Spaces:
Build error
Build error
File size: 2,654 Bytes
bd24fca | 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 | import ast
from typing import List
import pandas as pd
from config import EXCHANGE_RATES
def convert_to_usd(value, country):
"""Convert local currency to USD using EXCHANGE_RATES"""
if pd.isna(value) or pd.isna(country):
return value
return value / EXCHANGE_RATES.get(country, 1.0)
def parse_emtec_list(txt: str) -> List[str]:
"""Safely parse the EMTEC list string"""
try:
return [] if pd.isna(txt) or txt in ('[]', '') else ast.literal_eval(txt)
except Exception:
return []
def create_multiple_classification_data(df: pd.DataFrame) -> pd.DataFrame:
"""Generate one row per every EMSEC × EMTEC combination (cross‑product)."""
expanded_rows: List[pd.Series] = []
for _, row in df.iterrows():
# ── 1) Collect valid EMSEC levels ------------------------------------------------
emsec_list = []
for i in range(1, 6):
emsec_code = row.get(f'EMSEC{i}')
if pd.notna(emsec_code):
emsec_list.append({
'sector': row.get(f'EMSEC{i}_Sector', 'Unclassified') or 'Unclassified',
'industry': row.get(f'EMSEC{i}_Industry', 'Unclassified') or 'Unclassified',
'sub_industry': emsec_code
})
# ── 2) Collect EMTEC hierarchy ---------------------------------------------------
lvl1 = parse_emtec_list(row.get('EMTEC_LEVEL1'))
lvl2 = parse_emtec_list(row.get('EMTEC_LEVEL2'))
lvl3 = parse_emtec_list(row.get('EMTEC_LEVEL3'))
emtec_combos: List[dict] = []
if lvl1:
for l1 in lvl1:
for l2 in (lvl2 or ['Unclassified']):
for l3 in (lvl3 or ['Unclassified']):
emtec_combos.append({'theme': l1, 'technology': l2, 'sub_technology': l3})
else:
emtec_combos.append({'theme': 'Unclassified', 'technology': 'Unclassified', 'sub_technology': 'Unclassified'})
# ── 3) Produce cross‑product rows ----------------------------------------------
for emsec in emsec_list:
for emtec in emtec_combos:
new_row = row.to_dict()
new_row.update({
'Sector': emsec['sector'],
'Industry': emsec['industry'],
'Sub_industry': emsec['sub_industry'],
'Theme': emtec['theme'],
'Technology': emtec['technology'],
'Sub_Technology': emtec['sub_technology'],
})
expanded_rows.append(new_row)
return pd.DataFrame(expanded_rows)
|