array is (800, ) dimension, each element is (240, ) dimension, how to change to (800, 240)












0















I have a np array which is (800,) in shape, and each element in this array is (240, ) in shape, how to reshape this array to (800, 240) dimension?






python numpy-ndarray







share|improve this question


















  • 1





    What is the output of a.shape (assuming your array is named a)?

    – Julian Peller
    Nov 21 '18 at 3:27











  • the a.shape is (800, ), a[0].shape is (240,), I would like to reshape this to an array with shape (800, 240)

    – Kevin Li
    Nov 21 '18 at 3:35






  • 1





    I can't figure out how you created an array of that shape, if you can provide a simple snippet of code it would be nice (maybe with lower dimensions :P). Just to check, have you tried: a.reshape((800, 240))?

    – Julian Peller
    Nov 21 '18 at 3:38













  • I tried to reshape, it does not work. Actually, I pull out this array from a pandas column. a = df['col'].values, then a.shape is (800, ), and each element in a, the shape is (240, ), so take first element as example, a[0].shape is (240, )

    – Kevin Li
    Nov 21 '18 at 3:49













  • Found something. Posted it as an answer!

    – Julian Peller
    Nov 21 '18 at 3:56


















0















I have a np array which is (800,) in shape, and each element in this array is (240, ) in shape, how to reshape this array to (800, 240) dimension?






python numpy-ndarray







share|improve this question


















  • 1





    What is the output of a.shape (assuming your array is named a)?

    – Julian Peller
    Nov 21 '18 at 3:27











  • the a.shape is (800, ), a[0].shape is (240,), I would like to reshape this to an array with shape (800, 240)

    – Kevin Li
    Nov 21 '18 at 3:35






  • 1





    I can't figure out how you created an array of that shape, if you can provide a simple snippet of code it would be nice (maybe with lower dimensions :P). Just to check, have you tried: a.reshape((800, 240))?

    – Julian Peller
    Nov 21 '18 at 3:38













  • I tried to reshape, it does not work. Actually, I pull out this array from a pandas column. a = df['col'].values, then a.shape is (800, ), and each element in a, the shape is (240, ), so take first element as example, a[0].shape is (240, )

    – Kevin Li
    Nov 21 '18 at 3:49













  • Found something. Posted it as an answer!

    – Julian Peller
    Nov 21 '18 at 3:56
















0












0








0








I have a np array which is (800,) in shape, and each element in this array is (240, ) in shape, how to reshape this array to (800, 240) dimension?






python numpy-ndarray







share|improve this question














I have a np array which is (800,) in shape, and each element in this array is (240, ) in shape, how to reshape this array to (800, 240) dimension?






python numpy-ndarray




python numpy-ndarray






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 21 '18 at 2:56









Kevin LiKevin Li

54




54








  • 1





    What is the output of a.shape (assuming your array is named a)?

    – Julian Peller
    Nov 21 '18 at 3:27











  • the a.shape is (800, ), a[0].shape is (240,), I would like to reshape this to an array with shape (800, 240)

    – Kevin Li
    Nov 21 '18 at 3:35






  • 1





    I can't figure out how you created an array of that shape, if you can provide a simple snippet of code it would be nice (maybe with lower dimensions :P). Just to check, have you tried: a.reshape((800, 240))?

    – Julian Peller
    Nov 21 '18 at 3:38













  • I tried to reshape, it does not work. Actually, I pull out this array from a pandas column. a = df['col'].values, then a.shape is (800, ), and each element in a, the shape is (240, ), so take first element as example, a[0].shape is (240, )

    – Kevin Li
    Nov 21 '18 at 3:49













  • Found something. Posted it as an answer!

    – Julian Peller
    Nov 21 '18 at 3:56
















  • 1





    What is the output of a.shape (assuming your array is named a)?

    – Julian Peller
    Nov 21 '18 at 3:27











  • the a.shape is (800, ), a[0].shape is (240,), I would like to reshape this to an array with shape (800, 240)

    – Kevin Li
    Nov 21 '18 at 3:35






  • 1





    I can't figure out how you created an array of that shape, if you can provide a simple snippet of code it would be nice (maybe with lower dimensions :P). Just to check, have you tried: a.reshape((800, 240))?

    – Julian Peller
    Nov 21 '18 at 3:38













  • I tried to reshape, it does not work. Actually, I pull out this array from a pandas column. a = df['col'].values, then a.shape is (800, ), and each element in a, the shape is (240, ), so take first element as example, a[0].shape is (240, )

    – Kevin Li
    Nov 21 '18 at 3:49













  • Found something. Posted it as an answer!

    – Julian Peller
    Nov 21 '18 at 3:56










1




1





What is the output of a.shape (assuming your array is named a)?

– Julian Peller
Nov 21 '18 at 3:27





What is the output of a.shape (assuming your array is named a)?

– Julian Peller
Nov 21 '18 at 3:27













the a.shape is (800, ), a[0].shape is (240,), I would like to reshape this to an array with shape (800, 240)

– Kevin Li
Nov 21 '18 at 3:35





the a.shape is (800, ), a[0].shape is (240,), I would like to reshape this to an array with shape (800, 240)

– Kevin Li
Nov 21 '18 at 3:35




1




1





I can't figure out how you created an array of that shape, if you can provide a simple snippet of code it would be nice (maybe with lower dimensions :P). Just to check, have you tried: a.reshape((800, 240))?

– Julian Peller
Nov 21 '18 at 3:38







I can't figure out how you created an array of that shape, if you can provide a simple snippet of code it would be nice (maybe with lower dimensions :P). Just to check, have you tried: a.reshape((800, 240))?

– Julian Peller
Nov 21 '18 at 3:38















I tried to reshape, it does not work. Actually, I pull out this array from a pandas column. a = df['col'].values, then a.shape is (800, ), and each element in a, the shape is (240, ), so take first element as example, a[0].shape is (240, )

– Kevin Li
Nov 21 '18 at 3:49







I tried to reshape, it does not work. Actually, I pull out this array from a pandas column. a = df['col'].values, then a.shape is (800, ), and each element in a, the shape is (240, ), so take first element as example, a[0].shape is (240, )

– Kevin Li
Nov 21 '18 at 3:49















Found something. Posted it as an answer!

– Julian Peller
Nov 21 '18 at 3:56







Found something. Posted it as an answer!

– Julian Peller
Nov 21 '18 at 3:56














1 Answer
1






active

oldest

votes


















0














Try with np.stack:



np.stack(a)





share|improve this answer
























  • yes, it is. But I am a little bit confused, why use stack here.

    – Kevin Li
    Nov 21 '18 at 4:00











  • I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

    – Julian Peller
    Nov 21 '18 at 4:14











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1 Answer
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1 Answer
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active

oldest

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0














Try with np.stack:



np.stack(a)





share|improve this answer
























  • yes, it is. But I am a little bit confused, why use stack here.

    – Kevin Li
    Nov 21 '18 at 4:00











  • I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

    – Julian Peller
    Nov 21 '18 at 4:14
















0














Try with np.stack:



np.stack(a)





share|improve this answer
























  • yes, it is. But I am a little bit confused, why use stack here.

    – Kevin Li
    Nov 21 '18 at 4:00











  • I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

    – Julian Peller
    Nov 21 '18 at 4:14














0












0








0







Try with np.stack:



np.stack(a)





share|improve this answer













Try with np.stack:



np.stack(a)






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 21 '18 at 3:54









Julian PellerJulian Peller

8941511




8941511













  • yes, it is. But I am a little bit confused, why use stack here.

    – Kevin Li
    Nov 21 '18 at 4:00











  • I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

    – Julian Peller
    Nov 21 '18 at 4:14



















  • yes, it is. But I am a little bit confused, why use stack here.

    – Kevin Li
    Nov 21 '18 at 4:00











  • I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

    – Julian Peller
    Nov 21 '18 at 4:14

















yes, it is. But I am a little bit confused, why use stack here.

– Kevin Li
Nov 21 '18 at 4:00





yes, it is. But I am a little bit confused, why use stack here.

– Kevin Li
Nov 21 '18 at 4:00













I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

– Julian Peller
Nov 21 '18 at 4:14





I'm a bit confused too. The object you brought is new to me and my first shots (reshape and resize) didn't work. This solution works, in general, for sequences of arrays.

– Julian Peller
Nov 21 '18 at 4:14


















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