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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 | import pandas as pd
import numpy as np
def compute_aggregate(sub_df, metric, agg_func, year_sel, group_sel, metric_main, metric_mode, base_col):
"""Exact logic from standalone Heatmap (v3.6)."""
if group_sel == "기업":
if metric_main == "기업수":
total = sub_df['Company'].nunique()
if total == 0:
return 0 if metric_mode != "결측 비율" else np.nan
if metric_mode == "결측 포함":
return total
elif metric_mode == "결측 미포함":
return sub_df.loc[~sub_df['has_missing_financials'], 'Company'].nunique()
elif metric_mode == "결측 비율":
missing = sub_df.loc[sub_df['has_missing_financials'], 'Company'].nunique()
return missing / total if total else np.nan
elif metric_main == "0이하비율":
arr = pd.to_numeric(sub_df[base_col], errors="coerce")
if metric_mode == "결측 제외":
arr = arr.dropna()
return (arr <= 0).sum() / len(arr) if len(arr) else np.nan
else: # 멀티플·재무비율
if agg_func == "AGG":
mc_col = 'Market Cap (2024-12-31)_USD'
if mc_col not in sub_df.columns or sub_df[mc_col].isna().all():
return np.nan
mc = sub_df[mc_col]
q1, q3 = mc.quantile(0.25), mc.quantile(0.75)
iqr = q3 - q1
lower, upper = q1 - 2*iqr, q3 + 2*iqr
filt = sub_df[(mc >= lower) & (mc <= upper)]
if filt.empty:
return np.nan
if metric in ['PER', 'PBR', 'EV_EBITDA']:
if metric == 'PER':
num, den = filt[mc_col].sum(), filt['Net_Income'].sum()
elif metric == 'PBR':
num, den = filt[mc_col].sum(), filt['Book'].sum()
else: # EV_EBITDA
num, den = filt['Enterprise Value (FQ0)_USD'].sum(), filt['EBITDA'].sum()
return num / den if den else np.nan
arr = filt[metric].dropna()
return arr.sum() if len(arr) else np.nan
else: # AVG / MED / HRM
arr = sub_df[metric].dropna()
if not len(arr):
return np.nan
if agg_func == 'AVG':
return arr.mean()
elif agg_func == 'MED':
return arr.median()
else: # HRM
arr = arr[arr > 0]
return len(arr) / (1/arr).sum() if len(arr) else np.nan
# ---------------------------------------------------------------------------
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