I agree with Cade Roux. This article should get you on the right track: Indexes in SQL Server 2005/2008 – Best Practices, Part 1 Indexes in SQL Server 2005/2008 – Part 2 – Internals One thing to note, clustered indexes should have a unique key (an identity column I would recommend) as the first column. … Read more
Label vs. Location The main distinction between the two methods is: loc gets rows (and/or columns) with particular labels. iloc gets rows (and/or columns) at integer locations. To demonstrate, consider a series s of characters with a non-monotonic integer index: >>> s = pd.Series(list(“abcdef”), index=[49, 48, 47, 0, 1, 2]) 49 a 48 b 47 … Read more
With a clustered index the rows are stored physically on the disk in the same order as the index. Therefore, there can be only one clustered index. With a non clustered index there is a second list that has pointers to the physical rows. You can have many non clustered indices, although each new index … Read more
Use del and specify the index of the element you want to delete: >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> del a[-1] >>> a [0, 1, 2, 3, 4, 5, 6, 7, 8] Also supports slices: >>> del a[2:4] >>> a [0, 1, 4, 5, 6, 7, 8, … Read more
some_list[-1] is the shortest and most Pythonic. In fact, you can do much more with this syntax. The some_list[-n] syntax gets the nth-to-last element. So some_list[-1] gets the last element, some_list[-2] gets the second to last, etc, all the way down to some_list[-len(some_list)], which gives you the first element. You can also set list elements … Read more
Why is it needed? When data is stored on disk-based storage devices, it is stored as blocks of data. These blocks are accessed in their entirety, making them the atomic disk access operation. Disk blocks are structured in much the same way as linked lists; both contain a section for data, a pointer to the … Read more