Table: Visits
+---------------+---------+ | Column Name | Type | +---------------+---------+ | user_id | int | | visit_date | date | +---------------+---------+ (user_id, visit_date) is the primary key for this table. Each row of this table indicates that user_id has visited the bank in visit_date.
Table: Transactions
+------------------+---------+ | Column Name | Type | +------------------+---------+ | user_id | int | | transaction_date | date | | amount | int | +------------------+---------+ There is no primary key for this table, it may contain duplicates. Each row of this table indicates that user_id has done a transaction of amount in transaction_date. It is guaranteed that the user has visited the bank in the transaction_date.(i.e The Visits table contains (user_id, transaction_date) in one row)
Write an SQL query to find how many users visited the bank and didn't do any transactions, how many visited the bank and did one transaction and so on.
The result table will contain two columns transactions_count which is the number of transactions done in one visit and visits_count which is the corresponding number of users who did transactions_count in one visit to the bank. transactions_count should take all values from 0 to max(transactions_count) done by one or more users.
Order the result table by transactions_count.
The query result format is in the following example:
Visits table: +---------+------------+ | user_id | visit_date | +---------+------------+ | 1 | 2020-01-01 | | 2 | 2020-01-02 | | 12 | 2020-01-01 | | 19 | 2020-01-03 | | 1 | 2020-01-02 | | 2 | 2020-01-03 | | 1 | 2020-01-04 | | 7 | 2020-01-11 | | 9 | 2020-01-25 | | 8 | 2020-01-28 | +---------+------------+ Transactions table: +---------+------------------+--------+ | user_id | transaction_date | amount | +---------+------------------+--------+ | 1 | 2020-01-02 | 120 | | 2 | 2020-01-03 | 22 | | 7 | 2020-01-11 | 232 | | 1 | 2020-01-04 | 7 | | 9 | 2020-01-25 | 33 | | 9 | 2020-01-25 | 66 | | 8 | 2020-01-28 | 1 | | 9 | 2020-01-25 | 99 | +---------+------------------+--------+ Result table: +--------------------+--------------+ | transactions_count | visits_count | +--------------------+--------------+ | 0 | 4 | | 1 | 5 | | 2 | 0 | | 3 | 1 | +--------------------+--------------+ Users 1, 2, 12 and 19 visited the bank in 2020-01-01, 2020-01-02, 2020-01-01 and 2020-01-03 respectively, and didn't do any transactions. So we have visits_count = 4 for transactions_count = 0. Users 2, 7 and 8 visited the bank in 2020-01-03, 2020-01-11 and 2020-01-28 respectively, and did one transaction. User 1 Also visited the bank in 2020-01-02 and 2020-01-04 and did one transaction each day. So we have total visits_count = 5 for transactions_count = 1. For transactions_count = 2 we don't have any users who visited the bank and did two transactions. For transactions_count = 3 we have user 9 who visited the bank in 2020-01-25 and did three transactions. Note that we stopped at transactions_count = 3 as this is the maximum number of transactions done by one user in one visit to the bank.
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