I have a ndarray like this one:
number_of_rows = 3
number_of_columns = 3
a = np.arange(number_of_rows*number_of_columns).reshape(number_of_rows,number_of_columns
For the continuous rows and columns case, you can use basic slicing like this:
In [634]: a
Out[634]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
In [635]: b = np.asarray([[100, 101],[102, 103]])
In [636]: a[:rows_to_keep[1]+1, columns_to_keep[0]:] = b
In [637]: a
Out[637]:
array([[ 0, 100, 101],
[ 3, 102, 103],
[ 6, 7, 8]])
Indexing with lists like [1,2]
is called advanced indexing. By itself it produces a copy, not a view. You have to use one indexing expression, not two to assign or change values. That is a[[1,2],:]
is a copy, a[[1,2],:][:,[1,2]] += 100
modifies that copy, not the original a
.
In [68]: arr = np.arange(12).reshape(3,4)
Indexing with slices; this is basic indexing:
In [69]: arr[1:,2:]
Out[69]:
array([[ 6, 7],
[10, 11]])
In [70]: arr[1:,2:] += 100
In [71]: arr
Out[71]:
array([[ 0, 1, 2, 3],
[ 4, 5, 106, 107],
[ 8, 9, 110, 111]])
Doing the same indexing with lists requires arrays that 'broadcast' against each other. ix_
is a handy way of generating these:
In [73]: arr[np.ix_([1,2],[2,3])]
Out[73]:
array([[106, 107],
[110, 111]])
In [74]: arr[np.ix_([1,2],[2,3])] -= 100
In [75]: arr
Out[75]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
Here's what ix_
produces - a tuple of arrays, one is (2,1) in shape, the other (1,2). Together they index a (2,2) block:
In [76]: np.ix_([1,2],[2,3])
Out[76]:
(array([[1],
[2]]), array([[2, 3]]))