfF8jSZShmlz2kyJKfLyylvn6LYJnN5KZLTZRolaQJAVHwyzj14gYblS+T/BcmiUBM6AAgMDES/fv1Qt25d1K9fH8HBwXj16hUGDBgAAOjbty/c3Nwwe/ZsAMDcuXMxdepU/Prrr/D09ER0dDQAwMrKClZWVoV2HkREpBtSmcC5By8Qm5gMJ2sz1C/rAEMDSWGHRQCEEIqkJ0VdYqWUMGWTfOVa5l25zOXTpIU/8EATsYnJuRfKB4We0PXs2RPPnj3D1KlTER0djVq1auHAgQOKgRKRkZEwMHjXMvzjjz8iNTUV3bt3V9rPtGnTMH369IIMnYiIdKyodjgvSoQQSJMK3dQ+Za3hylpGvp9M64oCIwMJTI0MYGZsCFMjA5hm+apYrvTeEGbGGV9NjQ1gprZ81jIZX02NDHD5cRz6/fJPrrE5Wavv05/fCn0euoLGeeiIiIomeYfzrH+U5HVzP35Wu0gldVKZyL4GKqfkSE0tldrarhyStSIwSwYkEigSnsyJj9rkSCnpylxeuYxKIqamjKmRAYwMC34IgFQm0GTuEUTHJ6vtRycBUMrWDCe/bqWzGmVtcpZCr6EjIiKSygSC9lzPtsO5BBkdzlt7l1L6YymTCaRKNaulytovKscyWcpn/ipfnl4UsipAkeSYGueSHGVOstTUZJlqUEuVeT/GhhJIJMWnKdzQQIJpAd4YtikcEkDpXpVfhWkB3oXWPYA1dETFEPsokabkzXvpMhnS0jOSp7RMr9T0t+vevldaJxVIS5chXfbuvdI6qezteoFHL18j7Ib6Rz5mVtLSBJBIFLVcqdKi0QRoYmigqH0yVal9yqEmSpPaqqzrstRWFaekqigoyG4BrKEjomyxj1Lhk8rE26RG9jbJyZwIqV+XKpUhPfO6TOvln1XX57Ct/PPbJCtdlpFwpUqVk7Ki1hH9+avUbNcZGkiy6Relfe2TSmKVQxkTIwP+Q1SMtK3mgtbepYrcP8WsoSMqRvStj5KmZDKBNNnbJCVdpvxekcS8qxFKk2Wz7m2Ck660XjXByVw7lbVGKj1TudR05e0yEitZkej/9D5MjAxgbCCBsZEBjA0NYGJoAGNDCYwNDd6+Mr03MoCJ0rqs6yUwMTRAdHwyfrvwONdjz+xSDXU97NU2JxZGvyqi/MQaOiJSoU0fJQMJkC4TSJdmaWJTaXLLktBkSnBUtlWT4GRNdt41yWVTY/U2hnSZvGZJKGqX9JmRgUSR6JgYvUt8jAwlb5Old0nQu/USGKlJpjLWS2Bk8O69Yt3bBMrY0ODteuVEK/N6Y4NM7zMdx9Agf/pNSWUCJ+8+z7XDee/6ZQq9JoSoKGJCR6TnZDKBxJR0JCanIeFNxtfE5HQkpmR8TXiT8fVObJLK42oyk0+KWfnb/XqfIEkkGX2aTN7WEMkTJnUJjpFSMpQ1wclImuTv5TVOmT8byROxTDVS8s85rZMnbMYGBjBgglLkO5wTFXVM6IgKkRACr1KlKslYQnIaEpIzJWfJysmZfFlCcjqSUtJ1GlN2yZyxoWqyY5ypNundekmmGp13SUzmGh+TTPsxNnq7zjBrE16mZZnWq6uxMjJQXsc/+vqpbTUX/PhZbZU+nqXYx5MoV0zoiPJICIHkNBkSktMUyZUi0XqjnIwlKH2Vl0lDUkq6zvpTmRgZwMbMCDZmxrA2M4L126/yz3Gv07A9PPc+Sst6+cC3XIkszXPFa3oCKjxFtcM5UVHHhI6KrZR0aZbEK12RnGW8V9+MmfmzrpomjQwksDGXJ2JGsDZ9m4yZv0vO1CZrmbYxNTLM8RhSmcCpe7n3UWpX3YV/PKlQGRpICuVZmET6jAkd6aU0qUy5KVKlVkw5OVNXU5aqo0fYGEigSLAy14rZyJMzM2PYmBvlUMYYZsb5P5cU+ygREX24mNBRgZPKBJIUCde7rzk1UWb0HXuXnL1Jk+osHmvT7BIvec3Yu882apIzSxNDvWmOZB8lIqIPExO6fPAhz8Ivkwm8Sk3PtonyXad91eRMvo0uO/FbmBgqNUlmTrRszI1UkrGsyZqVqdEH873RFPsoERF9eJjQ6VhRnoVfCIE3aVJFohWfSxOlPDlTqkFLSYeupqI2NTJQqhWzUUq8siRnWZsqzY1gZWrEiUTziH2UiIg+LEzodCi7Wfij45MxbFP4e8/Cn/w2GVPbRPkmSxNl1uQsOQ1JOuzEb2woUZN4qdaC2ZirqTl7+9XEiMkYERGRLjCh0xFNZuGftvsaKjpZ47V83rGckrMU1ZoyXT2E2kCCd6Mjs4ymzKmJMnMZPhCaiIio6GBCpyPnHrzIdRb+mIQUfLTor/c6jkQCWJmqS7zUTWWhOprS2swIFnrUiZ+IiIhyx4ROR2ITs0/mMjM1lMDe0jTbxCvja/bJmZWJER8TREREREqY0OmIk7WZRuXWDfRlZ3QiIiLSKfZK15H6ZR3gYmuG7OrOJMgY7Vq/rENBhkVERETFABM6HZHPwg9AJanjLPxERESUn5jQ6ZB8Fv5StsrNr6Vszd57yhIiIiKi7LAPnY5xFn4iIiIqaEzo8gFn4SciIqKCxCZXIiIiIj3HhI6IiIhIzzGhIyIiItJzTOiIiIiI9BwTOiIiIiI9x4SOiIiISM8xoSMiIiLSc0zoiIiIiPQcEzoiIiIiPceEjoiIiEjPFbtHfwkhAAAJCQmFHAkRERFR9uS5ijx3yUmxS+gSExMBAO7u7oUcCREREVHuEhMTYWtrm2MZidAk7fuAyGQyPH36FNbW1pBIJPl2nISEBLi7u+PRo0ewsbHJt+MUF7yeusdrqlu8nrrF66l7vKa6VRDXUwiBxMREuLq6wsAg515yxa6GzsDAAKVLly6w49nY2PAHR4d4PXWP11S3eD11i9dT93hNdSu/r2duNXNyHBRBREREpOeY0BERERHpOSZ0+cTU1BTTpk2DqalpYYfyQeD11D1eU93i9dQtXk/d4zXVraJ2PYvdoAgiIiKiDw1r6IiIiIj0HBM6IiIiIj3HhI6IiIhIzxX7hG727NmoV68erK2t4eTkhC5duuDWrVtKZZKTkzFixAiUKFECVlZW6NatG2JiYhTrL1++jF69esHd3R3m5uaoUqUKlixZorSPqKgo9O7dG5UqVYKBgQHGjBmjUXyRkZHo0KEDLCws4OTkhAkTJiA9Pf29zzu/FPXrKZFIVF4hISHvfd75paCuZ2hoKFq3bg1HR0fY2NigYcOGOHjwYK7x/fvvv2jatCnMzMzg7u6OefPm6ebE81FRvqYRERFq79G///5bdxdAxwrqep48eRKNGzdGiRIlYG5uDi8vLyxevDjX+HiP6vaa8h7N/npmdurUKRgZGaFWrVq5xqeze1QUc/7+/mLt2rXi6tWr4tKlS6J9+/aiTJkyIikpSVFm6NChwt3dXYSFhYnz58+LBg0aiEaNGinWr1mzRowaNUocO3ZM3Lt3T2zcuFGYm5uLpUuXKso8ePBAjBo1Sqxfv17UqlVLjB49OtfY0tPTRbVq1YSfn5+4ePGi2LdvnyhZsqSYNGmSTq+BLhXl6ymEEADE2rVrRVRUlOL15s0bnZ2/rhXU9Rw9erSYO3euOHfunLh9+7aYNGmSMDY2FuHh4dnGFh8fL5ydnUWfPn3E1atXxZYtW4S5ublYtWpV/lwMHSnK1/TBgwcCgDh8+LDSPZqampo/F0MHCup6hoeHi19//VVcvXpVPHjwQGzcuFFYWFjkeL/xHtX9NeU9mv31lHv58qUoV66caNOmjahZs2aOsenyHi32CV1WsbGxAoD466+/hBBCxMXFCWNjY/Hbb78pyty4cUMAEGfOnMl2P8OHDxctW7ZUu6558+YaJSD79u0TBgYGIjo6WrHsxx9/FDY2NiIlJUXDMypcRel6CpGR0O3cuVPj+Iuagriect7e3iIoKCjb9StWrBD29vZK9+LXX38tKleurOnpFAlF6ZrK/1hevHhRu5MoQgryen788cfis88+y3Y971FlurimvEffye569uzZU3z77bdi2rRpuSZ0urxHi32Ta1bx8fEAAAcHBwDAhQsXkJaWBj8/P0UZLy8vlClTBmfOnMlxP/J95NWZM2dQvXp1ODs7K5b5+/sjISEB165de699F5SidD3lRowYgZIlS6J+/fr45ZdfIPRo5p6Cup4ymQyJiYk5ljlz5gyaNWsGExMTxTJ/f3/cunULL1++1PicCltRuqZynTp1gpOTE5o0aYLdu3dreipFQkFdz4sXL+L06dNo3rx5tmV4j6ru532vqRzvUfXXc+3atbh//z6mTZumUSy6vEeL3bNccyKTyTBmzBg0btwY1apVAwBER0fDxMQEdnZ2SmWdnZ0RHR2tdj+nT5/G1q1b8ccff7xXPNHR0UrJnPy48nVFXVG7ngAwY8YMtGrVChYWFvjzzz8xfPhwJCUlYdSoUe+97/xWkNdzwYIFSEpKQo8ePbItEx0djbJly6ocV77O3t5ek9MqVEXtmlpZWWHhwoVo3LgxDAwMsGPHDnTp0gW7du1Cp06dtD/BAlYQ17N06dJ49uwZ0tPTMX36dAwePDjbeHiPvqOra8p7NIO663nnzh1MnDgRJ06cgJGRZumVLu9RJnSZjBgxAlevXsXJkyfzvI+rV6+ic+fOmDZtGtq0aaPD6PRPUbyeU6ZMUbz38fHBq1evMH/+fL1I6Arqev76668ICgrC77//DicnpzwfSx8UtWtasmRJBAYGKj7Xq1cPT58+xfz58/Xij2VBXM8TJ04gKSkJf//9NyZOnIgKFSqgV69e7xN2kVbUrinvUfXXUyqVonfv3ggKCkKlSpV0Fa52tG6k/UCNGDFClC5dWty/f19peVhYmAAgXr58qbS8TJkyYtGiRUrLrl27JpycnMQ333yT47E07fM1ZcoUlfb3+/fvCwA5dqwuCori9VRn7969AoBITk7O0/YFpaCup7xD7t69e3ON6fPPPxedO3dWWnbkyBEBQLx48SLX7QtbUbym6ixbtkyUKlUqT9sWpIL8mZebOXOmqFSpUrbreY/q/pqqw3s0YyAEAGFoaKh4SSQSxbKwsDC1MenyHi32CZ1MJhMjRowQrq6u4vbt2yrr5Z0lt2/frlh28+ZNlc6SV69eFU5OTmLChAm5HlPbQRExMTGKZatWrRI2NjZFNgEpytdTne+++07Y29vnaduCUJDX89dffxVmZmZi165dGsUm78ybeXTbpEmTinyH86J8TdUZPHiw8PHxyfP2+a0wfublgoKChIeHR7breY/q/pqqw3tUCKlUKq5cuaL0GjZsmKhcubK4cuWK0ojazHR5jxb7hG7YsGHC1tZWHDt2TGkI9uvXrxVlhg4dKsqUKSOOHDkizp8/Lxo2bCgaNmyoWH/lyhXh6OgoPvvsM6V9xMbGKh3r4sWL4uLFi6JOnTqid+/e4uLFi+LatWuK9aGhoUrfRPm0JW3atBGXLl0SBw4cEI6OjkV62pKifD13794tVq9eLa5cuSLu3LkjVqxYISwsLMTUqVPz8Yq8n4K6nps3bxZGRkZi+fLlSmXi4uIUZZYuXSpatWql+BwXFyecnZ3F559/Lq5evSpCQkJynfKgKCjK13TdunXi119/FTdu3BA3btwQ33//vTAwMBC//PJLPl+VvCuo67ls2TKxe/ducfv2bXH79m3x888/C2trazF58mRFGd6j+X9NeY/m/HcpM3WjXPPzHi32CR0Ata+1a9cqyrx580YMHz5c2NvbCwsLC/Hxxx+LqKgoxfpp06ap3UfW/3JyK7N27VqRtRU8IiJCtGvXTpibm4uSJUuKcePGibS0tPy4FDpRlK/n/v37Ra1atYSVlZWwtLQUNWvWFCtXrhRSqTS/Lsd7K6jr2bx5c7Vl+vXrp7SfrN+Dy5cviyZNmghTU1Ph5uYm5syZk09XQneK8jVdt26dqFKlirCwsBA2Njaifv36SlMpFEUFdT1/+OEHUbVqVcW18fHxEStWrFD6+eU9mv/XlPdozn+XMlOX0OXnPSp5e7JEREREpKc4Dx0RERGRnmNCR0RERKTnmNARERER6TkmdERERER6jgkdERERkZ5jQkdERESk55jQEREREek5JnREREREeo4JHREREZGeY0JHRJSJEAJ+fn7w9/dXWbdixQrY2dnh8ePHhRAZEVH2mNAREWUikUiwdu1anD17FqtWrVIsf/DgAb766issXboUpUuX1ukx09LSdLo/Iip+mNAREWXh7u6OJUuWYPz48Xjw4AGEEBg0aBDatGkDHx8ftGvXDlZWVnB2dsbnn3+O58+fK7Y9cOAAmjRpAjs7O5QoUQIdO3bEvXv3FOsjIiIgkUiwdetWNG/eHGZmZti8eXNhnCYRfUAkQghR2EEQERVFXbp0QXx8PLp27YqZM2fi2rVrqFq1KgYPHoy+ffvizZs3+Prrr5Geno4jR44AAHbs2AGJRIIaNWogKSkJU6dORUREBC5dugQDAwNERESgbNmy8PT0xMKFC+Hj4wMzMzO4uLgU8tkSkT5jQkdElI3Y2FhUrVoVL168wI4dO3D16lWcOHECBw8eVJR5/Pgx3N3dcevWLVSqVEllH8+fP4ejoyOuXLmCatWqKRK64OBgjB49uiBPh4g+YGxyJSLKhpOTE7744gtUqVIFXbp0weXLl3H06FFYWVkpXl5eXgCgaFa9c+cOevXqhXLlysHGxgaenp4AgMjISKV9161bt0DPhYg+bEaFHQARUVFmZGQEI6OMX5VJSUkICAjA3LlzVcrJm0wDAgLg4eGB1atXw9XVFTKZDNWqVUNqaqpSeUtLy/wPnoiKDSZ0REQaql27Nnbs2AFPT09FkpfZf//9h1u3bmH16tVo2rQpAODkyZMFHSYRFUNsciUi0tCIESPw4sUL9OrVC//88w/u3buHgwcPYsCAAZBKpbC3t0eJEiXw008/4e7duzhy5AgCAwMLO2wiKgaY0BERacjV1RWnTp2CVCpFmzZtUL16dYwZMwZ2dnYwMDCAgYEBQkJCcOHCBVSrVg1jx47F/PnzCztsIioGOMqViIiISM+xho6IiIhIzzGhIyIiItJzTOiIiIiI9BwTOiIiIiI9x4SOiIiISM8xoSMiIiLSc0zoiIiIiPQcEzoiIiIiPceEjoiIiEjPMaEjIiIi0nNM6IiIiIj0HBM6IiIiIj33f07DbgChgdRwAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["time_summary = (\n","    analysis_complete\n","    .groupby(\"survey_year\")[[\"sports_spending_dollars\", \"se_spending_dollars\", \"sports_to_se_ratio\"]]\n","    .mean()\n",")\n","\n","print(time_summary)\n","\n","time_summary[[\"sports_spending_dollars\", \"se_spending_dollars\"]].plot(marker=\"o\")\n","plt.title(\"Average Sports vs S&E Spending Over Time\")\n","plt.xlabel(\"Year\")\n","plt.ylabel(\"Average Spending in Dollars\")\n","plt.tight_layout()\n","plt.show()\n","\n","time_summary[\"sports_to_se_ratio\"].plot(marker=\"o\")\n","plt.title(\"Average Sports-to-S&E Spending Ratio Over Time\")\n","plt.xlabel(\"Year\")\n","plt.ylabel(\"Sports Spending / S&E Spending\")\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"T7TyMtg4Qstj"},"source":["### Analysis 3: Political Party and Spending\n","This compares spending by congressional majority party at the time."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"iruEESR3Qstj","executionInfo":{"status":"ok","timestamp":1778087417317,"user_tz":240,"elapsed":362,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}},"outputId":"81a1986f-b455-42f4-b6c2-561cd37e5854"},"outputs":[{"output_type":"stream","name":"stdout","text":["                sports_spending_dollars  se_spending_dollars  \\\n","majority_party                                                 \n","Divided                    1.746066e+08         1.514353e+09   \n","Democrats                  1.260819e+08         1.290837e+09   \n","\n","                sports_to_se_ratio  \n","majority_party                      \n","Divided                   0.123107  \n","Democrats                 0.103569  \n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["party_summary = (\n","    analysis_complete\n","    .groupby(\"majority_party\")[[\"sports_spending_dollars\", \"se_spending_dollars\", \"sports_to_se_ratio\"]]\n","    .mean()\n","    .sort_values(\"sports_to_se_ratio\", ascending=False)\n",")\n","\n","print(party_summary)\n","\n","party_summary[[\"sports_spending_dollars\", \"se_spending_dollars\"]].plot(kind=\"bar\")\n","plt.title(\"Average Sports vs S&E Spending by Political Party\")\n","plt.xlabel(\"Political Party\")\n","plt.ylabel(\"Average Spending in Dollars\")\n","plt.xticks(rotation=0)\n","plt.tight_layout()\n","plt.show()\n","\n","party_summary[\"sports_to_se_ratio\"].plot(kind=\"bar\")\n","plt.title(\"Average Sports-to-S&E Spending Ratio by Political Party\")\n","plt.xlabel(\"Political Party\")\n","plt.ylabel(\"Sports Spending / S&E Spending\")\n","plt.xticks(rotation=0)\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"raSLyqqrQstj"},"source":["### Analysis 4: Correlation\n","This tests whether schools with higher S&E spending also tend to have higher athletics spending."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"p36YBM9aQstj","executionInfo":{"status":"ok","timestamp":1778087419973,"user_tz":240,"elapsed":783,"user":{"displayName":"Mondukpe Somakpo","userId":"00722705140799482011"}},"outputId":"93000c6b-bb5c-4eba-9ac7-57f722074613"},"outputs":[{"output_type":"stream","name":"stdout","text":["Correlation between sports spending and S&E spending: 0.6207772181435235\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["correlation_value = analysis_complete[\"sports_spending_dollars\"].corr(analysis_complete[\"se_spending_dollars\"])\n","print(\"Correlation between sports spending and S&E spending:\", correlation_value)\n","\n","plt.scatter(analysis_complete[\"se_spending_dollars\"], analysis_complete[\"sports_spending_dollars\"])\n","plt.title(\"Correlation Between S&E Spending and Sports Spending\")\n","plt.xlabel(\"S&E Spending in Dollars\")\n","plt.ylabel(\"Sports Spending in Dollars\")\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","source":[],"metadata":{"id":"FJrf5zfKRNxA"},"execution_count":null,"outputs":[]}]}