I've seen DBAs, when counting the number of rows, run SELECT COUNT(1) FROM table;. What is the 1 in the query? I've tried putting in other numbers (2, 0, -1) and result is always the same as using 1.

2008年10月8日 · Also count(1) here 1 is not coloumn no, it is a expression. e.g) select 1 from table1; will print 1 no of times for no of rows that table has. – Kanagavelu Sugumar Commented Feb 13, 2013 at 12:13

2013年6月19日 · Yes. Count doesn't count NULL values, so you can do this: select COUNT('x') as Everything, COUNT(case when OutcomeID = 36 then 'x' else NULL end) as Sales, COUNT(case when OutcomeID <> 36 then 'x' else NULL end) as Other from YourTable Alternatively, you can use SUM, like bluefeet demonstrated.

2014年2月21日 · Transaction count after EXECUTE indicates a mismatching number of BEGIN and COMMIT statements. Previous count = 1, current count = 0. I have read the answers in other such questions and am unable to find where exactly the commit count is getting messed up.

2022年2月19日 · SELECT Name, COUNT(1) as Cnt FROM Table1 GROUP BY Name UNION ALL SELECT 'SUM' Name, COUNT(1) FROM Table1 That said, I would recomend that the total be added by your presentation layer, and not by the database. This is a bit more of a SQL SERVER Version using Summarizing Data Using ROLLUP. SQL Fiddle DEMO

2012年8月31日 · To add a filter to just the Count Of column select the cell above and the cell containing the title and then choose the Filter option from the menus as shown... To only count data if it fulfills a certain condition, I think you would need to add another column called OK to the source data, with a formula like IF(Status="OK",1,0). Then add this ...

In 0.18.1 groupby together with count does not give the frequency of unique values: >>> df a 0 a 1 b 2 s 3 s 4 b 5 a 6 b >>> df.groupby('a').count() Empty DataFrame Columns: [] Index: [a, b, s] However, the unique values and their frequencies are easily determined using size: >>> df.groupby('a').size() a a 2 b 3 s 2

2015年4月24日 · Solutions with better performance should be GroupBy.transform with size for count per groups to Series with same size like original df, so possible filter by boolean indexing: df1 = df[df.groupby("A")['A'].transform('size') > 1]

SELECT count(*) AS count_1 FROM (SELECT table.col1 as col1, table.col2 as col2, ... from table) which is significantly slower in MySQL with InnoDB. I am looking for a solution that doesn't require the table to have a known primary key, as suggested in Get the number of rows in table using SQLAlchemy .

Count all the DISTINCT program names by program type and push number. SELECT program_type AS [Type], Count(DISTINCT program_name) AS [Count], FROM cm_production WHERE push_number = @push_number GROUP BY program_type DISTINCT COUNT(*) will return a row for each unique count.