import pandas as pd import numpy as np import os import re import gc from datetime import datetime from concurrent.futures import ProcessPoolExecutor, as_completed from tqdm import tqdm # --- CONFIGURATION --- RAW_DATA_DIR = "data/raw" CBP_DIR = os.path.join(RAW_DATA_DIR, "district_industries") NAICS_DIR = os.path.join(RAW_DATA_DIR, "industry_codes_NAICS") OUTPUT_FILE = "data/processed/events_geographical_industry.csv" MAX_WORKERS = os.cpu_count() - 1 or 1 STATE_ABBREV = { 'Alabama': 'AL', 'Alaska': 'AK', 'Arizona': 'AZ', 'Arkansas': 'AR', 'California': 'CA', 'Colorado': 'CO', 'Connecticut': 'CT', 'Delaware': 'DE', 'Florida': 'FL', 'Georgia': 'GA', 'Hawaii': 'HI', 'Idaho': 'ID', 'Illinois': 'IL', 'Indiana': 'IN', 'Iowa': 'IA', 'Kansas': 'KS', 'Kentucky': 'KY', 'Louisiana': 'LA', 'Maine': 'ME', 'Maryland': 'MD', 'Massachusetts': 'MA', 'Michigan': 'MI', 'Minnesota': 'MN', 'Mississippi': 'MS', 'Missouri': 'MO', 'Montana': 'MT', 'Nebraska': 'NE', 'Nevada': 'NV', 'New Hampshire': 'NH', 'New Jersey': 'NJ', 'New Mexico': 'NM', 'New York': 'NY', 'North Carolina': 'NC', 'North Dakota': 'ND', 'Ohio': 'OH', 'Oklahoma': 'OK', 'Oregon': 'OR', 'Pennsylvania': 'PA', 'Rhode Island': 'RI', 'South Carolina': 'SC', 'South Dakota': 'SD', 'Tennessee': 'TN', 'Texas': 'TX', 'Utah': 'UT', 'Vermont': 'VT', 'Virginia': 'VA', 'Washington': 'WA', 'West Virginia': 'WV', 'Wisconsin': 'WI', 'Wyoming': 'WY', 'District of Columbia': 'DC', 'Puerto Rico': 'PR' } def load_crosswalks(): print("[INFO] Loading NAICS-to-SIC Crosswalks...") cw_2012 = pd.read_csv(os.path.join(NAICS_DIR, "2012-NAICS-to-SIC-Crosswalk.csv"), dtype=str) cw_2017 = pd.read_csv(os.path.join(NAICS_DIR, "2017-NAICS-to-SIC-Crosswalk.csv"), dtype=str) dict_2012 = cw_2012.groupby('NAICS')['SIC'].apply(list).to_dict() dict_2017 = cw_2017.groupby('NAICS')['SIC'].apply(list).to_dict() return dict_2012, dict_2017 def load_release_dates(): print("[INFO] Loading Survey Release Dates...") release_df = pd.read_csv(os.path.join(CBP_DIR, "survey_release_dates.csv")) release_df['date'] = pd.to_datetime(release_df['date'], format='mixed') return dict(zip(release_df['survey_reference_year'], release_df['date'])) def load_legislators(): print("[INFO] Loading Legislator Metadata...") terms_df = pd.read_csv(os.path.join(RAW_DATA_DIR, "congress_terms_all_github.csv"), low_memory=False) terms_df['start'] = pd.to_datetime(terms_df['start']) terms_df['end'] = pd.to_datetime(terms_df['end']) terms_df['district'] = terms_df['district'].replace('At Large', 0) terms_df['district'] = pd.to_numeric(terms_df['district'], errors='coerce') return terms_df def parse_geography(name_str): if pd.isna(name_str): return None, None match = re.search(r'(?:Congressional District (\d+)|District \(At Large\)).*?,\s*(.*)', str(name_str), re.IGNORECASE) if match: dist_str = match.group(1) district = int(dist_str) if dist_str else 0 state_name = match.group(2).strip() state_abbr = STATE_ABBREV.get(state_name, None) return state_abbr, district return None, None def get_active_legislator(terms_df, state, district, release_date): if state is None or pd.isna(district): return None active = terms_df[ (terms_df['state'] == state) & (terms_df['district'] == district) & (terms_df['start'] <= release_date) & (terms_df['end'] >= release_date) & (terms_df['type'] == 'rep') ] if not active.empty: return active.iloc[0]['id_bioguide'] return None def process_chunk(chunk, year, release_date, crosswalk, terms_df): edges = [] cols = chunk.columns # Safely find columns dynamically to handle schema evolution col_name = next((c for c in cols if 'NAME' in c), None) col_naics = next((c for c in cols if 'NAICS' in c and 'LABEL' not in c), None) col_estab = next((c for c in cols if 'ESTAB' in c), None) col_emp = next((c for c in cols if 'EMP' in c and 'EMPSZES' not in c), None) col_payann = next((c for c in cols if 'PAYANN' in c), None) if not all([col_name, col_naics, col_estab, col_emp, col_payann]): return pd.DataFrame() # --- THE FIX: FILTER OUT GRANULAR ROWS --- naics_raw = chunk[col_naics].astype(str).str.strip() # Match ONLY top-level 2-digit sectors (e.g., '11', '11----') or hyphenated groups ('31-33') is_top_level = naics_raw.str.match(r'^(\d{2}-*|\d{2}-\d{2}-*)$') is_not_total = ~naics_raw.str.startswith('00') # Exclude the "All Sectors" total # This perfectly standardizes 2010-2012 to match the 2013+ methodology chunk = chunk[is_top_level & is_not_total].copy() # Clean the NAICS codes to the standard format for our crosswalk # Removes trailing dashes the Census sometimes uses (e.g. '11----' -> '11') chunk[col_naics] = chunk[col_naics].astype(str).str.replace(r'-+$', '', regex=True).str.strip() for c in [col_estab, col_emp, col_payann]: chunk[c] = chunk[c].astype(str).str.replace(',', '') chunk[c] = pd.to_numeric(chunk[c], errors='coerce') chunk = chunk.dropna(subset=[col_estab, col_emp, col_payann], how='all') # Local cache to prevent redundant crosswalk scans inside the chunk resolved_sics_cache = {} for _, row in chunk.iterrows(): naics_code = str(row[col_naics]).strip() # --- PREFIX-MATCHING LOGIC --- if naics_code not in resolved_sics_cache: sics = set() # Handle hyphenated aggregated sectors (e.g. "31-33") if '-' in naics_code: try: start, end = naics_code.split('-') prefixes = tuple(str(p) for p in range(int(start), int(end)+1)) except: prefixes = (naics_code,) else: prefixes = (naics_code,) # Scan crosswalk for any 6-digit NAICS that starts with this prefix for cw_naics, cw_sics in crosswalk.items(): if str(cw_naics).startswith(prefixes): sics.update(cw_sics) resolved_sics_cache[naics_code] = list(sics) sic_list = resolved_sics_cache[naics_code] if not sic_list: continue state, dist = parse_geography(row[col_name]) bioguide_id = get_active_legislator(terms_df, state, dist, release_date) if bioguide_id: for sic in sic_list: edges.append({ 'bioguide_id': bioguide_id, 'sic_code': sic, 'release_date': release_date, 'reference_year': year, 'establishments': row[col_estab], 'employment': row[col_emp], 'annual_payroll': row[col_payann] }) return pd.DataFrame(edges) def process_cbp_file(file_path, year, release_date, crosswalk, terms_df): print(f"\n[INFO] Reading Year {year} (Release: {release_date.date()})") chunk_size = 25000 chunks = [] for chunk in pd.read_csv(file_path, chunksize=chunk_size, dtype=str): chunks.append(chunk) print(f" Spawned {len(chunks)} chunks. Processing across {MAX_WORKERS} cores...") results = [] with ProcessPoolExecutor(max_workers=MAX_WORKERS) as executor: futures = { executor.submit(process_chunk, c, year, release_date, crosswalk, terms_df): i for i, c in enumerate(chunks) } for future in tqdm(as_completed(futures), total=len(chunks), desc=f"Year {year}", unit="chunk"): res_df = future.result() if not res_df.empty: results.append(res_df) if results: return pd.concat(results, ignore_index=True) return pd.DataFrame() def main(): print("=====================================================") print(" BUILDING INDUSTRY-GEOGRAPHICAL EDGES (CBP)") print(f" Mode: Multiprocessing enabled ({MAX_WORKERS} Workers)") print("=====================================================") cw_2012, cw_2017 = load_crosswalks() release_dates = load_release_dates() terms_df = load_legislators() all_edges = [] for year in range(2010, 2024): file_name = f"{year}_CB_estimates.csv" if year <= 2012 else f"{year}_CB_survey.csv" file_path = os.path.join(CBP_DIR, file_name) if not os.path.exists(file_path): continue release_date = release_dates.get(year) if not release_date: continue active_crosswalk = cw_2012 if year <= 2012 else cw_2017 df_edges = process_cbp_file(file_path, year, release_date, active_crosswalk, terms_df) if not df_edges.empty: all_edges.append(df_edges) print("\n[INFO] Concatenating and saving final edge list...") if all_edges: final_df = pd.concat(all_edges, ignore_index=True) os.makedirs(os.path.dirname(OUTPUT_FILE), exist_ok=True) final_df.to_csv(OUTPUT_FILE, index=False) print(f"[SUCCESS] Saved {len(final_df)} geographical-industry edges to {OUTPUT_FILE}.") print(f"[STATS] Unique Legislators Mapped: {final_df['bioguide_id'].nunique()}") print(f"[STATS] Unique SIC Sectors Mapped: {final_df['sic_code'].nunique()}") else: print("[WARNING] No edges generated.") if __name__ == "__main__": main()