File size: 4,191 Bytes
3609368 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import pandas as pd
import glob
import os
from tqdm import tqdm
import sys
# Get the absolute path to the project root (two directories up from data_prep)
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
if project_root not in sys.path:
sys.path.append(project_root)
import config
def load_legislator_map():
if os.path.exists(config.LEGISLATORS_CROSSWALK_PATH):
# Added low_memory=False to suppress DtypeWarnings
df = pd.read_csv(config.LEGISLATORS_CROSSWALK_PATH, low_memory=False)
if 'id_opensecrets' in df.columns and 'id_bioguide' in df.columns:
# Drop duplicates to prevent InvalidIndexError during mapping
mapping_df = df[['id_opensecrets', 'id_bioguide']].dropna().drop_duplicates(subset=['id_opensecrets'])
return mapping_df.set_index('id_opensecrets')['id_bioguide'].to_dict()
print(f"WARNING: Legislator crosswalk not found at {config.LEGISLATORS_CROSSWALK_PATH}.")
return {}
def build_campaign_events():
print("Building Campaign Finance Event Stream (Using Filing Dates)...")
cid_to_bioguide = load_legislator_map()
all_events = []
# --- Step 1: Corporate PACs ---
pac_files = glob.glob(str(config.CAMPAIGN_FINANCE_DIR / config.CAMPAIGN_PACS_PATTERN))
print(f"Found {len(pac_files)} PAC files.")
for f in tqdm(pac_files, desc="Processing PAC Files"):
df = pd.read_csv(f, on_bad_lines='skip', low_memory=False)
if 'estimated_filing_date' not in df.columns:
continue
df = df.dropna(subset=['CID', 'RealCode', 'estimated_filing_date'])
# Safely map to BioGuide IDs using the deduplicated dictionary
df['bioguide_id'] = df['CID'].map(cid_to_bioguide)
df = df.dropna(subset=['bioguide_id'])
df = df[['estimated_filing_date', 'RealCode', 'bioguide_id', 'Amount']].copy()
all_events.append(df)
# --- Step 2: 527 Expenditures ---
if os.path.exists(config.DATA_527_EXPENDITURES_PATH):
print("\nProcessing 527 Expenditures...")
exp_df = pd.read_csv(config.DATA_527_EXPENDITURES_PATH, on_bad_lines='skip', low_memory=False)
cmtes_df = pd.read_csv(config.DATA_527_COMMITTEES_PATH, on_bad_lines='skip', low_memory=False)
if 'estimated_filing_date' in exp_df.columns:
ein_to_industry = cmtes_df.set_index('EIN')['PrimCode'].to_dict()
exp_df['bioguide_id'] = exp_df['RecipID'].map(cid_to_bioguide)
exp_df['RealCode'] = exp_df['EIN'].map(ein_to_industry)
exp_df = exp_df.dropna(subset=['bioguide_id', 'RealCode', 'estimated_filing_date'])
exp_df = exp_df[['estimated_filing_date', 'RealCode', 'bioguide_id', 'Amount']].copy()
all_events.append(exp_df)
else:
print("Skipping 527 Expenditures: 'estimated_filing_date' missing.")
# --- Step 3: Aggregation ---
if all_events:
print("\nConcatenating and Aggregating Events...")
full_df = pd.concat(all_events, ignore_index=True)
# Parse dates
tqdm.pandas(desc="Parsing Dates")
full_df['estimated_filing_date'] = pd.to_datetime(full_df['estimated_filing_date'], errors='coerce')
full_df = full_df.dropna(subset=['estimated_filing_date'])
# Aggregate to Weekly "Pulses"
full_df = full_df.set_index('estimated_filing_date')
# Group and sum
agg_df = full_df.groupby([pd.Grouper(freq='W'), 'RealCode', 'bioguide_id'])['Amount'].sum().reset_index()
agg_df.rename(columns={'estimated_filing_date': 'date', 'RealCode': 'industry_code', 'Amount': 'weight'}, inplace=True)
agg_df['event_type'] = 'DONATION'
agg_df.to_csv(config.CAMPAIGN_FINANCE_EVENTS_PATH, index=False)
print(f"\nSUCCESS: Saved {len(agg_df)} Campaign Events to {config.CAMPAIGN_FINANCE_EVENTS_PATH}")
else:
print("\nNo Campaign Events found.")
if __name__ == "__main__":
build_campaign_events() |