Tutorial by Examples: dense

from scipy.sparse import csr_matrix A = csr_matrix([[1,0,2],[0,3,0]]) >>>A <2x3 sparse matrix of type '<type 'numpy.int64'>' with 3 stored elements in Compressed Sparse Row format> >>> A.todense() matrix([[1, 0, 2], [0, 3, 0]]) >>&g...

Same as that of RANK(). It returns rank without any gaps: Select Studentid, Name,Subject,Marks, DENSE_RANK() over(partition by name order by Marks desc)Rank From Exam order by name Studentid Name Subject Marks Rank 101 Ivan Science 80 1 101 Ivan ...

here you can find the functions. With the table wf_example created in previous example, run: select i , dense_rank() over (order by i) , row_number() over () , rank() over (order by i) from wf_example The result is: i | dense_rank | row_number | rank ---+------------+------------+-...