Predict: unknown prices in a filter

Predict the products returned by a filter for list prices other than 1.25.

what to return

Columns: stock_codelist_price in this order
Rows: any order
Watch for: Use only the sample rows shown. NULL is not zero or empty text. Keep duplicate rows.
Skill: Reason about the exact result of a fixed SQL query. Refresher: Understand NULL →

data you can query

Retail dataset: the first 6 rows of each table. Under each column name: the kind of value it holds, and which table it links to. Your queries run on all rows.

price_sample2 columns · 5 rows

stock_codetext	list_pricedecimal
A	1.25
B	2.5
C	NULL
D	1.25
E	NULL

stock_codetext: A
list_pricedecimal: 1.25

stock_codetext: B
list_pricedecimal: 2.5

stock_codetext: C
list_pricedecimal: NULL

stock_codetext: D
list_pricedecimal: 1.25

stock_codetext: E
list_pricedecimal: NULL

all 5 rows

predict.sql

SELECT stock_code, list_price
FROM price_sample
WHERE list_price <> 1.25

Read the query · predict its result

SELECT stock_code, list_price FROM price_sample WHERE list_price <> 1.25

Look at the sample tables and SQL. Enter the rows you think the query returns, in this column order: stock_code, list_price. Put each row in square brackets, then wrap all rows in another pair of brackets (JSON format). For example, [[1,null]] is one row with 2 values. Replace these with your predicted values. Use numbers without quotes, text in double quotes, and null for SQL NULL. Keep repeated rows. Use [] for no rows. Rows can be in any order.

Your predicted rows

Showing the result or using a hint means a correct prediction counts as solved with help. Check prediction tests your answer without showing the result.

Hints and the answer are always available. Using them counts the question as solved with help.