"""Cannibalization analysis: NextGen Protein 2 kg promo (POL12880 Vanilla + POL12881 Cookies & Cream) vs. its sibling pool. Compares CW19/CW20 2026 actual sales (RCM channel only — wholesale excluded per request) against the 12-week pre-promo baseline of retail + webshop sales from sales_clean.csv. Output: docs/NextGen_Cannibalization_CW19-20.xlsx Run: py analyze_nextgen_promo.py """ from __future__ import annotations from collections import defaultdict from datetime import datetime from pathlib import Path import openpyxl from openpyxl.styles import Alignment, Border, Font, PatternFill, Side from openpyxl.utils import get_column_letter import pandas as pd ROOT = Path(__file__).parent DATA = ROOT / "data" # Three ERP exports — HR (main), Austria, Slovenia. Schemas differ # slightly (column indices for SKU / qty / rev are different), so each # has its own column-map declared below. SOURCES = [ { "name": "HR", "file": DATA / "Rekapitulacijazaakciju.xlsx", "sku_col": 5, # 0-indexed: col 6 "Artikal" "qty_col": 29, # col 30 "Količina" "rev_col": 37, # col 38 "Vrijednost €" "date_col": 1, "dok_col": 0, }, { "name": "AT", "file": DATA / "akcijaaut.xlsx", "sku_col": 9, # col 10 "Artikal" "qty_col": 45, # col 46 "Količina" "rev_col": 53, # col 54 "Vrijednost EUR" "date_col": 1, "dok_col": 0, }, { "name": "SLO", "file": DATA / "akcijaslo.xlsx", "sku_col": 9, "qty_col": 45, "rev_col": 53, "date_col": 1, "dok_col": 0, }, ] OUT = ROOT / "docs" / "NextGen_Cannibalization_CW19-20.xlsx" OUT.parent.mkdir(parents=True, exist_ok=True) # ============================================================ # SKU universe # ============================================================ PROMO_SKUS = [ ("POL12880", "Polleo NextGen Protein 2 kg Vanilla"), ("POL12881", "Polleo NextGen Protein 2 kg Cookies & Cream"), ] # Sibling pool from the planner cannibalization screenshot SIBLINGS = [ ("POL09747", "Polleo 1st Whey 2.27kg Vanilla Madagascar"), ("POL09746", "Polleo 1st Whey 2.27kg Swiss Chocolate Supreme"), ("ON00243", "ON Gold Whey 2.28kg Vanilla Ice Cream"), ("ON00298", "ON Gold Whey 2.28kg French Vanilla Creme"), ("POL09901", "Polleo 1st Whey 2.27kg White Choco Bueno"), ("POL09750", "Polleo 1st Whey 2.27kg Unflavoured"), ("POL09748", "Polleo 1st Whey 2.27kg Cookies & Cream"), ("POL09900", "Polleo 1st Whey 2.27kg Chocolate-Hazelnut"), ("POL09899", "Polleo 1st Whey 2.27kg Chocolate-Banana"), ] PROMO_PERIOD_YWS = [202619, 202620] BASELINE_LAST_N = 20 # target clean weeks (up from 12) BASELINE_LOOKBACK = 52 # look back up to 1 year to collect them OOS_FLOOR_MULT = 0.30 # weeks below 30% of initial median = OOS, dropped # ============================================================ # Load reference data # ============================================================ plan = pd.read_csv(DATA / "sku_plan_list.csv") prices = pd.read_csv(DATA / "sku_prices.csv") costs = pd.read_csv(DATA / "sku_costs.csv") sales = pd.read_csv(DATA / "sales_clean.csv") sales["yw"] = sales["year"].astype(int) * 100 + sales["week"].astype(int) # ERP promo calendar — authoritative source of "this SKU was on promo". # sales_clean's is_any_promo flag misses some campaigns (e.g. "Akcije # 03/26" on POL09747/46 was unmasked), so we union both signals. erp_promo_df = pd.read_csv(DATA / "erp_promo_calendar.csv") erp_promo_df["yw"] = erp_promo_df["year"].astype(int) * 100 + erp_promo_df["week"].astype(int) ERP_PROMO_YWS = { (str(r["sku"]), int(r["yw"])) for _, r in erp_promo_df[erp_promo_df["is_erp_promo"] == 1].iterrows() } # Post-promo "bleed" window — 2 weeks after each promo ends often still # carries elevated baseline (forward-buy effect during promo dampens # subsequent demand). Drop those too. ERP_BLEED_YWS = set() for (sku, yw) in ERP_PROMO_YWS: for offset in (1, 2): bleed_yw = yw + offset if (sku, bleed_yw) not in ERP_PROMO_YWS: ERP_BLEED_YWS.add((sku, bleed_yw)) DISC_PCT_PROMO_THRESHOLD = 5.0 # |retail_discount_pct| or |web_discount_pct| above this → treat as promo price_map = dict(zip(prices["sku"].astype(str), pd.to_numeric(prices["normal_retail_ppp"], errors="coerce").fillna(0))) avg_sell_map = dict(zip(prices["sku"].astype(str), pd.to_numeric(prices["avg_sell_price"], errors="coerce").fillna(0))) cost_map = dict(zip(costs["sku"].astype(str), pd.to_numeric(costs["cost_price"], errors="coerce").fillna(0))) name_map = dict(zip(plan["sku"].astype(str), plan["name"])) cat_map = dict(zip(plan["sku"].astype(str), plan["cat"])) tier_map = dict(zip(plan["sku"].astype(str), plan["oznaka"])) def _is_clean_week(sku: str, row) -> bool: """A clean week = no ERP-calendar promo, no bleed window, no is_any_promo flag, AND no absolute discount > threshold on either retail or webshop channel.""" yw = int(row["yw"]) if (sku, yw) in ERP_PROMO_YWS: return False if (sku, yw) in ERP_BLEED_YWS: return False if int(row.get("is_any_promo", 0) or 0): return False rd = abs(float(row.get("retail_discount_pct", 0) or 0)) wd = abs(float(row.get("webshop_discount_pct", 0) or 0)) if rd > DISC_PCT_PROMO_THRESHOLD or wd > DISC_PCT_PROMO_THRESHOLD: return False return True def _rcm_web_qty(row) -> float: """Retail + webshop (no wholesale). Mirrors the channels we sum from the Excel actuals (RCM + WSA/B/C).""" return float(row.get("qty_retail", 0) or 0) + float(row.get("qty_webshop", 0) or 0) def baseline_12w(sku: str) -> dict: """Return {avg, median, n, weeks, excluded_reasons} from up to BASELINE_LAST_N CLEAN weeks of RETAIL + WEB sales (no wholesale) before the promo window. Cleanness: - NOT in ERP promo calendar for this SKU - NOT in 2-week post-promo bleed window - sales_clean is_any_promo == 0 - |retail_discount_pct| ≤ 5% AND |webshop_discount_pct| ≤ 5% - NOT a zero-sale or near-zero week (OOS suspect) Looks back up to BASELINE_LOOKBACK weeks to collect them.""" sub = sales[(sales["sku"] == sku) & (sales["yw"] < min(PROMO_PERIOD_YWS))] sub = sub.sort_values("yw", ascending=False).head(BASELINE_LOOKBACK) candidate_rows = [] excluded = {"erp_promo": 0, "bleed": 0, "flag": 0, "discount_anomaly": 0, "oos": 0} for _, r in sub.iterrows(): yw = int(r["yw"]) if (sku, yw) in ERP_PROMO_YWS: excluded["erp_promo"] += 1 continue if (sku, yw) in ERP_BLEED_YWS: excluded["bleed"] += 1 continue if int(r.get("is_any_promo", 0) or 0): excluded["flag"] += 1 continue rd = abs(float(r.get("retail_discount_pct", 0) or 0)) wd = abs(float(r.get("webshop_discount_pct", 0) or 0)) if rd > DISC_PCT_PROMO_THRESHOLD or wd > DISC_PCT_PROMO_THRESHOLD: excluded["discount_anomaly"] += 1 continue candidate_rows.append(r) if len(candidate_rows) >= BASELINE_LAST_N * 2: break # OOS filter: drop zero-sale weeks AND weeks below 30% of # provisional median (likely stockout / sharp-drop). if candidate_rows: rcm_web_qtys = [_rcm_web_qty(r) for r in candidate_rows] prov_med = pd.Series(rcm_web_qtys).median() threshold = max(1.0, OOS_FLOOR_MULT * prov_med) clean = [] for r in candidate_rows: q = _rcm_web_qty(r) if q <= 0 or q < threshold: excluded["oos"] += 1 continue clean.append(r) if len(clean) >= BASELINE_LAST_N: break else: clean = [] qty = pd.Series([_rcm_web_qty(r) for r in clean]) weeks = [(int(r["yw"]), _rcm_web_qty(r)) for r in clean] return { "avg": float(qty.mean()) if len(qty) else 0.0, "median": float(qty.median()) if len(qty) else 0.0, "n": int(len(qty)), "weeks": sorted(weeks), "excluded": excluded, } # ============================================================ # Pull actuals from HR + AT + SLO Excels (RCM + WSA/B/C channels) # ============================================================ # Polleo document codes — confirmed by CM: # RCM = retail (physical store sales) # WSA/B/C = WEB sales (different web sub-channels, NOT wholesale) # Wholesale is in a separate ERP export we don't have here, so it's # naturally excluded just by using these Excel files. ALL_CHANNELS = ("RCM", "WSA", "WSB", "WSC") act_qty = defaultdict(lambda: defaultdict(float)) # sku -> yw -> qty act_rev = defaultdict(lambda: defaultdict(float)) # actual € booked # Also break out by source country for traceability in the output act_qty_by_src = defaultdict(lambda: defaultdict(lambda: defaultdict(float))) # sku -> src -> yw -> qty ALL_SKUS = {s for s, _ in PROMO_SKUS} | {s for s, _ in SIBLINGS} for src in SOURCES: p = src["file"] if not p.exists(): print(f" [skip] {src['name']}: {p.name} not found.") continue wb_raw = openpyxl.load_workbook(p, data_only=True, read_only=True) ws_raw = wb_raw.active n_matched = 0 for row in ws_raw.iter_rows(min_row=2, values_only=True): dok = str(row[src["dok_col"]] or "").upper() if not any(dok.startswith(pf) for pf in ALL_CHANNELS): continue dt = row[src["date_col"]] sku = row[src["sku_col"]] if sku not in ALL_SKUS: continue if not isinstance(dt, datetime): continue iso = dt.isocalendar() yw = iso[0] * 100 + iso[1] qty = float(row[src["qty_col"]] or 0) rev = float(row[src["rev_col"]] or 0) act_qty[sku][yw] += qty act_rev[sku][yw] += rev act_qty_by_src[sku][src["name"]][yw] += qty n_matched += 1 wb_raw.close() print(f" [{src['name']}] {p.name}: {n_matched:,} matching rows added.") # ============================================================ # Compose rows for both 2-week and 1-week (CW20 only) views # ============================================================ def build_rows(period_yws: list[int], label: str) -> list[dict]: """Build per-SKU row dicts for a given promo period (list of yws). Returns list ordered PROMO first then SIBLINGS.""" out = [] n_weeks = len(period_yws) for sku, name_fallback in PROMO_SKUS + SIBLINGS: role = "PROMO" if any(s == sku for s, _ in PROMO_SKUS) else "SIBLING" bl = baseline_12w(sku) base_avg = bl["avg"] n_clean = bl["n"] actual_qty = sum(act_qty[sku].get(yw, 0.0) for yw in period_yws) actual_rev = sum(act_rev[sku].get(yw, 0.0) for yw in period_yws) list_price = price_map.get(sku, 0.0) avg_price = avg_sell_map.get(sku, 0.0) if avg_price <= 0: avg_price = list_price cost = cost_map.get(sku, 0.0) baseline_rev_wk = base_avg * avg_price baseline_ruc_wk = base_avg * max(0.0, avg_price - cost) # Effective price during period eff_price = (actual_rev / actual_qty) if actual_qty > 0 else 0.0 actual_ruc = actual_qty * max(0.0, eff_price - cost) expected_qty = base_avg * n_weeks expected_rev = baseline_rev_wk * n_weeks expected_ruc = baseline_ruc_wk * n_weeks delta_qty = actual_qty - expected_qty delta_rev = actual_rev - expected_rev delta_ruc = actual_ruc - expected_ruc drop_pct = (delta_qty / expected_qty * 100) if expected_qty else 0.0 out.append({ "role": role, "sku": sku, "name": name_map.get(sku, name_fallback), "tier": tier_map.get(sku, ""), "n_clean": n_clean, "baseline_avg_wk": base_avg, "list_price": list_price, "avg_sell_price": avg_price, "cost": cost, "expected_qty": expected_qty, "expected_rev": expected_rev, "expected_ruc": expected_ruc, "actual_qty": actual_qty, "actual_rev": actual_rev, "actual_ruc": actual_ruc, "eff_price": eff_price, "delta_qty": delta_qty, "delta_rev": delta_rev, "delta_ruc": delta_ruc, "drop_pct": drop_pct, "baseline_weeks": bl["weeks"], "cw19_qty": act_qty[sku].get(202619, 0.0), "cw20_qty": act_qty[sku].get(202620, 0.0), "label": label, "n_weeks": n_weeks, }) return out rows_2w = build_rows([202619, 202620], "2-week (CW19+CW20)") rows_1w = build_rows([202620], "1-week (CW20 — current)") # ============================================================ # Write Excel with two sheets: Summary + Weekly detail # ============================================================ THIN = Side(border_style="thin", color="CCCCCC") HEADER_FILL = PatternFill("solid", fgColor="0C2340") ALT_FILL = PatternFill("solid", fgColor="F1F4F8") PROMO_FILL = PatternFill("solid", fgColor="FFE9D5") WIN_FONT = Font(bold=True, color="10B981") NEG_FONT = Font(bold=True, color="DC2626") def write_header(ws, headers, row): for c, h in enumerate(headers, start=1): cell = ws.cell(row, c, h) cell.font = Font(bold=True, color="FFFFFF") cell.fill = HEADER_FILL cell.alignment = Alignment(horizontal="center", vertical="center", wrap_text=True) cell.border = Border(left=THIN, right=THIN, top=THIN, bottom=THIN) def fmt_eur(v): return f"{v:,.0f} €" if v else "—" def fmt_int(v): return f"{int(round(v)):,}" if v else "0" wb = openpyxl.Workbook() ws = wb.active ws.title = "Summary" # Top banner ws.merge_cells("A1:Q1") title_cell = ws.cell(1, 1, "NextGen Protein 2 kg Promo — Cannibalization Analysis (CW19–CW20 2026)") title_cell.font = Font(bold=True, size=14, color="FFFFFF") title_cell.fill = HEADER_FILL title_cell.alignment = Alignment(horizontal="center", vertical="center") ws.row_dimensions[1].height = 28 ws.cell(2, 1, "Baseline = MEAN of last 12 non-promo weeks (qty_total = retail + webshop + wholesale from sales_clean.csv). " "Actual = ALL channels (RCM + WSA/B/C) from Excel export.").font = Font(italic=True, color="666666") ws.row_dimensions[2].height = 18 ws.merge_cells("A2:Q2") def render_block(start_row: int, block_rows: list[dict], block_title: str) -> int: """Render one period block (2-week or 1-week) starting at `start_row`. Returns the row index AFTER the block (with 1 empty row spacing).""" n_w = block_rows[0]["n_weeks"] if block_rows else 0 # Section header ws.merge_cells(start_row=start_row, start_column=1, end_row=start_row, end_column=17) h = ws.cell(start_row, 1, f"📊 {block_title} · period span: {n_w} week(s)") h.font = Font(bold=True, size=12, color="FFFFFF") h.fill = PatternFill("solid", fgColor="1F3A5F") h.alignment = Alignment(horizontal="left", vertical="center", indent=1) ws.row_dimensions[start_row].height = 22 # Column headers headers = [ "Role", "SKU", "Name", "Tier", "Baseline avg/wk", "List €", "Avg sell €", f"Expected {n_w}w qty", f"Actual {n_w}w qty", "Δ qty", "Drop %", f"Expected {n_w}w rev €", f"Actual {n_w}w rev €", "Δ rev €", f"Expected {n_w}w RUC €", f"Actual {n_w}w RUC €", "Δ RUC €", ] write_header(ws, headers, start_row + 1) row = start_row + 2 for r in block_rows: fill = PROMO_FILL if r["role"] == "PROMO" else (ALT_FILL if row % 2 == 0 else None) values = [ r["role"], r["sku"], r["name"], r["tier"], round(r["baseline_avg_wk"], 1), round(r["list_price"], 2), round(r["avg_sell_price"], 2), round(r["expected_qty"], 1), round(r["actual_qty"], 1), round(r["delta_qty"], 1), round(r["drop_pct"], 1), round(r["expected_rev"], 0), round(r["actual_rev"], 0), round(r["delta_rev"], 0), round(r["expected_ruc"], 0), round(r["actual_ruc"], 0), round(r["delta_ruc"], 0), ] for c, v in enumerate(values, start=1): cell = ws.cell(row, c, v) if fill: cell.fill = fill cell.border = Border(left=THIN, right=THIN, top=THIN, bottom=THIN) if c == 1: cell.font = Font(bold=True, color=("E8734A" if r["role"] == "PROMO" else "1F3A5F")) if c in (10, 11, 14, 17): # delta + drop % try: num = float(v) if num > 0: cell.font = WIN_FONT elif num < 0: cell.font = NEG_FONT except Exception: pass if c in (5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17): cell.alignment = Alignment(horizontal="right") if c in (12, 13, 14, 15, 16, 17): cell.number_format = "#,##0 €" row += 1 # Totals totals_row = row ws.cell(totals_row, 1, "TOTAL") def sm(f): return sum(r[f] for r in block_rows) def smr(f, role): return sum(r[f] for r in block_rows if r["role"] == role) ws.cell(totals_row, 8, round(sm("expected_qty"), 1)) ws.cell(totals_row, 9, round(sm("actual_qty"), 1)) ws.cell(totals_row, 10, round(sm("delta_qty"), 1)) ws.cell(totals_row, 12, round(sm("expected_rev"), 0)) ws.cell(totals_row, 13, round(sm("actual_rev"), 0)) ws.cell(totals_row, 14, round(sm("delta_rev"), 0)) ws.cell(totals_row, 15, round(sm("expected_ruc"), 0)) ws.cell(totals_row, 16, round(sm("actual_ruc"), 0)) ws.cell(totals_row, 17, round(sm("delta_ruc"), 0)) for c in range(1, 18): cc = ws.cell(totals_row, c) cc.font = Font(bold=True, color="FFFFFF") cc.fill = HEADER_FILL cc.border = Border(left=THIN, right=THIN, top=THIN, bottom=THIN) if c in (8, 9, 10, 12, 13, 14, 15, 16, 17): cc.alignment = Alignment(horizontal="right") if c in (12, 13, 14, 15, 16, 17): cc.number_format = "#,##0 €" # Net by role nr_row = totals_row + 2 ws.cell(nr_row, 1, "Net by role").font = Font(bold=True, size=11, color="0C2340") ws.cell(nr_row + 1, 1, "PROMO Δ rev").font = Font(bold=True) ws.cell(nr_row + 1, 2, round(smr("delta_rev", "PROMO"), 0)).number_format = "#,##0 €" ws.cell(nr_row + 1, 3, "PROMO Δ RUC").font = Font(bold=True) ws.cell(nr_row + 1, 4, round(smr("delta_ruc", "PROMO"), 0)).number_format = "#,##0 €" ws.cell(nr_row + 2, 1, "SIBLINGS Δ rev").font = Font(bold=True) ws.cell(nr_row + 2, 2, round(smr("delta_rev", "SIBLING"), 0)).number_format = "#,##0 €" ws.cell(nr_row + 2, 3, "SIBLINGS Δ RUC").font = Font(bold=True) ws.cell(nr_row + 2, 4, round(smr("delta_ruc", "SIBLING"), 0)).number_format = "#,##0 €" nx = nr_row + 3 net_r = smr("delta_rev", "PROMO") + smr("delta_rev", "SIBLING") net_u = smr("delta_ruc", "PROMO") + smr("delta_ruc", "SIBLING") ws.cell(nx, 1, "NET category Δ rev").font = Font(bold=True) nc = ws.cell(nx, 2, round(net_r, 0)) nc.number_format = "#,##0 €"; nc.font = WIN_FONT if net_r >= 0 else NEG_FONT ws.cell(nx, 3, "NET category Δ RUC").font = Font(bold=True) nc2 = ws.cell(nx, 4, round(net_u, 0)) nc2.number_format = "#,##0 €"; nc2.font = WIN_FONT if net_u >= 0 else NEG_FONT return nx + 3 # blank rows before next block next_row = render_block(4, rows_2w, "2-week period (CW19 + CW20) — full promo so far") next_row = render_block(next_row, rows_1w, "1-week period (CW20 — current week only)") # Caveat about partial week ws.cell(next_row + 1, 1, "⚠️ CW20 may be a partial week if today's date is mid-week. Drop % can look exaggerated for that reason." ).font = Font(italic=True, color="DC2626") ws.merge_cells(start_row=next_row + 1, start_column=1, end_row=next_row + 1, end_column=17) # Column widths widths = [10, 12, 50, 11, 14, 9, 11, 15, 16, 9, 9, 17, 17, 12, 17, 17, 12] for i, w in enumerate(widths, start=1): ws.column_dimensions[get_column_letter(i)].width = w ws.freeze_panes = "C5" # ---------- Sheet 2: Weekly detail ---------- ws2 = wb.create_sheet("Weekly detail") ws2.merge_cells("A1:R1") title2 = ws2.cell(1, 1, "Weekly detail — sales_clean baseline (qty_total) vs CW19/CW20 actuals (RCM + WSA/B/C)") title2.font = Font(bold=True, size=12, color="FFFFFF") title2.fill = HEADER_FILL title2.alignment = Alignment(horizontal="center") # Build week columns: last 12 baseline weeks + CW19 + CW20 baseline_yws = set() for r in rows_2w: for (yw, _) in r["baseline_weeks"]: baseline_yws.add(yw) baseline_yws = sorted(baseline_yws) all_weeks = baseline_yws + [202619, 202620] headers2 = ["Role", "SKU", "Name"] + [f"CW{yw % 100}/{yw // 100}" for yw in all_weeks] + ["Baseline avg", "CW19+20 total"] write_header(ws2, headers2, 3) row = 4 for r in rows_2w: fill = PROMO_FILL if r["role"] == "PROMO" else None weeks_dict = dict(r["baseline_weeks"]) ws2.cell(row, 1, r["role"]).font = Font(bold=True, color=("E8734A" if r["role"] == "PROMO" else "1F3A5F")) ws2.cell(row, 2, r["sku"]) ws2.cell(row, 3, r["name"]) col = 4 for yw in baseline_yws: ws2.cell(row, col, round(weeks_dict.get(yw, 0), 1)) col += 1 ws2.cell(row, col, round(r["cw19_qty"], 1)) ws2.cell(row, col + 1, round(r["cw20_qty"], 1)) col += 2 ws2.cell(row, col, round(r["baseline_avg_wk"], 1)).font = Font(bold=True) ws2.cell(row, col + 1, round(r["actual_qty"], 1)).font = Font(bold=True) for c in range(1, col + 2): cell = ws2.cell(row, c) if fill: cell.fill = fill cell.border = Border(left=THIN, right=THIN, top=THIN, bottom=THIN) if c >= 4: cell.alignment = Alignment(horizontal="right") row += 1 ws2.column_dimensions["A"].width = 10 ws2.column_dimensions["B"].width = 12 ws2.column_dimensions["C"].width = 45 for i in range(4, 4 + len(all_weeks) + 2): ws2.column_dimensions[get_column_letter(i)].width = 11 ws2.freeze_panes = "D4" # Save def _safe_save(wb, path: Path) -> Path: try: wb.save(path) return path except PermissionError: i = 1 while True: alt = path.with_name(f"{path.stem}_v{i}{path.suffix}") if not alt.exists(): break i += 1 wb.save(alt) return alt written = _safe_save(wb, OUT) print(f"Generated: {written} ({written.stat().st_size // 1024} KB)") # Console mini-summary — 2-week block def _print_block(rs, label): print() print(f"=== {label} ===") print(f"{'SKU':10s} {'Role':8s} {'Baseline/wk':>11s} " f"{'Actual':>7s} {'Drop %':>7s} {'Δ rev €':>11s} {'Δ RUC €':>11s}") print("-" * 80) for r in rs: print(f"{r['sku']:10s} {r['role']:8s} {r['baseline_avg_wk']:>11.1f} " f"{r['actual_qty']:>7.0f} {r['drop_pct']:>6.1f}% " f"{r['delta_rev']:>11,.0f} {r['delta_ruc']:>11,.0f}") print("-" * 80) print(f"{'TOTAL':10s} {'':8s} {'':>11s} {sum(r['actual_qty'] for r in rs):>7.0f} " f"{'':>7s} {sum(r['delta_rev'] for r in rs):>11,.0f} " f"{sum(r['delta_ruc'] for r in rs):>11,.0f}") _print_block(rows_2w, "2-week (CW19 + CW20) — all channels") _print_block(rows_1w, "1-week (CW20 only — current week)")