average-transaction-value-stats

name

Average_Transaction_Value_Stats

description

Solve dabstep Average_Transaction_Value_Stats questions that ask for average transaction value (eur_amount) grouped by a dimension (shopper_interaction, issuing_country, acquirer_country, aci) for a specific merchant and card scheme over a date range. Use this skill whenever a question asks for average transaction value/amount broken down by a grouping variable with merchant and/or card scheme filters on the payments dataset.

Average Transaction Value Stats — Solver Guide

Task Pattern

Questions in this category ask:

"What is the average transaction value grouped by [dimension] for [merchant]'s [card_scheme] transactions between [month_start] and [month_end] [year]?"

Dimensions include: shopper_interaction, issuing_country, acquirer_country, aci, or email_address.

Dataset

Use only payments.csv. Key columns:

• merchant — exact merchant name (e.g., Crossfit_Hanna, Golfclub_Baron_Friso, Belles_cookbook_store, Rafa_AI, Martinis_Fine_Steakhouse)
• card_scheme — one of: NexPay, GlobalCard, SwiftCharge, TransactPlus
• year — all data is 2023
• day_of_year — integer 1–365 (no actual date column exists; convert months to day ranges)
• eur_amount — transaction amount in euros
• shopper_interaction — Ecommerce or POS
• issuing_country, acquirer_country — country codes (SE, NL, LU, IT, BE, FR, GR, ES, US, ...)
• aci — Authorization Characteristics Indicator: A, B, C, D, E, F, G
• email_address — hashed email (may contain NaN)

2023 Month → day_of_year Mapping (Non-Leap Year)

Month	day_of_year range
January	1–31
February	32–59
March	60–90
April	91–120
May	121–151
June	152–181
July	182–212
August	213–243
September	244–273
October	274–304
November	305–334
December	335–365

For a multi-month range (e.g., "between May and June"), use the start of the first month through the end of the last month.

Standard Solution Steps

import pandas as pd

df = pd.read_csv('<data_path>/payments.csv')

# Step 1: Apply ALL filters — both merchant AND card_scheme are required
filtered = df[
    (df['merchant'] == 'Merchant_Name') &
    (df['card_scheme'] == 'CardSchemeName') &
    (df['day_of_year'] >= START_DAY) &
    (df['day_of_year'] <= END_DAY)
]
# year filter not needed if all data is 2023, but add (df['year'] == 2023) if uncertain

# Step 2: Group and compute mean
avg = filtered.groupby('dimension_column')['eur_amount'].mean()

# Step 3: Sort ascending by value, round to 2 decimal places
avg_sorted = avg.sort_values(ascending=True).round(2)

# Step 4: Format as list of "KEY: VALUE" strings with exactly 2 decimal places
result = [f"{k}: {v:.2f}" for k, v in avg_sorted.items()]
print(result)

Critical Rules

Always filter by card_scheme

The question says "Merchant's CardScheme transactions" — both filters are mandatory. Omitting card_scheme filter yields wrong results (different transaction population).

Use exact 2 decimal place formatting

Use f"{value:.2f}" not round(value, 2) in format strings. Python drops trailing zeros (75.7 ≠ 75.70 as a string), but answers require 75.70.

Output format

Produce a list of strings: ['BE: 86.39', 'SE: 91.89', ...] — not tuples. The question specifies [grouping_i: amount_i, ] format.

Sort ascending by amount

Always sort by the computed average value, not alphabetically by the group key.

Special Case: "Average transaction amount per unique email"

When the question asks for "average transaction amount per unique email":

• Correct interpretation: For each unique email, compute the average of its transactions; then take the mean of those per-email averages.
• Wrong interpretation: total_eur_amount / count_of_unique_emails

# Correct
per_email_avg = df.groupby('email_address')['eur_amount'].mean()
result = per_email_avg.mean().round(3)

NaN email addresses are automatically excluded by groupby.

Common Errors to Avoid

1. Missing card_scheme filter — The most frequent source of wrong answers. Always apply it.
2. Wrong date boundaries — Double-check month boundaries using the table above. "Between January and April" = days 1–120.
3. Tuple format instead of string format — Output ['FR: 71.18', ...] not [('FR', 71.18), ...].
4. Floating-point display — Use :.2f format; round(x, 2) alone won't guarantee two decimal places in string output.

Example

Query: "Average transaction value grouped by issuing_country for Golfclub_Baron_Friso's NexPay transactions between May and June 2023?"

filtered = df[
    (df['merchant'] == 'Golfclub_Baron_Friso') &
    (df['card_scheme'] == 'NexPay') &
    (df['day_of_year'] >= 121) &   # May 1
    (df['day_of_year'] <= 181)     # June 30
]
avg = filtered.groupby('issuing_country')['eur_amount'].mean().sort_values()
result = [f"{k}: {v:.2f}" for k, v in avg.items()]
# → ['FR: 71.18', 'GR: 77.56', 'NL: 86.79', 'IT: 90.72', 'BE: 95.83', 'SE: 105.05', 'ES: 109.18', 'LU: 143.98']