Count unique values per row (over index axis, not column wise) - mysql

Question

I have the following table:
WITH data AS (
SELECT 10 AS A, 10 AS B, 10 AS C
UNION ALL
SELECT 20 AS A, 10 AS B, 20 AS C
UNION ALL
SELECT 30 AS A, 20 AS B, 10 AS C
UNION ALL
SELECT 40 AS A, 40 AS B, 40 AS C
UNION ALL
SELECT 50 AS A, 20 AS B, 20 AS C)
SELECT * FROM data;
A B C
0 10 10 10
1 20 10 20
2 30 20 10
3 40 40 40
4 50 20 20
Now I want to count the number if unique values per row and store this in a new column called Unique_count
So my expected output would be:
A B C Unique_count
0 10 10 10 1
1 20 10 20 2
2 30 20 10 3
3 40 40 40 1
4 50 20 20 2
I am familiar with SELECT DISTINCT. But these are all column wise operations. I can't figure out how to count per row in SQL.
With the pandas module in Python it would simply be:
data['Unique_count'] = data.nunique(axis=1)
I have access to a MS SQL SERVER or MySQL SERVER so answers in both dialects are accepted.

Answer 1

In SQL Server, use a lateral join -- apply keyword`:
select t.*, v.unique_count
from t cross apply
(select count(distinct col) as unique_count
from (values (t.a), (t.b), (t.c)) v(col)
) v;
A lateral join is a lot like a correlated subquery in the from clause -- but more general because the subquery can return more than one column and more than one row.
This version does exactly what it looks like: it unpivots the columns and then uses count(distinct) to count the number of unique values.

Answer 2

In MySQL, you can use conditional logic:
select
t.*,
1 + (a <> b) + (a <> c and b<>c) unique_count
from data t
This works because MySQL evaluates true/false conditions as 1/0 in numeric context (this features saves us from lengthy case expressions here).
Demo on DB Fiddle:
| A | B | C | unique_count |
| --- | --- | --- | ------------ |
| 10 | 10 | 10 | 1 |
| 20 | 10 | 20 | 2 |
| 30 | 20 | 10 | 3 |
| 40 | 40 | 40 | 1 |
| 50 | 20 | 20 | 2 |

Answer 3

Add an id column to the table. Then you can use UNION to pivot the columns into rows, then COUNT(*) to get the counts. Then join that with the original table.
Note that you don't need to use COUNT(DISTINCT) because UNION DISTINCT removes duplicates.
WITH data AS (
SELECT 0 AS id, 10 AS A, 10 AS B, 10 AS C
UNION ALL
SELECT 1 AS id, 20 AS A, 10 AS B, 20 AS C
UNION ALL
SELECT 2 AS id, 30 AS A, 20 AS B, 10 AS C
UNION ALL
SELECT 3 AS id, 40 AS A, 40 AS B, 40 AS C
UNION ALL
SELECT 4 AS id, 50 AS A, 20 AS B, 20 AS C)
SELECT t1.*, t2.unique_count
FROM data AS t1
JOIN (
SELECT id, COUNT(*) AS unique_count
FROM (
SELECT id, A AS datum FROM data
UNION DISTINCT
SELECT id, B AS datum FROM data
UNION DISTINCT
SELECT id, C AS datum FROM data) AS x
GROUP BY id) AS t2
ON t1.id = t2.id