overriding partially an numpy array does not work
I tried to override a numpy array partially
does anyone know how to do that in such comfort indexing way?
Thanks!
python numpy numpy-ndarray
asked Nov 20 '18 at 19:19
malocho
3618
add a comment |
I tried to override a numpy array partially
does anyone know how to do that in such comfort indexing way?
Thanks!
python numpy numpy-ndarray
asked Nov 20 '18 at 19:19
malocho
3618
add a comment |
I tried to override a numpy array partially
does anyone know how to do that in such comfort indexing way?
Thanks!
python numpy numpy-ndarray
asked Nov 20 '18 at 19:19
malocho
3618
I tried to override a numpy array partially
does anyone know how to do that in such comfort indexing way?
Thanks!
python numpy numpy-ndarray
python numpy numpy-ndarray
asked Nov 20 '18 at 19:19
malocho
3618
asked Nov 20 '18 at 19:19
malocho
3618
asked Nov 20 '18 at 19:19
malocho
3618
asked Nov 20 '18 at 19:19
malocho
3618
asked Nov 20 '18 at 19:19
malocho
3618
3618
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Setup
a = np.array([[1,2,3], [1,1,1], [1,1,1]])
b = np.array([[888,888], [99, 99]])
You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:
>>> a[np.ix_([1,2], [0,1])] = b
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
add a comment |
You can also use this sort of indexing with the : separating your starting and ending indices:
>>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
# 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
>>> a[1:,:2] = np.array([[888,888], [99, 99]])
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:29
sacul
29.9k41740
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Setup
a = np.array([[1,2,3], [1,1,1], [1,1,1]])
b = np.array([[888,888], [99, 99]])
You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:
>>> a[np.ix_([1,2], [0,1])] = b
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
add a comment |
Setup
a = np.array([[1,2,3], [1,1,1], [1,1,1]])
b = np.array([[888,888], [99, 99]])
You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:
>>> a[np.ix_([1,2], [0,1])] = b
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
add a comment |
Setup
a = np.array([[1,2,3], [1,1,1], [1,1,1]])
b = np.array([[888,888], [99, 99]])
You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:
>>> a[np.ix_([1,2], [0,1])] = b
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
Setup
a = np.array([[1,2,3], [1,1,1], [1,1,1]])
b = np.array([[888,888], [99, 99]])
You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:
>>> a[np.ix_([1,2], [0,1])] = b
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
answered Nov 20 '18 at 19:26
user3483203
30.2k82354
30.2k82354
add a comment |
add a comment |
You can also use this sort of indexing with the : separating your starting and ending indices:
>>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
# 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
>>> a[1:,:2] = np.array([[888,888], [99, 99]])
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:29
sacul
29.9k41740
add a comment |
You can also use this sort of indexing with the : separating your starting and ending indices:
>>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
# 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
>>> a[1:,:2] = np.array([[888,888], [99, 99]])
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:29
sacul
29.9k41740
add a comment |
You can also use this sort of indexing with the : separating your starting and ending indices:
>>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
# 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
>>> a[1:,:2] = np.array([[888,888], [99, 99]])
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:29
sacul
29.9k41740
You can also use this sort of indexing with the : separating your starting and ending indices:
>>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
# 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
>>> a[1:,:2] = np.array([[888,888], [99, 99]])
>>> a
array([[ 1, 2, 3],
[888, 888, 1],
[ 99, 99, 1]])
answered Nov 20 '18 at 19:29
sacul
29.9k41740
edited Nov 20 '18 at 19:41
answered Nov 20 '18 at 19:29
sacul
29.9k41740
answered Nov 20 '18 at 19:29
sacul
29.9k41740
answered Nov 20 '18 at 19:29
sacul
29.9k41740
29.9k41740
add a comment |
add a comment |
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