converting numpy array of string fields to numerical format
I have an array of strings grouped into three fields:
x = np.array([(-1, 0, 1),
(-1, 1, 0),
(0, 1, -1),
(0, -1, 1)],
dtype=[('a', 'S2'),
('b', 'S2'),
('c', 'S2')])
I would like to convert to a numerical array (of type np.int8 for a preference, but not required), shaped 4x3, instead of the fields.
My general approach is to transform into a 4x3 array of type 'S2', then use astype to make it numerical. The only problem is that the only approach I can think of involves both view and np.lib.stride_tricks.as_strided, which doesn't seem like a very robust solution:
y = np.lib.stride_tricks.as_strided(x.view(dtype='S2'),
shape=(4, 3), strides=(6, 2))
z = y.astype(np.int8)
This works for the toy case shown here, but I feel like there must be a simpler way to unpack an array with fields all having the same dtype. What is a more robust alternative?
python numpy
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
add a comment |
I have an array of strings grouped into three fields:
x = np.array([(-1, 0, 1),
(-1, 1, 0),
(0, 1, -1),
(0, -1, 1)],
dtype=[('a', 'S2'),
('b', 'S2'),
('c', 'S2')])
I would like to convert to a numerical array (of type np.int8 for a preference, but not required), shaped 4x3, instead of the fields.
My general approach is to transform into a 4x3 array of type 'S2', then use astype to make it numerical. The only problem is that the only approach I can think of involves both view and np.lib.stride_tricks.as_strided, which doesn't seem like a very robust solution:
y = np.lib.stride_tricks.as_strided(x.view(dtype='S2'),
shape=(4, 3), strides=(6, 2))
z = y.astype(np.int8)
This works for the toy case shown here, but I feel like there must be a simpler way to unpack an array with fields all having the same dtype. What is a more robust alternative?
python numpy
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
3
Using lists is the most robust way of converting a structured array to a simple dtype one:np.array(x.tolist(),'int8')
– hpaulj
Dec 28 '18 at 5:50
@hpaulij. I'd probably select that answer, although it bothers me that I now need a third copy of the data.
– Mad Physicist
Dec 28 '18 at 5:57
Why S2 in the first place? How are you reading this in?
– Andy Hayden
Dec 28 '18 at 6:09
@Andy. stackoverflow.com/q/53953116/2988730. I asked this question to shamelessly rip off the answer hpaulj provided.
– Mad Physicist
Dec 28 '18 at 6:14
@hpaulj. I've used the technique you suggest: stackoverflow.com/a/53954336/2988730
– Mad Physicist
Dec 28 '18 at 6:15
add a comment |
I have an array of strings grouped into three fields:
x = np.array([(-1, 0, 1),
(-1, 1, 0),
(0, 1, -1),
(0, -1, 1)],
dtype=[('a', 'S2'),
('b', 'S2'),
('c', 'S2')])
I would like to convert to a numerical array (of type np.int8 for a preference, but not required), shaped 4x3, instead of the fields.
My general approach is to transform into a 4x3 array of type 'S2', then use astype to make it numerical. The only problem is that the only approach I can think of involves both view and np.lib.stride_tricks.as_strided, which doesn't seem like a very robust solution:
y = np.lib.stride_tricks.as_strided(x.view(dtype='S2'),
shape=(4, 3), strides=(6, 2))
z = y.astype(np.int8)
This works for the toy case shown here, but I feel like there must be a simpler way to unpack an array with fields all having the same dtype. What is a more robust alternative?
python numpy
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
I have an array of strings grouped into three fields:
x = np.array([(-1, 0, 1),
(-1, 1, 0),
(0, 1, -1),
(0, -1, 1)],
dtype=[('a', 'S2'),
('b', 'S2'),
('c', 'S2')])
I would like to convert to a numerical array (of type np.int8 for a preference, but not required), shaped 4x3, instead of the fields.
My general approach is to transform into a 4x3 array of type 'S2', then use astype to make it numerical. The only problem is that the only approach I can think of involves both view and np.lib.stride_tricks.as_strided, which doesn't seem like a very robust solution:
y = np.lib.stride_tricks.as_strided(x.view(dtype='S2'),
shape=(4, 3), strides=(6, 2))
z = y.astype(np.int8)
This works for the toy case shown here, but I feel like there must be a simpler way to unpack an array with fields all having the same dtype. What is a more robust alternative?
python numpy
python numpy
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
edited Dec 28 '18 at 5:56
Mad Physicist
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
asked Dec 28 '18 at 5:43
Mad PhysicistMad Physicist
34.2k156895
34.2k156895
3
Using lists is the most robust way of converting a structured array to a simple dtype one:np.array(x.tolist(),'int8')
– hpaulj
Dec 28 '18 at 5:50
@hpaulij. I'd probably select that answer, although it bothers me that I now need a third copy of the data.
– Mad Physicist
Dec 28 '18 at 5:57
Why S2 in the first place? How are you reading this in?
– Andy Hayden
Dec 28 '18 at 6:09
@Andy. stackoverflow.com/q/53953116/2988730. I asked this question to shamelessly rip off the answer hpaulj provided.
– Mad Physicist
Dec 28 '18 at 6:14
@hpaulj. I've used the technique you suggest: stackoverflow.com/a/53954336/2988730
– Mad Physicist
Dec 28 '18 at 6:15
add a comment |
3
Using lists is the most robust way of converting a structured array to a simple dtype one:np.array(x.tolist(),'int8')
– hpaulj
Dec 28 '18 at 5:50
@hpaulij. I'd probably select that answer, although it bothers me that I now need a third copy of the data.
– Mad Physicist
Dec 28 '18 at 5:57
Why S2 in the first place? How are you reading this in?
– Andy Hayden
Dec 28 '18 at 6:09
@Andy. stackoverflow.com/q/53953116/2988730. I asked this question to shamelessly rip off the answer hpaulj provided.
– Mad Physicist
Dec 28 '18 at 6:14
@hpaulj. I've used the technique you suggest: stackoverflow.com/a/53954336/2988730
– Mad Physicist
Dec 28 '18 at 6:15
3
3
Using lists is the most robust way of converting a structured array to a simple dtype one:
np.array(x.tolist(),'int8')– hpaulj
Dec 28 '18 at 5:50
Using lists is the most robust way of converting a structured array to a simple dtype one:
np.array(x.tolist(),'int8')– hpaulj
Dec 28 '18 at 5:50
@hpaulij. I'd probably select that answer, although it bothers me that I now need a third copy of the data.
– Mad Physicist
Dec 28 '18 at 5:57
@hpaulij. I'd probably select that answer, although it bothers me that I now need a third copy of the data.
– Mad Physicist
Dec 28 '18 at 5:57
Why S2 in the first place? How are you reading this in?
– Andy Hayden
Dec 28 '18 at 6:09
Why S2 in the first place? How are you reading this in?
– Andy Hayden
Dec 28 '18 at 6:09
@Andy. stackoverflow.com/q/53953116/2988730. I asked this question to shamelessly rip off the answer hpaulj provided.
– Mad Physicist
Dec 28 '18 at 6:14
@Andy. stackoverflow.com/q/53953116/2988730. I asked this question to shamelessly rip off the answer hpaulj provided.
– Mad Physicist
Dec 28 '18 at 6:14
@hpaulj. I've used the technique you suggest: stackoverflow.com/a/53954336/2988730
– Mad Physicist
Dec 28 '18 at 6:15
@hpaulj. I've used the technique you suggest: stackoverflow.com/a/53954336/2988730
– Mad Physicist
Dec 28 '18 at 6:15
add a comment |
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3
Using lists is the most robust way of converting a structured array to a simple dtype one:
np.array(x.tolist(),'int8')– hpaulj
Dec 28 '18 at 5:50
@hpaulij. I'd probably select that answer, although it bothers me that I now need a third copy of the data.
– Mad Physicist
Dec 28 '18 at 5:57
Why S2 in the first place? How are you reading this in?
– Andy Hayden
Dec 28 '18 at 6:09
@Andy. stackoverflow.com/q/53953116/2988730. I asked this question to shamelessly rip off the answer hpaulj provided.
– Mad Physicist
Dec 28 '18 at 6:14
@hpaulj. I've used the technique you suggest: stackoverflow.com/a/53954336/2988730
– Mad Physicist
Dec 28 '18 at 6:15