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a78209f | 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 | import pandas as pd
from typing import List, Dict, Optional, Any
class FinancialTableExtractor:
def clean_dataframe(self, data: List[List[str]]) -> pd.DataFrame:
"""Converts list of lists to cleaned DataFrame."""
if not data:
return pd.DataFrame()
# Assume first row is header
headers = data[0]
rows = data[1:]
# Handle duplicate headers if any
headers = [h if h else f"Col_{i}" for i, h in enumerate(headers)]
df = pd.DataFrame(rows, columns=headers)
return df
def identify_financial_tables(self, tables: List[Dict[str, Any]]) -> Dict[str, pd.DataFrame]:
"""Heuristic to identify key statements."""
identified = {
'balance_sheet': None,
'income_statement': None,
'cash_flow': None
}
for table_info in tables:
data = table_info['data']
# Flatten to string to search keywords
content_str = str(data).lower()
df = self.clean_dataframe(data)
# Simple keyword scoring
if 'balance sheet' in content_str or ('assets' in content_str and 'liabilities' in content_str):
if identified['balance_sheet'] is None: # take first match
identified['balance_sheet'] = df
elif 'profit' in content_str and 'loss' in content_str and 'revenue' in content_str:
if identified['income_statement'] is None:
identified['income_statement'] = df
elif 'cash flow' in content_str and 'operating' in content_str:
if identified['cash_flow'] is None:
identified['cash_flow'] = df
return identified
def table_to_text(self, df: pd.DataFrame, table_type: str) -> str:
"""Converts table to LLM-readable text."""
if df is None or df.empty:
return ""
return f"--- {table_type.replace('_', ' ').upper()} ---\n" + df.to_string()
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