total-fees-calculation

name	Total_Fees_Calculation
description	Solve dabstep questions that ask for the total payment processing fees a merchant should pay over a specific time period (a day, month, or year). Use this skill whenever the question involves computing total fees for a named merchant over a date range using the dabstep dataset. Trigger on phrases like "total fees", "fees that [merchant] should pay", "fees paid in [month/year]".

Total Fees Calculation

These questions ask: "What are the total fees (in euros) that [Merchant] paid/should pay in [time period]?"

The answer is computed by: filtering transactions → matching each to exactly one fee rule → summing fee = fixed_amount + rate * eur_amount / 10000.

Do NOT read manual.md or payments-readme.md — all required knowledge is in this skill. Skip directly to the algorithm.

Dataset Files

File	Purpose
payments.csv	One row per transaction; key columns: merchant, year, day_of_year, card_scheme, is_credit, eur_amount, issuing_country, acquirer_country, aci, has_fraudulent_dispute, is_refused_by_adyen
merchant_data.json	Merchant objects: merchant, account_type, capture_delay, merchant_category_code
fees.json	~1000 fee rule objects (see matching logic below)

Complete Algorithm

Run this in one code block — adapt the merchant name, year, and time filter for the question:

import json, pandas as pd
from datetime import date, timedelta

# ── Load data ──────────────────────────────────────────────────────────
with open('fees.json') as f: fees = json.load(f)
with open('merchant_data.json') as f: merchant_data = json.load(f)
merchant_map = {m['merchant']: m for m in merchant_data}
payments = pd.read_csv('payments.csv')

def doy_to_month(doy, year):
    return (date(year, 1, 1) + timedelta(days=doy - 1)).month

payments['month'] = payments.apply(lambda r: doy_to_month(r['day_of_year'], r['year']), axis=1)

# ── Merchant attributes ────────────────────────────────────────────────
merchant_name = 'MERCHANT_NAME'   # ← fill in
year = 2023                        # ← fill in

m = merchant_map[merchant_name]
merchant_at  = m['account_type']
merchant_mcc = m['merchant_category_code']

def map_capture_delay(cd):
    if cd in ('immediate', 'manual'): return cd
    n = int(cd)
    return '<3'  n <   (  n <=   )

merchant_cd = map_capture_delay(m[])


 ():
     ():
        v = v.strip().replace(, )
         v.endswith():  (v[:-]) * 
         v.endswith():  (v[:-]) * 
         (v) /   pct  (v)
     s.startswith():  (, val(s[:]))
     s.startswith():  (val(s[:]), ())
    a, b = s.split()
     (val(a), val(b))


 ():
    ic = (tx[] == tx[])
    candidates = []
     rule  fees:
         rule[] != tx[]: 
        
         rule[]  merchant_at   rule[]: 
         rule[]  merchant_mcc   rule[]: 
         rule[]  tx[]   rule[]: 
        
         rule[]     rule[] != merchant_cd: 
         rule[]     rule[] != tx[]: 
         rule[]   :
             rule[] != (  ic  ): 
         rule[]   :
            lo, hi = parse_range(rule[], pct=)
              (lo <= monthly_fraud_rate < hi): 
         rule[]   :
            lo, hi = parse_range(rule[])
              (lo <= monthly_volume < hi): 
        candidates.append(rule)
      candidates:  
    
     (candidates, key= r: ([
        (r[]), r[]   ,
        (r[]), r[]   ,
        (r[]), r[]   ,
        r[]   , r[]   
    ]))





target_day =    
txs = payments[(payments[] == merchant_name) &
               (payments[] == year) &
               (payments[] == target_day) &
               (payments[] == )]

target_month = doy_to_month(target_day, year)















all_merchant = payments[(payments[] == merchant_name) & (payments[] == year)]

monthly_txs = all_merchant[all_merchant[] == target_month]
monthly_volume     = monthly_txs[].()
fraud_volume       = monthly_txs[monthly_txs[] == ][].()
monthly_fraud_rate = fraud_volume / monthly_volume  monthly_volume >   



total_fee = 
 _, row  txs.iterrows():
    rule = find_rule(row.to_dict(), monthly_volume, monthly_fraud_rate)
     rule:
        total_fee += rule[] + rule[] * row[] / 

((total_fee, ))

Key Rules

Rule selection — when multiple rules match, pick the most specific one (the rule with the most non-null / non-empty fields). Never use the first/lowest-ID rule.

Empty list [] in account_type, merchant_category_code, aci means the rule applies to ALL values (same as null).

No matching rule → fee = 0 for that transaction. This is expected; many transaction-type combinations are not covered by the fee schedule.

Monthly stats scope — always compute monthly_volume and monthly_fraud_rate over the full natural calendar month using all merchant transactions (including refused ones), even when the question asks about a single day within that month.

intracountry — compute directly as issuing_country == acquirer_country from payments.csv; do not look up the acquirer's country from merchant_data.json.

Refused transactions — exclude is_refused_by_adyen == True from the fee-paying transactions, but include them when computing monthly stats.

Expected Output

A single number rounded to 2 decimal places. If no applicable answer exists, output Not Applicable.