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This approach utilizes SQL LEFT JOIN to find all visits that do not have corresponding transactions and GROUP BY to count the occurrences.
Time Complexity: O(N + M), where N is the number of rows in the Visits table and M is the number of rows in the Transactions table.
Space Complexity: O(K), where K is the number of unique customer_id entries in the result set.
1import sqlite3
2
3# Connect to SQLite database
4connection = sqlite3.connect('your_database.db')
5cursor = connection.cursor()
6
7# Execute the SQL query
8query = '''
9SELECT customer_id, COUNT(visit_id) AS count_no_trans
10FROM Visits
11LEFT JOIN Transactions ON Visits.visit_id = Transactions.visit_id
12WHERE Transactions.transaction_id IS NULL
13GROUP BY customer_id;'''
14
15cursor.execute(query)
16rows = cursor.fetchall()
17
18# Output the results
19for row in rows:
20 print(f"customer_id: {row[0]}, count_no_trans: {row[1]}")
21
22# Close the connection
23connection.close()This Python solution utilizes the sqlite3 library to run an SQL query that joins Visits and Transactions tables, filters for null transactions, and groups the results. Python's sqlite3 is useful for embedding SQL queries in a Python script for easier data processing.
This approach uses a subquery with NOT EXISTS to find all visits by customers that do not appear in the Transactions table.
Time Complexity: O(N * M), where N is the number of rows in the Visits table and M is the number of rows in the Transactions table.
Space Complexity: O(K), where K is the number of unique customer_id entries in the result set.
Utilizing Python with the sqlite3 library, this code performs a SQL query using subquery logic with NOT EXISTS to detect visits without matching transactions, yielding results grouped by customer_id. It demonstrates efficient database interaction and records processing.