How to rows from an numpy array using single indices and a range of indices

Question

Imagine, I have a 2D NumPy array X with 100 columns and 100 rows. Then, how can I extract the following rows and column using indexing?

rows= 1, 5, 15, 16 to 35, 45, 55 to 75
columns = 1, 2, 10 to 30, 42, 50

I know in MATLAB, we can do this using

X([1,5,15,16:35,45,55:75],[1,2,10:30,42,50]).

How can we do this in Python?

Does the MATLAB give a block of values? A set of rows and a set of columns? numpy handles the block versus sub2ind cases differently. — hpaulj
– hpaulj, Commented May 23, 2020 at 3:55
yes, as I mentioned in the question, we can extract a set of rows (say indices, idxR) and a set of columns (idxC) simultaneously from an array X in MATLAB using X([idxR], [idxC]). — Punit
– Punit, Commented Jun 3, 2020 at 20:11
Use np.ix_(list1, list2) to see the kinds arrays needed to index a block. To understand arr[ idx1, idx2] in numpy you have to understand how idx1 broadcasts with idx2. These are the same rules as used when adding two arrays. — hpaulj
– hpaulj, Commented Jun 3, 2020 at 21:02

Quang Hoang · Accepted Answer · 2020-05-23 01:10:27Z

1

You can use np.r_:

rows = np.r_[1, 5, 15, 16:35, 45, 55:75]
cols = np.r_[1, 2, 10:30, 42, 50]

X[rows,cols]

Note that, in Python, 16:35 usually does not include 35. You may want to do 16:36 if you want row 35 as well.

answered May 23, 2020 at 1:10

Quang Hoang

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