If you are using SQL Server 2005+, then you can use the PIVOT function to transform the data from rows into columns. I have read the stuff on MS pivot tables and I am still having problems getting this correct. Connect and share knowledge within a single location that is structured and easy to search. The answer to that question involves a situation where pivot without aggregation is needed so an example of doing it is part of the solution. If your full data has duplicate combinations of labels (Country, Year, Indicator), you can use .pivot_table. To squash back into a flat table, use .rename_axis to remove “Indicator” and .reset_index to convert Country and Year back to normal columns.

• Store numbers down the side and weeks across the top.
• Apparently, I had previously opened and pivoted an older version of the same spreadsheet.
• It is as simple as specifying the key column (Store), pivoting column (Week), and the calculated metric (sum(xCount)).

How to pivot a dataframe in Pandas?

By hitting the ‘refresh’ button on the options tab under PivotTable Tools in the ribbon, my pivot was updated to the proper data. I have a table in csv format that looks like this. I would like to transpose the table so that the values in the indicator column are the new columns. Just give you some idea how other databases solve this problem. DolphinDB also has built-in support for pivoting and the sql looks much more intuitive and neat. It is as simple as specifying the key column (Store), pivoting column (Week), and the calculated metric (sum(xCount)).

Answers 10

It sounds like you will need to use dynamic sql if the weeks are unknown but it is easier to see the correct code using a hard-coded version initially. I have a temp table that is being created, we will say that column 1 is a Store number, and column 2 is a week number and lastly column 3 is a total of some type. Also the Week numbers are dynamic, the store numbers are static.

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My anecdotal results are that running this query over a couple of thousand rows completed in less than one second, and I actually had 7 subqueries. Then I had Forex Trading for beginners to use a while statement and build the above statement as a varchar and use dynmaic sql. To pivot this table you want three arguments in your Pandas “pivot”.

• The answer to that question involves a situation where pivot without aggregation is needed so an example of doing it is part of the solution.
• Also the Week numbers are dynamic, the store numbers are static.
• To squash back into a flat table, use .rename_axis to remove “Indicator” and .reset_index to convert Country and Year back to normal columns.
• Just give you some idea how other databases solve this problem.
• The mismatch in values may occur due to change in format in which the data is stored.

Why is my pivot table showing different values than the table it’s based on?

If I want to filter this data based on the types of product (Speaker, Glass, Headset) by each customer, then use Pivot operator. Store numbers down the side and weeks across the top. Find centralized, trusted content and collaborate around the technologies you use most.

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The mismatch in values may occur due to change in format in which the data is stored. Having a to build @fulltext using a while loop and select the distinct column names out of the table. Apparently, I had previously opened and pivoted an older version of the same spreadsheet. When creating the pivot table for the later version, some cache must have existed that it built the pivot off of instead of the newest data.