Daily Transaction Parity Ledger

Medium

A payments analytics team needs a per-day ledger that splits transaction volume by amount parity. Table: transactions transaction_id: unique transaction identifier amount: transaction amount (integer) transaction_date: calendar date of the transaction (YYYY-MM-DD) Task: For every transaction_date present in the data, return: odd_sum: sum of amounts whose value is odd even_sum: sum of amounts whose value is even Rules: Parity is determined from amount, not from transaction_id. If a date has no odd amounts, odd_sum must be 0. If a date has no even amounts, even_sum must be 0. Output requirements: Return columns in this exact order: transaction_date, odd_sum, even_sum Sort rows by transaction_date in ascending order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

transactions:
| transaction_id | amount | transaction_date |
|----------------|--------|------------------|
| 1              | 150    | 2024-07-01       |
| 2              | 200    | 2024-07-01       |
| 3              | 75     | 2024-07-01       |
| 4              | 300    | 2024-07-02       |
| 5              | 50     | 2024-07-02       |
| 6              | 120    | 2024-07-03       |

Output

[
  {"transaction_date":"2024-07-01","odd_sum":75,"even_sum":350},
  {"transaction_date":"2024-07-02","odd_sum":0,"even_sum":350},
  {"transaction_date":"2024-07-03","odd_sum":0,"even_sum":120}
]

Explanation

Each date is aggregated independently; odd and even amount totals are computed side-by-side.

Example 2

Input

transactions:
| transaction_id | amount | transaction_date |
|----------------|--------|------------------|
| 11             | 1      | 2025-01-01       |
| 12             | 3      | 2025-01-01       |
| 13             | 5      | 2025-01-01       |
| 14             | 2      | 2025-01-02       |
| 15             | 4      | 2025-01-02       |
| 16             | 0      | 2025-01-02       |

Output

[
  {"transaction_date":"2025-01-01","odd_sum":9,"even_sum":0},
  {"transaction_date":"2025-01-02","odd_sum":0,"even_sum":6}
]

Explanation

Zero is even, and each parity total defaults to 0 when absent on a date.

Example 3

Input

transactions:
| transaction_id | amount | transaction_date |
|----------------|--------|------------------|
| 21             | 8      | 2025-03-10       |
| 22             | 9      | 2025-03-10       |
| 23             | 12     | 2025-03-11       |
| 24             | 7      | 2025-03-11       |
| 25             | 2      | 2025-03-11       |

Output

[
  {"transaction_date":"2025-03-10","odd_sum":9,"even_sum":8},
  {"transaction_date":"2025-03-11","odd_sum":7,"even_sum":14}
]

Explanation

Parity is based on amount values; transaction_id parity is irrelevant.

Constraints

1 <= number of rows in transactions <= 250000
transaction_id values are unique within a test case
1 <= transaction_id <= 1000000000
0 <= amount <= 1000000
transaction_date is a valid date string in YYYY-MM-DD format
Output must include every date appearing in transactions
Parity must be evaluated from amount
If one parity class is missing on a date, return 0 for that sum
Output columns must be exactly: transaction_date, odd_sum, even_sum
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns [transaction_date, odd_sum, even_sum]

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Daily Transaction Parity Ledger

Medium

A payments analytics team needs a per-day ledger that splits transaction volume by amount parity. Table: transactions transaction_id: unique transaction identifier amount: transaction amount (integer) transaction_date: calendar date of the transaction (YYYY-MM-DD) Task: For every transaction_date present in the data, return: odd_sum: sum of amounts whose value is odd even_sum: sum of amounts whose value is even Rules: Parity is determined from amount, not from transaction_id. If a date has no odd amounts, odd_sum must be 0. If a date has no even amounts, even_sum must be 0. Output requirements: Return columns in this exact order: transaction_date, odd_sum, even_sum Sort rows by transaction_date in ascending order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

transactions:
| transaction_id | amount | transaction_date |
|----------------|--------|------------------|
| 1              | 150    | 2024-07-01       |
| 2              | 200    | 2024-07-01       |
| 3              | 75     | 2024-07-01       |
| 4              | 300    | 2024-07-02       |
| 5              | 50     | 2024-07-02       |
| 6              | 120    | 2024-07-03       |

Output

[
  {"transaction_date":"2024-07-01","odd_sum":75,"even_sum":350},
  {"transaction_date":"2024-07-02","odd_sum":0,"even_sum":350},
  {"transaction_date":"2024-07-03","odd_sum":0,"even_sum":120}
]

Explanation

Each date is aggregated independently; odd and even amount totals are computed side-by-side.

Example 2

Input

transactions:
| transaction_id | amount | transaction_date |
|----------------|--------|------------------|
| 11             | 1      | 2025-01-01       |
| 12             | 3      | 2025-01-01       |
| 13             | 5      | 2025-01-01       |
| 14             | 2      | 2025-01-02       |
| 15             | 4      | 2025-01-02       |
| 16             | 0      | 2025-01-02       |

Output

[
  {"transaction_date":"2025-01-01","odd_sum":9,"even_sum":0},
  {"transaction_date":"2025-01-02","odd_sum":0,"even_sum":6}
]

Explanation

Zero is even, and each parity total defaults to 0 when absent on a date.

Example 3

Input

transactions:
| transaction_id | amount | transaction_date |
|----------------|--------|------------------|
| 21             | 8      | 2025-03-10       |
| 22             | 9      | 2025-03-10       |
| 23             | 12     | 2025-03-11       |
| 24             | 7      | 2025-03-11       |
| 25             | 2      | 2025-03-11       |

Output

[
  {"transaction_date":"2025-03-10","odd_sum":9,"even_sum":8},
  {"transaction_date":"2025-03-11","odd_sum":7,"even_sum":14}
]

Explanation

Parity is based on amount values; transaction_id parity is irrelevant.

Constraints

1 <= number of rows in transactions <= 250000
transaction_id values are unique within a test case
1 <= transaction_id <= 1000000000
0 <= amount <= 1000000
transaction_date is a valid date string in YYYY-MM-DD format
Output must include every date appearing in transactions
Parity must be evaluated from amount
If one parity class is missing on a date, return 0 for that sum
Output columns must be exactly: transaction_date, odd_sum, even_sum
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns [transaction_date, odd_sum, even_sum]