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You should note that SQL Server's INFORMATION_SCHEMA views include views that report on database constraints, however DEFAULTs cannot be found within them.
As mentioned earlier, the ANSI-SQL standard defines constraints as a column property.
With the nice indexing methods in Pandas I have no problems extracting data in various ways. Data Frame(, index=) # this overwrites the first row but we want to update the second # df.update(df2) # this does not update anything df.loc[df.filename == 'test2.dat'].update(df2) print(df) So first of all, pandas updates using the index.
On the other hand I am still confused about how to change data in an existing Data Frame. When an update command does not update anything, check both left-hand side and right-hand side.
As a result, you cannot rely on using an an auto-generated name statically within a static DROP CONSTRAINT statement.
In these cases, the only way to modify DEFAULT constraints with auto-generated names is to capture the name from SQL Server's system catalog and to run a dynamic SQL query to perform the DROP.
Would you like to answer one of these unanswered questions instead?
In these cases, make sure you document where you're reading system tables directly.
In the following code I have two Data Frames and my goal is to update values in a specific row in the first df from values of the second df. If for some reason you are too lazy to update the indices to follow your identification logic, you can do something along the lines of There are probably a few ways to do this, but one approach would be to merge the two dataframes together on the filename/m column, then populate the column 'n' from the right dataframe if a match was found.