Mixing
Numpy ValueError broadcasting list of tuples into an array
3
I'm observing some odd behaviour using numpy broadcasting. The problem is illustrated below, where running the first piece of code produces an error:
A = np.ones((10))
B = np.ones((10, 4))
C = np.ones((10))
np.asarray([A, B, C])
ValueError: could not broadcast input array from shape (10,4) into shape (10)
If I instead expand the dimensions of B, using B = np.expand_dims(B, axis=0), it will successfully create the array, but it now has (not surprisingly) the wrong dimensions:
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=float32)
Why does it fail to broadcast the first example, and how can I end up with an array like below (notice only double brackets around the second array)? Any feedback is much appreciated.
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
python arrays numpy
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
asked Nov 19 '18 at 14:32
andkir
205
|
show 1 more comment
3
I'm observing some odd behaviour using numpy broadcasting. The problem is illustrated below, where running the first piece of code produces an error:
A = np.ones((10))
B = np.ones((10, 4))
C = np.ones((10))
np.asarray([A, B, C])
ValueError: could not broadcast input array from shape (10,4) into shape (10)
If I instead expand the dimensions of B, using B = np.expand_dims(B, axis=0), it will successfully create the array, but it now has (not surprisingly) the wrong dimensions:
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=float32)
Why does it fail to broadcast the first example, and how can I end up with an array like below (notice only double brackets around the second array)? Any feedback is much appreciated.
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
python arrays numpy
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
asked Nov 19 '18 at 14:32
andkir
205
np.hstack([A[:,None], B, C[:,None]])?
– Divakar
Nov 19 '18 at 14:38
This doesn't quite work as it creates (in the example above) a new array of shape(10,6)and not(3,)or(1,3)as I need.
– andkir
Nov 19 '18 at 14:41
So, you need an object array with a shape of (3,)?
– Divakar
Nov 19 '18 at 14:42
Yep, or to be precise, once I join (or append) 5 of these together I need them to be of shape (5, 3)
– andkir
Nov 19 '18 at 14:49
1
The common first dimensions (10) in all the arrays sendsnp.arraydown a faulty path, trying to create a size (10,?) array. Keep in mind the default behavior fornp.arrayis to create a multidimensional (numeric) array. Creating an object array is a fall back option. With this error yet another possibility.
– hpaulj
Nov 19 '18 at 17:15
|
show 1 more comment
3
3
3
2
I'm observing some odd behaviour using numpy broadcasting. The problem is illustrated below, where running the first piece of code produces an error:
A = np.ones((10))
B = np.ones((10, 4))
C = np.ones((10))
np.asarray([A, B, C])
ValueError: could not broadcast input array from shape (10,4) into shape (10)
If I instead expand the dimensions of B, using B = np.expand_dims(B, axis=0), it will successfully create the array, but it now has (not surprisingly) the wrong dimensions:
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=float32)
Why does it fail to broadcast the first example, and how can I end up with an array like below (notice only double brackets around the second array)? Any feedback is much appreciated.
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
python arrays numpy
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
asked Nov 19 '18 at 14:32
andkir
205
I'm observing some odd behaviour using numpy broadcasting. The problem is illustrated below, where running the first piece of code produces an error:
A = np.ones((10))
B = np.ones((10, 4))
C = np.ones((10))
np.asarray([A, B, C])
ValueError: could not broadcast input array from shape (10,4) into shape (10)
If I instead expand the dimensions of B, using B = np.expand_dims(B, axis=0), it will successfully create the array, but it now has (not surprisingly) the wrong dimensions:
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=float32)
Why does it fail to broadcast the first example, and how can I end up with an array like below (notice only double brackets around the second array)? Any feedback is much appreciated.
array([array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]),
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
python arrays numpy
python arrays numpy
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
asked Nov 19 '18 at 14:32
andkir
205
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
asked Nov 19 '18 at 14:32
andkir
205
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
edited Nov 19 '18 at 20:49
Ulrich Stern
5,08712350
5,08712350
asked Nov 19 '18 at 14:32
andkir
205
asked Nov 19 '18 at 14:32
andkir
205
asked Nov 19 '18 at 14:32
andkir
205
205
np.hstack([A[:,None], B, C[:,None]])?
– Divakar
Nov 19 '18 at 14:38
This doesn't quite work as it creates (in the example above) a new array of shape(10,6)and not(3,)or(1,3)as I need.
– andkir
Nov 19 '18 at 14:41
So, you need an object array with a shape of (3,)?
– Divakar
Nov 19 '18 at 14:42
Yep, or to be precise, once I join (or append) 5 of these together I need them to be of shape (5, 3)
– andkir
Nov 19 '18 at 14:49
1
The common first dimensions (10) in all the arrays sendsnp.arraydown a faulty path, trying to create a size (10,?) array. Keep in mind the default behavior fornp.arrayis to create a multidimensional (numeric) array. Creating an object array is a fall back option. With this error yet another possibility.
– hpaulj
Nov 19 '18 at 17:15
|
show 1 more comment
np.hstack([A[:,None], B, C[:,None]])?
– Divakar
Nov 19 '18 at 14:38
This doesn't quite work as it creates (in the example above) a new array of shape(10,6)and not(3,)or(1,3)as I need.
– andkir
Nov 19 '18 at 14:41
So, you need an object array with a shape of (3,)?
– Divakar
Nov 19 '18 at 14:42
Yep, or to be precise, once I join (or append) 5 of these together I need them to be of shape (5, 3)
– andkir
Nov 19 '18 at 14:49
1
The common first dimensions (10) in all the arrays sendsnp.arraydown a faulty path, trying to create a size (10,?) array. Keep in mind the default behavior fornp.arrayis to create a multidimensional (numeric) array. Creating an object array is a fall back option. With this error yet another possibility.
– hpaulj
Nov 19 '18 at 17:15
np.hstack([A[:,None], B, C[:,None]])?– Divakar
Nov 19 '18 at 14:38
np.hstack([A[:,None], B, C[:,None]])?– Divakar
Nov 19 '18 at 14:38
This doesn't quite work as it creates (in the example above) a new array of shape
(10,6) and not (3,)or (1,3) as I need.– andkir
Nov 19 '18 at 14:41
This doesn't quite work as it creates (in the example above) a new array of shape
(10,6) and not (3,)or (1,3) as I need.– andkir
Nov 19 '18 at 14:41
So, you need an object array with a shape of (3,)?
– Divakar
Nov 19 '18 at 14:42
So, you need an object array with a shape of (3,)?
– Divakar
Nov 19 '18 at 14:42
Yep, or to be precise, once I join (or append) 5 of these together I need them to be of shape (5, 3)
– andkir
Nov 19 '18 at 14:49
Yep, or to be precise, once I join (or append) 5 of these together I need them to be of shape (5, 3)
– andkir
Nov 19 '18 at 14:49
1
1
The common first dimensions (10) in all the arrays sends
np.array down a faulty path, trying to create a size (10,?) array. Keep in mind the default behavior for np.array is to create a multidimensional (numeric) array. Creating an object array is a fall back option. With this error yet another possibility.– hpaulj
Nov 19 '18 at 17:15
The common first dimensions (10) in all the arrays sends
np.array down a faulty path, trying to create a size (10,?) array. Keep in mind the default behavior for np.array is to create a multidimensional (numeric) array. Creating an object array is a fall back option. With this error yet another possibility.– hpaulj
Nov 19 '18 at 17:15
|
show 1 more comment
1 Answer
1
active
oldest
votes
3
Including, say, None prevents the broadcasting, so this workaround is an option:
np.asarray([A, B, C, None])[:-1]
Here the outcome:
array([array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]]),
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
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active
oldest
votes
active
oldest
votes
3
Including, say, None prevents the broadcasting, so this workaround is an option:
np.asarray([A, B, C, None])[:-1]
Here the outcome:
array([array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]]),
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
add a comment |
3
Including, say, None prevents the broadcasting, so this workaround is an option:
np.asarray([A, B, C, None])[:-1]
Here the outcome:
array([array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]]),
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
add a comment |
3
3
3
Including, say, None prevents the broadcasting, so this workaround is an option:
np.asarray([A, B, C, None])[:-1]
Here the outcome:
array([array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]]),
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
Including, say, None prevents the broadcasting, so this workaround is an option:
np.asarray([A, B, C, None])[:-1]
Here the outcome:
array([array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]]),
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])], dtype=object)
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
answered Nov 19 '18 at 16:40
Ulrich Stern
5,08712350
5,08712350
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
add a comment |
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
Worked beautifully, cheers! Learn something new every day.
– andkir
Nov 19 '18 at 19:25
add a comment |
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np.hstack([A[:,None], B, C[:,None]])?– Divakar
Nov 19 '18 at 14:38
This doesn't quite work as it creates (in the example above) a new array of shape
(10,6)and not(3,)or(1,3)as I need.– andkir
Nov 19 '18 at 14:41
So, you need an object array with a shape of (3,)?
– Divakar
Nov 19 '18 at 14:42
Yep, or to be precise, once I join (or append) 5 of these together I need them to be of shape (5, 3)
– andkir
Nov 19 '18 at 14:49
1
The common first dimensions (10) in all the arrays sends
np.arraydown a faulty path, trying to create a size (10,?) array. Keep in mind the default behavior fornp.arrayis to create a multidimensional (numeric) array. Creating an object array is a fall back option. With this error yet another possibility.– hpaulj
Nov 19 '18 at 17:15