Quick ways to manipulate Numpy array in array












3















I would like to find a way to quickly manipulate an array of arrays in Numpy like this one, which has a shape of (10,):



[array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]


For instance, I'd like to compute the total number of array elements, which is 16 for the array above, but without doing a for loop since in practice my "nested array" will be quite large.



Thanks!






python arrays numpy







share|improve this question




















  • 1





    Define "manipulation". If all you want is the length, it would be better to flatten into a single array. Otherwise your only choice s iteration because these arrays are uneven length.

    – coldspeed
    Dec 31 '18 at 7:58






  • 1





    That's a list of arrays - or object dtype array. Loops, list comprehensions are the normal tools. np.frompyfunc may be useful in some cases, with a modest speed difference.

    – hpaulj
    Dec 31 '18 at 8:04











  • As shown in the answer, 'flattening' the list into one array with concatenate way help. It depends on the manipulation.

    – hpaulj
    Dec 31 '18 at 16:57
















3















I would like to find a way to quickly manipulate an array of arrays in Numpy like this one, which has a shape of (10,):



[array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]


For instance, I'd like to compute the total number of array elements, which is 16 for the array above, but without doing a for loop since in practice my "nested array" will be quite large.



Thanks!






python arrays numpy







share|improve this question




















  • 1





    Define "manipulation". If all you want is the length, it would be better to flatten into a single array. Otherwise your only choice s iteration because these arrays are uneven length.

    – coldspeed
    Dec 31 '18 at 7:58






  • 1





    That's a list of arrays - or object dtype array. Loops, list comprehensions are the normal tools. np.frompyfunc may be useful in some cases, with a modest speed difference.

    – hpaulj
    Dec 31 '18 at 8:04











  • As shown in the answer, 'flattening' the list into one array with concatenate way help. It depends on the manipulation.

    – hpaulj
    Dec 31 '18 at 16:57














3












3








3








I would like to find a way to quickly manipulate an array of arrays in Numpy like this one, which has a shape of (10,):



[array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]


For instance, I'd like to compute the total number of array elements, which is 16 for the array above, but without doing a for loop since in practice my "nested array" will be quite large.



Thanks!






python arrays numpy







share|improve this question
















I would like to find a way to quickly manipulate an array of arrays in Numpy like this one, which has a shape of (10,):



[array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]


For instance, I'd like to compute the total number of array elements, which is 16 for the array above, but without doing a for loop since in practice my "nested array" will be quite large.



Thanks!






python arrays numpy




python arrays numpy






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Dec 31 '18 at 7:57









coldspeed

129k23133219




129k23133219










asked Dec 31 '18 at 7:55









nusjjsunnusjjsun

263




263








  • 1





    Define "manipulation". If all you want is the length, it would be better to flatten into a single array. Otherwise your only choice s iteration because these arrays are uneven length.

    – coldspeed
    Dec 31 '18 at 7:58






  • 1





    That's a list of arrays - or object dtype array. Loops, list comprehensions are the normal tools. np.frompyfunc may be useful in some cases, with a modest speed difference.

    – hpaulj
    Dec 31 '18 at 8:04











  • As shown in the answer, 'flattening' the list into one array with concatenate way help. It depends on the manipulation.

    – hpaulj
    Dec 31 '18 at 16:57














  • 1





    Define "manipulation". If all you want is the length, it would be better to flatten into a single array. Otherwise your only choice s iteration because these arrays are uneven length.

    – coldspeed
    Dec 31 '18 at 7:58






  • 1





    That's a list of arrays - or object dtype array. Loops, list comprehensions are the normal tools. np.frompyfunc may be useful in some cases, with a modest speed difference.

    – hpaulj
    Dec 31 '18 at 8:04











  • As shown in the answer, 'flattening' the list into one array with concatenate way help. It depends on the manipulation.

    – hpaulj
    Dec 31 '18 at 16:57








1




1





Define "manipulation". If all you want is the length, it would be better to flatten into a single array. Otherwise your only choice s iteration because these arrays are uneven length.

– coldspeed
Dec 31 '18 at 7:58





Define "manipulation". If all you want is the length, it would be better to flatten into a single array. Otherwise your only choice s iteration because these arrays are uneven length.

– coldspeed
Dec 31 '18 at 7:58




1




1





That's a list of arrays - or object dtype array. Loops, list comprehensions are the normal tools. np.frompyfunc may be useful in some cases, with a modest speed difference.

– hpaulj
Dec 31 '18 at 8:04





That's a list of arrays - or object dtype array. Loops, list comprehensions are the normal tools. np.frompyfunc may be useful in some cases, with a modest speed difference.

– hpaulj
Dec 31 '18 at 8:04













As shown in the answer, 'flattening' the list into one array with concatenate way help. It depends on the manipulation.

– hpaulj
Dec 31 '18 at 16:57





As shown in the answer, 'flattening' the list into one array with concatenate way help. It depends on the manipulation.

– hpaulj
Dec 31 '18 at 16:57












1 Answer
1






active

oldest

votes


















5














One way to find the length of the array in your case is to ravel the nested numpy arrays and then find the length as below:



a = [array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]



len(np.concatenate(a).ravel())

#Here we expand the numpy arrays and then flatten it to find the length.


Output:



16


As per my knowledge, ravel has a better timeit performance time in comparison to for loop.






share|improve this answer
























  • I don't think you need the ravel here.

    – hpaulj
    Dec 31 '18 at 13:50











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1 Answer
1






active

oldest

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1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









5














One way to find the length of the array in your case is to ravel the nested numpy arrays and then find the length as below:



a = [array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]



len(np.concatenate(a).ravel())

#Here we expand the numpy arrays and then flatten it to find the length.


Output:



16


As per my knowledge, ravel has a better timeit performance time in comparison to for loop.






share|improve this answer
























  • I don't think you need the ravel here.

    – hpaulj
    Dec 31 '18 at 13:50
















5














One way to find the length of the array in your case is to ravel the nested numpy arrays and then find the length as below:



a = [array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]



len(np.concatenate(a).ravel())

#Here we expand the numpy arrays and then flatten it to find the length.


Output:



16


As per my knowledge, ravel has a better timeit performance time in comparison to for loop.






share|improve this answer
























  • I don't think you need the ravel here.

    – hpaulj
    Dec 31 '18 at 13:50














5












5








5







One way to find the length of the array in your case is to ravel the nested numpy arrays and then find the length as below:



a = [array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]



len(np.concatenate(a).ravel())

#Here we expand the numpy arrays and then flatten it to find the length.


Output:



16


As per my knowledge, ravel has a better timeit performance time in comparison to for loop.






share|improve this answer













One way to find the length of the array in your case is to ravel the nested numpy arrays and then find the length as below:



a = [array([0, 1, 3]) ,array([0, 1, 7]), array([2]), array([0, 3]), array([4]),

 array([5]), array([6]) ,array([1, 7]), array([8]), array([9])]



len(np.concatenate(a).ravel())

#Here we expand the numpy arrays and then flatten it to find the length.


Output:



16


As per my knowledge, ravel has a better timeit performance time in comparison to for loop.







share|improve this answer












share|improve this answer



share|improve this answer










answered Dec 31 '18 at 8:25









Jim ToddJim Todd

42737




42737













  • I don't think you need the ravel here.

    – hpaulj
    Dec 31 '18 at 13:50



















  • I don't think you need the ravel here.

    – hpaulj
    Dec 31 '18 at 13:50

















I don't think you need the ravel here.

– hpaulj
Dec 31 '18 at 13:50





I don't think you need the ravel here.

– hpaulj
Dec 31 '18 at 13:50


















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