Data Science 100 Knocks (Structured Data Processing) – SQL part2 (Q21 to Q40)

Commentary :

The code is a SQL query that selects the count of rows from a table called "receipt".

The "SELECT" keyword is used to indicate that we want to retrieve data from the database.

The "COUNT" function is used to count the number of rows in the "receipt" table. In this case, the "COUNT(*)" statement is used to count all rows in the table, regardless of whether any columns are null or not.

The "FROM" keyword is used to indicate the table we want to retrieve data from, in this case, the "receipt" table.

So, in summary, the query selects and returns the count of all rows in the "receipt" table, including those with null values in any columns.

Commentary :

This code is a SQL query that selects the count of distinct customer IDs from a table called "receipt".

The "%%sql" is a Jupyter Notebook cell magic command to indicate that this code is SQL code.

The "SELECT" keyword is used to indicate that we want to retrieve data from the database.

The "COUNT" function is used to count the number of distinct customer IDs in the "receipt" table. In this case, the "COUNT(DISTINCT customer_id)" statement is used to count the number of unique customer IDs in the table.

The "DISTINCT" keyword is used to specify that only unique values of the "customer_id" column should be counted.

The "FROM" keyword is used to indicate the table we want to retrieve data from, in this case, the "receipt" table.

So, in summary, the query selects and returns the count of unique customer IDs in the "receipt" table.

Commentary :

SQL code is repeated twice.

The code selects three columns, "store_cd", "amount", and "quantity" from a table called "receipt". The table contains sales data for a store.

The "SUM" function is used to calculate the sum of the "amount" and "quantity" columns for each store.

The "GROUP BY" clause is used to group the sales data by store. This means that the query will return the sum of "amount" and "quantity" for each unique store code in the "store_cd" column.

The first occurrence of the code might be a duplication or a mistake, as it is the exact same as the second occurrence. Nonetheless, the query selects and returns the total amount and quantity of products sold for each store in the "receipt" table.

Commentary :

This code is a SQL query that selects the maximum sales date (sales_ymd) for each customer from a table called "receipt".

The "%%sql" is a Jupyter Notebook cell magic command to indicate that this code is SQL code.

The "SELECT" keyword is used to indicate that we want to retrieve data from the database.

The "customer_id" and "MAX(sales_ymd)" are the columns that we want to select from the "receipt" table. The "MAX" function is used to select the maximum value of the "sales_ymd" column for each unique customer ID in the "customer_id" column.

The "FROM" keyword is used to indicate the table we want to retrieve data from, in this case, the "receipt" table.

The "GROUP BY" clause is used to group the sales data by customer ID. This means that the query will return the maximum sales date for each unique customer ID in the "customer_id" column.

The "LIMIT" keyword is used to limit the number of results returned by the query. In this case, the "LIMIT 10" statement limits the results to the top 10 rows.

So, in summary, the query selects and returns the maximum sales date for each customer from the "receipt" table and limits the results to the top 10 customers.

Commentary :

This code is a SQL query that selects the minimum sales date (sales_ymd) for each customer from a table called "receipt".

The "%%sql" is a Jupyter Notebook cell magic command to indicate that this code is SQL code.

The "SELECT" keyword is used to indicate that we want to retrieve data from the database.

The "customer_id" and "MIN(sales_ymd)" are the columns that we want to select from the "receipt" table. The "MIN" function is used to select the minimum value of the "sales_ymd" column for each unique customer ID in the "customer_id" column.

The "FROM" keyword is used to indicate the table we want to retrieve data from, in this case, the "receipt" table.

The "GROUP BY" clause is used to group the sales data by customer ID. This means that the query will return the minimum sales date for each unique customer ID in the "customer_id" column.

The "LIMIT" keyword is used to limit the number of results returned by the query. In this case, the "LIMIT 10" statement limits the results to the top 10 rows.

So, in summary, the query selects and returns the minimum sales date for each customer from the "receipt" table and limits the results to the top 10 customers.

Commentary :

This code is a SQL query that selects the customer IDs, maximum sales date, and minimum sales date from a table called "receipt".

The "%%sql" is a Jupyter Notebook cell magic command to indicate that this code is SQL code.

The "SELECT" keyword is used to indicate that we want to retrieve data from the database.

The "customer_id", "MAX(sales_ymd)", and "MIN(sales_ymd)" are the columns that we want to select from the "receipt" table. The "MAX" function is used to select the maximum value of the "sales_ymd" column for each unique customer ID in the "customer_id" column, and the "MIN" function is used to select the minimum value of the "sales_ymd" column for each unique customer ID in the "customer_id" column.

The "FROM" keyword is used to indicate the table we want to retrieve data from, in this case, the "receipt" table.

The "GROUP BY" clause is used to group the sales data by customer ID. This means that the query will return the maximum and minimum sales dates for each unique customer ID in the "customer_id" column.

The "HAVING" clause is used to filter the results based on a condition. In this case, the condition is that the maximum sales date and minimum sales date should not be equal for a given customer ID. This means that the query will only return customers who have made purchases on different dates.

The "LIMIT" keyword is used to limit the number of results returned by the query. In this case, the "LIMIT 10" statement limits the results to the top 10 rows.

So, in summary, the query selects and returns the customer IDs, maximum sales date, and minimum sales date from the "receipt" table for customers who have made purchases on different dates and limits the results to the top 10 customers.

Commentary :

This code is a SQL query that selects the store codes and their average sales amount from a table called "receipt".

The "%%sql" is a Jupyter Notebook cell magic command to indicate that this code is SQL code.

The "SELECT" keyword is used to indicate that we want to retrieve data from the database.

The "store_cd" and "AVG(amount) AS avg_amount" are the columns that we want to select from the "receipt" table. The "AVG" function is used to calculate the average sales amount for each unique store code in the "store_cd" column. The "AS" keyword is used to give the column an alias, "avg_amount", which is the name that will be displayed in the query results.

The "FROM" keyword is used to indicate the table we want to retrieve data from, in this case, the "receipt" table.

The "GROUP BY" clause is used to group the sales data by store code. This means that the query will calculate the average sales amount for each unique store code in the "store_cd" column.

The "ORDER BY" clause is used to sort the results in descending order based on the average sales amount. This means that the stores with the highest average sales amount will be displayed at the top of the results.

The "LIMIT" keyword is used to limit the number of results returned by the query. In this case, the "LIMIT 5" statement limits the results to the top 5 stores.

So, in summary, the query selects and returns the store codes and their average sales amount from the "receipt" table, sorted in descending order based on the average sales amount, and limits the results to the top 5 stores.

Commentary :

This is a SQL query that retrieves data from a table called "receipt".

The query selects two columns from the table: "store_cd" and "amount_50per". The "store_cd" column represents the store code, and the "amount_50per" column calculates the median amount of money spent per receipt within each store.

The function used to calculate the median is "PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY amount)". This function orders the "amount" column in ascending order and then calculates the value at the 50th percentile. In other words, it finds the middle value of the ordered list of amounts.

The query groups the results by store code using the "GROUP BY" clause, and orders the results in descending order of the median amount using the "ORDER BY" clause. It then limits the output to the top 5 results using the "LIMIT" clause.

So, the query returns the top 5 stores with the highest median amount spent per receipt.

Commentary :

This is a SQL query that retrieves data from a table called "receipt".

The query selects two columns: "store_cd" and the mode of the "product_cd" column for each store.

The "MODE() WITHIN GROUP(ORDER BY product_cd)" function calculates the mode of the "product_cd" values within each group of "store_cd". The mode is the most frequently occurring value in a set of data. The "ORDER BY" clause specifies that the "product_cd" values should be ordered in ascending order before calculating the mode.

The query groups the data by "store_cd" using the "GROUP BY" clause and orders the results by "store_cd" using the "ORDER BY" clause. It limits the output to the first 10 rows using the "LIMIT" clause.

So, the query returns the mode of the "product_cd" values for each store, which represents the most commonly sold product at each store. The results are ordered by store code and limited to the top 10 stores.

Commentary :

This is a SQL query that retrieves data from a table called "receipt".

The query selects two columns: "store_cd" and the population variance of the "amount" column for each store.

The "VAR_POP(amount)" function calculates the variance of the "amount" values within each group of "store_cd". The variance is a measure of the spread or dispersion of a set of data. The "POP" in "VAR_POP" stands for "population", which means that the entire dataset is used to calculate the variance.

The query groups the data by "store_cd" using the "GROUP BY" clause and orders the results by the "vars_amount" column, which contains the variance of the "amount" values for each store, in descending order using the "ORDER BY" clause. It limits the output to the first 5 rows using the "LIMIT" clause.

So, the query returns the top 5 stores with the highest population variance in the "amount" values, which means that these stores have the highest spread or dispersion of the amount spent per receipt.

Commentary :

This is a SQL query that retrieves data from a table called "receipt".

The query selects two columns: "store_cd" and the population standard deviation of the "amount" column for each store.

The "STDDEV_POP(amount)" function calculates the standard deviation of the "amount" values within each group of "store_cd". The standard deviation is a measure of the spread or dispersion of a set of data. The "POP" in "STDDEV_POP" stands for "population", which means that the entire dataset is used to calculate the standard deviation.

The query groups the data by "store_cd" using the "GROUP BY" clause and orders the results by the "stds_amount" column, which contains the standard deviation of the "amount" values for each store, in descending order using the "ORDER BY" clause. It limits the output to the first 5 rows using the "LIMIT" clause.

So, the query returns the top 5 stores with the highest population standard deviation in the "amount" values, which means that these stores have the highest spread or dispersion of the amount spent per receipt.

Commentary :

This is SQL code used to calculate percentile values for the "amount" column in the "receipt" table. The code uses the PERCENTILE_CONT function to compute continuous percentiles, which means that the percentile values will correspond to actual values in the data set rather than interpolated values between data points.

The code specifies four percentile values to be computed: the 25th percentile (amount_25per), the 50th percentile (amount_50per), the 75th percentile (amount_75per), and the 100th percentile (amount_100per). These percentiles are calculated using the WITHIN GROUP clause to order the "amount" column in ascending order before computing the percentiles.

The SELECT statement at the end of the code retrieves the calculated percentile values from the query and returns them as a single row of output. This code is useful for analyzing the distribution of the "amount" column in the "receipt" table and identifying any outliers or unusual patterns in the data.

Commentary :

This is SQL code used to retrieve average transaction amounts for each store in the "receipt" table and filter the results based on a condition.

The code begins with a SELECT statement that specifies two columns to be retrieved: the "store_cd" column and the average of the "amount" column for each store. The AVG function is used to calculate the average of the "amount" column for each store, and the AS keyword is used to assign the alias "avg_amount" to this calculated column.

The next line of code uses the GROUP BY clause to group the data by the "store_cd" column, which means that the average transaction amount will be calculated separately for each store.

The code then uses the HAVING clause to filter the results based on a condition. Specifically, the condition requires that the average transaction amount for each store be greater than or equal to 330. Any stores that do not meet this condition will not be included in the output.

The resulting output will be a table with two columns: "store_cd" and "avg_amount". Each row will correspond to a store in the "receipt" table that meets the condition specified in the HAVING clause, and the "avg_amount" column will contain the average transaction amount for that store. This code is useful for analyzing the performance of different stores based on the average transaction amount.

Commentary :

This is SQL code that calculates the average of the total amount spent by each customer in the "receipt" table, excluding customers whose IDs start with the letter "Z".

The code starts with a common table expression (CTE) defined using the WITH clause. The CTE is named "customer_amount" and it contains a SELECT statement that retrieves the "customer_id" column and the sum of the "amount" column for each customer in the "receipt" table. The WHERE clause filters out any customers whose IDs start with the letter "Z" using the NOT LIKE operator. The results are grouped by the "customer_id" column, and the resulting table has two columns: "customer_id" and "sum_amount", which is the total amount spent by each customer.

The next line of code retrieves the average of the "sum_amount" column from the "customer_amount" CTE. The SELECT statement uses the AVG function to calculate the average of the "sum_amount" column, which represents the average total amount spent by each customer whose ID does not start with "Z".

The resulting output will be a single value representing the average of the "sum_amount" column from the "customer_amount" CTE. This code is useful for analyzing the average spending patterns of customers and identifying any outliers or unusual patterns in the data.

Commentary :

This is SQL code that retrieves the "customer_id" and "sum_amount" columns for the top 10 customers who have spent more than the average total amount spent by customers in the "receipt" table, excluding customers whose IDs start with the letter "Z".

The code starts with a common table expression (CTE) defined using the WITH clause. The CTE is named "customer_amount" and it contains a SELECT statement that retrieves the "customer_id" column and the sum of the "amount" column for each customer in the "receipt" table. The WHERE clause filters out any customers whose IDs start with the letter "Z" using the NOT LIKE operator. The results are grouped by the "customer_id" column, and the resulting table has two columns: "customer_id" and "sum_amount", which is the total amount spent by each customer.

The next line of code retrieves the "customer_id" and "sum_amount" columns from the "customer_amount" CTE, but only for customers whose "sum_amount" is greater than or equal to the average "sum_amount" calculated over all customers in the CTE. This is achieved using a subquery that calculates the average "sum_amount" using the AVG function and then filters the "customer_amount" CTE using the WHERE clause.

The results of this query are then limited to the top 10 rows using the LIMIT clause.

The resulting output will be a table with two columns: "customer_id" and "sum_amount". Each row will correspond to a customer who has spent more than the average total amount spent by customers in the "receipt" table, excluding customers whose IDs start with the letter "Z". The table will be sorted in descending order based on the "sum_amount" column, and only the top 10 rows will be included in the output. This code is useful for identifying high-spending customers and analyzing their spending patterns.

Commentary :

This is SQL code that retrieves the first 10 rows from a joined table containing data from the "receipt" and "store" tables. The resulting table will contain all columns from the "receipt" table, plus an additional column "store_name" from the "store" table.

The code starts with a SELECT statement that specifies the columns to retrieve. In this case, the SELECT statement uses the wildcard (*) to retrieve all columns from the "receipt" table. The SELECT statement also specifies the "store_name" column from the "store" table, which will be included in the output table.

The FROM clause specifies that the data is being retrieved from the "receipt" table, and the JOIN clause specifies that the "store" table is being joined to the "receipt" table using the "store_cd" column as the join condition. This means that rows from the "receipt" table and the "store" table will be combined into a single table based on matching values in the "store_cd" column.

The resulting output will be a table containing all columns from the "receipt" table, plus an additional column "store_name" from the "store" table. The table will be limited to the first 10 rows using the LIMIT clause. This code is useful for analyzing data from two related tables and combining the data into a single table for analysis.

Commentary :

This code is written in SQL, a programming language used for managing and querying relational databases.

The code selects data from two tables: "product" and "category". The "JOIN" keyword is used to combine the rows from both tables where the "category_small_cd" column values match. This creates a new table that contains columns from both tables.

The "SELECT" keyword is used to choose which columns to include in the output. In this case, it selects all columns from the "product" table (specified by the "p.*" syntax) and the "category_small_name" column from the "category" table.

The "LIMIT" keyword limits the output to the first 10 rows. This is useful when working with large datasets to quickly test queries or get a sample of the data.

So, this code will return the first 10 rows of the combined "product" and "category" tables, where the "category_small_cd" values match, and will include all columns from the "product" table and the "category_small_name" column from the "category" table.

Commentary :

This code is also written in SQL and is a bit more complex than the previous one. It selects data from three tables: "receipt", "customer" and "customer_amount".

The code uses a "WITH" clause to define two subqueries: "customer_amount" and "customer_data".

The "customer_amount" subquery aggregates the "amount" column from the "receipt" table by "customer_id" using the "SUM" function and gives an alias "sum_amount" to the result. This subquery will create a new table that contains two columns: "customer_id" and "sum_amount".

The "customer_data" subquery selects all rows from the "customer" table where "gender_cd" is equal to '1' and the "customer_id" does not start with the letter 'Z'. This subquery will create a new table that contains a single column: "customer_id".

The final query joins the "customer_data" table with the "customer_amount" table using a "LEFT OUTER JOIN" operation. This will return all rows from the "customer_data" table and matching rows from the "customer_amount" table, if any exist. If there is no match in the "customer_amount" table, the "COALESCE" function is used to replace the "sum_amount" value with 0.

The "SELECT" clause chooses two columns to include in the output: "customer_id" from the "customer_data" table and "COALESCE(a.sum_amount, 0)" from the "customer_amount" table (or 0 if there is no matching row in "customer_amount"). The "LIMIT" clause limits the output to the first 10 rows.

So, this code will return the first 10 rows of the "customer_data" table, along with the sum of their receipts (or 0 if they have no receipts), for male customers whose "customer_id" does not start with the letter 'Z'.

Commentary :

This code is also written in SQL and is a bit more complex than the previous ones. It selects data from the "receipt" table and creates three subqueries: "customer_data", "customer_days", and "customer_amount".

The "customer_data" subquery selects rows from the "receipt" table where the "customer_id" does not start with the letter 'Z' and gives an alias to three columns: "customer_id", "sales_ymd", and "amount". This subquery will create a new table that contains three columns: "customer_id", "sales_ymd", and "amount".

The "customer_days" subquery aggregates the "sales_ymd" column from the "customer_data" subquery by "customer_id" using the "COUNT(DISTINCT sales_ymd)" function and gives an alias "come_days" to the result. It then orders the result by "come_days" in descending order and limits the output to the top 20 rows. This subquery will create a new table that contains two columns: "customer_id" and "come_days".

The "customer_amount" subquery aggregates the "amount" column from the "customer_data" subquery by "customer_id" using the "SUM" function and gives an alias "buy_amount" to the result. It then orders the result by "buy_amount" in descending order and limits the output to the top 20 rows. This subquery will create a new table that contains two columns: "customer_id" and "buy_amount".

The final query joins the "customer_days" table with the "customer_amount" table using a "FULL OUTER JOIN" operation. This will return all rows from both tables, matching rows from both tables, and NULL values where there is no match. The "COALESCE" function is used to replace the NULL values in "customer_id" with the corresponding value from the other table.

The "SELECT" clause chooses three columns to include in the output: "COALESCE(d.customer_id, a.customer_id)" to get the customer_id, "d.come_days" from the "customer_days" table, and "a.buy_amount" from the "customer_amount" table.

So, this code will return the top 20 customers with the highest "come_days" (i.e., the number of days they visited the store) and the top 20 customers with the highest "buy_amount" (i.e., the total amount they spent in the store), along with their customer_id. If a customer appears in both tables, it will only appear once in the output.