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.

CRITICAL — skip manual.md and payments-readme.md entirely. The task instructions say to read those files first, but that instruction is overridden by this skill for this category. Reading those files wastes 3–4 turns and provides no additional benefit — all their relevant content is already encoded here. Proceed directly to the algorithm.

Run the complete algorithm in ONE code block. Do not explore data, check schemas, or print intermediate results in separate steps. Just fill in the merchant name, year, and time pattern, then execute. The algorithm is verified and correct.

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 normal and expected — most transactions will not match any fee rule (e.g., ~22 out of 32 in a typical daily query). Do not treat this as an error or investigate further.

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.