Average_Transaction_Value_Stats

Average Transaction Value Stats

This skill covers computing mean eur_amount from payments.csv, with optional filters on merchant, card scheme, and month range, grouped by a categorical column.

Dataset Overview

payments.csv — core transaction table. Key columns:

• merchant: merchant name (e.g., Crossfit_Hanna, Rafa_AI, Golfclub_Baron_Friso, Belles_cookbook_store, Martinis_Fine_Steakhouse)
• card_scheme: payment network (NexPay, GlobalCard, SwiftCharge, TransactPlus)
• year: always 2023 in this dataset
• day_of_year: 1–365 (no month column — compute month ranges manually)
• eur_amount: transaction amount in euros
• shopper_interaction: Ecommerce or POS
• issuing_country, acquirer_country, ip_country: country codes (SE, NL, LU, IT, BE, FR, GR, ES)
• aci: Authorization Characteristics Indicator (A–G)
• email_address: hashed email (may have NaN values)

Month → day_of_year Mapping (2023, non-leap year)

Use (df['day_of_year'] >= start) & (df['day_of_year'] <= end) to filter date ranges.

Critical Filtering Rules

Card scheme is a filter column, not part of the merchant name

When a question says "Merchant_X's TransactPlus transactions", it means:

df[(df['merchant'] == 'Merchant_X') & (df['card_scheme'] == 'TransactPlus')]

Never omit the card_scheme filter when a card scheme is mentioned in the question. This is the most common source of wrong answers.

Multi-month ranges

"Between January and April" means months January through April inclusive (day_of_year 1–120). Include both endpoint months.

Standard Solution Pattern

import pandas as pd

df = pd.read_csv('/path/to/payments.csv')

# 1. Filter (apply all conditions that the question specifies)
mask = pd.Series([True] * len(df), index=df.index)

# Optional: filter by merchant
mask &= (df['merchant'] == 'Merchant_Name')

# Optional: filter by card scheme
mask &= (df['card_scheme'] == 'CardSchemeName')

# Optional: filter by date range (use day_of_year ranges from the table above)
mask &= (df['day_of_year'] >= DAY_START) & (df['day_of_year'] <= DAY_END)

filtered = df[mask]

# 2. Group by the specified column and compute mean eur_amount
result = filtered.groupby('grouping_column')['eur_amount'].mean()

# 3. Round (default: 2 decimal places unless question specifies otherwise)
result = result.round(2)

# 4. Sort ascending by value
result = result.sort_values(ascending=True)

# 5. Format as list of strings
answer = [f"{idx}: {val}" for idx, val in result.items()]
print(answer)

Output Format

• List of grouped averages: ['GroupA: 71.18', 'GroupB: 86.79', ...]
  • Elements sorted in ascending order by amount
  • Amounts rounded to 2 decimal places (unless otherwise specified)
  • Use the grouping key exactly as it appears in the data (e.g., country codes like FR, SE)
• Single scalar average (e.g., "average transaction amount per unique email"): return as a number (e.g., 90.696)

Interpreting "Average per Unique X"

When the question asks for "average transaction amount per unique email" (or similar per-unique-entity phrasing):

• Compute the average transaction amount for each entity, then take the mean of those per-entity averages:

  result = df.groupby('email_address')['eur_amount'].mean().mean()
  

• This is not total_amount / count_of_unique_entities (which gives a different, incorrect result).

Common Mistakes to Avoid

1. Missing card_scheme filter: "NexPay transactions" = card_scheme == 'NexPay', not just merchant filter.
2. Wrong date range: Check the month table. September–October is days 244–304, not 244–273.
3. Sorting direction: Always sort ascending by eur_amount unless the question says descending.
4. Rounding: Use .round(2) for 2 decimal places. For display ensure f"{val:.2f}" to show trailing zeros.
5. NaN in email_address: When grouping by email_address, pandas groupby automatically excludes NaN keys — no need to drop them explicitly.