Number of Transactions per Visit

Imagine you're analyzing website traffic data to understand user behavior. You want to determine how many transactions (e.g., purchases, sign-ups) occur during each visit to your site. This information can help you identify areas for improvement in your user experience and optimize conversion rates.

Problem Description

You are given a log of user interactions on a website. Each interaction is represented as a record containing a visit_id (unique identifier for a user's session) and an event_type (e.g., "page_view", "add_to_cart", "purchase"). Your task is to calculate the number of transactions (defined as events with event_type equal to "purchase") that occur during each unique visit_id.

What needs to be achieved:

• Process a list of interaction records.
• Group interactions by visit_id.
• Count the number of "purchase" events within each visit_id group.
• Return a mapping (e.g., dictionary, hashmap) where the keys are visit_ids and the values are the corresponding number of purchases.

Key Requirements:

• The input is a list of records, where each record is a dictionary/map with keys visit_id and event_type.
• The visit_id is a string.
• The event_type is a string.
• The output should be a mapping (dictionary/hashmap) where keys are visit_ids and values are the number of purchases for that visit.
• If a visit_id has no purchases, its corresponding value in the output should be 0.

Expected Behavior:

The solution should correctly count purchases for each visit, even if visits have multiple page views or other non-purchase events. It should handle cases where a visit has no purchases.

Edge Cases to Consider:

• Empty input list.
• visit_id values that are empty strings.
• event_type values that are not "purchase" (should be ignored).
• Duplicate records with the same visit_id and event_type. (Count each purchase only once).

Examples

Example 1:

Input: [
    {"visit_id": "A123", "event_type": "page_view"},
    {"visit_id": "A123", "event_type": "add_to_cart"},
    {"visit_id": "A123", "event_type": "purchase"},
    {"visit_id": "B456", "event_type": "page_view"},
    {"visit_id": "B456", "event_type": "purchase"},
    {"visit_id": "C789", "event_type": "page_view"},
    {"visit_id": "C789", "event_type": "page_view"},
    {"visit_id": "C789", "event_type": "purchase"},
    {"visit_id": "C789", "event_type": "purchase"}
]
Output: {"A123": 1, "B456": 1, "C789": 2}
Explanation: Visit A123 has 1 purchase, B456 has 1 purchase, and C789 has 2 purchases.

Example 2:

Input: [
    {"visit_id": "X999", "event_type": "page_view"},
    {"visit_id": "Y000", "event_type": "page_view"},
    {"visit_id": "Z111", "event_type": "page_view"}
]
Output: {"X999": 0, "Y000": 0, "Z111": 0}
Explanation: None of the visits have any purchases, so the count for each is 0.

Example 3: (Edge Case)

Input: []
Output: {}
Explanation: The input list is empty, so the output is an empty mapping.

Constraints

• The input list can contain up to 10,000 records.
• visit_id and event_type are strings with a maximum length of 255 characters.
• The solution should have a time complexity of O(n), where n is the number of records in the input list.
• The solution should use a reasonable amount of memory (e.g., avoid storing the entire input list in memory if possible).

Notes

Consider using a hash map (dictionary) to efficiently group interactions by visit_id. Iterate through the input list once, updating the purchase count for each visit_id as you encounter it. Remember to handle the case where a visit_id is encountered for the first time. Focus on clarity and efficiency in your solution.