Introduction

In this blog post, we will explore how to solve the LeetCode problem "1169" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like Chatmagic, we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

Problem Statement

A transaction is possibly invalid if: the amount exceeds $1000, or; if it occurs within (and including) 60 minutes of another transaction with the same name in a different city. You are given an array of strings transaction where transactions[i] consists of comma-separated values representing the name, time (in minutes), amount, and city of the transaction. Return a list of transactions that are possibly invalid. You may return the answer in any order. Example 1: Input: transactions = ["alice,20,800,mtv","alice,50,100,beijing"] Output: ["alice,20,800,mtv","alice,50,100,beijing"] Explanation: The first transaction is invalid because the second transaction occurs within a difference of 60 minutes, have the same name and is in a different city. Similarly the second one is invalid too. Example 2: Input: transactions = ["alice,20,800,mtv","alice,50,1200,mtv"] Output: ["alice,50,1200,mtv"] Example 3: Input: transactions = ["alice,20,800,mtv","bob,50,1200,mtv"] Output: ["bob,50,1200,mtv"] Constraints: transactions.length <= 1000 Each transactions[i] takes the form "{name},{time},{amount},{city}" Each {name} and {city} consist of lowercase English letters, and have lengths between 1 and 10. Each {time} consist of digits, and represent an integer between 0 and 1000. Each {amount} consist of digits, and represent an integer between 0 and 2000.

Explanation

Here's the breakdown of the solution:

• Parse Transactions: Convert the input strings into a list of transaction objects (dictionaries or tuples) for easier manipulation.
• Validity Checks: Iterate through each transaction and check for both invalidity conditions: amount exceeding $1000 and proximity to other transactions with the same name in different cities.

• Mark Invalid: Maintain a boolean array to mark transactions as invalid during the checking process.

• Runtime Complexity: O(n^2), where n is the number of transactions.

• Storage Complexity: O(n)

Code

    def invalidTransactions(transactions):
    """
    Identifies and returns a list of potentially invalid transactions.

    Args:
        transactions: A list of strings, where each string represents a transaction
                      in the format "name,time,amount,city".

    Returns:
        A list of strings representing the invalid transactions.
    """

    n = len(transactions)
    transactions_data = []
    invalid = [False] * n
    result = []

    for i in range(n):
        name, time, amount, city = transactions[i].split(',')
        transactions_data.append({
            'name': name,
            'time': int(time),
            'amount': int(amount),
            'city': city,
            'original_index': i
        })

        if int(amount) > 1000:
            invalid[i] = True

    for i in range(n):
        for j in range(n):
            if i != j:
                if transactions_data[i]['name'] == transactions_data[j]['name'] and \
                   transactions_data[i]['city'] != transactions_data[j]['city'] and \
                   abs(transactions_data[i]['time'] - transactions_data[j]['time']) <= 60:
                    invalid[i] = True
                    invalid[j] = True

    for i in range(n):
        if invalid[i]:
            result.append(transactions[i])

    return result