consider the row-echelon matrix A =
[1 0 2 3]
[0 1 1 4]
[0 0 0 1]
[0 0 0 0]
the "pivot columns" are the columns that contain the leading 1's of the rows. in this example, they are columns 1,2 and 4. "non-pivot columns" are linearly dependent on preceding ones. for example column 3 is twice column one plus column two.
pivot columns are important, because they form a basis for the column space, which has dimension = rank(A). the number of pivot columns in an mxn matrix is always equal to the number of non-zero rows in a row-reduced matrix.
the concept only applies to row-reduced matrices (row-reduction is how we reveal which columns are independent). but having row-reduced a matrix, we can use the pivot columns of the reduced form, to identify a basis for the column space of the original matrix, by choosing the same columns as basis vectors. in our example, if we had started with some other matrix, B, and row-reduced it to A, the 1st, 2nd and 4th columns of B would form a basis for the column space of B (also known as the range, or image of B).